From e9e513ab5974a2c0ab3aaa2f1058818240047a3d Mon Sep 17 00:00:00 2001 From: "Dennis(Zhenhuan) Liu" Date: Tue, 14 Apr 2026 13:50:55 +0800 Subject: [PATCH 01/72] feat(ci): add strict review mode to Claude review workflow (#4197) Co-authored-by: Claude Opus 4.6 (1M context) --- .github/workflows/claude_review.yml | 182 +++++++++++++++++++++++++++- 1 file changed, 179 insertions(+), 3 deletions(-) diff --git a/.github/workflows/claude_review.yml b/.github/workflows/claude_review.yml index b1d2ab12c87..99c6cdf4ac8 100644 --- a/.github/workflows/claude_review.yml +++ b/.github/workflows/claude_review.yml @@ -5,8 +5,12 @@ on: types: [created] jobs: - review-on-comment: - name: Claude Review (comment trigger) + # ────────────────────────────────────────────────────────────────── + # Light review: quick pass for obvious bugs, typos, and test gaps + # Trigger: /claude review + # ────────────────────────────────────────────────────────────────── + light-review: + name: Claude Light Review if: | github.event_name == 'issue_comment' && github.event.issue.pull_request && @@ -39,7 +43,7 @@ jobs: --method POST \ -f content='eyes' - - name: Run Claude Code Review + - name: Run Claude Light Review uses: anthropics/claude-code-action@v1 with: anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }} @@ -77,3 +81,175 @@ jobs: It's perfectly acceptable to not have anything to comment on. If you do not have anything to comment on, approve the PR with: gh pr review $PR_NUMBER --repo $REPO --approve --body "LGTM" + + # ────────────────────────────────────────────────────────────────── + # Strict review: comprehensive Megatron-LM focused analysis + # covering precision, parallelism correctness, performance, + # backward compatibility, and code quality + # Trigger: /claude strict-review + # ────────────────────────────────────────────────────────────────── + strict-review: + name: Claude Strict Review + if: | + github.event_name == 'issue_comment' && + github.event.issue.pull_request && + contains(github.event.comment.body, '/claude strict-review') + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: write + issues: write + id-token: write + env: + GH_TOKEN: ${{ github.token }} + REPO: ${{ github.repository }} + PR_NUMBER: ${{ github.event.issue.number }} + steps: + - name: Get PR info + id: pr-info + run: | + PR_DATA=$(gh pr view $PR_NUMBER --repo $REPO --json headRefOid,baseRefName) + echo "sha=$(echo $PR_DATA | jq -r .headRefOid)" >> $GITHUB_OUTPUT + echo "base_ref=$(echo $PR_DATA | jq -r .baseRefName)" >> $GITHUB_OUTPUT + + - name: Checkout repository + uses: actions/checkout@v6 + with: + fetch-depth: 1 + ref: ${{ steps.pr-info.outputs.sha }} + + - name: Fetch base branch for diff analysis + run: git fetch origin ${{ steps.pr-info.outputs.base_ref }} + + - name: React to trigger comment + run: | + gh api repos/$REPO/issues/comments/${{ github.event.comment.id }}/reactions \ + --method POST \ + -f content='eyes' + + - name: Run Claude Strict Review + uses: anthropics/claude-code-action@v1 + with: + anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }} + trigger_phrase: "/claude strict-review" + show_full_output: true + claude_args: | + --allowedTools "mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*),Bash(gh pr review:*),Bash(git diff:*),Bash(git show:*),Bash(git log:*)" + --model "claude-opus-4-6" + prompt: | + REPO: ${{ env.REPO }} + PR NUMBER: ${{ env.PR_NUMBER }} + BASE REF: origin/${{ steps.pr-info.outputs.base_ref }} + + You are performing a strict, comprehensive code review on a **Megatron-LM** Pull Request. + Megatron-LM is NVIDIA's large-scale distributed training framework for LLMs. + Review the diff with a focus on **implementation correctness**, **training performance**, and **backward compatibility**. + + ## Review Procedure + + 1. Get PR metadata: `gh pr view $PR_NUMBER --repo $REPO --json title,body,baseRefName,headRefName,files,additions,deletions,changedFiles,author` + 2. Get the full diff: `gh pr diff $PR_NUMBER --repo $REPO` + - For large PRs (>50 files), prioritize source code over config/lock/auto-generated files. + 3. For each significant changed file, read the full file for surrounding context. + 4. Trace data flow and dtype through computation paths to verify correctness. + 5. For each newly introduced variable/argument/field, verify it has a meaningful runtime use path (see Mandatory Check below). + 6. Post findings as inline comments with severity and category tags. + + ## Critical Issues (Must Fix) + + ### Implementation Correctness + - **dtype handling**: Verify operations use the correct dtype at each computation stage — explicit casts must be present at mixed-precision boundaries (e.g. fp16 compute → fp32 accumulation → fp16 output) + - **Loss scaling logic**: Verify DynamicLossScaler changes correctly detect inf/nan, adjust scale factor, and skip optimizer steps — incorrect logic causes training divergence or silent underflow + - **Reduction operations**: Verify reductions (sum, mean, allreduce) use correct dtype, reduction dimension, and normalization factor — wrong dimension or missing fp32 upcast produces silently wrong gradients + - **Normalization layers**: Verify LayerNorm/RMSNorm compute variance and mean on the correct dimension, with correct epsilon placement and upcast before rsqrt + - **Attention computation**: Verify QK^T scaling factor, softmax input dtype, causal mask application, and dropout placement match the intended algorithm + - **Residual connections**: Verify the correct tensor is added (pre-norm vs post-norm) with appropriate dtype for accumulation + - **Optimizer updates**: Verify state updates follow the correct formula — momentum/variance update order, bias correction, weight decay application + - **Gradient clipping**: Verify norm computation uses correct parameter set, norm type (L2 vs inf), and fp32 dtype + - **Embedding/output layer**: Verify weight tying is correctly wired, logit projection uses the right matrix, and output dtype matches expectation + - **MoE routing/aux loss**: Verify expert routing logic (top-k selection, capacity enforcement, token dropping) and auxiliary loss computation follow the intended algorithm + + ### Correctness + - **Tensor parallel**: Incorrect scatter/gather or allreduce placement — silent wrong results across TP ranks + - **Pipeline parallel**: Wrong microbatch scheduling, missing send/recv synchronization, incorrect grad accumulation across pipeline stages + - **Sequence parallel**: Incorrect sequence dimension partitioning or missing allgather/reduce-scatter in SP regions + - **Context parallel**: Incorrect KV cache partitioning or ring attention implementation errors + - **Expert parallel**: Token routing/dispatch errors across EP ranks, incorrect capacity factor handling + - **Gradient accumulation**: Missing no_sync() context or incorrect division factor when accumulating across microbatches + - **Checkpoint save/load**: State dict key mismatch, missing optimizer states, incorrect RNG state restoration — causes silent divergence after resume + - **RNG state management**: Incorrect random seed handling across TP/PP/DP ranks, causing correlated dropout masks or data sampling + + ## Important Issues (Should Fix) + + ### Training Performance + - **Unnecessary CPU-GPU sync**: .item(), .cpu(), torch.cuda.synchronize(), Python-side tensor value checks in training loop — kills throughput + - **Redundant communication**: Allreduce/allgather that could be fused, overlapped with compute, or eliminated + - **Memory inefficiency**: Missing activation checkpointing on memory-heavy layers, unnecessary tensor clones or .contiguous() calls + - **Communication-computation overlap**: Missed opportunities to overlap allreduce with backward, or allgather with forward + - **Kernel launch overhead**: Python loops over small ops that should be fused into a single kernel + - **CUDA graph compatibility**: Dynamic shapes, Python-side conditionals on tensor values, host-device sync inside captured region + + ### Backward Compatibility + - **Config/argument changes**: Renamed or removed arguments without deprecation path — breaks existing training scripts + - **Checkpoint format changes**: Modified state dict keys/structure without migration logic — makes existing checkpoints unloadable + - **Default value changes**: Changed defaults for training hyperparameters or parallelism settings — silently alters behavior for users relying on defaults + - **API contract changes**: Changed function signatures, return types, or side effects in megatron/core/ without backward-compat shim + - **Model architecture changes**: Altered layer ordering, initialization, or normalization placement — existing pretrained weights become incompatible + + ### Mandatory Check: Unused New Variables / Arguments + - For each changed file, list newly added identifiers (function args, config fields, locals). + - Verify each has a meaningful read/use path — not just declaration/docstring or discard assignment (_ = new_arg). + - Use Grep to search for usage beyond declaration sites. + - Treat placeholder discard patterns as findings unless explicitly documented as temporary migration shim. + - If usage is intentionally deferred, flag and request explicit TODO + migration note. + + ## Suggestions (Nice to Have) + + ### Naming + - Name must describe what the thing *is*, not what it's *used for* + - No abbreviations in parallel/distributed code — use full names (token_dispatcher, routing_map, comm_manager, world_size) + - Naming consistency within scope for variables serving the same role + + ### Function/Method Decomposition + - Functions over ~50 lines mixing data collection, reduction, computation, and I/O should be split + - Non-trivial logic blocks embedded in a method with different primary purpose should be extracted + + ### Simplification + - Redundant operations (e.g. .reshape(()) on 0-dim tensor, two-step constructions where one suffices) + - Setup constant across training should not run on every forward pass — move to __init__ + - Dead complexity that doesn't achieve its stated purpose + - Unnecessary intermediate aliases adding indirection with no abstraction value + + ### Other + - Stale, imprecise, or misleading comments/docstrings — a wrong docstring is worse than none + - Missing shape/dtype assertions at parallelism boundaries + + ## What NOT to Comment On + - Style/formatting issues (leave to linters) + - Test code that is reasonably clear + - Clearly intentional design decisions by the author + - Pure refactoring that preserves identical behavior (verify via diff) + - Findings invalidated by deeper analysis — drop them entirely rather than hedging + + ## Comment Format + + Prefix each comment with severity and category tag: + - `**[CRITICAL Implementation]**`, `**[CRITICAL Correctness]**` + - `**[IMPORTANT Performance]**`, `**[IMPORTANT Compatibility]**` + - `**[SUGGESTION Naming]**`, `**[SUGGESTION Simplification]**` + + For each finding, explain: (1) what the issue is, (2) why it matters (impact/risk), (3) specific suggestion for fix. + + Only use inline ```suggestion blocks for simple, self-contained line replacements (typos, + renames, single-line fixes). For structural changes that add, remove, or reorganize blocks + of code, use a top-level PR comment with a code block showing the proposed change instead. + + ## Completion + + After posting all inline comments, post a summary PR comment: + - List total findings by severity (CRITICAL: N, IMPORTANT: N, SUGGESTION: N) + - Highlight the most impactful findings + - Overall assessment of the PR's risk level + + If no significant issues are found, approve the PR: + gh pr review $PR_NUMBER --repo $REPO --approve --body "Strict review passed — no significant issues found. LGTM" From cd03c3e28e6ab6a1af9441a786355a301f62f30b Mon Sep 17 00:00:00 2001 From: Philip Petrakian Date: Mon, 13 Apr 2026 23:15:14 -0700 Subject: [PATCH 02/72] Fix stale approvals (#4280) Co-authored-by: Claude Opus 4.6 (1M context) --- .../python_scripts/swap_pr_labels.py | 65 ++++++++++++++----- 1 file changed, 49 insertions(+), 16 deletions(-) diff --git a/tests/test_utils/python_scripts/swap_pr_labels.py b/tests/test_utils/python_scripts/swap_pr_labels.py index 1fde4520a89..d2e78eea786 100644 --- a/tests/test_utils/python_scripts/swap_pr_labels.py +++ b/tests/test_utils/python_scripts/swap_pr_labels.py @@ -12,6 +12,7 @@ from dataclasses import dataclass from typing import List +import requests from github import Github logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') @@ -39,6 +40,7 @@ class PRReviewTracker: EXCLUDED_TEAMS = {"core-adlr", "core-nemo"} def __init__(self, token: str, repo_name: str, pr_number: str): + self.token = token self.github = Github(token) self.repo = self.github.get_repo(repo_name) self.pr = self.repo.get_pull(pr_number) @@ -135,6 +137,48 @@ def _get_teams_members(self, slugs): members.update(self._get_team_members(slug)) return members + def _get_latest_reviews(self, pr_number): + """Get the active review state per reviewer using GraphQL latestReviews. + + Unlike the REST API's get_reviews(), this returns only the current + review per reviewer — matching the green/red checkmarks in the UI. + Dismissed or removed approvals are not included. + """ + owner, name = self.repo.full_name.split("/") + query = """ + query($owner: String!, $name: String!, $number: Int!) { + repository(owner: $owner, name: $name) { + pullRequest(number: $number) { + latestReviews(first: 100) { + nodes { + author { login } + state + } + } + } + } + } + """ + reviews = {} + try: + resp = requests.post( + "https://api.github.com/graphql", + json={ + "query": query, + "variables": {"owner": owner, "name": name, "number": pr_number}, + }, + headers={"Authorization": f"Bearer {self.token}"}, + ) + resp.raise_for_status() + nodes = resp.json()["data"]["repository"]["pullRequest"]["latestReviews"]["nodes"] + for node in nodes: + if node["author"]: + reviews[node["author"]["login"]] = node["state"] + except Exception as e: + logger.warning(f"Could not get latest reviews for PR #{pr_number}: {e}") + logger.info(f"Active reviews: {reviews}") + return reviews + def swap_labels(self): """Evaluate review state and update labels accordingly.""" pr = self.pr @@ -142,24 +186,13 @@ def swap_labels(self): logger.info(f"PR #{pr.number} is a draft. Skipping label swap.") return - # 1. Get the latest review state for everyone who has submitted a review - latest_reviews = {} - try: - for review in pr.get_reviews(): - if not review.user: - continue - if review.state in ("APPROVED", "CHANGES_REQUESTED"): - if ( - review.user.login not in latest_reviews - or review.submitted_at > latest_reviews[review.user.login].submitted_at - ): - latest_reviews[review.user.login] = review - except Exception as e: - logger.warning(f"Could not get reviews for PR #{pr.number}: {e}") + # 1. Get the active review state per reviewer via GraphQL latestReviews, + # which reflects dismissed/removed approvals the same way the GitHub UI does. + latest_reviews = self._get_latest_reviews(pr.number) - approvers = {user for user, review in latest_reviews.items() if review.state == "APPROVED"} + approvers = {user for user, state in latest_reviews.items() if state == "APPROVED"} non_approvers = { - user for user, review in latest_reviews.items() if review.state == "CHANGES_REQUESTED" + user for user, state in latest_reviews.items() if state == "CHANGES_REQUESTED" } # 2. Determine required teams from CODEOWNERS + changed files From eb80b749126481371a3b6f5e6c2dcee2b280dc17 Mon Sep 17 00:00:00 2001 From: Xin Yao Date: Tue, 14 Apr 2026 16:14:46 +0800 Subject: [PATCH 03/72] [MoE] Add a new score function to the router (#3673) Signed-off-by: Xin Yao --- megatron/core/transformer/moe/moe_layer.py | 7 +- megatron/core/transformer/moe/moe_utils.py | 44 +- .../core/transformer/transformer_config.py | 14 +- .../test_cases/common/moe_perf/__main__.py | 16 +- .../golden_values_dev_dgx_h100.json | 490 ++-- .../golden_values_dev_dgx_h100.json | 2476 ++++++++--------- .../golden_values_dev_dgx_h100.json | 2336 ++++++++-------- .../golden_values_dev_dgx_h100.json | 2 +- .../golden_values_dev_dgx_h100.json | 2 +- .../golden_values_dev_dgx_h100.json | 2 +- 10 files changed, 2709 insertions(+), 2680 deletions(-) diff --git a/megatron/core/transformer/moe/moe_layer.py b/megatron/core/transformer/moe/moe_layer.py index 35b567679fe..a64afee719f 100644 --- a/megatron/core/transformer/moe/moe_layer.py +++ b/megatron/core/transformer/moe/moe_layer.py @@ -30,7 +30,7 @@ ) from megatron.core.transformer.transformer_config import TransformerConfig from megatron.core.typed_torch import apply_module, not_none -from megatron.core.utils import internal_api +from megatron.core.utils import internal_api, nvtx_range_pop, nvtx_range_push try: import flashinfer # pylint: disable=unused-import @@ -716,8 +716,9 @@ def backward(ctx, *grad_outputs): wgrad_stream.wait_event(event) with torch.cuda.stream(wgrad_stream): - with torch.cuda.nvtx.range("delayed_expert_wgrad"): - module.backward_dw(routed_experts=True, shared_experts=False) + nvtx_range_push("delayed_expert_wgrad") + module.backward_dw(routed_experts=True, shared_experts=False) + nvtx_range_pop("delayed_expert_wgrad") event.record(wgrad_stream) torch.cuda.current_stream().wait_event(event) diff --git a/megatron/core/transformer/moe/moe_utils.py b/megatron/core/transformer/moe/moe_utils.py index b903f59856b..896495d0710 100644 --- a/megatron/core/transformer/moe/moe_utils.py +++ b/megatron/core/transformer/moe/moe_utils.py @@ -696,8 +696,8 @@ def topk_routing_with_score_function( group_topk (int, optional): Number of selected groups for each token. Defaults to None. scaling_factor (float, optional): Scaling factor of routing score in top-k selection. Defaults to None. - score_function (str, optional): The score function to use. Can be either "softmax" or - "sigmoid". Defaults to "softmax". + score_function (str, optional): The score function to use. Can be "softmax", "sigmoid" + or "sqrtsoftplus". Defaults to "softmax". expert_bias (torch.Tensor, optional): The bias added to logits for expert routing. Defaults to None. fused (bool, optional): Whether to use the fused version. Defaults to False. @@ -731,6 +731,11 @@ def topk_routing_with_score_function( raise ValueError( "fused_topk_with_score_function is not available. Please install TE >= 2.6.0." ) + if score_function == "sqrtsoftplus" and not is_te_min_version("2.13.0"): + raise ValueError( + "Fused sqrtsoftplus score function requires TE >= 2.13.0. " + "Please upgrade Transformer Engine or disable moe_router_fusion." + ) return fused_topk_with_score_function( logits=logits, topk=topk, @@ -783,19 +788,26 @@ def compute_topk(scores, topk, num_groups=None, group_topk=None): scores, topk, num_groups, group_topk, _compute_topk ) + # Precision notes: + # - Logits are converted to fp32 for score functions. + # - All the intermediate calculations are in fp32. + # - The final probs are casted to the same dtype as the logits. if score_function == "softmax": if use_pre_softmax: - scores = torch.softmax(logits, dim=-1, dtype=torch.float32).type_as(logits) + scores = torch.softmax(logits, dim=-1, dtype=torch.float32) probs, top_indices = compute_topk(scores, topk, num_groups, group_topk) else: scores, top_indices = compute_topk(logits, topk, num_groups, group_topk) - probs = torch.softmax(scores, dim=-1, dtype=torch.float32).type_as(logits) - elif score_function == "sigmoid": - scores = torch.sigmoid(logits.float()).type_as(logits) + probs = torch.softmax(scores, dim=-1, dtype=torch.float32) + elif score_function in ("sigmoid", "sqrtsoftplus"): + if score_function == "sigmoid": + scores = torch.sigmoid(logits.float()) + else: + scores = torch.nn.functional.softplus(logits.float()).sqrt() if expert_bias is not None: - scores_for_routing = scores + expert_bias + scores_for_routing = scores + expert_bias.float() _, top_indices = compute_topk(scores_for_routing, topk, num_groups, group_topk) - scores = torch.gather(scores, dim=1, index=top_indices).type_as(logits) + scores = torch.gather(scores, dim=1, index=top_indices) else: scores, top_indices = compute_topk(scores, topk, num_groups, group_topk) probs = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) if topk > 1 else scores @@ -805,6 +817,8 @@ def compute_topk(scores, topk, num_groups=None, group_topk=None): if scaling_factor: probs = probs * scaling_factor + probs = probs.type_as(logits) + if dense_output: return probs, top_indices @@ -839,7 +853,8 @@ def compute_routing_scores_for_aux_loss( Args: logits (torch.Tensor): The logits tensor after gating, shape: [num_tokens, num_experts]. topk (int): The number of top-k indices to compute. - score_function (str): The score function to use. Can be either "softmax" or "sigmoid". + score_function (str): The score function to use. Can be "softmax", "sigmoid" + or "sqrtsoftplus". fused (bool, optional): Whether to use the fused version. Defaults to False. padding_mask (torch.Tensor, optional): Boolean mask indicating non-padding tokens. Shape in [num_tokens]. True for valid tokens, @@ -853,6 +868,11 @@ def compute_routing_scores_for_aux_loss( raise ValueError( "fused_compute_score_for_moe_aux_loss is not available. Please install TE >= 2.6.0." ) + if score_function == "sqrtsoftplus" and not is_te_min_version("2.13.0"): + raise ValueError( + "Fused sqrtsoftplus score function requires TE >= 2.13.0. " + "Please upgrade Transformer Engine or disable moe_router_fusion." + ) routing_map, scores = fused_compute_score_for_moe_aux_loss( logits=logits, topk=topk, score_function=score_function ) @@ -860,8 +880,10 @@ def compute_routing_scores_for_aux_loss( if score_function == "softmax": scores = torch.softmax(logits, dim=-1, dtype=torch.float32) elif score_function == "sigmoid": - # Cast logits to float32 before sigmoid for stability - scores = torch.sigmoid(logits.to(torch.float32)) + scores = torch.sigmoid(logits.float()) + scores = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) + elif score_function == "sqrtsoftplus": + scores = torch.nn.functional.softplus(logits.float()).sqrt() scores = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) else: raise ValueError(f"Invalid score_function: {score_function}") diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py index 48610a0078e..e198d07161c 100644 --- a/megatron/core/transformer/transformer_config.py +++ b/megatron/core/transformer/transformer_config.py @@ -695,8 +695,8 @@ class TransformerConfig(ModelParallelConfig): """Scaling factor for routing score in top-k selection, only works when moe_router_pre_softmax enabled. Defaults to None, which means no scaling.""" - moe_router_score_function: Literal['softmax', 'sigmoid'] = "softmax" - """Score function for MoE routing. Can be "softmax" or "sigmoid".""" + moe_router_score_function: Literal['softmax', 'sigmoid', 'sqrtsoftplus'] = "softmax" + """Score function for MoE routing. Can be "softmax", "sigmoid" or "sqrtsoftplus".""" moe_router_dtype: Optional[Literal['fp32', 'fp64']] = None """Data type for routing and expert output weighted averaging. Using fp32 or fp64 can @@ -1831,10 +1831,14 @@ def __post_init__(self): self.expert_tensor_parallel_size == 1 ), "Bias in Moe is only supported when ETP==1" - if self.moe_router_enable_expert_bias and self.moe_router_score_function != "sigmoid": + if self.moe_router_enable_expert_bias and self.moe_router_score_function not in ( + "sigmoid", + "sqrtsoftplus", + ): raise ValueError( - "Expert bias for aux-loss-free routing only supports sigmoid score function." - "Please set --moe-router-score-function sigmoid for sigmoid score function." + "Expert bias for aux-loss-free routing only supports 'sigmoid' and 'sqrtsoftplus' " + "score functions. Please set --moe-router-score-function to 'sigmoid' or " + "'sqrtsoftplus', or unset --moe-router-enable-expert-bias." ) if self.num_moe_experts and self.fp8: diff --git a/tests/functional_tests/test_cases/common/moe_perf/__main__.py b/tests/functional_tests/test_cases/common/moe_perf/__main__.py index 6cc33c36b43..803d0c11a26 100644 --- a/tests/functional_tests/test_cases/common/moe_perf/__main__.py +++ b/tests/functional_tests/test_cases/common/moe_perf/__main__.py @@ -23,7 +23,7 @@ from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.moe.moe_utils import RandomSTE from megatron.core.transformer.transformer_config import TransformerConfig -from megatron.core.utils import is_te_min_version, nvtx_range_pop, nvtx_range_push +from megatron.core.utils import nvtx_range_pop, nvtx_range_push from megatron.training.initialize import _set_random_seed from tests.unit_tests.test_utilities import Utils @@ -211,10 +211,11 @@ def _benchmark_moe_layer(layer: MoELayer, case: MoEPerformanceCase): nvtx_iter_msg = f"({case.name}) iteration {iteration}" nvtx_range_push(nvtx_iter_msg) # Use a long CUDA kernel to hide the router launch overhead - with torch.cuda.nvtx.range("(dummy GEMM)"): - dummy_tensor = torch.randn(8192, 8192, device="cuda") - torch.matmul(dummy_tensor, dummy_tensor) - del dummy_tensor + nvtx_range_push("(dummy GEMM)") + dummy_tensor = torch.randn(8192, 8192, device="cuda") + torch.matmul(dummy_tensor, dummy_tensor) + del dummy_tensor + nvtx_range_pop("(dummy GEMM)") input_tensor.grad = None layer.zero_grad(set_to_none=True) torch.cuda.reset_peak_memory_stats() @@ -345,8 +346,9 @@ def test_moe_layer_performance(perf_case: MoEPerformanceCase, debug_mode: bool = _set_random_seed(seed_=123, data_parallel_random_init=False) torch.cuda.reset_peak_memory_stats() layer = _prepare_moe_layer(perf_case) - with torch.cuda.nvtx.range(f"({perf_case.name})"): - metrics = _benchmark_moe_layer(layer, perf_case) + nvtx_range_push(f"({perf_case.name})") + metrics = _benchmark_moe_layer(layer, perf_case) + nvtx_range_pop(f"({perf_case.name})") summary = ( f"MoE layer performance ({perf_case.name}): forward {metrics['forward_ms']:.3f} ms " diff --git a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json index 3f022ffde7e..0b92de260a0 100644 --- a/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt_dynamic_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json @@ -34,8 +34,8 @@ 1394, 1636 ], - 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Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [0.9536803364753723, 0.016934143379330635, 0.016783935949206352, 0.016745438799262047, 0.0167548805475235, 0.01675168052315712, 0.016725631430745125, 0.016744224354624748, 0.016771871596574783, 0.01673772744834423, 0.01671331189572811, 0.016742143779993057, 0.016752831637859344, 0.016735071316361427, 0.016708416864275932, 0.01676086336374283, 0.01672108843922615, 0.01675747148692608, 0.01672768034040928, 0.01673593558371067, 0.01673644781112671, 0.016768928617239, 0.016721663996577263, 0.01674390397965908, 0.016715550795197487, 0.01673286408185959, 0.016974497586488724, 0.016759486868977547, 0.01675366424024105, 0.017026912420988083], "latency": 1.4477943610399961, "logprobs": [-10.737512588500977, -3.6907906532287598, -2.8354406356811523, -1.241209626197815, -0.23157396912574768, -1.8544931411743164, -2.3058578968048096, -1.965078592300415, -2.131650447845459, -6.1445770263671875, -0.8567591905593872, -2.4118287563323975, -3.49544095993042, -4.07368278503418, -1.9927215576171875, -1.8116189241409302, -2.220911979675293, -7.154321193695068, -0.041270580142736435, -1.897834300994873, -5.07651424407959, -8.796056747436523, -9.88559341430664, -0.7177769541740417, -4.775661468505859, -0.8519912362098694, -2.3280630111694336, -0.018436847254633904, -0.038715627044439316, -3.4831454753875732, -8.691339492797852, -1.2537559270858765, -6.647421836853027, -3.785327911376953, -3.7754249572753906, -4.1729302406311035, -2.268207550048828, -1.0762473344802856, -0.22336173057556152, -0.7773433923721313, -4.659046173095703, -9.07835865020752, -0.01364840567111969, -3.1569409370422363, -1.3136285543441772, -3.999577760696411, -0.8146029710769653, -0.0020667400676757097, -2.9257936477661133, -10.559369087219238, -3.301023483276367, -1.1468515396118164, -4.866663455963135, -0.20965954661369324, -0.06276518106460571, -1.3678232431411743, -2.2146267890930176, -4.369752883911133, -0.35328271985054016, -4.076470851898193, -0.39479735493659973, -0.14124885201454163, -2.7151336669921875, -10.645881652832031, -0.05100790411233902, -3.277879238128662, -0.859420657157898, -4.694356918334961, -0.26331964135169983, -2.601013660430908, -0.8340632319450378, -1.5944981575012207, -5.802148818969727, -16.992801666259766, -2.9630136489868164, -0.12174151837825775, -7.421735763549805, -1.090034008026123, -2.115244150161743, -1.592454195022583, -0.297377347946167, -5.607227325439453, -0.006619194056838751, -7.817281723022461, -2.701261043548584, -2.988292932510376, -2.9706215858459473, -2.4563350677490234, -0.3993130624294281, -1.5173310041427612, -2.269473075866699, -0.6111201047897339, -1.313757300376892, -1.9383023977279663, -1.6797527074813843, -0.7714957594871521, -0.49417543411254883, -1.2875804901123047, -1.5885818004608154, -1.018824577331543, -0.4005858302116394, -0.46394026279449463, -0.04470847547054291, -1.2914193868637085, -2.2410547733306885, -2.740159273147583, -0.7651359438896179, -0.4316181242465973, -2.7719383239746094, -1.5367236137390137, -1.652032732963562, -0.051836322993040085, -1.3689777851104736, -1.342658519744873, -1.26646089553833, -1.3113347291946411, -0.5160548686981201]}} \ No newline at end of file +{"1": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [0.9375802278518677, 0.01699276641011238, 0.0168936625123024, 0.016852224245667458, 0.01684313639998436, 0.0167868472635746, 0.01678694412112236, 0.016788223758339882, 0.01683913543820381, 0.016797984018921852, 0.01679254323244095, 0.01679065451025963, 0.01676851324737072, 0.016781216487288475, 0.016814494505524635, 0.016818752512335777, 0.016785727813839912, 0.01677747070789337, 0.016795936971902847, 0.016792094334959984, 0.017113056033849716, 0.01678374409675598, 0.016811968758702278, 0.01680857688188553, 0.01679401658475399, 0.016806943342089653, 0.017167072743177414, 0.016829216852784157, 0.016787424683570862, 0.016795679926872253], "latency": 1.4339574556797743, "logprobs": [-10.285516738891602, -3.7254064083099365, -2.8119518756866455, -1.2440053224563599, -0.2537177503108978, -1.7926565408706665, -2.373988628387451, -1.9992115497589111, -2.156676769256592, -6.0899152755737305, -0.814454197883606, -2.4737956523895264, -3.5019524097442627, -4.121853828430176, -1.9726070165634155, -1.8766462802886963, -2.2752578258514404, -7.124467849731445, -0.04110218212008476, -1.985706090927124, -5.001168727874756, -8.708745956420898, -9.837553024291992, -0.7836530804634094, -4.778284072875977, -0.8594533801078796, -2.295872211456299, -0.01927023194730282, -0.03589310497045517, -3.4844179153442383, -8.688593864440918, -1.2540490627288818, -6.682423114776611, -3.651825428009033, -3.790018081665039, -4.23261833190918, -2.183030128479004, -1.0943747758865356, -0.23566779494285583, -0.7870454788208008, -4.739112377166748, -9.07720947265625, -0.013387431390583515, -3.2162814140319824, -1.224939227104187, -3.9755403995513916, -0.749676525592804, -0.002204370917752385, -2.970001697540283, -10.52740478515625, -2.9543890953063965, -1.14937162399292, -4.980201721191406, -0.210438534617424, -0.06334561854600906, -1.3424071073532104, -2.2933273315429688, -4.389246940612793, -0.35113364458084106, -4.080296516418457, -0.36735761165618896, -0.1487821638584137, -2.7155423164367676, -10.591231346130371, -0.047830305993556976, -3.2783076763153076, -0.9070821404457092, -4.689446926116943, -0.2631016671657562, -2.5976767539978027, -0.8437033295631409, -1.703050136566162, -5.785486221313477, -16.94239616394043, -2.6509127616882324, -0.13801372051239014, -7.448275089263916, -1.05545175075531, -2.1010847091674805, -1.579776644706726, -0.2949126064777374, -5.616103649139404, -0.006375213619321585, -7.791621685028076, -2.7076175212860107, -3.008326768875122, -3.0655322074890137, -2.351105213165283, -0.4216800332069397, -1.4136691093444824, -2.2709290981292725, -0.6092997789382935, -1.3407301902770996, -1.9368633031845093, -1.6967312097549438, -0.7726529240608215, -0.5008315443992615, -1.2634307146072388, -1.4748260974884033, -0.9624079465866089, -0.39953532814979553, -0.4278743863105774, -0.0418093241751194, -1.2810207605361938, -2.2278497219085693, -2.7257916927337646, -0.8166881799697876, -0.40065163373947144, -2.907078742980957, -1.5525448322296143, -1.6864242553710938, -0.050371624529361725, -1.3529325723648071, -1.3700380325317383, -1.2650587558746338, -1.2854982614517212, -0.5030314326286316]}} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json index 16ce3cb46a0..1702fd48270 100644 --- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp1_pp1_ep1_16B_logitsmatch/golden_values_dev_dgx_h100.json @@ -1 +1 @@ -{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [2.545225143432617, 0.8161460161209106, 0.08185821026563644, 0.08545549213886261, 0.0806993618607521, 0.08108274638652802, 0.08104605227708817, 0.08051212877035141, 0.08013814687728882, 0.08076290786266327, 0.08076870441436768, 0.08029650896787643, 0.07956188917160034, 0.07908589392900467, 0.07883257418870926, 0.0789686068892479, 0.07868800312280655, 0.0788155198097229, 0.07876496016979218, 0.07869625836610794, 0.07890035212039948, 0.08047452569007874, 0.08083260804414749, 0.08091676980257034, 0.08101955056190491, 0.08092495799064636, 0.08079087734222412, 0.08215625584125519, 0.08282729238271713, 0.08078553527593613], "latency": 5.632539719343185, "logprobs": [-10.748703956604004, -3.6647238731384277, -2.829481363296509, -1.2498102188110352, -0.2529161274433136, -1.8659480810165405, -2.381495237350464, -1.9656293392181396, -2.1487326622009277, -6.079752445220947, -0.854481041431427, -2.4210457801818848, -3.5035147666931152, -4.105377674102783, -1.9736212491989136, -1.812272310256958, -2.2293882369995117, -7.158683776855469, -0.04035309702157974, -1.8912277221679688, -5.030168056488037, -8.83056640625, -9.887261390686035, -0.783703088760376, -4.775330066680908, -0.8503050208091736, -2.3185184001922607, -0.019133294001221657, -0.035927604883909225, -3.367396831512451, -8.677278518676758, -1.249742031097412, -6.646797180175781, -3.7622885704040527, -3.5719683170318604, -4.217543125152588, -2.1486034393310547, -1.0877041816711426, -0.21807751059532166, -0.779091477394104, -4.703271865844727, -9.254538536071777, -0.013438244350254536, -3.1536498069763184, -1.347169041633606, -3.951521873474121, -0.7524824142456055, -0.0020295039284974337, -2.977275848388672, -10.547475814819336, -3.2425384521484375, -1.1805994510650635, -4.853910446166992, -0.20160463452339172, -0.06345974653959274, -1.3051351308822632, -2.185082197189331, -4.38629150390625, -0.36718395352363586, -4.047024726867676, -0.35912153124809265, -0.14624275267124176, -2.715745687484741, -10.57785415649414, -0.057624127715826035, -3.278026819229126, -0.8596221804618835, -4.68856954574585, -0.2627037465572357, -2.6609294414520264, -0.8223639726638794, -1.599161148071289, -5.80098295211792, -16.973386764526367, -2.616450548171997, -0.13072170317173004, -7.462261199951172, -1.082613229751587, -2.122760534286499, -1.5544897317886353, -0.2857922911643982, -5.874238967895508, -0.006397482007741928, -7.670827865600586, -2.7104744911193848, -2.9125661849975586, -3.0234169960021973, -2.3567330837249756, -0.39910370111465454, -1.4856780767440796, -2.293515920639038, -0.6103871464729309, -1.3627440929412842, -1.9288791418075562, -1.683215618133545, -0.7717607021331787, -0.5037432312965393, -1.2485487461090088, -1.559265375137329, -1.0295413732528687, -0.4404847025871277, -0.46411699056625366, -0.04514020308852196, -1.3507211208343506, -2.1256327629089355, -2.687516927719116, -0.759257435798645, -0.41867509484291077, -2.854245185852051, -1.5189608335494995, -1.5710458755493164, -0.05560074374079704, -1.357395052909851, -1.3819431066513062, -1.2458440065383911, -1.2788543701171875, -0.5129148364067078]}} \ No newline at end of file +{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [2.4694507122039795, 0.8105818629264832, 0.07978953421115875, 0.08307728171348572, 0.0785929262638092, 0.07809542119503021, 0.07851853221654892, 0.07843484729528427, 0.07814009487628937, 0.07834912091493607, 0.07996083050966263, 0.07863132655620575, 0.07814034819602966, 0.07732026278972626, 0.07675567269325256, 0.07662268728017807, 0.07645461708307266, 0.07643615454435349, 0.07646707445383072, 0.07645142078399658, 0.07822445034980774, 0.0770794227719307, 0.07666249573230743, 0.07645964622497559, 0.07742467522621155, 0.07828329503536224, 0.0784420445561409, 0.07834690809249878, 0.07829263806343079, 0.07826755195856094], "latency": 5.4831181932240725, "logprobs": [-10.095212936401367, -3.663025379180908, -2.818272829055786, -1.2482454776763916, -0.2589055001735687, -1.7956947088241577, -2.3784303665161133, -1.9685165882110596, -2.1426568031311035, -6.07860803604126, -0.8911423087120056, -2.457338333129883, -3.5499165058135986, -4.096373558044434, -1.9708608388900757, -1.8492865562438965, -2.2776310443878174, -7.12592887878418, -0.04183332994580269, -1.9820369482040405, -5.0428290367126465, -8.705978393554688, -9.927094459533691, -0.8530583381652832, -4.768869400024414, -0.8547260761260986, -2.3348214626312256, -0.01930426061153412, -0.03659956902265549, -3.378457546234131, -8.80008602142334, -1.3498220443725586, -6.705995559692383, -3.6526565551757812, -3.7612364292144775, -4.295293807983398, -2.1751351356506348, -1.105019450187683, -0.23961491882801056, -0.7740461826324463, -4.707608699798584, -8.981962203979492, -0.01310709398239851, -3.1597490310668945, -1.3147504329681396, -4.0162248611450195, -0.7506906390190125, -0.0020567469764500856, -2.982682704925537, -10.58509349822998, -3.2262539863586426, -1.164378046989441, -4.873621940612793, -0.21488438546657562, -0.06360495090484619, -1.3561501502990723, -2.2166733741760254, -4.372846603393555, -0.3606990873813629, -4.01932430267334, -0.3661923408508301, -0.1414143443107605, -2.7147157192230225, -10.585433006286621, -0.05091376230120659, -3.3839571475982666, -0.8235527276992798, -4.721957206726074, -0.2624366581439972, -2.6822218894958496, -0.7976292967796326, -1.7078050374984741, -5.78978967666626, -17.028467178344727, -3.0127875804901123, -0.12382228672504425, -7.439476490020752, -1.1189169883728027, -2.107215404510498, -1.584132194519043, -0.2924787998199463, -5.607152938842773, -0.006540084723383188, -7.795315265655518, -2.7055139541625977, -3.128197193145752, -3.0823655128479004, -2.4160594940185547, -0.39977338910102844, -1.438467264175415, -2.29890775680542, -0.5784891247749329, -1.3163944482803345, -1.966688632965088, -1.6879653930664062, -0.7745707035064697, -0.5166807174682617, -1.2876402139663696, -1.4877150058746338, -1.0908961296081543, -0.4440205693244934, -0.47916895151138306, -0.04499730095267296, -1.2864679098129272, -2.157663345336914, -2.706785202026367, -0.7605403661727905, -0.41501256823539734, -2.88466477394104, -1.547515630722046, -1.5868767499923706, -0.05144042894244194, -1.3736324310302734, -1.3632761240005493, -1.2712645530700684, -1.2568752765655518, -0.5050450563430786]}} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json index 9374ead29ab..07159e4d6cd 100644 --- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json @@ -1 +1 @@ -{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [13.711557388305664, 0.9041259288787842, 0.09681683033704758, 0.09153660386800766, 0.09111235290765762, 0.09335695952177048, 0.09111097455024719, 0.09149472415447235, 0.09076278656721115, 0.09109959006309509, 0.09105174243450165, 0.09161145985126495, 0.09026294946670532, 0.09116349369287491, 0.09077664464712143, 0.09046704322099686, 0.09124940633773804, 0.09130454063415527, 0.09034591913223267, 0.09066786617040634, 0.0906708836555481, 0.09116076678037643, 0.09075567871332169, 0.09169074892997742, 0.0911671370267868, 0.09112399816513062, 0.09118761122226715, 0.09077664464712143, 0.09082793444395065, 0.09155046194791794], "latency": 17.191568877082318, "logprobs": [-10.737512588500977, -3.6907906532287598, -2.8354406356811523, -1.241209626197815, -0.23157396912574768, -1.8544931411743164, -2.3058578968048096, -1.965078592300415, -2.131650447845459, -6.1445770263671875, -0.8567591905593872, -2.4118287563323975, -3.49544095993042, -4.07368278503418, -1.9927215576171875, -1.8116189241409302, -2.220911979675293, -7.154321193695068, -0.041270580142736435, -1.897834300994873, -5.07651424407959, -8.796056747436523, -9.88559341430664, -0.7177769541740417, -4.775661468505859, -0.8519912362098694, -2.3280630111694336, -0.018436847254633904, -0.038715627044439316, -3.4831454753875732, -8.691339492797852, -1.2537559270858765, -6.647421836853027, -3.785327911376953, -3.7754249572753906, -4.1729302406311035, -2.268207550048828, -1.0762473344802856, -0.22336173057556152, -0.7773433923721313, -4.659046173095703, -9.07835865020752, -0.01364840567111969, -3.1569409370422363, -1.3136285543441772, -3.999577760696411, -0.8146029710769653, -0.0020667400676757097, -2.9257936477661133, -10.559369087219238, -3.301023483276367, -1.1468515396118164, -4.866663455963135, -0.20965954661369324, -0.06276518106460571, -1.3678232431411743, -2.2146267890930176, -4.369752883911133, -0.35328271985054016, -4.076470851898193, -0.39479735493659973, -0.14124885201454163, -2.7151336669921875, -10.645881652832031, -0.05100790411233902, -3.277879238128662, -0.859420657157898, -4.694356918334961, -0.26331964135169983, -2.601013660430908, -0.8340632319450378, -1.5944981575012207, -5.802148818969727, -16.992801666259766, -2.9630136489868164, -0.12174151837825775, -7.421735763549805, -1.090034008026123, -2.115244150161743, -1.592454195022583, -0.297377347946167, -5.607227325439453, -0.006619194056838751, -7.817281723022461, -2.701261043548584, -2.988292932510376, -2.9706215858459473, -2.4563350677490234, -0.3993130624294281, -1.5173310041427612, -2.269473075866699, -0.6111201047897339, -1.313757300376892, -1.9383023977279663, -1.6797527074813843, -0.7714957594871521, -0.49417543411254883, -1.2875804901123047, -1.5885818004608154, -1.018824577331543, -0.4005858302116394, -0.46394026279449463, -0.04470847547054291, -1.2914193868637085, -2.2410547733306885, -2.740159273147583, -0.7651359438896179, -0.4316181242465973, -2.7719383239746094, -1.5367236137390137, -1.652032732963562, -0.051836322993040085, -1.3689777851104736, -1.342658519744873, -1.26646089553833, -1.3113347291946411, -0.5160548686981201]}} \ No newline at end of file +{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [13.663225173950195, 0.9431529641151428, 0.09179968386888504, 0.08776150643825531, 0.08934815973043442, 0.08766365051269531, 0.0875975638628006, 0.08973637968301773, 0.08724931627511978, 0.08813049644231796, 0.08782428503036499, 0.08671878278255463, 0.08726608008146286, 0.08730233460664749, 0.08764249086380005, 0.08764160424470901, 0.08731506764888763, 0.08679869025945663, 0.08846284449100494, 0.08785020560026169, 0.08797827363014221, 0.08753775805234909, 0.08817724883556366, 0.08719398826360703, 0.08734601736068726, 0.08738765120506287, 0.0870174691081047, 0.08738476783037186, 0.08912406116724014, 0.08691645413637161], "latency": 17.085081157274544, "logprobs": [-10.285516738891602, -3.7254064083099365, -2.8119518756866455, -1.2440053224563599, -0.2537177503108978, -1.7926565408706665, -2.373988628387451, -1.9992115497589111, -2.156676769256592, -6.0899152755737305, -0.814454197883606, -2.4737956523895264, -3.5019524097442627, -4.121853828430176, -1.9726070165634155, -1.8766462802886963, -2.2752578258514404, -7.124467849731445, -0.04110218212008476, -1.985706090927124, -5.001168727874756, -8.708745956420898, -9.837553024291992, -0.7836530804634094, -4.778284072875977, -0.8594533801078796, -2.295872211456299, -0.01927023194730282, -0.03589310497045517, -3.4844179153442383, -8.688593864440918, -1.2540490627288818, -6.682423114776611, -3.651825428009033, -3.790018081665039, -4.23261833190918, -2.183030128479004, -1.0943747758865356, -0.23566779494285583, -0.7870454788208008, -4.739112377166748, -9.07720947265625, -0.013387431390583515, -3.2162814140319824, -1.224939227104187, -3.9755403995513916, -0.749676525592804, -0.002204370917752385, -2.970001697540283, -10.52740478515625, -2.9543890953063965, -1.14937162399292, -4.980201721191406, -0.210438534617424, -0.06334561854600906, -1.3424071073532104, -2.2933273315429688, -4.389246940612793, -0.35113364458084106, -4.080296516418457, -0.36735761165618896, -0.1487821638584137, -2.7155423164367676, -10.591231346130371, -0.047830305993556976, -3.2783076763153076, -0.9070821404457092, -4.689446926116943, -0.2631016671657562, -2.5976767539978027, -0.8437033295631409, -1.703050136566162, -5.785486221313477, -16.94239616394043, -2.6509127616882324, -0.13801372051239014, -7.448275089263916, -1.05545175075531, -2.1010847091674805, -1.579776644706726, -0.2949126064777374, -5.616103649139404, -0.006375213619321585, -7.791621685028076, -2.7076175212860107, -3.008326768875122, -3.0655322074890137, -2.351105213165283, -0.4216800332069397, -1.4136691093444824, -2.2709290981292725, -0.6092997789382935, -1.3407301902770996, -1.9368633031845093, -1.6967312097549438, -0.7726529240608215, -0.5008315443992615, -1.2634307146072388, -1.4748260974884033, -0.9624079465866089, -0.39953532814979553, -0.4278743863105774, -0.0418093241751194, -1.2810207605361938, -2.2278497219085693, -2.7257916927337646, -0.8166881799697876, -0.40065163373947144, -2.907078742980957, -1.5525448322296143, -1.6864242553710938, -0.050371624529361725, -1.3529325723648071, -1.3700380325317383, -1.2650587558746338, -1.2854982614517212, -0.5030314326286316]}} \ No newline at end of file From ebfa13852f3d6bdb61c8571d68ccab13cb239089 Mon Sep 17 00:00:00 2001 From: "Dennis(Zhenhuan) Liu" Date: Tue, 14 Apr 2026 17:28:50 +0800 Subject: [PATCH 04/72] [MoE] Improvement of shared expert overlap, support shared expert overlap for FlexDispatcher (#2207) Co-authored-by: Claude Opus 4.6 (1M context) --- megatron/core/tensor_parallel/mappings.py | 45 +++-- .../core/transformer/moe/shared_experts.py | 106 +++++++++-- .../core/transformer/moe/token_dispatcher.py | 73 +++++-- .../core/transformer/transformer_config.py | 5 +- .../transformer/moe/test_shared_experts.py | 179 ++++++++---------- 5 files changed, 264 insertions(+), 144 deletions(-) diff --git a/megatron/core/tensor_parallel/mappings.py b/megatron/core/tensor_parallel/mappings.py index 9ff69c9dc31..121f7c45a8e 100644 --- a/megatron/core/tensor_parallel/mappings.py +++ b/megatron/core/tensor_parallel/mappings.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import torch @@ -419,11 +419,12 @@ def backward(ctx, grad_output): class _AllToAll(torch.autograd.Function): @staticmethod - def forward(ctx, group, input, output_split_sizes, input_split_sizes): + def forward(ctx, group, input, output_split_sizes, input_split_sizes, use_nccl_stream=False): """Forward function.""" ctx.group = group ctx.output_split_sizes = output_split_sizes ctx.input_split_sizes = input_split_sizes + ctx.use_nccl_stream = use_nccl_stream world_size = group.size() # Bypass the function if we are using only 1 GPU. @@ -441,13 +442,24 @@ def forward(ctx, group, input, output_split_sizes, input_split_sizes): dtype=input.dtype, device=torch.cuda.current_device(), ) - torch.distributed.all_to_all_single( - output, - input, - output_split_sizes=output_split_sizes, - input_split_sizes=input_split_sizes, - group=group, - ) + if use_nccl_stream: + handle = torch.distributed.all_to_all_single( + output, + input, + output_split_sizes=output_split_sizes, + input_split_sizes=input_split_sizes, + group=group, + async_op=True, + ) + handle.wait() + else: + torch.distributed.all_to_all_single( + output, + input, + output_split_sizes=output_split_sizes, + input_split_sizes=input_split_sizes, + group=group, + ) return output @staticmethod @@ -455,7 +467,14 @@ def backward(ctx, *grad_output): """Backward function.""" return ( None, - _AllToAll.apply(ctx.group, *grad_output, ctx.input_split_sizes, ctx.output_split_sizes), + _AllToAll.apply( + ctx.group, + *grad_output, + ctx.input_split_sizes, + ctx.output_split_sizes, + ctx.use_nccl_stream, + ), + None, None, None, ) @@ -532,10 +551,12 @@ def reduce_scatter_last_dim_to_tensor_parallel_region(input_, group=None): return _ReduceScatterToTensorParallelRegion.apply(input_, group) -def all_to_all(group, input_, output_split_sizes_=None, input_split_sizes=None): +def all_to_all( + group, input_, output_split_sizes_=None, input_split_sizes=None, use_nccl_stream=False +): """Wrapper for autograd function""" assert group is not None, "group should not be None" - return _AllToAll.apply(group, input_, output_split_sizes_, input_split_sizes) + return _AllToAll.apply(group, input_, output_split_sizes_, input_split_sizes, use_nccl_stream) def all_to_all_sp2hp(input_, group=None): diff --git a/megatron/core/transformer/moe/shared_experts.py b/megatron/core/transformer/moe/shared_experts.py index ed754748bb3..61ea47955b8 100644 --- a/megatron/core/transformer/moe/shared_experts.py +++ b/megatron/core/transformer/moe/shared_experts.py @@ -1,7 +1,9 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import warnings -from copy import copy +from copy import deepcopy +from enum import Enum +from functools import wraps from typing import Optional import torch @@ -34,6 +36,63 @@ TELinear, set_save_original_input = None, None +class SharedExpertState(Enum): + """State machine states for SharedExpertMLP overlapped forward pass.""" + + IDLE = 0 + PRE_FORWARD_COMM_DONE = 1 + FC1_FORWARD_DONE = 2 + FC2_FORWARD_DONE = 3 + POST_FORWARD_COMM_DONE = 4 + + +def overlap_state_check(required_state: "SharedExpertState", next_state: "SharedExpertState"): + """ + Decorator to validate overlap state and cached variables before method execution, + and update state after method execution. + + Args: + required_state: The expected SharedExpertState before this method runs. + next_state: The SharedExpertState to transition to after method execution. + """ + + def decorator(method): + @wraps(method) + def wrapper(self, *args, **kwargs): + # Check overlap is enabled + assert ( + self.config.moe_shared_expert_overlap + ), f"{method.__name__} requires --moe-shared-expert-overlap to be set" + # Check state machine + assert self._overlap_state == required_state, ( + f"{method.__name__} must be called from {required_state.name} state, " + f"but current state is {self._overlap_state.name}" + ) + # Execute method + result = method(self, *args, **kwargs) + # Update state after method execution + self._overlap_state = next_state + return result + + return wrapper + + return decorator + + +class _BackwardStreamWait(torch.autograd.Function): + @staticmethod + def forward(ctx, input, stream): + """forward""" + ctx.stream = stream + return input + + @staticmethod + def backward(ctx, grad_output): + """backward with stream wait""" + ctx.stream.wait_stream(torch.cuda.current_stream()) + return grad_output, None + + class SharedExpertMLP(MLP): """ MLP layer for Shared Experts. @@ -50,7 +109,7 @@ def __init__( gate: bool, pg_collection: Optional[ProcessGroupCollection] = None, ): - config = copy(config) + config = deepcopy(config) assert config.add_bias_linear == False, "bias is not supported in the shared experts, " "please set '--disable-bias-linear' instead." @@ -115,6 +174,9 @@ def __init__( self.cached_output = None self.gate_score = None + # State machine to ensure correct calling order of overlapped forward methods + self._overlap_state = SharedExpertState.IDLE + if self.__class__.stream is None: self.__class__.stream = torch.cuda.Stream() self.stream = self.__class__.stream @@ -148,15 +210,19 @@ def sharded_state_dict( sharded_state_dict.update(sub_sd) return sharded_state_dict - def pre_forward_comm(self, input): + def wait_current_stream(self): + """Wait for the current stream to complete.""" + self.stream.wait_stream(torch.cuda.current_stream()) + + @overlap_state_check(SharedExpertState.IDLE, SharedExpertState.PRE_FORWARD_COMM_DONE) + def pre_forward_comm(self, input, wait_current_stream=True): """ All Gather for SP before forward. This function is used to overlap shared experts with the dispatcher. It is only useful when --moe-shared-expert-overlap is set and may be changed. """ - assert self.config.moe_shared_expert_overlap - assert self.cached_output is None - self.stream.wait_stream(torch.cuda.current_stream()) + if wait_current_stream: + self.wait_current_stream() with torch.cuda.stream(self.stream): if self.use_shared_expert_gate: logits = torch.nn.functional.linear(input, self.gate_weight) @@ -169,16 +235,15 @@ def pre_forward_comm(self, input): self.cached_fc1_input = copy_to_tensor_model_parallel_region(input) set_tensor_grad_fn_sequence_sr(self.cached_fc1_input, torch.iinfo(torch.int).max) + @overlap_state_check( + SharedExpertState.PRE_FORWARD_COMM_DONE, SharedExpertState.FC1_FORWARD_DONE + ) def linear_fc1_forward_and_act(self, overlapped_comm_output=None): """ Do Linear FC1 and activation function forward. This function is used to overlap shared experts with the dispatcher. It is only useful when --moe-shared-expert-overlap is set and may be changed. """ - assert self.config.moe_shared_expert_overlap - assert self.cached_fc1_input is not None - if overlapped_comm_output is not None: - set_tensor_grad_fn_sequence_sr(overlapped_comm_output, torch.iinfo(torch.int).max) with torch.cuda.stream(self.stream): # [s, b, 4 * h/p] intermediate_parallel, bias_parallel = apply_module(self.linear_fc1)( @@ -221,15 +286,22 @@ def glu(x): intermediate_parallel = self.activation_func(intermediate_parallel) self.cached_fc2_input = intermediate_parallel + # Tensor sequence number is used to control the backward order. + # Decrease the sequence number of the expert output to make the comm launched first + # in the backward order. + if overlapped_comm_output is not None and overlapped_comm_output.grad_fn is not None: + target_sequence_nr = overlapped_comm_output.grad_fn._sequence_nr() - 1 + set_tensor_grad_fn_sequence_sr(intermediate_parallel, target_sequence_nr) + # Make sure the shared expert fc1 backward is launched after the routed fc1 backward + self.cached_fc2_input = _BackwardStreamWait.apply(intermediate_parallel, self.stream) + @overlap_state_check(SharedExpertState.FC1_FORWARD_DONE, SharedExpertState.FC2_FORWARD_DONE) def linear_fc2_forward(self, overlapped_comm_output=None): """ Do Linear FC2 forward. This function is used to overlap shared experts with the dispatcher. It is only useful when --moe-shared-expert-overlap is set and may be changed. """ - assert self.config.moe_shared_expert_overlap - assert self.cached_fc2_input is not None if overlapped_comm_output is not None: set_tensor_grad_fn_sequence_sr(overlapped_comm_output, torch.iinfo(torch.int).max) with torch.cuda.stream(self.stream): @@ -237,14 +309,15 @@ def linear_fc2_forward(self, overlapped_comm_output=None): self.cached_fc2_output, _ = apply_module(self.linear_fc2)(self.cached_fc2_input) self.cached_fc2_input = None + @overlap_state_check( + SharedExpertState.FC2_FORWARD_DONE, SharedExpertState.POST_FORWARD_COMM_DONE + ) def post_forward_comm(self): """ Reduce scatter for SP after forward. This function is used to overlap shared experts with the dispatcher. It is only useful when --moe-shared-expert-overlap is set and may be changed. """ - assert self.config.moe_shared_expert_overlap - assert self.cached_fc2_output is not None with torch.cuda.stream(self.stream): if self.config.sequence_parallel: self.cached_output = reduce_scatter_to_sequence_parallel_region( @@ -257,14 +330,13 @@ def post_forward_comm(self): self.cached_fc2_output = None set_tensor_grad_fn_sequence_sr(self.cached_output, torch.iinfo(torch.int).max) + @overlap_state_check(SharedExpertState.POST_FORWARD_COMM_DONE, SharedExpertState.IDLE) def get_output(self): """ Gets the module forward output. This function is used to overlap shared experts with the dispatcher. It is only useful when --moe-shared-expert-overlap is set and may be changed. """ - assert self.config.moe_shared_expert_overlap - assert self.cached_output is not None with torch.cuda.stream(self.stream): if self.use_shared_expert_gate: assert self.gate_score is not None diff --git a/megatron/core/transformer/moe/token_dispatcher.py b/megatron/core/transformer/moe/token_dispatcher.py index 2466ffc0825..15fc8b984b3 100644 --- a/megatron/core/transformer/moe/token_dispatcher.py +++ b/megatron/core/transformer/moe/token_dispatcher.py @@ -67,6 +67,8 @@ def __init__( """ self.config = config self.shared_experts: Optional[SharedExpertMLP] = None + # Whether to use NCCL stream for A2A communication, otherwise default stream is used. + self.use_nccl_stream = False # Will be set to True when shared_experts is set. self.ep_group = pg_collection.ep # use pg_collection.expt_tp_group as tensor parallel group in this module. @@ -203,6 +205,7 @@ def set_shared_experts(self, shared_experts): """Set shared expert to the dispatcher.""" assert self.config.moe_shared_expert_overlap self.shared_experts = shared_experts + self.use_nccl_stream = True class MoEAllGatherTokenDispatcher(MoETokenDispatcher): @@ -663,16 +666,33 @@ def token_dispatch(self, permutated_local_input_tokens, permuted_probs): Returns: A tuple of tokens and probabilities after All-to-All. """ - + # Make sure the shared experts fc1 is overlapped with dispatch A2A + # when CUDA_DEVICE_MAX_CONNECTIONS>1. + if self.shared_experts is not None: + self.shared_experts.wait_current_stream() # Perform expert parallel AlltoAll communication self.tokens_per_expert = self._maybe_dtoh_and_synchronize( "before_ep_alltoall", self.tokens_per_expert ) global_input_tokens = all_to_all( - self.ep_group, permutated_local_input_tokens, self.output_splits, self.input_splits + self.ep_group, + permutated_local_input_tokens, + self.output_splits, + self.input_splits, + use_nccl_stream=self.use_nccl_stream, ) + # Move the shared experts fc1 right after the tokens A2A, to prevent the probs A2A + # block the launch of fc1 GEMM when CUDA_DEVICE_MAX_CONNECTIONS=1. + # Forward launch order: tokens A2A -> shared experts fc1 -> probs A2A + # Backward launch order: probs A2A -> tokens A2A -> shared experts fc1 + if self.shared_experts is not None: + self.shared_experts.linear_fc1_forward_and_act(global_input_tokens) global_probs = all_to_all( - self.ep_group, permuted_probs, self.output_splits, self.input_splits + self.ep_group, + permuted_probs, + self.output_splits, + self.input_splits, + use_nccl_stream=self.use_nccl_stream, ) return global_input_tokens, global_probs @@ -690,9 +710,6 @@ def dispatch_postprocess(self, global_input_tokens, global_probs): Returns: A tuple of processed tokens, token counts per expert, and processed probabilities. """ - if self.shared_experts is not None: - self.shared_experts.linear_fc1_forward_and_act(global_input_tokens) - if self.tp_size > 1: if self.output_splits_tp is None: output_split_sizes = None @@ -810,11 +827,22 @@ def token_combine( Returns: Tokens after the All-to-All communication for combining. """ + # Make sure the shared experts fc2 is not overlapped with routed experts fc1 + # when CUDA_DEVICE_MAX_CONNECTIONS>1. + if self.shared_experts is not None: + self.shared_experts.wait_current_stream() # Perform expert parallel AlltoAll communication # hidden_states: [SEQL, H] -> [SEQL, H/TP] permutated_local_input_tokens = all_to_all( - self.ep_group, hidden_states, self.input_splits, self.output_splits + self.ep_group, + hidden_states, + self.input_splits, + self.output_splits, + use_nccl_stream=self.use_nccl_stream, ) + if self.shared_experts is not None: + self.shared_experts.linear_fc2_forward(permutated_local_input_tokens) + self.shared_experts.post_forward_comm() return permutated_local_input_tokens def combine_postprocess(self, permutated_local_input_tokens): @@ -830,9 +858,6 @@ def combine_postprocess(self, permutated_local_input_tokens): Returns: The final MoE layer output reshaped to its original dimensions. """ - if self.shared_experts is not None: - self.shared_experts.linear_fc2_forward(permutated_local_input_tokens) - self.shared_experts.post_forward_comm() # Unpermutation 1: AlltoAll output to output output = unpermute( @@ -1251,6 +1276,9 @@ def combine( ) # Release the handle after combine operation self.handle = None + # Manually release the metadata to avoid memory leak. + self.dispatched_indices = None + self.dispatched_probs = None return hidden_states def _pad_routing_map( @@ -1382,11 +1410,6 @@ def __init__( "--moe-flex-dispatcher-backend=hybridep" ) - def set_shared_experts(self, shared_experts): - raise NotImplementedError( - "Shared expert overlap is not supported in Flex Token Dispatcher." - ) - def _initialize_metadata(self, routing_map: torch.Tensor, probs: torch.Tensor) -> torch.Tensor: """ Initialize the routing map and probs to a unified format covering the TPxEP group. @@ -1465,10 +1488,16 @@ def token_dispatch( Returns: A tuple of dispatched tokens and probabilities. """ - return ( - self._comm_manager.dispatch(hidden_states, async_finish, allocate_on_comm_stream), - self._comm_manager.dispatched_probs, + if self.shared_experts is not None: + self.shared_experts.wait_current_stream() + dispatched_hidden_states = self._comm_manager.dispatch( + hidden_states, async_finish, allocate_on_comm_stream ) + if self.shared_experts is not None: + self.shared_experts.pre_forward_comm(hidden_states, wait_current_stream=False) + self.shared_experts.linear_fc1_forward_and_act(dispatched_hidden_states) + + return dispatched_hidden_states, self._comm_manager.dispatched_probs def dispatch_postprocess(self, hidden_states: torch.Tensor, probs: torch.Tensor): """Converts dispatched tokens to a per-expert format for expert processing. @@ -1516,6 +1545,10 @@ def token_combine( Returns: Combined tokens after fused un-permutation and communication. """ + # Make sure the shared experts fc2 is not overlapped with routed experts GEMM + # when CUDA_DEVICE_MAX_CONNECTIONS>1. + if self.shared_experts is not None: + self.shared_experts.wait_current_stream() return self._comm_manager.combine(hidden_states, async_finish, allocate_on_comm_stream) def combine_postprocess(self, hidden_states: torch.Tensor): @@ -1531,4 +1564,8 @@ def combine_postprocess(self, hidden_states: torch.Tensor): Returns: The final MoE layer output reshaped to its original dimensions. """ + if self.shared_experts is not None: + self.shared_experts.linear_fc2_forward(hidden_states) + self.shared_experts.post_forward_comm() + hidden_states += self.shared_experts.get_output() return hidden_states.view(self.hidden_shape) diff --git a/megatron/core/transformer/transformer_config.py b/megatron/core/transformer/transformer_config.py index e198d07161c..40c1a745493 100644 --- a/megatron/core/transformer/transformer_config.py +++ b/megatron/core/transformer/transformer_config.py @@ -1267,10 +1267,11 @@ def __post_init__(self): f"but got {self.moe_shared_expert_intermediate_size}" ) if self.moe_shared_expert_overlap and self.moe_token_dispatcher_type not in [ - "alltoall" + "alltoall", + "flex", ]: raise ValueError( - f"moe_shared_expert_overlap only works with alltoall token dispatcher." + f"moe_shared_expert_overlap only works with alltoall or flex token dispatcher." ) if isinstance(self.moe_router_load_balancing_type, list): diff --git a/tests/unit_tests/transformer/moe/test_shared_experts.py b/tests/unit_tests/transformer/moe/test_shared_experts.py index d99dc1a0b05..a96c6450af4 100644 --- a/tests/unit_tests/transformer/moe/test_shared_experts.py +++ b/tests/unit_tests/transformer/moe/test_shared_experts.py @@ -1,9 +1,12 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +import dataclasses import pytest import torch from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_local_submodules +from megatron.core.parallel_state import get_tensor_model_parallel_world_size from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer.moe.moe_layer import MoELayer from megatron.core.transformer.transformer_config import TransformerConfig @@ -11,117 +14,103 @@ class TestSharedExperts: - def setup_method(self, method): - pass - - def teardown_method(self, method): - Utils.destroy_model_parallel() - - @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - @pytest.mark.internal - @pytest.mark.parametrize("shared_expert_gate", [False, True]) - def test_gpu_forward(self, shared_expert_gate): - Utils.initialize_model_parallel(1, 1) - model_parallel_cuda_manual_seed(123) - print("done intializing") - num_moe_experts = 2 - transformer_config = TransformerConfig( + self.config = TransformerConfig( num_layers=1, - hidden_size=12, + hidden_size=32, num_attention_heads=4, - num_moe_experts=num_moe_experts, + num_moe_experts=16, moe_shared_expert_intermediate_size=32, + moe_shared_expert_overlap=False, + moe_token_dispatcher_type="alltoall", use_cpu_initialization=True, activation_func=torch.nn.functional.silu, gated_linear_unit=True, bias_activation_fusion=True, - moe_router_load_balancing_type="sinkhorn", - moe_router_topk=1, + moe_router_load_balancing_type="aux_loss", + moe_router_topk=4, add_bias_linear=False, - moe_shared_expert_gate=shared_expert_gate, ) + + def get_moe_layer(self, **kargs) -> MoELayer: submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + num_experts=self.config.num_moe_experts, moe_grouped_gemm=False ) - self.moe_layer = MoELayer(transformer_config, submodules.mlp.submodules) - - assert isinstance(self.moe_layer, MoELayer) - - num_weights = sum([p.numel() for p in self.moe_layer.parameters()]) - if shared_expert_gate: - assert num_weights == 3480 + 1152 + 12 # 12 is the weight of the gate - else: - assert num_weights == 3480 + 1152 - assert self.moe_layer.shared_experts is not None - assert self.moe_layer.shared_experts.stream is None - assert self.moe_layer.token_dispatcher.shared_experts is None - - moe_layer = self.moe_layer + new_config = dataclasses.replace(self.config, **kargs) + if get_tensor_model_parallel_world_size() > 1: + new_config.sequence_parallel = True + moe_layer = MoELayer(new_config, submodules.mlp.submodules) moe_layer.cuda() - # [sequence length, batch size, hidden size] - hidden_states = torch.ones((32, 2, moe_layer.config.hidden_size)) - hidden_states = hidden_states.cuda() - output, _ = moe_layer(hidden_states) - assert output.shape[0] == 32 - assert output.shape[1] == 2 - assert output.shape[2] == moe_layer.config.hidden_size - assert output.dtype == torch.float32 - assert output.device.type == 'cuda' - - -class TestSharedExpertsOverlap: - - def setup_method(self, method): - pass + return moe_layer def teardown_method(self, method): Utils.destroy_model_parallel() - @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") @pytest.mark.internal - def test_gpu_forward(self): - Utils.initialize_model_parallel(1, 1) - model_parallel_cuda_manual_seed(123) - print("done intializing") - num_moe_experts = 2 - transformer_config = TransformerConfig( - num_layers=1, - hidden_size=12, - num_attention_heads=4, - num_moe_experts=num_moe_experts, - moe_shared_expert_intermediate_size=32, - moe_shared_expert_overlap=True, - moe_token_dispatcher_type="alltoall", - use_cpu_initialization=True, - activation_func=torch.nn.functional.silu, - gated_linear_unit=True, - bias_activation_fusion=True, - moe_router_load_balancing_type="sinkhorn", - moe_router_topk=1, - add_bias_linear=False, - ) - submodules = get_gpt_layer_local_submodules( - num_experts=num_moe_experts, moe_grouped_gemm=False + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + @pytest.mark.parametrize("dispatcher_type", ["alltoall"]) + @pytest.mark.parametrize("tp_size, ep_size", [[1, 1], [4, 1], [1, 4], [2, 4]]) + def test_shared_expert_forward_backward(self, dispatcher_type: str, tp_size, ep_size): + """ + Tests that the MoELayer with and without shared expert overlap produce + identical outputs and gradients. + """ + Utils.initialize_model_parallel( + tensor_model_parallel_size=tp_size, expert_model_parallel_size=ep_size ) - self.moe_layer = MoELayer(transformer_config, submodules.mlp.submodules) - - assert isinstance(self.moe_layer, MoELayer) - - num_weights = sum([p.numel() for p in self.moe_layer.parameters()]) - assert num_weights == 3480 + 1152 - assert self.moe_layer.shared_experts is not None - assert self.moe_layer.shared_experts.stream is not None - assert self.moe_layer.token_dispatcher.shared_experts is not None + # Create MoE layer with shared expert overlap enabled. + model_parallel_cuda_manual_seed(123) + moe_layer_overlap = self.get_moe_layer( + moe_shared_expert_overlap=True, moe_token_dispatcher_type=dispatcher_type + ).to(dtype=torch.bfloat16) - moe_layer = self.moe_layer - moe_layer.cuda() - # [sequence length, batch size, hidden size] - hidden_states = torch.ones((32, 2, moe_layer.config.hidden_size)) - hidden_states = hidden_states.cuda() - output, _ = moe_layer(hidden_states) - assert output.shape[0] == 32 - assert output.shape[1] == 2 - assert output.shape[2] == moe_layer.config.hidden_size - assert output.dtype == torch.float32 - assert output.device.type == 'cuda' + # Create MoE layer with shared expert overlap disabled. + model_parallel_cuda_manual_seed(123) + moe_layer_no_overlap = self.get_moe_layer( + moe_shared_expert_overlap=False, moe_token_dispatcher_type=dispatcher_type + ).to(dtype=torch.bfloat16) + moe_layer_no_overlap.load_state_dict(moe_layer_overlap.state_dict()) + + # Sanity check that the weights are identical. + for p_overlap, p_no_overlap in zip( + moe_layer_overlap.parameters(), moe_layer_no_overlap.parameters() + ): + assert torch.equal(p_overlap, p_no_overlap) + + # Verify attributes of the MoE layers. + num_weights_overlap = sum([p.numel() for p in moe_layer_overlap.parameters()]) + num_weights_no_overlap = sum([p.numel() for p in moe_layer_no_overlap.parameters()]) + assert num_weights_overlap == num_weights_no_overlap + + assert moe_layer_overlap.shared_experts is not None + assert moe_layer_overlap.shared_experts.stream is not None + assert moe_layer_overlap.token_dispatcher.shared_experts is not None + + assert moe_layer_no_overlap.shared_experts is not None + assert moe_layer_no_overlap.token_dispatcher.shared_experts is None + + # Create a dummy input tensor. + hidden_states = torch.randn( + (32, 2, self.config.hidden_size), + requires_grad=True, + device="cuda", + dtype=torch.bfloat16, + ) + hidden_states_no_overlap = hidden_states.clone().detach().requires_grad_(True) + + # Forward pass. + output_overlap, _ = moe_layer_overlap(hidden_states) + output_no_overlap, _ = moe_layer_no_overlap(hidden_states_no_overlap) + torch.testing.assert_close(output_overlap, output_no_overlap) + + # Backward pass. + output_overlap.mean().backward() + output_no_overlap.mean().backward() + + # Check gradients. + for p_overlap, p_no_overlap in zip( + moe_layer_overlap.parameters(), moe_layer_no_overlap.parameters() + ): + assert torch.allclose( + p_overlap.grad, p_no_overlap.grad + ), f"max diff: {torch.max(torch.abs(p_overlap.grad - p_no_overlap.grad))}" From 123645bb7812525515d6a70b22da0f6e184f0f0a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 14 Apr 2026 15:10:42 +0200 Subject: [PATCH 05/72] build: bump DeepEP to 34152ae (#4228) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- docker/Dockerfile.ci.dev | 2 +- tests/unit_tests/conftest.py | 2 +- tests/unit_tests/test_utilities.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docker/Dockerfile.ci.dev b/docker/Dockerfile.ci.dev index 7a8b69c1297..5e3d7419b3a 100644 --- a/docker/Dockerfile.ci.dev +++ b/docker/Dockerfile.ci.dev @@ -71,7 +71,7 @@ RUN bash -ex <<"EOF" git clone --branch hybrid-ep https://github.com/deepseek-ai/DeepEP.git pushd DeepEP - git checkout eb9cee7de5a24193bf09500668d3a619d3d3f3fb + git checkout 34152ae28f80bcc3ee38d7a12cb2ad87cfd4ea72 patch -p1 < /workspace/deepep.patch popd TORCH_CUDA_ARCH_LIST="9.0 10.0 12.0" uv pip install --no-build-isolation -v DeepEP/. diff --git a/tests/unit_tests/conftest.py b/tests/unit_tests/conftest.py index e24494b6e49..10aec38b15c 100644 --- a/tests/unit_tests/conftest.py +++ b/tests/unit_tests/conftest.py @@ -44,7 +44,7 @@ def cleanup(): yield if torch.distributed.is_initialized(): try: - torch.distributed.barrier(timeout=timedelta(seconds=30)) + torch.distributed.barrier(timeout=timedelta(seconds=300)) except Exception: return torch.distributed.destroy_process_group() diff --git a/tests/unit_tests/test_utilities.py b/tests/unit_tests/test_utilities.py index 9b56e75d078..f8fad3325f5 100644 --- a/tests/unit_tests/test_utilities.py +++ b/tests/unit_tests/test_utilities.py @@ -96,7 +96,7 @@ def destroy_model_parallel(): # Flush pending CUDA work before the barrier so slow ranks don't # time out while fast ranks tear down process groups. torch.cuda.synchronize() - torch.distributed.barrier(timeout=timedelta(seconds=30)) + torch.distributed.barrier(timeout=timedelta(seconds=300)) except Exception: Utils.inited = False return From 3d7a70127f5670c917c3fdf936f47b590c1ca145 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 14 Apr 2026 17:27:21 +0200 Subject: [PATCH 06/72] ci: mark test_fused_indexer_loss_gradient_tp_consistency as flaky_in_dev (#4299) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .../experimental_attention_variant/test_attention_variant_dsa.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py b/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py index 96253a4ca10..45470d4dd6c 100644 --- a/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py +++ b/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py @@ -423,6 +423,7 @@ def test_fused_indexer_loss_gradient_matches_autograd(self, seqlen_and_topk, spa class TestFusedDSAIndexerLossGradientTP: """Test FusedDSAIndexerLoss gradient consistency across different TP sizes.""" + @pytest.mark.flaky_in_dev @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_fused_indexer_loss_gradient_tp_consistency( self, tensor_model_parallel_size, sparse_loss From 4cef23ca73ddea1067b048f075de0e499ac8175b Mon Sep 17 00:00:00 2001 From: Cory Ye <44509866+cspades@users.noreply.github.com> Date: Tue, 14 Apr 2026 08:30:14 -0700 Subject: [PATCH 07/72] Fix typo in PR4133. (#4277) Signed-off-by: Cory Ye --- megatron/core/optimizer/optimizer.py | 2 +- .../megatron_fsdp/test_mcore_fully_sharded_data_parallel.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/megatron/core/optimizer/optimizer.py b/megatron/core/optimizer/optimizer.py index f18d09f84e7..9ae23bb4b7f 100644 --- a/megatron/core/optimizer/optimizer.py +++ b/megatron/core/optimizer/optimizer.py @@ -1377,7 +1377,7 @@ def step(self): or ( # Megatron-FSDP always uses decoupled_grad with FusedAdam. self.config.use_precision_aware_optimizer - and getattr(params[0], "__fsdp_param__", False) + and getattr(parameters[0], "__fsdp_param__", False) ) ), ) diff --git a/tests/unit_tests/distributed/megatron_fsdp/test_mcore_fully_sharded_data_parallel.py b/tests/unit_tests/distributed/megatron_fsdp/test_mcore_fully_sharded_data_parallel.py index 62ef035dc7d..04f7c5c6482 100644 --- a/tests/unit_tests/distributed/megatron_fsdp/test_mcore_fully_sharded_data_parallel.py +++ b/tests/unit_tests/distributed/megatron_fsdp/test_mcore_fully_sharded_data_parallel.py @@ -773,7 +773,6 @@ def _training_loop(seed=42, **kwargs): return outputs - @pytest.mark.flaky_in_dev @pytest.mark.skipif( not is_torch_min_version("2.4.0"), reason="Test needs to be updated for torch >= 2.4.0" ) @@ -816,6 +815,7 @@ def _training_loop(seed=42, **kwargs): data_parallel_sharding_strategy="optim_grads_params", megatron_fsdp_main_params_dtype=torch.float32, use_precision_aware_optimizer=True, + fp8="hybrid", fp8_recipe="delayed", fp8_param_gather=True, bf16=True, From bcec6184cc54168732b5459bed938847b3539818 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 14 Apr 2026 17:30:27 +0200 Subject: [PATCH 08/72] ci: add retry loop to apt-get update to handle transient mirror sync failures (#4209) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .github/actions/action.yml | 6 +++++- .github/workflows/cicd-main.yml | 12 ++++++++++-- 2 files changed, 15 insertions(+), 3 deletions(-) diff --git a/.github/actions/action.yml b/.github/actions/action.yml index 11cc3633808..823cacd302f 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -78,7 +78,11 @@ runs: - name: Setup python and install dependencies shell: bash -x -e -u -o pipefail {0} run: | - apt-get update + for i in 1 2 3; do + apt-get update && break + echo "apt-get update failed (attempt $i/3), retrying in 10s..." + sleep 10 + done apt-get install -y python3.12 python3.12-venv uuid-runtime curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/local/bin sh diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index 0d2d5b9577e..f4540b42c02 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -488,7 +488,11 @@ jobs: - name: Install GH CLI shell: bash -x -e -u -o pipefail {0} run: | - apt-get update + for i in 1 2 3; do + apt-get update && break + echo "apt-get update failed (attempt $i/3), retrying in 10s..." + sleep 10 + done apt-get install -y gh - name: Download test data @@ -502,7 +506,11 @@ jobs: - name: Install GH CLI shell: bash run: | - apt-get update + for i in 1 2 3; do + apt-get update && break + echo "apt-get update failed (attempt $i/3), retrying in 10s..." + sleep 10 + done apt-get install -y gh - name: Get last merged PR From 4e85d74ae9b40dd3c44bd690745e0be71907b640 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 14 Apr 2026 19:48:09 +0200 Subject: [PATCH 09/72] fix: enforce correct pass thresholds for deterministic and approximate tests (#4238) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- megatron/core/datasets/bert_dataset.py | 2 +- .../python_test_utils/common.py | 13 +- .../python_test_utils/test_common.py | 261 ++++ .../test_pretraining_regular_pipeline.py | 3 +- .../golden_values_dev_dgx_h100.json | 400 +++--- .../golden_values_dev_dgx_h100.json | 366 +++--- .../golden_values_dev_dgx_h100.json | 334 ++--- .../golden_values_dev_dgx_h100.json | 368 +++--- .../golden_values_dev_dgx_h100.json | 794 ++++++------ .../golden_values_dev_dgx_h100.json | 770 +++++------ .../golden_values_dev_dgx_h100.json | 298 ++--- .../golden_values_dev_dgx_gb200.json | 136 +- .../golden_values_dev_dgx_h100.json | 100 +- .../golden_values_dev_dgx_gb200.json | 24 +- .../golden_values_dev_dgx_h100.json | 84 +- .../golden_values_dev_dgx_h100.json | 12 +- .../golden_values_dev_dgx_gb200.json | 98 +- .../golden_values_dev_dgx_h100.json | 228 ++-- .../golden_values_dev_dgx_gb200.json | 98 +- .../golden_values_dev_dgx_h100.json | 220 ++-- .../golden_values_lts_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 526 ++++---- .../golden_values_dev_dgx_h100.json | 616 ++++----- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_gb200.json | 496 ++++---- .../golden_values_dev_dgx_gb200_2nd.json | 298 ++--- .../golden_values_dev_dgx_h100.json | 910 ++++++------- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_gb200.json | 596 ++++----- .../golden_values_dev_dgx_gb200_2nd.json | 298 ++--- .../golden_values_dev_dgx_h100.json | 510 ++++---- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_dev_dgx_gb200.json | 594 ++++----- .../golden_values_dev_dgx_gb200_2nd.json | 298 ++--- .../golden_values_dev_dgx_h100.json | 520 ++++---- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_gb200.json | 200 +-- .../golden_values_dev_dgx_gb200_2nd.json | 298 ++--- .../golden_values_dev_dgx_h100.json | 536 ++++---- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- 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| 296 ++--- .../golden_values_dev_dgx_gb200.json | 198 +-- .../golden_values_dev_dgx_h100.json | 536 ++++---- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_gb200.json | 200 +-- .../golden_values_dev_dgx_gb200_2nd.json | 300 ++--- .../golden_values_dev_dgx_h100.json | 622 ++++----- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_lts_dgx_a100.json | 276 ++-- .../golden_values_dev_dgx_gb200.json | 596 ++++----- .../golden_values_dev_dgx_gb200_2nd.json | 318 ++--- .../golden_values_dev_dgx_h100.json | 934 +++++++------- .../golden_values_dev_dgx_h100_2nd.json | 500 ++++---- .../golden_values_lts_dgx_a100.json | 598 ++++----- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_lts_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_gb200.json | 298 ++--- .../golden_values_dev_dgx_h100.json | 248 ++-- .../golden_values_lts_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 196 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.../golden_values_dev_dgx_gb200.json | 198 +-- .../golden_values_dev_dgx_h100.json | 298 ++--- .../golden_values_dev_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 198 +-- .../golden_values_dev_dgx_h100.json | 298 ++--- .../golden_values_dev_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 198 +-- .../golden_values_dev_dgx_h100.json | 198 +-- .../golden_values_lts_dgx_a100.json | 98 +- .../golden_values_dev_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 198 +-- .../golden_values_dev_dgx_h100.json | 198 +-- .../golden_values_lts_dgx_a100.json | 98 +- .../golden_values_dev_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 298 ++--- .../golden_values_dev_dgx_h100.json | 298 ++--- .../golden_values_lts_dgx_a100.json | 98 +- .../golden_values_dev_dgx_gb200.json | 920 +++++++------- .../golden_values_dev_dgx_h100.json | 966 +++++++------- .../golden_values_dev_dgx_h100_2nd.json | 498 ++++---- .../golden_values_dev_dgx_gb200.json | 598 ++++----- .../golden_values_dev_dgx_gb200_2nd.json | 537 ++++++++ .../golden_values_dev_dgx_h100.json | 930 +++++++------- .../golden_values_dev_dgx_h100_2nd.json | 500 ++++---- .../golden_values_dev_dgx_gb200.json | 936 +++++++------- .../golden_values_dev_dgx_h100.json | 986 +++++++------- .../golden_values_dev_dgx_h100_2nd.json | 498 ++++---- .../golden_values_dev_dgx_h100.json | 516 ++++---- .../golden_values_dev_dgx_h100.json | 372 +++--- .../golden_values_dev_dgx_gb200.json | 754 +++++------ .../golden_values_dev_dgx_h100.json | 764 +++++------ .../golden_values_dev_dgx_h100_2nd.json | 400 +++--- .../golden_values_dev_dgx_gb200.json | 598 ++++----- .../golden_values_dev_dgx_gb200_2nd.json | 644 ++++++++++ .../golden_values_dev_dgx_h100.json | 1130 ++++++++--------- .../golden_values_dev_dgx_h100_2nd.json | 600 ++++----- .../model_config.yaml | 4 +- .../golden_values_dev_dgx_h100.json | 16 +- .../golden_values_dev_dgx_h100.json | 254 ++-- .../golden_values_dev_dgx_h100.json | 450 +++---- .../golden_values_dev_dgx_h100.json | 96 +- .../golden_values_dev_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_dev_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_dev_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../golden_values_dev_dgx_a100.json | 198 +-- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100.json | 598 ++++----- .../golden_values_dev_dgx_h100.json | 596 ++++----- .../golden_values_dev_dgx_h100_2nd.json | 300 ++--- .../launch_nemo_run_workload.py | 11 + ...oe-dynamic-inference-with-coordinator.yaml | 2 +- tests/test_utils/recipes/h100/moe.yaml | 10 - tests/test_utils/recipes/h100/unit-tests.yaml | 14 + tools/bisect.md | 65 + tools/bisect.sh | 75 ++ 324 files changed, 64411 insertions(+), 61004 deletions(-) create mode 100644 tests/functional_tests/python_test_utils/test_common.py create mode 100644 tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200_2nd.json create mode 100644 tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200_2nd.json create mode 100644 tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200_2nd.json create mode 100644 tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_gb200_2nd.json create mode 100644 tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/golden_values_dev_dgx_gb200_2nd.json create mode 100644 tools/bisect.md create mode 100755 tools/bisect.sh diff --git a/megatron/core/datasets/bert_dataset.py b/megatron/core/datasets/bert_dataset.py index aa0ba7501cf..be4fc048e26 100644 --- a/megatron/core/datasets/bert_dataset.py +++ b/megatron/core/datasets/bert_dataset.py @@ -54,7 +54,7 @@ def __init__( indexed_dataset, dataset_path, indexed_indices, num_samples, index_split, config ) - self.token_lookup = list(self.config.tokenizer.inv_vocab.keys()) + self.token_lookup = sorted(self.config.tokenizer.inv_vocab.keys()) # Account for the single and two token ids self.sample_index = self._build_sample_index( self.config.sequence_length - 3, 2 if self.config.classification_head else 1 diff --git a/tests/functional_tests/python_test_utils/common.py b/tests/functional_tests/python_test_utils/common.py index 8fb3ebc511a..21a003d39b1 100644 --- a/tests/functional_tests/python_test_utils/common.py +++ b/tests/functional_tests/python_test_utils/common.py @@ -1,3 +1,4 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import enum import glob import json @@ -226,8 +227,16 @@ def pipeline( # Tolerance check is_close = np.isclose(actual, golden, rtol=test.rtol, atol=test.atol) - num_failing_steps_allowed = min(max(total_steps_evaluated // 100, 1), 50) - passing = np.mean(is_close) >= (num_failing_steps_allowed / total_steps_evaluated) + if ( + test.type_of_test_result == TypeOfTestResult.DETERMINISTIC + or total_steps_evaluated == 1 + ): + passing = bool(np.all(is_close)) + else: + num_failing_steps_allowed = min(max(total_steps_evaluated // 100, 1), 50) + passing = np.mean(is_close) >= 1 - ( + num_failing_steps_allowed / total_steps_evaluated + ) if not passing: logger.info( diff --git a/tests/functional_tests/python_test_utils/test_common.py b/tests/functional_tests/python_test_utils/test_common.py new file mode 100644 index 00000000000..b2fbefcb022 --- /dev/null +++ b/tests/functional_tests/python_test_utils/test_common.py @@ -0,0 +1,261 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +import pytest + +from tests.functional_tests.python_test_utils.common import ( + ApproximateTest, + DeterministicTest, + GoldenValueMetric, + MissingTensorboardLogsError, + NotApproximateError, + NotDeterminsticError, + TypeOfTestResult, + _filter_checks, + pipeline, +) + +# ── helpers ────────────────────────────────────────────────────────────────── + + +def make_metric(values: dict, step_interval: int = 1) -> GoldenValueMetric: + steps = sorted(values) + return GoldenValueMetric( + start_step=steps[0], end_step=steps[-1], step_interval=step_interval, values=values + ) + + +def run(golden, actual, checks, compare_approximate=False): + pipeline( + compare_approximate_results=compare_approximate, + golden_values=golden, + actual_values=actual, + checks=checks, + ) + + +# ── ApproximateTest ─────────────────────────────────────────────────────────── + + +class TestApproximateTest: + def test_defaults(self): + t = ApproximateTest() + assert t.atol == 0 + assert t.rtol == 1e-5 + + def test_type(self): + assert ApproximateTest().type_of_test_result == TypeOfTestResult.APPROXIMATE + + def test_error_message_type(self): + assert isinstance(ApproximateTest().error_message("m"), NotApproximateError) + + +# ── DeterministicTest ───────────────────────────────────────────────────────── + + +class TestDeterministicTest: + def test_tolerances_are_zero(self): + t = DeterministicTest() + assert t.atol == 0 + assert t.rtol == 0.0 + + def test_type(self): + assert DeterministicTest().type_of_test_result == TypeOfTestResult.DETERMINISTIC + + def test_error_message_type(self): + assert isinstance(DeterministicTest().error_message("m"), NotDeterminsticError) + + +# ── _filter_checks ──────────────────────────────────────────────────────────── + + +class TestFilterChecks: + def test_returns_only_approximate(self): + checks = [ApproximateTest(), DeterministicTest(), ApproximateTest()] + result = _filter_checks(checks, TypeOfTestResult.APPROXIMATE) + assert len(result) == 2 + assert all(c.type_of_test_result == TypeOfTestResult.APPROXIMATE for c in result) + + def test_returns_only_deterministic(self): + checks = [ApproximateTest(), DeterministicTest()] + result = _filter_checks(checks, TypeOfTestResult.DETERMINISTIC) + assert len(result) == 1 + assert result[0].type_of_test_result == TypeOfTestResult.DETERMINISTIC + + def test_empty_when_no_match(self): + assert _filter_checks([ApproximateTest()], TypeOfTestResult.DETERMINISTIC) == [] + + +# ── pipeline — deterministic ────────────────────────────────────────────────── + + +class TestPipelineDeterministic: + def test_exact_match_passes(self): + gv = make_metric({1: 1.0, 2: 2.0, 3: 3.0}) + run({"loss": gv}, {"loss": gv}, {"loss": [DeterministicTest()]}) + + def test_single_mismatch_fails(self): + golden = make_metric({1: 1.0, 2: 2.0, 3: 3.0}) + actual = make_metric({1: 1.0, 2: 2.0, 3: 3.1}) + with pytest.raises(AssertionError, match="loss"): + run({"loss": golden}, {"loss": actual}, {"loss": [DeterministicTest()]}) + + def test_skipped_in_compare_approximate_mode(self): + # Deterministic checks must be silently skipped when + # compare_approximate_results=True, even if values differ wildly. + golden = make_metric({1: 1.0}) + actual = make_metric({1: 999.0}) + run( + {"loss": golden}, + {"loss": actual}, + {"loss": [DeterministicTest()]}, + compare_approximate=True, + ) + + +# ── pipeline — approximate ──────────────────────────────────────────────────── + + +class TestPipelineApproximate: + def test_within_rtol_passes(self): + golden = make_metric({1: 1.0, 2: 2.0}) + actual = make_metric({1: 1.04, 2: 2.04}) # 4 % < 5 % rtol + run({"loss": golden}, {"loss": actual}, {"loss": [ApproximateTest(rtol=0.05)]}) + + def test_outside_rtol_fails(self): + golden = make_metric({1: 1.0, 2: 2.0}) + actual = make_metric({1: 1.2, 2: 2.4}) # 20 % > 5 % rtol + with pytest.raises(AssertionError, match="loss"): + run({"loss": golden}, {"loss": actual}, {"loss": [ApproximateTest(rtol=0.05)]}) + + def test_within_atol_passes(self): + golden = make_metric({1: 0.0, 2: 0.0}) + actual = make_metric({1: 0.5, 2: 0.5}) # within atol=1 + run({"loss": golden}, {"loss": actual}, {"loss": [ApproximateTest(atol=1, rtol=0)]}) + + def test_outside_atol_fails(self): + golden = make_metric({1: 0.0}) + actual = make_metric({1: 2.0}) # outside atol=1 + with pytest.raises(AssertionError): + run({"loss": golden}, {"loss": actual}, {"loss": [ApproximateTest(atol=1, rtol=0)]}) + + def test_single_bad_step_in_large_run_passes(self): + # With 1000 steps: total_steps_evaluated=1001, num_failing_allowed=10. + # 1 bad step → mean(is_close) = 999/1000 = 0.999, well above threshold. + n = 1000 + golden = {i: 1.0 for i in range(1, n + 1)} + actual = {**golden, n: 999.0} + run( + {"loss": make_metric(golden)}, + {"loss": make_metric(actual)}, + {"loss": [ApproximateTest(rtol=0.05)]}, + ) + + def test_majority_bad_steps_fails(self): + n = 1000 + golden = {i: 1.0 for i in range(1, n + 1)} + actual = {i: 999.0 for i in range(1, n + 1)} # all wrong + with pytest.raises(AssertionError): + run( + {"loss": make_metric(golden)}, + {"loss": make_metric(actual)}, + {"loss": [ApproximateTest(rtol=0.05)]}, + ) + + +# ── pipeline — missing metric ───────────────────────────────────────────────── + + +class TestPipelineMissingMetric: + def test_raises_when_metric_absent_from_actual(self): + gv = make_metric({1: 1.0}) + with pytest.raises(MissingTensorboardLogsError): + run({"loss": gv}, {}, {"loss": [DeterministicTest()]}) + + +# ── pipeline — iteration-time special case ──────────────────────────────────── + + +class TestPipelineIterationTime: + def test_uses_median_not_per_step_values(self): + # Per-step values diverge but medians match — should pass. + golden = make_metric({1: 0.25, 2: 0.25, 3: 0.25, 4: 0.25}) + # median([0.10, 0.25, 0.26, 100.0]) = (0.25+0.26)/2 = 0.255 ≈ 0.25 ✓ + actual = make_metric({1: 0.10, 2: 0.25, 3: 0.26, 4: 100.0}) + run( + {"iteration-time": golden}, + {"iteration-time": actual}, + {"iteration-time": [ApproximateTest(rtol=0.05)]}, + ) + + def test_diverging_medians_fails(self): + golden = make_metric({1: 0.25, 2: 0.25, 3: 0.25}) + actual = make_metric({1: 0.50, 2: 0.50, 3: 0.50}) # median 0.50, 100 % off + with pytest.raises(AssertionError, match="iteration-time"): + run( + {"iteration-time": golden}, + {"iteration-time": actual}, + {"iteration-time": [ApproximateTest(rtol=0.05)]}, + ) + + def test_nan_warmup_step_does_not_break_median(self): + # Step 1 is "nan" (warm-up). Median of [inf, 0.25, 0.25, 0.25] = 0.25. + golden = make_metric({1: "nan", 2: 0.25, 3: 0.25, 4: 0.25}) + actual = make_metric({1: "nan", 2: 0.25, 3: 0.25, 4: 0.25}) + run( + {"iteration-time": golden}, + {"iteration-time": actual}, + {"iteration-time": [ApproximateTest(rtol=0.05)]}, + ) + + +# ── pipeline — "nan" string handling ───────────────────────────────────────── + + +class TestPipelineNanHandling: + def test_matching_nan_strings_pass(self): + # "nan" → np.inf; np.isclose(inf, inf) is True (numpy treats same-sign + # infinities as close), so matching "nan" steps are not penalised. + golden = make_metric({1: "nan", 2: 1.0, 3: 1.0}) + actual = make_metric({1: "nan", 2: 1.0, 3: 1.0}) + run({"loss": golden}, {"loss": actual}, {"loss": [ApproximateTest(rtol=0.05)]}) + + def test_nan_in_golden_but_not_actual_fails_deterministic(self): + golden = make_metric({1: 1.0, 2: "nan"}) + actual = make_metric({1: 1.0, 2: 1.0}) + with pytest.raises(AssertionError): + run({"loss": golden}, {"loss": actual}, {"loss": [DeterministicTest()]}) + + +# ── pipeline — multiple metrics ─────────────────────────────────────────────── + + +class TestPipelineMultipleMetrics: + def test_all_pass(self): + gv = make_metric({1: 1.0, 2: 2.0}) + run( + {"loss": gv, "num-zeros": gv}, + {"loss": gv, "num-zeros": gv}, + {"loss": [DeterministicTest()], "num-zeros": [DeterministicTest()]}, + ) + + def test_one_failing_metric_fails_overall(self): + good = make_metric({1: 1.0}) + bad_golden = make_metric({1: 1.0}) + bad_actual = make_metric({1: 999.0}) + with pytest.raises(AssertionError, match="num-zeros"): + run( + {"loss": good, "num-zeros": bad_golden}, + {"loss": good, "num-zeros": bad_actual}, + {"loss": [DeterministicTest()], "num-zeros": [DeterministicTest()]}, + ) + + def test_failure_message_lists_all_failed_metrics(self): + bad_golden = make_metric({1: 1.0}) + bad_actual = make_metric({1: 999.0}) + with pytest.raises(AssertionError, match="loss") as exc_info: + run( + {"loss": bad_golden, "num-zeros": bad_golden}, + {"loss": bad_actual, "num-zeros": bad_actual}, + {"loss": [DeterministicTest()], "num-zeros": [DeterministicTest()]}, + ) + assert "num-zeros" in str(exc_info.value) diff --git a/tests/functional_tests/python_test_utils/test_pretraining_regular_pipeline.py b/tests/functional_tests/python_test_utils/test_pretraining_regular_pipeline.py index 58311542ee9..68aa0db5622 100644 --- a/tests/functional_tests/python_test_utils/test_pretraining_regular_pipeline.py +++ b/tests/functional_tests/python_test_utils/test_pretraining_regular_pipeline.py @@ -10,13 +10,14 @@ logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) + CHECK_THRESHOLDS = { "iteration-time": [common.ApproximateTest(atol=0, rtol=0.05)], "mem-allocated-bytes": [common.ApproximateTest(atol=0, rtol=0.05)], "mem-max-allocated-bytes": [common.ApproximateTest(atol=0, rtol=0.05)], "lm loss": [common.DeterministicTest(), common.ApproximateTest(atol=0, rtol=0.05)], "mtp_1 loss": [common.DeterministicTest(), common.ApproximateTest(atol=0, rtol=0.05)], - "num-zeros": [common.DeterministicTest(), common.ApproximateTest(atol=0, rtol=0.05)], + "num-zeros": [common.DeterministicTest(), common.ApproximateTest(atol=0, rtol=0.20)], "generated_tokens": [common.DeterministicTest(), common.ApproximateTest(atol=0, rtol=0.05)], "logprobs": [common.DeterministicTest(), common.ApproximateTest(atol=0, rtol=0.05)], } diff --git a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json index f60a93a743e..dbf0e83da8b 100644 --- a/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/bert/bert_mcore_tp1_pp2/golden_values_dev_dgx_h100.json @@ -4,56 +4,56 @@ "end_step": 50, "step_interval": 1, "values": { - "1": 10.51672, - "2": 10.53131, - "3": 10.51935, - "4": 10.51511, - "5": 10.50629, - "6": 10.50923, - "7": 10.52495, - "8": 10.52196, - "9": 10.52622, - "10": 10.50761, - "11": 10.50978, - "12": 10.50534, - "13": 10.50289, - "14": 10.50516, - "15": 10.50423, - "16": 10.48072, - "17": 10.47586, - "18": 10.47023, - "19": 10.46435, - "20": 10.4739, - "21": 10.45166, - "22": 10.39165, - "23": 10.3991, - "24": 10.35476, - "25": 10.35141, - "26": 10.3495, - "27": 10.31286, - "28": 10.32875, - "29": 10.3109, - "30": 10.14329, - "31": 10.1053, - "32": 10.08058, - "33": 10.10543, - "34": 10.0341, - "35": 10.02688, - "36": 10.00705, - "37": 10.00331, - "38": 9.96057, - "39": 9.88869, - "40": 9.85233, - "41": 9.78278, - "42": 9.71943, - "43": 9.6861, - "44": 9.65707, - "45": 9.65007, - "46": 9.57377, - "47": 9.59432, - "48": 9.58108, - "49": 9.52836, - "50": 9.49644 + "1": 10.51634, + "2": 10.53019, + "3": 10.52, + "4": 10.51533, + "5": 10.5071, + "6": 10.51129, + "7": 10.52361, + "8": 10.52273, + "9": 10.5252, + "10": 10.50774, + "11": 10.50979, + "12": 10.50502, + "13": 10.50216, + "14": 10.50577, + "15": 10.5025, + "16": 10.48019, + "17": 10.47359, + "18": 10.46993, + "19": 10.4638, + "20": 10.477, + "21": 10.45297, + "22": 10.39172, + "23": 10.39755, + "24": 10.35454, + "25": 10.35256, + "26": 10.35023, + "27": 10.31139, + "28": 10.32871, + "29": 10.30886, + "30": 10.14452, + "31": 10.10669, + "32": 10.07861, + "33": 10.10435, + "34": 10.03457, + "35": 10.02754, + "36": 10.01022, + "37": 10.00285, + "38": 9.96123, + "39": 9.89017, + "40": 9.85272, + "41": 9.78441, + "42": 9.71989, + "43": 9.6866, + "44": 9.65531, + "45": 9.65013, + "46": 9.57506, + "47": 9.59255, + "48": 9.58283, + "49": 9.52845, + "50": 9.49562 } }, "num-zeros": { @@ -61,56 +61,56 @@ "end_step": 50, "step_interval": 1, "values": { - 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"1": 10.86791 + "1": 29.29548 } }, "num-zeros": { @@ -12,7 +12,7 @@ "end_step": 1, "step_interval": 1, "values": { - "1": 152866448.0 + "1": 152955200.0 } }, "mem-allocated-bytes": { @@ -30,13 +30,5 @@ "values": { "1": 67277205504.0 } - }, - "iteration-time": { - "start_step": 1, - "end_step": 1, - "step_interval": 1, - "values": { - "1": 14.45281 - } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json index 49a1208665c..6cfef695086 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset/golden_values_dev_dgx_gb200.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 3.95902, - 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"latency": 0.33650875091552734, + "latency": 0.23165392875671387, + "ttft": 0.005468606948852539, "cuda_graph_request_count_map": { - "32": 29 + "112": 1, + "56": 29 }, "step_count": 30, "top_n_logprobs": null, @@ -283,14 +285,15 @@ ] }, "throughput": [ - 112.34141028159206, - 143.47299899774578, - 136.9190123220356, - 133.0550750138523, - 140.54753942350868, - 142.31278267940777, - 142.60535677655014, - 142.2477862300286 + 103.52352681774204, + 127.15637827644959, + 128.61952049718647, + 127.64189012353444, + 127.67465716938344, + 128.14012719394276, + 127.31630172039594, + 126.7018422896914 ], - "mem-max-allocated-bytes": 23014038016 -} + "mem-max-allocated-bytes": 23794105344, + "lifetime_prefill_token_count": 88 +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_logitsmatch_decode_graphs_only/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_logitsmatch_decode_graphs_only/golden_values_dev_dgx_h100.json index 51437664cf7..1b68ff57b19 100644 --- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_logitsmatch_decode_graphs_only/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_logitsmatch_decode_graphs_only/golden_values_dev_dgx_h100.json @@ -34,9 +34,10 @@ 1321, 1402 ], - "latency": 0.4249272346496582, + "latency": 0.30296945571899414, + "ttft": 0.09055852890014648, "cuda_graph_request_count_map": { - "32": 29 + "56": 29 }, "step_count": 30, "top_n_logprobs": null, @@ -283,13 +284,15 @@ ] }, "throughput": [ - 3.8530017644378955, - 98.13326345491087, - 100.89859151541394, - 100.80208030416277, - 100.18034658518215, - 100.88831730291241, - 100.4922180479951, - 101.13060027776349 - ] -} + 74.3662571164981, + 97.68188847278893, + 98.03883558687363, + 97.82967891613008, + 97.7321144241897, + 97.38289476026367, + 97.87107342365256, + 98.0357802325201 + ], + "mem-max-allocated-bytes": 22908388864, + "lifetime_prefill_token_count": 88 +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_dp8_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_dp8_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json index 07adf271434..92eb5c6cab0 100644 --- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_dp8_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_dp8_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json @@ -1,158 +1,158 @@ { - "0": { - "input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", - "generated_text": " And that this is the place where you can be yourself, and be yourself in the most beautiful way. And that this is the place where you can", - "generated_tokens": [ - 3060, - 1455, - 1593, - 1395, - 1278, - 3535, - 2478, - 1636, - 1710, - 1402, - 14019, - 1044, - 1321, - 1402, - 14019, - 1294, - 1278, - 2725, - 15568, - 3039, - 1046, - 3060, - 1455, - 1593, - 1395, - 1278, - 3535, - 2478, - 1636, - 1710 - ], - "latency": 2.020272731781006, - "logprobs": [ - -9.358587265014648, - -2.7594826221466064, - -4.608366012573242, - -1.4093360900878906, - -0.6152952313423157, - -1.7217562198638916, - -2.496668815612793, - -2.0547454357147217, - -2.441960573196411, - -6.280838966369629, - -1.5643692016601562, - -3.462346076965332, - -4.428728103637695, - -3.8633861541748047, - -1.9936373233795166, - -1.8929449319839478, - -3.796365737915039, - -6.8360137939453125, - -0.2901247441768646, - -0.9246833324432373, - -6.633338928222656, - -7.166708469390869, - -12.771251678466797, - -2.198296308517456, - -3.7778120040893555, - -0.4983733296394348, - -4.381269454956055, - -0.0666784718632698, - -0.09580295532941818, - -3.2437636852264404, - -10.079947471618652, - -1.172220230102539, - -5.977442741394043, - -5.046236038208008, - -3.855658531188965, - -2.5585858821868896, - -3.356245994567871, - -5.557229518890381, - -1.6787731647491455, - -5.483290672302246, - -12.218501091003418, - -12.61402702331543, - -0.09662941098213196, - -2.5431432723999023, - -1.4071024656295776, - -2.9154715538024902, - -1.1964417695999146, - -0.006458481773734093, - -3.3625335693359375, - -13.262511253356934, - -4.314079761505127, - -2.617699146270752, - -5.987792015075684, - -0.778266429901123, - -0.048888545483350754, - -1.548882007598877, - -1.1381981372833252, - -5.627166748046875, - -0.4078553318977356, - -4.958505630493164, - -0.6187160611152649, - -0.7174848914146423, - -2.469533920288086, - -13.620073318481445, - -0.09088654816150665, - -3.526974678039551, - -1.4195809364318848, - -6.402483940124512, - -0.5898402333259583, - -3.565917491912842, - -0.8561318516731262, - -1.6140165328979492, - -5.370549201965332, - -17.159223556518555, - -6.583524703979492, - -0.8855001926422119, - -4.19431209564209, - -1.2012220621109009, - -2.2563133239746094, - -1.7674944400787354, - -0.22064533829689026, - -9.292220115661621, - -0.12445646524429321, - -7.29617977142334, - -2.526529312133789, - -4.071560859680176, - -3.5568013191223145, - -1.926215410232544, - -2.349026918411255, - -2.2132363319396973, - -0.3125414550304413, - -1.4718132019042969, - -2.149106740951538, - -1.0855519771575928, - -1.631832242012024, - -1.3751734495162964, - -1.9396103620529175, - -1.5293723344802856, - -0.8444125056266785, - -1.2414811849594116, - -1.9522171020507812, - -2.4338042736053467, - -1.5651824474334717, - -0.9498789310455322, - -1.8044980764389038, - -2.356677770614624, - -1.247452974319458, - -1.550165057182312, - -0.5635553598403931, - -0.6177330017089844, - -0.4778785705566406, - -0.020452087745070457, - -0.48500269651412964, - -0.23854275047779083, - -0.06543659418821335, - -0.11837350577116013, - -0.0585334412753582 - ] - } + "0": { + "input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", + "generated_text": " And that this is the place where you can be yourself, and be yourself in the most beautiful way. And that this is the place where you can", + "generated_tokens": [ + 3060, + 1455, + 1593, + 1395, + 1278, + 3535, + 2478, + 1636, + 1710, + 1402, + 14019, + 1044, + 1321, + 1402, + 14019, + 1294, + 1278, + 2725, + 15568, + 3039, + 1046, + 3060, + 1455, + 1593, + 1395, + 1278, + 3535, + 2478, + 1636, + 1710 + ], + "latency": 1.964757651090622, + "logprobs": [ + -9.358587265014648, + -2.7594826221466064, + -4.608366012573242, + -1.4093360900878906, + -0.6152952313423157, + -1.7217562198638916, + -2.496668815612793, + -2.0547454357147217, + -2.441960573196411, + -6.280838966369629, + -1.5643692016601562, + -3.462346076965332, + -4.428728103637695, + -3.8633861541748047, + -1.9936373233795166, + -1.8929449319839478, + -3.796365737915039, + -6.8360137939453125, + -0.2901247441768646, + -0.9246833324432373, + -6.633338928222656, + -7.166708469390869, + -12.771251678466797, + -2.198296308517456, + -3.7778120040893555, + -0.4983733296394348, + -4.381269454956055, + -0.0666784718632698, + -0.09580295532941818, + -3.2437636852264404, + -10.079947471618652, + -1.172220230102539, + -5.977442741394043, + -5.046236038208008, + -3.855658531188965, + -2.5585858821868896, + -3.356245994567871, + -5.557229518890381, + -1.6787731647491455, + -5.483290672302246, + -12.218501091003418, + -12.61402702331543, + -0.09662941098213196, + -2.5431432723999023, + -1.4071024656295776, + -2.9154715538024902, + -1.1964417695999146, + -0.006458481773734093, + -3.3625335693359375, + -13.262511253356934, + -4.314079761505127, + -2.617699146270752, + -5.987792015075684, + -0.778266429901123, + -0.048888545483350754, + -1.548882007598877, + -1.1381981372833252, + -5.627166748046875, + -0.4078553318977356, + -4.958505630493164, + -0.6187160611152649, + -0.7174848914146423, + -2.469533920288086, + -13.620073318481445, + -0.09088654816150665, + -3.526974678039551, + -1.4195809364318848, + -6.402483940124512, + -0.5898402333259583, + -3.565917491912842, + -0.8561318516731262, + -1.6140165328979492, + -5.370549201965332, + -17.159223556518555, + -6.583524703979492, + -0.8855001926422119, + -4.19431209564209, + -1.2012220621109009, + -2.2563133239746094, + -1.7674944400787354, + -0.22064533829689026, + -9.292220115661621, + -0.12445646524429321, + -7.29617977142334, + -2.526529312133789, + -4.071560859680176, + -3.5568013191223145, + -1.926215410232544, + -2.349026918411255, + -2.2132363319396973, + -0.3125414550304413, + -1.4718132019042969, + -2.149106740951538, + -1.0855519771575928, + -1.631832242012024, + -1.3751734495162964, + -1.9396103620529175, + -1.5293723344802856, + -0.8444125056266785, + -1.2414811849594116, + -1.9522171020507812, + -2.4338042736053467, + -1.5651824474334717, + -0.9498789310455322, + -1.8044980764389038, + -2.356677770614624, + -1.247452974319458, + -1.550165057182312, + -0.5635553598403931, + -0.6177330017089844, + -0.4778785705566406, + -0.020452087745070457, + -0.48500269651412964, + -0.23854275047779083, + -0.06543659418821335, + -0.11837350577116013, + -0.0585334412753582 + ] + } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json index 07adf271434..4a2afe7b67a 100644 --- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp8_dp1_583m_logitsmatch_zmq/golden_values_dev_dgx_h100.json @@ -1,158 +1,158 @@ { - "0": { - "input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", - "generated_text": " And that this is the place where you can be yourself, and be yourself in the most beautiful way. 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Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", + "generated_text": " And that this is the place where you can be yourself, and be yourself in the most beautiful way. 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a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json index 52a17a100e8..77f58723ba9 100644 --- a/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt_grpo_tp4_pp1_dp2_8b_cudagraphs_throughput/golden_values_dev_dgx_h100.json @@ -58,26 +58,26 @@ "end_step": 20, "step_interval": 1, "values": { - "1": 61233643520.0, - "2": 61300752384.0, - "3": 61300748288.0, - "4": 61300748288.0, - "5": 61300748288.0, - "6": 61300744192.0, - "7": 61300748288.0, - "8": 61300744192.0, - "9": 61300744192.0, - "10": 61300740096.0, - "11": 61300744192.0, - "12": 61300744192.0, - "13": 61300744192.0, - "14": 61300744192.0, - "15": 61300744192.0, - "16": 61300744192.0, - "17": 61300744192.0, - "18": 61300744192.0, - "19": 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This is New York at the end", "generated_tokens": [3060, 2430, 1636, 2012, 1317, 1278, 2362, 1307, 1278, 16070, 1044, 1321, 1636, 23067, 1455, 1593, 1395, 1605, 3140, 5152, 1513, 1747, 1046, 2409, 1395, 3140, 5152, 1513, 1278, 2362], "tpot": [0.6098978519439697, 0.00587167963385582, 0.00553337624296546, 0.005388895981013775, 0.0052880640141665936, 0.005359936039894819, 0.00534518389031291, 0.005303360056132078, 0.0053532798774540424, 0.005232864059507847, 0.0053773121908307076, 0.005341055803000927, 0.0052644480019807816, 0.005387584213167429, 0.005375008098781109, 0.00524944020435214, 0.0053992001339793205, 0.005333151668310165, 0.0052451519295573235, 0.005348992068320513, 0.005396031774580479, 0.0052389120683074, 0.005332960281521082, 0.005230464041233063, 0.005353568121790886, 0.005343679804354906, 0.005257599987089634, 0.005404096096754074, 0.005395135842263699, 0.005260608159005642], "latency": 0.769633749499917, "logprobs": [-9.358616828918457, -2.7474308013916016, -4.628000259399414, -1.5015846490859985, -0.6537986993789673, -1.6720777750015259, -2.478705883026123, -2.0523874759674072, -2.4486241340637207, -6.257688522338867, -1.4695018529891968, -3.4444499015808105, -4.394474029541016, -3.875497817993164, -2.0133562088012695, -1.8832889795303345, -3.8004486560821533, -6.784910678863525, -0.2949134111404419, -0.9851954579353333, -6.626471519470215, -7.186152458190918, -12.800604820251465, -2.2686400413513184, -3.7816011905670166, -0.4978560209274292, -4.371628284454346, -0.0696188285946846, -0.09487748891115189, -3.2375073432922363, -10.075444221496582, -1.138173222541809, -5.97689151763916, -5.093283653259277, -3.874396324157715, -2.6073620319366455, -3.466899871826172, -5.642228126525879, -1.6154727935791016, -5.416567325592041, -12.158267974853516, -12.610607147216797, -0.09664110094308853, -2.5213418006896973, -1.3747841119766235, -2.8510401248931885, -1.1877963542938232, -0.006288621574640274, -3.382380962371826, -13.207911491394043, -4.477662086486816, -2.5299136638641357, -6.053747653961182, -0.7650555372238159, -0.04903985932469368, -1.5557448863983154, -1.1315535306930542, -5.610307216644287, -0.4059771001338959, -4.961302280426025, -0.5701270699501038, -0.7174267172813416, -2.4735305309295654, -13.610812187194824, -0.09192369878292084, -3.5248732566833496, -1.3797900676727295, -6.429551124572754, -0.541852593421936, -3.5403199195861816, -0.8477706909179688, -1.5764057636260986, -5.343497276306152, -17.19588851928711, -6.635483741760254, -0.8923014402389526, -4.114314556121826, -1.2193646430969238, -2.2128424644470215, -1.7673423290252686, -0.22567729651927948, -9.320298194885254, -0.1282224804162979, -7.3249101638793945, -2.511319875717163, -4.0696563720703125, -3.5427517890930176, -1.9300249814987183, -2.347038507461548, -1.5178614854812622, -2.366441249847412, -1.744020938873291, -1.1570327281951904, -3.0150983333587646, -0.5272141098976135, -0.4669455885887146, -1.7157398462295532, -0.8362292051315308, -0.41491177678108215, -0.9386503100395203, -1.5008316040039062, -0.4635278284549713, -1.6312834024429321, -0.5320357084274292, -1.2249717712402344, -1.1707526445388794, -0.0023814670275896788, -1.1655761003494263, -0.006950841750949621, -0.7309689521789551, -0.7428325414657593, -0.042878177016973495, -0.8572992086410522, -0.01948782242834568, -2.0537290573120117, -1.2817553281784058, -0.8235744833946228]}} \ No newline at end of file +{"1": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. 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Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " And then you get to the end of the movie, and you realize that this is not New York at all. 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1.906998872756958, - "ttft": 0.08748054504394531, + "latency": 1.7664601802825928, + "ttft": 0.09903407096862793, "cuda_graph_request_count_map": null, "step_count": 30, "top_n_logprobs": null, @@ -281,6 +281,6 @@ -2.262563943862915 ] }, - "mem-max-allocated-bytes": 48828583424, + "mem-max-allocated-bytes": 53317222912, "lifetime_prefill_token_count": 88 } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/golden_values_dev_dgx_h100.json index 928d335177a..83954b271ae 100644 --- a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m_chunked_prefill/golden_values_dev_dgx_h100.json @@ -34,8 +34,8 @@ 1032, 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a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json index c9a9f0c18e3..9b0c71e3de0 100644 --- a/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/hybrid/hybrid_mr_mcore_te_tp2_pp1_cp1_dgx_a100_1N8G/golden_values_dev_dgx_h100.json @@ -4,56 +4,56 @@ "end_step": 50, "step_interval": 1, "values": { - "1": 10.96115, - "2": 10.95442, - "3": 10.96815, - "4": 10.94185, - "5": 10.9912, - "6": 10.99106, - "7": 10.97905, - "8": 10.95656, - "9": 10.95286, - "10": 10.92841, - "11": 10.97363, - "12": 10.94886, - "13": 10.94986, - "14": 10.97176, - "15": 10.84445, - "16": 10.84452, - "17": 10.79535, - "18": 10.81592, - "19": 10.81097, - "20": 10.61854, - "21": 10.56479, - "22": 10.32903, - "23": 10.59978, - 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a/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/mimo/mimo_vlm_pretrain_convergence_tp1_pp1_cp1_dp8_seq_packing/golden_values_dev_dgx_h100.json @@ -8,102 +8,102 @@ "2": 7.94009, "3": 7.64718, "4": 7.11692, - "5": 6.09843, - "6": 5.8091, - "7": 5.59573, - "8": 5.39367, - "9": 5.12575, - "10": 5.09298, - "11": 4.8586, - "12": 4.76892, - "13": 4.68905, - "14": 4.57558, - "15": 4.49718, - "16": 4.46151, - "17": 4.33643, - "18": 4.28784, - "19": 4.22689, - "20": 4.12088, - "21": 4.15883, - "22": 4.17278, - "23": 4.01549, - "24": 3.78218, - "25": 3.84438, - "26": 3.63675, - "27": 3.60623, - "28": 3.78552, - "29": 3.73417, - "30": 3.4335, - "31": 3.60412, - "32": 3.43946, - "33": 3.37099, - "34": 3.43735, - "35": 3.51764, - "36": 3.32475, - "37": 3.41112, - "38": 3.3566, - "39": 3.46701, - "40": 3.3994, - "41": 3.36013, - "42": 3.50815, - "43": 3.49041, - "44": 3.33243, - 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b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json index b804ba57a90..e429e763e8c 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_gb200.json @@ -4,106 +4,106 @@ "end_step": 100, "step_interval": 1, "values": { - "1": 10.81772, - "2": 10.82538, - "3": 10.82315, - "4": 10.7893, - "5": 10.84414, - "6": 10.85629, - "7": 10.82859, - "8": 10.8301, - "9": 10.84207, - "10": 10.78601, - "11": 10.85733, - "12": 10.84663, - "13": 10.86562, - "14": 10.86914, - "15": 10.81232, - "16": 10.80865, - "17": 10.77965, - "18": 10.80508, - "19": 10.79288, - "20": 10.74264, - "21": 10.72495, - "22": 10.58933, - "23": 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b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json index 615b1b90939..0bfc537856b 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json @@ -54,56 +54,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 9.92297, - "52": 9.8284, - "53": 10.14624, - "54": 10.04331, - "55": 9.96248, - "56": 9.70547, - "57": 9.58553, - "58": 9.91673, - "59": 9.66059, - "60": 9.60402, - "61": 9.77812, - "62": 10.06258, - "63": 9.47179, - "64": 9.85361, - "65": 9.02415, - "66": 9.79391, - "67": 9.43341, - "68": 9.85341, - "69": 9.84716, - "70": 9.81035, - "71": 9.68402, - "72": 9.65988, - "73": 9.57308, - 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--git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json index 9b6990b963d..5aad06d2d81 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer/golden_values_dev_dgx_h100_2nd.json @@ -54,56 +54,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 9.90528, - "52": 9.80364, - "53": 10.12728, - "54": 10.00036, - "55": 9.9362, - "56": 9.68506, - "57": 9.55805, - "58": 9.90514, - "59": 9.63857, - "60": 9.57451, - "61": 9.76864, - "62": 10.03802, - "63": 9.44503, - "64": 9.82796, - "65": 9.00712, - "66": 9.77422, - "67": 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a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_gb200.json @@ -325,106 +325,106 @@ "end_step": 100, "step_interval": 1, "values": { - "1": 2064059392.0, - "2": 2545253376.0, - "3": 2545253376.0, - "4": 2546730496.0, - "5": 2546730496.0, - "6": 2546730496.0, - "7": 2546730496.0, - "8": 2546730496.0, - "9": 2546730496.0, - "10": 2546730496.0, - "11": 2546730496.0, - "12": 2546730496.0, - "13": 2546730496.0, - "14": 2546730496.0, - "15": 2546730496.0, - "16": 2546730496.0, - "17": 2546730496.0, - "18": 2546730496.0, - "19": 2546730496.0, - "20": 2546730496.0, - "21": 2546730496.0, - "22": 2546730496.0, - "23": 2546730496.0, - "24": 2546730496.0, - "25": 2546730496.0, - "26": 2546730496.0, - "27": 2546730496.0, - "28": 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a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json index b7b8787cd8f..3d55824e8d4 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/golden_values_dev_dgx_h100.json @@ -13,97 +13,97 @@ "7": 10.9275, "8": 10.91969, "9": 10.92241, - "10": 10.92325, - "11": 10.90758, - "12": 10.92281, - "13": 10.90611, - "14": 10.88927, - "15": 10.89641, - "16": 10.87453, - "17": 10.88139, - "18": 10.8804, - "19": 10.87739, - "20": 10.82169, - "21": 10.82193, - "22": 10.79733, - 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a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json index bfa04ba36c1..e3c735919a1 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM/golden_values_dev_dgx_gb200.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 6.93465, - "3": 0.37132, - "4": 0.35547, - "5": 0.34878, - "6": 0.34941, - "7": 0.34636, - "8": 0.3479, - "9": 0.34395, - "10": 0.33918, - "11": 0.33839, - "12": 0.343, - "13": 0.34069, - "14": 0.34137, - "15": 0.34111, - "16": 0.33814, 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a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json index 325158cf8b0..68cf5ade642 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_cp2_pp2_ep2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 10.37873, - "3": 0.72867, - "4": 0.67217, - "5": 0.67782, - "6": 0.67218, - "7": 0.67276, - "8": 0.67632, - "9": 0.67091, - "10": 0.67173, - "11": 0.67206, - "12": 0.67116, - "13": 0.67171, - "14": 0.66016, - "15": 0.65984, - "16": 0.65917, - "17": 0.66007, - "18": 0.65877, - "19": 0.65945, - "20": 0.66087, - "21": 0.66115, - "22": 0.65938, - "23": 0.665, - "24": 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b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json index 18613d708ea..29c154228bd 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel/golden_values_dev_dgx_a100.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 4.69151, - "3": 0.59356, - "4": 0.54, - "5": 0.53982, - "6": 0.53949, - "7": 0.53957, - "8": 0.53813, - "9": 0.53669, - "10": 0.53761, - "11": 0.53768, - "12": 0.53875, - "13": 0.53832, - "14": 0.53845, - "15": 0.538, - "16": 0.53727, - "17": 0.53748, - "18": 0.53724, - "19": 0.53663, - "20": 0.53757, - "21": 0.54985, - "22": 0.53892, - "23": 0.53695, - "24": 0.5381, - "25": 0.53689, - "26": 0.53883, - "27": 0.5407, - "28": 0.53977, - "29": 0.53959, - "30": 0.5377, - "31": 0.53839, - "32": 0.53827, - "33": 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a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_a100.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 6.64838, - "3": 0.59273, - "4": 0.59711, - "5": 0.5402, - "6": 0.53723, - "7": 0.53584, - "8": 0.53725, - "9": 0.54007, - "10": 0.54442, - "11": 0.53441, - "12": 0.53343, - "13": 0.53394, - "14": 0.53514, - "15": 0.53447, - "16": 0.53338, - "17": 0.53391, - "18": 0.53317, - "19": 0.54677, - "20": 0.53627, - "21": 0.5476, - "22": 0.5373, - "23": 0.54152, - "24": 0.54359, - "25": 0.5345, - "26": 0.53408, - "27": 0.53691, - "28": 0.53476, - "29": 0.53733, - "30": 0.53556, - "31": 0.53549, - "32": 0.53561, - "33": 0.53604, - "34": 0.5347, - "35": 0.53437, - "36": 0.5438, - "37": 0.53444, - "38": 0.53479, - "39": 0.53361, - "40": 0.53404, - "41": 0.53843, - 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diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json index a5f910db0cc..9fb4464c4f6 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_tp2_pp2_ep2_etp2_te_4experts2parallel_dp_last/golden_values_dev_dgx_gb200.json @@ -175,56 +175,56 @@ "end_step": 50, "step_interval": 1, "values": { - "1": 1059942400.0, - "2": 1224771584.0, - "3": 1226285056.0, - "4": 1226285056.0, - "5": 1226285056.0, - "6": 1226285056.0, - "7": 1226285056.0, - "8": 1226285056.0, - "9": 1226285056.0, - "10": 1226285056.0, - "11": 1226285056.0, - "12": 1226285056.0, - "13": 1226285056.0, - "14": 1226465792.0, - "15": 1226465792.0, - "16": 1226465792.0, - "17": 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a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_h100_2nd.json +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon/golden_values_dev_dgx_h100_2nd.json @@ -54,56 +54,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 10.82235, - "52": 10.80371, - "53": 10.83231, - "54": 10.8151, - "55": 10.82578, - "56": 10.77729, - "57": 10.75325, - "58": 10.80742, - "59": 10.79087, - "60": 10.73998, - "61": 10.79954, - "62": 10.81284, - "63": 10.72011, - "64": 10.78598, - "65": 10.68981, - "66": 10.76066, - "67": 10.73402, - "68": 10.8022, - "69": 10.78312, - "70": 10.77711, - "71": 10.76626, - "72": 10.73591, - "73": 10.72919, - "74": 10.62192, - "75": 10.69079, - "76": 10.65398, - "77": 10.82162, - "78": 10.76368, - "79": 10.70473, - "80": 10.69368, - "81": 10.72419, - "82": 10.74233, - "83": 10.66786, - "84": 10.6983, - "85": 10.714, - "86": 10.6383, - "87": 10.71809, - "88": 10.73508, - "89": 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b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json index a452733bca6..40cb1f6f814 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon/golden_values_dev_dgx_gb200.json @@ -6,104 +6,104 @@ "values": { "1": 10.94254, "2": 10.91055, - "3": 10.92749, - "4": 10.9172, - "5": 10.92125, - "6": 10.92734, - "7": 10.92252, - "8": 10.9202, - "9": 10.93241, - "10": 10.92732, - "11": 10.91806, - "12": 10.9348, - "13": 10.92014, - "14": 10.91043, - "15": 10.91833, - "16": 10.92081, - "17": 10.93475, - "18": 10.91652, - "19": 10.92803, - "20": 10.90288, - "21": 10.92619, - "22": 10.92311, - "23": 10.92973, - "24": 10.90455, - "25": 10.9139, - "26": 10.91431, - "27": 10.92941, - "28": 10.91943, - "29": 10.92005, - "30": 10.91149, - "31": 10.90651, - "32": 10.91828, - "33": 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b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json index c9b7e4b8b1d..7a7c3bdeab7 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading/golden_values_dev_dgx_h100.json @@ -6,54 +6,54 @@ "values": { "1": 11.06362, "2": 11.07891, - "3": 9.91543, - "4": 10.0055, - "5": 10.20278, - "6": 10.4242, - "7": 10.80496, - "8": 10.80271, - "9": 10.31584, - "10": 10.19739, - "11": 10.22741, - "12": 10.08478, - "13": 9.95048, - "14": 10.0836, - "15": 10.05826, - "16": 10.13331, - "17": 9.96659, - "18": 10.05414, - "19": 10.03153, - "20": 10.12479, - "21": 10.07847, - "22": 10.07475, - "23": 10.08324, - "24": 10.04691, - "25": 10.08554, - "26": 10.05148, - "27": 10.07512, - "28": 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--git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json index f529a646a7e..4af3201b402 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100.json @@ -11,98 +11,98 @@ "5": 10.90484, "6": 10.90203, "7": 10.89753, - "8": 10.91294, - "9": 10.91701, - "10": 10.91028, - "11": 10.90124, - "12": 10.89698, - "13": 10.88788, - "14": 10.89478, - "15": 10.87488, - "16": 10.87022, - "17": 10.86892, - "18": 10.85196, - "19": 10.87008, - "20": 10.7881, - "21": 10.77222, - "22": 10.7669, - "23": 10.75865, - "24": 10.71955, - "25": 10.71987, - 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a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100_2nd.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100_2nd.json index 78918e95bae..f30437bac4a 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100_2nd.json +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/golden_values_dev_dgx_h100_2nd.json @@ -54,56 +54,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 9.85363, - "52": 9.74288, - "53": 10.07163, - "54": 9.96275, - "55": 9.88233, - "56": 9.63455, - "57": 9.48649, - "58": 9.84879, - "59": 9.589, - "60": 9.5109, - "61": 9.703, - "62": 9.99634, - "63": 9.40054, - "64": 9.78477, - "65": 8.95365, - "66": 9.71813, - "67": 9.36915, - "68": 9.79814, - "69": 9.79674, 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"81": 10.66904, + "82": 10.65016, + "83": 10.6267, + "84": 10.61015, + "85": 10.60262, + "86": 10.6432, + "87": 10.63641, + "88": 10.63101, + "89": 10.59551, + "90": 10.58424, + "91": 10.60768, + "92": 10.58305, + "93": 10.56222, + "94": 10.59342, + "95": 10.57615, + "96": 10.57208, + "97": 10.55416, + "98": 10.55921, + "99": 10.55818, + "100": 10.5284 } }, "iteration-time": { @@ -589,56 +589,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 57.04071, - "52": 1.40134, - "53": 1.32404, - "54": 3.89868, - "55": 0.67679, - "56": 0.684, - "57": 0.68825, - "58": 0.68465, - "59": 0.68607, - "60": 0.68633, - "61": 0.6798, - "62": 0.68281, - "63": 0.68253, - "64": 0.68011, - "65": 0.6766, - "66": 0.67533, - "67": 0.67885, - "68": 0.67126, - "69": 0.6756, - "70": 0.67255, - "71": 0.67556, - "72": 0.67135, - "73": 0.66897, - "74": 0.66783, - "75": 0.66944, - "76": 0.66908, - "77": 0.66904, - "78": 0.67839, - "79": 0.6752, - "80": 0.67644, - "81": 0.6727, - "82": 0.67278, - "83": 0.66999, - "84": 0.67287, - "85": 0.67248, - "86": 0.6678, - "87": 0.67191, - "88": 0.66961, - "89": 0.67168, - "90": 0.67021, - "91": 0.66676, - "92": 0.66871, - "93": 0.67204, - "94": 0.67233, - "95": 0.66905, - "96": 0.6735, - "97": 0.67671, - "98": 0.67137, - "99": 0.67053, - "100": 0.67168 + "51": "nan", + "52": 20.08109, + "53": 1.44514, + "54": 3.53908, + "55": 0.6804, + "56": 0.67925, + "57": 0.67899, + "58": 0.68158, + "59": 0.67854, + "60": 0.67794, + "61": 0.68106, + "62": 0.68357, + "63": 0.67949, + "64": 0.68018, + "65": 0.68161, + "66": 0.68619, + "67": 0.67989, + "68": 0.67966, + "69": 0.6946, + "70": 0.67236, + "71": 0.67144, + "72": 0.66944, + "73": 0.67325, + "74": 0.66956, + "75": 0.66889, + "76": 0.66874, + "77": 0.67063, + "78": 0.67241, + "79": 0.67054, + "80": 0.6696, + "81": 0.67387, + "82": 0.67081, + "83": 0.67698, + "84": 0.66975, + "85": 0.67749, + "86": 0.66897, + "87": 0.66989, + "88": 0.672, + "89": 0.6687, + "90": 0.66866, + "91": 0.67163, + "92": 0.67312, + "93": 0.66877, + "94": 0.66989, + "95": 0.67069, + "96": 0.66771, + "97": 0.6697, + "98": 0.67028, + "99": 0.6707, + "100": 0.67357 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml index badd7a0b91a..cfd0c2b7132 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph/model_config.yaml @@ -93,6 +93,6 @@ METRICS: # - "iteration-time" - "lm loss" - "num-zeros" - - "mem-allocated-bytes" - - "mem-max-allocated-bytes" + # - "mem-allocated-bytes" + # - "mem-max-allocated-bytes" - "mtp_1 loss" diff --git a/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/golden_values_dev_dgx_h100.json index fdc5a1ae42b..456a85e7466 100644 --- a/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt_grpo_tp8tp4_pp1_ep8ep2_dp8_throughputtest/golden_values_dev_dgx_h100.json @@ -16,10 +16,10 @@ "end_step": 5, "step_interval": 1, "values": { - "1": 622876864.0, - "2": 622889216.0, + "1": 622876928.0, + "2": 622889280.0, "3": 623018176.0, - "4": 622966976.0, + "4": 622967040.0, "5": 623036672.0 } }, @@ -53,10 +53,10 @@ "step_interval": 1, "values": { "1": "nan", - "2": 139.78513, - "3": 88.37674, - "4": 89.09523, - "5": 84.93382 + "2": 136.37686, + "3": 88.63863, + "4": 88.5902, + "5": 84.06419 } } -} +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json index 00ed37733ae..2ce7eb10c7d 100644 --- a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp1_pp1/golden_values_dev_dgx_h100.json @@ -13,46 +13,46 @@ "7": 9.14378, "8": 9.14363, "9": 9.15072, - "10": 9.15239, - "11": 9.14608, - "12": 9.14125, + "10": 9.15235, + "11": 9.14607, + "12": 9.14126, "13": 9.14146, - "14": 9.14247, + "14": 9.14248, "15": 9.13422, - "16": 9.12606, - "17": 9.12413, + "16": 9.1261, + "17": 9.12414, "18": 9.12057, - "19": 9.1179, - "20": 9.09773, - "21": 9.0695, - "22": 9.06984, - "23": 9.07077, - "24": 9.06045, - "25": 9.05509, - "26": 9.05714, - "27": 9.04089, - "28": 9.0186, - "29": 9.00351, - "30": 8.99698, - "31": 8.9948, - "32": 8.98417, - "33": 8.97761, - "34": 8.9862, - "35": 8.94992, - "36": 8.9456, - "37": 8.92135, - "38": 8.94106, - "39": 8.92485, - "40": 8.87125, - "41": 8.89626, - "42": 8.87604, - "43": 8.87414, - "44": 8.84111, - "45": 8.81225, - "46": 8.79568, - "47": 8.84576, - "48": 8.77194, - "49": 8.78043, + "19": 9.11792, + "20": 9.09776, + "21": 9.06951, + "22": 9.06982, + "23": 9.07076, + "24": 9.06042, + "25": 9.05507, + "26": 9.05713, + "27": 9.04088, + "28": 9.01861, + "29": 9.00349, + "30": 8.99699, + "31": 8.99481, + "32": 8.98414, + "33": 8.97759, + "34": 8.98615, + "35": 8.94993, + "36": 8.94559, + "37": 8.92131, + "38": 8.94104, + "39": 8.9248, + "40": 8.87126, + "41": 8.89625, + "42": 8.87602, + "43": 8.87416, + "44": 8.84109, + "45": 8.81223, + "46": 8.79561, + "47": 8.84581, + "48": 8.77192, + "49": 8.78042, "50": 8.76196 } }, @@ -69,48 +69,48 @@ "6": 3584604.0, "7": 3478292.0, "8": 3414214.0, - "9": 3511551.0, - "10": 3544353.0, - "11": 3475513.0, - "12": 3519004.0, - "13": 3591792.0, - "14": 3549575.0, - "15": 3421322.0, - "16": 3383312.0, - "17": 3424142.0, - "18": 3509310.0, - "19": 3426210.0, - "20": 3465844.0, - "21": 3699866.0, - "22": 3474417.0, - "23": 3693512.0, - "24": 3405590.0, - "25": 3457789.0, - "26": 3479283.0, - "27": 3555496.0, - "28": 3497078.0, - "29": 3561734.0, - "30": 3708144.0, - "31": 3397570.0, - "32": 3467832.0, - "33": 3515682.0, - "34": 3501518.0, - "35": 3432575.0, - "36": 3454076.0, - "37": 3958864.0, - "38": 3488540.0, - "39": 3410013.0, - "40": 3614392.0, - "41": 3425670.0, - "42": 3643700.0, - "43": 3472723.0, - "44": 3448423.0, - "45": 3452103.0, - "46": 3585686.0, - "47": 3467299.0, - "48": 3462916.0, - "49": 3529603.0, - "50": 3411958.0 + "9": 3511615.0, + "10": 3544254.0, + "11": 3475542.0, + "12": 3518903.0, + "13": 3591808.0, + "14": 3549558.0, + "15": 3421139.0, + "16": 3383262.0, + "17": 3424156.0, + "18": 3509340.0, + "19": 3426265.0, + "20": 3465857.0, + "21": 3700002.0, + "22": 3474391.0, + "23": 3693456.0, + "24": 3405739.0, + "25": 3457807.0, + "26": 3479005.0, + "27": 3555423.0, + "28": 3497020.0, + "29": 3561639.0, + "30": 3708154.0, + "31": 3397641.0, + "32": 3467990.0, + "33": 3515524.0, + "34": 3501397.0, + "35": 3432545.0, + "36": 3453998.0, + "37": 3958872.0, + "38": 3488400.0, + "39": 3409868.0, + "40": 3614396.0, + "41": 3425689.0, + "42": 3643696.0, + "43": 3472836.0, + "44": 3448208.0, + "45": 3452155.0, + "46": 3585725.0, + "47": 3467335.0, + "48": 3462958.0, + "49": 3529637.0, + "50": 3412012.0 } }, "mem-allocated-bytes": { @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 4.3752, - "3": 0.18438, - "4": 0.17408, - "5": 0.17426, - "6": 0.17324, - "7": 0.17437, - "8": 0.16591, - "9": 0.16608, - "10": 0.16563, - "11": 0.16603, - "12": 0.1654, - "13": 0.16746, - "14": 0.16557, - "15": 0.16692, - "16": 0.16648, - "17": 0.16679, - "18": 0.1661, - "19": 0.16846, - "20": 0.17421, - "21": 0.16648, - "22": 0.16578, - "23": 0.16601, - "24": 0.16672, - "25": 0.16647, - "26": 0.16552, - "27": 0.16788, - "28": 0.16751, - "29": 0.1673, - "30": 0.1667, - "31": 0.16952, - "32": 0.16693, + "2": 2.95145, + "3": 0.19273, + "4": 0.16459, + "5": 0.16606, + "6": 0.16483, + "7": 0.16676, + "8": 0.16547, + "9": 0.16685, + "10": 0.16451, + "11": 0.16465, + "12": 0.16671, + "13": 0.16644, + "14": 0.16525, + "15": 0.16592, + "16": 0.16522, + "17": 0.1653, + "18": 0.1669, + "19": 0.16529, + "20": 0.16792, + "21": 0.16517, + "22": 0.1647, + "23": 0.16463, + "24": 0.16511, + "25": 0.16609, + "26": 0.16519, + "27": 0.165, + "28": 0.16473, + "29": 0.16478, + "30": 0.165, + "31": 0.165, + "32": 0.16449, "33": 0.16603, - "34": 0.17134, - "35": 0.16658, - "36": 0.16674, - "37": 0.16801, - "38": 0.16643, - "39": 0.1679, - "40": 0.16548, - "41": 0.16743, - "42": 0.16511, - "43": 0.16745, - "44": 0.16536, - "45": 0.16772, - "46": 0.16513, - "47": 0.16764, - "48": 0.16926, - "49": 0.16982, - "50": 0.16849 + "34": 0.16692, + "35": 0.166, + "36": 0.1666, + "37": 0.16698, + "38": 0.16698, + "39": 0.16518, + "40": 0.1664, + "41": 0.16497, + "42": 0.16636, + "43": 0.16518, + "44": 0.16521, + "45": 0.16639, + "46": 0.16697, + "47": 0.16507, + "48": 0.16548, + "49": 0.16581, + "50": 0.16511 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json index 8fb704f4745..b9a5b65a999 100644 --- a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json @@ -16,44 +16,44 @@ "10": 9.28289, "11": 9.28325, "12": 9.27371, - "13": 9.27127, - "14": 9.27213, - "15": 9.253, - "16": 9.24492, - "17": 9.24859, - "18": 9.22958, - "19": 9.23149, + "13": 9.27125, + "14": 9.27206, + "15": 9.25306, + "16": 9.24488, + "17": 9.24845, + "18": 9.22948, + "19": 9.23152, "20": 9.20816, - "21": 9.17058, - "22": 9.15059, - "23": 9.16841, - "24": 9.15088, - "25": 9.14428, - "26": 9.14731, - "27": 9.12298, - "28": 9.09703, - "29": 9.09381, - "30": 9.07824, - "31": 8.97182, - "32": 9.03154, - "33": 9.02015, - "34": 8.98673, - "35": 8.95912, - "36": 8.97146, - "37": 8.91452, - "38": 8.88791, - "39": 8.88878, - "40": 8.90648, - "41": 8.81814, - "42": 8.87399, - "43": 8.85661, - "44": 8.81711, - "45": 8.8137, - "46": 8.84467, - "47": 8.73715, - "48": 8.66933, - "49": 8.70117, - "50": 8.73514 + "21": 9.17053, + "22": 9.15063, + "23": 9.16842, + "24": 9.15083, + "25": 9.14427, + "26": 9.14735, + "27": 9.12292, + "28": 9.09717, + "29": 9.09386, + "30": 9.07828, + "31": 8.97181, + "32": 9.03161, + "33": 9.02026, + "34": 8.98671, + "35": 8.9592, + "36": 8.9714, + "37": 8.9145, + "38": 8.88788, + "39": 8.88875, + "40": 8.90647, + "41": 8.81818, + "42": 8.87409, + "43": 8.8566, + "44": 8.81693, + "45": 8.81374, + "46": 8.84461, + "47": 8.73716, + "48": 8.6692, + "49": 8.70086, + "50": 8.73502 } }, "num-zeros": { @@ -72,45 +72,45 @@ "9": 6624555.0, "10": 6529053.0, "11": 7466628.0, - "12": 6471518.0, - "13": 6003450.0, - "14": 8071967.0, - "15": 6529964.0, - "16": 7526726.0, - "17": 6035087.0, - "18": 6289754.0, - "19": 6162432.0, - "20": 6527695.0, - "21": 6981984.0, - "22": 7132788.0, - "23": 5928504.0, - "24": 6210076.0, - "25": 6993073.0, - "26": 6471296.0, - "27": 6355325.0, - "28": 6877023.0, - "29": 6380286.0, - "30": 6468637.0, - "31": 8165049.0, - "32": 6765693.0, - "33": 6355607.0, - "34": 6662298.0, - "35": 7065050.0, - "36": 6077046.0, - "37": 7785469.0, - "38": 6727037.0, - "39": 7315675.0, - "40": 6555109.0, - "41": 7314543.0, - "42": 6591977.0, - "43": 6927941.0, - "44": 7274324.0, - "45": 6680295.0, - "46": 6232304.0, - "47": 6496696.0, - "48": 6809772.0, - "49": 6753632.0, - "50": 6238092.0 + "12": 6471690.0, + "13": 6003426.0, + "14": 8072059.0, + "15": 6529973.0, + "16": 7526909.0, 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file diff --git a/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json index fc04ffa9c18..9249ba81c91 100644 --- a/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/t5/t5_11b_mcore_tp4_pp1/golden_values_dev_dgx_h100.json @@ -101,30 +101,30 @@ "step_interval": 1, "values": { "1": 40735715328.0, - "2": 44989894656.0, - "3": 44989894656.0, - "4": 44989894656.0, - "5": 44989894656.0, - "6": 44989894656.0, - "7": 44989894656.0, - "8": 44989894656.0, - "9": 44989894656.0, - "10": 44989894656.0, - "11": 44989894656.0, - "12": 44989894656.0, - "13": 44989894656.0, - "14": 44989894656.0, - "15": 44989894656.0, - "16": 44989894656.0, - "17": 44989894656.0, - "18": 44989894656.0, - "19": 44989894656.0, - "20": 44989894656.0, - "21": 44989894656.0, - "22": 44989894656.0, - "23": 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0.48014, + "100": 0.48024 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/t5/t5_mcore_tp4_pp1_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json b/tests/functional_tests/test_cases/t5/t5_mcore_tp4_pp1_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json index a890b5a0f5d..b7f3d727b59 100644 --- a/tests/functional_tests/test_cases/t5/t5_mcore_tp4_pp1_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json +++ b/tests/functional_tests/test_cases/t5/t5_mcore_tp4_pp1_resume_torch_dist/golden_values_dev_dgx_h100_2nd.json @@ -54,56 +54,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 7.36469, - "52": 7.12044, - "53": 7.09167, - "54": 7.22712, - "55": 7.13495, - "56": 7.20751, - "57": 7.31287, - "58": 6.99063, - "59": 7.09849, - "60": 7.12665, - "61": 7.10047, - "62": 7.23974, - "63": 7.14358, - "64": 7.06717, - "65": 6.98408, - "66": 7.03692, - "67": 7.02875, - "68": 7.12914, - "69": 7.01425, - "70": 7.04954, - "71": 6.89312, - "72": 6.98513, - "73": 6.96734, - "74": 6.90236, - "75": 7.05611, - "76": 6.95986, - "77": 7.06862, - "78": 7.0204, - "79": 6.8505, - "80": 6.92019, - "81": 6.95982, - "82": 7.04575, - "83": 6.98617, - "84": 6.99991, - "85": 6.83511, - "86": 7.04087, - "87": 6.96604, - "88": 6.90125, - "89": 6.80345, - "90": 7.22384, - "91": 6.70505, - "92": 7.03979, - "93": 6.8857, - "94": 7.04044, - "95": 6.84746, - "96": 6.9546, - "97": 6.94425, - "98": 6.86865, - "99": 6.9948, - "100": 6.96761 + "51": 7.10048, + "52": 6.98607, + "53": 6.90881, + "54": 6.89677, + "55": 6.86698, + "56": 6.9645, + "57": 6.91749, + "58": 6.83125, + "59": 6.8964, + "60": 6.74492, + "61": 6.88675, + "62": 6.82126, + "63": 6.88112, + "64": 6.91615, + "65": 6.77238, + "66": 6.80372, + "67": 6.71038, + "68": 6.72329, + "69": 6.8732, + "70": 6.6612, + "71": 6.85317, + "72": 6.77809, + "73": 6.805, + "74": 6.73143, + "75": 6.73546, + "76": 6.8296, + "77": 6.7623, + "78": 6.69475, + "79": 6.81605, + "80": 6.83984, + "81": 6.79472, + "82": 6.72263, + "83": 6.77789, + "84": 6.75974, + "85": 6.75429, + "86": 6.71001, + "87": 6.76412, + "88": 6.75749, + "89": 6.73619, + "90": 6.59343, + "91": 6.71047, + "92": 6.70172, + "93": 6.74908, + "94": 6.76105, + "95": 6.68659, + "96": 6.7752, + "97": 6.78365, + "98": 6.65691, + "99": 6.66343, + "100": 6.76127 } }, "num-zeros": { @@ -161,56 +161,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 41133.0, - "52": 43849.0, - "53": 43899.0, - "54": 41704.0, - "55": 43863.0, - "56": 43205.0, - "57": 42636.0, - "58": 43835.0, - "59": 44623.0, - "60": 41226.0, - "61": 39705.0, - "62": 44732.0, - "63": 44659.0, - "64": 45371.0, - "65": 44682.0, - "66": 45341.0, - "67": 43169.0, - "68": 42486.0, - "69": 43829.0, - "70": 45529.0, - "71": 43294.0, - "72": 44745.0, - "73": 45364.0, - "74": 42463.0, - "75": 44679.0, - "76": 43882.0, - "77": 42042.0, - "78": 40356.0, - "79": 38928.0, - "80": 41079.0, - "81": 45349.0, - "82": 43226.0, - "83": 38474.0, - "84": 42415.0, - "85": 43989.0, - "86": 45673.0, - "87": 40850.0, - "88": 41756.0, - "89": 41065.0, - "90": 44686.0, - "91": 46135.0, - "92": 41609.0, - "93": 43267.0, - "94": 39525.0, - "95": 43921.0, - "96": 44683.0, - "97": 45412.0, - "98": 41832.0, - "99": 45416.0, - "100": 42457.0 + "51": 41930.0, + "52": 42006.0, + "53": 47081.0, + "54": 44629.0, + "55": 44005.0, + "56": 44061.0, + "57": 43438.0, + "58": 44694.0, + "59": 42423.0, + "60": 43183.0, + "61": 44619.0, + "62": 43910.0, + "63": 42402.0, + "64": 43992.0, + "65": 45500.0, + "66": 42718.0, + "67": 43914.0, + "68": 43937.0, + "69": 41633.0, + "70": 41675.0, + "71": 45458.0, + "72": 45363.0, + "73": 42689.0, + "74": 44738.0, + "75": 43871.0, + "76": 43312.0, + "77": 41639.0, + "78": 43205.0, + "79": 45341.0, + "80": 45443.0, + "81": 42465.0, + "82": 39202.0, + "83": 45665.0, + "84": 43452.0, + "85": 43953.0, + "86": 43840.0, + "87": 41213.0, + "88": 43973.0, + "89": 40417.0, + "90": 39832.0, + "91": 46216.0, + "92": 46230.0, + "93": 43856.0, + "94": 45489.0, + "95": 41776.0, + "96": 41464.0, + "97": 42010.0, + "98": 43357.0, + "99": 39063.0, + "100": 39452.0 } }, "mem-allocated-bytes": { @@ -482,56 +482,56 @@ "48": "nan", "49": "nan", "50": "nan", - "51": 23.79487, - "52": 0.54117, - "53": 0.50294, - "54": 0.49381, - "55": 0.49765, - "56": 0.49437, - "57": 0.48794, - "58": 0.4927, - "59": 0.492, - "60": 0.50378, - "61": 0.49484, - "62": 0.49441, - "63": 0.49721, - "64": 0.49973, - "65": 0.49641, - "66": 0.49959, - "67": 0.49735, - "68": 0.49554, - "69": 0.4954, - "70": 0.49556, - "71": 0.49515, - "72": 0.49547, - "73": 0.49564, - "74": 0.50072, - "75": 0.50384, - "76": 0.50256, - "77": 0.49599, - "78": 0.49854, - "79": 0.49618, - "80": 0.5065, - "81": 0.50877, - "82": 0.49521, - "83": 0.51145, - "84": 0.49943, - "85": 0.49798, - "86": 0.49691, - "87": 0.49859, - "88": 0.50159, - "89": 0.49713, - "90": 0.49297, - "91": 0.49503, - "92": 0.49824, - "93": 0.49313, - "94": 0.4893, - "95": 0.48841, - "96": 0.49, - "97": 0.48974, - "98": 0.4896, - "99": 0.49265, - "100": 0.49225 + "51": "nan", + "52": 3.95932, + "53": 0.5131, + "54": 0.49076, + "55": 0.4923, + "56": 0.4931, + "57": 0.48957, + "58": 0.49119, + "59": 0.48912, + "60": 0.49067, + "61": 0.48911, + "62": 0.48803, + "63": 0.49583, + "64": 0.48428, + "65": 0.48302, + "66": 0.48338, + "67": 0.47974, + "68": 0.479, + "69": 0.47871, + "70": 0.47971, + "71": 0.4815, + "72": 0.4856, + "73": 0.4776, + "74": 0.48946, + "75": 0.47819, + "76": 0.47925, + "77": 0.48574, + "78": 0.49203, + "79": 0.47861, + "80": 0.47941, + "81": 0.47659, + "82": 0.47842, + "83": 0.48257, + "84": 0.47812, + "85": 0.4755, + "86": 0.47512, + "87": 0.4779, + "88": 0.47739, + "89": 0.47688, + "90": 0.47982, + "91": 0.48352, + "92": 0.47912, + "93": 0.47527, + "94": 0.4742, + "95": 0.47706, + "96": 0.47895, + "97": 0.47689, + "98": 0.47567, + "99": 0.47606, + "100": 0.47613 } } } \ No newline at end of file diff --git a/tests/test_utils/python_scripts/launch_nemo_run_workload.py b/tests/test_utils/python_scripts/launch_nemo_run_workload.py index cdb730fae63..78c8635f2d1 100644 --- a/tests/test_utils/python_scripts/launch_nemo_run_workload.py +++ b/tests/test_utils/python_scripts/launch_nemo_run_workload.py @@ -128,6 +128,17 @@ def main( "NCCL_DEBUG_FILE": "/opt/megatron-lm/assets_dir/logs/nccl_debug.log", "HF_HOME": "/mnt/hf_home", "TRANSFORMERS_OFFLINE": "1", + "NCCL_PROTO": "simple", + "NCCL_ALGO": "Ring", + "NCCL_COLLNET_ENABLE": "0", + "NCCL_NVLS_ENABLE": "0", + # Disable the EFA tuner plugin so NCCL_ALGO/NCCL_PROTO are + # actually respected instead of being overridden at runtime. + "NCCL_TUNER_PLUGIN": "", + # Match the NCCL default (4 MB) so buffer-chunking behaviour + # is the same as on Slurm nodes. + "NCCL_BUFFSIZE": "4194304", + "TORCH_NCCL_AVOID_RECORD_STREAMS": "1", }, packager=run.Packager(), volumes=artifacts, diff --git a/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml b/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml index bb6d056dfd4..bc75ed1f9a6 100644 --- a/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml +++ b/tests/test_utils/recipes/h100/moe-dynamic-inference-with-coordinator.yaml @@ -68,5 +68,5 @@ products: - test_case: [gpt_dynamic_inference_tp4_etp1_pp1_ep8_16B_logitsmatch_zmq_suspend_resume] products: - environment: [dev] - scope: [mr, mr-github] + scope: [mr-broken, mr-github-broken] platforms: [dgx_h100] diff --git a/tests/test_utils/recipes/h100/moe.yaml b/tests/test_utils/recipes/h100/moe.yaml index fc7ad97e983..6aeee162ab8 100644 --- a/tests/test_utils/recipes/h100/moe.yaml +++ b/tests/test_utils/recipes/h100/moe.yaml @@ -168,16 +168,6 @@ products: - environment: [dev] scope: [mr-broken, mr-github-broken] platforms: [dgx_h100] - - test_case: [gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_fine_grained_offloading] - products: - - environment: [dev] - scope: [mr] - platforms: [dgx_h100] - - test_case: [gpt3_moe_mcore_te_tp2_pp2_ep4_etp1_no_mtp_no_a2a_ovlp_fine_grained_offloading] - products: - - environment: [dev] - scope: [mr] - platforms: [dgx_h100] - test_case: [gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective] products: - environment: [dev] diff --git a/tests/test_utils/recipes/h100/unit-tests.yaml b/tests/test_utils/recipes/h100/unit-tests.yaml index b2dba522546..f90704b1264 100644 --- a/tests/test_utils/recipes/h100/unit-tests.yaml +++ b/tests/test_utils/recipes/h100/unit-tests.yaml @@ -185,3 +185,17 @@ products: scope: [unit-tests] n_repeat: [1] time_limit: [1800] + - test_case: [tests/unit_tests/transformer/test_multi_latent_attention.py] + products: + - environment: [lts, dev] + tag: [latest, legacy] + scope: [unit-tests] + n_repeat: [1] + time_limit: [1800] + - test_case: [tests/unit_tests/transformer/test_multi_token_prediction.py] + products: + - environment: [lts, dev] + tag: [latest, legacy] + scope: [unit-tests] + n_repeat: [1] + time_limit: [1800] diff --git a/tools/bisect.md b/tools/bisect.md new file mode 100644 index 00000000000..0e1f4adafd3 --- /dev/null +++ b/tools/bisect.md @@ -0,0 +1,65 @@ +# Bisect: Golden-Value Update Script + +`tools/bisect.sh` checks out a specific commit, runs the `gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic` functional test to generate fresh golden values, copies those values into the test-case directory, then re-runs the test to confirm it passes against the new baseline. + +It is used during a manual `git bisect` workflow to determine which commit first broke checkpoint-resume determinism. + +## Usage + +```bash +./tools/bisect.sh +``` + +| Argument | Description | +|----------|-------------| +| `` | Any ref (SHA, branch, tag) that `git checkout` can resolve | +| `` | Model subdirectory under `test_cases/` (e.g. `gpt`) | +| `` | Test-case name (e.g. `gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic`) | + +Example: + +```bash +./tools/bisect.sh abc1234 gpt gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic +``` + +## What it does + +| Step | Action | +|------|--------| +| 1 | *(commented out)* Stash local changes | +| 2 | *(commented out)* Fetch and checkout `` | +| 3 | *(commented out)* Cherry-pick each commit in `CHERRYPICK_SHAS` onto the tested commit, in order | +| 4 | Generate local job configs (`generate_local_jobs --environment dev --scope mr`) | +| 5 | Run `test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic.sh`, tee output to `log.txt` | +| 6 | Parse `OUTPUT_PATH` from the line `This test wrote results into …` | +| 7 | Copy `golden_values*.json` from `OUTPUT_PATH` into `tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/` | +| 8 | Re-run the test to validate against the newly copied golden values | + +Steps 1–3 are commented out because during a `git bisect run` workflow the caller is expected to handle the checkout externally. + +## Manual bisect workflow + +Because `git bisect run` conflicts with the pre-installed megatron-lm at `/opt/megatron-lm` inside the NeMo container, use a manual binary-search approach: + +1. Collect the commit list between GOOD and BAD, oldest-first. +2. Pick the midpoint commit. +3. Run `bisect.sh ` inside the container. +4. Mark the commit as GOOD or BAD based on the exit code / log output. +5. Narrow the range and repeat. + +## Key constants (as of 2026-04-10) + +| Label | SHA | +|-------|-----| +| GOOD | `696f164de1076f46c87c904e58ca293108459572` | +| BAD | `d30c3ae5469fe3f6a64d4fd2e63b6e7f7844ea81` | +| Cherry-picks (applied in order) | `bfa0f308aa5f2df76eb24e6b9fb86de5b39b5334` | + +## Critical caveat — clone location + +The NeMo 25.11 image ships megatron-lm pre-installed at `/opt/megatron-lm`. Cloning into `/opt/megatron-lm` silently reuses the existing directory (which is not a git repo), causing every bisect step to test the image's bundled code instead of the target commit. Always clone into `/tmp/megatron-lm`. + +## Output + +- `log.txt` — cumulative log for both test runs (appended with `tee -a`). +- `tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic/golden_values*.json` — updated golden values copied from the first run. diff --git a/tools/bisect.sh b/tools/bisect.sh new file mode 100755 index 00000000000..e50a2b82551 --- /dev/null +++ b/tools/bisect.sh @@ -0,0 +1,75 @@ +#!/usr/bin/env bash +# bisect.sh — checkout a commit, run a functional test to generate golden values, +# copy them into the test case directory, then run the test again to validate. +# +# Usage: +# ./bisect.sh +# +# Example: +# ./bisect.sh abc1234 gpt gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic + +set -uo pipefail + +COMMIT="${1:?Usage: $0 }" +MODEL="${2:?Usage: $0 }" +TESTCASE="${3:?Usage: $0 }" +# Commits cherry-picked onto the tested commit before running the test. +# Applied in order; add entries here to layer additional fixes on every bisect step. +CHERRYPICK_SHAS=( + "bfa0f308aa5f2df76eb24e6b9fb86de5b39b5334" +) +TEST_SCRIPT="test_cases/${MODEL}/${TESTCASE}.sh" +GOLDEN_DIR="tests/functional_tests/test_cases/${MODEL}/${TESTCASE}" +LOG_FILE="log.txt" + +REPO_ROOT="$(git rev-parse --show-toplevel)" + +log() { echo "[update-golden-values $(date +%H:%M:%S)] $*"; } +die() { log "FATAL: $*"; exit 1; } + +cd "${REPO_ROOT}" + +#1. Stash any local changes +log "Stashing local changes..." +git stash + +#2. Fetch and checkout the target commit +log "Fetching and checking out ${COMMIT}..." +git fetch origin "${COMMIT}" +git checkout "${COMMIT}" + +#3. Cherry-pick each patch commit onto the tested commit, in order +log "Cherry-picking ${#CHERRYPICK_SHAS[@]} commit(s)..." +for sha in "${CHERRYPICK_SHAS[@]}"; do + log " cherry-pick ${sha}..." + git fetch origin "${sha}" + git cherry-pick "${sha}" +done + +python -m tests.test_utils.python_scripts.generate_local_jobs --environment dev --scope mr + +# 4. Run the test and tee output to log.txt +log "Running ${TEST_SCRIPT} (output → ${LOG_FILE})..." +bash "${TEST_SCRIPT}" 2>&1 | tee -a "${LOG_FILE}" + +# 5. Parse OUTPUT_PATH from the last few lines of log.txt +log "Extracting OUTPUT_PATH from ${LOG_FILE}..." +OUTPUT_PATH="$(tail -20 "${LOG_FILE}" \ + | grep 'This test wrote results into' \ + | tail -1 \ + | sed 's/.*This test wrote results into //')" + +[[ -n "${OUTPUT_PATH}" ]] || die "Could not extract OUTPUT_PATH from ${LOG_FILE}" +log "OUTPUT_PATH=${OUTPUT_PATH}" + +# 6. Copy golden_values*.json into the test case directory +log "Copying golden values from ${OUTPUT_PATH} → ${GOLDEN_DIR}..." +mkdir -p "${GOLDEN_DIR}" +cp "${OUTPUT_PATH}"/golden_values*.json "${GOLDEN_DIR}/" +log "Copied: $(ls "${that sGOLDEN_DIR}"/golden_values*.json)" + +# 7. Run the test again to validate against the new golden values +log "Re-running ${TEST_SCRIPT} to validate..." +bash "${TEST_SCRIPT}" 2>&1 | tee -a "${LOG_FILE}" + +log "Done." \ No newline at end of file From d245c4438fb09f6b3b96e449400187c8b9063195 Mon Sep 17 00:00:00 2001 From: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Date: Tue, 14 Apr 2026 20:37:10 +0300 Subject: [PATCH 10/72] remove legacy biencoder and realm models (#4205) Signed-off-by: dimapihtar --- megatron/legacy/model/biencoder_model.py | 329 ----------------------- megatron/legacy/model/realm_model.py | 205 -------------- 2 files changed, 534 deletions(-) delete mode 100644 megatron/legacy/model/biencoder_model.py delete mode 100644 megatron/legacy/model/realm_model.py diff --git a/megatron/legacy/model/biencoder_model.py b/megatron/legacy/model/biencoder_model.py deleted file mode 100644 index df787686b41..00000000000 --- a/megatron/legacy/model/biencoder_model.py +++ /dev/null @@ -1,329 +0,0 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -import os -import torch -import sys - -from megatron.training import get_args, print_rank_0, get_tokenizer -from megatron.core import mpu -from megatron.training.checkpointing import fix_query_key_value_ordering -from megatron.training.checkpointing import get_checkpoint_tracker_filename -from megatron.training.checkpointing import get_checkpoint_name -from megatron.legacy.model.bert_model import bert_position_ids -from megatron.legacy.model.enums import AttnMaskType -from megatron.legacy.model.language_model import get_language_model -from megatron.legacy.model.utils import get_linear_layer -from megatron.legacy.model.utils import init_method_normal -from megatron.legacy.model.utils import scaled_init_method_normal -from .module import MegatronModule - -def get_model_provider(only_query_model=False, only_context_model=False, - biencoder_shared_query_context_model=False): - - def model_provider(pre_process=True, post_process=True): - """Build the model.""" - - print_rank_0('building Bienoder model ...') - model = biencoder_model_provider(only_query_model=only_query_model, - only_context_model = only_context_model, - biencoder_shared_query_context_model = \ - biencoder_shared_query_context_model, - pre_process=pre_process, post_process=post_process) - - return model - - return model_provider - - -def biencoder_model_provider(only_query_model=False, - only_context_model=False, - biencoder_shared_query_context_model=False, - pre_process=True, - post_process=True): - """Build the model.""" - - assert mpu.get_tensor_model_parallel_world_size() == 1 and \ - mpu.get_pipeline_model_parallel_world_size() == 1, \ - "Model parallel size > 1 not supported for ICT" - - print_rank_0('building BiEncoderModel...') - - # simpler to just keep using 2 tokentypes since - # the LM we initialize with has 2 tokentypes - model = BiEncoderModel( - num_tokentypes=2, - parallel_output=False, - only_query_model=only_query_model, - only_context_model=only_context_model, - biencoder_shared_query_context_model=\ - biencoder_shared_query_context_model, - pre_process=pre_process, - post_process=post_process) - - return model - - -class BiEncoderModel(MegatronModule): - """Bert-based module for Biencoder model.""" - - def __init__(self, - num_tokentypes=1, - parallel_output=True, - only_query_model=False, - only_context_model=False, - biencoder_shared_query_context_model=False, - pre_process=True, - post_process=True): - super(BiEncoderModel, self).__init__() - args = get_args() - - bert_kwargs = dict( - num_tokentypes=num_tokentypes, - parallel_output=parallel_output, - pre_process=pre_process, - post_process=post_process) - - self.biencoder_shared_query_context_model = \ - biencoder_shared_query_context_model - assert not (only_context_model and only_query_model) - self.use_context_model = not only_query_model - self.use_query_model = not only_context_model - self.biencoder_projection_dim = args.biencoder_projection_dim - - if self.biencoder_shared_query_context_model: - self.model = PretrainedBertModel(**bert_kwargs) - self._model_key = 'shared_model' - self.query_model, self.context_model = self.model, self.model - else: - if self.use_query_model: - # this model embeds (pseudo-)queries - Embed_input in the paper - self.query_model = PretrainedBertModel(**bert_kwargs) - self._query_key = 'query_model' - - if self.use_context_model: - # this model embeds evidence blocks - Embed_doc in the paper - self.context_model = PretrainedBertModel(**bert_kwargs) - self._context_key = 'context_model' - - def set_input_tensor(self, input_tensor): - """See megatron.legacy.model.transformer.set_input_tensor()""" - # this is just a placeholder and will be needed when model - # parallelism will be used - # self.language_model.set_input_tensor(input_tensor) - return - - def forward(self, query_tokens, query_attention_mask, query_types, - context_tokens, context_attention_mask, context_types): - """Run a forward pass for each of the models and - return the respective embeddings.""" - - if self.use_query_model: - query_logits = self.embed_text(self.query_model, - query_tokens, - query_attention_mask, - query_types) - else: - raise ValueError("Cannot embed query without the query model.") - if self.use_context_model: - context_logits = self.embed_text(self.context_model, - context_tokens, - context_attention_mask, - context_types) - else: - raise ValueError("Cannot embed block without the block model.") - return query_logits, context_logits - - @staticmethod - def embed_text(model, tokens, attention_mask, token_types): - """Embed a batch of tokens using the model""" - logits = model(tokens, - attention_mask, - token_types) - return logits - - def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): - """Save dict with state dicts of each of the models.""" - state_dict_ = {} - if self.biencoder_shared_query_context_model: - state_dict_[self._model_key] = \ - self.model.state_dict_for_save_checkpoint( - prefix=prefix, keep_vars=keep_vars) - else: - if self.use_query_model: - state_dict_[self._query_key] = \ - self.query_model.state_dict_for_save_checkpoint( - prefix=prefix, keep_vars=keep_vars) - - if self.use_context_model: - state_dict_[self._context_key] = \ - self.context_model.state_dict_for_save_checkpoint( - prefix=prefix, keep_vars=keep_vars) - - return state_dict_ - - def load_state_dict(self, state_dict, strict=True): - """Load the state dicts of each of the models""" - if self.biencoder_shared_query_context_model: - print_rank_0("Loading shared query-context model") - self.model.load_state_dict(state_dict[self._model_key], \ - strict=strict) - else: - if self.use_query_model: - print_rank_0("Loading query model") - self.query_model.load_state_dict( \ - state_dict[self._query_key], strict=strict) - - if self.use_context_model: - print_rank_0("Loading context model") - self.context_model.load_state_dict( \ - state_dict[self._context_key], strict=strict) - - def init_state_dict_from_bert(self): - """Initialize the state from a pretrained BERT model - on iteration zero of ICT pretraining""" - args = get_args() - - if args.bert_load is None: - print_rank_0("bert-load argument is None") - return - - tracker_filename = get_checkpoint_tracker_filename(args.bert_load) - if not os.path.isfile(tracker_filename): - raise FileNotFoundError("Could not find BERT checkpoint") - with open(tracker_filename, 'r') as f: - iteration = int(f.read().strip()) - assert iteration > 0 - - checkpoint_name = get_checkpoint_name(args.bert_load, iteration, False) - if mpu.get_data_parallel_rank() == 0: - print('global rank {} is loading BERT checkpoint {}'.format( - torch.distributed.get_rank(), checkpoint_name)) - - # Load the checkpoint. - try: - state_dict = torch.load(checkpoint_name, map_location='cpu') - except ModuleNotFoundError: - from megatron.legacy.fp16_deprecated import loss_scaler - # For backward compatibility. - print_rank_0(' > deserializing using the old code structure ...') - sys.modules['fp16.loss_scaler'] = sys.modules[ - 'megatron.fp16_deprecated.loss_scaler'] - sys.modules['megatron.fp16.loss_scaler'] = sys.modules[ - 'megatron.fp16_deprecated.loss_scaler'] - state_dict = torch.load(checkpoint_name, map_location='cpu') - sys.modules.pop('fp16.loss_scaler', None) - sys.modules.pop('megatron.fp16.loss_scaler', None) - except Exception: - print_rank_0('could not load the BERT checkpoint') - sys.exit() - - checkpoint_version = state_dict.get('checkpoint_version', 0) - - # load the LM state dict into each model - model_dict = state_dict['model']['language_model'] - - if self.biencoder_shared_query_context_model: - self.model.language_model.load_state_dict(model_dict) - fix_query_key_value_ordering(self.model, checkpoint_version) - else: - if self.use_query_model: - self.query_model.language_model.load_state_dict(model_dict) - # give each model the same ict_head to begin with as well - if self.biencoder_projection_dim > 0: - query_proj_state_dict = \ - self.state_dict_for_save_checkpoint()\ - [self._query_key]['projection_enc'] - fix_query_key_value_ordering(self.query_model, checkpoint_version) - - if self.use_context_model: - self.context_model.language_model.load_state_dict(model_dict) - if self.query_model is not None and \ - self.biencoder_projection_dim > 0: - self.context_model.projection_enc.load_state_dict\ - (query_proj_state_dict) - fix_query_key_value_ordering(self.context_model, checkpoint_version) - - -class PretrainedBertModel(MegatronModule): - """BERT-based encoder for queries or contexts used for - learned information retrieval.""" - - def __init__(self, num_tokentypes=2, - parallel_output=True, pre_process=True, post_process=True): - super(PretrainedBertModel, self).__init__() - - args = get_args() - tokenizer = get_tokenizer() - self.pad_id = tokenizer.pad - self.biencoder_projection_dim = args.biencoder_projection_dim - self.parallel_output = parallel_output - self.pre_process = pre_process - self.post_process = post_process - init_method = init_method_normal(args.init_method_std) - scaled_init_method = scaled_init_method_normal( - args.init_method_std, args.num_layers) - - self.language_model, self._language_model_key = get_language_model( - num_tokentypes=num_tokentypes, - add_pooler=False, - encoder_attn_mask_type=AttnMaskType.padding, - init_method=init_method, - scaled_init_method=scaled_init_method, - pre_process=self.pre_process, - post_process=self.post_process) - - if args.biencoder_projection_dim > 0: - self.projection_enc = get_linear_layer(args.hidden_size, - args.biencoder_projection_dim, - init_method) - self._projection_enc_key = 'projection_enc' - - def forward(self, input_ids, attention_mask, tokentype_ids=None): - extended_attention_mask = attention_mask.unsqueeze(1) - #extended_attention_mask = bert_extended_attention_mask(attention_mask) - position_ids = bert_position_ids(input_ids) - - lm_output = self.language_model(input_ids, - position_ids, - extended_attention_mask, - tokentype_ids=tokentype_ids) - # This mask will be used in average-pooling and max-pooling - pool_mask = (input_ids == self.pad_id).unsqueeze(2) - - # Taking the representation of the [CLS] token of BERT - pooled_output = lm_output[0, :, :] - - # Converting to float16 dtype - pooled_output = pooled_output.to(lm_output.dtype) - - # Output. - if self.biencoder_projection_dim: - pooled_output = self.projection_enc(pooled_output) - - return pooled_output - - def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): - """For easy load when model is combined with other heads, - add an extra key.""" - - state_dict_ = {} - state_dict_[self._language_model_key] \ - = self.language_model.state_dict_for_save_checkpoint( - prefix=prefix, keep_vars=keep_vars) - - if self.biencoder_projection_dim > 0: - state_dict_[self._projection_enc_key] = \ - self.projection_enc.state_dict(prefix=prefix, - keep_vars=keep_vars) - - return state_dict_ - - def load_state_dict(self, state_dict, strict=True): - """Customized load.""" - print_rank_0("loading pretrained weights") - self.language_model.load_state_dict( - state_dict[self._language_model_key], strict=strict) - - if self.biencoder_projection_dim > 0: - print_rank_0("loading projection head weights") - self.projection_enc.load_state_dict( - state_dict[self._projection_enc_key], strict=strict) diff --git a/megatron/legacy/model/realm_model.py b/megatron/legacy/model/realm_model.py deleted file mode 100644 index 1999cdb07cd..00000000000 --- a/megatron/legacy/model/realm_model.py +++ /dev/null @@ -1,205 +0,0 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -import os -import torch - -from megatron.training import get_args, print_rank_0 -from megatron.training.checkpointing import get_checkpoint_tracker_filename, get_checkpoint_name -from megatron.legacy.model import BertModel -from .module import MegatronModule -from megatron.core import mpu -from megatron.legacy.model.enums import AttnMaskType -from megatron.legacy.model.utils import get_linear_layer -from megatron.legacy.model.utils import init_method_normal -from megatron.legacy.model.language_model import get_language_model -from megatron.legacy.model.utils import scaled_init_method_normal -from megatron.legacy.model.bert_model import bert_extended_attention_mask, bert_position_ids - - -def general_ict_model_provider(only_query_model=False, only_block_model=False): - """Build the model.""" - args = get_args() - assert args.ict_head_size is not None, \ - "Need to specify --ict-head-size to provide an ICTBertModel" - assert mpu.get_tensor_model_parallel_world_size() == 1 and mpu.get_pipeline_model_parallel_world_size() == 1, \ - "Model parallel size > 1 not supported for ICT" - - print_rank_0('building ICTBertModel...') - - # simpler to just keep using 2 tokentypes since the LM we initialize with has 2 tokentypes - model = ICTBertModel( - ict_head_size=args.ict_head_size, - num_tokentypes=2, - parallel_output=True, - only_query_model=only_query_model, - only_block_model=only_block_model) - - return model - - -class ICTBertModel(MegatronModule): - """Bert-based module for Inverse Cloze task.""" - def __init__(self, - ict_head_size, - num_tokentypes=1, - parallel_output=True, - only_query_model=False, - only_block_model=False): - super(ICTBertModel, self).__init__() - bert_kwargs = dict( - ict_head_size=ict_head_size, - num_tokentypes=num_tokentypes, - parallel_output=parallel_output - ) - assert not (only_block_model and only_query_model) - self.use_block_model = not only_query_model - self.use_query_model = not only_block_model - - if self.use_query_model: - # this model embeds (pseudo-)queries - Embed_input in the paper - self.query_model = IREncoderBertModel(**bert_kwargs) - self._query_key = 'question_model' - - if self.use_block_model: - # this model embeds evidence blocks - Embed_doc in the paper - self.block_model = IREncoderBertModel(**bert_kwargs) - self._block_key = 'context_model' - - def forward(self, query_tokens, query_attention_mask, block_tokens, block_attention_mask): - """Run a forward pass for each of the models and return the respective embeddings.""" - query_logits = self.embed_query(query_tokens, query_attention_mask) - block_logits = self.embed_block(block_tokens, block_attention_mask) - return query_logits, block_logits - - def embed_query(self, query_tokens, query_attention_mask): - """Embed a batch of tokens using the query model""" - if self.use_query_model: - query_types = torch.cuda.LongTensor(*query_tokens.shape).fill_(0) - query_ict_logits, _ = self.query_model.forward(query_tokens, query_attention_mask, query_types) - return query_ict_logits - else: - raise ValueError("Cannot embed query without query model.") - - def embed_block(self, block_tokens, block_attention_mask): - """Embed a batch of tokens using the block model""" - if self.use_block_model: - block_types = torch.cuda.LongTensor(*block_tokens.shape).fill_(0) - block_ict_logits, _ = self.block_model.forward(block_tokens, block_attention_mask, block_types) - return block_ict_logits - else: - raise ValueError("Cannot embed block without block model.") - - def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): - """Save dict with state dicts of each of the models.""" - state_dict_ = {} - if self.use_query_model: - state_dict_[self._query_key] \ - = self.query_model.state_dict_for_save_checkpoint( - prefix=prefix, keep_vars=keep_vars) - - if self.use_block_model: - state_dict_[self._block_key] \ - = self.block_model.state_dict_for_save_checkpoint( - prefix=prefix, keep_vars=keep_vars) - - return state_dict_ - - def load_state_dict(self, state_dict, strict=True): - """Load the state dicts of each of the models""" - if self.use_query_model: - print("Loading ICT query model", flush=True) - self.query_model.load_state_dict( - state_dict[self._query_key], strict=strict) - - if self.use_block_model: - print("Loading ICT block model", flush=True) - self.block_model.load_state_dict( - state_dict[self._block_key], strict=strict) - - def init_state_dict_from_bert(self): - """Initialize the state from a pretrained BERT model on iteration zero of ICT pretraining""" - args = get_args() - tracker_filename = get_checkpoint_tracker_filename(args.bert_load) - if not os.path.isfile(tracker_filename): - raise FileNotFoundError("Could not find BERT load for ICT") - with open(tracker_filename, 'r') as f: - iteration = int(f.read().strip()) - assert iteration > 0 - - checkpoint_name = get_checkpoint_name(args.bert_load, iteration, False) - if mpu.get_data_parallel_rank() == 0: - print('global rank {} is loading checkpoint {}'.format( - torch.distributed.get_rank(), checkpoint_name)) - - try: - state_dict = torch.load(checkpoint_name, map_location='cpu') - except Exception: - raise ValueError("Could not load checkpoint") - - # load the LM state dict into each model - model_dict = state_dict['model']['language_model'] - self.query_model.language_model.load_state_dict(model_dict) - self.block_model.language_model.load_state_dict(model_dict) - - # give each model the same ict_head to begin with as well - query_ict_head_state_dict = self.state_dict_for_save_checkpoint()[self._query_key]['ict_head'] - self.block_model.ict_head.load_state_dict(query_ict_head_state_dict) - - -class IREncoderBertModel(MegatronModule): - """BERT-based encoder for queries or blocks used for learned information retrieval.""" - def __init__(self, ict_head_size, num_tokentypes=2, parallel_output=True): - super(IREncoderBertModel, self).__init__() - args = get_args() - - self.ict_head_size = ict_head_size - self.parallel_output = parallel_output - init_method = init_method_normal(args.init_method_std) - scaled_init_method = scaled_init_method_normal(args.init_method_std, - args.num_layers) - - self.language_model, self._language_model_key = get_language_model( - num_tokentypes=num_tokentypes, - add_pooler=True, - encoder_attn_mask_type=AttnMaskType.padding, - init_method=init_method, - scaled_init_method=scaled_init_method) - - self.ict_head = get_linear_layer(args.hidden_size, ict_head_size, init_method) - self._ict_head_key = 'ict_head' - - def forward(self, input_ids, attention_mask, tokentype_ids=None): - extended_attention_mask = bert_extended_attention_mask( - attention_mask, next(self.language_model.parameters()).dtype) - position_ids = bert_position_ids(input_ids) - - lm_output, pooled_output = self.language_model( - input_ids, - position_ids, - extended_attention_mask, - tokentype_ids=tokentype_ids) - - # Output. - ict_logits = self.ict_head(pooled_output) - return ict_logits, None - - def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False): - """For easy load when model is combined with other heads, - add an extra key.""" - - state_dict_ = {} - state_dict_[self._language_model_key] \ - = self.language_model.state_dict_for_save_checkpoint(prefix=prefix, - keep_vars=keep_vars) - state_dict_[self._ict_head_key] \ - = self.ict_head.state_dict(prefix=prefix, - keep_vars=keep_vars) - return state_dict_ - - def load_state_dict(self, state_dict, strict=True): - """Customized load.""" - self.language_model.load_state_dict( - state_dict[self._language_model_key], strict=strict) - self.ict_head.load_state_dict( - state_dict[self._ict_head_key], strict=strict) - - From 6636eb073359387f20c8a207f72c5282c22a6660 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 14 Apr 2026 23:52:49 +0200 Subject: [PATCH 11/72] ci: add configurable launcher support for functional tests (ft_launcher / torchrun) (#4298) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .../shell_test_utils/_run_training.sh | 23 +++++++++++++++---- .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + 7 files changed, 24 insertions(+), 5 deletions(-) diff --git a/tests/functional_tests/shell_test_utils/_run_training.sh b/tests/functional_tests/shell_test_utils/_run_training.sh index 8f848a24add..654faac31d4 100644 --- a/tests/functional_tests/shell_test_utils/_run_training.sh +++ b/tests/functional_tests/shell_test_utils/_run_training.sh @@ -159,10 +159,13 @@ MASTER_PORT=${MASTER_PORT:-6000} NUM_NODES=${NUM_NODES:-${SLURM_NNODES:-1}} GPUS_PER_NODE=${GPUS_PER_NODE:-8} NODE_RANK=${SLURM_NODEID:-${SLURM_NODEID:-0}} -LAST_RANK=$((GPUS_PER_NODE - 1)) +LAST_RANK=$((GPUS_PER_NODE - 1)) export LOG_DIR=$OUTPUT_PATH/logs/$REPEAT mkdir -p $LOG_DIR +# Read launcher type from model config (default: torchrun) +LAUNCHER=$(/usr/local/bin/yq '.LAUNCHER // "torchrun"' "$TRAINING_PARAMS_PATH") + DISTRIBUTED_ARGS=( --nproc_per_node $GPUS_PER_NODE --nnodes $NUM_NODES @@ -176,11 +179,21 @@ DISTRIBUTED_ARGS=( # Start training if [[ "$IS_NEMO_TEST" == "true" ]]; then - uv run --no-sync python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} \ - --no-python /opt/venv/bin/$TRAINING_SCRIPT_PATH "${PARAMS[@]}" && EXIT_CODE=0 || EXIT_CODE=$? + if [[ "$LAUNCHER" == "ft_launcher" ]]; then + ft_launcher ${DISTRIBUTED_ARGS[@]} \ + --no-python /opt/venv/bin/$TRAINING_SCRIPT_PATH "${PARAMS[@]}" && EXIT_CODE=0 || EXIT_CODE=$? + else + uv run --no-sync python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} \ + --no-python /opt/venv/bin/$TRAINING_SCRIPT_PATH "${PARAMS[@]}" && EXIT_CODE=0 || EXIT_CODE=$? + fi else - uv run --no-sync python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} \ - $TRAINING_SCRIPT_PATH "${PARAMS[@]}" && EXIT_CODE=0 || EXIT_CODE=$? + if [[ "$LAUNCHER" == "ft_launcher" ]]; then + ft_launcher ${DISTRIBUTED_ARGS[@]} \ + $TRAINING_SCRIPT_PATH "${PARAMS[@]}" && EXIT_CODE=0 || EXIT_CODE=$? + else + uv run --no-sync python -m torch.distributed.run ${DISTRIBUTED_ARGS[@]} \ + $TRAINING_SCRIPT_PATH "${PARAMS[@]}" && EXIT_CODE=0 || EXIT_CODE=$? + fi fi # Run after script diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml index bc18bf3e2d8..4f2d19ce559 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml @@ -61,3 +61,4 @@ MODEL_ARGS: --async-save: true --use-persistent-ckpt-worker: true TEST_TYPE: regular # Usually ckpt-resume, but as a WAR to #513 set to regular +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/model_config.yaml index 42c361d5f8a..d226ebb89c5 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances/model_config.yaml @@ -57,3 +57,4 @@ MODEL_ARGS: --async-save: true --use-persistent-ckpt-worker: true TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml index 6cc70da70fc..12acfaf8402 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml @@ -57,3 +57,4 @@ MODEL_ARGS: --async-strategy: mcore --use-persistent-ckpt-worker: true TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/model_config.yaml index 329639ad0c9..e885bc255aa 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/model_config.yaml +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer/model_config.yaml @@ -57,3 +57,4 @@ MODEL_ARGS: --no-bias-gelu-fusion: true --log-memory-to-tensorboard: true TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/model_config.yaml index 159a7d1f4c2..37224a25cd2 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/model_config.yaml +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances/model_config.yaml @@ -63,3 +63,4 @@ MODEL_ARGS: --async-save: true --use-persistent-ckpt-worker: true TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml index 7c292c0fa53..ff52550c238 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml @@ -67,3 +67,4 @@ MODEL_ARGS: --async-save: true --use-persistent-ckpt-worker: true TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher From d530a042eaaebad65230719c29e41129639b2afb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Wed, 15 Apr 2026 00:09:05 +0200 Subject: [PATCH 12/72] chore: document --target main for local Docker builds (#4307) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .claude/skills/build-and-test/SKILL.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.claude/skills/build-and-test/SKILL.md b/.claude/skills/build-and-test/SKILL.md index 52270b15fe4..6bd823a42e1 100644 --- a/.claude/skills/build-and-test/SKILL.md +++ b/.claude/skills/build-and-test/SKILL.md @@ -47,9 +47,14 @@ docker pull ${GITLAB_HOST}/adlr/megatron-lm/mcore_ci_dev:main **Option B — Build from scratch (works for everyone)** +> ⚠️ `Dockerfile.ci.dev` has two stages: `main` and `jet`. The `jet` stage +> requires an internal build secret and will fail without it. Always pass +> `--target main` to stop at the public stage. + ```bash # dev image (default) docker build \ + --target main \ --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev) \ --build-arg IMAGE_TYPE=dev \ -f docker/Dockerfile.ci.dev \ @@ -57,6 +62,7 @@ docker build \ # lts image docker build \ + --target main \ --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.lts) \ --build-arg IMAGE_TYPE=lts \ -f docker/Dockerfile.ci.dev \ @@ -450,3 +456,4 @@ PR's changes or is a pre-existing issue on `main`. | `No space left on device` during uv ops | Cache fills container's `/root/.cache/` | Mount a host cache dir via `-v $HOME/.cache/uv:/root/.cache/uv` | | Pre-commit fails with linting errors | Code style violations | Run `BASE_REF=main CHECK_ONLY=false bash tools/autoformat.sh` | | Port collision on multi-GPU runs | torchrun binding conflicts | Use `torch.distributed.run` via the container entry point | +| `docker build` fails with secret-related error | `Dockerfile.ci.dev` has a `jet` stage that requires an internal secret | Add `--target main` to stop before the `jet` stage | From 97aca2f8878f9a59ed506981d5495da32d7a4ba3 Mon Sep 17 00:00:00 2001 From: Maanu Grover <109391026+maanug-nv@users.noreply.github.com> Date: Tue, 14 Apr 2026 16:10:24 -0700 Subject: [PATCH 13/72] Extract args init to launch scripts (#4225) Signed-off-by: Maanu Grover Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com> --- .../inference/gpt/gpt_dynamic_inference.py | 6 +-- .../gpt_dynamic_inference_with_coordinator.py | 5 ++- .../inference/gpt/gpt_static_inference.py | 6 +-- examples/mimo/train.py | 4 +- megatron/training/arguments.py | 36 ++++++++++++++++ megatron/training/initialize.py | 41 +------------------ megatron/training/training.py | 4 -- pretrain_bert.py | 5 ++- pretrain_gpt.py | 8 ++-- pretrain_mamba.py | 7 +++- pretrain_t5.py | 4 +- pretrain_vlm.py | 8 ++-- train_rl.py | 8 ++-- 13 files changed, 73 insertions(+), 69 deletions(-) diff --git a/examples/inference/gpt/gpt_dynamic_inference.py b/examples/inference/gpt/gpt_dynamic_inference.py index f02aae9c221..02a257c1b46 100644 --- a/examples/inference/gpt/gpt_dynamic_inference.py +++ b/examples/inference/gpt/gpt_dynamic_inference.py @@ -11,6 +11,7 @@ from collections import defaultdict from typing import Dict, List, Optional +from megatron.training.arguments import parse_and_validate_args import torch from tqdm import tqdm @@ -279,10 +280,11 @@ def _process_step_result(result): def main(): """Run dynamic inference.""" # Initialize Megatron. - initialize_megatron( + args = parse_and_validate_args( extra_args_provider=add_inference_args, args_defaults={'no_load_rng': True, 'no_load_optim': True}, ) + initialize_megatron() # Start Nsight profiler. if os.environ.get("NSIGHT_PREFIX"): @@ -294,8 +296,6 @@ def main(): configure_nvtx_profiling(True) - args = get_args() - # Build tokenizer tokenizer = build_tokenizer(args) diff --git a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py index d34749e3a5a..247404d537e 100644 --- a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py +++ b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py @@ -9,6 +9,7 @@ from collections import defaultdict from typing import List +from megatron.training.arguments import parse_and_validate_args import torch import torch.distributed as dist @@ -202,12 +203,12 @@ async def main( # enable inference mode in the very beginning as some fp8 optimizations # check for it. with torch.inference_mode(): - initialize_megatron( + args = parse_and_validate_args( extra_args_provider=add_inference_args, args_defaults={'no_load_rng': True, 'no_load_optim': True}, ) + initialize_megatron() - args = get_args() tokenizer = get_tokenizer() # Sampling params. diff --git a/examples/inference/gpt/gpt_static_inference.py b/examples/inference/gpt/gpt_static_inference.py index 17cf7c53b05..d3dd619eaa1 100644 --- a/examples/inference/gpt/gpt_static_inference.py +++ b/examples/inference/gpt/gpt_static_inference.py @@ -5,6 +5,7 @@ import time from argparse import Namespace +from megatron.training.arguments import parse_and_validate_args import torch from megatron.core.inference.contexts import StaticInferenceContext @@ -121,7 +122,7 @@ def main(): # Note: The default args passed here can be overwritten by using appropriate params (check arguments.py file) # Micro batch size is not needed to be set by user. (It is calculated based on inference-batch-times-seqlen-threshold argument) - initialize_megatron( + args = parse_and_validate_args( extra_args_provider=add_static_inference_args, args_defaults={ 'no_load_rng': True, @@ -130,8 +131,7 @@ def main(): 'exit_on_missing_checkpoint': True, }, ) - - args = get_args() + initialize_megatron() model = get_model_for_inference() diff --git a/examples/mimo/train.py b/examples/mimo/train.py index f4bab99d80f..a89c83728e0 100644 --- a/examples/mimo/train.py +++ b/examples/mimo/train.py @@ -9,6 +9,7 @@ from functools import partial from typing import Any, Dict, Iterator +from megatron.training.arguments import parse_and_validate_args import torch from megatron.training import get_args, pretrain, print_rank_0 @@ -275,11 +276,10 @@ def model_provider( if __name__ == "__main__": train_valid_test_datasets_provider.is_distributed = True + parse_and_validate_args(args_defaults={}, extra_args_provider=add_mimo_args) pretrain( train_valid_test_datasets_provider, model_provider, ModelType.encoder_or_decoder, forward_step, - args_defaults={}, - extra_args_provider=add_mimo_args, ) diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index bca2c602587..31433704b6b 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -31,6 +31,7 @@ ) from megatron.core.activations import squared_relu from megatron.core.fusions.fused_bias_geglu import quick_gelu +from megatron.training.global_vars import set_global_variables from megatron.training.utils import ( get_device_arch_version, update_use_dist_ckpt, @@ -85,6 +86,35 @@ def add_megatron_arguments(parser: argparse.ArgumentParser): return parser +def parse_and_validate_args(extra_args_provider=None, ignore_unknown_args=False, args_defaults={}): + args = parse_args(extra_args_provider, ignore_unknown_args) + + if args.use_checkpoint_args or args_defaults.get("use_checkpoint_args", False): + from megatron.training.checkpointing import load_args_from_checkpoint + + assert args.load is not None or args.pretrained_checkpoint is not None, "--use-checkpoint-args requires --load or --pretrained-checkpoint argument" + assert args.non_persistent_ckpt_type != "local", ( + "--use-checkpoint-args is not supported with --non_persistent_ckpt_type=local. " + "Two-stage checkpoint loading is not implemented, and all arguments must be defined " + "before initializing LocalCheckpointManager." + ) + load_args_from_checkpoint(args, load_arg='pretrained_checkpoint') + load_args_from_checkpoint(args) + + if args.yaml_cfg is not None: + from megatron.training.yaml_arguments import validate_yaml + + args = validate_yaml(args, args_defaults) + else: + validate_args(args, args_defaults) + + # set global args, build tokenizer, and set adlr-autoresume, + # tensorboard-writer, and timers. + set_global_variables(args) + + return args + + def parse_args(extra_args_provider=None, ignore_unknown_args=False): """Parse all arguments.""" parser = argparse.ArgumentParser(description='Megatron-LM Arguments', @@ -293,6 +323,12 @@ def tuple_type(x): def validate_args(args, defaults={}): + # Prep for checkpoint conversion. + if args.ckpt_convert_format is not None: + assert args.ckpt_convert_save is not None + assert args.load is not None + args.exit_on_missing_checkpoint = True + # Temporary assert args.non_persistent_ckpt_type in ['global', 'local', None], \ 'Currently only global and local checkpoints are supported' diff --git a/megatron/training/initialize.py b/megatron/training/initialize.py index 212d76e8b35..ff655502019 100644 --- a/megatron/training/initialize.py +++ b/megatron/training/initialize.py @@ -32,25 +32,17 @@ get_tensorboard_writer, inprocess_restart, ) -from megatron.training.arguments import parse_args, validate_args from megatron.training.async_utils import init_persistent_async_worker -from megatron.training.checkpointing import load_args_from_checkpoint -from megatron.training.global_vars import set_global_variables from megatron.training.utils import is_rank0, print_rank_0, warn_rank_0 -from megatron.training.yaml_arguments import validate_yaml logger = logging.getLogger(__name__) def initialize_megatron( - extra_args_provider=None, - args_defaults={}, - ignore_unknown_args=False, allow_no_cuda=False, skip_mpu_initialization=False, get_embedding_ranks=None, get_position_embedding_ranks=None, - parsed_args=None, store=None, ): """Set global variables, initialize distributed, and @@ -65,38 +57,7 @@ def initialize_megatron( # Make sure cuda is available. assert torch.cuda.is_available(), "Megatron requires CUDA." - # Parse arguments - if parsed_args is None: - args = parse_args(extra_args_provider, ignore_unknown_args) - else: - args = parsed_args - - # Prep for checkpoint conversion. - if args.ckpt_convert_format is not None: - assert args.ckpt_convert_save is not None - assert args.load is not None - args.exit_on_missing_checkpoint = True - - if args.use_checkpoint_args or args_defaults.get("use_checkpoint_args", False): - assert ( - args.load is not None or args.pretrained_checkpoint is not None - ), "--use-checkpoint-args requires --load or --pretrained-checkpoint argument" - assert args.non_persistent_ckpt_type != "local", ( - "--use-checkpoint-args is not supported with --non_persistent_ckpt_type=local. " - "Two-stage checkpoint loading is not implemented, and all arguments must be defined " - "before initializing LocalCheckpointManager." - ) - load_args_from_checkpoint(args, load_arg='pretrained_checkpoint') - load_args_from_checkpoint(args) - - if args.yaml_cfg is not None: - args = validate_yaml(args, args_defaults) - else: - validate_args(args, args_defaults) - - # set global args, build tokenizer, and set adlr-autoresume, - # tensorboard-writer, and timers. - set_global_variables(args) + args = get_args() # set logging level setup_logging() diff --git a/megatron/training/training.py b/megatron/training/training.py index e3b80224716..7fa00c048b7 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -827,8 +827,6 @@ def pretrain( model_type, forward_step_func, process_non_loss_data_func=None, - extra_args_provider=None, - args_defaults={}, get_embedding_ranks=None, get_position_embedding_ranks=None, non_loss_data_func=None, @@ -894,8 +892,6 @@ def pretrain( # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron( - extra_args_provider=extra_args_provider, - args_defaults=args_defaults, get_embedding_ranks=get_embedding_ranks, get_position_embedding_ranks=get_position_embedding_ranks, store=store, diff --git a/pretrain_bert.py b/pretrain_bert.py index 3a66a124b6e..a5a75d87879 100644 --- a/pretrain_bert.py +++ b/pretrain_bert.py @@ -15,7 +15,7 @@ from megatron.core.models.bert.bert_model import BertModel from megatron.training import pretrain from megatron.training.utils import average_losses_across_data_parallel_group -from megatron.training.arguments import core_transformer_config_from_args +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args from megatron.core.transformer.spec_utils import import_module from megatron.core.models.bert.bert_layer_specs import bert_layer_with_transformer_engine_spec, bert_layer_local_spec from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer @@ -181,6 +181,7 @@ def train_valid_test_datasets_provider(train_val_test_num_samples, vp_stage=None # Temporary for transition to core datasets train_valid_test_datasets_provider.is_distributed = True + parse_and_validate_args(args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) pretrain(train_valid_test_datasets_provider, model_provider, ModelType.encoder_or_decoder, - forward_step, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) + forward_step) diff --git a/pretrain_gpt.py b/pretrain_gpt.py index 31eee0f4dc6..929a9d0f866 100644 --- a/pretrain_gpt.py +++ b/pretrain_gpt.py @@ -41,7 +41,7 @@ ) from megatron.training.datasets.sft_dataset import SFTDataset from megatron.core.transformer.multi_token_prediction import mtp_on_this_rank, get_mtp_ranks -from megatron.training.arguments import core_transformer_config_from_args +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args from megatron.training.datasets.fim_dataset import GPTFIMDataset, GPTFIMDatasetConfig from megatron.training.utils import ( get_batch_on_this_cp_rank, @@ -409,13 +409,15 @@ def get_embedding_ranks(pp_ranks: List[int]): # Optionally enable inprocess restart on pretrain pretrain, store = inprocess_restart.maybe_wrap_for_inprocess_restart(pretrain) + args = parse_and_validate_args( + extra_args_provider=add_modelopt_args if has_nvidia_modelopt else None, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + ) pretrain( train_valid_test_datasets_provider, partial(model_provider, gpt_builder), ModelType.encoder_or_decoder, forward_step, - args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, - extra_args_provider=add_modelopt_args if has_nvidia_modelopt else None, store=store, get_embedding_ranks=get_embedding_ranks, ) diff --git a/pretrain_mamba.py b/pretrain_mamba.py index a30979af714..590eb92ab28 100644 --- a/pretrain_mamba.py +++ b/pretrain_mamba.py @@ -42,6 +42,7 @@ print_rank_0, set_startup_timestamps, ) +from megatron.training.arguments import parse_and_validate_args from megatron.training.datasets.sft_dataset import SFTDataset from megatron.training.utils import ( get_batch_on_this_cp_rank, @@ -356,11 +357,13 @@ def train_valid_test_datasets_provider(train_val_test_num_samples, vp_stage=None # Optionally enable inprocess restart on pretrain pretrain, store = inprocess_restart.maybe_wrap_for_inprocess_restart(pretrain) + args = parse_and_validate_args( + extra_args_provider=add_modelopt_args if has_nvidia_modelopt else None, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + ) pretrain(train_valid_test_datasets_provider, partial(model_provider, mamba_builder), ModelType.encoder_or_decoder, forward_step, - args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, store=store, - extra_args_provider=add_modelopt_args if has_nvidia_modelopt else None, ) diff --git a/pretrain_t5.py b/pretrain_t5.py index 7a88251d0b9..2b10fa3ffe3 100644 --- a/pretrain_t5.py +++ b/pretrain_t5.py @@ -26,7 +26,7 @@ get_t5_encoder_with_transformer_engine_block_spec, ) from megatron.training import get_args, get_timers, pretrain, print_rank_0 -from megatron.training.arguments import core_transformer_config_from_args +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args from pretrain_gpt import loss_func """ @@ -269,12 +269,12 @@ def t5_position_embedding_ranks(pp_ranks): # Temporary for transition to core datasets train_valid_test_datasets_provider.is_distributed = True + parse_and_validate_args(args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) pretrain( train_valid_test_datasets_provider, model_provider, ModelType.encoder_or_decoder, forward_step, - args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}, get_embedding_ranks=t5_embedding_ranks, get_position_embedding_ranks=t5_position_embedding_ranks, ) diff --git a/pretrain_vlm.py b/pretrain_vlm.py index 9da1afa669f..a6aef770002 100644 --- a/pretrain_vlm.py +++ b/pretrain_vlm.py @@ -38,7 +38,7 @@ pretrain, print_rank_0, ) -from megatron.training.arguments import core_transformer_config_from_args +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args from pretrain_gpt import loss_func @@ -439,13 +439,15 @@ def llava_position_embedding_ranks(pp_ranks): if __name__ == "__main__": train_valid_test_datasets_provider.is_distributed = True + parse_and_validate_args( + extra_args_provider=add_vlm_extra_args, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, + ) pretrain( train_valid_test_datasets_provider, model_provider, ModelType.encoder_or_decoder, forward_step, - args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, - extra_args_provider=add_vlm_extra_args, get_embedding_ranks=llava_embedding_ranks, get_position_embedding_ranks=llava_position_embedding_ranks, ) diff --git a/train_rl.py b/train_rl.py index f75bba12997..8bcee5f096d 100644 --- a/train_rl.py +++ b/train_rl.py @@ -23,7 +23,7 @@ ) from megatron.training import get_args, get_timers, pretrain, print_rank_0 from megatron.training.utils import is_hybrid_model -from megatron.training.arguments import core_transformer_config_from_args +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args from model_provider import model_provider from megatron.core.packed_seq_params import PackedSeqParams @@ -410,11 +410,13 @@ def _model_builder( pg_collection=pg_collection, ) + parse_and_validate_args( + extra_args_provider=add_inference_args, + args_defaults={}, + ) pretrain( None, # we don't need to build any datasets for RL training partial(model_provider, _model_builder), ModelType.encoder_or_decoder, forward_step, - args_defaults={}, - extra_args_provider=add_inference_args, ) From c2d1a8f7e508f216b4974b10c7cbf6c05f97da3d Mon Sep 17 00:00:00 2001 From: Li Tao Date: Wed, 15 Apr 2026 10:36:27 +0800 Subject: [PATCH 14/72] [Main] Fix TE version check for retain_pinned_cpu_buffers in cpu offload (#4267) Co-authored-by: Claude Opus 4.6 (1M context) --- megatron/core/extensions/transformer_engine.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/megatron/core/extensions/transformer_engine.py b/megatron/core/extensions/transformer_engine.py index c2817cfe691..419c85b404d 100644 --- a/megatron/core/extensions/transformer_engine.py +++ b/megatron/core/extensions/transformer_engine.py @@ -2557,8 +2557,8 @@ def get_cpu_offload_context( retain_pinned_cpu_buffers, ): """Get CPU offload context and sync function.""" - if is_te_min_version("2.5.0"): - # Enables the additional double buffering switch for activations during LLM training + if is_te_min_version("2.10.0"): + # TE 2.10+ supports retain_pinned_cpu_buffers context, sync_func = _get_cpu_offload_context( enabled, num_layers, @@ -2568,6 +2568,16 @@ def get_cpu_offload_context( double_buffering, retain_pinned_cpu_buffers=retain_pinned_cpu_buffers, ) + elif is_te_min_version("2.5.0"): + # TE 2.5-2.9 supports double_buffering but not retain_pinned_cpu_buffers + context, sync_func = _get_cpu_offload_context( + enabled, + num_layers, + model_layers, + activation_offloading, + weight_offloading, + double_buffering, + ) elif is_te_min_version("1.10.0.dev0"): context, sync_func = _get_cpu_offload_context( enabled, num_layers, model_layers, activation_offloading, weight_offloading From b3426025fcf36f33d916590f2dee81e0e425ddd5 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 15 Apr 2026 09:34:00 +0000 Subject: [PATCH 15/72] chore: rotate oncall schedule --- .github/oncall_schedule.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/.github/oncall_schedule.json b/.github/oncall_schedule.json index 23f2699cfa1..d89323c3dc3 100644 --- a/.github/oncall_schedule.json +++ b/.github/oncall_schedule.json @@ -1,8 +1,4 @@ [ - { - "user": "ilml", - "date": "2026-04-08" - }, { "user": "Phlip79", "date": "2026-04-15" From 1d344ae7fec596a021a4dd586ac5e9cb4b1e1e4e Mon Sep 17 00:00:00 2001 From: janEbert Date: Wed, 15 Apr 2026 15:58:17 +0000 Subject: [PATCH 16/72] Fix documented shape (#3486) --- megatron/core/models/multimodal/llava_model.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/megatron/core/models/multimodal/llava_model.py b/megatron/core/models/multimodal/llava_model.py index 86ce04521a7..7d238f84979 100644 --- a/megatron/core/models/multimodal/llava_model.py +++ b/megatron/core/models/multimodal/llava_model.py @@ -479,7 +479,8 @@ def _preprocess_data( Currently, we assume the language model uses a causal mask. Returns: - final_embedding (torch.Tensor): image and text embeddings [combined_seq_len, b, h]. + final_embedding (torch.Tensor): image and text embeddings [combined_seq_len, b, h] if + context_parallel_lm == 1 else [b, combined_seq_len, h]. final_labels (torch.Tensor): labels for image and text positions [b, combined_seq_len]. final_loss_mask (torch.Tensor): loss mask [b, combined_seq_len]. """ @@ -913,6 +914,10 @@ def forward( if num_image_tiles is None and images is not None: num_image_tiles = torch.ones(images.shape[0], dtype=torch.int, device=input_ids.device) + # if context_parallel_lm == 1: + # [combined_seq_len, b, h_language], [b, combined_seq_len], [b, combined_seq_len] + # else: + # [b, combined_seq_len, h_language], [b, combined_seq_len], [b, combined_seq_len] combined_embeddings, new_labels, new_loss_mask = self._preprocess_data( image_embeddings, language_embeddings, @@ -923,7 +928,7 @@ def forward( inference_context, image_token_index if image_token_index is not None else self.image_token_index, num_image_tiles, - ) # [combined_seq_len, b, h_language], [b, combined_seq_len], [b, combined_seq_len] + ) if self.context_parallel_lm > 1 or self.sequence_parallel_lm: combined_embeddings, new_labels, new_loss_mask, packed_seq_params = ( From 4a795369c0526a438d54e26a4f7c236891e74870 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Thu, 16 Apr 2026 01:46:46 +0200 Subject: [PATCH 17/72] =?UTF-8?q?ci:=20add=20sync-skills=20workflow,=20ren?= =?UTF-8?q?ame=20CLAUDE.md=20=E2=86=92=20AGENTS.md,=20move=20.claude/skill?= =?UTF-8?q?s=20=E2=86=92=20skills/=20(#4303)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- .github/workflows/sync-skills.yml | 29 +++++++++++++++++++ AGENTS.md | 14 ++++++++- CLAUDE.md | 13 --------- .../skills => skills}/build-and-test/SKILL.md | 0 .../respond-to-issue/SKILL.md | 0 {.claude/skills => skills}/split-pr/SKILL.md | 0 6 files changed, 42 insertions(+), 14 deletions(-) create mode 100644 .github/workflows/sync-skills.yml delete mode 100644 CLAUDE.md rename {.claude/skills => skills}/build-and-test/SKILL.md (100%) rename {.claude/skills => skills}/respond-to-issue/SKILL.md (100%) rename {.claude/skills => skills}/split-pr/SKILL.md (100%) diff --git a/.github/workflows/sync-skills.yml b/.github/workflows/sync-skills.yml new file mode 100644 index 00000000000..75b8c20dca0 --- /dev/null +++ b/.github/workflows/sync-skills.yml @@ -0,0 +1,29 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +name: Sync skills → agent dirs + +on: + workflow_dispatch: + push: + branches: + - main + paths: + - "skills/**" + - "AGENTS.md" + +jobs: + sync: + uses: NVIDIA-NeMo/FW-CI-templates/.github/workflows/_sync_skills.yml@v0.91.0 + secrets: + PAT: ${{ secrets.PAT }} diff --git a/AGENTS.md b/AGENTS.md index f2ae889e21b..24d5d846238 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1 +1,13 @@ -See [CLAUDE.md](CLAUDE.md) for all repository guidelines. +# Repository Guidelines + +## Contributing + +### Pull Requests + +- All PRs must be created as **drafts**. Use `gh pr create --draft` or the GitHub UI draft option. +- Never push branches directly to `https://github.com/NVIDIA/Megatron-LM`. You must push your branch to a personal fork (e.g. `https://github.com//Megatron-LM`), then open a PR from the fork's branch against `NVIDIA/Megatron-LM`. +- Read [docs/developer/contribute.md](docs/developer/contribute.md) for the full contribution policy, including code style, commit message conventions, and issue guidelines. + +### Code Quality + +- After editing imports in any Python files, always run `uv run isort` on those files to fix import order before committing. diff --git a/CLAUDE.md b/CLAUDE.md deleted file mode 100644 index 24d5d846238..00000000000 --- a/CLAUDE.md +++ /dev/null @@ -1,13 +0,0 @@ -# Repository Guidelines - -## Contributing - -### Pull Requests - -- All PRs must be created as **drafts**. Use `gh pr create --draft` or the GitHub UI draft option. -- Never push branches directly to `https://github.com/NVIDIA/Megatron-LM`. You must push your branch to a personal fork (e.g. `https://github.com//Megatron-LM`), then open a PR from the fork's branch against `NVIDIA/Megatron-LM`. -- Read [docs/developer/contribute.md](docs/developer/contribute.md) for the full contribution policy, including code style, commit message conventions, and issue guidelines. - -### Code Quality - -- After editing imports in any Python files, always run `uv run isort` on those files to fix import order before committing. diff --git a/.claude/skills/build-and-test/SKILL.md b/skills/build-and-test/SKILL.md similarity index 100% rename from .claude/skills/build-and-test/SKILL.md rename to skills/build-and-test/SKILL.md diff --git a/.claude/skills/respond-to-issue/SKILL.md b/skills/respond-to-issue/SKILL.md similarity index 100% rename from .claude/skills/respond-to-issue/SKILL.md rename to skills/respond-to-issue/SKILL.md diff --git a/.claude/skills/split-pr/SKILL.md b/skills/split-pr/SKILL.md similarity index 100% rename from .claude/skills/split-pr/SKILL.md rename to skills/split-pr/SKILL.md From 57fc3ae35511b67cc12f61e63ed7505859955dc4 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Thu, 16 Apr 2026 00:28:40 +0000 Subject: [PATCH 18/72] =?UTF-8?q?chore(beep=20boop=20=F0=9F=A4=96):=20syml?= =?UTF-8?q?ink=20skills/=20=E2=86=92=20.claude/skills,=20.agents/skills=20?= =?UTF-8?q?and=20AGENTS.md=20=E2=86=92=20CLAUDE.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .agents/skills | 1 + .claude/skills | 1 + CLAUDE.md | 1 + 3 files changed, 3 insertions(+) create mode 120000 .agents/skills create mode 120000 .claude/skills create mode 120000 CLAUDE.md diff --git a/.agents/skills b/.agents/skills new file mode 120000 index 00000000000..42c5394a18a --- /dev/null +++ b/.agents/skills @@ -0,0 +1 @@ +../skills \ No newline at end of file diff --git a/.claude/skills b/.claude/skills new file mode 120000 index 00000000000..42c5394a18a --- /dev/null +++ b/.claude/skills @@ -0,0 +1 @@ +../skills \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md new file mode 120000 index 00000000000..47dc3e3d863 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1 @@ +AGENTS.md \ No newline at end of file From 23265d2db37efa883b3983bce321c631898822f3 Mon Sep 17 00:00:00 2001 From: Shifang Xu Date: Thu, 16 Apr 2026 12:48:16 +0800 Subject: [PATCH 19/72] Get `device` correctly when module returns a dict instead of individual tensor (#4265) --- megatron/core/pipeline_parallel/schedules.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/megatron/core/pipeline_parallel/schedules.py b/megatron/core/pipeline_parallel/schedules.py index 0c9c650c9df..48963eb0e1d 100644 --- a/megatron/core/pipeline_parallel/schedules.py +++ b/megatron/core/pipeline_parallel/schedules.py @@ -219,6 +219,13 @@ def custom_backward(output, grad_output): ) +def get_tensor_device(tensor: Union[torch.Tensor, Dict[str, torch.Tensor]]): + """Get the device of a tensor or a dictionary of tensors.""" + if isinstance(tensor, dict): + return next(iter(tensor.values())).device + return tensor.device + + def forward_step_calc_loss( model, output_tensor, @@ -284,10 +291,11 @@ def forward_step_calc_loss( # explicitly. if hasattr(config, 'num_moe_experts') and config.num_moe_experts is not None: # Calculate the loss scale based on the grad_scale_func if available, else default to 1. + device = get_tensor_device(output_tensor) loss_scale = ( - config.grad_scale_func(torch.ones(1, device=output_tensor.device)) + config.grad_scale_func(torch.ones(1, device=device)) if config.grad_scale_func is not None - else torch.ones(1, device=output_tensor.device) + else torch.ones(1, device=device) ) # Set the loss scale if config.calculate_per_token_loss: @@ -299,10 +307,11 @@ def forward_step_calc_loss( # Set the loss scale for Multi-Token Prediction (MTP) loss. if hasattr(config, 'mtp_num_layers') and config.mtp_num_layers is not None: # Calculate the loss scale based on the grad_scale_func if available, else default to 1. + device = get_tensor_device(output_tensor) loss_scale = ( - config.grad_scale_func(torch.ones(1, device=output_tensor.device)) + config.grad_scale_func(torch.ones(1, device=device)) if config.grad_scale_func is not None - else torch.ones(1, device=output_tensor.device) + else torch.ones(1, device=device) ) # Set the loss scale if config.calculate_per_token_loss: From e69cf431a6c7b97d6c2f5f52d4345537a72f4fe1 Mon Sep 17 00:00:00 2001 From: Dmytro Pykhtar <37850217+dimapihtar@users.noreply.github.com> Date: Thu, 16 Apr 2026 14:55:24 +0300 Subject: [PATCH 20/72] remove vision legacy code (#4202) Signed-off-by: dimapihtar --- .../legacy/model/vision/classification.py | 86 -- megatron/legacy/model/vision/dino.py | 291 ------ .../model/vision/esvit_swin_backbone.py | 849 ------------------ megatron/legacy/model/vision/inpainting.py | 152 ---- megatron/legacy/model/vision/knn_monitor.py | 130 --- megatron/legacy/model/vision/mit_backbone.py | 415 --------- megatron/legacy/model/vision/swin_backbone.py | 625 ------------- megatron/legacy/model/vision/utils.py | 28 - megatron/legacy/model/vision/vit_backbone.py | 248 ----- megatron/training/training.py | 5 - 10 files changed, 2829 deletions(-) delete mode 100644 megatron/legacy/model/vision/classification.py delete mode 100644 megatron/legacy/model/vision/dino.py delete mode 100644 megatron/legacy/model/vision/esvit_swin_backbone.py delete mode 100644 megatron/legacy/model/vision/inpainting.py delete mode 100644 megatron/legacy/model/vision/knn_monitor.py delete mode 100644 megatron/legacy/model/vision/mit_backbone.py delete mode 100644 megatron/legacy/model/vision/swin_backbone.py delete mode 100644 megatron/legacy/model/vision/utils.py delete mode 100644 megatron/legacy/model/vision/vit_backbone.py diff --git a/megatron/legacy/model/vision/classification.py b/megatron/legacy/model/vision/classification.py deleted file mode 100644 index f9419c71dea..00000000000 --- a/megatron/legacy/model/vision/classification.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - -"""Vision Transformer(VIT) model.""" - -import torch -from torch.nn.init import trunc_normal_ -from megatron.training import get_args -from megatron.legacy.model.utils import get_linear_layer -from megatron.legacy.model.vision.vit_backbone import VitBackbone, VitMlpHead -from megatron.legacy.model.vision.mit_backbone import mit_b3_avg -from megatron.legacy.model.module import MegatronModule - -class VitClassificationModel(MegatronModule): - """Vision Transformer Model.""" - - def __init__(self, config, num_classes, finetune=False, - pre_process=True, post_process=True): - super(VitClassificationModel, self).__init__() - args = get_args() - self.config = config - - self.hidden_size = args.hidden_size - self.num_classes = num_classes - self.finetune = finetune - self.pre_process = pre_process - self.post_process = post_process - self.backbone = VitBackbone( - config=config, - pre_process=self.pre_process, - post_process=self.post_process, - single_token_output=True - ) - - if self.post_process: - if not self.finetune: - self.head = VitMlpHead(config, self.hidden_size, self.num_classes) - else: - self.head = get_linear_layer( - self.hidden_size, - self.num_classes, - torch.nn.init.zeros_ - ) - - def set_input_tensor(self, input_tensor): - """See megatron.legacy.model.transformer.set_input_tensor()""" - self.backbone.set_input_tensor(input_tensor) - - def forward(self, input): - hidden_states = self.backbone(input) - - if self.post_process: - hidden_states = self.head(hidden_states) - - return hidden_states - - -class MitClassificationModel(MegatronModule): - """Mix vision Transformer Model.""" - - def __init__(self, num_classes, - pre_process=True, post_process=True): - super(MitClassificationModel, self).__init__() - args = get_args() - - self.hidden_size = args.hidden_size - self.num_classes = num_classes - - self.backbone = mit_b3_avg() - self.head = torch.nn.Linear(512, num_classes) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, torch.nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, torch.nn.Linear) and m.bias is not None: - torch.nn.init.constant_(m.bias, 0) - - def set_input_tensor(self, input_tensor): - """See megatron.legacy.model.transformer.set_input_tensor()""" - pass - - def forward(self, input): - hidden_states = self.backbone(input) - hidden_states = self.head(hidden_states) - - return hidden_states diff --git a/megatron/legacy/model/vision/dino.py b/megatron/legacy/model/vision/dino.py deleted file mode 100644 index 20ca2100f65..00000000000 --- a/megatron/legacy/model/vision/dino.py +++ /dev/null @@ -1,291 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the Apache license found in the -# LICENSE file in the root directory of this source tree. - -# copied from https://github.com/facebookresearch/dino/blob/main/main_dino.py -# reworked/refactored some parts to make it run in Megatron. -import math -import apex -import einops -import torch -import numpy as np -import torch.nn.functional as F -from torch.nn.init import trunc_normal_ -from megatron.training import get_args, print_rank_0 -from megatron.legacy.model.utils import get_linear_layer -from megatron.legacy.model.vision.vit_backbone import VitBackbone -from megatron.legacy.model.module import MegatronModule -from megatron.legacy.model.vision.mit_backbone import mit_b5_avg -from megatron.legacy.model.vision.esvit_swin_backbone import get_swin - - -class DINOLoss(torch.nn.Module): - def __init__(self, out_dim, ncrops, warmup_teacher_temp, teacher_temp, - warmup_teacher_temp_epochs, nepochs, student_temp=0.1, - center_momentum=0.9): - super().__init__() - self.student_temp = student_temp - self.center_momentum = center_momentum - self.ncrops = ncrops - self.register_buffer("center", torch.zeros(1, out_dim)) - # we apply a warm up for the teacher temperature because - # a too high temperature makes the training instable at the beginning - self.teacher_temp_schedule = np.concatenate(( - np.linspace(warmup_teacher_temp, - teacher_temp, warmup_teacher_temp_epochs), - np.ones(nepochs - warmup_teacher_temp_epochs) * teacher_temp - )) - self.teacher_temp = teacher_temp - - def forward(self, student_output, teacher_output, iteration): - """ - Cross-entropy between softmax outputs of the teacher - and student network. - """ - args = get_args() - student_out = student_output / self.student_temp - student_out = student_out.chunk(self.ncrops) - - epoch = iteration // args.iter_per_epoch - - # teacher centering and sharpening - temp = self.teacher_temp_schedule[epoch] - teacher_out = F.softmax((teacher_output - self.center) / temp, dim=-1) - - teacher_out = teacher_out.detach().chunk(2) - - total_loss = 0 - n_loss_terms = 0 - for iq, q in enumerate(teacher_out): - for v in range(len(student_out)): - if v == iq: - # we skip cases where student and teacher operate on the same view - continue - loss = torch.sum(-q * F.log_softmax(student_out[v], dim=-1), dim=-1) - total_loss += loss.mean() - n_loss_terms += 1 - total_loss /= n_loss_terms - self.update_center(teacher_output) - return total_loss - - @torch.no_grad() - def update_center(self, teacher_output): - """ - Update center used for teacher output. - """ - batch_center = torch.sum(teacher_output, dim=0, keepdim=True) - torch.distributed.all_reduce(batch_center) - batch_center = batch_center / (len(teacher_output) * torch.distributed.get_world_size()) - self.center = self.center * self.center_momentum + batch_center * (1 - self.center_momentum) - -class DINOHead(torch.nn.Module): - def __init__(self, in_dim, out_dim, norm_last_layer=True, nlayers=3): - super().__init__() - args = get_args() - hidden_dim = args.dino_head_hidden_size - bottleneck_dim = args.dino_bottleneck_size - nlayers = max(nlayers, 1) - if nlayers == 1: - self.mlp = torch.nn.Linear(in_dim, bottleneck_dim) - else: - layers = [torch.nn.Linear(in_dim, hidden_dim)] - layers.append(torch.nn.GELU()) - for _ in range(nlayers - 2): - layers.append(torch.nn.Linear(hidden_dim, hidden_dim)) - layers.append(torch.nn.GELU()) - layers.append(torch.nn.Linear(hidden_dim, bottleneck_dim)) - self.mlp = torch.nn.Sequential(*layers) - self.apply(self._init_weights) - self.last_layer = torch.nn.utils.weight_norm(torch.nn.Linear(bottleneck_dim, out_dim, bias=False)) - self.last_layer.weight_g.data.fill_(1) - if norm_last_layer: - self.last_layer.weight_g.requires_grad = False - - def _init_weights(self, m): - if isinstance(m, torch.nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, torch.nn.Linear) and m.bias is not None: - torch.nn.init.constant_(m.bias, 0) - - def forward(self, x): - x = self.mlp(x) - x = torch.nn.functional.normalize(x, dim=-1, p=2) - x = self.last_layer(x) - return x - - -class MultiCropWrapper(MegatronModule): - - """ - Perform forward pass separately on each resolution input. - The inputs corresponding to a single resolution are clubbed and single - forward is run on the same resolution inputs. Hence we do several - forward passes = number of different resolutions used. We then - concatenate all the output features and run the head forward on these - concatenated features. - """ - def __init__(self, backbone, head): - super(MultiCropWrapper, self).__init__() - # disable layers dedicated to ImageNet labels classification - #backbone.fc, backbone.head = torch.nn.Identity(), torch.nn.Identity() - self.backbone = backbone - self.head = head - - def forward(self, x): - # convert to list - if not isinstance(x, list): - x = [x] - idx_crops = torch.cumsum(torch.unique_consecutive( - torch.tensor([inp.shape[-1] for inp in x]), - return_counts=True, - )[1], 0) - - start_idx = 0 - for end_idx in idx_crops: - _out = self.backbone(torch.cat(x[start_idx: end_idx])) - if start_idx == 0: - output = _out - else: - output = torch.cat((output, _out)) - start_idx = end_idx - # Run the head forward on the concatenated features. - if self.training: - return self.head(output) - else: - return output - - -def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, - warmup_epochs=0, start_warmup_value=0): - warmup_schedule = np.array([]) - warmup_iters = warmup_epochs * niter_per_ep - if warmup_epochs > 0: - warmup_schedule = \ - np.linspace(start_warmup_value, base_value, warmup_iters) - - iters = np.arange(epochs * niter_per_ep - warmup_iters) - schedule = final_value + 0.5 * (base_value - final_value) \ - * (1 + np.cos(np.pi * iters / len(iters))) - - schedule = np.concatenate((warmup_schedule, schedule)) - assert len(schedule) == epochs * niter_per_ep - return schedule - - -def get_student_backbone_and_num_features(config, pre_process=True, post_process=True): - args = get_args() - - if args.vision_backbone_type == 'vit': - student = VitBackbone(config, - pre_process=pre_process, - post_process=post_process, - drop_path_rate=0.1, - single_token_output=True) - num_features = args.hidden_size - elif args.vision_backbone_type == 'mit': - student = mit_b5_avg(drop_path_rate=0.1) - num_features = 512 - elif args.vision_backbone_type == 'swin': - student = get_swin() - num_features = student.num_features - else: - raise Exception('{} vision backbone is not supported.'.format( - args.vision_backbone_type)) - - return student, num_features - -def get_teacher_backbone_and_num_features(config, pre_process=True, post_process=True): - args = get_args() - - if args.vision_backbone_type == 'vit': - teacher = VitBackbone(config, - pre_process=pre_process, - post_process=post_process, - single_token_output=True) - num_features = args.hidden_size - elif args.vision_backbone_type == 'mit': - teacher = mit_b5_avg(drop_path_rate=0.0) - num_features = 512 - elif args.vision_backbone_type == 'swin': - teacher = get_swin(is_teacher=True) - num_features = teacher.num_features - else: - raise Exception('{} vision backbone is not supported.'.format( - args.vision_backbone_type)) - return teacher, num_features - - -class DINOPretrainModel(MegatronModule): - def __init__(self, config, pre_process=True, post_process=True): - super(DINOPretrainModel, self).__init__() - args = get_args() - self.config = config - self.out_dim = 65536 - - self.dino_loss = DINOLoss( - self.out_dim, - args.dino_local_crops_number + 2, - args.dino_warmup_teacher_temp, - args.dino_teacher_temp, - args.dino_warmup_teacher_temp_epochs, - 300, - ) - - self.pre_process = pre_process - self.post_process = post_process - self.momentum_teacher = 0.996 - - student_backbone, num_features = \ - get_student_backbone_and_num_features(config, pre_process, post_process) - - self.student = MultiCropWrapper( - student_backbone, - DINOHead(num_features, self.out_dim, - norm_last_layer=args.dino_norm_last_layer) - ) - - self.momentum_schedule = cosine_scheduler( - self.momentum_teacher, 1, - args.train_iters // args.iter_per_epoch, - args.iter_per_epoch - ) - - teacher_backbone, num_features = \ - get_teacher_backbone_and_num_features(config, pre_process, post_process) - self.teacher = MultiCropWrapper( - teacher_backbone, - DINOHead(num_features, self.out_dim) - ) - self.teacher.load_state_dict(self.student.state_dict()) - - for p in self.teacher.parameters(): - if hasattr(p, "requires_grad") and p.requires_grad is not None: - p.requires_grad = False - - def set_input_tensor(self, tensor): - pass - - def forward(self, input): - student_output = None - if self.training: - student_output = self.student(input) - teacher_output = self.teacher(input[:2]) - else: - teacher_output = self.teacher(input) - return student_output, teacher_output - - def cancel_gradients_last_layer(self, iteration): - args = get_args() - epoch = iteration // args.iter_per_epoch - if epoch < args.dino_freeze_last_layer: - for n, p in self.student.named_parameters(): - if "last_layer" in n: - p.grad = None - - def update_momentum(self, iteration): - with torch.no_grad(): - m = self.momentum_schedule[iteration] - for param_q, param_k in zip(self.student.parameters(), self.teacher.parameters()): - param_k.data.mul_(m).add_((1 - m) * param_q.detach().data) - diff --git a/megatron/legacy/model/vision/esvit_swin_backbone.py b/megatron/legacy/model/vision/esvit_swin_backbone.py deleted file mode 100644 index c7c67b40c64..00000000000 --- a/megatron/legacy/model/vision/esvit_swin_backbone.py +++ /dev/null @@ -1,849 +0,0 @@ -# Copyright (c) 2021 Microsoft -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# Modified by Chunyuan Li (chunyl@microsoft.com) -# Swin Transformer -# -------------------------------------------------------- - -import os -import logging -import torch -import torch.nn as nn -import torch.nn.functional as F -from functools import partial -import torch.distributed as dist -from torch.nn.init import trunc_normal_ -from megatron.legacy.model.transformer import DropPath -from megatron.training import get_args -from megatron.legacy.model import LayerNorm -import numpy as np -from math import sqrt - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, - out_features=None, act_layer=nn.GELU, drop=0.): - super(Mlp, self).__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r"""Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super(WindowAttention, self).__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2 Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0).type(attn.type()) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn_out = attn - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x, attn_out - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - @staticmethod - def compute_macs(module, input, output): - B, N, C = input[0].shape - - module.__flops__ += module.flops(N) * B - - -class SwinTransformerBlock(nn.Module): - r"""Swin Transformer Block. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.H = input_resolution[0] - self.W = input_resolution[1] - - self.attn_mask_dict = {} - - - def create_attn_mask(self, H, W): - # calculate attention mask for SW-MSA - - Hp = int(np.ceil(H / self.window_size)) * self.window_size - Wp = int(np.ceil(W / self.window_size)) * self.window_size - img_mask = torch.zeros((1, Hp, Wp, 1)) # 1 Hp Wp 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - - def forward(self, x): - B, L, C = x.shape - H = int(sqrt(L)) - W = H - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # pad feature maps to multiples of window size - pad_l = pad_t = 0 - pad_r = (self.window_size - W % self.window_size) % self.window_size - pad_b = (self.window_size - H % self.window_size) % self.window_size - x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b)) - _, Hp, Wp, _ = x.shape - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - - if H in self.attn_mask_dict.keys(): - attn_mask = self.attn_mask_dict[H] - else: - self.attn_mask_dict[H] = self.create_attn_mask(self.H, self.W).to(x.device) - attn_mask = self.attn_mask_dict[H] - - else: - shifted_x = x - attn_mask = None - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows, attn = self.attn(x_windows, attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - - if pad_r > 0 or pad_b > 0: - x = x[:, :H, :W, :].contiguous() - - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x, attn - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ - f"window_size={self.window_size}, shift_size={self.shift_size} mlp_ratio={self.mlp_ratio}" - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r"""Patch Merging Layer. - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ Forward function. - Args: - x: Input feature, tensor size (B, H*W, C). - H, W: Spatial resolution of the input feature. - """ - B, L, C = x.shape - H = int(sqrt(L)) - W = H - - x = x.view(B, H, W, C) - - # padding - pad_input = (H % 2 == 1) or (W % 2 == 1) - if pad_input: - x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2)) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.dim - flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """A basic Swin Transformer layer for one stage. - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - - self.blocks = nn.ModuleList([ - SwinTransformerBlock(dim=dim, input_resolution=input_resolution, - num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x): - for blk in self.blocks: - x, _ = blk(x) - if self.downsample is not None: - x = self.downsample(x) - return x - - def forward_with_features(self, x): - fea = [] - for blk in self.blocks: - x, _ = blk(x) - fea.append(x) - if self.downsample is not None: - x = self.downsample(x) - return x, fea - - def forward_with_attention(self, x): - attns = [] - for blk in self.blocks: - x, attn = blk(x) - attns.append(attn) - if self.downsample is not None: - x = self.downsample(x) - return x, attns - - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class PatchEmbed(nn.Module): - """ Image to Patch Embedding - """ - - def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None): - super().__init__() - img_size = (img_size, img_size) - patch_size = (patch_size, patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - B, C, H, W = x.shape - - x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - - def flops(self): - Ho, Wo = self.patches_resolution - flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) - if self.norm is not None: - flops += Ho * Wo * self.embed_dim - return flops - -class SwinTransformer(nn.Module): - r""" Swin Transformer - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - Args: - img_size (int | tuple(int)): Input image size. - patch_size (int | tuple(int)): Patch size. - in_chans (int): Number of input channels. - num_classes (int): Number of classes for classification head. - embed_dim (int): Embedding dimension. - depths (tuple(int)): Depth of Swin Transformer layers. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: Truee - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. - drop_rate (float): Dropout rate. - attn_drop_rate (float): Attention dropout rate. - drop_path_rate (float): Stochastic depth rate. - norm_layer (nn.Module): normalization layer. - ape (bool): If True, add absolute position embedding to the patch embedding. - patch_norm (bool): If True, add normalization after patch embedding. - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000, - embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], - window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, - norm_layer=nn.LayerNorm, ape=False, patch_norm=True, **kwargs): - super().__init__() - - self.num_classes = num_classes - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) - self.mlp_ratio = mlp_ratio - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), - input_resolution=(patches_resolution[0] // (2 ** i_layer), - patches_resolution[1] // (2 ** i_layer)), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], - norm_layer=norm_layer, - downsample=PatchMerging if (i_layer < self.num_layers - 1) else None) - self.layers.append(layer) - - self.norm = norm_layer(self.num_features) - self.avgpool = nn.AdaptiveAvgPool1d(1) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - # todo: to be implemented - return {'relative_position_bias_table'} - - def forward(self, x): - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x) - - x_region = self.norm(x) # B L C - x = self.avgpool(x_region.transpose(1, 2)) # B C 1 - x = torch.flatten(x, 1) - - return x - - - def forward_feature_maps(self, x): - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x) - - x_grid = self.norm(x) # B L C - x = self.avgpool(x_grid.transpose(1, 2)) # B C 1 - x = torch.flatten(x, 1) - - return x, x_grid - - - def forward_selfattention(self, x, n=1): - # n=1 return the last layer attn map; otherwise return attn maps in all layers - - - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - if n==1: - return self.forward_last_selfattention(x) - else: - return self.forward_all_selfattention(x) - - def forward_last_selfattention(self, x): - - for i, layer in enumerate(self.layers): - if i < len(self.layers) - 1: - x = layer(x) - else: - x, attns = layer.forward_with_attention(x) - return attns[-1] - - def forward_all_selfattention(self, x): - attn_out = [] - - for layer in self.layers: - x, attns = layer.forward_with_attention(x) - attn_out += attns - - return attn_out - - - def forward_return_n_last_blocks(self, x, n=1, return_patch_avgpool=False, depth=[]): - - num_blks = sum(depth) - start_idx = num_blks - n - - sum_cur = 0 - for i, d in enumerate(depth): - sum_cur_new = sum_cur + d - if start_idx >= sum_cur and start_idx < sum_cur_new: - start_stage = i - start_blk = start_idx - sum_cur - sum_cur = sum_cur_new - - - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - # we will return the averaged token features from the `n` last blocks - # note: there is no [CLS] token in Swin Transformer - output = [] - s = 0 - for i, layer in enumerate(self.layers): - x, fea = layer.forward_with_features(x) - - if i >= start_stage: - for x_ in fea[start_blk:]: - - if i == len(self.layers)-1: # use the norm in the last stage - x_ = self.norm(x_) - - x_avg = torch.flatten(self.avgpool(x_.transpose(1, 2)), 1) # B C - # print(f'Stage {i}, x_avg {x_avg.shape}') - output.append(x_avg) - - start_blk = 0 - - return torch.cat(output, dim=-1) - - - - def flops(self): - flops = 0 - flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): - flops += layer.flops() - if dist.get_rank() == 0: - print(f"GFLOPs layer_{i}: {layer.flops() / 1e9}") - flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) - flops += self.num_features * self.num_classes - return flops - - def init_weights(self, pretrained='', pretrained_layers=[], verbose=True): - if os.path.isfile(pretrained): - pretrained_dict = torch.load(pretrained, map_location='cpu') - logging.info(f'=> loading pretrained model {pretrained}') - model_dict = self.state_dict() - pretrained_dict = { - k: v for k, v in pretrained_dict.items() - if k in model_dict.keys() - } - need_init_state_dict = {} - for k, v in pretrained_dict.items(): - need_init = ( - k.split('.')[0] in pretrained_layers - or pretrained_layers[0] == '*' - or 'relative_position_index' not in k - or 'attn_mask' not in k - ) - - if need_init: - if verbose: - logging.info(f'=> init {k} from {pretrained}') - - if 'relative_position_bias_table' in k and v.size() != model_dict[k].size(): - relative_position_bias_table_pretrained = v - relative_position_bias_table_current = model_dict[k] - L1, nH1 = relative_position_bias_table_pretrained.size() - L2, nH2 = relative_position_bias_table_current.size() - if nH1 != nH2: - logging.info(f"Error in loading {k}, passing") - else: - if L1 != L2: - logging.info( - '=> load_pretrained: resized variant: {} to {}' - .format((L1, nH1), (L2, nH2)) - ) - S1 = int(L1 ** 0.5) - S2 = int(L2 ** 0.5) - relative_position_bias_table_pretrained_resized = torch.nn.functional.interpolate( - relative_position_bias_table_pretrained.permute(1, 0).view(1, nH1, S1, S1), - size=(S2, S2), - mode='bicubic') - v = relative_position_bias_table_pretrained_resized.view(nH2, L2).permute(1, 0) - - if 'absolute_pos_embed' in k and v.size() != model_dict[k].size(): - absolute_pos_embed_pretrained = v - absolute_pos_embed_current = model_dict[k] - _, L1, C1 = absolute_pos_embed_pretrained.size() - _, L2, C2 = absolute_pos_embed_current.size() - if C1 != C1: - logging.info(f"Error in loading {k}, passing") - else: - if L1 != L2: - logging.info( - '=> load_pretrained: resized variant: {} to {}' - .format((1, L1, C1), (1, L2, C2)) - ) - S1 = int(L1 ** 0.5) - S2 = int(L2 ** 0.5) - absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.reshape(-1, S1, S1, C1) - absolute_pos_embed_pretrained = absolute_pos_embed_pretrained.permute(0, 3, 1, 2) - absolute_pos_embed_pretrained_resized = torch.nn.functional.interpolate( - absolute_pos_embed_pretrained, size=(S2, S2), mode='bicubic') - v = absolute_pos_embed_pretrained_resized.permute(0, 2, 3, 1).flatten(1, 2) - - need_init_state_dict[k] = v - self.load_state_dict(need_init_state_dict, strict=False) - - def freeze_pretrained_layers(self, frozen_layers=[]): - for name, module in self.named_modules(): - if ( - name.split('.')[0] in frozen_layers - or '.'.join(name.split('.')[0:2]) in frozen_layers - or (len(frozen_layers) > 0 and frozen_layers[0] == '*') - ): - for _name, param in module.named_parameters(): - param.requires_grad = False - logging.info( - '=> set param {} requires grad to False' - .format(name) - ) - for name, param in self.named_parameters(): - if ( - name.split('.')[0] in frozen_layers - or (len(frozen_layers) > 0 and frozen_layers[0] == '*') - and param.requires_grad is True - ): - param.requires_grad = False - logging.info( - '=> set param {} requires grad to False' - .format(name) - ) - return self - - -def get_swin(is_teacher=False): - args = get_args() - - if args.swin_backbone_type == "tiny": - embed_dim = 96 - depths = [2, 2, 6, 2] - num_heads = [3, 6, 12, 24] - drop_path_rate = 0.1 - elif args.swin_backbone_type == 'h3': - embed_dim = 384 - depths = [2, 2, 18, 2] - num_heads = [6, 12, 24, 48] - drop_path_rate = 0.2 - else: - embed_dim = 128 - depths = [2, 2, 18, 2] - num_heads = [4, 8, 16, 32] - drop_path_rate = 0.2 - - swin = SwinTransformer( - img_size=224, - in_chans=3, - num_classes=1000, - patch_size=4, - embed_dim=embed_dim, - depths=depths, - num_heads=num_heads, - window_size=7, - mlp_ratio=4, - qkv_bias=True, - drop_rate=0, - attn_drop_rate=0, - drop_path_rate=(0.0 if is_teacher else drop_path_rate), - norm_layer=partial(LayerNorm, eps=1e-6), - ape=False, - patch_norm=True, - ) - - return swin - diff --git a/megatron/legacy/model/vision/inpainting.py b/megatron/legacy/model/vision/inpainting.py deleted file mode 100644 index f71f5e32091..00000000000 --- a/megatron/legacy/model/vision/inpainting.py +++ /dev/null @@ -1,152 +0,0 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. -# -# This source code is licensed under the BSD license found in the -# LICENSE file in the root directory of this source tree. - -import math -import apex -import einops -import torch -import torch.nn.functional as F -from megatron.training import get_args, print_rank_0 -from megatron.legacy.model.utils import get_linear_layer -from megatron.legacy.model.vision.vit_backbone import VitBackbone -from megatron.legacy.model.module import MegatronModule -from megatron.legacy.model.vision.mit_backbone import mit_b3 -from megatron.legacy.model.vision.utils import resize - - -class VitInpaintingModel(MegatronModule): - - def __init__(self, config, pre_process=True, post_process=True): - super(VitInpaintingModel, self).__init__() - args = get_args() - - self.config = config - self.pre_process = pre_process - self.post_process = post_process - self.hidden_size = config.hidden_size - self.backbone = VitBackbone( - config=config, - pre_process=self.pre_process, - post_process=self.post_process, - class_token=False, - ) - self.patch_dim = args.patch_dim - self.img_h = args.img_h - self.img_w = args.img_w - self.seq_length = args.seq_length - # full mask - - if self.post_process: - self.linear_decoder = get_linear_layer( - self.hidden_size, - self.backbone.flatten_dim, - torch.nn.init.zeros_ - ) - - def set_input_tensor(self, input_tensor): - self.backbone.set_input_tensor(input_tensor) - - def forward(self, input): - - hidden_states = self.backbone(input) - - if not self.post_process: - return hidden_states - decoded_output = self.linear_decoder(hidden_states) - output = einops.rearrange( - decoded_output, - "b (h w) (p1 p2 c) -> b c (h p1) (w p2)", - p1=self.patch_dim, - p2=self.patch_dim, - h=self.img_h//self.patch_dim, - w=self.img_w//self.patch_dim, - ) - - return output - - -class MLP(torch.nn.Module): - """ - Linear Embedding - """ - def __init__(self, input_dim=2048, embed_dim=768): - super().__init__() - self.proj = torch.nn.Linear(input_dim, embed_dim) - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) - x = self.proj(x) - return x - - -class MitInpaintingModel(MegatronModule): - """Mix vision Transformer Model.""" - - def __init__(self, pre_process=True, post_process=True): - super(MitInpaintingModel, self).__init__() - self.pre_process = pre_process - self.post_process = post_process - - args = get_args() - self.patch_dim = args.patch_dim - self.img_h = args.img_h - self.img_w = args.img_w - self.flatten_dim = self.patch_dim * self.patch_dim * 3 - self.backbone = mit_b3() - - self.in_channels = [64, 128, 320, 512] - self.embedding_dim = 768 - - c1_in_channels, c2_in_channels, c3_in_channels, c4_in_channels = self.in_channels - - self.linear_c4 = MLP(input_dim=c4_in_channels, embed_dim=self.embedding_dim) - self.linear_c3 = MLP(input_dim=c3_in_channels, embed_dim=self.embedding_dim) - self.linear_c2 = MLP(input_dim=c2_in_channels, embed_dim=self.embedding_dim) - self.linear_c1 = MLP(input_dim=c1_in_channels, embed_dim=self.embedding_dim) - - self.conv_fuse = torch.nn.Conv2d(self.embedding_dim*4, self.embedding_dim, 1, 1, bias=False) - self.norm = apex.parallel.SyncBatchNorm(self.embedding_dim) - self.dropout = torch.nn.Dropout2d(0.1) - - self.linear_pred = torch.nn.Conv2d(self.embedding_dim, self.flatten_dim, kernel_size=1) - - def set_input_tensor(self, input_tensor): - """See megatron.legacy.model.transformer.set_input_tensor()""" - pass - - def forward(self, input): - c1, c2, c3, c4 = self.backbone(input) - - n, _, h, w = c4.shape - _c4 = self.linear_c4(c4).permute(0, 2, 1).reshape(n, -1, c4.shape[2], c4.shape[3]) - _c4 = resize(_c4, size=c1.size()[2:], mode='bilinear', align_corners=False) - - _c3 = self.linear_c3(c3).permute(0, 2, 1).reshape(n, -1, c3.shape[2], c3.shape[3]) - _c3 = resize(_c3, size=c1.size()[2:], mode='bilinear', align_corners=False) - - _c2 = self.linear_c2(c2).permute(0, 2, 1).reshape(n, -1, c2.shape[2], c2.shape[3]) - _c2 = resize(_c2, size=c1.size()[2:], mode='bilinear', align_corners=False) - - _c1 = self.linear_c1(c1).permute(0, 2, 1).reshape(n, -1, c1.shape[2], c1.shape[3]) - - _c = torch.cat([_c4, _c3, _c2, _c1], dim=1) - _c = self.conv_fuse(_c) - - x = self.norm(_c) - x = F.relu(x, inplace=True) - x = self.dropout(x) - - x = self.linear_pred(x) - - output = einops.rearrange( - x, - "b (c p1 p2) h w -> b c (h p1) (w p2)", - p1=self.patch_dim, - p2=self.patch_dim, - h=self.img_h//self.patch_dim, - w=self.img_w//self.patch_dim, - ) - - return output diff --git a/megatron/legacy/model/vision/knn_monitor.py b/megatron/legacy/model/vision/knn_monitor.py deleted file mode 100644 index 54e726854dd..00000000000 --- a/megatron/legacy/model/vision/knn_monitor.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -import torch.nn.functional as F -import torch -from megatron.training import print_rank_0, get_args -from megatron.core import mpu -from megatron.legacy.data.vit_dataset import ClassificationTransform -from megatron.legacy.data.image_folder import ImageFolder - -_FEATURE_BANK = None - - -def build_data_loader(dataset, drop_last=True, shuffle=False): - """Data loader. Note that batch-size is the local (per GPU) batch-size.""" - # Sampler. - args = get_args() - micro_batch_size = 16 - num_workers = args.num_workers - world_size = mpu.get_data_parallel_world_size() - rank = mpu.get_data_parallel_rank() - sampler = torch.utils.data.distributed.DistributedSampler( - dataset, num_replicas=world_size, rank=rank, - drop_last=drop_last, shuffle=shuffle - ) - - # Data loader. Note that batch size is the per GPU batch size. - data_loader = torch.utils.data.DataLoader( - dataset, - batch_size=micro_batch_size, - sampler=sampler, - shuffle=False, - num_workers=num_workers, - drop_last=not drop_last, - pin_memory=True, - ) - return data_loader - - -def compute_feature_bank(model): - args = get_args() - global _FEATURE_BANK - feature_bank = [] - feature_label = [] - - train_ds = ImageFolder( - root=args.data_path[0], - transform=ClassificationTransform((args.img_h, args.img_w), train=False), - data_per_class_fraction=1.0 - ) - classes = len(train_ds.classes) - dataloader = build_data_loader(train_ds) - - for m in model: - m.eval() - - with torch.no_grad(): - for i, batch in enumerate(dataloader): - images = batch[0].cuda().contiguous() - labels = batch[1].cuda().contiguous() - student_feature, teacher_feature = model[0](images) - feature = F.normalize(teacher_feature.float(), dim=1) - feature_bank.append(feature) - feature_label.append(labels) - - for m in model: - m.train() - - # [N', D] - feature_bank = torch.cat(feature_bank, dim=0).contiguous() - feature_label = torch.cat(feature_label, dim=0).contiguous() - - feature_banks = [torch.zeros_like(feature_bank) - for i in range(mpu.get_data_parallel_world_size())] - torch.distributed.all_gather(feature_banks, - feature_bank, - group=mpu.get_data_parallel_group()) - - assert torch.all(torch.eq(feature_banks[mpu.get_data_parallel_rank()], - feature_bank)) - - feature_labels = [torch.zeros_like(feature_label) - for i in range(mpu.get_data_parallel_world_size())] - torch.distributed.all_gather(feature_labels, - feature_label, - group=mpu.get_data_parallel_group()) - - # [D, N] - feature_banks = torch.cat(feature_banks, dim=0).t().contiguous() - # [N] - feature_labels = torch.cat(feature_labels, dim=0).contiguous() - print_rank_0("feature_banks size is {}".format(feature_banks.size())) - print_rank_0("feature labels size is {}".format(feature_labels.size())) - - _FEATURE_BANK = (feature_banks, feature_labels, classes) - - -def get_feature_bank(): - global _FEATURE_BANK - assert _FEATURE_BANK is not None - return _FEATURE_BANK - - -# knn monitor as in InstDisc https://arxiv.org/abs/1805.01978 -# implementation follows http://github.com/zhirongw/lemniscate.pytorch and -# https://github.com/leftthomas/SimCLR -def knn_predict(feature, feature_bank, feature_labels, classes, knn_k, knn_t): - # compute cos similarity between each feature vector and feature bank ---> [B, N] - sim_matrix = torch.mm(feature, feature_bank) - # [B, K] - sim_weight, sim_indices = sim_matrix.topk(k=knn_k, dim=-1) - # [B, K] - sim_labels = torch.gather(feature_labels.expand(feature.size(0), -1), - dim=-1, - index=sim_indices) - sim_weight = (sim_weight / knn_t).exp() - - # counts for each class - one_hot_label = torch.zeros(feature.size(0) * knn_k, - classes, - device=sim_labels.device) - # [B*K, C] - one_hot_label = one_hot_label.scatter(dim=-1, - index=sim_labels.view(-1, 1), - value=1.0) - # weighted score ---> [B, C] - pred_scores = torch.sum( - one_hot_label.view(feature.size(0), -1, classes) * sim_weight.unsqueeze(dim=-1), - dim=1) - - pred_labels = pred_scores.argsort(dim=-1, descending=True) - return pred_labels diff --git a/megatron/legacy/model/vision/mit_backbone.py b/megatron/legacy/model/vision/mit_backbone.py deleted file mode 100644 index 3ca2303c30a..00000000000 --- a/megatron/legacy/model/vision/mit_backbone.py +++ /dev/null @@ -1,415 +0,0 @@ -# Copyright (c) 2023, NVIDIA Corporation. All rights reserved. - -import math -import torch -import torch.nn as nn -import torch.nn.functional as F -from functools import partial -from torch.nn.init import trunc_normal_ -from megatron.legacy.model.transformer import DropPath -from megatron.legacy.model import LayerNorm - - -class Mlp(nn.Module): - def __init__(self, - in_features, - hidden_features=None, - out_features=None, - act_layer=nn.GELU, - drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.dwconv = DWConv(hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x, H, W): - x = self.fc1(x) - x = self.dwconv(x, H, W) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -class Attention(nn.Module): - def __init__(self, - dim, - num_heads=8, - qkv_bias=False, - qk_scale=None, - attn_drop=0., - proj_drop=0., - sr_ratio=1): - super().__init__() - assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." - - self.dim = dim - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - self.q = nn.Linear(dim, dim, bias=qkv_bias) - self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - self.sr_ratio = sr_ratio - if sr_ratio > 1: - self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) - self.norm = LayerNorm(dim) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x, H, W): - B, N, C = x.shape - q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) - - if self.sr_ratio > 1: - x_ = x.permute(0, 2, 1).reshape(B, C, H, W) - x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) - x_ = self.norm(x_) - kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - else: - kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - k, v = kv[0], kv[1] - - attn = (q @ k.transpose(-2, -1)) * self.scale - attn = attn.softmax(dim=-1) - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B, N, C) - x = self.proj(x) - x = self.proj_drop(x) - - return x - - -class Block(nn.Module): - - def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., act_layer=nn.GELU, norm_layer=LayerNorm, sr_ratio=1): - super().__init__() - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, - num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, - attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio) - # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x, H, W): - x = x + self.drop_path(self.attn(self.norm1(x), H, W)) - x = x + self.drop_path(self.mlp(self.norm2(x), H, W)) - - return x - - -class OverlapPatchEmbed(nn.Module): - """ Image to Patch Embedding - """ - - def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768): - super().__init__() - img_size = (img_size, img_size) - patch_size = (patch_size, patch_size) - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride, - padding=(patch_size[0] // 2, patch_size[1] // 2)) - self.norm = LayerNorm(embed_dim) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def forward(self, x): - x = self.proj(x) - _, _, H, W = x.shape - x = x.flatten(2).transpose(1, 2) - x = self.norm(x) - - return x, H, W - - -class MixVisionTransformer(nn.Module): - def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512], - num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0., - attn_drop_rate=0., drop_path_rate=0., norm_layer=LayerNorm, - depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], output_avg=False): - super().__init__() - self.num_classes = num_classes - self.depths = depths - self.output_avg = output_avg - - # patch_embed - self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans, - embed_dim=embed_dims[0]) - self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0], - embed_dim=embed_dims[1]) - self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1], - embed_dim=embed_dims[2]) - self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2], - embed_dim=embed_dims[3]) - - # transformer encoder - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - cur = 0 - self.block1 = nn.ModuleList([Block( - dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[0]) - for i in range(depths[0])]) - self.norm1 = norm_layer(embed_dims[0]) - - cur += depths[0] - self.block2 = nn.ModuleList([Block( - dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[1]) - for i in range(depths[1])]) - self.norm2 = norm_layer(embed_dims[1]) - - cur += depths[1] - self.block3 = nn.ModuleList([Block( - dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[2]) - for i in range(depths[2])]) - self.norm3 = norm_layer(embed_dims[2]) - - cur += depths[2] - self.block4 = nn.ModuleList([Block( - dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, - sr_ratio=sr_ratios[3]) - for i in range(depths[3])]) - self.norm4 = norm_layer(embed_dims[3]) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - elif isinstance(m, nn.Conv2d): - fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - fan_out //= m.groups - m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) - if m.bias is not None: - m.bias.data.zero_() - - def reset_drop_path(self, drop_path_rate): - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] - cur = 0 - for i in range(self.depths[0]): - self.block1[i].drop_path.drop_prob = dpr[cur + i] - - cur += self.depths[0] - for i in range(self.depths[1]): - self.block2[i].drop_path.drop_prob = dpr[cur + i] - - cur += self.depths[1] - for i in range(self.depths[2]): - self.block3[i].drop_path.drop_prob = dpr[cur + i] - - cur += self.depths[2] - for i in range(self.depths[3]): - self.block4[i].drop_path.drop_prob = dpr[cur + i] - - def freeze_patch_emb(self): - self.patch_embed1.requires_grad = False - - def forward_features(self, x): - B = x.shape[0] - outs = [] - - # stage 1 - x, H, W = self.patch_embed1(x) - for i, blk in enumerate(self.block1): - x = blk(x, H, W) - x = self.norm1(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - # stage 2 - x, H, W = self.patch_embed2(x) - for i, blk in enumerate(self.block2): - x = blk(x, H, W) - x = self.norm2(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - # stage 3 - x, H, W = self.patch_embed3(x) - for i, blk in enumerate(self.block3): - x = blk(x, H, W) - x = self.norm3(x) - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - # stage 4 - x, H, W = self.patch_embed4(x) - for i, blk in enumerate(self.block4): - x = blk(x, H, W) - x = self.norm4(x) - if not self.output_avg: - x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() - outs.append(x) - - return outs - - def forward(self, x): - x = self.forward_features(x) - - if self.output_avg: - x = x[3].mean(dim=1) - - return x - - -class DWConv(nn.Module): - def __init__(self, dim=768): - super(DWConv, self).__init__() - self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) - - def forward(self, x, H, W): - B, N, C = x.shape - x = x.transpose(1, 2).view(B, C, H, W) - x = self.dwconv(x) - x = x.flatten(2).transpose(1, 2) - - return x - -class mit_b0(MixVisionTransformer): - def __init__(self, **kwargs): - super(mit_b0, self).__init__( - patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - - -class mit_b1(MixVisionTransformer): - def __init__(self, **kwargs): - super(mit_b1, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - - -class mit_b2(MixVisionTransformer): - def __init__(self, **kwargs): - super(mit_b2, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - - -class mit_b3(MixVisionTransformer): - def __init__(self, **kwargs): - super(mit_b3, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - -class mit_b3_avg(MixVisionTransformer): - def __init__(self, drop_path_rate=0.1, **kwargs): - super(mit_b3_avg, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=drop_path_rate, output_avg=True) - -class mit_b4(MixVisionTransformer): - def __init__(self, **kwargs): - super(mit_b4, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - -class mit_b5(MixVisionTransformer): - def __init__(self, **kwargs): - super(mit_b5, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=0.1) - -class mit_b5_avg(MixVisionTransformer): - def __init__(self, drop_path_rate=0.1, **kwargs): - super(mit_b5_avg, self).__init__( - patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], - qkv_bias=True, norm_layer=partial(LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1], - drop_rate=0.0, drop_path_rate=drop_path_rate, output_avg=True) - diff --git a/megatron/legacy/model/vision/swin_backbone.py b/megatron/legacy/model/vision/swin_backbone.py deleted file mode 100644 index 231802c8f2c..00000000000 --- a/megatron/legacy/model/vision/swin_backbone.py +++ /dev/null @@ -1,625 +0,0 @@ -# Copyright (c) 2021 Microsoft -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# Swin Transformer -# -------------------------------------------------------- - -import torch -import torch.nn as nn -import torch.utils.checkpoint as checkpoint -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ -from math import sqrt - -from megatron.training import get_args -from functools import partial - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, - out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r""" Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r""" Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - self.H = input_resolution[0] - self.W = input_resolution[1] - - self.attn_mask_dict = {} - - def create_attn_mask(self, H, W): - # calculate attention mask for SW-MSA - - Hp = int(np.ceil(H / self.window_size)) * self.window_size - Wp = int(np.ceil(W / self.window_size)) * self.window_size - img_mask = torch.zeros((1, Hp, Wp, 1)) # 1 Hp Wp 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - - def forward(self, x): - B, L, C = x.shape - H = int(sqrt(L)) - W = H - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA - attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r""" Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: B, H*W, C - """ - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." - - x = x.view(B, H, W, C) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.dim - flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock(dim=dim, input_resolution=input_resolution, - num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x) - else: - x = blk(x) - x_b4_ds = x - if self.downsample is not None: - x = self.downsample(x) - return x_b4_ds, x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class PatchEmbed(nn.Module): - r""" Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - B, C, H, W = x.shape - # FIXME look at relaxing size constraints - assert H == self.img_size[0] and W == self.img_size[1], \ - f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." - x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - Ho, Wo = self.patches_resolution - flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) - if self.norm is not None: - flops += Ho * Wo * self.embed_dim - return flops - - -class SwinTransformer(nn.Module): - r""" Swin Transformer - A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - - https://arxiv.org/pdf/2103.14030 - - Args: - img_size (int | tuple(int)): Input image size. Default 224 - patch_size (int | tuple(int)): Patch size. Default: 4 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, - embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], - window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0.3, - norm_layer=partial(nn.LayerNorm, eps=1e-6), ape=False, patch_norm=True, - use_checkpoint=False, output_avg=False, **kwargs): - super().__init__() - - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = int(embed_dim * 2 ** (self.num_layers - 1)) - self.mlp_ratio = mlp_ratio - self.img_size = to_2tuple(img_size) - self.patch_size = to_2tuple(patch_size) - self.output_avg = output_avg - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build layers - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = BasicLayer(dim=int(embed_dim * 2 ** i_layer), - input_resolution=(patches_resolution[0] // (2 ** i_layer), - patches_resolution[1] // (2 ** i_layer)), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], - norm_layer=norm_layer, - downsample=PatchMerging if (i_layer < self.num_layers - 1) else None, - use_checkpoint=use_checkpoint) - self.layers.append(layer) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - def forward(self, x): - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - h = self.img_size[0] // self.patch_size[0] - w = self.img_size[1] // self.patch_size[1] - outs = [] - - for i, layer in enumerate(self.layers): - px, x = layer(x) - b, n, c = px.shape - - if i != len(self.layers) - 1 or not self.output_avg: - px = px.permute(0, 2, 1).contiguous() - px = px.reshape(b, c, h, w) - # is this a fair assumption ?? i think it's baked into the architecture - h, w = h//2, w//2 - outs.append(px) - - if self.output_avg: - return outs[-1].mean(dim=1) - - return outs - - def flops(self): - flops = 0 - flops += self.patch_embed.flops() - for i, layer in enumerate(self.layers): - flops += layer.flops() - flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) - flops += self.num_features * self.num_classes - return flops - - -def get_swin(drop_path_rate=0.3, output_avg=False): - args = get_args() - - window_size = 7 - embed_dim = 128 - depths = [2, 2, 18, 2] - num_heads = [4, 8, 16, 32] - swin = SwinTransformer( - img_size=(args.img_h, args.img_w,), - in_chans=3, - patch_size=args.patch_dim, - embed_dim=embed_dim, - depths=depths, - num_heads=num_heads, - window_size=window_size, - drop_path_rate=drop_path_rate, - output_avg=output_avg, - ) - - return swin - diff --git a/megatron/legacy/model/vision/utils.py b/megatron/legacy/model/vision/utils.py deleted file mode 100644 index 6d29a877f1e..00000000000 --- a/megatron/legacy/model/vision/utils.py +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -import warnings -import torch -import torch.nn.functional as F - - -def resize(input, - size=None, - scale_factor=None, - mode='nearest', - align_corners=None, - warning=True): - if warning: - if size is not None and align_corners: - input_h, input_w = tuple(int(x) for x in input.shape[2:]) - output_h, output_w = tuple(int(x) for x in size) - if output_h > input_h or output_w > output_h: - if ((output_h > 1 and output_w > 1 and input_h > 1 - and input_w > 1) and (output_h - 1) % (input_h - 1) - and (output_w - 1) % (input_w - 1)): - warnings.warn( - f'When align_corners={align_corners}, ' - 'the output would more aligned if ' - f'input size {(input_h, input_w)} is `x+1` and ' - f'out size {(output_h, output_w)} is `nx+1`') - if isinstance(size, torch.Size): - size = tuple(int(x) for x in size) - return F.interpolate(input, size, scale_factor, mode, align_corners) diff --git a/megatron/legacy/model/vision/vit_backbone.py b/megatron/legacy/model/vision/vit_backbone.py deleted file mode 100644 index b46f6f74d7d..00000000000 --- a/megatron/legacy/model/vision/vit_backbone.py +++ /dev/null @@ -1,248 +0,0 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. - -"""Vision Transformer(VIT) model.""" - -import math -import einops -import torch -import apex -import torch.nn.functional as F -from megatron.training import get_args -from megatron.legacy.model.transformer import ParallelTransformer -from megatron.legacy.model.utils import ( - get_linear_layer, - init_method_normal, - scaled_init_method_normal, -) -from megatron.legacy.model.module import MegatronModule - -CLASS_TOKEN_LENGTH = 8 - -class VitMlpHead(MegatronModule): - """Pooler layer. - - Pool hidden states of a specific token (for example start of the - sequence) and add a linear transformation followed by a tanh. - - Args: - hidden_size: hidden size - init_method: weight initialization method for the linear layer. - bias is set to zero. - """ - - def __init__(self, config, hidden_size, num_classes): - super(VitMlpHead, self).__init__() - self.config = config - self.dense_in = torch.nn.Linear(hidden_size, hidden_size) - self.relu = torch.nn.ReLU() - self.dense_out = torch.nn.Linear(hidden_size, num_classes) - torch.nn.init.constant_(self.dense_out.bias, -10) - - def forward(self, hidden_states): - # hidden_states: [b, 1, h] - # sequence_index: index of the token to pool. - dense_in_result = self.dense_in(hidden_states) - tanh_result = torch.tanh(dense_in_result) - dense_out_result = self.dense_out(tanh_result) - return dense_out_result - - -def isPerfectSquare(x): - if(x >= 0): - sr = math.sqrt(x) - return (int(sr) * int(sr) == x) - return False - - -def twod_interpolate_position_embeddings_hook( - state_dict, - prefix, - local_metadata, - strict, - missing_keys, - unexpected_keys, - error_msgs, -): - - args = get_args() - num_patches_per_dim_h = args.img_h // args.patch_dim - num_patches_per_dim_w = args.img_w // args.patch_dim - num_patches = num_patches_per_dim_h * num_patches_per_dim_w - hidden_size = args.hidden_size - - key = prefix + "weight" - - assert key in state_dict - if key in state_dict: - input_param = state_dict[key] - - input_seq_len = input_param.shape[0] - assert(isPerfectSquare(input_seq_len) or isPerfectSquare(input_seq_len - CLASS_TOKEN_LENGTH)) - input_has_class_token = not isPerfectSquare(input_seq_len) - num_tok_input = input_seq_len - CLASS_TOKEN_LENGTH if input_has_class_token else input_seq_len - num_tok_output = num_patches - output_has_class_token = args.class_token_present - - # update input_param and load it to state_dict[key] - if input_has_class_token: - input_param_tok = input_param[:CLASS_TOKEN_LENGTH, :] - input_param_grid = input_param[CLASS_TOKEN_LENGTH:, :] - else: - input_param_tok = torch.zeros(CLASS_TOKEN_LENGTH, hidden_size) - input_param_grid = input_param - - assert input_param.shape[1] == hidden_size - - if num_tok_input != num_tok_output: - - gs_input = int(math.sqrt(num_tok_input)) - gs_new = (num_patches_per_dim_h, num_patches_per_dim_w) - - input_param_grid = input_param_grid.transpose(0, 1).contiguous() - input_param_grid = input_param_grid.reshape( - (1, -1, gs_input, gs_input) - ) - input_param_grid = input_param_grid.float() - scale_factor = (gs_new[0] / gs_input, gs_new[1] / gs_input) - - input_param_grid = F.interpolate( - input_param_grid, scale_factor=scale_factor, mode="bilinear" - ) - - input_param_grid = input_param_grid.half() - input_param_grid = input_param_grid.reshape((-1, num_tok_output)) - input_param_grid = input_param_grid.transpose(0, 1).contiguous() - - assert input_param_grid.shape[1] == hidden_size - - input_param = input_param_grid - assert ( - input_param.shape[0] == num_tok_output - and input_param.shape[1] == hidden_size - ) - - if output_has_class_token: - input_param = torch.cat((input_param_tok, input_param), dim=0) - - state_dict[key] = input_param - - -class VitBackbone(MegatronModule): - """Vision Transformer Model.""" - - def __init__(self, - config, - pre_process=True, - post_process=True, - class_token=True, - single_token_output=False, - post_layer_norm=True, - drop_path_rate=0.0): - super(VitBackbone, self).__init__(share_embeddings_and_output_weights=False) - args = get_args() - self.config = config - - self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy - - self.pre_process = pre_process - self.post_process = post_process - self.class_token = class_token - self.post_layer_norm = post_layer_norm - self.hidden_size = args.hidden_size - self.patch_dim = args.patch_dim - self.img_h = args.img_h - self.img_w = args.img_w - self.micro_batch_size = args.micro_batch_size - self.single_token_output = single_token_output - self.drop_path_rate = drop_path_rate - - assert self.img_h % self.patch_dim == 0 - assert self.img_w % self.patch_dim == 0 - self.num_patches_per_dim_h = self.img_h // self.patch_dim - self.num_patches_per_dim_w = self.img_w // self.patch_dim - self.num_patches = self.num_patches_per_dim_h * self.num_patches_per_dim_w - self.seq_length = self.num_patches + (CLASS_TOKEN_LENGTH if self.class_token else 0) - self.flatten_dim = self.patch_dim * self.patch_dim * args.num_channels - self.input_tensor = None - self.position_ids = None - - if self.pre_process: - # cls_token - if self.class_token: - self.cls_token = torch.nn.Parameter( - torch.randn(1, CLASS_TOKEN_LENGTH, self.hidden_size) - ) - torch.nn.init.zeros_(self.cls_token) - self.position_ids = torch.arange(self.seq_length).expand(1, -1).cuda() - - # Linear encoder - self.linear_encoder = torch.nn.Linear( - self.flatten_dim, self.hidden_size - ) - - # embedding - self.position_embeddings = torch.nn.Embedding( - self.seq_length, self.hidden_size - ) - init_method_normal(args.init_method_std)( - self.position_embeddings.weight - ) - - args.class_token_present = self.class_token - self.position_embeddings._register_load_state_dict_pre_hook( - twod_interpolate_position_embeddings_hook - ) - - self.embedding_dropout = torch.nn.Dropout(args.hidden_dropout) - - # Transformer - self.transformer = ParallelTransformer( - config, - model_type=args.model_type, - pre_process=self.pre_process, - post_process=self.post_process, - post_layer_norm=self.post_layer_norm, - drop_path_rate=self.drop_path_rate - ) - - def set_input_tensor(self, input_tensor): - """See megatron.legacy.model.transformer.set_input_tensor()""" - self.transformer.set_input_tensor(input_tensor) - - def forward(self, input): - - if self.pre_process: - rearranged_input = einops.rearrange( - input, - "b c (h p1) (w p2) -> b (h w) (p1 p2 c)", - p1=self.patch_dim, - p2=self.patch_dim, - ) - - assert rearranged_input.dtype == torch.half - encoder_output = self.linear_encoder(rearranged_input) - - concatenated_tokens = encoder_output - if self.class_token: - cls_tokens = self.cls_token.expand(encoder_output.shape[0], -1, -1) - concatenated_tokens = torch.cat((cls_tokens, encoder_output), dim=1) - - token_embeddings = concatenated_tokens + \ - self.position_embeddings(self.position_ids[:, :concatenated_tokens.shape[1]]) - # [b, s, h] => [s, b, h] - token_embeddings = token_embeddings.transpose(0, 1).contiguous() - hidden_states = self.embedding_dropout(token_embeddings) - else: - hidden_states = input - - hidden_states = self.transformer(hidden_states, None) - - if self.post_process: - # [s b h] => [b s h] - if self.single_token_output: - hidden_states = hidden_states[0] - else: - hidden_states = hidden_states.transpose(0, 1).contiguous() - - return hidden_states - diff --git a/megatron/training/training.py b/megatron/training/training.py index 7fa00c048b7..7c66812c67a 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -3289,11 +3289,6 @@ def evaluate( timers('evaluate', log_level=0).start(barrier=True) - if args.vision_pretraining and args.vision_pretraining_type == "dino": - from megatron.legacy.model.vision.knn_monitor import compute_feature_bank - - compute_feature_bank(model) - # Turn on evaluation mode which disables dropout. for model_module in model: model_module.eval() From f098fe84828f3e07c445130ff7c9405f958dc001 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Thu, 16 Apr 2026 16:00:02 +0200 Subject: [PATCH 21/72] feat: long convergence resiliency for release tests (#4335) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .../golden_values_dev_dgx_gb200.json | 20042 ++ .../golden_values_dev_dgx_h100.json | 32400 +-- .../bert/bert_release/model_config.yaml | 1 + .../bert/bert_release_sm/model_config.yaml | 1 + .../golden_values_dev_dgx_h100.json | 51043 ++++- .../gpt/gpt3_15b_8t_release/model_config.yaml | 1 + .../model_config.yaml | 1 + .../gpt3_15b_8t_release_sm/model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../golden_values_dev_dgx_gb200.json | 57266 ++++++ .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../golden_values_dev_dgx_h100.json | 240 +- .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../model_config.yaml | 1 + .../golden_values_dev_dgx_h100.json | 322 +- .../model_config.yaml | 1 + .../golden_values_dev_dgx_gb200.json | 100042 +++++++++ .../golden_values_dev_dgx_h100.json | 159994 +++++++-------- .../t5/t5_release/model_config.yaml | 1 + .../t5/t5_release_sm/model_config.yaml | 1 + .../python_scripts/launch_jet_workload.py | 57 + 25 files changed, 324943 insertions(+), 96479 deletions(-) create mode 100644 tests/functional_tests/test_cases/bert/bert_release/golden_values_dev_dgx_gb200.json create mode 100644 tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/golden_values_dev_dgx_gb200.json create mode 100644 tests/functional_tests/test_cases/t5/t5_release/golden_values_dev_dgx_gb200.json diff --git a/tests/functional_tests/test_cases/bert/bert_release/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/bert/bert_release/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..375eec110d1 --- /dev/null +++ b/tests/functional_tests/test_cases/bert/bert_release/golden_values_dev_dgx_gb200.json @@ -0,0 +1,20042 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 20000, + "step_interval": 5, + "values": { + "1": 10.57639, + "5": 10.57231, + "10": 10.59066, + "15": 10.56928, + "20": 10.13589, + "25": 9.49392, + "30": 9.40566, + "35": 9.162, + "40": 9.08603, + "45": 8.96494, + "50": 8.6706, + "55": 8.77766, + "60": 8.52106, + "65": 8.52296, + "70": 8.20839, + "75": 8.31602, + "80": 8.01203, + "85": 7.79661, + "90": 7.71545, + "95": 7.5761, + "100": 7.53743, + "105": 7.35462, + "110": 7.44917, + "115": 7.20902, + "120": 6.98281, + "125": 7.27623, + "130": 7.0138, + "135": 6.82292, + "140": 7.21816, + "145": 6.99859, + "150": 7.10006, + "155": 6.97982, + "160": 7.01529, + "165": 7.04774, + "170": 6.92045, + "175": 6.88784, + "180": 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-16216,7 +16216,7 @@ "885": "nan", "890": "nan", "895": "nan", - "900": 0.30737, + "900": 0.27481, "905": "nan", "910": "nan", "915": "nan", @@ -16236,7 +16236,7 @@ "985": "nan", "990": "nan", "995": "nan", - "1000": 0.29366, + "1000": 0.28689, "1005": "nan", "1010": "nan", "1015": "nan", @@ -16256,7 +16256,7 @@ "1085": "nan", "1090": "nan", "1095": "nan", - "1100": 0.30117, + "1100": 0.2811, "1105": "nan", "1110": "nan", "1115": "nan", @@ -16276,7 +16276,7 @@ "1185": "nan", "1190": "nan", "1195": "nan", - "1200": 0.30515, + "1200": 0.28428, "1205": "nan", "1210": "nan", "1215": "nan", @@ -16296,7 +16296,7 @@ "1285": "nan", "1290": "nan", "1295": "nan", - "1300": 0.29886, + "1300": 0.29028, "1305": "nan", "1310": "nan", "1315": "nan", @@ -16316,7 +16316,7 @@ "1385": "nan", "1390": "nan", "1395": "nan", - "1400": 0.31284, + "1400": 0.28008, "1405": "nan", "1410": "nan", "1415": "nan", @@ -16336,7 +16336,7 @@ "1485": "nan", "1490": "nan", "1495": "nan", - "1500": 0.30994, + "1500": 0.2846, "1505": "nan", "1510": "nan", "1515": "nan", @@ -16356,7 +16356,7 @@ "1585": "nan", "1590": "nan", "1595": "nan", - "1600": 0.29741, + "1600": 0.29918, "1605": "nan", "1610": "nan", "1615": "nan", @@ -16376,7 +16376,7 @@ "1685": "nan", "1690": "nan", "1695": "nan", - "1700": 0.29425, + "1700": 0.29317, "1705": "nan", "1710": "nan", "1715": "nan", @@ -16396,7 +16396,7 @@ "1785": "nan", "1790": "nan", "1795": "nan", - "1800": 0.29731, + "1800": 0.32139, "1805": "nan", "1810": "nan", "1815": "nan", @@ -16416,7 +16416,7 @@ "1885": "nan", "1890": "nan", "1895": "nan", - "1900": 0.29135, + "1900": 0.32037, "1905": "nan", "1910": "nan", "1915": "nan", @@ -16436,7 +16436,7 @@ "1985": "nan", "1990": "nan", "1995": "nan", - "2000": 0.29396, + "2000": 0.33062, "2005": "nan", "2010": "nan", "2015": "nan", @@ -16456,7 +16456,7 @@ "2085": "nan", "2090": "nan", "2095": "nan", - "2100": 0.29868, + "2100": 0.28773, "2105": "nan", "2110": "nan", "2115": "nan", @@ -16476,7 +16476,7 @@ "2185": "nan", "2190": "nan", "2195": "nan", - "2200": 0.29659, + "2200": 0.29525, "2205": "nan", "2210": "nan", "2215": "nan", @@ -16496,7 +16496,7 @@ "2285": "nan", "2290": "nan", "2295": "nan", - "2300": 0.30668, + "2300": 0.28619, "2305": "nan", "2310": "nan", "2315": "nan", @@ -16516,7 +16516,7 @@ "2385": "nan", "2390": "nan", "2395": "nan", - "2400": 0.29147, + "2400": 0.2912, "2405": "nan", "2410": "nan", "2415": "nan", @@ -16536,7 +16536,7 @@ "2485": "nan", "2490": "nan", "2495": "nan", - "2500": 0.30306, + "2500": 0.2873, "2505": "nan", "2510": "nan", "2515": "nan", @@ -16556,7 +16556,7 @@ "2585": "nan", "2590": "nan", "2595": "nan", - "2600": 0.29399, + "2600": 0.28991, "2605": "nan", "2610": "nan", "2615": "nan", @@ -16576,7 +16576,7 @@ "2685": "nan", "2690": "nan", "2695": "nan", - "2700": 0.30369, + "2700": 0.28878, "2705": "nan", "2710": "nan", "2715": "nan", @@ -16596,7 +16596,7 @@ "2785": "nan", "2790": "nan", "2795": "nan", - "2800": 0.30933, + "2800": 0.28712, "2805": "nan", "2810": "nan", "2815": "nan", @@ -16616,7 +16616,7 @@ "2885": "nan", "2890": "nan", "2895": "nan", - "2900": 0.29764, + "2900": 0.29521, "2905": "nan", "2910": "nan", "2915": "nan", @@ -16636,7 +16636,7 @@ "2985": "nan", "2990": "nan", "2995": "nan", - "3000": 0.29713, + "3000": 0.31242, "3005": "nan", "3010": "nan", "3015": "nan", @@ -16656,7 +16656,7 @@ "3085": "nan", "3090": "nan", "3095": "nan", - "3100": 0.30356, + "3100": 0.28824, "3105": "nan", "3110": "nan", "3115": "nan", @@ -16676,7 +16676,7 @@ "3185": "nan", "3190": "nan", "3195": "nan", - "3200": 0.29333, + "3200": 0.27284, "3205": "nan", "3210": "nan", "3215": "nan", @@ -16696,7 +16696,7 @@ "3285": "nan", "3290": "nan", "3295": "nan", - "3300": 0.28855, + "3300": 0.28528, "3305": "nan", "3310": "nan", "3315": "nan", @@ -16716,7 +16716,7 @@ "3385": "nan", "3390": "nan", "3395": "nan", - "3400": 0.30944, + "3400": 0.28046, "3405": "nan", "3410": "nan", "3415": "nan", @@ -16736,7 +16736,7 @@ "3485": "nan", "3490": "nan", "3495": "nan", - "3500": 0.29328, + "3500": 0.2967, "3505": "nan", "3510": "nan", "3515": "nan", @@ -16756,7 +16756,7 @@ "3585": "nan", "3590": "nan", "3595": "nan", - "3600": 0.2964, + "3600": 0.28516, "3605": "nan", "3610": "nan", "3615": "nan", @@ -16776,7 +16776,7 @@ "3685": "nan", "3690": "nan", "3695": "nan", - "3700": 0.30483, + "3700": 0.28726, "3705": "nan", "3710": "nan", "3715": "nan", @@ -16796,7 +16796,7 @@ "3785": "nan", "3790": "nan", "3795": "nan", - "3800": 0.29341, + "3800": 0.31605, "3805": "nan", "3810": "nan", "3815": "nan", @@ -16816,7 +16816,7 @@ "3885": "nan", "3890": "nan", "3895": "nan", - "3900": 0.28771, + "3900": 0.28669, "3905": "nan", "3910": "nan", "3915": "nan", @@ -16836,7 +16836,7 @@ "3985": "nan", "3990": "nan", "3995": "nan", - "4000": 0.29046, + "4000": 0.2812, "4005": "nan", "4010": "nan", "4015": "nan", @@ -16856,7 +16856,7 @@ "4085": "nan", "4090": "nan", "4095": "nan", - "4100": 0.30601, + "4100": 0.30522, "4105": "nan", "4110": "nan", "4115": "nan", @@ -16876,7 +16876,7 @@ "4185": "nan", "4190": "nan", "4195": "nan", - "4200": 0.29169, + "4200": 0.28685, "4205": "nan", "4210": "nan", "4215": "nan", @@ -16896,7 +16896,7 @@ "4285": "nan", "4290": "nan", "4295": "nan", - "4300": 0.29779, + "4300": 0.29183, "4305": "nan", "4310": "nan", "4315": "nan", @@ -16916,7 +16916,7 @@ "4385": "nan", "4390": "nan", "4395": "nan", - "4400": 0.31312, + "4400": 0.29355, "4405": "nan", "4410": "nan", "4415": "nan", @@ -16936,7 +16936,7 @@ "4485": "nan", "4490": "nan", "4495": "nan", - "4500": 0.29557, + "4500": 0.28678, "4505": "nan", "4510": "nan", "4515": "nan", @@ -16956,7 +16956,7 @@ "4585": "nan", "4590": "nan", "4595": "nan", - "4600": 0.29602, + "4600": 0.28569, "4605": "nan", "4610": "nan", "4615": "nan", @@ -16976,7 +16976,7 @@ "4685": "nan", "4690": "nan", "4695": "nan", - "4700": 0.29316, + "4700": 0.30304, "4705": "nan", "4710": "nan", "4715": "nan", @@ -16996,7 +16996,7 @@ "4785": "nan", "4790": "nan", "4795": "nan", - "4800": 0.29488, + "4800": 0.29146, "4805": "nan", "4810": "nan", "4815": "nan", @@ -17016,7 +17016,7 @@ "4885": "nan", "4890": "nan", "4895": "nan", - "4900": 0.29017, + "4900": 0.28628, "4905": "nan", "4910": "nan", "4915": "nan", @@ -17036,7 +17036,7 @@ "4985": "nan", "4990": "nan", "4995": "nan", - "5000": 0.29473, + "5000": 0.3007, "5005": "nan", "5010": "nan", "5015": "nan", @@ -17056,7 +17056,7 @@ "5085": "nan", "5090": "nan", "5095": "nan", - "5100": 0.2914, + "5100": 0.26905, "5105": "nan", "5110": "nan", "5115": "nan", @@ -17076,7 +17076,7 @@ "5185": "nan", "5190": "nan", "5195": "nan", - "5200": 0.30349, + "5200": 0.29106, "5205": "nan", "5210": "nan", "5215": "nan", @@ -17096,7 +17096,7 @@ "5285": "nan", "5290": "nan", "5295": "nan", - "5300": 0.29229, + "5300": 0.29612, "5305": "nan", "5310": "nan", "5315": "nan", @@ -17116,7 +17116,7 @@ "5385": "nan", "5390": "nan", "5395": "nan", - "5400": 0.30084, + "5400": 0.31672, "5405": "nan", "5410": "nan", "5415": "nan", @@ -17136,7 +17136,7 @@ "5485": "nan", "5490": "nan", "5495": "nan", - "5500": 0.30346, + "5500": 0.28967, "5505": "nan", "5510": "nan", "5515": "nan", @@ -17156,7 +17156,7 @@ "5585": "nan", "5590": "nan", "5595": "nan", - "5600": 0.29987, + "5600": 0.26251, "5605": "nan", "5610": "nan", "5615": "nan", @@ -17176,7 +17176,7 @@ "5685": "nan", "5690": "nan", "5695": "nan", - "5700": 0.29203, + "5700": 0.28716, "5705": "nan", "5710": "nan", "5715": "nan", @@ -17196,7 +17196,7 @@ "5785": "nan", "5790": "nan", "5795": "nan", - "5800": 0.29203, + "5800": 0.28044, "5805": "nan", "5810": "nan", "5815": "nan", @@ -17216,7 +17216,7 @@ "5885": "nan", "5890": "nan", "5895": "nan", - "5900": 0.29442, + "5900": 0.27484, "5905": "nan", "5910": "nan", "5915": "nan", @@ -17236,7 +17236,7 @@ "5985": "nan", "5990": "nan", "5995": "nan", - "6000": 0.29503, + "6000": 0.2867, "6005": "nan", "6010": "nan", "6015": "nan", @@ -17256,7 +17256,7 @@ "6085": "nan", "6090": "nan", "6095": "nan", - "6100": 0.30417, + "6100": 0.28145, "6105": "nan", "6110": "nan", "6115": "nan", @@ -17276,7 +17276,7 @@ "6185": "nan", "6190": "nan", "6195": "nan", - "6200": 0.29847, + "6200": 0.29309, "6205": "nan", "6210": "nan", "6215": "nan", @@ -17296,7 +17296,7 @@ "6285": "nan", "6290": "nan", "6295": "nan", - "6300": 0.2933, + "6300": 0.29298, "6305": "nan", "6310": "nan", "6315": "nan", @@ -17316,7 +17316,7 @@ "6385": "nan", "6390": "nan", "6395": "nan", - "6400": 0.28914, + "6400": 0.27946, "6405": "nan", "6410": "nan", "6415": "nan", @@ -17336,7 +17336,7 @@ "6485": "nan", "6490": "nan", "6495": "nan", - "6500": 0.29461, + "6500": 0.28294, "6505": "nan", "6510": "nan", "6515": "nan", @@ -17356,7 +17356,7 @@ "6585": "nan", "6590": "nan", "6595": "nan", - "6600": 0.29601, + "6600": 0.30915, "6605": "nan", "6610": "nan", "6615": "nan", @@ -17376,7 +17376,7 @@ "6685": "nan", "6690": "nan", "6695": "nan", - "6700": 0.2975, + "6700": 0.31789, "6705": "nan", "6710": "nan", "6715": "nan", @@ -17396,7 +17396,7 @@ "6785": "nan", "6790": "nan", "6795": "nan", - "6800": 0.29903, + "6800": 0.29939, "6805": "nan", "6810": "nan", "6815": "nan", @@ -17416,7 +17416,7 @@ "6885": "nan", "6890": "nan", "6895": "nan", - "6900": 0.29481, + "6900": 0.28546, "6905": "nan", "6910": "nan", "6915": "nan", @@ -17436,7 +17436,7 @@ "6985": "nan", "6990": "nan", "6995": "nan", - "7000": 0.29695, + "7000": 0.27714, "7005": "nan", "7010": "nan", "7015": "nan", @@ -17456,7 +17456,7 @@ "7085": "nan", "7090": "nan", "7095": "nan", - "7100": 0.29247, + "7100": 0.29118, "7105": "nan", "7110": "nan", "7115": "nan", @@ -17476,7 +17476,7 @@ "7185": "nan", "7190": "nan", "7195": "nan", - "7200": 0.29084, + "7200": 0.28091, "7205": "nan", "7210": "nan", "7215": "nan", @@ -17496,7 +17496,7 @@ "7285": "nan", "7290": "nan", "7295": "nan", - "7300": 0.29315, + "7300": 0.29459, "7305": "nan", "7310": "nan", "7315": "nan", @@ -17516,7 +17516,7 @@ "7385": "nan", "7390": "nan", "7395": "nan", - "7400": 0.298, + "7400": 0.28179, "7405": "nan", "7410": "nan", "7415": "nan", @@ -17536,7 +17536,7 @@ "7485": "nan", "7490": "nan", "7495": "nan", - "7500": 0.30765, + "7500": 0.28983, "7505": "nan", "7510": "nan", "7515": "nan", @@ -17556,7 +17556,7 @@ "7585": "nan", "7590": "nan", "7595": "nan", - "7600": 0.29214, + "7600": 0.28968, "7605": "nan", "7610": "nan", "7615": "nan", @@ -17576,7 +17576,7 @@ "7685": "nan", "7690": "nan", "7695": "nan", - "7700": 0.29013, + "7700": 0.27493, "7705": "nan", "7710": "nan", "7715": "nan", @@ -17596,7 +17596,7 @@ "7785": "nan", "7790": "nan", "7795": "nan", - "7800": 0.29279, + "7800": 0.31367, "7805": "nan", "7810": "nan", "7815": "nan", @@ -17616,7 +17616,7 @@ "7885": "nan", "7890": "nan", "7895": "nan", - "7900": 0.29941, + "7900": 0.3018, "7905": "nan", "7910": "nan", "7915": "nan", @@ -17636,7 +17636,7 @@ "7985": "nan", "7990": "nan", "7995": "nan", - "8000": 0.28757, + "8000": 0.28204, "8005": "nan", "8010": "nan", "8015": "nan", @@ -17656,7 +17656,7 @@ "8085": "nan", "8090": "nan", "8095": "nan", - "8100": 0.29861, + "8100": 0.2962, "8105": "nan", "8110": "nan", "8115": "nan", @@ -17676,7 +17676,7 @@ "8185": "nan", "8190": "nan", "8195": "nan", - "8200": 0.29215, + "8200": 0.27538, "8205": "nan", "8210": "nan", "8215": "nan", @@ -17696,7 +17696,7 @@ "8285": "nan", "8290": "nan", "8295": "nan", - "8300": 0.28995, + "8300": 0.31745, "8305": "nan", "8310": "nan", "8315": "nan", @@ -17716,7 +17716,7 @@ "8385": "nan", "8390": "nan", "8395": "nan", - "8400": 0.29279, + "8400": 0.27227, "8405": "nan", "8410": "nan", "8415": "nan", @@ -17736,7 +17736,7 @@ "8485": "nan", "8490": "nan", "8495": "nan", - "8500": 0.29218, + "8500": 0.2811, "8505": "nan", "8510": "nan", "8515": "nan", @@ -17756,7 +17756,7 @@ "8585": "nan", "8590": "nan", "8595": "nan", - "8600": 0.28848, + "8600": 0.27835, "8605": "nan", "8610": "nan", "8615": "nan", @@ -17776,7 +17776,7 @@ "8685": "nan", "8690": "nan", "8695": "nan", - "8700": 0.29589, + "8700": 0.28569, "8705": "nan", "8710": "nan", "8715": "nan", @@ -17796,7 +17796,7 @@ "8785": "nan", "8790": "nan", "8795": "nan", - "8800": 0.29437, + "8800": 0.28861, "8805": "nan", "8810": "nan", "8815": "nan", @@ -17816,7 +17816,7 @@ "8885": "nan", "8890": "nan", "8895": "nan", - "8900": 0.29367, + "8900": 0.27491, "8905": "nan", "8910": "nan", "8915": "nan", @@ -17836,7 +17836,7 @@ "8985": "nan", "8990": "nan", "8995": "nan", - "9000": 0.29567, + "9000": 0.3198, "9005": "nan", "9010": "nan", "9015": "nan", @@ -17856,7 +17856,7 @@ "9085": "nan", "9090": "nan", "9095": "nan", - "9100": 0.29096, + "9100": 0.28393, "9105": "nan", "9110": "nan", "9115": "nan", @@ -17876,7 +17876,7 @@ "9185": "nan", "9190": "nan", "9195": "nan", - "9200": 0.28403, + "9200": 0.2752, "9205": "nan", "9210": "nan", "9215": "nan", @@ -17896,7 +17896,7 @@ "9285": "nan", "9290": "nan", "9295": "nan", - "9300": 0.29074, + "9300": 0.29169, "9305": "nan", "9310": "nan", "9315": "nan", @@ -17916,7 +17916,7 @@ "9385": "nan", "9390": "nan", "9395": "nan", - "9400": 0.2955, + "9400": 0.28014, "9405": "nan", "9410": "nan", "9415": "nan", @@ -17936,7 +17936,7 @@ "9485": "nan", "9490": "nan", "9495": "nan", - "9500": 0.30297, + "9500": 0.29135, "9505": "nan", "9510": "nan", "9515": "nan", @@ -17956,7 +17956,7 @@ "9585": "nan", "9590": "nan", "9595": "nan", - "9600": 0.29919, + "9600": 0.27701, "9605": "nan", "9610": "nan", "9615": "nan", @@ -17976,7 +17976,7 @@ "9685": "nan", "9690": "nan", "9695": "nan", - "9700": 0.30329, + "9700": 0.28543, "9705": "nan", "9710": "nan", "9715": "nan", @@ -17996,7 +17996,7 @@ "9785": "nan", "9790": "nan", "9795": "nan", - "9800": 0.2953, + "9800": 0.28535, "9805": "nan", "9810": "nan", "9815": "nan", @@ -18016,7 +18016,7 @@ "9885": "nan", "9890": "nan", "9895": "nan", - "9900": 0.29335, + "9900": 0.26417, "9905": "nan", "9910": "nan", "9915": "nan", @@ -18036,7 +18036,7 @@ "9985": "nan", "9990": "nan", "9995": "nan", - "10000": 0.29575, + "10000": 0.28957, "10005": "nan", "10010": "nan", "10015": "nan", @@ -18056,7 +18056,7 @@ "10085": "nan", "10090": "nan", "10095": "nan", - "10100": 0.30017, + "10100": 0.29871, "10105": "nan", "10110": "nan", "10115": "nan", @@ -18076,7 +18076,7 @@ "10185": "nan", "10190": "nan", "10195": "nan", - "10200": 0.28788, + "10200": 0.28593, "10205": "nan", "10210": "nan", "10215": "nan", @@ -18096,7 +18096,7 @@ "10285": "nan", "10290": "nan", "10295": "nan", - "10300": 0.29328, + "10300": 0.30617, "10305": "nan", "10310": "nan", "10315": "nan", @@ -18116,7 +18116,7 @@ "10385": "nan", "10390": "nan", "10395": "nan", - "10400": 0.29073, + "10400": 0.27488, "10405": "nan", "10410": "nan", "10415": "nan", @@ -18136,7 +18136,7 @@ "10485": "nan", "10490": "nan", "10495": "nan", - "10500": 0.2916, + "10500": 0.32544, "10505": "nan", "10510": "nan", "10515": "nan", @@ -18156,7 +18156,7 @@ "10585": "nan", "10590": "nan", "10595": "nan", - "10600": 0.29251, + "10600": 0.28688, "10605": "nan", "10610": "nan", "10615": "nan", @@ -18176,7 +18176,7 @@ "10685": "nan", "10690": "nan", "10695": "nan", - "10700": 0.29815, + "10700": 0.28388, "10705": "nan", "10710": "nan", "10715": "nan", @@ -18196,7 +18196,7 @@ "10785": "nan", "10790": "nan", "10795": "nan", - "10800": 0.2964, + "10800": 0.28719, "10805": "nan", "10810": "nan", "10815": "nan", @@ -18216,7 +18216,7 @@ "10885": "nan", "10890": "nan", "10895": "nan", - "10900": 0.29354, + "10900": 0.28169, "10905": "nan", "10910": "nan", "10915": "nan", @@ -18236,7 +18236,7 @@ "10985": "nan", "10990": "nan", "10995": "nan", - "11000": 0.29398, + "11000": 0.28574, "11005": "nan", "11010": "nan", "11015": "nan", @@ -18256,7 +18256,7 @@ "11085": "nan", "11090": "nan", "11095": "nan", - "11100": 0.28933, + "11100": 0.27024, "11105": "nan", "11110": "nan", "11115": "nan", @@ -18276,7 +18276,7 @@ "11185": "nan", "11190": "nan", "11195": "nan", - "11200": 0.29307, + "11200": 0.2662, "11205": "nan", "11210": "nan", "11215": "nan", @@ -18296,7 +18296,7 @@ "11285": "nan", "11290": "nan", "11295": "nan", - "11300": 0.29257, + "11300": 0.30471, "11305": "nan", "11310": "nan", "11315": "nan", @@ -18316,7 +18316,7 @@ "11385": "nan", "11390": "nan", "11395": "nan", - "11400": 0.29479, + "11400": 0.27888, "11405": "nan", "11410": "nan", "11415": "nan", @@ -18336,7 +18336,7 @@ "11485": "nan", "11490": "nan", "11495": "nan", - "11500": 0.30464, + "11500": 0.29849, "11505": "nan", "11510": "nan", "11515": "nan", @@ -18356,7 +18356,7 @@ "11585": "nan", "11590": "nan", "11595": "nan", - "11600": 0.29822, + "11600": 0.28281, "11605": "nan", "11610": "nan", "11615": "nan", @@ -18376,7 +18376,7 @@ "11685": "nan", "11690": "nan", "11695": "nan", - "11700": 0.29887, + "11700": 0.28254, "11705": "nan", "11710": "nan", "11715": "nan", @@ -18396,7 +18396,7 @@ "11785": "nan", "11790": "nan", "11795": "nan", - "11800": 0.3027, + "11800": 0.28002, "11805": "nan", "11810": "nan", "11815": "nan", @@ -18416,7 +18416,7 @@ "11885": "nan", "11890": "nan", "11895": "nan", - "11900": 0.29833, + "11900": 0.29103, "11905": "nan", "11910": "nan", "11915": "nan", @@ -18436,7 +18436,7 @@ "11985": "nan", "11990": "nan", "11995": "nan", - "12000": 0.29865, + "12000": 0.2742, "12005": "nan", "12010": "nan", "12015": "nan", @@ -18456,7 +18456,7 @@ "12085": "nan", "12090": "nan", "12095": "nan", - "12100": 0.29085, + "12100": 0.29374, "12105": "nan", "12110": "nan", "12115": "nan", @@ -18476,7 +18476,7 @@ "12185": "nan", "12190": "nan", "12195": "nan", - "12200": 0.29165, + "12200": 0.29465, "12205": "nan", "12210": "nan", "12215": "nan", @@ -18496,7 +18496,7 @@ "12285": "nan", "12290": "nan", "12295": "nan", - "12300": 0.28632, + "12300": 0.27247, "12305": "nan", "12310": "nan", "12315": "nan", @@ -18516,7 +18516,7 @@ "12385": "nan", "12390": "nan", "12395": "nan", - "12400": 0.29291, + "12400": 0.29147, "12405": "nan", "12410": "nan", "12415": "nan", @@ -18536,7 +18536,7 @@ "12485": "nan", "12490": "nan", "12495": "nan", - "12500": 0.29211, + "12500": 0.28301, "12505": "nan", "12510": "nan", "12515": "nan", @@ -18556,7 +18556,7 @@ "12585": "nan", "12590": "nan", "12595": "nan", - "12600": 0.2955, + "12600": 0.33632, "12605": "nan", "12610": "nan", "12615": "nan", @@ -18576,7 +18576,7 @@ "12685": "nan", "12690": "nan", "12695": "nan", - "12700": 0.2851, + "12700": 0.2786, "12705": "nan", "12710": "nan", "12715": "nan", @@ -18596,7 +18596,7 @@ "12785": "nan", "12790": "nan", "12795": "nan", - "12800": 0.29696, + "12800": 0.2923, "12805": "nan", "12810": "nan", "12815": "nan", @@ -18616,7 +18616,7 @@ "12885": "nan", "12890": "nan", "12895": "nan", - "12900": 0.29112, + "12900": 0.28043, "12905": "nan", "12910": "nan", "12915": "nan", @@ -18636,7 +18636,7 @@ "12985": "nan", "12990": "nan", "12995": "nan", - "13000": 0.29889, + "13000": 0.29934, "13005": "nan", "13010": "nan", "13015": "nan", @@ -18656,7 +18656,7 @@ "13085": "nan", "13090": "nan", "13095": "nan", - "13100": 0.29889, + "13100": 0.28845, "13105": "nan", "13110": "nan", "13115": "nan", @@ -18676,7 +18676,7 @@ "13185": "nan", "13190": "nan", "13195": "nan", - "13200": 0.29419, + "13200": 0.30194, "13205": "nan", "13210": "nan", "13215": "nan", @@ -18696,7 +18696,7 @@ "13285": "nan", "13290": "nan", "13295": "nan", - "13300": 0.288, + "13300": 0.29776, "13305": "nan", "13310": "nan", "13315": "nan", @@ -18716,7 +18716,7 @@ "13385": "nan", "13390": "nan", "13395": "nan", - "13400": 0.29757, + "13400": 0.29691, "13405": "nan", "13410": "nan", "13415": "nan", @@ -18736,7 +18736,7 @@ "13485": "nan", "13490": "nan", "13495": "nan", - "13500": 0.29383, + "13500": 0.28372, "13505": "nan", "13510": "nan", "13515": "nan", @@ -18756,7 +18756,7 @@ "13585": "nan", "13590": "nan", "13595": "nan", - "13600": 0.29566, + "13600": 0.29299, "13605": "nan", "13610": "nan", "13615": "nan", @@ -18776,7 +18776,7 @@ "13685": "nan", "13690": "nan", "13695": "nan", - "13700": 0.2974, + "13700": 0.28074, "13705": "nan", "13710": "nan", "13715": "nan", @@ -18796,7 +18796,7 @@ "13785": "nan", "13790": "nan", "13795": "nan", - "13800": 0.28893, + "13800": 0.28752, "13805": "nan", "13810": "nan", "13815": "nan", @@ -18816,7 +18816,7 @@ "13885": "nan", "13890": "nan", "13895": "nan", - "13900": 0.28373, + "13900": 0.2797, "13905": "nan", "13910": "nan", "13915": "nan", @@ -18836,7 +18836,7 @@ "13985": "nan", "13990": "nan", "13995": "nan", - "14000": 0.33844, + "14000": 0.28777, "14005": "nan", "14010": "nan", "14015": "nan", @@ -18856,7 +18856,7 @@ "14085": "nan", "14090": "nan", "14095": "nan", - "14100": 0.30085, + "14100": 0.29172, "14105": "nan", "14110": "nan", "14115": "nan", @@ -18876,7 +18876,7 @@ "14185": "nan", "14190": "nan", "14195": "nan", - "14200": 0.29411, + "14200": 0.2964, "14205": "nan", "14210": "nan", "14215": "nan", @@ -18896,7 +18896,7 @@ "14285": "nan", "14290": "nan", "14295": "nan", - "14300": 0.288, + "14300": 0.33113, "14305": "nan", "14310": "nan", "14315": "nan", @@ -18916,7 +18916,7 @@ "14385": "nan", "14390": "nan", "14395": "nan", - "14400": 0.29446, + "14400": 0.27404, "14405": "nan", "14410": "nan", "14415": "nan", @@ -18936,7 +18936,7 @@ "14485": "nan", "14490": "nan", "14495": "nan", - "14500": 0.28518, + "14500": 0.27554, "14505": "nan", "14510": "nan", "14515": "nan", @@ -18956,7 +18956,7 @@ "14585": "nan", "14590": "nan", "14595": "nan", - "14600": 0.28954, + "14600": 0.28132, "14605": "nan", "14610": "nan", "14615": "nan", @@ -18976,7 +18976,7 @@ "14685": "nan", "14690": "nan", "14695": "nan", - "14700": 0.29251, + "14700": 0.2705, "14705": "nan", "14710": "nan", "14715": "nan", @@ -18996,7 +18996,7 @@ "14785": "nan", "14790": "nan", "14795": "nan", - "14800": 0.29451, + "14800": 0.28462, "14805": "nan", "14810": "nan", "14815": "nan", @@ -19016,7 +19016,7 @@ "14885": "nan", "14890": "nan", "14895": "nan", - "14900": 0.29061, + "14900": 0.27772, "14905": "nan", "14910": "nan", "14915": "nan", @@ -19036,7 +19036,7 @@ "14985": "nan", "14990": "nan", "14995": "nan", - "15000": 0.29414, + "15000": 0.28815, "15005": "nan", "15010": "nan", "15015": "nan", @@ -19056,7 +19056,7 @@ "15085": "nan", "15090": "nan", "15095": "nan", - "15100": 0.28785, + "15100": 0.28358, "15105": "nan", "15110": "nan", "15115": "nan", @@ -19076,7 +19076,7 @@ "15185": "nan", "15190": "nan", "15195": "nan", - "15200": 0.28665, + "15200": 0.28012, "15205": "nan", "15210": "nan", "15215": "nan", @@ -19096,7 +19096,7 @@ "15285": "nan", "15290": "nan", "15295": "nan", - "15300": 0.29015, + "15300": 0.2902, "15305": "nan", "15310": "nan", "15315": "nan", @@ -19116,7 +19116,7 @@ "15385": "nan", "15390": "nan", "15395": "nan", - "15400": 0.29171, + "15400": 0.28724, "15405": "nan", "15410": "nan", "15415": "nan", @@ -19136,7 +19136,7 @@ "15485": "nan", "15490": "nan", "15495": "nan", - "15500": 0.28957, + "15500": 0.29231, "15505": "nan", "15510": "nan", "15515": "nan", @@ -19156,7 +19156,7 @@ "15585": "nan", "15590": "nan", "15595": "nan", - "15600": 0.29499, + "15600": 0.28444, "15605": "nan", "15610": "nan", "15615": "nan", @@ -19176,7 +19176,7 @@ "15685": "nan", "15690": "nan", "15695": "nan", - "15700": 0.30239, + "15700": 0.28849, "15705": "nan", "15710": "nan", "15715": "nan", @@ -19196,7 +19196,7 @@ "15785": "nan", "15790": "nan", "15795": "nan", - "15800": 0.29441, + "15800": 0.28184, "15805": "nan", "15810": "nan", "15815": "nan", @@ -19216,7 +19216,7 @@ "15885": "nan", "15890": "nan", "15895": "nan", - "15900": 0.28379, + "15900": 0.27786, "15905": "nan", "15910": "nan", "15915": "nan", @@ -19236,7 +19236,7 @@ "15985": "nan", "15990": "nan", "15995": "nan", - "16000": 0.30864, + "16000": 0.31719, "16005": "nan", "16010": "nan", "16015": "nan", @@ -19256,7 +19256,7 @@ "16085": "nan", "16090": "nan", "16095": "nan", - "16100": 0.28342, + "16100": 0.32443, "16105": "nan", "16110": "nan", "16115": "nan", @@ -19276,7 +19276,7 @@ "16185": "nan", "16190": "nan", "16195": "nan", - "16200": 0.28317, + "16200": 0.2938, "16205": "nan", "16210": "nan", "16215": "nan", @@ -19296,7 +19296,7 @@ "16285": "nan", "16290": "nan", "16295": "nan", - "16300": 0.28554, + "16300": 0.28927, "16305": "nan", "16310": "nan", "16315": "nan", @@ -19316,7 +19316,7 @@ "16385": "nan", "16390": "nan", "16395": "nan", - "16400": 0.32254, + "16400": 0.28561, "16405": "nan", "16410": "nan", "16415": "nan", @@ -19336,7 +19336,7 @@ "16485": "nan", "16490": "nan", "16495": "nan", - "16500": 0.29199, + "16500": 0.28411, "16505": "nan", "16510": "nan", "16515": "nan", @@ -19356,7 +19356,7 @@ "16585": "nan", "16590": "nan", "16595": "nan", - "16600": 0.28704, + "16600": 0.29357, "16605": "nan", "16610": "nan", "16615": "nan", @@ -19376,7 +19376,7 @@ "16685": "nan", "16690": "nan", "16695": "nan", - "16700": 0.29115, + "16700": 0.27951, "16705": "nan", "16710": "nan", "16715": "nan", @@ -19396,7 +19396,7 @@ "16785": "nan", "16790": "nan", "16795": "nan", - "16800": 0.2992, + "16800": 0.28239, "16805": "nan", "16810": "nan", "16815": "nan", @@ -19416,7 +19416,7 @@ "16885": "nan", "16890": "nan", "16895": "nan", - "16900": 0.29073, + "16900": 0.29186, "16905": "nan", "16910": "nan", "16915": "nan", @@ -19436,7 +19436,7 @@ "16985": "nan", "16990": "nan", "16995": "nan", - "17000": 0.29589, + "17000": 0.2832, "17005": "nan", "17010": "nan", "17015": "nan", @@ -19456,7 +19456,7 @@ "17085": "nan", "17090": "nan", "17095": "nan", - "17100": 0.29431, + "17100": 0.29194, "17105": "nan", "17110": "nan", "17115": "nan", @@ -19476,7 +19476,7 @@ "17185": "nan", "17190": "nan", "17195": "nan", - "17200": 0.29774, + "17200": 0.28352, "17205": "nan", "17210": "nan", "17215": "nan", @@ -19496,7 +19496,7 @@ "17285": "nan", "17290": "nan", "17295": "nan", - "17300": 0.29168, + "17300": 0.31235, "17305": "nan", "17310": "nan", "17315": "nan", @@ -19516,7 +19516,7 @@ "17385": "nan", "17390": "nan", "17395": "nan", - "17400": 0.2873, + "17400": 0.28146, "17405": "nan", "17410": "nan", "17415": "nan", @@ -19536,7 +19536,7 @@ "17485": "nan", "17490": "nan", "17495": "nan", - "17500": 0.28948, + "17500": 0.29137, "17505": "nan", "17510": "nan", "17515": "nan", @@ -19556,7 +19556,7 @@ "17585": "nan", "17590": "nan", "17595": "nan", - "17600": 0.28878, + "17600": 0.27947, "17605": "nan", "17610": "nan", "17615": "nan", @@ -19576,7 +19576,7 @@ "17685": "nan", "17690": "nan", "17695": "nan", - "17700": 0.29628, + "17700": 0.2821, "17705": "nan", "17710": "nan", "17715": "nan", @@ -19596,7 +19596,7 @@ "17785": "nan", "17790": "nan", "17795": "nan", - "17800": 0.30409, + "17800": 0.27562, "17805": "nan", "17810": "nan", "17815": "nan", @@ -19616,7 +19616,7 @@ "17885": "nan", "17890": "nan", "17895": "nan", - "17900": 0.28961, + "17900": 0.28776, "17905": "nan", "17910": "nan", "17915": "nan", @@ -19636,7 +19636,7 @@ "17985": "nan", "17990": "nan", "17995": "nan", - "18000": 0.29833, + "18000": 0.2836, "18005": "nan", "18010": "nan", "18015": "nan", @@ -19656,7 +19656,7 @@ "18085": "nan", "18090": "nan", "18095": "nan", - "18100": 0.29411, + "18100": 0.28181, "18105": "nan", "18110": "nan", "18115": "nan", @@ -19676,7 +19676,7 @@ "18185": "nan", "18190": "nan", "18195": "nan", - "18200": 0.29609, + "18200": 0.28189, "18205": "nan", "18210": "nan", "18215": "nan", @@ -19696,7 +19696,7 @@ "18285": "nan", "18290": "nan", "18295": "nan", - "18300": 0.2835, + "18300": 0.27348, "18305": "nan", "18310": "nan", "18315": "nan", @@ -19716,7 +19716,7 @@ "18385": "nan", "18390": "nan", "18395": "nan", - "18400": 0.29893, + "18400": 0.2874, "18405": "nan", "18410": "nan", "18415": "nan", @@ -19736,7 +19736,7 @@ "18485": "nan", "18490": "nan", "18495": "nan", - "18500": 0.29928, + "18500": 0.27998, "18505": "nan", "18510": "nan", "18515": "nan", @@ -19756,7 +19756,7 @@ "18585": "nan", "18590": "nan", "18595": "nan", - "18600": 0.28746, + "18600": 0.30183, "18605": "nan", "18610": "nan", "18615": "nan", @@ -19776,7 +19776,7 @@ "18685": "nan", "18690": "nan", "18695": "nan", - "18700": 0.29459, + "18700": 0.29265, "18705": "nan", "18710": "nan", "18715": "nan", @@ -19796,7 +19796,7 @@ "18785": "nan", "18790": "nan", "18795": "nan", - "18800": 0.29505, + "18800": 0.28703, "18805": "nan", "18810": "nan", "18815": "nan", @@ -19816,7 +19816,7 @@ "18885": "nan", "18890": "nan", "18895": "nan", - "18900": 0.29133, + "18900": 0.28378, "18905": "nan", "18910": "nan", "18915": "nan", @@ -19836,7 +19836,7 @@ "18985": "nan", "18990": "nan", "18995": "nan", - "19000": 0.30679, + "19000": 0.29403, "19005": "nan", "19010": "nan", "19015": "nan", @@ -19856,7 +19856,7 @@ "19085": "nan", "19090": "nan", "19095": "nan", - "19100": 0.29257, + "19100": 0.27985, "19105": "nan", "19110": "nan", "19115": "nan", @@ -19876,7 +19876,7 @@ "19185": "nan", "19190": "nan", "19195": "nan", - "19200": 0.30812, + "19200": 0.27797, "19205": "nan", "19210": "nan", "19215": "nan", @@ -19896,7 +19896,7 @@ "19285": "nan", "19290": "nan", "19295": "nan", - "19300": 0.29739, + "19300": 0.28199, "19305": "nan", "19310": "nan", "19315": "nan", @@ -19916,7 +19916,7 @@ "19385": "nan", "19390": "nan", "19395": "nan", - "19400": 0.2924, + "19400": 0.28282, "19405": "nan", "19410": "nan", "19415": "nan", @@ -19936,7 +19936,7 @@ "19485": "nan", "19490": "nan", "19495": "nan", - "19500": 0.30931, + "19500": 0.28138, "19505": "nan", "19510": "nan", "19515": "nan", @@ -19956,7 +19956,7 @@ "19585": "nan", "19590": "nan", "19595": "nan", - "19600": 0.3085, + "19600": 0.30043, "19605": "nan", "19610": "nan", "19615": "nan", @@ -19976,7 +19976,7 @@ "19685": "nan", "19690": "nan", "19695": "nan", - "19700": 0.30079, + "19700": 0.28241, "19705": "nan", "19710": "nan", "19715": "nan", @@ -19996,7 +19996,7 @@ "19785": "nan", "19790": "nan", "19795": "nan", - "19800": 0.30041, + "19800": 0.29117, "19805": "nan", "19810": "nan", "19815": "nan", @@ -20016,7 +20016,7 @@ "19885": "nan", "19890": "nan", "19895": "nan", - "19900": 0.29519, + "19900": 0.28627, "19905": "nan", "19910": "nan", "19915": "nan", @@ -20036,7 +20036,7 @@ "19985": "nan", "19990": "nan", "19995": "nan", - "20000": 0.29517 + "20000": 0.30346 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/bert/bert_release/model_config.yaml b/tests/functional_tests/test_cases/bert/bert_release/model_config.yaml index 860215bd473..3c7a1a0bc95 100644 --- a/tests/functional_tests/test_cases/bert/bert_release/model_config.yaml +++ b/tests/functional_tests/test_cases/bert/bert_release/model_config.yaml @@ -3,6 +3,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: "1" NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Bert model args --num-layers: 24 diff --git a/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml b/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml index 1d982960246..b365e18f837 100644 --- a/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/bert/bert_release_sm/model_config.yaml @@ -3,6 +3,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: "1" NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Bert model args --num-layers: 24 diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/golden_values_dev_dgx_h100.json index 9684fbaf1bc..de1180457df 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/golden_values_dev_dgx_h100.json @@ -1 +1,51042 @@ -{"lm loss": {"start_step": 1, "end_step": 51000, "step_interval": 5, "values": {"1": 12.95636, "5": 12.91492, "10": 12.06071, "15": 11.39909, "20": 10.42783, "25": 9.97711, "30": 9.6286, "35": 9.36624, "40": 9.17254, "45": 9.00255, "50": 8.8422, "55": 8.63834, "60": 8.63846, "65": 8.45294, "70": 8.43263, "75": 8.26644, "80": 8.09687, "85": 8.11139, "90": 7.96479, "95": 7.93325, "100": 7.79677, "105": 7.67345, "110": 7.56066, "115": 7.50422, "120": 7.46202, "125": 7.44329, "130": 7.26251, "135": 7.2425, "140": 7.2044, "145": 7.00436, "150": 7.15832, "155": 7.0238, "160": 6.90308, "165": 6.88146, "170": 6.79477, "175": 6.82432, "180": 6.79919, "185": 6.73194, "190": 6.67383, "195": 6.63462, "200": 6.65786, "205": 6.62088, "210": 6.52161, "215": 6.48652, "220": 6.5137, "225": 6.49893, "230": 6.47324, "235": 6.41688, "240": 6.36555, "245": 6.34188, "250": 6.29649, "255": 6.41633, "260": 6.34333, "265": 6.25441, "270": 6.20479, "275": 6.22791, "280": 6.19939, "285": 6.20459, "290": 6.15582, "295": 6.13042, "300": 6.11567, "305": 6.03889, "310": 6.09181, "315": 6.08567, "320": 5.96246, "325": 5.94091, "330": 6.0021, "335": 6.00696, "340": 5.923, "345": 5.91037, "350": 5.90166, "355": 5.83938, "360": 5.86421, "365": 5.83851, "370": 5.79736, "375": 5.88069, "380": 5.84632, "385": 5.79522, "390": 5.78997, "395": 5.68485, "400": 5.65935, "405": 5.67324, "410": 5.67915, "415": 5.70348, "420": 5.62598, "425": 5.69901, "430": 5.63413, "435": 5.57726, "440": 5.62055, "445": 5.56107, "450": 5.59801, "455": 5.52289, "460": 5.52019, "465": 5.59069, "470": 5.55559, "475": 5.51408, "480": 5.46367, "485": 5.4963, "490": 5.47827, "495": 5.47537, "500": 5.40917, "505": 5.39348, "510": 5.44131, "515": 5.4227, "520": 5.42475, "525": 5.30792, "530": 5.32991, "535": 5.32045, "540": 5.33396, "545": 5.37814, "550": 5.33432, "555": 5.22468, "560": 5.34448, "565": 5.26025, "570": 5.25496, "575": 5.26508, "580": 5.2261, "585": 5.18523, "590": 5.23531, "595": 5.21597, "600": 5.25481, "605": 5.22827, "610": 5.24152, "615": 5.16897, "620": 5.20393, "625": 5.21192, "630": 5.12559, "635": 5.0983, "640": 5.06269, "645": 5.11363, "650": 5.11504, "655": 5.10817, "660": 5.05975, "665": 5.07345, "670": 5.01161, "675": 5.00973, "680": 5.00039, "685": 4.98384, "690": 4.9873, "695": 4.96491, "700": 4.94539, "705": 4.93223, "710": 4.94081, "715": 4.83795, "720": 4.83274, "725": 4.78491, "730": 4.8244, "735": 4.81083, "740": 4.79976, "745": 4.68904, "750": 4.72059, "755": 4.74714, 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"50780": "nan", + "50785": "nan", + "50790": "nan", + "50795": "nan", + "50800": 1.44053, + "50805": "nan", + "50810": "nan", + "50815": "nan", + "50820": "nan", + "50825": "nan", + "50830": "nan", + "50835": "nan", + "50840": "nan", + "50845": "nan", + "50850": "nan", + "50855": "nan", + "50860": "nan", + "50865": "nan", + "50870": "nan", + "50875": "nan", + "50880": "nan", + "50885": "nan", + "50890": "nan", + "50895": "nan", + "50900": "nan", + "50905": "nan", + "50910": "nan", + "50915": "nan", + "50920": "nan", + "50925": "nan", + "50930": "nan", + "50935": "nan", + "50940": "nan", + "50945": "nan", + "50950": "nan", + "50955": "nan", + "50960": "nan", + "50965": "nan", + "50970": "nan", + "50975": "nan", + "50980": "nan", + "50985": "nan", + "50990": "nan", + "50995": "nan", + "51000": "nan" + } + } +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml index 91c8cf977a7..3bb12c99539 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml @@ -8,6 +8,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml index 3227158130d..8eb57abf308 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml @@ -8,6 +8,7 @@ ENV_VARS: PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True USE_MNNVL: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml index 3bde7ac0d2d..300e0777917 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml @@ -8,6 +8,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml index b41e49d1185..2f045810a87 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml @@ -8,6 +8,7 @@ ENV_VARS: PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True USE_MNNVL: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml index e504bcb1320..dec4cb383d7 100644 --- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release/model_config.yaml @@ -14,6 +14,7 @@ ENV_VARS: NVSHMEM_IB_ENABLE_IBGDA: 0 CUDA_DEVICE_MAX_CONNECTIONS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml index 49cca71a596..811708b0b87 100644 --- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp1pp4emp16etp1cp1_release_sm/model_config.yaml @@ -14,6 +14,7 @@ ENV_VARS: NVSHMEM_IB_ENABLE_IBGDA: 0 CUDA_DEVICE_MAX_CONNECTIONS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..b8428d7886f --- /dev/null +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/golden_values_dev_dgx_gb200.json @@ -0,0 +1,57266 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 47680, + "step_interval": 5, + "values": { + "1": 13.90137, + "5": 13.90368, + "10": 13.87471, + "15": 13.86113, + "20": 13.76255, + "25": 13.72661, + "30": 13.42473, + "35": 13.35275, + "40": 13.27005, + "45": 13.15996, + "50": 12.56243, + "55": 12.38226, + "60": 12.19767, + "65": 12.0316, + "70": 11.85951, + "75": 11.55688, + "80": 11.35375, + "85": 11.13817, + "90": 10.99329, + "95": 10.83889, + "100": 10.62257, + "105": 10.49203, + "110": 10.27981, + "115": 10.0683, + "120": 9.91669, + "125": 9.77349, + "130": 9.68534, + "135": 9.6063, + "140": 9.37937, + "145": 9.37151, + "150": 9.21304, + "155": 9.13383, + "160": 9.05558, + "165": 8.93807, + "170": 8.88928, + "175": 8.8445, + "180": 8.70272, + "185": 8.74079, + "190": 8.62076, + "195": 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"47565": 7.43853, + "47570": 7.47677, + "47575": 7.40758, + "47580": 7.46809, + "47585": 7.44232, + "47590": 7.44324, + "47595": 7.41541, + "47600": 7.47872, + "47605": 7.41449, + "47610": 7.43374, + "47615": 7.43397, + "47620": 7.42997, + "47625": 7.45155, + "47630": 7.47191, + "47635": 7.4349, + "47640": 7.44237, + "47645": 7.43831, + "47650": 7.51044, + "47655": 7.43901, + "47660": 7.48147, + "47665": 7.44504, + "47670": 7.42308, + "47675": 7.49591, + "47680": 7.45771 + } + } +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml index 4452ad22987..aef697ad9c4 100644 --- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release/model_config.yaml @@ -16,6 +16,7 @@ ENV_VARS: NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 8 USE_MNNVL: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml index 36cd34bf752..c8492799abd 100644 --- a/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/deepseekv3_proxy_flex_tp2pp2emp16etp1cp1_gb_200_release_sm/model_config.yaml @@ -16,6 +16,7 @@ ENV_VARS: NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN: 8 USE_MNNVL: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json index 65b1c44baec..7ea880db0c2 100644 --- a/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/golden_values_dev_dgx_h100.json @@ -6,35 +6,35 @@ "values": { "1": 1.73932, "5": 1.7625, - "10": 1.71024, - "15": 1.71696, - "20": 1.7093, - "25": 1.65693, - "30": 1.70056, - "35": 1.59849, - "40": 1.61145, - "45": 1.60968, - "50": 1.57245, - "55": 1.58498, - "60": 1.58013, - "65": 1.57, - "70": 1.55073, - "75": 1.57846, - "80": 1.56971, - "85": 1.55965, - "90": 1.57538, - "95": 1.56214, - "100": 1.5347, - "105": 1.52615, - "110": 1.54244, - "115": 1.52564, - "120": 1.53258, - "125": 1.53894, - "130": 1.57133, - "135": 1.52407, - "140": 1.53167, - "145": 1.50728, - "150": 1.5337 + "10": 1.71033, + "15": 1.71706, + "20": 1.70935, + "25": 1.65698, + "30": 1.70078, + "35": 1.59936, + "40": 1.61261, + "45": 1.61485, + "50": 1.57579, + "55": 1.58778, + "60": 1.5825, + "65": 1.57224, + "70": 1.55247, + "75": 1.58048, + "80": 1.57105, + "85": 1.56082, + "90": 1.57658, + "95": 1.56306, + "100": 1.53549, + "105": 1.52714, + "110": 1.54327, + "115": 1.52636, + "120": 1.53322, + "125": 1.53966, + "130": 1.57194, + "135": 1.52451, + "140": 1.53219, + "145": 1.50763, + "150": 1.53412 } }, "num-zeros": { @@ -42,37 +42,37 @@ "end_step": 150, "step_interval": 5, "values": { - "1": 45157168.0, - "5": 48252968.0, - "10": 45833172.0, - "15": 48763448.0, - "20": 49512632.0, - "25": 49549132.0, - "30": 46404516.0, - "35": 46939324.0, - "40": 46914204.0, - "45": 47307464.0, - "50": 46042896.0, - "55": 50101452.0, - "60": 47000316.0, - "65": 49044716.0, - "70": 45219092.0, - "75": 46238420.0, - "80": 48393404.0, - "85": 46354672.0, - "90": 48178604.0, - "95": 47343576.0, - "100": 47841268.0, - "105": 49604104.0, - "110": 49585320.0, - "115": 43063064.0, - "120": 44880512.0, - "125": 47404648.0, - "130": 48068848.0, - "135": 48339044.0, - "140": 47952152.0, - "145": 45427352.0, - "150": 48922408.0 + "1": 45157112.0, + "5": 48252600.0, + "10": 45833012.0, + "15": 48762876.0, + "20": 49512032.0, + "25": 49548760.0, + "30": 46403960.0, + "35": 46938924.0, + "40": 46913400.0, + "45": 47306876.0, + "50": 46042344.0, + "55": 50101216.0, + "60": 47000408.0, + "65": 49044316.0, + "70": 45219008.0, + "75": 46238368.0, + "80": 48393860.0, + "85": 46354920.0, + "90": 48178088.0, + "95": 47343092.0, + "100": 47840968.0, + "105": 49603996.0, + "110": 49585436.0, + "115": 43063444.0, + "120": 44880372.0, + "125": 47404608.0, + "130": 48068500.0, + "135": 48338584.0, + "140": 47951860.0, + "145": 45426960.0, + "150": 48922104.0 } }, "mem-allocated-bytes": { @@ -119,36 +119,36 @@ "step_interval": 5, "values": { "1": 23002945536.0, - "5": 28229746688.0, - "10": 28431153152.0, - "15": 28431153152.0, - "20": 28431153152.0, - "25": 28431153152.0, - "30": 28431153152.0, - "35": 28431153152.0, - "40": 28526688256.0, - "45": 28526688256.0, - "50": 28534185984.0, - "55": 28534185984.0, - "60": 28534185984.0, - "65": 28534185984.0, - "70": 28534185984.0, - "75": 28534185984.0, - "80": 28534185984.0, - "85": 28534185984.0, - "90": 28534185984.0, - "95": 28534185984.0, - "100": 28534185984.0, - "105": 28534185984.0, - "110": 28534185984.0, - "115": 28534185984.0, - "120": 28534185984.0, - "125": 28534185984.0, - "130": 28534185984.0, - "135": 28534185984.0, - "140": 28534185984.0, - "145": 28534185984.0, - "150": 28534185984.0 + "5": 28230793216.0, + "10": 28431276032.0, + "15": 28431276032.0, + "20": 28431276032.0, + "25": 28431276032.0, + "30": 28431276032.0, + "35": 28431276032.0, + "40": 28522264576.0, + "45": 28522264576.0, + "50": 28522264576.0, + "55": 28522264576.0, + "60": 28533706752.0, + "65": 28533706752.0, + "70": 28533706752.0, + "75": 28533706752.0, + "80": 28533706752.0, + "85": 28533706752.0, + "90": 28533706752.0, + "95": 28533706752.0, + "100": 28533706752.0, + "105": 28533706752.0, + "110": 28533706752.0, + "115": 28533706752.0, + "120": 28533706752.0, + "125": 28533706752.0, + "130": 28533706752.0, + "135": 28533706752.0, + "140": 28533706752.0, + "145": 28533706752.0, + "150": 28533706752.0 } }, "iteration-time": { @@ -157,36 +157,36 @@ "step_interval": 5, "values": { "1": "nan", - "5": 9.33326, - "10": 7.45877, - "15": 6.55202, - "20": 7.14405, - "25": 6.74357, - "30": 7.73142, - "35": 7.1059, - "40": 7.15075, - "45": 6.68013, - "50": 6.27464, - "55": 6.4961, - "60": 6.20048, - "65": 7.0085, - "70": 6.96858, - "75": 6.99176, - "80": 6.97374, - "85": 7.59797, - "90": 5.99314, - "95": 7.31466, - "100": 6.1496, - "105": 6.1462, - "110": 6.52008, - "115": 6.14035, - "120": 6.48433, - "125": 5.7572, - "130": 8.06756, - "135": 7.32705, - "140": 6.87268, - "145": 7.35193, - "150": 7.70667 + "5": 6.12929, + "10": 6.15867, + "15": 7.20055, + "20": 6.73851, + "25": 6.52474, + "30": 6.21672, + "35": 7.62268, + "40": 6.60842, + "45": 7.43233, + "50": 6.73307, + "55": 7.86167, + "60": 7.53877, + "65": 8.3, + "70": 7.49531, + "75": 7.20237, + "80": 6.84819, + "85": 6.98051, + "90": 7.10691, + "95": 6.2307, + "100": 7.83817, + "105": 7.0374, + "110": 6.66377, + "115": 8.36971, + "120": 6.59261, + "125": 6.18435, + "130": 7.07609, + "135": 8.21835, + "140": 6.63968, + "145": 6.56568, + "150": 6.7847 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/model_config.yaml index efe39998065..7c49a75f354 100644 --- a/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x22b_tp2pp8ep8vpp1_release/model_config.yaml @@ -6,6 +6,7 @@ ENV_VARS: NCCL_NVLS_ENABLE: 0 NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release/model_config.yaml index f4476c712f2..bf433836b9f 100644 --- a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release/model_config.yaml @@ -8,6 +8,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release_sm/model_config.yaml b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release_sm/model_config.yaml index cfeb7709839..b66f49ca6bd 100644 --- a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_alltoall_tp2pp4ep4_release_sm/model_config.yaml @@ -8,6 +8,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json index 8f120988635..f8b5c16f7ee 100644 --- a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/golden_values_dev_dgx_h100.json @@ -5,46 +5,46 @@ "step_interval": 5, "values": { "1": 1.80632, - "5": 1.83184, - "10": 1.77661, - "15": 1.78762, - "20": 1.79521, - "25": 1.75237, - "30": 1.8052, - "35": 1.7058, - "40": 1.72645, - "45": 1.72384, - "50": 1.68568, - "55": 1.69619, - "60": 1.69328, - "65": 1.68708, - "70": 1.65636, - "75": 1.6844, - "80": 1.67457, - "85": 1.66171, - "90": 1.68318, - "95": 1.66504, - "100": 1.64206, - "105": 1.62512, - "110": 1.63696, - "115": 1.63439, - "120": 1.63309, - "125": 1.64257, - "130": 1.66907, - "135": 1.62597, - "140": 1.64125, - "145": 1.6082, - "150": 1.62734, - "155": 1.61977, - "160": 1.61879, - "165": 1.62264, - "170": 1.62567, - "175": 1.60156, - "180": 1.60662, - "185": 1.67142, - "190": 1.6127, - "195": 1.64064, - "200": 1.62423 + "5": 1.83171, + "10": 1.77652, + "15": 1.78758, + "20": 1.79514, + "25": 1.7523, + "30": 1.80523, + "35": 1.70566, + "40": 1.72623, + "45": 1.72374, + "50": 1.68562, + "55": 1.69612, + "60": 1.69343, + "65": 1.68699, + "70": 1.65632, + "75": 1.68431, + "80": 1.6747, + "85": 1.66184, + "90": 1.6833, + "95": 1.66515, + "100": 1.64228, + "105": 1.62522, + "110": 1.63712, + "115": 1.63452, + "120": 1.63324, + "125": 1.64268, + "130": 1.66917, + "135": 1.62613, + "140": 1.6413, + "145": 1.60826, + "150": 1.62746, + "155": 1.61972, + "160": 1.61896, + "165": 1.62267, + "170": 1.62569, + "175": 1.6017, + "180": 1.60673, + "185": 1.67144, + "190": 1.61266, + "195": 1.64073, + "200": 1.62436 } }, "num-zeros": { @@ -52,47 +52,47 @@ "end_step": 200, "step_interval": 5, "values": { - "1": 30107410.0, - "5": 32170588.0, - "10": 30557604.0, - "15": 32510750.0, - "20": 33010342.0, - "25": 33034544.0, - "30": 30938648.0, - "35": 31294908.0, - "40": 31277976.0, - "45": 31540080.0, - "50": 30697116.0, - "55": 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31563868.0, + "100": 31895832.0, + "105": 33071062.0, + "110": 33058322.0, + "115": 28710484.0, + "120": 29922046.0, + "125": 31604670.0, + "130": 32047268.0, + "135": 32227440.0, + "140": 31969476.0, + "145": 30286526.0, + "150": 32616498.0, + "155": 33017152.0, + "160": 31523068.0, + "165": 33045780.0, + "170": 31870778.0, + "175": 31379784.0, + "180": 30556930.0, + "185": 31366544.0, + "190": 32951690.0, + "195": 31584238.0, + "200": 30109926.0 } }, "mem-allocated-bytes": { @@ -149,46 +149,46 @@ "step_interval": 5, "values": { "1": 29394774016.0, - "5": 35404840960.0, - "10": 35417063424.0, - "15": 35463147520.0, - "20": 35497099264.0, - "25": 35497099264.0, - "30": 35508408320.0, - "35": 35508408320.0, - "40": 35508408320.0, - "45": 35508408320.0, - "50": 35508408320.0, - "55": 35508408320.0, - "60": 35508408320.0, - "65": 35508408320.0, - "70": 35508408320.0, - "75": 35508408320.0, - "80": 35508408320.0, - "85": 35508408320.0, - "90": 35508408320.0, - "95": 35508408320.0, - "100": 35508408320.0, - "105": 35508408320.0, - "110": 35508408320.0, - "115": 35508408320.0, - "120": 35508408320.0, - "125": 35508408320.0, - "130": 35508408320.0, - "135": 35508408320.0, - "140": 35508408320.0, - "145": 35508408320.0, - "150": 35508408320.0, - "155": 35508408320.0, - "160": 35508408320.0, - "165": 35508408320.0, - "170": 35508408320.0, - "175": 35508408320.0, - "180": 35508408320.0, - "185": 35508408320.0, - "190": 35508408320.0, - "195": 35508408320.0, - "200": 35508408320.0 + "5": 35408363520.0, + "10": 35420778496.0, + "15": 35466379264.0, + "20": 35493052416.0, + "25": 35493052416.0, + "30": 35505844224.0, + "35": 35505844224.0, + "40": 35505844224.0, + "45": 35505844224.0, + "50": 35505844224.0, + "55": 35505844224.0, + "60": 35505844224.0, + "65": 35505844224.0, + "70": 35505844224.0, + "75": 35505844224.0, + "80": 35505844224.0, + "85": 35505844224.0, + "90": 35505844224.0, + "95": 35505844224.0, + "100": 35505844224.0, + "105": 35505844224.0, + "110": 35505844224.0, + "115": 35505844224.0, + "120": 35505844224.0, + "125": 35505844224.0, + "130": 35505844224.0, + "135": 35505844224.0, + "140": 35505844224.0, + "145": 35505844224.0, + "150": 35505844224.0, + "155": 35505844224.0, + "160": 35505844224.0, + "165": 35505844224.0, + "170": 35505844224.0, + "175": 35505844224.0, + "180": 35505844224.0, + "185": 35505844224.0, + "190": 35505844224.0, + "195": 35505844224.0, + "200": 35505844224.0 } }, "iteration-time": { @@ -197,46 +197,46 @@ "step_interval": 5, "values": { "1": "nan", - "5": 3.47451, - "10": 3.31169, - "15": 3.4018, - "20": 3.32494, - "25": 3.99476, - "30": 3.31795, - "35": 3.30721, - "40": 3.2989, - "45": 3.29245, - "50": 3.73078, - "55": 4.2852, - "60": 3.2697, - "65": 3.27747, - "70": 3.49739, - "75": 3.28191, - "80": 3.26247, - "85": 3.27631, - "90": 3.2584, - "95": 3.26058, - "100": 3.26095, - "105": 3.2444, - "110": 3.24824, - "115": 3.25435, - "120": 3.23352, - "125": 3.2335, - "130": 3.22, - "135": 3.23003, - "140": 3.22953, - "145": 3.22956, - "150": 3.26259, - "155": 3.21536, - "160": 3.23325, - "165": 3.91941, - "170": 3.22964, - "175": 3.22433, - "180": 3.21506, - "185": 3.567, - "190": 3.59199, - "195": 3.22657, - "200": 3.56689 + "5": 3.80368, + "10": 3.89725, + "15": 4.54906, + "20": 4.498, + "25": 5.11874, + "30": 4.14905, + "35": 3.90342, + "40": 3.31327, + "45": 3.73961, + "50": 3.37841, + "55": 3.31795, + "60": 3.30589, + "65": 3.3119, + "70": 3.34672, + "75": 3.29959, + "80": 3.27096, + "85": 3.26952, + "90": 3.27423, + "95": 3.2623, + "100": 3.26629, + "105": 3.26448, + "110": 3.26589, + "115": 3.25984, + "120": 3.32184, + "125": 3.26079, + "130": 3.31507, + "135": 3.26124, + "140": 3.25768, + "145": 3.64192, + "150": 3.26537, + "155": 3.25123, + "160": 3.24949, + "165": 3.23512, + "170": 3.39667, + "175": 3.23471, + "180": 3.23931, + "185": 3.23349, + "190": 3.26019, + "195": 3.68123, + "200": 4.09189 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/model_config.yaml b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/model_config.yaml index 29dcefadf0e..2ccdad35bdc 100644 --- a/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/model_config.yaml +++ b/tests/functional_tests/test_cases/mixtral/mixtral_8x7b_tp1pp4ep8vpp8_release/model_config.yaml @@ -6,6 +6,7 @@ ENV_VARS: NCCL_NVLS_ENABLE: 0 NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # Distributed args --distributed-timeout-minutes: 60 diff --git a/tests/functional_tests/test_cases/t5/t5_release/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/t5/t5_release/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..4f61240e0e2 --- /dev/null +++ b/tests/functional_tests/test_cases/t5/t5_release/golden_values_dev_dgx_gb200.json @@ -0,0 +1,100042 @@ +{ + "lm loss": { + 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"nan", "2995": "nan", - "3000": 0.15292, + "3000": 0.1974, "3005": "nan", "3010": "nan", "3015": "nan", @@ -80656,7 +80656,7 @@ "3085": "nan", "3090": "nan", "3095": "nan", - "3100": 0.15296, + "3100": 0.19981, "3105": "nan", "3110": "nan", "3115": "nan", @@ -80676,7 +80676,7 @@ "3185": "nan", "3190": "nan", "3195": "nan", - "3200": 0.18595, + "3200": 0.19931, "3205": "nan", "3210": "nan", "3215": "nan", @@ -80696,7 +80696,7 @@ "3285": "nan", "3290": "nan", "3295": "nan", - "3300": 0.15439, + "3300": 0.22452, "3305": "nan", "3310": "nan", "3315": "nan", @@ -80716,7 +80716,7 @@ "3385": "nan", "3390": "nan", "3395": "nan", - "3400": 0.15413, + "3400": 0.18107, "3405": "nan", "3410": "nan", "3415": "nan", @@ -80736,7 +80736,7 @@ "3485": "nan", "3490": "nan", "3495": "nan", - "3500": 0.15552, + "3500": 0.18249, "3505": "nan", "3510": "nan", "3515": "nan", @@ -80756,7 +80756,7 @@ "3585": "nan", "3590": "nan", "3595": "nan", - "3600": 0.18438, + "3600": 0.23635, "3605": "nan", "3610": "nan", "3615": "nan", @@ -80776,7 +80776,7 @@ "3685": "nan", "3690": "nan", "3695": "nan", - "3700": 0.15542, + "3700": 0.20435, "3705": "nan", "3710": "nan", "3715": "nan", @@ -80796,7 +80796,7 @@ "3785": "nan", "3790": "nan", "3795": "nan", - "3800": 0.15551, + "3800": 0.19647, "3805": "nan", "3810": "nan", "3815": "nan", @@ -80816,7 +80816,7 @@ "3885": "nan", "3890": "nan", "3895": "nan", - "3900": 0.17681, + "3900": 0.19675, "3905": "nan", "3910": "nan", "3915": "nan", @@ -80836,7 +80836,7 @@ "3985": "nan", "3990": "nan", "3995": "nan", - "4000": 0.17262, + "4000": 0.1909, "4005": "nan", "4010": "nan", "4015": "nan", @@ -80856,7 +80856,7 @@ "4085": "nan", "4090": "nan", "4095": "nan", - "4100": 0.1703, + "4100": 0.22291, "4105": "nan", "4110": "nan", "4115": "nan", @@ -80876,7 +80876,7 @@ "4185": "nan", "4190": "nan", "4195": "nan", - "4200": 0.15612, + "4200": 0.20401, "4205": "nan", "4210": "nan", "4215": "nan", @@ -80896,7 +80896,7 @@ "4285": "nan", "4290": "nan", "4295": "nan", - "4300": 0.15366, + "4300": 0.19594, "4305": "nan", "4310": "nan", "4315": "nan", @@ -80916,7 +80916,7 @@ "4385": "nan", "4390": "nan", "4395": "nan", - "4400": 0.17231, + "4400": 0.19146, "4405": "nan", "4410": "nan", "4415": "nan", @@ -80936,7 +80936,7 @@ "4485": "nan", "4490": "nan", "4495": "nan", - "4500": 0.17523, + "4500": 0.1968, "4505": "nan", "4510": "nan", "4515": "nan", @@ -80956,7 +80956,7 @@ "4585": "nan", "4590": "nan", "4595": "nan", - "4600": 0.15517, + "4600": 0.20682, "4605": "nan", "4610": "nan", "4615": "nan", @@ -80976,7 +80976,7 @@ "4685": "nan", "4690": "nan", "4695": "nan", - "4700": 0.15281, + "4700": 0.18744, "4705": "nan", "4710": "nan", "4715": "nan", @@ -80996,7 +80996,7 @@ "4785": "nan", "4790": "nan", "4795": "nan", - "4800": 0.17469, + "4800": 0.22815, "4805": "nan", "4810": "nan", "4815": "nan", @@ -81016,7 +81016,7 @@ "4885": "nan", "4890": "nan", "4895": "nan", - "4900": 0.16884, + "4900": 0.22312, "4905": "nan", "4910": "nan", "4915": "nan", @@ -81036,7 +81036,7 @@ "4985": "nan", "4990": "nan", "4995": "nan", - "5000": 0.15718, + "5000": 0.20029, "5005": "nan", "5010": "nan", "5015": "nan", @@ -81056,7 +81056,7 @@ "5085": "nan", "5090": "nan", "5095": "nan", - "5100": 0.156, + "5100": 0.19754, "5105": "nan", "5110": "nan", "5115": "nan", @@ -81076,7 +81076,7 @@ "5185": "nan", "5190": "nan", "5195": "nan", - "5200": 0.18057, + "5200": 0.20239, "5205": "nan", "5210": "nan", "5215": "nan", @@ -81096,7 +81096,7 @@ "5285": "nan", "5290": "nan", "5295": "nan", - "5300": 0.17064, + "5300": 0.20375, "5305": "nan", "5310": "nan", "5315": "nan", @@ -81116,7 +81116,7 @@ "5385": "nan", "5390": "nan", "5395": "nan", - "5400": 0.15997, + "5400": 0.19725, "5405": "nan", "5410": "nan", "5415": "nan", @@ -81136,7 +81136,7 @@ "5485": "nan", "5490": "nan", "5495": "nan", - "5500": 0.15481, + "5500": 0.20149, "5505": "nan", "5510": "nan", "5515": "nan", @@ -81156,7 +81156,7 @@ "5585": "nan", "5590": "nan", "5595": "nan", - "5600": 0.17681, + "5600": 0.24029, "5605": "nan", "5610": "nan", "5615": "nan", @@ -81176,7 +81176,7 @@ "5685": "nan", "5690": "nan", "5695": "nan", - "5700": 0.17584, + "5700": 0.19491, "5705": "nan", "5710": "nan", "5715": "nan", @@ -81196,7 +81196,7 @@ "5785": "nan", "5790": "nan", "5795": "nan", - "5800": 0.15193, + "5800": 0.20885, "5805": "nan", "5810": "nan", "5815": "nan", @@ -81216,7 +81216,7 @@ "5885": "nan", "5890": "nan", "5895": "nan", - "5900": 0.17743, + "5900": 0.20635, "5905": "nan", "5910": "nan", "5915": "nan", @@ -81236,7 +81236,7 @@ "5985": "nan", "5990": "nan", "5995": "nan", - "6000": 0.15199, + "6000": 0.20484, "6005": "nan", "6010": "nan", "6015": "nan", @@ -81256,7 +81256,7 @@ "6085": "nan", "6090": "nan", "6095": "nan", - "6100": 0.15643, + "6100": 0.22317, "6105": "nan", "6110": "nan", "6115": "nan", @@ -81276,7 +81276,7 @@ "6185": "nan", "6190": "nan", "6195": "nan", - "6200": 0.15258, + "6200": 0.18686, "6205": "nan", "6210": "nan", "6215": "nan", @@ -81296,7 +81296,7 @@ "6285": "nan", "6290": "nan", "6295": "nan", - "6300": 0.1617, + "6300": 0.22036, "6305": "nan", "6310": "nan", "6315": "nan", @@ -81316,7 +81316,7 @@ "6385": "nan", "6390": "nan", "6395": "nan", - "6400": 0.18858, + "6400": 0.20347, "6405": "nan", "6410": "nan", "6415": "nan", @@ -81336,7 +81336,7 @@ "6485": "nan", "6490": "nan", "6495": "nan", - "6500": 0.15556, + "6500": 0.20953, "6505": "nan", "6510": "nan", "6515": "nan", @@ -81356,7 +81356,7 @@ "6585": "nan", "6590": "nan", "6595": "nan", - "6600": 0.15572, + "6600": 0.19283, "6605": "nan", "6610": "nan", "6615": "nan", @@ -81376,7 +81376,7 @@ "6685": "nan", "6690": "nan", "6695": "nan", - "6700": 0.16115, + "6700": 0.20544, "6705": "nan", "6710": "nan", "6715": "nan", @@ -81396,7 +81396,7 @@ "6785": "nan", "6790": "nan", "6795": "nan", - "6800": 0.18028, + "6800": 0.21166, "6805": "nan", "6810": "nan", "6815": "nan", @@ -81416,7 +81416,7 @@ "6885": "nan", "6890": "nan", "6895": "nan", - "6900": 0.15556, + "6900": 0.22191, "6905": "nan", "6910": "nan", "6915": "nan", @@ -81436,7 +81436,7 @@ "6985": "nan", "6990": "nan", "6995": "nan", - "7000": 0.15864, + "7000": 0.20235, "7005": "nan", "7010": "nan", "7015": "nan", @@ -81456,7 +81456,7 @@ "7085": "nan", "7090": "nan", "7095": "nan", - "7100": 0.15751, + "7100": 0.22049, "7105": "nan", "7110": "nan", "7115": "nan", @@ -81476,7 +81476,7 @@ "7185": "nan", "7190": "nan", "7195": "nan", - "7200": 0.18131, + "7200": 0.21227, "7205": "nan", "7210": "nan", "7215": "nan", @@ -81496,7 +81496,7 @@ "7285": "nan", "7290": "nan", "7295": "nan", - "7300": 0.15237, + "7300": 0.18694, "7305": "nan", "7310": "nan", "7315": "nan", @@ -81516,7 +81516,7 @@ "7385": "nan", "7390": "nan", "7395": "nan", - "7400": 0.15881, + "7400": 0.20079, "7405": "nan", "7410": "nan", "7415": "nan", @@ -81536,7 +81536,7 @@ "7485": "nan", "7490": "nan", "7495": "nan", - "7500": 0.16252, + "7500": 0.20809, "7505": "nan", "7510": "nan", "7515": "nan", @@ -81556,7 +81556,7 @@ "7585": "nan", "7590": "nan", "7595": "nan", - "7600": 0.18421, + "7600": 0.24188, "7605": "nan", "7610": "nan", "7615": "nan", @@ -81576,7 +81576,7 @@ "7685": "nan", "7690": "nan", "7695": "nan", - "7700": 0.15868, + "7700": 0.19682, "7705": "nan", "7710": "nan", "7715": "nan", @@ -81596,7 +81596,7 @@ "7785": "nan", "7790": "nan", "7795": "nan", - "7800": 0.15528, + "7800": 0.21306, "7805": "nan", "7810": "nan", "7815": "nan", @@ -81616,7 +81616,7 @@ "7885": "nan", "7890": "nan", "7895": "nan", - "7900": 0.1839, + "7900": 0.22107, "7905": "nan", "7910": "nan", "7915": "nan", @@ -81636,7 +81636,7 @@ "7985": "nan", "7990": "nan", "7995": "nan", - "8000": 0.16289, + "8000": 0.21012, "8005": "nan", "8010": "nan", "8015": "nan", @@ -81656,7 +81656,7 @@ "8085": "nan", "8090": "nan", "8095": "nan", - "8100": 0.16425, + "8100": 0.22561, "8105": "nan", "8110": "nan", "8115": "nan", @@ -81676,7 +81676,7 @@ "8185": "nan", "8190": "nan", "8195": "nan", - "8200": 0.15299, + "8200": 0.2107, "8205": "nan", "8210": "nan", "8215": "nan", @@ -81696,7 +81696,7 @@ "8285": "nan", "8290": "nan", "8295": "nan", - "8300": 0.15351, + "8300": 0.21176, "8305": "nan", "8310": "nan", "8315": "nan", @@ -81716,7 +81716,7 @@ "8385": "nan", "8390": "nan", "8395": "nan", - "8400": 0.1843, + "8400": 0.18168, "8405": "nan", "8410": "nan", "8415": "nan", @@ -81736,7 +81736,7 @@ "8485": "nan", "8490": "nan", "8495": "nan", - "8500": 0.16649, + "8500": 0.20799, "8505": "nan", "8510": "nan", "8515": "nan", @@ -81756,7 +81756,7 @@ "8585": "nan", "8590": "nan", "8595": "nan", - "8600": 0.15273, + "8600": 0.19127, "8605": "nan", "8610": "nan", "8615": "nan", @@ -81776,7 +81776,7 @@ "8685": "nan", "8690": "nan", "8695": "nan", - "8700": 0.15643, + "8700": 0.18728, "8705": "nan", "8710": "nan", "8715": "nan", @@ -81796,7 +81796,7 @@ "8785": "nan", "8790": "nan", "8795": "nan", - "8800": 0.17973, + "8800": 0.20783, "8805": "nan", "8810": "nan", "8815": "nan", @@ -81816,7 +81816,7 @@ "8885": "nan", "8890": "nan", "8895": "nan", - "8900": 0.16811, + "8900": 0.23318, "8905": "nan", "8910": "nan", "8915": "nan", @@ -81836,7 +81836,7 @@ "8985": "nan", "8990": "nan", "8995": "nan", - "9000": 0.15637, + "9000": 0.23087, "9005": "nan", "9010": "nan", "9015": "nan", @@ -81856,7 +81856,7 @@ "9085": "nan", "9090": "nan", "9095": "nan", - "9100": 0.15588, + "9100": 0.19787, "9105": "nan", "9110": "nan", "9115": "nan", @@ -81876,7 +81876,7 @@ "9185": "nan", "9190": "nan", "9195": "nan", - "9200": 0.17723, + "9200": 0.1833, "9205": "nan", "9210": "nan", "9215": "nan", @@ -81896,7 +81896,7 @@ "9285": "nan", "9290": "nan", "9295": "nan", - "9300": 0.16311, + "9300": 0.21294, "9305": "nan", "9310": "nan", "9315": "nan", @@ -81916,7 +81916,7 @@ "9385": "nan", "9390": "nan", "9395": "nan", - "9400": 0.15332, + "9400": 0.21802, "9405": "nan", "9410": "nan", "9415": "nan", @@ -81936,7 +81936,7 @@ "9485": "nan", "9490": "nan", "9495": "nan", - "9500": 0.16059, + "9500": 0.20728, "9505": "nan", "9510": "nan", "9515": "nan", @@ -81956,7 +81956,7 @@ "9585": "nan", "9590": "nan", "9595": "nan", - "9600": 0.17798, + "9600": 0.21483, "9605": "nan", "9610": "nan", "9615": "nan", @@ -81976,7 +81976,7 @@ "9685": "nan", "9690": "nan", "9695": "nan", - "9700": 0.16691, + "9700": 0.20885, "9705": "nan", "9710": "nan", "9715": "nan", @@ -81996,7 +81996,7 @@ "9785": "nan", "9790": "nan", "9795": "nan", - "9800": 0.15352, + "9800": 0.2129, "9805": "nan", "9810": "nan", "9815": "nan", @@ -82016,7 +82016,7 @@ "9885": "nan", "9890": "nan", "9895": "nan", - "9900": 0.17738, + "9900": 0.22588, "9905": "nan", "9910": "nan", "9915": "nan", @@ -82036,7 +82036,7 @@ "9985": "nan", "9990": "nan", "9995": "nan", - "10000": 0.15419, + "10000": 0.21634, "10005": "nan", "10010": "nan", "10015": "nan", @@ -82056,7 +82056,7 @@ "10085": "nan", "10090": "nan", "10095": "nan", - "10100": 0.15659, + "10100": 0.21282, "10105": "nan", "10110": "nan", "10115": "nan", @@ -82076,7 +82076,7 @@ "10185": "nan", "10190": "nan", "10195": "nan", - "10200": 0.1522, + "10200": 0.21337, "10205": "nan", "10210": "nan", "10215": "nan", @@ -82096,7 +82096,7 @@ "10285": "nan", "10290": "nan", "10295": "nan", - "10300": 0.15928, + "10300": 0.21322, "10305": "nan", "10310": "nan", "10315": "nan", @@ -82116,7 +82116,7 @@ "10385": "nan", "10390": "nan", "10395": "nan", - "10400": 0.18485, + "10400": 0.18523, "10405": "nan", "10410": "nan", "10415": "nan", @@ -82136,7 +82136,7 @@ "10485": "nan", "10490": "nan", "10495": "nan", - "10500": 0.15823, + "10500": 0.2268, "10505": "nan", "10510": "nan", "10515": "nan", @@ -82156,7 +82156,7 @@ "10585": "nan", "10590": "nan", "10595": "nan", - "10600": 0.15627, + "10600": 0.17977, "10605": "nan", "10610": "nan", "10615": "nan", @@ -82176,7 +82176,7 @@ "10685": "nan", "10690": "nan", "10695": "nan", - "10700": 0.15834, + "10700": 0.18799, "10705": "nan", "10710": "nan", "10715": "nan", @@ -82196,7 +82196,7 @@ "10785": "nan", "10790": "nan", "10795": "nan", - "10800": 0.18277, + "10800": 0.21279, "10805": "nan", "10810": "nan", "10815": "nan", @@ -82216,7 +82216,7 @@ "10885": "nan", "10890": "nan", "10895": "nan", - "10900": 0.15369, + "10900": 0.26221, "10905": "nan", "10910": "nan", "10915": "nan", @@ -82236,7 +82236,7 @@ "10985": "nan", "10990": "nan", "10995": "nan", - "11000": 0.15771, + "11000": 0.19667, "11005": "nan", "11010": "nan", "11015": "nan", @@ -82256,7 +82256,7 @@ "11085": "nan", "11090": "nan", "11095": "nan", - "11100": 0.16348, + "11100": 0.20301, "11105": "nan", "11110": "nan", "11115": "nan", @@ -82276,7 +82276,7 @@ "11185": "nan", "11190": "nan", "11195": "nan", - "11200": 0.18215, + "11200": 0.20313, "11205": "nan", "11210": "nan", "11215": "nan", @@ -82296,7 +82296,7 @@ "11285": "nan", "11290": "nan", "11295": "nan", - "11300": 0.15406, + "11300": 0.22663, "11305": "nan", "11310": "nan", "11315": "nan", @@ -82316,7 +82316,7 @@ "11385": "nan", "11390": "nan", "11395": "nan", - "11400": 0.15279, + "11400": 0.18738, "11405": "nan", "11410": "nan", "11415": "nan", @@ -82336,7 +82336,7 @@ "11485": "nan", "11490": "nan", "11495": "nan", - "11500": 0.16506, + "11500": 0.19425, "11505": "nan", "11510": "nan", "11515": "nan", @@ -82356,7 +82356,7 @@ "11585": "nan", "11590": "nan", "11595": "nan", - "11600": 0.18587, + "11600": 0.23067, "11605": "nan", "11610": "nan", "11615": "nan", @@ -82376,7 +82376,7 @@ "11685": "nan", "11690": "nan", "11695": "nan", - "11700": 0.15529, + "11700": 0.22543, "11705": "nan", "11710": "nan", "11715": "nan", @@ -82396,7 +82396,7 @@ "11785": "nan", "11790": "nan", "11795": "nan", - "11800": 0.15385, + "11800": 0.23654, "11805": "nan", "11810": "nan", "11815": "nan", @@ -82416,7 +82416,7 @@ "11885": "nan", "11890": "nan", "11895": "nan", - "11900": 0.17824, + "11900": 0.21627, "11905": "nan", "11910": "nan", "11915": "nan", @@ -82436,7 +82436,7 @@ "11985": "nan", "11990": "nan", "11995": "nan", - "12000": 0.16401, + "12000": 0.21596, "12005": "nan", "12010": "nan", "12015": "nan", @@ -82456,7 +82456,7 @@ "12085": "nan", "12090": "nan", "12095": "nan", - "12100": 0.16745, + "12100": 0.20806, "12105": "nan", "12110": "nan", "12115": "nan", @@ -82476,7 +82476,7 @@ "12185": "nan", "12190": "nan", "12195": "nan", - "12200": 0.15396, + "12200": 0.21136, "12205": "nan", "12210": "nan", "12215": "nan", @@ -82496,7 +82496,7 @@ "12285": "nan", "12290": "nan", "12295": "nan", - "12300": 0.15284, + "12300": 0.19415, "12305": "nan", "12310": "nan", "12315": "nan", @@ -82516,7 +82516,7 @@ "12385": "nan", "12390": "nan", "12395": "nan", - "12400": 0.1737, + "12400": 0.18297, "12405": "nan", "12410": "nan", "12415": "nan", @@ -82536,7 +82536,7 @@ "12485": "nan", "12490": "nan", "12495": "nan", - "12500": 0.1657, + "12500": 0.22712, "12505": "nan", "12510": "nan", "12515": "nan", @@ -82556,7 +82556,7 @@ "12585": "nan", "12590": "nan", "12595": "nan", - "12600": 0.15621, + "12600": 0.17793, "12605": "nan", "12610": "nan", "12615": "nan", @@ -82576,7 +82576,7 @@ "12685": "nan", "12690": "nan", "12695": "nan", - "12700": 0.15225, + "12700": 0.19445, "12705": "nan", "12710": "nan", "12715": "nan", @@ -82596,7 +82596,7 @@ "12785": "nan", "12790": "nan", "12795": "nan", - "12800": 0.17023, + "12800": 0.20485, "12805": "nan", "12810": "nan", "12815": "nan", @@ -82616,7 +82616,7 @@ "12885": "nan", "12890": "nan", "12895": "nan", - "12900": 0.16969, + "12900": 0.24549, "12905": "nan", "12910": "nan", "12915": "nan", @@ -82636,7 +82636,7 @@ "12985": "nan", "12990": "nan", "12995": "nan", - "13000": 0.15727, + "13000": 0.21045, "13005": "nan", "13010": "nan", "13015": "nan", @@ -82656,7 +82656,7 @@ "13085": "nan", "13090": "nan", "13095": "nan", - "13100": 0.15469, + "13100": 0.19411, "13105": "nan", "13110": "nan", "13115": "nan", @@ -82676,7 +82676,7 @@ "13185": "nan", "13190": "nan", "13195": "nan", - "13200": 0.17026, + "13200": 0.19851, "13205": "nan", "13210": "nan", "13215": "nan", @@ -82696,7 +82696,7 @@ "13285": "nan", "13290": "nan", "13295": "nan", - "13300": 0.16864, + "13300": 0.21258, "13305": "nan", "13310": "nan", "13315": "nan", @@ -82716,7 +82716,7 @@ "13385": "nan", "13390": "nan", "13395": "nan", - "13400": 0.16643, + "13400": 0.2155, "13405": "nan", "13410": "nan", "13415": "nan", @@ -82736,7 +82736,7 @@ "13485": "nan", "13490": "nan", "13495": "nan", - "13500": 0.1568, + "13500": 0.21267, "13505": "nan", "13510": "nan", "13515": "nan", @@ -82756,7 +82756,7 @@ "13585": "nan", "13590": "nan", "13595": "nan", - "13600": 0.16924, + "13600": 0.23061, "13605": "nan", "13610": "nan", "13615": "nan", @@ -82776,7 +82776,7 @@ "13685": "nan", "13690": "nan", "13695": "nan", - "13700": 0.17003, + "13700": 0.23291, "13705": "nan", "13710": "nan", "13715": "nan", @@ -82796,7 +82796,7 @@ "13785": "nan", "13790": "nan", "13795": "nan", - "13800": 0.15268, + "13800": 0.2079, "13805": "nan", "13810": "nan", "13815": "nan", @@ -82816,7 +82816,7 @@ "13885": "nan", "13890": "nan", "13895": "nan", - "13900": 0.17672, + "13900": 0.21576, "13905": "nan", "13910": "nan", "13915": "nan", @@ -82836,7 +82836,7 @@ "13985": "nan", "13990": "nan", "13995": "nan", - "14000": 0.15631, + "14000": 0.19896, "14005": "nan", "14010": "nan", "14015": "nan", @@ -82856,7 +82856,7 @@ "14085": "nan", "14090": "nan", "14095": "nan", - "14100": 0.15761, + "14100": 0.2257, "14105": "nan", "14110": "nan", "14115": "nan", @@ -82876,7 +82876,7 @@ "14185": "nan", "14190": "nan", "14195": "nan", - "14200": 0.15867, + "14200": 0.21462, "14205": "nan", "14210": "nan", "14215": "nan", @@ -82896,7 +82896,7 @@ "14285": "nan", "14290": "nan", "14295": "nan", - "14300": 0.15953, + "14300": 0.20244, "14305": "nan", "14310": "nan", "14315": "nan", @@ -82916,7 +82916,7 @@ "14385": "nan", "14390": "nan", "14395": "nan", - "14400": 0.17725, + "14400": 0.19314, "14405": "nan", "14410": "nan", "14415": "nan", @@ -82936,7 +82936,7 @@ "14485": "nan", "14490": "nan", "14495": "nan", - "14500": 0.1541, + "14500": 0.20305, "14505": "nan", "14510": "nan", "14515": "nan", @@ -82956,7 +82956,7 @@ "14585": "nan", "14590": "nan", "14595": "nan", - "14600": 0.15523, + "14600": 0.18281, "14605": "nan", "14610": "nan", "14615": "nan", @@ -82976,7 +82976,7 @@ "14685": "nan", "14690": "nan", "14695": "nan", - "14700": 0.16087, + "14700": 0.19583, "14705": "nan", "14710": "nan", "14715": "nan", @@ -82996,7 +82996,7 @@ "14785": "nan", "14790": "nan", "14795": "nan", - "14800": 0.17672, + "14800": 0.21935, "14805": "nan", "14810": "nan", "14815": "nan", @@ -83016,7 +83016,7 @@ "14885": "nan", "14890": "nan", "14895": "nan", - "14900": 0.15473, + "14900": 0.2618, "14905": "nan", "14910": "nan", "14915": "nan", @@ -83036,7 +83036,7 @@ "14985": "nan", "14990": "nan", "14995": "nan", - "15000": 0.1576, + "15000": 0.2006, "15005": "nan", "15010": "nan", "15015": "nan", @@ -83056,7 +83056,7 @@ "15085": "nan", "15090": "nan", "15095": "nan", - "15100": 0.16069, + "15100": 0.18925, "15105": "nan", "15110": "nan", "15115": "nan", @@ -83076,7 +83076,7 @@ "15185": "nan", "15190": "nan", "15195": "nan", - "15200": 0.1831, + "15200": 0.19535, "15205": "nan", "15210": "nan", "15215": "nan", @@ -83096,7 +83096,7 @@ "15285": "nan", "15290": "nan", "15295": "nan", - "15300": 0.15159, + "15300": 0.22515, "15305": "nan", "15310": "nan", "15315": "nan", @@ -83116,7 +83116,7 @@ "15385": "nan", "15390": "nan", "15395": "nan", - "15400": 0.15571, + "15400": 0.19523, "15405": "nan", "15410": "nan", "15415": "nan", @@ -83136,7 +83136,7 @@ "15485": "nan", "15490": "nan", "15495": "nan", - "15500": 0.15999, + "15500": 0.18991, "15505": "nan", "15510": "nan", "15515": "nan", @@ -83156,7 +83156,7 @@ "15585": "nan", "15590": "nan", "15595": "nan", - "15600": 0.17957, + "15600": 0.22419, "15605": "nan", "15610": "nan", "15615": "nan", @@ -83176,7 +83176,7 @@ "15685": "nan", "15690": "nan", "15695": "nan", - "15700": 0.15147, + "15700": 0.21846, "15705": "nan", "15710": "nan", "15715": "nan", @@ -83196,7 +83196,7 @@ "15785": "nan", "15790": "nan", "15795": "nan", - "15800": 0.1525, + "15800": 0.1921, "15805": "nan", "15810": "nan", "15815": "nan", @@ -83216,7 +83216,7 @@ "15885": "nan", "15890": "nan", "15895": "nan", - "15900": 0.17704, + "15900": 0.21536, "15905": "nan", "15910": "nan", "15915": "nan", @@ -83236,7 +83236,7 @@ "15985": "nan", "15990": "nan", "15995": "nan", - "16000": 0.1638, + "16000": 0.20072, "16005": "nan", "16010": "nan", "16015": "nan", @@ -83256,7 +83256,7 @@ "16085": "nan", "16090": "nan", "16095": "nan", - "16100": 0.16202, + "16100": 0.20597, "16105": "nan", "16110": "nan", "16115": "nan", @@ -83276,7 +83276,7 @@ "16185": "nan", "16190": "nan", "16195": "nan", - "16200": 0.15438, + "16200": 0.17432, "16205": "nan", "16210": "nan", "16215": "nan", @@ -83296,7 +83296,7 @@ "16285": "nan", "16290": "nan", "16295": "nan", - "16300": 0.15418, + "16300": 0.1926, "16305": "nan", "16310": "nan", "16315": "nan", @@ -83316,7 +83316,7 @@ "16385": "nan", "16390": "nan", "16395": "nan", - "16400": 0.17518, + "16400": 0.20842, "16405": "nan", "16410": "nan", "16415": "nan", @@ -83336,7 +83336,7 @@ "16485": "nan", "16490": "nan", "16495": "nan", - "16500": 0.16033, + "16500": 0.20674, "16505": "nan", "16510": "nan", "16515": "nan", @@ -83356,7 +83356,7 @@ "16585": "nan", "16590": "nan", "16595": "nan", - "16600": 0.15165, + "16600": 0.19059, "16605": "nan", "16610": "nan", "16615": "nan", @@ -83376,7 +83376,7 @@ "16685": "nan", "16690": "nan", "16695": "nan", - "16700": 0.15466, + "16700": 0.1846, "16705": "nan", "16710": "nan", "16715": "nan", @@ -83396,7 +83396,7 @@ "16785": "nan", "16790": "nan", "16795": "nan", - "16800": 0.17698, + "16800": 0.18171, "16805": "nan", "16810": "nan", "16815": "nan", @@ -83416,7 +83416,7 @@ "16885": "nan", "16890": "nan", "16895": "nan", - "16900": 0.15934, + "16900": 0.21235, "16905": "nan", "16910": "nan", "16915": "nan", @@ -83436,7 +83436,7 @@ "16985": "nan", "16990": "nan", "16995": "nan", - "17000": 0.15654, + "17000": 0.18101, "17005": "nan", "17010": "nan", "17015": "nan", @@ -83456,7 +83456,7 @@ "17085": "nan", "17090": "nan", "17095": "nan", - "17100": 0.15382, + "17100": 0.17649, "17105": "nan", "17110": "nan", "17115": "nan", @@ -83476,7 +83476,7 @@ "17185": "nan", "17190": "nan", "17195": "nan", - "17200": 0.17821, + "17200": 0.18094, "17205": "nan", "17210": "nan", "17215": "nan", @@ -83496,7 +83496,7 @@ "17285": "nan", "17290": "nan", "17295": "nan", - "17300": 0.16105, + "17300": 0.20933, "17305": "nan", "17310": "nan", "17315": "nan", @@ -83516,7 +83516,7 @@ "17385": "nan", "17390": "nan", "17395": "nan", - "17400": 0.15345, + "17400": 0.19283, "17405": "nan", "17410": "nan", "17415": "nan", @@ -83536,7 +83536,7 @@ "17485": "nan", "17490": "nan", "17495": "nan", - "17500": 0.15707, + "17500": 0.18297, "17505": "nan", "17510": "nan", "17515": "nan", @@ -83556,7 +83556,7 @@ "17585": "nan", "17590": "nan", "17595": "nan", - "17600": 0.17171, + "17600": 0.21305, "17605": "nan", "17610": "nan", "17615": "nan", @@ -83576,7 +83576,7 @@ "17685": "nan", "17690": "nan", "17695": "nan", - "17700": 0.16041, + "17700": 0.20367, "17705": "nan", "17710": "nan", "17715": "nan", @@ -83596,7 +83596,7 @@ "17785": "nan", "17790": "nan", "17795": "nan", - "17800": 0.15329, + "17800": 0.19504, "17805": "nan", "17810": "nan", "17815": "nan", @@ -83616,7 +83616,7 @@ "17885": "nan", "17890": "nan", "17895": "nan", - "17900": 0.17693, + "17900": 0.1794, "17905": "nan", "17910": "nan", "17915": "nan", @@ -83636,7 +83636,7 @@ "17985": "nan", "17990": "nan", "17995": "nan", - "18000": 0.1527, + "18000": 0.21125, "18005": "nan", "18010": "nan", "18015": "nan", @@ -83656,7 +83656,7 @@ "18085": "nan", "18090": "nan", "18095": "nan", - "18100": 0.15658, + "18100": 0.18998, "18105": "nan", "18110": "nan", "18115": "nan", @@ -83676,7 +83676,7 @@ "18185": "nan", "18190": "nan", "18195": "nan", - "18200": 0.152, + "18200": 0.19993, "18205": "nan", "18210": "nan", "18215": "nan", @@ -83696,7 +83696,7 @@ "18285": "nan", "18290": "nan", "18295": "nan", - "18300": 0.16032, + "18300": 0.18904, "18305": "nan", "18310": "nan", "18315": "nan", @@ -83716,7 +83716,7 @@ "18385": "nan", "18390": "nan", "18395": "nan", - "18400": 0.19547, + "18400": 0.1977, "18405": "nan", "18410": "nan", "18415": "nan", @@ -83736,7 +83736,7 @@ "18485": "nan", "18490": "nan", "18495": "nan", - "18500": 0.15517, + "18500": 0.20315, "18505": "nan", "18510": "nan", "18515": "nan", @@ -83756,7 +83756,7 @@ "18585": "nan", "18590": "nan", "18595": "nan", - "18600": 0.15082, + "18600": 0.17326, "18605": "nan", "18610": "nan", "18615": "nan", @@ -83776,7 +83776,7 @@ "18685": "nan", "18690": "nan", "18695": "nan", - "18700": 0.1597, + "18700": 0.18986, "18705": "nan", "18710": "nan", "18715": "nan", @@ -83796,7 +83796,7 @@ "18785": "nan", "18790": "nan", "18795": "nan", - "18800": 0.18373, + "18800": 0.2101, "18805": "nan", "18810": "nan", "18815": "nan", @@ -83816,7 +83816,7 @@ "18885": "nan", "18890": "nan", "18895": "nan", - "18900": 0.15182, + "18900": 0.20269, "18905": "nan", "18910": "nan", "18915": "nan", @@ -83836,7 +83836,7 @@ "18985": "nan", "18990": "nan", "18995": "nan", - "19000": 0.15288, + "19000": 0.19377, "19005": "nan", "19010": "nan", "19015": "nan", @@ -83856,7 +83856,7 @@ "19085": "nan", "19090": "nan", "19095": "nan", - "19100": 0.15912, + "19100": 0.21069, "19105": "nan", "19110": "nan", "19115": "nan", @@ -83876,7 +83876,7 @@ "19185": "nan", "19190": "nan", "19195": "nan", - "19200": 0.18536, + "19200": 0.18597, "19205": "nan", "19210": "nan", "19215": "nan", @@ -83896,7 +83896,7 @@ "19285": "nan", "19290": "nan", "19295": "nan", - "19300": 0.15139, + "19300": 0.18268, "19305": "nan", "19310": "nan", "19315": "nan", @@ -83916,7 +83916,7 @@ "19385": "nan", "19390": "nan", "19395": "nan", - "19400": 0.15255, + "19400": 0.18361, "19405": "nan", "19410": "nan", "19415": "nan", @@ -83936,7 +83936,7 @@ "19485": "nan", "19490": "nan", "19495": "nan", - "19500": 0.16608, + "19500": 0.2201, "19505": "nan", "19510": "nan", "19515": "nan", @@ -83956,7 +83956,7 @@ "19585": "nan", "19590": "nan", "19595": "nan", - "19600": 0.17983, + "19600": 0.19412, "19605": "nan", "19610": "nan", "19615": "nan", @@ -83976,7 +83976,7 @@ "19685": "nan", "19690": "nan", "19695": "nan", - "19700": 0.15479, + "19700": 0.20698, "19705": "nan", "19710": "nan", "19715": "nan", @@ -83996,7 +83996,7 @@ "19785": "nan", "19790": "nan", "19795": "nan", - "19800": 0.15313, + "19800": 0.19302, "19805": "nan", "19810": "nan", "19815": "nan", @@ -84016,7 +84016,7 @@ "19885": "nan", "19890": "nan", "19895": "nan", - "19900": 0.18407, + "19900": 0.21296, "19905": "nan", "19910": "nan", "19915": "nan", @@ -84036,7 +84036,7 @@ "19985": "nan", "19990": "nan", "19995": "nan", - "20000": 0.16254, + "20000": 0.20645, "20005": "nan", "20010": "nan", "20015": "nan", @@ -84056,7 +84056,7 @@ "20085": "nan", "20090": "nan", "20095": "nan", - "20100": 0.16532, + "20100": 0.22113, "20105": "nan", "20110": "nan", "20115": "nan", @@ -84076,7 +84076,7 @@ "20185": "nan", "20190": "nan", "20195": "nan", - "20200": 0.15249, + "20200": 0.20043, "20205": "nan", "20210": "nan", "20215": "nan", @@ -84096,7 +84096,7 @@ "20285": "nan", "20290": "nan", "20295": "nan", - "20300": 0.15084, + "20300": 0.18736, "20305": "nan", "20310": "nan", "20315": "nan", @@ -84116,7 +84116,7 @@ "20385": "nan", "20390": "nan", "20395": "nan", - "20400": 0.18001, + "20400": 0.20287, "20405": "nan", "20410": "nan", "20415": "nan", @@ -84136,7 +84136,7 @@ "20485": "nan", "20490": "nan", "20495": "nan", - "20500": 0.16971, + "20500": 0.19171, "20505": "nan", "20510": "nan", "20515": "nan", @@ -84156,7 +84156,7 @@ "20585": "nan", "20590": "nan", "20595": "nan", - "20600": 0.15131, + "20600": 0.20726, "20605": "nan", "20610": "nan", "20615": "nan", @@ -84176,7 +84176,7 @@ "20685": "nan", "20690": "nan", "20695": "nan", - "20700": 0.15243, + "20700": 0.19562, "20705": "nan", "20710": "nan", "20715": "nan", @@ -84196,7 +84196,7 @@ "20785": "nan", "20790": "nan", "20795": "nan", - "20800": 0.18387, + "20800": 0.2016, "20805": "nan", "20810": "nan", "20815": "nan", @@ -84216,7 +84216,7 @@ "20885": "nan", "20890": "nan", "20895": "nan", - "20900": 0.16569, + "20900": 0.20514, "20905": "nan", "20910": "nan", "20915": "nan", @@ -84236,7 +84236,7 @@ "20985": "nan", "20990": "nan", "20995": "nan", - "21000": 0.15357, + "21000": 0.19607, "21005": "nan", "21010": "nan", "21015": "nan", @@ -84256,7 +84256,7 @@ "21085": "nan", "21090": "nan", "21095": "nan", - "21100": 0.15064, + "21100": 0.19137, "21105": "nan", "21110": "nan", "21115": "nan", @@ -84276,7 +84276,7 @@ "21185": "nan", "21190": "nan", "21195": "nan", - "21200": 0.17613, + "21200": 0.19856, "21205": "nan", "21210": "nan", "21215": "nan", @@ -84296,7 +84296,7 @@ "21285": "nan", "21290": "nan", "21295": "nan", - "21300": 0.16651, + "21300": 0.19191, "21305": "nan", "21310": "nan", "21315": "nan", @@ -84316,7 +84316,7 @@ "21385": "nan", "21390": "nan", "21395": "nan", - "21400": 0.15154, + "21400": 0.21074, "21405": "nan", "21410": "nan", "21415": "nan", @@ -84336,7 +84336,7 @@ "21485": "nan", "21490": "nan", "21495": "nan", - "21500": 0.16003, + "21500": 0.20729, "21505": "nan", "21510": "nan", "21515": "nan", @@ -84356,7 +84356,7 @@ "21585": "nan", "21590": "nan", "21595": "nan", - "21600": 0.17069, + "21600": 0.20098, "21605": "nan", "21610": "nan", "21615": "nan", @@ -84376,7 +84376,7 @@ "21685": "nan", "21690": "nan", "21695": "nan", - "21700": 0.16417, + "21700": 0.20309, "21705": "nan", "21710": "nan", "21715": "nan", @@ -84396,7 +84396,7 @@ "21785": "nan", "21790": "nan", "21795": "nan", - "21800": 0.14998, + "21800": 0.18595, "21805": "nan", "21810": "nan", "21815": "nan", @@ -84416,7 +84416,7 @@ "21885": "nan", "21890": "nan", "21895": "nan", - "21900": 0.17571, + "21900": 0.19776, "21905": "nan", "21910": "nan", "21915": "nan", @@ -84436,7 +84436,7 @@ "21985": "nan", "21990": "nan", "21995": "nan", - "22000": 0.1522, + "22000": 0.19074, "22005": "nan", "22010": "nan", "22015": "nan", @@ -84456,7 +84456,7 @@ "22085": "nan", "22090": "nan", "22095": "nan", - "22100": 0.15519, + "22100": 0.21278, "22105": "nan", "22110": "nan", "22115": "nan", @@ -84476,7 +84476,7 @@ "22185": "nan", "22190": "nan", "22195": "nan", - "22200": 0.15912, + "22200": 0.17702, "22205": "nan", "22210": "nan", "22215": "nan", @@ -84496,7 +84496,7 @@ "22285": "nan", "22290": "nan", "22295": "nan", - "22300": 0.16469, + "22300": 0.19933, "22305": "nan", "22310": "nan", "22315": "nan", @@ -84516,7 +84516,7 @@ "22385": "nan", "22390": "nan", "22395": "nan", - "22400": 0.18448, + "22400": 0.20962, "22405": "nan", "22410": "nan", "22415": "nan", @@ -84536,7 +84536,7 @@ "22485": "nan", "22490": "nan", "22495": "nan", - "22500": 0.15598, + "22500": 0.18994, "22505": "nan", "22510": "nan", "22515": "nan", @@ -84556,7 +84556,7 @@ "22585": "nan", "22590": "nan", "22595": "nan", - "22600": 0.15434, + "22600": 0.18645, "22605": "nan", "22610": "nan", "22615": "nan", @@ -84576,7 +84576,7 @@ "22685": "nan", "22690": "nan", "22695": "nan", - "22700": 0.16363, + "22700": 0.17195, "22705": "nan", "22710": "nan", "22715": "nan", @@ -84596,7 +84596,7 @@ "22785": "nan", "22790": "nan", "22795": "nan", - "22800": 0.18342, + "22800": 0.20737, "22805": "nan", "22810": "nan", "22815": "nan", @@ -84616,7 +84616,7 @@ "22885": "nan", "22890": "nan", "22895": "nan", - "22900": 0.15625, + "22900": 0.2043, "22905": "nan", "22910": "nan", "22915": "nan", @@ -84636,7 +84636,7 @@ "22985": "nan", "22990": "nan", "22995": "nan", - "23000": 0.15414, + "23000": 0.19373, "23005": "nan", "23010": "nan", "23015": "nan", @@ -84656,7 +84656,7 @@ "23085": "nan", "23090": "nan", "23095": "nan", - "23100": 0.17371, + "23100": 0.21203, "23105": "nan", "23110": "nan", "23115": "nan", @@ -84676,7 +84676,7 @@ "23185": "nan", "23190": "nan", "23195": "nan", - "23200": 0.18029, + "23200": 0.19856, "23205": "nan", "23210": "nan", "23215": "nan", @@ -84696,7 +84696,7 @@ "23285": "nan", "23290": "nan", "23295": "nan", - "23300": 0.15361, + "23300": 0.19204, "23305": "nan", "23310": "nan", "23315": "nan", @@ -84716,7 +84716,7 @@ "23385": "nan", "23390": "nan", "23395": "nan", - "23400": 0.15395, + "23400": 0.18165, "23405": "nan", "23410": "nan", "23415": "nan", @@ -84736,7 +84736,7 @@ "23485": "nan", "23490": "nan", "23495": "nan", - "23500": 0.17138, + "23500": 0.19878, "23505": "nan", "23510": "nan", "23515": "nan", @@ -84756,7 +84756,7 @@ "23585": "nan", "23590": "nan", "23595": "nan", - "23600": 0.17886, + "23600": 0.22127, "23605": "nan", "23610": "nan", "23615": "nan", @@ -84776,7 +84776,7 @@ "23685": "nan", "23690": "nan", "23695": "nan", - "23700": 0.15637, + "23700": 0.20375, "23705": "nan", "23710": "nan", "23715": "nan", @@ -84796,7 +84796,7 @@ "23785": "nan", "23790": "nan", "23795": "nan", - "23800": 0.15613, + "23800": 0.20655, "23805": "nan", "23810": "nan", "23815": "nan", @@ -84816,7 +84816,7 @@ "23885": "nan", "23890": "nan", "23895": "nan", - "23900": 0.18666, + "23900": 0.18454, "23905": "nan", "23910": "nan", "23915": "nan", @@ -84836,7 +84836,7 @@ "23985": "nan", "23990": "nan", "23995": "nan", - "24000": 0.16686, + "24000": 0.19839, "24005": "nan", "24010": "nan", "24015": "nan", @@ -84856,7 +84856,7 @@ "24085": "nan", "24090": "nan", "24095": "nan", - "24100": 0.16277, + "24100": 0.19727, "24105": "nan", "24110": "nan", "24115": "nan", @@ -84876,7 +84876,7 @@ "24185": "nan", "24190": "nan", "24195": "nan", - "24200": 0.15181, + "24200": 0.19164, "24205": "nan", "24210": "nan", "24215": "nan", @@ -84896,7 +84896,7 @@ "24285": "nan", "24290": "nan", "24295": "nan", - "24300": 0.15381, + "24300": 0.20884, "24305": "nan", "24310": "nan", "24315": "nan", @@ -84916,7 +84916,7 @@ "24385": "nan", "24390": "nan", "24395": "nan", - "24400": 0.18234, + "24400": 0.18698, "24405": "nan", "24410": "nan", "24415": "nan", @@ -84936,7 +84936,7 @@ "24485": "nan", "24490": "nan", "24495": "nan", - "24500": 0.1645, + "24500": 0.19055, "24505": "nan", "24510": "nan", "24515": "nan", @@ -84956,7 +84956,7 @@ "24585": "nan", "24590": "nan", "24595": "nan", - "24600": 0.15351, + "24600": 0.1818, "24605": "nan", "24610": "nan", "24615": "nan", @@ -84976,7 +84976,7 @@ "24685": "nan", "24690": "nan", "24695": "nan", - "24700": 0.15447, + "24700": 0.18114, "24705": "nan", "24710": "nan", "24715": "nan", @@ -84996,7 +84996,7 @@ "24785": "nan", "24790": "nan", "24795": "nan", - "24800": 0.17278, + "24800": 0.20103, "24805": "nan", "24810": "nan", "24815": "nan", @@ -85016,7 +85016,7 @@ "24885": "nan", "24890": "nan", "24895": "nan", - "24900": 0.16861, + "24900": 0.21732, "24905": "nan", "24910": "nan", "24915": "nan", @@ -85036,7 +85036,7 @@ "24985": "nan", "24990": "nan", "24995": "nan", - "25000": 0.15726, + "25000": 0.20355, "25005": "nan", "25010": "nan", "25015": "nan", @@ -85056,7 +85056,7 @@ "25085": "nan", "25090": "nan", "25095": "nan", - "25100": 0.15173, + "25100": 0.18684, "25105": "nan", "25110": "nan", "25115": "nan", @@ -85076,7 +85076,7 @@ "25185": "nan", "25190": "nan", "25195": "nan", - "25200": 0.17245, + "25200": 0.18868, "25205": "nan", "25210": "nan", "25215": "nan", @@ -85096,7 +85096,7 @@ "25285": "nan", "25290": "nan", "25295": "nan", - "25300": 0.1691, + "25300": 0.2035, "25305": "nan", "25310": "nan", "25315": "nan", @@ -85116,7 +85116,7 @@ "25385": "nan", "25390": "nan", "25395": "nan", - "25400": 0.1551, + "25400": 0.18362, "25405": "nan", "25410": "nan", "25415": "nan", @@ -85136,7 +85136,7 @@ "25485": "nan", "25490": "nan", "25495": "nan", - "25500": 0.15455, + "25500": 0.187, "25505": "nan", "25510": "nan", "25515": "nan", @@ -85156,7 +85156,7 @@ "25585": "nan", "25590": "nan", "25595": "nan", - "25600": 0.17131, + "25600": 0.21299, "25605": "nan", "25610": "nan", "25615": "nan", @@ -85176,7 +85176,7 @@ "25685": "nan", "25690": "nan", "25695": "nan", - "25700": 0.16699, + "25700": 0.19641, "25705": "nan", "25710": "nan", "25715": "nan", @@ -85196,7 +85196,7 @@ "25785": "nan", "25790": "nan", "25795": "nan", - "25800": 0.15366, + "25800": 0.20639, "25805": "nan", "25810": "nan", "25815": "nan", @@ -85216,7 +85216,7 @@ "25885": "nan", "25890": "nan", "25895": "nan", - "25900": 0.1731, + "25900": 0.19809, "25905": "nan", "25910": "nan", "25915": "nan", @@ -85236,7 +85236,7 @@ "25985": "nan", "25990": "nan", "25995": "nan", - "26000": 0.15377, + "26000": 0.20758, "26005": "nan", "26010": "nan", "26015": "nan", @@ -85256,7 +85256,7 @@ "26085": "nan", "26090": "nan", "26095": "nan", - "26100": 0.15394, + "26100": 0.21027, "26105": "nan", "26110": "nan", "26115": "nan", @@ -85276,7 +85276,7 @@ "26185": "nan", "26190": "nan", "26195": "nan", - "26200": 0.15375, + "26200": 0.18721, "26205": "nan", "26210": "nan", "26215": "nan", @@ -85296,7 +85296,7 @@ "26285": "nan", "26290": "nan", "26295": "nan", - "26300": 0.16645, + "26300": 0.1913, "26305": "nan", "26310": "nan", "26315": "nan", @@ -85316,7 +85316,7 @@ "26385": "nan", "26390": "nan", "26395": "nan", - "26400": 0.18079, + "26400": 0.18587, "26405": "nan", "26410": "nan", "26415": "nan", @@ -85336,7 +85336,7 @@ "26485": "nan", "26490": "nan", "26495": "nan", - "26500": 0.15438, + "26500": 0.2028, "26505": "nan", "26510": "nan", "26515": "nan", @@ -85356,7 +85356,7 @@ "26585": "nan", "26590": "nan", "26595": "nan", - "26600": 0.15316, + "26600": 0.19788, "26605": "nan", "26610": "nan", "26615": "nan", @@ -85376,7 +85376,7 @@ "26685": "nan", "26690": "nan", "26695": "nan", - "26700": 0.16402, + "26700": 0.17144, "26705": "nan", "26710": "nan", "26715": "nan", @@ -85396,7 +85396,7 @@ "26785": "nan", "26790": "nan", "26795": "nan", - "26800": 0.17126, + "26800": 0.18992, "26805": "nan", "26810": "nan", "26815": "nan", @@ -85416,7 +85416,7 @@ "26885": "nan", "26890": "nan", "26895": "nan", - "26900": 0.15249, + "26900": 0.20731, "26905": "nan", "26910": "nan", "26915": "nan", @@ -85436,7 +85436,7 @@ "26985": "nan", "26990": "nan", "26995": "nan", - "27000": 0.15825, + "27000": 0.20644, "27005": "nan", "27010": "nan", "27015": "nan", @@ -85456,7 +85456,7 @@ "27085": "nan", "27090": "nan", "27095": "nan", - "27100": 0.16844, + "27100": 0.19668, "27105": "nan", "27110": "nan", "27115": "nan", @@ -85476,7 +85476,7 @@ "27185": "nan", "27190": "nan", "27195": "nan", - "27200": 0.17296, + "27200": 0.18899, "27205": "nan", "27210": "nan", "27215": "nan", @@ -85496,7 +85496,7 @@ "27285": "nan", "27290": "nan", "27295": "nan", - "27300": 0.15123, + "27300": 0.19245, "27305": "nan", "27310": "nan", "27315": "nan", @@ -85516,7 +85516,7 @@ "27385": "nan", "27390": "nan", "27395": "nan", - "27400": 0.15242, + "27400": 0.20168, "27405": "nan", "27410": "nan", "27415": "nan", @@ -85536,7 +85536,7 @@ "27485": "nan", "27490": "nan", "27495": "nan", - "27500": 0.16973, + "27500": 0.18901, "27505": "nan", "27510": "nan", "27515": "nan", @@ -85556,7 +85556,7 @@ "27585": "nan", "27590": "nan", "27595": "nan", - "27600": 0.17414, + "27600": 0.20975, "27605": "nan", "27610": "nan", "27615": "nan", @@ -85576,7 +85576,7 @@ "27685": "nan", "27690": "nan", "27695": "nan", - "27700": 0.15185, + "27700": 0.20307, "27705": "nan", "27710": "nan", "27715": "nan", @@ -85596,7 +85596,7 @@ "27785": "nan", "27790": "nan", "27795": "nan", - "27800": 0.15274, + "27800": 0.19204, "27805": "nan", "27810": "nan", "27815": "nan", @@ -85616,7 +85616,7 @@ "27885": "nan", "27890": "nan", "27895": "nan", - "27900": 0.1784, + "27900": 0.20734, "27905": "nan", "27910": "nan", "27915": "nan", @@ -85636,7 +85636,7 @@ "27985": "nan", "27990": "nan", "27995": "nan", - "28000": 0.1596, + "28000": 0.18819, "28005": "nan", "28010": "nan", "28015": "nan", @@ -85656,7 +85656,7 @@ "28085": "nan", "28090": "nan", "28095": "nan", - "28100": 0.1649, + "28100": 0.19724, "28105": "nan", "28110": "nan", "28115": "nan", @@ -85676,7 +85676,7 @@ "28185": "nan", "28190": "nan", "28195": "nan", - "28200": 0.15146, + "28200": 0.17861, "28205": "nan", "28210": "nan", "28215": "nan", @@ -85696,7 +85696,7 @@ "28285": "nan", "28290": "nan", "28295": "nan", - "28300": 0.15277, + "28300": 0.18676, "28305": "nan", "28310": "nan", "28315": "nan", @@ -85716,7 +85716,7 @@ "28385": "nan", "28390": "nan", "28395": "nan", - "28400": 0.17967, + "28400": 0.18834, "28405": "nan", "28410": "nan", "28415": "nan", @@ -85736,7 +85736,7 @@ "28485": "nan", "28490": "nan", "28495": "nan", - "28500": 0.1689, + "28500": 0.21457, "28505": "nan", "28510": "nan", "28515": "nan", @@ -85756,7 +85756,7 @@ "28585": "nan", "28590": "nan", "28595": "nan", - "28600": 0.1509, + "28600": 0.17913, "28605": "nan", "28610": "nan", "28615": "nan", @@ -85776,7 +85776,7 @@ "28685": "nan", "28690": "nan", "28695": "nan", - "28700": 0.15042, + "28700": 0.17766, "28705": "nan", "28710": "nan", "28715": "nan", @@ -85796,7 +85796,7 @@ "28785": "nan", "28790": "nan", "28795": "nan", - "28800": 0.17736, + "28800": 0.22725, "28805": "nan", "28810": "nan", "28815": "nan", @@ -85816,7 +85816,7 @@ "28885": "nan", "28890": "nan", "28895": "nan", - "28900": 0.16374, + "28900": 0.2212, "28905": "nan", "28910": "nan", "28915": "nan", @@ -85836,7 +85836,7 @@ "28985": "nan", "28990": "nan", "28995": "nan", - "29000": 0.15676, + "29000": 0.20995, "29005": "nan", "29010": "nan", "29015": "nan", @@ -85856,7 +85856,7 @@ "29085": "nan", "29090": "nan", "29095": "nan", - "29100": 0.25681, + "29100": 0.17482, "29105": "nan", "29110": "nan", "29115": "nan", @@ -85876,7 +85876,7 @@ "29185": "nan", "29190": "nan", "29195": "nan", - "29200": 0.17759, + "29200": 0.18979, "29205": "nan", "29210": "nan", "29215": "nan", @@ -85896,7 +85896,7 @@ "29285": "nan", "29290": "nan", "29295": "nan", - "29300": 0.16645, + "29300": 0.19559, "29305": "nan", "29310": "nan", "29315": "nan", @@ -85916,7 +85916,7 @@ "29385": "nan", "29390": "nan", "29395": "nan", - "29400": 0.15176, + "29400": 0.19862, "29405": "nan", "29410": "nan", "29415": "nan", @@ -85936,7 +85936,7 @@ "29485": "nan", "29490": "nan", "29495": "nan", - "29500": 0.15576, + "29500": 0.18479, "29505": "nan", "29510": "nan", "29515": "nan", @@ -85956,7 +85956,7 @@ "29585": "nan", "29590": "nan", "29595": "nan", - "29600": 0.17465, + "29600": 0.2149, "29605": "nan", "29610": "nan", "29615": "nan", @@ -85976,7 +85976,7 @@ "29685": "nan", "29690": "nan", "29695": "nan", - "29700": 0.17163, + "29700": 0.2104, "29705": "nan", "29710": "nan", "29715": "nan", @@ -85996,7 +85996,7 @@ "29785": "nan", "29790": "nan", "29795": "nan", - "29800": 0.15474, + "29800": 0.18382, "29805": "nan", "29810": "nan", "29815": "nan", @@ -86016,7 +86016,7 @@ "29885": "nan", "29890": "nan", "29895": "nan", - "29900": 0.1828, + "29900": 0.19745, "29905": "nan", "29910": "nan", "29915": "nan", @@ -86036,7 +86036,7 @@ "29985": "nan", "29990": "nan", "29995": "nan", - "30000": 0.1562, + "30000": 0.2263, "30005": "nan", "30010": "nan", "30015": "nan", @@ -86056,7 +86056,7 @@ "30085": "nan", "30090": "nan", "30095": "nan", - "30100": 0.15584, + "30100": 0.21258, "30105": "nan", "30110": "nan", "30115": "nan", @@ -86076,7 +86076,7 @@ "30185": "nan", "30190": "nan", "30195": "nan", - "30200": 0.15477, + "30200": 0.18702, "30205": "nan", "30210": "nan", "30215": "nan", @@ -86096,7 +86096,7 @@ "30285": "nan", "30290": "nan", "30295": "nan", - "30300": 0.15246, + "30300": 0.19762, "30305": "nan", "30310": "nan", "30315": "nan", @@ -86116,7 +86116,7 @@ "30385": "nan", "30390": "nan", "30395": "nan", - "30400": 0.17607, + "30400": 0.20001, "30405": "nan", "30410": "nan", "30415": "nan", @@ -86136,7 +86136,7 @@ "30485": "nan", "30490": "nan", "30495": "nan", - "30500": 0.15609, + "30500": 0.19168, "30505": "nan", "30510": "nan", "30515": "nan", @@ -86156,7 +86156,7 @@ "30585": "nan", "30590": "nan", "30595": "nan", - "30600": 0.15444, + "30600": 0.18897, "30605": "nan", "30610": "nan", "30615": "nan", @@ -86176,7 +86176,7 @@ "30685": "nan", "30690": "nan", "30695": "nan", - "30700": 0.15423, + "30700": 0.18604, "30705": "nan", "30710": "nan", "30715": "nan", @@ -86196,7 +86196,7 @@ "30785": "nan", "30790": "nan", "30795": "nan", - "30800": 0.18052, + "30800": 0.20646, "30805": "nan", "30810": "nan", "30815": "nan", @@ -86216,7 +86216,7 @@ "30885": "nan", "30890": "nan", "30895": "nan", - "30900": 0.15308, + "30900": 0.21086, "30905": "nan", "30910": "nan", "30915": "nan", @@ -86236,7 +86236,7 @@ "30985": "nan", "30990": "nan", "30995": "nan", - "31000": 0.15398, + "31000": 0.22421, "31005": "nan", "31010": "nan", "31015": "nan", @@ -86256,7 +86256,7 @@ "31085": "nan", "31090": "nan", "31095": "nan", - "31100": 0.15752, + "31100": 0.17709, "31105": "nan", "31110": "nan", "31115": "nan", @@ -86276,7 +86276,7 @@ "31185": "nan", "31190": "nan", "31195": "nan", - "31200": 0.17466, + "31200": 0.19515, "31205": "nan", "31210": "nan", "31215": "nan", @@ -86296,7 +86296,7 @@ "31285": "nan", "31290": "nan", "31295": "nan", - "31300": 0.1542, + "31300": 0.19718, "31305": "nan", "31310": "nan", "31315": "nan", @@ -86316,7 +86316,7 @@ "31385": "nan", "31390": "nan", "31395": "nan", - "31400": 0.15297, + "31400": 0.20606, "31405": "nan", "31410": "nan", "31415": "nan", @@ -86336,7 +86336,7 @@ "31485": "nan", "31490": "nan", "31495": "nan", - "31500": 0.1625, + "31500": 0.18022, "31505": "nan", "31510": "nan", "31515": "nan", @@ -86356,7 +86356,7 @@ "31585": "nan", "31590": "nan", "31595": "nan", - "31600": 0.17666, + "31600": 0.22451, "31605": "nan", "31610": "nan", "31615": "nan", @@ -86376,7 +86376,7 @@ "31685": "nan", "31690": "nan", "31695": "nan", - "31700": 0.15761, + "31700": 0.19776, "31705": "nan", "31710": "nan", "31715": "nan", @@ -86396,7 +86396,7 @@ "31785": "nan", "31790": "nan", "31795": "nan", - "31800": 0.15621, + "31800": 0.20865, "31805": "nan", "31810": "nan", "31815": "nan", @@ -86416,7 +86416,7 @@ "31885": "nan", "31890": "nan", "31895": "nan", - "31900": 0.17119, + "31900": 0.1987, "31905": "nan", "31910": "nan", "31915": "nan", @@ -86436,7 +86436,7 @@ "31985": "nan", "31990": "nan", "31995": "nan", - "32000": 0.1629, + "32000": 0.2116, "32005": "nan", "32010": "nan", "32015": "nan", @@ -86456,7 +86456,7 @@ "32085": "nan", "32090": "nan", "32095": "nan", - "32100": 0.16303, + "32100": 0.20624, "32105": "nan", "32110": "nan", "32115": "nan", @@ -86476,7 +86476,7 @@ "32185": "nan", "32190": "nan", "32195": "nan", - "32200": 0.15629, + "32200": 0.21014, "32205": "nan", "32210": "nan", "32215": "nan", @@ -86496,7 +86496,7 @@ "32285": "nan", "32290": "nan", "32295": "nan", - "32300": 0.15291, + "32300": 0.20974, "32305": "nan", "32310": "nan", "32315": "nan", @@ -86516,7 +86516,7 @@ "32385": "nan", "32390": "nan", "32395": "nan", - "32400": 0.17618, + "32400": 0.18505, "32405": "nan", "32410": "nan", "32415": "nan", @@ -86536,7 +86536,7 @@ "32485": "nan", "32490": "nan", "32495": "nan", - "32500": 0.16832, + "32500": 0.20548, "32505": "nan", "32510": "nan", "32515": "nan", @@ -86556,7 +86556,7 @@ "32585": "nan", "32590": "nan", "32595": "nan", - "32600": 0.15683, + "32600": 0.18397, "32605": "nan", "32610": "nan", "32615": "nan", @@ -86576,7 +86576,7 @@ "32685": "nan", "32690": "nan", "32695": "nan", - "32700": 0.15626, + "32700": 0.17823, "32705": "nan", "32710": "nan", "32715": "nan", @@ -86596,7 +86596,7 @@ "32785": "nan", "32790": "nan", "32795": "nan", - "32800": 0.17689, + "32800": 0.19329, "32805": "nan", "32810": "nan", "32815": "nan", @@ -86616,7 +86616,7 @@ "32885": "nan", "32890": "nan", "32895": "nan", - "32900": 0.16557, + "32900": 0.21345, "32905": "nan", "32910": "nan", "32915": "nan", @@ -86636,7 +86636,7 @@ "32985": "nan", "32990": "nan", "32995": "nan", - "33000": 0.15678, + "33000": 0.21818, "33005": "nan", "33010": "nan", "33015": "nan", @@ -86656,7 +86656,7 @@ "33085": "nan", "33090": "nan", "33095": "nan", - "33100": 0.15488, + "33100": 0.18876, "33105": "nan", "33110": "nan", "33115": "nan", @@ -86676,7 +86676,7 @@ "33185": "nan", "33190": "nan", "33195": "nan", - "33200": 0.17359, + "33200": 0.20029, "33205": "nan", "33210": "nan", "33215": "nan", @@ -86696,7 +86696,7 @@ "33285": "nan", "33290": "nan", "33295": "nan", - "33300": 0.16856, + "33300": 0.21576, "33305": "nan", "33310": "nan", "33315": "nan", @@ -86716,7 +86716,7 @@ "33385": "nan", "33390": "nan", "33395": "nan", - "33400": 0.15367, + "33400": 0.19576, "33405": "nan", "33410": "nan", "33415": "nan", @@ -86736,7 +86736,7 @@ "33485": "nan", "33490": "nan", "33495": "nan", - "33500": 0.15785, + "33500": 0.18279, "33505": "nan", "33510": "nan", "33515": "nan", @@ -86756,7 +86756,7 @@ "33585": "nan", "33590": "nan", "33595": "nan", - "33600": 0.17198, + "33600": 0.22076, "33605": "nan", "33610": "nan", "33615": "nan", @@ -86776,7 +86776,7 @@ "33685": "nan", "33690": "nan", "33695": "nan", - "33700": 0.16779, + "33700": 0.20661, "33705": "nan", "33710": "nan", "33715": "nan", @@ -86796,7 +86796,7 @@ "33785": "nan", "33790": "nan", "33795": "nan", - "33800": 0.15203, + "33800": 0.19723, "33805": "nan", "33810": "nan", "33815": "nan", @@ -86816,7 +86816,7 @@ "33885": "nan", "33890": "nan", "33895": "nan", - "33900": 0.17591, + "33900": 0.21218, "33905": "nan", "33910": "nan", "33915": "nan", @@ -86836,7 +86836,7 @@ "33985": "nan", "33990": "nan", "33995": "nan", - "34000": 0.15025, + "34000": 0.2123, "34005": "nan", "34010": "nan", "34015": "nan", @@ -86856,7 +86856,7 @@ "34085": "nan", "34090": "nan", "34095": "nan", - "34100": 0.15435, + "34100": 0.20264, "34105": "nan", "34110": "nan", "34115": "nan", @@ -86876,7 +86876,7 @@ "34185": "nan", "34190": "nan", "34195": "nan", - "34200": 0.15081, + "34200": 0.18918, "34205": "nan", "34210": "nan", "34215": "nan", @@ -86896,7 +86896,7 @@ "34285": "nan", "34290": "nan", "34295": "nan", - "34300": 0.15293, + "34300": 0.2246, "34305": "nan", "34310": "nan", "34315": "nan", @@ -86916,7 +86916,7 @@ "34385": "nan", "34390": "nan", "34395": "nan", - "34400": 0.1914, + "34400": 0.18971, "34405": "nan", "34410": "nan", "34415": "nan", @@ -86936,7 +86936,7 @@ "34485": "nan", "34490": "nan", "34495": "nan", - "34500": 0.15362, + "34500": 0.19823, "34505": "nan", "34510": "nan", "34515": "nan", @@ -86956,7 +86956,7 @@ "34585": "nan", "34590": "nan", "34595": "nan", - "34600": 0.15033, + "34600": 0.18422, "34605": "nan", "34610": "nan", "34615": "nan", @@ -86976,7 +86976,7 @@ "34685": "nan", "34690": "nan", "34695": "nan", - "34700": 0.15569, + "34700": 0.2024, "34705": "nan", "34710": "nan", "34715": "nan", @@ -86996,7 +86996,7 @@ "34785": "nan", "34790": "nan", "34795": "nan", - "34800": 0.18385, + "34800": 0.19751, "34805": "nan", "34810": "nan", "34815": "nan", @@ -87016,7 +87016,7 @@ "34885": "nan", "34890": "nan", "34895": "nan", - "34900": 0.15038, + "34900": 0.19211, "34905": "nan", "34910": "nan", "34915": "nan", @@ -87036,7 +87036,7 @@ "34985": "nan", "34990": "nan", "34995": "nan", - "35000": 0.15339, + "35000": 0.21882, "35005": "nan", "35010": "nan", "35015": "nan", @@ -87056,7 +87056,7 @@ "35085": "nan", "35090": "nan", "35095": "nan", - "35100": 0.15323, + "35100": 0.19631, "35105": "nan", "35110": "nan", "35115": "nan", @@ -87076,7 +87076,7 @@ "35185": "nan", "35190": "nan", "35195": "nan", - "35200": 0.19205, + "35200": 0.20922, "35205": "nan", "35210": "nan", "35215": "nan", @@ -87096,7 +87096,7 @@ "35285": "nan", "35290": "nan", "35295": "nan", - "35300": 0.15098, + "35300": 0.192, "35305": "nan", "35310": "nan", "35315": "nan", @@ -87116,7 +87116,7 @@ "35385": "nan", "35390": "nan", "35395": "nan", - "35400": 0.15389, + "35400": 0.20134, "35405": "nan", "35410": "nan", "35415": "nan", @@ -87136,7 +87136,7 @@ "35485": "nan", "35490": "nan", "35495": "nan", - "35500": 0.1613, + "35500": 0.18937, "35505": "nan", "35510": "nan", "35515": "nan", @@ -87156,7 +87156,7 @@ "35585": "nan", "35590": "nan", "35595": "nan", - "35600": 0.18466, + "35600": 0.21688, "35605": "nan", "35610": "nan", "35615": "nan", @@ -87176,7 +87176,7 @@ "35685": "nan", "35690": "nan", "35695": "nan", - "35700": 0.15071, + "35700": 0.20146, "35705": "nan", "35710": "nan", "35715": "nan", @@ -87196,7 +87196,7 @@ "35785": "nan", "35790": "nan", "35795": "nan", - "35800": 0.15492, + "35800": 0.20761, "35805": "nan", "35810": "nan", "35815": "nan", @@ -87216,7 +87216,7 @@ "35885": "nan", "35890": "nan", "35895": "nan", - "35900": 0.1764, + "35900": 0.20194, "35905": "nan", "35910": "nan", "35915": "nan", @@ -87236,7 +87236,7 @@ "35985": "nan", "35990": "nan", "35995": "nan", - "36000": 0.16786, + "36000": 0.21252, "36005": "nan", "36010": "nan", "36015": "nan", @@ -87256,7 +87256,7 @@ "36085": "nan", "36090": "nan", "36095": "nan", - "36100": 0.16974, + "36100": 0.21188, "36105": "nan", "36110": "nan", "36115": "nan", @@ -87276,7 +87276,7 @@ "36185": "nan", "36190": "nan", "36195": "nan", - "36200": 0.1529, + "36200": 0.19543, "36205": "nan", "36210": "nan", "36215": "nan", @@ -87296,7 +87296,7 @@ "36285": "nan", "36290": "nan", "36295": "nan", - "36300": 0.15135, + "36300": 0.19453, "36305": "nan", "36310": "nan", "36315": "nan", @@ -87316,7 +87316,7 @@ "36385": "nan", "36390": "nan", "36395": "nan", - "36400": 0.17695, + "36400": 0.18534, "36405": "nan", "36410": "nan", "36415": "nan", @@ -87336,7 +87336,7 @@ "36485": "nan", "36490": "nan", "36495": "nan", - "36500": 0.17077, + "36500": 0.20009, "36505": "nan", "36510": "nan", "36515": "nan", @@ -87356,7 +87356,7 @@ "36585": "nan", "36590": "nan", "36595": "nan", - "36600": 0.1536, + "36600": 0.18784, "36605": "nan", "36610": "nan", "36615": "nan", @@ -87376,7 +87376,7 @@ "36685": "nan", "36690": "nan", "36695": "nan", - "36700": 0.15037, + "36700": 0.18852, "36705": "nan", "36710": "nan", "36715": "nan", @@ -87396,7 +87396,7 @@ "36785": "nan", "36790": "nan", "36795": "nan", - "36800": 0.17362, + "36800": 0.20653, "36805": "nan", "36810": "nan", "36815": "nan", @@ -87416,7 +87416,7 @@ "36885": "nan", "36890": "nan", "36895": "nan", - "36900": 0.16909, + "36900": 0.21836, "36905": "nan", "36910": "nan", "36915": "nan", @@ -87436,7 +87436,7 @@ "36985": "nan", "36990": "nan", "36995": "nan", - "37000": 0.15357, + "37000": 0.20669, "37005": "nan", "37010": "nan", "37015": "nan", @@ -87456,7 +87456,7 @@ "37085": "nan", "37090": "nan", "37095": "nan", - "37100": 0.15049, + "37100": 0.20393, "37105": "nan", "37110": "nan", "37115": "nan", @@ -87476,7 +87476,7 @@ "37185": "nan", "37190": "nan", "37195": "nan", - "37200": 0.17361, + "37200": 0.20864, "37205": "nan", "37210": "nan", "37215": "nan", @@ -87496,7 +87496,7 @@ "37285": "nan", "37290": "nan", "37295": "nan", - "37300": 0.17103, + "37300": 0.19397, "37305": "nan", "37310": "nan", "37315": "nan", @@ -87516,7 +87516,7 @@ "37385": "nan", "37390": "nan", "37395": "nan", - "37400": 0.15326, + "37400": 0.19683, "37405": "nan", "37410": "nan", "37415": "nan", @@ -87536,7 +87536,7 @@ "37485": "nan", "37490": "nan", "37495": "nan", - "37500": 0.15984, + "37500": 0.2023, "37505": "nan", "37510": "nan", "37515": "nan", @@ -87556,7 +87556,7 @@ "37585": "nan", "37590": "nan", "37595": "nan", - "37600": 0.18555, + "37600": 0.20575, "37605": "nan", "37610": "nan", "37615": "nan", @@ -87576,7 +87576,7 @@ "37685": "nan", "37690": "nan", "37695": "nan", - "37700": 0.16937, + "37700": 0.22192, "37705": "nan", "37710": "nan", "37715": "nan", @@ -87596,7 +87596,7 @@ "37785": "nan", "37790": "nan", "37795": "nan", - "37800": 0.15335, + "37800": 0.18413, "37805": "nan", "37810": "nan", "37815": "nan", @@ -87616,7 +87616,7 @@ "37885": "nan", "37890": "nan", "37895": "nan", - "37900": 0.17593, + "37900": 0.21563, "37905": "nan", "37910": "nan", "37915": "nan", @@ -87636,7 +87636,7 @@ "37985": "nan", "37990": "nan", "37995": "nan", - "38000": 0.1528, + "38000": 0.20028, "38005": "nan", "38010": "nan", "38015": "nan", @@ -87656,7 +87656,7 @@ "38085": "nan", "38090": "nan", "38095": "nan", - "38100": 0.15269, + "38100": 0.20842, "38105": "nan", "38110": "nan", "38115": "nan", @@ -87676,7 +87676,7 @@ "38185": "nan", "38190": "nan", "38195": "nan", - "38200": 0.15513, + "38200": 0.19753, "38205": "nan", "38210": "nan", "38215": "nan", @@ -87696,7 +87696,7 @@ "38285": "nan", "38290": "nan", "38295": "nan", - "38300": 0.1528, + "38300": 0.21674, "38305": "nan", "38310": "nan", "38315": "nan", @@ -87716,7 +87716,7 @@ "38385": "nan", "38390": "nan", "38395": "nan", - "38400": 0.19079, + "38400": 0.19563, "38405": "nan", "38410": "nan", "38415": "nan", @@ -87736,7 +87736,7 @@ "38485": "nan", "38490": "nan", "38495": "nan", - "38500": 0.15348, + "38500": 0.18788, "38505": "nan", "38510": "nan", "38515": "nan", @@ -87756,7 +87756,7 @@ "38585": "nan", "38590": "nan", "38595": "nan", - "38600": 0.15235, + "38600": 0.19979, "38605": "nan", "38610": "nan", "38615": "nan", @@ -87776,7 +87776,7 @@ "38685": "nan", "38690": "nan", "38695": "nan", - "38700": 0.15335, + "38700": 0.18588, "38705": "nan", "38710": "nan", "38715": "nan", @@ -87796,7 +87796,7 @@ "38785": "nan", "38790": "nan", "38795": "nan", - "38800": 0.18159, + "38800": 0.20239, "38805": "nan", "38810": "nan", "38815": "nan", @@ -87816,7 +87816,7 @@ "38885": "nan", "38890": "nan", "38895": "nan", - "38900": 0.15439, + "38900": 0.22215, "38905": "nan", "38910": "nan", "38915": "nan", @@ -87836,7 +87836,7 @@ "38985": "nan", "38990": "nan", "38995": "nan", - "39000": 0.15339, + "39000": 0.21238, "39005": "nan", "39010": "nan", "39015": "nan", @@ -87856,7 +87856,7 @@ "39085": "nan", "39090": "nan", "39095": "nan", - "39100": 0.15625, + "39100": 0.19004, "39105": "nan", "39110": "nan", "39115": "nan", @@ -87876,7 +87876,7 @@ "39185": "nan", "39190": "nan", "39195": "nan", - "39200": 0.18791, + "39200": 0.18858, "39205": "nan", "39210": "nan", "39215": "nan", @@ -87896,7 +87896,7 @@ "39285": "nan", "39290": "nan", "39295": "nan", - "39300": 0.15224, + "39300": 0.19867, "39305": "nan", "39310": "nan", "39315": "nan", @@ -87916,7 +87916,7 @@ "39385": "nan", "39390": "nan", "39395": "nan", - "39400": 0.15311, + "39400": 0.19548, "39405": "nan", "39410": "nan", "39415": "nan", @@ -87936,7 +87936,7 @@ "39485": "nan", "39490": "nan", "39495": "nan", - "39500": 0.15575, + "39500": 0.18529, "39505": "nan", "39510": "nan", "39515": "nan", @@ -87956,7 +87956,7 @@ "39585": "nan", "39590": "nan", "39595": "nan", - "39600": 0.1875, + "39600": 0.22124, "39605": "nan", "39610": "nan", "39615": "nan", @@ -87976,7 +87976,7 @@ "39685": "nan", "39690": "nan", "39695": "nan", - "39700": 0.15588, + "39700": 0.20398, "39705": "nan", "39710": "nan", "39715": "nan", @@ -87996,7 +87996,7 @@ "39785": "nan", "39790": "nan", "39795": "nan", - "39800": 0.15152, + "39800": 0.20945, "39805": "nan", "39810": "nan", "39815": "nan", @@ -88016,7 +88016,7 @@ "39885": "nan", "39890": "nan", "39895": "nan", - "39900": 0.17408, + "39900": 0.19777, "39905": "nan", "39910": "nan", "39915": "nan", @@ -88036,7 +88036,7 @@ "39985": "nan", "39990": "nan", "39995": "nan", - "40000": 0.16574, + "40000": 0.21122, "40005": "nan", "40010": "nan", "40015": "nan", @@ -88056,7 +88056,7 @@ "40085": "nan", "40090": "nan", "40095": "nan", - "40100": 0.16471, + "40100": 0.20932, "40105": "nan", "40110": "nan", "40115": "nan", @@ -88076,7 +88076,7 @@ "40185": "nan", "40190": "nan", "40195": "nan", - "40200": 0.15041, + "40200": 0.20395, "40205": "nan", "40210": "nan", "40215": "nan", @@ -88096,7 +88096,7 @@ "40285": "nan", "40290": "nan", "40295": "nan", - "40300": 0.15228, + "40300": 0.20855, "40305": "nan", "40310": "nan", "40315": "nan", @@ -88116,7 +88116,7 @@ "40385": "nan", "40390": "nan", "40395": "nan", - "40400": 0.17696, + "40400": 0.17838, "40405": "nan", "40410": "nan", "40415": "nan", @@ -88136,7 +88136,7 @@ "40485": "nan", "40490": "nan", "40495": "nan", - "40500": 0.16867, + "40500": 0.20803, "40505": "nan", "40510": "nan", "40515": "nan", @@ -88156,7 +88156,7 @@ "40585": "nan", "40590": "nan", "40595": "nan", - "40600": 0.156, + "40600": 0.18104, "40605": "nan", "40610": "nan", "40615": "nan", @@ -88176,7 +88176,7 @@ "40685": "nan", "40690": "nan", "40695": "nan", - "40700": 0.15451, + "40700": 0.18771, "40705": "nan", "40710": "nan", "40715": "nan", @@ -88196,7 +88196,7 @@ "40785": "nan", "40790": "nan", "40795": "nan", - "40800": 0.17714, + "40800": 0.19322, "40805": "nan", "40810": "nan", "40815": "nan", @@ -88216,7 +88216,7 @@ "40885": "nan", "40890": "nan", "40895": "nan", - "40900": 0.17031, + "40900": 0.21878, "40905": "nan", "40910": "nan", "40915": "nan", @@ -88236,7 +88236,7 @@ "40985": "nan", "40990": "nan", "40995": "nan", - "41000": 0.16108, + "41000": 0.21537, "41005": "nan", "41010": "nan", "41015": "nan", @@ -88256,7 +88256,7 @@ "41085": "nan", "41090": "nan", "41095": "nan", - "41100": 0.15349, + "41100": 0.19254, "41105": "nan", "41110": "nan", "41115": "nan", @@ -88276,7 +88276,7 @@ "41185": "nan", "41190": "nan", "41195": "nan", - "41200": 0.17408, + "41200": 0.19309, "41205": "nan", "41210": "nan", "41215": "nan", @@ -88296,7 +88296,7 @@ "41285": "nan", "41290": "nan", "41295": "nan", - "41300": 0.16937, + "41300": 0.1931, "41305": "nan", "41310": "nan", "41315": "nan", @@ -88316,7 +88316,7 @@ "41385": "nan", "41390": "nan", "41395": "nan", - "41400": 0.15275, + "41400": 0.20029, "41405": "nan", "41410": "nan", "41415": "nan", @@ -88336,7 +88336,7 @@ "41485": "nan", "41490": "nan", "41495": "nan", - "41500": 0.15593, + "41500": 0.18963, "41505": "nan", "41510": "nan", "41515": "nan", @@ -88356,7 +88356,7 @@ "41585": "nan", "41590": "nan", "41595": "nan", - "41600": 0.17254, + "41600": 0.21548, "41605": "nan", "41610": "nan", "41615": "nan", @@ -88376,7 +88376,7 @@ "41685": "nan", "41690": "nan", "41695": "nan", - "41700": 0.16982, + "41700": 0.22203, "41705": "nan", "41710": "nan", "41715": "nan", @@ -88396,7 +88396,7 @@ "41785": "nan", "41790": "nan", "41795": "nan", - "41800": 0.15251, + "41800": 0.19633, "41805": "nan", "41810": "nan", "41815": "nan", @@ -88416,7 +88416,7 @@ "41885": "nan", "41890": "nan", "41895": "nan", - "41900": 0.17823, + "41900": 0.20271, "41905": "nan", "41910": "nan", "41915": "nan", @@ -88436,7 +88436,7 @@ "41985": "nan", "41990": "nan", "41995": "nan", - "42000": 0.15072, + "42000": 0.21455, "42005": "nan", "42010": "nan", "42015": "nan", @@ -88456,7 +88456,7 @@ "42085": "nan", "42090": "nan", "42095": "nan", - "42100": 0.15669, + "42100": 0.21199, "42105": "nan", "42110": "nan", "42115": "nan", @@ -88476,7 +88476,7 @@ "42185": "nan", "42190": "nan", "42195": "nan", - "42200": 0.15604, + "42200": 0.18094, "42205": "nan", "42210": "nan", "42215": "nan", @@ -88496,7 +88496,7 @@ "42285": "nan", "42290": "nan", "42295": "nan", - "42300": 0.15744, + "42300": 0.20721, "42305": "nan", "42310": "nan", "42315": "nan", @@ -88516,7 +88516,7 @@ "42385": "nan", "42390": "nan", "42395": "nan", - "42400": 0.18372, + "42400": 0.19245, "42405": "nan", "42410": "nan", "42415": "nan", @@ -88536,7 +88536,7 @@ "42485": "nan", "42490": "nan", "42495": "nan", - "42500": 0.15297, + "42500": 0.21536, "42505": "nan", "42510": "nan", "42515": "nan", @@ -88556,7 +88556,7 @@ "42585": "nan", "42590": "nan", "42595": "nan", - "42600": 0.15143, + "42600": 0.17228, "42605": "nan", "42610": "nan", "42615": "nan", @@ -88576,7 +88576,7 @@ "42685": "nan", "42690": "nan", "42695": "nan", - "42700": 0.15838, + "42700": 0.19784, "42705": "nan", "42710": "nan", "42715": "nan", @@ -88596,7 +88596,7 @@ "42785": "nan", "42790": "nan", "42795": "nan", - "42800": 0.18152, + "42800": 0.20904, "42805": "nan", "42810": "nan", "42815": "nan", @@ -88616,7 +88616,7 @@ "42885": "nan", "42890": "nan", "42895": "nan", - "42900": 0.15325, + "42900": 0.21566, "42905": "nan", "42910": "nan", "42915": "nan", @@ -88636,7 +88636,7 @@ "42985": "nan", "42990": "nan", "42995": "nan", - "43000": 0.15581, + "43000": 0.21586, "43005": "nan", "43010": "nan", "43015": "nan", @@ -88656,7 +88656,7 @@ "43085": "nan", "43090": "nan", "43095": "nan", - "43100": 0.16132, + "43100": 0.1934, "43105": "nan", "43110": "nan", "43115": "nan", @@ -88676,7 +88676,7 @@ "43185": "nan", "43190": "nan", "43195": "nan", - "43200": 0.18275, + "43200": 0.21248, "43205": "nan", "43210": "nan", "43215": "nan", @@ -88696,7 +88696,7 @@ "43285": "nan", "43290": "nan", "43295": "nan", - "43300": 0.15322, + "43300": 0.1808, "43305": "nan", "43310": "nan", "43315": "nan", @@ -88716,7 +88716,7 @@ "43385": "nan", "43390": "nan", "43395": "nan", - "43400": 0.15058, + "43400": 0.19731, "43405": "nan", "43410": "nan", "43415": "nan", @@ -88736,7 +88736,7 @@ "43485": "nan", "43490": "nan", "43495": "nan", - "43500": 0.16008, + "43500": 0.17523, "43505": "nan", "43510": "nan", "43515": "nan", @@ -88756,7 +88756,7 @@ "43585": "nan", "43590": "nan", "43595": "nan", - "43600": 0.18244, + "43600": 0.21742, "43605": "nan", "43610": "nan", "43615": "nan", @@ -88776,7 +88776,7 @@ "43685": "nan", "43690": "nan", "43695": "nan", - "43700": 0.14997, + "43700": 0.22128, "43705": "nan", "43710": "nan", "43715": "nan", @@ -88796,7 +88796,7 @@ "43785": "nan", "43790": "nan", "43795": "nan", - "43800": 0.1533, + "43800": 0.19158, "43805": "nan", "43810": "nan", "43815": "nan", @@ -88816,7 +88816,7 @@ "43885": "nan", "43890": "nan", "43895": "nan", - "43900": 0.18506, + "43900": 0.216, "43905": "nan", "43910": "nan", "43915": "nan", @@ -88836,7 +88836,7 @@ "43985": "nan", "43990": "nan", "43995": "nan", - "44000": 0.16773, + "44000": 0.20233, "44005": "nan", "44010": "nan", "44015": "nan", @@ -88856,7 +88856,7 @@ "44085": "nan", "44090": "nan", "44095": "nan", - "44100": 0.16858, + "44100": 0.21212, "44105": "nan", "44110": "nan", "44115": "nan", @@ -88876,7 +88876,7 @@ "44185": "nan", "44190": "nan", "44195": "nan", - "44200": 0.15073, + "44200": 0.21199, "44205": "nan", "44210": "nan", "44215": "nan", @@ -88896,7 +88896,7 @@ "44285": "nan", "44290": "nan", "44295": "nan", - "44300": 0.15531, + "44300": 0.20902, "44305": "nan", "44310": "nan", "44315": "nan", @@ -88916,7 +88916,7 @@ "44385": "nan", "44390": "nan", "44395": "nan", - "44400": 0.17836, + "44400": 0.18192, "44405": "nan", "44410": "nan", "44415": "nan", @@ -88936,7 +88936,7 @@ "44485": "nan", "44490": "nan", "44495": "nan", - "44500": 0.17104, + "44500": 0.19501, "44505": "nan", "44510": "nan", "44515": "nan", @@ -88956,7 +88956,7 @@ "44585": "nan", "44590": "nan", "44595": "nan", - "44600": 0.15127, + "44600": 0.17833, "44605": "nan", "44610": "nan", "44615": "nan", @@ -88976,7 +88976,7 @@ "44685": "nan", "44690": "nan", "44695": "nan", - "44700": 0.15451, + "44700": 0.19157, "44705": "nan", "44710": "nan", "44715": "nan", @@ -88996,7 +88996,7 @@ "44785": "nan", "44790": "nan", "44795": "nan", - "44800": 0.17124, + "44800": 0.19134, "44805": "nan", "44810": "nan", "44815": "nan", @@ -89016,7 +89016,7 @@ "44885": "nan", "44890": "nan", "44895": "nan", - "44900": 0.16972, + "44900": 0.23445, "44905": "nan", "44910": "nan", "44915": "nan", @@ -89036,7 +89036,7 @@ "44985": "nan", "44990": "nan", "44995": "nan", - "45000": 0.15857, + "45000": 0.19687, "45005": "nan", "45010": "nan", "45015": "nan", @@ -89056,7 +89056,7 @@ "45085": "nan", "45090": "nan", "45095": "nan", - "45100": 0.15329, + "45100": 0.20632, "45105": "nan", "45110": "nan", "45115": "nan", @@ -89076,7 +89076,7 @@ "45185": "nan", "45190": "nan", "45195": "nan", - "45200": 0.16947, + "45200": 0.18957, "45205": "nan", "45210": "nan", "45215": "nan", @@ -89096,7 +89096,7 @@ "45285": "nan", "45290": "nan", "45295": "nan", - "45300": 0.16756, + "45300": 0.19691, "45305": "nan", "45310": "nan", "45315": "nan", @@ -89116,7 +89116,7 @@ "45385": "nan", "45390": "nan", "45395": "nan", - "45400": 0.14965, + "45400": 0.19599, "45405": "nan", "45410": "nan", "45415": "nan", @@ -89136,7 +89136,7 @@ "45485": "nan", "45490": "nan", "45495": "nan", - "45500": 0.15717, + "45500": 0.1881, "45505": "nan", "45510": "nan", "45515": "nan", @@ -89156,7 +89156,7 @@ "45585": "nan", "45590": "nan", "45595": "nan", - "45600": 0.16944, + "45600": 0.22133, "45605": "nan", "45610": "nan", "45615": "nan", @@ -89176,7 +89176,7 @@ "45685": "nan", "45690": "nan", "45695": "nan", - "45700": 0.17051, + "45700": 0.2216, "45705": "nan", "45710": "nan", "45715": "nan", @@ -89196,7 +89196,7 @@ "45785": "nan", "45790": "nan", "45795": "nan", - "45800": 0.15167, + "45800": 0.19922, "45805": "nan", "45810": "nan", "45815": "nan", @@ -89216,7 +89216,7 @@ "45885": "nan", "45890": "nan", "45895": "nan", - "45900": 0.17662, + "45900": 0.1996, "45905": "nan", "45910": "nan", "45915": "nan", @@ -89236,7 +89236,7 @@ "45985": "nan", "45990": "nan", "45995": "nan", - "46000": 0.15327, + "46000": 0.2034, "46005": "nan", "46010": "nan", "46015": "nan", @@ -89256,7 +89256,7 @@ "46085": "nan", "46090": "nan", "46095": "nan", - "46100": 0.15539, + "46100": 0.22878, "46105": "nan", "46110": "nan", "46115": "nan", @@ -89276,7 +89276,7 @@ "46185": "nan", "46190": "nan", "46195": "nan", - "46200": 0.15197, + "46200": 0.18496, "46205": "nan", "46210": "nan", "46215": "nan", @@ -89296,7 +89296,7 @@ "46285": "nan", "46290": "nan", "46295": "nan", - "46300": 0.16004, + "46300": 0.18725, "46305": "nan", "46310": "nan", "46315": "nan", @@ -89316,7 +89316,7 @@ "46385": "nan", "46390": "nan", "46395": "nan", - "46400": 0.18734, + "46400": 0.19417, "46405": "nan", "46410": "nan", "46415": "nan", @@ -89336,7 +89336,7 @@ "46485": "nan", "46490": "nan", "46495": "nan", - "46500": 0.15529, + "46500": 0.19233, "46505": "nan", "46510": "nan", "46515": "nan", @@ -89356,7 +89356,7 @@ "46585": "nan", "46590": "nan", "46595": "nan", - "46600": 0.15341, + "46600": 0.19249, "46605": "nan", "46610": "nan", "46615": "nan", @@ -89376,7 +89376,7 @@ "46685": "nan", "46690": "nan", "46695": "nan", - "46700": 0.15791, + "46700": 0.18312, "46705": "nan", "46710": "nan", "46715": "nan", @@ -89396,7 +89396,7 @@ "46785": "nan", "46790": "nan", "46795": "nan", - "46800": 0.18302, + "46800": 0.22157, "46805": "nan", "46810": "nan", "46815": "nan", @@ -89416,7 +89416,7 @@ "46885": "nan", "46890": "nan", "46895": "nan", - "46900": 0.15272, + "46900": 0.21119, "46905": "nan", "46910": "nan", "46915": "nan", @@ -89436,7 +89436,7 @@ "46985": "nan", "46990": "nan", "46995": "nan", - "47000": 0.15277, + "47000": 0.21338, "47005": "nan", "47010": "nan", "47015": "nan", @@ -89456,7 +89456,7 @@ "47085": "nan", "47090": "nan", "47095": "nan", - "47100": 0.15816, + "47100": 0.19139, "47105": "nan", "47110": "nan", "47115": "nan", @@ -89476,7 +89476,7 @@ "47185": "nan", "47190": "nan", "47195": "nan", - "47200": 0.18535, + "47200": 0.18554, "47205": "nan", "47210": "nan", "47215": "nan", @@ -89496,7 +89496,7 @@ "47285": "nan", "47290": "nan", "47295": "nan", - "47300": 0.15279, + "47300": 0.2004, "47305": "nan", "47310": "nan", "47315": "nan", @@ -89516,7 +89516,7 @@ "47385": "nan", "47390": "nan", "47395": "nan", - "47400": 0.15123, + "47400": 0.21327, "47405": "nan", "47410": "nan", "47415": "nan", @@ -89536,7 +89536,7 @@ "47485": "nan", "47490": "nan", "47495": "nan", - "47500": 0.16725, + "47500": 0.18323, "47505": "nan", "47510": "nan", "47515": "nan", @@ -89556,7 +89556,7 @@ "47585": "nan", "47590": "nan", "47595": "nan", - "47600": 0.1799, + "47600": 0.22777, "47605": "nan", "47610": "nan", "47615": "nan", @@ -89576,7 +89576,7 @@ "47685": "nan", "47690": "nan", "47695": "nan", - "47700": 0.15265, + "47700": 0.21967, "47705": "nan", "47710": "nan", "47715": "nan", @@ -89596,7 +89596,7 @@ "47785": "nan", "47790": "nan", "47795": "nan", - "47800": 0.15082, + "47800": 0.19451, "47805": "nan", "47810": "nan", "47815": "nan", @@ -89616,7 +89616,7 @@ "47885": "nan", "47890": "nan", "47895": "nan", - "47900": 0.18235, + "47900": 0.2064, "47905": "nan", "47910": "nan", "47915": "nan", @@ -89636,7 +89636,7 @@ "47985": "nan", "47990": "nan", "47995": "nan", - "48000": 0.16094, + "48000": 0.20484, "48005": "nan", "48010": "nan", "48015": "nan", @@ -89656,7 +89656,7 @@ "48085": "nan", "48090": "nan", "48095": "nan", - "48100": 0.16477, + "48100": 0.22002, "48105": "nan", "48110": "nan", "48115": "nan", @@ -89676,7 +89676,7 @@ "48185": "nan", "48190": "nan", "48195": "nan", - "48200": 0.15059, + "48200": 0.18647, "48205": "nan", "48210": "nan", "48215": "nan", @@ -89696,7 +89696,7 @@ "48285": "nan", "48290": "nan", "48295": "nan", - "48300": 0.15309, + "48300": 0.21017, "48305": "nan", "48310": "nan", "48315": "nan", @@ -89716,7 +89716,7 @@ "48385": "nan", "48390": "nan", "48395": "nan", - "48400": 0.17763, + "48400": 0.2003, "48405": "nan", "48410": "nan", "48415": "nan", @@ -89736,7 +89736,7 @@ "48485": "nan", "48490": "nan", "48495": "nan", - "48500": 0.16942, + "48500": 0.19258, "48505": "nan", "48510": "nan", "48515": "nan", @@ -89756,7 +89756,7 @@ "48585": "nan", "48590": "nan", "48595": "nan", - "48600": 0.14989, + "48600": 0.1866, "48605": "nan", "48610": "nan", "48615": "nan", @@ -89776,7 +89776,7 @@ "48685": "nan", "48690": "nan", "48695": "nan", - "48700": 0.15502, + "48700": 0.19485, "48705": "nan", "48710": "nan", "48715": "nan", @@ -89796,7 +89796,7 @@ "48785": "nan", "48790": "nan", "48795": "nan", - "48800": 0.17031, + "48800": 0.20738, "48805": "nan", "48810": "nan", "48815": "nan", @@ -89816,7 +89816,7 @@ "48885": "nan", "48890": "nan", "48895": "nan", - "48900": 0.16891, + "48900": 0.20193, "48905": "nan", "48910": "nan", "48915": "nan", @@ -89836,7 +89836,7 @@ "48985": "nan", "48990": "nan", "48995": "nan", - "49000": 0.15617, + "49000": 0.21885, "49005": "nan", "49010": "nan", "49015": "nan", @@ -89856,7 +89856,7 @@ "49085": "nan", "49090": "nan", "49095": "nan", - "49100": 0.15441, + "49100": 0.2033, "49105": "nan", "49110": "nan", "49115": "nan", @@ -89876,7 +89876,7 @@ "49185": "nan", "49190": "nan", "49195": "nan", - "49200": 0.17194, + "49200": 0.19553, "49205": "nan", "49210": "nan", "49215": "nan", @@ -89896,7 +89896,7 @@ "49285": "nan", "49290": "nan", "49295": "nan", - "49300": 0.16998, + "49300": 0.19443, "49305": "nan", "49310": "nan", "49315": "nan", @@ -89916,7 +89916,7 @@ "49385": "nan", "49390": "nan", "49395": "nan", - "49400": 0.15385, + "49400": 0.19148, "49405": "nan", "49410": "nan", "49415": "nan", @@ -89936,7 +89936,7 @@ "49485": "nan", "49490": "nan", "49495": "nan", - "49500": 0.15639, + "49500": 0.18145, "49505": "nan", "49510": "nan", "49515": "nan", @@ -89956,7 +89956,7 @@ "49585": "nan", "49590": "nan", "49595": "nan", - "49600": 0.17176, + "49600": 0.22113, "49605": "nan", "49610": "nan", "49615": "nan", @@ -89976,7 +89976,7 @@ "49685": "nan", "49690": "nan", "49695": "nan", - "49700": 0.16892, + "49700": 0.20983, "49705": "nan", "49710": "nan", "49715": "nan", @@ -89996,7 +89996,7 @@ "49785": "nan", "49790": "nan", "49795": "nan", - "49800": 0.15387, + "49800": 0.20076, "49805": "nan", "49810": "nan", "49815": "nan", @@ -90016,7 +90016,7 @@ "49885": "nan", "49890": "nan", "49895": "nan", - "49900": 0.17332, + "49900": 0.20998, "49905": "nan", "49910": "nan", "49915": "nan", @@ -90036,7 +90036,7 @@ "49985": "nan", "49990": "nan", "49995": "nan", - "50000": 0.1529, + "50000": 0.21681, "50005": "nan", "50010": "nan", "50015": "nan", @@ -90056,7 +90056,7 @@ "50085": "nan", "50090": "nan", "50095": "nan", - "50100": 0.15603, + "50100": 0.20354, "50105": "nan", "50110": "nan", "50115": "nan", @@ -90076,7 +90076,7 @@ "50185": "nan", "50190": "nan", "50195": "nan", - "50200": 0.15145, + "50200": 0.2143, "50205": "nan", "50210": "nan", "50215": "nan", @@ -90096,7 +90096,7 @@ "50285": "nan", "50290": "nan", "50295": "nan", - "50300": 0.16579, + "50300": 0.19943, "50305": "nan", "50310": "nan", "50315": "nan", @@ -90116,7 +90116,7 @@ "50385": "nan", "50390": "nan", "50395": "nan", - "50400": 0.18151, + "50400": 0.18365, "50405": "nan", "50410": "nan", "50415": "nan", @@ -90136,7 +90136,7 @@ "50485": "nan", "50490": "nan", "50495": "nan", - "50500": 0.15231, + "50500": 0.18688, "50505": "nan", "50510": "nan", "50515": "nan", @@ -90156,7 +90156,7 @@ "50585": "nan", "50590": "nan", "50595": "nan", - "50600": 0.15436, + "50600": 0.20001, "50605": "nan", "50610": "nan", "50615": "nan", @@ -90176,7 +90176,7 @@ "50685": "nan", "50690": "nan", "50695": "nan", - "50700": 0.16145, + "50700": 0.17046, "50705": "nan", "50710": "nan", "50715": "nan", @@ -90196,7 +90196,7 @@ "50785": "nan", "50790": "nan", "50795": "nan", - "50800": 0.17816, + "50800": 0.18352, "50805": "nan", "50810": "nan", "50815": "nan", @@ -90216,7 +90216,7 @@ "50885": "nan", "50890": "nan", "50895": "nan", - "50900": 0.14963, + "50900": 0.223, "50905": "nan", "50910": "nan", "50915": "nan", @@ -90236,7 +90236,7 @@ "50985": "nan", "50990": "nan", "50995": "nan", - "51000": 0.15399, + "51000": 0.21276, "51005": "nan", "51010": "nan", "51015": "nan", @@ -90256,7 +90256,7 @@ "51085": "nan", "51090": "nan", "51095": "nan", - "51100": 0.16278, + "51100": 0.20126, "51105": "nan", "51110": "nan", "51115": "nan", @@ -90276,7 +90276,7 @@ "51185": "nan", "51190": "nan", "51195": "nan", - "51200": 0.17797, + "51200": 0.18675, "51205": "nan", "51210": "nan", "51215": "nan", @@ -90296,7 +90296,7 @@ "51285": "nan", "51290": "nan", "51295": "nan", - "51300": 0.15446, + "51300": 0.19048, "51305": "nan", "51310": "nan", "51315": "nan", @@ -90316,7 +90316,7 @@ "51385": "nan", "51390": "nan", "51395": "nan", - "51400": 0.1516, + "51400": 0.19545, "51405": "nan", "51410": "nan", "51415": "nan", @@ -90336,7 +90336,7 @@ "51485": "nan", "51490": "nan", "51495": "nan", - "51500": 0.16839, + "51500": 0.18405, "51505": "nan", "51510": "nan", "51515": "nan", @@ -90356,7 +90356,7 @@ "51585": "nan", "51590": "nan", "51595": "nan", - "51600": 0.17752, + "51600": 0.21784, "51605": "nan", "51610": "nan", "51615": "nan", @@ -90376,7 +90376,7 @@ "51685": "nan", "51690": "nan", "51695": "nan", - "51700": 0.1556, + "51700": 0.22797, "51705": "nan", "51710": "nan", "51715": "nan", @@ -90396,7 +90396,7 @@ "51785": "nan", "51790": "nan", "51795": "nan", - "51800": 0.15418, + "51800": 0.20089, "51805": "nan", "51810": "nan", "51815": "nan", @@ -90416,7 +90416,7 @@ "51885": "nan", "51890": "nan", "51895": "nan", - "51900": 0.18226, + "51900": 0.20316, "51905": "nan", "51910": "nan", "51915": "nan", @@ -90436,7 +90436,7 @@ "51985": "nan", "51990": "nan", "51995": "nan", - "52000": 0.16072, + "52000": 0.20826, "52005": "nan", "52010": "nan", "52015": "nan", @@ -90456,7 +90456,7 @@ "52085": "nan", "52090": "nan", "52095": "nan", - "52100": 0.16221, + "52100": 0.20269, "52105": "nan", "52110": "nan", "52115": "nan", @@ -90476,7 +90476,7 @@ "52185": "nan", "52190": "nan", "52195": "nan", - "52200": 0.15095, + "52200": 0.19574, "52205": "nan", "52210": "nan", "52215": "nan", @@ -90496,7 +90496,7 @@ "52285": "nan", "52290": "nan", "52295": "nan", - "52300": 0.16006, + "52300": 0.18746, "52305": "nan", "52310": "nan", "52315": "nan", @@ -90516,7 +90516,7 @@ "52385": "nan", "52390": "nan", "52395": "nan", - "52400": 0.18015, + "52400": 0.1893, "52405": "nan", "52410": "nan", "52415": "nan", @@ -90536,7 +90536,7 @@ "52485": "nan", "52490": "nan", "52495": "nan", - "52500": 0.16616, + "52500": 0.19746, "52505": "nan", "52510": "nan", "52515": "nan", @@ -90556,7 +90556,7 @@ "52585": "nan", "52590": "nan", "52595": "nan", - "52600": 0.15257, + "52600": 0.18797, "52605": "nan", "52610": "nan", "52615": "nan", @@ -90576,7 +90576,7 @@ "52685": "nan", "52690": "nan", "52695": "nan", - "52700": 0.1497, + "52700": 0.17978, "52705": "nan", "52710": "nan", "52715": "nan", @@ -90596,7 +90596,7 @@ "52785": "nan", "52790": "nan", "52795": "nan", - "52800": 0.18105, + "52800": 0.21054, "52805": "nan", "52810": "nan", "52815": "nan", @@ -90616,7 +90616,7 @@ "52885": "nan", "52890": "nan", "52895": "nan", - "52900": 0.16528, + "52900": 0.22386, "52905": "nan", "52910": "nan", "52915": "nan", @@ -90636,7 +90636,7 @@ "52985": "nan", "52990": "nan", "52995": "nan", - "53000": 0.15342, + "53000": 0.20457, "53005": "nan", "53010": "nan", "53015": "nan", @@ -90656,7 +90656,7 @@ "53085": "nan", "53090": "nan", "53095": "nan", - "53100": 0.15034, + "53100": 0.18768, "53105": "nan", "53110": "nan", "53115": "nan", @@ -90676,7 +90676,7 @@ "53185": "nan", "53190": "nan", "53195": "nan", - "53200": 0.17818, + "53200": 0.18721, "53205": "nan", "53210": "nan", "53215": "nan", @@ -90696,7 +90696,7 @@ "53285": "nan", "53290": "nan", "53295": "nan", - "53300": 0.16604, + "53300": 0.19524, "53305": "nan", "53310": "nan", "53315": "nan", @@ -90716,7 +90716,7 @@ "53385": "nan", "53390": "nan", "53395": "nan", - "53400": 0.15192, + "53400": 0.19894, "53405": "nan", "53410": "nan", "53415": "nan", @@ -90736,7 +90736,7 @@ "53485": "nan", "53490": "nan", "53495": "nan", - "53500": 0.1566, + "53500": 0.20589, "53505": "nan", "53510": "nan", "53515": "nan", @@ -90756,7 +90756,7 @@ "53585": "nan", "53590": "nan", "53595": "nan", - "53600": 0.18133, + "53600": 0.21563, "53605": "nan", "53610": "nan", "53615": "nan", @@ -90776,7 +90776,7 @@ "53685": "nan", "53690": "nan", "53695": "nan", - "53700": 0.16654, + "53700": 0.22556, "53705": "nan", "53710": "nan", "53715": "nan", @@ -90796,7 +90796,7 @@ "53785": "nan", "53790": "nan", "53795": "nan", - "53800": 0.15402, + "53800": 0.19132, "53805": "nan", "53810": "nan", "53815": "nan", @@ -90816,7 +90816,7 @@ "53885": "nan", "53890": "nan", "53895": "nan", - "53900": 0.1752, + "53900": 0.20526, "53905": "nan", "53910": "nan", "53915": "nan", @@ -90836,7 +90836,7 @@ "53985": "nan", "53990": "nan", "53995": "nan", - "54000": 0.15765, + "54000": 0.21847, "54005": "nan", "54010": "nan", "54015": "nan", @@ -90856,7 +90856,7 @@ "54085": "nan", "54090": "nan", "54095": "nan", - "54100": 0.15858, + "54100": 0.20972, "54105": "nan", "54110": "nan", "54115": "nan", @@ -90876,7 +90876,7 @@ "54185": "nan", "54190": "nan", "54195": "nan", - "54200": 0.15824, + "54200": 0.18632, "54205": "nan", "54210": "nan", "54215": "nan", @@ -90896,7 +90896,7 @@ "54285": "nan", "54290": "nan", "54295": "nan", - "54300": 0.15774, + "54300": 0.21361, "54305": "nan", "54310": "nan", "54315": "nan", @@ -90916,7 +90916,7 @@ "54385": "nan", "54390": "nan", "54395": "nan", - "54400": 0.1808, + "54400": 0.19982, "54405": "nan", "54410": "nan", "54415": "nan", @@ -90936,7 +90936,7 @@ "54485": "nan", "54490": "nan", "54495": "nan", - "54500": 0.15402, + "54500": 0.19212, "54505": "nan", "54510": "nan", "54515": "nan", @@ -90956,7 +90956,7 @@ "54585": "nan", "54590": "nan", "54595": "nan", - "54600": 0.15497, + "54600": 0.18383, "54605": "nan", "54610": "nan", "54615": "nan", @@ -90976,7 +90976,7 @@ "54685": "nan", "54690": "nan", "54695": "nan", - "54700": 0.15371, + "54700": 0.20072, "54705": "nan", "54710": "nan", "54715": "nan", @@ -90996,7 +90996,7 @@ "54785": "nan", "54790": "nan", "54795": "nan", - "54800": 0.17483, + "54800": 0.21002, "54805": "nan", "54810": "nan", "54815": "nan", @@ -91016,7 +91016,7 @@ "54885": "nan", "54890": "nan", "54895": "nan", - "54900": 0.15411, + "54900": 0.20876, "54905": "nan", "54910": "nan", "54915": "nan", @@ -91036,7 +91036,7 @@ "54985": "nan", "54990": "nan", "54995": "nan", - "55000": 0.15644, + "55000": 0.21114, "55005": "nan", "55010": "nan", "55015": "nan", @@ -91056,7 +91056,7 @@ "55085": "nan", "55090": "nan", "55095": "nan", - "55100": 0.15492, + "55100": 0.19844, "55105": "nan", "55110": "nan", "55115": "nan", @@ -91076,7 +91076,7 @@ "55185": "nan", "55190": "nan", "55195": "nan", - "55200": 0.17769, + "55200": 0.19167, "55205": "nan", "55210": "nan", "55215": "nan", @@ -91096,7 +91096,7 @@ "55285": "nan", "55290": "nan", "55295": "nan", - "55300": 0.1576, + "55300": 0.19784, "55305": "nan", "55310": "nan", "55315": "nan", @@ -91116,7 +91116,7 @@ "55385": "nan", "55390": "nan", "55395": "nan", - "55400": 0.15211, + "55400": 0.19368, "55405": "nan", "55410": "nan", "55415": "nan", @@ -91136,7 +91136,7 @@ "55485": "nan", "55490": "nan", "55495": "nan", - "55500": 0.15935, + "55500": 0.18485, "55505": "nan", "55510": "nan", "55515": "nan", @@ -91156,7 +91156,7 @@ "55585": "nan", "55590": "nan", "55595": "nan", - "55600": 0.17614, + "55600": 0.20715, "55605": "nan", "55610": "nan", "55615": "nan", @@ -91176,7 +91176,7 @@ "55685": "nan", "55690": "nan", "55695": "nan", - "55700": 0.15159, + "55700": 0.21234, "55705": "nan", "55710": "nan", "55715": "nan", @@ -91196,7 +91196,7 @@ "55785": "nan", "55790": "nan", "55795": "nan", - "55800": 0.15198, + "55800": 0.19749, "55805": "nan", "55810": "nan", "55815": "nan", @@ -91216,7 +91216,7 @@ "55885": "nan", "55890": "nan", "55895": "nan", - "55900": 0.16969, + "55900": 0.20923, "55905": "nan", "55910": "nan", "55915": "nan", @@ -91236,7 +91236,7 @@ "55985": "nan", "55990": "nan", "55995": "nan", - "56000": 0.16568, + "56000": 0.19953, "56005": "nan", "56010": "nan", "56015": "nan", @@ -91256,7 +91256,7 @@ "56085": "nan", "56090": "nan", "56095": "nan", - "56100": 0.16564, + "56100": 0.21456, "56105": "nan", "56110": "nan", "56115": "nan", @@ -91276,7 +91276,7 @@ "56185": "nan", "56190": "nan", "56195": "nan", - "56200": 0.15133, + "56200": 0.19768, "56205": "nan", "56210": "nan", "56215": "nan", @@ -91296,7 +91296,7 @@ "56285": "nan", "56290": "nan", "56295": "nan", - "56300": 0.15341, + "56300": 0.21493, "56305": "nan", "56310": "nan", "56315": "nan", @@ -91316,7 +91316,7 @@ "56385": "nan", "56390": "nan", "56395": "nan", - "56400": 0.16863, + "56400": 0.20311, "56405": "nan", "56410": "nan", "56415": "nan", @@ -91336,7 +91336,7 @@ "56485": "nan", "56490": "nan", "56495": "nan", - "56500": 0.1713, + "56500": 0.1913, "56505": "nan", "56510": "nan", "56515": "nan", @@ -91356,7 +91356,7 @@ "56585": "nan", "56590": "nan", "56595": "nan", - "56600": 0.15502, + "56600": 0.22814, "56605": "nan", "56610": "nan", "56615": "nan", @@ -91376,7 +91376,7 @@ "56685": "nan", "56690": "nan", "56695": "nan", - "56700": 0.1528, + "56700": 0.17806, "56705": "nan", "56710": "nan", "56715": "nan", @@ -91396,7 +91396,7 @@ "56785": "nan", "56790": "nan", "56795": "nan", - "56800": 0.17029, + "56800": 0.19448, "56805": "nan", "56810": "nan", "56815": "nan", @@ -91416,7 +91416,7 @@ "56885": "nan", "56890": "nan", "56895": "nan", - "56900": 0.16638, + "56900": 0.18231, "56905": "nan", "56910": "nan", "56915": "nan", @@ -91436,7 +91436,7 @@ "56985": "nan", "56990": "nan", "56995": "nan", - "57000": 0.15573, + "57000": 0.18603, "57005": "nan", "57010": "nan", "57015": "nan", @@ -91456,7 +91456,7 @@ "57085": "nan", "57090": "nan", "57095": "nan", - "57100": 0.15286, + "57100": 0.18946, "57105": "nan", "57110": "nan", "57115": "nan", @@ -91476,7 +91476,7 @@ "57185": "nan", "57190": "nan", "57195": "nan", - "57200": 0.17211, + "57200": 0.19383, "57205": "nan", "57210": "nan", "57215": "nan", @@ -91496,7 +91496,7 @@ "57285": "nan", "57290": "nan", "57295": "nan", - "57300": 0.16985, + "57300": 0.21829, "57305": "nan", "57310": "nan", "57315": "nan", @@ -91516,7 +91516,7 @@ "57385": "nan", "57390": "nan", "57395": "nan", - "57400": 0.15263, + "57400": 0.18408, "57405": "nan", "57410": "nan", "57415": "nan", @@ -91536,7 +91536,7 @@ "57485": "nan", "57490": "nan", "57495": "nan", - "57500": 0.1535, + "57500": 0.19825, "57505": "nan", "57510": "nan", "57515": "nan", @@ -91556,7 +91556,7 @@ "57585": "nan", "57590": "nan", "57595": "nan", - "57600": 0.17179, + "57600": 0.18758, "57605": "nan", "57610": "nan", "57615": "nan", @@ -91576,7 +91576,7 @@ "57685": "nan", "57690": "nan", "57695": "nan", - "57700": 0.16634, + "57700": 0.20207, "57705": "nan", "57710": "nan", "57715": "nan", @@ -91596,7 +91596,7 @@ "57785": "nan", "57790": "nan", "57795": "nan", - "57800": 0.15297, + "57800": 0.19611, "57805": "nan", "57810": "nan", "57815": "nan", @@ -91616,7 +91616,7 @@ "57885": "nan", "57890": "nan", "57895": "nan", - "57900": 0.1729, + "57900": 0.20767, "57905": "nan", "57910": "nan", "57915": "nan", @@ -91636,7 +91636,7 @@ "57985": "nan", "57990": "nan", "57995": "nan", - "58000": 0.15333, + "58000": 0.21857, "58005": "nan", "58010": "nan", "58015": "nan", @@ -91656,7 +91656,7 @@ "58085": "nan", "58090": "nan", "58095": "nan", - "58100": 0.15463, + "58100": 0.22602, "58105": "nan", "58110": "nan", "58115": "nan", @@ -91676,7 +91676,7 @@ "58185": "nan", "58190": "nan", "58195": "nan", - "58200": 0.15011, + "58200": 0.20415, "58205": "nan", "58210": "nan", "58215": "nan", @@ -91696,7 +91696,7 @@ "58285": "nan", "58290": "nan", "58295": "nan", - "58300": 0.15527, + "58300": 0.18488, "58305": "nan", "58310": "nan", "58315": "nan", @@ -91716,7 +91716,7 @@ "58385": "nan", "58390": "nan", "58395": "nan", - "58400": 0.18927, + "58400": 0.21369, "58405": "nan", "58410": "nan", "58415": "nan", @@ -91736,7 +91736,7 @@ "58485": "nan", "58490": "nan", "58495": "nan", - "58500": 0.15345, + "58500": 0.21292, "58505": "nan", "58510": "nan", "58515": "nan", @@ -91756,7 +91756,7 @@ "58585": "nan", "58590": "nan", "58595": "nan", - "58600": 0.15023, + "58600": 0.18231, "58605": "nan", "58610": "nan", "58615": "nan", @@ -91776,7 +91776,7 @@ "58685": "nan", "58690": "nan", "58695": "nan", - "58700": 0.15566, + "58700": 0.2033, "58705": "nan", "58710": "nan", "58715": "nan", @@ -91796,7 +91796,7 @@ "58785": "nan", "58790": "nan", "58795": "nan", - "58800": 0.19849, + "58800": 0.20517, "58805": "nan", "58810": "nan", "58815": "nan", @@ -91816,7 +91816,7 @@ "58885": "nan", "58890": "nan", "58895": "nan", - "58900": 0.15449, + "58900": 0.19355, "58905": "nan", "58910": "nan", "58915": "nan", @@ -91836,7 +91836,7 @@ "58985": "nan", "58990": "nan", "58995": "nan", - "59000": 0.15551, + "59000": 0.19494, "59005": "nan", "59010": "nan", "59015": "nan", @@ -91856,7 +91856,7 @@ "59085": "nan", "59090": "nan", "59095": "nan", - "59100": 0.15567, + "59100": 0.20443, "59105": "nan", "59110": "nan", "59115": "nan", @@ -91876,7 +91876,7 @@ "59185": "nan", "59190": "nan", "59195": "nan", - "59200": 0.186, + "59200": 0.19392, "59205": "nan", "59210": "nan", "59215": "nan", @@ -91896,7 +91896,7 @@ "59285": "nan", "59290": "nan", "59295": "nan", - "59300": 0.15085, + "59300": 0.20183, "59305": "nan", "59310": "nan", "59315": "nan", @@ -91916,7 +91916,7 @@ "59385": "nan", "59390": "nan", "59395": "nan", - "59400": 0.15479, + "59400": 0.18609, "59405": "nan", "59410": "nan", "59415": "nan", @@ -91936,7 +91936,7 @@ "59485": "nan", "59490": "nan", "59495": "nan", - "59500": 0.16291, + "59500": 0.20059, "59505": "nan", "59510": "nan", "59515": "nan", @@ -91956,7 +91956,7 @@ "59585": "nan", "59590": "nan", "59595": "nan", - "59600": 0.18199, + "59600": 0.21173, "59605": "nan", "59610": "nan", "59615": "nan", @@ -91976,7 +91976,7 @@ "59685": "nan", "59690": "nan", "59695": "nan", - "59700": 0.15076, + "59700": 0.20259, "59705": "nan", "59710": "nan", "59715": "nan", @@ -91996,7 +91996,7 @@ "59785": "nan", "59790": "nan", "59795": "nan", - "59800": 0.15322, + "59800": 0.19715, "59805": "nan", "59810": "nan", "59815": "nan", @@ -92016,7 +92016,7 @@ "59885": "nan", "59890": "nan", "59895": "nan", - "59900": 0.17786, + "59900": 0.18127, "59905": "nan", "59910": "nan", "59915": "nan", @@ -92036,7 +92036,7 @@ "59985": "nan", "59990": "nan", "59995": "nan", - "60000": 0.16591, + "60000": 0.19715, "60005": "nan", "60010": "nan", "60015": "nan", @@ -92056,7 +92056,7 @@ "60085": "nan", "60090": "nan", "60095": "nan", - "60100": 0.17121, + "60100": 0.19853, "60105": "nan", "60110": "nan", "60115": "nan", @@ -92076,7 +92076,7 @@ "60185": "nan", "60190": "nan", "60195": "nan", - "60200": 0.15216, + "60200": 0.19854, "60205": "nan", "60210": "nan", "60215": "nan", @@ -92096,7 +92096,7 @@ "60285": "nan", "60290": "nan", "60295": "nan", - "60300": 0.15402, + "60300": 0.20147, "60305": "nan", "60310": "nan", "60315": "nan", @@ -92116,7 +92116,7 @@ "60385": "nan", "60390": "nan", "60395": "nan", - "60400": 0.17998, + "60400": 0.18116, "60405": "nan", "60410": "nan", "60415": "nan", @@ -92136,7 +92136,7 @@ "60485": "nan", "60490": "nan", "60495": "nan", - "60500": 0.17442, + "60500": 0.20927, "60505": "nan", "60510": "nan", "60515": "nan", @@ -92156,7 +92156,7 @@ "60585": "nan", "60590": "nan", "60595": "nan", - "60600": 0.151, + "60600": 0.18872, "60605": "nan", "60610": "nan", "60615": "nan", @@ -92176,7 +92176,7 @@ "60685": "nan", "60690": "nan", "60695": "nan", - "60700": 0.15693, + "60700": 0.17249, "60705": "nan", "60710": "nan", "60715": "nan", @@ -92196,7 +92196,7 @@ "60785": "nan", "60790": "nan", "60795": "nan", - "60800": 0.173, + "60800": 0.20514, "60805": "nan", "60810": "nan", "60815": "nan", @@ -92216,7 +92216,7 @@ "60885": "nan", "60890": "nan", "60895": "nan", - "60900": 0.16995, + "60900": 0.19177, "60905": "nan", "60910": "nan", "60915": "nan", @@ -92236,7 +92236,7 @@ "60985": "nan", "60990": "nan", "60995": "nan", - "61000": 0.15519, + "61000": 0.20398, "61005": "nan", "61010": "nan", "61015": "nan", @@ -92256,7 +92256,7 @@ "61085": "nan", "61090": "nan", "61095": "nan", - "61100": 0.15373, + "61100": 0.19527, "61105": "nan", "61110": "nan", "61115": "nan", @@ -92276,7 +92276,7 @@ "61185": "nan", "61190": "nan", "61195": "nan", - "61200": 0.1698, + "61200": 0.20847, "61205": "nan", "61210": "nan", "61215": "nan", @@ -92296,7 +92296,7 @@ "61285": "nan", "61290": "nan", "61295": "nan", - "61300": 0.16773, + "61300": 0.18507, "61305": "nan", "61310": "nan", "61315": "nan", @@ -92316,7 +92316,7 @@ "61385": "nan", "61390": "nan", "61395": "nan", - "61400": 0.15025, + "61400": 0.19305, "61405": "nan", "61410": "nan", "61415": "nan", @@ -92336,7 +92336,7 @@ "61485": "nan", "61490": "nan", "61495": "nan", - "61500": 0.15745, + "61500": 0.19107, "61505": "nan", "61510": "nan", "61515": "nan", @@ -92356,7 +92356,7 @@ "61585": "nan", "61590": "nan", "61595": "nan", - "61600": 0.16528, + "61600": 0.20263, "61605": "nan", "61610": "nan", "61615": "nan", @@ -92376,7 +92376,7 @@ "61685": "nan", "61690": "nan", "61695": "nan", - "61700": 0.16781, + "61700": 0.22317, "61705": "nan", "61710": "nan", "61715": "nan", @@ -92396,7 +92396,7 @@ "61785": "nan", "61790": "nan", "61795": "nan", - "61800": 0.15207, + "61800": 0.18914, "61805": "nan", "61810": "nan", "61815": "nan", @@ -92416,7 +92416,7 @@ "61885": "nan", "61890": "nan", "61895": "nan", - "61900": 0.17157, + "61900": 0.19108, "61905": "nan", "61910": "nan", "61915": "nan", @@ -92436,7 +92436,7 @@ "61985": "nan", "61990": "nan", "61995": "nan", - "62000": 0.1521, + "62000": 0.21335, "62005": "nan", "62010": "nan", "62015": "nan", @@ -92456,7 +92456,7 @@ "62085": "nan", "62090": "nan", "62095": "nan", - "62100": 0.15594, + "62100": 0.20076, "62105": "nan", "62110": "nan", "62115": "nan", @@ -92476,7 +92476,7 @@ "62185": "nan", "62190": "nan", "62195": "nan", - "62200": 0.14962, + "62200": 0.19859, "62205": "nan", "62210": "nan", "62215": "nan", @@ -92496,7 +92496,7 @@ "62285": "nan", "62290": "nan", "62295": "nan", - "62300": 0.15446, + "62300": 0.1873, "62305": "nan", "62310": "nan", "62315": "nan", @@ -92516,7 +92516,7 @@ "62385": "nan", "62390": "nan", "62395": "nan", - "62400": 0.18085, + "62400": 0.19441, "62405": "nan", "62410": "nan", "62415": "nan", @@ -92536,7 +92536,7 @@ "62485": "nan", "62490": "nan", "62495": "nan", - "62500": 0.15573, + "62500": 0.19584, "62505": "nan", "62510": "nan", "62515": "nan", @@ -92556,7 +92556,7 @@ "62585": "nan", "62590": "nan", "62595": "nan", - "62600": 0.15106, + "62600": 0.18033, "62605": "nan", "62610": "nan", "62615": "nan", @@ -92576,7 +92576,7 @@ "62685": "nan", "62690": "nan", "62695": "nan", - "62700": 0.15478, + "62700": 0.1869, "62705": "nan", "62710": "nan", "62715": "nan", @@ -92596,7 +92596,7 @@ "62785": "nan", "62790": "nan", "62795": "nan", - "62800": 0.18384, + "62800": 0.2109, "62805": "nan", "62810": "nan", "62815": "nan", @@ -92616,7 +92616,7 @@ "62885": "nan", "62890": "nan", "62895": "nan", - "62900": 0.15149, + "62900": 0.19442, "62905": "nan", "62910": "nan", "62915": "nan", @@ -92636,7 +92636,7 @@ "62985": "nan", "62990": "nan", "62995": "nan", - "63000": 0.15242, + "63000": 0.19647, "63005": "nan", "63010": "nan", "63015": "nan", @@ -92656,7 +92656,7 @@ "63085": "nan", "63090": "nan", "63095": "nan", - "63100": 0.1554, + "63100": 0.20385, "63105": "nan", "63110": "nan", "63115": "nan", @@ -92676,7 +92676,7 @@ "63185": "nan", "63190": "nan", "63195": "nan", - "63200": 0.18854, + "63200": 0.18398, "63205": "nan", "63210": "nan", "63215": "nan", @@ -92696,7 +92696,7 @@ "63285": "nan", "63290": "nan", "63295": "nan", - "63300": 0.15133, + "63300": 0.19057, "63305": "nan", "63310": "nan", "63315": "nan", @@ -92716,7 +92716,7 @@ "63385": "nan", "63390": "nan", "63395": "nan", - "63400": 0.15139, + "63400": 0.20945, "63405": "nan", "63410": "nan", "63415": "nan", @@ -92736,7 +92736,7 @@ "63485": "nan", "63490": "nan", "63495": "nan", - "63500": 0.15913, + "63500": 0.21774, "63505": "nan", "63510": "nan", "63515": "nan", @@ -92756,7 +92756,7 @@ "63585": "nan", "63590": "nan", "63595": "nan", - "63600": 0.18966, + "63600": 0.20327, "63605": "nan", "63610": "nan", "63615": "nan", @@ -92776,7 +92776,7 @@ "63685": "nan", "63690": "nan", "63695": "nan", - "63700": 0.1542, + "63700": 0.18916, "63705": "nan", "63710": "nan", "63715": "nan", @@ -92796,7 +92796,7 @@ "63785": "nan", "63790": "nan", "63795": "nan", - "63800": 0.1546, + "63800": 0.18167, "63805": "nan", "63810": "nan", "63815": "nan", @@ -92816,7 +92816,7 @@ "63885": "nan", "63890": "nan", "63895": "nan", - "63900": 0.18192, + "63900": 0.19806, "63905": "nan", "63910": "nan", "63915": "nan", @@ -92836,7 +92836,7 @@ "63985": "nan", "63990": "nan", "63995": "nan", - "64000": 0.16875, + "64000": 0.19379, "64005": "nan", "64010": "nan", "64015": "nan", @@ -92856,7 +92856,7 @@ "64085": "nan", "64090": "nan", "64095": "nan", - "64100": 0.16156, + "64100": 0.18587, "64105": "nan", "64110": "nan", "64115": "nan", @@ -92876,7 +92876,7 @@ "64185": "nan", "64190": "nan", "64195": "nan", - "64200": 0.15114, + "64200": 0.18889, "64205": "nan", "64210": "nan", "64215": "nan", @@ -92896,7 +92896,7 @@ "64285": "nan", "64290": "nan", "64295": "nan", - "64300": 0.14947, + "64300": 0.18811, "64305": "nan", "64310": "nan", "64315": "nan", @@ -92916,7 +92916,7 @@ "64385": "nan", "64390": "nan", "64395": "nan", - "64400": 0.17629, + "64400": 0.20031, "64405": "nan", "64410": "nan", "64415": "nan", @@ -92936,7 +92936,7 @@ "64485": "nan", "64490": "nan", "64495": "nan", - "64500": 0.16671, + "64500": 0.18512, "64505": "nan", "64510": "nan", "64515": "nan", @@ -92956,7 +92956,7 @@ "64585": "nan", "64590": "nan", "64595": "nan", - "64600": 0.15358, + "64600": 0.18547, "64605": "nan", "64610": "nan", "64615": "nan", @@ -92976,7 +92976,7 @@ "64685": "nan", "64690": "nan", "64695": "nan", - "64700": 0.15231, + "64700": 0.19437, "64705": "nan", "64710": "nan", "64715": "nan", @@ -92996,7 +92996,7 @@ "64785": "nan", "64790": "nan", "64795": "nan", - "64800": 0.17696, + "64800": 0.21992, "64805": "nan", "64810": "nan", "64815": "nan", @@ -93016,7 +93016,7 @@ "64885": "nan", "64890": "nan", "64895": "nan", - "64900": 0.16406, + "64900": 0.19982, "64905": "nan", "64910": "nan", "64915": "nan", @@ -93036,7 +93036,7 @@ "64985": "nan", "64990": "nan", "64995": "nan", - "65000": 0.15254, + "65000": 0.20462, "65005": "nan", "65010": "nan", "65015": "nan", @@ -93056,7 +93056,7 @@ "65085": "nan", "65090": "nan", "65095": "nan", - "65100": 0.14976, + "65100": 0.19905, "65105": "nan", "65110": "nan", "65115": "nan", @@ -93076,7 +93076,7 @@ "65185": "nan", "65190": "nan", "65195": "nan", - "65200": 0.17237, + "65200": 0.18746, "65205": "nan", "65210": "nan", "65215": "nan", @@ -93096,7 +93096,7 @@ "65285": "nan", "65290": "nan", "65295": "nan", - "65300": 0.16694, + "65300": 0.20003, "65305": "nan", "65310": "nan", "65315": "nan", @@ -93116,7 +93116,7 @@ "65385": "nan", "65390": "nan", "65395": "nan", - "65400": 0.1523, + "65400": 0.18844, "65405": "nan", "65410": "nan", "65415": "nan", @@ -93136,7 +93136,7 @@ "65485": "nan", "65490": "nan", "65495": "nan", - "65500": 0.15813, + "65500": 0.2006, "65505": "nan", "65510": "nan", "65515": "nan", @@ -93156,7 +93156,7 @@ "65585": "nan", "65590": "nan", "65595": "nan", - "65600": 0.17353, + "65600": 0.20895, "65605": "nan", "65610": "nan", "65615": "nan", @@ -93176,7 +93176,7 @@ "65685": "nan", "65690": "nan", "65695": "nan", - "65700": 0.17058, + "65700": 0.21703, "65705": "nan", "65710": "nan", "65715": "nan", @@ -93196,7 +93196,7 @@ "65785": "nan", "65790": "nan", "65795": "nan", - "65800": 0.15268, + "65800": 0.17779, "65805": "nan", "65810": "nan", "65815": "nan", @@ -93216,7 +93216,7 @@ "65885": "nan", "65890": "nan", "65895": "nan", - "65900": 0.17633, + "65900": 0.19885, "65905": "nan", "65910": "nan", "65915": "nan", @@ -93236,7 +93236,7 @@ "65985": "nan", "65990": "nan", "65995": "nan", - "66000": 0.15463, + "66000": 0.19535, "66005": "nan", "66010": "nan", "66015": "nan", @@ -93256,7 +93256,7 @@ "66085": "nan", "66090": "nan", "66095": "nan", - "66100": 0.15323, + "66100": 0.212, "66105": "nan", "66110": "nan", "66115": "nan", @@ -93276,7 +93276,7 @@ "66185": "nan", "66190": "nan", "66195": "nan", - "66200": 0.15134, + "66200": 0.19181, "66205": "nan", "66210": "nan", "66215": "nan", @@ -93296,7 +93296,7 @@ "66285": "nan", "66290": "nan", "66295": "nan", - "66300": 0.16316, + "66300": 0.19787, "66305": "nan", "66310": "nan", "66315": "nan", @@ -93316,7 +93316,7 @@ "66385": "nan", "66390": "nan", "66395": "nan", - "66400": 0.18663, + "66400": 0.19596, "66405": "nan", "66410": "nan", "66415": "nan", @@ -93336,7 +93336,7 @@ "66485": "nan", "66490": "nan", "66495": "nan", - "66500": 0.15592, + "66500": 0.19141, "66505": "nan", "66510": "nan", "66515": "nan", @@ -93356,7 +93356,7 @@ "66585": "nan", "66590": "nan", "66595": "nan", - "66600": 0.15222, + "66600": 0.1958, "66605": "nan", "66610": "nan", "66615": "nan", @@ -93376,7 +93376,7 @@ "66685": "nan", "66690": "nan", "66695": "nan", - "66700": 0.16092, + "66700": 0.19071, "66705": "nan", "66710": "nan", "66715": "nan", @@ -93396,7 +93396,7 @@ "66785": "nan", "66790": "nan", "66795": "nan", - "66800": 0.18124, + "66800": 0.21095, "66805": "nan", "66810": "nan", "66815": "nan", @@ -93416,7 +93416,7 @@ "66885": "nan", "66890": "nan", "66895": "nan", - "66900": 0.15111, + "66900": 0.20036, "66905": "nan", "66910": "nan", "66915": "nan", @@ -93436,7 +93436,7 @@ "66985": "nan", "66990": "nan", "66995": "nan", - "67000": 0.16125, + "67000": 0.19569, "67005": "nan", "67010": "nan", "67015": "nan", @@ -93456,7 +93456,7 @@ "67085": "nan", "67090": "nan", "67095": "nan", - "67100": 0.15682, + "67100": 0.19174, "67105": "nan", "67110": "nan", "67115": "nan", @@ -93476,7 +93476,7 @@ "67185": "nan", "67190": "nan", "67195": "nan", - "67200": 0.18386, + "67200": 0.18486, "67205": "nan", "67210": "nan", "67215": "nan", @@ -93496,7 +93496,7 @@ "67285": "nan", "67290": "nan", "67295": "nan", - "67300": 0.15048, + "67300": 0.1858, "67305": "nan", "67310": "nan", "67315": "nan", @@ -93516,7 +93516,7 @@ "67385": "nan", "67390": "nan", "67395": "nan", - "67400": 0.15343, + "67400": 0.20445, "67405": "nan", "67410": "nan", "67415": "nan", @@ -93536,7 +93536,7 @@ "67485": "nan", "67490": "nan", "67495": "nan", - "67500": 0.16299, + "67500": 0.18093, "67505": "nan", "67510": "nan", "67515": "nan", @@ -93556,7 +93556,7 @@ "67585": "nan", "67590": "nan", "67595": "nan", - "67600": 0.18905, + "67600": 0.23021, "67605": "nan", "67610": "nan", "67615": "nan", @@ -93576,7 +93576,7 @@ "67685": "nan", "67690": "nan", "67695": "nan", - "67700": 0.14952, + "67700": 0.18395, "67705": "nan", "67710": "nan", "67715": "nan", @@ -93596,7 +93596,7 @@ "67785": "nan", "67790": "nan", "67795": "nan", - "67800": 0.15283, + "67800": 0.19929, "67805": "nan", "67810": "nan", "67815": "nan", @@ -93616,7 +93616,7 @@ "67885": "nan", "67890": "nan", "67895": "nan", - "67900": 0.17548, + "67900": 0.17655, "67905": "nan", "67910": "nan", "67915": "nan", @@ -93636,7 +93636,7 @@ "67985": "nan", "67990": "nan", "67995": "nan", - "68000": 0.16415, + "68000": 0.21782, "68005": "nan", "68010": "nan", "68015": "nan", @@ -93656,7 +93656,7 @@ "68085": "nan", "68090": "nan", "68095": "nan", - "68100": 0.16368, + "68100": 0.22902, "68105": "nan", "68110": "nan", "68115": "nan", @@ -93676,7 +93676,7 @@ "68185": "nan", "68190": "nan", "68195": "nan", - "68200": 0.14952, + "68200": 0.17988, "68205": "nan", "68210": "nan", "68215": "nan", @@ -93696,7 +93696,7 @@ "68285": "nan", "68290": "nan", "68295": "nan", - "68300": 0.15143, + "68300": 0.17286, "68305": "nan", "68310": "nan", "68315": "nan", @@ -93716,7 +93716,7 @@ "68385": "nan", "68390": "nan", "68395": "nan", - "68400": 0.17805, + "68400": 0.20365, "68405": "nan", "68410": "nan", "68415": "nan", @@ -93736,7 +93736,7 @@ "68485": "nan", "68490": "nan", "68495": "nan", - "68500": 0.16235, + "68500": 0.20051, "68505": "nan", "68510": "nan", "68515": "nan", @@ -93756,7 +93756,7 @@ "68585": "nan", "68590": "nan", "68595": "nan", - "68600": 0.15032, + "68600": 0.18916, "68605": "nan", "68610": "nan", "68615": "nan", @@ -93776,7 +93776,7 @@ "68685": "nan", "68690": "nan", "68695": "nan", - "68700": 0.15249, + "68700": 0.17917, "68705": "nan", "68710": "nan", "68715": "nan", @@ -93796,7 +93796,7 @@ "68785": "nan", "68790": "nan", "68795": "nan", - "68800": 0.17254, + "68800": 0.22765, "68805": "nan", "68810": "nan", "68815": "nan", @@ -93816,7 +93816,7 @@ "68885": "nan", "68890": "nan", "68895": "nan", - "68900": 0.16589, + "68900": 0.21745, "68905": "nan", "68910": "nan", "68915": "nan", @@ -93836,7 +93836,7 @@ "68985": "nan", "68990": "nan", "68995": "nan", - "69000": 0.1541, + "69000": 0.16887, "69005": "nan", "69010": "nan", "69015": "nan", @@ -93856,7 +93856,7 @@ "69085": "nan", "69090": "nan", "69095": "nan", - "69100": 0.14878, + "69100": 0.20866, "69105": "nan", "69110": "nan", "69115": "nan", @@ -93876,7 +93876,7 @@ "69185": "nan", "69190": "nan", "69195": "nan", - "69200": 0.17134, + "69200": 0.19842, "69205": "nan", "69210": "nan", "69215": "nan", @@ -93896,7 +93896,7 @@ "69285": "nan", "69290": "nan", "69295": "nan", - "69300": 0.16138, + "69300": 0.18601, "69305": "nan", "69310": "nan", "69315": "nan", @@ -93916,7 +93916,7 @@ "69385": "nan", "69390": "nan", "69395": "nan", - "69400": 0.15042, + "69400": 0.19171, "69405": "nan", "69410": "nan", "69415": "nan", @@ -93936,7 +93936,7 @@ "69485": "nan", "69490": "nan", "69495": "nan", - "69500": 0.15418, + "69500": 0.19464, "69505": "nan", "69510": "nan", "69515": "nan", @@ -93956,7 +93956,7 @@ "69585": "nan", "69590": "nan", "69595": "nan", - "69600": 0.18896, + "69600": 0.20657, "69605": "nan", "69610": "nan", "69615": "nan", @@ -93976,7 +93976,7 @@ "69685": "nan", "69690": "nan", "69695": "nan", - "69700": 0.31926, + "69700": 0.19336, "69705": "nan", "69710": "nan", "69715": "nan", @@ -93996,7 +93996,7 @@ "69785": "nan", "69790": "nan", "69795": "nan", - "69800": 0.15283, + "69800": 0.19146, "69805": "nan", "69810": "nan", "69815": "nan", @@ -94016,7 +94016,7 @@ "69885": "nan", "69890": "nan", "69895": "nan", - "69900": 0.16968, + "69900": 0.17408, "69905": "nan", "69910": "nan", "69915": "nan", @@ -94036,7 +94036,7 @@ "69985": "nan", "69990": "nan", "69995": "nan", - "70000": 0.15394, + "70000": 0.20878, "70005": "nan", "70010": "nan", "70015": "nan", @@ -94056,7 +94056,7 @@ "70085": "nan", "70090": "nan", "70095": "nan", - "70100": 0.15532, + "70100": 0.20464, "70105": "nan", "70110": "nan", "70115": "nan", @@ -94076,7 +94076,7 @@ "70185": "nan", "70190": "nan", "70195": "nan", - "70200": 0.14936, + "70200": 0.18608, "70205": "nan", "70210": "nan", "70215": "nan", @@ -94096,7 +94096,7 @@ "70285": "nan", "70290": "nan", "70295": "nan", - "70300": 0.15375, + "70300": 0.17651, "70305": "nan", "70310": "nan", "70315": "nan", @@ -94116,7 +94116,7 @@ "70385": "nan", "70390": "nan", "70395": "nan", - "70400": 0.18291, + "70400": 0.18443, "70405": "nan", "70410": "nan", "70415": "nan", @@ -94136,7 +94136,7 @@ "70485": "nan", "70490": "nan", "70495": "nan", - "70500": 0.15172, + "70500": 0.20083, "70505": "nan", "70510": "nan", "70515": "nan", @@ -94156,7 +94156,7 @@ "70585": "nan", "70590": "nan", "70595": "nan", - "70600": 0.14905, + "70600": 0.18184, "70605": "nan", "70610": "nan", "70615": "nan", @@ -94176,7 +94176,7 @@ "70685": "nan", "70690": "nan", "70695": "nan", - "70700": 0.15349, + "70700": 0.17939, "70705": "nan", "70710": "nan", "70715": "nan", @@ -94196,7 +94196,7 @@ "70785": "nan", "70790": "nan", "70795": "nan", - "70800": 0.18363, + "70800": 0.26139, "70805": "nan", "70810": "nan", "70815": "nan", @@ -94216,7 +94216,7 @@ "70885": "nan", "70890": "nan", "70895": "nan", - "70900": 0.14835, + "70900": 0.1837, "70905": "nan", "70910": "nan", "70915": "nan", @@ -94236,7 +94236,7 @@ "70985": "nan", "70990": "nan", "70995": "nan", - "71000": 0.15877, + "71000": 0.17841, "71005": "nan", "71010": "nan", "71015": "nan", @@ -94256,7 +94256,7 @@ "71085": "nan", "71090": "nan", "71095": "nan", - "71100": 0.19136, + "71100": 0.20198, "71105": "nan", "71110": "nan", "71115": "nan", @@ -94276,7 +94276,7 @@ "71185": "nan", "71190": "nan", "71195": "nan", - "71200": 0.18428, + "71200": 0.1872, "71205": "nan", "71210": "nan", "71215": "nan", @@ -94296,7 +94296,7 @@ "71285": "nan", "71290": "nan", "71295": "nan", - "71300": 0.14854, + "71300": 0.18148, "71305": "nan", "71310": "nan", "71315": "nan", @@ -94316,7 +94316,7 @@ "71385": "nan", "71390": "nan", "71395": "nan", - "71400": 0.15244, + "71400": 0.1977, "71405": "nan", "71410": "nan", "71415": "nan", @@ -94336,7 +94336,7 @@ "71485": "nan", "71490": "nan", "71495": "nan", - "71500": 0.15811, + "71500": 0.2081, "71505": "nan", "71510": "nan", "71515": "nan", @@ -94356,7 +94356,7 @@ "71585": "nan", "71590": "nan", "71595": "nan", - "71600": 0.1826, + "71600": 0.22408, "71605": "nan", "71610": "nan", "71615": "nan", @@ -94376,7 +94376,7 @@ "71685": "nan", "71690": "nan", "71695": "nan", - "71700": 0.14892, + "71700": 0.18082, "71705": "nan", "71710": "nan", "71715": "nan", @@ -94396,7 +94396,7 @@ "71785": "nan", "71790": "nan", "71795": "nan", - "71800": 0.15261, + "71800": 0.19741, "71805": "nan", "71810": "nan", "71815": "nan", @@ -94416,7 +94416,7 @@ "71885": "nan", "71890": "nan", "71895": "nan", - "71900": 0.17961, + "71900": 0.19568, "71905": "nan", "71910": "nan", "71915": "nan", @@ -94436,7 +94436,7 @@ "71985": "nan", "71990": "nan", "71995": "nan", - "72000": 0.16477, + "72000": 0.19264, "72005": "nan", "72010": "nan", "72015": "nan", @@ -94456,7 +94456,7 @@ "72085": "nan", "72090": "nan", "72095": "nan", - "72100": 0.16671, + "72100": 0.21009, "72105": "nan", "72110": "nan", "72115": "nan", @@ -94476,7 +94476,7 @@ "72185": "nan", "72190": "nan", "72195": "nan", - "72200": 0.15267, + "72200": 0.18215, "72205": "nan", "72210": "nan", "72215": "nan", @@ -94496,7 +94496,7 @@ "72285": "nan", "72290": "nan", "72295": "nan", - "72300": 0.1533, + "72300": 0.18524, "72305": "nan", "72310": "nan", "72315": "nan", @@ -94516,7 +94516,7 @@ "72385": "nan", "72390": "nan", "72395": "nan", - "72400": 0.18111, + "72400": 0.20299, "72405": "nan", "72410": "nan", "72415": "nan", @@ -94536,7 +94536,7 @@ "72485": "nan", "72490": "nan", "72495": "nan", - "72500": 0.16763, + "72500": 0.20848, "72505": "nan", "72510": "nan", "72515": "nan", @@ -94556,7 +94556,7 @@ "72585": "nan", "72590": "nan", "72595": "nan", - "72600": 0.15115, + "72600": 0.1786, "72605": "nan", "72610": "nan", "72615": "nan", @@ -94576,7 +94576,7 @@ "72685": "nan", "72690": "nan", "72695": "nan", - "72700": 0.15127, + "72700": 0.17886, "72705": "nan", "72710": "nan", "72715": "nan", @@ -94596,7 +94596,7 @@ "72785": "nan", "72790": "nan", "72795": "nan", - "72800": 0.17342, + "72800": 0.21825, "72805": "nan", "72810": "nan", "72815": "nan", @@ -94616,7 +94616,7 @@ "72885": "nan", "72890": "nan", "72895": "nan", - "72900": 0.166, + "72900": 0.19517, "72905": "nan", "72910": "nan", "72915": "nan", @@ -94636,7 +94636,7 @@ "72985": "nan", "72990": "nan", "72995": "nan", - "73000": 0.15166, + "73000": 0.19702, "73005": "nan", "73010": "nan", "73015": "nan", @@ -94656,7 +94656,7 @@ "73085": "nan", "73090": "nan", "73095": "nan", - "73100": 0.15099, + "73100": 0.18073, "73105": "nan", "73110": "nan", "73115": "nan", @@ -94676,7 +94676,7 @@ "73185": "nan", "73190": "nan", "73195": "nan", - "73200": 0.17308, + "73200": 0.21513, "73205": "nan", "73210": "nan", "73215": "nan", @@ -94696,7 +94696,7 @@ "73285": "nan", "73290": "nan", "73295": "nan", - "73300": 0.16661, + "73300": 0.18503, "73305": "nan", "73310": "nan", "73315": "nan", @@ -94716,7 +94716,7 @@ "73385": "nan", "73390": "nan", "73395": "nan", - "73400": 0.14965, + "73400": 0.18261, "73405": "nan", "73410": "nan", "73415": "nan", @@ -94736,7 +94736,7 @@ "73485": "nan", "73490": "nan", "73495": "nan", - "73500": 0.15715, + "73500": 0.20597, "73505": "nan", "73510": "nan", "73515": "nan", @@ -94756,7 +94756,7 @@ "73585": "nan", "73590": "nan", "73595": "nan", - "73600": 0.1678, + "73600": 0.22125, "73605": "nan", "73610": "nan", "73615": "nan", @@ -94776,7 +94776,7 @@ "73685": "nan", "73690": "nan", "73695": "nan", - "73700": 0.16851, + "73700": 0.19344, "73705": "nan", "73710": "nan", "73715": "nan", @@ -94796,7 +94796,7 @@ "73785": "nan", "73790": "nan", "73795": "nan", - "73800": 0.15177, + "73800": 0.1873, "73805": "nan", "73810": "nan", "73815": "nan", @@ -94816,7 +94816,7 @@ "73885": "nan", "73890": "nan", "73895": "nan", - "73900": 0.1703, + "73900": 0.19419, "73905": "nan", "73910": "nan", "73915": "nan", @@ -94836,7 +94836,7 @@ "73985": "nan", "73990": "nan", "73995": "nan", - "74000": 0.15146, + "74000": 0.20607, "74005": "nan", "74010": "nan", "74015": "nan", @@ -94856,7 +94856,7 @@ "74085": "nan", "74090": "nan", "74095": "nan", - "74100": 0.15684, + "74100": 0.20011, "74105": "nan", "74110": "nan", "74115": "nan", @@ -94876,7 +94876,7 @@ "74185": "nan", "74190": "nan", "74195": "nan", - "74200": 0.14948, + "74200": 0.19427, "74205": "nan", "74210": "nan", "74215": "nan", @@ -94896,7 +94896,7 @@ "74285": "nan", "74290": "nan", "74295": "nan", - "74300": 0.15896, + "74300": 0.1782, "74305": "nan", "74310": "nan", "74315": "nan", @@ -94916,7 +94916,7 @@ "74385": "nan", "74390": "nan", "74395": "nan", - "74400": 0.18242, + "74400": 0.19844, "74405": "nan", "74410": "nan", "74415": "nan", @@ -94936,7 +94936,7 @@ "74485": "nan", "74490": "nan", "74495": "nan", - "74500": 0.15431, + "74500": 0.1913, "74505": "nan", "74510": "nan", "74515": "nan", @@ -94956,7 +94956,7 @@ "74585": "nan", "74590": "nan", "74595": "nan", - "74600": 0.14947, + "74600": 0.1807, "74605": "nan", "74610": "nan", "74615": "nan", @@ -94976,7 +94976,7 @@ "74685": "nan", "74690": "nan", "74695": "nan", - "74700": 0.1599, + "74700": 0.182, "74705": "nan", "74710": "nan", "74715": "nan", @@ -94996,7 +94996,7 @@ "74785": "nan", "74790": "nan", "74795": "nan", - "74800": 0.18411, + "74800": 0.21644, "74805": "nan", "74810": "nan", "74815": "nan", @@ -95016,7 +95016,7 @@ "74885": "nan", "74890": "nan", "74895": "nan", - "74900": 0.14925, + "74900": 0.20567, "74905": "nan", "74910": "nan", "74915": "nan", @@ -95036,7 +95036,7 @@ "74985": "nan", "74990": "nan", "74995": "nan", - "75000": 0.153, + "75000": 0.20281, "75005": "nan", "75010": "nan", "75015": "nan", @@ -95056,7 +95056,7 @@ "75085": "nan", "75090": "nan", "75095": "nan", - "75100": 0.15911, + "75100": 0.18803, "75105": "nan", "75110": "nan", "75115": "nan", @@ -95076,7 +95076,7 @@ "75185": "nan", "75190": "nan", "75195": "nan", - "75200": 0.18582, + "75200": 0.19912, "75205": "nan", "75210": "nan", "75215": "nan", @@ -95096,7 +95096,7 @@ "75285": "nan", "75290": "nan", "75295": "nan", - "75300": 0.14969, + "75300": 0.1865, "75305": "nan", "75310": "nan", "75315": "nan", @@ -95116,7 +95116,7 @@ "75385": "nan", "75390": "nan", "75395": "nan", - "75400": 0.15128, + "75400": 0.19919, "75405": "nan", "75410": "nan", "75415": "nan", @@ -95136,7 +95136,7 @@ "75485": "nan", "75490": "nan", "75495": "nan", - "75500": 0.18805, + "75500": 0.20285, "75505": "nan", "75510": "nan", "75515": "nan", @@ -95156,7 +95156,7 @@ "75585": "nan", "75590": "nan", "75595": "nan", - "75600": 0.17696, + "75600": 0.21169, "75605": "nan", "75610": "nan", "75615": "nan", @@ -95176,7 +95176,7 @@ "75685": "nan", "75690": "nan", "75695": "nan", - "75700": 0.14954, + "75700": 0.1961, "75705": "nan", "75710": "nan", "75715": "nan", @@ -95196,7 +95196,7 @@ "75785": "nan", "75790": "nan", "75795": "nan", - "75800": 0.14853, + "75800": 0.1885, "75805": "nan", "75810": "nan", "75815": "nan", @@ -95216,7 +95216,7 @@ "75885": "nan", "75890": "nan", "75895": "nan", - "75900": 0.1797, + "75900": 0.20838, "75905": "nan", "75910": "nan", "75915": "nan", @@ -95236,7 +95236,7 @@ "75985": "nan", "75990": "nan", "75995": "nan", - "76000": 0.16379, + "76000": 0.1847, "76005": "nan", "76010": "nan", "76015": "nan", @@ -95256,7 +95256,7 @@ "76085": "nan", "76090": "nan", "76095": "nan", - "76100": 0.16627, + "76100": 0.31789, "76105": "nan", "76110": "nan", "76115": "nan", @@ -95276,7 +95276,7 @@ "76185": "nan", "76190": "nan", "76195": "nan", - "76200": 0.15137, + "76200": 0.17246, "76205": "nan", "76210": "nan", "76215": "nan", @@ -95296,7 +95296,7 @@ "76285": "nan", "76290": "nan", "76295": "nan", - "76300": 0.14816, + "76300": 0.20762, "76305": "nan", "76310": "nan", "76315": "nan", @@ -95316,7 +95316,7 @@ "76385": "nan", "76390": "nan", "76395": "nan", - "76400": 0.18263, + "76400": 0.20049, "76405": "nan", "76410": "nan", "76415": "nan", @@ -95336,7 +95336,7 @@ "76485": "nan", "76490": "nan", "76495": "nan", - "76500": 0.16538, + "76500": 0.20003, "76505": "nan", "76510": "nan", "76515": "nan", @@ -95356,7 +95356,7 @@ "76585": "nan", "76590": "nan", "76595": "nan", - "76600": 0.15301, + "76600": 0.1841, "76605": "nan", "76610": "nan", "76615": "nan", @@ -95376,7 +95376,7 @@ "76685": "nan", "76690": "nan", "76695": "nan", - "76700": 0.14832, + "76700": 0.19898, "76705": "nan", "76710": "nan", "76715": "nan", @@ -95396,7 +95396,7 @@ "76785": "nan", "76790": "nan", "76795": "nan", - "76800": 0.17889, + "76800": 0.18544, "76805": "nan", "76810": "nan", "76815": "nan", @@ -95416,7 +95416,7 @@ "76885": "nan", "76890": "nan", "76895": "nan", - "76900": 0.16352, + "76900": 0.20576, "76905": "nan", "76910": "nan", "76915": "nan", @@ -95436,7 +95436,7 @@ "76985": "nan", "76990": "nan", "76995": "nan", - "77000": 0.15162, + "77000": 0.17949, "77005": "nan", "77010": "nan", "77015": "nan", @@ -95456,7 +95456,7 @@ "77085": "nan", "77090": "nan", "77095": "nan", - "77100": 0.14869, + "77100": 0.19104, "77105": "nan", "77110": "nan", "77115": "nan", @@ -95476,7 +95476,7 @@ "77185": "nan", "77190": "nan", "77195": "nan", - "77200": 0.17278, + "77200": 0.21376, "77205": "nan", "77210": "nan", "77215": "nan", @@ -95496,7 +95496,7 @@ "77285": "nan", "77290": "nan", "77295": "nan", - "77300": 0.16583, + "77300": 0.2083, "77305": "nan", "77310": "nan", "77315": "nan", @@ -95516,7 +95516,7 @@ "77385": "nan", "77390": "nan", "77395": "nan", - "77400": 0.14878, + "77400": 0.18542, "77405": "nan", "77410": "nan", "77415": "nan", @@ -95536,7 +95536,7 @@ "77485": "nan", "77490": "nan", "77495": "nan", - "77500": 0.15529, + "77500": 0.17212, "77505": "nan", "77510": "nan", "77515": "nan", @@ -95556,7 +95556,7 @@ "77585": "nan", "77590": "nan", "77595": "nan", - "77600": 0.16882, + "77600": 0.19793, "77605": "nan", "77610": "nan", "77615": "nan", @@ -95576,7 +95576,7 @@ "77685": "nan", "77690": "nan", "77695": "nan", - "77700": 0.16609, + "77700": 0.20663, "77705": "nan", "77710": "nan", "77715": "nan", @@ -95596,7 +95596,7 @@ "77785": "nan", "77790": "nan", "77795": "nan", - "77800": 0.1494, + "77800": 0.18248, "77805": "nan", "77810": "nan", "77815": "nan", @@ -95616,7 +95616,7 @@ "77885": "nan", "77890": "nan", "77895": "nan", - "77900": 0.17592, + "77900": 0.19244, "77905": "nan", "77910": "nan", "77915": "nan", @@ -95636,7 +95636,7 @@ "77985": "nan", "77990": "nan", "77995": "nan", - "78000": 0.15203, + "78000": 0.20619, "78005": "nan", "78010": "nan", "78015": "nan", @@ -95656,7 +95656,7 @@ "78085": "nan", "78090": "nan", "78095": "nan", - "78100": 0.155, + "78100": 0.21509, "78105": "nan", "78110": "nan", "78115": "nan", @@ -95676,7 +95676,7 @@ "78185": "nan", "78190": "nan", "78195": "nan", - "78200": 0.14984, + "78200": 0.20854, "78205": "nan", "78210": "nan", "78215": "nan", @@ -95696,7 +95696,7 @@ "78285": "nan", "78290": "nan", "78295": "nan", - "78300": 0.15322, + "78300": 0.17541, "78305": "nan", "78310": "nan", "78315": "nan", @@ -95716,7 +95716,7 @@ "78385": "nan", "78390": "nan", "78395": "nan", - "78400": 0.18189, + "78400": 0.20092, "78405": "nan", "78410": "nan", "78415": "nan", @@ -95736,7 +95736,7 @@ "78485": "nan", "78490": "nan", "78495": "nan", - "78500": 0.15146, + "78500": 0.20821, "78505": "nan", "78510": "nan", "78515": "nan", @@ -95756,7 +95756,7 @@ "78585": "nan", "78590": "nan", "78595": "nan", - "78600": 0.15252, + "78600": 0.19807, "78605": "nan", "78610": "nan", "78615": "nan", @@ -95776,7 +95776,7 @@ "78685": "nan", "78690": "nan", "78695": "nan", - "78700": 0.15605, + "78700": 0.1984, "78705": "nan", "78710": "nan", "78715": "nan", @@ -95796,7 +95796,7 @@ "78785": "nan", "78790": "nan", "78795": "nan", - "78800": 0.17936, + "78800": 0.20921, "78805": "nan", "78810": "nan", "78815": "nan", @@ -95816,7 +95816,7 @@ "78885": "nan", "78890": "nan", "78895": "nan", - "78900": 0.14985, + "78900": 0.19334, "78905": "nan", "78910": "nan", "78915": "nan", @@ -95836,7 +95836,7 @@ "78985": "nan", "78990": "nan", "78995": "nan", - "79000": 0.15369, + "79000": 0.20098, "79005": "nan", "79010": "nan", "79015": "nan", @@ -95856,7 +95856,7 @@ "79085": "nan", "79090": "nan", "79095": "nan", - "79100": 0.15197, + "79100": 0.20221, "79105": "nan", "79110": "nan", "79115": "nan", @@ -95876,7 +95876,7 @@ "79185": "nan", "79190": "nan", "79195": "nan", - "79200": 0.1849, + "79200": 0.18478, "79205": "nan", "79210": "nan", "79215": "nan", @@ -95896,7 +95896,7 @@ "79285": "nan", "79290": "nan", "79295": "nan", - "79300": 0.1515, + "79300": 0.19254, "79305": "nan", "79310": "nan", "79315": "nan", @@ -95916,7 +95916,7 @@ "79385": "nan", "79390": "nan", "79395": "nan", - "79400": 0.15152, + "79400": 0.20068, "79405": "nan", "79410": "nan", "79415": "nan", @@ -95936,7 +95936,7 @@ "79485": "nan", "79490": "nan", "79495": "nan", - "79500": 0.15689, + "79500": 0.20751, "79505": "nan", "79510": "nan", "79515": "nan", @@ -95956,7 +95956,7 @@ "79585": "nan", "79590": "nan", "79595": "nan", - "79600": 0.18066, + "79600": 0.21138, "79605": "nan", "79610": "nan", "79615": "nan", @@ -95976,7 +95976,7 @@ "79685": "nan", "79690": "nan", "79695": "nan", - "79700": 0.14936, + "79700": 0.19264, "79705": "nan", "79710": "nan", "79715": "nan", @@ -95996,7 +95996,7 @@ "79785": "nan", "79790": "nan", "79795": "nan", - "79800": 0.15557, + "79800": 0.19669, "79805": "nan", "79810": "nan", "79815": "nan", @@ -96016,7 +96016,7 @@ "79885": "nan", "79890": "nan", "79895": "nan", - "79900": 0.16844, + "79900": 0.19728, "79905": "nan", "79910": "nan", "79915": "nan", @@ -96036,7 +96036,7 @@ "79985": "nan", "79990": "nan", "79995": "nan", - "80000": 0.16658, + "80000": 0.20964, "80005": "nan", "80010": "nan", "80015": "nan", @@ -96056,7 +96056,7 @@ "80085": "nan", "80090": "nan", "80095": "nan", - "80100": 0.16801, + "80100": 0.18768, "80105": "nan", "80110": "nan", "80115": "nan", @@ -96076,7 +96076,7 @@ "80185": "nan", "80190": "nan", "80195": "nan", - "80200": 0.15497, + "80200": 0.19872, "80205": "nan", "80210": "nan", "80215": "nan", @@ -96096,7 +96096,7 @@ "80285": "nan", "80290": "nan", "80295": "nan", - "80300": 0.15176, + "80300": 0.18925, "80305": "nan", "80310": "nan", "80315": "nan", @@ -96116,7 +96116,7 @@ "80385": "nan", "80390": "nan", "80395": "nan", - "80400": 0.1778, + "80400": 0.19875, "80405": "nan", "80410": "nan", "80415": "nan", @@ -96136,7 +96136,7 @@ "80485": "nan", "80490": "nan", "80495": "nan", - "80500": 0.16772, + "80500": 0.20639, "80505": "nan", "80510": "nan", "80515": "nan", @@ -96156,7 +96156,7 @@ "80585": "nan", "80590": "nan", "80595": "nan", - "80600": 0.1521, + "80600": 0.18434, "80605": "nan", "80610": "nan", "80615": "nan", @@ -96176,7 +96176,7 @@ "80685": "nan", "80690": "nan", "80695": "nan", - "80700": 0.15176, + "80700": 0.18059, "80705": "nan", "80710": "nan", "80715": "nan", @@ -96196,7 +96196,7 @@ "80785": "nan", "80790": "nan", "80795": "nan", - "80800": 0.16892, + "80800": 0.19642, "80805": "nan", "80810": "nan", "80815": "nan", @@ -96216,7 +96216,7 @@ "80885": "nan", "80890": "nan", "80895": "nan", - "80900": 0.16356, + "80900": 0.21062, "80905": "nan", "80910": "nan", "80915": "nan", @@ -96236,7 +96236,7 @@ "80985": "nan", "80990": "nan", "80995": "nan", - "81000": 0.15224, + "81000": 0.1943, "81005": "nan", "81010": "nan", "81015": "nan", @@ -96256,7 +96256,7 @@ "81085": "nan", "81090": "nan", "81095": "nan", - "81100": 0.15452, + "81100": 0.18358, "81105": "nan", "81110": "nan", "81115": "nan", @@ -96276,7 +96276,7 @@ "81185": "nan", "81190": "nan", "81195": "nan", - "81200": 0.16997, + "81200": 0.19455, "81205": "nan", "81210": "nan", "81215": "nan", @@ -96296,7 +96296,7 @@ "81285": "nan", "81290": "nan", "81295": "nan", - "81300": 0.16349, + "81300": 0.20322, "81305": "nan", "81310": "nan", "81315": "nan", @@ -96316,7 +96316,7 @@ "81385": "nan", "81390": "nan", "81395": "nan", - "81400": 0.15069, + "81400": 0.1961, "81405": "nan", "81410": "nan", "81415": "nan", @@ -96336,7 +96336,7 @@ "81485": "nan", "81490": "nan", "81495": "nan", - "81500": 0.1603, + "81500": 0.19548, "81505": "nan", "81510": "nan", "81515": "nan", @@ -96356,7 +96356,7 @@ "81585": "nan", "81590": "nan", "81595": "nan", - "81600": 0.39915, + "81600": 0.21721, "81605": "nan", "81610": "nan", "81615": "nan", @@ -96376,7 +96376,7 @@ "81685": "nan", "81690": "nan", "81695": "nan", - "81700": 0.16653, + "81700": 0.19033, "81705": "nan", "81710": "nan", "81715": "nan", @@ -96396,7 +96396,7 @@ "81785": "nan", "81790": "nan", "81795": "nan", - "81800": 0.16048, + "81800": 0.20043, "81805": "nan", "81810": "nan", "81815": "nan", @@ -96416,7 +96416,7 @@ "81885": "nan", "81890": "nan", "81895": "nan", - "81900": 0.17232, + "81900": 0.18573, "81905": "nan", "81910": "nan", "81915": "nan", @@ -96436,7 +96436,7 @@ "81985": "nan", "81990": "nan", "81995": "nan", - "82000": 0.15802, + "82000": 0.20662, "82005": "nan", "82010": "nan", "82015": "nan", @@ -96456,7 +96456,7 @@ "82085": "nan", "82090": "nan", "82095": "nan", - "82100": 0.16269, + "82100": 0.20564, "82105": "nan", "82110": "nan", "82115": "nan", @@ -96476,7 +96476,7 @@ "82185": "nan", "82190": "nan", "82195": "nan", - "82200": 0.15551, + "82200": 0.18989, "82205": "nan", "82210": "nan", "82215": "nan", @@ -96496,7 +96496,7 @@ "82285": "nan", "82290": "nan", "82295": "nan", - "82300": 0.15656, + "82300": 0.18299, "82305": "nan", "82310": "nan", "82315": "nan", @@ -96516,7 +96516,7 @@ "82385": "nan", "82390": "nan", "82395": "nan", - "82400": 0.18103, + "82400": 0.21316, "82405": "nan", "82410": "nan", "82415": "nan", @@ -96536,7 +96536,7 @@ "82485": "nan", "82490": "nan", "82495": "nan", - "82500": 0.1724, + "82500": 0.20572, "82505": "nan", "82510": "nan", "82515": "nan", @@ -96556,7 +96556,7 @@ "82585": "nan", "82590": "nan", "82595": "nan", - "82600": 0.15623, + "82600": 0.18384, "82605": "nan", "82610": "nan", "82615": "nan", @@ -96576,7 +96576,7 @@ "82685": "nan", "82690": "nan", "82695": "nan", - "82700": 0.15432, + "82700": 0.18217, "82705": "nan", "82710": "nan", "82715": "nan", @@ -96596,7 +96596,7 @@ "82785": "nan", "82790": "nan", "82795": "nan", - "82800": 0.1586, + "82800": 0.19583, "82805": "nan", "82810": "nan", "82815": "nan", @@ -96616,7 +96616,7 @@ "82885": "nan", "82890": "nan", "82895": "nan", - "82900": 0.19203, + "82900": 0.23179, "82905": "nan", "82910": "nan", "82915": "nan", @@ -96636,7 +96636,7 @@ "82985": "nan", "82990": "nan", "82995": "nan", - "83000": 0.15308, + "83000": 0.20425, "83005": "nan", "83010": "nan", "83015": "nan", @@ -96656,7 +96656,7 @@ "83085": "nan", "83090": "nan", "83095": "nan", - "83100": 0.15611, + "83100": 0.19321, "83105": "nan", "83110": "nan", "83115": "nan", @@ -96676,7 +96676,7 @@ "83185": "nan", "83190": "nan", "83195": "nan", - "83200": 0.15625, + "83200": 0.19514, "83205": "nan", "83210": "nan", "83215": "nan", @@ -96696,7 +96696,7 @@ "83285": "nan", "83290": "nan", "83295": "nan", - "83300": 0.18187, + "83300": 0.20658, "83305": "nan", "83310": "nan", "83315": "nan", @@ -96716,7 +96716,7 @@ "83385": "nan", "83390": "nan", "83395": "nan", - "83400": 0.1683, + "83400": 0.1878, "83405": "nan", "83410": "nan", "83415": "nan", @@ -96736,7 +96736,7 @@ "83485": "nan", "83490": "nan", "83495": "nan", - "83500": 0.15279, + "83500": 0.18788, "83505": "nan", "83510": "nan", "83515": "nan", @@ -96756,7 +96756,7 @@ "83585": "nan", "83590": "nan", "83595": "nan", - "83600": 0.15575, + "83600": 0.2092, "83605": "nan", "83610": "nan", "83615": "nan", @@ -96776,7 +96776,7 @@ "83685": "nan", "83690": "nan", "83695": "nan", - "83700": 0.18372, + "83700": 0.21816, "83705": "nan", "83710": "nan", "83715": "nan", @@ -96796,7 +96796,7 @@ "83785": "nan", "83790": "nan", "83795": "nan", - "83800": 0.16726, + "83800": 0.21121, "83805": "nan", "83810": "nan", "83815": "nan", @@ -96816,7 +96816,7 @@ "83885": "nan", "83890": "nan", "83895": "nan", - "83900": 0.15491, + "83900": 0.18241, "83905": "nan", "83910": "nan", "83915": "nan", @@ -96836,7 +96836,7 @@ "83985": "nan", "83990": "nan", "83995": "nan", - "84000": 0.15467, + "84000": 0.20311, "84005": "nan", "84010": "nan", "84015": "nan", @@ -96856,7 +96856,7 @@ "84085": "nan", "84090": "nan", "84095": "nan", - "84100": 0.15846, + "84100": 0.20683, "84105": "nan", "84110": "nan", "84115": "nan", @@ -96876,7 +96876,7 @@ "84185": "nan", "84190": "nan", "84195": "nan", - "84200": 0.15432, + "84200": 0.19576, "84205": "nan", "84210": "nan", "84215": "nan", @@ -96896,7 +96896,7 @@ "84285": "nan", "84290": "nan", "84295": "nan", - "84300": 0.1542, + "84300": 0.20512, "84305": "nan", "84310": "nan", "84315": "nan", @@ -96916,7 +96916,7 @@ "84385": "nan", "84390": "nan", "84395": "nan", - "84400": 0.17036, + "84400": 0.20049, "84405": "nan", "84410": "nan", "84415": "nan", @@ -96936,7 +96936,7 @@ "84485": "nan", "84490": "nan", "84495": "nan", - "84500": 0.17424, + "84500": 0.19826, "84505": "nan", "84510": "nan", "84515": "nan", @@ -96956,7 +96956,7 @@ "84585": "nan", "84590": "nan", "84595": "nan", - "84600": 0.15524, + "84600": 0.18532, "84605": "nan", "84610": "nan", "84615": "nan", @@ -96976,7 +96976,7 @@ "84685": "nan", "84690": "nan", "84695": "nan", - "84700": 0.15622, + "84700": 0.18336, "84705": "nan", "84710": "nan", "84715": "nan", @@ -96996,7 +96996,7 @@ "84785": "nan", "84790": "nan", "84795": "nan", - "84800": 0.17654, + "84800": 0.23346, "84805": "nan", "84810": "nan", "84815": "nan", @@ -97016,7 +97016,7 @@ "84885": "nan", "84890": "nan", "84895": "nan", - "84900": 0.17874, + "84900": 0.21009, "84905": "nan", "84910": "nan", "84915": "nan", @@ -97036,7 +97036,7 @@ "84985": "nan", "84990": "nan", "84995": "nan", - "85000": 0.15588, + "85000": 0.19217, "85005": "nan", "85010": "nan", "85015": "nan", @@ -97056,7 +97056,7 @@ "85085": "nan", "85090": "nan", "85095": "nan", - "85100": 0.15977, + "85100": 0.19475, "85105": "nan", "85110": "nan", "85115": "nan", @@ -97076,7 +97076,7 @@ "85185": "nan", "85190": "nan", "85195": "nan", - "85200": 0.17841, + "85200": 0.19981, "85205": "nan", "85210": "nan", "85215": "nan", @@ -97096,7 +97096,7 @@ "85285": "nan", "85290": "nan", "85295": "nan", - "85300": 0.16498, + "85300": 0.20338, "85305": "nan", "85310": "nan", "85315": "nan", @@ -97116,7 +97116,7 @@ "85385": "nan", "85390": "nan", "85395": "nan", - "85400": 0.16538, + "85400": 0.19647, "85405": "nan", "85410": "nan", "85415": "nan", @@ -97136,7 +97136,7 @@ "85485": "nan", "85490": "nan", "85495": "nan", - "85500": 0.15297, + "85500": 0.18001, "85505": "nan", "85510": "nan", "85515": "nan", @@ -97156,7 +97156,7 @@ "85585": "nan", "85590": "nan", "85595": "nan", - "85600": 0.17199, + "85600": 0.22409, "85605": "nan", "85610": "nan", "85615": "nan", @@ -97176,7 +97176,7 @@ "85685": "nan", "85690": "nan", "85695": "nan", - "85700": 0.16621, + "85700": 0.20038, "85705": "nan", "85710": "nan", "85715": "nan", @@ -97196,7 +97196,7 @@ "85785": "nan", "85790": "nan", "85795": "nan", - "85800": 0.17116, + "85800": 0.20253, "85805": "nan", "85810": "nan", "85815": "nan", @@ -97216,7 +97216,7 @@ "85885": "nan", "85890": "nan", "85895": "nan", - "85900": 0.15305, + "85900": 0.19535, "85905": "nan", "85910": "nan", "85915": "nan", @@ -97236,7 +97236,7 @@ "85985": "nan", "85990": "nan", "85995": "nan", - "86000": 0.15797, + "86000": 0.21039, "86005": "nan", "86010": "nan", "86015": "nan", @@ -97256,7 +97256,7 @@ "86085": "nan", "86090": "nan", "86095": "nan", - "86100": 0.15756, + "86100": 0.20374, "86105": "nan", "86110": "nan", "86115": "nan", @@ -97276,7 +97276,7 @@ "86185": "nan", "86190": "nan", "86195": "nan", - "86200": 0.1558, + "86200": 0.19039, "86205": "nan", "86210": "nan", "86215": "nan", @@ -97296,7 +97296,7 @@ "86285": "nan", "86290": "nan", "86295": "nan", - "86300": 0.16521, + "86300": 0.19353, "86305": "nan", "86310": "nan", "86315": "nan", @@ -97316,7 +97316,7 @@ "86385": "nan", "86390": "nan", "86395": "nan", - "86400": 0.16745, + "86400": 0.21562, "86405": "nan", "86410": "nan", "86415": "nan", @@ -97336,7 +97336,7 @@ "86485": "nan", "86490": "nan", "86495": "nan", - "86500": 0.18033, + "86500": 0.20083, "86505": "nan", "86510": "nan", "86515": "nan", @@ -97356,7 +97356,7 @@ "86585": "nan", "86590": "nan", "86595": "nan", - "86600": 0.15953, + "86600": 0.19359, "86605": "nan", "86610": "nan", "86615": "nan", @@ -97376,7 +97376,7 @@ "86685": "nan", "86690": "nan", "86695": "nan", - "86700": 0.16505, + "86700": 0.1723, "86705": "nan", "86710": "nan", "86715": "nan", @@ -97396,7 +97396,7 @@ "86785": "nan", "86790": "nan", "86795": "nan", - "86800": 0.15972, + "86800": 0.20057, "86805": "nan", "86810": "nan", "86815": "nan", @@ -97416,7 +97416,7 @@ "86885": "nan", "86890": "nan", "86895": "nan", - "86900": 0.16956, + "86900": 0.22587, "86905": "nan", "86910": "nan", "86915": "nan", @@ -97436,7 +97436,7 @@ "86985": "nan", "86990": "nan", "86995": "nan", - "87000": 0.16112, + "87000": 0.20701, "87005": "nan", "87010": "nan", "87015": "nan", @@ -97456,7 +97456,7 @@ "87085": "nan", "87090": "nan", "87095": "nan", - "87100": 0.1674, + "87100": 0.18524, "87105": "nan", "87110": "nan", "87115": "nan", @@ -97476,7 +97476,7 @@ "87185": "nan", "87190": "nan", "87195": "nan", - "87200": 0.15938, + "87200": 0.19321, "87205": "nan", "87210": "nan", "87215": "nan", @@ -97496,7 +97496,7 @@ "87285": "nan", "87290": "nan", "87295": "nan", - "87300": 0.1609, + "87300": 0.2158, "87305": "nan", "87310": "nan", "87315": "nan", @@ -97516,7 +97516,7 @@ "87385": "nan", "87390": "nan", "87395": "nan", - "87400": 0.17055, + "87400": 0.18877, "87405": "nan", "87410": "nan", "87415": "nan", @@ -97536,7 +97536,7 @@ "87485": "nan", "87490": "nan", "87495": "nan", - "87500": 0.16414, + "87500": 0.17869, "87505": "nan", "87510": "nan", "87515": "nan", @@ -97556,7 +97556,7 @@ "87585": "nan", "87590": "nan", "87595": "nan", - "87600": 0.16367, + "87600": 0.20433, "87605": "nan", "87610": "nan", "87615": "nan", @@ -97576,7 +97576,7 @@ "87685": "nan", "87690": "nan", "87695": "nan", - "87700": 0.16379, + "87700": 0.22539, "87705": "nan", "87710": "nan", "87715": "nan", @@ -97596,7 +97596,7 @@ "87785": "nan", "87790": "nan", "87795": "nan", - "87800": 0.17194, + "87800": 0.19448, "87805": "nan", "87810": "nan", "87815": "nan", @@ -97616,7 +97616,7 @@ "87885": "nan", "87890": "nan", "87895": "nan", - "87900": 0.15872, + "87900": 0.20403, "87905": "nan", "87910": "nan", "87915": "nan", @@ -97636,7 +97636,7 @@ "87985": "nan", "87990": "nan", "87995": "nan", - "88000": 0.1665, + "88000": 0.20783, "88005": "nan", "88010": "nan", "88015": "nan", @@ -97656,7 +97656,7 @@ "88085": "nan", "88090": "nan", "88095": "nan", - "88100": 0.15819, + "88100": 0.20135, "88105": "nan", "88110": "nan", "88115": "nan", @@ -97676,7 +97676,7 @@ "88185": "nan", "88190": "nan", "88195": "nan", - "88200": 0.15989, + "88200": 0.19286, "88205": "nan", "88210": "nan", "88215": "nan", @@ -97696,7 +97696,7 @@ "88285": "nan", "88290": "nan", "88295": "nan", - "88300": 0.15871, + "88300": 0.18406, "88305": "nan", "88310": "nan", "88315": "nan", @@ -97716,7 +97716,7 @@ "88385": "nan", "88390": "nan", "88395": "nan", - "88400": 0.16091, + "88400": 0.21556, "88405": "nan", "88410": "nan", "88415": "nan", @@ -97736,7 +97736,7 @@ "88485": "nan", "88490": "nan", "88495": "nan", - "88500": 0.19372, + "88500": 0.20031, "88505": "nan", "88510": "nan", "88515": "nan", @@ -97756,7 +97756,7 @@ "88585": "nan", "88590": "nan", "88595": "nan", - "88600": 0.1595, + "88600": 0.19188, "88605": "nan", "88610": "nan", "88615": "nan", @@ -97776,7 +97776,7 @@ "88685": "nan", "88690": "nan", "88695": "nan", - "88700": 0.15752, + "88700": 0.18323, "88705": "nan", "88710": "nan", "88715": "nan", @@ -97796,7 +97796,7 @@ "88785": "nan", "88790": "nan", "88795": "nan", - "88800": 0.16004, + "88800": 0.21037, "88805": "nan", "88810": "nan", "88815": "nan", @@ -97816,7 +97816,7 @@ "88885": "nan", "88890": "nan", "88895": "nan", - "88900": 0.1864, + "88900": 0.22782, "88905": "nan", "88910": "nan", "88915": "nan", @@ -97836,7 +97836,7 @@ "88985": "nan", "88990": "nan", "88995": "nan", - "89000": 0.16104, + "89000": 0.18287, "89005": "nan", "89010": "nan", "89015": "nan", @@ -97856,7 +97856,7 @@ "89085": "nan", "89090": "nan", "89095": "nan", - "89100": 0.15988, + "89100": 0.19858, "89105": "nan", "89110": "nan", "89115": "nan", @@ -97876,7 +97876,7 @@ "89185": "nan", "89190": "nan", "89195": "nan", - "89200": 0.15826, + "89200": 0.20431, "89205": "nan", "89210": "nan", "89215": "nan", @@ -97896,7 +97896,7 @@ "89285": "nan", "89290": "nan", "89295": "nan", - "89300": 0.16719, + "89300": 0.20797, "89305": "nan", "89310": "nan", "89315": "nan", @@ -97916,7 +97916,7 @@ "89385": "nan", "89390": "nan", "89395": "nan", - "89400": 0.17537, + "89400": 0.17488, "89405": "nan", "89410": "nan", "89415": "nan", @@ -97936,7 +97936,7 @@ "89485": "nan", "89490": "nan", "89495": "nan", - "89500": 0.15462, + "89500": 0.18706, "89505": "nan", "89510": "nan", "89515": "nan", @@ -97956,7 +97956,7 @@ "89585": "nan", "89590": "nan", "89595": "nan", - "89600": 0.1633, + "89600": 0.2215, "89605": "nan", "89610": "nan", "89615": "nan", @@ -97976,7 +97976,7 @@ "89685": "nan", "89690": "nan", "89695": "nan", - "89700": 0.17343, + "89700": 0.20219, "89705": "nan", "89710": "nan", "89715": "nan", @@ -97996,7 +97996,7 @@ "89785": "nan", "89790": "nan", "89795": "nan", - "89800": 0.17327, + "89800": 0.1912, "89805": "nan", "89810": "nan", "89815": "nan", @@ -98016,7 +98016,7 @@ "89885": "nan", "89890": "nan", "89895": "nan", - "89900": 0.15683, + "89900": 0.20147, "89905": "nan", "89910": "nan", "89915": "nan", @@ -98036,7 +98036,7 @@ "89985": "nan", "89990": "nan", "89995": "nan", - "90000": 0.15748, + "90000": 0.21357, "90005": "nan", "90010": "nan", "90015": "nan", @@ -98056,7 +98056,7 @@ "90085": "nan", "90090": "nan", "90095": "nan", - "90100": 0.15803, + "90100": 0.22499, "90105": "nan", "90110": "nan", "90115": "nan", @@ -98076,7 +98076,7 @@ "90185": "nan", "90190": "nan", "90195": "nan", - "90200": 0.1555, + "90200": 0.187, "90205": "nan", "90210": "nan", "90215": "nan", @@ -98096,7 +98096,7 @@ "90285": "nan", "90290": "nan", "90295": "nan", - "90300": 0.15755, + "90300": 0.19237, "90305": "nan", "90310": "nan", "90315": "nan", @@ -98116,7 +98116,7 @@ "90385": "nan", "90390": "nan", "90395": "nan", - "90400": 0.16296, + "90400": 0.19444, "90405": "nan", "90410": "nan", "90415": "nan", @@ -98136,7 +98136,7 @@ "90485": "nan", "90490": "nan", "90495": "nan", - "90500": 0.18121, + "90500": 0.18983, "90505": "nan", "90510": "nan", "90515": "nan", @@ -98156,7 +98156,7 @@ "90585": "nan", "90590": "nan", "90595": "nan", - "90600": 0.15638, + "90600": 0.1943, "90605": "nan", "90610": "nan", "90615": "nan", @@ -98176,7 +98176,7 @@ "90685": "nan", "90690": "nan", "90695": "nan", - "90700": 0.1567, + "90700": 0.17439, "90705": "nan", "90710": "nan", "90715": "nan", @@ -98196,7 +98196,7 @@ "90785": "nan", "90790": "nan", "90795": "nan", - "90800": 0.16387, + "90800": 0.20672, "90805": "nan", "90810": "nan", "90815": "nan", @@ -98216,7 +98216,7 @@ "90885": "nan", "90890": "nan", "90895": "nan", - "90900": 0.17914, + "90900": 0.21671, "90905": "nan", "90910": "nan", "90915": "nan", @@ -98236,7 +98236,7 @@ "90985": "nan", "90990": "nan", "90995": "nan", - "91000": 0.15492, + "91000": 0.22172, "91005": "nan", "91010": "nan", "91015": "nan", @@ -98256,7 +98256,7 @@ "91085": "nan", "91090": "nan", "91095": "nan", - "91100": 0.15846, + "91100": 0.19496, "91105": "nan", "91110": "nan", "91115": "nan", @@ -98276,7 +98276,7 @@ "91185": "nan", "91190": "nan", "91195": "nan", - "91200": 0.16389, + "91200": 0.18817, "91205": "nan", "91210": "nan", "91215": "nan", @@ -98296,7 +98296,7 @@ "91285": "nan", "91290": "nan", "91295": "nan", - "91300": 0.17372, + "91300": 0.19218, "91305": "nan", "91310": "nan", "91315": "nan", @@ -98316,7 +98316,7 @@ "91385": "nan", "91390": "nan", "91395": "nan", - "91400": 0.1648, + "91400": 0.21854, "91405": "nan", "91410": "nan", "91415": "nan", @@ -98336,7 +98336,7 @@ "91485": "nan", "91490": "nan", "91495": "nan", - "91500": 0.15395, + "91500": 0.202, "91505": "nan", "91510": "nan", "91515": "nan", @@ -98356,7 +98356,7 @@ "91585": "nan", "91590": "nan", "91595": "nan", - "91600": 0.16317, + "91600": 0.24093, "91605": "nan", "91610": "nan", "91615": "nan", @@ -98376,7 +98376,7 @@ "91685": "nan", "91690": "nan", "91695": "nan", - "91700": 0.17541, + "91700": 0.2092, "91705": "nan", "91710": "nan", "91715": "nan", @@ -98396,7 +98396,7 @@ "91785": "nan", "91790": "nan", "91795": "nan", - "91800": 0.16891, + "91800": 0.21848, "91805": "nan", "91810": "nan", "91815": "nan", @@ -98416,7 +98416,7 @@ "91885": "nan", "91890": "nan", "91895": "nan", - "91900": 0.15677, + "91900": 0.19801, "91905": "nan", "91910": "nan", "91915": "nan", @@ -98436,7 +98436,7 @@ "91985": "nan", "91990": "nan", "91995": "nan", - "92000": 0.15698, + "92000": 0.19375, "92005": "nan", "92010": "nan", "92015": "nan", @@ -98456,7 +98456,7 @@ "92085": "nan", "92090": "nan", "92095": "nan", - "92100": 0.16055, + "92100": 0.2193, "92105": "nan", "92110": "nan", "92115": "nan", @@ -98476,7 +98476,7 @@ "92185": "nan", "92190": "nan", "92195": "nan", - "92200": 0.15171, + "92200": 0.19915, "92205": "nan", "92210": "nan", "92215": "nan", @@ -98496,7 +98496,7 @@ "92285": "nan", "92290": "nan", "92295": "nan", - "92300": 0.15469, + "92300": 0.20026, "92305": "nan", "92310": "nan", "92315": "nan", @@ -98516,7 +98516,7 @@ "92385": "nan", "92390": "nan", "92395": "nan", - "92400": 0.16439, + "92400": 0.19356, "92405": "nan", "92410": "nan", "92415": "nan", @@ -98536,7 +98536,7 @@ "92485": "nan", "92490": "nan", "92495": "nan", - "92500": 0.19158, + "92500": 0.19071, "92505": "nan", "92510": "nan", "92515": "nan", @@ -98556,7 +98556,7 @@ "92585": "nan", "92590": "nan", "92595": "nan", - "92600": 0.15798, + "92600": 0.19633, "92605": "nan", "92610": "nan", "92615": "nan", @@ -98576,7 +98576,7 @@ "92685": "nan", "92690": "nan", "92695": "nan", - "92700": 0.15483, + "92700": 0.17993, "92705": "nan", "92710": "nan", "92715": "nan", @@ -98596,7 +98596,7 @@ "92785": "nan", "92790": "nan", "92795": "nan", - "92800": 0.16761, + "92800": 0.21076, "92805": "nan", "92810": "nan", "92815": "nan", @@ -98616,7 +98616,7 @@ "92885": "nan", "92890": "nan", "92895": "nan", - "92900": 0.18405, + "92900": 0.20976, "92905": "nan", "92910": "nan", "92915": "nan", @@ -98636,7 +98636,7 @@ "92985": "nan", "92990": "nan", "92995": "nan", - "93000": 0.1566, + "93000": 0.20657, "93005": "nan", "93010": "nan", "93015": "nan", @@ -98656,7 +98656,7 @@ "93085": "nan", "93090": "nan", "93095": "nan", - "93100": 0.15772, + "93100": 0.21599, "93105": "nan", "93110": "nan", "93115": "nan", @@ -98676,7 +98676,7 @@ "93185": "nan", "93190": "nan", "93195": "nan", - "93200": 0.16467, + "93200": 0.19414, "93205": "nan", "93210": "nan", "93215": "nan", @@ -98696,7 +98696,7 @@ "93285": "nan", "93290": "nan", "93295": "nan", - "93300": 0.17084, + "93300": 0.19306, "93305": "nan", "93310": "nan", "93315": "nan", @@ -98716,7 +98716,7 @@ "93385": "nan", "93390": "nan", "93395": "nan", - "93400": 0.17499, + "93400": 0.19821, "93405": "nan", "93410": "nan", "93415": "nan", @@ -98736,7 +98736,7 @@ "93485": "nan", "93490": "nan", "93495": "nan", - "93500": 0.15493, + "93500": 0.1739, "93505": "nan", "93510": "nan", "93515": "nan", @@ -98756,7 +98756,7 @@ "93585": "nan", "93590": "nan", "93595": "nan", - "93600": 0.16596, + "93600": 0.21068, "93605": "nan", "93610": "nan", "93615": "nan", @@ -98776,7 +98776,7 @@ "93685": "nan", "93690": "nan", "93695": "nan", - "93700": 0.17172, + "93700": 0.22337, "93705": "nan", "93710": "nan", "93715": "nan", @@ -98796,7 +98796,7 @@ "93785": "nan", "93790": "nan", "93795": "nan", - "93800": 0.17513, + "93800": 0.19377, "93805": "nan", "93810": "nan", "93815": "nan", @@ -98816,7 +98816,7 @@ "93885": "nan", "93890": "nan", "93895": "nan", - "93900": 0.15446, + "93900": 0.20535, "93905": "nan", "93910": "nan", "93915": "nan", @@ -98836,7 +98836,7 @@ "93985": "nan", "93990": "nan", "93995": "nan", - "94000": 0.15755, + "94000": 0.21655, "94005": "nan", "94010": "nan", "94015": "nan", @@ -98856,7 +98856,7 @@ "94085": "nan", "94090": "nan", "94095": "nan", - "94100": 0.15832, + "94100": 0.20716, "94105": "nan", "94110": "nan", "94115": "nan", @@ -98876,7 +98876,7 @@ "94185": "nan", "94190": "nan", "94195": "nan", - "94200": 0.16546, + "94200": 0.20982, "94205": "nan", "94210": "nan", "94215": "nan", @@ -98896,7 +98896,7 @@ "94285": "nan", "94290": "nan", "94295": "nan", - "94300": 0.16172, + "94300": 0.19997, "94305": "nan", "94310": "nan", "94315": "nan", @@ -98916,7 +98916,7 @@ "94385": "nan", "94390": "nan", "94395": "nan", - "94400": 0.1628, + "94400": 0.2078, "94405": "nan", "94410": "nan", "94415": "nan", @@ -98936,7 +98936,7 @@ "94485": "nan", "94490": "nan", "94495": "nan", - "94500": 0.19235, + "94500": 0.20121, "94505": "nan", "94510": "nan", "94515": "nan", @@ -98956,7 +98956,7 @@ "94585": "nan", "94590": "nan", "94595": "nan", - "94600": 0.15863, + "94600": 0.21419, "94605": "nan", "94610": "nan", "94615": "nan", @@ -98976,7 +98976,7 @@ "94685": "nan", "94690": "nan", "94695": "nan", - "94700": 0.16202, + "94700": 0.1797, "94705": "nan", "94710": "nan", "94715": "nan", @@ -98996,7 +98996,7 @@ "94785": "nan", "94790": "nan", "94795": "nan", - "94800": 0.1687, + "94800": 0.21337, "94805": "nan", "94810": "nan", "94815": "nan", @@ -99016,7 +99016,7 @@ "94885": "nan", "94890": "nan", "94895": "nan", - "94900": 0.18953, + "94900": 0.21581, "94905": "nan", "94910": "nan", "94915": "nan", @@ -99036,7 +99036,7 @@ "94985": "nan", "94990": "nan", "94995": "nan", - "95000": 0.15418, + "95000": 0.18799, "95005": "nan", "95010": "nan", "95015": "nan", @@ -99056,7 +99056,7 @@ "95085": "nan", "95090": "nan", "95095": "nan", - "95100": 0.16844, + "95100": 0.2256, "95105": "nan", "95110": "nan", "95115": "nan", @@ -99076,7 +99076,7 @@ "95185": "nan", "95190": "nan", "95195": "nan", - "95200": 0.16422, + "95200": 0.19905, "95205": "nan", "95210": "nan", "95215": "nan", @@ -99096,7 +99096,7 @@ "95285": "nan", "95290": "nan", "95295": "nan", - "95300": 0.16772, + "95300": 0.20083, "95305": "nan", "95310": "nan", "95315": "nan", @@ -99116,7 +99116,7 @@ "95385": "nan", "95390": "nan", "95395": "nan", - "95400": 0.17732, + "95400": 0.1884, "95405": "nan", "95410": "nan", "95415": "nan", @@ -99136,7 +99136,7 @@ "95485": "nan", "95490": "nan", "95495": "nan", - "95500": 0.16231, + "95500": 0.18295, "95505": "nan", "95510": "nan", "95515": "nan", @@ -99156,7 +99156,7 @@ "95585": "nan", "95590": "nan", "95595": "nan", - "95600": 0.16766, + "95600": 0.19786, "95605": "nan", "95610": "nan", "95615": "nan", @@ -99176,7 +99176,7 @@ "95685": "nan", "95690": "nan", "95695": "nan", - "95700": 0.16902, + "95700": 0.22575, "95705": "nan", "95710": "nan", "95715": "nan", @@ -99196,7 +99196,7 @@ "95785": "nan", "95790": "nan", "95795": "nan", - "95800": 0.17664, + "95800": 0.19841, "95805": "nan", "95810": "nan", "95815": "nan", @@ -99216,7 +99216,7 @@ "95885": "nan", "95890": "nan", "95895": "nan", - "95900": 0.15346, + "95900": 0.19804, "95905": "nan", "95910": "nan", "95915": "nan", @@ -99236,7 +99236,7 @@ "95985": "nan", "95990": "nan", "95995": "nan", - "96000": 0.16647, + "96000": 0.20848, "96005": "nan", "96010": "nan", "96015": "nan", @@ -99256,7 +99256,7 @@ "96085": "nan", "96090": "nan", "96095": "nan", - "96100": 0.16102, + "96100": 0.22458, "96105": "nan", "96110": "nan", "96115": "nan", @@ -99276,7 +99276,7 @@ "96185": "nan", "96190": "nan", "96195": "nan", - "96200": 0.15395, + "96200": 0.19755, "96205": "nan", "96210": "nan", "96215": "nan", @@ -99296,7 +99296,7 @@ "96285": "nan", "96290": "nan", "96295": "nan", - "96300": 0.15901, + "96300": 0.18568, "96305": "nan", "96310": "nan", "96315": "nan", @@ -99316,7 +99316,7 @@ "96385": "nan", "96390": "nan", "96395": "nan", - "96400": 0.16267, + "96400": 0.27061, "96405": "nan", "96410": "nan", "96415": "nan", @@ -99336,7 +99336,7 @@ "96485": "nan", "96490": "nan", "96495": "nan", - "96500": 0.19625, + "96500": 0.19549, "96505": "nan", "96510": "nan", "96515": "nan", @@ -99356,7 +99356,7 @@ "96585": "nan", "96590": "nan", "96595": "nan", - "96600": 0.15741, + "96600": 0.19121, "96605": "nan", "96610": "nan", "96615": "nan", @@ -99376,7 +99376,7 @@ "96685": "nan", "96690": "nan", "96695": "nan", - "96700": 0.16208, + "96700": 0.19703, "96705": "nan", "96710": "nan", "96715": "nan", @@ -99396,7 +99396,7 @@ "96785": "nan", "96790": "nan", "96795": "nan", - "96800": 0.16409, + "96800": 0.2121, "96805": "nan", "96810": "nan", "96815": "nan", @@ -99416,7 +99416,7 @@ "96885": "nan", "96890": "nan", "96895": "nan", - "96900": 0.19617, + "96900": 0.20763, "96905": "nan", "96910": "nan", "96915": "nan", @@ -99436,7 +99436,7 @@ "96985": "nan", "96990": "nan", "96995": "nan", - "97000": 0.15368, + "97000": 0.2028, "97005": "nan", "97010": "nan", "97015": "nan", @@ -99456,7 +99456,7 @@ "97085": "nan", "97090": "nan", "97095": "nan", - "97100": 0.16305, + "97100": 0.21315, "97105": "nan", "97110": "nan", "97115": "nan", @@ -99476,7 +99476,7 @@ "97185": "nan", "97190": "nan", "97195": "nan", - "97200": 0.16351, + "97200": 0.18651, "97205": "nan", "97210": "nan", "97215": "nan", @@ -99496,7 +99496,7 @@ "97285": "nan", "97290": "nan", "97295": "nan", - "97300": 0.17839, + "97300": 0.19985, "97305": "nan", "97310": "nan", "97315": "nan", @@ -99516,7 +99516,7 @@ "97385": "nan", "97390": "nan", "97395": "nan", - "97400": 0.1672, + "97400": 0.18575, "97405": "nan", "97410": "nan", "97415": "nan", @@ -99536,7 +99536,7 @@ "97485": "nan", "97490": "nan", "97495": "nan", - "97500": 0.15571, + "97500": 0.20064, "97505": "nan", "97510": "nan", "97515": "nan", @@ -99556,7 +99556,7 @@ "97585": "nan", "97590": "nan", "97595": "nan", - "97600": 0.16586, + "97600": 0.20635, "97605": "nan", "97610": "nan", "97615": "nan", @@ -99576,7 +99576,7 @@ "97685": "nan", "97690": "nan", "97695": "nan", - "97700": 0.17699, + "97700": 0.20942, "97705": "nan", "97710": "nan", "97715": "nan", @@ -99596,7 +99596,7 @@ "97785": "nan", "97790": "nan", "97795": "nan", - "97800": 0.17113, + "97800": 0.20628, "97805": "nan", "97810": "nan", "97815": "nan", @@ -99616,7 +99616,7 @@ "97885": "nan", "97890": "nan", "97895": "nan", - "97900": 0.15608, + "97900": 0.19575, "97905": "nan", "97910": "nan", "97915": "nan", @@ -99636,7 +99636,7 @@ "97985": "nan", "97990": "nan", "97995": "nan", - "98000": 0.15694, + "98000": 0.21765, "98005": "nan", "98010": "nan", "98015": "nan", @@ -99656,7 +99656,7 @@ "98085": "nan", "98090": "nan", "98095": "nan", - "98100": 0.15732, + "98100": 0.21052, "98105": "nan", "98110": "nan", "98115": "nan", @@ -99676,7 +99676,7 @@ "98185": "nan", "98190": "nan", "98195": "nan", - "98200": 0.15306, + "98200": 0.18999, "98205": "nan", "98210": "nan", "98215": "nan", @@ -99696,7 +99696,7 @@ "98285": "nan", "98290": "nan", "98295": "nan", - "98300": 0.15673, + "98300": 0.20272, "98305": "nan", "98310": "nan", "98315": "nan", @@ -99716,7 +99716,7 @@ "98385": "nan", "98390": "nan", "98395": "nan", - "98400": 0.16629, + "98400": 0.20415, "98405": "nan", "98410": "nan", "98415": "nan", @@ -99736,7 +99736,7 @@ "98485": "nan", "98490": "nan", "98495": "nan", - "98500": 0.18962, + "98500": 0.20408, "98505": "nan", "98510": "nan", "98515": "nan", @@ -99756,7 +99756,7 @@ "98585": "nan", "98590": "nan", "98595": "nan", - "98600": 0.15502, + "98600": 0.19101, "98605": "nan", "98610": "nan", "98615": "nan", @@ -99776,7 +99776,7 @@ "98685": "nan", "98690": "nan", "98695": "nan", - "98700": 0.15627, + "98700": 0.198, "98705": "nan", "98710": "nan", "98715": "nan", @@ -99796,7 +99796,7 @@ "98785": "nan", "98790": "nan", "98795": "nan", - "98800": 0.16372, + "98800": 0.19103, "98805": "nan", "98810": "nan", "98815": "nan", @@ -99816,7 +99816,7 @@ "98885": "nan", "98890": "nan", "98895": "nan", - "98900": 0.1812, + "98900": 0.1963, "98905": "nan", "98910": "nan", "98915": "nan", @@ -99836,7 +99836,7 @@ "98985": "nan", "98990": "nan", "98995": "nan", - "99000": 0.16022, + "99000": 0.19488, "99005": "nan", "99010": "nan", "99015": "nan", @@ -99856,7 +99856,7 @@ "99085": "nan", "99090": "nan", "99095": "nan", - "99100": 0.16284, + "99100": 0.20409, "99105": "nan", "99110": "nan", "99115": "nan", @@ -99876,7 +99876,7 @@ "99185": "nan", "99190": "nan", "99195": "nan", - "99200": 0.16703, + "99200": 0.19089, "99205": "nan", "99210": "nan", "99215": "nan", @@ -99896,7 +99896,7 @@ "99285": "nan", "99290": "nan", "99295": "nan", - "99300": 0.17802, + "99300": 0.20466, "99305": "nan", "99310": "nan", "99315": "nan", @@ -99916,7 +99916,7 @@ "99385": "nan", "99390": "nan", "99395": "nan", - "99400": 0.16665, + "99400": 0.19627, "99405": "nan", "99410": "nan", "99415": "nan", @@ -99936,7 +99936,7 @@ "99485": "nan", "99490": "nan", "99495": "nan", - "99500": 0.15867, + "99500": 0.18733, "99505": "nan", "99510": "nan", "99515": "nan", @@ -99956,7 +99956,7 @@ "99585": "nan", "99590": "nan", "99595": "nan", - "99600": 0.16696, + "99600": 0.2, "99605": "nan", "99610": "nan", "99615": "nan", @@ -99976,7 +99976,7 @@ "99685": "nan", "99690": "nan", "99695": "nan", - "99700": 0.17461, + "99700": 0.21645, "99705": "nan", "99710": "nan", "99715": "nan", @@ -99996,7 +99996,7 @@ "99785": "nan", "99790": "nan", "99795": "nan", - "99800": 0.16267, + "99800": 0.19608, "99805": "nan", "99810": "nan", "99815": "nan", @@ -100016,7 +100016,7 @@ "99885": "nan", "99890": "nan", "99895": "nan", - "99900": 0.1544, + "99900": 0.18837, "99905": "nan", "99910": "nan", "99915": "nan", @@ -100036,7 +100036,7 @@ "99985": "nan", "99990": "nan", "99995": "nan", - "100000": 0.15732 + "100000": 0.21379 } } } \ No newline at end of file diff --git a/tests/functional_tests/test_cases/t5/t5_release/model_config.yaml b/tests/functional_tests/test_cases/t5/t5_release/model_config.yaml index 485b14cbfa2..f483d005b82 100644 --- a/tests/functional_tests/test_cases/t5/t5_release/model_config.yaml +++ b/tests/functional_tests/test_cases/t5/t5_release/model_config.yaml @@ -3,6 +3,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: "1" NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # T5 model args --encoder-num-layers: 12 diff --git a/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml b/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml index 6feba73c0a1..ebd21a651f9 100644 --- a/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/t5/t5_release_sm/model_config.yaml @@ -3,6 +3,7 @@ ENV_VARS: NVTE_ALLOW_NONDETERMINISTIC_ALGO: "1" NON_DETERMINSTIC_RESULTS: 1 TEST_TYPE: "release" +LAUNCHER: ft_launcher MODEL_ARGS: # T5 model args --encoder-num-layers: 12 diff --git a/tests/test_utils/python_scripts/launch_jet_workload.py b/tests/test_utils/python_scripts/launch_jet_workload.py index 7f60ceb12d6..bb78562eba5 100644 --- a/tests/test_utils/python_scripts/launch_jet_workload.py +++ b/tests/test_utils/python_scripts/launch_jet_workload.py @@ -6,6 +6,7 @@ import pathlib import re import signal +import subprocess import sys import time import uuid @@ -32,6 +33,44 @@ logger = logging.getLogger(__name__) +def send_slack_alert(test_case: str, context: str, n_iteration: int, n_attempts: int) -> None: + """Send a Slack alert via notify.py for the current release pipeline state. + + Args: + test_case: Name of the release test case being run. + context: Human-readable context string appended to the pipeline context label. + n_iteration: Current training iteration (pipeline relaunch count). + n_attempts: Current attempt count within this iteration. + """ + pipeline_id = os.getenv("PARENT_PIPELINE_ID") + pipeline_created_at = os.getenv("CI_PIPELINE_CREATED_AT", "") + + if not pipeline_id or not pipeline_created_at: + logger.info("Missing PARENT_PIPELINE_ID or CI_PIPELINE_CREATED_AT, skipping Slack alert.") + return + + pipeline_context = f"{test_case} | iteration={n_iteration} | attempt={n_attempts} | {context}" + + try: + subprocess.run( + [ + sys.executable, + str(BASE_PATH / "notify.py"), + "--pipeline-id", + pipeline_id, + "--check-for", + "functional-tests", + "--pipeline-context", + pipeline_context, + "--pipeline-created-at", + pipeline_created_at, + ], + check=False, + ) + except Exception as e: + logger.warning("Failed to send Slack alert: %s", e) + + def register_pipeline_terminator(pipeline: jetclient.JETPipeline): def sigterm_handler(_signo, _stack_frame): print(f"Trying to terminate pipeline {pipeline.jet_id}") @@ -520,14 +559,32 @@ def main( or "exiting program at iteration" in concat_allranks_logs ): logger.info("Release training finished") + send_slack_alert( + test_case=test_case, + context="training finished", + n_iteration=n_iteration, + n_attempts=n_attempts, + ) sys.exit(int(not success)) # invert for exit 0 if parse_failed_job(logs=mainrank_log): + send_slack_alert( + test_case=test_case, + context="pipeline failed, retrying", + n_iteration=n_iteration, + n_attempts=n_attempts, + ) n_attempts += 1 continue n_iteration += 1 + send_slack_alert( + test_case=test_case, + context="max attempts exhausted", + n_iteration=n_iteration, + n_attempts=n_attempts, + ) telemetrics_and_exit( success=False, test_case=test_case, From 8681ebbad357efd3e2fdd8dd130914180495a27a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Thu, 16 Apr 2026 18:40:03 +0200 Subject: [PATCH 22/72] ci(action): improve GitHub Actions output UX (#4337) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .github/actions/action.yml | 67 ++++++++++++++++++++++++++++---------- 1 file changed, 49 insertions(+), 18 deletions(-) diff --git a/.github/actions/action.yml b/.github/actions/action.yml index 823cacd302f..923173bf103 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -157,10 +157,7 @@ runs: - name: Set timeout shell: bash -x -e -u -o pipefail {0} id: timeout_in_seconds - run: | - echo "::group::Set timeout" - echo "main=$(( ${{ inputs.timeout }} * 60 ))" | tee -a "$GITHUB_OUTPUT" - echo "::endgroup::" + run: echo "main=$(( ${{ inputs.timeout }} * 60 ))" | tee -a "$GITHUB_OUTPUT" - name: Pull container shell: bash -x -e -u -o pipefail {0} @@ -173,27 +170,31 @@ runs: shell: bash -x -e -u -o pipefail {0} id: run-main-script run: | - echo "::group::Run main script" + { set +x; } 2>/dev/null + echo -e "\033[1;34m┌─ launching test ─────────────────────────────────────────────────────────┐\033[0m" + echo -e "\033[1;34m│ test case : ${{ inputs.test_case }}\033[0m" + echo -e "\033[1;34m│ platform : ${{ inputs.platform }} scope: ${{ inputs.scope }}\033[0m" + echo -e "\033[1;34m│ container : ${{ inputs.container-image }}\033[0m" + echo -e "\033[1;34m└──────────────────────────────────────────────────────────────────────────┘\033[0m" + { set -x; } 2>/dev/null + echo "::group::Logs" EXIT_CODE=0 /bin/bash job.sh || EXIT_CODE=$? echo "exit_code=$EXIT_CODE" | tee -a "$GITHUB_OUTPUT" - exit $EXIT_CODE echo "::endgroup::" + exit $EXIT_CODE - name: Check result id: check - shell: bash -x -e -u -o pipefail {0} + shell: bash -e -u -o pipefail {0} if: always() env: IS_UNIT_TEST: ${{ inputs.is_unit_test == 'true' }} run: | - echo "::group::Check result" - logs_report=logs-${{ inputs.test_case }}-${{ github.run_id }}-$(uuidgen) echo "logs_report=$logs_report" | sed 's/\//-/g' | sed 's/\*/-/g' | tee -a "$GITHUB_OUTPUT" - if [[ "$IS_UNIT_TEST" == "true" ]]; then - coverage_report=coverage-${{ inputs.is_unit_test == 'true' && 'unit-test' || 'e2e' }}-${{ github.run_id }}-$(uuidgen) + coverage_report=coverage-unit-test-${{ github.run_id }}-$(uuidgen) else coverage_report=none fi @@ -203,21 +204,51 @@ runs: IS_SUCCESS=$([[ "$EXIT_CODE" -eq 0 ]] && echo "true" || echo "false") if [[ "$IS_SUCCESS" == "false" && "${{ inputs.is-optional }}" == "true" ]]; then - echo "::warning:: Test failed, but displayed as successful because it is marked as optional." + echo "::warning::Test failed but is marked optional — treating as success." IS_SUCCESS=true fi - if [[ "$IS_SUCCESS" == "false" ]]; then - echo Test did not finish successfully. - exit 1 + LOG_BASE=$([[ "$IS_UNIT_TEST" == "true" ]] && echo "assets_dir/logs" || echo "assets_dir") + LATEST_LOG="" + if [[ -d "$LOG_BASE" ]]; then + LATEST_LOG=$(find "$LOG_BASE" -maxdepth 3 -name "*.log" ! -name "nccl_debug.log" -type f 2>/dev/null \ + | xargs -r ls -t 2>/dev/null | head -1 || true) + fi + if [[ -n "$LATEST_LOG" ]]; then + echo -e "\033[1;36m\n📋 ── log excerpt ───────────────────────────────────────────────────────\033[0m" + echo -e "\033[1;36m ${LATEST_LOG} — last 40 lines\033[0m" + echo -e "\033[1;36m────────────────────────────────────────────────────────────────────────\033[0m" + tail -40 "$LATEST_LOG" + echo -e "\033[1;36m────────────────────────────────────────────────────────────────────────\033[0m\n" + else + echo -e "\033[33m⚠ no log file found in ${LOG_BASE}\033[0m" fi if [[ "$coverage_report" != "none" ]]; then - uv run coverage report -i + echo "::group::Coverage report" + uv run coverage report -i || true + echo "::endgroup::" fi - exit $EXIT_CODE - echo "::endgroup::" + if [[ "$IS_SUCCESS" == "true" ]]; then + echo -e "\033[1;32m╔══════════════════════════════════════════════════════════════════════════╗\033[0m" + echo -e "\033[1;32m║ ║\033[0m" + echo -e "\033[1;32m║ ✅ PASSED ║\033[0m" + echo -e "\033[1;32m║ ${{ inputs.test_case }}\033[0m" + echo -e "\033[1;32m║ ║\033[0m" + echo -e "\033[1;32m╚══════════════════════════════════════════════════════════════════════════╝\033[0m" + echo "::notice title=Result::✅ ${{ inputs.test_case }} — PASSED" + exit 0 + else + echo -e "\033[1;31m╔══════════════════════════════════════════════════════════════════════════╗\033[0m" + echo -e "\033[1;31m║ ║\033[0m" + echo -e "\033[1;31m║ ❌ FAILED (exit code: ${EXIT_CODE}) ║\033[0m" + echo -e "\033[1;31m║ ${{ inputs.test_case }}\033[0m" + echo -e "\033[1;31m║ ║\033[0m" + echo -e "\033[1;31m╚══════════════════════════════════════════════════════════════════════════╝\033[0m" + echo "::error title=Result::❌ ${{ inputs.test_case }} — FAILED (exit $EXIT_CODE)" + exit 1 + fi - name: Upload coverage uses: actions/upload-artifact@v4 From ceac269466f81dfbd0a15b007e64385a9c2df0cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Thu, 16 Apr 2026 18:40:49 +0200 Subject: [PATCH 23/72] build: bump TransformerEngine to release_v2.14 (#4331) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .github/actions/action.yml | 5 +- .github/workflows/cicd-main.yml | 10 +- pyproject.toml | 2 +- uv.lock | 178 +++++++++----------------------- 4 files changed, 57 insertions(+), 138 deletions(-) diff --git a/.github/actions/action.yml b/.github/actions/action.yml index 923173bf103..d500a5896d0 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -79,11 +79,10 @@ runs: shell: bash -x -e -u -o pipefail {0} run: | for i in 1 2 3; do - apt-get update && break - echo "apt-get update failed (attempt $i/3), retrying in 10s..." + apt-get update && apt-get install -y python3.12 python3.12-venv uuid-runtime && break + echo "apt attempt $i failed, retrying..." sleep 10 done - apt-get install -y python3.12 python3.12-venv uuid-runtime curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/local/bin sh - name: Create run-script (unit test) diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml index f4540b42c02..1e62f6b3016 100644 --- a/.github/workflows/cicd-main.yml +++ b/.github/workflows/cicd-main.yml @@ -489,11 +489,10 @@ jobs: shell: bash -x -e -u -o pipefail {0} run: | for i in 1 2 3; do - apt-get update && break - echo "apt-get update failed (attempt $i/3), retrying in 10s..." + apt-get update && apt-get install -y gh && break + echo "apt attempt $i failed, retrying..." sleep 10 done - apt-get install -y gh - name: Download test data shell: bash @@ -507,11 +506,10 @@ jobs: shell: bash run: | for i in 1 2 3; do - apt-get update && break - echo "apt-get update failed (attempt $i/3), retrying in 10s..." + apt-get update && apt-get install -y gh && break + echo "apt attempt $i failed, retrying..." sleep 10 done - apt-get install -y gh - name: Get last merged PR id: cache_from diff --git a/pyproject.toml b/pyproject.toml index 2070dbbfcb9..7733456ecc8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -202,7 +202,7 @@ override-dependencies = [ flash_mla = [ { git = "https://github.com/deepseek-ai/FlashMLA", rev = "9edee0c022cd0938148a18e334203b0aab43aa19" }, ] -transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "71bbefbf153418f943640df0f7373625dc93fa46" } +transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "f031cf87bd054c7558b887df7bed93975456667f" } nemo-run = { git = "https://github.com/NVIDIA-NeMo/Run.git", rev = "17ae86b64d7f75653351664f5d8c9e466faede00" } emerging_optimizers = { git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git", rev = "v0.2.0" } nvidia-resiliency-ext = { git = "https://github.com/NVIDIA/nvidia-resiliency-ext.git", rev = "15a851565a4ce846c04431ecb0cf09903ab4837e" } diff --git a/uv.lock b/uv.lock index 374d76e6249..78f40208716 100644 --- a/uv.lock +++ b/uv.lock @@ -77,8 +77,7 @@ dependencies = [ { name = "huggingface-hub" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "psutil" }, { name = "pyyaml" }, { name = "safetensors" }, @@ -545,8 +544,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "click" }, { name = "mypy-extensions" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pathspec" }, { name = "platformdirs" }, ] @@ -624,8 +622,7 @@ version = "1.6.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ninja" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts'" }, + { name = "packaging" }, { name = "torch", marker = "sys_platform == 'never'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/63/15/ec51d77a2df03ee93410f8ee97fceeb7181da213813c51243e9dd6d7e144/causal_conv1d-1.6.1.tar.gz", hash = "sha256:e4a697ec2db3906f012e675125569f8b510b4559bc53e3095143d91369e1221b", size = 29426, upload-time = "2026-03-10T08:56:35.305Z" } @@ -1088,8 +1085,7 @@ dependencies = [ { name = "multiprocess", marker = "python_full_version >= '3.14' and sys_platform == 'win32'" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "packaging", marker = "python_full_version >= '3.14' and sys_platform == 'win32'" }, { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "pandas", version = "3.0.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-lts') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "pyarrow", marker = "python_full_version >= '3.14' and sys_platform == 'win32'" }, @@ -1134,8 +1130,7 @@ dependencies = [ { name = "multiprocess", marker = "python_full_version < '3.14' or sys_platform != 'win32'" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-lts') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-lts') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, + { name = "packaging", marker = "python_full_version < '3.14' or sys_platform != 'win32'" }, { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "pandas", version = "3.0.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.14' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-lts') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "pyarrow", marker = "python_full_version < '3.14' or sys_platform != 'win32'" }, @@ -1410,8 +1405,7 @@ dependencies = [ { name = "nvidia-cudnn-frontend" }, { name = "nvidia-cutlass-dsl" }, { name = "nvidia-ml-py" }, - 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{ name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pyyaml" }, { name = "requests" }, { name = "tqdm" }, @@ -2255,21 +2247,20 @@ wheels = [ [[package]] name = "langchain-core" -version = "0.3.83" +version = "0.3.76" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jsonpatch" }, { name = "langsmith" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging" }, { name = "pydantic" }, { name = "pyyaml" }, { name = "tenacity" }, { name = "typing-extensions" }, - { name = "uuid-utils" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/21/a4/24f2d787bfcf56e5990924cacefe6f6e7971a3629f97c8162fc7a2a3d851/langchain_core-0.3.83.tar.gz", hash = "sha256:a0a4c7b6ea1c446d3b432116f405dc2afa1fe7891c44140d3d5acca221909415", size = 597965, upload-time = "2026-01-13T01:19:23.854Z" } +sdist = { url = "https://files.pythonhosted.org/packages/4f/4d/5e2ea7754ee0a1f524c412801c6ba9ad49318ecb58b0d524903c3d9efe0a/langchain_core-0.3.76.tar.gz", hash = "sha256:71136a122dd1abae2c289c5809d035cf12b5f2bb682d8a4c1078cd94feae7419", size = 573568, upload-time = "2025-09-10T14:49:39.863Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/5a/db/d71b80d3bd6193812485acea4001cdf86cf95a44bbf942f7a240120ff762/langchain_core-0.3.83-py3-none-any.whl", hash = "sha256:8c92506f8b53fc1958b1c07447f58c5783eb8833dd3cb6dc75607c80891ab1ae", size = 458890, upload-time = "2026-01-13T01:19:21.748Z" }, + { url = "https://files.pythonhosted.org/packages/77/b5/501c0ffcb09c734457ceaa86bc7b1dd37b6a261147bd653add03b838aacb/langchain_core-0.3.76-py3-none-any.whl", hash = "sha256:46e0eb48c7ac532432d51f8ca1ece1804c82afe9ae3dcf027b867edadf82b3ec", size = 447508, upload-time = "2025-09-10T14:49:38.179Z" }, ] [[package]] @@ -2305,7 +2296,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "httpx" }, { name = "orjson", marker = "platform_python_implementation != 'PyPy'" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging" }, { name = "pydantic" }, { name = "requests" }, { name = "requests-toolbelt" }, @@ -2457,8 +2448,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "einops" }, { name = "ninja" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts'" }, + { name = "packaging" }, { name = "setuptools" }, { name = "torch", marker = "sys_platform == 'never'" }, { name = "transformers" }, @@ -2611,8 +2601,7 @@ source = { editable = "." } dependencies = [ { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, ] @@ -2687,8 +2676,7 @@ build = [ { name = "cython" }, { name = "hatchling" }, { name = "nvidia-mathdx" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pybind11" }, { name = "setuptools" }, { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, @@ -2785,7 +2773,7 @@ requires-dist = [ { name = "torch", specifier = ">=2.6.0" }, { name = "tqdm", marker = "extra == 'dev'" }, { name = "tqdm", marker = "extra == 'lts'" }, - { name = "transformer-engine", extras = ["core-cu13", "pytorch"], marker = "extra == 'dev'", git = "https://github.com/NVIDIA/TransformerEngine.git?rev=71bbefbf153418f943640df0f7373625dc93fa46" }, + { name = "transformer-engine", extras = ["core-cu13", "pytorch"], marker = "extra == 'dev'", git = "https://github.com/NVIDIA/TransformerEngine.git?rev=f031cf87bd054c7558b887df7bed93975456667f" }, { name = "transformers", marker = "extra == 'mlm'" }, { name = "transformers", marker = "extra == 'training'" }, { name = "wandb", marker = "extra == 'mlm'" }, @@ -3210,8 +3198,7 @@ dependencies = [ { name = "leptonai" }, { name = "networkx" }, { name = "omegaconf" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "rich" }, { name = "toml" }, { name = "torchx" }, @@ -3636,7 +3623,7 @@ dependencies = [ { name = "ninja" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" } }, { name = "nvidia-ml-py" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging" }, { name = "pulp" }, { name = "pydantic" }, { name = "regex" }, @@ -3700,7 +3687,7 @@ dependencies = [ { name = "logsage" }, { name = "mcp" }, { name = "nvidia-ml-py" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging" }, { name = "protobuf" }, { name = "psutil" }, { name = "pyyaml" }, @@ -3941,7 +3928,7 @@ dependencies = [ { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" }, { name = "onnx", version = "1.19.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" }, { name = "onnx-ir", version = "0.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" }, + { name = "packaging", marker = "python_full_version >= '3.13'" }, { name = "typing-extensions", marker = "python_full_version >= '3.13'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/f5/2f/0bb2b6ca727e4d5173f640527f402ab4225def4bc8d667269b83047be8c4/onnxscript-0.5.0.tar.gz", hash = "sha256:4aba215e1f80fbcd07ba0d97d6bca96797fc3e9639eacb5434d35317ce1406aa", size = 588762, upload-time = "2025-09-12T16:57:46.484Z" } @@ -3979,8 +3966,7 @@ dependencies = [ { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts'" }, { name = "onnx", version = "1.21.0", source = { registry = "https://pypi.org/simple" } }, { name = "onnx-ir", version = "0.2.0", source = { registry = "https://pypi.org/simple" } }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - 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{ name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pluggy" }, ] sdist = { url = "https://files.pythonhosted.org/packages/ae/3c/c9d525a414d506893f0cd8a8d0de7706446213181570cdbd766691164e40/pytest-8.3.5.tar.gz", hash = "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845", size = 1450891, upload-time = "2025-03-02T12:54:54.503Z" } @@ -5456,8 +5383,7 @@ dependencies = [ { name = "filelock" }, { name = "jsonschema" }, { name = "msgpack" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "protobuf" }, { name = "pyyaml" }, { name = "requests" }, @@ -5480,10 +5406,10 @@ default = [ { name = "grpcio" }, { name = "opencensus" }, { name = "opentelemetry-exporter-prometheus", version = "0.54b1", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev' or extra == 'extra-13-megatron-core-lts'" }, - { name = "opentelemetry-exporter-prometheus", version = "0.61b0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts')" }, + { name = "opentelemetry-exporter-prometheus", version = "0.62b0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts')" }, { name = "opentelemetry-proto" }, { name = "opentelemetry-sdk", version = "1.33.1", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev' or extra == 'extra-13-megatron-core-lts'" }, - { name = "opentelemetry-sdk", version = "1.40.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts')" }, + { name = "opentelemetry-sdk", version = "1.41.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts')" }, { name = "prometheus-client" }, { name = "py-spy" }, { name = "pydantic" }, @@ -6138,8 +6064,7 @@ dependencies = [ { name = "docutils" }, { name = "imagesize" }, { name = "jinja2" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pygments" }, { name = "requests" }, { name = "roman-numerals" }, @@ -6364,8 +6289,7 @@ dependencies = [ { name = "markdown" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pillow" }, { name = "protobuf" }, { name = "setuptools" }, @@ -6681,8 +6605,8 @@ wheels = [ [[package]] name = "transformer-engine" -version = "2.14.0+71bbefbf" -source = { git = "https://github.com/NVIDIA/TransformerEngine.git?rev=71bbefbf153418f943640df0f7373625dc93fa46#71bbefbf153418f943640df0f7373625dc93fa46" } +version = "2.14.0+f031cf87" +source = { git = "https://github.com/NVIDIA/TransformerEngine.git?rev=f031cf87bd054c7558b887df7bed93975456667f#f031cf87bd054c7558b887df7bed93975456667f" } dependencies = [ { name = "einops" }, { name = "importlib-metadata" }, @@ -6691,7 +6615,7 @@ dependencies = [ { name = "onnx", version = "1.21.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "onnxscript", version = "0.5.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "onnxscript", version = "0.6.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging" }, { name = "pydantic" }, { name = "torch", marker = "sys_platform == 'never'" }, ] @@ -6705,8 +6629,7 @@ dependencies = [ { name = "huggingface-hub" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "pyyaml" }, { name = "regex" }, { name = "requests" }, @@ -6869,8 +6792,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "click" }, { name = "gitpython" }, - { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, - { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, + { name = "packaging" }, { name = "platformdirs" }, { name = "protobuf" }, { name = "pydantic" }, From efbe7a1f05fa8164ff0b94224db20c97d13b34dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Thu, 16 Apr 2026 21:59:44 +0200 Subject: [PATCH 24/72] feat: add create-issue skill (#4338) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- skills/create-issue/SKILL.md | 186 +++++++++++++++++++++++++++++++++++ 1 file changed, 186 insertions(+) create mode 100644 skills/create-issue/SKILL.md diff --git a/skills/create-issue/SKILL.md b/skills/create-issue/SKILL.md new file mode 100644 index 00000000000..d974fcb438e --- /dev/null +++ b/skills/create-issue/SKILL.md @@ -0,0 +1,186 @@ +--- +name: create-issue +description: Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure. Use when the user shares a GitHub Actions URL and wants to file a bug report for the CI failure. +user_invocable: true +argument: "" +--- + +# Triage CI Failure into a GitHub Issue + +Investigate a failing GitHub Actions job, extract the root cause, and file a +well-structured bug issue against `NVIDIA/Megatron-LM`. + +## Workflow + +### 1. Parse the URL + +The argument is a GitHub Actions URL. It will be one of: + +- **Job URL**: `https://github.com///actions/runs//job/` +- **Run URL**: `https://github.com///actions/runs/` + +Extract `run_id` and, if present, `job_id`. + +### 2. Identify failed jobs + +- If a `job_id` was provided, use that job directly. +- If only a `run_id` was provided, list all failed jobs in the run: + + ```bash + gh run view --repo NVIDIA/Megatron-LM --json jobs \ + --jq '[.jobs[] | select(.conclusion == "failure") | {id: .databaseId, name: .name, url: .url}]' + ``` + + If multiple jobs failed, ask the user which one to triage, or triage all of them if they say so. + +### 3. Fetch the failure logs + +For each failed job, retrieve the logs and narrow them down to the failure: + +```bash +# Pull the raw log and keep only error-bearing lines +gh api repos/NVIDIA/Megatron-LM/actions/jobs//logs 2>&1 \ + | grep -E "(FAILED|ERROR|\bError\b|assert|Traceback|Exception|##\[error\])" \ + | head -200 +``` + +Also capture the full job name: + +```bash +gh run view --job --repo NVIDIA/Megatron-LM --json name --jq .name +``` + +If the grep output is sparse, download the full logs and look for the pytest +`FAILURES` section or the last non-zero exit signal. + +### 4. Resolve the triggering PR and test author + +**Triggering PR**: the run's head branch follows the pattern `pull-request/`. +Extract it and resolve the PR: + +```bash +gh run view --repo NVIDIA/Megatron-LM --json headBranch --jq .headBranch +# → e.g. "pull-request/4332" +# Extract PR number and fetch metadata: +gh pr view --repo NVIDIA/Megatron-LM --json number,title,url \ + --jq '{number: .number, title: .title, url: .url}' +``` + +**Test file author**: find the GitHub login of whoever last touched the failing +test file. The file may not exist on `main` — first determine the PR's base +branch, then search from there: + +```bash +# 1. Get the PR's base branch (e.g. "main", "dev", "release/X.Y") +gh pr view --repo NVIDIA/Megatron-LM --json baseRefName --jq .baseRefName + +# 2. Search commits on that base branch +gh api "repos/NVIDIA/Megatron-LM/commits?path=&sha=&per_page=1" \ + --jq '.[0] | {login: .author.login, name: .commit.author.name, sha: .sha}' +``` + +If the result is empty (file was introduced by the PR itself), query the PR's +commits instead: + +```bash +gh api "repos/NVIDIA/Megatron-LM/pulls//commits" \ + --jq '[.[] | select(.files? // [] | any(.filename == ""))] | .[0].author.login' +``` + +As a last resort, list the PR commits and pick the author of the commit whose +message most closely relates to the failing test file. + +### 5. Extract the root cause + +From the logs, identify: + +- **Failed test(s)**: lines matching `FAILED tests/...::...` give the exact pytest node IDs. +- **Error message**: the assertion failure, exception type, or first meaningful + traceback frame — keep it under ~30 lines. +- **Job name**: the GitHub Actions job name (e.g. `tests/unit_tests/transformer/moe/**/*.py - latest`). +- **Run / job URLs** and **PR URL**: for linking in the issue. + +### 6. Check for duplicate issues + +Search for open issues that already cover the same test: + +```bash +gh issue list --repo NVIDIA/Megatron-LM \ + --state open \ + --search "" \ + --json number,title,url \ + --limit 10 +``` + +- If a matching open issue exists, **do not create a new one**. Report the + existing issue to the user and stop. +- If no match is found, proceed to file a new issue. + +### 7. Create the issue + +Pass `--assignee ` to assign the issue to the test file's +author. Include the triggering PR URL in the issue body. + +```bash +gh issue create \ + --repo NVIDIA/Megatron-LM \ + --title "🐛 CI failure: " \ + --label "bug" \ + --assignee "" \ + --body "..." +``` + +Use the bug-report template body structure: + +```markdown +**Describe the bug** + +CI test `` failed in job [``](). +Tag the [@mcore-oncall](https://github.com/orgs/NVIDIA/teams/mcore-oncall) to get oncall's attention to this issue. + +**Failing run** + +| Field | Value | +|-------|-------| +| PR | [#: ]() | +| Run | []() | +| Job | []() | + +**Error** + +``` + +``` + +**Steps/Code to reproduce bug** + +Re-run the failing CI job linked above, or locally inside the dev container: + +```bash +pytest +``` + +**Additional context** + +Triaged automatically via `/triage-issue`. +``` + +If multiple tests failed in the same job, list each one as a separate bullet +under "Describe the bug" and include the combined error snippets. Assign the +issue to the author of whichever test file appears first in the failure list. + +### 8. Report back to the user + +Print the URL of the newly created issue (or the duplicate, if found) so the +user can review or share it. + +## Important guidelines + +- Never create an issue if a duplicate already exists — link the existing one instead. +- Always include the triggering PR link in the issue body. +- Always assign the issue to the test file's most recent author. If the author + lookup fails (e.g. the commit was made by a bot or the login is unavailable), + skip `--assignee` and note it in the "Additional context" section. +- Keep the error snippet concise (≤30 lines). Truncate long tracebacks and note that the full log is available via the job URL. +- Do not guess the root cause — quote the actual log output verbatim. +- If the job is still in progress or the logs are unavailable, say so and ask the user to retry once the run completes. From 97f9ab6f2361690266f11fb65d72cd7f6ed05921 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Thu, 16 Apr 2026 22:26:05 +0200 Subject: [PATCH 25/72] Set megatron-fsdp to 0.5.0 --- .../core/distributed/fsdp/src/megatron_fsdp/package_info.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/package_info.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/package_info.py index a3a282a01c0..c3c7fc18ca5 100644 --- a/megatron/core/distributed/fsdp/src/megatron_fsdp/package_info.py +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/package_info.py @@ -2,7 +2,7 @@ MAJOR = 0 -MINOR = 3 +MINOR = 5 PATCH = 0 PRE_RELEASE = 'rc0' From 01eb7e87efd5ce3d724172b03fc37df7db28b3f3 Mon Sep 17 00:00:00 2001 From: Shifang Xu Date: Fri, 17 Apr 2026 02:56:01 +0800 Subject: [PATCH 26/72] M4 leftover for TE cuda graph (#3137) --- megatron/core/transformer/cuda_graphs.py | 89 +++++++++++++++++------- 1 file changed, 65 insertions(+), 24 deletions(-) diff --git a/megatron/core/transformer/cuda_graphs.py b/megatron/core/transformer/cuda_graphs.py index c7631519e43..067f6055015 100644 --- a/megatron/core/transformer/cuda_graphs.py +++ b/megatron/core/transformer/cuda_graphs.py @@ -20,8 +20,8 @@ import torch from torch.utils._pytree import tree_map as tree_map_pyt -from megatron.core import parallel_state from megatron.core.num_microbatches_calculator import get_num_microbatches +from megatron.core.process_groups_config import ProcessGroupCollection from megatron.core.tensor_parallel.random import ( CudaRNGStatesTracker, get_all_rng_states, @@ -1409,7 +1409,12 @@ class CudaGraphManager(torch.nn.Module): global_mempool = None def __init__( - self, config: TransformerConfig, base_module=None, function_name=None, need_backward=True + self, + config: TransformerConfig, + base_module=None, + function_name=None, + need_backward=True, + pg_collection=None, ): super().__init__() """Creates a CudaGraphManager to manage CUDA graphs for a Megatron module. @@ -1418,6 +1423,9 @@ def __init__( config: TransformerConfig object containing CUDA graph settings for memory pooling, graph retention, gradient accumulation, FP8/FP4, and warmup steps. """ + if pg_collection is None: + pg_collection = ProcessGroupCollection.use_mpu_process_groups() + self.pg_collection = pg_collection rng_tracker = get_cuda_rng_tracker() self.need_backward = need_backward @@ -1463,7 +1471,7 @@ def wrapped_func(*args, **kwargs): # Without pipeline parallelism, microbatches execute one at a time. # Therefore modules will always execute in the same order, so cudagraphs # can both be reused and share a single mempool. - self.reuse_cudagraphs = parallel_state.get_pipeline_model_parallel_world_size() == 1 + self.reuse_cudagraphs = self.pg_collection.pp.size() == 1 if CudaGraphManager.global_mempool is None: CudaGraphManager.global_mempool = torch.cuda.graph_pool_handle() # Cudagraph stream capture requires no operations on the default stream prior to the @@ -1711,7 +1719,9 @@ class TECudaGraphHelper: parameters that are covered by cudagraphs. """ - def __init__(self, model, config, seq_length, micro_batch_size, optimizers=[]): + def __init__( + self, model, config, seq_length, micro_batch_size, optimizers=[], pg_collection=None + ): assert HAVE_TE_GRAPHS, "CUDA Graphs are not supported without TE." assert ( config.cuda_graph_impl == "transformer_engine" @@ -1733,6 +1743,15 @@ def __init__(self, model, config, seq_length, micro_batch_size, optimizers=[]): self.seq_length = seq_length self.micro_batch_size = micro_batch_size self.optimizers = optimizers + self.pg_collection = pg_collection + if self.pg_collection is None: + self.pg_collection = ProcessGroupCollection.use_mpu_process_groups() + self.tp_group = self.pg_collection.tp + self.dp_cp_group = self.pg_collection.dp_cp + self.pp_group = self.pg_collection.pp + from megatron.core.pipeline_parallel.p2p_communication import P2PCommunicator + + self.p2p_communicator = P2PCommunicator(pp_group=self.pp_group, config=self.config) self.num_model_chunks = len(model) # Number of microbatches to capture. The value will be set in _get_cuda_graph_input_data(). @@ -1764,8 +1783,8 @@ def _discover_layers(self): num_graphable_layers = 0 log_on_each_pipeline_stage( logger=logger, - tp_group=None, - dp_cp_group=None, + tp_group=self.tp_group, + dp_cp_group=self.dp_cp_group, level=logging.DEBUG, msg=f'Rank {torch.distributed.get_rank()}: ' f'No valid layer in model chunk {chunk_number}.', @@ -1792,8 +1811,8 @@ def _discover_layers(self): callables_is_mtp.append(True) log_on_each_pipeline_stage( logger=logger, - tp_group=None, - dp_cp_group=None, + tp_group=self.tp_group, + dp_cp_group=self.dp_cp_group, level=logging.DEBUG, msg=f'Rank {torch.distributed.get_rank()}: ' f'{num_decoder_layers} decoder layers and {num_mtp_layers} MTP layers in ' @@ -1815,8 +1834,8 @@ def _discover_layers(self): log_on_each_pipeline_stage( logger=logger, - tp_group=None, - dp_cp_group=None, + tp_group=self.tp_group, + dp_cp_group=self.dp_cp_group, level=logging.INFO, msg=f'Rank {torch.distributed.get_rank()}: ' f'{len(self.flattened_callables)} graphable layers.', @@ -2086,6 +2105,23 @@ def get_rotary_pos_emb(transformer_module, transformer_input): return sample_args, sample_kwargs + def _get_amax_reduction_group(self, with_context_parallel=False, tp_only_amax_red=False): + """Get the FP8 amax reduction group the caller rank belongs to.""" + if with_context_parallel: + if not tp_only_amax_red: + assert self.pg_collection.tp_dp_cp is not None + return self.pg_collection.tp_dp_cp + else: + assert self.pg_collection.tp_cp is not None + return self.pg_collection.tp_cp + else: + if not tp_only_amax_red: + assert self.pg_collection.tp_dp is not None + return self.pg_collection.tp_dp + else: + assert self.pg_collection.tp is not None + return self.pg_collection.tp + def _get_cuda_graph_input_data(self): """ Create the CUDA Graph capturing input data. @@ -2099,10 +2135,7 @@ def _get_cuda_graph_input_data(self): ) # If PP is not enabled, we only need to capture one microbatch. - if ( - parallel_state.get_pipeline_model_parallel_world_size() == 1 - and not self.config.overlap_moe_expert_parallel_comm - ): + if self.pp_group.size() == 1 and not self.config.overlap_moe_expert_parallel_comm: assert ( self.num_model_chunks == 1 ), "If PP is not enabled, there should be only one model chunk." @@ -2114,7 +2147,8 @@ def _get_cuda_graph_input_data(self): self.num_microbatches, self.num_model_chunks, self.config.microbatch_group_size_per_vp_stage, - False, + forward_only=False, + p2p_communicator=self.p2p_communicator, ) schedule_table = get_schedule_table( self.num_microbatches, @@ -2126,8 +2160,8 @@ def _get_cuda_graph_input_data(self): ) log_on_each_pipeline_stage( logger=logger, - tp_group=None, - dp_cp_group=None, + tp_group=self.tp_group, + dp_cp_group=self.dp_cp_group, level=logging.DEBUG, msg=f'Rank {torch.distributed.get_rank()}: ORDER {order}', ) @@ -2152,8 +2186,8 @@ def _get_cuda_graph_input_data(self): self.num_microbatches = len(_order_without_wgrad) // self.num_model_chunks // 2 log_on_each_pipeline_stage( logger=logger, - tp_group=None, - dp_cp_group=None, + tp_group=self.tp_group, + dp_cp_group=self.dp_cp_group, level=logging.DEBUG, msg=f'Rank {torch.distributed.get_rank()}: ' f'ORDER after overlap_moe_expert_parallel_comm {order}', @@ -2224,8 +2258,12 @@ def _get_fp8_enabled(): get_fp8_recipe(self.config) if self.config.fp8 else get_fp4_recipe(self.config) ) kwargs['fp8_weight_caching'] = True - if is_te_min_version("1.14.0") and parallel_state.model_parallel_is_initialized(): - kwargs['fp8_group'] = parallel_state.get_amax_reduction_group( + if ( + is_te_min_version("1.14.0") + and self.pg_collection is not None + and self.pg_collection.tp is not None + ): + kwargs['fp8_group'] = self._get_amax_reduction_group( with_context_parallel=True, tp_only_amax_red=self.config.tp_only_amax_red ) else: @@ -2364,8 +2402,8 @@ def delete_cuda_graphs(self): log_on_each_pipeline_stage( logger=logger, - tp_group=None, - dp_cp_group=None, + tp_group=self.tp_group, + dp_cp_group=self.dp_cp_group, level=logging.INFO, msg=f'Rank {torch.distributed.get_rank()}: ' f'{graphs_reset} graphs deleted with explicit reset, ' @@ -2649,8 +2687,11 @@ def __init__( vision_seq_length: int, micro_batch_size: int, num_microbatches: int = 1, + pg_collection=None, ): - super().__init__(model, vision_config, vision_seq_length, micro_batch_size) + super().__init__( + model, vision_config, vision_seq_length, micro_batch_size, pg_collection=pg_collection + ) # Vision encoder concatenates all images along the sequence dimension # with a fixed batch dimension of 1, regardless of the training MBS. self.micro_batch_size = 1 From 260cba71338c83717d3f644c1f77cb1ccd8c7f8d Mon Sep 17 00:00:00 2001 From: Zhiyu Li Date: Thu, 16 Apr 2026 15:39:06 -0700 Subject: [PATCH 27/72] fix: wait for async P2P send before deallocating output tensor (#4047) --- megatron/core/pipeline_parallel/schedules.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/megatron/core/pipeline_parallel/schedules.py b/megatron/core/pipeline_parallel/schedules.py index 48963eb0e1d..14fc6041574 100644 --- a/megatron/core/pipeline_parallel/schedules.py +++ b/megatron/core/pipeline_parallel/schedules.py @@ -1565,6 +1565,11 @@ def forward_backward_helper_wrapper( ) if "recv_prev" in fwd_wait_handles: recv_prev_wait_handles.append(fwd_wait_handles.pop("recv_prev")) + # isend() copies asynchronously; wait until the copy is done before + # freeing the source buffer, otherwise the next PP stage gets corrupted data. + if send_next_wait_handle is not None and config.deallocate_pipeline_outputs: + send_next_wait_handle.wait() + send_next_wait_handle = None deallocate_output_tensor(output_tensor, config.deallocate_pipeline_outputs) if recv_prev: @@ -1687,6 +1692,11 @@ def pp_post_forward(output_tensor, vp_stage=None): ) if "recv_prev" in fwd_wait_handles: recv_prev_wait_handles.append(fwd_wait_handles.pop("recv_prev")) + # isend() copies asynchronously; wait until the copy is done before + # freeing the source buffer, otherwise the next PP stage gets corrupted data. + if send_next_wait_handle is not None and config.deallocate_pipeline_outputs: + send_next_wait_handle.wait() + send_next_wait_handle = None # assert fwd_wait_handles is not None # Put input_tensor and output_tensor_grad in data structures in the From 2aeaf56cb73f95823928bdeab47b2a0c68e2647a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Fri, 17 Apr 2026 02:16:54 +0200 Subject: [PATCH 28/72] ci(gb200): add 1-node mr-github functional test variants (#4334) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Signed-off-by: Oliver Koenig Co-authored-by: Claude Sonnet 4.6 --- skills/build-and-dependency/SKILL.md | 211 ++++++ skills/onboard-gb200-1node-tests/SKILL.md | 150 ++++ .../{build-and-test => testsystem}/SKILL.md | 342 ++++------ .../model_config.yaml | 90 +++ 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create mode 100644 tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph_1node/model_config.yaml create mode 100644 tests/test_utils/recipes/gb200/gpt-1node.yaml diff --git a/skills/build-and-dependency/SKILL.md b/skills/build-and-dependency/SKILL.md new file mode 100644 index 00000000000..c7dc89ec1ad --- /dev/null +++ b/skills/build-and-dependency/SKILL.md @@ -0,0 +1,211 @@ +--- +name: build-and-dependency +description: Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, updating uv.lock, and linting. +TRIGGER when: user asks to add, remove, or update a dependency; user edits or asks about pyproject.toml or uv.lock; uv.lock has a merge conflict; user asks to set up a dev environment or pull/build the CI container; user hits a container build error or uv error; user asks to run linting or autoformat. +DO NOT TRIGGER when: user is only running tests, investigating CI failures, or opening a PR (use testsystem instead). +--- + +# Build & Dependency Guide + +The core principle: **build and develop inside containers** — the CI container +ships the correct CUDA toolkit, PyTorch build, and pre-compiled native extensions +(TransformerEngine, DeepEP, …) that cannot be reproduced on a bare host. + +--- + +## Why Containers + +Megatron-LM depends on CUDA, NCCL, PyTorch with GPU support, TransformerEngine, +and optional components like ModelOpt and DeepEP. Installing these on a bare host +is fragile and hard to reproduce. The project ships Dockerfiles that pin every +dependency. + +**Use the container as your development environment.** This guarantees: + +- Identical CUDA / NCCL / cuDNN versions across all developers and CI. +- `uv.lock` resolves the same way locally and in CI. +- GPU-dependent operations (training, testing) work out of the box. + +--- + +## Step 1 — Acquire an Image + +**Option A — NVIDIA-internal: pull a CI-built image** + +> ⚠️ Requires access to the internal GitLab instance. +> See `tools/trigger_internal_ci.md` for setup (adding the git remote, obtaining a token). + +The internal GitLab CI publishes images to its container registry. +Derive the registry host from your configured `gitlab` remote — the same +host you use for `trigger_internal_ci.py`: + +```bash +# Derive host from your 'gitlab' remote: +GITLAB_HOST=$(git remote get-url gitlab | sed 's/.*@\(.*\):.*/\1/') + +docker pull ${GITLAB_HOST}/adlr/megatron-lm/mcore_ci_dev:main +``` + +**Option B — Build from scratch (works for everyone)** + +> ⚠️ `Dockerfile.ci.dev` has two stages: `main` and `jet`. The `jet` stage +> requires an internal build secret and will fail without it. Always pass +> `--target main` to stop at the public stage. + +```bash +# dev image (default) +docker build \ + --target main \ + --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev) \ + --build-arg IMAGE_TYPE=dev \ + -f docker/Dockerfile.ci.dev \ + -t megatron-lm:local . + +# lts image +docker build \ + --target main \ + --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.lts) \ + --build-arg IMAGE_TYPE=lts \ + -f docker/Dockerfile.ci.dev \ + -t megatron-lm:local-lts . +``` + +Which image variant is used is controlled by the PR label `container::lts`; +absent that label, `dev` is used. + +--- + +## Step 2 — Launch the Container + +**Option A — Local Docker runtime** + +```bash +docker run --rm --gpus all \ + -v $(pwd):/workspace \ + -w /workspace \ + megatron-lm:local \ + bash -c "" +``` + +**Option B — Slurm cluster (for those without a local Docker runtime)** + +NVIDIA clusters typically use [Pyxis](https://github.com/NVIDIA/pyxis) + +[enroot](https://github.com/NVIDIA/enroot). Request an interactive session: + +```bash +srun \ + --nodes=1 --gpus-per-node=8 \ + --container-image megatron-lm:local \ + --container-mounts $(pwd):/workspace \ + --container-workdir /workspace \ + --pty bash +``` + +For clusters that require a `.sqsh` archive first: + +```bash +enroot import -o megatron-lm.sqsh dockerd://megatron-lm:local +srun \ + --nodes=1 --gpus-per-node=8 \ + --container-image $(pwd)/megatron-lm.sqsh \ + --container-mounts $(pwd):/workspace \ + --container-workdir /workspace \ + --pty bash +``` + +--- + +## Dependency Management + +Dependencies are declared in `pyproject.toml`. The venv lives at `/opt/venv` +inside the container (already on `PATH`). + +> **All `uv` operations must be run inside the container.** +> Never run `uv sync` / `uv pip install` on the host. + +### uv Dependency Groups + +| Group | Purpose | +|-------|---------| +| `training` | Runtime training extras | +| `dev` | Full dev environment (TransformerEngine, ModelOpt, …) | +| `lts` | LTS-safe subset (no ModelOpt) | +| `test` | pytest, coverage, nemo-run | +| `linting` | ruff, black, isort, pylint | +| `build` | Cython, pybind11, nvidia-mathdx | + +Install commands (inside the container): + +```bash +# Full dev + test environment +uv sync --locked --group dev --group test + +# Linting only +uv sync --locked --only-group linting + +# LTS environment +uv sync --locked --group lts --group test +``` + +Several dependencies are sourced directly from git (TransformerEngine, nemo-run, +FlashMLA, Emerging-Optimizers, nvidia-resiliency-ext). The locked `uv.lock` file +pins exact revisions; update it with `uv lock` when changing `pyproject.toml`. + +### Adding a New Dependency + +Follow this three-step workflow: + +1. **Acquire a container image** — see [Step 1](#step-1--acquire-an-image) above. +2. **Launch the container interactively** — see [Step 2](#step-2--launch-the-container) above. +3. **Update the lock file inside the container**, then commit it: + + ```bash + # Inside the container: + uv add # adds to pyproject.toml and resolves + uv lock # regenerates uv.lock + # Exit the container, then on the host: + git add pyproject.toml uv.lock + git commit -S -s -m "build: add dependency" + ``` + +### Resolving a merge conflict in uv.lock + +`uv.lock` is machine-generated; never resolve conflicts manually. Instead: + +```bash +git checkout origin/main -- uv.lock # take main's version as the base +# then inside the container: +uv lock # re-resolve on top of your pyproject.toml changes +``` + +--- + +## Linting + +Run before opening a PR: + +```bash +# Check mode (no changes applied) +BASE_REF=main CHECK_ONLY=true SKIP_DOCS=false bash tools/autoformat.sh + +# Fix mode +BASE_REF=main CHECK_ONLY=false bash tools/autoformat.sh +``` + +Tools invoked: `black`, `isort`, `pylint`, `ruff`, `mypy`. + +After editing imports in any Python files, always run `uv run isort` on those +files before committing (repo CLAUDE.md requirement). + +--- + +## Common Pitfalls + +| Problem | Cause | Fix | +|---------|-------|-----| +| `uv sync --locked` fails | Dependency conflict or stale `uv.lock` | Re-run `uv lock` inside the container and commit updated lock | +| `ModuleNotFoundError` after pip install | pip installed outside the uv-managed venv | Use `uv add` and `uv sync`, never bare `pip install` | +| `uv: command not found` inside container | Wrong container image | Use the `megatron-lm` image built from `Dockerfile.ci.dev` | +| `No space left on device` during uv ops | Cache fills container's `/root/.cache/` | Mount a host cache dir via `-v $HOME/.cache/uv:/root/.cache/uv` | +| Pre-commit fails with linting errors | Code style violations | Run `BASE_REF=main CHECK_ONLY=false bash tools/autoformat.sh` | +| `docker build` fails with secret-related error | `Dockerfile.ci.dev` has a `jet` stage that requires an internal secret | Add `--target main` to stop before the `jet` stage | diff --git a/skills/onboard-gb200-1node-tests/SKILL.md b/skills/onboard-gb200-1node-tests/SKILL.md new file mode 100644 index 00000000000..ef5642f73fb --- /dev/null +++ b/skills/onboard-gb200-1node-tests/SKILL.md @@ -0,0 +1,150 @@ +--- +name: onboard-gb200-1node-tests +description: Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests. Use when the user asks to add GB200 github-mr tests, create single-node variants of existing tests, or expand CI coverage for GB200. +user_invocable: true +argument: "[model-yaml] # optional: gpt, moe, or both (default: both)" +TRIGGER when: user asks to add GB200 mr-github tests, create 1-node variants of functional tests, or onboard tests for GitHub CI on GB200. +DO NOT TRIGGER when: user is asking about H100 tests or making unrelated changes to existing test configs. +--- + +# Onboard GB200 1-Node GitHub MR Tests + +Create 1-node (`mr-github`) variants of existing 2-node (`mr`-scoped) GB200 functional tests. +Each GB200 node has **4 GPUs**. A 2-node test uses 8 GPUs total; the 1-node variant uses 4. + +--- + +## Background + +GB200 functional tests live in `tests/test_utils/recipes/gb200/`: + +| Recipe file | Notes | +|-------------|-------| +| `gpt.yaml` | GPT dense tests, `nodes: 2, gpus: 4` (8 total) | +| `moe.yaml` | MoE tests, `nodes: 2, gpus: 4` (8 total) | +| `moe-1node.yaml` | Existing 1-node MoE tests, `nodes: 1, gpus: 4` (4 total) | +| `gpt-1node.yaml` | 1-node GPT tests (create if not present) | + +Model configs live at: +`tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml` + +1-node test cases use the `_1node` suffix: +`tests/functional_tests/test_cases/{model}/{test_case}_1node/model_config.yaml` + +--- + +## Workflow + +### Step 1 — Find candidate tests + +Scan the `products:` block in `gpt.yaml` and `moe.yaml` for entries with `scope: [mr, ...]` or `scope: [mr-slim, ...]`. These are the 2-node tests that need 1-node `mr-github` counterparts. + +Ignore tests already covered in `*-1node.yaml` files, and ignore `nightly`, `weekly`, `mr-broken` scopes. + +### Step 2 — Read each model config + +For each candidate, read its `model_config.yaml` and extract the key parallelism arguments: + +``` +--tensor-model-parallel-size (TP) +--pipeline-model-parallel-size (PP) +--expert-model-parallel-size (EP) +--expert-tensor-parallel-size (ETP) +--context-parallel-size (CP) +--global-batch-size +--micro-batch-size +``` + +### Step 3 — Classify: trivial copy vs. needs adaptation + +The world size formula is: `world_size = TP × PP × DP` where `DP ≥ EP`. + +Going from 8 GPUs → 4 GPUs: + +| Condition | Action | +|-----------|--------| +| `TP × PP ≤ 4` | **Trivial copy.** Config unchanged; DP is halved automatically. | +| `TP × PP = 8` (e.g. tp4 pp2) | **Reduce PP.** Set `PP = PP / 2` (e.g. pp2→1). Verify `TP × PP_new ≤ 4`. | +| `EP > 4` (e.g. ep8 with tp1 pp1) | **Reduce EP.** Set `EP = 4`. Experts stay at `num-experts` (each EP rank holds more experts). | +| `EP > 4` **and** `TP × PP > 4` | Reduce both PP and EP as above. | +| ETP test (ep × etp ≤ TP × DP) | Check `EP × ETP ≤ TP × DP_new` after PP reduction. Usually satisfied when pp→1. | + +**Do not change GBS** — let gradient accumulation absorb the reduced DP. + +### Step 4 — Create `_1node` model config directories + +```bash +# Trivial copy +mkdir -p tests/functional_tests/test_cases/{model}/{test_case}_1node +cp tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml \ + tests/functional_tests/test_cases/{model}/{test_case}_1node/model_config.yaml + +# Then apply any parallelism changes (EP or PP) with Edit tool +``` + +### Step 5 — Create or update recipe files + +**For GPT tests** — create `tests/test_utils/recipes/gb200/gpt-1node.yaml` (if absent) by cloning `gpt.yaml`'s spec block with `nodes: 1`. Use this template for the spec: + +```yaml +type: basic +format_version: 1 +maintainers: [mcore] +loggers: [stdout] +spec: + name: "{test_case}_{environment}_{platforms}" + model: gpt # or moe + build: mcore-pyt-{environment} + nodes: 1 + gpus: 4 + n_repeat: 5 + platforms: dgx_gb200 + script_setup: | # copy verbatim from gpt.yaml / moe.yaml + ... + script: |- # copy verbatim from gpt.yaml / moe.yaml + ... +``` + +**For MoE tests** — append entries to the existing `moe-1node.yaml`. + +### Step 6 — Add products entries + +Scope convention: +- **1–2 most representative tests** per recipe: `scope: [mr-github, mr-github-slim]` +- **All other tests**: `scope: [mr-github]` + +```yaml +products: + - test_case: [_1node] + products: + - environment: [dev] + scope: [mr-github, mr-github-slim] # or [mr-github] + platforms: [dgx_gb200] +``` + +--- + +## Quick parallelism reference + +| Original (8 GPUs) | 1-node config (4 GPUs) | Notes | +|-------------------|----------------------|-------| +| tp1 pp1 ep1 → dp8 | tp1 pp1 ep1 → dp4 | trivial | +| tp2 pp1 ep1 → dp4 | tp2 pp1 ep1 → dp2 | trivial | +| tp1 pp2 ep1 → dp4 | tp1 pp2 ep1 → dp2 | trivial | +| tp4 pp1 ep1 → dp2 | tp4 pp1 ep1 → dp1 | trivial | +| tp1 pp4 ep1 → dp2 | tp1 pp4 ep1 → dp1 | trivial | +| tp1 pp1 ep8 → dp8 | tp1 pp1 ep4 → dp4 | ep 8→4 | +| tp4 pp2 ep2 etp2 → dp1 | tp4 pp1 ep2 etp2 → dp1 | pp 2→1 | + +--- + +## Checklist + +- [ ] Identified all `mr`-scoped tests in `gpt.yaml` and `moe.yaml` not yet in `*-1node.yaml` +- [ ] Read model config for each candidate +- [ ] Classified trivial vs. adaptation needed +- [ ] Created `_1node/model_config.yaml` for each test +- [ ] Applied EP or PP reductions where needed +- [ ] Created/updated recipe YAML with `nodes: 1, gpus: 4` +- [ ] Assigned `mr-github` scope (+ `mr-github-slim` for 1–2 representative tests per recipe) +- [ ] Verified no `mr-github-slim` overload (slim suite should stay small) diff --git a/skills/build-and-test/SKILL.md b/skills/testsystem/SKILL.md similarity index 51% rename from skills/build-and-test/SKILL.md rename to skills/testsystem/SKILL.md index 6bd823a42e1..3b3d6175915 100644 --- a/skills/build-and-test/SKILL.md +++ b/skills/testsystem/SKILL.md @@ -1,193 +1,24 @@ --- -name: build-and-test -description: Developer environment setup, CI/CD workflows, and CI failure debugging for Megatron-LM. Covers container-based development, uv package management, linting, running tests, CI failure investigation, and common pitfalls. -TRIGGER when: user asks to add, remove, or update a dependency; user edits or asks about pyproject.toml or uv.lock; user asks to set up a dev environment or run tests; user asks about a CI failure or build error. -DO NOT TRIGGER when: user is only reading or discussing code unrelated to dependencies, build, or CI. +name: testsystem +description: Test system, CI pipeline, and CI failure investigation for Megatron-LM. Covers test layout, recipe YAML structure, adding unit and functional tests, CI scope labels, triggering internal GitLab CI, pipeline structure, and debugging CI failures. +TRIGGER when: user asks to run tests, add a test, investigate a CI failure, understand the CI pipeline, or work with test recipes; user opens or pushes to a PR and needs to know which CI label to attach; user wants to trigger the internal GitLab CI pipeline; user asks to download golden values or references a pipeline/run ID in the context of golden values. +DO NOT TRIGGER when: user is only setting up the dev environment or managing dependencies (use build-and-dependency instead). --- -# Developer Guide - -This guide covers the recommended development workflow for Megatron-LM. -The core principle: **build and develop inside containers** — the CI container -ships the correct CUDA toolkit, PyTorch build, and pre-compiled native extensions -(TransformerEngine, DeepEP, …) that cannot be reproduced on a bare host. - ---- - -## Why Containers - -Megatron-LM depends on CUDA, NCCL, PyTorch with GPU support, TransformerEngine, -and optional components like ModelOpt and DeepEP. Installing these on a bare host -is fragile and hard to reproduce. The project ships Dockerfiles that pin every -dependency. - -**Use the container as your development environment.** This guarantees: - -- Identical CUDA / NCCL / cuDNN versions across all developers and CI. -- `uv.lock` resolves the same way locally and in CI. -- GPU-dependent operations (training, testing) work out of the box. - -### Step 1 — Acquire an Image - -**Option A — NVIDIA-internal: pull a CI-built image** - -> ⚠️ Requires access to the internal GitLab instance. -> See `tools/trigger_internal_ci.md` for setup (adding the git remote, obtaining a token). - -The internal GitLab CI publishes images to its container registry. -Derive the registry host from your configured `gitlab` remote — the same -host you use for `trigger_internal_ci.py`: - -```bash -# Derive host from your 'gitlab' remote: -GITLAB_HOST=$(git remote get-url gitlab | sed 's/.*@\(.*\):.*/\1/') - -docker pull ${GITLAB_HOST}/adlr/megatron-lm/mcore_ci_dev:main -``` - -**Option B — Build from scratch (works for everyone)** - -> ⚠️ `Dockerfile.ci.dev` has two stages: `main` and `jet`. The `jet` stage -> requires an internal build secret and will fail without it. Always pass -> `--target main` to stop at the public stage. - -```bash -# dev image (default) -docker build \ - --target main \ - --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.dev) \ - --build-arg IMAGE_TYPE=dev \ - -f docker/Dockerfile.ci.dev \ - -t megatron-lm:local . - -# lts image -docker build \ - --target main \ - --build-arg FROM_IMAGE_NAME=$(cat docker/.ngc_version.lts) \ - --build-arg IMAGE_TYPE=lts \ - -f docker/Dockerfile.ci.dev \ - -t megatron-lm:local-lts . -``` - -Which image variant is used is controlled by the PR label `container::lts`; -absent that label, `dev` is used. - -### Step 2 — Launch the Container - -**Option A — Local Docker runtime** - -```bash -docker run --rm --gpus all \ - -v $(pwd):/workspace \ - -w /workspace \ - megatron-lm:local \ - bash -c "" -``` - -**Option B — Slurm cluster (for those without a local Docker runtime)** - -NVIDIA clusters typically use [Pyxis](https://github.com/NVIDIA/pyxis) + -[enroot](https://github.com/NVIDIA/enroot). Request an interactive session: - -```bash -srun \ - --nodes=1 --gpus-per-node=8 \ - --container-image megatron-lm:local \ - --container-mounts $(pwd):/workspace \ - --container-workdir /workspace \ - --pty bash -``` - -For clusters that require a `.sqsh` archive first: - -```bash -enroot import -o megatron-lm.sqsh dockerd://megatron-lm:local -srun \ - --nodes=1 --gpus-per-node=8 \ - --container-image $(pwd)/megatron-lm.sqsh \ - --container-mounts $(pwd):/workspace \ - --container-workdir /workspace \ - --pty bash -``` +# Test System & CI Guide --- -## Dependency Management - -Dependencies are declared in `pyproject.toml`. The venv lives at `/opt/venv` -inside the container (already on `PATH`). - -> **All `uv` operations must be run inside the container.** -> Never run `uv sync` / `uv pip install` on the host. - -### uv Dependency Groups - -| Group | Purpose | -|-------|---------| -| `training` | Runtime training extras | -| `dev` | Full dev environment (TransformerEngine, ModelOpt, …) | -| `lts` | LTS-safe subset (no ModelOpt) | -| `test` | pytest, coverage, nemo-run | -| `linting` | ruff, black, isort, pylint | -| `build` | Cython, pybind11, nvidia-mathdx | - -Install commands (inside the container): - -```bash -# Full dev + test environment -uv sync --locked --group dev --group test - -# Linting only -uv sync --locked --only-group linting - -# LTS environment -uv sync --locked --group lts --group test -``` - -Several dependencies are sourced directly from git (TransformerEngine, nemo-run, -FlashMLA, Emerging-Optimizers, nvidia-resiliency-ext). The locked `uv.lock` file -pins exact revisions; update it with `uv lock` when changing `pyproject.toml`. - -### Adding a New Dependency - -Follow this three-step workflow: - -1. **Acquire a container image** — see [Step 1](#step-1--acquire-an-image) above. -2. **Launch the container interactively** — see [Step 2](#step-2--launch-the-container) above. -3. **Update the lock file inside the container**, then commit it: - - ```bash - # Inside the container: - uv add # adds to pyproject.toml and resolves - uv lock # regenerates uv.lock - # Exit the container, then on the host: - git add pyproject.toml uv.lock - git commit -S -s -m "build: add dependency" - ``` - ---- - -## Linting - -Run before opening a PR: - -```bash -# Check mode (no changes applied) -BASE_REF=main CHECK_ONLY=true SKIP_DOCS=false bash tools/autoformat.sh -``` - -Tools invoked: `black`, `isort`, `pylint`, `ruff`, `mypy`. - ---- - -## Running Tests - -### Test Layout +## Test Layout ```text tests/ ├── unit_tests/ # pytest, 1 node × 8 GPUs, torch.distributed runner ├── functional_tests/ # end-to-end shell + training scripts +│ └── test_cases/ +│ └── {model}/{test_case}/ +│ ├── model_config.yaml # training args +│ └── golden_values_{env}_{platform}.json └── test_utils/ ├── recipes/ │ ├── h100/ # YAML recipes for H100 jobs @@ -195,12 +26,13 @@ tests/ └── python_scripts/ # helpers (recipe_parser, golden-value download, …) ``` -### How Tests Execute +--- + +## How Tests Execute -All tests run on a **single DGX H100 node (8 GPUs)**. The GitHub Actions runner -invokes `launch_nemo_run_workload.py`, which uses **nemo-run** to launch a -`DockerExecutor` container. The repo is bind-mounted at `/opt/megatron-lm`; -training data is mounted at `/mnt/artifacts`. +The GitHub Actions runner invokes `launch_nemo_run_workload.py`, which uses +**nemo-run** to launch a `DockerExecutor` container. The repo is bind-mounted +at `/opt/megatron-lm`; training data is mounted at `/mnt/artifacts`. **Unit tests** are dispatched through `torch.distributed.run`: @@ -216,7 +48,9 @@ pytest validation step; training output from all ranks is uploaded as an artifac **3 times** for known transient patterns (NCCL timeout, ECC error, segfault, HuggingFace connectivity, …) before declaring a genuine failure. -### Recipe YAML Structure +--- + +## Recipe YAML Structure Recipes live in `tests/test_utils/recipes/` and are parsed by `tests/test_utils/python_scripts/recipe_parser.py`. Each file expands a @@ -225,34 +59,89 @@ cartesian `products` block into individual workload specs: ```yaml type: basic format_version: 1 +maintainers: [mcore] +loggers: [stdout] spec: - name: "{test_case}_{environment}_{platforms}_{tag}" - model: gpt + name: "{test_case}_{environment}_{platforms}" + model: gpt # maps to tests/functional_tests/test_cases/{model}/ + build: mcore-pyt-{environment} nodes: 1 gpus: 8 + n_repeat: 5 platforms: dgx_h100 time_limit: 1800 script_setup: | ... script: |- - bash tests/unit_tests/run_ci_test.sh \ - --tag {tag} \ - --environment {environment} \ - --bucket "tests/unit_tests/models/**/*.py" \ - --log-dir {assets_dir}/logs/1/ + bash tests/functional_tests/shell_test_utils/run_ci_test.sh ... products: - test_case: [my_test] - environment: [dev, lts] - tag: [latest, legacy] - scope: [mr-github] - n_repeat: [1] - time_limit: [1800] + products: + - environment: [dev, lts] + scope: [mr-github] + platforms: [dgx_h100] ``` Key runtime placeholders: `{assets_dir}`, `{artifacts_dir}`, `{test_case}`, -`{environment}`, `{platforms}`, `{tag}`, `{n_repeat}`. +`{environment}`, `{platforms}`, `{n_repeat}`. + +### CI Test Scope Labels + +The CI pipeline reads PR labels to decide test scope, n_repeat, and container image. + +**Decision tree (first match wins):** -### Adding a Unit Test +| Condition | `scope` | `n_repeat` | `lightweight` | Notes | +|-----------|---------|-----------|---------------|-------| +| Merge group | `mr-github` | 1 | false | Automatic, no label needed | +| Label: **`Run tests`** | `mr-github` | 1 | **true** | Trains 4 steps, no golden-value compare | +| Label: **`Run functional tests`** | `mr-github` | 5 | **false** | Trains 100 steps, golden-value compare | +| _(no label)_ | `mr-github-slim` | 5 | false | Slim subset only | + +**Orthogonal image label:** + +| Label | Effect | +|-------|--------| +| **`container::lts`** | Use the LTS base image instead of `dev` (combinable with any scope label) | +| **`Run MBridge tests`** | Also triggers the MBridge L1 test suite | + +### Disabling a Test Without Deleting It + +To temporarily disable a test case in a recipe YAML, suffix its `scope` value +with `-broken` — **do not delete the entry**: + +```yaml +# before (test runs in CI) +scope: [mr-github] + +# after (test is skipped; entry preserved for easy re-enable) +scope: [mr-github-broken] +``` + +This applies to any scope token (`mr-github`, `mr-github-slim`, `mr-gitlab`, +etc.). Deleting the entry entirely would require recreating the test case +definition when the fix lands. + +### Which label to attach when opening a PR + +Apply this logic based on what the PR changes: + +| Changed paths / nature of change | Label to attach | +|----------------------------------|-----------------| +| Docs only (`docs/`, `*.md`, docstrings) | _(none)_ | +| CI/tooling only (`.github/`, `tools/`, `Makefile`) | _(none)_ | +| Test files only (`tests/`) — existing tests, no new golden values | `Run tests` | +| **New test cases added** (no golden values exist yet) | `Run functional tests` | +| Non-numerical library code (logging, error handling, CLI flags, refactors) | `Run tests` | +| Could affect training numerics (model arch, attention, optimizer, distributed, MoE routing) | `Run functional tests` | +| Container or dependency changes (`docker/`, `pyproject.toml`, `uv.lock`) | `Run tests` + `container::lts` | +| Touches MBridge integration | add `Run MBridge tests` | + +**Rule of thumb:** default to `Run tests`. Always use `Run functional tests` when the PR adds new test cases (golden values must be generated) or when the change could plausibly shift loss curves. + +--- + +## Adding a Unit Test 1. Create `tests/unit_tests//test_.py`. 2. Use fixtures from `tests/unit_tests/conftest.py`. @@ -260,7 +149,7 @@ Key runtime placeholders: `{assets_dir}`, `{artifacts_dir}`, `{test_case}`, - `@pytest.mark.internal` — skipped on `legacy` tag - `@pytest.mark.flaky` — skipped in `lts` environment - `@pytest.mark.experimental` — `latest` tag only -4. Verify the test runs locally inside the container: +4. Verify locally inside the container: ```bash pytest -xvs tests/unit_tests//test_.py @@ -269,10 +158,12 @@ Key runtime placeholders: `{assets_dir}`, `{artifacts_dir}`, `{test_case}`, 5. If the test needs a dedicated CI bucket, add an entry to `tests/test_utils/recipes/h100/unit-tests.yaml`. -### Adding a Functional / Integration Test +--- + +## Adding a Functional / Integration Test 1. Create `tests/functional_tests/test_cases///`. -2. Write the shell test script; use `{assets_dir}` for all output paths. +2. Write `model_config.yaml` with `MODEL_ARGS`, `ENV_VARS`, and `TEST_TYPE`. 3. Add a YAML recipe under `tests/test_utils/recipes/h100/` (and `gb200/` if needed). Required fields: `scope`, `environment`, `platform`, `n_repeat`, `time_limit`. @@ -286,14 +177,38 @@ Key runtime placeholders: `{assets_dir}`, `{artifacts_dir}`, `{test_case}`, 6. Commit the downloaded golden values. -### CI Test Scope Labels +--- + +## Triggering Internal CI + +Use `tools/trigger_internal_ci.py` to push the current branch to the internal +GitLab remote and trigger a pipeline — without touching the GitLab UI. +Full setup and usage details: `tools/trigger_internal_ci.md`. + +**Prerequisites** (one-time): + +```bash +# 1. Add the internal GitLab remote +git remote add gitlab git@:ADLR/Megatron-LM.git + +# 2. Create a personal access token with 'api' scope on your GitLab profile, +# then store it: +export GITLAB_TOKEN=glpat- +``` + +**Usage:** + +```bash +python tools/trigger_internal_ci.py \ + --gitlab-origin gitlab \ + [--functional-test-scope mr] \ + [--functional-test-repeat 5] \ + [--functional-test-cases all] \ + [--dry-run] +``` -| PR label | Scope | Behaviour | -|----------|-------|-----------| -| _(none)_ | `mr-github-slim` | Lightweight subset, fast feedback | -| `Run tests` | `mr-github` | Full suite, lightweight mode (4 steps, no golden compare) | -| `Run functional tests` | `mr-github` | Full suite, 100-step training + golden compare, n_repeat=5 | -| `container::lts` | _(any)_ | Use the LTS base image instead of dev | +The script force-pushes the current branch as `pull-request/` and +prints the resulting pipeline URL. --- @@ -450,10 +365,7 @@ PR's changes or is a pre-existing issue on `main`. | Problem | Cause | Fix | |---------|-------|-----| -| `uv sync --locked` fails | Dependency conflict or stale `uv.lock` | Re-run `uv lock` inside the container and commit updated lock | -| `ModuleNotFoundError` after pip install | pip installed outside the uv-managed venv | Use `uv add` and `uv sync`, never bare `pip install` | -| `uv: command not found` inside container | Wrong container image | Use the `megatron-lm` image built from `Dockerfile.ci.dev` | -| `No space left on device` during uv ops | Cache fills container's `/root/.cache/` | Mount a host cache dir via `-v $HOME/.cache/uv:/root/.cache/uv` | -| Pre-commit fails with linting errors | Code style violations | Run `BASE_REF=main CHECK_ONLY=false bash tools/autoformat.sh` | | Port collision on multi-GPU runs | torchrun binding conflicts | Use `torch.distributed.run` via the container entry point | -| `docker build` fails with secret-related error | `Dockerfile.ci.dev` has a `jet` stage that requires an internal secret | Add `--target main` to stop before the `jet` stage | +| Test passes locally but fails in CI | Different environment or data path | Check `DATA_PATH`, `DATA_CACHE_PATH`, and the `environment` tag (`dev` vs `lts`) | +| Golden value mismatch after a code change | Numerical regression | Download new golden values via `download_golden_values.py` after a clean run | +| `cicd-integration-tests-gb200` not triggered | GB200 jobs require maintainer status | Ask a maintainer to trigger, or add the `Run functional tests` label | diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_reruns_persistent_1_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_reruns_persistent_1_1node/model_config.yaml new file mode 100644 index 00000000000..daa5093f0a4 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_reruns_persistent_1_1node/model_config.yaml @@ -0,0 +1,90 @@ +ENV_VARS: + SKIP_PYTEST: 1 + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + # generic training settings + --deterministic-mode: true + --bf16: true + --train-iters: 50 + --eval-iters: 0 + --manual-gc: true + --use-mcore-models: true + --distributed-backend: nccl + # parallelism settings + --sequence-parallel: true + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 2 + --micro-batch-size: 1 + --global-batch-size: 128 + # embedding settings + --untie-embeddings-and-output-weights: true + --position-embedding-type: rope + --rotary-percent: 1.0 + --max-position-embeddings: 4096 + # transformer settings + --num-layers: 32 + --hidden-size: 3072 + --ffn-hidden-size: 8192 + --num-attention-heads: 32 + --num-query-groups: 8 + --seq-length: 4096 + --kv-channels: 128 + --ffn-hidden-size: 8192 + --group-query-attention: true + --normalization: RMSNorm + --swiglu: true + --attention-dropout: 0.0 + --hidden-dropout: 0.0 + --no-create-attention-mask-in-dataloader: true + --transformer-impl: transformer_engine + --disable-bias-linear: true + # gradient & optimizer settings + --clip-grad: 1.0 + --overlap-grad-reduce: true + --overlap-param-gather: true + --lr: 3e-4 + --lr-warmup-samples: 0 + --adam-beta1: 0.9 + --adam-beta2: 0.95 + --adam-eps: 1e-8 + --use-distributed-optimizer: true + --split: 949,50,1 + --no-gradient-accumulation-fusion: true + # checkpoint settings + --save-interval: 10000 + --eval-interval: 1000 + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + # data settings + --data-cache-path: ${DATA_CACHE_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + # logging settings + --tensorboard-dir: ${TENSORBOARD_PATH} + --timing-log-level: 0 + --log-interval: 1 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --log-memory-to-tensorboard: true + # rerun settings + --rerun-mode: validate_results + --error-injection-rate: 100 + --error-injection-type: persistent_error + --async-save: true + --use-persistent-ckpt-worker: true +AFTER_SCRIPT: | + check_log() { if [[ -z $(grep -r $1 "$2" $LOG_DIR) ]]; then exit 1; else echo OK; fi } + check_log -F "Result validation enabled" + check_log -F "Injecting error type Persistent error" + check_log -F "First rerun: unexpected result is reproducible within the tolerance" + check_log -F "Saving a checkpoint and exiting now. Please resume the job from the checkpoint to rerun the last iteration and establish a diagnostic" + EXIT_CODE=0 +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..4a618a0422f --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.88433, + "2": 10.88455, + "3": 10.88309, + "4": 10.88497, + "5": 10.87886, + "6": 10.87416, + "7": 10.88029, + "8": 10.87382, + "9": 10.88138, + "10": 10.88114, + "11": 10.87549, + "12": 10.86262, + "13": 10.87418, + "14": 10.86552, + "15": 10.83746, + "16": 10.83285, + "17": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset_1node/model_config.yaml new file mode 100644 index 00000000000..86039c4d7f2 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset_1node/model_config.yaml @@ -0,0 +1,58 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --fim-data: true + --fim-rate: 0.5 + --fim-spm-rate: 0.5 + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_h100.json index 9497508565b..a99191f0165 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files/golden_values_dev_dgx_h100.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 3.79403, - "3": 0.07704, - "4": 0.05712, - "5": 0.05704, - "6": 0.0572, - "7": 0.05716, - "8": 0.05679, - "9": 0.05811, - "10": 0.05723, - "11": 0.05946, - "12": 0.0569, - "13": 0.05852, - "14": 0.05673, - "15": 0.0581, 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${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --no-mmap-bin-files: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..2307d5176ee --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + 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--data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..529a9e27376 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.8851, + "2": 10.88271, + "3": 10.88387, + "4": 10.88623, + "5": 10.87916, + "6": 10.87661, + "7": 10.87963, + "8": 10.87211, + "9": 10.88437, + "10": 10.87858, + "11": 10.87354, + "12": 10.86326, + "13": 10.87879, + "14": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 1 + --recompute-granularity: full + --recompute-method: uniform + --recompute-num-layers: 1 + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..5e8a1e44b89 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.90185, + "2": 10.90476, + "3": 10.91298, + "4": 10.90732, + "5": 10.90338, + "6": 10.88352, + "7": 10.89466, + "8": 10.9003, + "9": 10.90241, + "10": 10.90124, + "11": 10.89365, + "12": 10.88403, + "13": 10.87658, + "14": 10.87083, + "15": 10.86053, + "16": 10.85047, + "17": 10.85366, + "18": 10.83505, + "19": 10.83504, + "20": 10.75556, + "21": 10.75273, + "22": 10.74048, + "23": 10.73225, + "24": 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--hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 2 + --position-embedding-type: rope + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..4a78de1149f --- /dev/null +++ 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0.58777 + } + } +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion_1node/model_config.yaml new file mode 100644 index 00000000000..b4e07b5b5e1 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 2 + --position-embedding-type: rope + --rotary-interleaved: true + --no-rope-fusion: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..c55077b5809 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear_1node/model_config.yaml new file mode 100644 index 00000000000..305e7eabf98 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear_1node/model_config.yaml @@ -0,0 +1,55 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --disable-bias-linear: true + --async-save: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..071b5fa17ad --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.92753, + "2": 10.92559, + "3": 10.93114, + "4": 10.93741, + "5": 10.93016, + "6": 10.92355, + "7": 10.93863, + "8": 10.92774, + "9": 10.93076, + "10": 10.92802, + "11": 10.91411, + "12": 10.92298, + "13": 10.92014, + "14": 10.90614, + "15": 10.88338, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear_1node/model_config.yaml @@ -0,0 +1,55 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --disable-bias-linear: true + --async-save: true + --use-persistent-ckpt-worker: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true +TEST_TYPE: ckpt-resume diff --git 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--log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --swiglu: true + --ckpt-fully-parallel-load: true + --async-save: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..b6f8a75f92d --- /dev/null +++ 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+ NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --untie-embeddings-and-output-weights: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/model_config.yaml new file mode 100644 index 00000000000..8d764f5a87a --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 + PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-layers-per-virtual-pipeline-stage: 1 + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..93d7bced52f --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.93805, + "2": 10.93722, + "3": 10.93622, + "4": 10.94202, + "5": 10.93341, + "6": 10.93239, + "7": 10.9378, + "8": 10.93336, + "9": 10.93523, + "10": 10.93708, + "11": 10.92541, + "12": 10.92478, + "13": 10.92498, + "14": 10.913, + "15": 10.89303, + "16": 10.88848, + "17": 10.89973, + "18": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer_1node/model_config.yaml new file mode 100644 index 00000000000..28248046b44 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer_1node/model_config.yaml @@ -0,0 +1,58 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-layers-per-virtual-pipeline-stage: 1 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --overlap-param-gather: true + --overlap-param-gather-with-optimizer-step: true + --check-weight-hash-across-dp-replicas-interval: 10 + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..d956f1fde7f --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.93832, + "2": 10.93769, + "3": 10.93625, + "4": 10.94232, + "5": 10.93349, + "6": 10.93269, + "7": 10.93796, + "8": 10.93356, + "9": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr_1node/model_config.yaml @@ -0,0 +1,54 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 + PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-layers-per-virtual-pipeline-stage: 1 + --decoupled-lr: 0.0002 + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --ckpt-format: torch + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true +TEST_TYPE: regular # Usually ckpt-resume, but as a WAR to #513 set to regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..ce2166701f3 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.93832, + "2": 10.93769, + "3": 10.93625, + "4": 10.9423, + "5": 10.93352, + "6": 10.93273, + "7": 10.93793, + "8": 10.93357, + "9": 10.93552, + "10": 10.93693, + "11": 10.92585, + "12": 10.92413, + "13": 10.9251, + "14": 10.91258, + "15": 10.89335, + "16": 10.88822, + "17": 10.89973, + "18": 10.87821, + "19": 10.87589, + "20": 10.79119, + "21": 10.78019, + "22": 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NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-layers-per-virtual-pipeline-stage: 1 + --calculate-per-token-loss: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node/golden_values_dev_dgx_gb200.json 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--num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-layers-per-virtual-pipeline-stage: 1 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap_1node/model_config.yaml new file mode 100644 index 00000000000..7dcdd335e8c --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap_1node/model_config.yaml @@ -0,0 +1,59 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 + PYTORCH_CUDA_ALLOC_CONF: expandable_segments:True +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 40 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-layers-per-virtual-pipeline-stage: 1 + --overlap-p2p-communication-warmup-flush: true + --microbatch-group-size-per-virtual-pipeline-stage: 5 + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..2e145054d9c --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.91778, + "2": 10.91602, + "3": 10.9198, + "4": 10.91999, + "5": 10.91201, + "6": 10.91954, + "7": 10.92034, + "8": 10.91471, + "9": 10.90787, + "10": 10.90661, + "11": 10.90292, + "12": 10.90754, + "13": 10.90318, + "14": 10.8975, + "15": 10.86511, + "16": 10.86752, + "17": 10.8629, + "18": 10.85425, + "19": 10.85849, + "20": 10.77184, + "21": 10.76543, + 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a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node/model_config.yaml new file mode 100644 index 00000000000..e068c864d10 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --untie-embeddings-and-output-weights: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --decoder-first-pipeline-num-layers: 2 + --decoder-last-pipeline-num-layers: 2 + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..c4a4f44df1d --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.90673, + "2": 10.91263, + "3": 10.91376, + "4": 10.90507, + "5": 10.90666, + "6": 10.9031, 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--log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 4 + --num-virtual-stages-per-pipeline-rank: 2 + --untie-embeddings-and-output-weights: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --account-for-embedding-in-pipeline-split: true + --account-for-loss-in-pipeline-split: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_gdn_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_gdn_1node/model_config.yaml new file mode 100644 index 00000000000..cc7211c9967 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_gdn_1node/model_config.yaml @@ -0,0 +1,82 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 + ENABLE_LIGHTWEIGHT_MODE: true +MODEL_ARGS: + # Add network size args + --untie-embeddings-and-output-weights: true + --num-layers: 6 + --hidden-size: 512 + --num-attention-heads: 8 + --group-query-attention: true + --num-query-groups: 2 + --swiglu: true + --position-embedding-type: rope + --rotary-percent: 0.5 + --no-rope-fusion: true #TODO: We can remove this once upgrading to the DEV container + --apply-layernorm-1p: true + --apply-wd-to-qk-layernorm: true + --attention-output-gate: true + --experimental-attention-variant: gated_delta_net + --linear-attention-freq: 3 + --linear-conv-kernel-dim: 4 + --linear-key-head-dim: 64 + --linear-value-head-dim: 64 + --linear-num-key-heads: 4 + --linear-num-value-heads: 8 + # Add MoE args + --num-experts: 32 + --moe-ffn-hidden-size: 64 + --moe-shared-expert-intermediate-size: 64 + --moe-shared-expert-gate: true + --moe-router-load-balancing-type: aux_loss + --moe-router-topk: 8 + --disable-bias-linear: true + --moe-router-dtype: fp32 + # Add logging args + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 25 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --sequence-parallel: true + --untie-embeddings-and-output-weights: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_modelopt_distill_resume_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_modelopt_distill_resume_1node/model_config.yaml new file mode 100644 index 00000000000..c75a5a81414 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_modelopt_distill_resume_1node/model_config.yaml @@ -0,0 +1,71 @@ +ENV_VARS: + SKIP_PYTEST: 1 + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 + ARTIFACTS_ROOT: /workspace/checkpoints + DISTILL_CONFIG: '{intermediate_layer_pairs: [["decoder.final_layernorm", "decoder.final_layernorm"]], logit_layers: ["output_layer", "output_layer"], skip_lm_loss: true, kd_loss_scale: 10.0}' +BEFORE_SCRIPT: | + mkdir -p ${DATA_CACHE_PATH}/distill && echo $DISTILL_CONFIG | yq -P > ${DATA_CACHE_PATH}/distill/distill_config.yaml +MODEL_ARGS: + --export-te-mcore-model: true + --export-kd-teacher-load: ${DATA_PATH}/model/gpt_dummy_pyt/ckpt/24.10.0_bf16_teacher + --export-kd-cfg: ${DATA_CACHE_PATH}/distill/distill_config.yaml + --auto-detect-ckpt-format: true + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --normalization: RMSNorm + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 2 + --global-batch-size: 16 + --seq-length: 1024 + --max-position-embeddings: 1024 + --position-embedding-type: rope + --no-rope-fusion: true #TODO: We can remove this once upgrading to the DEV container + --rotary-percent: 0.5 + --swiglu: true + --untie-embeddings-and-output-weights: true + --disable-bias-linear: true + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --use-distributed-optimizer: true + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --sequence-parallel: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --use-checkpoint-opt_param-scheduler: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..698afb0e549 --- /dev/null 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diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic_1node/model_config.yaml new file mode 100644 index 00000000000..bd303906bd6 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..e4c41e6a17d --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.95267, + "2": 10.9487, + "3": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances_1node/model_config.yaml new file mode 100644 index 00000000000..d226ebb89c5 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances_1node/model_config.yaml @@ -0,0 +1,60 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --num-distributed-optimizer-instances: 2 + --overlap-grad-reduce: true + --overlap-param-gather: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..ea82be620bf --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95181, + "4": 10.93701, + "5": 10.94341, + "6": 10.9381, + "7": 10.94906, + "8": 10.92913, + "9": 10.94291, + "10": 10.93268, + "11": 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a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_1node/model_config.yaml new file mode 100644 index 00000000000..7be86a80447 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_1node/model_config.yaml @@ -0,0 +1,55 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..cc303f41c1b --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95181, + "4": 10.93701, + "5": 10.94341, + "6": 10.9381, + "7": 10.94906, + "8": 10.92913, + "9": 10.94291, + "10": 10.93268, + "11": 10.9326, + "12": 10.92193, + "13": 10.91841, + "14": 10.91226, + "15": 10.891, + "16": 10.87484, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_1node/model_config.yaml new file mode 100644 index 00000000000..27fd45b5701 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_1node/model_config.yaml @@ -0,0 +1,55 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --calculate-per-token-loss: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..298678e05a1 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95179, + "4": 10.93702, + "5": 10.94339, + "6": 10.93813, + "7": 10.9491, + "8": 10.92914, + "9": 10.94288, + "10": 10.9327, + "11": 10.93261, + "12": 10.92195, + "13": 10.91843, + "14": 10.91224, + "15": 10.89099, + "16": 10.87486, + "17": 10.88663, + "18": 10.86792, + "19": 10.87316, + "20": 10.77511, 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100644 index 00000000000..27ff315894a --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic_1node/model_config.yaml @@ -0,0 +1,55 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --calculate-per-token-loss: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..d93eb6028ee --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95181, + "4": 10.93701, + "5": 10.94341, + "6": 10.9381, + "7": 10.94906, + "8": 10.92913, + "9": 10.94291, + "10": 10.93268, + "11": 10.9326, + "12": 10.92193, + "13": 10.91841, + "14": 10.91226, + "15": 10.891, + "16": 10.87484, + "17": 10.88661, + "18": 10.86792, + "19": 10.87319, + "20": 10.77508, + "21": 10.76177, + "22": 10.75197, + "23": 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--- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last_1node/model_config.yaml @@ -0,0 +1,58 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --use-tp-pp-dp-mapping: true + --expert-tensor-parallel-size: 4 + --calculate-per-token-loss: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..a981e98b820 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95183, + "4": 10.93701, + "5": 10.94343, + "6": 10.93812, + "7": 10.94908, + "8": 10.92911, + "9": 10.94294, + "10": 10.9327, + "11": 10.93263, + "12": 10.92198, + "13": 10.91843, + "14": 10.91225, + "15": 10.89099, + "16": 10.87483, + "17": 10.88665, + "18": 10.86793, + "19": 10.87316, + "20": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last_1node/model_config.yaml new file mode 100644 index 00000000000..c1087788693 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --use-tp-pp-dp-mapping: true + --expert-tensor-parallel-size: 4 + --calculate-per-token-loss: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..d47c2287f05 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95181, + "4": 10.93701, + "5": 10.94341, + "6": 10.9381, + "7": 10.94906, + "8": 10.92913, + "9": 10.94291, + "10": 10.93268, + "11": 10.9326, + "12": 10.92193, + "13": 10.91841, + "14": 10.91226, + "15": 10.891, + "16": 10.87484, + "17": 10.88661, + "18": 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00000000000..67831941bf5 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --use-tp-pp-dp-mapping: true + --expert-tensor-parallel-size: 4 + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..db21b12ea1a --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95177, + "4": 10.93704, + "5": 10.94344, + "6": 10.93813, + "7": 10.94906, + "8": 10.92916, + "9": 10.94292, + "10": 10.93268, + "11": 10.93263, + "12": 10.92191, + "13": 10.91842, + "14": 10.91226, + "15": 10.89099, + "16": 10.87486, + "17": 10.88661, + "18": 10.86794, + "19": 10.87322, + "20": 10.77507, + "21": 10.76177, + "22": 10.75195, + "23": 10.73999, + "24": 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last_1node/model_config.yaml @@ -0,0 +1,57 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --use-tp-pp-dp-mapping: true + --expert-tensor-parallel-size: 4 + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..8e1404f1d9e --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.95727, + "2": 10.94891, + "3": 10.95182, + "4": 10.93703, + "5": 10.9434, + "6": 10.93812, + "7": 10.94907, + "8": 10.92916, + "9": 10.94293, + "10": 10.93267, + "11": 10.93258, + "12": 10.92195, + "13": 10.9184, + "14": 10.91222, + "15": 10.89099, + "16": 10.87484, + "17": 10.88662, + "18": 10.8679, + "19": 10.87319, + "20": 10.7751, + "21": 10.7618, + "22": 10.75194, + "23": 10.73997, + "24": 10.70111, + "25": 10.71372, + "26": 10.68547, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cp2_nondeterministic_1node/model_config.yaml @@ -0,0 +1,56 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..d5f4d1bf850 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": { + "1": 10.93259, + "2": 10.92912, + "3": 10.92985, + "4": 10.92992, + "5": 10.93096, + "6": 10.92837, + "7": 10.9248, + "8": 10.9227, + "9": 10.92097, + "10": 10.92135, + "11": 10.91167, + "12": 10.9192, + "13": 10.91617, + "14": 10.90004, + "15": 10.88222, + "16": 10.86996, + "17": 10.8703, + "18": 10.86541, + "19": 10.86384, + "20": 10.77636, + "21": 10.75923, + "22": 10.75923, + "23": 10.74899, + "24": 10.7154, + "25": 10.71529, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion_1node/model_config.yaml @@ -0,0 +1,51 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --cross-entropy-loss-fusion: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_mla_1node/golden_values_dev_dgx_gb200.json new file 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CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 4 + --hidden-size: 512 + --num-attention-heads: 8 + --multi-latent-attention: true + --q-lora-rank: 192 + --kv-lora-rank: 64 + --qk-head-dim: 16 + --qk-pos-emb-head-dim: 8 + --v-head-dim: 16 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 25 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --sequence-parallel: true + --untie-embeddings-and-output-weights: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..236b42d4a3d --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.93086, + "2": 10.92833, + "3": 10.92866, + "4": 10.9288, + "5": 10.93088, + "6": 10.92771, + "7": 10.92336, + "8": 10.92159, + "9": 10.92031, + "10": 10.91964, + "11": 10.91027, + "12": 10.91814, + "13": 10.91523, + "14": 10.89825, + "15": 10.88042, + "16": 10.86841, + "17": 10.86882, + "18": 10.86258, + "19": 10.86185, + "20": 10.77348, + "21": 10.75516, + "22": 10.75521, + "23": 10.74508, + "24": 10.71106, + "25": 10.71082, + "26": 10.69968, + 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--log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..aab973f1fdd --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic_1node/golden_values_dev_dgx_gb200.json @@ -0,0 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic_1node/model_config.yaml new file mode 100644 index 00000000000..c682b78eedc --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic_1node/model_config.yaml @@ -0,0 +1,56 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --context-parallel-size: 2 + --sequence-parallel: true + --hidden-dropout: 0.0 + --attention-dropout: 0.0 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: flash + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..402f169bae6 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.93259, + "2": 10.92912, + "3": 10.92983, + "4": 10.92995, + "5": 10.93095, + "6": 10.9284, + "7": 10.92478, + "8": 10.9227, + "9": 10.92096, + "10": 10.92136, + "11": 10.91166, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion_1node/model_config.yaml @@ -0,0 +1,53 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --cross-entropy-loss-fusion: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..a21aff2a8af --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.93086, + "2": 10.92833, + "3": 10.92866, + "4": 10.9288, + "5": 10.93088, + "6": 10.92771, + "7": 10.92336, + "8": 10.92159, + "9": 10.92031, + "10": 10.91964, + "11": 10.91027, + "12": 10.91814, + "13": 10.91523, + "14": 10.89825, + "15": 10.88042, + "16": 10.86841, + "17": 10.86882, + "18": 10.86258, + "19": 10.86185, + "20": 10.77348, + "21": 10.75516, + "22": 10.75521, 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NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --ddp-average-in-collective: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute_1node/golden_values_dev_dgx_gb200.json new file mode 100644 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a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute_1node/model_config.yaml new file mode 100644 index 00000000000..f600f587ae9 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute_1node/model_config.yaml @@ -0,0 +1,55 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --defer-embedding-wgrad-compute: true + --wgrad-deferral-limit: 2 + --deterministic-mode: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..35e03d6e4ca --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.93259, + "2": 10.92912, + "3": 10.92982, + "4": 10.92995, + "5": 10.93097, + "6": 10.92838, + "7": 10.92478, + "8": 10.92268, + "9": 10.92099, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone_1node/model_config.yaml @@ -0,0 +1,52 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor_1node/golden_values_dev_dgx_gb200.json 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--log-memory-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --use-megatron-fsdp: true + --calculate-per-token-loss: true + --data-parallel-sharding-strategy: optim_grads_params + --use-distributed-optimizer: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: fsdp_dtensor + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..40726e38439 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + 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--log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --overlap-param-gather: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..2f50d1852c0 --- /dev/null +++ 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3.01584, + "100": 2.02465 + } + } +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node/model_config.yaml new file mode 100644 index 00000000000..bd6f526c209 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node/model_config.yaml @@ -0,0 +1,56 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..1dc40207f4e --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + 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a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node/model_config.yaml new file mode 100644 index 00000000000..12acfaf8402 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node/model_config.yaml @@ -0,0 +1,60 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --overlap-param-gather: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --async-strategy: mcore + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..eb68302393d --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ 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a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node/model_config.yaml new file mode 100644 index 00000000000..addb335475f --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node/model_config.yaml @@ -0,0 +1,56 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --qk-layernorm: true + --test-mode: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..e80138a7f38 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + "values": { + "1": 10.89526, + "2": 10.88355, + "3": 10.88128, + "4": 10.88343, + "5": 10.88378, + "6": 10.87665, + "7": 10.8776, + "8": 10.88243, + "9": 10.88104, + 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b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone_1node/model_config.yaml @@ -0,0 +1,54 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --use-distributed-optimizer: true + --async-save: true + --ckpt-fully-parallel-save: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --log-memory-to-tensorboard: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp2_pp2_uninstall_te_1node/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp2_pp2_uninstall_te_1node/model_config.yaml new file mode 100644 index 00000000000..d44645ba5e3 --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_tp2_pp2_uninstall_te_1node/model_config.yaml @@ -0,0 +1,59 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 + SKIP_PYTEST: 1 +BEFORE_SCRIPT: | + pip uninstall -y transformer_engine || true + pip uninstall -y Apex || true ## TODO: remove once Apex dependency has been removed completely +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: local + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 2 + --no-persist-layer-norm: true + --no-masked-softmax-fusion: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --fp16: true + --apply-query-key-layer-scaling: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/golden_values_dev_dgx_h100.json index f126da7402c..df80109e0dc 100644 --- a/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/hybrid/hybrid_dynamic_inference_tp1_pp1_dp8_583m/golden_values_dev_dgx_h100.json @@ -34,8 +34,8 @@ 1261, 15353 ], - "latency": 1.7664601802825928, - "ttft": 0.09903407096862793, + "latency": 2.3884456157684326, + "ttft": 0.10706686973571777, "cuda_graph_request_count_map": null, "step_count": 30, "top_n_logprobs": null, diff --git a/tests/functional_tests/test_cases/moe/deepseek_proxy_fsdp_ep2_fsdp2_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/deepseek_proxy_fsdp_ep2_fsdp2_1node/golden_values_dev_dgx_gb200.json index f3be3ae48a3..c825c2f98ce 100644 --- a/tests/functional_tests/test_cases/moe/deepseek_proxy_fsdp_ep2_fsdp2_1node/golden_values_dev_dgx_gb200.json +++ b/tests/functional_tests/test_cases/moe/deepseek_proxy_fsdp_ep2_fsdp2_1node/golden_values_dev_dgx_gb200.json @@ -233,55 +233,55 @@ "step_interval": 1, "values": { "1": "nan", - "2": 7.9241, - "3": 0.14054, - "4": 0.14617, - "5": 0.13812, - "6": 0.13462, - "7": 0.13823, - "8": 0.12549, - "9": 0.13315, - "10": 0.13723, - "11": 0.13167, - "12": 0.12839, - "13": 0.12284, - "14": 0.12524, - "15": 0.13108, - "16": 0.12445, - "17": 0.12506, - "18": 0.1217, - "19": 0.12602, - "20": 0.12442, - "21": 0.12158, - "22": 0.12384, - "23": 0.12268, - "24": 0.1232, - "25": 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--git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer_1node/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer_1node/model_config.yaml new file mode 100644 index 00000000000..e885bc255aa --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer_1node/model_config.yaml @@ -0,0 +1,60 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 1 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 2 + --expert-model-parallel-size: 2 + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --moe-router-load-balancing-type: sinkhorn + --moe-router-topk: 1 + --ckpt-fully-parallel-save: true + --ckpt-fully-parallel-load: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --disable-bias-linear: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true +TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..0b34c84f14b --- /dev/null +++ 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--log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 2 + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --moe-router-load-balancing-type: sinkhorn + --moe-router-topk: 1 + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-optim-fully-reshardable: true + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --ckpt-assume-constant-structure: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --disable-bias-linear: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..9e26dfeeb6e --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node/model_config.yaml new file mode 100644 index 00000000000..19b6015967b --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node/model_config.yaml @@ -0,0 +1,66 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 1 + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --moe-router-load-balancing-type: sinkhorn + --moe-router-topk: 1 + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --ckpt-assume-constant-structure: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --disable-bias-linear: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --num-distributed-optimizer-instances: 2 + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..4e880a431f4 --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 50, + "step_interval": 1, + "values": 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--log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 2 + --no-ckpt-fully-parallel-save: true + --moe-grouped-gemm: true + --disable-bias-linear: true + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --moe-router-load-balancing-type: sinkhorn + --moe-router-topk: 1 + --overlap-grad-reduce: true + --overlap-param-gather: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM_1node/golden_values_dev_dgx_gb200.json 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b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM_1node/model_config.yaml new file mode 100644 index 00000000000..8ced6e37a52 --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM_1node/model_config.yaml @@ -0,0 +1,66 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 2 + --no-ckpt-fully-parallel-save: true + --moe-grouped-gemm: true + --disable-bias-linear: true + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --moe-router-load-balancing-type: sinkhorn + --moe-router-topk: 1 + --overlap-grad-reduce: true + --overlap-param-gather: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git a/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..7e76648b5a2 --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm loss": { + "start_step": 1, + 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--log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --train-iters: 50 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 10000 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 2 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 4 + --expert-tensor-parallel-size: 1 + --disable-bias-linear: true + --sequence-parallel: true + --num-experts: 8 + --moe-router-load-balancing-type: aux_loss + --moe-router-topk: 2 + --moe-aux-loss-coeff: 1e-2 + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --moe-grouped-gemm: true + --moe-token-dispatcher-type: alltoall + --overlap-moe-expert-parallel-comm: true + --attention-softmax-in-fp32: true + --use-mcore-models: true + --ckpt-format: torch_dist + --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --attention-backend: unfused + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --exit-interval: 50 + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: regular diff --git 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NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --disable-bias-linear: true + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 4 + --num-experts: 8 + --moe-token-dispatcher-type: allgather + --moe-router-load-balancing-type: aux_loss + --moe-router-topk: 2 + --moe-router-dtype: fp32 + --moe-ffn-hidden-size: 1024 + --moe-grouped-gemm: true + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --optimizer: muon + --muon-momentum: 0.9 + --muon-extra-scale-factor: 0.2 + --muon-scale-mode: spectral + --async-save: true + 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b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer_1node/model_config.yaml new file mode 100644 index 00000000000..85b4ea629df --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer_1node/model_config.yaml @@ -0,0 +1,67 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --disable-bias-linear: true + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 4 + --num-experts: 8 + --use-distributed-optimizer: true + --moe-token-dispatcher-type: allgather + --moe-router-load-balancing-type: aux_loss + --moe-router-topk: 2 + --moe-router-dtype: fp32 + --moe-ffn-hidden-size: 1024 + --moe-grouped-gemm: true + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --ckpt-assume-constant-structure: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --async-save: true + --async-strategy: mcore + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..cdfed84f204 --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,537 @@ +{ + "lm loss": { + "start_step": 1, + "end_step": 100, + "step_interval": 1, + 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} + } +} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node/model_config.yaml new file mode 100644 index 00000000000..e1653ce7ed4 --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node/model_config.yaml @@ -0,0 +1,70 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 12 + --hidden-size: 512 + --num-attention-heads: 8 + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --disable-bias-linear: true + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 1 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 4 + --num-experts: 8 + --moe-token-dispatcher-type: allgather + --moe-router-load-balancing-type: aux_loss + --moe-router-topk: 2 + --moe-router-dtype: fp32 + --moe-ffn-hidden-size: 1024 + --moe-grouped-gemm: true + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --ckpt-assume-constant-structure: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --optimizer: muon + --muon-momentum: 0.9 + --muon-extra-scale-factor: 0.2 + --muon-scale-mode: spectral + --check-weight-hash-across-dp-replicas-interval: 1 + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer_1node/golden_values_dev_dgx_gb200.json new file mode 100644 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--log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --micro-batch-size: 4 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --disable-bias-linear: true + --train-iters: 100 + --timing-log-level: 0 + --lr-decay-iters: 320000 + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --log-interval: 1 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 2 + --expert-tensor-parallel-size: 2 + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --moe-token-dispatcher-type: alltoall + --moe-router-load-balancing-type: aux_loss + --moe-router-topk: 2 + --moe-router-dtype: fp32 + --moe-ffn-hidden-size: 1024 + --moe-shared-expert-intermediate-size: 512 + --moe-grouped-gemm: true + --ckpt-fully-parallel-load: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --ckpt-format: torch_dist + --ckpt-assume-constant-structure: true + --data-cache-path: ${DATA_CACHE_PATH} + --bf16: true + --no-bias-gelu-fusion: true + --log-memory-to-tensorboard: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume +LAUNCHER: ft_launcher diff --git 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new file mode 100644 index 00000000000..a33ffbe1018 --- /dev/null +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph_1node/model_config.yaml @@ -0,0 +1,97 @@ +ENV_VARS: + CUDA_DEVICE_MAX_CONNECTIONS: 1 + NVTE_ALLOW_NONDETERMINISTIC_ALGO: 0 + NCCL_ALGO: Ring + CUBLAS_WORKSPACE_CONFIG: :4096:8 +MODEL_ARGS: + --num-layers: 13 + --hidden-size: 512 + --num-attention-heads: 8 + --mtp-num-layers: 1 + --micro-batch-size: 2 + --global-batch-size: 32 + --seq-length: 1024 + --max-position-embeddings: 1024 + --position-embedding-type: rope + --rotary-base: 10000 + --untie-embeddings-and-output-weights: true + --disable-bias-linear: true + --attention-dropout: 0.0 + --hidden-dropout: 0.0 + --train-iters: 100 + --lr-decay-iters: 320000 + --split: 949,50,1 + --distributed-backend: nccl + --lr: 0.00015 + --lr-decay-style: cosine + --min-lr: 1.0e-5 + --weight-decay: 1e-2 + --clip-grad: 1.0 + --lr-warmup-fraction: .01 + --transformer-impl: transformer_engine + --tensor-model-parallel-size: 4 + --pipeline-model-parallel-size: 1 + --expert-model-parallel-size: 2 + --expert-tensor-parallel-size: 2 + --sequence-parallel: true + --num-experts: 8 + --use-distributed-optimizer: true + --overlap-grad-reduce: true + --overlap-param-gather: true + --moe-token-dispatcher-type: alltoall + --moe-router-load-balancing-type: global_aux_loss + --moe-router-topk: 2 + --moe-router-dtype: fp32 + --moe-router-fusion: true + --moe-router-enable-expert-bias: true + --moe-router-score-function: sigmoid + --moe-router-pre-softmax: true + --moe-ffn-hidden-size: 1024 + --moe-shared-expert-intermediate-size: 512 + --moe-grouped-gemm: true + --moe-layer-freq: ([0]*4+[1]*9) + --moe-permute-fusion: true + --deterministic-mode: true + --no-gradient-accumulation-fusion: true + --attention-softmax-in-fp32: true + --use-checkpoint-opt_param-scheduler: true + --use-mcore-models: true + --bf16: true + --fp8-format: hybrid + --fp8-recipe: blockwise + --first-last-layers-bf16: true + --no-bias-gelu-fusion: true + --recompute-granularity: selective + --recompute-modules: "[moe_act]" + --cuda-graph-impl: transformer_engine + --cuda-graph-scope: "[attn mlp moe_router moe_preprocess]" + --log-memory-to-tensorboard: true + --log-params-norm: true + --log-num-zeros-in-grad: true + --log-validation-ppl-to-tensorboard: true + --log-timers-to-tensorboard: true + --tensorboard-dir: ${TENSORBOARD_PATH} + --log-interval: 1 + --timing-log-level: 0 + --save-interval: 50 + --eval-interval: 1000 + --eval-iters: 10 + --data-path: ${DATA_PATH}/text/common_pile/v01_filtered_data/my-gpt3_00_text_document + --data-cache-path: ${DATA_CACHE_PATH} + --vocab-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/vocab.json + --merge-file: ${DATA_PATH}/text/common_pile/v01_filtered_data/bpe/merges.txt + --save: ${CHECKPOINT_SAVE_PATH} + --load: ${CHECKPOINT_LOAD_PATH} + --ckpt-fully-parallel-load: true + --ckpt-format: torch_dist + --ckpt-assume-constant-structure: true + --async-save: true + --use-persistent-ckpt-worker: true +TEST_TYPE: ckpt-resume +METRICS: + # - "iteration-time" + - "lm loss" + - "num-zeros" + # - "mem-allocated-bytes" + # - "mem-max-allocated-bytes" + - "mtp_1 loss" diff --git a/tests/test_utils/recipes/gb200/gpt-1node.yaml b/tests/test_utils/recipes/gb200/gpt-1node.yaml new file mode 100644 index 00000000000..67375f6a97b --- /dev/null +++ b/tests/test_utils/recipes/gb200/gpt-1node.yaml @@ -0,0 +1,320 @@ +type: basic +format_version: 1 +maintainers: [mcore] +loggers: [stdout] +spec: + name: "{test_case}_{environment}_{platforms}" + model: gpt + build: mcore-pyt-{environment} + nodes: 1 + gpus: 4 + n_repeat: 5 + platforms: dgx_gb200 + script_setup: | + unset https_proxy + echo "machine gitlab-master.nvidia.com login okoenig password $RO_API_TOKEN" | tee -a /root/.netrc + + # Checkout latest + cd /opt + rm -rf /opt/megatron-lm; mkdir megatron-lm; cd megatron-lm + git init + git remote add origin $MCORE_REPO + git fetch origin '+refs/merge-requests/*:refs/remotes/merge-requests/*' + git fetch origin $MCORE_MR_COMMIT + git checkout $MCORE_MR_COMMIT + git rev-parse HEAD + + # Checkout backwards-ref + cd /opt + rm -rf /opt/megatron-lm-legacy; mkdir megatron-lm-legacy; cd megatron-lm-legacy + git init + git remote add origin $MCORE_REPO + git fetch origin $MCORE_BACKWARDS_COMMIT + git checkout $MCORE_BACKWARDS_COMMIT + git rev-parse HEAD + rm -rf megatron; cp -a /opt/megatron-lm/megatron ./ + script: |- + ls + cd /opt/megatron-lm + + NAME=$(echo {test_case}_{environment} | sed 's/dgx_gb200/dgx_a100/g') + export GPUS_PER_NODE={gpus} + + ARGUMENTS=( + "DATA_PATH=/mnt/artifacts" + "DATA_CACHE_PATH=/lustre/fsw/coreai_dlalgo_mcore/mcore_ci/data/$RUN_ID/cache/" + "OUTPUT_PATH={assets_dir}" + "TENSORBOARD_PATH={assets_dir}/tensorboard" + "CHECKPOINT_SAVE_PATH={artifacts_dir}/checkpoints" + "CHECKPOINT_LOAD_PATH=/mnt/artifacts/" + "TRAINING_SCRIPT_PATH=pretrain_gpt.py" + "TRAINING_PARAMS_PATH=./tests/functional_tests/test_cases/{model}/{test_case}/model_config.yaml" + "GOLDEN_VALUES_PATH=./tests/functional_tests/test_cases/{model}/{test_case}/golden_values_{environment}_{platforms}.json" + "N_REPEAT={n_repeat}" + "ENABLE_LIGHTWEIGHT_MODE=${{ENABLE_LIGHTWEIGHT_MODE}}" + "RECORD_CHECKPOINTS=${{RECORD_CHECKPOINTS}}" + ) + + set +x + bash ./tests/functional_tests/shell_test_utils/run_ci_test.sh ${{ARGUMENTS[@]}} + exit_code=$? + echo "Exit code: $exit_code" + rm -rf /lustre/fsw/coreai_dlalgo_mcore/mcore_ci/data/$RUN_ID || true + set -x + exit $exit_code + +products: + - test_case: [gpt3_mcore_te_tp1_pp1_resume_torch_dist_dist_optimizer_1node] + products: + - environment: [dev] + scope: [mr-github, mr-github-slim] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_gdn_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_reruns_persistent_1_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_dist_optimizer_overlap_grad_reduce_param_gather_overlap_optimizer_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather_1node] + products: + - environment: [dev] + scope: [mr-github, mr-github-slim] + platforms: [dgx_gb200] + ####################################################################### + # tp1_pp1 variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp1_pp1_dist_optimizer_no_mmap_bin_files_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp1_dist_optimizer_fim_dataset_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp1_resume_torch_dist_uniform_full_recompute_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp1_pp2 variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_rope_embeddings_interleaved_no_fusion_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp1_pp4 variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_disable_bias_linear_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_persistent_disable_bias_linear_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_swiglu_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_resume_torch_dist_untie_embeddings_and_outputs_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_1node] + products: + - environment: [dev] + scope: [mr-github-broken] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_calculate_per_token_loss_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap_1node] + products: + - environment: [dev] + scope: [mr-github-broken] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp1_pp4_vp2_account_for_embedding_loss_in_pipeline_split_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp2_pp1 variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_cp2_nondeterministic_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_multi_dist_optimizer_instances_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_zp_z3_resume_fsdp_dtensor_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_modelopt_distill_resume_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp2_pp2 (no cp) variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp2_pp2_cross_entropy_loss_fusion_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_cross_entropy_loss_fusion_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_mla_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_ddp_average_in_collective_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_defer_embedding_wgrad_compute_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_reshard_1x4xNone_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_tp2_pp2_uninstall_te_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp2_pp2_cp2 variants (PP reduced 2→1 for 1-node) # + ####################################################################### + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_dp_last_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_dp_last_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_nondeterministic_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_nondeterministic_dp_last_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_calculate_per_token_loss_nondeterministic_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_cp2_etp4_calculate_per_token_loss_nondeterministic_dp_last_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp2_resume_torch_dist_cp2_nondeterministic_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp4_pp1 variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp4_pp1_resume_torch_dist_qk_layernorm_test_mode_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp4_pp1_dist_optimizer_overlap_grad_reduce_param_gather_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp4_pp2 variant (PP reduced 2→1 for 1-node) # + ####################################################################### + - test_case: [gpt3_mcore_te_tp4_pp2_resume_torch_dist_reshard_8x1xNone_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] diff --git a/tests/test_utils/recipes/gb200/moe-1node.yaml b/tests/test_utils/recipes/gb200/moe-1node.yaml index 10fe3219c28..cd1e8fe0555 100644 --- a/tests/test_utils/recipes/gb200/moe-1node.yaml +++ b/tests/test_utils/recipes/gb200/moe-1node.yaml @@ -63,3 +63,70 @@ products: - environment: [dev] scope: [mr-github, mr-github-slim] platforms: [dgx_gb200] + - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_muon_1node] + products: + - environment: [dev] + scope: [mr-github, mr-github-slim] + platforms: [dgx_gb200] + - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_muon_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp1_pp2 MoE variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp1_pp2_resume_torch_dist_reshard_2x1x4_te_8experts2parallel_dist_optimizer_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp2_pp1 MoE variants # + ####################################################################### + - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_dist_optimizer_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_resume_torch_dist_te_8experts2parallel_multi_dist_optimizer_instances_1node] + products: + - environment: [dev] + scope: [mr-github-broken] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_te_8experts2parallel_overlap_grad_reduce_param_gather_groupedGEMM_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_te_a2a_ovlp_8experts_etp1_ep4_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + - test_case: [gpt3_mcore_te_tp2_pp1_te_8experts2parallel_ddp_average_in_collective_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # ep8 variant (EP reduced 8→4 for 1-node) # + ####################################################################### + - test_case: [gpt3_moe_mcore_te_ep8_resume_torch_dist_dist_optimizer_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] + ####################################################################### + # tp4_ep2_etp2_pp2 variant (PP reduced 2→1 for 1-node) # + ####################################################################### + - test_case: [gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_scoped_cudagraph_1node] + products: + - environment: [dev] + scope: [mr-github] + platforms: [dgx_gb200] From ded22f42849e679dd65c1fba832951d7c712a3c7 Mon Sep 17 00:00:00 2001 From: melon <49278241+ezioliao@users.noreply.github.com> Date: Fri, 17 Apr 2026 14:50:52 +0800 Subject: [PATCH 29/72] Fix potential coredump issue that occurs when saving a checkpoint (#1871) Co-authored-by: liaoyang Co-authored-by: Eric Harper Co-authored-by: Deepak Narayanan --- megatron/core/energy_monitor.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/megatron/core/energy_monitor.py b/megatron/core/energy_monitor.py index 4334cfe3873..c6f14ee269a 100644 --- a/megatron/core/energy_monitor.py +++ b/megatron/core/energy_monitor.py @@ -59,7 +59,11 @@ def resume(self) -> None: def _get_energy(self) -> int: """Get current energy consumption from NVML.""" try: - return nvmlDeviceGetTotalEnergyConsumption(self._handle) + # Passing None to nvmlDeviceGetTotalEnergyConsumption can cause a core + # dump, so short circuit if self._handle is None. + if self._handle is not None: + return nvmlDeviceGetTotalEnergyConsumption(self._handle) + return self._last_energy except NVMLError: return self._last_energy # return *something* if it errors From 30bc2306ec591b1456f723f1918747d54372bace Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Fri, 17 Apr 2026 15:40:32 +0200 Subject: [PATCH 30/72] docs: bump versions1.json to 0.17.0 (latest) (#4360) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- docs/versions1.json | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/docs/versions1.json b/docs/versions1.json index d55416e0a95..b0bc489fa71 100644 --- a/docs/versions1.json +++ b/docs/versions1.json @@ -5,10 +5,15 @@ "url": "https://docs.nvidia.com/megatron-core/developer-guide/nightly/" }, { - "name": "0.16.0 (latest)", - "version": "0.16.0", + "name": "0.17.0 (latest)", + "version": "0.17.0", "url": "https://docs.nvidia.com/megatron-core/developer-guide/latest/" }, + { + "name": "0.16.0", + "version": "0.16.0", + "url": "https://docs.nvidia.com/megatron-core/developer-guide/0.16.0/" + }, { "name": "0.15.0", "version": "0.15.0", From a00e9443ca53a3b782543ad3c0aaf781ba58a447 Mon Sep 17 00:00:00 2001 From: janEbert Date: Fri, 17 Apr 2026 13:33:35 +0000 Subject: [PATCH 31/72] Port DeepSeek Sparse Attention to `MambaModel` (#3553) --- .../inference/contexts/dynamic_context.py | 16 +- ...rimental_attention_variant_module_specs.py | 3 + .../core/models/mamba/mamba_layer_specs.py | 82 +++ megatron/core/models/mamba/mamba_model.py | 6 +- megatron/core/ssm/mamba_block.py | 13 +- .../core/ssm/mamba_hybrid_layer_allocation.py | 49 +- megatron/core/transformer/attention.py | 4 +- .../transformer/multi_latent_attention.py | 4 + megatron/training/arguments.py | 15 +- .../models/test_dsa_gpt_mamba_equivalence.py | 644 ++++++++++++++++++ tests/unit_tests/ssm/test_mamba_block.py | 52 ++ .../ssm/test_mamba_hybrid_layer_allocation.py | 118 +++- .../test_attention_variant_dsa.py | 73 ++ tools/checkpoint/remap_gpt_dsa_to_mamba.py | 179 +++++ 14 files changed, 1214 insertions(+), 44 deletions(-) create mode 100644 tests/unit_tests/models/test_dsa_gpt_mamba_equivalence.py create mode 100644 tools/checkpoint/remap_gpt_dsa_to_mamba.py diff --git a/megatron/core/inference/contexts/dynamic_context.py b/megatron/core/inference/contexts/dynamic_context.py index 1b0cf16315c..7ac46d42e05 100644 --- a/megatron/core/inference/contexts/dynamic_context.py +++ b/megatron/core/inference/contexts/dynamic_context.py @@ -2,6 +2,7 @@ import logging import math +import operator import warnings from contextlib import nullcontext from typing import List, Optional, Sequence, Tuple @@ -29,7 +30,10 @@ from megatron.core.inference.utils import device_memory_summary, tensor_swap from megatron.core.models.common.embeddings.rope_utils import apply_rotary_pos_emb from megatron.core.package_info import __version__ as mcore_version -from megatron.core.ssm.mamba_hybrid_layer_allocation import get_layer_maps_from_layer_type_list +from megatron.core.ssm.mamba_hybrid_layer_allocation import ( + Symbols, + get_layer_maps_from_layer_type_list, +) from megatron.core.transformer import MLATransformerConfig, TransformerConfig from megatron.core.utils import deprecate_args from megatron.core.utils import divide as core_divide @@ -332,16 +336,18 @@ def __init__(self, model_config: TransformerConfig, inference_config: InferenceC # For hybrid models, the layer map converts the global layer index to the # corresponding attention layer index or Mamba layer index depending on the # layer type. - mamba_layer_map, gdn_layer_map, attention_layer_map, _, _ = ( - get_layer_maps_from_layer_type_list(mamba_inference_state_config.layer_type_list) + attention_layer_map, dsa_layer_map, gdn_layer_map, mamba_layer_map = ( + operator.itemgetter( + Symbols.ATTENTION, Symbols.DS_ATTENTION, Symbols.GDN, Symbols.MAMBA + )(get_layer_maps_from_layer_type_list(mamba_inference_state_config.layer_type_list)) ) if len(gdn_layer_map) > 0: raise NotImplementedError("GDN layers are not supported for inference.") - self.num_attention_layers = len(attention_layer_map) + self.num_attention_layers = len(attention_layer_map) + len(dsa_layer_map) self.num_mamba_layers = len(mamba_layer_map) - self.layer_map = attention_layer_map | mamba_layer_map + self.layer_map = attention_layer_map | dsa_layer_map | mamba_layer_map else: # The layer map is the identity function for pure Transformer models. self.num_attention_layers = model_config.num_layers // pp_size diff --git a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py index 6608073136c..1b03b935639 100644 --- a/megatron/core/models/gpt/experimental_attention_variant_module_specs.py +++ b/megatron/core/models/gpt/experimental_attention_variant_module_specs.py @@ -123,6 +123,7 @@ def get_dsa_module_spec_for_backend( q_layernorm=IdentityOp, kv_layernorm=IdentityOp, ), + metainfo={"fuse_input_layernorm": False}, ) return attention @@ -138,6 +139,8 @@ def get_experimental_attention_variant_module_spec( if config.experimental_attention_variant == "gated_delta_net": return get_gated_delta_net_module_spec(config=config, backend=backend) + elif config.experimental_attention_variant == "dsa": + return get_dsa_module_spec_for_backend(config=config, backend=backend) else: raise ValueError( f"Invalid experimental attention variant: {config.experimental_attention_variant}" diff --git a/megatron/core/models/mamba/mamba_layer_specs.py b/megatron/core/models/mamba/mamba_layer_specs.py index 48f25bdbab9..96b2e13e2d4 100755 --- a/megatron/core/models/mamba/mamba_layer_specs.py +++ b/megatron/core/models/mamba/mamba_layer_specs.py @@ -4,6 +4,7 @@ TEColumnParallelLinear, TEDotProductAttention, TELayerNormColumnParallelLinear, + TELinear, TENorm, TERowParallelLinear, ) @@ -24,7 +25,18 @@ ) from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.experimental_attention_variant.dsa import ( + DSAIndexer, + DSAIndexerSubmodules, + DSAttention, + DSAttentionSubmodules, +) +from megatron.core.transformer.identity_op import IdentityOp from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.multi_latent_attention import ( + MLASelfAttention, + MLASelfAttentionSubmodules, +) from megatron.core.transformer.multi_token_prediction import ( MultiTokenPredictionBlock, MultiTokenPredictionBlockSubmodules, @@ -117,6 +129,41 @@ self_attn_bda=get_bias_dropout_add, ), ), + dsa_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=TENorm, + self_attention=ModuleSpec( + module=MLASelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=MLASelfAttentionSubmodules( + linear_q_proj=TEColumnParallelLinear, + linear_q_down_proj=TELinear, + linear_q_up_proj=TEColumnParallelLinear, + linear_kv_down_proj=TELinear, + linear_kv_up_proj=TEColumnParallelLinear, + core_attention=ModuleSpec( + module=DSAttention, + submodules=DSAttentionSubmodules( + indexer=ModuleSpec( + module=DSAIndexer, + submodules=DSAIndexerSubmodules( + linear_wq_b=TELinear, + linear_wk=TELinear, + k_norm=TENorm, + linear_weights_proj=TELinear, + ), + ) + ), + ), + linear_proj=TERowParallelLinear, + q_layernorm=IdentityOp, + kv_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), # Started with spec from gpt_layer_specs.py # Using the TE spec because we had problems getting the non-TE spec # working @@ -177,6 +224,41 @@ self_attn_bda=get_bias_dropout_add, ), ), + dsa_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=TENorm, + self_attention=ModuleSpec( + module=MLASelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=MLASelfAttentionSubmodules( + linear_q_proj=TEColumnParallelLinear, + linear_q_down_proj=TELinear, + linear_q_up_proj=TEColumnParallelLinear, + linear_kv_down_proj=TELinear, + linear_kv_up_proj=TEColumnParallelLinear, + core_attention=ModuleSpec( + module=DSAttention, + submodules=DSAttentionSubmodules( + indexer=ModuleSpec( + module=DSAIndexer, + submodules=DSAIndexerSubmodules( + linear_wq_b=TELinear, + linear_wk=TELinear, + k_norm=TENorm, + linear_weights_proj=TELinear, + ), + ) + ), + ), + linear_proj=InferenceRowParallelLinear, + q_layernorm=IdentityOp, + kv_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), # Started with spec from gpt_layer_specs.py # Using the TE spec because we had problems getting the non-TE spec # working diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index 817db8789d3..d0458988dcc 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -217,7 +217,9 @@ def __init__( tp_group=self.pg_collection.tp, ) - if self.position_embedding_type == 'rope': + # MLA (also used by DeepSeek Sparse Attention) uses its own decoupled RoPE, therefore we do + # not build standard RoPE here when using MLA. + if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention: self.rotary_pos_emb = RotaryEmbedding( kv_channels=self.config.kv_channels, rotary_percent=rotary_percent, @@ -373,7 +375,7 @@ def forward( decoder_input = None rotary_pos_emb = None - if self.position_embedding_type == 'rope': + if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention: rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( inference_context, self.decoder, decoder_input, self.config, packed_seq_params ) diff --git a/megatron/core/ssm/mamba_block.py b/megatron/core/ssm/mamba_block.py index f42f3542c3d..fa262b2293f 100644 --- a/megatron/core/ssm/mamba_block.py +++ b/megatron/core/ssm/mamba_block.py @@ -41,6 +41,7 @@ class MambaStackSubmodules: mamba_layer: Union[ModuleSpec, type] = IdentityOp gdn_layer: Union[ModuleSpec, type] = IdentityOp attention_layer: Union[ModuleSpec, type] = IdentityOp + dsa_layer: Union[ModuleSpec, type] = IdentityOp mlp_layer: Union[ModuleSpec, type] = IdentityOp moe_layer: Union[ModuleSpec, type] = IdentityOp mtp_block_spec: Optional[ModuleSpec] = None @@ -135,6 +136,16 @@ def __init__( add_layer_offset=False, pp_layer_offset=pp_layer_offset, ) + elif layer_type == LayerSymbols.DS_ATTENTION: + layer = build_module( + submodules.dsa_layer, + config=self.config, + layer_number=layer_number, + pg_collection=pg_collection, + is_mtp_layer=is_mtp_layer, + add_layer_offset=False, + pp_layer_offset=pp_layer_offset, + ) elif layer_type == LayerSymbols.MLP: layer = build_module( submodules.mlp_layer, @@ -161,7 +172,7 @@ def __init__( add_layer_offset=False, ) else: - assert False, "unexpected layer_type" + raise ValueError("unexpected layer_type") self.layers.append(layer) # Required for activation recomputation diff --git a/megatron/core/ssm/mamba_hybrid_layer_allocation.py b/megatron/core/ssm/mamba_hybrid_layer_allocation.py index 1cb1e4a31d1..1947dbc39f1 100644 --- a/megatron/core/ssm/mamba_hybrid_layer_allocation.py +++ b/megatron/core/ssm/mamba_hybrid_layer_allocation.py @@ -17,11 +17,24 @@ class Symbols: MAMBA = "M" GDN = 'G' ATTENTION = "*" + DS_ATTENTION = "D" MLP = "-" MOE = 'E' PIPE = '|' MTP_SEPARATOR = "/" - VALID_LAYERS = {MAMBA, GDN, ATTENTION, MLP, MOE} + VALID_LAYERS = {MAMBA, GDN, ATTENTION, DS_ATTENTION, MLP, MOE} + + @classmethod + def name_sorted_valid_layer_symbols(cls) -> list[str]: + """Return the valid layer symbols sorted lexicographically by their public attribute + name. + """ + valid_layer_attrs = [] + for name, value in vars(cls).items(): + if not name.startswith('_') and value in cls.VALID_LAYERS: + valid_layer_attrs.append((name, value)) + valid_layer_attrs.sort() + return [value for (_, value) in valid_layer_attrs] @dataclass @@ -155,24 +168,18 @@ def get_hybrid_layer_counts(pattern: str) -> Dict[str, int]: pattern: Full hybrid layer pattern string. Returns: - Dictionary mapping layer symbol to count. Keys are Symbols.MAMBA, - Symbols.GDN, Symbols.ATTENTION, Symbols.MLP, and Symbols.MOE. + Dictionary mapping layer symbol to count. Keys are all valid layer symbols + (Symbols.VALID_LAYERS). Examples: >>> get_hybrid_layer_counts("M*M*") - {'M': 2, 'G': 0, '*': 2, '-': 0, 'E': 0} + {'*': 2, 'G': 0, 'D': 0, 'M': 2, '-': 0, 'E': 0} >>> get_hybrid_layer_counts("M-M-|M-M*-/MM/MM") - {'M': 8, 'G': 0, '*': 1, '-': 4, 'E': 0} + {'*': 1, 'G': 0, 'D': 0, 'M': 8, '-': 4, 'E': 0} """ parsed = parse_hybrid_pattern(pattern) - counts = { - Symbols.MAMBA: 0, - Symbols.GDN: 0, - Symbols.ATTENTION: 0, - Symbols.MLP: 0, - Symbols.MOE: 0, - } + counts = {symbol: 0 for symbol in Symbols.name_sorted_valid_layer_symbols()} # Count main decoder layers (skip '|' pipe separators) if parsed.main_pattern: @@ -285,6 +292,10 @@ def _validate_pattern(pattern: str, pattern_name: str, allow_pipe: bool = False) f"Valid symbols are: {valid_chars}" ) + # Disallow Attention + MLA/DSA hybridity. + if Symbols.ATTENTION in pattern and Symbols.DS_ATTENTION in pattern: + raise ValueError("Not supported to have both Attention and MLA/DSA in one model") + def validate_segment_layers(segment: str) -> List[str]: """Validate and convert a single pipeline segment pattern to a layer type list. @@ -308,6 +319,10 @@ def validate_segment_layers(segment: str) -> List[str]: f"In hybrid layer pattern segment, '{layer_char}' is not " f"one of {Symbols.VALID_LAYERS}" ) + + # Disallow Attention + MLA/DSA hybridity. + if Symbols.ATTENTION in segment and Symbols.DS_ATTENTION in segment: + raise ValueError("Not supported to have both Attention and MLA/DSA in one model") return layer_type_list @@ -468,17 +483,15 @@ def select_pipeline_segment( return layer_type_list, layer_offset -def get_layer_maps_from_layer_type_list( - layer_type_list: List[str], -) -> Tuple[Dict[int, int], Dict[int, int], Dict[int, int], Dict[int, int], Dict[int, int]]: +def get_layer_maps_from_layer_type_list(layer_type_list: list[str]) -> dict[str, dict[int, int]]: """ Returns maps from global layer index to the corresponding layer index - for each layer type in [Mamba, GDN, Attention, MLP, MoE] given a layer type list. + for each valid layer type (those in Symbols.VALID_LAYERS) given a layer type list. """ - layer_types = [Symbols.MAMBA, Symbols.GDN, Symbols.ATTENTION, Symbols.MLP, Symbols.MOE] + layer_types = [symbol for symbol in Symbols.name_sorted_valid_layer_symbols()] layer_maps = {layer_type: {} for layer_type in layer_types} for global_layer_idx, layer_type in enumerate(layer_type_list): layer_map = layer_maps[layer_type] local_layer_idx = len(layer_map) layer_map[global_layer_idx] = local_layer_idx - return [layer_maps[layer_type] for layer_type in layer_types] + return layer_maps diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py index 605cb585779..0e5cec991e8 100644 --- a/megatron/core/transformer/attention.py +++ b/megatron/core/transformer/attention.py @@ -867,7 +867,9 @@ def flash_decode_and_prefill( q = q.reshape(num_requests, tokens_per_request, q.shape[2], q.shape[3]) # If using MLA we use the FlashMLA kernel - if isinstance(self.config, MLATransformerConfig): + # The `softmax_scale` attribute check is to find out whether this is an MLA layer or + # standard Attention. + if isinstance(self.config, MLATransformerConfig) and hasattr(self, "softmax_scale"): softmax_scale = self.softmax_scale num_heads_k = 1 # Only a single head for MLA Flash diff --git a/megatron/core/transformer/multi_latent_attention.py b/megatron/core/transformer/multi_latent_attention.py index a27fed14d6b..601ae89fae1 100644 --- a/megatron/core/transformer/multi_latent_attention.py +++ b/megatron/core/transformer/multi_latent_attention.py @@ -138,6 +138,7 @@ def __init__( attention_type: str, cp_comm_type: Optional[str] = None, pg_collection: Optional[ProcessGroupCollection] = None, + pp_layer_offset: Optional[int] = None, ) -> None: super().__init__( @@ -147,6 +148,7 @@ def __init__( attention_type=attention_type, attn_mask_type=attn_mask_type, pg_collection=pg_collection, + pp_layer_offset=pp_layer_offset, ) self.config: MLATransformerConfig @@ -473,6 +475,7 @@ def __init__( attn_mask_type=AttnMaskType.padding, cp_comm_type: Optional[str] = None, pg_collection: Optional[ProcessGroupCollection] = None, + pp_layer_offset: Optional[int] = None, ): if pg_collection is None: pg_collection = ProcessGroupCollection.use_mpu_process_groups() @@ -485,6 +488,7 @@ def __init__( attention_type="self", cp_comm_type=cp_comm_type, pg_collection=pg_collection, + pp_layer_offset=pp_layer_offset, ) if self.config.q_lora_rank is None: diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 31433704b6b..0413ea518c9 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -799,6 +799,11 @@ def validate_args(args, defaults={}): "This argument will be ignored.", args.rank ) + + # Infer use of MLA from unified pattern + if args.hybrid_layer_pattern and Symbols.DS_ATTENTION in args.hybrid_layer_pattern: + args.multi_latent_attention = True + # === End of hybrid layer pattern: deprecation handling and validation === # Uneven virtual pipeline parallelism @@ -1763,6 +1768,9 @@ def core_transformer_config_from_args(args, config_class=None): kw_args['cp_comm_type'] = args.cp_comm_type[0] if args.hybrid_layer_pattern is not None: kw_args['is_hybrid_model'] = True + from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols + if Symbols.DS_ATTENTION in args.hybrid_layer_pattern: + kw_args['experimental_attention_variant'] = 'dsa' kw_args['inference_sampling_seed'] = args.seed @@ -3286,9 +3294,10 @@ def _add_experimental_args(parser): '`transformer_block.py`, or `transformer_layer.py`') group.add_argument('--hybrid-layer-pattern', type=str, default=None, help='Specify a hybrid layer pattern using M (mamba), G (gdn), ' - '* (attention), - (mlp), E (moe). Use | to define pipeline stage ' - 'boundaries for flexible virtual pipeline parallel (fVPP). Use / to ' - 'separate MTP patterns. Example: "M-M-|M-M*-|M-M-|M-M*-" or "M-M-|M-M*-/MM/MM". ' + '* (attention), D (dsa), - (mlp), E (moe). Use | to define pipeline ' + 'stage boundaries for flexible virtual pipeline parallel (fVPP). ' + 'Use / to separate MTP patterns. ' + 'Example: "M-M-|M-M*-|M-M-|M-M*-" or "M-M-|M-M*-/MM/MM". ' 'When this flag is used, it is the sole indicator that a hybrid model ' 'is being run.') group.add_argument('--hybrid-override-pattern', type=str, default=None, diff --git a/tests/unit_tests/models/test_dsa_gpt_mamba_equivalence.py b/tests/unit_tests/models/test_dsa_gpt_mamba_equivalence.py new file mode 100644 index 00000000000..96b782fad85 --- /dev/null +++ b/tests/unit_tests/models/test_dsa_gpt_mamba_equivalence.py @@ -0,0 +1,644 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. +""" +Equivalence tests: GPTModel with DSA vs MambaModel with DSA pattern. + +A small DeepSeek-V3.2 proxy model (4 GPT layers / 8 Mamba layers) is built, +weights are remapped GPT→Mamba, and logprobs are compared to verify they are +numerically identical. + +Architecture equivalence +------------------------ +GPTModel layer N (combined attention + MLP in one TransformerLayer) + ≡ MambaModel layer 2N (D, DSA TransformerLayer: input_layernorm + MLASelfAttention) + + MambaModel layer 2N+1 (-, MLPLayer: fused-norm MLP) + +Run with:: + + torchrun --nproc-per-node=2 -m pytest \\ + tests/unit_tests/models/test_dsa_gpt_mamba_equivalence.py -v +""" + +import copy +import json +import math +import os +from pathlib import Path +from typing import Dict, Optional +from unittest.mock import patch + +import pytest +import torch +import torch.distributed as dist + +import megatron.core.parallel_state as mpu +from megatron.core.models.gpt.experimental_attention_variant_module_specs import ( + get_transformer_block_with_experimental_attention_variant_spec, +) +from megatron.core.models.gpt.gpt_model import GPTModel +from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec +from megatron.core.models.mamba.mamba_model import MambaModel +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.ssm.mamba_hybrid_layer_allocation import validate_segment_layers +from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed +from megatron.core.transformer.transformer_config import MLATransformerConfig +from megatron.rl.rl_utils import selective_log_softmax +from tests.unit_tests.test_utilities import Utils + +try: + from fast_hadamard_transform import hadamard_transform as _hadamard_transform + + HAVE_HADAMARD = True +except ImportError: + HAVE_HADAMARD = False + + +# --------------------------------------------------------------------------- +# Hadamard mock (used when the library is not installed) +# --------------------------------------------------------------------------- + + +def _mock_hadamard_transform(x: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + """Identity-scale mock for hadamard_transform used in DSA.""" + return x * scale + + +@pytest.fixture(autouse=True) +def _patch_hadamard_if_needed(): + """Patch hadamard_transform in the DSA module when the library is absent.""" + if not HAVE_HADAMARD: + with patch( + 'megatron.core.transformer.experimental_attention_variant.dsa.hadamard_transform', + _mock_hadamard_transform, + ): + yield + else: + yield + + +# --------------------------------------------------------------------------- +# Proxy model constants +# --------------------------------------------------------------------------- + +_VOCAB_SIZE = 256 +_MAX_SEQ_LEN = 64 +_SEQ_LEN = 32 +_BATCH_SIZE = 2 +_NUM_GPT_LAYERS = 4 +_MAMBA_PATTERN = "D-D-D-D-" # len=8 = 2 * _NUM_GPT_LAYERS + +# MoE variant: first 2 GPT layers dense, last 2 MoE → 8 Mamba layers +_MOE_MAMBA_PATTERN = "D-D-DEDE" # 2 dense (D-) + 2 MoE (DE) + + +# --------------------------------------------------------------------------- +# Model construction helpers +# --------------------------------------------------------------------------- + + +def _make_dsa_config(num_layers: int, tp: int = 1, pp: int = 1) -> MLATransformerConfig: + """Return a small DeepSeek-V3.2 proxy MLATransformerConfig.""" + return MLATransformerConfig( + num_layers=num_layers, + hidden_size=256, + num_attention_heads=16, + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + normalization="RMSNorm", + bf16=True, + params_dtype=torch.bfloat16, + pipeline_dtype=torch.bfloat16, + add_bias_linear=False, + use_cpu_initialization=True, + rope_type='rope', + experimental_attention_variant="dsa", + hidden_dropout=0.0, + attention_dropout=0.0, + tensor_model_parallel_size=tp, + pipeline_model_parallel_size=pp, + ) + + +def _make_dsa_moe_config(num_layers: int, tp: int = 1, pp: int = 1) -> MLATransformerConfig: + """Return a small DeepSeek-V3 proxy MLATransformerConfig with MoE layers. + + Mirrors the DeepSeek-V3 pattern: first 2 GPT layers are dense, last 2 are MoE. + ``moe_layer_freq=[0, 0, 1, 1]`` controls which GPT layers become MoE layers. + """ + return MLATransformerConfig( + num_layers=num_layers, + hidden_size=256, + num_attention_heads=16, + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + normalization="RMSNorm", + bf16=True, + params_dtype=torch.bfloat16, + pipeline_dtype=torch.bfloat16, + add_bias_linear=False, + use_cpu_initialization=True, + rope_type='rope', + experimental_attention_variant="dsa", + hidden_dropout=0.0, + attention_dropout=0.0, + tensor_model_parallel_size=tp, + # Enable sequence parallelism when TP is used, otherwise we + # error out due to use of MoE. + sequence_parallel=tp > 1, + pipeline_model_parallel_size=pp, + # MoE fields + num_moe_experts=4, + moe_router_topk=2, + moe_grouped_gemm=True, + moe_token_dispatcher_type="allgather", + moe_router_load_balancing_type="aux_loss", + moe_aux_loss_coeff=0.0, + moe_ffn_hidden_size=512, + moe_shared_expert_intermediate_size=512, + moe_layer_freq=[0, 0, 1, 1], # first 2 layers dense, last 2 MoE + ) + + +def _build_gpt_model( + config: MLATransformerConfig, pre_process: bool = True, post_process: bool = True +) -> GPTModel: + """Build a GPTModel with the DSA transformer block spec.""" + spec = get_transformer_block_with_experimental_attention_variant_spec(config) + model = GPTModel( + config=config, + transformer_layer_spec=spec, + vocab_size=_VOCAB_SIZE, + max_sequence_length=_MAX_SEQ_LEN, + pre_process=pre_process, + post_process=post_process, + parallel_output=False, # Gather logits across TP for easy comparison + position_embedding_type='rope', + ) + return model.cuda() + + +def _build_mamba_model( + config: MLATransformerConfig, + layer_pattern: str, + pre_process: bool = True, + post_process: bool = True, +) -> MambaModel: + """Build a MambaModel with the given hybrid layer pattern.""" + layer_type_list = validate_segment_layers(layer_pattern) + mamba_config = copy.deepcopy(config) + mamba_config.num_layers = len(layer_type_list) + assert mamba_config.num_layers == _NUM_GPT_LAYERS * 2 + model = MambaModel( + config=mamba_config, + mamba_stack_spec=mamba_stack_spec, + vocab_size=_VOCAB_SIZE, + max_sequence_length=_MAX_SEQ_LEN, + pre_process=pre_process, + post_process=post_process, + parallel_output=False, + hybrid_layer_pattern=layer_pattern, + position_embedding_type='rope', + ) + return model.cuda() + + +# --------------------------------------------------------------------------- +# Weight remapping +# --------------------------------------------------------------------------- + + +def _remap_gpt_to_mamba_state_dict( + gpt_sd: Dict[str, torch.Tensor], num_local_gpt_layers: int +) -> Dict[str, torch.Tensor]: + """Remap a GPTModel state_dict to a MambaModel state_dict. + + GPTModel layer N (combined attention + MLP) maps to: + * MambaModel layer 2N – DSA attention (input_layernorm + self_attention) + * MambaModel layer 2N+1 – MLP (mlp.*) + + Additionally, ``decoder.final_layernorm.*`` (TransformerBlock naming) is + remapped to ``decoder.final_norm.*`` (MambaStack naming). + + All other keys (embedding, output_layer, rotary_pos_emb, …) are unchanged. + + Args: + gpt_sd: State dict obtained from GPTModel.state_dict(). + num_local_gpt_layers: Number of GPT decoder layers on the current + pipeline stage (i.e. ``len(gpt_model.decoder.layers)``). + + Returns: + Remapped state dict ready for MambaModel.load_state_dict(strict=True). + """ + mamba_sd: Dict[str, torch.Tensor] = {} + layer_prefix = "decoder.layers." + final_ln_prefix = "decoder.final_layernorm." + + for key, value in gpt_sd.items(): + # ---- final layernorm rename ---- + if key.startswith(final_ln_prefix): + suffix = key[len(final_ln_prefix) :] + mamba_sd[f"decoder.final_norm.{suffix}"] = value + continue + + # ---- non-layer keys pass through unchanged ---- + if not key.startswith(layer_prefix): + mamba_sd[key] = value + continue + + # ---- parse "decoder.layers.{N}.{rest}" ---- + remainder = key[len(layer_prefix) :] + dot_idx = remainder.index('.') + layer_n = int(remainder[:dot_idx]) + rest = remainder[dot_idx + 1 :] # e.g. "self_attention.linear_q_proj.weight" + + assert ( + 0 <= layer_n < num_local_gpt_layers + ), f"Layer index {layer_n} out of range [0, {num_local_gpt_layers}) in key '{key}'" + + if rest.startswith("input_layernorm.") or rest.startswith("self_attention."): + # Attention sub-module → DSA layer 2N + mamba_sd[f"{layer_prefix}{2 * layer_n}.{rest}"] = value + elif rest.startswith("mlp."): + # MLP sub-module → MLP layer 2N+1 + mamba_sd[f"{layer_prefix}{2 * layer_n + 1}.{rest}"] = value + elif rest.startswith("pre_mlp_layernorm."): + # pre_mlp_layernorm → MoE layer 2N+1 (MoETransformerLayer has TENorm) + # Dense layers use IdentityOp for pre_mlp_layernorm (no state dict keys), + # so this branch only fires for MoE layers. + mamba_sd[f"{layer_prefix}{2 * layer_n + 1}.{rest}"] = value + else: + # self_attn_bda / mlp_bda are callables (no weights). + # Anything else is unexpected. + raise ValueError( + f"Unexpected sub-key '{rest}' in GPT layer {layer_n} (full key='{key}'). " + "Expected: input_layernorm.*, self_attention.*, pre_mlp_layernorm.*, mlp.*" + ) + + return mamba_sd + + +# --------------------------------------------------------------------------- +# Forward-pass helpers +# --------------------------------------------------------------------------- + + +def _make_inputs(tokens: torch.Tensor): + """Return position_ids and attention_mask for a token batch.""" + batch_size, seq_len = tokens.shape + position_ids = ( + torch.arange(seq_len, device=tokens.device).unsqueeze(0).expand(batch_size, seq_len) + ) + attention_mask = torch.ones( + batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=tokens.device + ) + return position_ids, attention_mask + + +def _forward_logprobs_pp1(model: torch.nn.Module, tokens: torch.Tensor) -> torch.Tensor: + """Single-stage (PP=1) forward returning logprobs [batch, seq-1].""" + position_ids, attention_mask = _make_inputs(tokens) + with torch.no_grad(): + logits = model(input_ids=tokens, position_ids=position_ids, attention_mask=attention_mask) + return selective_log_softmax(logits[:, :-1, :], tokens[:, 1:]) + + +def _forward_logprobs_pp2(model: torch.nn.Module, tokens: torch.Tensor) -> Optional[torch.Tensor]: + """Two-stage (PP=2) forward using point-to-point communication. + + Returns logprobs on the last PP stage; None on the first stage. + The caller must invoke this function for both GPT and Mamba models in the + *same order* on all ranks to avoid deadlocks. + """ + batch_size, seq_len = tokens.shape + hidden_size = model.config.hidden_size + position_ids, attention_mask = _make_inputs(tokens) + + pp_rank = mpu.get_pipeline_model_parallel_rank() + next_rank = mpu.get_pipeline_model_parallel_next_rank() + prev_rank = mpu.get_pipeline_model_parallel_prev_rank() + + if pp_rank == 0: + # First stage: embedding + local layers → hidden states [seq, batch, hidden] + with torch.no_grad(): + hidden = model( + input_ids=tokens, position_ids=position_ids, attention_mask=attention_mask + ) + dist.send(hidden.contiguous(), dst=next_rank) + return None + else: + # Last stage: receive hidden states, run remaining layers → logits + hidden_buf = torch.empty( + seq_len, batch_size, hidden_size, dtype=torch.bfloat16, device=tokens.device + ) + dist.recv(hidden_buf, src=prev_rank) + model.set_input_tensor(hidden_buf) + with torch.no_grad(): + logits = model( + input_ids=tokens, position_ids=position_ids, attention_mask=attention_mask + ) + return selective_log_softmax(logits[:, :-1, :], tokens[:, 1:]) + + +# --------------------------------------------------------------------------- +# Golden-value comparison +# --------------------------------------------------------------------------- + + +def _compare_against_golden_values( + logprobs: torch.Tensor, golden_logprobs: torch.Tensor, abs_tol: float = 1e-3 +): + """Assert logprobs match the golden values JSON within *abs_tol*.""" + golden_lp = golden_logprobs[0].float().tolist() + actual_lp = logprobs[0].float().tolist() + assert len(actual_lp) == len( + golden_lp + ), f"Logprob length mismatch: actual={len(actual_lp)}, golden={len(golden_lp)}" + for i, (a, g) in enumerate(zip(actual_lp, golden_lp)): + assert math.isclose( + a, g, abs_tol=abs_tol + ), f"Logprob mismatch at position {i}: actual={a:.6f}, golden={g:.6f}" + + +# --------------------------------------------------------------------------- +# Test class +# --------------------------------------------------------------------------- + +_GOLDEN_BASE = Path(__file__).parent.parent.parent / ("functional_tests/test_cases/hybrid") + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +@pytest.mark.parametrize("tp,pp", [(1, 1), (2, 1), (1, 2)]) +class TestDSAGPTMambaEquivalence: + """Verify logprob equivalence between GPTModel+DSA and MambaModel+DSA. + + For each distributed configuration (TP, PP), the test: + 1. Builds a GPTModel with 4 DSA layers. + 2. Builds a MambaModel with pattern "D-D-D-D-" (8 layers). + 3. Remaps and loads GPT weights into MambaModel (strict=True). + 4. Runs the same random tokens through both models. + 5. Asserts logprob tensors are numerically close. + """ + + def teardown_method(self, method): + Utils.destroy_model_parallel() + + def _skip_if_insufficient_gpus(self, tp: int, pp: int) -> None: + world_size = int(os.environ.get('WORLD_SIZE', '1')) + required = tp * pp + if world_size < required: + pytest.skip( + f"Test tp={tp} pp={pp} requires {required} GPU(s), " f"but WORLD_SIZE={world_size}" + ) + + def test_dsa_logprobs_match(self, tp: int, pp: int) -> None: + """Build both models, transfer weights, compare logprobs.""" + self._skip_if_insufficient_gpus(tp, pp) + Utils.initialize_model_parallel(tp, pp) + model_parallel_cuda_manual_seed(42) + + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + + # ---- Build GPTModel ---- + gpt_config = _make_dsa_config(num_layers=_NUM_GPT_LAYERS, tp=tp, pp=pp) + gpt_model = _build_gpt_model(gpt_config, pre_process=pre_process, post_process=post_process) + num_local_gpt_layers = len(gpt_model.decoder.layers) + gpt_sd = gpt_model.state_dict() + + # ---- Build MambaModel ---- + mamba_model = _build_mamba_model( + gpt_config, _MAMBA_PATTERN, pre_process=pre_process, post_process=post_process + ) + + # ---- Remap GPT weights → Mamba ---- + mamba_sd = _remap_gpt_to_mamba_state_dict(gpt_sd, num_local_gpt_layers) + missing, unexpected = mamba_model.load_state_dict(mamba_sd, strict=True) + assert not missing, f"Missing keys after weight remap: {missing}" + assert not unexpected, f"Unexpected keys after weight remap: {unexpected}" + + # ---- Create identical inputs on all ranks ---- + torch.manual_seed(99) + tokens = torch.randint(0, _VOCAB_SIZE, (_BATCH_SIZE, _SEQ_LEN), device='cuda') + + # ---- Forward pass ---- + if pp == 1: + gpt_logprobs = _forward_logprobs_pp1(gpt_model, tokens) + mamba_logprobs = _forward_logprobs_pp1(mamba_model, tokens) + # Both models have full logits; compare on all TP ranks + torch.testing.assert_close( + gpt_logprobs, + mamba_logprobs, + atol=1e-5, + rtol=1e-5, + msg=f"Logprob mismatch for tp={tp} pp={pp}", + ) + else: + # PP=2: manual pipeline communication + # Run GPT then Mamba in the same order on all ranks to avoid deadlocks. + gpt_logprobs = _forward_logprobs_pp2(gpt_model, tokens) + mamba_logprobs = _forward_logprobs_pp2(mamba_model, tokens) + if post_process: + torch.testing.assert_close( + gpt_logprobs, + mamba_logprobs, + atol=1e-5, + rtol=1e-5, + msg=f"Logprob mismatch for tp={tp} pp={pp}", + ) + + def test_weight_loading_strict(self, tp: int, pp: int) -> None: + """Verify that strict=True weight loading succeeds (no missing/unexpected keys).""" + self._skip_if_insufficient_gpus(tp, pp) + Utils.initialize_model_parallel(tp, pp) + model_parallel_cuda_manual_seed(42) + + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + + gpt_config = _make_dsa_config(num_layers=_NUM_GPT_LAYERS, tp=tp, pp=pp) + gpt_model = _build_gpt_model(gpt_config, pre_process=pre_process, post_process=post_process) + mamba_model = _build_mamba_model( + gpt_config, _MAMBA_PATTERN, pre_process=pre_process, post_process=post_process + ) + + gpt_sd = gpt_model.state_dict() + num_local_gpt_layers = len(gpt_model.decoder.layers) + mamba_sd = _remap_gpt_to_mamba_state_dict(gpt_sd, num_local_gpt_layers) + missing, unexpected = mamba_model.load_state_dict(mamba_sd, strict=True) + + assert not missing, f"Missing keys: {missing}" + assert not unexpected, f"Unexpected keys: {unexpected}" + + def test_record_and_compare_golden_values(self, tp: int, pp: int) -> None: + """Record GPTModel logprobs as golden values, then compare MambaModel against them. + + Golden values are written to the functional test directory so they can be + committed and used by the CI inference golden-value tests. + """ + self._skip_if_insufficient_gpus(tp, pp) + # Only run for TP=1, PP=1 (the canonical golden-value configuration) + if tp != 1 or pp != 1: + pytest.skip("Golden-value recording only runs for tp=1, pp=1") + + Utils.initialize_model_parallel(1, 1) + model_parallel_cuda_manual_seed(42) + + gpt_config = _make_dsa_config(num_layers=_NUM_GPT_LAYERS, tp=1, pp=1) + gpt_model = _build_gpt_model(gpt_config) + mamba_model = _build_mamba_model(gpt_config, _MAMBA_PATTERN) + + gpt_sd = gpt_model.state_dict() + mamba_sd = _remap_gpt_to_mamba_state_dict(gpt_sd, len(gpt_model.decoder.layers)) + mamba_model.load_state_dict(mamba_sd, strict=True) + + torch.manual_seed(99) + tokens = torch.randint(0, _VOCAB_SIZE, (_BATCH_SIZE, _SEQ_LEN), device='cuda') + + gpt_logprobs = _forward_logprobs_pp1(gpt_model, tokens) + mamba_logprobs = _forward_logprobs_pp1(mamba_model, tokens) + + # Verify MambaModel matches golden values + _compare_against_golden_values(mamba_logprobs, gpt_logprobs, abs_tol=1e-3) + + +# --------------------------------------------------------------------------- +# MoE test class +# --------------------------------------------------------------------------- + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +@pytest.mark.parametrize("tp,pp", [(1, 1), (2, 1), (1, 2)]) +class TestDSAMoEGPTMambaEquivalence: + """Verify logprob equivalence between GPTModel+DSA+MoE and MambaModel+DSA+MoE. + + Architecture: 4 GPT layers with moe_layer_freq=[0,0,1,1] (first 2 dense, last 2 MoE) + maps to 8 Mamba layers with pattern "D-D-DEDE": + GPT layer 0 (dense) → Mamba layers 0 (D) + 1 (-) + GPT layer 1 (dense) → Mamba layers 2 (D) + 3 (-) + GPT layer 2 (MoE) → Mamba layers 4 (D) + 5 (E) + GPT layer 3 (MoE) → Mamba layers 6 (D) + 7 (E) + """ + + def teardown_method(self, method): + Utils.destroy_model_parallel() + + def _skip_if_insufficient_gpus(self, tp: int, pp: int) -> None: + world_size = int(os.environ.get('WORLD_SIZE', '1')) + required = tp * pp + if world_size < required: + pytest.skip( + f"Test tp={tp} pp={pp} requires {required} GPU(s), but WORLD_SIZE={world_size}" + ) + + def test_dsa_moe_logprobs_match(self, tp: int, pp: int) -> None: + """Build both models with MoE, transfer weights, compare logprobs.""" + self._skip_if_insufficient_gpus(tp, pp) + Utils.initialize_model_parallel(tp, pp) + model_parallel_cuda_manual_seed(42) + + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + + # ---- Build GPTModel with MoE ---- + gpt_config = _make_dsa_moe_config(num_layers=_NUM_GPT_LAYERS, tp=tp, pp=pp) + gpt_model = _build_gpt_model(gpt_config, pre_process=pre_process, post_process=post_process) + num_local_gpt_layers = len(gpt_model.decoder.layers) + gpt_sd = gpt_model.state_dict() + + # ---- Build MambaModel with MoE pattern ---- + mamba_model = _build_mamba_model( + gpt_config, _MOE_MAMBA_PATTERN, pre_process=pre_process, post_process=post_process + ) + + # ---- Remap GPT weights → Mamba (includes pre_mlp_layernorm.* for MoE layers) ---- + mamba_sd = _remap_gpt_to_mamba_state_dict(gpt_sd, num_local_gpt_layers) + missing, unexpected = mamba_model.load_state_dict(mamba_sd, strict=True) + assert not missing, f"Missing keys after weight remap: {missing}" + assert not unexpected, f"Unexpected keys after weight remap: {unexpected}" + + # ---- Create identical inputs on all ranks ---- + torch.manual_seed(99) + tokens = torch.randint(0, _VOCAB_SIZE, (_BATCH_SIZE, _SEQ_LEN), device='cuda') + + # ---- Forward pass ---- + if pp == 1: + gpt_logprobs = _forward_logprobs_pp1(gpt_model, tokens) + mamba_logprobs = _forward_logprobs_pp1(mamba_model, tokens) + torch.testing.assert_close( + gpt_logprobs, + mamba_logprobs, + atol=1e-5, + rtol=1e-5, + msg=f"MoE logprob mismatch for tp={tp} pp={pp}", + ) + else: + gpt_logprobs = _forward_logprobs_pp2(gpt_model, tokens) + mamba_logprobs = _forward_logprobs_pp2(mamba_model, tokens) + if post_process: + torch.testing.assert_close( + gpt_logprobs, + mamba_logprobs, + atol=1e-5, + rtol=1e-5, + msg=f"MoE logprob mismatch for tp={tp} pp={pp}", + ) + + def test_moe_weight_loading_strict(self, tp: int, pp: int) -> None: + """Verify that strict=True weight loading succeeds with MoE keys.""" + self._skip_if_insufficient_gpus(tp, pp) + Utils.initialize_model_parallel(tp, pp) + model_parallel_cuda_manual_seed(42) + + pre_process = mpu.is_pipeline_first_stage() + post_process = mpu.is_pipeline_last_stage() + + gpt_config = _make_dsa_moe_config(num_layers=_NUM_GPT_LAYERS, tp=tp, pp=pp) + gpt_model = _build_gpt_model(gpt_config, pre_process=pre_process, post_process=post_process) + mamba_model = _build_mamba_model( + gpt_config, _MOE_MAMBA_PATTERN, pre_process=pre_process, post_process=post_process + ) + + gpt_sd = gpt_model.state_dict() + num_local_gpt_layers = len(gpt_model.decoder.layers) + mamba_sd = _remap_gpt_to_mamba_state_dict(gpt_sd, num_local_gpt_layers) + missing, unexpected = mamba_model.load_state_dict(mamba_sd, strict=True) + + assert not missing, f"Missing keys: {missing}" + assert not unexpected, f"Unexpected keys: {unexpected}" + + def test_moe_record_and_compare_golden_values(self, tp: int, pp: int) -> None: + """Record GPTModel+MoE logprobs as golden values, then compare MambaModel+MoE.""" + self._skip_if_insufficient_gpus(tp, pp) + if tp != 1 or pp != 1: + pytest.skip("Golden-value recording only runs for tp=1, pp=1") + + Utils.initialize_model_parallel(1, 1) + model_parallel_cuda_manual_seed(42) + + gpt_config = _make_dsa_moe_config(num_layers=_NUM_GPT_LAYERS, tp=1, pp=1) + gpt_model = _build_gpt_model(gpt_config) + mamba_model = _build_mamba_model(gpt_config, _MOE_MAMBA_PATTERN) + + gpt_sd = gpt_model.state_dict() + mamba_sd = _remap_gpt_to_mamba_state_dict(gpt_sd, len(gpt_model.decoder.layers)) + mamba_model.load_state_dict(mamba_sd, strict=True) + + torch.manual_seed(99) + tokens = torch.randint(0, _VOCAB_SIZE, (_BATCH_SIZE, _SEQ_LEN), device='cuda') + + gpt_logprobs = _forward_logprobs_pp1(gpt_model, tokens) + mamba_logprobs = _forward_logprobs_pp1(mamba_model, tokens) + + # Verify MambaModel matches golden values + _compare_against_golden_values(mamba_logprobs, gpt_logprobs, abs_tol=1e-3) diff --git a/tests/unit_tests/ssm/test_mamba_block.py b/tests/unit_tests/ssm/test_mamba_block.py index 7b743afbfad..65d42ea3c17 100644 --- a/tests/unit_tests/ssm/test_mamba_block.py +++ b/tests/unit_tests/ssm/test_mamba_block.py @@ -12,7 +12,10 @@ from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig from megatron.core.transformer.attention import SelfAttention +from megatron.core.transformer.experimental_attention_variant.dsa import DSAttention from megatron.core.transformer.mlp import MLP +from megatron.core.transformer.multi_latent_attention import MLASelfAttention +from megatron.core.transformer.transformer_config import MLATransformerConfig from megatron.core.transformer.transformer_layer import TransformerLayer from tests.unit_tests.test_utilities import Utils @@ -46,6 +49,38 @@ def get_mamba_block(self, layer_pattern): pg_collection=self.get_pg_collection(), ) + def get_dsa_mamba_block(self, layer_pattern): + layer_type_list = validate_segment_layers(layer_pattern) + transformer_config = MLATransformerConfig( + hidden_size=256, # The Mamba layer places several constraints on this + # Need to specify num_attention_heads and num_layers or TransformerConfig + # will generate errors. + num_layers=len(layer_type_list), + num_attention_heads=16, + use_cpu_initialization=True, + bf16=True, + params_dtype=torch.bfloat16, + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + rope_type='rope', + rotary_base=10000, + rotary_percent=1.0, + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + ) + modules = mamba_stack_spec.submodules + return MambaStack( + transformer_config, + modules, + layer_type_list=layer_type_list, + pp_layer_offset=0, + pg_collection=self.get_pg_collection(), + ) + def teardown_method(self, method): Utils.destroy_model_parallel() @@ -138,3 +173,20 @@ def test_gdn_gpu_forward(self): assert output.shape[1] == micro_batch_size assert output.shape[2] == block.config.hidden_size assert output.dtype == torch.float32 + + def test_dsa_layer_types(self): + """D symbol creates a TransformerLayer with MLASelfAttention.""" + layer_pattern = Symbols.MAMBA + Symbols.DS_ATTENTION + Symbols.MAMBA + block = self.get_dsa_mamba_block(layer_pattern) + layers = block.layers + assert isinstance(layers[0], MambaLayer) + assert isinstance(layers[1], TransformerLayer) + assert isinstance(layers[1].self_attention, MLASelfAttention) + assert isinstance(layers[1].self_attention.core_attention, DSAttention) + assert isinstance(layers[2], MambaLayer) + + def test_mixed_attention_and_dsa_layer_types(self): + """* and D in the same block fail.""" + layer_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.DS_ATTENTION + Symbols.MAMBA + with pytest.raises(ValueError): + block = self.get_dsa_mamba_block(layer_pattern) diff --git a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py b/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py index 440c843bc27..915ba9dd34f 100644 --- a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py +++ b/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py @@ -1,5 +1,6 @@ # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +import operator from unittest.mock import patch import pytest @@ -10,6 +11,7 @@ get_hybrid_layer_counts, get_hybrid_total_layer_count, get_hybrid_total_pipeline_segment_count, + get_layer_maps_from_layer_type_list, parse_hybrid_pattern, pattern_from_ratios, select_pipeline_segment, @@ -75,6 +77,7 @@ def test_valid_patterns(self): ("", []), ("GGG*GGG*", ['G', 'G', 'G', '*', 'G', 'G', 'G', '*']), ("GEGEGE*E", ['G', 'E', 'G', 'E', 'G', 'E', '*', 'E']), + ("MDMD", ['M', 'D', 'M', 'D']), ] for pattern, expected in test_cases: result = validate_segment_layers(pattern) @@ -95,6 +98,9 @@ def test_invalid_symbols_cause_failure(self): validate_segment_layers("M|M") # pipe not valid in a segment with pytest.raises(ValueError): validate_segment_layers("M/M") # MTP separator not valid in a segment + with pytest.raises(ValueError): + # Not allowed to have both standard Attention and MLA/DSA + validate_segment_layers("MDM*-") @pytest.mark.internal @@ -155,6 +161,8 @@ def test_main_pattern_only(self): ("E", "E"), ("GGG*GGG*", "GGG*GGG*"), ("GEGEGE*E", "GEGEGE*E"), + ("MDMD", "MDMD"), + ("DM", "DM"), ] for pattern, expected_main in test_cases: result = parse_hybrid_pattern(pattern) @@ -277,6 +285,8 @@ def test_complex_patterns(self): ("MEME/MM/MM", "MEME", "MM", 2), # GDN+MoE main pattern with GDN MTP ("GEGEGE*E/GG/GG", "GEGEGE*E", "GG", 2), + # DSA in main pattern with MTP + ("MDMD/MD/MD", "MDMD", "MD", 2), ] for pattern, expected_main, expected_mtp, expected_depths in test_cases: result = parse_hybrid_pattern(pattern) @@ -295,47 +305,74 @@ def test_dataclass_equality(self): class TestGetHybridLayerCounts: def test_simple_pattern(self): - assert get_hybrid_layer_counts("M*M*") == {'M': 2, 'G': 0, '*': 2, '-': 0, 'E': 0} + assert get_hybrid_layer_counts("M*M*") == {'*': 2, 'D': 0, 'G': 0, 'M': 2, '-': 0, 'E': 0} def test_all_layer_types(self): - assert get_hybrid_layer_counts("MG*-E") == {'M': 1, 'G': 1, '*': 1, '-': 1, 'E': 1} + # Not allowed to have both standard Attention and MLA/DSA, so we do separate asserts. + assert get_hybrid_layer_counts("MG*-E") == {'*': 1, 'D': 0, 'G': 1, 'M': 1, '-': 1, 'E': 1} + assert get_hybrid_layer_counts("MGD-E") == {'*': 0, 'D': 1, 'G': 1, 'M': 1, '-': 1, 'E': 1} def test_with_pipes(self): # Pipes should be skipped in counting - assert get_hybrid_layer_counts("M*|M*") == {'M': 2, 'G': 0, '*': 2, '-': 0, 'E': 0} - assert get_hybrid_layer_counts("M-M-|M-M*-") == {'M': 4, 'G': 0, '*': 1, '-': 4, 'E': 0} + assert get_hybrid_layer_counts("M*|M*") == {'*': 2, 'D': 0, 'G': 0, 'M': 2, '-': 0, 'E': 0} + assert get_hybrid_layer_counts("M-M-|M-M*-") == { + '*': 1, + 'D': 0, + 'G': 0, + 'M': 4, + '-': 4, + 'E': 0, + } def test_with_mtp(self): # MTP pattern "MM" repeated 2 depths -> 4 extra mamba layers - assert get_hybrid_layer_counts("M*M*/MM/MM") == {'M': 6, 'G': 0, '*': 2, '-': 0, 'E': 0} + assert get_hybrid_layer_counts("M*M*/MM/MM") == { + '*': 2, + 'D': 0, + 'G': 0, + 'M': 6, + '-': 0, + 'E': 0, + } def test_with_pipes_and_mtp(self): # Main: M-M-|M-M*- -> 1 attn, 4 mamba, 4 mlp # MTP: MM x 2 depths -> +4 mamba assert get_hybrid_layer_counts("M-M-|M-M*-/MM/MM") == { - 'M': 8, - 'G': 0, '*': 1, + 'D': 0, + 'G': 0, + 'M': 8, '-': 4, 'E': 0, } def test_moe_pattern(self): - assert get_hybrid_layer_counts("MEME") == {'M': 2, 'G': 0, '*': 0, '-': 0, 'E': 2} + assert get_hybrid_layer_counts("MEME") == {'*': 0, 'D': 0, 'G': 0, 'M': 2, '-': 0, 'E': 2} def test_mtp_with_attention(self): # MTP pattern "*M" repeated 3 depths -> 3 attn + 3 mamba from MTP - assert get_hybrid_layer_counts("MMMM/*M/*M/*M") == {'M': 7, 'G': 0, '*': 3, '-': 0, 'E': 0} - - def test_empty_pattern(self): - assert get_hybrid_layer_counts("") == {'M': 0, 'G': 0, '*': 0, '-': 0, 'E': 0} + assert get_hybrid_layer_counts("MMMM/*M/*M/*M") == { + '*': 3, + 'D': 0, + 'G': 0, + 'M': 7, + '-': 0, + 'E': 0, + } def test_gdn_pattern(self): - assert get_hybrid_layer_counts("GMGM") == {'M': 2, 'G': 2, '*': 0, '-': 0, 'E': 0} + assert get_hybrid_layer_counts("GMGM") == {'*': 0, 'D': 0, 'G': 2, 'M': 2, '-': 0, 'E': 0} def test_gdn_hybrid_pattern(self): # GDN + Mamba + Attention - assert get_hybrid_layer_counts("G*GM*") == {'M': 1, 'G': 2, '*': 2, '-': 0, 'E': 0} + assert get_hybrid_layer_counts("G*GM*") == {'*': 2, 'D': 0, 'G': 2, 'M': 1, '-': 0, 'E': 0} + + def test_dsa_pattern(self): + assert get_hybrid_layer_counts("DMDM") == {'*': 0, 'D': 2, 'G': 0, 'M': 2, '-': 0, 'E': 0} + + def test_empty_pattern(self): + assert get_hybrid_layer_counts("") == {'*': 0, 'D': 0, 'G': 0, 'M': 0, '-': 0, 'E': 0} @pytest.mark.internal @@ -596,3 +633,56 @@ def test_all_ranks_cover_full_pattern(self): assert offset == len(all_layers) all_layers.extend(layers) assert all_layers == ['M', '*', 'M', '*', 'M', '*'] + + +@pytest.mark.internal +class TestGetLayerMapsFromLayerTypeList: + """Tests for get_layer_maps_from_layer_type_list.""" + + def test_standard_layer_types(self): + """Standard symbols each produce a single-entry map at local index 0.""" + maps = get_layer_maps_from_layer_type_list(["*", "M", "-", "E"]) + # We always get all symbols returned, not only those contained in the pattern. + assert len(maps) == 6 + attention_map, mamba_map, mlp_map, moe_map = operator.itemgetter( + Symbols.ATTENTION, Symbols.MAMBA, Symbols.MLP, Symbols.MOE + )(maps) + assert attention_map == {0: 0} + assert mamba_map == {1: 0} + assert mlp_map == {2: 0} + assert moe_map == {3: 0} + + def test_dsa(self): + """D (DSA) layers are treated as separate layers for KV cache mapping.""" + maps = get_layer_maps_from_layer_type_list(["D", "M", "D", "M"]) + attention_map, dsa_map, mamba_map, mlp_map, moe_map = operator.itemgetter( + Symbols.ATTENTION, Symbols.DS_ATTENTION, Symbols.MAMBA, Symbols.MLP, Symbols.MOE + )(maps) + assert attention_map == {} + assert dsa_map == {0: 0, 2: 1} + assert mamba_map == {1: 0, 3: 1} + assert mlp_map == {} + assert moe_map == {} + + def test_mixed_attention_and_dsa(self): + """Both * and D contribute to the different maps with non-consecutive local indices.""" + maps = get_layer_maps_from_layer_type_list(["*", "D", "M", "-"]) + attention_map, dsa_map, mamba_map, mlp_map, moe_map = operator.itemgetter( + Symbols.ATTENTION, Symbols.DS_ATTENTION, Symbols.MAMBA, Symbols.MLP, Symbols.MOE + )(maps) + assert attention_map == {0: 0} + assert dsa_map == {1: 0} + assert mamba_map == {2: 0} + assert mlp_map == {3: 0} + assert moe_map == {} + + def test_all_mamba(self): + """All-mamba pattern leaves attention, mlp, and moe maps empty.""" + maps = get_layer_maps_from_layer_type_list(["M", "M", "M"]) + attention_map, mamba_map, mlp_map, moe_map = operator.itemgetter( + Symbols.ATTENTION, Symbols.MAMBA, Symbols.MLP, Symbols.MOE + )(maps) + assert attention_map == {} + assert mamba_map == {0: 0, 1: 1, 2: 2} + assert mlp_map == {} + assert moe_map == {} diff --git a/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py b/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py index 45470d4dd6c..6d2426a42d1 100644 --- a/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py +++ b/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py @@ -6,6 +6,10 @@ import torch import megatron.core.parallel_state as parallel_state +from megatron.core.models.gpt.experimental_attention_variant_module_specs import ( + get_dsa_module_spec_for_backend, + get_experimental_attention_variant_module_spec, +) from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec from megatron.core.process_groups_config import ProcessGroupCollection from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed @@ -23,6 +27,7 @@ fused_qk_topk_naive, rotate_activation, ) +from megatron.core.transformer.multi_latent_attention import MLASelfAttention from megatron.core.transformer.transformer_config import MLATransformerConfig from tests.unit_tests.test_utilities import Utils @@ -1587,3 +1592,71 @@ def test_dsa_gradient_sync( ), f"Indexer gradient for {name} differs between TP rank 0 and rank {i} after TP sync" Utils.destroy_model_parallel() + + +@pytest.mark.internal +class TestDSAModuleSpecDispatch: + """Tests for get_dsa_module_spec_for_backend and get_experimental_attention_variant_module_spec.""" + + @pytest.fixture(scope='function', autouse=True) + def setup_method(self): + Utils.initialize_model_parallel( + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 + ) + yield + Utils.destroy_model_parallel() + + def _make_dsa_config(self, **kwargs): + return MLATransformerConfig( + num_layers=2, + hidden_size=256, + num_attention_heads=16, + use_cpu_initialization=True, + bf16=True, + params_dtype=torch.bfloat16, + q_lora_rank=64, + kv_lora_rank=64, + qk_head_dim=64, + qk_pos_emb_head_dim=32, + v_head_dim=64, + rope_type='rope', + rotary_base=10000, + rotary_percent=1.0, + dsa_indexer_n_heads=8, + dsa_indexer_head_dim=64, + dsa_indexer_topk=32, + **kwargs, + ) + + def test_get_experimental_attention_variant_module_spec_dsa(self): + """get_experimental_attention_variant_module_spec dispatches to DSA for variant='dsa'.""" + config = self._make_dsa_config(experimental_attention_variant="dsa") + spec = get_experimental_attention_variant_module_spec(config) + assert spec.module == MLASelfAttention + assert spec.submodules.core_attention.module == DSAttention + + def test_get_dsa_module_spec_for_backend(self): + """get_dsa_module_spec_for_backend returns the correct full spec structure.""" + from megatron.core.extensions.transformer_engine_spec_provider import TESpecProvider + + config = self._make_dsa_config() + backend = TESpecProvider() + spec = get_dsa_module_spec_for_backend(config, backend=backend) + assert spec.module == MLASelfAttention + assert spec.submodules.core_attention.module == DSAttention + assert spec.submodules.core_attention.submodules.indexer.module == DSAIndexer + assert spec.params["attn_mask_type"] == AttnMaskType.causal + + def test_get_dsa_module_spec_requires_mla(self): + """get_dsa_module_spec_for_backend rejects configs without MLA.""" + from megatron.core.transformer import TransformerConfig as _TransformerConfig + + config = _TransformerConfig(num_layers=2, hidden_size=256, num_attention_heads=4) + with pytest.raises(AssertionError, match="only MLA supports sparse attention"): + get_dsa_module_spec_for_backend(config, backend=None) + + def test_get_dsa_module_spec_rejects_qk_l2_norm(self): + """get_dsa_module_spec_for_backend rejects configs with qk_l2_norm=True.""" + config = self._make_dsa_config(qk_l2_norm=True) + with pytest.raises(AssertionError, match="qk_l2_norm is not supported"): + get_dsa_module_spec_for_backend(config, backend=None) diff --git a/tools/checkpoint/remap_gpt_dsa_to_mamba.py b/tools/checkpoint/remap_gpt_dsa_to_mamba.py new file mode 100644 index 00000000000..8a6888d1dc7 --- /dev/null +++ b/tools/checkpoint/remap_gpt_dsa_to_mamba.py @@ -0,0 +1,179 @@ +#!/usr/bin/env python3 +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. +"""Convert a GPTModel DSA checkpoint to a MambaModel-compatible checkpoint. + +A GPTModel with ``--experimental-attention-variant dsa`` uses one combined +TransformerLayer per model layer (attention + MLP). The equivalent MambaModel +with pattern ``D-D-...`` stores them as two separate layers: + +* Layer 2N – DSA attention (TransformerLayer: input_layernorm + MLASelfAttention) +* Layer 2N+1 – MLP (MLPLayer: fused-norm MLP) + +This script loads a GPTModel Distributed Checkpoint (DCP), remaps the state-dict +keys, and saves a new DCP that can be loaded by MambaModel. + +Usage +----- +:: + + python tools/checkpoint/remap_gpt_dsa_to_mamba.py \\ + --input /path/to/gpt_dsa_dcp_checkpoint \\ + --output /path/to/mamba_dsa_dcp_checkpoint \\ + --num-gpt-layers 4 + +Key remapping rules +------------------- +* ``decoder.layers.{N}.input_layernorm.*`` → ``decoder.layers.{2N}.input_layernorm.*`` +* ``decoder.layers.{N}.self_attention.*`` → ``decoder.layers.{2N}.self_attention.*`` +* ``decoder.layers.{N}.pre_mlp_layernorm.*`` → ``decoder.layers.{2N+1}.pre_mlp_layernorm.*`` +* ``decoder.layers.{N}.mlp.*`` → ``decoder.layers.{2N+1}.mlp.*`` +* ``decoder.final_layernorm.*`` → ``decoder.final_norm.*`` +* All other keys → unchanged + +Note: ``pre_mlp_layernorm`` only appears for MoE layers (where it is a real TENorm). +Dense layers use ``IdentityOp`` for ``pre_mlp_layernorm``, which produces no state dict keys. +""" + +import argparse +import os +import re +import shutil +from pathlib import Path +from typing import Dict + + +def _remap_key(key: str, num_gpt_layers: int) -> str: + """Return the MambaModel state-dict key corresponding to *key* from GPTModel. + + Args: + key: A key from the GPTModel state dict. + num_gpt_layers: Total number of GPT decoder layers (across all PP stages). + + Returns: + The remapped key for MambaModel. + + Raises: + ValueError: If an unexpected sub-key is encountered in a decoder layer. + """ + layer_prefix = "decoder.layers." + final_ln_prefix = "decoder.final_layernorm." + + # Final layernorm name differs between TransformerBlock and MambaStack + if key.startswith(final_ln_prefix): + return "decoder.final_norm." + key[len(final_ln_prefix):] + + if not key.startswith(layer_prefix): + return key # embedding, output_layer, rotary_pos_emb, etc. + + # Parse "decoder.layers.{N}.{rest}" + remainder = key[len(layer_prefix):] + dot_idx = remainder.index('.') + layer_n = int(remainder[:dot_idx]) + rest = remainder[dot_idx + 1:] + + assert 0 <= layer_n < num_gpt_layers, f"Layer index {layer_n} out of range [0, {num_gpt_layers}) in key '{key}'" + + if rest.startswith("input_layernorm.") or rest.startswith("self_attention."): + return f"{layer_prefix}{2 * layer_n}.{rest}" + elif rest.startswith("mlp."): + return f"{layer_prefix}{2 * layer_n + 1}.{rest}" + elif rest.startswith("pre_mlp_layernorm."): + # MoE layers have a real TENorm for pre_mlp_layernorm (not fused); + # it maps to MoETransformerLayer 2N+1. + return f"{layer_prefix}{2 * layer_n + 1}.{rest}" + else: + raise ValueError( + f"Unexpected sub-key '{rest}' in GPT layer {layer_n} (full key='{key}'). " + "Expected: input_layernorm.*, self_attention.*, pre_mlp_layernorm.*, mlp.*" + ) + + +def _remap_state_dict( + gpt_sd: Dict, num_gpt_layers: int +) -> Dict: + """Apply key remapping to the full GPTModel state dict.""" + return {_remap_key(k, num_gpt_layers): v for k, v in gpt_sd.items()} + + +def convert(input_path: Path, output_path: Path, num_gpt_layers: int) -> None: + """Load a GPTModel DCP checkpoint, remap keys, and save as MambaModel DCP. + + Args: + input_path: Path to the GPTModel DCP checkpoint directory. + output_path: Destination directory for the MambaModel DCP checkpoint. + num_gpt_layers: Number of GPT decoder layers in the original model. + """ + try: + import torch + import torch.distributed.checkpoint as dcp + from torch.distributed.checkpoint.format_utils import ( + dcp_to_torch_save, + torch_save_to_dcp, + ) + except ImportError as exc: + raise SystemExit( + "PyTorch distributed checkpoint (torch.distributed.checkpoint) is required. " + "Please upgrade to PyTorch >= 2.0." + ) from exc + + print(f"Loading GPTModel checkpoint from: {input_path}") + + # --- Load the flat state dict from DCP --- + # We use dcp_to_torch_save to materialize the DCP into a regular .pt file, + # then remap keys, then convert back to DCP. + tmp_flat = output_path.parent / "_tmp_gpt_flat.pt" + try: + dcp_to_torch_save(str(input_path), str(tmp_flat)) + gpt_sd = torch.load(tmp_flat, map_location="cpu") + print(f"Loaded {len(gpt_sd)} keys from GPTModel checkpoint.") + + # --- Remap keys --- + mamba_sd = _remap_state_dict(gpt_sd, num_gpt_layers) + print( + f"Remapped state dict: {len(gpt_sd)} GPT keys → {len(mamba_sd)} Mamba keys." + ) + + # --- Save remapped state dict as a new flat .pt then convert to DCP --- + tmp_mamba = output_path.parent / "_tmp_mamba_flat.pt" + torch.save(mamba_sd, tmp_mamba) + + output_path.mkdir(parents=True, exist_ok=True) + torch_save_to_dcp(str(tmp_mamba), str(output_path)) + print(f"MambaModel DCP checkpoint saved to: {output_path}") + + finally: + for tmp in (tmp_flat, output_path.parent / "_tmp_mamba_flat.pt"): + if tmp.exists(): + tmp.unlink() + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Convert GPTModel DSA checkpoint to MambaModel-compatible format." + ) + parser.add_argument( + "--input", required=True, type=Path, + help="Path to the source GPTModel DCP checkpoint directory.", + ) + parser.add_argument( + "--output", required=True, type=Path, + help="Destination path for the MambaModel DCP checkpoint.", + ) + parser.add_argument( + "--num-gpt-layers", required=True, type=int, + help="Number of decoder layers in the GPTModel (e.g. 4).", + ) + args = parser.parse_args() + + if not args.input.exists(): + raise SystemExit(f"Input checkpoint not found: {args.input}") + if args.output.exists(): + print(f"Warning: output path already exists and will be overwritten: {args.output}") + shutil.rmtree(args.output) + + convert(args.input, args.output, args.num_gpt_layers) + print("Conversion complete.") + + +if __name__ == "__main__": + main() From 23663a870ab4d99f1a6d84976d94e15d0af110a2 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Fri, 17 Apr 2026 09:30:36 -0500 Subject: [PATCH 32/72] Add tables and histogram for RL staleness (#4097) --- megatron/rl/rl_utils.py | 72 +++++++++++++++++++--------- tests/unit_tests/rl/test_rl_utils.py | 14 ++++-- 2 files changed, 60 insertions(+), 26 deletions(-) diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index 30c941d61dc..8ae4b6c0469 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -801,8 +801,8 @@ def compute_group_stats( group_traj_lengths.append(sum(roll_turn_lens)) assert rollout.policy_epoch, "Rollout has no policy_epoch data" assert rollout.kv_cache_epoch, "Rollout has no kv_cache_epoch data" - group_policy_epoch.append(min(turn[0][1] for turn in rollout.policy_epoch)) - group_kv_epoch.append(min(turn[0][1] for turn in rollout.kv_cache_epoch)) + group_policy_epoch.append([epoch for turn in rollout.policy_epoch for _, epoch in turn]) + group_kv_epoch.append([epoch for turn in rollout.kv_cache_epoch for _, epoch in turn]) group_completed_epochs.extend(turn[-1][1] for turn in rollout.policy_epoch) group_num_evictions.append(sum(rollout.num_evictions)) all_policy_epoch.append(group_policy_epoch) @@ -852,8 +852,8 @@ def prep_wandb_metrics( rewards: List[List[float]], num_turns: List[List[int]], advantages: List[float], - policy_epoch: List[List[int]], - kv_cache_epoch: List[List[int]], + policy_epoch: List[List[List[int]]], + kv_cache_epoch: List[List[List[int]]], completed_epochs: List[List[int]], num_evictions: List[List[int]], current_iteration: int, @@ -870,8 +870,8 @@ def prep_wandb_metrics( rewards: Grouped list of rewards. num_turns: Grouped list of number of turns in the trajectories. advantages: Flattened list of advantages. - policy_epoch: Grouped list of per-rollout min policy epoch stamps. - kv_cache_epoch: Grouped list of per-rollout min KV cache epoch stamps. + policy_epoch: Grouped list of per-token policy epoch stamps. + kv_cache_epoch: Grouped list of per-token KV cache epoch stamps. completed_epochs: Grouped list of per-turn max policy epoch stamps. num_evictions: Grouped list of per-rollout number of evictions. current_iteration: Current training iteration. @@ -884,8 +884,15 @@ def prep_wandb_metrics( data=[[np.mean(g), np.std(g)] for g in rewards], ) - true_policy_staleness = [current_iteration - s for g in policy_epoch for s in g] - true_kv_staleness = [current_iteration - s for g in kv_cache_epoch for s in g] + # Per-rollout staleness (oldest token) + rollout_policy_staleness = [current_iteration - r[0] for g in policy_epoch for r in g] + rollout_kv_staleness = [current_iteration - r[0] for g in kv_cache_epoch for r in g] + # Per-rollout staleness (newest token) + rollout_policy_last_token_staleness = [current_iteration - r[-1] for g in policy_epoch for r in g] + rollout_kv_last_token_staleness = [current_iteration - r[-1] for g in kv_cache_epoch for r in g] + # Per-token staleness + per_token_policy_staleness = [current_iteration - e for g in policy_epoch for r in g for e in r] + per_token_kv_staleness = [current_iteration - e for g in kv_cache_epoch for r in g for e in r] metrics = { 'group_means_hist': wandb_writer.plot.histogram( @@ -907,13 +914,26 @@ def prep_wandb_metrics( 'advantages', 'Advantages' ), 'rollout_table': wandb_writer.Table( - columns=['reward', 'traj_length', 'num_evictions'], + columns=[ + 'reward', 'traj_length', 'num_evictions', + 'policy_staleness', 'kv_staleness', + 'policy_last_token_staleness', 'kv_last_token_staleness', + ], data=list(zip( [r for g in rewards for r in g], [l for g in traj_lens for l in g], [e for g in num_evictions for e in g], + rollout_policy_staleness, + rollout_kv_staleness, + rollout_policy_last_token_staleness, + rollout_kv_last_token_staleness, )), ), + # NOTE: This table can get very large (one row per token across all rollouts). + 'per_token_table': wandb_writer.Table( + columns=['policy_staleness', 'kv_staleness'], + data=list(zip(per_token_policy_staleness, per_token_kv_staleness)), + ), 'mean_turn_length': np.mean([np.mean(g) for g in turn_lens]), 'mean_turn_length_std': np.mean([np.std(g) for g in turn_lens]), 'max_turn_length': max([max(g) for g in turn_lens]), @@ -929,15 +949,29 @@ def prep_wandb_metrics( 'mean_advantage': np.mean(advantages), 'nonzero_groups_ratio': np.count_nonzero(advantages) / len(advantages), - 'mean_policy_staleness': np.mean(true_policy_staleness), - 'max_policy_staleness': max(true_policy_staleness), - 'min_policy_staleness': min(true_policy_staleness), - 'mean_kv_cache_staleness': np.mean(true_kv_staleness), - 'max_kv_cache_staleness': max(true_kv_staleness), - 'min_kv_cache_staleness': min(true_kv_staleness), + 'mean_policy_staleness': np.mean(rollout_policy_staleness), + 'max_policy_staleness': max(rollout_policy_staleness), + 'min_policy_staleness': min(rollout_policy_staleness), + 'mean_kv_cache_staleness': np.mean(rollout_kv_staleness), + 'max_kv_cache_staleness': max(rollout_kv_staleness), + 'min_kv_cache_staleness': min(rollout_kv_staleness), + 'mean_policy_last_token_staleness': np.mean(rollout_policy_last_token_staleness), + 'max_policy_last_token_staleness': max(rollout_policy_last_token_staleness), + 'min_policy_last_token_staleness': min(rollout_policy_last_token_staleness), + 'mean_kv_cache_last_token_staleness': np.mean(rollout_kv_last_token_staleness), + 'max_kv_cache_last_token_staleness': max(rollout_kv_last_token_staleness), + 'min_kv_cache_last_token_staleness': min(rollout_kv_last_token_staleness), 'total_eviction_count': sum([sum(g) for g in num_evictions]), 'max_num_evictions': max([max(g) for g in num_evictions]), 'mean_completion_gap': np.mean([current_iteration - s for g in completed_epochs for s in g]), + 'per_token_policy_staleness_hist': wandb_writer.plot.histogram( + wandb_writer.Table(columns=['staleness'], data=[[s] for s in per_token_policy_staleness]), + 'staleness', 'Per-Token Policy Staleness' + ), + 'per_token_kv_staleness_hist': wandb_writer.plot.histogram( + wandb_writer.Table(columns=['staleness'], data=[[s] for s in per_token_kv_staleness]), + 'staleness', 'Per-Token KV Cache Staleness' + ), } if example_group: if tokenizer is None: @@ -1516,14 +1550,6 @@ def prepare_data_for_update( data = TensorDataset(*dataset_tensors) loader = DataLoader(data, batch_size=args.micro_batch_size) - with nvtx_range("rl/log-wandb-tb", time=True): - maybe_log_training_metrics( - group_stats=group_stats, - current_iteration=args.curr_iteration, - tokenizer=tokenizer, - example_groups=example_groups, - ) - return RerunDataIterator(itertools.cycle(loader)), group_stats, example_groups diff --git a/tests/unit_tests/rl/test_rl_utils.py b/tests/unit_tests/rl/test_rl_utils.py index 7d05708e27c..6bf6e994ffb 100644 --- a/tests/unit_tests/rl/test_rl_utils.py +++ b/tests/unit_tests/rl/test_rl_utils.py @@ -902,9 +902,9 @@ def test_prep_wandb_metrics(self, initialize_model_parallel): rewards = [[1, 1], [-1, 2]] num_turns = [[42, 2], [10, 8]] advantages = [0, 1] - # Per-rollout min iteration stamps (2 rollouts in group 1, 2 in group 2) - policy_epoch = [[4, 2], [5, 0]] - kv_cache_epoch = [[4, 3], [5, 1]] + # Per-token epoch stamps, grouped by group then rollout + policy_epoch = [[[4, 5], [2, 3]], [[5], [0, 1]]] + kv_cache_epoch = [[[4, 5], [3, 4]], [[5], [1, 2]]] # Per-turn max epoch stamps (when each turn completed) completed_epochs = [[5, 3], [5, 1]] num_evictions = [[0, 1], [0, 0]] @@ -944,6 +944,14 @@ def test_prep_wandb_metrics(self, initialize_model_parallel): assert metrics["mean_kv_cache_staleness"] == np.mean([2, 3, 1, 5]) assert metrics["max_kv_cache_staleness"] == 5 assert metrics["min_kv_cache_staleness"] == 1 + # last_token (max epoch per rollout): policy=[5, 3, 5, 1] -> staleness=[1, 3, 1, 5] + assert metrics["mean_policy_last_token_staleness"] == np.mean([1, 3, 1, 5]) + assert metrics["max_policy_last_token_staleness"] == 5 + assert metrics["min_policy_last_token_staleness"] == 1 + # last_token (max epoch per rollout): kv=[5, 4, 5, 2] -> staleness=[1, 2, 1, 4] + assert metrics["mean_kv_cache_last_token_staleness"] == np.mean([1, 2, 1, 4]) + assert metrics["max_kv_cache_last_token_staleness"] == 4 + assert metrics["min_kv_cache_last_token_staleness"] == 1 assert metrics["total_eviction_count"] == 1 assert metrics["max_num_evictions"] == 1 # mean_completion_gap = mean([6-5, 6-3, 6-5, 6-1]) = mean([1, 3, 1, 5]) = 2.5 From 4ece77d906ee13c9189ef47d827ee5366214b969 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Fri, 17 Apr 2026 17:58:27 +0200 Subject: [PATCH 33/72] [docs] ci: use parent-relative json_url for version picker (#4367) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 9bf0b99c706..a83ced9a93c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -99,7 +99,7 @@ html_theme = "nvidia_sphinx_theme" html_theme_options = { "switcher": { - "json_url": "versions1.json", + "json_url": "../versions1.json", "version_match": release, }, "icon_links": [ From ed5de26a49f921a1e434c8931ed343fa7b2d8094 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Fri, 17 Apr 2026 12:47:32 -0500 Subject: [PATCH 34/72] Fix bug with non-partial rollouts (#3964) --- megatron/rl/agent/api.py | 2 +- megatron/rl/rl_utils.py | 2 +- tests/unit_tests/rl/test_grouped_rollouts.py | 5 +++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/megatron/rl/agent/api.py b/megatron/rl/agent/api.py index ed50427d00a..2f3a31db445 100644 --- a/megatron/rl/agent/api.py +++ b/megatron/rl/agent/api.py @@ -247,7 +247,7 @@ async def generate_and_enqueue(batch_id, index_in_batch): @trace_async_exceptions(verbose=True) async def generate_task(): nonlocal submitted_groups - while request.streaming or submitted_groups < self.parallel_generation_tasks: + while request.streaming or submitted_groups < request.num_groups: await submission_gate.acquire() batch_id = submitted_groups // groups_per_worker submitted_groups += groups_per_worker diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index 8ae4b6c0469..516dc89ab11 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -460,7 +460,7 @@ def get_rollout_generator(args, inference_interface, n_prompts, samples_per_grou if not (streaming := args.rl_partial_rollouts) or _ROLLOUT_GENERATOR is None: agent = get_agent(args, parallel_generation_tasks=args.rl_parallel_generation_tasks) request = GroupedRolloutRequest( - num_groups=args.rl_generation_batch_size, + num_groups=args.rl_generation_batch_size if streaming else n_prompts, streaming=streaming, rollouts_per_group=samples_per_group, inference_interface=inference_interface, diff --git a/tests/unit_tests/rl/test_grouped_rollouts.py b/tests/unit_tests/rl/test_grouped_rollouts.py index 95974c5d91b..7e3aa102c29 100644 --- a/tests/unit_tests/rl/test_grouped_rollouts.py +++ b/tests/unit_tests/rl/test_grouped_rollouts.py @@ -52,8 +52,9 @@ class TestGroupedRollouts: @pytest.mark.parametrize( "num_slow_calls, streaming, num_groups, expected_count, expected_batch_ids", [ - pytest.param(0, False, 1, 8, None, id="non_batched"), - pytest.param(4, False, 2, 8, [0, 0, 1, 1, 2, 2, 3, 3], id="batched_submission_order"), + pytest.param(0, False, 8, 8, None, id="non_batched"), + pytest.param(0, False, 4, 4, None, id="non_streaming_fewer_than_parallel"), + pytest.param(4, True, 2, 8, [0, 0, 1, 1, 2, 2, 3, 3], id="batched_submission_order"), pytest.param(0, True, 1, 10, None, id="streaming"), ], ) From e15ec3c0434186d6ae8fe33009221ad99478f24f Mon Sep 17 00:00:00 2001 From: Philip Petrakian Date: Fri, 17 Apr 2026 15:08:23 -0700 Subject: [PATCH 35/72] Add QK layernorm support for dot-product attention in MambaModel (#4067) Co-authored-by: Claude Opus 4.6 (1M context) Co-authored-by: Yuzhong Wang --- megatron/core/transformer/attention.py | 51 +++++-- tests/unit_tests/models/test_mamba_model.py | 129 ++++++++++++++++++ .../unit_tests/transformer/test_attention.py | 71 ++++++++++ .../transformer/test_spec_customization.py | 6 +- 4 files changed, 245 insertions(+), 12 deletions(-) diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py index 0e5cec991e8..c53664d037d 100644 --- a/megatron/core/transformer/attention.py +++ b/megatron/core/transformer/attention.py @@ -35,7 +35,7 @@ from megatron.core.transformer.identity_op import IdentityOp from megatron.core.transformer.module import MegatronModule from megatron.core.transformer.spec_utils import ModuleSpec, build_module -from megatron.core.transformer.torch_norm import LayerNormBuilder +from megatron.core.transformer.torch_norm import L2Norm, LayerNormBuilder from megatron.core.typed_torch import apply_module, not_none from megatron.core.utils import ( deprecate_inference_params, @@ -102,10 +102,11 @@ from megatron.core.extensions.transformer_engine import ( SplitAlongDim, TELinear, + TENorm, set_save_original_input, ) else: - SplitAlongDim, TELinear, set_save_original_input = None, None, None + SplitAlongDim, TELinear, TENorm, set_save_original_input = None, None, None, None try: from transformer_engine.pytorch.attention.rope import apply_fused_qkv_rotary_pos_emb @@ -1328,23 +1329,51 @@ def __init__( tp_group=self.pg_collection.tp, ) - if submodules.q_layernorm is not None: - self.q_layernorm = submodules.q_layernorm( + # Resolve which norm class to use for Q and K. + # Config selects the default norm class; spec overrides if set. + if self.config.qk_l2_norm: + q_norm_cls = submodules.q_layernorm or L2Norm + k_norm_cls = submodules.k_layernorm or L2Norm + elif self.config.qk_layernorm: + # TODO(yuzhongw, janpabloe): Support local backend. + q_norm_cls = submodules.q_layernorm or TENorm + k_norm_cls = submodules.k_layernorm or TENorm + if q_norm_cls is None or k_norm_cls is None: + raise ValueError( + "qk_layernorm requires Transformer Engine (for TENorm) or " + "q_layernorm/k_layernorm set in the spec." + ) + else: + if submodules.q_layernorm not in (None, IdentityOp): + raise ValueError( + f"spec sets q_layernorm={submodules.q_layernorm} but " + "qk_layernorm/qk_l2_norm are disabled" + ) + if submodules.k_layernorm not in (None, IdentityOp): + raise ValueError( + f"spec sets k_layernorm={submodules.k_layernorm} but " + "qk_layernorm/qk_l2_norm are disabled" + ) + q_norm_cls = k_norm_cls = None + + self.q_layernorm = ( + q_norm_cls( hidden_size=self.hidden_size_per_attention_head, config=self.config, eps=self.config.layernorm_epsilon, ) - else: - self.q_layernorm = None - - if submodules.k_layernorm is not None: - self.k_layernorm = submodules.k_layernorm( + if q_norm_cls is not None + else None + ) + self.k_layernorm = ( + k_norm_cls( hidden_size=self.hidden_size_per_attention_head, config=self.config, eps=self.config.layernorm_epsilon, ) - else: - self.k_layernorm = None + if k_norm_cls is not None + else None + ) def run_realtime_tests(self): """Performs a consistency check. diff --git a/tests/unit_tests/models/test_mamba_model.py b/tests/unit_tests/models/test_mamba_model.py index 6f1c27ca70c..1139debd07d 100644 --- a/tests/unit_tests/models/test_mamba_model.py +++ b/tests/unit_tests/models/test_mamba_model.py @@ -292,6 +292,135 @@ def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size): assert logits.shape[2] == divide(model.vocab_size, tp_size) +class TestMambaQKLayernorm: + + def setup_method(self, method): + Utils.initialize_model_parallel(1, 1) + model_parallel_cuda_manual_seed(123) + + def teardown_method(self, method): + Utils.destroy_model_parallel() + + def _build_model(self, **config_overrides): + config = TransformerConfig( + num_layers=3, + hidden_size=256, + num_attention_heads=4, + use_cpu_initialization=True, + **config_overrides, + ) + return MambaModel( + config=config, + mamba_stack_spec=mamba_stack_spec, + vocab_size=100, + max_sequence_length=4, + hybrid_layer_pattern="M*-", + ) + + def _get_attention_layer(self, model): + """Return the SelfAttention submodule from the attention layer.""" + for layer in model.decoder.layers: + if hasattr(layer, 'self_attention') and hasattr(layer.self_attention, 'q_layernorm'): + return layer.self_attention + return None + + def test_no_qk_norm_by_default(self): + """Without qk_layernorm, attention has no q/k layernorm.""" + model = self._build_model() + attn = self._get_attention_layer(model) + assert attn is not None + assert attn.q_layernorm is None + assert attn.k_layernorm is None + + def test_qk_layernorm_from_config(self): + """config.qk_layernorm=True creates q/k layernorm even with static spec.""" + model = self._build_model(qk_layernorm=True) + attn = self._get_attention_layer(model) + assert attn is not None + # TENorm is a factory (__new__ returns a TE LayerNorm/RMSNorm), so we + # verify the norm was created rather than checking for a specific type. + assert attn.q_layernorm is not None + assert attn.k_layernorm is not None + + def test_qk_l2_norm_from_config(self): + """config.qk_l2_norm=True creates L2Norm q/k layernorm.""" + from megatron.core.transformer.torch_norm import L2Norm + + model = self._build_model(qk_l2_norm=True) + attn = self._get_attention_layer(model) + assert attn is not None + assert isinstance(attn.q_layernorm, L2Norm) + assert isinstance(attn.k_layernorm, L2Norm) + + def test_spec_provided_norm_not_overwritten(self): + """When the spec already provides q/k layernorm, config doesn't override it.""" + import copy + + from megatron.core.extensions.transformer_engine import ( + TEDotProductAttention, + TELayerNormColumnParallelLinear, + TERowParallelLinear, + ) + from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules + from megatron.core.transformer.enums import AttnMaskType + from megatron.core.transformer.identity_op import IdentityOp + from megatron.core.transformer.spec_utils import ModuleSpec + from megatron.core.transformer.transformer_layer import ( + TransformerLayer, + TransformerLayerSubmodules, + ) + + # Build a spec that explicitly sets q/k layernorm to IdentityOp + spec = copy.deepcopy(mamba_stack_spec) + spec.submodules.attention_layer.submodules.self_attention.submodules.q_layernorm = ( + IdentityOp + ) + spec.submodules.attention_layer.submodules.self_attention.submodules.k_layernorm = ( + IdentityOp + ) + + config = TransformerConfig( + num_layers=3, + hidden_size=256, + num_attention_heads=4, + use_cpu_initialization=True, + qk_layernorm=True, + ) + model = MambaModel( + config=config, + mamba_stack_spec=spec, + vocab_size=100, + max_sequence_length=4, + hybrid_layer_pattern="M*-", + ) + attn = self._get_attention_layer(model) + assert attn is not None + assert isinstance(attn.q_layernorm, IdentityOp) + assert isinstance(attn.k_layernorm, IdentityOp) + + def test_forward_with_qk_layernorm(self): + """MambaModel forward pass works with qk_layernorm enabled.""" + model = self._build_model(qk_layernorm=True) + model.cuda() + + sequence_length = 4 + micro_batch_size = 2 + data = list(range(sequence_length)) + input_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + position_ids = torch.tensor(data, dtype=torch.int64).repeat((micro_batch_size, 1)).cuda() + attention_mask = torch.ones( + (micro_batch_size, 1, sequence_length, sequence_length), dtype=bool + ).cuda() + + logits = model.forward( + input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask + ) + + assert logits.shape[0] == micro_batch_size + assert logits.shape[1] == sequence_length + assert logits.shape[2] == 100 + + class TestMambaWithDynamicInference: """Tests MambaModel with dynamic inference.""" diff --git a/tests/unit_tests/transformer/test_attention.py b/tests/unit_tests/transformer/test_attention.py index 15d95128bac..ab5a33aa61b 100644 --- a/tests/unit_tests/transformer/test_attention.py +++ b/tests/unit_tests/transformer/test_attention.py @@ -724,3 +724,74 @@ def test_parallel_attention_correctness_num_query_groups_less_than_tp_size( seed=123, sequence_length=256, ) + + +def test_qk_layernorm_from_config_fallback(): + """config.qk_layernorm=True with spec q/k_layernorm=None builds TENorm.""" + te_pytorch = pytest.importorskip("transformer_engine.pytorch") + from dataclasses import replace + + Utils.initialize_model_parallel(1, 1) + model_parallel_cuda_manual_seed(123) + try: + config = TransformerConfig( + num_layers=1, + hidden_size=128, + num_attention_heads=4, + use_cpu_initialization=True, + qk_layernorm=True, + ) + base = get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules + submodules = replace(base, q_layernorm=None, k_layernorm=None) + attn = SelfAttention(config, submodules, layer_number=1) + assert isinstance(attn.q_layernorm, te_pytorch.LayerNorm) + assert isinstance(attn.k_layernorm, te_pytorch.LayerNorm) + finally: + Utils.destroy_model_parallel() + + +def test_qk_l2_norm_from_config_fallback(): + """config.qk_l2_norm=True with spec q/k_layernorm=None builds L2Norm.""" + pytest.importorskip("transformer_engine.pytorch") + from dataclasses import replace + + from megatron.core.transformer.torch_norm import L2Norm + + Utils.initialize_model_parallel(1, 1) + model_parallel_cuda_manual_seed(123) + try: + config = TransformerConfig( + num_layers=1, + hidden_size=128, + num_attention_heads=4, + use_cpu_initialization=True, + qk_l2_norm=True, + ) + base = get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules + submodules = replace(base, q_layernorm=None, k_layernorm=None) + attn = SelfAttention(config, submodules, layer_number=1) + assert isinstance(attn.q_layernorm, L2Norm) + assert isinstance(attn.k_layernorm, L2Norm) + finally: + Utils.destroy_model_parallel() + + +def test_qk_layernorm_spec_config_mismatch_raises(): + """Spec sets a concrete norm but config disables qk_layernorm/qk_l2_norm -> ValueError.""" + pytest.importorskip("transformer_engine") + from dataclasses import replace + + from megatron.core.transformer.torch_norm import L2Norm + + Utils.initialize_model_parallel(1, 1) + model_parallel_cuda_manual_seed(123) + try: + config = TransformerConfig( + num_layers=1, hidden_size=128, num_attention_heads=4, use_cpu_initialization=True + ) + base = get_gpt_layer_with_transformer_engine_submodules().self_attention.submodules + submodules = replace(base, q_layernorm=L2Norm, k_layernorm=L2Norm) + with pytest.raises(ValueError, match="qk_layernorm"): + SelfAttention(config, submodules, layer_number=1) + finally: + Utils.destroy_model_parallel() diff --git a/tests/unit_tests/transformer/test_spec_customization.py b/tests/unit_tests/transformer/test_spec_customization.py index 84fb2ee706c..bc2e6edee74 100755 --- a/tests/unit_tests/transformer/test_spec_customization.py +++ b/tests/unit_tests/transformer/test_spec_customization.py @@ -34,7 +34,11 @@ def setup_method(self, method): Utils.initialize_model_parallel(1, 1) model_parallel_cuda_manual_seed(123) self.config = TransformerConfig( - num_layers=2, hidden_size=12, num_attention_heads=4, use_cpu_initialization=True + num_layers=2, + hidden_size=12, + num_attention_heads=4, + use_cpu_initialization=True, + qk_l2_norm=True, ) # specify Transformer Layer spec with all identity ops From 75a28781fd2c8e375027cf23af8108f06954eefb Mon Sep 17 00:00:00 2001 From: Dhinesh Ponnarasan <160256912+DhineshPonnarasan@users.noreply.github.com> Date: Fri, 17 Apr 2026 19:09:12 -0400 Subject: [PATCH 36/72] Docs: improve docstrings and comments in example training loop (#4041) --- examples/run_simple_mcore_train_loop.py | 57 +++++++++++++++---------- 1 file changed, 35 insertions(+), 22 deletions(-) diff --git a/examples/run_simple_mcore_train_loop.py b/examples/run_simple_mcore_train_loop.py index 24aea52b2b2..1ba5e10cfc9 100644 --- a/examples/run_simple_mcore_train_loop.py +++ b/examples/run_simple_mcore_train_loop.py @@ -7,7 +7,6 @@ from functools import partial from pathlib import Path from typing import Any, Callable, Dict, Tuple, Iterator - from megatron.core import parallel_state from megatron.core import dist_checkpointing from megatron.core.pipeline_parallel.schedules import get_forward_backward_func @@ -25,19 +24,17 @@ from megatron.core.distributed.finalize_model_grads import finalize_model_grads from megatron.core.tokenizers import MegatronTokenizer - _SEQUENCE_LENGTH: int = 64 - def initialize_distributed( tensor_model_parallel_size: int = 1, pipeline_model_parallel_size: int = 1 ) -> None: """ - Initialize torch.distributed and Megatron-Core model parallel groups. + Set up torch.distributed and Megatron-Core model parallel groups. Args: - tensor_model_parallel_size: Number of GPUs for tensor model parallelism. - pipeline_model_parallel_size: Number of GPUs for pipeline model parallelism. + tensor_model_parallel_size (int): Number of GPUs to use for tensor model parallelism. + pipeline_model_parallel_size (int): Number of GPUs to use for pipeline model parallelism. """ parallel_state.destroy_model_parallel() @@ -59,10 +56,10 @@ def initialize_distributed( def model_provider() -> GPTModel: """ - Build and return a simple GPT model for demonstration. + Construct a minimal GPT model for demonstration and testing purposes. Returns: - GPTModel: A small GPT model with 2 layers for testing. + GPTModel: A small GPT model instance with 2 layers. """ transformer_config: TransformerConfig = TransformerConfig( num_layers=2, @@ -84,10 +81,14 @@ def model_provider() -> GPTModel: def get_train_data_iterator() -> Iterator: """ - Create a mock dataset and return a data iterator. + Initialize and return an iterator over the training dataset for the GPT model. + + This function sets up a mock dataset using the provided configuration and tokenizer, builds the dataset, + and returns an iterator for use in the training loop. It ensures that helper functions are compiled + across distributed processes if running in a distributed environment. Returns: - Iterator: Data iterator for training batches. + Iterator: An iterator that yields training batches for the GPT model. """ if torch.distributed.is_available() and torch.distributed.is_initialized(): if torch.distributed.get_rank() == 0: @@ -124,15 +125,20 @@ def forward_step_func( data_iterator: Iterator, model: torch.nn.Module ) -> Tuple[torch.Tensor, Callable]: """ - Forward step function that computes model output and returns loss function. + Perform a forward pass on a batch of training data and return the model output and loss function. + + This function retrieves the next batch from the data iterator, moves all tensors to the appropriate device, + and computes the model's output tensor. It also defines and returns a loss function, partially applied with the + current loss mask, for use in the training loop. Args: - data_iterator: Iterator providing training batches. - model: The GPT model to train. + data_iterator (Iterator): Iterator yielding training batches as dictionaries of tensors. + model (torch.nn.Module): The GPT model to be trained. Returns: - Tuple of (output_tensor, loss_function) where loss_function is a partial - function that will compute the final loss when called. + Tuple[torch.Tensor, Callable]: + - output_tensor: The output tensor from the model's forward pass. + - loss_function: A callable that computes the loss when invoked with the model output. """ def loss_func( @@ -164,11 +170,15 @@ def save_distributed_checkpoint( checkpoint_path: str, gpt_model: torch.nn.Module ) -> None: """ - Save model checkpoint using Megatron-Core distributed checkpointing. + Save a distributed checkpoint of the GPT model using Megatron-Core utilities. + + This function extracts the underlying model if wrapped with DistributedDataParallel (DDP), + obtains its sharded state dictionary, and saves it to the specified directory using + Megatron-Core's distributed checkpointing mechanism. Args: - checkpoint_path: Directory path to save checkpoint. - gpt_model: The model to checkpoint (may be wrapped with DDP). + checkpoint_path (str): Directory path where the checkpoint will be saved. + gpt_model (torch.nn.Module): The GPT model to checkpoint (may be wrapped with DDP). """ # Access underlying model if wrapped with DDP model: torch.nn.Module = ( @@ -184,14 +194,17 @@ def load_distributed_checkpoint( checkpoint_path: str, gpt_model: torch.nn.Module ) -> torch.nn.Module: """ - Load model checkpoint using Megatron-Core distributed checkpointing. + Load a distributed checkpoint into the GPT model using Megatron-Core utilities. + + This function extracts the underlying model if wrapped with DistributedDataParallel (DDP), + loads the checkpoint from the specified directory, and updates the model's state dictionary. Args: - checkpoint_path: Directory path to load checkpoint from. - gpt_model: The model to load into (may be wrapped with DDP). + checkpoint_path (str): Directory path from which to load the checkpoint. + gpt_model (torch.nn.Module): The GPT model to load the checkpoint into (may be wrapped with DDP). Returns: - The model with loaded checkpoint weights. + torch.nn.Module: The model with loaded checkpoint weights. """ # Access underlying model if wrapped with DDP model: torch.nn.Module = ( From 86b7218e09dc66cf55df152eee158651325efaca Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Sat, 18 Apr 2026 00:20:14 +0000 Subject: [PATCH 37/72] Update copy-pr-bot.yaml [skip ci] --- .github/copy-pr-bot.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml index 149574b0217..7a002db7063 100644 --- a/.github/copy-pr-bot.yaml +++ b/.github/copy-pr-bot.yaml @@ -1,4 +1,4 @@ enabled: true auto_sync_draft: false auto_sync_ready: true -trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "Connor-XY", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "WanZzzzzz", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "fitsumreda", "frsun-nvda", "gautham-kollu", "gdengk", "guihong-nv", "guyueh1", "hexinw-nvidia", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kajalj22", "kanz-nv", "keshavb96", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "minitu", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "sheliang-nv", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sraman-rgb", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "wujingyue", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"] +trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "Connor-XY", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Mellonta", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "WanZzzzzz", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "fitsumreda", "frsun-nvda", "gautham-kollu", "gdengk", "guihong-nv", "guyueh1", "hexinw-nvidia", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kajalj22", "kanz-nv", "keshavb96", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "minitu", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "sheliang-nv", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sraman-rgb", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "wujingyue", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"] From 997896883b04dee1259d92889e7344be528eefa8 Mon Sep 17 00:00:00 2001 From: Seonmyeong Bak Date: Fri, 17 Apr 2026 22:59:57 -0700 Subject: [PATCH 38/72] feat(ckpt): add --async-ckpt-use-cpu-shm argument (#4355) Co-authored-by: Claude Sonnet 4.6 --- .../dist_checkpointing/strategies/torch.py | 19 +++ megatron/training/async_utils.py | 28 ++- megatron/training/checkpointing.py | 57 +++++-- megatron/training/config/training_config.py | 6 + pyproject.toml | 2 +- .../model_config.yaml | 2 + .../model_config.yaml | 1 + uv.lock | 160 ++++++++++++++---- 8 files changed, 223 insertions(+), 52 deletions(-) diff --git a/megatron/core/dist_checkpointing/strategies/torch.py b/megatron/core/dist_checkpointing/strategies/torch.py index 58e1e563bc8..3e467c1f9dd 100644 --- a/megatron/core/dist_checkpointing/strategies/torch.py +++ b/megatron/core/dist_checkpointing/strategies/torch.py @@ -1,6 +1,7 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. """ Strategies using PyTorch distributed.checkpoint as an underlying format. """ +import inspect import io import os import pickle @@ -600,6 +601,7 @@ def __init__( thread_count: int = 1, cached_metadata: bool = False, separation_hint: Optional[str] = None, + cpu_shm_mode: bool = False, ): """Adds parameters specific to PyT Distributed format Args: @@ -614,6 +616,10 @@ def __init__( gathering local metadata every checkpointing invocation separation_hint(str, optional): If provided, all tensors whose keys have this prefix will be saved to a separate file. + cpu_shm_mode (bool, optional): Copy GPU tensors to CPU shared-memory in the + training process before handing off to the async worker. Avoids CUDA IPC / + NVLink fabric handles in the worker subprocess. Only applies with nvrx async + strategy. """ self.backend = backend self.version = version @@ -640,6 +646,7 @@ def __init__( self.cached_global_metadata: Optional[Metadata] = None self.separation_hint = separation_hint + self.cpu_shm_mode = cpu_shm_mode self.validated_loaded_metadata_reuse = False @@ -698,6 +705,18 @@ def async_save( self._metadata_cache.set_cached_global_metadata(self.cached_global_metadata) # Define additional arguments async_writer_kwargs["use_cached_data_structure"] = self.use_cached_ckpt_structure + if self.cpu_shm_mode: + if ( + "use_cpu_shm_for_gpu_tensors" + in inspect.signature(async_writer.__init__).parameters + ): + async_writer_kwargs["use_cpu_shm_for_gpu_tensors"] = True + else: + raise AssertionError( + "Installed nvidia-resiliency-ext does not support " + "use_cpu_shm_for_gpu_tensors. Update nvidia-resiliency-ext " + "to enable cpu_shm_mode." + ) state_dict_saver_kwargs["enable_cache"] = self.use_cached_ckpt_structure state_dict_saver_kwargs["metadata_cache"] = self._metadata_cache else: diff --git a/megatron/training/async_utils.py b/megatron/training/async_utils.py index a085a7caf74..3d283c056fa 100644 --- a/megatron/training/async_utils.py +++ b/megatron/training/async_utils.py @@ -4,9 +4,9 @@ This module provides a singleton instance of AsyncCallsQueue which manages the async checkpoint save calls. """ +import inspect import logging import time - from abc import ABC from megatron.core.dist_checkpointing.strategies.async_utils import AsyncRequest @@ -17,10 +17,14 @@ try: from nvidia_resiliency_ext.checkpointing.async_ckpt.core import AsyncRequest as NVRxAsyncRequest from nvidia_resiliency_ext.checkpointing.async_ckpt.filesystem_async import _results_queue - from nvidia_resiliency_ext.checkpointing.async_ckpt.state_dict_saver import save_state_dict_async_finalize + from nvidia_resiliency_ext.checkpointing.async_ckpt.state_dict_saver import ( + save_state_dict_async_finalize, + ) except (ImportError, ModuleNotFoundError): from megatron.core.dist_checkpointing.strategies.filesystem_async import _results_queue - from megatron.core.dist_checkpointing.strategies.state_dict_saver import save_state_dict_async_finalize + from megatron.core.dist_checkpointing.strategies.state_dict_saver import ( + save_state_dict_async_finalize, + ) NVRxAsyncRequest = ABC @@ -56,12 +60,28 @@ def init_persistent_async_worker(rank: int, mp_mode: str = 'spawn'): time_start = time.time() if rank == 0: print(f"init_persistent_async_worker: {rank}, Starting Async Caller", flush=True) - _async_calls_queue = AsyncCallsQueue(persistent=True) + _async_calls_queue = AsyncCallsQueue( + persistent=True, + **( + {"cpu_shm_mode": args.async_ckpt_use_cpu_shm} + if async_strategy == "nvrx" + and "cpu_shm_mode" in inspect.signature(AsyncCallsQueue.__init__).parameters + else {} + ), + ) # initialize the persistent caller with QoS priorities from args kwargs = {} if async_strategy == "mcore": # Note: nvidia-resiliency-ext uses is_daemon instead of mp_mode (always spawns) kwargs["mp_mode"] = mp_mode + elif async_strategy == "nvrx": + if "cpu_shm_mode" in inspect.signature(AsyncCallsQueue.warmup_persistent_caller).parameters: + kwargs["cpu_shm_mode"] = args.async_ckpt_use_cpu_shm + elif args.async_ckpt_use_cpu_shm: + raise AssertionError( + "Installed nvidia-resiliency-ext does not support cpu_shm_mode. " + "Update nvidia-resiliency-ext to use --async-ckpt-use-cpu-shm." + ) AsyncCallsQueue.warmup_persistent_caller( rank, cpu_priority=args.async_ckpt_cpu_priority, diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py index caf32b2a7bc..1441a71518d 100644 --- a/megatron/training/checkpointing.py +++ b/megatron/training/checkpointing.py @@ -3,6 +3,7 @@ """Input/output checkpointing.""" import contextlib +import inspect import multiprocessing import os import random @@ -16,41 +17,45 @@ from logging import getLogger from pathlib import Path from time import time +from typing import Any, Dict, List, Optional, Union import numpy as np import torch -from typing import Optional, Union, List, Dict, Any from torch.distributed.checkpoint import FileSystemReader, default_planner from megatron.core import dist_checkpointing, mpu, tensor_parallel from megatron.core.dist_checkpointing.mapping import ShardedObject -from megatron.core.dist_checkpointing.strategies.torch import TorchDistLoadShardedStrategy, TorchDistSaveShardedStrategy +from megatron.core.dist_checkpointing.strategies.async_utils import _disable_gc from megatron.core.dist_checkpointing.strategies.fully_parallel import ( FullyParallelLoadStrategyWrapper, FullyParallelSaveStrategyWrapper, ) +from megatron.core.dist_checkpointing.strategies.torch import ( + TorchDistLoadShardedStrategy, + TorchDistSaveShardedStrategy, +) from megatron.core.msc_utils import MultiStorageClientFeature, open_file from megatron.core.num_microbatches_calculator import update_num_microbatches -from megatron.core.utils import get_pg_rank, get_pg_size from megatron.core.optimizer import DistributedOptimizer from megatron.core.rerun_state_machine import get_rerun_state_machine -from megatron.core.utils import get_torch_version, is_torch_min_version +from megatron.core.utils import get_pg_rank, get_pg_size, get_torch_version, is_torch_min_version from ..core.dist_checkpointing.utils import _clean_metadata_for_serialization from . import ft_integration, wandb_utils from .async_utils import get_save_and_finalize_callbacks, is_empty_async_queue, schedule_async_save -from megatron.core.dist_checkpointing.strategies.async_utils import _disable_gc from .global_vars import get_args from .one_logger_utils import on_save_checkpoint_start, on_save_checkpoint_success from .utils import append_to_progress_log, is_last_rank, print_rank_0, unwrap_model try: - from megatron.core.distributed.fsdp.src.megatron_fsdp.uneven_dtensor import preprocess_state_dict_for_uneven_dtensor + from megatron.core.distributed.fsdp.src.megatron_fsdp.uneven_dtensor import ( + preprocess_state_dict_for_uneven_dtensor, + ) from megatron.core.transformer.fsdp_dtensor_checkpoint import ( - print_diff_in_state_dicts, + handle_experts_in_state_dict, handle_fp8_extra_state_case, handle_swiglu_in_state_dict, - handle_experts_in_state_dict, + print_diff_in_state_dicts, ) HAVE_MEGATRON_FSDP = True except ImportError: @@ -60,6 +65,7 @@ # [ModelOpt]: Import try: from modelopt.torch.opt.plugins import save_modelopt_state, save_sharded_modelopt_state + from megatron.post_training.utils import print_distributed_quant_summary has_nvidia_modelopt = True except Exception: @@ -67,8 +73,12 @@ try: - from nvidia_resiliency_ext.checkpointing.async_ckpt.filesystem_async import FileSystemWriterAsync - from nvidia_resiliency_ext.checkpointing.async_ckpt.state_dict_saver import save_state_dict_async_plan + from nvidia_resiliency_ext.checkpointing.async_ckpt.filesystem_async import ( + FileSystemWriterAsync, + ) + from nvidia_resiliency_ext.checkpointing.async_ckpt.state_dict_saver import ( + save_state_dict_async_plan, + ) HAVE_NVRX = True except (ImportError, ModuleNotFoundError): @@ -631,7 +641,9 @@ def save_checkpoint(iteration, model, optimizer, opt_param_scheduler, num_floati validate_sharding_integrity = not args.ckpt_assume_constant_structure else: validate_sharding_integrity = True - save_strategy = TorchDistSaveShardedStrategy() + save_strategy = TorchDistSaveShardedStrategy( + cpu_shm_mode=getattr(args, 'async_ckpt_use_cpu_shm', False) + ) if args.ckpt_assume_constant_structure and args.ckpt_format == 'torch_dist': save_strategy.use_cached_ckpt_structure = args.ckpt_assume_constant_structure if args.async_save: @@ -681,8 +693,25 @@ def save_checkpoint(iteration, model, optimizer, opt_param_scheduler, num_floati if args.async_save: planner = torch.distributed.checkpoint.DefaultSavePlanner() coordinator_rank = 0 + _cpu_shm = getattr(args, 'async_ckpt_use_cpu_shm', False) + _writer_kwargs = {} + if _cpu_shm: + if ( + "use_cpu_shm_for_gpu_tensors" + in inspect.signature(FileSystemWriterAsync.__init__).parameters + ): + _writer_kwargs["use_cpu_shm_for_gpu_tensors"] = True + else: + raise AssertionError( + "Installed nvidia-resiliency-ext does not support " + "use_cpu_shm_for_gpu_tensors. Update nvidia-resiliency-ext " + "to use --async-ckpt-use-cpu-shm." + ) fs_storage_writer = FileSystemWriterAsync( - checkpoint_name, thread_count=args.dist_ckpt_workers, use_msc=args.enable_msc + checkpoint_name, + thread_count=args.dist_ckpt_workers, + use_msc=args.enable_msc, + **_writer_kwargs, ) save_state_dict_ret = save_state_dict_async_plan( @@ -707,7 +736,9 @@ def save_checkpoint(iteration, model, optimizer, opt_param_scheduler, num_floati logger.debug(f"rank: {rank}, takes {end_ckpt - start_ckpt} to prepare state dict for ckpt ") if ckpt_type == CheckpointType.LOCAL: try: - from megatron.core.dist_checkpointing.tensor_aware_state_dict import MCoreTensorAwareStateDict + from megatron.core.dist_checkpointing.tensor_aware_state_dict import ( + MCoreTensorAwareStateDict, + ) except ModuleNotFoundError: raise RuntimeError("The 'nvidia_resiliency_ext' module is required for local " "checkpointing but was not found. Please ensure it is installed.") diff --git a/megatron/training/config/training_config.py b/megatron/training/config/training_config.py index a494391dde3..2d5806bcb57 100644 --- a/megatron/training/config/training_config.py +++ b/megatron/training/config/training_config.py @@ -477,6 +477,12 @@ class CheckpointConfig: async_ckpt_io_priority: Optional[int] = 3 """I/O scheduling class (0-3, 3=idle) for the async checkpoint writer process.""" + async_ckpt_use_cpu_shm: bool = False + """Copy GPU tensors to CPU shared-memory in the training process before handing off to + the async checkpoint worker. Avoids CUDA IPC / NVLink fabric handles in the worker + subprocess. Useful on MNNVL systems where fabric resources are exhausted. + Only applies with the nvrx async strategy.""" + fully_parallel_load: bool = field(default=False, metadata={"argparse_meta": {"arg_names": ["--ckpt-fully-parallel-load"], "dest": "ckpt_fully_parallel_load"}}) """Apply full load parallelization across DP for distributed checkpoints.""" diff --git a/pyproject.toml b/pyproject.toml index 7733456ecc8..5b09cbd2a66 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -205,7 +205,7 @@ flash_mla = [ transformer-engine = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "f031cf87bd054c7558b887df7bed93975456667f" } nemo-run = { git = "https://github.com/NVIDIA-NeMo/Run.git", rev = "17ae86b64d7f75653351664f5d8c9e466faede00" } emerging_optimizers = { git = "https://github.com/NVIDIA-NeMo/Emerging-Optimizers.git", rev = "v0.2.0" } -nvidia-resiliency-ext = { git = "https://github.com/NVIDIA/nvidia-resiliency-ext.git", rev = "15a851565a4ce846c04431ecb0cf09903ab4837e" } +nvidia-resiliency-ext = { git = "https://github.com/NVIDIA/nvidia-resiliency-ext.git", rev = "b2bb3d728a18795807d9f76c535e005a609a1b01" } [tool.isort] profile = "black" # black-compatible diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml index 12acfaf8402..31d04e480ba 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp4_pp1_resume_torch_dist_dist_optimizer_overlap_grad_reduce_param_gather/model_config.yaml @@ -50,11 +50,13 @@ MODEL_ARGS: --ckpt-format: torch_dist --dist-ckpt-optim-fully-reshardable: true --dist-ckpt-strictness: log_all # backward compatibility for TE changes + --ckpt-assume-constant-structure: true --data-cache-path: ${DATA_CACHE_PATH} --bf16: true --log-memory-to-tensorboard: true --async-save: true --async-strategy: mcore --use-persistent-ckpt-worker: true + --async-ckpt-use-cpu-shm: true TEST_TYPE: ckpt-resume LAUNCHER: ft_launcher diff --git a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml index ff52550c238..faf717c7821 100644 --- a/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml +++ b/tests/functional_tests/test_cases/moe/gpt3_moe_mcore_te_tp4_ep2_etp2_pp2_resume_torch_dist_dist_optimizer/model_config.yaml @@ -66,5 +66,6 @@ MODEL_ARGS: --log-memory-to-tensorboard: true --async-save: true --use-persistent-ckpt-worker: true + --async-ckpt-use-cpu-shm: true TEST_TYPE: ckpt-resume LAUNCHER: ft_launcher diff --git a/uv.lock b/uv.lock index 78f40208716..821f353b11b 100644 --- a/uv.lock +++ b/uv.lock @@ -77,7 +77,8 @@ dependencies = [ { name = "huggingface-hub" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "psutil" }, { name = "pyyaml" }, { name = "safetensors" }, @@ -544,7 +545,8 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "click" }, { name = "mypy-extensions" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pathspec" }, { name = "platformdirs" }, ] @@ -622,7 +624,8 @@ version = "1.6.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "ninja" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts'" }, { name = "torch", marker = "sys_platform == 'never'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/63/15/ec51d77a2df03ee93410f8ee97fceeb7181da213813c51243e9dd6d7e144/causal_conv1d-1.6.1.tar.gz", hash = "sha256:e4a697ec2db3906f012e675125569f8b510b4559bc53e3095143d91369e1221b", size = 29426, upload-time = "2026-03-10T08:56:35.305Z" } @@ -1085,7 +1088,8 @@ dependencies = [ { name = "multiprocess", marker = "python_full_version >= '3.14' and sys_platform == 'win32'" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-13-megatron-core-dev') or (python_full_version < '3.14' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts') or (sys_platform != 'win32' and extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.14' and 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'extra-13-megatron-core-lts'" }, { name = "requests" }, { name = "tabulate" }, { name = "torch", marker = "sys_platform == 'never'" }, @@ -1842,7 +1848,8 @@ name = "hatchling" version = "1.29.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pathspec" }, { name = "pluggy" }, { name = "trove-classifiers" }, @@ -1958,7 +1965,8 @@ dependencies = [ { name = "fsspec", version = "2026.2.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.14' or sys_platform != 'win32' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "fsspec", 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}, ] +[[package]] +name = "langchain-openai" +version = "0.3.35" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "langchain-core" }, + { name = "openai" }, + { name = "tiktoken" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/fb/96/06d0d25a37e05a0ff2d918f0a4b0bf0732aed6a43b472b0b68426ce04ef8/langchain_openai-0.3.35.tar.gz", hash = "sha256:fa985fd041c3809da256a040c98e8a43e91c6d165b96dcfeb770d8bd457bf76f", size = 786635, upload-time = "2025-10-06T15:09:28.463Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d8/d5/c90c5478215c20ee71d8feaf676f7ffd78d0568f8c98bd83f81ce7562ed7/langchain_openai-0.3.35-py3-none-any.whl", hash = "sha256:76d5707e6e81fd461d33964ad618bd326cb661a1975cef7c1cb0703576bdada5", size = 75952, upload-time = "2025-10-06T15:09:27.137Z" }, +] + [[package]] name = "langchain-text-splitters" version = "0.3.11" @@ -2296,7 +2319,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = 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marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "torch", marker = "sys_platform == 'never' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, ] @@ -2676,7 +2701,8 @@ build = [ { name = "cython" }, { name = "hatchling" }, { name = "nvidia-mathdx" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 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and sys_platform != 'win32' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", + "python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", + "python_full_version < '3.13' and sys_platform == 'win32' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", + "python_full_version < '3.13' and sys_platform == 'emscripten' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", + "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-13-megatron-core-dev' and extra != 'extra-13-megatron-core-lts'", +] sdist = { url = "https://files.pythonhosted.org/packages/65/ee/299d360cdc32edc7d2cf530f3accf79c4fca01e96ffc950d8a52213bd8e4/packaging-26.0.tar.gz", hash = "sha256:00243ae351a257117b6a241061796684b084ed1c516a08c48a3f7e147a9d80b4", size = 143416, upload-time = "2026-01-21T20:50:39.064Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" }, @@ -5144,7 +5230,8 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "iniconfig" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pluggy" }, ] sdist = { url = "https://files.pythonhosted.org/packages/ae/3c/c9d525a414d506893f0cd8a8d0de7706446213181570cdbd766691164e40/pytest-8.3.5.tar.gz", hash = "sha256:f4efe70cc14e511565ac476b57c279e12a855b11f48f212af1080ef2263d3845", size = 1450891, upload-time = "2025-03-02T12:54:54.503Z" } @@ -5383,7 +5470,8 @@ dependencies = [ { name = "filelock" }, { name = "jsonschema" }, { name = "msgpack" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "protobuf" }, { name = "pyyaml" }, { name = "requests" }, @@ -6064,7 +6152,8 @@ dependencies = [ { name = "docutils" }, { name = "imagesize" }, { name = "jinja2" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pygments" }, { name = "requests" }, { name = "roman-numerals" }, @@ -6289,7 +6378,8 @@ dependencies = [ { name = "markdown" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pillow" }, { name = "protobuf" }, { name = "setuptools" }, @@ -6615,7 +6705,7 @@ dependencies = [ { name = "onnx", version = "1.21.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "onnxscript", version = "0.5.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, { name = "onnxscript", version = "0.6.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and extra == 'extra-13-megatron-core-dev') or (extra == 'extra-13-megatron-core-dev' and extra == 'extra-13-megatron-core-lts')" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" } }, { name = "pydantic" }, { name = "torch", marker = "sys_platform == 'never'" }, ] @@ -6629,7 +6719,8 @@ dependencies = [ { name = "huggingface-hub" }, { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "pyyaml" }, { name = "regex" }, { name = "requests" }, @@ -6792,7 +6883,8 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "click" }, { name = "gitpython" }, - { name = "packaging" }, + { name = "packaging", version = "25.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-dev'" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-13-megatron-core-lts' or extra != 'extra-13-megatron-core-dev'" }, { name = "platformdirs" }, { name = "protobuf" }, { name = "pydantic" }, From 664baa8a0216ce2e1d76230db184cb6923c7d11c Mon Sep 17 00:00:00 2001 From: Charlie Truong Date: Sat, 18 Apr 2026 03:55:02 -0500 Subject: [PATCH 39/72] cp: Fix UT timeout (#4310) (#4373) Signed-off-by: Charlie Truong Co-authored-by: Kunlun Li <94586211+kunlunl@users.noreply.github.com> Co-authored-by: Kunlun Li --- .../test_attention_variant_dsa.py | 1415 +++++++++-------- 1 file changed, 714 insertions(+), 701 deletions(-) diff --git a/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py b/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py index 6d2426a42d1..757b9dd283a 100644 --- a/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py +++ b/tests/unit_tests/transformer/experimental_attention_variant/test_attention_variant_dsa.py @@ -64,7 +64,7 @@ def patch_hadamard_if_needed(): class TestRotateActivation: """Test rotate_activation function.""" - @pytest.fixture(scope='function', autouse=True) + @pytest.fixture(scope='class', autouse=True) def setup_method(self): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 @@ -96,12 +96,14 @@ def test_rotate_activation_dtype_check(self): class TestComputeDSAIndexerLoss: """Test compute_dsa_indexer_loss function.""" - @pytest.fixture(scope='function', autouse=True) - def setup_method(self): + @pytest.fixture(scope='class', autouse=True) + def setup_method(self, request): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 ) - self.pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp']) + request.cls.pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp'] + ) yield Utils.destroy_model_parallel() @@ -214,7 +216,7 @@ def test_dsa_indexer_loss_sparse(self, seqlen_and_topk): class TestDSAIndexerLossAutoScaler: """Test DSAIndexerLossAutoScaler autograd function.""" - @pytest.fixture(scope='function', autouse=True) + @pytest.fixture(scope='class', autouse=True) def setup_method(self): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 @@ -273,28 +275,26 @@ def test_backward_pass(self): ), f"Gradient should be scaled by loss scale, expected {expected_grad_per_element}, got {dummy_input.grad[0].item()}" -@pytest.mark.parametrize("seqlen_and_topk", [[16, 8], [32, 16], [64, 32]]) -@pytest.mark.parametrize("sparse_loss", [False, True]) class TestFusedDSAIndexerLossGradient: """Test that FusedDSAIndexerLoss manual backward matches autograd backward.""" - @pytest.fixture(scope='function', autouse=True) - def setup_method(self): + @pytest.fixture(scope='class', autouse=True) + def setup_method(self, request): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 ) - self.pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp']) + request.cls.pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp'] + ) yield Utils.destroy_model_parallel() @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_fused_indexer_loss_gradient_matches_autograd(self, seqlen_and_topk, sparse_loss): + def test_fused_indexer_loss_gradient_matches_autograd(self): """ Test that the manually written backward in FusedDSAIndexerLoss produces the same gradients as PyTorch autograd on the unfused implementation. """ - seqlen = seqlen_and_topk[0] - index_topk = seqlen_and_topk[1] batch_size = 2 num_heads = 4 head_dim = 64 @@ -303,139 +303,118 @@ def test_fused_indexer_loss_gradient_matches_autograd(self, seqlen_and_topk, spa softmax_scale = head_dim**-0.5 loss_coeff = 1.0 - torch.manual_seed(42) - - # Create inputs for indexer - # q: [seqlen, batch, index_n_heads, index_head_dim] - q_ref = ( - torch.randn(seqlen, batch_size, index_n_heads, index_head_dim, dtype=torch.float32) - .cuda() - .requires_grad_(True) - ) - # weights: [seqlen, batch, index_n_heads] - weights_ref = ( - torch.randn(seqlen, batch_size, index_n_heads, dtype=torch.float32) - .cuda() - .requires_grad_(True) - ) - # k: [seqlen, batch, index_head_dim] - k_ref = ( - torch.randn(seqlen, batch_size, index_head_dim, dtype=torch.float32) - .cuda() - .requires_grad_(True) - ) - # query: [seqlen, batch, num_heads, head_dim] - detached, not trained - query = torch.randn(seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() - # key: [seqlen, batch, num_heads, head_dim] - detached, not trained - key = torch.randn(seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16).cuda() - - # Create causal mask - mask = torch.triu( - torch.full((seqlen, seqlen), float('-inf'), dtype=torch.float32).cuda(), diagonal=1 - ) - - # ============================================= - # Method 1: Autograd (reference) - # ============================================= - # Compute index scores and apply mask (matches fused_qk_topk_naive behavior) - index_scores_ref = _compute_index_scores(q_ref, weights_ref, k_ref) - # Apply mask - index_scores_masked = index_scores_ref + mask.unsqueeze(0) - # Get topk indices from masked scores - topk_k = min(index_topk, seqlen) - topk_indices = index_scores_masked.topk(topk_k, dim=-1)[1] - - # Compute loss using autograd - loss_ref = compute_dsa_indexer_loss( - index_scores=index_scores_masked, - topk_indices=topk_indices, - query=query, - key=key, - softmax_scale=softmax_scale, - loss_coeff=loss_coeff, - sparse_loss=sparse_loss, - pg_collection=self.pg_collection, - ) - - # Backward with autograd - loss_ref.backward() - - # Save reference gradients - grad_q_ref = q_ref.grad.clone() - grad_weights_ref = weights_ref.grad.clone() - grad_k_ref = k_ref.grad.clone() - - # ============================================= - # Method 2: FusedDSAIndexerLoss (manual backward) - # ============================================= - # Clone tensors from ref (detach and require grad again) - q_fused = q_ref.detach().clone().requires_grad_(True) - weights_fused = weights_ref.detach().clone().requires_grad_(True) - k_fused = k_ref.detach().clone().requires_grad_(True) - - # Use FusedDSAIndexerLoss - topk_indices_fused, loss_fused = FusedDSAIndexerLoss.apply( - q_fused, - weights_fused, - k_fused, - query.detach(), - key.detach(), - softmax_scale, - index_topk, - loss_coeff, - mask, - sparse_loss, - self.pg_collection, - ) + for seqlen, index_topk in [[16, 8], [32, 16], [64, 32]]: + for sparse_loss in [False, True]: + tag = f"[seqlen={seqlen}, topk={index_topk}, sparse={sparse_loss}]" + torch.manual_seed(42) + + q_ref = ( + torch.randn( + seqlen, batch_size, index_n_heads, index_head_dim, dtype=torch.float32 + ) + .cuda() + .requires_grad_(True) + ) + weights_ref = ( + torch.randn(seqlen, batch_size, index_n_heads, dtype=torch.float32) + .cuda() + .requires_grad_(True) + ) + k_ref = ( + torch.randn(seqlen, batch_size, index_head_dim, dtype=torch.float32) + .cuda() + .requires_grad_(True) + ) + query = torch.randn( + seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + key = torch.randn( + seqlen, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + mask = torch.triu( + torch.full((seqlen, seqlen), float('-inf'), dtype=torch.float32).cuda(), + diagonal=1, + ) - # Backward with manual implementation - loss_fused.backward() + # Method 1: Autograd (reference) + index_scores_ref = _compute_index_scores(q_ref, weights_ref, k_ref) + index_scores_masked = index_scores_ref + mask.unsqueeze(0) + topk_k = min(index_topk, seqlen) + topk_indices = index_scores_masked.topk(topk_k, dim=-1)[1] + + loss_ref = compute_dsa_indexer_loss( + index_scores=index_scores_masked, + topk_indices=topk_indices, + query=query, + key=key, + softmax_scale=softmax_scale, + loss_coeff=loss_coeff, + sparse_loss=sparse_loss, + pg_collection=self.pg_collection, + ) + loss_ref.backward() + + grad_q_ref = q_ref.grad.clone() + grad_weights_ref = weights_ref.grad.clone() + grad_k_ref = k_ref.grad.clone() + + # Method 2: FusedDSAIndexerLoss (manual backward) + q_fused = q_ref.detach().clone().requires_grad_(True) + weights_fused = weights_ref.detach().clone().requires_grad_(True) + k_fused = k_ref.detach().clone().requires_grad_(True) + + topk_indices_fused, loss_fused = FusedDSAIndexerLoss.apply( + q_fused, + weights_fused, + k_fused, + query.detach(), + key.detach(), + softmax_scale, + index_topk, + loss_coeff, + mask, + sparse_loss, + self.pg_collection, + ) + loss_fused.backward() - # Get fused gradients - grad_q_fused = q_fused.grad - grad_weights_fused = weights_fused.grad - grad_k_fused = k_fused.grad + grad_q_fused = q_fused.grad + grad_weights_fused = weights_fused.grad + grad_k_fused = k_fused.grad - # ============================================= - # Compare gradients - # ============================================= - # Check loss values match - assert torch.allclose( - loss_fused, loss_ref, rtol=1e-5, atol=1e-5 - ), f"Loss mismatch: fused={loss_fused.item()}, ref={loss_ref.item()}" + # Compare + assert torch.allclose( + loss_fused, loss_ref, rtol=1e-5, atol=1e-5 + ), f"{tag} Loss mismatch: fused={loss_fused.item()}, ref={loss_ref.item()}" - # Check topk indices match - assert torch.equal( - topk_indices_fused, topk_indices - ), "Top-k indices mismatch between fused and reference" + assert torch.equal( + topk_indices_fused, topk_indices + ), f"{tag} Top-k indices mismatch between fused and reference" - # Check gradients match - assert torch.allclose( - grad_q_fused, grad_q_ref, rtol=1e-5, atol=1e-5 - ), f"grad_q mismatch: max diff = {(grad_q_fused - grad_q_ref).abs().max().item()}" + assert torch.allclose( + grad_q_fused, grad_q_ref, rtol=1e-5, atol=1e-5 + ), f"{tag} grad_q mismatch: max diff = {(grad_q_fused - grad_q_ref).abs().max().item()}" - assert torch.allclose( - grad_weights_fused, grad_weights_ref, rtol=1e-5, atol=1e-5 - ), f"grad_weights mismatch: max diff = {(grad_weights_fused - grad_weights_ref).abs().max().item()}" + assert torch.allclose( + grad_weights_fused, grad_weights_ref, rtol=1e-5, atol=1e-5 + ), f"{tag} grad_weights mismatch: max diff = {(grad_weights_fused - grad_weights_ref).abs().max().item()}" - assert torch.allclose( - grad_k_fused, grad_k_ref, rtol=1e-5, atol=1e-5 - ), f"grad_k mismatch: max diff = {(grad_k_fused - grad_k_ref).abs().max().item()}" + assert torch.allclose( + grad_k_fused, grad_k_ref, rtol=1e-5, atol=1e-5 + ), f"{tag} grad_k mismatch: max diff = {(grad_k_fused - grad_k_ref).abs().max().item()}" -@pytest.mark.parametrize("tensor_model_parallel_size", [2, 4]) -@pytest.mark.parametrize("sparse_loss", [False, True]) class TestFusedDSAIndexerLossGradientTP: """Test FusedDSAIndexerLoss gradient consistency across different TP sizes.""" - @pytest.mark.flaky_in_dev @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_fused_indexer_loss_gradient_tp_consistency( - self, tensor_model_parallel_size, sparse_loss - ): + def test_fused_indexer_loss_gradient_tp_consistency(self): """ Test that FusedDSAIndexerLoss produces consistent gradients across TP ranks and matches TP=1 baseline. + + Tests all combinations of sparse_loss=[False, True] and TP=[2, 4] in a + single test to minimise process-group init/destroy overhead. """ seqlen = 64 index_topk = 32 @@ -448,7 +427,7 @@ def test_fused_indexer_loss_gradient_tp_consistency( loss_coeff = 1.0 # ============================================= - # First run with TP=1 to get baseline + # Compute TP=1 baselines for both sparse_loss values # ============================================= Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 @@ -458,7 +437,7 @@ def test_fused_indexer_loss_gradient_tp_consistency( pg_collection_tp1 = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp']) - # Create inputs + # Create inputs (shared across all variants) q_input = torch.randn( seqlen, batch_size, index_n_heads, index_head_dim, dtype=torch.float32 ).cuda() @@ -474,126 +453,134 @@ def test_fused_indexer_loss_gradient_tp_consistency( torch.full((seqlen, seqlen), float('-inf'), dtype=torch.float32).cuda(), diagonal=1 ) - # Clone for TP=1 - q_tp1 = q_input.clone().requires_grad_(True) - weights_tp1 = weights_input.clone().requires_grad_(True) - k_tp1 = k_input.clone().requires_grad_(True) - - # Forward and backward with TP=1 - topk_indices_tp1, loss_tp1 = FusedDSAIndexerLoss.apply( - q_tp1, - weights_tp1, - k_tp1, - query_input.detach(), - key_input.detach(), - softmax_scale, - index_topk, - loss_coeff, - mask, - sparse_loss, - pg_collection_tp1, - ) - loss_tp1.backward() - - # Save TP=1 results - grad_q_tp1 = q_tp1.grad.clone() - grad_weights_tp1 = weights_tp1.grad.clone() - grad_k_tp1 = k_tp1.grad.clone() - loss_tp1_value = loss_tp1.detach().clone() + # {sparse_loss: (topk_indices, loss, grad_q, grad_weights, grad_k)} + baselines = {} + for sparse_loss in [False, True]: + q_tp1 = q_input.clone().requires_grad_(True) + weights_tp1 = weights_input.clone().requires_grad_(True) + k_tp1 = k_input.clone().requires_grad_(True) + + topk_indices_tp1, loss_tp1 = FusedDSAIndexerLoss.apply( + q_tp1, + weights_tp1, + k_tp1, + query_input.detach(), + key_input.detach(), + softmax_scale, + index_topk, + loss_coeff, + mask, + sparse_loss, + pg_collection_tp1, + ) + loss_tp1.backward() + + baselines[sparse_loss] = ( + topk_indices_tp1.clone(), + loss_tp1.detach().clone(), + q_tp1.grad.clone(), + weights_tp1.grad.clone(), + k_tp1.grad.clone(), + ) Utils.destroy_model_parallel() # ============================================= - # Run with target TP size + # Test each TP size against baselines # ============================================= - Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(42) - model_parallel_cuda_manual_seed(42) + for tensor_model_parallel_size in [2, 4]: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) + torch.manual_seed(42) + model_parallel_cuda_manual_seed(42) - pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp']) - tp_rank = parallel_state.get_tensor_model_parallel_rank() - - # Clone inputs for TP=N (same values as TP=1) - q_tpn = q_input.clone().requires_grad_(True) - weights_tpn = weights_input.clone().requires_grad_(True) - k_tpn = k_input.clone().requires_grad_(True) - - # query and key need to be split along heads for TP - head_per_rank = num_heads // tensor_model_parallel_size - start_head = tp_rank * head_per_rank - end_head = (tp_rank + 1) * head_per_rank - query_tpn = query_input[:, :, start_head:end_head, :].clone() - key_tpn = key_input[:, :, start_head:end_head, :].clone() - - # Forward and backward with TP=N - topk_indices_tpn, loss_tpn = FusedDSAIndexerLoss.apply( - q_tpn, - weights_tpn, - k_tpn, - query_tpn.detach(), - key_tpn.detach(), - softmax_scale, - index_topk, - loss_coeff, - mask, - sparse_loss, - pg_collection_tpn, - ) - loss_tpn.backward() + pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp']) + tp_rank = parallel_state.get_tensor_model_parallel_rank() - # ============================================= - # Compare results - # ============================================= - # Loss should be the same - assert torch.allclose( - loss_tpn, loss_tp1_value, rtol=1e-5, atol=1e-5 - ), f"Loss mismatch: TP={tensor_model_parallel_size} got {loss_tpn.item()}, TP=1 got {loss_tp1_value.item()}" + # query and key split along heads for TP + head_per_rank = num_heads // tensor_model_parallel_size + start_head = tp_rank * head_per_rank + end_head = (tp_rank + 1) * head_per_rank + query_tpn = query_input[:, :, start_head:end_head, :].clone() + key_tpn = key_input[:, :, start_head:end_head, :].clone() - # Top-k indices should be the same - assert torch.equal( - topk_indices_tpn, topk_indices_tp1 - ), "Top-k indices mismatch between TP=1 and TP=N" + for sparse_loss in [False, True]: + topk_indices_tp1, loss_tp1_value, grad_q_tp1, grad_weights_tp1, grad_k_tp1 = ( + baselines[sparse_loss] + ) + tag = f"[TP={tensor_model_parallel_size}, sparse_loss={sparse_loss}]" + + q_tpn = q_input.clone().requires_grad_(True) + weights_tpn = weights_input.clone().requires_grad_(True) + k_tpn = k_input.clone().requires_grad_(True) + + topk_indices_tpn, loss_tpn = FusedDSAIndexerLoss.apply( + q_tpn, + weights_tpn, + k_tpn, + query_tpn.detach(), + key_tpn.detach(), + softmax_scale, + index_topk, + loss_coeff, + mask, + sparse_loss, + pg_collection_tpn, + ) + loss_tpn.backward() - # Gradients should match exactly (indexer params are duplicated across TP) - assert torch.allclose( - q_tpn.grad, grad_q_tp1, rtol=1e-5, atol=1e-5 - ), f"grad_q mismatch: max diff = {(q_tpn.grad - grad_q_tp1).abs().max().item()}" + # Loss should be the same + assert torch.allclose( + loss_tpn, loss_tp1_value, rtol=1e-5, atol=1e-5 + ), f"{tag} Loss mismatch: got {loss_tpn.item()}, TP=1 got {loss_tp1_value.item()}" - assert torch.allclose( - weights_tpn.grad, grad_weights_tp1, rtol=1e-5, atol=1e-5 - ), f"grad_weights mismatch: max diff = {(weights_tpn.grad - grad_weights_tp1).abs().max().item()}" + # Top-k indices should be the same + assert torch.equal( + topk_indices_tpn, topk_indices_tp1 + ), f"{tag} Top-k indices mismatch between TP=1 and TP=N" - assert torch.allclose( - k_tpn.grad, grad_k_tp1, rtol=1e-5, atol=1e-5 - ), f"grad_k mismatch: max diff = {(k_tpn.grad - grad_k_tp1).abs().max().item()}" - - # Check gradients are identical across all TP ranks - tp_size = parallel_state.get_tensor_model_parallel_world_size() - if tp_size > 1: - for grad_tensor, name in [ - (q_tpn.grad, "grad_q"), - (weights_tpn.grad, "grad_weights"), - (k_tpn.grad, "grad_k"), - ]: - grad_list = [torch.zeros_like(grad_tensor) for _ in range(tp_size)] - torch.distributed.all_gather(grad_list, grad_tensor, group=pg_collection_tpn.tp) - - for i in range(1, tp_size): - assert torch.allclose( - grad_list[0], grad_list[i], rtol=0, atol=0 - ), f"{name} differs between TP rank 0 and rank {i}" + # Gradients should match (indexer params are duplicated across TP) + assert torch.allclose( + q_tpn.grad, grad_q_tp1, rtol=1e-5, atol=1e-5 + ), f"{tag} grad_q mismatch: max diff = {(q_tpn.grad - grad_q_tp1).abs().max().item()}" - Utils.destroy_model_parallel() + assert torch.allclose( + weights_tpn.grad, grad_weights_tp1, rtol=1e-5, atol=1e-5 + ), f"{tag} grad_weights mismatch: max diff = {(weights_tpn.grad - grad_weights_tp1).abs().max().item()}" + + assert torch.allclose( + k_tpn.grad, grad_k_tp1, rtol=1e-5, atol=1e-5 + ), f"{tag} grad_k mismatch: max diff = {(k_tpn.grad - grad_k_tp1).abs().max().item()}" + + # Check gradients are identical across all TP ranks + tp_size = parallel_state.get_tensor_model_parallel_world_size() + if tp_size > 1: + for grad_tensor, name in [ + (q_tpn.grad, "grad_q"), + (weights_tpn.grad, "grad_weights"), + (k_tpn.grad, "grad_k"), + ]: + grad_list = [torch.zeros_like(grad_tensor) for _ in range(tp_size)] + torch.distributed.all_gather( + grad_list, grad_tensor, group=pg_collection_tpn.tp + ) + + for i in range(1, tp_size): + assert torch.allclose( + grad_list[0], grad_list[i], rtol=0, atol=0 + ), f"{tag} {name} differs between TP rank 0 and rank {i}" + + Utils.destroy_model_parallel() @pytest.mark.parametrize("seqlen", [16, 64]) class TestDSAIndexer: """Test DSA Indexer module basic functionality with TP=1.""" - @pytest.fixture(scope='function', autouse=True) - def setup_method(self): + @pytest.fixture(scope='class', autouse=True) + def setup_method(self, request): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 ) @@ -601,8 +588,9 @@ def setup_method(self): model_parallel_cuda_manual_seed(123) # Create MLA config with sparse attention parameters - self.index_topk = 32 - self.config = MLATransformerConfig( + cls = request.cls + cls.index_topk = 32 + cls.config = MLATransformerConfig( num_layers=2, hidden_size=256, num_attention_heads=16, @@ -621,7 +609,7 @@ def setup_method(self): # Sparse attention specific configs dsa_indexer_n_heads=8, dsa_indexer_head_dim=64, - dsa_indexer_topk=self.index_topk, + dsa_indexer_topk=cls.index_topk, ) # Create indexer submodules spec @@ -635,10 +623,8 @@ def setup_method(self): linear_weights_proj=ModuleSpec(module=TELinear), ) - self.pg_collection = ProcessGroupCollection.use_mpu_process_groups( - required_pgs=['tp', 'cp'] - ) - self.indexer = DSAIndexer(self.config, indexer_submodules, self.pg_collection) + cls.pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) + cls.indexer = DSAIndexer(cls.config, indexer_submodules, cls.pg_collection) yield Utils.destroy_model_parallel() @@ -730,16 +716,17 @@ def test_dsa_indexer_with_mask(self, seqlen): class TestDSAttention: """Test DSAttention module basic functionality with TP=1.""" - @pytest.fixture(scope='function', autouse=True) - def setup_method(self): + @pytest.fixture(scope='class', autouse=True) + def setup_method(self, request): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 ) torch.manual_seed(123) model_parallel_cuda_manual_seed(123) + cls = request.cls # Create MLA config with sparse attention parameters - self.config = MLATransformerConfig( + cls.config = MLATransformerConfig( num_layers=2, hidden_size=256, num_attention_heads=16, @@ -776,17 +763,15 @@ def setup_method(self): indexer_spec = ModuleSpec(module=DSAIndexer, submodules=indexer_submodules) sparse_attention_submodules = DSAttentionSubmodules(indexer=indexer_spec) - self.pg_collection = ProcessGroupCollection.use_mpu_process_groups( - required_pgs=['tp', 'cp'] - ) + cls.pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - self.sparse_attention = DSAttention( - config=self.config, + cls.sparse_attention = DSAttention( + config=cls.config, submodules=sparse_attention_submodules, layer_number=1, attn_mask_type=AttnMaskType.causal, attention_type='self', - pg_collection=self.pg_collection, + pg_collection=cls.pg_collection, ) yield @@ -959,11 +944,12 @@ def test_dsa_topk_selection(self): # ====================================================================================== -@pytest.mark.parametrize("tensor_model_parallel_size", [2, 4, 8]) -@pytest.mark.parametrize("sequence_parallel", [False, True]) class TestIndexerTensorParallel: """Test DSA Indexer with different TP sizes and SP settings, compare with TP=1 baseline.""" + TP_SIZES = [2, 4, 8] + SP_VALUES = [False, True] + def _create_config(self, sequence_parallel=False): """Helper to create MLA config.""" # Get TP size from parallel_state @@ -1008,54 +994,56 @@ def _create_indexer(self, config, pg_collection): return DSAIndexer(config, indexer_submodules, pg_collection) @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_dsa_indexer_weight_consistency(self, tensor_model_parallel_size, sequence_parallel): + def test_dsa_indexer_weight_consistency(self): """Test that indexer weights are identical across ALL GPUs.""" - Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) + for tensor_model_parallel_size in self.TP_SIZES: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) + world_size = torch.distributed.get_world_size() - config = self._create_config(sequence_parallel=sequence_parallel) - pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - indexer = self._create_indexer(config, pg_collection).cuda() + for sequence_parallel in self.SP_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) - # Check that all weights are identical across ALL ranks (not just TP group) - world_size = torch.distributed.get_world_size() - world_rank = torch.distributed.get_rank() + config = self._create_config(sequence_parallel=sequence_parallel) + pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + indexer = self._create_indexer(config, pg_collection).cuda() + tag = f"[TP={tensor_model_parallel_size}, SP={sequence_parallel}]" - if world_size > 1: - for name, param in indexer.named_parameters(): - # Gather weights from ALL ranks in WORLD group - param_list = [torch.zeros_like(param.data) for _ in range(world_size)] - torch.distributed.all_gather(param_list, param.data) + # Check that all weights are identical across ALL ranks + if world_size > 1: + for name, param in indexer.named_parameters(): + param_list = [torch.zeros_like(param.data) for _ in range(world_size)] + torch.distributed.all_gather(param_list, param.data) - # All weights should be identical across all GPUs - for i in range(1, world_size): - assert torch.allclose( - param_list[0], param_list[i], rtol=0, atol=0 - ), f"Parameter {name} differs between rank 0 and rank {i} (world)" + for i in range(1, world_size): + assert torch.allclose( + param_list[0], param_list[i], rtol=0, atol=0 + ), f"{tag} Parameter {name} differs between rank 0 and rank {i} (world)" - Utils.destroy_model_parallel() + Utils.destroy_model_parallel() @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_dsa_indexer_forward_consistency(self, tensor_model_parallel_size, sequence_parallel): + def test_dsa_indexer_forward_consistency(self): """Test that indexer gives consistent results across different TP sizes and SP settings.""" - # First run with TP=1 to get baseline + seq_len = 64 + batch_size = 2 + + # TP=1 baseline (once for all TP sizes; TP=1 doesn't use SP) Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 ) torch.manual_seed(123) model_parallel_cuda_manual_seed(123) - config_tp1 = self._create_config(sequence_parallel=False) # TP=1 doesn't use SP + config_tp1 = self._create_config(sequence_parallel=False) pg_collection_tp1 = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) indexer_tp1 = self._create_indexer(config_tp1, pg_collection_tp1).cuda() - seq_len = 64 - batch_size = 2 - - # Create one common input (all ranks create same input with same seed) x_input = torch.randn( seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 ).cuda() @@ -1063,14 +1051,10 @@ def test_dsa_indexer_forward_consistency(self, tensor_model_parallel_size, seque seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 ).cuda() - # Forward pass with gradients enabled index_scores_tp1, topk_indices_tp1 = indexer_tp1.forward_with_scores(x_input, qr_input) - - # Backward pass loss_tp1 = index_scores_tp1.sum() loss_tp1.backward() - # Save gradients from TP=1 indexer_tp1_grads = { name: param.grad.clone().cpu() for name, param in indexer_tp1.named_parameters() @@ -1079,129 +1063,130 @@ def test_dsa_indexer_forward_consistency(self, tensor_model_parallel_size, seque Utils.destroy_model_parallel() - # Now run with target TP size - Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) - - config_tpn = self._create_config(sequence_parallel=sequence_parallel) - pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - indexer_tpn = self._create_indexer(config_tpn, pg_collection_tpn).cuda() - - # Prepare input: split along seqlen if SP is enabled - if sequence_parallel: - tp_rank = parallel_state.get_tensor_model_parallel_rank() - seq_per_rank = seq_len // tensor_model_parallel_size - start_idx = tp_rank * seq_per_rank - end_idx = (tp_rank + 1) * seq_per_rank - x_tpn = x_input[start_idx:end_idx] - qr_tpn = qr_input[start_idx:end_idx] - else: - # No SP: all TP ranks see full input - x_tpn = x_input - qr_tpn = qr_input - - # Forward pass with gradients enabled - index_scores_tpn, topk_indices_tpn = indexer_tpn.forward_with_scores(x_tpn, qr_tpn) - - # Backward pass - loss_tpn = index_scores_tpn.sum() - loss_tpn.backward() - - # Compare forward outputs - assert index_scores_tpn.shape == index_scores_tp1.shape - assert topk_indices_tpn.shape == topk_indices_tp1.shape - - # Check that index scores are close (allow for floating point accumulation errors) - assert torch.allclose( - index_scores_tpn, index_scores_tp1, rtol=0, atol=0 - ), f"Index scores mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}" + # Test each TP size with both SP values + for tensor_model_parallel_size in self.TP_SIZES: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) - # Check that topk indices are exactly the same - assert torch.equal( - topk_indices_tpn, topk_indices_tp1 - ), f"Top-k indices mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}" + for sequence_parallel in self.SP_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) - # Compare gradients - indexer grads should be identical (duplicated weights) - for name, param in indexer_tpn.named_parameters(): - if param.grad is not None and name in indexer_tp1_grads: + config_tpn = self._create_config(sequence_parallel=sequence_parallel) + pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + indexer_tpn = self._create_indexer(config_tpn, pg_collection_tpn).cuda() + tag = f"[TP={tensor_model_parallel_size}, SP={sequence_parallel}]" + + if sequence_parallel: + tp_rank = parallel_state.get_tensor_model_parallel_rank() + seq_per_rank = seq_len // tensor_model_parallel_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x_tpn = x_input[start_idx:end_idx] + qr_tpn = qr_input[start_idx:end_idx] + else: + x_tpn = x_input + qr_tpn = qr_input + + index_scores_tpn, topk_indices_tpn = indexer_tpn.forward_with_scores(x_tpn, qr_tpn) + loss_tpn = index_scores_tpn.sum() + loss_tpn.backward() + + assert index_scores_tpn.shape == index_scores_tp1.shape + assert topk_indices_tpn.shape == topk_indices_tp1.shape assert torch.allclose( - param.grad.cpu(), indexer_tp1_grads[name], rtol=0, atol=0 - ), f"Indexer gradient {name} mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}" + index_scores_tpn, index_scores_tp1, rtol=0, atol=0 + ), f"{tag} Index scores mismatch vs TP=1" + assert torch.equal( + topk_indices_tpn, topk_indices_tp1 + ), f"{tag} Top-k indices mismatch vs TP=1" + + for name, param in indexer_tpn.named_parameters(): + if param.grad is not None and name in indexer_tp1_grads: + assert torch.allclose( + param.grad.cpu(), indexer_tp1_grads[name], rtol=0, atol=0 + ), f"{tag} Indexer gradient {name} mismatch vs TP=1" - Utils.destroy_model_parallel() + Utils.destroy_model_parallel() @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_dsa_indexer_gradient_sync(self, tensor_model_parallel_size, sequence_parallel): + def test_dsa_indexer_gradient_sync(self): """Test that gradients are properly synchronized within TP group.""" - Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) - - config = self._create_config(sequence_parallel=sequence_parallel) - pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - indexer = self._create_indexer(config, pg_collection).cuda() - seq_len = 64 batch_size = 2 - # Create one common input (all ranks create same input with same seed) - x_input = torch.randn(seq_len, batch_size, config.hidden_size, dtype=torch.bfloat16).cuda() - qr_input = torch.randn(seq_len, batch_size, config.q_lora_rank, dtype=torch.bfloat16).cuda() - - # Prepare input: split along seqlen if SP is enabled - if sequence_parallel: - tp_rank = parallel_state.get_tensor_model_parallel_rank() - tp_size = parallel_state.get_tensor_model_parallel_world_size() - seq_per_rank = seq_len // tp_size - start_idx = tp_rank * seq_per_rank - end_idx = (tp_rank + 1) * seq_per_rank - x = x_input[start_idx:end_idx] - qr = qr_input[start_idx:end_idx] - else: - # No SP: all TP ranks see full input - x = x_input - qr = qr_input - - # Forward and backward - index_scores, topk_indices = indexer.forward_with_scores(x, qr) - loss = index_scores.sum() - loss.backward() + for tensor_model_parallel_size in self.TP_SIZES: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) - # Check that all parameters have gradients - for name, param in indexer.named_parameters(): - if param.requires_grad: - assert param.grad is not None, f"Parameter {name} has no gradient" - - # After TP sync, check that gradients are identical within TP group - # Note: We only check TP group because DDP sync happens separately - tp_size = parallel_state.get_tensor_model_parallel_world_size() - if tp_size > 1: - for name, param in indexer.named_parameters(): - if param.requires_grad and param.grad is not None: - # Gather gradients from all ranks in TP group only - grad_list = [torch.zeros_like(param.grad) for _ in range(tp_size)] - torch.distributed.all_gather(grad_list, param.grad, group=pg_collection.tp) - - # All gradients should be identical within TP group after sync - for i in range(1, tp_size): - assert torch.allclose( - grad_list[0], grad_list[i], rtol=0, atol=0 - ), f"Gradient for {name} differs between TP rank 0 and rank {i} after TP sync" + for sequence_parallel in self.SP_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) - Utils.destroy_model_parallel() + config = self._create_config(sequence_parallel=sequence_parallel) + pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + indexer = self._create_indexer(config, pg_collection).cuda() + tag = f"[TP={tensor_model_parallel_size}, SP={sequence_parallel}]" + + x_input = torch.randn( + seq_len, batch_size, config.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input = torch.randn( + seq_len, batch_size, config.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + + if sequence_parallel: + tp_rank = parallel_state.get_tensor_model_parallel_rank() + tp_size = parallel_state.get_tensor_model_parallel_world_size() + seq_per_rank = seq_len // tp_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x = x_input[start_idx:end_idx] + qr = qr_input[start_idx:end_idx] + else: + x = x_input + qr = qr_input + + index_scores, topk_indices = indexer.forward_with_scores(x, qr) + loss = index_scores.sum() + loss.backward() + + for name, param in indexer.named_parameters(): + if param.requires_grad: + assert param.grad is not None, f"{tag} Parameter {name} has no gradient" + + tp_size = parallel_state.get_tensor_model_parallel_world_size() + if tp_size > 1: + for name, param in indexer.named_parameters(): + if param.requires_grad and param.grad is not None: + grad_list = [torch.zeros_like(param.grad) for _ in range(tp_size)] + torch.distributed.all_gather( + grad_list, param.grad, group=pg_collection.tp + ) + + for i in range(1, tp_size): + assert torch.allclose( + grad_list[0], grad_list[i], rtol=0, atol=0 + ), f"{tag} Gradient for {name} differs between TP rank 0 and rank {i}" + + Utils.destroy_model_parallel() -@pytest.mark.parametrize("tensor_model_parallel_size", [2, 4]) -@pytest.mark.parametrize("sequence_parallel", [False, True]) -@pytest.mark.parametrize("use_sparse_indexer_loss", [False, True]) class TestDSAttentionTensorParallel: """Test DSAttention with different TP sizes, SP settings, and sparse indexer loss.""" + TP_SIZES = [2, 4] + SP_VALUES = [False, True] + SPARSE_VALUES = [False, True] + def _create_config(self, sequence_parallel=False, use_sparse_indexer_loss=False): """Helper to create MLA config.""" # Get TP size from parallel_state @@ -1257,348 +1242,376 @@ def _create_sparse_attention(self, config, pg_collection): ) @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_dsa_weight_consistency( - self, tensor_model_parallel_size, sequence_parallel, use_sparse_indexer_loss - ): + def test_dsa_weight_consistency(self): """Test that sparse attention indexer weights are identical across ALL GPUs.""" - Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) - - config = self._create_config( - sequence_parallel=sequence_parallel, use_sparse_indexer_loss=use_sparse_indexer_loss - ) - pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - sparse_attention = self._create_sparse_attention(config, pg_collection).cuda() - - # Check that all indexer weights are identical across ALL ranks - world_size = torch.distributed.get_world_size() - world_rank = torch.distributed.get_rank() - - if world_size > 1: - for name, param in sparse_attention.indexer.named_parameters(): - # Gather weights from ALL ranks in WORLD group - param_list = [torch.zeros_like(param.data) for _ in range(world_size)] - torch.distributed.all_gather(param_list, param.data) - - # All weights should be identical across all GPUs - for i in range(1, world_size): - torch.testing.assert_close(param_list[0], param_list[i], rtol=0, atol=0) - - Utils.destroy_model_parallel() + for tensor_model_parallel_size in self.TP_SIZES: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) + world_size = torch.distributed.get_world_size() + + for sequence_parallel in self.SP_VALUES: + for use_sparse_indexer_loss in self.SPARSE_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config = self._create_config( + sequence_parallel=sequence_parallel, + use_sparse_indexer_loss=use_sparse_indexer_loss, + ) + pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + sparse_attention = self._create_sparse_attention(config, pg_collection).cuda() + tag = f"[TP={tensor_model_parallel_size}, SP={sequence_parallel}, sparse={use_sparse_indexer_loss}]" + + if world_size > 1: + for name, param in sparse_attention.indexer.named_parameters(): + param_list = [torch.zeros_like(param.data) for _ in range(world_size)] + torch.distributed.all_gather(param_list, param.data) + + for i in range(1, world_size): + torch.testing.assert_close( + param_list[0], param_list[i], rtol=0, atol=0 + ) + + Utils.destroy_model_parallel() @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_dsa_forward_consistency( - self, tensor_model_parallel_size, sequence_parallel, use_sparse_indexer_loss - ): + def test_dsa_forward_consistency(self): """Test that sparse attention gives consistent results across different TP, SP, and sparse loss settings.""" - # First run with TP=1 to get baseline - Utils.initialize_model_parallel( - tensor_model_parallel_size=1, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) - - config_tp1 = self._create_config( - sequence_parallel=False, use_sparse_indexer_loss=use_sparse_indexer_loss - ) # TP=1 doesn't use SP - pg_collection_tp1 = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - sparse_attention_tp1 = self._create_sparse_attention(config_tp1, pg_collection_tp1).cuda() + from megatron.core.tensor_parallel.mappings import gather_from_tensor_model_parallel_region seq_len = 64 batch_size = 2 - num_heads = config_tp1.num_attention_heads - head_dim = config_tp1.hidden_size // num_heads - # Create one common input (all ranks create same input with same seed) - query_input = ( - torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) - .cuda() - .requires_grad_(True) - ) - key_input = ( - torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) - .cuda() - .requires_grad_(True) - ) - value_input = ( - torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) - .cuda() - .requires_grad_(True) - ) - x_input = torch.randn( - seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 - ).cuda() - qr_input = torch.randn( - seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 - ).cuda() - attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() - attention_mask = torch.tril(attention_mask) - - # Forward pass with gradients enabled - sparse_attention_tp1.train() - output_tp1 = sparse_attention_tp1( - query=query_input, - key=key_input, - value=value_input, - x=x_input, - qr=qr_input, - attention_mask=attention_mask, - attn_mask_type=AttnMaskType.causal, - ) - - # Backward pass - loss_tp1 = output_tp1.sum() - loss_tp1.backward() - - # Save gradients from TP=1 - indexer_tp1_grads = { - name: param.grad.clone() - for name, param in sparse_attention_tp1.indexer.named_parameters() - if param.grad is not None - } - query_tp1_grad = query_input.grad.clone().cpu() - key_tp1_grad = key_input.grad.clone().cpu() - value_tp1_grad = value_input.grad.clone().cpu() - - Utils.destroy_model_parallel() - - # Now run with target TP size + # TP=1 baselines: one per use_sparse_indexer_loss value (SP is always False for TP=1) Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) - - config_tpn = self._create_config( - sequence_parallel=sequence_parallel, use_sparse_indexer_loss=use_sparse_indexer_loss + tensor_model_parallel_size=1, pipeline_model_parallel_size=1 ) - pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - sparse_attention_tpn = self._create_sparse_attention(config_tpn, pg_collection_tpn).cuda() - # Create one common input (all ranks create same input with same seed) - query_input = torch.randn( - seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 - ).cuda() - key_input = torch.randn( - seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 - ).cuda() - value_input = torch.randn( - seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 - ).cuda() - x_input = torch.randn( - seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 - ).cuda() - qr_input = torch.randn( - seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 - ).cuda() - attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() - attention_mask = torch.tril(attention_mask) + baselines = {} # {sparse_loss: (output, indexer_grads, q_grad, k_grad, v_grad)} + for use_sparse_indexer_loss in self.SPARSE_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) - # Prepare input: split along seqlen if SP is enabled - tp_rank = parallel_state.get_tensor_model_parallel_rank() - if sequence_parallel: - seq_per_rank = seq_len // tensor_model_parallel_size - start_idx = tp_rank * seq_per_rank - end_idx = (tp_rank + 1) * seq_per_rank - x_tpn = x_input[start_idx:end_idx] - qr_tpn = qr_input[start_idx:end_idx] - else: - x_tpn = x_input - qr_tpn = qr_input - - query_input = query_input.detach() - key_input = key_input.detach() - value_input = value_input.detach() - head_per_rank = num_heads // tensor_model_parallel_size - start_head = tp_rank * head_per_rank - end_head = (tp_rank + 1) * head_per_rank - query_tpn = query_input[:, :, start_head:end_head, :].clone().requires_grad_(True) - key_tpn = key_input[:, :, start_head:end_head, :].clone().requires_grad_(True) - value_tpn = value_input[:, :, start_head:end_head, :].clone().requires_grad_(True) - attention_mask_tpn = attention_mask - - # Forward pass with gradients enabled - sparse_attention_tpn.train() - output_tpn = sparse_attention_tpn( - query=query_tpn, - key=key_tpn, - value=value_tpn, - x=x_tpn, - qr=qr_tpn, - attention_mask=attention_mask_tpn, - attn_mask_type=AttnMaskType.causal, - ) + config_tp1 = self._create_config( + sequence_parallel=False, use_sparse_indexer_loss=use_sparse_indexer_loss + ) + pg_collection_tp1 = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + sparse_attention_tp1 = self._create_sparse_attention( + config_tp1, pg_collection_tp1 + ).cuda() - # Backward pass - loss_tpn = output_tpn.sum() - loss_tpn.backward() + num_heads = config_tp1.num_attention_heads + head_dim = config_tp1.hidden_size // num_heads - from megatron.core.tensor_parallel.mappings import gather_from_tensor_model_parallel_region + query_input = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + key_input = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + value_input = ( + torch.randn(seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16) + .cuda() + .requires_grad_(True) + ) + x_input = torch.randn( + seq_len, batch_size, config_tp1.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input = torch.randn( + seq_len, batch_size, config_tp1.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() + attention_mask = torch.tril(attention_mask) + + sparse_attention_tp1.train() + output_tp1 = sparse_attention_tp1( + query=query_input, + key=key_input, + value=value_input, + x=x_input, + qr=qr_input, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) - output_tpn_gathered = gather_from_tensor_model_parallel_region( - output_tpn, group=pg_collection_tpn.tp - ) - assert output_tpn_gathered.shape == output_tp1.shape - assert torch.allclose( - output_tpn_gathered.detach(), output_tp1.detach(), rtol=0, atol=0 - ), f"Sparse attention outputs mismatch between TP=1 and TP={tensor_model_parallel_size}, SP={sequence_parallel}, sparse_loss={use_sparse_indexer_loss}" - - # 1. Check indexer gradients. - for name, param in sparse_attention_tpn.indexer.named_parameters(): - if param.grad is not None and name in indexer_tp1_grads: - torch.testing.assert_close( - param.grad, indexer_tp1_grads[name], rtol=1e-5, atol=1e-5 - ) + loss_tp1 = output_tp1.sum() + loss_tp1.backward() + + baselines[use_sparse_indexer_loss] = ( + output_tp1.detach().clone(), + { + name: param.grad.clone() + for name, param in sparse_attention_tp1.indexer.named_parameters() + if param.grad is not None + }, + query_input.grad.clone().cpu(), + key_input.grad.clone().cpu(), + value_input.grad.clone().cpu(), + num_heads, + head_dim, + ) - # 2. Query/Key/Value gradients need to be gathered along num_heads dim (dim 2) if SP is enabled - # Flatten last two dims: [seq_len, batch, num_heads, head_dim] -> [seq_len, batch, num_heads * head_dim] - sq, b, nh, hd = query_tpn.grad.shape - query_grad_flat = query_tpn.grad.reshape(sq, b, nh * hd) - key_grad_flat = key_tpn.grad.reshape(sq, b, nh * hd) - value_grad_flat = value_tpn.grad.reshape(sq, b, nh * hd) + Utils.destroy_model_parallel() - # Gather along last dim - query_grad_gathered_flat = gather_from_tensor_model_parallel_region( - query_grad_flat, group=pg_collection_tpn.tp - ) - key_grad_gathered_flat = gather_from_tensor_model_parallel_region( - key_grad_flat, group=pg_collection_tpn.tp - ) - value_grad_gathered_flat = gather_from_tensor_model_parallel_region( - value_grad_flat, group=pg_collection_tpn.tp - ) + # Test each TP size with all (SP, sparse) combos + for tensor_model_parallel_size in self.TP_SIZES: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) - # Reshape back: [seq_len, batch, num_heads * head_dim] -> [seq_len, batch, num_heads, head_dim] - query_tpn_grad_gathered = query_grad_gathered_flat.reshape(sq, b, num_heads, hd) - key_tpn_grad_gathered = key_grad_gathered_flat.reshape(sq, b, num_heads, hd) - value_tpn_grad_gathered = value_grad_gathered_flat.reshape(sq, b, num_heads, hd) + for sequence_parallel in self.SP_VALUES: + for use_sparse_indexer_loss in self.SPARSE_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + ( + output_tp1, + indexer_tp1_grads, + query_tp1_grad, + key_tp1_grad, + value_tp1_grad, + num_heads, + head_dim, + ) = baselines[use_sparse_indexer_loss] + + config_tpn = self._create_config( + sequence_parallel=sequence_parallel, + use_sparse_indexer_loss=use_sparse_indexer_loss, + ) + pg_collection_tpn = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + sparse_attention_tpn = self._create_sparse_attention( + config_tpn, pg_collection_tpn + ).cuda() + tag = f"[TP={tensor_model_parallel_size}, SP={sequence_parallel}, sparse={use_sparse_indexer_loss}]" + + query_input_tpn = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + key_input_tpn = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + value_input_tpn = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + x_input_tpn = torch.randn( + seq_len, batch_size, config_tpn.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input_tpn = torch.randn( + seq_len, batch_size, config_tpn.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + attention_mask_tpn = torch.ones( + batch_size, 1, seq_len, seq_len, dtype=torch.bool + ).cuda() + attention_mask_tpn = torch.tril(attention_mask_tpn) + + tp_rank = parallel_state.get_tensor_model_parallel_rank() + if sequence_parallel: + seq_per_rank = seq_len // tensor_model_parallel_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x_tpn = x_input_tpn[start_idx:end_idx] + qr_tpn = qr_input_tpn[start_idx:end_idx] + else: + x_tpn = x_input_tpn + qr_tpn = qr_input_tpn + + head_per_rank = num_heads // tensor_model_parallel_size + start_head = tp_rank * head_per_rank + end_head = (tp_rank + 1) * head_per_rank + query_tpn = ( + query_input_tpn[:, :, start_head:end_head, :].clone().requires_grad_(True) + ) + key_tpn = ( + key_input_tpn[:, :, start_head:end_head, :].clone().requires_grad_(True) + ) + value_tpn = ( + value_input_tpn[:, :, start_head:end_head, :].clone().requires_grad_(True) + ) + + sparse_attention_tpn.train() + output_tpn = sparse_attention_tpn( + query=query_tpn, + key=key_tpn, + value=value_tpn, + x=x_tpn, + qr=qr_tpn, + attention_mask=attention_mask_tpn, + attn_mask_type=AttnMaskType.causal, + ) + + loss_tpn = output_tpn.sum() + loss_tpn.backward() + + output_tpn_gathered = gather_from_tensor_model_parallel_region( + output_tpn, group=pg_collection_tpn.tp + ) + assert output_tpn_gathered.shape == output_tp1.shape + assert torch.allclose( + output_tpn_gathered.detach(), output_tp1, rtol=0, atol=0 + ), f"{tag} Sparse attention outputs mismatch vs TP=1" + + for name, param in sparse_attention_tpn.indexer.named_parameters(): + if param.grad is not None and name in indexer_tp1_grads: + torch.testing.assert_close( + param.grad, indexer_tp1_grads[name], rtol=1e-5, atol=1e-5 + ) + + sq, b, nh, hd = query_tpn.grad.shape + query_grad_gathered = gather_from_tensor_model_parallel_region( + query_tpn.grad.reshape(sq, b, nh * hd), group=pg_collection_tpn.tp + ).reshape(sq, b, num_heads, hd) + key_grad_gathered = gather_from_tensor_model_parallel_region( + key_tpn.grad.reshape(sq, b, nh * hd), group=pg_collection_tpn.tp + ).reshape(sq, b, num_heads, hd) + value_grad_gathered = gather_from_tensor_model_parallel_region( + value_tpn.grad.reshape(sq, b, nh * hd), group=pg_collection_tpn.tp + ).reshape(sq, b, num_heads, hd) - assert torch.allclose( - query_tpn_grad_gathered.cpu(), query_tp1_grad, rtol=0, atol=0 - ), f"Query gradient mismatch between TP=1 and TP={tensor_model_parallel_size}" - assert torch.allclose( - key_tpn_grad_gathered.cpu(), key_tp1_grad, rtol=0, atol=0 - ), f"Key gradient mismatch between TP=1 and TP={tensor_model_parallel_size}" - assert torch.allclose( - value_tpn_grad_gathered.cpu(), value_tp1_grad, rtol=0, atol=0 - ), f"Value gradient mismatch between TP=1 and TP={tensor_model_parallel_size}" + assert torch.allclose( + query_grad_gathered.cpu(), query_tp1_grad, rtol=0, atol=0 + ), f"{tag} Query gradient mismatch vs TP=1" + assert torch.allclose( + key_grad_gathered.cpu(), key_tp1_grad, rtol=0, atol=0 + ), f"{tag} Key gradient mismatch vs TP=1" + assert torch.allclose( + value_grad_gathered.cpu(), value_tp1_grad, rtol=0, atol=0 + ), f"{tag} Value gradient mismatch vs TP=1" - Utils.destroy_model_parallel() + Utils.destroy_model_parallel() @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") - def test_dsa_gradient_sync( - self, tensor_model_parallel_size, sequence_parallel, use_sparse_indexer_loss - ): + def test_dsa_gradient_sync(self): """Test that indexer gradients are properly synchronized within TP group.""" - Utils.initialize_model_parallel( - tensor_model_parallel_size=tensor_model_parallel_size, pipeline_model_parallel_size=1 - ) - torch.manual_seed(123) - model_parallel_cuda_manual_seed(123) - - config = self._create_config( - sequence_parallel=sequence_parallel, use_sparse_indexer_loss=use_sparse_indexer_loss - ) - pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) - sparse_attention = self._create_sparse_attention(config, pg_collection).cuda() - sparse_attention.train() - seq_len = 64 batch_size = 2 - num_heads = config.num_attention_heads - head_dim = config.hidden_size // num_heads - # Create one common input (all ranks create same input with same seed) - query_input = torch.randn( - seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 - ).cuda() - key_input = torch.randn( - seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 - ).cuda() - value_input = torch.randn( - seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 - ).cuda() - x_input = torch.randn(seq_len, batch_size, config.hidden_size, dtype=torch.bfloat16).cuda() - qr_input = torch.randn(seq_len, batch_size, config.q_lora_rank, dtype=torch.bfloat16).cuda() - - # Prepare input: split along seqlen if SP is enabled - tp_rank = parallel_state.get_tensor_model_parallel_rank() - if sequence_parallel: - tp_size = parallel_state.get_tensor_model_parallel_world_size() - seq_per_rank = seq_len // tp_size - start_idx = tp_rank * seq_per_rank - end_idx = (tp_rank + 1) * seq_per_rank - x = x_input[start_idx:end_idx] - qr = qr_input[start_idx:end_idx] - else: - x = x_input - qr = qr_input - - # query, key, value should be split along num_heads dim - head_per_rank = num_heads // tensor_model_parallel_size - start_head = tp_rank * head_per_rank - end_head = (tp_rank + 1) * head_per_rank - query = query_input[:, :, start_head:end_head, :] - key = key_input[:, :, start_head:end_head, :] - value = value_input[:, :, start_head:end_head, :] - - attention_mask = torch.ones(batch_size, 1, seq_len, seq_len, dtype=torch.bool).cuda() - attention_mask = torch.tril(attention_mask) - - query.requires_grad_(True) - key.requires_grad_(True) - value.requires_grad_(True) - - # Forward and backward - output = sparse_attention( - query=query, - key=key, - value=value, - x=x, - qr=qr, - attention_mask=attention_mask, - attn_mask_type=AttnMaskType.causal, - ) - - loss = output.sum() - loss.backward() - - # Check that gradients exist before sync - assert query.grad is not None - assert key.grad is not None - assert value.grad is not None - - # Check that indexer parameters have gradients - for name, param in sparse_attention.indexer.named_parameters(): - if param.requires_grad: - assert param.grad is not None, f"Indexer parameter {name} has no gradient" - - # Check that indexer gradients are identical within TP group - tp_size = parallel_state.get_tensor_model_parallel_world_size() - if tp_size > 1: - for name, param in sparse_attention.indexer.named_parameters(): - if param.requires_grad and param.grad is not None: - # Gather gradients from all ranks in TP group only - grad_list = [torch.zeros_like(param.grad) for _ in range(tp_size)] - torch.distributed.all_gather(grad_list, param.grad, group=pg_collection.tp) - - # All gradients should be identical within TP group after sync - for i in range(1, tp_size): - assert torch.allclose( - grad_list[0], grad_list[i], rtol=0, atol=0 - ), f"Indexer gradient for {name} differs between TP rank 0 and rank {i} after TP sync" + for tensor_model_parallel_size in self.TP_SIZES: + Utils.initialize_model_parallel( + tensor_model_parallel_size=tensor_model_parallel_size, + pipeline_model_parallel_size=1, + ) - Utils.destroy_model_parallel() + for sequence_parallel in self.SP_VALUES: + for use_sparse_indexer_loss in self.SPARSE_VALUES: + torch.manual_seed(123) + model_parallel_cuda_manual_seed(123) + + config = self._create_config( + sequence_parallel=sequence_parallel, + use_sparse_indexer_loss=use_sparse_indexer_loss, + ) + pg_collection = ProcessGroupCollection.use_mpu_process_groups( + required_pgs=['tp', 'cp'] + ) + sparse_attention = self._create_sparse_attention(config, pg_collection).cuda() + sparse_attention.train() + tag = f"[TP={tensor_model_parallel_size}, SP={sequence_parallel}, sparse={use_sparse_indexer_loss}]" + + num_heads = config.num_attention_heads + head_dim = config.hidden_size // num_heads + + query_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + key_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + value_input = torch.randn( + seq_len, batch_size, num_heads, head_dim, dtype=torch.bfloat16 + ).cuda() + x_input = torch.randn( + seq_len, batch_size, config.hidden_size, dtype=torch.bfloat16 + ).cuda() + qr_input = torch.randn( + seq_len, batch_size, config.q_lora_rank, dtype=torch.bfloat16 + ).cuda() + + tp_rank = parallel_state.get_tensor_model_parallel_rank() + if sequence_parallel: + tp_size = parallel_state.get_tensor_model_parallel_world_size() + seq_per_rank = seq_len // tp_size + start_idx = tp_rank * seq_per_rank + end_idx = (tp_rank + 1) * seq_per_rank + x = x_input[start_idx:end_idx] + qr = qr_input[start_idx:end_idx] + else: + x = x_input + qr = qr_input + + head_per_rank = num_heads // tensor_model_parallel_size + start_head = tp_rank * head_per_rank + end_head = (tp_rank + 1) * head_per_rank + query = query_input[:, :, start_head:end_head, :] + key = key_input[:, :, start_head:end_head, :] + value = value_input[:, :, start_head:end_head, :] + + attention_mask = torch.ones( + batch_size, 1, seq_len, seq_len, dtype=torch.bool + ).cuda() + attention_mask = torch.tril(attention_mask) + + query.requires_grad_(True) + key.requires_grad_(True) + value.requires_grad_(True) + + output = sparse_attention( + query=query, + key=key, + value=value, + x=x, + qr=qr, + attention_mask=attention_mask, + attn_mask_type=AttnMaskType.causal, + ) + + loss = output.sum() + loss.backward() + + assert query.grad is not None + assert key.grad is not None + assert value.grad is not None + + for name, param in sparse_attention.indexer.named_parameters(): + if param.requires_grad: + assert ( + param.grad is not None + ), f"{tag} Indexer parameter {name} has no gradient" + + tp_size = parallel_state.get_tensor_model_parallel_world_size() + if tp_size > 1: + for name, param in sparse_attention.indexer.named_parameters(): + if param.requires_grad and param.grad is not None: + grad_list = [torch.zeros_like(param.grad) for _ in range(tp_size)] + torch.distributed.all_gather( + grad_list, param.grad, group=pg_collection.tp + ) + + for i in range(1, tp_size): + assert torch.allclose( + grad_list[0], grad_list[i], rtol=0, atol=0 + ), f"{tag} Gradient for {name} differs between TP rank 0 and rank {i}" + + Utils.destroy_model_parallel() @pytest.mark.internal class TestDSAModuleSpecDispatch: """Tests for get_dsa_module_spec_for_backend and get_experimental_attention_variant_module_spec.""" - @pytest.fixture(scope='function', autouse=True) + @pytest.fixture(scope='class', autouse=True) def setup_method(self): Utils.initialize_model_parallel( tensor_model_parallel_size=1, pipeline_model_parallel_size=1 From e4d3a4c4f9255d31c97c76077e87523cdeeca189 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Sat, 18 Apr 2026 05:51:23 -0500 Subject: [PATCH 40/72] Fix RL reward due to stop token (#4096) --- .../environments/countdown/countdown_agent.py | 2 +- examples/rl/environments/math/aime_agent.py | 6 +- .../rl/environments/math/bigmath_agent.py | 6 +- examples/rl/environments/math/dapo_agent.py | 6 +- examples/rl/environments/math/gsm8k_agent.py | 4 +- examples/rl/environments/math/math_agent.py | 43 ++++--- .../rl/environments/math/openmath_agent.py | 6 +- megatron/rl/agent/reward_only_agent.py | 10 +- megatron/rl/inference/api.py | 1 + megatron/rl/inference/megatron.py | 1 + tests/unit_tests/rl/test_rl_agents.py | 107 ++++++++++++++++++ 11 files changed, 155 insertions(+), 37 deletions(-) create mode 100644 tests/unit_tests/rl/test_rl_agents.py diff --git a/examples/rl/environments/countdown/countdown_agent.py b/examples/rl/environments/countdown/countdown_agent.py index e14ac6c6d7e..9cfdb850bce 100644 --- a/examples/rl/environments/countdown/countdown_agent.py +++ b/examples/rl/environments/countdown/countdown_agent.py @@ -40,5 +40,5 @@ async def get_prompt(self, validation=False) -> tuple[str, dict]: golden = dataset[random.randrange(len(dataset))] return self.make_prefix(**golden), golden - async def get_reward(self, response, golden: dict) -> float: + async def get_reward(self, response, golden: dict, finish_reason: str) -> float: return compute_score(response, golden) diff --git a/examples/rl/environments/math/aime_agent.py b/examples/rl/environments/math/aime_agent.py index a6a6dc0bfdf..25d13b73f02 100644 --- a/examples/rl/environments/math/aime_agent.py +++ b/examples/rl/environments/math/aime_agent.py @@ -1,3 +1,5 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + import datasets from .math_agent import MathAgent @@ -41,5 +43,5 @@ async def get_prompt(self, validation=False) -> tuple[str, dict]: prompt = self.make_prefix(**golden, problem_key="Problem") return prompt, golden - async def get_reward(self, response, golden: dict) -> float: - return self.compute_score(response, golden, golden_key="Answer") + async def get_reward(self, response, golden: dict, finish_reason: str) -> float: + return self.compute_score(response, golden, golden_key="Answer", finish_reason=finish_reason) diff --git a/examples/rl/environments/math/bigmath_agent.py b/examples/rl/environments/math/bigmath_agent.py index aa79152d4b3..5895e07e996 100644 --- a/examples/rl/environments/math/bigmath_agent.py +++ b/examples/rl/environments/math/bigmath_agent.py @@ -1,3 +1,5 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + import random import datasets @@ -34,5 +36,5 @@ async def get_prompt(self, validation=False) -> tuple[str, dict]: prompt = self.make_prefix(**golden) return prompt, golden - async def get_reward(self, response, golden: dict) -> float: - return self.compute_score(response, golden, golden_key="answer") + async def get_reward(self, response, golden: dict, finish_reason: str) -> float: + return self.compute_score(response, golden, golden_key="answer", finish_reason=finish_reason) diff --git a/examples/rl/environments/math/dapo_agent.py b/examples/rl/environments/math/dapo_agent.py index 5a56d861f5b..7339b384bc1 100644 --- a/examples/rl/environments/math/dapo_agent.py +++ b/examples/rl/environments/math/dapo_agent.py @@ -1,3 +1,5 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + import random import datasets @@ -47,5 +49,5 @@ async def get_prompt(self, validation=False) -> tuple[str, dict]: prompt = self.make_prefix(**golden) return prompt, golden - async def get_reward(self, response, golden: dict) -> float: - return self.compute_score(response, golden, golden_key="answer") + async def get_reward(self, response, golden: dict, finish_reason: str) -> float: + return self.compute_score(response, golden, golden_key="answer", finish_reason=finish_reason) diff --git a/examples/rl/environments/math/gsm8k_agent.py b/examples/rl/environments/math/gsm8k_agent.py index 6cdfb4f926e..278f88bd57c 100644 --- a/examples/rl/environments/math/gsm8k_agent.py +++ b/examples/rl/environments/math/gsm8k_agent.py @@ -64,8 +64,8 @@ async def get_prompt(self, validation=False) -> tuple[str, dict]: prompt = self.make_prefix(**golden) return prompt, golden - async def get_reward(self, response, golden: dict) -> float: - return self.compute_score(response, golden, golden_key="numeric_answer") + async def get_reward(self, response, golden: dict, finish_reason: str) -> float: + return self.compute_score(response, golden, golden_key="numeric_answer", finish_reason=finish_reason) # pytest diff --git a/examples/rl/environments/math/math_agent.py b/examples/rl/environments/math/math_agent.py index 0747254d1ad..f5610c0d742 100644 --- a/examples/rl/environments/math/math_agent.py +++ b/examples/rl/environments/math/math_agent.py @@ -34,7 +34,8 @@ def __init__(self, answer_format (str): Which answer format is expected: "tagged" for tags, or "boxed" for \boxed{} LaTeX formatting. negative_reward (float): Reward assigned for a clearly incorrect or unparseable answer. - partial_end_reward (float): Reward when the answer is correct but an expected end token is not matched exactly. + partial_end_reward (float): Reward when the answer is correct and nothing follows it, + but generation did not intentionally stop via an end-of-text token/string. **kwargs: Additional arguments for the base RewardOnlyAgent. """ super().__init__(**kwargs) @@ -46,23 +47,28 @@ def __init__(self, self.negative_reward = negative_reward self.partial_end_reward = partial_end_reward - def compute_score(self, response: str, golden: dict, golden_key: str = "answer") -> float: + def compute_score( + self, + response: str, + golden: dict, + finish_reason: str, + golden_key: str = "answer", + ) -> float: """Take a response and a golden answer and return a score. Supports tagged or boxed answers. Uses the final answer in the response string to compute the score. """ - # Allow tags or \boxed{} tags (this is a bit of cheating in favor of deepseek distilled models I think) - matched_format = None - end_tokens = ["<|end_of_text|>", "<|endoftext|>", "", "<|eot_id|>", "<|im_end|>"] + # Generation that stopped cleanly (EOD or stop word) rather than + # hitting the token limit is eligible for full reward. + stopped = finish_reason != "length" - # Only an answer immediately followed by a known end token yields 1.0 reward. answer_tag_pattern = r'(.*?)' answer_tag_match = list(re.finditer(answer_tag_pattern, response, re.DOTALL)) if answer_tag_match: # Only consider the last occurrence last_match = answer_tag_match[-1] final_answer = last_match.group(1).strip() - after = response[last_match.end():].lstrip() # strip whitespace between and token + after = response[last_match.end():].lstrip() try: parsed_answer = parse(final_answer) @@ -73,15 +79,10 @@ def compute_score(self, response: str, golden: dict, golden_key: str = "answer") correct_answer = verify(str(golden[golden_key]), parsed_answer) if correct_answer: - # Accept either <|end_of_text|> or <|endoftext|> as valid terminators, for flexibility. - for token in end_tokens: - if after.startswith(token): - return 1.0 - # If the end token is present later (extra text before it), give partial credit. - for token in end_tokens: - if token in after: - return self.partial_end_reward - # If a correct answer but missing immediate end, give format reward (not NEGATIVE_REWARD). + if stopped and not after: + return 1.0 + if stopped: + return self.partial_end_reward return self.format_reward else: # Incorrect answer, regardless of format/end-of-text @@ -103,12 +104,10 @@ def compute_score(self, response: str, golden: dict, golden_key: str = "answer") correct_answer = verify(str(golden[golden_key]), parsed_answer) if correct_answer: - for token in end_tokens: - if after.startswith(token): - return 1.0 - for token in end_tokens: - if token in after: - return self.partial_end_reward + if stopped and not after: + return 1.0 + if stopped: + return self.partial_end_reward return self.format_reward else: # Formatting is correct but the answer is incorrect diff --git a/examples/rl/environments/math/openmath_agent.py b/examples/rl/environments/math/openmath_agent.py index df7511e0e2f..98f9ae22d0c 100644 --- a/examples/rl/environments/math/openmath_agent.py +++ b/examples/rl/environments/math/openmath_agent.py @@ -1,3 +1,5 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + import random import datasets @@ -34,5 +36,5 @@ async def get_prompt(self, validation=False) -> tuple[str, dict]: prompt = self.make_prefix(**golden) return prompt, golden - async def get_reward(self, response, golden: dict) -> float: - return self.compute_score(response, golden, golden_key="expected_answer") + async def get_reward(self, response, golden: dict, finish_reason: str) -> float: + return self.compute_score(response, golden, golden_key="expected_answer", finish_reason=finish_reason) diff --git a/megatron/rl/agent/reward_only_agent.py b/megatron/rl/agent/reward_only_agent.py index 42e3b2b08f4..9755da48112 100644 --- a/megatron/rl/agent/reward_only_agent.py +++ b/megatron/rl/agent/reward_only_agent.py @@ -43,7 +43,9 @@ def get_dataset(self, validation: bool = False): """Return validation or train dataset.""" raise NotImplementedError("Derived class must implement get_dataset.") - async def get_reward(self, response: str, golden: Any) -> float: + async def get_reward( + self, response: str, golden: Any, finish_reason: str + ) -> float: """Given the LLM response and the golden data, provide a reward.""" raise NotImplementedError("Derived class must implement get_reward") @@ -99,7 +101,7 @@ async def rollout_from_response( ] rollout = TokenRollout( trajectory=[response.token_ids], - reward=await self.get_reward(response_text, golden), + reward=await self.get_reward(response_text, golden, response.finish_reason), logprobs=[logprobs], generation_mask=[generation_mask], env_id=self.env_id, @@ -111,7 +113,7 @@ async def rollout_from_response( else: rollout = Rollout( trajectory=[raw_text], - reward=await self.get_reward(response_text, golden), + reward=await self.get_reward(response_text, golden, response.finish_reason), env_id=self.env_id, problem_id=golden['problem_id'] if 'problem_id' in golden else None, policy_epoch=[response.policy_epoch], @@ -159,7 +161,7 @@ async def _evaluation( env_id=self.env_id, prompt=[prompt] if isinstance(prompt, LLMChatMessage) else prompt, response=response.response, - reward=await self.get_reward(response_text, golden), + reward=await self.get_reward(response_text, golden, response.finish_reason), problem_id=golden['problem_id'] if 'problem_id' in golden else None, ) diff --git a/megatron/rl/inference/api.py b/megatron/rl/inference/api.py index 48c5ec0a82d..7455a8791a8 100644 --- a/megatron/rl/inference/api.py +++ b/megatron/rl/inference/api.py @@ -23,6 +23,7 @@ class InferenceResponse(BaseModel): token_ids: list[int] | None = None prompt_length: int | None = None logprobs: list[float] | None = None + finish_reason: str policy_epoch: list[tuple[int, int]] kv_cache_epoch: list[tuple[int, int]] num_evictions: int diff --git a/megatron/rl/inference/megatron.py b/megatron/rl/inference/megatron.py index 005acfc4ffd..cec693a138f 100644 --- a/megatron/rl/inference/megatron.py +++ b/megatron/rl/inference/megatron.py @@ -75,6 +75,7 @@ async def base_generate(self, request: InferenceRequest) -> InferenceResponse: raw_text=choice.raw_text, token_ids=choice.prompt_token_ids + choice.generation_token_ids, logprobs=choice.generation_log_probs, + finish_reason=choice.finish_reason, prompt_length=len(choice.prompt_token_ids), policy_epoch=choice.policy_epoch, kv_cache_epoch=choice.kv_cache_epoch, diff --git a/tests/unit_tests/rl/test_rl_agents.py b/tests/unit_tests/rl/test_rl_agents.py new file mode 100644 index 00000000000..23f3a65e549 --- /dev/null +++ b/tests/unit_tests/rl/test_rl_agents.py @@ -0,0 +1,107 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +import sys +from unittest.mock import MagicMock + +import pytest + + +def _mock_math_verify(): + """Patch math_verify so MathAgent can be imported without the real package.""" + mock_module = MagicMock() + mock_module.parse = lambda x: x + mock_module.verify = lambda golden, parsed: str(golden).strip() == str(parsed).strip() + return mock_module + + +# Ensure math_verify mock is in place before any MathAgent import at module level. +sys.modules.setdefault("math_verify", _mock_math_verify()) + +from examples.rl.environments.countdown.countdown import compute_score as countdown_compute_score +from examples.rl.environments.math.math_agent import MathAgent + + +def _make_math_agent( + answer_format="tagged", format_reward=0.05, negative_reward=-0.5, partial_end_reward=0.1 +): + return MathAgent( + answer_format=answer_format, + format_reward=format_reward, + negative_reward=negative_reward, + partial_end_reward=partial_end_reward, + ) + + +class TestRLAgentRewards: + """Test reward computation for all RL environment agents. + + Covers MathAgent.compute_score (used by AIME, BigMath, DAPO, GSM8K, OpenMath) + and CountdownAgent / countdown.compute_score. + """ + + @pytest.mark.parametrize( + "answer_format, correct, wrong", + [ + pytest.param("tagged", "42", "99", id="tagged"), + pytest.param("boxed", r"\boxed{42}", r"\boxed{99}", id="boxed"), + ], + ) + @pytest.mark.parametrize( + "response_fn, finish_reason, expected_reward", + [ + # Correct answer, stopped cleanly, nothing trailing. + pytest.param(lambda c, w: c, "stop", 1.0, id="correct-stop-clean"), + # Correct answer, hit token limit, nothing trailing. + pytest.param(lambda c, w: c, "length", 0.05, id="correct-length-clean"), + # Correct answer, stopped cleanly, but trailing text. + pytest.param(lambda c, w: c + " extra", "stop", 0.1, id="correct-stop-trailing"), + # Wrong answer, correct format. + pytest.param(lambda c, w: w, "stop", 0.05, id="wrong-answer"), + # No answer tag at all. + pytest.param(lambda c, w: "I think 42", "stop", -0.5, id="no-tag"), + ], + ) + def test_math_agent_compute_score( + self, answer_format, correct, wrong, response_fn, finish_reason, expected_reward + ): + agent = _make_math_agent(answer_format=answer_format) + response = response_fn(correct, wrong) + score = agent.compute_score(response, {"answer": "42"}, finish_reason=finish_reason) + assert score == pytest.approx(expected_reward), ( + f"compute_score({response!r}, finish_reason={finish_reason!r}) " + f"= {score}, expected {expected_reward}" + ) + + @pytest.mark.parametrize( + "response, ground_truth, expected_reward", + [ + # Correct equation + pytest.param( + "(10 + 5) * 2", + {"target": 30, "nums": [10, 5, 2]}, + 1.0, + id="countdown-correct", + ), + # Wrong result + pytest.param( + "10 + 5 + 2", + {"target": 30, "nums": [10, 5, 2]}, + 0.1, + id="countdown-wrong-result", + ), + # No answer tag + pytest.param( + "I have no idea", {"target": 30, "nums": [10, 5, 2]}, 0, id="countdown-no-tag" + ), + # Uses a number not in the set + pytest.param( + "30 * 1", + {"target": 30, "nums": [10, 5, 2]}, + 0.1, + id="countdown-invalid-numbers", + ), + ], + ) + def test_countdown_compute_score(self, response, ground_truth, expected_reward): + score = countdown_compute_score(response, ground_truth) + assert score == pytest.approx(expected_reward) From 76ac7c24b4641b40a26da0c0c591056767b17272 Mon Sep 17 00:00:00 2001 From: wdykas <73254672+wdykas@users.noreply.github.com> Date: Sat, 18 Apr 2026 16:33:49 -0400 Subject: [PATCH 41/72] FA4 Inference (#4186) Co-authored-by: William Dykas Co-authored-by: root Co-authored-by: root --- megatron/core/transformer/attention.py | 82 ++++++++++++++++++++------ 1 file changed, 64 insertions(+), 18 deletions(-) diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py index c53664d037d..f89259be442 100644 --- a/megatron/core/transformer/attention.py +++ b/megatron/core/transformer/attention.py @@ -81,6 +81,13 @@ except ImportError as e: pass +try: + from flash_attn.cute import flash_attn_varlen_func as flash_attn4_varlen_func + + HAVE_FA4 = True +except ImportError: + HAVE_FA4 = False + try: from flash_mla import flash_mla_with_kvcache, get_mla_metadata @@ -828,7 +835,21 @@ def flash_decode_and_prefill( softmax_scale = self.softmax_scale else: softmax_scale = q.shape[-1] ** -0.5 - if HAVE_FA3: + if HAVE_FA4: + output_total, _ = flash_attn4_varlen_func( + q, + k, + v, + cu_seqlens_q=cu_seqlens_q, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + seqused_k=seqlens_k, + page_table=block_table, + softmax_scale=softmax_scale, + causal=True, + num_splits=1, + ) + elif HAVE_FA3: # TODO(ksanthanam): Replace with call to flash_attn_varlen_func once # it accepts block_table output_total = self._flash_attention_3_forward_wrapper( @@ -898,22 +919,47 @@ def flash_decode_and_prefill( causal=True, ) else: - flash_attn_args = { - "q": q, - "k_cache": k, - "v_cache": v, - "cache_seqlens": seqlens_k, - "causal": True, - "page_table" if HAVE_FA3 else "block_table": block_table, - "num_splits": 0 if not self.batch_invariant_mode else 1, - } - if HAVE_FA3: - output_total = flash_attn3_with_kvcache(**flash_attn_args) + if HAVE_FA4: + if getattr(self, "softmax_scale", None) is not None: + softmax_scale = self.softmax_scale + else: + softmax_scale = q.shape[-1] ** -0.5 + # Reshape q from (B, S, H, D) to (B*S, H, D) for varlen interface + q_varlen = q.reshape(-1, q.shape[-2], q.shape[-1]) + output_total, _ = flash_attn4_varlen_func( + q_varlen, + k, + v, + cu_seqlens_q=cu_seqlens_q, + max_seqlen_q=tokens_per_request, + max_seqlen_k=max_seqlen_k, + seqused_k=seqlens_k, + page_table=block_table, + softmax_scale=softmax_scale, + causal=True, + num_splits=1, + ) + # Reshape back to (B, S, H, D) + output_total = output_total.reshape( + num_requests, tokens_per_request, *output_total.shape[1:] + ) else: - assert ( - not self.batch_invariant_mode - ), "Batch invariant mode is not supported for flash attention 2" - output_total = flash_attn_with_kvcache(**flash_attn_args) + flash_attn_args = { + "q": q, + "k_cache": k, + "v_cache": v, + "cache_seqlens": seqlens_k, + "causal": True, + "page_table" if HAVE_FA3 else "block_table": block_table, + "num_splits": 0 if not self.batch_invariant_mode else 1, + } + if HAVE_FA3: + output_total = flash_attn3_with_kvcache(**flash_attn_args) + else: + assert ( + not self.batch_invariant_mode + ), "Batch invariant mode is not supported for flash attention 2" + output_total = flash_attn_with_kvcache(**flash_attn_args) # Reshape back to (B*S, 1, H, D) for consistent output shape. output_total = output_total.reshape( @@ -973,8 +1019,8 @@ def forward( inference_context = deprecate_inference_params(inference_context, inference_params) if inference_context and inference_context.is_dynamic_batching(): - assert HAVE_FA3 or is_fa_min_version( - "2.7.3" + assert ( + HAVE_FA4 or HAVE_FA3 or is_fa_min_version("2.7.3") ), "flash attn verion v2.7.3 and above is required for dynamic batching." # hidden_states: [sq, b, h] From 3315c86bc3e32a45536e46dcbaa46a19f128b2a0 Mon Sep 17 00:00:00 2001 From: Deepak Narayanan Date: Sat, 18 Apr 2026 13:48:59 -0700 Subject: [PATCH 42/72] Make param_index_map always use unpacked (full numel) offsets (#4328) Co-authored-by: Claude Opus 4.6 --- .../core/distributed/param_and_grad_buffer.py | 346 +++++++++++------- megatron/core/optimizer/distrib_optimizer.py | 7 +- .../dist_checkpointing/test_optimizer.py | 6 +- .../distributed/test_param_and_grad_buffer.py | 217 ++++++++++- 4 files changed, 431 insertions(+), 145 deletions(-) diff --git a/megatron/core/distributed/param_and_grad_buffer.py b/megatron/core/distributed/param_and_grad_buffer.py index 33eb335c1fa..fe0a51b86b7 100644 --- a/megatron/core/distributed/param_and_grad_buffer.py +++ b/megatron/core/distributed/param_and_grad_buffer.py @@ -867,25 +867,63 @@ def _pad_start_of_param_if_needed(param_start_index: int) -> int: per_bucket_numel_unpadded = [] bucket_id = 0 - def _update_bucket_metadata(param_end_index: int) -> int: + def _update_bucket_metadata( + param_end_index: int, + bucket_start_index: int, + bucket_indices: list, + numel_unpadded_list: list, + ) -> int: """ - Record metadata for the bucket starting at bucket_start_index and ending with the - passed-in param_end_index. Returns the bucket's end_index. + Record metadata for a bucket. Returns the bucket's (padded) end_index. + + Args: + param_end_index: End index of the last param in this bucket (unpadded). + bucket_start_index: Start index of this bucket. + bucket_indices: List to append (start, end) bucket boundaries to. + numel_unpadded_list: List to append unpadded bucket numel to. + + Returns: + The bucket's end index, padded if using distributed optimizer. """ - nonlocal bucket_start_index, bucket_params, bucket_id - per_bucket_numel_unpadded.append(param_end_index - bucket_start_index) + numel_unpadded_list.append(param_end_index - bucket_start_index) bucket_end_index = _pad_end_of_bucket_if_needed(param_end_index) + bucket_indices.append((bucket_start_index, bucket_end_index)) + return bucket_end_index + + def _finalize_bucket_all_index_spaces( + param_end_index: int, + bucket_start_index: int, + nvfp4_packed_param_end_index: int = None, + nvfp4_packed_bucket_start_index: int = None, + ) -> tuple: + """ + Record metadata for the current bucket across both main and (if applicable) + NVFP4 packed index spaces. Also resets bucket_params and increments bucket_id. - # Record metadata of new bucket. - self.bucket_indices.append((bucket_start_index, bucket_end_index)) - bucket_start_index = bucket_end_index + Args: + param_end_index: End index of the last param in the bucket (full numel). + bucket_start_index: Start index of the bucket (full numel). + nvfp4_packed_param_end_index: End index in packed space (NVFP4 only). + nvfp4_packed_bucket_start_index: Bucket start in packed space (NVFP4 only). - # Prepare for next bucket. + Returns: + Tuple of (bucket_end_index, nvfp4_packed_bucket_end_index). + """ + nonlocal bucket_params, bucket_id + bucket_end_index = _update_bucket_metadata( + param_end_index, bucket_start_index, self.bucket_indices, per_bucket_numel_unpadded + ) + nvfp4_packed_bucket_end_index = None + if self.has_nvfp4_params: + nvfp4_packed_bucket_end_index = _update_bucket_metadata( + nvfp4_packed_param_end_index, + nvfp4_packed_bucket_start_index, + self.nvfp4_packed_bucket_indices, + nvfp4_packed_per_bucket_numel_unpadded, + ) bucket_params = set() bucket_id += 1 - - # Return the potentially padded bucket_end_index. - return bucket_end_index + return bucket_end_index, nvfp4_packed_bucket_end_index def _does_param_require_new_bucket(param): """ @@ -915,82 +953,110 @@ def _does_param_require_new_bucket(param): # Grad buffer: [g0, g1, g2, g3, ...] numel = N # # We therefore maintain two index maps: - # - param_index_map: offsets into the packed param buffer (numel // 2) - # - nvfp4_unpacked_param_index_map: offsets using full (unpacked) numel + # - param_index_map: offsets using full numel. + # - nvfp4_packed_param_index_map: offsets into the packed param buffer (numel // 2). # self.has_nvfp4_params = any(is_nvfp4tensor(p) for p in self.params) - self.nvfp4_unpacked_param_index_map = {} - grad_start_index = 0 if self.has_nvfp4_params else None - grad_bucket_start_index = 0 if self.has_nvfp4_params else None - grad_bucket_end_index = None + # Secondary (packed) index map, counters, and bucket tracking for NVFP4. + self.nvfp4_packed_param_index_map = {} if self.has_nvfp4_params else None + nvfp4_packed_param_start_index = 0 if self.has_nvfp4_params else None + nvfp4_packed_param_end_index = None + nvfp4_packed_bucket_start_index = 0 if self.has_nvfp4_params else None + self.nvfp4_packed_bucket_indices = [] if self.has_nvfp4_params else None + nvfp4_packed_per_bucket_numel_unpadded = [] if self.has_nvfp4_params else None for param, _ in params_with_names[::-1]: # Iterate through parameters in reverse order to roughly follow backprop order. - full_numel = param.data.nelement() - # NVFP4 params are packed (2 values per byte), so the param buffer uses - # half the logical numel. Non-NVFP4 params use the full numel for both. - if self.has_nvfp4_params and is_nvfp4tensor(param): - assert ( - full_numel % 2 == 0 - ), f"NVFP4 requires even numel for packing, got {full_numel}" - param_numel = full_numel // 2 - else: - param_numel = full_numel param_start_index = _pad_start_of_param_if_needed(param_start_index) + if self.has_nvfp4_params: + nvfp4_packed_param_start_index = _pad_start_of_param_if_needed( + nvfp4_packed_param_start_index + ) # Create bucket with collected parameters if current param needs its own bucket. if _does_param_require_new_bucket(param) and len(bucket_params) > 0: - # Ensure this param accounts for the new padding introduced at end of - # previous bucket. - param_start_index = _update_bucket_metadata(param_start_index) + # Finalize the current bucket and update start indices for the next bucket. + bucket_start_index, nvfp4_packed_bucket_start_index = ( + _finalize_bucket_all_index_spaces( + param_start_index, + bucket_start_index, + nvfp4_packed_param_start_index, + nvfp4_packed_bucket_start_index, + ) + ) + param_start_index = bucket_start_index + if self.has_nvfp4_params: + nvfp4_packed_param_start_index = nvfp4_packed_bucket_start_index + # Primary index computation: always uses full param numel. + param_numel = param.data.nelement() param_end_index = param_start_index + param_numel self.param_index_map[param] = (param_start_index, param_end_index, bucket_id) - bucket_params.add(param) - # For NVFP4, the grad buffer is sized at full numel (not packed), so we - # maintain a parallel index map using full_numel for every param. + # Secondary (packed) index computation for NVFP4. if self.has_nvfp4_params: - grad_start_index = _pad_start_of_param_if_needed(grad_start_index) - grad_end_index = grad_start_index + full_numel - self.nvfp4_unpacked_param_index_map[param] = ( - grad_start_index, - grad_end_index, + if is_nvfp4tensor(param): + assert ( + param_numel % 2 == 0 + ), f"NVFP4 requires even numel for packing, got {param_numel}" + # NVFP4 packs two FP4 values into one byte, so packed numel is half. + nvfp4_packed_param_end_index = nvfp4_packed_param_start_index + param_numel // 2 + else: + nvfp4_packed_param_end_index = nvfp4_packed_param_start_index + param_numel + self.nvfp4_packed_param_index_map[param] = ( + nvfp4_packed_param_start_index, + nvfp4_packed_param_end_index, bucket_id, ) - grad_start_index = grad_end_index + + bucket_params.add(param) # If we have enough elements already or the current param is part of the shared # embedding layer and needs a separate bucket, form a new bucket. if ( bucket_size is not None and (param_end_index - bucket_start_index) >= bucket_size ) or _does_param_require_new_bucket(param): - bucket_end_index = _update_bucket_metadata(param_end_index) - param_start_index = bucket_end_index + # Finalize the current bucket and update start indices for the next bucket. + bucket_start_index, nvfp4_packed_bucket_start_index = ( + _finalize_bucket_all_index_spaces( + param_end_index, + bucket_start_index, + nvfp4_packed_param_end_index, + nvfp4_packed_bucket_start_index, + ) + ) + param_start_index = bucket_start_index + if self.has_nvfp4_params: + nvfp4_packed_param_start_index = nvfp4_packed_bucket_start_index else: param_start_index = param_end_index + if self.has_nvfp4_params: + nvfp4_packed_param_start_index = nvfp4_packed_param_end_index # Add remaining params to a new bucket. if len(bucket_params) > 0: - bucket_end_index = _update_bucket_metadata(param_end_index) + _finalize_bucket_all_index_spaces( + param_end_index, + bucket_start_index, + nvfp4_packed_param_end_index, + nvfp4_packed_bucket_start_index, + ) # Next, create underlying storage for buffer (with numel elements that includes # padding as necessary). - self.numel = bucket_end_index + self.numel = self.bucket_indices[-1][1] self.numel_unpadded = sum(per_bucket_numel_unpadded) - - # For NVFP4, grad buffer needs full size (roughly 2x the packed param buffer). if self.has_nvfp4_params: - self.grad_numel = grad_start_index - if self.ddp_config.use_distributed_optimizer: - self.grad_numel = _pad(self.grad_numel, self.data_parallel_world_size) - else: - self.grad_numel = self.numel + self.nvfp4_packed_numel = self.nvfp4_packed_bucket_indices[-1][1] + self.nvfp4_packed_numel_unpadded = sum(nvfp4_packed_per_bucket_numel_unpadded) assert self.numel_unpadded <= self.numel if self.ddp_config.use_distributed_optimizer: assert self.numel % self.data_parallel_world_size == 0 + if self.has_nvfp4_params: + assert self.nvfp4_packed_numel_unpadded <= self.nvfp4_packed_numel + assert self.nvfp4_packed_numel % self.data_parallel_world_size == 0 else: assert self.numel == self.numel_unpadded @@ -1044,16 +1110,15 @@ def _does_param_require_new_bucket(param): else: # Only re-map param tensors if using distributed optimizer. if self.ddp_config.use_distributed_optimizer: + numel = self.nvfp4_packed_numel if self.has_nvfp4_params else self.numel self.param_data = torch.zeros( - self.numel, + numel, dtype=self.param_dtype, device=torch.cuda.current_device(), requires_grad=False, ) - # For NVFP4, grad buffer uses full size (grad_numel), - # param buffer uses packed size (numel) self.grad_data = torch.zeros( - self.grad_numel, + self.numel, dtype=self.grad_dtype, device=torch.cuda.current_device(), requires_grad=False, @@ -1064,12 +1129,46 @@ def _does_param_require_new_bucket(param): self.param_data_cpu = None # Finally, map param.data and param.main_grad fields to buffers. + def _create_bucket(bucket_id, bucket_params, bucket_params_with_extra_main_grads): + """ + Look up precomputed bucket indices and create a new bucket. + + Args: + bucket_id: ID of the bucket to create. + bucket_params: List of parameters in this bucket. + bucket_params_with_extra_main_grads: List of parameters with + extra FP32 main_grads. + + Returns: + A new _ParamAndGradBucket instance. + """ + bucket_start_index, bucket_end_index = self.bucket_indices[bucket_id] + if self.has_nvfp4_params: + nvfp4_packed_start_index, nvfp4_packed_end_index = self.nvfp4_packed_bucket_indices[ + bucket_id + ] + else: + nvfp4_packed_start_index, nvfp4_packed_end_index = None, None + return self._new_bucket( + bucket_params=bucket_params, + start_index=bucket_start_index, + end_index=bucket_end_index, + numel_unpadded=per_bucket_numel_unpadded[bucket_id], + bucket_id=bucket_id, + nvfp4_packed_start_index=nvfp4_packed_start_index, + nvfp4_packed_end_index=nvfp4_packed_end_index, + bucket_params_with_extra_main_grads=bucket_params_with_extra_main_grads, + ) + bucket_params = [] bucket_params_with_extra_main_grads = [] - bucket_start_index = 0 cur_bucket_id = 0 for param, param_name in params_with_names[::-1]: + # Get parameter indices computed in previous loop. param_start_index, param_end_index, bucket_id = self.param_index_map[param] + nvfp4_packed_param_start_index = None + if self.has_nvfp4_params: + nvfp4_packed_param_start_index, _, _ = self.nvfp4_packed_param_index_map[param] # For MXFP8 param: # we only need to map bf16 weights (layernorm, embedding, etc) to the buffer. if not self.ddp_config.reuse_grad_buf_for_mxfp8_param_ag or not is_mxfp8tensor(param): @@ -1082,17 +1181,31 @@ def _does_param_require_new_bucket(param): packed_shape = get_nvfp4_rowwise_packed_shape(param.data.shape) rowwise_bytes_view = self._get( - packed_shape, param_start_index, buffer_type=BufferType.PARAM + packed_shape, + nvfp4_packed_param_start_index, + buffer_type=BufferType.PARAM, ) modify_nvfp4_rowwise_storage(param, rowwise_bytes_view) elif is_float8tensor(param): new_param_data = self._get( - param.data.shape, param_start_index, buffer_type=BufferType.PARAM + param.data.shape, + ( + nvfp4_packed_param_start_index + if self.has_nvfp4_params + else param_start_index + ), + buffer_type=BufferType.PARAM, ) modify_underlying_storage(param, new_param_data) else: new_param_data = self._get( - param.data.shape, param_start_index, buffer_type=BufferType.PARAM + param.data.shape, + ( + nvfp4_packed_param_start_index + if self.has_nvfp4_params + else param_start_index + ), + buffer_type=BufferType.PARAM, ) old_param_data = param.data param.data = new_param_data @@ -1101,16 +1214,10 @@ def _does_param_require_new_bucket(param): param.data.detach().copy_(old_param_data) del old_param_data - # For NVFP4, use grad_index_map for main_grad (full numel offsets) - if self.has_nvfp4_params: - grad_start, grad_end, _ = self.nvfp4_unpacked_param_index_map[param] - param.main_grad = self._get( - param.data.shape, grad_start, buffer_type=BufferType.GRAD - ) - else: - param.main_grad = self._get( - param.data.shape, param_start_index, buffer_type=BufferType.GRAD - ) + # Grad buffer always uses full-numel offsets from param_index_map. + param.main_grad = self._get( + param.data.shape, param_start_index, buffer_type=BufferType.GRAD + ) # Create FP32 copy of .main_grads if necessary. promote_main_grads_to_higher_precision = False for param_name_pattern in ddp_config.param_name_patterns_for_fp32_local_accumulation: @@ -1135,24 +1242,11 @@ def _does_param_require_new_bucket(param): self.extra_main_grads.append(param.main_grad) if bucket_id != cur_bucket_id: - bucket_end_index = _pad_end_of_bucket_if_needed(param_start_index) - if self.has_nvfp4_params: - grad_bucket_end_index = _pad_end_of_bucket_if_needed(grad_start) self.buckets.append( - self._new_bucket( - bucket_params=bucket_params, - start_index=bucket_start_index, - end_index=bucket_end_index, - numel_unpadded=per_bucket_numel_unpadded[cur_bucket_id], - bucket_id=cur_bucket_id, - grad_start_index=grad_bucket_start_index, - grad_end_index=grad_bucket_end_index, - bucket_params_with_extra_main_grads=bucket_params_with_extra_main_grads, + _create_bucket( + cur_bucket_id, bucket_params, bucket_params_with_extra_main_grads ) ) - if self.has_nvfp4_params: - grad_bucket_start_index = grad_bucket_end_index - bucket_start_index = bucket_end_index bucket_params = [] bucket_params_with_extra_main_grads = [] assert cur_bucket_id + 1 == len(self.buckets) @@ -1165,20 +1259,8 @@ def _does_param_require_new_bucket(param): # Add remaining params to a new bucket. if len(bucket_params) > 0: - bucket_end_index = _pad_end_of_bucket_if_needed(param_end_index) - if self.has_nvfp4_params: - grad_bucket_end_index = self.grad_numel self.buckets.append( - self._new_bucket( - bucket_params=bucket_params, - start_index=bucket_start_index, - end_index=bucket_end_index, - numel_unpadded=per_bucket_numel_unpadded[cur_bucket_id], - bucket_id=cur_bucket_id, - grad_start_index=grad_bucket_start_index, - grad_end_index=grad_bucket_end_index, - bucket_params_with_extra_main_grads=bucket_params_with_extra_main_grads, - ) + _create_bucket(cur_bucket_id, bucket_params, bucket_params_with_extra_main_grads) ) # Log buckets for all PP stages. log_strs = [] @@ -1210,22 +1292,6 @@ def scale_gradients(self, scaling_factor: float) -> None: for grad in self.extra_main_grads: grad *= scaling_factor - def get_unpacked_index_map(self) -> Dict[torch.nn.Parameter, tuple[int, int, int]]: - """ - Return the index map using unpacked (full) numel for each parameter. - - For NVFP4 buffers, param_index_map uses packed numel (half the logical size), - so this returns nvfp4_unpacked_param_index_map which has full-numel indices instead. - For other buffers, packed and unpacked indices are identical, so param_index_map - is returned directly. - - The distributed optimizer uses this to determine which rank owns which portion - of each parameter's data. - """ - if self.has_nvfp4_params: - return self.nvfp4_unpacked_param_index_map - return self.param_index_map - def _get(self, shape: torch.Size, start_index: int, buffer_type: BufferType) -> torch.Tensor: """ Return a tensor with the input `shape` as a view into the 1-D data starting at @@ -1233,11 +1299,12 @@ def _get(self, shape: torch.Size, start_index: int, buffer_type: BufferType) -> """ end_index = start_index + shape.numel() if buffer_type == BufferType.PARAM: - assert end_index <= self.numel, "Requested tensor is out of param buffer range" + numel = self.nvfp4_packed_numel if self.has_nvfp4_params else self.numel + assert end_index <= numel, "Requested tensor is out of param buffer range" assert self.param_data is not None buffer_tensor = self.param_data[start_index:end_index] elif buffer_type == BufferType.GRAD: - assert end_index <= self.grad_numel, "Requested tensor is out of grad buffer range" + assert end_index <= self.numel, "Requested tensor is out of grad buffer range" buffer_tensor = self.grad_data[start_index:end_index] else: raise Exception("Illegal buffer type provided to GradBuffer._get() function") @@ -1252,14 +1319,15 @@ def _new_bucket( numel_unpadded: int, bucket_id: int, bucket_params_with_extra_main_grads: List[torch.Tensor], - grad_start_index: int = None, - grad_end_index: int = None, + nvfp4_packed_start_index: int = None, + nvfp4_packed_end_index: int = None, ) -> _ParamAndGradBucket: """ Helper function that creates a new bucket. Also updates param->bucket mapping. - For NVFP4 buffers, grad_start_index and grad_end_index are provided separately - because grad buffer uses full numel while param buffer uses packed numel. + For NVFP4 buffers, nvfp4_packed_start_index and nvfp4_packed_end_index + are provided separately because the param buffer uses packed numel while + the grad buffer uses full numel. """ # Assert that indices are correctly padded (if needed), and that bucket @@ -1269,32 +1337,36 @@ def _new_bucket( assert start_index % self.data_parallel_world_size == 0 assert end_index % self.data_parallel_world_size == 0 assert (start_index, end_index) == self.bucket_indices[bucket_id] + if nvfp4_packed_start_index is not None: + assert ( + nvfp4_packed_start_index, + nvfp4_packed_end_index, + ) == self.nvfp4_packed_bucket_indices[bucket_id] # Get appropriate view into global _ParamAndGradBuffer. + # For NVFP4, param buffer uses packed offsets; otherwise same as start/end. bucketed_param_data = None if self.param_data is not None: - bucketed_param_data = self._get( - torch.Size([end_index - start_index]), start_index, buffer_type=BufferType.PARAM - ) - # For NVFP4, use separate grad buffer offsets - if grad_start_index is not None and grad_end_index is not None: - bucketed_grad_data = self._get( - torch.Size([grad_end_index - grad_start_index]), - grad_start_index, - buffer_type=BufferType.GRAD, - ) - else: - bucketed_grad_data = self._get( - torch.Size([end_index - start_index]), start_index, buffer_type=BufferType.GRAD - ) - # For NVFP4, use grad buffer offset for bucket.offset since distrib_optimizer - # uses it for grad buffer operations. For non-NVFP4, param and grad offsets are same. - bucket_offset = grad_start_index if grad_start_index is not None else start_index + if nvfp4_packed_start_index is not None: + assert nvfp4_packed_end_index is not None + bucketed_param_data = self._get( + torch.Size([nvfp4_packed_end_index - nvfp4_packed_start_index]), + nvfp4_packed_start_index, + buffer_type=BufferType.PARAM, + ) + else: + bucketed_param_data = self._get( + torch.Size([end_index - start_index]), start_index, buffer_type=BufferType.PARAM + ) + # Grad buffer always uses full-numel offsets. + bucketed_grad_data = self._get( + torch.Size([end_index - start_index]), start_index, buffer_type=BufferType.GRAD + ) bucket = _ParamAndGradBucket( params=bucket_params, param_data=bucketed_param_data, grad_data=bucketed_grad_data, - offset=bucket_offset, + offset=start_index, numel_unpadded=numel_unpadded, gradient_scaling_factor=self.gradient_scaling_factor, bucket_id=bucket_id, diff --git a/megatron/core/optimizer/distrib_optimizer.py b/megatron/core/optimizer/distrib_optimizer.py index a45eceefe35..b4d52e6b56b 100644 --- a/megatron/core/optimizer/distrib_optimizer.py +++ b/megatron/core/optimizer/distrib_optimizer.py @@ -218,10 +218,8 @@ def _build_model_gbuf_range(cls, param_and_grad_buffer: _ParamAndGradBuffer, buc gbuf_world_range = gbuf_world_all_ranges[data_parallel_rank] # Get each param's ranges. - # Use get_unpacked_index_map() which returns full-numel indices for NVFP4 params - # (from nvfp4_unpacked_param_index_map) and normal indices for other params. param_range_map = cls._build_model_gbuf_param_range_map( - param_and_grad_buffer.get_unpacked_index_map(), gbuf_world_range, bucket.offset + param_and_grad_buffer.param_index_map, gbuf_world_range, bucket.offset ) # Group into dict. @@ -1504,6 +1502,9 @@ def sharded_param_state_fully_reshardable( for dtype, gbuf_range_map_for_all_buckets in gbuf_range_maps.items(): world_tensors = dp_zero_state_dict[gbuf_idx][dtype] world_tensor_keys = world_tensors.keys() + # Note: for NVFP4, param_index_map uses unpacked (full numel) + # offsets, which is correct here since optimizer states + # (fp32_param, exp_avg, exp_avg_sq) are in unpacked space. for model_param, ( param_world_start, param_world_end, diff --git a/tests/unit_tests/dist_checkpointing/test_optimizer.py b/tests/unit_tests/dist_checkpointing/test_optimizer.py index 97928bd5954..1db844dd9a8 100644 --- a/tests/unit_tests/dist_checkpointing/test_optimizer.py +++ b/tests/unit_tests/dist_checkpointing/test_optimizer.py @@ -591,7 +591,7 @@ def test_bucket_space_optimizer_save_load( # Init model and optimizer with "src" bucket padding with patch('megatron.core.distributed.param_and_grad_buffer.math.lcm') as lcm_mock: lcm_mock.return_value = src_bucket_pad_divisor - assert len(lcm_mock.mock_calls) == 0 + model_A, optimizer_A = setup_model_and_optimizer( seed=2, tp=src_tp_pp[0], @@ -600,7 +600,6 @@ def test_bucket_space_optimizer_save_load( dist_opt=True, initialize_fn=initialize_pp_agnostic_model, ) - assert len(lcm_mock.mock_calls) > 1 metadata = {'distrib_optim_sharding_type': 'dp_reshardable'} @@ -618,7 +617,7 @@ def test_bucket_space_optimizer_save_load( # Init model and optimizer with "dest" bucket padding with patch('megatron.core.distributed.param_and_grad_buffer.math.lcm') as lcm_mock: lcm_mock.return_value = dest_bucket_pad_divisor - assert len(lcm_mock.mock_calls) == 0 + model_B, optimizer_B = setup_model_and_optimizer( seed=3, tp=dest_tp_pp[0], @@ -627,7 +626,6 @@ def test_bucket_space_optimizer_save_load( dist_opt=True, initialize_fn=initialize_pp_agnostic_model, ) - assert len(lcm_mock.mock_calls) > 1 model_sharded_sd = model_B[0].sharded_state_dict() load_sharded_state_dict = optimizer_B.sharded_state_dict( diff --git a/tests/unit_tests/distributed/test_param_and_grad_buffer.py b/tests/unit_tests/distributed/test_param_and_grad_buffer.py index 720c5a2d1af..223383bba64 100644 --- a/tests/unit_tests/distributed/test_param_and_grad_buffer.py +++ b/tests/unit_tests/distributed/test_param_and_grad_buffer.py @@ -10,7 +10,7 @@ from megatron.core import parallel_state from megatron.core.distributed import DistributedDataParallel, DistributedDataParallelConfig -from megatron.core.distributed.param_and_grad_buffer import partition_buckets +from megatron.core.distributed.param_and_grad_buffer import _ParamAndGradBuffer, partition_buckets from megatron.core.transformer import TransformerConfig from tests.unit_tests.test_utilities import TestModel, Utils @@ -669,3 +669,218 @@ def test_grad_sync_copies_to_and_from_comm_buffer(self): ) Utils.destroy_model_parallel() + + +class TestNVFP4IndexMaps: + """Tests for NVFP4 dual index map (param_index_map and nvfp4_packed_param_index_map). + + These tests mock NVFP4 functions and CUDA so they run on CPU without GPUs. + The mocking replaces is_nvfp4tensor (to treat regular bf16 params as NVFP4), + get_nvfp4_rowwise_packed_shape (to halve last dim), modify_nvfp4_rowwise_storage + (no-op), and torch.cuda.current_device (to allocate on CPU). + """ + + @staticmethod + def _make_buffer( + param_shapes, + nvfp4_param_indices=None, + use_distributed_optimizer=False, + bucket_size=None, + dp_world_size=1, + ): + """Create a _ParamAndGradBuffer with some params mocked as NVFP4. + + Args: + param_shapes: List of (name, shape) tuples for each parameter. + nvfp4_param_indices: Set of indices into param_shapes to treat as NVFP4. + use_distributed_optimizer: Whether to use distributed optimizer. + bucket_size: Bucket size for splitting. + dp_world_size: Simulated data parallel world size. + + Returns: + (buffer, params) where params is the ordered list of nn.Parameters. + """ + params = [] + params_with_names = [] + param_to_name = {} + for name, shape in param_shapes: + param = torch.nn.Parameter(torch.randn(shape, dtype=torch.bfloat16)) + params.append(param) + params_with_names.append((param, name)) + param_to_name[param] = name + + if nvfp4_param_indices is None: + nvfp4_param_indices = set() + nvfp4_params = {params[i] for i in nvfp4_param_indices} + has_nvfp4 = len(nvfp4_params) > 0 + + def mock_is_nvfp4(t): + return any(t is p for p in nvfp4_params) + + def mock_packed_shape(shape): + packed = list(shape) + packed[-1] = packed[-1] // 2 + return torch.Size(packed) + + mock_dp_group = mock.MagicMock() + mock_dp_group.size.return_value = dp_world_size + mock_pg = mock.MagicMock() + + ddp_config = DistributedDataParallelConfig( + use_distributed_optimizer=use_distributed_optimizer, + overlap_grad_reduce=False, + bucket_size=bucket_size, + average_in_collective=False, + ) + + with ( + mock.patch( + 'megatron.core.distributed.param_and_grad_buffer.is_nvfp4tensor', + side_effect=mock_is_nvfp4, + ), + mock.patch( + 'megatron.core.distributed.param_and_grad_buffer.get_nvfp4_rowwise_packed_shape', + side_effect=mock_packed_shape, + ), + mock.patch('megatron.core.fp4_utils.modify_nvfp4_rowwise_storage'), + mock.patch('torch.cuda.current_device', return_value='cpu'), + mock.patch( + 'megatron.core.distributed.param_and_grad_buffer.log_on_each_pipeline_stage' + ), + ): + buffer = _ParamAndGradBuffer( + ddp_config=ddp_config, + param_dtype=torch.uint8 if has_nvfp4 else torch.bfloat16, + grad_dtype=torch.bfloat16, + params_with_names=params_with_names, + data_parallel_group=mock_dp_group, + bucket_size=bucket_size, + param_to_name=param_to_name, + gradient_scaling_factor=1.0, + param_indices=list(range(len(params))), + nccl_ub=False, + pg_collection=mock_pg, + ) + + return buffer, params + + def test_exact_index_values_no_padding(self): + """Verify exact index map values for a simple case without distributed optimizer.""" + param_shapes = [('layer0.weight', (100, 100)), ('layer1.weight', (100, 100))] + buffer, params = self._make_buffer(param_shapes, nvfp4_param_indices={0, 1}) + + # Buffer processes params in reverse order: params[1] first, params[0] second. + assert buffer.param_index_map[params[1]] == (0, 10000, 0) + assert buffer.param_index_map[params[0]] == (10000, 20000, 0) + assert buffer.nvfp4_packed_param_index_map[params[1]] == (0, 5000, 0) + assert buffer.nvfp4_packed_param_index_map[params[0]] == (5000, 10000, 0) + + assert buffer.numel == 20000 + assert buffer.nvfp4_packed_numel == 10000 + + def test_non_nvfp4_exact_values_match_original(self): + """Non-NVFP4 param_index_map values should be identical to original behavior.""" + param_shapes = [('layer0.weight', (100, 100)), ('layer1.weight', (100, 100))] + buffer, params = self._make_buffer(param_shapes) + + # Without NVFP4 or distributed optimizer, no padding: offsets are contiguous. + assert buffer.param_index_map[params[1]] == (0, 10000, 0) + assert buffer.param_index_map[params[0]] == (10000, 20000, 0) + assert buffer.numel == 20000 + + def test_nvfp4_multi_bucket_param_to_index(self): + """param_to_index in each bucket should be relative to that bucket's full-numel offset.""" + param_shapes = [ + ('layer0.weight', (100, 100)), + ('layer1.weight', (100, 100)), + ('layer2.weight', (100, 100)), + ('layer3.weight', (100, 100)), + ] + # bucket_size=15000: each param is 10000 full numel, so 2 params per bucket. + buffer, params = self._make_buffer( + param_shapes, nvfp4_param_indices={0, 1, 2, 3}, bucket_size=15000 + ) + + assert len(buffer.buckets) == 2 + for bucket in buffer.buckets: + for param in bucket.params_list: + global_start, global_end, _ = buffer.param_index_map[param] + local_start, local_end = bucket.param_to_index[param] + assert local_start == global_start - bucket.offset + assert local_end == global_end - bucket.offset + assert local_end - local_start == param.data.nelement() + + @pytest.mark.parametrize("dp_world_size", [1, 2, 4, 8]) + def test_nvfp4_with_distributed_optimizer(self, dp_world_size): + """With distributed optimizer, both packed and unpacked indices should be padded.""" + param_shapes = [('layer0.weight', (98, 101)), ('layer1.weight', (98, 101))] + buffer, params = self._make_buffer( + param_shapes, + nvfp4_param_indices={0, 1}, + use_distributed_optimizer=True, + dp_world_size=dp_world_size, + ) + + # Param starts should be 64-aligned in both maps. + for param in params: + start, end, _ = buffer.param_index_map[param] + assert start % 64 == 0, f"Unpacked start {start} should be 64-aligned" + assert end - start == param.data.nelement() + + for param in params: + start, end, _ = buffer.nvfp4_packed_param_index_map[param] + assert start % 64 == 0, f"Packed start {start} should be 64-aligned" + assert end - start == param.data.nelement() // 2 + + # Buffer numel should be divisible by dp_world_size. + assert buffer.numel % dp_world_size == 0 + assert buffer.nvfp4_packed_numel % dp_world_size == 0 + + def test_nvfp4_mixed_params(self): + """Test buffer with a mix of NVFP4 and non-NVFP4 params.""" + param_shapes = [ + ('linear.weight', (100, 100)), # NVFP4. + ('layernorm.weight', (100,)), # Non-NVFP4 (bf16). + ] + buffer, params = self._make_buffer(param_shapes, nvfp4_param_indices={0}) + + # Non-NVFP4 param should have same span in both maps. + packed_start, packed_end, _ = buffer.nvfp4_packed_param_index_map[params[1]] + unpacked_start, unpacked_end, _ = buffer.param_index_map[params[1]] + assert packed_end - packed_start == unpacked_end - unpacked_start == 100 + + # NVFP4 param should have half the span in packed map. + packed_start, packed_end, _ = buffer.nvfp4_packed_param_index_map[params[0]] + unpacked_start, unpacked_end, _ = buffer.param_index_map[params[0]] + assert packed_end - packed_start == 5000 # numel // 2. + assert unpacked_end - unpacked_start == 10000 # Full numel. + + def test_nvfp4_varied_param_sizes(self): + """Test with different param sizes to verify offsets accumulate correctly.""" + param_shapes = [('small.weight', (10, 20)), ('large.weight', (100, 200))] + buffer, params = self._make_buffer(param_shapes, nvfp4_param_indices={0, 1}) + + # Reversed order: large (params[1]) processed first, then small (params[0]). + large_packed_start = 0 + large_packed_end = 100 * 200 // 2 # 10000. + small_packed_start = large_packed_end + small_packed_end = small_packed_start + 10 * 20 // 2 # 10100. + + assert buffer.nvfp4_packed_param_index_map[params[1]] == ( + large_packed_start, + large_packed_end, + 0, + ) + assert buffer.nvfp4_packed_param_index_map[params[0]] == ( + small_packed_start, + small_packed_end, + 0, + ) + + large_unpacked_start = 0 + large_unpacked_end = 100 * 200 # 20000. + small_unpacked_start = large_unpacked_end + small_unpacked_end = small_unpacked_start + 10 * 20 # 20200. + + assert buffer.param_index_map[params[1]] == (large_unpacked_start, large_unpacked_end, 0) + assert buffer.param_index_map[params[0]] == (small_unpacked_start, small_unpacked_end, 0) From 8be1e79d160c8dc3ecb097bea7dc2bc4a27552e6 Mon Sep 17 00:00:00 2001 From: Haoran Zhang Date: Sat, 18 Apr 2026 14:15:29 -0700 Subject: [PATCH 43/72] Add activation logging and tokens per expert logging (#3842) Co-authored-by: Charlie Truong --- megatron/training/activation_logging.py | 260 ++++++++++++++++++++ megatron/training/arguments.py | 2 + megatron/training/config/training_config.py | 9 + megatron/training/dgrad_logging.py | 14 +- megatron/training/training.py | 49 +++- tests/unit_tests/test_activation_logging.py | 100 ++++++++ 6 files changed, 419 insertions(+), 15 deletions(-) create mode 100644 megatron/training/activation_logging.py create mode 100644 tests/unit_tests/test_activation_logging.py diff --git a/megatron/training/activation_logging.py b/megatron/training/activation_logging.py new file mode 100644 index 00000000000..8f790a116c9 --- /dev/null +++ b/megatron/training/activation_logging.py @@ -0,0 +1,260 @@ +# Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + +"""Forward activation logging using forward hooks.""" + +from collections import defaultdict +import json +import logging +import os +import re +from typing import Callable, List, Tuple + +import torch +import torch.nn as nn + +logger = logging.getLogger(__name__) + +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.moe.router import Router + +from .checkpointing import save_grads +from .utils import unwrap_model + + +def _discover_te_types(): + """Discover available Transformer Engine layer types. + + Returns (all_types, grouped_types) where grouped_types is the subset of + TEGroupedLinear variants used for tokens-per-expert capture. + """ + all_types = [] + grouped_types = [] + + try: + from megatron.core.extensions.transformer_engine import ( + TELinear, + TENorm, + TEColumnParallelLinear, + TERowParallelLinear, + TELayerNormColumnParallelLinear, + ) + all_types.extend([TELinear, TENorm, TEColumnParallelLinear, TERowParallelLinear, + TELayerNormColumnParallelLinear]) + except ImportError: + pass + + try: + from megatron.core.extensions.transformer_engine import ( + TEGroupedLinear, + TEColumnParallelGroupedLinear, + TERowParallelGroupedLinear, + ) + if TEGroupedLinear is not None: + grouped = [TEGroupedLinear, TEColumnParallelGroupedLinear, + TERowParallelGroupedLinear] + all_types.extend(grouped) + grouped_types.extend(grouped) + except ImportError: + pass + + return tuple(all_types), tuple(grouped_types) + + +_TE_TYPES, _GROUPED_LINEAR_TYPES = _discover_te_types() + +LINEAR_TYPES = (nn.Linear, nn.Embedding, ColumnParallelLinear, RowParallelLinear, + Router, *_TE_TYPES) + + +def _register_hooks(model, module_types, hook_factory, *, name_filter=None): + """Walk *model* and register a forward hook on every module matching *module_types*. + + Args: + model: Iterable of model chunks (possibly wrapped). + module_types: Tuple of types to match via ``isinstance``. + hook_factory: ``(model_chunk_name, module_name) -> hook_fn``. + name_filter: Optional ``str -> bool`` predicate on the module name. + + Returns: + List of hook handles. + """ + handles = [] + for model_chunk_id, model_chunk in enumerate(model): + model_chunk_name = f"model_chunk{model_chunk_id}" + unwrapped = unwrap_model(model_chunk) + for module_name, module in unwrapped.named_modules(): + if isinstance(module, module_types) and (name_filter is None or name_filter(module_name)): + hook_fn = hook_factory(model_chunk_name, module_name) + if hook_fn is None: + continue + handle = module.register_forward_hook(hook_fn, with_kwargs=True) + handles.append(handle) + return handles + +class ActivationLogger: + """Captures and saves forward activations using forward hooks. + + Manages two independent hook sets: + + - **Full activation hooks** capture all inputs / outputs / kwargs for every + ``LINEAR_TYPES`` module. + - **Tokens-per-expert (TPE) hooks** are lightweight hooks that only capture + the tokens-per-expert routing metadata from MoE. + """ + + def __init__(self, save_dir: str): + self._save_dir = save_dir + + # Full activation state. + self._activations_state_dict: defaultdict = defaultdict(dict) + self._activation_hooks: List[torch.utils.hooks.RemovableHook] = [] + + # Tokens-per-expert state: layer -> list of per-microbatch token counts. + self._tpe_records: dict[str, list] = defaultdict(list) + self._tpe_hooks: List[torch.utils.hooks.RemovableHook] = [] + + # ------------------------------------------------------------------ + # Full activation hooks + # ------------------------------------------------------------------ + + def _make_activation_hook(self, model_chunk_name: str, module_name: str) -> Callable: + """Forward hook that captures all inputs, outputs and kwargs.""" + sd = self._activations_state_dict + + def hook(_, args, kwargs, output): + input_tuple = args if isinstance(args, tuple) else (args,) + for idx, inp in enumerate(input_tuple): + if inp is None: + continue + key = f"{module_name}/input{idx}" + sd[model_chunk_name][key] = inp.detach().cpu() if isinstance(inp, torch.Tensor) else inp + for idx, out in enumerate(output if isinstance(output, tuple) else (output,)): + if out is not None and isinstance(out, torch.Tensor): + sd[model_chunk_name][f"{module_name}/output{idx}"] = out.detach().cpu() + for kwarg_key, kwarg_value in kwargs.items(): + key = f"{module_name}/{kwarg_key}" + sd[model_chunk_name][key] = ( + kwarg_value.detach().cpu() if isinstance(kwarg_value, torch.Tensor) else kwarg_value + ) + + return hook + + def register_activation_hooks(self, model): + assert not self._activation_hooks + self._activation_hooks = _register_hooks(model, LINEAR_TYPES, self._make_activation_hook) + + def remove_activation_hooks(self): + for hook in self._activation_hooks: + hook.remove() + self._activation_hooks.clear() + + def save_activations(self, iteration: int): + if not self._activations_state_dict: + return + save_grads(self._save_dir, self._activations_state_dict, iteration, "activations") + self._activations_state_dict.clear() + + # ------------------------------------------------------------------ + # Tokens-per-expert hooks + # ------------------------------------------------------------------ + + def _make_tpe_hook(self, _model_chunk_name: str, module_name: str) -> Callable: + """Forward hook that captures only the non-Tensor ``input1`` (tokens_per_expert). + + The layer number is extracted from *module_name* + (e.g. ``decoder.layers.3.mlp.experts.linear_fc1`` → ``3``). + """ + m = re.search(r'\.layers\.(\d+)\.', module_name) + if not m: + logger.warning( + "Cannot extract layer number from module name: %r — " + "skipping tokens-per-expert hook for this module", module_name + ) + return None + layer = m.group(1) + + def hook(_, args, kwargs, output): + input_tuple = args if isinstance(args, tuple) else (args,) + if len(input_tuple) > 1 and input_tuple[1] is not None: + inp = input_tuple[1] + if not isinstance(inp, torch.Tensor): + self._tpe_records[layer].append(list(inp)) + + return hook + + def register_tpe_hooks(self, model): + assert not self._tpe_hooks + self._tpe_hooks = _register_hooks( + model, _GROUPED_LINEAR_TYPES, self._make_tpe_hook, + name_filter=lambda name: name.endswith("linear_fc1"), + ) + + def remove_tpe_hooks(self): + for hook in self._tpe_hooks: + hook.remove() + self._tpe_hooks.clear() + + def save_tpe(self, iteration: int): + """Append captured tokens-per-expert records as JSON Lines. + + Each rank writes to its own file under ``{save_dir}/tokens_per_expert/``, + e.g. ``rank0.jsonl``, ``rank1.jsonl``. Each line is a JSON object:: + + {"iter": 100, "layer": 3, "tpe": [[128, 64], [96, 80]]} + """ + if not self._tpe_records: + return + rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 + tpe_dir = os.path.join(self._save_dir, "tokens_per_expert") + os.makedirs(tpe_dir, exist_ok=True) + filepath = os.path.join(tpe_dir, f"rank{rank}.jsonl") + lines = "".join( + json.dumps({"iter": iteration, "layer": int(layer), + "tpe": microbatches}) + "\n" + for layer, microbatches in sorted(self._tpe_records.items()) + ) + with open(filepath, "a") as f: + f.write(lines) + self._tpe_records.clear() + +_LOGGER: ActivationLogger | None = None + + +def _get_logger(save_dir: str) -> ActivationLogger: + global _LOGGER + if _LOGGER is None: + _LOGGER = ActivationLogger(save_dir) + return _LOGGER + + +def _require_logger() -> ActivationLogger: + assert _LOGGER is not None, "No ActivationLogger has been initialised" + return _LOGGER + + +# -- Full activation logging ------------------------------------------- + +def enable_activation_logging(model: torch.nn.Module, save_dir: str): + _get_logger(save_dir).register_activation_hooks(model) + + +def disable_activation_logging(): + _require_logger().remove_activation_hooks() + + +def save_activations(iteration: int): + _require_logger().save_activations(iteration) + + +# -- Tokens-per-expert logging ---------------------------------------- + +def enable_tokens_per_expert_logging(model: torch.nn.Module, save_dir: str): + _get_logger(save_dir).register_tpe_hooks(model) + + +def disable_tokens_per_expert_logging(): + _require_logger().remove_tpe_hooks() + + +def save_tokens_per_expert(iteration: int): + _require_logger().save_tpe(iteration) diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 0413ea518c9..684a8ace6fb 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -1194,6 +1194,8 @@ def validate_args(args, defaults={}): if args.save_retain_interval is not None: assert args.save_retain_interval > 0 assert args.save_retain_interval % args.save_interval == 0 + if args.save_params_interval is not None: + assert not args.overlap_param_gather if args.log_memory_interval is not None: assert args.log_memory_interval % args.log_interval == 0 # Mixed precision checks. diff --git a/megatron/training/config/training_config.py b/megatron/training/config/training_config.py index 2d5806bcb57..6bc59668789 100644 --- a/megatron/training/config/training_config.py +++ b/megatron/training/config/training_config.py @@ -352,6 +352,15 @@ class CheckpointConfig: save_interval: int | None = field(default=None, metadata={"argparse_meta": {"arg_names": ["--save-interval", "--persistent-save-interval"]}}) """Number of iterations between persistent checkpoint saves.""" + save_params_interval: int | None = None + """Number of iterations between param.name->param.data mapping saves.""" + + save_activations_interval: int | None = None + """Number of iterations between act.name->act.data mapping saves.""" + + save_tokens_per_expert_interval: int | None = None + """Number of iterations between tokens-per-expert routing metadata saves.""" + save_wgrads_interval: int | None = None """Number of iterations between wgrad (main_grad) saves.""" diff --git a/megatron/training/dgrad_logging.py b/megatron/training/dgrad_logging.py index c046b4709fb..4fcfead6d22 100644 --- a/megatron/training/dgrad_logging.py +++ b/megatron/training/dgrad_logging.py @@ -7,6 +7,7 @@ import torch.nn as nn from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.moe.router import Router from .checkpointing import save_grads from .utils import unwrap_model @@ -14,17 +15,18 @@ def _get_linear_types(): """Build tuple of linear layer types to capture gradients from.""" - types = [nn.Linear, nn.Embedding, ColumnParallelLinear, RowParallelLinear] + types = [nn.Linear, nn.Embedding, ColumnParallelLinear, RowParallelLinear, Router] # Add Transformer Engine layers if available. try: from megatron.core.extensions.transformer_engine import ( TELinear, + TENorm, TEColumnParallelLinear, TERowParallelLinear, TELayerNormColumnParallelLinear, ) - types.extend([TELinear, TEColumnParallelLinear, TERowParallelLinear, + types.extend([TELinear, TENorm, TEColumnParallelLinear, TERowParallelLinear, TELayerNormColumnParallelLinear]) except ImportError: pass @@ -63,12 +65,12 @@ def _make_hook(self, model_chunk_name: str, module_name: str): def hook(_, grad_input, grad_output): for idx, grad in enumerate(grad_output): if grad is not None: - grad_name = f"{module_name}/output{idx}" - self._dgrads_state_dict[model_chunk_name][grad_name] = grad.detach().cpu() + key = f"{module_name}/output{idx}" + self._dgrads_state_dict[model_chunk_name][key] = grad.detach().cpu() for idx, grad in enumerate(grad_input): if grad is not None: - grad_name = f"{module_name}/input{idx}" - self._dgrads_state_dict[model_chunk_name][grad_name] = grad.detach().cpu() + key = f"{module_name}/input{idx}" + self._dgrads_state_dict[model_chunk_name][key] = grad.detach().cpu() return hook def save(self, iteration: int): diff --git a/megatron/training/training.py b/megatron/training/training.py index 7c66812c67a..d476bd22a17 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -252,6 +252,14 @@ def set_startup_timestamps(program_start=None, main_entry=None): get_energy_monitor, ) from . import one_logger_utils +from .activation_logging import ( + enable_activation_logging, + disable_activation_logging, + save_activations, + enable_tokens_per_expert_logging, + disable_tokens_per_expert_logging, + save_tokens_per_expert, +) from .dgrad_logging import enable_dgrad_logging, disable_dgrad_logging, save_dgrads from . import ft_integration @@ -1782,10 +1790,16 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch timers = get_timers() rerun_state_machine = get_rerun_state_machine() - save_dgrads_in_this_iteration = (args.save_dgrads_interval is not None and - (iteration + 1) % args.save_dgrads_interval == 0) + save_params_in_this_iteration = (args.save_params_interval is not None and + (iteration + 1) % args.save_params_interval == 0) + save_activations_in_this_iteration = (args.save_activations_interval is not None and + (iteration + 1) % args.save_activations_interval == 0) + save_tpe_in_this_iteration = (args.save_tokens_per_expert_interval is not None and + (iteration + 1) % args.save_tokens_per_expert_interval == 0) save_wgrads_in_this_iteration = (args.save_wgrads_interval is not None and (iteration + 1) % args.save_wgrads_interval == 0) + save_dgrads_in_this_iteration = (args.save_dgrads_interval is not None and + (iteration + 1) % args.save_dgrads_interval == 0) while rerun_state_machine.should_run_forward_backward(data_iterator): # Set grad to zero. for model_chunk in model: @@ -1823,6 +1837,10 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch optim_instance._copy_main_params_to_param_buffer() # Forward pass. + if save_activations_in_this_iteration: + enable_activation_logging(model, args.save) + if save_tpe_in_this_iteration: + enable_tokens_per_expert_logging(model, args.save) if save_dgrads_in_this_iteration: enable_dgrad_logging(model, args.save) losses_reduced = forward_backward_func( @@ -1837,6 +1855,12 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch adjust_tensor_shapes_fn=adjust_tensor_shapes_fn, force_all_reduce=save_wgrads_in_this_iteration, ) + if save_activations_in_this_iteration: + save_activations(iteration + 1) + disable_activation_logging() + if save_tpe_in_this_iteration: + save_tokens_per_expert(iteration + 1) + disable_tokens_per_expert_logging() if save_dgrads_in_this_iteration: save_dgrads(iteration + 1) disable_dgrad_logging() @@ -1845,20 +1869,23 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch for model_chunk in model: model_chunk.force_all_reduce = False - # Checkpoint main_grads. - if save_wgrads_in_this_iteration: - # Collect state_dict of wgrads (each param's .main_grad field). + def _save_state_dict(attr_name, label): + # Collect state_dict of the given attribute for each parameter. state_dict = defaultdict(dict) for model_chunk_id, model_chunk in enumerate(model): model_chunk_name = f"model_chunk{model_chunk_id}" unwrapped_model_chunk = unwrap_model(model_chunk) for param_name, param in unwrapped_model_chunk.named_parameters(): - if getattr(param, "main_grad", None) is not None: - main_grad_on_cpu = param.main_grad.cpu() - state_dict[model_chunk_name][param_name] = main_grad_on_cpu + if getattr(param, attr_name, None) is not None: + tensor_on_cpu = getattr(param, attr_name).cpu() + state_dict[model_chunk_name][param_name] = tensor_on_cpu # iteration is 0-indexed, move to 1-indexed for checkpoint name and logging. - save_grads(args.save, state_dict, iteration + 1, "wgrads") + save_grads(args.save, state_dict, iteration + 1, label) + + # Checkpoint wgrads with parameter names. + if save_wgrads_in_this_iteration: + _save_state_dict(attr_name="main_grad", label="wgrads") should_checkpoint, should_exit, exit_code = rerun_state_machine.should_checkpoint_and_exit() if should_exit: @@ -1886,6 +1913,10 @@ def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_sch timers('optimizer').stop() + # Checkpoint params with parameter names. + if save_params_in_this_iteration: + _save_state_dict(attr_name="data", label="params") + # when freezing sub-models we may have a mixture of successful and unsucessful ranks, # so we must gather across mp ranks update_successful = logical_and_across_model_parallel_group(update_successful) diff --git a/tests/unit_tests/test_activation_logging.py b/tests/unit_tests/test_activation_logging.py new file mode 100644 index 00000000000..cc7634d6558 --- /dev/null +++ b/tests/unit_tests/test_activation_logging.py @@ -0,0 +1,100 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + +import json +import logging + +import pytest +import torch +import torch.nn as nn + +from megatron.training.activation_logging import ActivationLogger + + +@pytest.fixture() +def logger(tmp_path): + return ActivationLogger(save_dir=str(tmp_path)) + + +@pytest.fixture() +def simple_model(): + """A minimal model with two nn.Linear layers, wrapped as a single-element model chunk list.""" + model = nn.Sequential(nn.Linear(16, 32), nn.Linear(32, 8)) + return [model] + + +class TestMakeTpeHook: + """Tests for _make_tpe_hook regex layer extraction.""" + + def test_extracts_layer_number(self, logger): + hook = logger._make_tpe_hook("chunk0", "decoder.layers.3.mlp.experts.linear_fc1") + assert hook is not None + fake_tpe = [128, 64, 96, 80] + hook(None, (torch.zeros(1), fake_tpe), {}, torch.zeros(1)) + assert logger._tpe_records["3"] == [fake_tpe] + + def test_returns_none_for_non_matching_name(self, logger, caplog): + with caplog.at_level(logging.WARNING): + hook = logger._make_tpe_hook("chunk0", "some.module.without.layer.number") + assert hook is None + assert "Cannot extract layer number" in caplog.text + + +class TestSaveTpe: + """Tests for save_tpe JSONL output.""" + + def test_creates_jsonl(self, tmp_path, logger): + logger._tpe_records["3"].append([128, 64, 96, 80]) + logger._tpe_records["3"].append([100, 90, 110, 70]) + logger._tpe_records["7"].append([200, 200]) + + logger.save_tpe(iteration=100) + + rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 + filepath = tmp_path / "tokens_per_expert" / f"rank{rank}.jsonl" + assert filepath.exists() + + records = [json.loads(line) for line in filepath.read_text().strip().split("\n")] + assert records == [ + {"iter": 100, "layer": 3, "tpe": [[128, 64, 96, 80], [100, 90, 110, 70]]}, + {"iter": 100, "layer": 7, "tpe": [[200, 200]]}, + ] + + def test_appends_across_calls(self, tmp_path, logger): + logger._tpe_records["0"].append([10, 20]) + logger.save_tpe(iteration=100) + + logger._tpe_records["0"].append([30, 40]) + logger.save_tpe(iteration=200) + + rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 + filepath = tmp_path / "tokens_per_expert" / f"rank{rank}.jsonl" + records = [json.loads(line) for line in filepath.read_text().strip().split("\n")] + assert len(records) == 2 + assert records[0]["iter"] == 100 + assert records[1]["iter"] == 200 + + +class TestActivationHookLifecycle: + """Tests for activation hook registration and removal.""" + + def test_register_and_remove(self, logger, simple_model): + logger.register_activation_hooks(simple_model) + assert len(logger._activation_hooks) == 2 + + logger.remove_activation_hooks() + assert len(logger._activation_hooks) == 0 + + def test_hooks_capture_activations(self, logger, simple_model): + logger.register_activation_hooks(simple_model) + + simple_model[0](torch.randn(2, 16)) + + assert len(logger._activations_state_dict) > 0 + logger.remove_activation_hooks() + + def test_removed_hooks_dont_capture(self, logger, simple_model): + logger.register_activation_hooks(simple_model) + logger.remove_activation_hooks() + + simple_model[0](torch.randn(2, 16)) + assert len(logger._activations_state_dict) == 0 From 98a51eb2c15f6c3e07fa4cf5011f4eedddc088d6 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Sat, 18 Apr 2026 17:44:11 -0400 Subject: [PATCH 44/72] Fix RL to once again work with --skip-train (#4249) --- megatron/training/training.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/megatron/training/training.py b/megatron/training/training.py index d476bd22a17..8014435b4af 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -1622,10 +1622,11 @@ def setup_model_and_optimizer( timers = get_timers() one_logger = get_one_logger() - # Skip optimizer when not training. In RL inference-only mode (skip_train + perform_rl_step), - # --no-load-optim controls whether the optimizer is skipped (saving memory) or created - # (required for --rl-offload-optimizer-during-inference). - skip_optimizer = args.skip_train and (not args.perform_rl_step or args.no_load_optim) + # Typically, --skip-train is the only thing needed to disable the optimizer. + has_normal_optimizer = not args.skip_train + # Even with --skip-train, RL still creates an optimizer unless --no-load-optim is set. + has_rl_optimizer = args.perform_rl_step and not args.no_load_optim + skip_optimizer = not (has_normal_optimizer or has_rl_optimizer) wrap_with_ddp = not skip_optimizer model = get_model(model_provider_func, model_type, wrap_with_ddp=wrap_with_ddp) unwrapped_model = unwrap_model(model) @@ -3776,8 +3777,16 @@ def _get_iterator(dataloader_type, dataloader): def should_disable_forward_pre_hook(args): """Block forward pre-hook for certain configurations.""" + # Typically, --skip-train is the only thing needed to disable the optimizer. + has_normal_optimizer = not args.skip_train + # Even with --skip-train, RL still creates an optimizer unless --no-load-optim is set. + has_rl_optimizer = args.perform_rl_step and not args.no_load_optim + # The forward pre-hooks are part of the distributed optimizer's overlapped param-gather; + # so in order to disable them, we must check that the optimizer actually exists. + has_optimizer = has_normal_optimizer or has_rl_optimizer return ( not args.use_megatron_fsdp + and has_optimizer and (args.use_distributed_optimizer or args.use_layer_wise_distributed_optimizer) and args.overlap_param_gather ) From afae25b5f0c04ae747d9d48e855bb7df2221ce52 Mon Sep 17 00:00:00 2001 From: Keshav Santhanam Date: Sat, 18 Apr 2026 16:09:15 -0700 Subject: [PATCH 45/72] Fix Megatron initialization with extra_args_provider (#4327) Signed-off-by: Keshav Santhanam --- .../detxoify_lm/generate_samples_gpt.py | 12 +++++++----- .../model_converter/vision_model_tester.py | 4 +++- examples/multimodal/run_text_generation.py | 4 +++- tools/run_dynamic_text_generation_server.py | 4 +++- tools/run_inference_performance_test.py | 4 +++- tools/run_text_generation_server.py | 4 +++- tools/run_vlm_text_generation.py | 4 +++- 7 files changed, 25 insertions(+), 11 deletions(-) diff --git a/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py b/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py index 895a45d0242..183c6c695bd 100644 --- a/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py +++ b/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py @@ -14,6 +14,7 @@ from megatron.training import print_rank_0 from megatron.training.checkpointing import load_checkpoint from megatron.core import mpu +from megatron.training.arguments import parse_and_validate_args from megatron.training.initialize import initialize_megatron from megatron.legacy.model import GPTModel from megatron.training import get_model @@ -232,11 +233,12 @@ def generate_and_write_samples_conditional(model): def main(): """Main program.""" - initialize_megatron(extra_args_provider=add_text_generate_args, - args_defaults={'tokenizer_type': 'GPT2BPETokenizer', - 'no_load_rng': True, - 'no_load_optim': True, - 'seq_length': 2048}) + parse_and_validate_args(extra_args_provider=add_text_generate_args, + args_defaults={'tokenizer_type': 'GPT2BPETokenizer', + 'no_load_rng': True, + 'no_load_optim': True, + 'seq_length': 2048}) + initialize_megatron() # Set up model and load checkpoint model = get_model(model_provider, wrap_with_ddp=False) diff --git a/examples/multimodal/model_converter/vision_model_tester.py b/examples/multimodal/model_converter/vision_model_tester.py index ef36dd5f9e0..36e2122b555 100644 --- a/examples/multimodal/model_converter/vision_model_tester.py +++ b/examples/multimodal/model_converter/vision_model_tester.py @@ -17,6 +17,7 @@ from examples.multimodal.model import model_provider from examples.multimodal.multimodal_args import add_multimodal_extra_args from megatron.training import get_model +from megatron.training.arguments import parse_and_validate_args from megatron.training.checkpointing import load_checkpoint from megatron.training.initialize import initialize_megatron @@ -50,7 +51,8 @@ def run_mcore_vision(model_path): f"--pretrained-checkpoint={model_path}", ] - initialize_megatron(extra_args_provider=add_multimodal_extra_args) + parse_and_validate_args(extra_args_provider=add_multimodal_extra_args) + initialize_megatron() def wrapped_model_provider(pre_process, post_process): return model_provider(pre_process, post_process, parallel_output=False) diff --git a/examples/multimodal/run_text_generation.py b/examples/multimodal/run_text_generation.py index e55679c1b2e..532d0771a94 100644 --- a/examples/multimodal/run_text_generation.py +++ b/examples/multimodal/run_text_generation.py @@ -39,6 +39,7 @@ VLMInferenceWrapper, ) from megatron.training import get_args, get_model, get_tokenizer, print_rank_0, is_last_rank +from megatron.training.arguments import parse_and_validate_args from megatron.training.checkpointing import load_checkpoint from megatron.training.initialize import initialize_megatron @@ -842,7 +843,8 @@ def run_evaluation_loop(model, configs, output_dir_override=None, iteration=None def eval_tasks(): """Vision language model text generation for single or batch tasks.""" - initialize_megatron(extra_args_provider=add_text_generation_args) + parse_and_validate_args(extra_args_provider=add_text_generation_args) + initialize_megatron() args = get_args() diff --git a/tools/run_dynamic_text_generation_server.py b/tools/run_dynamic_text_generation_server.py index 74fbfd8e3d3..ed5ab473d63 100644 --- a/tools/run_dynamic_text_generation_server.py +++ b/tools/run_dynamic_text_generation_server.py @@ -14,6 +14,7 @@ from megatron.inference.utils import add_inference_args, get_dynamic_inference_engine from megatron.post_training.arguments import add_modelopt_args from megatron.training import get_args +from megatron.training.arguments import parse_and_validate_args from megatron.training.initialize import initialize_megatron @@ -76,10 +77,11 @@ async def run_text_generation_server( if __name__ == "__main__": with torch.inference_mode(): - initialize_megatron( + parse_and_validate_args( extra_args_provider=add_text_generation_server_args, args_defaults={'no_load_rng': True, 'no_load_optim': True}, ) + initialize_megatron() # Enable return_log_probs to allow prompt logprobs computation for echo=True requests # This sets materialize_only_last_token_logits=False in the inference context, diff --git a/tools/run_inference_performance_test.py b/tools/run_inference_performance_test.py index a1feef380ef..ac9e92d3639 100644 --- a/tools/run_inference_performance_test.py +++ b/tools/run_inference_performance_test.py @@ -39,6 +39,7 @@ from megatron.core import mpu from megatron.training import get_args, get_model, get_tokenizer from megatron.training.checkpointing import load_checkpoint +from megatron.training.arguments import parse_and_validate_args from megatron.training.initialize import initialize_megatron REQUEST_ID = 0 @@ -151,7 +152,7 @@ def generate_dynamic( def main(): """Main program.""" - initialize_megatron( + parse_and_validate_args( extra_args_provider=add_inference_benchmarking_args, args_defaults={ 'no_load_rng': True, @@ -160,6 +161,7 @@ def main(): 'exit_on_missing_checkpoint': True, }, ) + initialize_megatron() args = get_args() diff --git a/tools/run_text_generation_server.py b/tools/run_text_generation_server.py index 83a0bbc2369..5a2940f1a4c 100644 --- a/tools/run_text_generation_server.py +++ b/tools/run_text_generation_server.py @@ -40,6 +40,7 @@ from megatron.core import mpu from megatron.training import get_args, get_model, get_tokenizer from megatron.training.checkpointing import load_checkpoint +from megatron.training.arguments import parse_and_validate_args from megatron.training.initialize import initialize_megatron @@ -113,7 +114,7 @@ def add_text_generate_args(parser): @torch.inference_mode() def main(model_type: str = "gpt"): """Runs the text generation server with the specified model type.""" - initialize_megatron( + parse_and_validate_args( extra_args_provider=add_text_generate_args, args_defaults={ 'no_load_rng': True, @@ -121,6 +122,7 @@ def main(model_type: str = "gpt"): 'exit_on_missing_checkpoint': True, }, ) + initialize_megatron() args = get_args() if args.num_layers_per_virtual_pipeline_stage is not None: print("Interleaved pipeline schedule is not yet supported for text generation.") diff --git a/tools/run_vlm_text_generation.py b/tools/run_vlm_text_generation.py index 3e9a6f785de..17c3b1030dc 100644 --- a/tools/run_vlm_text_generation.py +++ b/tools/run_vlm_text_generation.py @@ -19,6 +19,7 @@ from megatron.inference.text_generation.api import generate_and_post_process from megatron.inference.text_generation.forward_step import ForwardStep from megatron.training import get_args, get_model, print_rank_0 +from megatron.training.arguments import parse_and_validate_args from megatron.training.checkpointing import load_checkpoint from megatron.training.initialize import initialize_megatron from pretrain_vlm import model_provider @@ -199,7 +200,8 @@ def main(): logging.getLogger(__name__).warning("Models using pipeline parallelism are not supported yet.") - initialize_megatron(extra_args_provider=add_text_generation_args) + parse_and_validate_args(extra_args_provider=add_text_generation_args) + initialize_megatron() # Set up model and load checkpoint. model = get_model(model_provider, wrap_with_ddp=False) From 15e07a2ddf6f7398022de29e84742b6e94b4d4c0 Mon Sep 17 00:00:00 2001 From: Philip Petrakian Date: Sun, 19 Apr 2026 13:04:55 -0700 Subject: [PATCH 46/72] Rename MambaModel/MambaStack to HybridModel/HybridStack (#4099) Co-authored-by: Claude Opus 4.6 (1M context) --- .github/CODEOWNERS | 2 + megatron/core/inference/config.py | 4 +- .../inference/contexts/dynamic_context.py | 4 +- megatron/core/models/gpt/moe_module_specs.py | 6 +- megatron/core/models/hybrid/__init__.py | 1 + megatron/core/models/hybrid/hybrid_block.py | 436 +++++++++++++++ .../models/hybrid/hybrid_layer_allocation.py | 498 +++++++++++++++++ .../core/models/hybrid/hybrid_layer_specs.py | 308 +++++++++++ megatron/core/models/hybrid/hybrid_model.py | 502 +++++++++++++++++ megatron/core/models/mamba/__init__.py | 8 +- .../core/models/mamba/mamba_layer_specs.py | 306 +---------- megatron/core/models/mamba/mamba_model.py | 508 +----------------- .../core/models/multimodal/llava_model.py | 8 +- .../post_training/modelopt/hybrid/__init__.py | 1 + .../modelopt/hybrid/model_specs.py | 145 +++++ .../modelopt/mamba/model_specs.py | 144 +---- megatron/core/ssm/mamba_block.py | 434 +-------------- .../core/ssm/mamba_hybrid_layer_allocation.py | 500 +---------------- .../transformer/multi_token_prediction.py | 70 ++- ...st_mamba_model.py => test_hybrid_model.py} | 46 +- ...hybrid_model_expert_parallel_inference.py} | 14 +- ..._moe_model.py => test_hybrid_moe_model.py} | 18 +- .../test_modelopt_module_spec.py | 34 +- ...st_mamba_block.py => test_hybrid_block.py} | 24 +- ...ion.py => test_hybrid_layer_allocation.py} | 38 +- tests/unit_tests/ssm/test_mamba_layer.py | 12 +- tests/unit_tests/ssm/test_mamba_mixer.py | 22 +- .../transformer/test_cuda_graphs.py | 14 +- .../test_multi_token_prediction.py | 20 +- 29 files changed, 2113 insertions(+), 2014 deletions(-) create mode 100644 megatron/core/models/hybrid/__init__.py create mode 100644 megatron/core/models/hybrid/hybrid_block.py create mode 100644 megatron/core/models/hybrid/hybrid_layer_allocation.py create mode 100755 megatron/core/models/hybrid/hybrid_layer_specs.py create mode 100644 megatron/core/models/hybrid/hybrid_model.py mode change 100755 => 100644 megatron/core/models/mamba/mamba_layer_specs.py create mode 100644 megatron/core/post_training/modelopt/hybrid/__init__.py create mode 100755 megatron/core/post_training/modelopt/hybrid/model_specs.py rename tests/unit_tests/models/{test_mamba_model.py => test_hybrid_model.py} (94%) rename tests/unit_tests/models/{test_mamba_model_expert_parallel_inference.py => test_hybrid_model_expert_parallel_inference.py} (97%) rename tests/unit_tests/models/{test_mamba_moe_model.py => test_hybrid_moe_model.py} (97%) rename tests/unit_tests/ssm/{test_mamba_block.py => test_hybrid_block.py} (92%) rename tests/unit_tests/ssm/{test_mamba_hybrid_layer_allocation.py => test_hybrid_layer_allocation.py} (94%) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 72d62d9e55e..2188c65aa0b 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -7,6 +7,8 @@ megatron/core/models/multimodal/ @NVIDIA/core-adlr @NVIDIA/core-nemo @NVIDIA/mul megatron/core/models/mamba/ @NVIDIA/core-adlr @NVIDIA/core-nemo @NVIDIA/hybrid-mamba megatron/core/ssm/ @NVIDIA/core-adlr @NVIDIA/core-nemo @NVIDIA/hybrid-mamba +megatron/core/models/hybrid/ @NVIDIA/core-adlr @NVIDIA/core-nemo @NVIDIA/hybrid-model + megatron/core/datasets/ @NVIDIA/core-adlr @NVIDIA/core-nemo @NVIDIA/datasets megatron/core/tokenizers/ @NVIDIA/core-adlr @NVIDIA/core-nemo @NVIDIA/tokenizers diff --git a/megatron/core/inference/config.py b/megatron/core/inference/config.py index 590377979ae..e1a36ff1563 100644 --- a/megatron/core/inference/config.py +++ b/megatron/core/inference/config.py @@ -24,7 +24,7 @@ class MambaInferenceStateConfig: layer_type_list: List[str] """ A list of strings that indicates the layer type (Mamba / Attention / MLP) for each layer. - See `megatron/core/ssm/mamba_hybrid_layer_allocation.py` for the list of symbols. + See `megatron/core/models/hybrid/hybrid_layer_allocation.py` for the list of symbols. """ conv_states_shape: Tuple[int] @@ -50,7 +50,7 @@ def from_model( ssm_states_dtype: Optional[torch.dtype] = None, ) -> Optional["MambaInferenceStateConfig"]: """Returns Mamba inference state config from the model if it is a hybrid model.""" - from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols + from megatron.core.models.hybrid.hybrid_layer_allocation import Symbols decoder = get_attr_wrapped_model(model, "decoder") layer_type_list = getattr(decoder, "layer_type_list", None) diff --git a/megatron/core/inference/contexts/dynamic_context.py b/megatron/core/inference/contexts/dynamic_context.py index 7ac46d42e05..5b264b36302 100644 --- a/megatron/core/inference/contexts/dynamic_context.py +++ b/megatron/core/inference/contexts/dynamic_context.py @@ -29,11 +29,11 @@ ) from megatron.core.inference.utils import device_memory_summary, tensor_swap from megatron.core.models.common.embeddings.rope_utils import apply_rotary_pos_emb -from megatron.core.package_info import __version__ as mcore_version -from megatron.core.ssm.mamba_hybrid_layer_allocation import ( +from megatron.core.models.hybrid.hybrid_layer_allocation import ( Symbols, get_layer_maps_from_layer_type_list, ) +from megatron.core.package_info import __version__ as mcore_version from megatron.core.transformer import MLATransformerConfig, TransformerConfig from megatron.core.utils import deprecate_args from megatron.core.utils import divide as core_divide diff --git a/megatron/core/models/gpt/moe_module_specs.py b/megatron/core/models/gpt/moe_module_specs.py index 53bca85f502..e82b6638f0b 100755 --- a/megatron/core/models/gpt/moe_module_specs.py +++ b/megatron/core/models/gpt/moe_module_specs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. from functools import partial from typing import Optional @@ -23,7 +23,7 @@ def get_moe_module_spec( ) -> ModuleSpec: """Helper function to get module spec for MoE. - Called by mamba_layer_specs.py for standard (non-inference) MoE specs. + Called by hybrid_layer_specs.py for standard (non-inference) MoE specs. The GPT layer specs call get_moe_module_spec_for_backend directly. Args: @@ -78,7 +78,7 @@ def get_inference_optimized_moe_spec() -> ModuleSpec: InferenceTopKRouter, InferenceGroupedMLP. MoELayer detects inference mode via config.transformer_impl and sets up the inference dispatcher internally. - Called by mamba_layer_specs.py and gpt_layer_specs.py. + Called by hybrid_layer_specs.py and gpt_layer_specs.py. """ backend = InferenceSpecProvider() activation_func = backend.activation_func() diff --git a/megatron/core/models/hybrid/__init__.py b/megatron/core/models/hybrid/__init__.py new file mode 100644 index 00000000000..d8a0a817ee3 --- /dev/null +++ b/megatron/core/models/hybrid/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. diff --git a/megatron/core/models/hybrid/hybrid_block.py b/megatron/core/models/hybrid/hybrid_block.py new file mode 100644 index 00000000000..5494d531e52 --- /dev/null +++ b/megatron/core/models/hybrid/hybrid_block.py @@ -0,0 +1,436 @@ +# Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2024, Tri Dao, Albert Gu. + +# Some of this code was adopted from https://github.com/state-spaces/mamba/ +# This source code is licensed under the Apache license found in the +# LICENSE file in the root directory of this source tree. + +from contextlib import nullcontext +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +from torch import Tensor, nn + +from megatron.core.dist_checkpointing.mapping import ShardedStateDict +from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding +from megatron.core.enums import Fp8Recipe +from megatron.core.extensions.transformer_engine import TENorm +from megatron.core.fp4_utils import get_fp4_context +from megatron.core.fp8_utils import get_fp8_context +from megatron.core.inference.contexts import BaseInferenceContext +from megatron.core.models.hybrid.hybrid_layer_allocation import Symbols as LayerSymbols +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.transformer import TransformerConfig +from megatron.core.transformer.enums import CudaGraphScope +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.module import GraphableMegatronModule, MegatronModule +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.transformer.transformer_layer import TransformerLayer +from megatron.core.transformer.utils import sharded_state_dict_default +from megatron.core.utils import WrappedTensor, deprecate_inference_params, make_viewless_tensor + + +@dataclass +class HybridStackSubmodules: + """ + A class for the module specs for the HybridStack. + """ + + mamba_layer: Union[ModuleSpec, type] = IdentityOp + gdn_layer: Union[ModuleSpec, type] = IdentityOp + attention_layer: Union[ModuleSpec, type] = IdentityOp + dsa_layer: Union[ModuleSpec, type] = IdentityOp + mlp_layer: Union[ModuleSpec, type] = IdentityOp + moe_layer: Union[ModuleSpec, type] = IdentityOp + mtp_block_spec: Optional[ModuleSpec] = None + + +class HybridStack(GraphableMegatronModule, MegatronModule): + """ + Constructor for the HybridStack class. + + Args: + config (TransformerConfig): the model configuration + submodules (HybridStackSubmodules): the submodules for the stack + pre_process (bool, optional): whether to include an embedding layer. + Defaults to True. + layer_type_list (list, optional): pre-computed list of layer type symbols for + this pipeline segment. When provided (by HybridModel), pipeline stage + selection has already been done via '|' separators in the pattern. + pp_layer_offset (int, optional): the global layer offset for this pipeline + segment. Defaults to 0. + post_layer_norm (bool, optional): whether to include a final layer norm. + Defaults to True. + post_process (bool, optional): whether to include an output layer. + Defaults to True. + device (optional): the device to use. Defaults to None. + dtype (optional): the data type to use. Defaults to None. + pg_collection (ProcessGroupCollection): the required model communication + process groups to use. + is_mtp_layer (bool, optional): whether this is an MTP layer. Defaults to False. + """ + + def __init__( + self, + config: TransformerConfig, + submodules: HybridStackSubmodules, + pre_process: bool = True, + layer_type_list: Optional[list[str]] = None, + pp_layer_offset: int = 0, + post_layer_norm: bool = True, + post_process: bool = True, + device=None, + dtype=None, + pg_collection: ProcessGroupCollection = None, + is_mtp_layer: bool = False, + ) -> None: + super().__init__(config=config) + self.pre_process = pre_process + self.post_layer_norm = post_layer_norm + self.post_process = post_process + self.is_mtp_layer = is_mtp_layer + + assert pg_collection is not None, "pg_collection must be provided for HybridStack" + + self.pp_group = pg_collection.pp + self.tp_group = pg_collection.tp + + # Required for pipeline parallel schedules + self.input_tensor = None + self.pg_collection = pg_collection + + assert layer_type_list is not None, ( + "layer_type_list must be provided. It should be pre-computed from " + "--hybrid-layer-pattern by HybridModel." + ) + self.layer_type_list = layer_type_list + + # Build layers from the pre-selected segment + self.layers = nn.ModuleList() + for i, layer_type in enumerate(self.layer_type_list): + layer_number = i + 1 + pp_layer_offset + if self.config.fp8: + quant_init_context = get_fp8_context(self.config, i + pp_layer_offset, is_init=True) + elif self.config.fp4: + quant_init_context = get_fp4_context(self.config, i + pp_layer_offset, is_init=True) + else: + quant_init_context = nullcontext() + with quant_init_context: + if layer_type == LayerSymbols.MAMBA: + layer = build_module( + submodules.mamba_layer, + config=self.config, + layer_number=layer_number, + pp_layer_offset=pp_layer_offset, + pg_collection=pg_collection, + ) + elif layer_type == LayerSymbols.ATTENTION: + layer = build_module( + submodules.attention_layer, + config=self.config, + layer_number=layer_number, + pg_collection=pg_collection, + is_mtp_layer=is_mtp_layer, + add_layer_offset=False, + pp_layer_offset=pp_layer_offset, + ) + elif layer_type == LayerSymbols.DS_ATTENTION: + layer = build_module( + submodules.dsa_layer, + config=self.config, + layer_number=layer_number, + pg_collection=pg_collection, + is_mtp_layer=is_mtp_layer, + add_layer_offset=False, + pp_layer_offset=pp_layer_offset, + ) + elif layer_type == LayerSymbols.MLP: + layer = build_module( + submodules.mlp_layer, + config=self.config, + layer_number=layer_number, + pg_collection=pg_collection, + add_layer_offset=False, + ) + elif layer_type == LayerSymbols.MOE: + layer = build_module( + submodules.moe_layer, + config=self.config, + layer_number=layer_number, + pg_collection=pg_collection, + add_layer_offset=False, + ) + elif layer_type == LayerSymbols.GDN: + layer = build_module( + submodules.gdn_layer, + config=self.config, + layer_number=layer_number, + pg_collection=pg_collection, + # Set to False as we do not want to change offset. + add_layer_offset=False, + ) + else: + raise ValueError("unexpected layer_type") + self.layers.append(layer) + + # Required for activation recomputation + self.num_layers_per_pipeline_rank = len(self.layers) + + if self.post_process and self.post_layer_norm: + # Final layer norm before output. + self.final_norm = TENorm( + config=self.config, + hidden_size=self.config.hidden_size, + eps=self.config.layernorm_epsilon, + ) + + def set_input_tensor(self, input_tensor: Tensor): + """Set input tensor to be used instead of forward()'s input. + + When doing pipeline parallelism the input from the previous + stage comes from communication, not from the input, so the + model's forward_step_func won't have it. This function is thus + used by internal code to bypass the input provided by the + forward_step_func""" + self.input_tensor = input_tensor + + def mamba_state_shapes_per_request(self) -> Optional[Tuple[Tuple[int], Tuple[int]]]: + """ + Returns the Mamba conv and ssm states shapes per input sequence + if this block contains Mamba layers (this may not be the case with PP > 1). + """ + for layer_type, layer in zip(self.layer_type_list, self.layers): + if layer_type == LayerSymbols.MAMBA: + return layer.mamba_state_shapes_per_request() + return None + + def _should_call_local_cudagraph(self, *args, **kwargs): + """ + Check if we should call the local cudagraph path. + """ + if ( + not self.training + and hasattr(self, 'cudagraph_manager') + and kwargs['attention_mask'] is None + and ( + kwargs.get('inference_context') is not None + or kwargs.get('inference_params') is not None + ) + and CudaGraphScope.full_iteration_inference in self.config.cuda_graph_scope + ): + if kwargs['inference_context'].is_static_batching(): + using_cuda_graph = kwargs['inference_context'].is_decode_only() + else: + using_cuda_graph = kwargs['inference_context'].using_cuda_graph_this_step() + + if using_cuda_graph: + return True + return False + + def __call__(self, *args, **kwargs): + if self._should_call_local_cudagraph(*args, **kwargs): + kwargs['hidden_states'] = ( + kwargs['hidden_states'].unwrap() + if isinstance(kwargs['hidden_states'], WrappedTensor) + else kwargs['hidden_states'] + ) + return super().__call__(*args, **kwargs)[0] + return super().__call__(*args, **kwargs) + + def forward( + self, + hidden_states: Union[Tensor, WrappedTensor], + attention_mask: Tensor, + inference_context: Optional[BaseInferenceContext] = None, + rotary_pos_emb: Optional[Tensor] = None, + *, + inference_params: Optional[BaseInferenceContext] = None, + packed_seq_params: Optional[PackedSeqParams] = None, + padding_mask=None, + ): + """ + Forward function of the HybridStack class. + + It either returns the Loss values if labels are given or the + final hidden units + + Args: + hidden_states (Union[Tensor, WrappedTensor]): the input tensor. + Can be passed as a WrappedTensor during inference to avoid an obsolete + reference in the calling function. + attention_mask (Tensor): the attention mask. + inference_context (BaseInferenceContext): the inference parameters. + rotary_pos_emb (Tensor, optional): the rotary positional embeddings. + Defaults to None. + Returns: + Tensor: the output tensor. + """ + + inference_context = deprecate_inference_params(inference_context, inference_params) + + if not self.pre_process: + # See set_input_tensor() + hidden_states = self.input_tensor + + # Delete the obsolete reference to the initial input tensor if necessary + if isinstance(hidden_states, WrappedTensor): + hidden_states = hidden_states.unwrap() + + if inference_context and inference_context.is_static_batching(): + # NOTE(bnorick): match BaseInferenceContext attributes for + # mamba_ssm.utils.generation.BaseInferenceContext, + # this hack supports eval + inference_context.max_seqlen = inference_context.max_sequence_length + inference_context.seqlen_offset = inference_context.sequence_len_offset + + if ( + ( + ( + self.config.cuda_graph_impl == "local" + and CudaGraphScope.full_iteration not in self.config.cuda_graph_scope + ) + or self.config.flash_decode + ) + and inference_context + and inference_context.is_static_batching() + and not self.training + ): + current_batch_size = hidden_states.shape[1] + sequence_len_offset = torch.tensor( + [inference_context.sequence_len_offset] * current_batch_size, + dtype=torch.int32, + device='cuda', + ) + else: + sequence_len_offset = None + + # If fp8_recipe is delayed, wrap the entire pass with get_fp8_context(), + # otherwise do nothing extra at the outer level + # if we are using other fp8 recipes, then the context manager enter&exit are free + # we can wrap fp8_context within the for loop over layers, so that we can fine-grained + # control which layer will be fp8 or bf16 + use_outer_fp8_context = self.config.fp8 and self.config.fp8_recipe == Fp8Recipe.delayed + use_inner_fp8_context = self.config.fp8 and self.config.fp8_recipe != Fp8Recipe.delayed + use_fp4_context = self.config.fp4 is not None + outer_fp8_context = get_fp8_context(self.config) if use_outer_fp8_context else nullcontext() + + if use_inner_fp8_context: + + def get_inner_quant_context(config, layer_number): + return get_fp8_context(config, layer_number) + + elif use_fp4_context: + + def get_inner_quant_context(config, layer_number): + return get_fp4_context(config, layer_number) + + else: + + def get_inner_quant_context(config, layer_number): + return nullcontext() + + with outer_fp8_context: + for layer in self.layers: + # Layers have 1-indexed layer numbers attribute. + inner_quant_context = get_inner_quant_context(self.config, layer.layer_number - 1) + with inner_quant_context: + if isinstance(layer, TransformerLayer): + hidden_states, _ = layer( + hidden_states=hidden_states, + attention_mask=attention_mask, + inference_context=inference_context, + rotary_pos_emb=rotary_pos_emb, + sequence_len_offset=sequence_len_offset, + packed_seq_params=packed_seq_params, + padding_mask=padding_mask, + ) + else: # MambaLayer, Expert, or MLP + hidden_states = layer( + hidden_states=hidden_states, + attention_mask=attention_mask, + inference_context=inference_context, + packed_seq_params=packed_seq_params, + ) + + # The attention layer (currently a simplified transformer layer) + # outputs a tuple of (hidden_states, context). Context is intended + # for cross-attention, and is not needed in our model. + if isinstance(hidden_states, tuple): + hidden_states = hidden_states[0] + + # Final layer norm. + if self.post_process and self.post_layer_norm: + hidden_states = self.final_norm(hidden_states) + + # Ensure that the tensor passed between pipeline parallel stages is + # viewless. See related notes in TransformerBlock and TransformerLayer + hidden_states = make_viewless_tensor( + inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True + ) + + return hidden_states + + def sharded_state_dict( + self, + prefix: str = '', + sharded_offsets: Optional[tuple] = None, + metadata: Optional[dict] = None, + ) -> ShardedStateDict: + """ + Returns a sharded state dictionary for the current object. + + This function constructs a sharded state dictionary by iterating over the layers + in the current object, computing the sharded state dictionary for each layer, + and combining the results into a single dictionary. + + Parameters: + prefix (str): The prefix to use for the state dictionary keys. + sharded_offsets (tuple): The sharded offsets to use for the state dictionary. + metadata (dict): Additional metadata to use when computing the sharded state dictionary. + + Returns: + dict: The sharded state dictionary for the current object. + """ + + sharded_state_dict = {} + layer_prefix = f'{prefix}layers.' + + for local_layer_idx, layer in enumerate(self.layers): + + global_layer_offset = layer.layer_number - 1 # self.layer_number starts at 1 + state_dict_prefix = ( + f'{layer_prefix}{local_layer_idx}.' # module list index in HybridStack + ) + + sharded_prefix = f'{layer_prefix}{global_layer_offset}.' + sharded_pp_offset = [] + + layer_sharded_state_dict = layer.sharded_state_dict( + state_dict_prefix, sharded_pp_offset, metadata + ) + + replace_prefix_for_sharding(layer_sharded_state_dict, state_dict_prefix, sharded_prefix) + + sharded_state_dict.update(layer_sharded_state_dict) + + # Add modules other than self.layers + for name, module in self.named_children(): + if not module is self.layers: + sharded_state_dict.update( + sharded_state_dict_default( + module, + f'{prefix}{name}.', + sharded_offsets, + metadata, + tp_group=self.tp_group, + ) + ) + + return sharded_state_dict + + +# Backward-compatible aliases +MambaStackSubmodules = HybridStackSubmodules +MambaStack = HybridStack diff --git a/megatron/core/models/hybrid/hybrid_layer_allocation.py b/megatron/core/models/hybrid/hybrid_layer_allocation.py new file mode 100644 index 00000000000..f1ba94ef7fa --- /dev/null +++ b/megatron/core/models/hybrid/hybrid_layer_allocation.py @@ -0,0 +1,498 @@ +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. + +import logging +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple + +import torch + +from megatron.core.utils import log_on_each_pipeline_stage, log_single_rank + +logger = logging.getLogger(__name__) + + +class Symbols: + """Symbols for different layer types and pattern separators.""" + + MAMBA = "M" + GDN = 'G' + ATTENTION = "*" + DS_ATTENTION = "D" + MLP = "-" + MOE = 'E' + PIPE = '|' + MTP_SEPARATOR = "/" + VALID_LAYERS = {MAMBA, GDN, ATTENTION, DS_ATTENTION, MLP, MOE} + + @classmethod + def name_sorted_valid_layer_symbols(cls) -> list[str]: + """Return the valid layer symbols sorted lexicographically by their public attribute + name. + """ + valid_layer_attrs = [] + for name, value in vars(cls).items(): + if not name.startswith('_') and value in cls.VALID_LAYERS: + valid_layer_attrs.append((name, value)) + valid_layer_attrs.sort() + return [value for (_, value) in valid_layer_attrs] + + +@dataclass +class ParsedHybridPattern: + """Result of parsing a unified hybrid pattern string. + + A unified pattern encodes both the main decoder pattern and the MTP pattern + in a single string using "/" as a separator. The main pattern may also + contain "|" pipe symbols to define pipeline stage boundaries for flexible + virtual pipeline parallelism (fVPP). + + Format: "///..." + + Examples: + - "M*M*" -> main="M*M*", mtp=None, depths=0 (no MTP) + - "M*M*/MM/MM" -> main="M*M*", mtp="MM", depths=2 + - "MMMM/*M/*M/*M" -> main="MMMM", mtp="*M", depths=3 + - "M-M-|M-M*-/MM/MM" -> main="M-M-|M-M*-" (2 PP stages), mtp="MM", depths=2 + + The "/" symbol introduces MTP patterns. Each repeated pattern after the main + decoder represents one MTP prediction depth. + + The "|" symbol in the main pattern defines pipeline stage boundaries. + + Attributes: + main_pattern: The main decoder layer pattern (e.g., "M*M*" or "M-M-|M-M*-") + mtp_pattern: The MTP layer pattern per depth (e.g., "MM"), or None if no MTP + mtp_num_depths: Number of MTP prediction depths (0 if no MTP) + """ + + main_pattern: Optional[str] + mtp_pattern: Optional[str] + mtp_num_depths: int + + +def pattern_from_ratios( + num_layers: int, attention_ratio: float = 0.0, mlp_ratio: float = 0.0 +) -> str: + """Convert deprecated ratio arguments to a layer pattern string. + + Generates an evenly-spaced hybrid layer pattern from target attention and MLP + ratios. This exists for backward compatibility with code that uses the deprecated + hybrid_attention_ratio and hybrid_mlp_ratio parameters. + + Args: + num_layers: Total number of layers. + attention_ratio: Target ratio of attention layers to total layers. + mlp_ratio: Target ratio of MLP layers to total layers. + + Returns: + A layer pattern string (e.g., "MMM*MMM*MM"). + """ + assert num_layers > 0 + assert 0.0 <= attention_ratio <= 1.0 + assert 0.0 <= mlp_ratio <= 1.0 + assert attention_ratio + mlp_ratio <= 1.0 + + # Allocate attention layers (evenly spaced, starting and ending with mamba) + attention_count = round(num_layers * attention_ratio) + mamba_count = num_layers - attention_count + sections = attention_count + 1 + section_len = mamba_count / sections + + layer_types = [Symbols.MAMBA] * num_layers + x = section_len + for i in range(num_layers): + if x < 0.5: + layer_types[i] = Symbols.ATTENTION + x += section_len + else: + x -= 1 + + # Allocate MLP layers (evenly distributed, not replacing attention) + mlp_count = round(num_layers * mlp_ratio) + if mlp_count > 0: + mamba_count -= mlp_count + ratio = mamba_count / mlp_count + x = ratio + for i in range(num_layers): + if layer_types[i] == Symbols.MAMBA: + if x < 0.5: + layer_types[i] = Symbols.MLP + x += ratio + else: + x -= 1 + + return ''.join(layer_types) + + +def get_hybrid_total_layer_count(pattern: str) -> int: + """Returns the total number of main decoder layers in a hybrid layer pattern. + + Extracts the main pattern (before the first MTP separator '/'), strips + pipeline stage separators '|', and returns the character count. + + Args: + pattern: Full hybrid layer pattern, possibly including MTP and pipe separators. + + Returns: + Total number of layers in the main decoder pattern. + """ + main_pattern = pattern.split(Symbols.MTP_SEPARATOR)[0] + _validate_pattern(main_pattern, "main", allow_pipe=True) + return len(main_pattern.replace(Symbols.PIPE, '')) + + +def get_hybrid_total_pipeline_segment_count(pattern: str) -> int: + """Returns the number of pipeline segments in a hybrid layer pattern. + + Extracts the main pattern (before the first MTP separator '/') and counts + the number of segments delimited by '|'. + + Args: + pattern: Full hybrid layer pattern, possibly including MTP and pipe separators. + + Returns: + Number of pipeline segments (pipe count + 1). + """ + main_pattern = pattern.split(Symbols.MTP_SEPARATOR)[0] + return main_pattern.count(Symbols.PIPE) + 1 + + +def get_hybrid_layer_counts(pattern: str) -> Dict[str, int]: + """Count layers by type across the full hybrid pattern (main + MTP). + + Parses the pattern to extract main and MTP components, then counts + each layer type. Main pattern '|' separators are skipped. MTP layers + are counted once per MTP depth. + + Args: + pattern: Full hybrid layer pattern string. + + Returns: + Dictionary mapping layer symbol to count. Keys are all valid layer symbols + (Symbols.VALID_LAYERS). + + Examples: + >>> get_hybrid_layer_counts("M*M*") + {'*': 2, 'G': 0, 'D': 0, 'M': 2, '-': 0, 'E': 0} + + >>> get_hybrid_layer_counts("M-M-|M-M*-/MM/MM") + {'*': 1, 'G': 0, 'D': 0, 'M': 8, '-': 4, 'E': 0} + """ + parsed = parse_hybrid_pattern(pattern) + counts = {symbol: 0 for symbol in Symbols.name_sorted_valid_layer_symbols()} + + # Count main decoder layers (skip '|' pipe separators) + if parsed.main_pattern: + for char in parsed.main_pattern: + if char in counts: + counts[char] += 1 + + # Count MTP layers (pattern repeated mtp_num_depths times) + if parsed.mtp_pattern and parsed.mtp_num_depths > 0: + for char in parsed.mtp_pattern: + if char in counts: + counts[char] += parsed.mtp_num_depths + + return counts + + +def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern: + """Parse a unified hybrid pattern string into main and MTP components. + + The pattern uses "/" as a separator between the main decoder pattern and + MTP patterns. Each MTP pattern after the separator represents one prediction + depth. The main pattern may contain "|" pipe symbols for pipeline stage + boundaries. + + Format: "///..." + + Args: + pattern: Unified pattern string, e.g., "M*M*/MM/MM" or just "M*M*" + + Returns: + ParsedHybridPattern with main_pattern, mtp_pattern, and mtp_num_depths + + Raises: + ValueError: If MTP patterns are inconsistent (all must be identical) + ValueError: If pattern contains invalid layer symbols + + Examples: + >>> parse_hybrid_pattern("M*M*") + ParsedHybridPattern(main_pattern="M*M*", mtp_pattern=None, mtp_num_depths=0) + + >>> parse_hybrid_pattern("M*M*/MM/MM") + ParsedHybridPattern(main_pattern="M*M*", mtp_pattern="MM", mtp_num_depths=2) + + >>> parse_hybrid_pattern("MMMM/*M/*M/*M") + ParsedHybridPattern(main_pattern="MMMM", mtp_pattern="*M", mtp_num_depths=3) + + >>> parse_hybrid_pattern("M-M-|M-M*-/MM/MM") + ParsedHybridPattern(main_pattern="M-M-|M-M*-", mtp_pattern="MM", mtp_num_depths=2) + """ + if pattern is None: + return ParsedHybridPattern(main_pattern=None, mtp_pattern=None, mtp_num_depths=0) + + parts = pattern.split(Symbols.MTP_SEPARATOR) + + if len(parts) == 1: + # No MTP separator found - pattern is main decoder only + main_pattern = parts[0] + _validate_pattern(main_pattern, "main", allow_pipe=True) + return ParsedHybridPattern(main_pattern=main_pattern, mtp_pattern=None, mtp_num_depths=0) + + # First part is main decoder pattern + main_pattern = parts[0] + if main_pattern: + _validate_pattern(main_pattern, "main", allow_pipe=True) + + # Remaining parts are MTP patterns (one per depth) + mtp_parts = parts[1:] + + if not mtp_parts or all(p == "" for p in mtp_parts): + # No MTP patterns after separator + return ParsedHybridPattern( + main_pattern=main_pattern if main_pattern else None, mtp_pattern=None, mtp_num_depths=0 + ) + + # Validate all MTP patterns are identical + mtp_pattern = mtp_parts[0] + for i, part in enumerate(mtp_parts[1:], start=2): + if part != mtp_pattern: + raise ValueError( + f"All MTP patterns must be identical. " + f"Pattern 1 is '{mtp_pattern}', but pattern {i} is '{part}'. " + f"Full pattern: '{pattern}'" + ) + + _validate_pattern(mtp_pattern, "MTP", allow_pipe=False) + + return ParsedHybridPattern( + main_pattern=main_pattern if main_pattern else None, + mtp_pattern=mtp_pattern, + mtp_num_depths=len(mtp_parts), + ) + + +def _validate_pattern(pattern: str, pattern_name: str, allow_pipe: bool = False) -> None: + """Validate that a pattern contains only valid layer symbols. + + Args: + pattern: Layer pattern string to validate + pattern_name: Name of pattern for error messages (e.g., "main" or "MTP") + allow_pipe: Whether to allow the pipe '|' separator (for main patterns) + + Raises: + ValueError: If pattern contains invalid symbols + """ + valid_chars = Symbols.VALID_LAYERS | {Symbols.PIPE} if allow_pipe else Symbols.VALID_LAYERS + for char in pattern: + if char not in valid_chars: + raise ValueError( + f"In {pattern_name} pattern, '{char}' is not a valid layer symbol. " + f"Valid symbols are: {valid_chars}" + ) + + # Disallow Attention + MLA/DSA hybridity. + if Symbols.ATTENTION in pattern and Symbols.DS_ATTENTION in pattern: + raise ValueError("Not supported to have both Attention and MLA/DSA in one model") + + +def validate_segment_layers(segment: str) -> List[str]: + """Validate and convert a single pipeline segment pattern to a layer type list. + + This is used after the main pattern has been split by '|' into segments. + Each segment should contain only valid layer symbols (no '|'). + + Args: + segment: A single pipeline segment pattern string (e.g., "M-M*-") + + Returns: + List of layer type characters. + + Raises: + ValueError: If segment contains invalid layer symbols. + """ + layer_type_list = list(segment) + for layer_char in layer_type_list: + if layer_char not in Symbols.VALID_LAYERS: + raise ValueError( + f"In hybrid layer pattern segment, '{layer_char}' is not " + f"one of {Symbols.VALID_LAYERS}" + ) + + # Disallow Attention + MLA/DSA hybridity. + if Symbols.ATTENTION in segment and Symbols.DS_ATTENTION in segment: + raise ValueError("Not supported to have both Attention and MLA/DSA in one model") + + return layer_type_list + + +def select_pipeline_segment( + main_pattern: str, + pp_group: Optional[torch.distributed.ProcessGroup], + vp_stage: Optional[int], + first_stage_layers: Optional[int] = None, + last_stage_layers: Optional[int] = None, +) -> Tuple[List[str], int]: + """Select and validate the pipeline segment for the given PP rank and VP stage. + + When the main pattern contains '|' pipe separators, splits by '|' into + pipeline segments and selects the segment for the current PP rank / VP stage. + + When the pattern has no pipes but pp_size > 1, falls back to runtime layer + slicing (for backwards compatibility), supporting both even and uneven PP splits + via first_stage_layers / last_stage_layers. + + Args: + main_pattern: Main decoder pattern (may contain '|' separators). + Empty string is allowed (produces one empty segment). + pp_group: Pipeline parallel process group, or None if not using PP. + vp_stage: Virtual pipeline stage, or None if not using VPP. + first_stage_layers: Number of layers on the first pipeline stage for + uneven PP. Only valid when the pattern has no pipe separators. + last_stage_layers: Number of layers on the last pipeline stage for + uneven PP. Only valid when the pattern has no pipe separators. + + Returns: + Tuple of (layer_type_list, layer_offset) where layer_type_list is + the list of layer type characters for this segment, and layer_offset + is the sum of layer counts from all preceding segments. + + Raises: + ValueError: If the segment contains invalid layer symbols, if + first/last_stage_layers are used with pipe separators, if VPP is + requested without pipe separators, or if layer counts are not + evenly divisible across pipeline stages. + """ + segments = main_pattern.split(Symbols.PIPE) if main_pattern else [''] + + pp_rank = torch.distributed.get_rank(pp_group) if pp_group is not None else 0 + pp_size = torch.distributed.get_world_size(pp_group) if pp_group is not None else 1 + + if len(segments) > 1 and (first_stage_layers is not None or last_stage_layers is not None): + raise ValueError( + "Cannot specify num_layers_in_first_pipeline_stage or " + "num_layers_in_last_pipeline_stage when hybrid_layer_pattern " + "contains pipe ('|') separators. The pipeline layout is already " + "explicitly defined by the pipe separators." + ) + + if len(segments) == 1 and pp_size > 1: + if vp_stage is not None: + raise ValueError( + "Virtual pipeline parallelism (vp_stage != None) is not supported " + "when hybrid_layer_pattern has no pipe ('|') separators. " + "Add '|' separators to define explicit pipeline/virtual-pipeline " + "stage boundaries." + ) + log_single_rank( + logger, + logging.WARNING, + "DEPRECATION: Using hybrid_layer_pattern without pipe ('|') separators " + "with pipeline_model_parallel_size > 1 is deprecated. Please add '|' " + "separators to explicitly define pipeline stage boundaries. " + "Example: 'M*M*M*M*' with pp_size=2 should become 'M*M*|M*M*'.", + ) + full_pattern = segments[0] + layer_type_list = validate_segment_layers(full_pattern) + num_layers = len(layer_type_list) + + if first_stage_layers is not None or last_stage_layers is not None: + first = first_stage_layers or 0 + last = last_stage_layers or 0 + middle_num_layers = num_layers - first - last + middle_stages = pp_size - sum( + 1 for x in (first_stage_layers, last_stage_layers) if x is not None + ) + if middle_stages > 0: + if middle_num_layers % middle_stages != 0: + raise ValueError( + f"Middle layers ({middle_num_layers}) must be evenly divisible " + f"by middle pipeline stages ({middle_stages})." + ) + layers_per_middle = middle_num_layers // middle_stages + else: + layers_per_middle = 0 + + is_first = first_stage_layers is not None and pp_rank == 0 + is_last = last_stage_layers is not None and pp_rank == pp_size - 1 + + if is_first: + offset = 0 + count = first + elif is_last: + offset = num_layers - last + count = last + else: + middle_rank = pp_rank if first_stage_layers is None else pp_rank - 1 + offset = middle_rank * layers_per_middle + first + count = layers_per_middle + else: + if num_layers % pp_size != 0: + raise ValueError( + f"Number of layers ({num_layers}) must be evenly divisible " + f"by pipeline-model-parallel-size ({pp_size}) when no pipe " + f"separators are specified in the pattern." + ) + layers_per_rank = num_layers // pp_size + offset = pp_rank * layers_per_rank + count = layers_per_rank + + selected = layer_type_list[offset : offset + count] + log_on_each_pipeline_stage( + logger, + logging.INFO, + f"HybridModel: pp_rank={pp_rank}/{pp_size}, vp_stage={vp_stage}, " + f"layers='{''.join(selected)}' ({len(selected)} layers), " + f"layer_offset={offset} (auto-split)", + ) + return selected, offset + + # Pipe-based segment selection + if len(segments) > 1 and len(segments) % pp_size != 0: + raise ValueError( + f"The number of pipe-delimited segments ({len(segments)}) in " + f"hybrid_layer_pattern must be evenly divisible by " + f"pipeline_model_parallel_size ({pp_size})." + ) + + vp_rel = vp_stage if vp_stage is not None else 0 + segment_index = vp_rel * pp_size + pp_rank + + if segment_index >= len(segments): + raise ValueError( + f"Pipeline segment index {segment_index} (pp_rank={pp_rank}, " + f"vp_stage={vp_rel}) is out of range for {len(segments)} segments. " + f"The pattern does not define enough pipe-delimited segments for " + f"the current PP/VPP configuration." + ) + + layer_offset = sum(len(segments[i]) for i in range(segment_index)) + my_segment = segments[segment_index] + + layer_type_list = validate_segment_layers(my_segment) + + log_on_each_pipeline_stage( + logger, + logging.INFO, + f"HybridModel: pp_rank={pp_rank}/{pp_size}, vp_stage={vp_rel}, " + f"segment_index={segment_index}/{len(segments)}, " + f"layers='{my_segment}' ({len(layer_type_list)} layers), " + f"layer_offset={layer_offset}", + ) + + return layer_type_list, layer_offset + + +def get_layer_maps_from_layer_type_list(layer_type_list: list[str]) -> dict[str, dict[int, int]]: + """ + Returns maps from global layer index to the corresponding layer index + for each valid layer type (those in Symbols.VALID_LAYERS) given a layer type list. + """ + layer_types = [symbol for symbol in Symbols.name_sorted_valid_layer_symbols()] + layer_maps = {layer_type: {} for layer_type in layer_types} + for global_layer_idx, layer_type in enumerate(layer_type_list): + layer_map = layer_maps[layer_type] + local_layer_idx = len(layer_map) + layer_map[global_layer_idx] = local_layer_idx + return layer_maps diff --git a/megatron/core/models/hybrid/hybrid_layer_specs.py b/megatron/core/models/hybrid/hybrid_layer_specs.py new file mode 100755 index 00000000000..a34a45a32ba --- /dev/null +++ b/megatron/core/models/hybrid/hybrid_layer_specs.py @@ -0,0 +1,308 @@ +# Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. + +from megatron.core.extensions.transformer_engine import ( + TEColumnParallelLinear, + TEDotProductAttention, + TELayerNormColumnParallelLinear, + TELinear, + TENorm, + TERowParallelLinear, +) +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.models.gpt.moe_module_specs import ( + get_inference_optimized_moe_spec, + get_moe_module_spec, +) +from megatron.core.models.hybrid.hybrid_block import HybridStack, HybridStackSubmodules +from megatron.core.ssm.gated_delta_net import GatedDeltaNet, GatedDeltaNetSubmodules +from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules +from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules +from megatron.core.ssm.mlp_layer import MLPLayer +from megatron.core.tensor_parallel import ( + InferenceColumnParallelLinear, + InferenceLayerNormColumnParallelLinear, + InferenceRowParallelLinear, +) +from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.experimental_attention_variant.dsa import ( + DSAIndexer, + DSAIndexerSubmodules, + DSAttention, + DSAttentionSubmodules, +) +from megatron.core.transformer.identity_op import IdentityOp +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.multi_latent_attention import ( + MLASelfAttention, + MLASelfAttentionSubmodules, +) +from megatron.core.transformer.multi_token_prediction import ( + MultiTokenPredictionBlock, + MultiTokenPredictionBlockSubmodules, + MultiTokenPredictionLayer, + MultiTokenPredictionLayerSubmodules, +) +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_layer import ( + MoETransformerLayer, + TransformerLayer, + TransformerLayerSubmodules, +) + +# This should be private and should not be used outside of this file. +moe = get_moe_module_spec( + use_te=True, + num_experts=8, # Can be any positive integer (must not be None). + moe_grouped_gemm=True, +) + +# Inference-optimized MoE spec +moe_inference = get_inference_optimized_moe_spec() + + +# MTP block spec - provides norms and projection only. +# Inner layers are built by MultiTokenPredictionLayer using nested HybridStack +_hybrid_mtp_block_spec = ModuleSpec( + module=MultiTokenPredictionBlock, + submodules=MultiTokenPredictionBlockSubmodules( + layer_specs=[ + ModuleSpec( + module=MultiTokenPredictionLayer, + submodules=MultiTokenPredictionLayerSubmodules( + enorm=TENorm, + hnorm=TENorm, + eh_proj=TEColumnParallelLinear, + mtp_model_layer=None, # Built via pattern + hybrid_submodules + layer_norm=TENorm, + ), + ) + ] + ), +) + + +hybrid_stack_spec = ModuleSpec( + module=HybridStack, + submodules=HybridStackSubmodules( + mamba_layer=ModuleSpec( + module=MambaLayer, + submodules=MambaLayerSubmodules( + mixer=ModuleSpec( + module=MambaMixer, + submodules=MambaMixerSubmodules( + in_proj=TELayerNormColumnParallelLinear, out_proj=TERowParallelLinear + ), + ), + mamba_bda=get_bias_dropout_add, + ), + ), + gdn_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=GatedDeltaNet, + submodules=GatedDeltaNetSubmodules( + in_proj=TELayerNormColumnParallelLinear, + out_norm=TENorm, + out_proj=TERowParallelLinear, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), + # Started with spec from gpt_layer_specs.py (with MLP removed) + # Using the TE spec because we had problems getting the non-TE spec + # working + attention_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=TELayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=TERowParallelLinear, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), + dsa_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=TENorm, + self_attention=ModuleSpec( + module=MLASelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=MLASelfAttentionSubmodules( + linear_q_proj=TEColumnParallelLinear, + linear_q_down_proj=TELinear, + linear_q_up_proj=TEColumnParallelLinear, + linear_kv_down_proj=TELinear, + linear_kv_up_proj=TEColumnParallelLinear, + core_attention=ModuleSpec( + module=DSAttention, + submodules=DSAttentionSubmodules( + indexer=ModuleSpec( + module=DSAIndexer, + submodules=DSAIndexerSubmodules( + linear_wq_b=TELinear, + linear_wk=TELinear, + k_norm=TENorm, + linear_weights_proj=TELinear, + ), + ) + ), + ), + linear_proj=TERowParallelLinear, + q_layernorm=IdentityOp, + kv_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), + # Started with spec from gpt_layer_specs.py + # Using the TE spec because we had problems getting the non-TE spec + # working + mlp_layer=ModuleSpec( + module=MLPLayer, + submodules=TransformerLayerSubmodules( + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear + ), + ), + mlp_bda=get_bias_dropout_add, + ), + ), + moe_layer=ModuleSpec( + module=MoETransformerLayer, + submodules=TransformerLayerSubmodules( + pre_mlp_layernorm=TENorm, mlp=moe, mlp_bda=get_bias_dropout_add + ), + ), + mtp_block_spec=_hybrid_mtp_block_spec, + ), +) + + +hybrid_inference_stack_spec = ModuleSpec( + module=HybridStack, + submodules=HybridStackSubmodules( + mamba_layer=ModuleSpec( + module=MambaLayer, + submodules=MambaLayerSubmodules( + mixer=ModuleSpec( + module=MambaMixer, + submodules=MambaMixerSubmodules( + in_proj=InferenceLayerNormColumnParallelLinear, + out_proj=InferenceRowParallelLinear, + ), + ), + mamba_bda=get_bias_dropout_add, + ), + ), + # Started with spec from gpt_layer_specs.py (with MLP removed) + # Using the TE spec because we had problems getting the non-TE spec + # working + attention_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=SelfAttentionSubmodules( + linear_qkv=InferenceLayerNormColumnParallelLinear, + core_attention=TEDotProductAttention, + linear_proj=InferenceRowParallelLinear, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), + dsa_layer=ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=TENorm, + self_attention=ModuleSpec( + module=MLASelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=MLASelfAttentionSubmodules( + linear_q_proj=TEColumnParallelLinear, + linear_q_down_proj=TELinear, + linear_q_up_proj=TEColumnParallelLinear, + linear_kv_down_proj=TELinear, + linear_kv_up_proj=TEColumnParallelLinear, + core_attention=ModuleSpec( + module=DSAttention, + submodules=DSAttentionSubmodules( + indexer=ModuleSpec( + module=DSAIndexer, + submodules=DSAIndexerSubmodules( + linear_wq_b=TELinear, + linear_wk=TELinear, + k_norm=TENorm, + linear_weights_proj=TELinear, + ), + ) + ), + ), + linear_proj=InferenceRowParallelLinear, + q_layernorm=IdentityOp, + kv_layernorm=IdentityOp, + ), + ), + self_attn_bda=get_bias_dropout_add, + ), + ), + # Started with spec from gpt_layer_specs.py + # Using the TE spec because we had problems getting the non-TE spec + # working + mlp_layer=ModuleSpec( + module=MLPLayer, + submodules=TransformerLayerSubmodules( + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=InferenceLayerNormColumnParallelLinear, + linear_fc2=InferenceRowParallelLinear, + ), + ), + mlp_bda=get_bias_dropout_add, + ), + ), + moe_layer=ModuleSpec( + # Use inference-optimized MoE layer for end-to-end CUDA graph support + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + pre_mlp_layernorm=TENorm, mlp=moe_inference, mlp_bda=get_bias_dropout_add + ), + ), + mtp_block_spec=ModuleSpec( + module=MultiTokenPredictionBlock, + submodules=MultiTokenPredictionBlockSubmodules( + layer_specs=[ + ModuleSpec( + module=MultiTokenPredictionLayer, + submodules=MultiTokenPredictionLayerSubmodules( + enorm=TENorm, + hnorm=TENorm, + eh_proj=InferenceColumnParallelLinear, + mtp_model_layer=None, # Built via pattern + hybrid_submodules + layer_norm=TENorm, + ), + ) + ] + ), + ), + ), +) + + +# Backward-compatible aliases +mamba_stack_spec = hybrid_stack_spec +mamba_inference_stack_spec = hybrid_inference_stack_spec diff --git a/megatron/core/models/hybrid/hybrid_model.py b/megatron/core/models/hybrid/hybrid_model.py new file mode 100644 index 00000000000..f1b3c102634 --- /dev/null +++ b/megatron/core/models/hybrid/hybrid_model.py @@ -0,0 +1,502 @@ +# Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. + +import logging +from typing import Literal, Optional + +from torch import Tensor + +from megatron.core import tensor_parallel +from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk +from megatron.core.inference.contexts import BaseInferenceContext +from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding +from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding +from megatron.core.models.common.language_module.language_module import LanguageModule +from megatron.core.packed_seq_params import PackedSeqParams +from megatron.core.pipeline_parallel.fine_grained_activation_offload import ( + FineGrainedActivationOffloadingInterface as off_interface, +) +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.quantization.utils import get_quant_config_or_none +from megatron.core.tensor_parallel import gather_from_sequence_parallel_region +from megatron.core.transformer import TransformerConfig +from megatron.core.transformer.enums import ModelType +from megatron.core.transformer.multi_token_prediction import ( + MultiTokenPredictionBlock, + mtp_on_this_rank, + process_mtp_loss, +) +from megatron.core.transformer.spec_utils import ModuleSpec, build_module +from megatron.core.utils import ( + WrappedTensor, + deprecate_inference_params, + is_using_quantization_scales, + log_single_rank, +) + +logger = logging.getLogger(__name__) + + +class HybridModel(LanguageModule): + """Hybrid language model. + + Args: + config (TransformerConfig): Model config + hybrid_stack_spec (ModuleSpec): Specifies the modules to use for the various layer types + vocab_size (int): Vocabulary size + max_sequence_length (int): maximum size of sequence. + This is used for positional embedding + hybrid_layer_pattern (str): Unified hybrid layer pattern with optional MTP and + pipeline stage boundaries. + Format: "///..." + The main pattern may contain "|" to define pipeline stage boundaries. + Examples: + - "M*M*" -> main decoder only, no MTP + - "M*M*/MM/MM" -> main="M*M*", mtp="MM", 2 depths + - "M-M-|M-M*-|M-M-|M-M*-" -> 4 pipeline segments + hybrid_attention_ratio (float, optional): Deprecated. Use hybrid_layer_pattern instead. + If set to a value > 0.0 and hybrid_layer_pattern is None, a pattern will be + generated from the ratio with a deprecation warning. + hybrid_mlp_ratio (float, optional): Deprecated. Use hybrid_layer_pattern instead. + If set to a value > 0.0 and hybrid_layer_pattern is None, a pattern will be + generated from the ratio with a deprecation warning. + hybrid_override_pattern (str, optional): Deprecated. Use hybrid_layer_pattern instead. + If set and hybrid_layer_pattern is None, the value is copied to hybrid_layer_pattern + with a deprecation warning. + pre_process (bool, optional): Include embedding layer + (used with pipeline parallelism). Defaults to True. + post_process (bool, optional): Include an output layer (used with pipeline parallelism). + Defaults to True. + fp16_lm_cross_entropy (bool, optional): Defaults to False. + parallel_output (bool, optional): Do not gather the outputs, keep them split across tensor + parallel ranks. Defaults to True. + share_embeddings_and_output_weights (bool, optional): When True, input embeddings and + output logit weights are shared. Defaults to False. + position_embedding_type (Literal[learned_absolute,rope,none], optional): Position + embedding type. Defaults to 'none'. + rotary_percent (float, optional): Percent of rotary dimension to use for rotary position + embeddings. Ignored unless position_embedding_type is 'rope'. Defaults to 1.0. + rotary_base (int, optional): Base period for rotary position embeddings. Ignored unless + position_embedding_type is 'rope'. Defaults to 10000. + seq_len_interpolation_factor (Optional[float], optional): scale of linearly + interpolating RoPE for longer sequences. The value must be a float larger than 1.0. + Defaults to None. + pg_collection (ProcessGroupCollection, optional): Model communication process groups. + vp_stage (Optional[int], optional): Virtual pipeline stage index. Defaults to None. + """ + + def __init__( + self, + config: TransformerConfig, + hybrid_stack_spec: ModuleSpec, + vocab_size: int, + max_sequence_length: int, + hybrid_layer_pattern: Optional[str] = None, + hybrid_attention_ratio: Optional[float] = None, + hybrid_mlp_ratio: Optional[float] = None, + hybrid_override_pattern: Optional[str] = None, + pre_process: bool = True, + post_process: bool = True, + fp16_lm_cross_entropy: bool = False, + parallel_output: bool = True, + share_embeddings_and_output_weights: bool = False, + # Mamba with no attention has no need for position embeddings, so none is default + position_embedding_type: Literal['learned_absolute', 'rope', 'none'] = 'none', + rotary_percent: float = 1.0, + rotary_base: int = 10000, + scatter_embedding_sequence_parallel: bool = True, + seq_len_interpolation_factor: Optional[float] = None, + pg_collection: Optional[ProcessGroupCollection] = None, + vp_stage: Optional[int] = None, + ) -> None: + super().__init__(config=config, pg_collection=pg_collection) + + if has_config_logger_enabled(config): + log_config_to_disk(config, locals(), prefix=type(self).__name__) + + if self.config.use_mup and not getattr(HybridModel, "mup_warning_printed", False): + log_single_rank( + logger, + logging.WARNING, + "MuP for HybridModel is experimental and not fully validated yet.", + ) + HybridModel.mup_warning_printed = True + + self.hybrid_stack_spec: ModuleSpec = hybrid_stack_spec + self.vocab_size = vocab_size + self.max_sequence_length = max_sequence_length + self.hybrid_layer_pattern = hybrid_layer_pattern + self.pre_process = pre_process + self.post_process = post_process + self.fp16_lm_cross_entropy = fp16_lm_cross_entropy + self.parallel_output = parallel_output + self.share_embeddings_and_output_weights = share_embeddings_and_output_weights + self.position_embedding_type = position_embedding_type + self.vp_stage = vp_stage + self.disable_param_offloading = True + + # Backward compatibility for deprecated hybrid parameters + if hybrid_override_pattern is not None: + if self.hybrid_layer_pattern is None: + log_single_rank( + logger, + logging.WARNING, + "hybrid_override_pattern has been deprecated. " + "Use hybrid_layer_pattern instead.", + ) + self.hybrid_layer_pattern = hybrid_override_pattern + else: + raise ValueError( + "hybrid_override_pattern and hybrid_layer_pattern cannot both be set. " + "hybrid_override_pattern has been deprecated; use hybrid_layer_pattern instead." + ) + if (hybrid_attention_ratio is not None and hybrid_attention_ratio > 0.0) or ( + hybrid_mlp_ratio is not None and hybrid_mlp_ratio > 0.0 + ): + if hybrid_layer_pattern is not None: + raise ValueError( + "hybrid_layer_pattern cannot be used together with " + "hybrid_attention_ratio or hybrid_mlp_ratio. " + "These ratios have been deprecated; use hybrid_layer_pattern alone." + ) + log_single_rank( + logger, + logging.WARNING, + "hybrid_attention_ratio and hybrid_mlp_ratio have been deprecated. " + "Use hybrid_layer_pattern instead.", + ) + if self.hybrid_layer_pattern is None: + from megatron.core.models.hybrid.hybrid_layer_allocation import pattern_from_ratios + + attn_ratio = hybrid_attention_ratio if hybrid_attention_ratio else 0.0 + mlp_ratio = hybrid_mlp_ratio if hybrid_mlp_ratio else 0.0 + self.hybrid_layer_pattern = pattern_from_ratios( + config.num_layers, attn_ratio, mlp_ratio + ) + + # Parse unified pattern to extract main and MTP components, and + # determine the pipeline segment for this model instance. + from megatron.core.models.hybrid.hybrid_layer_allocation import ( + parse_hybrid_pattern, + select_pipeline_segment, + ) + + parsed = parse_hybrid_pattern(self.hybrid_layer_pattern) + self.mtp_pattern = parsed.mtp_pattern + self.mtp_num_depths = parsed.mtp_num_depths + + layer_type_list, layer_offset = select_pipeline_segment( + parsed.main_pattern or '', + self.pg_collection.pp, + vp_stage, + first_stage_layers=self.config.num_layers_in_first_pipeline_stage, + last_stage_layers=self.config.num_layers_in_last_pipeline_stage, + ) + + # Determine if MTP is needed (based on pattern parsing) + self.mtp_process = ( + self.mtp_pattern is not None + and self.mtp_num_depths > 0 + # The following forces MTP to be on the final pipeline stage. It might be more optimal + # to split the hybrid layer pattern into pipeline stages before parsing the pattern for + # the current pipeline stage. This could also enable MTP standalone (MTP in a pipeline + # stage separate from loss) to be supported in the hybrid model. + and mtp_on_this_rank(self.config, ignore_virtual=False, vp_stage=self.vp_stage) + ) + + # megatron core pipelining currently depends on model type + # TODO: remove this dependency ? + self.model_type = ModelType.encoder_or_decoder + + if self.pre_process or self.mtp_process: + self.embedding = LanguageModelEmbedding( + config=self.config, + vocab_size=self.vocab_size, + max_sequence_length=self.max_sequence_length, + position_embedding_type=position_embedding_type, + scatter_to_sequence_parallel=scatter_embedding_sequence_parallel, + tp_group=self.pg_collection.tp, + ) + + # MLA (also used by DeepSeek Sparse Attention) uses its own decoupled RoPE, therefore we do + # not build standard RoPE here when using MLA. + if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention: + self.rotary_pos_emb = RotaryEmbedding( + kv_channels=self.config.kv_channels, + rotary_percent=rotary_percent, + seq_len_interpolation_factor=seq_len_interpolation_factor, + rotary_base=rotary_base, + use_cpu_initialization=self.config.use_cpu_initialization, + cp_group=self.pg_collection.cp, + ) + + self.decoder = build_module( + hybrid_stack_spec, + self.config, + pre_process=self.pre_process, + layer_type_list=layer_type_list, + pp_layer_offset=layer_offset, + post_process=self.post_process, + dtype=config.params_dtype, + pg_collection=self.pg_collection, + ) + + # MTP block - uses mtp_block_spec from hybrid_stack_spec.submodules + if self.mtp_process: + hybrid_submodules = hybrid_stack_spec.submodules + mtp_block_spec = hybrid_submodules.mtp_block_spec + assert mtp_block_spec is not None, ( + "MTP pattern specified but mtp_block_spec is None in hybrid_stack_spec.submodules. " + "Ensure hybrid_stack_spec includes mtp_block_spec for MTP support." + ) + + self.mtp = MultiTokenPredictionBlock( + config=self.config, + spec=mtp_block_spec, + pg_collection=self.pg_collection, + vp_stage=self.vp_stage, + mtp_layer_pattern=self.mtp_pattern, + mtp_num_depths=self.mtp_num_depths, + hybrid_submodules=hybrid_submodules, + ) + + # Output + if post_process or self.mtp_process: + self.output_layer = tensor_parallel.ColumnParallelLinear( + config.hidden_size, + self.vocab_size, + config=config, + init_method=( + config.embedding_init_method + if config.use_mup and not self.share_embeddings_and_output_weights + else config.init_method + ), + bias=False, + skip_bias_add=False, + gather_output=not self.parallel_output, + skip_weight_param_allocation=self.pre_process + and self.share_embeddings_and_output_weights, + tp_group=self.pg_collection.tp, + ) + + if self.pre_process or self.post_process or self.mtp_process: + self.setup_embeddings_and_output_layer() + + for name, module in self.named_modules(): + if hasattr(module, 'finish_init'): + quant_config = get_quant_config_or_none(name, self.config.quant_recipe) + module.finish_init(quant_config) + + def set_input_tensor(self, input_tensor: Tensor) -> None: + """Sets input tensor to the model. + + See megatron.model.transformer.set_input_tensor() + + Args: + input_tensor (Tensor): Sets the input tensor for the model. + """ + # This is usually handled in schedules.py but some inference code still + # gives us non-lists or None + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + + assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt/bert' + self.decoder.set_input_tensor(input_tensor[0]) + + def preprocess_for_fine_grained_offloading(self): + """Preprocess for fine-grained activation offloading.""" + off_interface.init_chunk_handler( + vp_size=self.config.virtual_pipeline_model_parallel_size, + vp_stage=self.vp_stage, + min_offloaded_tensor_size=self.config.min_offloaded_tensor_size, + ) + if self.disable_param_offloading: + for param in self.decoder.parameters(): + off_interface.mark_not_offloadable(param) + if self.mtp_process: + for param in self.mtp.parameters(): + off_interface.mark_not_offloadable(param) + if self.post_process: + for param in self.output_layer.parameters(): + off_interface.mark_not_offloadable(param) + self.disable_param_offloading = False + + def forward( + self, + input_ids: Tensor, + position_ids: Tensor, + attention_mask: Tensor, + decoder_input: Tensor = None, + labels: Tensor = None, + inference_context: BaseInferenceContext = None, + runtime_gather_output: Optional[bool] = None, + *, + inference_params: Optional[BaseInferenceContext] = None, + loss_mask: Optional[Tensor] = None, + packed_seq_params: Optional[PackedSeqParams] = None, + padding_mask: Optional[Tensor] = None, + is_spec_decode: Optional[bool] = None, + ) -> Tensor: + """Forward function of the Hybrid model. This function passes the input tensors + through the embedding layer, and then the decoder and finally into the post + processing layer (optional). + + It either returns the Loss values if labels are given or the final hidden units + """ + # If decoder_input is provided (not None), then input_ids and position_ids are ignored. + # Otherwise, apply embedding layer on input_ids and position_ids to get decoder_input. + + if self.config.fine_grained_activation_offloading: + self.preprocess_for_fine_grained_offloading() + + inference_context = deprecate_inference_params(inference_context, inference_params) + + in_inference_mode = inference_context is not None and not self.training + + if in_inference_mode: + assert runtime_gather_output, "Inference must always gather TP logits" + + # Decoder embedding. + if decoder_input is not None: + pass + elif self.pre_process: + decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids) + + # Clear the outputs for padding tokens when using dynamic batching with + # quantization scales to avoid corrupting amax calculations + if ( + in_inference_mode + and inference_context.is_dynamic_batching() + and is_using_quantization_scales(self.config) + ): + decoder_input[inference_context.padding_slice] = 0.0 + else: + # intermediate stage of pipeline + # decoder will get hidden_states from encoder.input_tensor + decoder_input = None + + rotary_pos_emb = None + if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention: + rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( + inference_context, self.decoder, decoder_input, self.config, packed_seq_params + ) + rotary_pos_emb = self.rotary_pos_emb( + rotary_seq_len, + packed_seq=packed_seq_params is not None and packed_seq_params.qkv_format == 'thd', + ) + + # Wrap decoder_input to allow the decoder (HybridStack) to delete the + # reference held by this caller function, enabling early garbage collection + # for inference. + if in_inference_mode: + decoder_input = WrappedTensor(decoder_input) + + # The following assert will currently fail when running inference. + # Commented out for now. + # TODO (duncan/rwaleffe): (1) confirm that the externally-generated + # attention mask is not needed and is ignored by the model in + # inference mode, (2) reduce the size of the externally-generated + # attention mask to prevent CPU OOM (as we did for training), (3) + # force the attention mask passed to the model in inference mode to + # be None, so this assert will succeed. + # assert attention_mask is None, "The attention mask is ignored and should be set to None" + + # Run decoder. + hidden_states = self.decoder( + hidden_states=decoder_input, + attention_mask=attention_mask, + inference_context=inference_context, + rotary_pos_emb=rotary_pos_emb, + packed_seq_params=packed_seq_params, + padding_mask=padding_mask, + ) + + output_weight = None + if self.share_embeddings_and_output_weights: + output_weight = self.shared_embedding_or_output_weight() + + # Check if speculative decoding is active. When it is, MTP must be + # computed *after* verification so that it is conditioned on verified + # tokens rather than stale speculative tokens from the previous step. + if is_spec_decode is None: + is_spec_decode = ( + in_inference_mode + and inference_context.is_dynamic_batching() + and inference_context.num_speculative_tokens > 0 + ) + + mtp_forward_ran = self.mtp_process and not (in_inference_mode or is_spec_decode) + if mtp_forward_ran: + hidden_states = self.mtp( + input_ids=input_ids, + position_ids=position_ids, + hidden_states=hidden_states, + attention_mask=attention_mask, + inference_params=inference_params, + rotary_pos_emb=rotary_pos_emb, + packed_seq_params=packed_seq_params, + embedding=self.embedding, + ) + + if not self.post_process: + return hidden_states + + if self.config.mtp_num_layers is not None and self.mtp_process: + assert self.config.mtp_num_layers > 0 + if in_inference_mode or is_spec_decode: + self._decoder_hidden_states_cache = hidden_states + else: + hidden_states = process_mtp_loss( + hidden_states=hidden_states, + labels=labels, + loss_mask=loss_mask, + output_layer=self.output_layer, + output_weight=output_weight, + runtime_gather_output=runtime_gather_output, + is_training=self.training, + compute_language_model_loss=self.compute_language_model_loss, + config=self.config, + cp_group=self.pg_collection.cp, + packed_seq_params=packed_seq_params, + scale_logits_fn=self._scale_logits if self.config.use_mup else None, + ) + sequence_parallel_override = False + if in_inference_mode and inference_context.config.materialize_only_last_token_logits: + if inference_context.is_static_batching(): + hidden_states = hidden_states[-1:, :, :] + else: + if self.output_layer.sequence_parallel: + # Perform the sequence parallel gather here instead of after the output layer + # because we need to slice the last token logits from the full view of the + # packed logits across all requests. + hidden_states = gather_from_sequence_parallel_region( + hidden_states, group=self.pg_collection.tp + ) + self.output_layer.sequence_parallel = False + sequence_parallel_override = True + + # Reshape [S, B, H] (with B=1) to [1, S, H] for logit extraction, + # then back to [S', B, H] for the output layer. + reshaped = hidden_states.squeeze(1).unsqueeze(0) + hidden_states = inference_context.last_token_logits(reshaped).unsqueeze(1) + + logits, _ = self.output_layer( + hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output + ) + logits = self._scale_logits(logits) + + # Restore sequence parallel execution to the output layer if necessary. + if sequence_parallel_override: + assert ( + in_inference_mode + and inference_context.is_dynamic_batching() + and inference_context.config.materialize_only_last_token_logits + ) + self.output_layer.sequence_parallel = True + + if labels is None: + # [s b h] => [b s h] + return logits.transpose(0, 1).contiguous() + + loss = self.compute_language_model_loss(labels, logits) + + return loss diff --git a/megatron/core/models/mamba/__init__.py b/megatron/core/models/mamba/__init__.py index 5aaf8524018..4de391a62c9 100644 --- a/megatron/core/models/mamba/__init__.py +++ b/megatron/core/models/mamba/__init__.py @@ -1,2 +1,6 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -from .mamba_model import MambaModel +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. + +# Backward-compatible re-exports. The canonical location is now +# megatron.core.models.hybrid. +from megatron.core.models.hybrid.hybrid_model import HybridModel +from megatron.core.models.mamba.mamba_model import MambaModel diff --git a/megatron/core/models/mamba/mamba_layer_specs.py b/megatron/core/models/mamba/mamba_layer_specs.py old mode 100755 new mode 100644 index 96b2e13e2d4..5fb9e49a0dd --- a/megatron/core/models/mamba/mamba_layer_specs.py +++ b/megatron/core/models/mamba/mamba_layer_specs.py @@ -1,303 +1,5 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. -from megatron.core.extensions.transformer_engine import ( - TEColumnParallelLinear, - TEDotProductAttention, - TELayerNormColumnParallelLinear, - TELinear, - TENorm, - TERowParallelLinear, -) -from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add -from megatron.core.models.gpt.moe_module_specs import ( - get_inference_optimized_moe_spec, - get_moe_module_spec, -) -from megatron.core.ssm.gated_delta_net import GatedDeltaNet, GatedDeltaNetSubmodules -from megatron.core.ssm.mamba_block import MambaStack, MambaStackSubmodules -from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules -from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules -from megatron.core.ssm.mlp_layer import MLPLayer -from megatron.core.tensor_parallel import ( - InferenceColumnParallelLinear, - InferenceLayerNormColumnParallelLinear, - InferenceRowParallelLinear, -) -from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules -from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.experimental_attention_variant.dsa import ( - DSAIndexer, - DSAIndexerSubmodules, - DSAttention, - DSAttentionSubmodules, -) -from megatron.core.transformer.identity_op import IdentityOp -from megatron.core.transformer.mlp import MLP, MLPSubmodules -from megatron.core.transformer.multi_latent_attention import ( - MLASelfAttention, - MLASelfAttentionSubmodules, -) -from megatron.core.transformer.multi_token_prediction import ( - MultiTokenPredictionBlock, - MultiTokenPredictionBlockSubmodules, - MultiTokenPredictionLayer, - MultiTokenPredictionLayerSubmodules, -) -from megatron.core.transformer.spec_utils import ModuleSpec -from megatron.core.transformer.transformer_layer import ( - MoETransformerLayer, - TransformerLayer, - TransformerLayerSubmodules, -) - -# This should be private and should not be used outside of this file. -moe = get_moe_module_spec( - use_te=True, - num_experts=8, # Can be any positive integer (must not be None). - moe_grouped_gemm=True, -) - -# Inference-optimized MoE spec -moe_inference = get_inference_optimized_moe_spec() - - -# MTP block spec for Mamba - provides norms and projection only. -# Inner layers are built by MultiTokenPredictionLayer using nested MambaStack -_mamba_mtp_block_spec = ModuleSpec( - module=MultiTokenPredictionBlock, - submodules=MultiTokenPredictionBlockSubmodules( - layer_specs=[ - ModuleSpec( - module=MultiTokenPredictionLayer, - submodules=MultiTokenPredictionLayerSubmodules( - enorm=TENorm, - hnorm=TENorm, - eh_proj=TEColumnParallelLinear, - mtp_model_layer=None, # Built via pattern + mamba_submodules - layer_norm=TENorm, - ), - ) - ] - ), -) - - -mamba_stack_spec = ModuleSpec( - module=MambaStack, - submodules=MambaStackSubmodules( - mamba_layer=ModuleSpec( - module=MambaLayer, - submodules=MambaLayerSubmodules( - mixer=ModuleSpec( - module=MambaMixer, - submodules=MambaMixerSubmodules( - in_proj=TELayerNormColumnParallelLinear, out_proj=TERowParallelLinear - ), - ), - mamba_bda=get_bias_dropout_add, - ), - ), - gdn_layer=ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - self_attention=ModuleSpec( - module=GatedDeltaNet, - submodules=GatedDeltaNetSubmodules( - in_proj=TELayerNormColumnParallelLinear, - out_norm=TENorm, - out_proj=TERowParallelLinear, - ), - ), - self_attn_bda=get_bias_dropout_add, - ), - ), - # Started with spec from gpt_layer_specs.py (with MLP removed) - # Using the TE spec because we had problems getting the non-TE spec - # working - attention_layer=ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - self_attention=ModuleSpec( - module=SelfAttention, - params={"attn_mask_type": AttnMaskType.causal}, - submodules=SelfAttentionSubmodules( - linear_qkv=TELayerNormColumnParallelLinear, - core_attention=TEDotProductAttention, - linear_proj=TERowParallelLinear, - ), - ), - self_attn_bda=get_bias_dropout_add, - ), - ), - dsa_layer=ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - input_layernorm=TENorm, - self_attention=ModuleSpec( - module=MLASelfAttention, - params={"attn_mask_type": AttnMaskType.causal}, - submodules=MLASelfAttentionSubmodules( - linear_q_proj=TEColumnParallelLinear, - linear_q_down_proj=TELinear, - linear_q_up_proj=TEColumnParallelLinear, - linear_kv_down_proj=TELinear, - linear_kv_up_proj=TEColumnParallelLinear, - core_attention=ModuleSpec( - module=DSAttention, - submodules=DSAttentionSubmodules( - indexer=ModuleSpec( - module=DSAIndexer, - submodules=DSAIndexerSubmodules( - linear_wq_b=TELinear, - linear_wk=TELinear, - k_norm=TENorm, - linear_weights_proj=TELinear, - ), - ) - ), - ), - linear_proj=TERowParallelLinear, - q_layernorm=IdentityOp, - kv_layernorm=IdentityOp, - ), - ), - self_attn_bda=get_bias_dropout_add, - ), - ), - # Started with spec from gpt_layer_specs.py - # Using the TE spec because we had problems getting the non-TE spec - # working - mlp_layer=ModuleSpec( - module=MLPLayer, - submodules=TransformerLayerSubmodules( - mlp=ModuleSpec( - module=MLP, - submodules=MLPSubmodules( - linear_fc1=TELayerNormColumnParallelLinear, linear_fc2=TERowParallelLinear - ), - ), - mlp_bda=get_bias_dropout_add, - ), - ), - moe_layer=ModuleSpec( - module=MoETransformerLayer, - submodules=TransformerLayerSubmodules( - pre_mlp_layernorm=TENorm, mlp=moe, mlp_bda=get_bias_dropout_add - ), - ), - mtp_block_spec=_mamba_mtp_block_spec, - ), -) - - -mamba_inference_stack_spec = ModuleSpec( - module=MambaStack, - submodules=MambaStackSubmodules( - mamba_layer=ModuleSpec( - module=MambaLayer, - submodules=MambaLayerSubmodules( - mixer=ModuleSpec( - module=MambaMixer, - submodules=MambaMixerSubmodules( - in_proj=InferenceLayerNormColumnParallelLinear, - out_proj=InferenceRowParallelLinear, - ), - ), - mamba_bda=get_bias_dropout_add, - ), - ), - # Started with spec from gpt_layer_specs.py (with MLP removed) - # Using the TE spec because we had problems getting the non-TE spec - # working - attention_layer=ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - self_attention=ModuleSpec( - module=SelfAttention, - params={"attn_mask_type": AttnMaskType.causal}, - submodules=SelfAttentionSubmodules( - linear_qkv=InferenceLayerNormColumnParallelLinear, - core_attention=TEDotProductAttention, - linear_proj=InferenceRowParallelLinear, - ), - ), - self_attn_bda=get_bias_dropout_add, - ), - ), - dsa_layer=ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - input_layernorm=TENorm, - self_attention=ModuleSpec( - module=MLASelfAttention, - params={"attn_mask_type": AttnMaskType.causal}, - submodules=MLASelfAttentionSubmodules( - linear_q_proj=TEColumnParallelLinear, - linear_q_down_proj=TELinear, - linear_q_up_proj=TEColumnParallelLinear, - linear_kv_down_proj=TELinear, - linear_kv_up_proj=TEColumnParallelLinear, - core_attention=ModuleSpec( - module=DSAttention, - submodules=DSAttentionSubmodules( - indexer=ModuleSpec( - module=DSAIndexer, - submodules=DSAIndexerSubmodules( - linear_wq_b=TELinear, - linear_wk=TELinear, - k_norm=TENorm, - linear_weights_proj=TELinear, - ), - ) - ), - ), - linear_proj=InferenceRowParallelLinear, - q_layernorm=IdentityOp, - kv_layernorm=IdentityOp, - ), - ), - self_attn_bda=get_bias_dropout_add, - ), - ), - # Started with spec from gpt_layer_specs.py - # Using the TE spec because we had problems getting the non-TE spec - # working - mlp_layer=ModuleSpec( - module=MLPLayer, - submodules=TransformerLayerSubmodules( - mlp=ModuleSpec( - module=MLP, - submodules=MLPSubmodules( - linear_fc1=InferenceLayerNormColumnParallelLinear, - linear_fc2=InferenceRowParallelLinear, - ), - ), - mlp_bda=get_bias_dropout_add, - ), - ), - moe_layer=ModuleSpec( - # Use inference-optimized MoE layer for end-to-end CUDA graph support - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - pre_mlp_layernorm=TENorm, mlp=moe_inference, mlp_bda=get_bias_dropout_add - ), - ), - mtp_block_spec=ModuleSpec( - module=MultiTokenPredictionBlock, - submodules=MultiTokenPredictionBlockSubmodules( - layer_specs=[ - ModuleSpec( - module=MultiTokenPredictionLayer, - submodules=MultiTokenPredictionLayerSubmodules( - enorm=TENorm, - hnorm=TENorm, - eh_proj=InferenceColumnParallelLinear, - mtp_model_layer=None, # Built via pattern + mamba_submodules - layer_norm=TENorm, - ), - ) - ] - ), - ), - ), -) +# Backward-compatible re-export. The canonical location is now +# megatron.core.models.hybrid.hybrid_layer_specs. +from megatron.core.models.hybrid.hybrid_layer_specs import * # noqa: F401,F403 diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py index d0458988dcc..13964286daf 100644 --- a/megatron/core/models/mamba/mamba_model.py +++ b/megatron/core/models/mamba/mamba_model.py @@ -1,502 +1,26 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2023-2026, NVIDIA CORPORATION. All rights reserved. import logging -from typing import Literal, Optional -from torch import Tensor - -from megatron.core import tensor_parallel -from megatron.core.config_logger import has_config_logger_enabled, log_config_to_disk -from megatron.core.inference.contexts import BaseInferenceContext -from megatron.core.models.common.embeddings.language_model_embedding import LanguageModelEmbedding -from megatron.core.models.common.embeddings.rotary_pos_embedding import RotaryEmbedding -from megatron.core.models.common.language_module.language_module import LanguageModule -from megatron.core.packed_seq_params import PackedSeqParams -from megatron.core.pipeline_parallel.fine_grained_activation_offload import ( - FineGrainedActivationOffloadingInterface as off_interface, -) -from megatron.core.process_groups_config import ProcessGroupCollection -from megatron.core.quantization.utils import get_quant_config_or_none -from megatron.core.tensor_parallel import gather_from_sequence_parallel_region -from megatron.core.transformer import TransformerConfig -from megatron.core.transformer.enums import ModelType -from megatron.core.transformer.multi_token_prediction import ( - MultiTokenPredictionBlock, - mtp_on_this_rank, - process_mtp_loss, -) -from megatron.core.transformer.spec_utils import ModuleSpec, build_module -from megatron.core.utils import ( - WrappedTensor, - deprecate_inference_params, - is_using_quantization_scales, - log_single_rank, -) +from megatron.core.models.hybrid.hybrid_model import * # noqa: F401,F403 # pylint: disable=unused-import +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.utils import log_single_rank logger = logging.getLogger(__name__) -class MambaModel(LanguageModule): - """Mamba language model. - - Args: - config (TransformerConfig): Model config - mamba_stack_spec (ModuleSpec): Specifies the modules to use for the various layer types - vocab_size (int): Vocabulary size - max_sequence_length (int): maximum size of sequence. - This is used for positional embedding - hybrid_layer_pattern (str): Unified hybrid layer pattern with optional MTP and - pipeline stage boundaries. - Format: "///..." - The main pattern may contain "|" to define pipeline stage boundaries. - Examples: - - "M*M*" -> main decoder only, no MTP - - "M*M*/MM/MM" -> main="M*M*", mtp="MM", 2 depths - - "M-M-|M-M*-|M-M-|M-M*-" -> 4 pipeline segments - hybrid_attention_ratio (float, optional): Deprecated. Use hybrid_layer_pattern instead. - If set to a value > 0.0 and hybrid_layer_pattern is None, a pattern will be - generated from the ratio with a deprecation warning. - hybrid_mlp_ratio (float, optional): Deprecated. Use hybrid_layer_pattern instead. - If set to a value > 0.0 and hybrid_layer_pattern is None, a pattern will be - generated from the ratio with a deprecation warning. - hybrid_override_pattern (str, optional): Deprecated. Use hybrid_layer_pattern instead. - If set and hybrid_layer_pattern is None, the value is copied to hybrid_layer_pattern - with a deprecation warning. - pre_process (bool, optional): Include embedding layer - (used with pipeline parallelism). Defaults to True. - post_process (bool, optional): Include an output layer (used with pipeline parallelism). - Defaults to True. - fp16_lm_cross_entropy (bool, optional): Defaults to False. - parallel_output (bool, optional): Do not gather the outputs, keep them split across tensor - parallel ranks. Defaults to True. - share_embeddings_and_output_weights (bool, optional): When True, input embeddings and - output logit weights are shared. Defaults to False. - position_embedding_type (Literal[learned_absolute,rope,none], optional): Position - embedding type. Defaults to 'none'. - rotary_percent (float, optional): Percent of rotary dimension to use for rotary position - embeddings. Ignored unless position_embedding_type is 'rope'. Defaults to 1.0. - rotary_base (int, optional): Base period for rotary position embeddings. Ignored unless - position_embedding_type is 'rope'. Defaults to 10000. - seq_len_interpolation_factor (Optional[float], optional): scale of linearly - interpolating RoPE for longer sequences. The value must be a float larger than 1.0. - Defaults to None. - pg_collection (ProcessGroupCollection, optional): Model communication process groups. - vp_stage (Optional[int], optional): Virtual pipeline stage index. Defaults to None. - """ - - def __init__( - self, - config: TransformerConfig, - mamba_stack_spec: ModuleSpec, - vocab_size: int, - max_sequence_length: int, - hybrid_layer_pattern: Optional[str] = None, - hybrid_attention_ratio: Optional[float] = None, - hybrid_mlp_ratio: Optional[float] = None, - hybrid_override_pattern: Optional[str] = None, - pre_process: bool = True, - post_process: bool = True, - fp16_lm_cross_entropy: bool = False, - parallel_output: bool = True, - share_embeddings_and_output_weights: bool = False, - # Mamba with no attention has no need for position embeddings, so none is default - position_embedding_type: Literal['learned_absolute', 'rope', 'none'] = 'none', - rotary_percent: float = 1.0, - rotary_base: int = 10000, - scatter_embedding_sequence_parallel: bool = True, - seq_len_interpolation_factor: Optional[float] = None, - pg_collection: Optional[ProcessGroupCollection] = None, - vp_stage: Optional[int] = None, - ) -> None: - super().__init__(config=config, pg_collection=pg_collection) - - if has_config_logger_enabled(config): - log_config_to_disk(config, locals(), prefix=type(self).__name__) - - if self.config.use_mup and not getattr(MambaModel, "mup_warning_printed", False): - log_single_rank( - logger, - logging.WARNING, - "MuP for MambaModel is experimental and not fully validated yet.", - ) - MambaModel.mup_warning_printed = True - - self.mamba_stack_spec: ModuleSpec = mamba_stack_spec - self.vocab_size = vocab_size - self.max_sequence_length = max_sequence_length - self.hybrid_layer_pattern = hybrid_layer_pattern - self.pre_process = pre_process - self.post_process = post_process - self.fp16_lm_cross_entropy = fp16_lm_cross_entropy - self.parallel_output = parallel_output - self.share_embeddings_and_output_weights = share_embeddings_and_output_weights - self.position_embedding_type = position_embedding_type - self.vp_stage = vp_stage - self.disable_param_offloading = True - - # Backward compatibility for deprecated hybrid parameters - if hybrid_override_pattern is not None: - if self.hybrid_layer_pattern is None: - log_single_rank( - logger, - logging.WARNING, - "hybrid_override_pattern has been deprecated. " - "Use hybrid_layer_pattern instead.", - ) - self.hybrid_layer_pattern = hybrid_override_pattern - else: - raise ValueError( - "hybrid_override_pattern and hybrid_layer_pattern cannot both be set. " - "hybrid_override_pattern has been deprecated; use hybrid_layer_pattern instead." - ) - if (hybrid_attention_ratio is not None and hybrid_attention_ratio > 0.0) or ( - hybrid_mlp_ratio is not None and hybrid_mlp_ratio > 0.0 - ): - if hybrid_layer_pattern is not None: - raise ValueError( - "hybrid_layer_pattern cannot be used together with " - "hybrid_attention_ratio or hybrid_mlp_ratio. " - "These ratios have been deprecated; use hybrid_layer_pattern alone." - ) - log_single_rank( - logger, - logging.WARNING, - "hybrid_attention_ratio and hybrid_mlp_ratio have been deprecated. " - "Use hybrid_layer_pattern instead.", - ) - if self.hybrid_layer_pattern is None: - from megatron.core.ssm.mamba_hybrid_layer_allocation import pattern_from_ratios - - attn_ratio = hybrid_attention_ratio if hybrid_attention_ratio else 0.0 - mlp_ratio = hybrid_mlp_ratio if hybrid_mlp_ratio else 0.0 - self.hybrid_layer_pattern = pattern_from_ratios( - config.num_layers, attn_ratio, mlp_ratio - ) +class MambaModel(HybridModel): + """Backward-compatible wrapper that accepts the deprecated mamba_stack_spec kwarg.""" - # Parse unified pattern to extract main and MTP components, and - # determine the pipeline segment for this model instance. - from megatron.core.ssm.mamba_hybrid_layer_allocation import ( - parse_hybrid_pattern, - select_pipeline_segment, + def __init__(self, *args, mamba_stack_spec: ModuleSpec = None, **kwargs): + log_single_rank( + logger, logging.WARNING, "MambaModel has been deprecated. Use HybridModel instead." ) - - parsed = parse_hybrid_pattern(self.hybrid_layer_pattern) - self.mtp_pattern = parsed.mtp_pattern - self.mtp_num_depths = parsed.mtp_num_depths - - layer_type_list, layer_offset = select_pipeline_segment( - parsed.main_pattern or '', - self.pg_collection.pp, - vp_stage, - first_stage_layers=self.config.num_layers_in_first_pipeline_stage, - last_stage_layers=self.config.num_layers_in_last_pipeline_stage, - ) - - # Determine if MTP is needed (based on pattern parsing) - self.mtp_process = ( - self.mtp_pattern is not None - and self.mtp_num_depths > 0 - # The following forces MTP to be on the final pipeline stage. It might be more optimal - # to split the hybrid layer pattern into pipeline stages before parsing the pattern for - # the current pipeline stage. This could also enable MTP standalone (MTP in a pipeline - # stage separate from loss) to be supported in the hybrid model. - and mtp_on_this_rank(self.config, ignore_virtual=False, vp_stage=self.vp_stage) - ) - - # megatron core pipelining currently depends on model type - # TODO: remove this dependency ? - self.model_type = ModelType.encoder_or_decoder - - if self.pre_process or self.mtp_process: - self.embedding = LanguageModelEmbedding( - config=self.config, - vocab_size=self.vocab_size, - max_sequence_length=self.max_sequence_length, - position_embedding_type=position_embedding_type, - scatter_to_sequence_parallel=scatter_embedding_sequence_parallel, - tp_group=self.pg_collection.tp, - ) - - # MLA (also used by DeepSeek Sparse Attention) uses its own decoupled RoPE, therefore we do - # not build standard RoPE here when using MLA. - if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention: - self.rotary_pos_emb = RotaryEmbedding( - kv_channels=self.config.kv_channels, - rotary_percent=rotary_percent, - seq_len_interpolation_factor=seq_len_interpolation_factor, - rotary_base=rotary_base, - use_cpu_initialization=self.config.use_cpu_initialization, - cp_group=self.pg_collection.cp, - ) - - self.decoder = build_module( - mamba_stack_spec, - self.config, - pre_process=self.pre_process, - layer_type_list=layer_type_list, - pp_layer_offset=layer_offset, - post_process=self.post_process, - dtype=config.params_dtype, - pg_collection=self.pg_collection, - ) - - # MTP block - uses mtp_block_spec from mamba_stack_spec.submodules - if self.mtp_process: - mamba_submodules = mamba_stack_spec.submodules - mtp_block_spec = mamba_submodules.mtp_block_spec - assert mtp_block_spec is not None, ( - "MTP pattern specified but mtp_block_spec is None in mamba_stack_spec.submodules. " - "Ensure mamba_stack_spec includes mtp_block_spec for MTP support." - ) - - self.mtp = MultiTokenPredictionBlock( - config=self.config, - spec=mtp_block_spec, - pg_collection=self.pg_collection, - vp_stage=self.vp_stage, - mtp_layer_pattern=self.mtp_pattern, - mtp_num_depths=self.mtp_num_depths, - mamba_submodules=mamba_submodules, - ) - - # Output - if post_process or self.mtp_process: - self.output_layer = tensor_parallel.ColumnParallelLinear( - config.hidden_size, - self.vocab_size, - config=config, - init_method=( - config.embedding_init_method - if config.use_mup and not self.share_embeddings_and_output_weights - else config.init_method - ), - bias=False, - skip_bias_add=False, - gather_output=not self.parallel_output, - skip_weight_param_allocation=self.pre_process - and self.share_embeddings_and_output_weights, - tp_group=self.pg_collection.tp, - ) - - if self.pre_process or self.post_process or self.mtp_process: - self.setup_embeddings_and_output_layer() - - for name, module in self.named_modules(): - if hasattr(module, 'finish_init'): - quant_config = get_quant_config_or_none(name, self.config.quant_recipe) - module.finish_init(quant_config) - - def set_input_tensor(self, input_tensor: Tensor) -> None: - """Sets input tensor to the model. - - See megatron.model.transformer.set_input_tensor() - - Args: - input_tensor (Tensor): Sets the input tensor for the model. - """ - # This is usually handled in schedules.py but some inference code still - # gives us non-lists or None - if not isinstance(input_tensor, list): - input_tensor = [input_tensor] - - assert len(input_tensor) == 1, 'input_tensor should only be length 1 for gpt/bert' - self.decoder.set_input_tensor(input_tensor[0]) - - def preprocess_for_fine_grained_offloading(self): - """Preprocess for fine-grained activation offloading.""" - off_interface.init_chunk_handler( - vp_size=self.config.virtual_pipeline_model_parallel_size, - vp_stage=self.vp_stage, - min_offloaded_tensor_size=self.config.min_offloaded_tensor_size, - ) - if self.disable_param_offloading: - for param in self.decoder.parameters(): - off_interface.mark_not_offloadable(param) - if self.mtp_process: - for param in self.mtp.parameters(): - off_interface.mark_not_offloadable(param) - if self.post_process: - for param in self.output_layer.parameters(): - off_interface.mark_not_offloadable(param) - self.disable_param_offloading = False - - def forward( - self, - input_ids: Tensor, - position_ids: Tensor, - attention_mask: Tensor, - decoder_input: Tensor = None, - labels: Tensor = None, - inference_context: BaseInferenceContext = None, - runtime_gather_output: Optional[bool] = None, - *, - inference_params: Optional[BaseInferenceContext] = None, - loss_mask: Optional[Tensor] = None, - packed_seq_params: Optional[PackedSeqParams] = None, - padding_mask: Optional[Tensor] = None, - is_spec_decode: Optional[bool] = None, - ) -> Tensor: - """Forward function of the Mamba model. This function passes the input tensors - through the embedding layer, and then the decoder and finally into the post - processing layer (optional). - - It either returns the Loss values if labels are given or the final hidden units - """ - # If decoder_input is provided (not None), then input_ids and position_ids are ignored. - # Otherwise, apply embedding layer on input_ids and position_ids to get decoder_input. - - if self.config.fine_grained_activation_offloading: - self.preprocess_for_fine_grained_offloading() - - inference_context = deprecate_inference_params(inference_context, inference_params) - - in_inference_mode = inference_context is not None and not self.training - - if in_inference_mode: - assert runtime_gather_output, "Inference must always gather TP logits" - - # Decoder embedding. - if decoder_input is not None: - pass - elif self.pre_process: - decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids) - - # Clear the outputs for padding tokens when using dynamic batching with - # quantization scales to avoid corrupting amax calculations - if ( - in_inference_mode - and inference_context.is_dynamic_batching() - and is_using_quantization_scales(self.config) - ): - decoder_input[inference_context.padding_slice] = 0.0 - else: - # intermediate stage of pipeline - # decoder will get hidden_states from encoder.input_tensor - decoder_input = None - - rotary_pos_emb = None - if self.position_embedding_type == 'rope' and not self.config.multi_latent_attention: - rotary_seq_len = self.rotary_pos_emb.get_rotary_seq_len( - inference_context, self.decoder, decoder_input, self.config, packed_seq_params - ) - rotary_pos_emb = self.rotary_pos_emb( - rotary_seq_len, - packed_seq=packed_seq_params is not None and packed_seq_params.qkv_format == 'thd', - ) - - # Wrap decoder_input to allow the decoder (MambaBlock) to delete the - # reference held by this caller function, enabling early garbage collection - # for inference. - if in_inference_mode: - decoder_input = WrappedTensor(decoder_input) - - # The following assert will currently fail when running inference. - # Commented out for now. - # TODO (duncan/rwaleffe): (1) confirm that the externally-generated - # attention mask is not needed and is ignored by the model in - # inference mode, (2) reduce the size of the externally-generated - # attention mask to prevent CPU OOM (as we did for training), (3) - # force the attention mask passed to the model in inference mode to - # be None, so this assert will succeed. - # assert attention_mask is None, "The attention mask is ignored and should be set to None" - - # Run decoder. - hidden_states = self.decoder( - hidden_states=decoder_input, - attention_mask=attention_mask, - inference_context=inference_context, - rotary_pos_emb=rotary_pos_emb, - packed_seq_params=packed_seq_params, - padding_mask=padding_mask, - ) - - output_weight = None - if self.share_embeddings_and_output_weights: - output_weight = self.shared_embedding_or_output_weight() - - # Check if speculative decoding is active. When it is, MTP must be - # computed *after* verification so that it is conditioned on verified - # tokens rather than stale speculative tokens from the previous step. - if is_spec_decode is None: - is_spec_decode = ( - in_inference_mode - and inference_context.is_dynamic_batching() - and inference_context.num_speculative_tokens > 0 - ) - - mtp_forward_ran = self.mtp_process and not (in_inference_mode or is_spec_decode) - if mtp_forward_ran: - hidden_states = self.mtp( - input_ids=input_ids, - position_ids=position_ids, - hidden_states=hidden_states, - attention_mask=attention_mask, - inference_params=inference_params, - rotary_pos_emb=rotary_pos_emb, - packed_seq_params=packed_seq_params, - embedding=self.embedding, - ) - - if not self.post_process: - return hidden_states - - if self.config.mtp_num_layers is not None and self.mtp_process: - assert self.config.mtp_num_layers > 0 - if in_inference_mode or is_spec_decode: - self._decoder_hidden_states_cache = hidden_states - else: - hidden_states = process_mtp_loss( - hidden_states=hidden_states, - labels=labels, - loss_mask=loss_mask, - output_layer=self.output_layer, - output_weight=output_weight, - runtime_gather_output=runtime_gather_output, - is_training=self.training, - compute_language_model_loss=self.compute_language_model_loss, - config=self.config, - cp_group=self.pg_collection.cp, - packed_seq_params=packed_seq_params, - scale_logits_fn=self._scale_logits if self.config.use_mup else None, + if mamba_stack_spec is not None: + if 'hybrid_stack_spec' in kwargs or (args and len(args) >= 2): + raise ValueError( + "Cannot specify both hybrid_stack_spec and mamba_stack_spec. " + "mamba_stack_spec has been deprecated; use hybrid_stack_spec instead." ) - sequence_parallel_override = False - if in_inference_mode and inference_context.config.materialize_only_last_token_logits: - if inference_context.is_static_batching(): - hidden_states = hidden_states[-1:, :, :] - else: - if self.output_layer.sequence_parallel: - # Perform the sequence parallel gather here instead of after the output layer - # because we need to slice the last token logits from the full view of the - # packed logits across all requests. - hidden_states = gather_from_sequence_parallel_region( - hidden_states, group=self.pg_collection.tp - ) - self.output_layer.sequence_parallel = False - sequence_parallel_override = True - - # Reshape [S, B, H] (with B=1) to [1, S, H] for logit extraction, - # then back to [S', B, H] for the output layer. - reshaped = hidden_states.squeeze(1).unsqueeze(0) - hidden_states = inference_context.last_token_logits(reshaped).unsqueeze(1) - - logits, _ = self.output_layer( - hidden_states, weight=output_weight, runtime_gather_output=runtime_gather_output - ) - logits = self._scale_logits(logits) - - # Restore sequence parallel execution to the output layer if necessary. - if sequence_parallel_override: - assert ( - in_inference_mode - and inference_context.is_dynamic_batching() - and inference_context.config.materialize_only_last_token_logits - ) - self.output_layer.sequence_parallel = True - - if labels is None: - # [s b h] => [b s h] - return logits.transpose(0, 1).contiguous() - - loss = self.compute_language_model_loss(labels, logits) - - return loss + kwargs['hybrid_stack_spec'] = mamba_stack_spec + super().__init__(*args, **kwargs) diff --git a/megatron/core/models/multimodal/llava_model.py b/megatron/core/models/multimodal/llava_model.py index 7d238f84979..a2e0eed68b2 100644 --- a/megatron/core/models/multimodal/llava_model.py +++ b/megatron/core/models/multimodal/llava_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import logging from collections import namedtuple from functools import partial @@ -11,7 +11,7 @@ from megatron.core.extensions.transformer_engine import HAVE_TE from megatron.core.inference.contexts import BaseInferenceContext from megatron.core.models.gpt import GPTModel -from megatron.core.models.mamba import MambaModel +from megatron.core.models.hybrid.hybrid_model import HybridModel from megatron.core.models.vision.clip_vit_model import CLIPViTModel, get_num_image_embeddings from megatron.core.models.vision.multimodal_projector import MultimodalProjector from megatron.core.models.vision.radio import RADIOViTModel @@ -196,9 +196,9 @@ def __init__( ) self.language_model = build_hf_model(language_transformer_config) elif language_model_type.startswith('nemotron5-hybrid'): - self.language_model = MambaModel( + self.language_model = HybridModel( config=language_transformer_config, - mamba_stack_spec=language_transformer_layer_spec, + hybrid_stack_spec=language_transformer_layer_spec, vocab_size=language_vocab_size, max_sequence_length=language_max_sequence_length, parallel_output=parallel_output, diff --git a/megatron/core/post_training/modelopt/hybrid/__init__.py b/megatron/core/post_training/modelopt/hybrid/__init__.py new file mode 100644 index 00000000000..e76ed74857b --- /dev/null +++ b/megatron/core/post_training/modelopt/hybrid/__init__.py @@ -0,0 +1 @@ +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. diff --git a/megatron/core/post_training/modelopt/hybrid/model_specs.py b/megatron/core/post_training/modelopt/hybrid/model_specs.py new file mode 100755 index 00000000000..59a509be718 --- /dev/null +++ b/megatron/core/post_training/modelopt/hybrid/model_specs.py @@ -0,0 +1,145 @@ +# Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. + +from megatron.core.extensions.transformer_engine import TEDotProductAttention +from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add +from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec +from megatron.core.models.hybrid.hybrid_block import HybridStack, HybridStackSubmodules +from megatron.core.post_training.modelopt.layers import Norm +from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules +from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules +from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear +from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules +from megatron.core.transformer.dot_product_attention import DotProductAttention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.mlp import MLP, MLPSubmodules +from megatron.core.transformer.spec_utils import ModuleSpec +from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules + + +# Use this spec for ModelOpt PTQ and TensorRT-LLM export +def get_hybrid_stack_modelopt_spec( + local_core_attention: bool = False, + remap_te_layernorm: bool = False, + use_default_te_spec: bool = False, +) -> ModuleSpec: + """Get the hybrid stack spec for ModelOpt PTQ and TensorRT-LLM export. + + When use_default_te_spec=False (default), this is the native local spec with TENorm + from Transformer-Engine for the layernorm implementation (since FusedLayerNorm from + apex has stopped supporting RMSNorm needed by llama). The remap_te_layernorm flag + can be used to add sharded state_dict key remapping for TE-compatible checkpoint + saving/loading. + + When use_default_te_spec=True, this returns the standard hybrid_stack_spec from + hybrid_layer_specs.py which uses full TE modules (TELayerNormColumnParallelLinear, + TERowParallelLinear, TEDotProductAttention, TENorm, moe_grouped_gemm=True). + + + Args: + local_core_attention: whether to use local DotProductAttention + (only for use_default_te_spec=False) + remap_te_layernorm: whether to perform sharded state_dict prefix mapping + on layernorm (only for use_default_te_spec=False) + use_default_te_spec: whether to use the default Transformer-Engine spec + """ + if use_default_te_spec: + from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec + + return hybrid_stack_spec + + return _get_hybrid_stack_local_spec( + local_core_attention=local_core_attention, remap_te_layernorm=remap_te_layernorm + ) + + +# Backward-compatible alias +get_mamba_stack_modelopt_spec = get_hybrid_stack_modelopt_spec + + +def _get_hybrid_stack_local_spec( + local_core_attention: bool = False, remap_te_layernorm: bool = False +) -> ModuleSpec: + """Get the hybrid stack spec with local (non-TE) modules. + + This is essentially the native local spec except for the layernorm implementation + is using TENorm from Transformer-Engine. + """ + mamba_state_dict_keys_map = {} + transformer_state_dict_keys_map = {} + if remap_te_layernorm: + mamba_state_dict_keys_map = {'norm.': 'mixer.in_proj.layer_norm_'} + transformer_state_dict_keys_map = { + 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', + 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', + } + + mamba_layer = ModuleSpec( + module=MambaLayer, + submodules=MambaLayerSubmodules( + norm=Norm, + mixer=ModuleSpec( + module=MambaMixer, + submodules=MambaMixerSubmodules( + in_proj=ColumnParallelLinear, out_proj=RowParallelLinear + ), + ), + mamba_bda=get_bias_dropout_add, + sharded_state_dict_keys_map=mamba_state_dict_keys_map, + ), + ) + + attn_mask_type = AttnMaskType.causal + core_attention = DotProductAttention if local_core_attention else TEDotProductAttention + attention_layer = ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + input_layernorm=Norm, + self_attention=ModuleSpec( + module=SelfAttention, + params={"attn_mask_type": attn_mask_type}, + submodules=SelfAttentionSubmodules( + linear_qkv=ColumnParallelLinear, + core_attention=core_attention, + linear_proj=RowParallelLinear, + ), + ), + self_attn_bda=get_bias_dropout_add, + sharded_state_dict_keys_map=transformer_state_dict_keys_map, + ), + ) + + mlp_layer = ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + pre_mlp_layernorm=Norm, + mlp=ModuleSpec( + module=MLP, + submodules=MLPSubmodules( + linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear + ), + ), + mlp_bda=get_bias_dropout_add, + sharded_state_dict_keys_map=transformer_state_dict_keys_map, + ), + ) + + moe_layer = ModuleSpec( + module=TransformerLayer, + submodules=TransformerLayerSubmodules( + pre_mlp_layernorm=Norm, + mlp=get_moe_module_spec( + use_te=False, num_experts=8, moe_grouped_gemm=False # Can be anything non None + ), + mlp_bda=get_bias_dropout_add, + ), + ) + + return ModuleSpec( + module=HybridStack, + submodules=HybridStackSubmodules( + mamba_layer=mamba_layer, + attention_layer=attention_layer, + mlp_layer=mlp_layer, + moe_layer=moe_layer, + ), + ) diff --git a/megatron/core/post_training/modelopt/mamba/model_specs.py b/megatron/core/post_training/modelopt/mamba/model_specs.py index e9f83b49f71..68907ed8bd9 100755 --- a/megatron/core/post_training/modelopt/mamba/model_specs.py +++ b/megatron/core/post_training/modelopt/mamba/model_specs.py @@ -1,141 +1,5 @@ -# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. -from megatron.core.extensions.transformer_engine import TEDotProductAttention -from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add -from megatron.core.models.gpt.moe_module_specs import get_moe_module_spec -from megatron.core.post_training.modelopt.layers import Norm -from megatron.core.ssm.mamba_block import MambaStack, MambaStackSubmodules -from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules -from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules -from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear -from megatron.core.transformer.attention import SelfAttention, SelfAttentionSubmodules -from megatron.core.transformer.dot_product_attention import DotProductAttention -from megatron.core.transformer.enums import AttnMaskType -from megatron.core.transformer.mlp import MLP, MLPSubmodules -from megatron.core.transformer.spec_utils import ModuleSpec -from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules - - -# Use this spec for ModelOpt PTQ and TensorRT-LLM export -def get_mamba_stack_modelopt_spec( - local_core_attention: bool = False, - remap_te_layernorm: bool = False, - use_default_te_spec: bool = False, -) -> ModuleSpec: - """Get the Mamba stack spec for ModelOpt PTQ and TensorRT-LLM export. - - When use_default_te_spec=False (default), this is the native local spec with TENorm - from Transformer-Engine for the layernorm implementation (since FusedLayerNorm from - apex has stopped supporting RMSNorm needed by llama). The remap_te_layernorm flag - can be used to add sharded state_dict key remapping for TE-compatible checkpoint - saving/loading. - - When use_default_te_spec=True, this returns the standard mamba_stack_spec from - mamba_layer_specs.py which uses full TE modules (TELayerNormColumnParallelLinear, - TERowParallelLinear, TEDotProductAttention, TENorm, moe_grouped_gemm=True). - - - Args: - local_core_attention: whether to use local DotProductAttention - (only for use_default_te_spec=False) - remap_te_layernorm: whether to perform sharded state_dict prefix mapping - on layernorm (only for use_default_te_spec=False) - use_default_te_spec: whether to use the default Transformer-Engine spec - """ - if use_default_te_spec: - from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec - - return mamba_stack_spec - - return _get_mamba_stack_local_spec( - local_core_attention=local_core_attention, remap_te_layernorm=remap_te_layernorm - ) - - -def _get_mamba_stack_local_spec( - local_core_attention: bool = False, remap_te_layernorm: bool = False -) -> ModuleSpec: - """Get the Mamba stack spec with local (non-TE) modules. - - This is essentially the native local spec except for the layernorm implementation - is using TENorm from Transformer-Engine. - """ - mamba_state_dict_keys_map = {} - transformer_state_dict_keys_map = {} - if remap_te_layernorm: - mamba_state_dict_keys_map = {'norm.': 'mixer.in_proj.layer_norm_'} - transformer_state_dict_keys_map = { - 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_', - 'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_', - } - - mamba_layer = ModuleSpec( - module=MambaLayer, - submodules=MambaLayerSubmodules( - norm=Norm, - mixer=ModuleSpec( - module=MambaMixer, - submodules=MambaMixerSubmodules( - in_proj=ColumnParallelLinear, out_proj=RowParallelLinear - ), - ), - mamba_bda=get_bias_dropout_add, - sharded_state_dict_keys_map=mamba_state_dict_keys_map, - ), - ) - - attn_mask_type = AttnMaskType.causal - core_attention = DotProductAttention if local_core_attention else TEDotProductAttention - attention_layer = ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - input_layernorm=Norm, - self_attention=ModuleSpec( - module=SelfAttention, - params={"attn_mask_type": attn_mask_type}, - submodules=SelfAttentionSubmodules( - linear_qkv=ColumnParallelLinear, - core_attention=core_attention, - linear_proj=RowParallelLinear, - ), - ), - self_attn_bda=get_bias_dropout_add, - sharded_state_dict_keys_map=transformer_state_dict_keys_map, - ), - ) - - mlp_layer = ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - pre_mlp_layernorm=Norm, - mlp=ModuleSpec( - module=MLP, - submodules=MLPSubmodules( - linear_fc1=ColumnParallelLinear, linear_fc2=RowParallelLinear - ), - ), - mlp_bda=get_bias_dropout_add, - sharded_state_dict_keys_map=transformer_state_dict_keys_map, - ), - ) - - moe_layer = ModuleSpec( - module=TransformerLayer, - submodules=TransformerLayerSubmodules( - pre_mlp_layernorm=Norm, - mlp=get_moe_module_spec( - use_te=False, num_experts=8, moe_grouped_gemm=False # Can be anything non None - ), - mlp_bda=get_bias_dropout_add, - ), - ) - - return ModuleSpec( - module=MambaStack, - submodules=MambaStackSubmodules( - mamba_layer=mamba_layer, - attention_layer=attention_layer, - mlp_layer=mlp_layer, - moe_layer=moe_layer, - ), - ) +# Backward-compatible re-export. The canonical location is now +# megatron.core.post_training.modelopt.hybrid.model_specs. +from megatron.core.post_training.modelopt.hybrid.model_specs import * # noqa: F401,F403 diff --git a/megatron/core/ssm/mamba_block.py b/megatron/core/ssm/mamba_block.py index fa262b2293f..bbfc3c62561 100644 --- a/megatron/core/ssm/mamba_block.py +++ b/megatron/core/ssm/mamba_block.py @@ -1,431 +1,5 @@ -# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# Copyright (c) 2024, Tri Dao, Albert Gu. +# Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# Some of this code was adopted from https://github.com/state-spaces/mamba/ -# This source code is licensed under the Apache license found in the -# LICENSE file in the root directory of this source tree. - -from contextlib import nullcontext -from dataclasses import dataclass -from typing import Optional, Tuple, Union - -import torch -from torch import Tensor, nn - -from megatron.core.dist_checkpointing.mapping import ShardedStateDict -from megatron.core.dist_checkpointing.utils import replace_prefix_for_sharding -from megatron.core.enums import Fp8Recipe -from megatron.core.extensions.transformer_engine import TENorm -from megatron.core.fp4_utils import get_fp4_context -from megatron.core.fp8_utils import get_fp8_context -from megatron.core.inference.contexts import BaseInferenceContext -from megatron.core.packed_seq_params import PackedSeqParams -from megatron.core.process_groups_config import ProcessGroupCollection -from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols as LayerSymbols -from megatron.core.transformer import TransformerConfig -from megatron.core.transformer.enums import CudaGraphScope -from megatron.core.transformer.identity_op import IdentityOp -from megatron.core.transformer.module import GraphableMegatronModule, MegatronModule -from megatron.core.transformer.spec_utils import ModuleSpec, build_module -from megatron.core.transformer.transformer_layer import TransformerLayer -from megatron.core.transformer.utils import sharded_state_dict_default -from megatron.core.utils import WrappedTensor, deprecate_inference_params, make_viewless_tensor - - -@dataclass -class MambaStackSubmodules: - """ - A class for the module specs for the MambaStack. - """ - - mamba_layer: Union[ModuleSpec, type] = IdentityOp - gdn_layer: Union[ModuleSpec, type] = IdentityOp - attention_layer: Union[ModuleSpec, type] = IdentityOp - dsa_layer: Union[ModuleSpec, type] = IdentityOp - mlp_layer: Union[ModuleSpec, type] = IdentityOp - moe_layer: Union[ModuleSpec, type] = IdentityOp - mtp_block_spec: Optional[ModuleSpec] = None - - -class MambaStack(GraphableMegatronModule, MegatronModule): - """ - Constructor for the MambaStack class. - - Args: - config (TransformerConfig): the model configuration - submodules (MambaStackSubmodules): the submodules for the stack - pre_process (bool, optional): whether to include an embedding layer. - Defaults to True. - layer_type_list (list, optional): pre-computed list of layer type symbols for - this pipeline segment. When provided (by MambaModel), pipeline stage - selection has already been done via '|' separators in the pattern. - pp_layer_offset (int, optional): the global layer offset for this pipeline - segment. Defaults to 0. - post_layer_norm (bool, optional): whether to include a final layer norm. - Defaults to True. - post_process (bool, optional): whether to include an output layer. - Defaults to True. - device (optional): the device to use. Defaults to None. - dtype (optional): the data type to use. Defaults to None. - pg_collection (ProcessGroupCollection): the required model communication - process groups to use. - is_mtp_layer (bool, optional): whether this is an MTP layer. Defaults to False. - """ - - def __init__( - self, - config: TransformerConfig, - submodules: MambaStackSubmodules, - pre_process: bool = True, - layer_type_list: Optional[list[str]] = None, - pp_layer_offset: int = 0, - post_layer_norm: bool = True, - post_process: bool = True, - device=None, - dtype=None, - pg_collection: ProcessGroupCollection = None, - is_mtp_layer: bool = False, - ) -> None: - super().__init__(config=config) - self.pre_process = pre_process - self.post_layer_norm = post_layer_norm - self.post_process = post_process - self.is_mtp_layer = is_mtp_layer - - assert pg_collection is not None, "pg_collection must be provided for MambaStack" - - self.pp_group = pg_collection.pp - self.tp_group = pg_collection.tp - - # Required for pipeline parallel schedules - self.input_tensor = None - self.pg_collection = pg_collection - - assert layer_type_list is not None, ( - "layer_type_list must be provided. It should be pre-computed from " - "--hybrid-layer-pattern by MambaModel." - ) - self.layer_type_list = layer_type_list - - # Build layers from the pre-selected segment - self.layers = nn.ModuleList() - for i, layer_type in enumerate(self.layer_type_list): - layer_number = i + 1 + pp_layer_offset - if self.config.fp8: - quant_init_context = get_fp8_context(self.config, i + pp_layer_offset, is_init=True) - elif self.config.fp4: - quant_init_context = get_fp4_context(self.config, i + pp_layer_offset, is_init=True) - else: - quant_init_context = nullcontext() - with quant_init_context: - if layer_type == LayerSymbols.MAMBA: - layer = build_module( - submodules.mamba_layer, - config=self.config, - layer_number=layer_number, - pp_layer_offset=pp_layer_offset, - pg_collection=pg_collection, - ) - elif layer_type == LayerSymbols.ATTENTION: - layer = build_module( - submodules.attention_layer, - config=self.config, - layer_number=layer_number, - pg_collection=pg_collection, - is_mtp_layer=is_mtp_layer, - add_layer_offset=False, - pp_layer_offset=pp_layer_offset, - ) - elif layer_type == LayerSymbols.DS_ATTENTION: - layer = build_module( - submodules.dsa_layer, - config=self.config, - layer_number=layer_number, - pg_collection=pg_collection, - is_mtp_layer=is_mtp_layer, - add_layer_offset=False, - pp_layer_offset=pp_layer_offset, - ) - elif layer_type == LayerSymbols.MLP: - layer = build_module( - submodules.mlp_layer, - config=self.config, - layer_number=layer_number, - pg_collection=pg_collection, - add_layer_offset=False, - ) - elif layer_type == LayerSymbols.MOE: - layer = build_module( - submodules.moe_layer, - config=self.config, - layer_number=layer_number, - pg_collection=pg_collection, - add_layer_offset=False, - ) - elif layer_type == LayerSymbols.GDN: - layer = build_module( - submodules.gdn_layer, - config=self.config, - layer_number=layer_number, - pg_collection=pg_collection, - # Set to False as we do not want to change offset. - add_layer_offset=False, - ) - else: - raise ValueError("unexpected layer_type") - self.layers.append(layer) - - # Required for activation recomputation - self.num_layers_per_pipeline_rank = len(self.layers) - - if self.post_process and self.post_layer_norm: - # Final layer norm before output. - self.final_norm = TENorm( - config=self.config, - hidden_size=self.config.hidden_size, - eps=self.config.layernorm_epsilon, - ) - - def set_input_tensor(self, input_tensor: Tensor): - """Set input tensor to be used instead of forward()'s input. - - When doing pipeline parallelism the input from the previous - stage comes from communication, not from the input, so the - model's forward_step_func won't have it. This function is thus - used by internal code to bypass the input provided by the - forward_step_func""" - self.input_tensor = input_tensor - - def mamba_state_shapes_per_request(self) -> Optional[Tuple[Tuple[int], Tuple[int]]]: - """ - Returns the Mamba conv and ssm states shapes per input sequence - if this block contains Mamba layers (this may not be the case with PP > 1). - """ - for layer_type, layer in zip(self.layer_type_list, self.layers): - if layer_type == LayerSymbols.MAMBA: - return layer.mamba_state_shapes_per_request() - return None - - def _should_call_local_cudagraph(self, *args, **kwargs): - """ - Check if we should call the local cudagraph path. - """ - if ( - not self.training - and hasattr(self, 'cudagraph_manager') - and kwargs['attention_mask'] is None - and ( - kwargs.get('inference_context') is not None - or kwargs.get('inference_params') is not None - ) - and CudaGraphScope.full_iteration_inference in self.config.cuda_graph_scope - ): - if kwargs['inference_context'].is_static_batching(): - using_cuda_graph = kwargs['inference_context'].is_decode_only() - else: - using_cuda_graph = kwargs['inference_context'].using_cuda_graph_this_step() - - if using_cuda_graph: - return True - return False - - def __call__(self, *args, **kwargs): - if self._should_call_local_cudagraph(*args, **kwargs): - kwargs['hidden_states'] = ( - kwargs['hidden_states'].unwrap() - if isinstance(kwargs['hidden_states'], WrappedTensor) - else kwargs['hidden_states'] - ) - return super().__call__(*args, **kwargs)[0] - return super().__call__(*args, **kwargs) - - def forward( - self, - hidden_states: Union[Tensor, WrappedTensor], - attention_mask: Tensor, - inference_context: Optional[BaseInferenceContext] = None, - rotary_pos_emb: Optional[Tensor] = None, - *, - inference_params: Optional[BaseInferenceContext] = None, - packed_seq_params: Optional[PackedSeqParams] = None, - padding_mask=None, - ): - """ - Forward function of the MambaStack class. - - It either returns the Loss values if labels are given or the - final hidden units - - Args: - hidden_states (Union[Tensor, WrappedTensor]): the input tensor. - Can be passed as a WrappedTensor during inference to avoid an obsolete - reference in the calling function. - attention_mask (Tensor): the attention mask. - inference_context (BaseInferenceContext): the inference parameters. - rotary_pos_emb (Tensor, optional): the rotary positional embeddings. - Defaults to None. - Returns: - Tensor: the output tensor. - """ - - inference_context = deprecate_inference_params(inference_context, inference_params) - - if not self.pre_process: - # See set_input_tensor() - hidden_states = self.input_tensor - - # Delete the obsolete reference to the initial input tensor if necessary - if isinstance(hidden_states, WrappedTensor): - hidden_states = hidden_states.unwrap() - - if inference_context and inference_context.is_static_batching(): - # NOTE(bnorick): match BaseInferenceContext attributes for - # mamba_ssm.utils.generation.BaseInferenceContext, - # this hack supports eval - inference_context.max_seqlen = inference_context.max_sequence_length - inference_context.seqlen_offset = inference_context.sequence_len_offset - - if ( - ( - ( - self.config.cuda_graph_impl == "local" - and CudaGraphScope.full_iteration not in self.config.cuda_graph_scope - ) - or self.config.flash_decode - ) - and inference_context - and inference_context.is_static_batching() - and not self.training - ): - current_batch_size = hidden_states.shape[1] - sequence_len_offset = torch.tensor( - [inference_context.sequence_len_offset] * current_batch_size, - dtype=torch.int32, - device='cuda', - ) - else: - sequence_len_offset = None - - # If fp8_recipe is delayed, wrap the entire pass with get_fp8_context(), - # otherwise do nothing extra at the outer level - # if we are using other fp8 recipes, then the context manager enter&exit are free - # we can wrap fp8_context within the for loop over layers, so that we can fine-grained - # control which layer will be fp8 or bf16 - use_outer_fp8_context = self.config.fp8 and self.config.fp8_recipe == Fp8Recipe.delayed - use_inner_fp8_context = self.config.fp8 and self.config.fp8_recipe != Fp8Recipe.delayed - use_fp4_context = self.config.fp4 is not None - outer_fp8_context = get_fp8_context(self.config) if use_outer_fp8_context else nullcontext() - - if use_inner_fp8_context: - - def get_inner_quant_context(config, layer_number): - return get_fp8_context(config, layer_number) - - elif use_fp4_context: - - def get_inner_quant_context(config, layer_number): - return get_fp4_context(config, layer_number) - - else: - - def get_inner_quant_context(config, layer_number): - return nullcontext() - - with outer_fp8_context: - for layer in self.layers: - # Layers have 1-indexed layer numbers attribute. - inner_quant_context = get_inner_quant_context(self.config, layer.layer_number - 1) - with inner_quant_context: - if isinstance(layer, TransformerLayer): - hidden_states, _ = layer( - hidden_states=hidden_states, - attention_mask=attention_mask, - inference_context=inference_context, - rotary_pos_emb=rotary_pos_emb, - sequence_len_offset=sequence_len_offset, - packed_seq_params=packed_seq_params, - padding_mask=padding_mask, - ) - else: # MambaLayer, Expert, or MLP - hidden_states = layer( - hidden_states=hidden_states, - attention_mask=attention_mask, - inference_context=inference_context, - packed_seq_params=packed_seq_params, - ) - - # The attention layer (currently a simplified transformer layer) - # outputs a tuple of (hidden_states, context). Context is intended - # for cross-attention, and is not needed in our model. - if isinstance(hidden_states, tuple): - hidden_states = hidden_states[0] - - # Final layer norm. - if self.post_process and self.post_layer_norm: - hidden_states = self.final_norm(hidden_states) - - # Ensure that the tensor passed between pipeline parallel stages is - # viewless. See related notes in TransformerBlock and TransformerLayer - hidden_states = make_viewless_tensor( - inp=hidden_states, requires_grad=hidden_states.requires_grad, keep_graph=True - ) - - return hidden_states - - def sharded_state_dict( - self, - prefix: str = '', - sharded_offsets: Optional[tuple] = None, - metadata: Optional[dict] = None, - ) -> ShardedStateDict: - """ - Returns a sharded state dictionary for the current object. - - This function constructs a sharded state dictionary by iterating over the layers - in the current object, computing the sharded state dictionary for each layer, - and combining the results into a single dictionary. - - Parameters: - prefix (str): The prefix to use for the state dictionary keys. - sharded_offsets (tuple): The sharded offsets to use for the state dictionary. - metadata (dict): Additional metadata to use when computing the sharded state dictionary. - - Returns: - dict: The sharded state dictionary for the current object. - """ - - sharded_state_dict = {} - layer_prefix = f'{prefix}layers.' - - for local_layer_idx, layer in enumerate(self.layers): - - global_layer_offset = layer.layer_number - 1 # self.layer_number starts at 1 - state_dict_prefix = ( - f'{layer_prefix}{local_layer_idx}.' # module list index in MambaBlock - ) - - sharded_prefix = f'{layer_prefix}{global_layer_offset}.' - sharded_pp_offset = [] - - layer_sharded_state_dict = layer.sharded_state_dict( - state_dict_prefix, sharded_pp_offset, metadata - ) - - replace_prefix_for_sharding(layer_sharded_state_dict, state_dict_prefix, sharded_prefix) - - sharded_state_dict.update(layer_sharded_state_dict) - - # Add modules other than self.layers - for name, module in self.named_children(): - if not module is self.layers: - sharded_state_dict.update( - sharded_state_dict_default( - module, - f'{prefix}{name}.', - sharded_offsets, - metadata, - tp_group=self.tp_group, - ) - ) - - return sharded_state_dict +# Backward-compatible re-export. The canonical location is now +# megatron.core.models.hybrid.hybrid_block. +from megatron.core.models.hybrid.hybrid_block import * # noqa: F401,F403 # pylint: disable=unused-import diff --git a/megatron/core/ssm/mamba_hybrid_layer_allocation.py b/megatron/core/ssm/mamba_hybrid_layer_allocation.py index 1947dbc39f1..fd43ed2aab9 100644 --- a/megatron/core/ssm/mamba_hybrid_layer_allocation.py +++ b/megatron/core/ssm/mamba_hybrid_layer_allocation.py @@ -1,497 +1,5 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -import logging -from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple - -import torch - -from megatron.core.utils import log_on_each_pipeline_stage, log_single_rank - -logger = logging.getLogger(__name__) - - -class Symbols: - """Symbols for different layer types and pattern separators.""" - - MAMBA = "M" - GDN = 'G' - ATTENTION = "*" - DS_ATTENTION = "D" - MLP = "-" - MOE = 'E' - PIPE = '|' - MTP_SEPARATOR = "/" - VALID_LAYERS = {MAMBA, GDN, ATTENTION, DS_ATTENTION, MLP, MOE} - - @classmethod - def name_sorted_valid_layer_symbols(cls) -> list[str]: - """Return the valid layer symbols sorted lexicographically by their public attribute - name. - """ - valid_layer_attrs = [] - for name, value in vars(cls).items(): - if not name.startswith('_') and value in cls.VALID_LAYERS: - valid_layer_attrs.append((name, value)) - valid_layer_attrs.sort() - return [value for (_, value) in valid_layer_attrs] - - -@dataclass -class ParsedHybridPattern: - """Result of parsing a unified hybrid pattern string. - - A unified pattern encodes both the main decoder pattern and the MTP pattern - in a single string using "/" as a separator. The main pattern may also - contain "|" pipe symbols to define pipeline stage boundaries for flexible - virtual pipeline parallelism (fVPP). - - Format: "///..." - - Examples: - - "M*M*" -> main="M*M*", mtp=None, depths=0 (no MTP) - - "M*M*/MM/MM" -> main="M*M*", mtp="MM", depths=2 - - "MMMM/*M/*M/*M" -> main="MMMM", mtp="*M", depths=3 - - "M-M-|M-M*-/MM/MM" -> main="M-M-|M-M*-" (2 PP stages), mtp="MM", depths=2 - - The "/" symbol introduces MTP patterns. Each repeated pattern after the main - decoder represents one MTP prediction depth. - - The "|" symbol in the main pattern defines pipeline stage boundaries. - - Attributes: - main_pattern: The main decoder layer pattern (e.g., "M*M*" or "M-M-|M-M*-") - mtp_pattern: The MTP layer pattern per depth (e.g., "MM"), or None if no MTP - mtp_num_depths: Number of MTP prediction depths (0 if no MTP) - """ - - main_pattern: Optional[str] - mtp_pattern: Optional[str] - mtp_num_depths: int - - -def pattern_from_ratios( - num_layers: int, attention_ratio: float = 0.0, mlp_ratio: float = 0.0 -) -> str: - """Convert deprecated ratio arguments to a layer pattern string. - - Generates an evenly-spaced hybrid layer pattern from target attention and MLP - ratios. This exists for backward compatibility with code that uses the deprecated - hybrid_attention_ratio and hybrid_mlp_ratio parameters. - - Args: - num_layers: Total number of layers. - attention_ratio: Target ratio of attention layers to total layers. - mlp_ratio: Target ratio of MLP layers to total layers. - - Returns: - A layer pattern string (e.g., "MMM*MMM*MM"). - """ - assert num_layers > 0 - assert 0.0 <= attention_ratio <= 1.0 - assert 0.0 <= mlp_ratio <= 1.0 - assert attention_ratio + mlp_ratio <= 1.0 - - # Allocate attention layers (evenly spaced, starting and ending with mamba) - attention_count = round(num_layers * attention_ratio) - mamba_count = num_layers - attention_count - sections = attention_count + 1 - section_len = mamba_count / sections - - layer_types = [Symbols.MAMBA] * num_layers - x = section_len - for i in range(num_layers): - if x < 0.5: - layer_types[i] = Symbols.ATTENTION - x += section_len - else: - x -= 1 - - # Allocate MLP layers (evenly distributed, not replacing attention) - mlp_count = round(num_layers * mlp_ratio) - if mlp_count > 0: - mamba_count -= mlp_count - ratio = mamba_count / mlp_count - x = ratio - for i in range(num_layers): - if layer_types[i] == Symbols.MAMBA: - if x < 0.5: - layer_types[i] = Symbols.MLP - x += ratio - else: - x -= 1 - - return ''.join(layer_types) - - -def get_hybrid_total_layer_count(pattern: str) -> int: - """Returns the total number of main decoder layers in a hybrid layer pattern. - - Extracts the main pattern (before the first MTP separator '/'), strips - pipeline stage separators '|', and returns the character count. - - Args: - pattern: Full hybrid layer pattern, possibly including MTP and pipe separators. - - Returns: - Total number of layers in the main decoder pattern. - """ - main_pattern = pattern.split(Symbols.MTP_SEPARATOR)[0] - _validate_pattern(main_pattern, "main", allow_pipe=True) - return len(main_pattern.replace(Symbols.PIPE, '')) - - -def get_hybrid_total_pipeline_segment_count(pattern: str) -> int: - """Returns the number of pipeline segments in a hybrid layer pattern. - - Extracts the main pattern (before the first MTP separator '/') and counts - the number of segments delimited by '|'. - - Args: - pattern: Full hybrid layer pattern, possibly including MTP and pipe separators. - - Returns: - Number of pipeline segments (pipe count + 1). - """ - main_pattern = pattern.split(Symbols.MTP_SEPARATOR)[0] - return main_pattern.count(Symbols.PIPE) + 1 - - -def get_hybrid_layer_counts(pattern: str) -> Dict[str, int]: - """Count layers by type across the full hybrid pattern (main + MTP). - - Parses the pattern to extract main and MTP components, then counts - each layer type. Main pattern '|' separators are skipped. MTP layers - are counted once per MTP depth. - - Args: - pattern: Full hybrid layer pattern string. - - Returns: - Dictionary mapping layer symbol to count. Keys are all valid layer symbols - (Symbols.VALID_LAYERS). - - Examples: - >>> get_hybrid_layer_counts("M*M*") - {'*': 2, 'G': 0, 'D': 0, 'M': 2, '-': 0, 'E': 0} - - >>> get_hybrid_layer_counts("M-M-|M-M*-/MM/MM") - {'*': 1, 'G': 0, 'D': 0, 'M': 8, '-': 4, 'E': 0} - """ - parsed = parse_hybrid_pattern(pattern) - counts = {symbol: 0 for symbol in Symbols.name_sorted_valid_layer_symbols()} - - # Count main decoder layers (skip '|' pipe separators) - if parsed.main_pattern: - for char in parsed.main_pattern: - if char in counts: - counts[char] += 1 - - # Count MTP layers (pattern repeated mtp_num_depths times) - if parsed.mtp_pattern and parsed.mtp_num_depths > 0: - for char in parsed.mtp_pattern: - if char in counts: - counts[char] += parsed.mtp_num_depths - - return counts - - -def parse_hybrid_pattern(pattern: Optional[str]) -> ParsedHybridPattern: - """Parse a unified hybrid pattern string into main and MTP components. - - The pattern uses "/" as a separator between the main decoder pattern and - MTP patterns. Each MTP pattern after the separator represents one prediction - depth. The main pattern may contain "|" pipe symbols for pipeline stage - boundaries. - - Format: "///..." - - Args: - pattern: Unified pattern string, e.g., "M*M*/MM/MM" or just "M*M*" - - Returns: - ParsedHybridPattern with main_pattern, mtp_pattern, and mtp_num_depths - - Raises: - ValueError: If MTP patterns are inconsistent (all must be identical) - ValueError: If pattern contains invalid layer symbols - - Examples: - >>> parse_hybrid_pattern("M*M*") - ParsedHybridPattern(main_pattern="M*M*", mtp_pattern=None, mtp_num_depths=0) - - >>> parse_hybrid_pattern("M*M*/MM/MM") - ParsedHybridPattern(main_pattern="M*M*", mtp_pattern="MM", mtp_num_depths=2) - - >>> parse_hybrid_pattern("MMMM/*M/*M/*M") - ParsedHybridPattern(main_pattern="MMMM", mtp_pattern="*M", mtp_num_depths=3) - - >>> parse_hybrid_pattern("M-M-|M-M*-/MM/MM") - ParsedHybridPattern(main_pattern="M-M-|M-M*-", mtp_pattern="MM", mtp_num_depths=2) - """ - if pattern is None: - return ParsedHybridPattern(main_pattern=None, mtp_pattern=None, mtp_num_depths=0) - - parts = pattern.split(Symbols.MTP_SEPARATOR) - - if len(parts) == 1: - # No MTP separator found - pattern is main decoder only - main_pattern = parts[0] - _validate_pattern(main_pattern, "main", allow_pipe=True) - return ParsedHybridPattern(main_pattern=main_pattern, mtp_pattern=None, mtp_num_depths=0) - - # First part is main decoder pattern - main_pattern = parts[0] - if main_pattern: - _validate_pattern(main_pattern, "main", allow_pipe=True) - - # Remaining parts are MTP patterns (one per depth) - mtp_parts = parts[1:] - - if not mtp_parts or all(p == "" for p in mtp_parts): - # No MTP patterns after separator - return ParsedHybridPattern( - main_pattern=main_pattern if main_pattern else None, mtp_pattern=None, mtp_num_depths=0 - ) - - # Validate all MTP patterns are identical - mtp_pattern = mtp_parts[0] - for i, part in enumerate(mtp_parts[1:], start=2): - if part != mtp_pattern: - raise ValueError( - f"All MTP patterns must be identical. " - f"Pattern 1 is '{mtp_pattern}', but pattern {i} is '{part}'. " - f"Full pattern: '{pattern}'" - ) - - _validate_pattern(mtp_pattern, "MTP", allow_pipe=False) - - return ParsedHybridPattern( - main_pattern=main_pattern if main_pattern else None, - mtp_pattern=mtp_pattern, - mtp_num_depths=len(mtp_parts), - ) - - -def _validate_pattern(pattern: str, pattern_name: str, allow_pipe: bool = False) -> None: - """Validate that a pattern contains only valid layer symbols. - - Args: - pattern: Layer pattern string to validate - pattern_name: Name of pattern for error messages (e.g., "main" or "MTP") - allow_pipe: Whether to allow the pipe '|' separator (for main patterns) - - Raises: - ValueError: If pattern contains invalid symbols - """ - valid_chars = Symbols.VALID_LAYERS | {Symbols.PIPE} if allow_pipe else Symbols.VALID_LAYERS - for char in pattern: - if char not in valid_chars: - raise ValueError( - f"In {pattern_name} pattern, '{char}' is not a valid layer symbol. " - f"Valid symbols are: {valid_chars}" - ) - - # Disallow Attention + MLA/DSA hybridity. - if Symbols.ATTENTION in pattern and Symbols.DS_ATTENTION in pattern: - raise ValueError("Not supported to have both Attention and MLA/DSA in one model") - - -def validate_segment_layers(segment: str) -> List[str]: - """Validate and convert a single pipeline segment pattern to a layer type list. - - This is used after the main pattern has been split by '|' into segments. - Each segment should contain only valid layer symbols (no '|'). - - Args: - segment: A single pipeline segment pattern string (e.g., "M-M*-") - - Returns: - List of layer type characters. - - Raises: - ValueError: If segment contains invalid layer symbols. - """ - layer_type_list = list(segment) - for layer_char in layer_type_list: - if layer_char not in Symbols.VALID_LAYERS: - raise ValueError( - f"In hybrid layer pattern segment, '{layer_char}' is not " - f"one of {Symbols.VALID_LAYERS}" - ) - - # Disallow Attention + MLA/DSA hybridity. - if Symbols.ATTENTION in segment and Symbols.DS_ATTENTION in segment: - raise ValueError("Not supported to have both Attention and MLA/DSA in one model") - return layer_type_list - - -def select_pipeline_segment( - main_pattern: str, - pp_group: Optional[torch.distributed.ProcessGroup], - vp_stage: Optional[int], - first_stage_layers: Optional[int] = None, - last_stage_layers: Optional[int] = None, -) -> Tuple[List[str], int]: - """Select and validate the pipeline segment for the given PP rank and VP stage. - - When the main pattern contains '|' pipe separators, splits by '|' into - pipeline segments and selects the segment for the current PP rank / VP stage. - - When the pattern has no pipes but pp_size > 1, falls back to runtime layer - slicing (for backwards compatibility), supporting both even and uneven PP splits - via first_stage_layers / last_stage_layers. - - Args: - main_pattern: Main decoder pattern (may contain '|' separators). - Empty string is allowed (produces one empty segment). - pp_group: Pipeline parallel process group, or None if not using PP. - vp_stage: Virtual pipeline stage, or None if not using VPP. - first_stage_layers: Number of layers on the first pipeline stage for - uneven PP. Only valid when the pattern has no pipe separators. - last_stage_layers: Number of layers on the last pipeline stage for - uneven PP. Only valid when the pattern has no pipe separators. - - Returns: - Tuple of (layer_type_list, layer_offset) where layer_type_list is - the list of layer type characters for this segment, and layer_offset - is the sum of layer counts from all preceding segments. - - Raises: - ValueError: If the segment contains invalid layer symbols, if - first/last_stage_layers are used with pipe separators, if VPP is - requested without pipe separators, or if layer counts are not - evenly divisible across pipeline stages. - """ - segments = main_pattern.split(Symbols.PIPE) if main_pattern else [''] - - pp_rank = torch.distributed.get_rank(pp_group) if pp_group is not None else 0 - pp_size = torch.distributed.get_world_size(pp_group) if pp_group is not None else 1 - - if len(segments) > 1 and (first_stage_layers is not None or last_stage_layers is not None): - raise ValueError( - "Cannot specify num_layers_in_first_pipeline_stage or " - "num_layers_in_last_pipeline_stage when hybrid_layer_pattern " - "contains pipe ('|') separators. The pipeline layout is already " - "explicitly defined by the pipe separators." - ) - - if len(segments) == 1 and pp_size > 1: - if vp_stage is not None: - raise ValueError( - "Virtual pipeline parallelism (vp_stage != None) is not supported " - "when hybrid_layer_pattern has no pipe ('|') separators. " - "Add '|' separators to define explicit pipeline/virtual-pipeline " - "stage boundaries." - ) - log_single_rank( - logger, - logging.WARNING, - "DEPRECATION: Using hybrid_layer_pattern without pipe ('|') separators " - "with pipeline_model_parallel_size > 1 is deprecated. Please add '|' " - "separators to explicitly define pipeline stage boundaries. " - "Example: 'M*M*M*M*' with pp_size=2 should become 'M*M*|M*M*'.", - ) - full_pattern = segments[0] - layer_type_list = validate_segment_layers(full_pattern) - num_layers = len(layer_type_list) - - if first_stage_layers is not None or last_stage_layers is not None: - first = first_stage_layers or 0 - last = last_stage_layers or 0 - middle_num_layers = num_layers - first - last - middle_stages = pp_size - sum( - 1 for x in (first_stage_layers, last_stage_layers) if x is not None - ) - if middle_stages > 0: - if middle_num_layers % middle_stages != 0: - raise ValueError( - f"Middle layers ({middle_num_layers}) must be evenly divisible " - f"by middle pipeline stages ({middle_stages})." - ) - layers_per_middle = middle_num_layers // middle_stages - else: - layers_per_middle = 0 - - is_first = first_stage_layers is not None and pp_rank == 0 - is_last = last_stage_layers is not None and pp_rank == pp_size - 1 - - if is_first: - offset = 0 - count = first - elif is_last: - offset = num_layers - last - count = last - else: - middle_rank = pp_rank if first_stage_layers is None else pp_rank - 1 - offset = middle_rank * layers_per_middle + first - count = layers_per_middle - else: - if num_layers % pp_size != 0: - raise ValueError( - f"Number of layers ({num_layers}) must be evenly divisible " - f"by pipeline-model-parallel-size ({pp_size}) when no pipe " - f"separators are specified in the pattern." - ) - layers_per_rank = num_layers // pp_size - offset = pp_rank * layers_per_rank - count = layers_per_rank - - selected = layer_type_list[offset : offset + count] - log_on_each_pipeline_stage( - logger, - logging.INFO, - f"MambaModel: pp_rank={pp_rank}/{pp_size}, vp_stage={vp_stage}, " - f"layers='{''.join(selected)}' ({len(selected)} layers), " - f"layer_offset={offset} (auto-split)", - ) - return selected, offset - - # Pipe-based segment selection - if len(segments) > 1 and len(segments) % pp_size != 0: - raise ValueError( - f"The number of pipe-delimited segments ({len(segments)}) in " - f"hybrid_layer_pattern must be evenly divisible by " - f"pipeline_model_parallel_size ({pp_size})." - ) - - vp_rel = vp_stage if vp_stage is not None else 0 - segment_index = vp_rel * pp_size + pp_rank - - if segment_index >= len(segments): - raise ValueError( - f"Pipeline segment index {segment_index} (pp_rank={pp_rank}, " - f"vp_stage={vp_rel}) is out of range for {len(segments)} segments. " - f"The pattern does not define enough pipe-delimited segments for " - f"the current PP/VPP configuration." - ) - - layer_offset = sum(len(segments[i]) for i in range(segment_index)) - my_segment = segments[segment_index] - - layer_type_list = validate_segment_layers(my_segment) - - log_on_each_pipeline_stage( - logger, - logging.INFO, - f"MambaModel: pp_rank={pp_rank}/{pp_size}, vp_stage={vp_rel}, " - f"segment_index={segment_index}/{len(segments)}, " - f"layers='{my_segment}' ({len(layer_type_list)} layers), " - f"layer_offset={layer_offset}", - ) - - return layer_type_list, layer_offset - - -def get_layer_maps_from_layer_type_list(layer_type_list: list[str]) -> dict[str, dict[int, int]]: - """ - Returns maps from global layer index to the corresponding layer index - for each valid layer type (those in Symbols.VALID_LAYERS) given a layer type list. - """ - layer_types = [symbol for symbol in Symbols.name_sorted_valid_layer_symbols()] - layer_maps = {layer_type: {} for layer_type in layer_types} - for global_layer_idx, layer_type in enumerate(layer_type_list): - layer_map = layer_maps[layer_type] - local_layer_idx = len(layer_map) - layer_map[global_layer_idx] = local_layer_idx - return layer_maps +# Backward-compatible re-export. The canonical location is now +# megatron.core.models.hybrid.hybrid_layer_allocation. +from megatron.core.models.hybrid.hybrid_layer_allocation import * # noqa: F401,F403 diff --git a/megatron/core/transformer/multi_token_prediction.py b/megatron/core/transformer/multi_token_prediction.py index 8fe7a2636b0..b70fdc3f28b 100755 --- a/megatron/core/transformer/multi_token_prediction.py +++ b/megatron/core/transformer/multi_token_prediction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. from __future__ import annotations import warnings @@ -37,7 +37,7 @@ ) if TYPE_CHECKING: - from megatron.core.ssm.mamba_block import MambaStackSubmodules + from megatron.core.models.hybrid.hybrid_block import HybridStackSubmodules if is_torch_min_version("1.13.0"): dist_all_gather_func = torch.distributed.all_gather_into_tensor @@ -736,11 +736,24 @@ def __init__( layer_number: int = 1, vp_stage: Optional[int] = None, pg_collection: Optional[ProcessGroupCollection] = None, - # For Mamba path - pattern and submodules to build inner layers directly + # For hybrid path - pattern and submodules to build inner layers directly mtp_layer_pattern: Optional[str] = None, - mamba_submodules: Optional[MambaStackSubmodules] = None, + hybrid_submodules: Optional[HybridStackSubmodules] = None, + mamba_submodules: Optional[HybridStackSubmodules] = None, ): super().__init__(config=config) + if mamba_submodules is not None: + if hybrid_submodules is not None: + raise ValueError( + "Cannot specify both hybrid_submodules and mamba_submodules. " + "mamba_submodules has been deprecated; use hybrid_submodules instead." + ) + warnings.warn( + "mamba_submodules has been deprecated. Use hybrid_submodules instead.", + DeprecationWarning, + stacklevel=2, + ) + hybrid_submodules = mamba_submodules self.sequence_parallel = config.sequence_parallel self.submodules = submodules self.layer_number = layer_number + get_mtp_layer_offset(self.config, vp_stage) @@ -753,11 +766,11 @@ def __init__( if self.submodules.mtp_model_layer is not None and hasattr( self.submodules.mtp_model_layer, 'submodules' ): - from megatron.core.ssm.mamba_block import MambaStackSubmodules + from megatron.core.models.hybrid.hybrid_block import HybridStackSubmodules from megatron.core.transformer.transformer_layer import TransformerLayerSubmodules layer_submodules = None - if isinstance(self.submodules.mtp_model_layer.submodules, MambaStackSubmodules): + if isinstance(self.submodules.mtp_model_layer.submodules, HybridStackSubmodules): attention_layer_spec = self.submodules.mtp_model_layer.submodules.attention_layer if hasattr(attention_layer_spec, 'submodules'): assert isinstance(attention_layer_spec.submodules, TransformerLayerSubmodules) @@ -809,15 +822,15 @@ def __init__( ) # Build inner layers: two possible paths - # 1. Mamba path: use MambaStack for hybrid pattern support + # 1. Hybrid path: use HybridStack for hybrid pattern support # 2. GPT path: single TransformerLayer - if mtp_layer_pattern is not None and mamba_submodules is not None: - from megatron.core.ssm.mamba_block import MambaStack - from megatron.core.ssm.mamba_hybrid_layer_allocation import validate_segment_layers + if mtp_layer_pattern is not None and hybrid_submodules is not None: + from megatron.core.models.hybrid.hybrid_block import HybridStack + from megatron.core.models.hybrid.hybrid_layer_allocation import validate_segment_layers - self.mtp_model_layer = MambaStack( + self.mtp_model_layer = HybridStack( config=self.config, - submodules=mamba_submodules, + submodules=hybrid_submodules, layer_type_list=validate_segment_layers(mtp_layer_pattern), pp_layer_offset=0, pre_process=True, # Always receives input from eh_proj @@ -1269,18 +1282,31 @@ def __init__( spec: Union[TransformerBlockSubmodules, ModuleSpec], vp_stage: Optional[int] = None, pg_collection: Optional[ProcessGroupCollection] = None, - # New: For Mamba path with unified pattern syntax + # New: For hybrid path with unified pattern syntax mtp_layer_pattern: Optional[str] = None, mtp_num_depths: int = 0, - mamba_submodules: Optional["MambaStackSubmodules"] = None, + hybrid_submodules: Optional["HybridStackSubmodules"] = None, + mamba_submodules: Optional["HybridStackSubmodules"] = None, ): super().__init__(config=config) + if mamba_submodules is not None: + if hybrid_submodules is not None: + raise ValueError( + "Cannot specify both hybrid_submodules and mamba_submodules. " + "mamba_submodules has been deprecated; use hybrid_submodules instead." + ) + warnings.warn( + "mamba_submodules has been deprecated. Use hybrid_submodules instead.", + DeprecationWarning, + stacklevel=2, + ) + hybrid_submodules = mamba_submodules self.submodules = _get_mtp_block_submodules(config, spec) self.mtp_loss_scaling_factor = config.mtp_loss_scaling_factor self.vp_stage = vp_stage self.mtp_layer_pattern = mtp_layer_pattern self.mtp_num_depths = mtp_num_depths - self.mamba_submodules = mamba_submodules + self.hybrid_submodules = hybrid_submodules self.mtp_use_repeated_layer = self.config.mtp_use_repeated_layer vp_size = config.virtual_pipeline_model_parallel_size @@ -1327,7 +1353,9 @@ def build_layer_legacy(layer_spec, layer_number): ) return module - def build_layer_with_pattern(layer_spec, layer_number, mtp_layer_pattern, mamba_submodules): + def build_layer_with_pattern( + layer_spec, layer_number, mtp_layer_pattern, hybrid_submodules + ): """Build layer using pattern-based approach (new Mamba path).""" fp8_init_context = get_fp8_context(self.config, is_init=True) with fp8_init_context: @@ -1338,12 +1366,12 @@ def build_layer_with_pattern(layer_spec, layer_number, mtp_layer_pattern, mamba_ vp_stage=self.vp_stage, pg_collection=pg_collection, mtp_layer_pattern=mtp_layer_pattern, - mamba_submodules=mamba_submodules, + hybrid_submodules=hybrid_submodules, ) return module - # New Mamba path: use mtp_layer_pattern and mamba_submodules - if self.mtp_layer_pattern is not None and self.mamba_submodules is not None: + # New Mamba path: use mtp_layer_pattern and hybrid_submodules + if self.mtp_layer_pattern is not None and self.hybrid_submodules is not None: if self.mtp_use_repeated_layer: # Shared/repeated layer: build one layer, use it for all depths layer_spec = self.submodules.layer_specs[0] @@ -1351,7 +1379,7 @@ def build_layer_with_pattern(layer_spec, layer_number, mtp_layer_pattern, mamba_ layer_spec, layer_number=1, mtp_layer_pattern=self.mtp_layer_pattern, - mamba_submodules=self.mamba_submodules, + hybrid_submodules=self.hybrid_submodules, ) self.layers = torch.nn.ModuleList([shared_layer]) else: @@ -1364,7 +1392,7 @@ def build_layer_with_pattern(layer_spec, layer_number, mtp_layer_pattern, mamba_ ], layer_number=i + 1, mtp_layer_pattern=self.mtp_layer_pattern, - mamba_submodules=self.mamba_submodules, + hybrid_submodules=self.hybrid_submodules, ) for i in range(num_depths) ] diff --git a/tests/unit_tests/models/test_mamba_model.py b/tests/unit_tests/models/test_hybrid_model.py similarity index 94% rename from tests/unit_tests/models/test_mamba_model.py rename to tests/unit_tests/models/test_hybrid_model.py index 1139debd07d..c4bdc147621 100644 --- a/tests/unit_tests/models/test_mamba_model.py +++ b/tests/unit_tests/models/test_hybrid_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import os from datetime import timedelta @@ -15,8 +15,8 @@ from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext from megatron.core.inference.inference_request import DynamicInferenceRequest from megatron.core.inference.sampling_params import SamplingParams -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec -from megatron.core.models.mamba.mamba_model import MambaModel +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec +from megatron.core.models.hybrid.hybrid_model import HybridModel from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig @@ -26,7 +26,7 @@ from tests.unit_tests.test_utilities import Utils -class TestMambaModel: +class TestHybridModel: def setup_method(self, method): Utils.initialize_model_parallel(1, 1) @@ -37,9 +37,9 @@ def setup_method(self, method): num_attention_heads=4, use_cpu_initialization=True, ) - self.model = MambaModel( + self.model = HybridModel( config=model_config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=100, max_sequence_length=4, hybrid_layer_pattern="M*-", # 1 Mamba, 1 attention, 1 MLP @@ -49,7 +49,7 @@ def teardown_method(self, method): Utils.destroy_model_parallel() def test_constructor(self): - assert isinstance(self.model, MambaModel) + assert isinstance(self.model, HybridModel) assert self.model.max_sequence_length == 4 @@ -105,9 +105,9 @@ def test_forward_packed_sequence(self): attention_backend=AttnBackend.flash, # Needed for packed sequence ) vocab_size = 100 - model = MambaModel( + model = HybridModel( config=model_config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=vocab_size, max_sequence_length=12, hybrid_layer_pattern="M*-", # 1 Mamba, 1 attention, 1 MLP @@ -212,7 +212,7 @@ def test_layer_numbers(self): ) @pytest.mark.parametrize("tp_size,cp_size,pp_size", [(2, 1, 4), (1, 1, 8), (8, 1, 1)]) def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size): - """Test MambaModel with custom process groups.""" + """Test HybridModel with custom process groups.""" Utils.initialize_model_parallel( tensor_model_parallel_size=tp_size, context_parallel_size=cp_size, @@ -261,9 +261,9 @@ def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size): pipeline_dtype=torch.bfloat16, ) - model = MambaModel( + model = HybridModel( config=model_config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=128, max_sequence_length=4, hybrid_layer_pattern=hybrid_layer_pattern, @@ -292,7 +292,7 @@ def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size): assert logits.shape[2] == divide(model.vocab_size, tp_size) -class TestMambaQKLayernorm: +class TestHybridQKLayernorm: def setup_method(self, method): Utils.initialize_model_parallel(1, 1) @@ -309,9 +309,9 @@ def _build_model(self, **config_overrides): use_cpu_initialization=True, **config_overrides, ) - return MambaModel( + return HybridModel( config=config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=100, max_sequence_length=4, hybrid_layer_pattern="M*-", @@ -371,7 +371,7 @@ def test_spec_provided_norm_not_overwritten(self): ) # Build a spec that explicitly sets q/k layernorm to IdentityOp - spec = copy.deepcopy(mamba_stack_spec) + spec = copy.deepcopy(hybrid_stack_spec) spec.submodules.attention_layer.submodules.self_attention.submodules.q_layernorm = ( IdentityOp ) @@ -386,9 +386,9 @@ def test_spec_provided_norm_not_overwritten(self): use_cpu_initialization=True, qk_layernorm=True, ) - model = MambaModel( + model = HybridModel( config=config, - mamba_stack_spec=spec, + hybrid_stack_spec=spec, vocab_size=100, max_sequence_length=4, hybrid_layer_pattern="M*-", @@ -399,7 +399,7 @@ def test_spec_provided_norm_not_overwritten(self): assert isinstance(attn.k_layernorm, IdentityOp) def test_forward_with_qk_layernorm(self): - """MambaModel forward pass works with qk_layernorm enabled.""" + """HybridModel forward pass works with qk_layernorm enabled.""" model = self._build_model(qk_layernorm=True) model.cuda() @@ -421,8 +421,8 @@ def test_forward_with_qk_layernorm(self): assert logits.shape[2] == 100 -class TestMambaWithDynamicInference: - """Tests MambaModel with dynamic inference.""" +class TestHybridWithDynamicInference: + """Tests HybridModel with dynamic inference.""" @torch.inference_mode() def setup_method(self, method): @@ -444,9 +444,9 @@ def setup_method(self, method): fp8_recipe="tensorwise", ) - self.model = MambaModel( + self.model = HybridModel( config=model_config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=128, max_sequence_length=DynamicInferenceContext.TOKEN_ROUNDER, hybrid_layer_pattern="M*", # 1 Mamba, 1 attention diff --git a/tests/unit_tests/models/test_mamba_model_expert_parallel_inference.py b/tests/unit_tests/models/test_hybrid_model_expert_parallel_inference.py similarity index 97% rename from tests/unit_tests/models/test_mamba_model_expert_parallel_inference.py rename to tests/unit_tests/models/test_hybrid_model_expert_parallel_inference.py index e7950cda58f..6fb3df43ad5 100644 --- a/tests/unit_tests/models/test_mamba_model_expert_parallel_inference.py +++ b/tests/unit_tests/models/test_hybrid_model_expert_parallel_inference.py @@ -1,6 +1,6 @@ # Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -"""Tests for full MambaModel inference with expert-parallel batch dimension sync. +"""Tests for full HybridModel inference with expert-parallel batch dimension sync. When expert parallelism > 1 with strict matching (hybrid models), batch dimensions are MAX-reduced across EP ranks. Different EP ranks can be in @@ -11,7 +11,7 @@ - PREFILL: 0 decode requests, >0 prefill requests - MIXED: >0 decode requests, >0 prefill requests -These tests verify that the full MambaModel produces correct output shapes +These tests verify that the full HybridModel produces correct output shapes for every combination of these states across EP ranks, using the real EP synchronization path (strict matching + MAX-reduce on batch dimensions). """ @@ -26,8 +26,8 @@ from megatron.core.inference.batch_dimensions_utils import InferenceBatchDimensions from megatron.core.inference.config import InferenceConfig, MambaInferenceStateConfig from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec -from megatron.core.models.mamba.mamba_model import MambaModel +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec +from megatron.core.models.hybrid.hybrid_model import HybridModel from megatron.core.ssm.mamba_mixer import _check_mamba_sequence_packing_support from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig @@ -71,7 +71,7 @@ @pytest.mark.internal class TestDynamicInference: - """Verify full MambaModel output shapes under EP strict matching scenarios.""" + """Verify full HybridModel output shapes under EP strict matching scenarios.""" HIDDEN_SIZE = 256 NUM_ATTN_HEADS = 4 @@ -117,9 +117,9 @@ def _build_model(self): moe_token_dispatcher_type="alltoall", cuda_graph_impl="local", ) - model = MambaModel( + model = HybridModel( config=config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=self.VOCAB_SIZE, max_sequence_length=self.MAX_SEQ_LEN, hybrid_layer_pattern="M*", diff --git a/tests/unit_tests/models/test_mamba_moe_model.py b/tests/unit_tests/models/test_hybrid_moe_model.py similarity index 97% rename from tests/unit_tests/models/test_mamba_moe_model.py rename to tests/unit_tests/models/test_hybrid_moe_model.py index ce594591503..01a46efe083 100644 --- a/tests/unit_tests/models/test_mamba_moe_model.py +++ b/tests/unit_tests/models/test_hybrid_moe_model.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import hashlib import inspect @@ -10,8 +10,8 @@ import pytest # type: ignore[import] import torch -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec -from megatron.core.models.mamba.mamba_model import MambaModel +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec +from megatron.core.models.hybrid.hybrid_model import HybridModel from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig @@ -390,14 +390,14 @@ def _diff_configs(expected: Mapping[str, Any], actual: Mapping[str, Any]) -> Tup return added, removed, changed -class TestMambaMoEModel: - """Test the initialization and use of an MoE Mamba model.""" +class TestHybridMoEModel: + """Test the initialization and use of an MoE Hybrid model.""" def create_test_args(self): destroy_global_vars() destroy_num_microbatches_calculator() - sys.argv = ['test_mamba_moe_model.py'] + sys.argv = ['test_hybrid_moe_model.py'] args = parse_args() # The following args would be set from the nano v3 checkpoint. @@ -425,7 +425,7 @@ def create_test_args(self): args.hidden_dropout = 0.0 args.hybrid_layer_pattern = "MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME" args.hybrid_override_pattern = None - args.spec = ["megatron.core.models.mamba.mamba_layer_specs", "mamba_stack_spec"] + args.spec = ["megatron.core.models.hybrid.hybrid_layer_specs", "hybrid_stack_spec"] args.num_experts = 128 args.moe_layer_freq = 1 args.moe_ffn_hidden_size = 1856 @@ -497,9 +497,9 @@ def setup_method(self, method): model_config = core_transformer_config_from_args(args, TransformerConfig) - self.model = MambaModel( + self.model = HybridModel( config=model_config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=args.vocab_size, max_sequence_length=args.seq_length, hybrid_layer_pattern=args.hybrid_layer_pattern, diff --git a/tests/unit_tests/post_training/test_modelopt_module_spec.py b/tests/unit_tests/post_training/test_modelopt_module_spec.py index 585be52f944..732dc08eca1 100644 --- a/tests/unit_tests/post_training/test_modelopt_module_spec.py +++ b/tests/unit_tests/post_training/test_modelopt_module_spec.py @@ -1,4 +1,4 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import inspect import tempfile @@ -13,13 +13,13 @@ get_gpt_layer_with_transformer_engine_spec, ) from megatron.core.models.gpt.gpt_model import GPTModel -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec -from megatron.core.models.mamba.mamba_model import MambaModel +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec +from megatron.core.models.hybrid.hybrid_model import HybridModel from megatron.core.post_training.modelopt.gpt.model_specs import get_gpt_modelopt_spec from megatron.core.post_training.modelopt.gpt.state_dict_hooks import ( mcore_gpt_load_te_state_dict_pre_hook, ) -from megatron.core.post_training.modelopt.mamba.model_specs import get_mamba_stack_modelopt_spec +from megatron.core.post_training.modelopt.hybrid.model_specs import get_hybrid_stack_modelopt_spec from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig from megatron.core.transformer.transformer_config import MLATransformerConfig @@ -197,7 +197,7 @@ def setup_method(self, method): ) -class TestModelOptMambaModel(TestModelOptGPTModel): +class TestModelOptHybridModel(TestModelOptGPTModel): def setup_method(self, method): Utils.initialize_model_parallel(1, 1) @@ -206,19 +206,19 @@ def setup_method(self, method): num_layers=3, hidden_size=256, num_attention_heads=4, use_cpu_initialization=True ) - # A Hybrid MambaModel using fused-TE spec (default) - self.default_model = MambaModel( + # A Hybrid HybridModel using fused-TE spec (default) + self.default_model = HybridModel( config=transformer_config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=100, max_sequence_length=4, hybrid_layer_pattern="M*-", ) - # A Hybrid MambaModel using ModelOpt spec (local + TENorm). - self.modelopt_model = MambaModel( + # A Hybrid HybridModel using ModelOpt spec (local + TENorm). + self.modelopt_model = HybridModel( config=transformer_config, - mamba_stack_spec=get_mamba_stack_modelopt_spec(remap_te_layernorm=True), + hybrid_stack_spec=get_hybrid_stack_modelopt_spec(remap_te_layernorm=True), vocab_size=100, max_sequence_length=4, hybrid_layer_pattern="M*-", @@ -264,9 +264,9 @@ def test_get_gpt_modelopt_spec_interface(): ), f"Default value of {sig_defaults[k]} does not match the expected value of {v} for parameter {k}." -def test_get_mamba_stack_modelopt_spec_interface(): +def test_get_hybrid_stack_modelopt_spec_interface(): # Get the function signature - sig = inspect.signature(get_mamba_stack_modelopt_spec) + sig = inspect.signature(get_hybrid_stack_modelopt_spec) # Define the expected signature expected_params = { @@ -298,7 +298,7 @@ def test_get_mamba_stack_modelopt_spec_interface(): ), f"Default value of {sig_defaults[k]} does not match the expected value of {v} for parameter {k}." -def test_get_mamba_stack_modelopt_spec_use_default_te_spec(): - """Test that use_default_te_spec=True returns the standard mamba_stack_spec.""" - spec = get_mamba_stack_modelopt_spec(use_default_te_spec=True) - assert spec is mamba_stack_spec +def test_get_hybrid_stack_modelopt_spec_use_default_te_spec(): + """Test that use_default_te_spec=True returns the standard hybrid_stack_spec.""" + spec = get_hybrid_stack_modelopt_spec(use_default_te_spec=True) + assert spec is hybrid_stack_spec diff --git a/tests/unit_tests/ssm/test_mamba_block.py b/tests/unit_tests/ssm/test_hybrid_block.py similarity index 92% rename from tests/unit_tests/ssm/test_mamba_block.py rename to tests/unit_tests/ssm/test_hybrid_block.py index 65d42ea3c17..08bf7f2bc28 100644 --- a/tests/unit_tests/ssm/test_mamba_block.py +++ b/tests/unit_tests/ssm/test_hybrid_block.py @@ -1,13 +1,13 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import pytest import torch -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec +from megatron.core.models.hybrid.hybrid_block import HybridStack +from megatron.core.models.hybrid.hybrid_layer_allocation import Symbols, validate_segment_layers +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec from megatron.core.process_groups_config import ProcessGroupCollection from megatron.core.ssm.gated_delta_net import GatedDeltaNet -from megatron.core.ssm.mamba_block import MambaStack -from megatron.core.ssm.mamba_hybrid_layer_allocation import Symbols, validate_segment_layers from megatron.core.ssm.mamba_layer import MambaLayer from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig @@ -21,7 +21,7 @@ @pytest.mark.internal -class TestMambaBlock: +class TestHybridBlock: def setup_method(self, method): Utils.initialize_model_parallel(1, 1) @@ -40,8 +40,8 @@ def get_mamba_block(self, layer_pattern): num_attention_heads=4, use_cpu_initialization=True, ) - modules = mamba_stack_spec.submodules - return MambaStack( + modules = hybrid_stack_spec.submodules + return HybridStack( transformer_config, modules, layer_type_list=layer_type_list, @@ -72,8 +72,8 @@ def get_dsa_mamba_block(self, layer_pattern): dsa_indexer_head_dim=64, dsa_indexer_topk=32, ) - modules = mamba_stack_spec.submodules - return MambaStack( + modules = hybrid_stack_spec.submodules + return HybridStack( transformer_config, modules, layer_type_list=layer_type_list, @@ -122,7 +122,7 @@ def test_invalid_layer_types_cause_failure(self): invalid_symbol = '+' assert invalid_symbol not in Symbols.VALID_LAYERS # sanity check. layer_pattern = Symbols.MAMBA + Symbols.ATTENTION + Symbols.MLP + invalid_symbol - # validate_segment_layers() in mamba_hybrid_layer_allocation.py throws a ValueError. + # validate_segment_layers() in hybrid_layer_allocation.py throws a ValueError. with pytest.raises(ValueError): block = self.get_mamba_block(layer_pattern) @@ -151,8 +151,8 @@ def test_gdn_gpu_forward(self): use_cpu_initialization=True, activation_func=torch.nn.functional.silu, ) - modules = mamba_stack_spec.submodules - block = MambaStack( + modules = hybrid_stack_spec.submodules + block = HybridStack( transformer_config, modules, layer_type_list=layer_type_list, diff --git a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py b/tests/unit_tests/ssm/test_hybrid_layer_allocation.py similarity index 94% rename from tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py rename to tests/unit_tests/ssm/test_hybrid_layer_allocation.py index 915ba9dd34f..fe0d7c2dc1e 100644 --- a/tests/unit_tests/ssm/test_mamba_hybrid_layer_allocation.py +++ b/tests/unit_tests/ssm/test_hybrid_layer_allocation.py @@ -5,7 +5,7 @@ import pytest -from megatron.core.ssm.mamba_hybrid_layer_allocation import ( +from megatron.core.models.hybrid.hybrid_layer_allocation import ( ParsedHybridPattern, Symbols, get_hybrid_layer_counts, @@ -383,28 +383,28 @@ class TestSelectPipelineSegment: is simply the vp_stage value. """ - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_single_segment_no_vp(self, mock_log): """Single segment, no VPP.""" layer_types, offset = select_pipeline_segment("M*M*", pp_group=None, vp_stage=None) assert layer_types == ['M', '*', 'M', '*'] assert offset == 0 - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_two_segments_vp0(self, mock_log): """Two segments, select first (vp_stage=0).""" layer_types, offset = select_pipeline_segment("M-M-|M-M*-", pp_group=None, vp_stage=0) assert layer_types == ['M', '-', 'M', '-'] assert offset == 0 - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_two_segments_vp1(self, mock_log): """Two segments, select second (vp_stage=1).""" layer_types, offset = select_pipeline_segment("M-M-|M-M*-", pp_group=None, vp_stage=1) assert layer_types == ['M', '-', 'M', '*', '-'] assert offset == 4 - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_four_segments(self, mock_log): """Four segments, verify each vp_stage selects correctly.""" pattern = "MM|M*|M-|ME" @@ -414,7 +414,7 @@ def test_four_segments(self, mock_log): assert layer_types == expected_layers, f"Failed for vp_stage={vp_stage}" assert offset == expected_offset, f"Failed for vp_stage={vp_stage}" - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_empty_segment(self, mock_log): """Empty segments are allowed for pipeline balancing.""" layer_types, offset = select_pipeline_segment("||M*", pp_group=None, vp_stage=0) @@ -425,7 +425,7 @@ def test_empty_segment(self, mock_log): assert layer_types == ['M', '*'] assert offset == 0 - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_uneven_segments(self, mock_log): """Segments of different lengths.""" pattern = "MMM|M|MMMMM" @@ -441,44 +441,44 @@ def test_uneven_segments(self, mock_log): assert len(layer_types) == 5 assert offset == 4 - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_empty_main_pattern(self, mock_log): """Empty main pattern produces one empty segment.""" layer_types, offset = select_pipeline_segment("", pp_group=None, vp_stage=None) assert layer_types == [] assert offset == 0 - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_invalid_segment_raises(self, mock_log): """Invalid layer symbols in a segment should raise ValueError.""" with pytest.raises(ValueError): select_pipeline_segment("MX|M*", pp_group=None, vp_stage=0) - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_out_of_range_segment_raises(self, mock_log): """Segment index out of range should raise ValueError.""" with pytest.raises(ValueError, match="out of range"): select_pipeline_segment("M*|M*", pp_group=None, vp_stage=5) - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_logging_is_called(self, mock_log): """Verify that log_on_each_pipeline_stage is called.""" select_pipeline_segment("M*M*", pp_group=None, vp_stage=None) mock_log.assert_called_once() - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_mutual_exclusivity_pipes_with_first_stage(self, mock_log): """Pipe separators + first_stage_layers should raise ValueError.""" with pytest.raises(ValueError, match="Cannot specify"): select_pipeline_segment("M*|M*", pp_group=None, vp_stage=0, first_stage_layers=1) - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_mutual_exclusivity_pipes_with_last_stage(self, mock_log): """Pipe separators + last_stage_layers should raise ValueError.""" with pytest.raises(ValueError, match="Cannot specify"): select_pipeline_segment("M*|M*", pp_group=None, vp_stage=0, last_stage_layers=1) - @patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage') + @patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage') def test_segment_count_not_divisible_by_pp_size(self, mock_log): """Segment count not divisible by pp_size should raise ValueError.""" mock_group = object() @@ -512,8 +512,8 @@ def _call_for_rank( with ( patch('torch.distributed.get_rank', return_value=pp_rank), patch('torch.distributed.get_world_size', return_value=pp_size), - patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage'), - patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_single_rank'), + patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage'), + patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_single_rank'), ): return select_pipeline_segment( pattern, @@ -615,8 +615,10 @@ def test_deprecation_warning_logged(self): with ( patch('torch.distributed.get_rank', return_value=0), patch('torch.distributed.get_world_size', return_value=2), - patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_on_each_pipeline_stage'), - patch('megatron.core.ssm.mamba_hybrid_layer_allocation.log_single_rank') as mock_warn, + patch('megatron.core.models.hybrid.hybrid_layer_allocation.log_on_each_pipeline_stage'), + patch( + 'megatron.core.models.hybrid.hybrid_layer_allocation.log_single_rank' + ) as mock_warn, ): select_pipeline_segment("M*M*", pp_group=mock_group, vp_stage=None) mock_warn.assert_called_once() diff --git a/tests/unit_tests/ssm/test_mamba_layer.py b/tests/unit_tests/ssm/test_mamba_layer.py index 26c7ba4d92f..8d6e0ab8c91 100644 --- a/tests/unit_tests/ssm/test_mamba_layer.py +++ b/tests/unit_tests/ssm/test_mamba_layer.py @@ -1,11 +1,11 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import pytest import torch -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec +from megatron.core.models.hybrid.hybrid_block import HybridStackSubmodules +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec from megatron.core.process_groups_config import ProcessGroupCollection -from megatron.core.ssm.mamba_block import MambaStackSubmodules from megatron.core.ssm.mamba_layer import MambaLayer, MambaLayerSubmodules from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed from megatron.core.transformer import TransformerConfig @@ -26,12 +26,12 @@ def setup_method(self, method): num_attention_heads=1, use_cpu_initialization=True, ) - assert isinstance(mamba_stack_spec.submodules, MambaStackSubmodules) - assert isinstance(mamba_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules) + assert isinstance(hybrid_stack_spec.submodules, HybridStackSubmodules) + assert isinstance(hybrid_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules) pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) self.layer = MambaLayer( transformer_config, - mamba_stack_spec.submodules.mamba_layer.submodules, + hybrid_stack_spec.submodules.mamba_layer.submodules, pg_collection=pg_collection, ) diff --git a/tests/unit_tests/ssm/test_mamba_mixer.py b/tests/unit_tests/ssm/test_mamba_mixer.py index f32b62146f8..184a00fcadd 100644 --- a/tests/unit_tests/ssm/test_mamba_mixer.py +++ b/tests/unit_tests/ssm/test_mamba_mixer.py @@ -1,12 +1,12 @@ -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2024-2026, NVIDIA CORPORATION. All rights reserved. import pytest import torch from megatron.core.inference.contexts.static_context import StaticInferenceContext -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec +from megatron.core.models.hybrid.hybrid_block import HybridStackSubmodules +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec from megatron.core.process_groups_config import ProcessGroupCollection -from megatron.core.ssm.mamba_block import MambaStackSubmodules from megatron.core.ssm.mamba_layer import MambaLayerSubmodules from megatron.core.ssm.mamba_mixer import MambaMixer, MambaMixerSubmodules from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed @@ -39,16 +39,16 @@ def get_mixer(self, tp_size=1, cp_size=1, use_mem_eff_path=True): use_cpu_initialization=True, use_mamba_mem_eff_path=use_mem_eff_path, ) - assert isinstance(mamba_stack_spec.submodules, MambaStackSubmodules) - assert isinstance(mamba_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules) + assert isinstance(hybrid_stack_spec.submodules, HybridStackSubmodules) + assert isinstance(hybrid_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules) assert isinstance( - mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, + hybrid_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, MambaMixerSubmodules, ) pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) mixer = MambaMixer( transformer_config, - mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, + hybrid_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, transformer_config.hidden_size, layer_number=1, pg_collection=pg_collection, @@ -126,17 +126,17 @@ def test_error_check(self, hidden_size, ngroups, tp_size, expected_error_message use_cpu_initialization=True, mamba_num_groups=ngroups, ) - assert isinstance(mamba_stack_spec.submodules, MambaStackSubmodules) - assert isinstance(mamba_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules) + assert isinstance(hybrid_stack_spec.submodules, HybridStackSubmodules) + assert isinstance(hybrid_stack_spec.submodules.mamba_layer.submodules, MambaLayerSubmodules) assert isinstance( - mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, + hybrid_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, MambaMixerSubmodules, ) pg_collection = ProcessGroupCollection.use_mpu_process_groups(required_pgs=['tp', 'cp']) with pytest.raises(AssertionError, match=expected_error_message): MambaMixer( transformer_config, - mamba_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, + hybrid_stack_spec.submodules.mamba_layer.submodules.mixer.submodules, transformer_config.hidden_size, pg_collection=pg_collection, ) diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py index bfde9ff9cf1..69f12e4d983 100644 --- a/tests/unit_tests/transformer/test_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_cuda_graphs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import gc import os @@ -15,15 +15,15 @@ get_gpt_mtp_block_spec, ) from megatron.core.models.gpt.gpt_model import GPTModel -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec +from megatron.core.models.hybrid.hybrid_block import HybridStack +from megatron.core.models.hybrid.hybrid_layer_allocation import validate_segment_layers +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec from megatron.core.num_microbatches_calculator import ( destroy_num_microbatches_calculator, init_num_microbatches_calculator, ) from megatron.core.pipeline_parallel.schedules import set_current_microbatch from megatron.core.process_groups_config import ProcessGroupCollection -from megatron.core.ssm.mamba_block import MambaStack -from megatron.core.ssm.mamba_hybrid_layer_allocation import validate_segment_layers from megatron.core.tensor_parallel.random import ( HAVE_TE, initialize_rng_tracker, @@ -462,7 +462,7 @@ def test_llava_cudagraph_is_last_layer_logic(self): del layer.cudagraph_manager.cudagraph_runners[0].bwd_graph -class TestParallelMambaBlockCudagraphs: +class TestParallelHybridBlockCudagraphs: def setup_method(self, method): # initialize parallel state initialize_rng_tracker(use_te_rng_tracker=True, force_reset=True) @@ -486,8 +486,8 @@ def get_mamba_block(hybrid_layer_pattern): use_cpu_initialization=True, cuda_graph_impl="local", ) - modules = mamba_stack_spec.submodules - return MambaStack( + modules = hybrid_stack_spec.submodules + return HybridStack( transformer_config, modules, layer_type_list=layer_type_list, diff --git a/tests/unit_tests/transformer/test_multi_token_prediction.py b/tests/unit_tests/transformer/test_multi_token_prediction.py index 57423da335b..d4d7edfe44b 100644 --- a/tests/unit_tests/transformer/test_multi_token_prediction.py +++ b/tests/unit_tests/transformer/test_multi_token_prediction.py @@ -1,4 +1,4 @@ -# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import os import sys @@ -14,8 +14,8 @@ get_gpt_mtp_block_spec, ) from megatron.core.models.gpt.gpt_model import GPTModel -from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec -from megatron.core.models.mamba.mamba_model import MambaModel +from megatron.core.models.hybrid.hybrid_layer_specs import hybrid_stack_spec +from megatron.core.models.hybrid.hybrid_model import HybridModel from megatron.core.num_microbatches_calculator import destroy_num_microbatches_calculator from megatron.core.packed_seq_params import PackedSeqParams from megatron.core.parallel_state import get_context_parallel_group @@ -686,7 +686,7 @@ def log(self, metrics, iteration): assert MTPLossLoggingHelper.tracker["avg_group"] is None -class TestMultiTokenPredictionMamba: +class TestMultiTokenPredictionHybrid: """Test Multi-Token Prediction with Mamba hybrid models.""" def setup_method(self, method): @@ -712,10 +712,10 @@ def model_provider(self, pre_process=True, post_process=True, **config_kwargs): config = core_transformer_config_from_args(args) # MTP is configured via unified pattern in hybrid_layer_pattern - # MambaModel creates the MTP block internally based on the parsed pattern - model = MambaModel( + # HybridModel creates the MTP block internally based on the parsed pattern + model = HybridModel( config=config, - mamba_stack_spec=mamba_stack_spec, + hybrid_stack_spec=hybrid_stack_spec, vocab_size=args.vocab_size, max_sequence_length=args.max_position_embeddings, pre_process=pre_process, @@ -735,7 +735,7 @@ def create_test_args( destroy_global_vars() destroy_num_microbatches_calculator() - sys.argv = ['test_multi_token_prediction_mamba.py'] + sys.argv = ['test_multi_token_prediction_hybrid.py'] args = parse_args() args.mtp_num_layers = 2 args.mtp_loss_scaling_factor = 0.1 @@ -763,7 +763,7 @@ def create_test_args( args.bf16 = True # Unified pattern: "main/mtp/mtp" - main decoder "M*M*", MTP pattern "M*" with 2 depths args.hybrid_layer_pattern = "M*M*/M*/M*" - args.spec = "megatron.core.models.mamba.mamba_layer_specs.mamba_stack_spec" + args.spec = "megatron.core.models.hybrid.hybrid_layer_specs.hybrid_stack_spec" if fp8 is not None: args.fp8 = 'e4m3' @@ -920,7 +920,7 @@ def test_attention_mask_validation_mamba(self): try: mamba_model = get_model(self.model_provider, ModelType.encoder_or_decoder) mamba_model = unwrap_model(mamba_model) - assert isinstance(mamba_model[0], MambaModel) + assert isinstance(mamba_model[0], HybridModel) assert mamba_model[0].mtp is not None except AssertionError as e: if "Multi-Token Prediction (MTP) is not yet supported" in str(e): From 30461822692a5da057c3db171d26fbee1d7c49d2 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Mon, 20 Apr 2026 11:37:54 +0000 Subject: [PATCH 47/72] =?UTF-8?q?chore(beep=20boop=20=F0=9F=A4=96):=20Bump?= =?UTF-8?q?=20=20(main)=20(2026-04-20)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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+++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/actions/action.yml b/.github/actions/action.yml index d500a5896d0..3913414a1c7 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -83,7 +83,11 @@ runs: echo "apt attempt $i failed, retrying..." sleep 10 done - curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/local/bin sh + for i in 1 2 3; do + curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/local/bin sh && break + echo "uv install attempt $i failed, retrying..." + sleep 10 + done - name: Create run-script (unit test) shell: bash -x -e -u -o pipefail {0} From 7928a84e86a79a783d70968660e067d2ab492f23 Mon Sep 17 00:00:00 2001 From: Ankur Srivastava <101727556+awsankur@users.noreply.github.com> Date: Mon, 20 Apr 2026 11:34:03 -0700 Subject: [PATCH 49/72] Call save_checkpoint_and_time() when saving checkpoint and compute elapsed duration for saving checkpoint before logging timer (#4263) Signed-off-by: Ankur Srivastava Co-authored-by: Ankur Srivastava Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com> Co-authored-by: Antoni-Joan Solergibert Co-authored-by: Philip Petrakian --- megatron/training/training.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/megatron/training/training.py b/megatron/training/training.py index 8014435b4af..8fed9afd570 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -1203,15 +1203,14 @@ def pretrain( print_datetime('after training is done') if not args.skip_train and args.save and iteration != 0 and iteration % args.save_interval != 0: - save_checkpoint( + save_checkpoint_and_time( iteration, model, optimizer, opt_param_scheduler, num_floating_point_operations_so_far, checkpointing_context, - train_data_iterator=train_data_iterator, - preprocess_common_state_dict_fn=preprocess_common_state_dict, + train_data_iterator=train_data_iterator ) one_logger and one_logger.log_metrics( @@ -2426,6 +2425,11 @@ def save_checkpoint_and_time( train_data_iterator=train_data_iterator, preprocess_common_state_dict_fn=preprocess_common_state_dict, ) + + # Stop timer and compute time elapsed to save checkpoint. Stop timer before timers.log() call as it resets the timer. + timers(timer_key).stop(barrier=True) + save_checkpoint_duration = timers(timer_key).elapsed(reset=False) + if should_report_memory: # Track memory after checkpoint save. report_memory(f"(after save_checkpoint for iteration {iteration})") @@ -2436,12 +2440,12 @@ def save_checkpoint_and_time( # dequantized bf16 tensors that were temporarily created during fp8 # model checkpoint saving. gc.collect() - timers(timer_key).stop(barrier=True) + timers.log([timer_key]) # Log E2E metrics after save-checkpoint one_logger_utils.track_e2e_metrics() - save_checkpoint_duration = timers(timer_key).elapsed() + one_logger_utils.on_save_checkpoint_end(save_checkpoint_duration, iteration, args.async_save) if args.log_progress and not non_persistent_ckpt: From ef1888bc5ce2868219e2db715362a935d2b6b7f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Mon, 20 Apr 2026 20:42:18 +0200 Subject: [PATCH 50/72] refactor(tests): move NCCL env vars from docker launcher to shell training script (#4390) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .../shell_test_utils/_run_training.sh | 10 + .../golden_values_dev_dgx_h100.json | 120 ++--- .../golden_values_dev_dgx_h100.json | 2 +- .../golden_values_dev_dgx_h100.json | 2 +- .../golden_values_dev_dgx_h100.json | 488 +++++++++--------- .../python_scripts/launch_jet_workload.py | 18 +- .../launch_nemo_run_workload.py | 11 - tests/test_utils/python_scripts/notify.py | 6 + 8 files changed, 331 insertions(+), 326 deletions(-) diff --git a/tests/functional_tests/shell_test_utils/_run_training.sh b/tests/functional_tests/shell_test_utils/_run_training.sh index 654faac31d4..cd22e9b1104 100644 --- a/tests/functional_tests/shell_test_utils/_run_training.sh +++ b/tests/functional_tests/shell_test_utils/_run_training.sh @@ -39,6 +39,16 @@ for mandatory_var in "${MANDATORY_VARS[@]}"; do fi done +export NCCL_PROTO="${NCCL_PROTO:-simple}" +export NCCL_ALGO="${NCCL_ALGO:-Ring}" +export NCCL_COLLNET_ENABLE="${NCCL_COLLNET_ENABLE:-0}" +export NCCL_NVLS_ENABLE="${NCCL_NVLS_ENABLE:-0}" +# Disable the EFA tuner plugin so NCCL_ALGO/NCCL_PROTO are actually respected instead of being overridden at runtime. +export NCCL_TUNER_PLUGIN="${NCCL_TUNER_PLUGIN:-}" +# Match the NCCL default (4 MB) so buffer-chunking behaviour is the same as on Slurm nodes. +export NCCL_BUFFSIZE="${NCCL_BUFFSIZE:-4194304}" +export TORCH_NCCL_AVOID_RECORD_STREAMS="${TORCH_NCCL_AVOID_RECORD_STREAMS:-1}" + set +x # Envsubst model_params cat $TRAINING_PARAMS_PATH | envsubst "$(env | cut -d= -f1 | sed -e 's/^/$/')" >$TRAINING_PARAMS_PATH.tmp diff --git a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_h100.json index cb4b154ded8..96491b449ff 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/gpt/gpt3_7b_tp4_pp1_memory_speed/golden_values_dev_dgx_h100.json @@ -6,29 +6,29 @@ "values": { "1": 12.59719, "2": 12.60069, - "3": 12.59724, - "4": 12.60016, - "5": 12.59009, - "6": 12.58836, - "7": 12.57598, - "8": 12.53622, - "9": 12.50748, - "10": 12.49077, - "11": 12.32627, - "12": 12.29355, - "13": 12.22601, - "14": 12.23027, - "15": 11.8209, - "16": 11.80583, - "17": 11.77001, - "18": 11.74939, - "19": 11.62181, - "20": 11.51695, - "21": 11.27106, - "22": 11.3897, - "23": 11.29559, - "24": 11.16599, - "25": 11.00354 + "3": 12.59714, + "4": 12.60017, + "5": 12.59, + "6": 12.58832, + "7": 12.5761, + "8": 12.53623, + "9": 12.50732, + "10": 12.49084, + "11": 12.32612, + "12": 12.29364, + "13": 12.22602, + "14": 12.23018, + "15": 11.82105, + "16": 11.8058, + "17": 11.77005, + "18": 11.74942, + "19": 11.62178, + "20": 11.51699, + "21": 11.27112, + "22": 11.38972, + "23": 11.29553, + "24": 11.16595, + "25": 11.00349 } }, "num-zeros": { @@ -36,31 +36,31 @@ "end_step": 25, "step_interval": 1, "values": { - "1": 523038112.0, - "2": 523667424.0, - "3": 522931840.0, - "4": 523230912.0, - "5": 523011200.0, - "6": 523366144.0, - "7": 523428576.0, - "8": 523073920.0, - "9": 523458656.0, - "10": 523184896.0, - "11": 524287072.0, - "12": 523446944.0, - "13": 523489504.0, - "14": 524476896.0, - "15": 523629120.0, - "16": 523461312.0, - "17": 523076512.0, - "18": 523362304.0, - "19": 523207936.0, - "20": 523230816.0, - "21": 524941312.0, - "22": 523656736.0, - "23": 523419296.0, - "24": 523494080.0, - "25": 525639488.0 + "1": 523038400.0, + "2": 523666144.0, + "3": 522932896.0, + "4": 523230784.0, + "5": 523010080.0, + "6": 523364672.0, + "7": 523427456.0, + "8": 523075072.0, + "9": 523459008.0, + "10": 523184256.0, + "11": 524287584.0, + "12": 523449408.0, + "13": 523490432.0, + "14": 524476160.0, + "15": 523629568.0, + "16": 523461216.0, + "17": 523075296.0, + "18": 523361760.0, + "19": 523208256.0, + "20": 523230272.0, + "21": 524940224.0, + "22": 523655680.0, + "23": 523418848.0, + "24": 523494112.0, + "25": 525637568.0 } }, "mem-allocated-bytes": { @@ -133,29 +133,29 @@ "step_interval": 1, "values": { "1": "nan", - "2": 6.05241, + "2": 6.3773, "3": "nan", - "4": 1.1044, + "4": 1.10598, "5": "nan", - "6": 1.09374, + "6": 1.09671, "7": "nan", - "8": 1.09724, + "8": 1.09861, "9": "nan", - "10": 1.09511, + "10": 1.09877, "11": "nan", - "12": 1.09777, + "12": 1.09983, "13": "nan", - "14": 1.09756, + "14": 1.10049, "15": "nan", - "16": 1.09624, + "16": 1.1003, "17": "nan", - "18": 1.09505, + "18": 1.1004, "19": "nan", - "20": 1.09743, + "20": 1.10004, "21": "nan", - "22": 1.09698, + "22": 1.28033, "23": "nan", - "24": 1.09519, + "24": 1.10055, "25": "nan" } } diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json index c5a0ab8d3e9..1f175733375 100644 --- a/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_cuda_graphs_pad_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json @@ -1 +1 @@ -{"1": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [0.9375802278518677, 0.01699276641011238, 0.0168936625123024, 0.016852224245667458, 0.01684313639998436, 0.0167868472635746, 0.01678694412112236, 0.016788223758339882, 0.01683913543820381, 0.016797984018921852, 0.01679254323244095, 0.01679065451025963, 0.01676851324737072, 0.016781216487288475, 0.016814494505524635, 0.016818752512335777, 0.016785727813839912, 0.01677747070789337, 0.016795936971902847, 0.016792094334959984, 0.017113056033849716, 0.01678374409675598, 0.016811968758702278, 0.01680857688188553, 0.01679401658475399, 0.016806943342089653, 0.017167072743177414, 0.016829216852784157, 0.016787424683570862, 0.016795679926872253], "latency": 1.4339574556797743, "logprobs": [-10.285516738891602, -3.7254064083099365, -2.8119518756866455, -1.2440053224563599, -0.2537177503108978, -1.7926565408706665, -2.373988628387451, -1.9992115497589111, -2.156676769256592, -6.0899152755737305, -0.814454197883606, -2.4737956523895264, -3.5019524097442627, -4.121853828430176, -1.9726070165634155, -1.8766462802886963, -2.2752578258514404, -7.124467849731445, -0.04110218212008476, -1.985706090927124, -5.001168727874756, -8.708745956420898, -9.837553024291992, -0.7836530804634094, -4.778284072875977, -0.8594533801078796, -2.295872211456299, -0.01927023194730282, -0.03589310497045517, -3.4844179153442383, -8.688593864440918, -1.2540490627288818, -6.682423114776611, -3.651825428009033, -3.790018081665039, -4.23261833190918, -2.183030128479004, -1.0943747758865356, -0.23566779494285583, -0.7870454788208008, -4.739112377166748, -9.07720947265625, -0.013387431390583515, -3.2162814140319824, -1.224939227104187, -3.9755403995513916, -0.749676525592804, -0.002204370917752385, -2.970001697540283, -10.52740478515625, -2.9543890953063965, -1.14937162399292, -4.980201721191406, -0.210438534617424, -0.06334561854600906, -1.3424071073532104, -2.2933273315429688, -4.389246940612793, -0.35113364458084106, -4.080296516418457, -0.36735761165618896, -0.1487821638584137, -2.7155423164367676, -10.591231346130371, -0.047830305993556976, -3.2783076763153076, -0.9070821404457092, -4.689446926116943, -0.2631016671657562, -2.5976767539978027, -0.8437033295631409, -1.703050136566162, -5.785486221313477, -16.94239616394043, -2.6509127616882324, -0.13801372051239014, -7.448275089263916, -1.05545175075531, -2.1010847091674805, -1.579776644706726, -0.2949126064777374, -5.616103649139404, -0.006375213619321585, -7.791621685028076, -2.7076175212860107, -3.008326768875122, -3.0655322074890137, -2.351105213165283, -0.4216800332069397, -1.4136691093444824, -2.2709290981292725, -0.6092997789382935, -1.3407301902770996, -1.9368633031845093, -1.6967312097549438, -0.7726529240608215, -0.5008315443992615, -1.2634307146072388, -1.4748260974884033, -0.9624079465866089, -0.39953532814979553, -0.4278743863105774, -0.0418093241751194, -1.2810207605361938, -2.2278497219085693, -2.7257916927337646, -0.8166881799697876, -0.40065163373947144, -2.907078742980957, -1.5525448322296143, -1.6864242553710938, -0.050371624529361725, -1.3529325723648071, -1.3700380325317383, -1.2650587558746338, -1.2854982614517212, -0.5030314326286316]}} \ No newline at end of file +{"1": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [0.9446943998336792, 0.01858755201101303, 0.01845088042318821, 0.01840316690504551, 0.01840025559067726, 0.018568480387330055, 0.018410207703709602, 0.018378719687461853, 0.01841052807867527, 0.018396640196442604, 0.018389441072940826, 0.01841011270880699, 0.018398208543658257, 0.018408993259072304, 0.018391264602541924, 0.018417054787278175, 0.018589023500680923, 0.018430015072226524, 0.018383968621492386, 0.01841052807867527, 0.018423520028591156, 0.01841152086853981, 0.018399743363261223, 0.01839520037174225, 0.018392670899629593, 0.01842377707362175, 0.01839977689087391, 0.018376000225543976, 0.01838582381606102, 0.01843372732400894], "latency": 1.4873389329877682, "logprobs": [-10.285516738891602, -3.660754680633545, -2.784210205078125, -1.2478915452957153, -0.25538313388824463, -1.7955950498580933, -2.393117904663086, -1.9708890914916992, -2.1845548152923584, -6.108627796173096, -0.8093583583831787, -2.479929208755493, -3.492928981781006, -4.095582962036133, -1.957276701927185, -1.8142306804656982, -2.216322422027588, -7.106796741485596, -0.041817210614681244, -1.895967960357666, -4.96872615814209, -8.66944694519043, -9.92969799041748, -0.8532882928848267, -4.771463871002197, -0.866722583770752, -2.3032634258270264, -0.018381956964731216, -0.03669184818863869, -3.478789806365967, -8.76733684539795, -1.2499185800552368, -6.596796989440918, -3.6890079975128174, -3.66709566116333, -4.238608360290527, -2.2527682781219482, -1.0958218574523926, -0.23835060000419617, -0.7813327312469482, -4.727116107940674, -8.907763481140137, -0.013596663251519203, -3.1760334968566895, -1.231803059577942, -3.9390676021575928, -0.7865883111953735, -0.002189026679843664, -2.8619141578674316, -10.677696228027344, -3.2620432376861572, -1.1773574352264404, -4.921055793762207, -0.2089274525642395, -0.06458104401826859, -1.3557488918304443, -2.268526792526245, -4.420165061950684, -0.3736952543258667, -4.021975994110107, -0.35798370838165283, -0.14103417098522186, -2.714251756668091, -10.528068542480469, -0.05847396329045296, -3.3989057540893555, -0.8518276810646057, -4.69785737991333, -0.26280850172042847, -2.659881114959717, -0.8425408005714417, -1.6949965953826904, -5.777432441711426, -16.946062088012695, -2.597249746322632, -0.1378251314163208, -7.378976821899414, -1.0831937789916992, -2.142360210418701, -1.5962045192718506, -0.29423344135284424, -5.565470218658447, -0.0060469298623502254, -7.74064302444458, -2.7711267471313477, -3.093285083770752, -3.0333504676818848, -2.406221866607666, -0.3982314467430115, -1.46319580078125, -2.2969329357147217, -0.6102715134620667, -1.3178201913833618, -1.999678134918213, -1.6960594654083252, -0.8376930952072144, -0.4746675491333008, -1.3011536598205566, -1.4845274686813354, -0.9787696599960327, -0.3996916115283966, -0.47013625502586365, -0.04292111471295357, -1.3171254396438599, -2.157470464706421, -2.698455810546875, -0.7798762917518616, -0.4310673475265503, -2.867293357849121, -1.5278971195220947, -1.676222324371338, -0.049695245921611786, -1.4178733825683594, -1.3098933696746826, -1.2817381620407104, -1.3194644451141357, -0.5006351470947266]}} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json index 07159e4d6cd..ae7ce4113b6 100644 --- a/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/moe/gpt_static_inference_tp4_pp1_ep4_16B_logitsmatch/golden_values_dev_dgx_h100.json @@ -1 +1 @@ -{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [13.663225173950195, 0.9431529641151428, 0.09179968386888504, 0.08776150643825531, 0.08934815973043442, 0.08766365051269531, 0.0875975638628006, 0.08973637968301773, 0.08724931627511978, 0.08813049644231796, 0.08782428503036499, 0.08671878278255463, 0.08726608008146286, 0.08730233460664749, 0.08764249086380005, 0.08764160424470901, 0.08731506764888763, 0.08679869025945663, 0.08846284449100494, 0.08785020560026169, 0.08797827363014221, 0.08753775805234909, 0.08817724883556366, 0.08719398826360703, 0.08734601736068726, 0.08738765120506287, 0.0870174691081047, 0.08738476783037186, 0.08912406116724014, 0.08691645413637161], "latency": 17.085081157274544, "logprobs": [-10.285516738891602, -3.7254064083099365, -2.8119518756866455, -1.2440053224563599, -0.2537177503108978, -1.7926565408706665, -2.373988628387451, -1.9992115497589111, -2.156676769256592, -6.0899152755737305, -0.814454197883606, -2.4737956523895264, -3.5019524097442627, -4.121853828430176, -1.9726070165634155, -1.8766462802886963, -2.2752578258514404, -7.124467849731445, -0.04110218212008476, -1.985706090927124, -5.001168727874756, -8.708745956420898, -9.837553024291992, -0.7836530804634094, -4.778284072875977, -0.8594533801078796, -2.295872211456299, -0.01927023194730282, -0.03589310497045517, -3.4844179153442383, -8.688593864440918, -1.2540490627288818, -6.682423114776611, -3.651825428009033, -3.790018081665039, -4.23261833190918, -2.183030128479004, -1.0943747758865356, -0.23566779494285583, -0.7870454788208008, -4.739112377166748, -9.07720947265625, -0.013387431390583515, -3.2162814140319824, -1.224939227104187, -3.9755403995513916, -0.749676525592804, -0.002204370917752385, -2.970001697540283, -10.52740478515625, -2.9543890953063965, -1.14937162399292, -4.980201721191406, -0.210438534617424, -0.06334561854600906, -1.3424071073532104, -2.2933273315429688, -4.389246940612793, -0.35113364458084106, -4.080296516418457, -0.36735761165618896, -0.1487821638584137, -2.7155423164367676, -10.591231346130371, -0.047830305993556976, -3.2783076763153076, -0.9070821404457092, -4.689446926116943, -0.2631016671657562, -2.5976767539978027, -0.8437033295631409, -1.703050136566162, -5.785486221313477, -16.94239616394043, -2.6509127616882324, -0.13801372051239014, -7.448275089263916, -1.05545175075531, -2.1010847091674805, -1.579776644706726, -0.2949126064777374, -5.616103649139404, -0.006375213619321585, -7.791621685028076, -2.7076175212860107, -3.008326768875122, -3.0655322074890137, -2.351105213165283, -0.4216800332069397, -1.4136691093444824, -2.2709290981292725, -0.6092997789382935, -1.3407301902770996, -1.9368633031845093, -1.6967312097549438, -0.7726529240608215, -0.5008315443992615, -1.2634307146072388, -1.4748260974884033, -0.9624079465866089, -0.39953532814979553, -0.4278743863105774, -0.0418093241751194, -1.2810207605361938, -2.2278497219085693, -2.7257916927337646, -0.8166881799697876, -0.40065163373947144, -2.907078742980957, -1.5525448322296143, -1.6864242553710938, -0.050371624529361725, -1.3529325723648071, -1.3700380325317383, -1.2650587558746338, -1.2854982614517212, -0.5030314326286316]}} \ No newline at end of file +{"0": {"input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.", "generated_text": " Wait for the moment when the music stops, and the lights come up, and the DJ says, \"I'm going to play a song for you", "generated_tokens": [32844, 1394, 1278, 4735, 2200, 1278, 7146, 30774, 1044, 1321, 1278, 26466, 3930, 2015, 1044, 1321, 1278, 30245, 8223, 1044, 1429, 1073, 4525, 4670, 1317, 3354, 1261, 6947, 1394, 1636], "tpot": [13.857564926147461, 0.9019312262535095, 0.09066630899906158, 0.08756758272647858, 0.08697913587093353, 0.08714726567268372, 0.08689986914396286, 0.08792512118816376, 0.08711794763803482, 0.08723466098308563, 0.08711709082126617, 0.08768505603075027, 0.08688262104988098, 0.0871850922703743, 0.08707337826490402, 0.08662214875221252, 0.08748595416545868, 0.08713395148515701, 0.0876602828502655, 0.08679766207933426, 0.08808169513940811, 0.0872347503900528, 0.08734342455863953, 0.08724310249090195, 0.08658534288406372, 0.0869181752204895, 0.08646786957979202, 0.08682256191968918, 0.08701737970113754, 0.08700713515281677], "latency": 17.222521955147386, "logprobs": [-10.285516738891602, -3.660754680633545, -2.784210205078125, -1.2478915452957153, -0.25538313388824463, -1.7955950498580933, -2.393117904663086, -1.9708890914916992, -2.1845548152923584, -6.108627796173096, -0.8093583583831787, -2.479929208755493, -3.492928981781006, -4.095582962036133, -1.957276701927185, -1.8142306804656982, -2.216322422027588, -7.106796741485596, -0.041817210614681244, -1.895967960357666, -4.96872615814209, -8.66944694519043, -9.92969799041748, -0.8532882928848267, -4.771463871002197, -0.866722583770752, -2.3032634258270264, -0.018381956964731216, -0.03669184818863869, -3.478789806365967, -8.76733684539795, -1.2499185800552368, -6.596796989440918, -3.6890079975128174, -3.66709566116333, -4.238608360290527, -2.2527682781219482, -1.0958218574523926, -0.23835060000419617, -0.7813327312469482, -4.727116107940674, -8.907763481140137, -0.013596663251519203, -3.1760334968566895, -1.231803059577942, -3.9390676021575928, -0.7865883111953735, -0.002189026679843664, -2.8619141578674316, -10.677696228027344, -3.2620432376861572, -1.1773574352264404, -4.921055793762207, -0.2089274525642395, -0.06458104401826859, -1.3557488918304443, -2.268526792526245, -4.420165061950684, -0.3736952543258667, -4.021975994110107, -0.35798370838165283, -0.14103417098522186, -2.714251756668091, -10.528068542480469, -0.05847396329045296, -3.3989057540893555, -0.8518276810646057, -4.69785737991333, -0.26280850172042847, -2.659881114959717, -0.8425408005714417, -1.6949965953826904, -5.777432441711426, -16.946062088012695, -2.597249746322632, -0.1378251314163208, -7.378976821899414, -1.0831937789916992, -2.142360210418701, -1.5962045192718506, -0.29423344135284424, -5.565470218658447, -0.0060469298623502254, -7.74064302444458, -2.7711267471313477, -3.093285083770752, -3.0333504676818848, -2.406221866607666, -0.3982314467430115, -1.46319580078125, -2.2969329357147217, -0.6102715134620667, -1.3178201913833618, -1.999678134918213, -1.6960594654083252, -0.8376930952072144, -0.4746675491333008, -1.3011536598205566, -1.4845274686813354, -0.9787696599960327, -0.3996916115283966, -0.47013625502586365, -0.04292111471295357, -1.3171254396438599, -2.157470464706421, -2.698455810546875, -0.7798762917518616, -0.4310673475265503, -2.867293357849121, -1.5278971195220947, -1.676222324371338, -0.049695245921611786, -1.4178733825683594, -1.3098933696746826, -1.2817381620407104, -1.3194644451141357, -0.5006351470947266]}} \ No newline at end of file diff --git a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json index b9a5b65a999..5b37f2fdb02 100644 --- a/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json +++ b/tests/functional_tests/test_cases/multimodal-llava/multimodal_llava_mcore_te_tp4_sp_cp2/golden_values_dev_dgx_h100.json @@ -4,56 +4,56 @@ "end_step": 50, "step_interval": 1, "values": { - 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res = requests.post( - DASHBOARD_ENDPOINT, - data=payload, - headers={"Content-Type": "application/json", "Accept-Charset": "UTF-8"}, - ) - - if not res.ok: - raise requests.exceptions.HTTPError( - f"Failed to make POST request. Received response: {res.status_code}" + try: + res = requests.post( + DASHBOARD_ENDPOINT, + data=payload, + headers={"Content-Type": "application/json", "Accept-Charset": "UTF-8"}, ) + if not res.ok: + logger.warning(f"Failed to make POST request. Received response: {res.status_code}") + except requests.exceptions.RequestException as e: + logger.warning(f"Failed to post telemetry: {e}") sys.exit(int(not success)) diff --git a/tests/test_utils/python_scripts/launch_nemo_run_workload.py b/tests/test_utils/python_scripts/launch_nemo_run_workload.py index 78c8635f2d1..cdb730fae63 100644 --- a/tests/test_utils/python_scripts/launch_nemo_run_workload.py +++ b/tests/test_utils/python_scripts/launch_nemo_run_workload.py @@ -128,17 +128,6 @@ def main( "NCCL_DEBUG_FILE": "/opt/megatron-lm/assets_dir/logs/nccl_debug.log", "HF_HOME": "/mnt/hf_home", "TRANSFORMERS_OFFLINE": "1", - "NCCL_PROTO": "simple", - "NCCL_ALGO": "Ring", - "NCCL_COLLNET_ENABLE": "0", - "NCCL_NVLS_ENABLE": "0", - # Disable the EFA tuner plugin so NCCL_ALGO/NCCL_PROTO are - # actually respected instead of being overridden at runtime. - "NCCL_TUNER_PLUGIN": "", - # Match the NCCL default (4 MB) so buffer-chunking behaviour - # is the same as on Slurm nodes. - "NCCL_BUFFSIZE": "4194304", - "TORCH_NCCL_AVOID_RECORD_STREAMS": "1", }, packager=run.Packager(), volumes=artifacts, diff --git a/tests/test_utils/python_scripts/notify.py b/tests/test_utils/python_scripts/notify.py index 7da00dc401a..cdad04644a7 100644 --- a/tests/test_utils/python_scripts/notify.py +++ b/tests/test_utils/python_scripts/notify.py @@ -1,3 +1,5 @@ +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + import logging import os @@ -94,6 +96,10 @@ def main(pipeline_id: int, check_for: str, pipeline_context: str, pipeline_creat messages.append("===============================================") + if not WEBHOOK_URL: + logger.info("No webhook URL configured, skipping Slack notification") + return + for message in messages: response = slack_sdk.webhook.WebhookClient(WEBHOOK_URL).send(text=message) logger.info(response.status_code) From b562151f4d87445edafcad4e934281183879f157 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Mon, 20 Apr 2026 15:19:45 -0500 Subject: [PATCH 51/72] Remove packed_attention_mask unused parameter (#3859) Co-authored-by: Jon Barker --- megatron/rl/sequence_packing_utils.py | 63 ++----------------- .../rl/test_sequence_packing_utils.py | 17 +++-- 2 files changed, 12 insertions(+), 68 deletions(-) diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py index 6f3e4711ed7..50ab2ca27de 100644 --- a/megatron/rl/sequence_packing_utils.py +++ b/megatron/rl/sequence_packing_utils.py @@ -51,7 +51,6 @@ class PackingContext: original_trajs: All trajectories before packing packed_trajs: Packed trajectories tensor [num_bins, bin_size] packed_position_ids: Position IDs for packed sequences [num_bins, bin_size] - packed_attention_mask: Attention mask for packed sequences [num_bins, 1, bin_size, bin_size] packed_loss_mask: Loss mask for packed sequences [num_bins, bin_size] original_inference_logprobs: Inference logprobs for all sequences before packing (optional) bin_advantages: List of advantage tensors for each bin @@ -64,7 +63,6 @@ class PackingContext: original_trajs: torch.Tensor packed_trajs: torch.Tensor packed_position_ids: torch.Tensor - packed_attention_mask: torch.Tensor packed_loss_mask: torch.Tensor original_inference_logprobs: Optional[torch.Tensor] = None bin_advantages: List[torch.Tensor] = field(default_factory=list) @@ -314,9 +312,8 @@ def create_empty_bins( packed_trajs : torch.Tensor, packed_position_ids : torch.Tensor, packed_loss_mask : torch.Tensor, - packed_attention_mask : torch.Tensor, tokenizer, -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, List[Dict[str, Any]]]: +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[Dict[str, Any]]]: """Create empty bins for padding to ensure all ranks have the same number of bins. Args: @@ -325,11 +322,10 @@ def create_empty_bins( packed_trajs: Packed trajectories tensor (for dtype/device reference) packed_position_ids: Packed position IDs tensor (for dtype/device reference) packed_loss_mask: Packed loss mask tensor (for dtype/device reference) - packed_attention_mask: Packed attention mask tensor (can be None) tokenizer: Tokenizer for pad token Returns: - Tuple of (empty_trajs, empty_position_ids, empty_loss_mask, empty_attention_mask, empty_packing_info_entries) + Tuple of (empty_trajs, empty_position_ids, empty_loss_mask, empty_packing_info_entries) """ device = packed_trajs.device @@ -337,7 +333,6 @@ def create_empty_bins( empty_bins = [] empty_position_ids_list = [] empty_loss_mask_list = [] - empty_attention_mask_list = [] empty_packing_info_entries = [] for i in range(num_empty_bins): @@ -355,14 +350,6 @@ def create_empty_bins( empty_loss = torch.zeros(1, bin_size, dtype=packed_loss_mask.dtype, device=device) empty_loss_mask_list.append(empty_loss) - # Zero attention mask if needed - if packed_attention_mask is not None: - # Attention mask is always 4D: [num_bins, 1, bin_size, bin_size] - empty_attn = torch.zeros( - 1, 1, bin_size, bin_size, dtype=packed_attention_mask.dtype, device=device - ) - empty_attention_mask_list.append(empty_attn) - # Empty packing info entries empty_packing_info_entries.append( { @@ -376,22 +363,15 @@ def create_empty_bins( empty_trajs = torch.cat(empty_bins, dim=0) empty_position_ids = torch.cat(empty_position_ids_list, dim=0) empty_loss_mask = torch.cat(empty_loss_mask_list, dim=0) - empty_attention_mask = ( - torch.cat(empty_attention_mask_list, dim=0) - if packed_attention_mask is not None - else None - ) else: empty_trajs = None empty_position_ids = None empty_loss_mask = None - empty_attention_mask = None return ( empty_trajs, empty_position_ids, empty_loss_mask, - empty_attention_mask, empty_packing_info_entries, ) @@ -708,9 +688,6 @@ def pack_sequences( position_ids = torch.zeros( (num_bins, self.bin_size), dtype=torch.long, device=device, requires_grad=False ) - attention_mask = torch.zeros( - (num_bins, 1, self.bin_size, self.bin_size), dtype=torch.bool, device=device - ) loss_mask = torch.zeros((num_bins, self.bin_size), dtype=torch.float, device=device) # Track packing information for unpacking later @@ -741,12 +718,6 @@ def pack_sequences( len(seq), device=device, requires_grad=False ) - # Causal attention mask within each sequence - seq_len = end - start - attention_mask[bin_idx, 0, start:end, start:end] = torch.tril( - torch.ones(seq_len, seq_len, dtype=torch.bool, device=device) - ) - # Loss mask (excluding padding) loss_mask[bin_idx, start:end] = 1.0 @@ -761,12 +732,6 @@ def pack_sequences( seq_starts.append(current_pos) seq_starts_dict[bin_idx] = seq_starts - # Note: We'll store the actual padded length later when we know it - # (it depends on the original trajectories passed to pack_sequences) - - # Invert attention mask, before inversion: (True = attend, False = mask) - attention_mask.bitwise_not_() - # Create the PackingInfo dataclass packing_info = PackingInfo( bin_seq_indices=bin_seq_indices, @@ -795,15 +760,14 @@ def pack_sequences( ) log_single_rank(logger, logging.DEBUG, f" - First 20 bins: {seq_per_bin[:20]}") - return packed_sequences, position_ids, attention_mask, loss_mask, packing_info + return packed_sequences, position_ids, loss_mask, packing_info def distribute_packed_bins( packed_trajs: torch.Tensor, packed_position_ids: torch.Tensor, - packed_attention_mask: torch.Tensor, packed_loss_mask: torch.Tensor, packing_info: PackingInfo, -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, PackingInfo]: +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, PackingInfo]: """Distribute packed bins across the data parallel ranks.""" rank = mpu.get_data_parallel_rank() world_size = mpu.get_data_parallel_world_size() @@ -840,7 +804,6 @@ def distribute_packed_bins( # Extract this rank's bins my_packed_trajs = [] my_packed_position_ids = [] - my_packed_attention_mask = [] my_packed_loss_mask = [] my_bin_seq_indices = [] my_seq_starts = {} @@ -850,8 +813,6 @@ def distribute_packed_bins( for new_idx, old_idx in enumerate(my_bin_indices): my_packed_trajs.append(packed_trajs[old_idx]) my_packed_position_ids.append(packed_position_ids[old_idx]) - if packed_attention_mask is not None: - my_packed_attention_mask.append(packed_attention_mask[old_idx]) my_packed_loss_mask.append(packed_loss_mask[old_idx]) my_bin_seq_indices.append(packing_info.bin_seq_indices[old_idx]) my_seq_starts[new_idx] = packing_info.seq_starts[old_idx] @@ -877,9 +838,6 @@ def distribute_packed_bins( device=packed_position_ids.device, ) ) - packed_attention_mask = ( - torch.stack(my_packed_attention_mask) if my_packed_attention_mask else None - ) packed_loss_mask = ( torch.stack(my_packed_loss_mask) if my_packed_loss_mask @@ -937,7 +895,6 @@ def distribute_packed_bins( empty_trajs, empty_position_ids, empty_loss_mask, - empty_attention_mask, empty_packing_entries, ) = create_empty_bins( num_empty_bins, @@ -945,7 +902,6 @@ def distribute_packed_bins( packed_trajs, packed_position_ids, packed_loss_mask, - packed_attention_mask, tokenizer, ) @@ -956,18 +912,13 @@ def distribute_packed_bins( ) packed_loss_mask = torch.cat([packed_loss_mask, empty_loss_mask], dim=0) - if packed_attention_mask is not None and empty_attention_mask is not None: - packed_attention_mask = torch.cat( - [packed_attention_mask, empty_attention_mask], dim=0 - ) - # Add empty entries to packing_info for i, entry in enumerate(empty_packing_entries): bin_idx = current_bins + i new_packing_info.bin_seq_indices.append(entry['bin_seq_indices']) new_packing_info.seq_starts[bin_idx] = entry['seq_starts'] - return packed_trajs, packed_position_ids, packed_attention_mask, packed_loss_mask, new_packing_info + return packed_trajs, packed_position_ids, packed_loss_mask, new_packing_info def pack_all_trajectories(trajs, generation_masks, inference_logprobs, global_advantages, bin_size, max_sequences_per_bin, packing_algo): @@ -1000,7 +951,6 @@ def _gather(data): ( packed_trajs, packed_position_ids, - packed_attention_mask, packed_loss_mask, packing_info, ) = packer.pack_sequences(trajs, generation_masks) @@ -1010,13 +960,11 @@ def _gather(data): ( packed_trajs, packed_position_ids, - packed_attention_mask, packed_loss_mask, packing_info, ) = distribute_packed_bins( packed_trajs, packed_position_ids, - packed_attention_mask, packed_loss_mask, packing_info, ) @@ -1053,7 +1001,6 @@ def _gather(data): original_trajs=trajs, packed_trajs=packed_trajs, packed_position_ids=packed_position_ids, - packed_attention_mask=packed_attention_mask, packed_loss_mask=packed_loss_mask, original_inference_logprobs=inference_logprobs, bin_advantages=bin_advantages, diff --git a/tests/unit_tests/rl/test_sequence_packing_utils.py b/tests/unit_tests/rl/test_sequence_packing_utils.py index 06e63adf217..0a4f4cd03f9 100644 --- a/tests/unit_tests/rl/test_sequence_packing_utils.py +++ b/tests/unit_tests/rl/test_sequence_packing_utils.py @@ -98,13 +98,12 @@ def test_sequence_packing_basic(): rewards = torch.tensor([1.0, 2.0, 3.0, 4.0]) sequences_tensor = torch.stack(sequences) - packed_trajs, packed_position_ids, packed_attention_mask, packed_loss_mask, packing_info = ( - packer.pack_sequences(sequences_tensor, generation_masks) + packed_trajs, packed_position_ids, packed_loss_mask, packing_info = packer.pack_sequences( + sequences_tensor, generation_masks ) assert packed_trajs is not None assert packed_position_ids is not None - assert packed_attention_mask is not None assert packed_loss_mask is not None assert packing_info is not None @@ -140,8 +139,8 @@ def test_sequence_packing_with_generation_masks(): ) padded_sequences_tensor = torch.stack(padded_sequences) - packed_trajs, packed_position_ids, packed_attention_mask, packed_loss_mask, packing_info = ( - packer.pack_sequences(padded_sequences_tensor, generation_masks) + packed_trajs, packed_position_ids, packed_loss_mask, packing_info = packer.pack_sequences( + padded_sequences_tensor, generation_masks ) assert packed_trajs.shape[0] == 1 @@ -162,16 +161,14 @@ def test_sequence_packing_empty_bins(): ) packed_position_ids = torch.tensor([[0, 1, 2, 3, 0, 0, 0, 0]]) packed_loss_mask = torch.tensor([[1, 1, 1, 1, 0, 0, 0, 0]], dtype=torch.float) - packed_attention_mask = torch.ones(1, bin_size, bin_size) - empty_trajs, empty_position_ids, empty_loss_mask, empty_attention_mask, empty_packing_info = ( + empty_trajs, empty_position_ids, empty_loss_mask, empty_packing_info = ( sequence_packing_utils.create_empty_bins( num_empty_bins=num_empty_bins, bin_size=bin_size, packed_trajs=packed_trajs, packed_position_ids=packed_position_ids, packed_loss_mask=packed_loss_mask, - packed_attention_mask=packed_attention_mask, tokenizer=tokenizer, ) ) @@ -220,8 +217,8 @@ def test_sequence_packing_integration(): ] sequences_tensor = torch.stack(sequences) - packed_trajs, packed_position_ids, packed_attention_mask, packed_loss_mask, packing_info = ( - packer.pack_sequences(sequences_tensor, generation_masks) + packed_trajs, packed_position_ids, packed_loss_mask, packing_info = packer.pack_sequences( + sequences_tensor, generation_masks ) assert packed_trajs is not None From 859b66a00390880ced45dac99f2feb5b4c661538 Mon Sep 17 00:00:00 2001 From: megnvidia Date: Mon, 20 Apr 2026 14:06:13 -0700 Subject: [PATCH 52/72] Second batch of audit edits (#4115) Signed-off-by: meg miranda Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com> --- docs/user-guide/features/context_parallel.md | 26 +++--- docs/user-guide/features/custom_fsdp.md | 91 ++++++++++--------- docs/user-guide/features/dist_optimizer.md | 28 +++--- .../fine_grained_activation_offloading.md | 44 +++++---- docs/user-guide/features/index.md | 2 +- docs/user-guide/features/megatron_energon.md | 28 +++--- docs/user-guide/features/megatron_rl.md | 20 ++-- .../features/multi_latent_attention.md | 10 +- .../features/multi_token_prediction.md | 33 +++---- .../features/pipeline_parallel_layout.md | 16 ++-- docs/user-guide/features/tokenizers.md | 55 ++++++----- 11 files changed, 185 insertions(+), 168 deletions(-) diff --git a/docs/user-guide/features/context_parallel.md b/docs/user-guide/features/context_parallel.md index c44366187be..890609ac7de 100644 --- a/docs/user-guide/features/context_parallel.md +++ b/docs/user-guide/features/context_parallel.md @@ -7,37 +7,37 @@ license agreement from NVIDIA CORPORATION is strictly prohibited. --> -# context_parallel package +# Context Parallel Package -## Context parallelism overview +## Context Parallelism Overview ```{figure} ../../images/context_parallel/CP_overview.png -:alt: cp_overview +:alt: Diagram of a transformer layer with tensor parallelism 2 and context parallelism 2, showing CP and TP communication patterns around attention and other blocks. :align: center Figure 1: A transformer layer running with TP2CP2. Communications next to Attention are for CP, others are for TP. (AG/RS: all-gather in forward and reduce-scatter in backward, RS/AG: reduce-scatter in forward and all-gather in backward, /AG: no-op in forward and all-gather in backward). ``` -Context Parallelism ("CP") is a parallelization scheme on the dimension of sequence length. Unlike prior SP (sequence parallelism) which only splits the sequence of Dropout and LayerNorm activations, CP partitions the network inputs and all activations along sequence dimension. With CP, all modules except attention (e.g., Linear, LayerNorm, etc.) can work as usual without any changes, because they do not have inter-token operations. As for attention, the Q (query) of each token needs to compute with the KV (key and value) of all tokens in the same sequence. Hence, CP requires additional all-gather across GPUs to collect the full sequence of KV. Correspondingly, reduce-scatter should be applied to the activation gradients of KV in backward propagation. To reduce activation memory footprint, each GPU only stores the KV of a sequence chunk in forward and gathers KV again in backward. KV communication happens between a GPU and its counterparts in other TP groups. The all-gather and reduce-scatter are transformed to point-to-point communications in ring topology under the hood. Exchanging KV also can leverage MQA/GQA to reduce communication volumes, as they only have one or few attention heads for KV. +Context Parallelism (CP) is a parallelization scheme on the sequence-length dimension. Unlike prior SP (sequence parallelism), which only splits the sequence of Dropout and LayerNorm activations, CP partitions the network inputs and all activations along the sequence dimension. With CP, all modules except attention (for example, Linear and LayerNorm) can work as usual without any changes, because they do not have inter-token operations. For attention, the Q (query) of each token must combine with the KV (key and value) of all tokens in the same sequence. CP therefore requires an additional all-gather across GPUs to collect the full sequence of KV. Correspondingly, reduce-scatter is applied to the activation gradients of KV in backward propagation. To reduce activation memory footprint, each GPU stores only the KV of a sequence chunk in forward and gathers KV again in backward. KV communication happens between a GPU and its counterparts in other TP groups. The all-gather and reduce-scatter are implemented as point-to-point communications in a ring topology. Exchanging KV can also leverage MQA or GQA to reduce communication volume, because those variants use one or a few attention heads for KV. -For example, in Figure 1, assuming sequence length is 8K, each GPU processes 4K tokens. GPU0 and GPU2 compose a CP group, they exchange KV with each other. Same thing also happens between GPU1 and GPU3. CP is similar to [Ring Attention](https://arxiv.org/abs/2310.01889) but provides better performance by (1) leveraging the latest OSS and cuDNN flash attention kernels; (2) removing unnecessary computation resulted from low-triangle causal masking and achieving optimal load balance among GPUs. +For example, in Figure 1, if the sequence length is 8K, each GPU processes 4K tokens. GPU0 and GPU2 form a CP group and exchange KV with each other; the same pattern applies between GPU1 and GPU3. CP is similar to [Ring Attention](https://arxiv.org/abs/2310.01889) but targets higher performance by (1) using current open-source and cuDNN flash attention kernels, and (2) avoiding extra work from lower-triangle causal masking while keeping load balanced across GPUs. -## Context parallelism benefits +## Context Parallelism Benefits ```{figure} ../../images/context_parallel/CP_results.png -:alt: cp_results +:alt: Chart of speedup for 175B GPT with different tensor parallelism and context parallelism combinations compared with full activation recomputation. :align: center -Figure 2: Speedup of 175B GPT with various TP+CP combinations vs. full recompute (i.e., TP8CP1). +Figure 2: Speedup of 175B GPT with various TP+CP combinations compared to full recomputation (that is, TP8CP1). ``` -LLM encounters OOM (out of memory) issue with long context (i.e., long sequence length) because of linearly increasing memory footprint of activations. Recomputing activations in backward can avoid OOM but also introduce significant overheads (~30% with full recompute). Enlarging TP (tensor model parallelism) can fix the OOM issue as well, but it potentially makes compute (e.g., Linear) too short to overlap communication latencies. To be clear, scaling out to more GPUs with bigger TP can hit the overlapping problem no matter if OOM happens. +An LLM can hit an out-of-memory (OOM) error on long contexts (long sequence lengths) because activation memory grows about linearly with sequence length. Recomputing activations in backward can avoid OOM but adds significant overhead (about 30 percent with full recomputation). Increasing TP (tensor model parallelism) can also fix OOM, but it can make compute in layers such as Linear too short to hide communication latency. Scaling to more GPUs with larger TP can hit that overlap limit even when OOM is not the driver. -CP can better address the issues. With CP, each GPU only computes on a part of the sequence, which reduces both computation and communication by CP times. Therefore, there are no concerns about the overlapping between them. The activation memory footprint per GPU is also CP times smaller, hence no OOM issue anymore. As Figure 2 shows, the combinations of TP and CP can achieve optimal performance by eliminating recompute overheads and making the best tradeoff between computation and communications. +CP addresses these tradeoffs. With CP, each GPU computes on part of the sequence, which scales down both compute and communication by the CP degree. Overlap between them is less of a concern. The activation memory footprint per GPU is also smaller by the CP degree, which reduces OOM risk. As Figure 2 shows, TP and CP together can outperform full recomputation by removing most recompute overhead and balancing compute against communication. -## Enabling context parallelism +## Enabling Context Parallelism -CP support has been added to GPT. All models that share GPT code path also should be able to benefit from CP, such as Llama. CP can work with TP (tensor model parallelism), PP (pipeline model parallelism), and DP (data parallelism), where the total number of GPUs equals TPxCPxPPxDP. CP also can work with different attention variants, including MHA/MQA/GQA, uni-directional and bi-directional masking. +CP support is included on the GPT code path. Other models that share that path, such as LLaMA, can use CP as well. CP works with TP (tensor model parallelism), PP (pipeline model parallelism), and DP (data parallelism). The total GPU count is TP × CP × PP × DP. CP also works with different attention variants, including MHA, MQA, and GQA, with unidirectional or bidirectional masking. -CP is enabled by simply setting context_parallel_size= in command line. Default context_parallel_size is 1, which means CP is disabled. Running with CP requires Megatron-Core (>=0.5.0) and Transformer Engine (>=1.1). +Enable CP by setting `context_parallel_size=` on the command line. The default `context_parallel_size` is 1, which disables CP. Running with CP requires Megatron Core (>=0.5.0) and Transformer Engine (>=1.1). diff --git a/docs/user-guide/features/custom_fsdp.md b/docs/user-guide/features/custom_fsdp.md index ab1a1efc402..3c774e5f493 100644 --- a/docs/user-guide/features/custom_fsdp.md +++ b/docs/user-guide/features/custom_fsdp.md @@ -9,11 +9,11 @@ # Megatron FSDP -**NOTE: In M-Core 0.14, the custom FSDP refactored its checkpoint implementation to use DTensor-based torch distributed checkpointing. The custom FSDP was also renamed Megatron FSDP. The relevant sections of this document are no longer applicable.** +**Note: In M-Core 0.14, the custom FSDP refactored its checkpoint implementation to use DTensor-based PyTorch distributed checkpointing. The custom FSDP was also renamed Megatron FSDP. The relevant sections of this document are no longer applicable.** -## How to use ? +## How to Use Megatron FSDP -Add these flag to enable MCore custom FSDP. +Add these flags to enable MCore custom FSDP. ```bash --use-megatron-fsdp @@ -22,19 +22,19 @@ Add these flag to enable MCore custom FSDP. --use-distributed-optimizer ``` -For a practical guide covering required configurations, checkpoint conversion, and example scripts, see the [Megatron-FSDP User Guide](../../discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md). +For a practical guide covering required configurations, checkpoint conversion, and example scripts, refer to the [Megatron-FSDP User Guide](../../discussions/megatron-fsdp-user-guide/megatron-fsdp-user-guide.md). ## Key Features -- **Sharding Strategy**: Efficiently shards optimizer states, gradients, and parameters to reduce memory consumption. -- **Communication and Computation Overlap**: Optimized to enable concurrent execution of communication and computation, enhancing overall efficiency. -- **Supports automatic mixed precision training**: Compatible with BF16 O1/O2/O3 recipes, as well as FP8 compute with FP32 parameters and FP8 parameter training, allowing for flexible precision configurations. -- **Tensor Parallelism (TP), Expert Parallelism (EP) and Context Parallelism (CP)**: Compatible with TP, EP and CP configurations, enabling efficient scaling of large language models. -- **Distributed Model Initialization with Meta Device**: Allows model initialization using meta device, followed by layer-by-layer initialization of distributed model weight buffers via the `Module.reset_parameters` API, facilitating the initialization of extremely large models. +- **Sharding Strategy**: Shards optimizer states, gradients, and parameters to reduce memory consumption. +- **Communication and Computation Overlap**: Overlaps communication with computation where possible during training. +- **Supports automatic mixed precision training**: Compatible with BF16 O1/O2/O3 recipes, as well as FP8 compute with FP32 parameters and FP8 parameter training, with several precision configuration options. +- **Tensor Parallelism (TP), Expert Parallelism (EP), and Context Parallelism (CP)**: Compatible with TP, EP, and CP configurations for scaling large language models. +- **Distributed Model Initialization with Meta Device**: Allows model initialization using the meta device, then layer-by-layer initialization of distributed model weight buffers through the `Module.reset_parameters` API, which supports very large models. ## Configuration Recommendations -### 1. Disable `CUDA_DEVICE_MAX_CONNECTIONS` +### Disable `CUDA_DEVICE_MAX_CONNECTIONS` To ensure full parallelization of FSDP communication and computation, disable the CUDA_DEVICE_MAX_CONNECTIONS environment variable. This step avoids potential bubble in CUDA stream. (But it may slow down TP and CP to some extent.) @@ -42,60 +42,60 @@ To ensure full parallelization of FSDP communication and computation, disable th unset CUDA_DEVICE_MAX_CONNECTIONS ``` -### 2. Add `--calculate-per-token-loss` +### Add `--calculate-per-token-loss` For gradients sharding mode optimization, include the `--calculate-per-token-loss` flag in your training script. This improves performance by reducing the frequency of gradient scaling, which is also a sizable drain on SM resources. ## Design of Custom FSDP -### 1. Overview +### Overview -The custom Fully Sharded Data Parallelism (FSDP) implementation in Megatron-Core is specifically designed to optimize memory consumption and performance for large language models. The core design principles include: +The custom Fully Sharded Data Parallelism (FSDP) implementation in Megatron Core targets memory use and throughput for large language models. The core design principles include: - - **Optimized for Large Language Models**: This custom FSDP implementation is tailored to efficiently scale with models containing billions of parameters, ensuring seamless execution and training of massive models. - - **Efficient Memory Consumption**: By strategically sharding optimizer states, gradients, and model parameters, the custom FSDP significantly reduces memory usage. This approach enables the training of models that would otherwise be too large to fit in memory. - - **Efficient Workflow & Overlapping Communication and Computation**: The implementation is engineered to minimize the number of communication steps required during training. It maximizes the overlap between communication and computation, thereby enhancing overall training efficiency and reducing latency. - - **Support for MCore's Efficient Training Methods**: The custom FSDP seamlessly integrates with Megatron-Core's advanced parallelism techniques, including tensor parallelism, expert parallelism and context parallelism. Additionally, it supports automatic mixed precision training, further optimizing training performance and efficiency. + - **Optimized for Large Language Models**: This custom FSDP implementation scales with models containing billions of parameters and supports training at that scale. + - **Efficient Memory Consumption**: By sharding optimizer states, gradients, and model parameters, the custom FSDP cuts memory use so models that would not fit on device with plain DDP can train. + - **Efficient Workflow and Overlapping Communication and Computation**: The implementation reduces communication steps during training and overlaps communication with computation where possible to reduce idle time. + - **Support for MCore's Efficient Training Methods**: The custom FSDP integrates with advanced parallelism in Megatron Core, including tensor parallelism, expert parallelism, and context parallelism. It also supports automatic mixed precision training. -The design of Custom FSDP draws inspiration from PyTorch FSDP [Zhao, Yanli, et al.](https://arxiv.org/pdf/2304.11277) and MCore's distributed optimizer. The introduction to PyTorch FSDP is referenced here to clarify the underlying concepts of the custom FSDP design. +The design of Custom FSDP draws inspiration from PyTorch FSDP [Zhao, Yanli, et al.](https://arxiv.org/pdf/2304.11277) and the MCore distributed optimizer. The following background on PyTorch FSDP clarifies the concepts behind the custom FSDP design. > In DistributedDataParallel, (DDP) training, each process/ worker owns a replica of the model and processes a batch of data, finally it uses all-reduce to sum up gradients over different workers. In DDP the model weights and optimizer states are replicated across all workers. FSDP is a type of data parallelism that shards model parameters, optimizer states and gradients across DDP ranks. > When training with FSDP, the GPU memory footprint is smaller than when training with DDP across all workers. This makes the training of some very large models feasible by allowing larger models or batch sizes to fit on device. This comes with the cost of increased communication volume. The communication overhead is reduced by internal optimizations like overlapping communication and computation. -![FSDP workflow](../../images/custom_fsdp/FSDP_workflow.png) +![Diagram of FSDP workflow showing all-gather, forward, discard, backward, and reduce-scatter across ranks](../../images/custom_fsdp/FSDP_workflow.png) -*Notice that the unit processed in workflow here is the “FSDP instance 1: N layers”, where an FSDP instance is the smallest FSDP processing unit (also a PyTorch module), which means that we can safely release this module weights after using it (executing the forward or backward of this module), and there will be no other computations computations relying on these weights. This capability is the foundation of FSDP's layer-by-layer execution and memory-saving strategy. An FSDP instance is also referred to as an **FSDP Unit**.* +The unit processed in the workflow here is the "FSDP instance 1: N layers", where an FSDP instance is the smallest FSDP processing unit (also a PyTorch module). You can release this module's weights after its forward or backward because no other computation depends on those weights. That behavior supports FSDP's layer-by-layer execution and memory-saving strategy. An FSDP instance is also called an **FSDP Unit**. -*It is worth noting that an FSDP instance can correspond to multiple FSDP parameter groups. These groups are separated by Data Parallel (DP) communication groups and the data type of the parameter or gradient. Consequently, an FSDP instance may require several parameter-gather tasks before execution (forward or backward). Each **FSDP parameter group** corresponds to one **Data Parallel Buffer** in custom FSDP.* +An FSDP instance can correspond to multiple FSDP parameter groups. These groups are separated by Data Parallel (DP) communication groups and the data type of the parameter or gradient. Consequently, an FSDP instance may require several parameter-gather tasks before execution (forward or backward). Each **FSDP parameter group** corresponds to one **Data Parallel Buffer** in custom FSDP. -At a high level FSDP works as follow: +At a high level, FSDP works as follows: -In constructor +In constructor: - Shard model parameters and each rank only keeps its own shard -In forward path +In forward path: - Run all_gather to collect all shards from all ranks to recover the full parameter in this FSDP unit - Run forward computation - Discard parameter shards it has just collected -In backward path +In backward path: - Run all_gather to collect all shards from all ranks to recover the full parameter in this FSDP unit - Run backward computation - Run reduce_scatter to sync gradients - - Discard parameters. + - Discard parameters One way to view FSDP’s sharding is to decompose the DDP gradient all-reduce into reduce-scatter and all-gather. Specifically, during the backward pass, FSDP reduces and scatters gradients, ensuring that each rank possesses a shard of the gradients. Then it updates the corresponding shard of the parameters in the optimizer step. Finally, in the subsequent forward pass, it performs an all-gather operation to collect and combine the updated parameter shards. -![FSDP Allreduce](../../images/custom_fsdp/FSDP_Allreduce.png) +![Diagram comparing DDP all-reduce with FSDP reduce-scatter and all-gather](../../images/custom_fsdp/FSDP_Allreduce.png) -### 2. Custom FSDP underlying data structure +### Custom FSDP Underlying Data Structure To implement the FSDP functionality described above, the custom FSDP is designed with the following Python classes and data structure: -![MCore Custom FSDP Class Diagram](../../images/custom_fsdp/MCore_Custom_FSDP_Class_Diagram.png) +![Class diagram of Megatron Core custom FSDP Python types](../../images/custom_fsdp/MCore_Custom_FSDP_Class_Diagram.png) -### 3. The custom FSDP interface: FullyShardedDataParallel +### The Custom FSDP Interface: FullyShardedDataParallel The custom FSDP provides the same programming interface as PyTorch's DistributedDataParallel (DDP) as FullyShardedDataParallel (FSDP). For example, you can apply FSDP to models as follows: @@ -135,14 +135,15 @@ def load_model_and_optimizer_state_dict(state_dict): optimizer.load_state_dict(state_dict["optimizer"]) ``` -**Key Notes:** - - You can configure which modules should be treated as FSDP units via the `fsdp_unit_modules` argument. This configuration is mandatory. +Key notes: + + - You can configure which modules should be treated as FSDP units through the `fsdp_unit_modules` argument. This configuration is mandatory. - The custom FSDP must be used with a distributed optimizer since it provides distributed checkpointing. - The data-parallel communication group for parameters is not explicitly shown. Custom FSDP configures these groups as either DP (data-parallel) or EDP (expert data-parallel) based on parameter markings. -#### 3.1 Initializing Models on the Meta Device +#### Initializing Models on the Meta Device -For training particularly large models with FSDP, you can initialize the model on the meta device. Using PyTorch's `reset_parameters` API, you can initialize model weights layer by layer during the construction of the `ParamAndGradBuffer`. Most PyTorch native modules and TransformerEngine modules support this API (e.g., [PyTorch Linear](https://github.com/pytorch/pytorch/blob/v2.6.0/torch/nn/modules/linear.py#L114), [TE LayerNormLinear](https://github.com/NVIDIA/TransformerEngine/blob/release_v2.0/transformer_engine/pytorch/module/layernorm_linear.py#L1107)). +For training particularly large models with FSDP, you can initialize the model on the meta device. Using PyTorch's `reset_parameters` API, you can initialize model weights layer by layer during the construction of the `ParamAndGradBuffer`. Most PyTorch native modules and TransformerEngine modules support this API (for example, [PyTorch Linear](https://github.com/pytorch/pytorch/blob/v2.6.0/torch/nn/modules/linear.py#L114), [TE LayerNormLinear](https://github.com/NVIDIA/TransformerEngine/blob/release_v2.0/transformer_engine/pytorch/module/layernorm_linear.py#L1107)). ```python # Initialize model on meta device @@ -158,10 +159,10 @@ model = FullyShardedDataParallel( ``` **Important Considerations:** -1. *Custom Modules*: If your model contains custom modules, ensure they implement the `reset_parameters` API. Otherwise, you may need to force parameter initialization on a CUDA or CPU device. -2. *Tensor Initialization*: Be cautious of tensors created during model initialization without a specified device—they will default to the meta device. To avoid issues, explicitly specify the device for these tensors to ensure compatibility with this function. +- *Custom Modules*: If your model contains custom modules, ensure they implement the `reset_parameters` API. Otherwise, you may need to force parameter initialization on a CUDA or CPU device. +- *Tensor Initialization*: Be cautious of tensors created during model initialization without a specified device; they default to the meta device. To avoid issues, explicitly specify the device for these tensors to ensure compatibility with this function. -### 4. Interaction between Custom FSDP and Model Forward/Backward Propagation +### Interaction Between Custom FSDP and Model Forward/Backward Propagation Custom FSDP implements Fully Sharded Data Parallelism (FSDP) through a series of module hooks, gradient hooks, or by adding functions between modules. This involves inserting communications and manipulating parameters and gradients during PyTorch's module forward or backward propagation. @@ -174,21 +175,21 @@ Module hooks summary: The gradient reduction pipeline maintains a map of gradients to FSDP parameter groups. If all gradients in an FSDP parameter group are ready, it launches a gradient reduction. Note that this assumes that the model's gradients are always generated in a certain order (reverse of `module.parameters()`), as otherwise, FSDP would maintain too many parameter group grad buffers, leading to excessive memory usage. -#### 4.1 Optimized for Activation Recompute +#### Optimized for Activation Recompute -Using the activation recompute will cause the same module to execute the forward function first and then the backward function in the backward prop, which will cause model weights unshard twice and model weights reshard twice. If we can tell program that this is a forward + backward operation, we can just call unshard once and reshard once. +Using activation recomputation runs the same module forward first and then its backward during backprop, which can unshard and reshard model weights twice. If the runtime can treat that as one forward-plus-backward region, it can unshard once and reshard once. -To make this determination, we keep track of the model's state with training_state, `FORWARD`, `PRE_BACKWARD`, `POST_BACKWARD`, `IDLE`. It's worth noting that pre-backward hook act before pre-forward hook, and we'll let pre-backward hook execute the model weight unshard, and then mark the model as `PRE_BACKWARD`, and when pre-forward hook sees this marking it will not perform the unshard operation. Similarly, for model weight reshard duplicate, post-forward hook act before post-backward function, and checking for the `PRE_BACKWARD` flag in the post-forward hook will cancel the unshard. +To make this determination, the implementation tracks the model state with `training_state`: `FORWARD`, `PRE_BACKWARD`, `POST_BACKWARD`, `IDLE`. It is worth noting that the pre-backward hook runs before the pre-forward hook: the pre-backward hook performs the model weight unshard, then marks the model as `PRE_BACKWARD`, and when the pre-forward hook observes that mark it skips unshard. Similarly, for duplicate reshard logic, the post-forward hook runs before the post-backward path, and checking for the `PRE_BACKWARD` flag in the post-forward hook can cancel unshard. -### 5. Memory Mechanisms and Features of Custom FSDP +### Memory Mechanisms and Features of Custom FSDP -FSDP can fully distribute the model parameters, gradients, and optimizer states, and for mixed-precision training, it can also fully distribute the high-precision main weights. This is pretty much distributes all the memory except for the activation memory, but FSDP will also face some memory issues. +FSDP can distribute model parameters, gradients, optimizer states, and (for mixed-precision training) high-precision main weights. That covers most memory outside activations, but FSDP can still hit allocator and spike issues. -FSDP frequently unshards and reshards model weights, which can lead to busy memory allocation and deallocation. This results in untimely tensor releases, causing memory spikes (or even out-of-memory errors), crashes of the PyTorch memory allocator cache, and a large number of `cudaMalloc` and `cudaFree` calls. These issues can significantly slow down the system. +FSDP frequently unshards and reshards model weights, which can lead to busy memory allocation and deallocation. This results in untimely tensor releases, causing memory spikes (or even out-of-memory errors), crashes of the PyTorch memory allocator cache, and many `cudaMalloc` and `cudaFree` calls. These issues can slow the system noticeably. -The problem of untimely tensor release can generally be addressed using the `tensor._typed_storage(). _resize_(0)` API, which immediately deallocates the storage's memory. Custom FSDP provides interfaces in `AllGatherPipeline` and `GradReducePipeline` to replace the temporary buffer memory allocator used for parameter gathering and gradient reduction with ` StorageResizeBasedBucketAllocator`. This replaces the tensor release operation with the `tensor._typed_storage(). _resize_(0)` API. +You can often address untimely tensor release with the `tensor._typed_storage()._resize_(0)` API, which deallocates storage immediately. Custom FSDP exposes hooks in `AllGatherPipeline` and `GradReducePipeline` to swap the temporary buffer allocator used for parameter gathering and gradient reduction with `StorageResizeBasedBucketAllocator`, using that `_resize_(0)` path for releases. -The PyTorch memory allocator cache crash is a complex issue that occurs frequently when the actual memory usage approaches the GPU memory limit, leading to poor performance. This problem is challenging and can only be mitigated by avoiding frequent hits on the GPU memory limit. Using a self-managed memory allocator like ` RotaryBucketAllocator` is another potential solution. However, note that `RotaryBucketAllocator` is not yet mature. +The PyTorch memory allocator cache can fail when real usage nears GPU capacity, which hurts performance. Mitigation is limited; avoiding repeated pressure at the memory limit helps. A self-managed allocator such as `RotaryBucketAllocator` is another option, though it is not yet mature. ## References diff --git a/docs/user-guide/features/dist_optimizer.md b/docs/user-guide/features/dist_optimizer.md index 4e47791c12f..bfea3b63a66 100644 --- a/docs/user-guide/features/dist_optimizer.md +++ b/docs/user-guide/features/dist_optimizer.md @@ -9,9 +9,9 @@ # Distributed Optimizer -The motivation for the distributed optimizer is to save memory by distributing the optimizer state evenly across data parallel ranks (https://arxiv.org/abs/1910.02054), versus the naive method of replicating the optimizer state across data parallel ranks. +The distributed optimizer saves memory by sharding optimizer state across data parallel ranks instead of replicating it on every rank, as described in the [ZeRO paper](https://arxiv.org/abs/1910.02054). -Theoretical memory savings vary depending on the combination of the datatype of the model's parameters (`param_dtype`) and main gradients accumulated across data-parallel replicas (`grad_dtype`). We always use `fp32` main parameters for optimizer steps. In the current implementation, the theoretical number of bytes per parameter is (where d is the data parallel size): +Theoretical memory savings depend on the data types of the model parameters (`param_dtype`) and of the main gradients accumulated across data-parallel replicas (`grad_dtype`). Optimizer steps always use `fp32` main parameters. In the current implementation, the theoretical number of bytes per parameter is as follows (where *d* is the data parallel size): | | Non-distributed optim | Distributed optim | | ------ | ------ | ------ | @@ -19,31 +19,31 @@ Theoretical memory savings vary depending on the combination of the datatype of | `bf16` parameters, `fp32` gradients | 18 | 6 + 12/d | | `fp32` parameters, `fp32` gradients | 16 | 8 + 8/d | -Our implementation of the distributed optimizer uses contiguous buffers for parameters and main gradients; model gradients are copied over to the main gradients as soon as they are fully computed. +This distributed optimizer uses contiguous buffers for parameters and main gradients. Model gradients copy into the main gradients as soon as they finish computing. -The figures below illustrate the distributed optimizer's sharding scheme, and the key steps of the distributed optimizer's parameter update: +The following figures show the sharding scheme and the main steps of the parameter update. -## Data flow +## Data Flow -![Data flow](../../images/distrib_optimizer/data_flow.png) +![Diagram of gradient and parameter data flow through reduce-scatter, optimizer step, and all-gather across data parallel ranks](../../images/distrib_optimizer/data_flow.png) -## Sharding scheme +## Sharding Scheme -![Sharding scheme](../../images/distrib_optimizer/sharding_scheme.png) +![Diagram of how optimizer state shards across data parallel ranks](../../images/distrib_optimizer/sharding_scheme.png) -## Key steps +## Key Steps -_(note: using illustrations above, assuming `bf16` model weights, `bf16` model gradients that are computed by the backward pass and `fp32` main gradients that are also used for optimizer steps; we always use `fp32` main weights for optimizer steps)_ +**Note:** The following steps match the illustrations above. They assume `bf16` model weights, `bf16` model gradients from the backward pass, and `fp32` main gradients for optimizer steps. Optimizer steps use `fp32` main weights. - Backward pass finishes (gradient buffer holds 16 `fp32` gradient elements). - Call reduce-scatter on each DP rank. -- Each DP rank now has 4 elements within the gradient buffer that are fully reduced (remaining 12 elements are garbage). +- Each DP rank now has four elements within the gradient buffer that are fully reduced (remaining 12 elements are garbage). - DP rank 0 has gradient values for elements [0:4]. - DP rank 1 has gradient values for elements [4:8]. - DP rank 2 has gradient values for elements [8:12]. - DP rank 3 has gradient values for elements [12:16]. - Optimizer.step(). -- Each DP rank copies its 4 `fp32` main parameter elements into the corresponding `bf16` parameter buffer (each element is cast from fp32 to fp16). +- Each DP rank copies its four `fp32` main parameter elements into the corresponding `bf16` parameter buffer (each element is cast from `fp32` to `bf16`). - Call all-gather on each DP rank. -- The parameter buffer now contains all 16, fully updated, `bf16` model parameter elements. Parameters in PyTorch modules already point to the appropriate locations in this parameter buffer, and thus forward passes are ready to run after the all-gather completes. -- At this point, the gradient buffer is also ready to be zero'd for the next iteration. +- The parameter buffer now contains all 16 updated `bf16` model parameter elements. Parameters in PyTorch modules already point to the correct views in this buffer, so forward passes can start after the all-gather completes. +- At this point, you can zero the gradient buffer for the next iteration. diff --git a/docs/user-guide/features/fine_grained_activation_offloading.md b/docs/user-guide/features/fine_grained_activation_offloading.md index 494674bd4f0..7b97c17cef9 100644 --- a/docs/user-guide/features/fine_grained_activation_offloading.md +++ b/docs/user-guide/features/fine_grained_activation_offloading.md @@ -7,34 +7,40 @@ license agreement from NVIDIA CORPORATION is strictly prohibited. --> -# Fine-grained Activation Offloading (collaborated with rednote) +# Fine-Grained Activation Offloading -Memory capacity is more and more important with the rising of extreme sparse MoE models like DeepSeek-V3 and Qwen3-235B. Fine-grained recomputing reduces the memory footprint at the cost of extra recomputation, while offloading could utilize the host-device bandwidth to achieve nearly zero-overhead. Fine-grained Activation Offloading targets at offloading the activation at the granularity of specific modules, so that we can calibrate the amount of offloading activation to maximize the training throughput. +Contributed in collaboration with RedNote. -Currently, the supported offloading modules are `"attn_norm", "core_attn", "attn_proj", "mlp_norm", "expert_fc1", "moe_act"`, which could work with fine-grained recomputation to release almost all activations of a transformer layer. +Memory is often the limiting factor for very large sparse MoE models such as DeepSeek-V3 and Qwen3-235B. Fine-grained recomputation lowers activation memory at the cost of extra compute. Offloading can use host-device bandwidth so that reload overlaps compute and keeps overhead small in many setups. Fine-grained activation offloading moves activations at module granularity so you can tune how much activation memory leaves the device and adjust training throughput. -**Features** -* Support PP=1/PP/Interleaved PP -* Compatible with fine-grained recomputation -* Support FP8 -* Support MTP -* Support mixed dense & moe layer -* Support A2A Overlap -* Support CUDA Graph - * (Temporary) cuda graph scope cannot contains the offloading modules +Supported offloading modules are `"attn_norm"`, `"core_attn"`, `"attn_proj"`, `"mlp_norm"`, `"expert_fc1"`, and `"moe_act"`. They can be combined with fine-grained recomputation to free almost all activations for a transformer layer on the device. + +## Features + +- Pipeline parallelism: PP=1, PP, and interleaved PP +- Compatible with fine-grained recomputation +- FP8 training +- MTP +- Mixed dense and MoE layers +- A2A overlap +- CUDA graphs + - **Note:** A CUDA graph capture cannot include the offloading modules (temporary limitation). + +## Usage -**Usage** ```bash # Enable fine-grained activation offloading --fine-grained-activation-offloading -# Specify which modules are going to offload its input +# Modules whose inputs are offloaded (refer to your training script for list or delimiter syntax). # Choices: "attn_norm", "core_attn", "attn_proj", "mlp_norm", "expert_fc1", "moe_act". --offload-modules expert_fc1 ``` -**Compatible with Fine-grained Recomputation** -- For modules with minor perf overhead like layernorm or moe_act, use recomputing to reduce memory footprint; -- For other modules, use offloading to reduce memory footprint; -- Make sure the offloading/reloading could be overlapped with computing; -![Fine-grained Activation Offloading and Fine-grained Recomputation](../../images/fine_grained_activation_offloading/offloading_and_recomputing.png) +## Compatible With Fine-Grained Recomputation + +- For low-overhead modules such as LayerNorm or `moe_act`, use recomputation to save activation memory. +- For other modules, use offloading to save activation memory. +- Overlap offload and reload with compute when possible. + +![Diagram comparing fine-grained activation offloading and fine-grained recomputation across a transformer layer](../../images/fine_grained_activation_offloading/offloading_and_recomputing.png) diff --git a/docs/user-guide/features/index.md b/docs/user-guide/features/index.md index 59cef95d574..d080fe3f256 100644 --- a/docs/user-guide/features/index.md +++ b/docs/user-guide/features/index.md @@ -9,7 +9,7 @@ # Advanced Features -Advanced feature guides for key Megatron Core capabilities. +Guides for Megatron Core training features. ```{toctree} :maxdepth: 2 diff --git a/docs/user-guide/features/megatron_energon.md b/docs/user-guide/features/megatron_energon.md index 9ebba72083a..c32b2d8facd 100644 --- a/docs/user-guide/features/megatron_energon.md +++ b/docs/user-guide/features/megatron_energon.md @@ -9,16 +9,16 @@ # Megatron Energon -Advanced multimodal dataloader for efficient loading of text, images, video, and audio at scale. +Multimodal dataloader for text, images, video, and audio at scale. ## Overview -[**Megatron Energon**](https://github.com/NVIDIA/Megatron-Energon) is purpose-built for large-scale multimodal training with: +[**Megatron Energon**](https://github.com/NVIDIA/Megatron-Energon) supports large-scale multimodal training with: -- **Multimodal support** - Text, images, video, audio -- **Distributed loading** - Optimized for multi-node training +- **Multimodal support** - Text, images, video, and audio +- **Distributed loading** - Suited to multi-node training - **Data blending** - Mix datasets with configurable weights -- **WebDataset format** - Efficient streaming from cloud storage +- **WebDataset format** - Streaming from cloud storage - **State management** - Save and restore training position ## Installation @@ -31,12 +31,12 @@ pip install megatron-energon ### Data Processing -- **Packing** - Optimize sequence length utilization -- **Grouping** - Smart batching of similar-length sequences +- **Packing** - Packs samples to use sequence length capacity +- **Grouping** - Batching of similar-length sequences - **Joining** - Combine multiple dataset sources -- **Object storage** - Stream from S3, GCS, Azure Blob Storage +- **Object storage** - Stream from S3, GCS, and Azure Blob Storage -### Production-Ready +### Production Use - Distributed loading across workers and nodes - Checkpoint data loading state @@ -106,6 +106,8 @@ WorkerConfig( ### Common Parameters +The following table summarizes frequently used dataset and loader parameters: + | Parameter | Description | |-----------|-------------| | `batch_size` | Samples per batch | @@ -132,10 +134,10 @@ for iteration, batch in enumerate(get_loader(train_ds)): ## Resources -- **[Megatron Energon GitHub](https://github.com/NVIDIA/Megatron-Energon)** - Documentation and examples -- **[Multimodal Examples](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/multimodal)** - Megatron-LM multimodal training +- **[Megatron Energon GitHub](https://github.com/NVIDIA/Megatron-Energon)**: Documentation and examples +- **[Multimodal Examples](https://github.com/NVIDIA/Megatron-LM/tree/main/examples/multimodal)**: Megatron-LM multimodal training ## Next Steps -- Check [Multimodal Models](../../models/multimodal.md) for supported architectures -- See [Training Examples](../training-examples.md) for integration examples +- Refer to [Multimodal Models](../../models/multimodal.md) for supported architectures +- Refer to [Training Examples](../training-examples.md) for integration examples diff --git a/docs/user-guide/features/megatron_rl.md b/docs/user-guide/features/megatron_rl.md index 653ecb92459..9cd46d79ae2 100644 --- a/docs/user-guide/features/megatron_rl.md +++ b/docs/user-guide/features/megatron_rl.md @@ -15,32 +15,32 @@ Reinforcement learning library for post-training large language models at scale. [**Megatron RL**](https://github.com/NVIDIA/Megatron-LM/tree/dev/megatron/rl) adds native reinforcement learning capabilities to Megatron-LM for large-scale RL-based post-training of foundation models. -> **Note**: Megatron RL is under active development and primarily designed for research teams exploring RL post-training on modern NVIDIA hardware. For production deployments, use [**NeMo RL**](https://github.com/NVIDIA-NeMo/RL). +> **Note:** Megatron RL is under active development and primarily designed for research teams exploring RL post-training on modern NVIDIA hardware. For production deployments, use [**NeMo RL**](https://github.com/NVIDIA-NeMo/RL). ## Key Features -- **Decoupled Design** - Clean separation between agent/environment logic and RL implementation -- **Flexible Inference** - Support for Megatron, OpenAI, and HuggingFace inference backends -- **Trainer/Evaluator** - Manages rollout generation and coordinates with inference systems +- **Decoupled Design** - Separates agent and environment logic from the core RL implementation +- **Inference Backends** - Megatron, OpenAI, and Hugging Face inference stacks +- **Trainer or Evaluator** - Manages rollout generation and coordinates with inference systems - **Megatron Integration** - Native integration with Megatron Core inference system ## Architecture ### Components -**Agents & Environments** +**Agents and Environments** - Accept inference handles - Return experience rollouts with rewards - Implement custom RL logic -**Trainer/Evaluator** +**Trainer or Evaluator** - Controls rollout generation - Coordinates with inference systems - Manages training loops **Inference Interface** -- Provides `.generate(prompt, **generation_args)` endpoint -- Supports multiple backends (Megatron, OpenAI, HuggingFace) +- Exposes a `.generate(prompt, **generation_args)` endpoint +- Supports multiple backends (Megatron, OpenAI, Hugging Face) ## Use Cases @@ -51,5 +51,5 @@ Reinforcement learning library for post-training large language models at scale. ## Resources -- **[Megatron RL GitHub](https://github.com/NVIDIA/Megatron-LM/tree/dev/megatron/rl)** - Source code and documentation -- **[Megatron Core Inference](../../api-guide/core/transformer.md)** - Native inference integration +- **[Megatron RL GitHub](https://github.com/NVIDIA/Megatron-LM/tree/dev/megatron/rl)**: Source code and documentation +- **[Megatron Core Inference](../../api-guide/core/transformer.md)**: Native inference integration diff --git a/docs/user-guide/features/multi_latent_attention.md b/docs/user-guide/features/multi_latent_attention.md index 4310843557a..65a1e573c6c 100644 --- a/docs/user-guide/features/multi_latent_attention.md +++ b/docs/user-guide/features/multi_latent_attention.md @@ -9,13 +9,13 @@ # Multi-Latent Attention -## Multi-Latent Attention overview +## Multi-Latent Attention Overview -Multi-Latent Attention ("MLA") is an innovative attention mechanism introduced by Deepseek team that enhances the efficiency of attention computation by leveraging multiple latent spaces. This approach is particularly beneficial for large language models (LLMs), as it reduces the computational burden associated with traditional attention mechanisms. According to Deepseek-V2 technical report, MLA achieves better performance compared to Multi-Head Attention (MHA) and requires smaller KV cache. +Multi-Latent Attention (MLA) is an attention variant from the DeepSeek team. It uses multiple latent spaces to change how attention is computed. That design often lowers cost for large language models (LLMs) compared with standard attention and can shrink the KV cache. The DeepSeek-V2 technical report compares MLA to Multi-Head Attention (MHA) on quality and cache size. ## Enabling Multi-Latent Attention -To enable MLA in Megatron-LM, set the following flags in command line: -- `--multi-latent-attention` to enable MLA in MLP. -- Set `MLATransformerConfig` to configure MLA. +To enable MLA in Megatron-LM, set the following on the command line: +- `--multi-latent-attention` to turn on MLA. +- Use `MLATransformerConfig` for MLA-specific model settings when you build the training configuration. diff --git a/docs/user-guide/features/multi_token_prediction.md b/docs/user-guide/features/multi_token_prediction.md index e16108bbcfa..e2d51c1b705 100644 --- a/docs/user-guide/features/multi_token_prediction.md +++ b/docs/user-guide/features/multi_token_prediction.md @@ -9,49 +9,50 @@ # Multi-Token Prediction (MTP) -Multi-Token Prediction (MTP) extends the prediction scope to multiple future tokens at each position. On the one hand, an MTP objective densifies the training signals and may improve -data efficiency. On the other hand, MTP may enable the model to pre-plan its representations for better prediction of future tokens. In this implementation of MTP, we sequentially predict additional tokens and keep the complete causal chain at each prediction depth. The following figure illustrates our implementation of MTP in [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3/). +Multi-Token Prediction (MTP) extends the prediction scope to several future tokens at each position. An MTP objective adds extra prediction targets, which can improve data efficiency. It may also encourage representations that anticipate later tokens. This implementation predicts additional tokens in sequence and preserves the causal dependency chain at each depth. The following figure illustrates MTP as used in [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3/). -![MTP_implementation](../../images/multi_token_prediction/MTP_implementation.png) +![Diagram of Multi-Token Prediction depth stack: shared embedding, projection, transformer block, and output head per depth](../../images/multi_token_prediction/MTP_implementation.png) -The k-th MTP module consists of a shared embedding layer, a projection matrix, a Transformer block, and a shared output head. For the i-th input token at the (k - 1)-th prediction depth, we first combine the representation of the i-th token and the embedding of the (i + K)-th token with the linear projection. The combined serves as the input of the Transformer block at the k-th depth to produce the output representation. +The *k*-th MTP module includes a shared embedding layer, a projection matrix, a Transformer block, and a shared output head. For the *i*-th input token at depth *k - 1*, the implementation combines the representation of the *i*-th token and the embedding of the *(i + K)*-th token with a linear projection. That combined representation is the input to the Transformer block at depth *k*, which produces the output representation. -For more information, refer to [DeepSeek-V3 Technical Report](https://arxiv.org/pdf/2412.19437.pdf) +For more detail, refer to the [DeepSeek-V3 technical report](https://arxiv.org/pdf/2412.19437.pdf). ## Related Arguments -We can train GPTModel like models with Multi-Token Prediction (MTP) by setting mtp_num_layers to be a positive integer. +Train `GPTModel`-style models with MTP by setting `mtp_num_layers` to a positive integer. + +The following table summarizes MTP configuration fields: | Item | Description | | --- | --- | -| mtp_num_layers | Number of Multi-Token Prediction (MTP) Layers. MTP extends the prediction scope to multiple future tokens at each position. This MTP implementation sequentially predict additional tokens by using D sequential modules to predict D additional tokens. Default is None. | -| mtp_loss_scaling_factor | Scaling factor of Multi-Token Prediction (MTP) loss. We compute the average of the MTP losses across all depths, and multiply it the scaling factor to obtain the overall MTP loss, which serves as an additional training objective. Default is 0.1. | +| `mtp_num_layers` | Number of MTP layers. MTP extends prediction to multiple future tokens at each position. This stack uses `mtp_num_layers` sequential modules to predict that many additional tokens per position. Default: `None`. | +| `mtp_loss_scaling_factor` | Weight for the MTP loss term. The implementation averages MTP losses across depths, multiplies by this factor, and adds the result to the training objective. Default: `0.1`. | ## Pipeline Parallel Layout for MTP -MTP supports flexible placement of MTP layers across pipeline stages using a custom `pipeline_model_parallel_layout`. By default, all MTP layers are placed on the last pipeline stage, but you can customize their placement. +MTP supports user-defined placement of MTP layers across pipeline stages through `pipeline_model_parallel_layout`. By default, all MTP layers sit on the last pipeline stage; you can override placement in the layout string. ### MTP Standalone Mode -When MTP layers are placed in a separate virtual pipeline (vpp) stage that is not on the last pipeline rank, the `mtp_standalone` flag is automatically set to `True`. This mode enables MTP to run independently in its own pipeline stage. +When MTP layers are placed in a separate virtual pipeline (VPP) stage that is not on the last pipeline rank, the `mtp_standalone` flag is automatically set to `True`. MTP then runs in its own pipeline stage. ### Layout Format -Use `m` to represent MTP layers in the pipeline layout string. For example: +Use `m` for MTP layers in the pipeline layout string. For example: - `"E|t*3|(t|)*5mL"` - MTP in the last stage - `"E|t*3|(t|)*4tm|L"` - MTP in the second-to-last stage with a decoder layer - `"E|t*3|(t|)*3tt|m|L"` - MTP in a standalone stage (second-to-last) with no other layers ### Constraints -- All MTP layers must be placed in the same one virtual pipeline stage. -- MTP layers cannot be placed on the first pipeline rank. +- Place all MTP layers in the same virtual pipeline stage. +- Do not place MTP layers on the first pipeline rank. ## Implementation Notes -- For models with MTP layers, the final layernorm is placed in the stage that contains the last decoder layer, rather than in the post-process stage. This may cause small numerical differences in gradient norm reduction when final layernorm is placed in different pipeline stages in deterministic mode. Bitwise alignment can be achieved by disabling gradient norm clipping. +- For models with MTP layers, the final LayerNorm sits in the stage that contains the last decoder layer, not in the post-process stage. That can change gradient norm reduction slightly in deterministic mode when LayerNorm would otherwise live in another stage. For bitwise alignment, disable gradient norm clipping. - MTP loss is computed in the post-processing stage. -## Precautions +## Unsupported Combinations -Do not use Context Parallel (CP), or arbitrary AttnMaskType, or learned absolute position embedding type with MTP. These use cases are not yet supported. +Context Parallel (CP), arbitrary `AttnMaskType`, and learned absolute position embeddings are not supported with MTP. diff --git a/docs/user-guide/features/pipeline_parallel_layout.md b/docs/user-guide/features/pipeline_parallel_layout.md index 96b00eca004..69ffb63da5a 100644 --- a/docs/user-guide/features/pipeline_parallel_layout.md +++ b/docs/user-guide/features/pipeline_parallel_layout.md @@ -11,13 +11,15 @@ *This is an experimental feature and may be changed.* -`--pipeline-model-parallel-layout` is a flexible API for defining the pipeline parallel partitioning, which is essential for balanced partitioning for an imbalanced model. For example, to partition DeepSeek-V3 (61 decoder layers + 1 mtp layer) with PP16VPP2, we can include the arguments as follows: +`--pipeline-model-parallel-layout` takes a string that defines pipeline parallel partitioning. Use it to balance partitioning for an imbalanced model. For example, to partition a DeepSeek-V3-style stack (61 decoder layers and one MTP layer) with PP16 and VPP2, pass arguments similar to the following: ```bash --pipeline-model-parallel-size 16 --pipeline-model-parallel-layout "Et*3|(tt|)*29,m|L" ``` +The table below shows one possible rank map for that layout: + | PP \ VPP rank | 0 | 1 | |---------------|-------------------------|---------------| | 0 | embedding + 3 × decoder | 2 × decoder | @@ -25,11 +27,11 @@ | 14 | 2 × decoder | mtp | | 15 | 2 × decoder | loss | -In the layout string, stages are split by '|'. Replicated stages or layers can be described with multiplication. Commas can be used cosmetically. Symbol choices: +In the layout string, stages are split by `|`. Replicated stages or layers use multiplication (for example, `t*3`). Commas are optional for readability. Symbols: -* `E` = embedding layer -* `t` = transformer decoder layer -* `m` = MTP layer -* `L` = loss calculation layer +* `E`: embedding layer +* `t`: transformer decoder layer +* `m`: MTP layer +* `L`: loss calculation layer -Note that it is legal to have empty stages, e.g., `E||t|L` (the second stage is empty). +**Note:** Empty stages are allowed, for example `E||t|L` (the second stage is empty). diff --git a/docs/user-guide/features/tokenizers.md b/docs/user-guide/features/tokenizers.md index bc1a47cec76..672f0f0cd98 100644 --- a/docs/user-guide/features/tokenizers.md +++ b/docs/user-guide/features/tokenizers.md @@ -9,22 +9,22 @@ # Tokenizers -Megatron Core provides a unified tokenizer system with a HuggingFace-style API for easy tokenizer management and configuration. +Megatron Core provides a unified tokenizer system with a Hugging Face-style API for configuration and loading. ## Overview -The `MegatronTokenizer` class offers a simple, familiar API for loading and managing tokenizers: +The `MegatronTokenizer` class uses the same entry points as many Hugging Face workflows for loading and managing tokenizers: -- **Automatic detection** - Load any tokenizer type without specifying the library -- **Metadata-based configuration** - Store tokenizer settings in JSON for easy reuse -- **HuggingFace-compatible API** - Familiar `.from_pretrained()` interface +- **Automatic detection** - Load tokenizer types without naming the backing library in code +- **Metadata-based configuration** - Store tokenizer settings in JSON for reuse across runs +- **Hugging Face-compatible API** - `.from_pretrained()`-style loading - **Custom tokenizer support** - Extend with model-specific tokenization logic ## Key Features ### Unified API -Use the same API regardless of tokenizer backend (SentencePiece, HuggingFace, TikToken, etc.): +Use the same API regardless of tokenizer backend (SentencePiece, Hugging Face, TikToken, and so on): ```python from megatron.core.tokenizers import MegatronTokenizer @@ -35,22 +35,25 @@ tokenizer = MegatronTokenizer.from_pretrained("/path/to/tokenizer") ### Tokenizer Metadata Configuration is stored in a JSON metadata file containing: -- Tokenizer library (HuggingFace, SentencePiece, TikToken, etc.) + +- Tokenizer library (Hugging Face, SentencePiece, TikToken, and so on) - Chat templates - Custom tokenizer class - Special token configurations -**Benefits:** +**Benefits** + - Set configuration once, reuse everywhere - No repeated CLI arguments -- Easy sharing - just copy the tokenizer directory +- Share setups by copying the tokenizer directory ### Automatic Library Detection -The correct tokenizer implementation is automatically selected: -- No need to specify `SentencePieceTokenizer`, `HuggingFaceTokenizer`, etc. -- Library type detected from metadata -- Seamless switching between tokenizer backends +The correct tokenizer implementation is selected automatically: + +- Avoids hard-coding `SentencePieceTokenizer`, `HuggingFaceTokenizer`, and related class names in user code +- Library type is read from metadata +- Change tokenizer backends by updating metadata and paths ## Basic Usage @@ -159,7 +162,7 @@ tokenizer = MegatronTokenizer.from_pretrained( ### Using with Training Scripts -The tokenizer system integrates seamlessly with Megatron-LM training: +The tokenizer system works with Megatron-LM training scripts: ```bash # Null tokenizer for testing @@ -170,7 +173,7 @@ torchrun --nproc_per_node=8 pretrain_gpt.py \ ``` ```bash -# HuggingFace tokenizer with metadata +# Hugging Face tokenizer with metadata torchrun --nproc_per_node=8 pretrain_gpt.py \ --tokenizer-type HuggingFaceTokenizer \ --tokenizer-model meta-llama/Meta-Llama-3-8B \ @@ -184,9 +187,11 @@ If `--tokenizer-metadata` is not specified, a default metadata file is generated ## Supported Tokenizer Libraries +The following table lists supported tokenizer backends: + | Library | Description | Use Case | |---------|-------------|----------| -| **HuggingFace** | Transformers tokenizers | Most modern LLMs (LLaMA, Mistral, etc.) | +| **Hugging Face** | Transformers tokenizers | Most modern LLMs, such as LLaMA and Mistral | | **SentencePiece** | Google's tokenizer | GPT-style models, custom vocabularies | | **TikToken** | OpenAI's tokenizer | GPT-3.5/GPT-4 style tokenization | | **Megatron** | Built-in tokenizers | Legacy GPT-2 BPE | @@ -214,16 +219,16 @@ MegatronTokenizer.write_metadata( ) ``` -## Best Practices +## Recommendations -1. **Always save metadata** - Create metadata once, reuse across training runs -2. **Use HuggingFace tokenizers** - When possible, for modern LLM compatibility -3. **Test tokenization** - Verify encode/decode before starting training -4. **Version control metadata** - Include `tokenizer_metadata.json` in your experiment configs -5. **Share tokenizer directories** - Include both model files and metadata for reproducibility +1. **Save metadata** - Create metadata once, then reuse across training runs +2. **Prefer Hugging Face tokenizers** - When the model ships one, it reduces integration work +3. **Test tokenization** - Verify encode and decode before long training jobs +4. **Version control metadata** - Track `tokenizer_metadata.json` with experiment configs +5. **Share tokenizer directories** - Ship model files and metadata together for reproducibility ## Next Steps -- **Prepare Data**: See [Data Preparation](../data-preparation.md) for preprocessing with tokenizers -- **Train Models**: Use tokenizers in [Training Examples](../training-examples.md) -- **Supported Models**: Check [Language Models](../../models/llms.md) for model-specific tokenizers +- **Prepare data**: Refer to [Data Preparation](../data-preparation.md) for preprocessing with tokenizers +- **Train models**: Refer to [Training Examples](../training-examples.md) +- **Supported models**: Refer to [Language Models](../../models/llms.md) for model-specific tokenizers From c9e03d0c3b5b437a619931503a47887bb44f743a Mon Sep 17 00:00:00 2001 From: "Mikail Khona (NVIDIA)" Date: Mon, 20 Apr 2026 16:28:17 -0700 Subject: [PATCH 53/72] Replace rampup batch size scheduler with custom step batch size schedules (#3779) Co-authored-by: Deepak Narayanan Co-authored-by: Claude Opus 4.6 --- examples/gpt3/gpt_config.yaml | 2 +- examples/gpt3/train_gpt3_175b_distributed.sh | 6 +- megatron/core/num_microbatches_calculator.py | 429 ++++++++++-------- megatron/legacy/model/transformer.py | 2 +- megatron/rl/rl_utils.py | 10 +- megatron/rl/sequence_packing_utils.py | 3 - megatron/training/arguments.py | 17 +- megatron/training/config/training_config.py | 20 +- megatron/training/global_vars.py | 6 +- megatron/training/training.py | 45 +- megatron/training/yaml_arguments.py | 12 +- tasks/finetune_utils.py | 3 - .../gpt/gpt3_15b_8t_release/model_config.yaml | 2 +- .../model_config.yaml | 2 +- .../gpt3_15b_8t_release_sm/model_config.yaml | 2 +- .../model_config.yaml | 2 +- .../test_pipeline_parallel_layout.py | 2 +- .../test_torch_fully_sharded_parallel.py | 2 +- .../pipeline_parallel/test_pipeline_layout.py | 2 +- .../rl/test_sequence_packing_utils.py | 8 +- tests/unit_tests/test_checkpointing.py | 2 +- .../test_num_microbatches_calculator.py | 152 ++++--- .../transformer/test_cuda_graphs.py | 1 - 23 files changed, 411 insertions(+), 321 deletions(-) diff --git a/examples/gpt3/gpt_config.yaml b/examples/gpt3/gpt_config.yaml index b7bc2bf4c4f..3c387eafe5a 100644 --- a/examples/gpt3/gpt_config.yaml +++ b/examples/gpt3/gpt_config.yaml @@ -136,7 +136,7 @@ use_legacy_models: False spec: null micro_batch_size: 2 global_batch_size: 128 -rampup_batch_size: [32, 32, 65324160] +step_batch_size_schedule: "0:32 90B:64 180B:96 270B:128" check_for_nan_in_loss_and_grad: True num_layers_per_virtual_pipeline_stage: null diff --git a/examples/gpt3/train_gpt3_175b_distributed.sh b/examples/gpt3/train_gpt3_175b_distributed.sh index 7d2c01b3157..be00d76120d 100755 --- a/examples/gpt3/train_gpt3_175b_distributed.sh +++ b/examples/gpt3/train_gpt3_175b_distributed.sh @@ -36,9 +36,9 @@ GPT_MODEL_ARGS=( TRAINING_ARGS=( --micro-batch-size 1 - --global-batch-size 1536 - --rampup-batch-size 16 16 5859375 - --train-iters 500000 + --global-batch-size 1536 + --step-batch-size-schedule "0:16 2.4B:320 4.8B:624 7.2B:928 9.6B:1232 12B:1536" + --train-iters 500000 --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.95 diff --git a/megatron/core/num_microbatches_calculator.py b/megatron/core/num_microbatches_calculator.py index ae7cea92e63..1056d3ebb70 100644 --- a/megatron/core/num_microbatches_calculator.py +++ b/megatron/core/num_microbatches_calculator.py @@ -4,14 +4,14 @@ import logging from abc import ABC, abstractmethod -from typing import List, Optional, Union +from typing import List, Optional, Tuple, Union logger = logging.getLogger(__name__) # TODO: global_var merge into mcore? _GLOBAL_NUM_MICROBATCHES_CALCULATOR: Union[ - 'ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator' -] = None + 'ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator' +] = None # type: ignore[assignment] def get_num_microbatches() -> int: @@ -63,20 +63,18 @@ def unset_num_microbatches_calculator(): def init_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, ) -> None: """Initialize number of microbatches calculator. Supporting backward compatibility. Args: rank (int): Rank of the GPU, only rank 0 will log the information. - rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of [start_global_batch_size, - batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -86,14 +84,24 @@ def init_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds are interpreted as samples unless seq_length is provided, in which case + thresholds are interpreted as tokens and converted to samples. + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ _configure_global_num_microbatches_calculator( rank, - rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, + step_batch_size_schedule, + seq_length, init=True, ) @@ -106,20 +114,18 @@ def destroy_num_microbatches_calculator(): def reconfigure_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, ) -> None: """Reconfigure number of microbatches calculator. Supporting backward compatibility. Args: rank (int): Rank of the GPU, only rank 0 will log the information. - rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of - [start_global_batch_size, batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -129,25 +135,34 @@ def reconfigure_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ _configure_global_num_microbatches_calculator( rank, - rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, + step_batch_size_schedule, + seq_length, init=False, ) def _configure_global_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, init: bool = False, ) -> None: """Configure number of microbatches calculator. Can be used for initialization and @@ -156,9 +171,6 @@ def _configure_global_num_microbatches_calculator( Args: rank (int): Rank of the GPU, only rank 0 will log the information. - rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of - [start_global_batch_size, batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -168,6 +180,13 @@ def _configure_global_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. init (bool, optional): If true, initialize the calculator. Defaults to False. """ @@ -180,43 +199,72 @@ def _configure_global_num_microbatches_calculator( _GLOBAL_NUM_MICROBATCHES_CALCULATOR = _build_num_microbatches_calculator( rank, - rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, + step_batch_size_schedule, + seq_length, ) def _build_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], - global_batch_size: int, + global_batch_size: Optional[int], micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool, -) -> Union['ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator']: + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, +) -> Union['ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator']: """Build number of microbatches calculator. Internal helper method. Args: rank (int): Rank of the GPU, only rank 0 will log the information. - rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of - [start_global_batch_size, batch_size_increment, ramup_samples]. - global_batch_size (int): - Global batch size for the model. + global_batch_size (Optional[int]): + Global batch size for the model. Required for constant mode. + Ignored when step_batch_size_schedule is provided. micro_batch_size (int): Micro batch size at initialization. data_parallel_size (int): Data parallel size. decrease_batch_size_if_needed (bool): If true, scale down batch size to ensure divisibility by DP size * microbatch size. - + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ - # Constant batch size. - if rampup_batch_size is None: + num_microbatches_calculator: Union[ + 'ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator' + ] + + # Step batch size schedule + if step_batch_size_schedule is not None: + if decrease_batch_size_if_needed: + raise ValueError( + 'Cannot specify both --step-batch-size-schedule and ' + '--decrease-batch-size-if-needed' + ) + num_microbatches_calculator = StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=micro_batch_size, + data_parallel_size=data_parallel_size, + decrease_batch_size_if_needed=decrease_batch_size_if_needed, + rank=rank, + schedule=step_batch_size_schedule, + seq_length=seq_length, + ) + + # Constant batch size + else: + assert ( + global_batch_size is not None + ), '--global-batch-size is required when not using --step-batch-size-schedule' num_microbatches_calculator = ConstantNumMicroBatchesCalculator( global_batch_size, micro_batch_size, @@ -228,32 +276,6 @@ def _build_num_microbatches_calculator( logger.info( f'setting number of microbatches to constant {num_microbatches_calculator.get()}' ) - # Batch size ramp up. - else: - assert len(rampup_batch_size) == 3, ( - 'expected the following ' - 'format: --rampup-batch-size ' - ' ' - ) - start_global_batch_size = int(rampup_batch_size[0]) - batch_size_increment = int(rampup_batch_size[1]) - ramup_samples = int(rampup_batch_size[2]) - if rank == 0: - logger.info( - f'will use batch size rampup starting from global batch size ' - f'{start_global_batch_size} to global batch size {global_batch_size} with batch' - f'size increments {batch_size_increment} over {ramup_samples} samples.' - ) - num_microbatches_calculator = RampupBatchsizeNumMicroBatchesCalculator( - global_batch_size, - micro_batch_size, - data_parallel_size, - decrease_batch_size_if_needed, - rank, - start_global_batch_size, - batch_size_increment, - ramup_samples, - ) return num_microbatches_calculator @@ -267,31 +289,35 @@ class NumMicroBatchesCalculator(ABC): """Base class for number of microbatches calculator.""" def __init__(self) -> None: - self.num_micro_batches = None - self.current_global_batch_size = None - self.micro_batch_size = None - self.current_running_global_batch_size = None + self.num_micro_batches: Optional[int] = None + self.current_global_batch_size: Optional[int] = None + self.micro_batch_size: Optional[int] = None + self.current_running_global_batch_size: Optional[int] = None def get(self) -> int: """Get number of microbatches.""" + assert self.num_micro_batches is not None return self.num_micro_batches def get_current_global_batch_size(self) -> int: """Get current global batch size.""" + assert self.current_global_batch_size is not None return self.current_global_batch_size def get_micro_batch_size(self) -> int: """Get current global batch size.""" + assert self.micro_batch_size is not None return self.micro_batch_size def get_current_running_global_batch_size(self) -> int: """Get current running global batch size. If decrease_batch_size_if_needed is False, this just equals global batch size.""" + assert self.current_running_global_batch_size is not None return self.current_running_global_batch_size @abstractmethod def update(self, consumed_samples, consistency_check, verbose=False) -> None: - """Update number of microbatches depending on batch size rampup.""" + """Update number of microbatches.""" pass @@ -320,6 +346,7 @@ def __init__( decrease_batch_size_if_needed: bool, rank: int, ) -> None: + super().__init__() micro_batch_times_data_parallel_size = micro_batch_size * data_parallel_size if decrease_batch_size_if_needed: @@ -333,9 +360,7 @@ def __init__( f'to keep divisiblity by micro_batch_size={micro_batch_size} * ' f'data_parallel_size={data_parallel_size}' ) - self.num_micro_batches = ( - running_global_batch_size // micro_batch_times_data_parallel_size - ) + num_micro_batches = running_global_batch_size // micro_batch_times_data_parallel_size else: assert global_batch_size % micro_batch_times_data_parallel_size == 0, ( 'global batch size ({}) is not divisible by micro batch size ({})' @@ -344,11 +369,12 @@ def __init__( ) ) running_global_batch_size = global_batch_size - self.num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size + num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size assert ( - self.num_micro_batches >= 1 - ), 'number of microbatches should be at least 1, got {}.'.format(self.num_micro_batches) + num_micro_batches >= 1 + ), 'number of microbatches should be at least 1, got {}.'.format(num_micro_batches) + self.num_micro_batches = num_micro_batches self.current_global_batch_size = global_batch_size self.current_running_global_batch_size = running_global_batch_size self.micro_batch_size = micro_batch_size @@ -357,152 +383,195 @@ def update(self, consumed_samples, consistency_check, verbose=False) -> None: pass -class RampupBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator): - """Calculator of number of microbatches with batch size rampup. - Over `steps = (global-batch-size - start-batch-size) / batch_size_increment` increment batch - size from start-batch-size to global-batch-size using rampup-samples / steps - samples. +class StepBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator): + """Calculator of number of microbatches with arbitrary step-wise batch size schedule. Args: - global_batch_size (int): - Global batch size post rampup. - micro_batch_size (int): - Micro batch size. - data_parallel_size (int): - Data parallel size. - decrease_batch_size_if_needed (bool): - If true, decrease batch size to ensure divisibility by DP size * microbatch size - (if needed). - rank (int): - Rank (to determine whether logging should be performed). - start_global_batch_size (int): - Global batch size to start with. - batch_size_increment (int): - Global batch size increments. - ramup_samples (int): - Number of samples to use ramp up global - batch size from `start_global_batch_size` to `global_batch_size`. + micro_batch_size (int): Micro batch size. + data_parallel_size (int): Data parallel size. + decrease_batch_size_if_needed (bool): Must be False. Step schedules do not support + decreasing batch size for divisibility. + rank (int): Rank for logging. + schedule (str): Schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Examples: + "0:768 250B:1536 500B:3072 750B:6144" (thresholds in tokens) + "0:768 61035156250:1536" (thresholds in samples) + seq_length (int, optional): Sequence length for token-to-sample conversion. + If provided, thresholds are interpreted as tokens and converted to samples. + If None, thresholds are interpreted as samples directly. """ def __init__( self, - global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool, rank: int, - start_global_batch_size: int, - batch_size_increment: int, - ramup_samples: int, + schedule: str, + seq_length: Optional[int] = None, ) -> None: - assert global_batch_size > 0, 'global batch size should be positive, got {}.'.format( - global_batch_size - ) - assert start_global_batch_size > 0, 'start batch size should be positive, got {}.'.format( - start_global_batch_size - ) - assert batch_size_increment > 0, 'batch size increment should be positive, got {}.'.format( - batch_size_increment - ) - assert ramup_samples >= 0, 'ramp-up samples should be non-negative, got {}.'.format( - ramup_samples - ) + super().__init__() + + if decrease_batch_size_if_needed: + raise ValueError( + 'Step batch size schedules do not support decrease_batch_size_if_needed' + ) - self.global_batch_size = global_batch_size self.micro_batch_size = micro_batch_size - self.data_parallel_size = data_parallel_size - self.decrease_batch_size_if_needed = decrease_batch_size_if_needed - self.rank = rank - self.start_global_batch_size = start_global_batch_size - self.batch_size_increment = batch_size_increment - self.ramup_samples = ramup_samples - - self.micro_batch_times_data_parallel_size = self.micro_batch_size * self.data_parallel_size + self.data_parallel_size: int = data_parallel_size + self.rank: int = rank + self.seq_length: Optional[int] = seq_length + + self.micro_batch_times_data_parallel_size: int = micro_batch_size * data_parallel_size assert self.micro_batch_times_data_parallel_size > 0 - self.current_global_batch_size = None - diff_batch_size = self.global_batch_size - self.start_global_batch_size - assert diff_batch_size >= 0, ( - 'expected global batch size to be greater than or equal to start batch size, ' - f'got {self.global_batch_size} and {self.start_global_batch_size}' - ) - assert diff_batch_size % batch_size_increment == 0, ( - 'expected ' - f'global batch size interval ({diff_batch_size}) to be divisible by global batch ' - f'size increment ({batch_size_increment})' - ) + # Parse schedule string + self.schedule = self._parse_schedule(schedule, seq_length) - num_increments = diff_batch_size // self.batch_size_increment - self.rampup_samples_per_increment = self.ramup_samples / num_increments + # Validate schedule + self._validate_schedule() - # Initialize number of microbatches. + self.global_batch_size: int = self.schedule[-1][1] + self.current_global_batch_size = None + + if rank == 0: + logger.info(f'> initializing step batch size schedule') + logger.info(f' raw schedule string: "{schedule}"') + unit = "tokens" if seq_length else "samples" + logger.info(f' seq_length: {seq_length}' f' (thresholds interpreted as {unit})') + logger.info(f' micro_batch_size: {micro_batch_size}') + logger.info(f' data_parallel_size: {data_parallel_size}') + logger.info(f'step batch size schedule ({len(self.schedule)} steps):') + for threshold, batch_size in self.schedule: + num_microbatches = batch_size // self.micro_batch_times_data_parallel_size + if seq_length: + tokens = threshold * seq_length + logger.info( + f' >= {tokens:,} tokens ({threshold:,} samples)' + f' -> batch_size={batch_size},' + f' num_microbatches={num_microbatches}' + ) + else: + logger.info( + f' >= {threshold:,} samples' + f' -> batch_size={batch_size},' + f' num_microbatches={num_microbatches}' + ) + # Initialize self.update(0, consistency_check=False, verbose=True) - def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None: - """Update number of microbatches. + @staticmethod + def _parse_numeric_value(value_str: str) -> int: + """Parse numeric value with optional suffix (K, M, B, T).""" + value_str = value_str.strip().upper() + + multiplier = 1 + if value_str.endswith('T'): + multiplier = 1_000_000_000_000 + value_str = value_str[:-1] + elif value_str.endswith('B'): + multiplier = 1_000_000_000 + value_str = value_str[:-1] + elif value_str.endswith('M'): + multiplier = 1_000_000 + value_str = value_str[:-1] + elif value_str.endswith('K'): + multiplier = 1_000 + value_str = value_str[:-1] + + return int(float(value_str) * multiplier) + + @classmethod + def _parse_schedule(cls, schedule_str: str, seq_length: Optional[int]) -> List[Tuple[int, int]]: + """Parse schedule string into list of (threshold_samples, batch_size) tuples. Args: - consumed_samples (int): Number of samples consumed. - consistency_check (bool): Option to check current schedule's consistency. - verbose (bool, optional): Option to control logging. Defaults to False. + schedule_str: Space-separated "THRESHOLD:BATCH_SIZE" pairs. + seq_length: If provided, convert thresholds from tokens to samples. + + Returns: + List of (threshold_samples, batch_size) tuples, sorted by threshold. """ + schedule = [] + entries = schedule_str.strip().replace(',', ' ').split() - # Update current global batch size. - global_batch_size_changed = False - old_current_global_batch_size = self.current_global_batch_size - if consumed_samples > self.ramup_samples: - self.current_global_batch_size = self.global_batch_size - else: - steps = int(consumed_samples / self.rampup_samples_per_increment) - self.current_global_batch_size = ( - self.start_global_batch_size + steps * self.batch_size_increment + for entry in entries: + if ':' not in entry: + raise ValueError( + f'Invalid schedule entry "{entry}". Expected format: "THRESHOLD:BATCH_SIZE"' + ) + + threshold_str, batch_size_str = entry.split(':', 1) + threshold = cls._parse_numeric_value(threshold_str) + batch_size = cls._parse_numeric_value(batch_size_str) + + # Convert tokens to samples if seq_length provided + if seq_length is not None: + threshold = threshold // seq_length + + schedule.append((threshold, batch_size)) + + # Sort by threshold ascending + schedule.sort(key=lambda x: x[0]) + + return schedule + + def _validate_schedule(self) -> None: + """Validate the parsed schedule.""" + assert len(self.schedule) > 0, 'schedule must have at least one entry' + assert ( + self.schedule[0][0] == 0 + ), f'first schedule entry must have threshold 0, got {self.schedule[0][0]}' + + # Check strictly increasing thresholds + for i in range(1, len(self.schedule)): + assert self.schedule[i][0] > self.schedule[i - 1][0], ( + f'schedule thresholds must be strictly increasing, ' + f'got {self.schedule[i - 1][0]} before {self.schedule[i][0]}' ) - assert self.current_global_batch_size <= self.global_batch_size - if old_current_global_batch_size != self.current_global_batch_size: - global_batch_size_changed = True - if self.rank == 0 and global_batch_size_changed and verbose: - if old_current_global_batch_size is None: - logger.info(f'setting initial batch size to {self.current_global_batch_size}') + # Validate batch sizes are positive. + # NOTE: divisibility by micro_batch_size * data_parallel_size is NOT checked here + # because early schedule entries may be smaller than the current GPU configuration + # (e.g., after scaling up GPUs mid-training). Divisibility of the CURRENT batch size + # is checked at runtime in update() when consistency_check=True (after checkpoint loading). + for threshold, batch_size in self.schedule: + assert batch_size > 0, f'batch size must be positive, got {batch_size}' + + def _get_batch_size_for_samples(self, consumed_samples: int) -> int: + """Get the batch size for the given number of consumed samples.""" + batch_size = self.schedule[0][1] + for threshold, bs in self.schedule: + if consumed_samples >= threshold: + batch_size = bs else: - logger.info( - f'ramping up batch size from {old_current_global_batch_size} to ' - f'{self.current_global_batch_size}' - ) + break + return batch_size - # Check consistency of the current global batch size. - if consistency_check and not self.decrease_batch_size_if_needed: + def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None: + """Update number of microbatches based on consumed samples. + + Args: + consumed_samples (int): Number of samples consumed. + consistency_check (bool): Check divisibility constraints. + verbose (bool): Enable logging. + """ + self.current_global_batch_size = self._get_batch_size_for_samples(consumed_samples) + assert self.current_global_batch_size is not None + + # Consistency check + if consistency_check: assert ( self.current_global_batch_size % self.micro_batch_times_data_parallel_size == 0 ), ( - 'current global ' - 'batch size ({}) is not divisible by micro-batch-size ({}) times' - 'data parallel size ({})'.format( - self.current_global_batch_size, self.micro_batch_size, self.data_parallel_size - ) + f'current global batch size ({self.current_global_batch_size}) is not divisible by ' + f'micro_batch_size ({self.micro_batch_size}) * ' + f'data_parallel_size ({self.data_parallel_size})' ) - if ( - self.decrease_batch_size_if_needed - and self.current_global_batch_size % self.micro_batch_times_data_parallel_size != 0 - ): - self.current_running_global_batch_size = _round( - self.current_global_batch_size, self.micro_batch_times_data_parallel_size - ) - if self.rank == 0 and global_batch_size_changed and verbose: - logger.info( - f'decreasing batch size from {self.current_global_batch_size} to ' - f'{self.current_running_global_batch_size} to keep divisiblity by ' - f'micro_batch_size={self.micro_batch_size} * ' - f'data_parallel_size={self.data_parallel_size}' - ) - assert ( - self.current_running_global_batch_size % self.micro_batch_times_data_parallel_size - == 0 - ) - else: - self.current_running_global_batch_size = self.current_global_batch_size - + self.current_running_global_batch_size = self.current_global_batch_size + assert self.current_running_global_batch_size is not None self.num_micro_batches = ( self.current_running_global_batch_size // self.micro_batch_times_data_parallel_size ) diff --git a/megatron/legacy/model/transformer.py b/megatron/legacy/model/transformer.py index ca3414eecdd..a2a85d3747d 100644 --- a/megatron/legacy/model/transformer.py +++ b/megatron/legacy/model/transformer.py @@ -1454,7 +1454,7 @@ def forward(self, hidden_states, attention_mask, ) if self.use_fp8 else nullcontext(): # Determine if the current iteration is first microbatch if self.num_microbatches_in_previous_step != get_num_microbatches(): - self.microbatch_count = 0 # Reset count on new batch size rampup interval + self.microbatch_count = 0 self.num_microbatches_in_previous_step = get_num_microbatches() is_first_microbatch = self.microbatch_count % get_num_microbatches() == 0 diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index 516dc89ab11..f82834606fd 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -1501,9 +1501,8 @@ def prepare_data_for_update( samples_ratio_per_step=samples_ratio_per_step, num_bins_this_rank = len(packing_context.packed_trajs), bin_seq_indices = packing_context.packing_info.bin_seq_indices, - global_batch_size=args.global_batch_size, - rampup_batch_size=args.rampup_batch_size, - micro_batch_size=args.micro_batch_size, + global_batch_size=args.global_batch_size, + micro_batch_size=args.micro_batch_size, decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, ) loader = get_microbatch_dataloader(len(packing_context.packed_trajs), args.micro_batch_size) @@ -1528,9 +1527,8 @@ def prepare_data_for_update( reconfigure_num_microbatches_calculator( rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, - global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), - rampup_batch_size=args.rampup_batch_size, - micro_batch_size=args.micro_batch_size, + global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), + micro_batch_size=args.micro_batch_size, decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, data_parallel_size=mpu.get_data_parallel_world_size(), ) diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py index 50ab2ca27de..9ceb806571c 100644 --- a/megatron/rl/sequence_packing_utils.py +++ b/megatron/rl/sequence_packing_utils.py @@ -1016,7 +1016,6 @@ def update_microbatch_calculator( num_bins_this_rank: int, bin_seq_indices: List[List[int]], global_batch_size: int, - rampup_batch_size: int, micro_batch_size: int, decrease_batch_size_if_needed: bool, ): @@ -1026,7 +1025,6 @@ def update_microbatch_calculator( num_bins_this_rank: Amount of packing bins that belongs to current rank. bin_seq_indices: Global seq indices in the bin, see PackingInfo. global_batch_size: Current global batch size. - rampup_batch_size: Rampup batch size. See num_microbatches_calculator.py for more. micro_batch_size: Micro batch size at init. decrease_batch_size_if_needed: Scale down batch size. See num_microbatches_calculator.py for more. @@ -1046,7 +1044,6 @@ def update_microbatch_calculator( old_num_microbatches = get_num_microbatches() reconfigure_num_microbatches_calculator( rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, - rampup_batch_size=rampup_batch_size, global_batch_size=bins_bs, micro_batch_size=micro_batch_size, data_parallel_size=dp_world_size, diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 684a8ace6fb..4e8de13bd54 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -132,6 +132,8 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False): else: args = parser.parse_args() + args._is_global_batch_size_explicitly_specified = args.global_batch_size is not None + # Experimental yaml if args.yaml_cfg is not None: from .yaml_arguments import load_yaml @@ -593,11 +595,16 @@ def validate_args(args, defaults={}): args.phase_transition_iterations = sorted( int(x.strip()) for x in args.phase_transition_iterations.split(",") ) - assert args.rampup_batch_size is None, "multi-phase training does not support batch size ramp-up" - # Batch size. assert args.micro_batch_size is not None assert args.micro_batch_size > 0 + is_global_batch_size_explicitly_specified = getattr( + args, '_is_global_batch_size_explicitly_specified', args.global_batch_size is not None + ) + if args.step_batch_size_schedule is not None and is_global_batch_size_explicitly_specified: + raise ValueError( + 'Cannot specify both --step-batch-size-schedule and --global-batch-size' + ) if args.global_batch_size is None: args.global_batch_size = args.micro_batch_size * args.data_parallel_size print_rank_0('setting global batch size to {}'.format(args.global_batch_size)) @@ -617,7 +624,6 @@ def validate_args(args, defaults={}): args.grpo_samples_per_iteration * args.grpo_iterations) # Ensure that the number of prompts we collect is a multiple of the global batch size. - # TODO: Make this account for batch size rampup? assert num_generated_samples_per_inference_iteration % args.global_batch_size == 0, \ f"grpo_group_size * grpo_prompts_per_step * grpo_iterations should be divisible by global_batch_size" @@ -1116,8 +1122,6 @@ def validate_args(args, defaults={}): 'expected iteration-based learning rate decay' assert args.lr_warmup_samples == 0, \ 'expected iteration-based learning rate warmup' - assert args.rampup_batch_size is None, \ - 'expected no batch-size rampup for iteration-based training' if args.lr_warmup_fraction is not None: assert args.lr_warmup_iters == 0, \ 'can only specify one of lr-warmup-fraction and lr-warmup-iters' @@ -2910,8 +2914,7 @@ def _add_data_args(parser): 'This argument is exclusive to the other independent --*-data-path arguments.') group.add_argument('--phase-transition-iterations', type=str, default=None, help='Comma-separated list of iterations where phase ' - 'transitions occur. Requires fixed global batch size across phases. ' - 'Does not support batch size ramp-up.') + 'transitions occur. Requires fixed global batch size across phases.') group.add_argument('--split', type=str, default=None, help='Comma-separated list of proportions for training,' ' validation, and test split. For example the split ' diff --git a/megatron/training/config/training_config.py b/megatron/training/config/training_config.py index 6bc59668789..8310060c525 100644 --- a/megatron/training/config/training_config.py +++ b/megatron/training/config/training_config.py @@ -16,21 +16,21 @@ class TrainingConfig: data-parallel-size. If this value is None, then use micro-batch-size * data-parallel-size as the global batch size. This choice will result in 1 for number of micro-batches.""" - rampup_batch_size: list[int] | None = field(default=None, metadata={"argparse_meta": {"nargs": 3}}) - """Batch size ramp up with the following values: , , - - For example: - rampup-batch-size = [16, 8, 300000] - global-batch-size 1024 - will start with global batch size 16 and over (1024 - 16) / 8 = 126 intervals will increase - the batch size linearly to 1024. In each interval we will use approximately - 300000 / 126 = 2380 samples. + step_batch_size_schedule: str | None = None + """Step-wise batch size schedule in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + If sequence length is provided, thresholds are interpreted as tokens; otherwise as samples. + Example: + step_batch_size_schedule = "0:768 250B:1536 500B:3072 750B:6144" + Cannot be used together with decrease_batch_size_if_needed. """ + decrease_batch_size_if_needed: bool = False """If set, decrease batch size if microbatch_size * dp_size does not divide batch_size. Old batch_size will be restored if training is re-started - with dp_size that divides batch_size // microbatch_size.""" + with dp_size that divides batch_size // microbatch_size. Not supported with + step-batch-size-schedule.""" empty_unused_memory_level: Literal[0, 1, 2] = 0 """Call torch.cuda.empty_cache() each iteration (training and eval), to reduce fragmentation. diff --git a/megatron/training/global_vars.py b/megatron/training/global_vars.py index ab4713c2624..a8c65103d16 100644 --- a/megatron/training/global_vars.py +++ b/megatron/training/global_vars.py @@ -121,13 +121,17 @@ def set_global_variables(args, build_tokenizer=True): _ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args') set_args(args) + if args.step_batch_size_schedule is not None: + print(f'> using step batch size schedule: {args.step_batch_size_schedule}') + init_num_microbatches_calculator( args.rank, - args.rampup_batch_size, args.global_batch_size, args.micro_batch_size, args.data_parallel_size, args.decrease_batch_size_if_needed, + step_batch_size_schedule=args.step_batch_size_schedule, + seq_length=args.seq_length, ) if build_tokenizer: _ = _build_tokenizer(args) diff --git a/megatron/training/training.py b/megatron/training/training.py index 8fed9afd570..c95d386c748 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -1292,29 +1292,20 @@ def update_train_iters(args): if args.train_iters: return - # Constant batch size with sample-based training. - if args.rampup_batch_size is None: - args.train_iters = args.train_samples // args.global_batch_size - - else: - # Sample based training with rampup batch size. + if args.step_batch_size_schedule is not None: + # Sample based training with step batch size schedule. iterations = 0 consumed_samples = 0 - # Rampup phase. - while ( - consumed_samples <= int(args.rampup_batch_size[2]) - and consumed_samples <= args.train_samples - ): + while consumed_samples < args.train_samples: update_num_microbatches(consumed_samples, consistency_check=False) consumed_samples += get_current_global_batch_size() iterations += 1 # Reset update_num_microbatches(0, consistency_check=False) - # Constant phase - # Note that we throw away any partial last batch. - if args.train_samples > consumed_samples: - iterations += (args.train_samples - consumed_samples) // args.global_batch_size args.train_iters = iterations + else: + # Constant batch size with sample-based training. + args.train_iters = args.train_samples // args.global_batch_size print_rank_0(f'setting training iterations to {args.train_iters}') @@ -1739,6 +1730,21 @@ def setup_model_and_optimizer( args.iteration = 0 args.num_floating_point_operations_so_far = 0 + # Validate that the world size can accommodate the current batch size. + # This catches the case where GPUs were scaled up mid-training but the + # current position in the batch size schedule yields a batch size that + # is too small for the number of data-parallel replicas. + num_microbatches = get_num_microbatches() + current_global_batch_size = get_current_global_batch_size() + data_parallel_size = mpu.get_data_parallel_world_size() + assert num_microbatches is not None and num_microbatches >= 1, ( + f'current global batch size ({current_global_batch_size}) is too small for ' + f'micro_batch_size ({args.micro_batch_size}) * data_parallel_size ({data_parallel_size}) = ' + f'{args.micro_batch_size * data_parallel_size}. The world size cannot accommodate the ' + f'batch size. This can happen when resuming with more GPUs than the current batch size ' + f'schedule entry supports.' + ) + # get model without FP16 and/or DDP wrappers if ( args.iteration == 0 @@ -2715,11 +2721,12 @@ def train( # Then initialize with the correct perform_rl_step=True context init_num_microbatches_calculator( args.rank, - args.rampup_batch_size, args.global_batch_size, args.micro_batch_size, mpu.get_data_parallel_world_size(), - args.decrease_batch_size_if_needed + args.decrease_batch_size_if_needed, + step_batch_size_schedule=args.step_batch_size_schedule, + seq_length=args.seq_length, ) print_rank_0(f"> GRPO training: num_microbatches set to {get_num_microbatches()}") @@ -2971,8 +2978,8 @@ def trace_handler(p): ) else: assert get_num_microbatches() > num_microbatches, ( - f"Number of microbatches should be increasing due to batch size rampup; " - f"instead going from {num_microbatches} to {get_num_microbatches()}" + f"Number of microbatches should not decrease; " + f"going from {num_microbatches} to {get_num_microbatches()}" ) if args.save is not None: save_checkpoint_and_time( diff --git a/megatron/training/yaml_arguments.py b/megatron/training/yaml_arguments.py index 3fa6dfa320a..3a2d04aadcf 100644 --- a/megatron/training/yaml_arguments.py +++ b/megatron/training/yaml_arguments.py @@ -103,6 +103,13 @@ def validate_yaml(args, defaults={}): # Batch size. assert args.micro_batch_size is not None assert args.micro_batch_size > 0 + is_global_batch_size_explicitly_specified = getattr( + args, '_is_global_batch_size_explicitly_specified', args.global_batch_size is not None + ) + if args.step_batch_size_schedule is not None and is_global_batch_size_explicitly_specified: + raise ValueError( + 'Cannot specify both --step-batch-size-schedule and --global-batch-size' + ) if args.global_batch_size is None: args.global_batch_size = args.micro_batch_size * args.data_parallel_size if args.rank == 0: @@ -188,8 +195,6 @@ def validate_yaml(args, defaults={}): 'expected iteration-based learning rate decay' assert args.lr_warmup_samples == 0, \ 'expected iteration-based learning rate warmup' - assert args.rampup_batch_size is None, \ - 'expected no batch-size rampup for iteration-based training' if args.lr_warmup_fraction is not None: assert args.lr_warmup_iters == 0, \ 'can only specify one of lr-warmup-fraction and lr-warmup-iters' @@ -430,5 +435,8 @@ def load_yaml(yaml_path): config_namespace = json.loads(json.dumps(config), object_hook=lambda item: SimpleNamespace(**item)) # Add config location to namespace config_namespace.yaml_cfg = yaml_path + config_namespace._is_global_batch_size_explicitly_specified = ( + getattr(config_namespace, "global_batch_size", None) is not None + ) return config_namespace diff --git a/tasks/finetune_utils.py b/tasks/finetune_utils.py index 6d20e55bd16..9507ba5dbf0 100644 --- a/tasks/finetune_utils.py +++ b/tasks/finetune_utils.py @@ -248,9 +248,6 @@ def finetune(train_valid_datasets_provider, model_provider, args = get_args() timers = get_timers() - assert args.rampup_batch_size is None, \ - 'batch size scaling is not supported for finetuning' - # Train and validation data loaders. timers('train/valid/test dataset/dataloder', log_level=0).start() if args.epochs > 0: diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml index 3bb12c99539..2baa92999e6 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml @@ -22,8 +22,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true # Transformer Engine args diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml index 8eb57abf308..d9b44500eca 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml @@ -24,8 +24,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true --cross-entropy-loss-fusion: true diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml index 300e0777917..4d686aba694 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml @@ -22,8 +22,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 4882812 --manual-gc: true # Transformer Engine args diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml index 2f045810a87..5f51a40ecc9 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml @@ -24,8 +24,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true --cross-entropy-loss-fusion: true diff --git a/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py b/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py index 8b03262714a..4d38f9a5051 100644 --- a/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py +++ b/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py @@ -305,7 +305,7 @@ def set_ckpt_path(ckpt_path): args.load = ckpt_path set_tp_pp_vpp(*src_tp_pp_vpp, pp_layout=src_pp_layout, destroy_first=False) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator(0, 1, 1, 1) iteration = 123 layer_spec_fn = get_gpt_decoder_block_spec if is_moe else gpt_te_spec diff --git a/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py b/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py index 51dc1d0bff6..e719327828d 100644 --- a/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py +++ b/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py @@ -39,7 +39,7 @@ def __init__(self, config: TransformerConfig): def init_model_parallel(): """Init torch distributed.""" Utils.initialize_model_parallel(1, 1) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator(0, 1, 1, 1) model_parallel_cuda_manual_seed(123) yield # Run the actual test. Utils.destroy_model_parallel() diff --git a/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py b/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py index e7fb8159af2..cf4a6c12cf1 100644 --- a/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py +++ b/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py @@ -226,7 +226,7 @@ def set_tp_pp_vpp(tp, pp, vpp=None, pp_layout=None, destroy_first=True): args.pipeline_model_parallel_layout = pp_layout set_tp_pp_vpp(*tp_pp_vpp, pp_layout=pp_layout, destroy_first=False) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator(0, 1, 1, 1) def forward_step_func(data_iterator, model: GPTModel): """Forward training step. Copied from `pretrain_gpt.py`""" diff --git a/tests/unit_tests/rl/test_sequence_packing_utils.py b/tests/unit_tests/rl/test_sequence_packing_utils.py index 0a4f4cd03f9..bb03be57cb3 100644 --- a/tests/unit_tests/rl/test_sequence_packing_utils.py +++ b/tests/unit_tests/rl/test_sequence_packing_utils.py @@ -465,12 +465,7 @@ def test_get_bins_bs_and_steps(ratio, local_bins, world, expected_bs): global_bs_in_seq = int(n_seqs * ratio) def side_eff( - rank, - rampup_batch_size, - global_batch_size, - micro_batch_size, - data_parallel_size, - decrease_batch_size_if_needed, + rank, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed ): # Inside of the get_microbatch_dataloader, we compute the batch size in bins. # We want to test this variable. @@ -488,7 +483,6 @@ def side_eff( num_bins_this_rank=local_bins, bin_seq_indices=[], global_batch_size=global_bs_in_seq, - rampup_batch_size=1, micro_batch_size=1, decrease_batch_size_if_needed=False, ) diff --git a/tests/unit_tests/test_checkpointing.py b/tests/unit_tests/test_checkpointing.py index 502e1ef700e..015cb964540 100644 --- a/tests/unit_tests/test_checkpointing.py +++ b/tests/unit_tests/test_checkpointing.py @@ -152,7 +152,7 @@ def create_ckpt_load_args(create_args): def init_model_parallel(): """Init torch distributed.""" Utils.initialize_model_parallel(1, 1) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator(0, 1, 1, 1) model_parallel_cuda_manual_seed(123) yield # Run the actual test. Utils.destroy_model_parallel() diff --git a/tests/unit_tests/test_num_microbatches_calculator.py b/tests/unit_tests/test_num_microbatches_calculator.py index 9b3356b8af5..0718f954dab 100644 --- a/tests/unit_tests/test_num_microbatches_calculator.py +++ b/tests/unit_tests/test_num_microbatches_calculator.py @@ -1,4 +1,4 @@ -from typing import List, Optional +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import pytest @@ -7,21 +7,21 @@ def test_init_num_microbatches_calculator(): mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator(0, 32, 8, 2, False) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 32 with pytest.raises(AssertionError): - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator(0, 32, 8, 2, False) mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 3, True) + mb_calculator.init_num_microbatches_calculator(0, 32, 8, 3, True) assert mb_calculator.get_num_microbatches() == 1 assert mb_calculator.get_current_global_batch_size() == 32 assert mb_calculator.get_current_running_global_batch_size() == 24 mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 33, 8, 2, True) + mb_calculator.init_num_microbatches_calculator(0, 33, 8, 2, True) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 33 assert mb_calculator.get_current_running_global_batch_size() == 32 @@ -29,68 +29,47 @@ def test_init_num_microbatches_calculator(): def test_reconfigure_num_microbatches_calculator(): mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator(0, 32, 8, 2, False) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 32 - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) - assert mb_calculator.get_num_microbatches() == 1 - assert mb_calculator.get_current_global_batch_size() == 16 - - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 16, 96], 32, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 8, 2, False) assert mb_calculator.get_num_microbatches() == 1 assert mb_calculator.get_current_global_batch_size() == 16 def test_get_num_microbatches(): - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 8, 2, False) assert mb_calculator.get_num_microbatches() == 1 - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 3, True) + mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 4, 3, True) assert mb_calculator.get_num_microbatches() == 1 def test_get_current_global_batch_size(): - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 4, 2, False) assert mb_calculator.get_current_global_batch_size() == 16 - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 3, True) + mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 4, 3, True) assert mb_calculator.get_current_global_batch_size() == 16 assert mb_calculator.get_current_running_global_batch_size() == 12 def test_get_micro_batch_size(): - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 8, 2, False) assert mb_calculator.get_micro_batch_size() == 8 -def test_update_num_microbatches(): - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 8, 96], 32, 4, 2, False) - assert mb_calculator.get_num_microbatches() == 2 - mb_calculator.update_num_microbatches(48, False) - assert mb_calculator.get_num_microbatches() == 3 - - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 8, 96], 32, 8, 2, False) - with pytest.raises(AssertionError): - mb_calculator.update_num_microbatches(49, True) - - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 32, 8, 2, False) - mb_calculator.update_num_microbatches(16) - assert mb_calculator.get_num_microbatches() == 2 - - def test_build_num_microbatches_calculator(): - temp_calculator = mb_calculator._build_num_microbatches_calculator(0, None, 32, 8, 2, False) + temp_calculator = mb_calculator._build_num_microbatches_calculator(0, 32, 8, 2, False) assert temp_calculator.get() == 2 assert temp_calculator.get_current_global_batch_size() == 32 assert type(temp_calculator) is mb_calculator.ConstantNumMicroBatchesCalculator - temp_calculator = mb_calculator._build_num_microbatches_calculator( - 0, [16, 16, 48], 32, 8, 2, False - ) - assert temp_calculator.get() == 1 - assert temp_calculator.get_current_global_batch_size() == 16 - assert type(temp_calculator) is mb_calculator.RampupBatchsizeNumMicroBatchesCalculator + with pytest.raises(ValueError): + mb_calculator._build_num_microbatches_calculator( + 0, None, 8, 2, True, step_batch_size_schedule="0:16 100:32" + ) class TestConstantNumMicroBatchesCalculator: @@ -110,38 +89,73 @@ def test_get_current_global_batch_size(self): assert self.mb_calculator.get_current_global_batch_size() == 32 -class TestRampupBatchsizeNumMicroBatchesCalculator: - def setup_method(self, method): - self.mb_calculator = mb_calculator.RampupBatchsizeNumMicroBatchesCalculator( - 32, 8, 2, False, 0, 16, 16, 48 - ) - - def test_constructor(self): - assert type(self.mb_calculator) is mb_calculator.RampupBatchsizeNumMicroBatchesCalculator - assert self.mb_calculator.global_batch_size == 32 - assert self.mb_calculator.micro_batch_size == 8 - assert self.mb_calculator.data_parallel_size == 2 - assert self.mb_calculator.start_global_batch_size == 16 - assert self.mb_calculator.batch_size_increment == 16 - assert self.mb_calculator.ramup_samples == 48 - assert self.mb_calculator.micro_batch_times_data_parallel_size == 16 - assert self.mb_calculator.num_micro_batches == 1 - - def test_get(self): - assert self.mb_calculator.get() == 1 +def test_step_batch_size_schedule_allows_past_entries_smaller_than_dp(): + """Test that step batch size schedule does not crash when early schedule entries + are smaller than micro_batch_size * data_parallel_size. + + This happens when scaling to more GPUs mid-training: the initial batch sizes + in the schedule are smaller than the new GPU count can support, but training + has progressed past those entries. The divisibility check should only apply + to the CURRENT batch size (after checkpoint loading), not all schedule entries. + """ + # micro=1, dp=512, micro*dp=512 + # Schedule starts at 256 which is < 512, but later entries are fine. + # This should NOT raise during construction. + calc = mb_calculator.StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=1, + data_parallel_size=512, + decrease_batch_size_if_needed=False, + rank=0, + schedule="0:256 200000:512 600000:1024 1500000:2048 3000000:4096 6000000:6144", + ) - def test_get_current_global_batch_size(self): - assert self.mb_calculator.get_current_global_batch_size() == 16 + # At init (consumed_samples=0), batch=256 < micro*dp=512. + # No consistency_check at init, so no error. + assert calc.current_global_batch_size == 256 + + # After training past the first entry, batch=512. Consistency check passes. + calc.update(200000, consistency_check=True) + assert calc.current_global_batch_size == 512 + assert calc.num_micro_batches == 1 + + # Later entries work fine. + calc.update(1500000, consistency_check=True) + assert calc.current_global_batch_size == 2048 + assert calc.num_micro_batches == 4 + + calc.update(6000000, consistency_check=True) + assert calc.current_global_batch_size == 6144 + assert calc.num_micro_batches == 12 + + +def test_step_batch_size_schedule_consistency_check_fails_when_batch_too_small(): + """Test that consistency_check=True raises when current batch size is smaller + than micro_batch_size * data_parallel_size. + + This is the scenario caught by the runtime check in setup_model_and_optimizer: + GPUs were scaled up but the current schedule entry yields a batch size too small + for the new world size. + """ + calc = mb_calculator.StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=1, + data_parallel_size=512, + decrease_batch_size_if_needed=False, + rank=0, + schedule="0:256 200000:512 600000:1024", + ) + # At consumed_samples=0, batch=256 < micro*dp=512. + # consistency_check=True should fail because 256 % 512 != 0. + with pytest.raises(AssertionError): + calc.update(0, consistency_check=True) -def test_ramp_up(): - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 16, 96], 32, 8, 2, False) - consumed_samples = 0 - count = 0 - expected_consumed_samples = [0, 16, 32, 48, 64, 80, 96, 128, 160, 192, 224, 256] - while consumed_samples < 256: - consumed_samples += mb_calculator.get_current_global_batch_size() - count += 1 - assert consumed_samples == expected_consumed_samples[count] - mb_calculator.update_num_microbatches(consumed_samples, True) +def test_step_batch_size_schedule_rejects_decrease_batch_size_if_needed(): + with pytest.raises(ValueError): + mb_calculator.StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=1, + data_parallel_size=512, + decrease_batch_size_if_needed=True, + rank=0, + schedule="0:256 200000:512 600000:1024", + ) diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py index 69f12e4d983..ee4ff7d152d 100644 --- a/tests/unit_tests/transformer/test_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_cuda_graphs.py @@ -575,7 +575,6 @@ def test_get_cuda_graph_input_data(self, num_microbatches, pp_size, vpp_size): # Initialize num_microbatches calculator init_num_microbatches_calculator( rank=0, - rampup_batch_size=None, global_batch_size=micro_batch_size * num_microbatches, micro_batch_size=micro_batch_size, data_parallel_size=1, From dc8785827b9d8f255b8361fb3bdff90a67bc4393 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 21 Apr 2026 00:23:18 +0000 Subject: [PATCH 54/72] Update copy-pr-bot.yaml [skip ci] --- .github/copy-pr-bot.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml index 7a002db7063..ce19144f988 100644 --- a/.github/copy-pr-bot.yaml +++ b/.github/copy-pr-bot.yaml @@ -1,4 +1,4 @@ enabled: true auto_sync_draft: false auto_sync_ready: true -trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "Connor-XY", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Mellonta", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "WanZzzzzz", "Wohox", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "fitsumreda", "frsun-nvda", "gautham-kollu", "gdengk", "guihong-nv", "guyueh1", "hexinw-nvidia", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kajalj22", "kanz-nv", "keshavb96", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "minitu", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "sheliang-nv", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sraman-rgb", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "wujingyue", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"] +trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "Connor-XY", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Mellonta", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "WanZzzzzz", "Wohox", "YangFei1990", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "fitsumreda", "frsun-nvda", "gautham-kollu", "gdengk", "guihong-nv", "guyueh1", "hexinw-nvidia", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kajalj22", "kanz-nv", "keshavb96", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "minitu", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "sheliang-nv", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sraman-rgb", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "wujingyue", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"] From a52112dae9557600ac1160522dcac5bc715d891d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 21 Apr 2026 10:12:21 +0200 Subject: [PATCH 55/72] revert: replace rampup batch size scheduler with custom step batch size schedules (#3779) (#4404) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- examples/gpt3/gpt_config.yaml | 2 +- examples/gpt3/train_gpt3_175b_distributed.sh | 6 +- megatron/core/num_microbatches_calculator.py | 429 ++++++++---------- megatron/legacy/model/transformer.py | 2 +- megatron/rl/rl_utils.py | 10 +- megatron/rl/sequence_packing_utils.py | 3 + megatron/training/arguments.py | 17 +- megatron/training/config/training_config.py | 20 +- megatron/training/global_vars.py | 6 +- megatron/training/training.py | 45 +- megatron/training/yaml_arguments.py | 12 +- tasks/finetune_utils.py | 3 + .../gpt/gpt3_15b_8t_release/model_config.yaml | 2 +- .../model_config.yaml | 2 +- .../gpt3_15b_8t_release_sm/model_config.yaml | 2 +- .../model_config.yaml | 2 +- .../test_pipeline_parallel_layout.py | 2 +- .../test_torch_fully_sharded_parallel.py | 2 +- .../pipeline_parallel/test_pipeline_layout.py | 2 +- .../rl/test_sequence_packing_utils.py | 8 +- tests/unit_tests/test_checkpointing.py | 2 +- .../test_num_microbatches_calculator.py | 152 +++---- .../transformer/test_cuda_graphs.py | 1 + 23 files changed, 321 insertions(+), 411 deletions(-) diff --git a/examples/gpt3/gpt_config.yaml b/examples/gpt3/gpt_config.yaml index 3c387eafe5a..b7bc2bf4c4f 100644 --- a/examples/gpt3/gpt_config.yaml +++ b/examples/gpt3/gpt_config.yaml @@ -136,7 +136,7 @@ use_legacy_models: False spec: null micro_batch_size: 2 global_batch_size: 128 -step_batch_size_schedule: "0:32 90B:64 180B:96 270B:128" +rampup_batch_size: [32, 32, 65324160] check_for_nan_in_loss_and_grad: True num_layers_per_virtual_pipeline_stage: null diff --git a/examples/gpt3/train_gpt3_175b_distributed.sh b/examples/gpt3/train_gpt3_175b_distributed.sh index be00d76120d..7d2c01b3157 100755 --- a/examples/gpt3/train_gpt3_175b_distributed.sh +++ b/examples/gpt3/train_gpt3_175b_distributed.sh @@ -36,9 +36,9 @@ GPT_MODEL_ARGS=( TRAINING_ARGS=( --micro-batch-size 1 - --global-batch-size 1536 - --step-batch-size-schedule "0:16 2.4B:320 4.8B:624 7.2B:928 9.6B:1232 12B:1536" - --train-iters 500000 + --global-batch-size 1536 + --rampup-batch-size 16 16 5859375 + --train-iters 500000 --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.95 diff --git a/megatron/core/num_microbatches_calculator.py b/megatron/core/num_microbatches_calculator.py index 1056d3ebb70..ae7cea92e63 100644 --- a/megatron/core/num_microbatches_calculator.py +++ b/megatron/core/num_microbatches_calculator.py @@ -4,14 +4,14 @@ import logging from abc import ABC, abstractmethod -from typing import List, Optional, Tuple, Union +from typing import List, Optional, Union logger = logging.getLogger(__name__) # TODO: global_var merge into mcore? _GLOBAL_NUM_MICROBATCHES_CALCULATOR: Union[ - 'ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator' -] = None # type: ignore[assignment] + 'ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator' +] = None def get_num_microbatches() -> int: @@ -63,18 +63,20 @@ def unset_num_microbatches_calculator(): def init_num_microbatches_calculator( rank: int, + rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, - step_batch_size_schedule: Optional[str] = None, - seq_length: Optional[int] = None, ) -> None: """Initialize number of microbatches calculator. Supporting backward compatibility. Args: rank (int): Rank of the GPU, only rank 0 will log the information. + rampup_batch_size (Optional[List[int]]): + Rampup batch size, should be in format of [start_global_batch_size, + batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -84,24 +86,14 @@ def init_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. - step_batch_size_schedule (Optional[str]): - Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". - Thresholds are interpreted as samples unless seq_length is provided, in which case - thresholds are interpreted as tokens and converted to samples. - Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). - Example: "0:768 250B:1536 500B:3072 750B:6144" - seq_length (Optional[int]): - Sequence length for token-to-sample conversion when using step_batch_size_schedule. - If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ _configure_global_num_microbatches_calculator( rank, + rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, - step_batch_size_schedule, - seq_length, init=True, ) @@ -114,18 +106,20 @@ def destroy_num_microbatches_calculator(): def reconfigure_num_microbatches_calculator( rank: int, + rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, - step_batch_size_schedule: Optional[str] = None, - seq_length: Optional[int] = None, ) -> None: """Reconfigure number of microbatches calculator. Supporting backward compatibility. Args: rank (int): Rank of the GPU, only rank 0 will log the information. + rampup_batch_size (Optional[List[int]]): + Rampup batch size, should be in format of + [start_global_batch_size, batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -135,34 +129,25 @@ def reconfigure_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. - step_batch_size_schedule (Optional[str]): - Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". - Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). - Example: "0:768 250B:1536 500B:3072 750B:6144" - seq_length (Optional[int]): - Sequence length for token-to-sample conversion when using step_batch_size_schedule. - If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ _configure_global_num_microbatches_calculator( rank, + rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, - step_batch_size_schedule, - seq_length, init=False, ) def _configure_global_num_microbatches_calculator( rank: int, + rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, - step_batch_size_schedule: Optional[str] = None, - seq_length: Optional[int] = None, init: bool = False, ) -> None: """Configure number of microbatches calculator. Can be used for initialization and @@ -171,6 +156,9 @@ def _configure_global_num_microbatches_calculator( Args: rank (int): Rank of the GPU, only rank 0 will log the information. + rampup_batch_size (Optional[List[int]]): + Rampup batch size, should be in format of + [start_global_batch_size, batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -180,13 +168,6 @@ def _configure_global_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. - step_batch_size_schedule (Optional[str]): - Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". - Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). - Example: "0:768 250B:1536 500B:3072 750B:6144" - seq_length (Optional[int]): - Sequence length for token-to-sample conversion when using step_batch_size_schedule. - If provided, thresholds are interpreted as tokens. If None, thresholds are samples. init (bool, optional): If true, initialize the calculator. Defaults to False. """ @@ -199,72 +180,43 @@ def _configure_global_num_microbatches_calculator( _GLOBAL_NUM_MICROBATCHES_CALCULATOR = _build_num_microbatches_calculator( rank, + rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, - step_batch_size_schedule, - seq_length, ) def _build_num_microbatches_calculator( rank: int, - global_batch_size: Optional[int], + rampup_batch_size: Optional[List[int]], + global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool, - step_batch_size_schedule: Optional[str] = None, - seq_length: Optional[int] = None, -) -> Union['ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator']: +) -> Union['ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator']: """Build number of microbatches calculator. Internal helper method. Args: rank (int): Rank of the GPU, only rank 0 will log the information. - global_batch_size (Optional[int]): - Global batch size for the model. Required for constant mode. - Ignored when step_batch_size_schedule is provided. + rampup_batch_size (Optional[List[int]]): + Rampup batch size, should be in format of + [start_global_batch_size, batch_size_increment, ramup_samples]. + global_batch_size (int): + Global batch size for the model. micro_batch_size (int): Micro batch size at initialization. data_parallel_size (int): Data parallel size. decrease_batch_size_if_needed (bool): If true, scale down batch size to ensure divisibility by DP size * microbatch size. - step_batch_size_schedule (Optional[str]): - Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". - Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). - Example: "0:768 250B:1536 500B:3072 750B:6144" - seq_length (Optional[int]): - Sequence length for token-to-sample conversion when using step_batch_size_schedule. - If provided, thresholds are interpreted as tokens. If None, thresholds are samples. - """ - - num_microbatches_calculator: Union[ - 'ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator' - ] - # Step batch size schedule - if step_batch_size_schedule is not None: - if decrease_batch_size_if_needed: - raise ValueError( - 'Cannot specify both --step-batch-size-schedule and ' - '--decrease-batch-size-if-needed' - ) - num_microbatches_calculator = StepBatchsizeNumMicroBatchesCalculator( - micro_batch_size=micro_batch_size, - data_parallel_size=data_parallel_size, - decrease_batch_size_if_needed=decrease_batch_size_if_needed, - rank=rank, - schedule=step_batch_size_schedule, - seq_length=seq_length, - ) + """ - # Constant batch size - else: - assert ( - global_batch_size is not None - ), '--global-batch-size is required when not using --step-batch-size-schedule' + # Constant batch size. + if rampup_batch_size is None: num_microbatches_calculator = ConstantNumMicroBatchesCalculator( global_batch_size, micro_batch_size, @@ -276,6 +228,32 @@ def _build_num_microbatches_calculator( logger.info( f'setting number of microbatches to constant {num_microbatches_calculator.get()}' ) + # Batch size ramp up. + else: + assert len(rampup_batch_size) == 3, ( + 'expected the following ' + 'format: --rampup-batch-size ' + ' ' + ) + start_global_batch_size = int(rampup_batch_size[0]) + batch_size_increment = int(rampup_batch_size[1]) + ramup_samples = int(rampup_batch_size[2]) + if rank == 0: + logger.info( + f'will use batch size rampup starting from global batch size ' + f'{start_global_batch_size} to global batch size {global_batch_size} with batch' + f'size increments {batch_size_increment} over {ramup_samples} samples.' + ) + num_microbatches_calculator = RampupBatchsizeNumMicroBatchesCalculator( + global_batch_size, + micro_batch_size, + data_parallel_size, + decrease_batch_size_if_needed, + rank, + start_global_batch_size, + batch_size_increment, + ramup_samples, + ) return num_microbatches_calculator @@ -289,35 +267,31 @@ class NumMicroBatchesCalculator(ABC): """Base class for number of microbatches calculator.""" def __init__(self) -> None: - self.num_micro_batches: Optional[int] = None - self.current_global_batch_size: Optional[int] = None - self.micro_batch_size: Optional[int] = None - self.current_running_global_batch_size: Optional[int] = None + self.num_micro_batches = None + self.current_global_batch_size = None + self.micro_batch_size = None + self.current_running_global_batch_size = None def get(self) -> int: """Get number of microbatches.""" - assert self.num_micro_batches is not None return self.num_micro_batches def get_current_global_batch_size(self) -> int: """Get current global batch size.""" - assert self.current_global_batch_size is not None return self.current_global_batch_size def get_micro_batch_size(self) -> int: """Get current global batch size.""" - assert self.micro_batch_size is not None return self.micro_batch_size def get_current_running_global_batch_size(self) -> int: """Get current running global batch size. If decrease_batch_size_if_needed is False, this just equals global batch size.""" - assert self.current_running_global_batch_size is not None return self.current_running_global_batch_size @abstractmethod def update(self, consumed_samples, consistency_check, verbose=False) -> None: - """Update number of microbatches.""" + """Update number of microbatches depending on batch size rampup.""" pass @@ -346,7 +320,6 @@ def __init__( decrease_batch_size_if_needed: bool, rank: int, ) -> None: - super().__init__() micro_batch_times_data_parallel_size = micro_batch_size * data_parallel_size if decrease_batch_size_if_needed: @@ -360,7 +333,9 @@ def __init__( f'to keep divisiblity by micro_batch_size={micro_batch_size} * ' f'data_parallel_size={data_parallel_size}' ) - num_micro_batches = running_global_batch_size // micro_batch_times_data_parallel_size + self.num_micro_batches = ( + running_global_batch_size // micro_batch_times_data_parallel_size + ) else: assert global_batch_size % micro_batch_times_data_parallel_size == 0, ( 'global batch size ({}) is not divisible by micro batch size ({})' @@ -369,12 +344,11 @@ def __init__( ) ) running_global_batch_size = global_batch_size - num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size + self.num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size assert ( - num_micro_batches >= 1 - ), 'number of microbatches should be at least 1, got {}.'.format(num_micro_batches) + self.num_micro_batches >= 1 + ), 'number of microbatches should be at least 1, got {}.'.format(self.num_micro_batches) - self.num_micro_batches = num_micro_batches self.current_global_batch_size = global_batch_size self.current_running_global_batch_size = running_global_batch_size self.micro_batch_size = micro_batch_size @@ -383,195 +357,152 @@ def update(self, consumed_samples, consistency_check, verbose=False) -> None: pass -class StepBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator): - """Calculator of number of microbatches with arbitrary step-wise batch size schedule. +class RampupBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator): + """Calculator of number of microbatches with batch size rampup. + Over `steps = (global-batch-size - start-batch-size) / batch_size_increment` increment batch + size from start-batch-size to global-batch-size using rampup-samples / steps + samples. Args: - micro_batch_size (int): Micro batch size. - data_parallel_size (int): Data parallel size. - decrease_batch_size_if_needed (bool): Must be False. Step schedules do not support - decreasing batch size for divisibility. - rank (int): Rank for logging. - schedule (str): Schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". - Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). - Examples: - "0:768 250B:1536 500B:3072 750B:6144" (thresholds in tokens) - "0:768 61035156250:1536" (thresholds in samples) - seq_length (int, optional): Sequence length for token-to-sample conversion. - If provided, thresholds are interpreted as tokens and converted to samples. - If None, thresholds are interpreted as samples directly. + global_batch_size (int): + Global batch size post rampup. + micro_batch_size (int): + Micro batch size. + data_parallel_size (int): + Data parallel size. + decrease_batch_size_if_needed (bool): + If true, decrease batch size to ensure divisibility by DP size * microbatch size + (if needed). + rank (int): + Rank (to determine whether logging should be performed). + start_global_batch_size (int): + Global batch size to start with. + batch_size_increment (int): + Global batch size increments. + ramup_samples (int): + Number of samples to use ramp up global + batch size from `start_global_batch_size` to `global_batch_size`. """ def __init__( self, + global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool, rank: int, - schedule: str, - seq_length: Optional[int] = None, + start_global_batch_size: int, + batch_size_increment: int, + ramup_samples: int, ) -> None: - super().__init__() - - if decrease_batch_size_if_needed: - raise ValueError( - 'Step batch size schedules do not support decrease_batch_size_if_needed' - ) + assert global_batch_size > 0, 'global batch size should be positive, got {}.'.format( + global_batch_size + ) + assert start_global_batch_size > 0, 'start batch size should be positive, got {}.'.format( + start_global_batch_size + ) + assert batch_size_increment > 0, 'batch size increment should be positive, got {}.'.format( + batch_size_increment + ) + assert ramup_samples >= 0, 'ramp-up samples should be non-negative, got {}.'.format( + ramup_samples + ) + self.global_batch_size = global_batch_size self.micro_batch_size = micro_batch_size - self.data_parallel_size: int = data_parallel_size - self.rank: int = rank - self.seq_length: Optional[int] = seq_length - - self.micro_batch_times_data_parallel_size: int = micro_batch_size * data_parallel_size + self.data_parallel_size = data_parallel_size + self.decrease_batch_size_if_needed = decrease_batch_size_if_needed + self.rank = rank + self.start_global_batch_size = start_global_batch_size + self.batch_size_increment = batch_size_increment + self.ramup_samples = ramup_samples + + self.micro_batch_times_data_parallel_size = self.micro_batch_size * self.data_parallel_size assert self.micro_batch_times_data_parallel_size > 0 + self.current_global_batch_size = None - # Parse schedule string - self.schedule = self._parse_schedule(schedule, seq_length) - - # Validate schedule - self._validate_schedule() + diff_batch_size = self.global_batch_size - self.start_global_batch_size + assert diff_batch_size >= 0, ( + 'expected global batch size to be greater than or equal to start batch size, ' + f'got {self.global_batch_size} and {self.start_global_batch_size}' + ) + assert diff_batch_size % batch_size_increment == 0, ( + 'expected ' + f'global batch size interval ({diff_batch_size}) to be divisible by global batch ' + f'size increment ({batch_size_increment})' + ) - self.global_batch_size: int = self.schedule[-1][1] - self.current_global_batch_size = None + num_increments = diff_batch_size // self.batch_size_increment + self.rampup_samples_per_increment = self.ramup_samples / num_increments - if rank == 0: - logger.info(f'> initializing step batch size schedule') - logger.info(f' raw schedule string: "{schedule}"') - unit = "tokens" if seq_length else "samples" - logger.info(f' seq_length: {seq_length}' f' (thresholds interpreted as {unit})') - logger.info(f' micro_batch_size: {micro_batch_size}') - logger.info(f' data_parallel_size: {data_parallel_size}') - logger.info(f'step batch size schedule ({len(self.schedule)} steps):') - for threshold, batch_size in self.schedule: - num_microbatches = batch_size // self.micro_batch_times_data_parallel_size - if seq_length: - tokens = threshold * seq_length - logger.info( - f' >= {tokens:,} tokens ({threshold:,} samples)' - f' -> batch_size={batch_size},' - f' num_microbatches={num_microbatches}' - ) - else: - logger.info( - f' >= {threshold:,} samples' - f' -> batch_size={batch_size},' - f' num_microbatches={num_microbatches}' - ) - # Initialize + # Initialize number of microbatches. self.update(0, consistency_check=False, verbose=True) - @staticmethod - def _parse_numeric_value(value_str: str) -> int: - """Parse numeric value with optional suffix (K, M, B, T).""" - value_str = value_str.strip().upper() - - multiplier = 1 - if value_str.endswith('T'): - multiplier = 1_000_000_000_000 - value_str = value_str[:-1] - elif value_str.endswith('B'): - multiplier = 1_000_000_000 - value_str = value_str[:-1] - elif value_str.endswith('M'): - multiplier = 1_000_000 - value_str = value_str[:-1] - elif value_str.endswith('K'): - multiplier = 1_000 - value_str = value_str[:-1] - - return int(float(value_str) * multiplier) - - @classmethod - def _parse_schedule(cls, schedule_str: str, seq_length: Optional[int]) -> List[Tuple[int, int]]: - """Parse schedule string into list of (threshold_samples, batch_size) tuples. + def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None: + """Update number of microbatches. Args: - schedule_str: Space-separated "THRESHOLD:BATCH_SIZE" pairs. - seq_length: If provided, convert thresholds from tokens to samples. - - Returns: - List of (threshold_samples, batch_size) tuples, sorted by threshold. + consumed_samples (int): Number of samples consumed. + consistency_check (bool): Option to check current schedule's consistency. + verbose (bool, optional): Option to control logging. Defaults to False. """ - schedule = [] - entries = schedule_str.strip().replace(',', ' ').split() - - for entry in entries: - if ':' not in entry: - raise ValueError( - f'Invalid schedule entry "{entry}". Expected format: "THRESHOLD:BATCH_SIZE"' - ) - - threshold_str, batch_size_str = entry.split(':', 1) - threshold = cls._parse_numeric_value(threshold_str) - batch_size = cls._parse_numeric_value(batch_size_str) - - # Convert tokens to samples if seq_length provided - if seq_length is not None: - threshold = threshold // seq_length - - schedule.append((threshold, batch_size)) - - # Sort by threshold ascending - schedule.sort(key=lambda x: x[0]) - return schedule - - def _validate_schedule(self) -> None: - """Validate the parsed schedule.""" - assert len(self.schedule) > 0, 'schedule must have at least one entry' - assert ( - self.schedule[0][0] == 0 - ), f'first schedule entry must have threshold 0, got {self.schedule[0][0]}' - - # Check strictly increasing thresholds - for i in range(1, len(self.schedule)): - assert self.schedule[i][0] > self.schedule[i - 1][0], ( - f'schedule thresholds must be strictly increasing, ' - f'got {self.schedule[i - 1][0]} before {self.schedule[i][0]}' + # Update current global batch size. + global_batch_size_changed = False + old_current_global_batch_size = self.current_global_batch_size + if consumed_samples > self.ramup_samples: + self.current_global_batch_size = self.global_batch_size + else: + steps = int(consumed_samples / self.rampup_samples_per_increment) + self.current_global_batch_size = ( + self.start_global_batch_size + steps * self.batch_size_increment ) + assert self.current_global_batch_size <= self.global_batch_size - # Validate batch sizes are positive. - # NOTE: divisibility by micro_batch_size * data_parallel_size is NOT checked here - # because early schedule entries may be smaller than the current GPU configuration - # (e.g., after scaling up GPUs mid-training). Divisibility of the CURRENT batch size - # is checked at runtime in update() when consistency_check=True (after checkpoint loading). - for threshold, batch_size in self.schedule: - assert batch_size > 0, f'batch size must be positive, got {batch_size}' - - def _get_batch_size_for_samples(self, consumed_samples: int) -> int: - """Get the batch size for the given number of consumed samples.""" - batch_size = self.schedule[0][1] - for threshold, bs in self.schedule: - if consumed_samples >= threshold: - batch_size = bs + if old_current_global_batch_size != self.current_global_batch_size: + global_batch_size_changed = True + if self.rank == 0 and global_batch_size_changed and verbose: + if old_current_global_batch_size is None: + logger.info(f'setting initial batch size to {self.current_global_batch_size}') else: - break - return batch_size - - def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None: - """Update number of microbatches based on consumed samples. - - Args: - consumed_samples (int): Number of samples consumed. - consistency_check (bool): Check divisibility constraints. - verbose (bool): Enable logging. - """ - self.current_global_batch_size = self._get_batch_size_for_samples(consumed_samples) - assert self.current_global_batch_size is not None + logger.info( + f'ramping up batch size from {old_current_global_batch_size} to ' + f'{self.current_global_batch_size}' + ) - # Consistency check - if consistency_check: + # Check consistency of the current global batch size. + if consistency_check and not self.decrease_batch_size_if_needed: assert ( self.current_global_batch_size % self.micro_batch_times_data_parallel_size == 0 ), ( - f'current global batch size ({self.current_global_batch_size}) is not divisible by ' - f'micro_batch_size ({self.micro_batch_size}) * ' - f'data_parallel_size ({self.data_parallel_size})' + 'current global ' + 'batch size ({}) is not divisible by micro-batch-size ({}) times' + 'data parallel size ({})'.format( + self.current_global_batch_size, self.micro_batch_size, self.data_parallel_size + ) ) - self.current_running_global_batch_size = self.current_global_batch_size - assert self.current_running_global_batch_size is not None + if ( + self.decrease_batch_size_if_needed + and self.current_global_batch_size % self.micro_batch_times_data_parallel_size != 0 + ): + self.current_running_global_batch_size = _round( + self.current_global_batch_size, self.micro_batch_times_data_parallel_size + ) + if self.rank == 0 and global_batch_size_changed and verbose: + logger.info( + f'decreasing batch size from {self.current_global_batch_size} to ' + f'{self.current_running_global_batch_size} to keep divisiblity by ' + f'micro_batch_size={self.micro_batch_size} * ' + f'data_parallel_size={self.data_parallel_size}' + ) + assert ( + self.current_running_global_batch_size % self.micro_batch_times_data_parallel_size + == 0 + ) + else: + self.current_running_global_batch_size = self.current_global_batch_size + self.num_micro_batches = ( self.current_running_global_batch_size // self.micro_batch_times_data_parallel_size ) diff --git a/megatron/legacy/model/transformer.py b/megatron/legacy/model/transformer.py index a2a85d3747d..ca3414eecdd 100644 --- a/megatron/legacy/model/transformer.py +++ b/megatron/legacy/model/transformer.py @@ -1454,7 +1454,7 @@ def forward(self, hidden_states, attention_mask, ) if self.use_fp8 else nullcontext(): # Determine if the current iteration is first microbatch if self.num_microbatches_in_previous_step != get_num_microbatches(): - self.microbatch_count = 0 + self.microbatch_count = 0 # Reset count on new batch size rampup interval self.num_microbatches_in_previous_step = get_num_microbatches() is_first_microbatch = self.microbatch_count % get_num_microbatches() == 0 diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index f82834606fd..516dc89ab11 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -1501,8 +1501,9 @@ def prepare_data_for_update( samples_ratio_per_step=samples_ratio_per_step, num_bins_this_rank = len(packing_context.packed_trajs), bin_seq_indices = packing_context.packing_info.bin_seq_indices, - global_batch_size=args.global_batch_size, - micro_batch_size=args.micro_batch_size, + global_batch_size=args.global_batch_size, + rampup_batch_size=args.rampup_batch_size, + micro_batch_size=args.micro_batch_size, decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, ) loader = get_microbatch_dataloader(len(packing_context.packed_trajs), args.micro_batch_size) @@ -1527,8 +1528,9 @@ def prepare_data_for_update( reconfigure_num_microbatches_calculator( rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, - global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), - micro_batch_size=args.micro_batch_size, + global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), + rampup_batch_size=args.rampup_batch_size, + micro_batch_size=args.micro_batch_size, decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, data_parallel_size=mpu.get_data_parallel_world_size(), ) diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py index 9ceb806571c..50ab2ca27de 100644 --- a/megatron/rl/sequence_packing_utils.py +++ b/megatron/rl/sequence_packing_utils.py @@ -1016,6 +1016,7 @@ def update_microbatch_calculator( num_bins_this_rank: int, bin_seq_indices: List[List[int]], global_batch_size: int, + rampup_batch_size: int, micro_batch_size: int, decrease_batch_size_if_needed: bool, ): @@ -1025,6 +1026,7 @@ def update_microbatch_calculator( num_bins_this_rank: Amount of packing bins that belongs to current rank. bin_seq_indices: Global seq indices in the bin, see PackingInfo. global_batch_size: Current global batch size. + rampup_batch_size: Rampup batch size. See num_microbatches_calculator.py for more. micro_batch_size: Micro batch size at init. decrease_batch_size_if_needed: Scale down batch size. See num_microbatches_calculator.py for more. @@ -1044,6 +1046,7 @@ def update_microbatch_calculator( old_num_microbatches = get_num_microbatches() reconfigure_num_microbatches_calculator( rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, + rampup_batch_size=rampup_batch_size, global_batch_size=bins_bs, micro_batch_size=micro_batch_size, data_parallel_size=dp_world_size, diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 4e8de13bd54..684a8ace6fb 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -132,8 +132,6 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False): else: args = parser.parse_args() - args._is_global_batch_size_explicitly_specified = args.global_batch_size is not None - # Experimental yaml if args.yaml_cfg is not None: from .yaml_arguments import load_yaml @@ -595,16 +593,11 @@ def validate_args(args, defaults={}): args.phase_transition_iterations = sorted( int(x.strip()) for x in args.phase_transition_iterations.split(",") ) + assert args.rampup_batch_size is None, "multi-phase training does not support batch size ramp-up" + # Batch size. assert args.micro_batch_size is not None assert args.micro_batch_size > 0 - is_global_batch_size_explicitly_specified = getattr( - args, '_is_global_batch_size_explicitly_specified', args.global_batch_size is not None - ) - if args.step_batch_size_schedule is not None and is_global_batch_size_explicitly_specified: - raise ValueError( - 'Cannot specify both --step-batch-size-schedule and --global-batch-size' - ) if args.global_batch_size is None: args.global_batch_size = args.micro_batch_size * args.data_parallel_size print_rank_0('setting global batch size to {}'.format(args.global_batch_size)) @@ -624,6 +617,7 @@ def validate_args(args, defaults={}): args.grpo_samples_per_iteration * args.grpo_iterations) # Ensure that the number of prompts we collect is a multiple of the global batch size. + # TODO: Make this account for batch size rampup? assert num_generated_samples_per_inference_iteration % args.global_batch_size == 0, \ f"grpo_group_size * grpo_prompts_per_step * grpo_iterations should be divisible by global_batch_size" @@ -1122,6 +1116,8 @@ def validate_args(args, defaults={}): 'expected iteration-based learning rate decay' assert args.lr_warmup_samples == 0, \ 'expected iteration-based learning rate warmup' + assert args.rampup_batch_size is None, \ + 'expected no batch-size rampup for iteration-based training' if args.lr_warmup_fraction is not None: assert args.lr_warmup_iters == 0, \ 'can only specify one of lr-warmup-fraction and lr-warmup-iters' @@ -2914,7 +2910,8 @@ def _add_data_args(parser): 'This argument is exclusive to the other independent --*-data-path arguments.') group.add_argument('--phase-transition-iterations', type=str, default=None, help='Comma-separated list of iterations where phase ' - 'transitions occur. Requires fixed global batch size across phases.') + 'transitions occur. Requires fixed global batch size across phases. ' + 'Does not support batch size ramp-up.') group.add_argument('--split', type=str, default=None, help='Comma-separated list of proportions for training,' ' validation, and test split. For example the split ' diff --git a/megatron/training/config/training_config.py b/megatron/training/config/training_config.py index 8310060c525..6bc59668789 100644 --- a/megatron/training/config/training_config.py +++ b/megatron/training/config/training_config.py @@ -16,21 +16,21 @@ class TrainingConfig: data-parallel-size. If this value is None, then use micro-batch-size * data-parallel-size as the global batch size. This choice will result in 1 for number of micro-batches.""" - step_batch_size_schedule: str | None = None - """Step-wise batch size schedule in format "THRESHOLD:BS THRESHOLD:BS ...". - Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). - If sequence length is provided, thresholds are interpreted as tokens; otherwise as samples. - Example: - step_batch_size_schedule = "0:768 250B:1536 500B:3072 750B:6144" - Cannot be used together with decrease_batch_size_if_needed. + rampup_batch_size: list[int] | None = field(default=None, metadata={"argparse_meta": {"nargs": 3}}) + """Batch size ramp up with the following values: , , + + For example: + rampup-batch-size = [16, 8, 300000] + global-batch-size 1024 + will start with global batch size 16 and over (1024 - 16) / 8 = 126 intervals will increase + the batch size linearly to 1024. In each interval we will use approximately + 300000 / 126 = 2380 samples. """ - decrease_batch_size_if_needed: bool = False """If set, decrease batch size if microbatch_size * dp_size does not divide batch_size. Old batch_size will be restored if training is re-started - with dp_size that divides batch_size // microbatch_size. Not supported with - step-batch-size-schedule.""" + with dp_size that divides batch_size // microbatch_size.""" empty_unused_memory_level: Literal[0, 1, 2] = 0 """Call torch.cuda.empty_cache() each iteration (training and eval), to reduce fragmentation. diff --git a/megatron/training/global_vars.py b/megatron/training/global_vars.py index a8c65103d16..ab4713c2624 100644 --- a/megatron/training/global_vars.py +++ b/megatron/training/global_vars.py @@ -121,17 +121,13 @@ def set_global_variables(args, build_tokenizer=True): _ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args') set_args(args) - if args.step_batch_size_schedule is not None: - print(f'> using step batch size schedule: {args.step_batch_size_schedule}') - init_num_microbatches_calculator( args.rank, + args.rampup_batch_size, args.global_batch_size, args.micro_batch_size, args.data_parallel_size, args.decrease_batch_size_if_needed, - step_batch_size_schedule=args.step_batch_size_schedule, - seq_length=args.seq_length, ) if build_tokenizer: _ = _build_tokenizer(args) diff --git a/megatron/training/training.py b/megatron/training/training.py index c95d386c748..8fed9afd570 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -1292,20 +1292,29 @@ def update_train_iters(args): if args.train_iters: return - if args.step_batch_size_schedule is not None: - # Sample based training with step batch size schedule. + # Constant batch size with sample-based training. + if args.rampup_batch_size is None: + args.train_iters = args.train_samples // args.global_batch_size + + else: + # Sample based training with rampup batch size. iterations = 0 consumed_samples = 0 - while consumed_samples < args.train_samples: + # Rampup phase. + while ( + consumed_samples <= int(args.rampup_batch_size[2]) + and consumed_samples <= args.train_samples + ): update_num_microbatches(consumed_samples, consistency_check=False) consumed_samples += get_current_global_batch_size() iterations += 1 # Reset update_num_microbatches(0, consistency_check=False) + # Constant phase + # Note that we throw away any partial last batch. + if args.train_samples > consumed_samples: + iterations += (args.train_samples - consumed_samples) // args.global_batch_size args.train_iters = iterations - else: - # Constant batch size with sample-based training. - args.train_iters = args.train_samples // args.global_batch_size print_rank_0(f'setting training iterations to {args.train_iters}') @@ -1730,21 +1739,6 @@ def setup_model_and_optimizer( args.iteration = 0 args.num_floating_point_operations_so_far = 0 - # Validate that the world size can accommodate the current batch size. - # This catches the case where GPUs were scaled up mid-training but the - # current position in the batch size schedule yields a batch size that - # is too small for the number of data-parallel replicas. - num_microbatches = get_num_microbatches() - current_global_batch_size = get_current_global_batch_size() - data_parallel_size = mpu.get_data_parallel_world_size() - assert num_microbatches is not None and num_microbatches >= 1, ( - f'current global batch size ({current_global_batch_size}) is too small for ' - f'micro_batch_size ({args.micro_batch_size}) * data_parallel_size ({data_parallel_size}) = ' - f'{args.micro_batch_size * data_parallel_size}. The world size cannot accommodate the ' - f'batch size. This can happen when resuming with more GPUs than the current batch size ' - f'schedule entry supports.' - ) - # get model without FP16 and/or DDP wrappers if ( args.iteration == 0 @@ -2721,12 +2715,11 @@ def train( # Then initialize with the correct perform_rl_step=True context init_num_microbatches_calculator( args.rank, + args.rampup_batch_size, args.global_batch_size, args.micro_batch_size, mpu.get_data_parallel_world_size(), - args.decrease_batch_size_if_needed, - step_batch_size_schedule=args.step_batch_size_schedule, - seq_length=args.seq_length, + args.decrease_batch_size_if_needed ) print_rank_0(f"> GRPO training: num_microbatches set to {get_num_microbatches()}") @@ -2978,8 +2971,8 @@ def trace_handler(p): ) else: assert get_num_microbatches() > num_microbatches, ( - f"Number of microbatches should not decrease; " - f"going from {num_microbatches} to {get_num_microbatches()}" + f"Number of microbatches should be increasing due to batch size rampup; " + f"instead going from {num_microbatches} to {get_num_microbatches()}" ) if args.save is not None: save_checkpoint_and_time( diff --git a/megatron/training/yaml_arguments.py b/megatron/training/yaml_arguments.py index 3a2d04aadcf..3fa6dfa320a 100644 --- a/megatron/training/yaml_arguments.py +++ b/megatron/training/yaml_arguments.py @@ -103,13 +103,6 @@ def validate_yaml(args, defaults={}): # Batch size. assert args.micro_batch_size is not None assert args.micro_batch_size > 0 - is_global_batch_size_explicitly_specified = getattr( - args, '_is_global_batch_size_explicitly_specified', args.global_batch_size is not None - ) - if args.step_batch_size_schedule is not None and is_global_batch_size_explicitly_specified: - raise ValueError( - 'Cannot specify both --step-batch-size-schedule and --global-batch-size' - ) if args.global_batch_size is None: args.global_batch_size = args.micro_batch_size * args.data_parallel_size if args.rank == 0: @@ -195,6 +188,8 @@ def validate_yaml(args, defaults={}): 'expected iteration-based learning rate decay' assert args.lr_warmup_samples == 0, \ 'expected iteration-based learning rate warmup' + assert args.rampup_batch_size is None, \ + 'expected no batch-size rampup for iteration-based training' if args.lr_warmup_fraction is not None: assert args.lr_warmup_iters == 0, \ 'can only specify one of lr-warmup-fraction and lr-warmup-iters' @@ -435,8 +430,5 @@ def load_yaml(yaml_path): config_namespace = json.loads(json.dumps(config), object_hook=lambda item: SimpleNamespace(**item)) # Add config location to namespace config_namespace.yaml_cfg = yaml_path - config_namespace._is_global_batch_size_explicitly_specified = ( - getattr(config_namespace, "global_batch_size", None) is not None - ) return config_namespace diff --git a/tasks/finetune_utils.py b/tasks/finetune_utils.py index 9507ba5dbf0..6d20e55bd16 100644 --- a/tasks/finetune_utils.py +++ b/tasks/finetune_utils.py @@ -248,6 +248,9 @@ def finetune(train_valid_datasets_provider, model_provider, args = get_args() timers = get_timers() + assert args.rampup_batch_size is None, \ + 'batch size scaling is not supported for finetuning' + # Train and validation data loaders. timers('train/valid/test dataset/dataloder', log_level=0).start() if args.epochs > 0: diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml index 2baa92999e6..3bb12c99539 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml @@ -22,8 +22,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 + --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 - --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true # Transformer Engine args diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml index d9b44500eca..8eb57abf308 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml @@ -24,8 +24,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 + --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 - --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true --cross-entropy-loss-fusion: true diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml index 4d686aba694..300e0777917 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml @@ -22,8 +22,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 + --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 - --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 4882812 --manual-gc: true # Transformer Engine args diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml index 5f51a40ecc9..2f045810a87 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml @@ -24,8 +24,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 + --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 - --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true --cross-entropy-loss-fusion: true diff --git a/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py b/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py index 4d38f9a5051..8b03262714a 100644 --- a/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py +++ b/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py @@ -305,7 +305,7 @@ def set_ckpt_path(ckpt_path): args.load = ckpt_path set_tp_pp_vpp(*src_tp_pp_vpp, pp_layout=src_pp_layout, destroy_first=False) - init_num_microbatches_calculator(0, 1, 1, 1) + init_num_microbatches_calculator(0, None, 1, 1, 1) iteration = 123 layer_spec_fn = get_gpt_decoder_block_spec if is_moe else gpt_te_spec diff --git a/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py b/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py index e719327828d..51dc1d0bff6 100644 --- a/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py +++ b/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py @@ -39,7 +39,7 @@ def __init__(self, config: TransformerConfig): def init_model_parallel(): """Init torch distributed.""" Utils.initialize_model_parallel(1, 1) - init_num_microbatches_calculator(0, 1, 1, 1) + init_num_microbatches_calculator(0, None, 1, 1, 1) model_parallel_cuda_manual_seed(123) yield # Run the actual test. Utils.destroy_model_parallel() diff --git a/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py b/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py index cf4a6c12cf1..e7fb8159af2 100644 --- a/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py +++ b/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py @@ -226,7 +226,7 @@ def set_tp_pp_vpp(tp, pp, vpp=None, pp_layout=None, destroy_first=True): args.pipeline_model_parallel_layout = pp_layout set_tp_pp_vpp(*tp_pp_vpp, pp_layout=pp_layout, destroy_first=False) - init_num_microbatches_calculator(0, 1, 1, 1) + init_num_microbatches_calculator(0, None, 1, 1, 1) def forward_step_func(data_iterator, model: GPTModel): """Forward training step. Copied from `pretrain_gpt.py`""" diff --git a/tests/unit_tests/rl/test_sequence_packing_utils.py b/tests/unit_tests/rl/test_sequence_packing_utils.py index bb03be57cb3..0a4f4cd03f9 100644 --- a/tests/unit_tests/rl/test_sequence_packing_utils.py +++ b/tests/unit_tests/rl/test_sequence_packing_utils.py @@ -465,7 +465,12 @@ def test_get_bins_bs_and_steps(ratio, local_bins, world, expected_bs): global_bs_in_seq = int(n_seqs * ratio) def side_eff( - rank, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed + rank, + rampup_batch_size, + global_batch_size, + micro_batch_size, + data_parallel_size, + decrease_batch_size_if_needed, ): # Inside of the get_microbatch_dataloader, we compute the batch size in bins. # We want to test this variable. @@ -483,6 +488,7 @@ def side_eff( num_bins_this_rank=local_bins, bin_seq_indices=[], global_batch_size=global_bs_in_seq, + rampup_batch_size=1, micro_batch_size=1, decrease_batch_size_if_needed=False, ) diff --git a/tests/unit_tests/test_checkpointing.py b/tests/unit_tests/test_checkpointing.py index 015cb964540..502e1ef700e 100644 --- a/tests/unit_tests/test_checkpointing.py +++ b/tests/unit_tests/test_checkpointing.py @@ -152,7 +152,7 @@ def create_ckpt_load_args(create_args): def init_model_parallel(): """Init torch distributed.""" Utils.initialize_model_parallel(1, 1) - init_num_microbatches_calculator(0, 1, 1, 1) + init_num_microbatches_calculator(0, None, 1, 1, 1) model_parallel_cuda_manual_seed(123) yield # Run the actual test. Utils.destroy_model_parallel() diff --git a/tests/unit_tests/test_num_microbatches_calculator.py b/tests/unit_tests/test_num_microbatches_calculator.py index 0718f954dab..9b3356b8af5 100644 --- a/tests/unit_tests/test_num_microbatches_calculator.py +++ b/tests/unit_tests/test_num_microbatches_calculator.py @@ -1,4 +1,4 @@ -# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +from typing import List, Optional import pytest @@ -7,21 +7,21 @@ def test_init_num_microbatches_calculator(): mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 32 with pytest.raises(AssertionError): - mb_calculator.init_num_microbatches_calculator(0, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, 32, 8, 3, True) + mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 3, True) assert mb_calculator.get_num_microbatches() == 1 assert mb_calculator.get_current_global_batch_size() == 32 assert mb_calculator.get_current_running_global_batch_size() == 24 mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, 33, 8, 2, True) + mb_calculator.init_num_microbatches_calculator(0, None, 33, 8, 2, True) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 33 assert mb_calculator.get_current_running_global_batch_size() == 32 @@ -29,47 +29,68 @@ def test_init_num_microbatches_calculator(): def test_reconfigure_num_microbatches_calculator(): mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 32 - mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) + assert mb_calculator.get_num_microbatches() == 1 + assert mb_calculator.get_current_global_batch_size() == 16 + + mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 16, 96], 32, 8, 2, False) assert mb_calculator.get_num_microbatches() == 1 assert mb_calculator.get_current_global_batch_size() == 16 def test_get_num_microbatches(): - mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) assert mb_calculator.get_num_microbatches() == 1 - mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 4, 3, True) + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 3, True) assert mb_calculator.get_num_microbatches() == 1 def test_get_current_global_batch_size(): - mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 4, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 2, False) assert mb_calculator.get_current_global_batch_size() == 16 - mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 4, 3, True) + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 3, True) assert mb_calculator.get_current_global_batch_size() == 16 assert mb_calculator.get_current_running_global_batch_size() == 12 def test_get_micro_batch_size(): - mb_calculator.reconfigure_num_microbatches_calculator(0, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) assert mb_calculator.get_micro_batch_size() == 8 +def test_update_num_microbatches(): + mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 8, 96], 32, 4, 2, False) + assert mb_calculator.get_num_microbatches() == 2 + mb_calculator.update_num_microbatches(48, False) + assert mb_calculator.get_num_microbatches() == 3 + + mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 8, 96], 32, 8, 2, False) + with pytest.raises(AssertionError): + mb_calculator.update_num_microbatches(49, True) + + mb_calculator.reconfigure_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.update_num_microbatches(16) + assert mb_calculator.get_num_microbatches() == 2 + + def test_build_num_microbatches_calculator(): - temp_calculator = mb_calculator._build_num_microbatches_calculator(0, 32, 8, 2, False) + temp_calculator = mb_calculator._build_num_microbatches_calculator(0, None, 32, 8, 2, False) assert temp_calculator.get() == 2 assert temp_calculator.get_current_global_batch_size() == 32 assert type(temp_calculator) is mb_calculator.ConstantNumMicroBatchesCalculator - with pytest.raises(ValueError): - mb_calculator._build_num_microbatches_calculator( - 0, None, 8, 2, True, step_batch_size_schedule="0:16 100:32" - ) + temp_calculator = mb_calculator._build_num_microbatches_calculator( + 0, [16, 16, 48], 32, 8, 2, False + ) + assert temp_calculator.get() == 1 + assert temp_calculator.get_current_global_batch_size() == 16 + assert type(temp_calculator) is mb_calculator.RampupBatchsizeNumMicroBatchesCalculator class TestConstantNumMicroBatchesCalculator: @@ -89,73 +110,38 @@ def test_get_current_global_batch_size(self): assert self.mb_calculator.get_current_global_batch_size() == 32 -def test_step_batch_size_schedule_allows_past_entries_smaller_than_dp(): - """Test that step batch size schedule does not crash when early schedule entries - are smaller than micro_batch_size * data_parallel_size. - - This happens when scaling to more GPUs mid-training: the initial batch sizes - in the schedule are smaller than the new GPU count can support, but training - has progressed past those entries. The divisibility check should only apply - to the CURRENT batch size (after checkpoint loading), not all schedule entries. - """ - # micro=1, dp=512, micro*dp=512 - # Schedule starts at 256 which is < 512, but later entries are fine. - # This should NOT raise during construction. - calc = mb_calculator.StepBatchsizeNumMicroBatchesCalculator( - micro_batch_size=1, - data_parallel_size=512, - decrease_batch_size_if_needed=False, - rank=0, - schedule="0:256 200000:512 600000:1024 1500000:2048 3000000:4096 6000000:6144", - ) +class TestRampupBatchsizeNumMicroBatchesCalculator: + def setup_method(self, method): + self.mb_calculator = mb_calculator.RampupBatchsizeNumMicroBatchesCalculator( + 32, 8, 2, False, 0, 16, 16, 48 + ) - # At init (consumed_samples=0), batch=256 < micro*dp=512. - # No consistency_check at init, so no error. - assert calc.current_global_batch_size == 256 - - # After training past the first entry, batch=512. Consistency check passes. - calc.update(200000, consistency_check=True) - assert calc.current_global_batch_size == 512 - assert calc.num_micro_batches == 1 - - # Later entries work fine. - calc.update(1500000, consistency_check=True) - assert calc.current_global_batch_size == 2048 - assert calc.num_micro_batches == 4 - - calc.update(6000000, consistency_check=True) - assert calc.current_global_batch_size == 6144 - assert calc.num_micro_batches == 12 - - -def test_step_batch_size_schedule_consistency_check_fails_when_batch_too_small(): - """Test that consistency_check=True raises when current batch size is smaller - than micro_batch_size * data_parallel_size. - - This is the scenario caught by the runtime check in setup_model_and_optimizer: - GPUs were scaled up but the current schedule entry yields a batch size too small - for the new world size. - """ - calc = mb_calculator.StepBatchsizeNumMicroBatchesCalculator( - micro_batch_size=1, - data_parallel_size=512, - decrease_batch_size_if_needed=False, - rank=0, - schedule="0:256 200000:512 600000:1024", - ) + def test_constructor(self): + assert type(self.mb_calculator) is mb_calculator.RampupBatchsizeNumMicroBatchesCalculator + assert self.mb_calculator.global_batch_size == 32 + assert self.mb_calculator.micro_batch_size == 8 + assert self.mb_calculator.data_parallel_size == 2 + assert self.mb_calculator.start_global_batch_size == 16 + assert self.mb_calculator.batch_size_increment == 16 + assert self.mb_calculator.ramup_samples == 48 + assert self.mb_calculator.micro_batch_times_data_parallel_size == 16 + assert self.mb_calculator.num_micro_batches == 1 - # At consumed_samples=0, batch=256 < micro*dp=512. - # consistency_check=True should fail because 256 % 512 != 0. - with pytest.raises(AssertionError): - calc.update(0, consistency_check=True) + def test_get(self): + assert self.mb_calculator.get() == 1 + + def test_get_current_global_batch_size(self): + assert self.mb_calculator.get_current_global_batch_size() == 16 -def test_step_batch_size_schedule_rejects_decrease_batch_size_if_needed(): - with pytest.raises(ValueError): - mb_calculator.StepBatchsizeNumMicroBatchesCalculator( - micro_batch_size=1, - data_parallel_size=512, - decrease_batch_size_if_needed=True, - rank=0, - schedule="0:256 200000:512 600000:1024", - ) +def test_ramp_up(): + mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 16, 96], 32, 8, 2, False) + consumed_samples = 0 + count = 0 + expected_consumed_samples = [0, 16, 32, 48, 64, 80, 96, 128, 160, 192, 224, 256] + + while consumed_samples < 256: + consumed_samples += mb_calculator.get_current_global_batch_size() + count += 1 + assert consumed_samples == expected_consumed_samples[count] + mb_calculator.update_num_microbatches(consumed_samples, True) diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py index ee4ff7d152d..69f12e4d983 100644 --- a/tests/unit_tests/transformer/test_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_cuda_graphs.py @@ -575,6 +575,7 @@ def test_get_cuda_graph_input_data(self, num_microbatches, pp_size, vpp_size): # Initialize num_microbatches calculator init_num_microbatches_calculator( rank=0, + rampup_batch_size=None, global_batch_size=micro_batch_size * num_microbatches, micro_batch_size=micro_batch_size, data_parallel_size=1, From 0b9bc2088b6d6c858275d02638064cc7e0bf8b70 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 21 Apr 2026 19:09:55 +0000 Subject: [PATCH 56/72] Update copy-pr-bot.yaml [skip ci] --- .github/copy-pr-bot.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/copy-pr-bot.yaml b/.github/copy-pr-bot.yaml index ce19144f988..618c7c4c9c9 100644 --- a/.github/copy-pr-bot.yaml +++ b/.github/copy-pr-bot.yaml @@ -1,4 +1,4 @@ enabled: true auto_sync_draft: false auto_sync_ready: true -trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "Connor-XY", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Mellonta", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "WanZzzzzz", "Wohox", "YangFei1990", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "fitsumreda", "frsun-nvda", "gautham-kollu", "gdengk", "guihong-nv", "guyueh1", "hexinw-nvidia", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kajalj22", "kanz-nv", "keshavb96", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "minitu", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "sheliang-nv", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sraman-rgb", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "wujingyue", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"] +trustees_override: ["AAnoosheh", "ArEsKay3", "Autumn1998", "BestJuly", "BoxiangW", "CarlosGomes98", "ChenhanYu", "Connor-XY", "FDecaYed", "HaochenYuan", "ISEEKYAN", "JRD971000", "Mellonta", "Phlip79", "QiZhangNV", "RPrenger", "ShriyaRishab", "Victarry", "WanZzzzzz", "Wohox", "YangFei1990", "ZhiyuLi-Nvidia", "ahmadki", "aklife97", "ananthsub", "aroshanghias-nvd", "asolergi-nv", "buptzyb", "chtruong814", "cjld", "cspades", "cuichenx", "deepakn94", "dimapihtar", "dingqingy-nv", "duncanriach", "erhoo82", "ericharper", "fanshiqing", "faradawn", "fitsumreda", "frsun-nvda", "gautham-kollu", "gdengk", "guihong-nv", "guyueh1", "hexinw-nvidia", "huvunvidia", "hxbai", "ilml", "jalbericiola", "janEbert", "jaredcasper", "jenchen13", "jiemingz", "jingqiny-99", "jkamalu", "jon-barker", "jstjohn", "kajalj22", "kanz-nv", "keshavb96", "kevalmorabia97", "ko3n1g", "ksivaman", "kunlunl", "kvareddy", "kwyss-nvidia", "layalir", "lhb8125", "lmcafee-nvidia", "maanug-nv", "mathemakitten", "matthieule", "mchrzanowski", "mehraakash", "minitu", "mkhona-nvidia", "nanz-nv", "parthmannan", "prajwal1210", "pthombre", "rhewett-nv", "rogerwaleffe", "sajadn", "sanandaraj5597", "sancha", "santhnm2", "sbak5", "shanmugamr1992", "sharathts", "sheliang-nv", "shengf-nv", "shifangx", "shjwudp", "sidsingh-nvidia", "skyw", "sraman-rgb", "sudhakarsingh27", "tdene", "theothermike", "thomasdhc", "tomlifu", "trintamaki", "tylerpoon", "wdykas", "wplf", "wujingyue", "xiaoyao0115", "xuwchen", "yanring", "yaox12", "yaoyu-33", "yashaswikarnati", "yeyu-nvidia", "yobibyte", "youngeunkwon0405", "yueshen2016", "yuzhongw-nvidia", "zhongbozhu"] From 532ad926b4f2e50770841c120efcf70f686f74d9 Mon Sep 17 00:00:00 2001 From: Deepak Narayanan Date: Tue, 21 Apr 2026 14:09:13 -0700 Subject: [PATCH 57/72] Replace rampup batch size scheduler with custom step batch size schedules (#4411) Co-authored-by: Mikail Khona (NVIDIA) Co-authored-by: Claude Opus 4.6 --- examples/gpt3/gpt_config.yaml | 2 +- examples/gpt3/train_gpt3_175b_distributed.sh | 6 +- megatron/core/num_microbatches_calculator.py | 485 ++++++++++-------- megatron/legacy/model/transformer.py | 2 +- megatron/rl/rl_utils.py | 10 +- megatron/rl/sequence_packing_utils.py | 3 - megatron/training/arguments.py | 17 +- megatron/training/config/training_config.py | 14 +- megatron/training/global_vars.py | 16 +- megatron/training/training.py | 53 +- megatron/training/yaml_arguments.py | 12 +- tasks/finetune_utils.py | 3 - .../gpt/gpt3_15b_8t_release/model_config.yaml | 2 +- .../model_config.yaml | 2 +- .../gpt3_15b_8t_release_sm/model_config.yaml | 2 +- .../model_config.yaml | 2 +- .../test_pipeline_parallel_layout.py | 4 +- .../test_torch_fully_sharded_parallel.py | 4 +- .../pipeline_parallel/test_pipeline_layout.py | 4 +- .../rl/test_sequence_packing_utils.py | 8 +- tests/unit_tests/test_checkpointing.py | 4 +- .../test_num_microbatches_calculator.py | 190 ++++--- .../transformer/test_cuda_graphs.py | 1 - 23 files changed, 505 insertions(+), 341 deletions(-) diff --git a/examples/gpt3/gpt_config.yaml b/examples/gpt3/gpt_config.yaml index b7bc2bf4c4f..3c387eafe5a 100644 --- a/examples/gpt3/gpt_config.yaml +++ b/examples/gpt3/gpt_config.yaml @@ -136,7 +136,7 @@ use_legacy_models: False spec: null micro_batch_size: 2 global_batch_size: 128 -rampup_batch_size: [32, 32, 65324160] +step_batch_size_schedule: "0:32 90B:64 180B:96 270B:128" check_for_nan_in_loss_and_grad: True num_layers_per_virtual_pipeline_stage: null diff --git a/examples/gpt3/train_gpt3_175b_distributed.sh b/examples/gpt3/train_gpt3_175b_distributed.sh index 7d2c01b3157..be00d76120d 100755 --- a/examples/gpt3/train_gpt3_175b_distributed.sh +++ b/examples/gpt3/train_gpt3_175b_distributed.sh @@ -36,9 +36,9 @@ GPT_MODEL_ARGS=( TRAINING_ARGS=( --micro-batch-size 1 - --global-batch-size 1536 - --rampup-batch-size 16 16 5859375 - --train-iters 500000 + --global-batch-size 1536 + --step-batch-size-schedule "0:16 2.4B:320 4.8B:624 7.2B:928 9.6B:1232 12B:1536" + --train-iters 500000 --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.95 diff --git a/megatron/core/num_microbatches_calculator.py b/megatron/core/num_microbatches_calculator.py index ae7cea92e63..033820f174a 100644 --- a/megatron/core/num_microbatches_calculator.py +++ b/megatron/core/num_microbatches_calculator.py @@ -4,14 +4,14 @@ import logging from abc import ABC, abstractmethod -from typing import List, Optional, Union +from typing import List, Optional, Tuple, Union logger = logging.getLogger(__name__) # TODO: global_var merge into mcore? _GLOBAL_NUM_MICROBATCHES_CALCULATOR: Union[ - 'ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator' -] = None + 'ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator' +] = None # type: ignore[assignment] def get_num_microbatches() -> int: @@ -63,11 +63,13 @@ def unset_num_microbatches_calculator(): def init_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], - global_batch_size: int, - micro_batch_size: int, - data_parallel_size: int, + rampup_batch_size: Optional[List[int]] = None, + global_batch_size: Optional[int] = None, + micro_batch_size: Optional[int] = None, + data_parallel_size: Optional[int] = None, decrease_batch_size_if_needed: bool = False, + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, ) -> None: """Initialize number of microbatches calculator. Supporting backward compatibility. @@ -75,25 +77,39 @@ def init_num_microbatches_calculator( rank (int): Rank of the GPU, only rank 0 will log the information. rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of [start_global_batch_size, - batch_size_increment, ramup_samples]. - global_batch_size (int): + Deprecated. This argument is ignored. Use step_batch_size_schedule instead. + global_batch_size (Optional[int]): Global batch size for the model. - micro_batch_size (int): + micro_batch_size (Optional[int]): Micro batch size at initialization. - data_parallel_size (int): + data_parallel_size (Optional[int]): Data parallel size. decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds are interpreted as samples unless seq_length is provided, in which case + thresholds are interpreted as tokens and converted to samples. + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ + if rampup_batch_size is not None and rank == 0: + logger.warning( + 'rampup_batch_size is deprecated and will be removed in a future release. ' + 'Use step_batch_size_schedule instead.' + ) _configure_global_num_microbatches_calculator( - rank, - rampup_batch_size, - global_batch_size, - micro_batch_size, - data_parallel_size, - decrease_batch_size_if_needed, + rank=rank, + global_batch_size=global_batch_size, + micro_batch_size=micro_batch_size, + data_parallel_size=data_parallel_size, + decrease_batch_size_if_needed=decrease_batch_size_if_needed, + step_batch_size_schedule=step_batch_size_schedule, + seq_length=seq_length, init=True, ) @@ -106,11 +122,13 @@ def destroy_num_microbatches_calculator(): def reconfigure_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], - global_batch_size: int, - micro_batch_size: int, - data_parallel_size: int, + rampup_batch_size: Optional[List[int]] = None, + global_batch_size: Optional[int] = None, + micro_batch_size: Optional[int] = None, + data_parallel_size: Optional[int] = None, decrease_batch_size_if_needed: bool = False, + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, ) -> None: """Reconfigure number of microbatches calculator. Supporting backward compatibility. @@ -118,36 +136,49 @@ def reconfigure_num_microbatches_calculator( rank (int): Rank of the GPU, only rank 0 will log the information. rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of - [start_global_batch_size, batch_size_increment, ramup_samples]. - global_batch_size (int): + Deprecated. This argument is ignored. Use step_batch_size_schedule instead. + global_batch_size (Optional[int]): Global batch size for the model. - micro_batch_size (int): + micro_batch_size (Optional[int]): Micro batch size at initialization. - data_parallel_size (int): + data_parallel_size (Optional[int]): Data parallel size. decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ + if rampup_batch_size is not None and rank == 0: + logger.warning( + 'rampup_batch_size is deprecated and will be removed in a future release. ' + 'Use step_batch_size_schedule instead.' + ) _configure_global_num_microbatches_calculator( - rank, - rampup_batch_size, - global_batch_size, - micro_batch_size, - data_parallel_size, - decrease_batch_size_if_needed, + rank=rank, + global_batch_size=global_batch_size, + micro_batch_size=micro_batch_size, + data_parallel_size=data_parallel_size, + decrease_batch_size_if_needed=decrease_batch_size_if_needed, + step_batch_size_schedule=step_batch_size_schedule, + seq_length=seq_length, init=False, ) def _configure_global_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool = False, + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, init: bool = False, ) -> None: """Configure number of microbatches calculator. Can be used for initialization and @@ -156,9 +187,6 @@ def _configure_global_num_microbatches_calculator( Args: rank (int): Rank of the GPU, only rank 0 will log the information. - rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of - [start_global_batch_size, batch_size_increment, ramup_samples]. global_batch_size (int): Global batch size for the model. micro_batch_size (int): @@ -168,6 +196,13 @@ def _configure_global_num_microbatches_calculator( decrease_batch_size_if_needed (bool, optional): If true, scale down batch size to ensure divisibility by DP size * microbatch size. Defaults to False. + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. init (bool, optional): If true, initialize the calculator. Defaults to False. """ @@ -180,43 +215,72 @@ def _configure_global_num_microbatches_calculator( _GLOBAL_NUM_MICROBATCHES_CALCULATOR = _build_num_microbatches_calculator( rank, - rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed, + step_batch_size_schedule, + seq_length, ) def _build_num_microbatches_calculator( rank: int, - rampup_batch_size: Optional[List[int]], - global_batch_size: int, + global_batch_size: Optional[int], micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool, -) -> Union['ConstantNumMicroBatchesCalculator', 'RampupBatchsizeNumMicroBatchesCalculator']: + step_batch_size_schedule: Optional[str] = None, + seq_length: Optional[int] = None, +) -> Union['ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator']: """Build number of microbatches calculator. Internal helper method. Args: rank (int): Rank of the GPU, only rank 0 will log the information. - rampup_batch_size (Optional[List[int]]): - Rampup batch size, should be in format of - [start_global_batch_size, batch_size_increment, ramup_samples]. - global_batch_size (int): - Global batch size for the model. + global_batch_size (Optional[int]): + Global batch size for the model. Required for constant mode. + Ignored when step_batch_size_schedule is provided. micro_batch_size (int): Micro batch size at initialization. data_parallel_size (int): Data parallel size. decrease_batch_size_if_needed (bool): If true, scale down batch size to ensure divisibility by DP size * microbatch size. - + step_batch_size_schedule (Optional[str]): + Step batch size schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Example: "0:768 250B:1536 500B:3072 750B:6144" + seq_length (Optional[int]): + Sequence length for token-to-sample conversion when using step_batch_size_schedule. + If provided, thresholds are interpreted as tokens. If None, thresholds are samples. """ - # Constant batch size. - if rampup_batch_size is None: + num_microbatches_calculator: Union[ + 'ConstantNumMicroBatchesCalculator', 'StepBatchsizeNumMicroBatchesCalculator' + ] + + # Step batch size schedule + if step_batch_size_schedule is not None: + if decrease_batch_size_if_needed: + raise ValueError( + 'Cannot specify both --step-batch-size-schedule and ' + '--decrease-batch-size-if-needed' + ) + num_microbatches_calculator = StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=micro_batch_size, + data_parallel_size=data_parallel_size, + decrease_batch_size_if_needed=decrease_batch_size_if_needed, + rank=rank, + schedule=step_batch_size_schedule, + seq_length=seq_length, + ) + + # Constant batch size + else: + assert ( + global_batch_size is not None + ), '--global-batch-size is required when not using --step-batch-size-schedule' num_microbatches_calculator = ConstantNumMicroBatchesCalculator( global_batch_size, micro_batch_size, @@ -228,32 +292,6 @@ def _build_num_microbatches_calculator( logger.info( f'setting number of microbatches to constant {num_microbatches_calculator.get()}' ) - # Batch size ramp up. - else: - assert len(rampup_batch_size) == 3, ( - 'expected the following ' - 'format: --rampup-batch-size ' - ' ' - ) - start_global_batch_size = int(rampup_batch_size[0]) - batch_size_increment = int(rampup_batch_size[1]) - ramup_samples = int(rampup_batch_size[2]) - if rank == 0: - logger.info( - f'will use batch size rampup starting from global batch size ' - f'{start_global_batch_size} to global batch size {global_batch_size} with batch' - f'size increments {batch_size_increment} over {ramup_samples} samples.' - ) - num_microbatches_calculator = RampupBatchsizeNumMicroBatchesCalculator( - global_batch_size, - micro_batch_size, - data_parallel_size, - decrease_batch_size_if_needed, - rank, - start_global_batch_size, - batch_size_increment, - ramup_samples, - ) return num_microbatches_calculator @@ -267,31 +305,35 @@ class NumMicroBatchesCalculator(ABC): """Base class for number of microbatches calculator.""" def __init__(self) -> None: - self.num_micro_batches = None - self.current_global_batch_size = None - self.micro_batch_size = None - self.current_running_global_batch_size = None + self.num_micro_batches: Optional[int] = None + self.current_global_batch_size: Optional[int] = None + self.micro_batch_size: Optional[int] = None + self.current_running_global_batch_size: Optional[int] = None def get(self) -> int: """Get number of microbatches.""" + assert self.num_micro_batches is not None return self.num_micro_batches def get_current_global_batch_size(self) -> int: """Get current global batch size.""" + assert self.current_global_batch_size is not None return self.current_global_batch_size def get_micro_batch_size(self) -> int: """Get current global batch size.""" + assert self.micro_batch_size is not None return self.micro_batch_size def get_current_running_global_batch_size(self) -> int: """Get current running global batch size. If decrease_batch_size_if_needed is False, this just equals global batch size.""" + assert self.current_running_global_batch_size is not None return self.current_running_global_batch_size @abstractmethod def update(self, consumed_samples, consistency_check, verbose=False) -> None: - """Update number of microbatches depending on batch size rampup.""" + """Update number of microbatches.""" pass @@ -320,6 +362,7 @@ def __init__( decrease_batch_size_if_needed: bool, rank: int, ) -> None: + super().__init__() micro_batch_times_data_parallel_size = micro_batch_size * data_parallel_size if decrease_batch_size_if_needed: @@ -333,9 +376,7 @@ def __init__( f'to keep divisiblity by micro_batch_size={micro_batch_size} * ' f'data_parallel_size={data_parallel_size}' ) - self.num_micro_batches = ( - running_global_batch_size // micro_batch_times_data_parallel_size - ) + num_micro_batches = running_global_batch_size // micro_batch_times_data_parallel_size else: assert global_batch_size % micro_batch_times_data_parallel_size == 0, ( 'global batch size ({}) is not divisible by micro batch size ({})' @@ -344,11 +385,12 @@ def __init__( ) ) running_global_batch_size = global_batch_size - self.num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size + num_micro_batches = global_batch_size // micro_batch_times_data_parallel_size assert ( - self.num_micro_batches >= 1 - ), 'number of microbatches should be at least 1, got {}.'.format(self.num_micro_batches) + num_micro_batches >= 1 + ), 'number of microbatches should be at least 1, got {}.'.format(num_micro_batches) + self.num_micro_batches = num_micro_batches self.current_global_batch_size = global_batch_size self.current_running_global_batch_size = running_global_batch_size self.micro_batch_size = micro_batch_size @@ -357,152 +399,195 @@ def update(self, consumed_samples, consistency_check, verbose=False) -> None: pass -class RampupBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator): - """Calculator of number of microbatches with batch size rampup. - Over `steps = (global-batch-size - start-batch-size) / batch_size_increment` increment batch - size from start-batch-size to global-batch-size using rampup-samples / steps - samples. +class StepBatchsizeNumMicroBatchesCalculator(NumMicroBatchesCalculator): + """Calculator of number of microbatches with arbitrary step-wise batch size schedule. Args: - global_batch_size (int): - Global batch size post rampup. - micro_batch_size (int): - Micro batch size. - data_parallel_size (int): - Data parallel size. - decrease_batch_size_if_needed (bool): - If true, decrease batch size to ensure divisibility by DP size * microbatch size - (if needed). - rank (int): - Rank (to determine whether logging should be performed). - start_global_batch_size (int): - Global batch size to start with. - batch_size_increment (int): - Global batch size increments. - ramup_samples (int): - Number of samples to use ramp up global - batch size from `start_global_batch_size` to `global_batch_size`. + micro_batch_size (int): Micro batch size. + data_parallel_size (int): Data parallel size. + decrease_batch_size_if_needed (bool): Must be False. Step schedules do not support + decreasing batch size for divisibility. + rank (int): Rank for logging. + schedule (str): Schedule string in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + Examples: + "0:768 250B:1536 500B:3072 750B:6144" (thresholds in tokens) + "0:768 61035156250:1536" (thresholds in samples) + seq_length (int, optional): Sequence length for token-to-sample conversion. + If provided, thresholds are interpreted as tokens and converted to samples. + If None, thresholds are interpreted as samples directly. """ def __init__( self, - global_batch_size: int, micro_batch_size: int, data_parallel_size: int, decrease_batch_size_if_needed: bool, rank: int, - start_global_batch_size: int, - batch_size_increment: int, - ramup_samples: int, + schedule: str, + seq_length: Optional[int] = None, ) -> None: - assert global_batch_size > 0, 'global batch size should be positive, got {}.'.format( - global_batch_size - ) - assert start_global_batch_size > 0, 'start batch size should be positive, got {}.'.format( - start_global_batch_size - ) - assert batch_size_increment > 0, 'batch size increment should be positive, got {}.'.format( - batch_size_increment - ) - assert ramup_samples >= 0, 'ramp-up samples should be non-negative, got {}.'.format( - ramup_samples - ) + super().__init__() + + if decrease_batch_size_if_needed: + raise ValueError( + 'Step batch size schedules do not support decrease_batch_size_if_needed' + ) - self.global_batch_size = global_batch_size self.micro_batch_size = micro_batch_size - self.data_parallel_size = data_parallel_size - self.decrease_batch_size_if_needed = decrease_batch_size_if_needed - self.rank = rank - self.start_global_batch_size = start_global_batch_size - self.batch_size_increment = batch_size_increment - self.ramup_samples = ramup_samples - - self.micro_batch_times_data_parallel_size = self.micro_batch_size * self.data_parallel_size + self.data_parallel_size: int = data_parallel_size + self.rank: int = rank + self.seq_length: Optional[int] = seq_length + + self.micro_batch_times_data_parallel_size: int = micro_batch_size * data_parallel_size assert self.micro_batch_times_data_parallel_size > 0 - self.current_global_batch_size = None - diff_batch_size = self.global_batch_size - self.start_global_batch_size - assert diff_batch_size >= 0, ( - 'expected global batch size to be greater than or equal to start batch size, ' - f'got {self.global_batch_size} and {self.start_global_batch_size}' - ) - assert diff_batch_size % batch_size_increment == 0, ( - 'expected ' - f'global batch size interval ({diff_batch_size}) to be divisible by global batch ' - f'size increment ({batch_size_increment})' - ) + # Parse schedule string + self.schedule = self._parse_schedule(schedule, seq_length) - num_increments = diff_batch_size // self.batch_size_increment - self.rampup_samples_per_increment = self.ramup_samples / num_increments + # Validate schedule + self._validate_schedule() - # Initialize number of microbatches. + self.global_batch_size: int = self.schedule[-1][1] + self.current_global_batch_size = None + + if rank == 0: + logger.info(f'> initializing step batch size schedule') + logger.info(f' raw schedule string: "{schedule}"') + unit = "tokens" if seq_length else "samples" + logger.info(f' seq_length: {seq_length}' f' (thresholds interpreted as {unit})') + logger.info(f' micro_batch_size: {micro_batch_size}') + logger.info(f' data_parallel_size: {data_parallel_size}') + logger.info(f'step batch size schedule ({len(self.schedule)} steps):') + for threshold, batch_size in self.schedule: + num_microbatches = batch_size // self.micro_batch_times_data_parallel_size + if seq_length: + tokens = threshold * seq_length + logger.info( + f' >= {tokens:,} tokens ({threshold:,} samples)' + f' -> batch_size={batch_size},' + f' num_microbatches={num_microbatches}' + ) + else: + logger.info( + f' >= {threshold:,} samples' + f' -> batch_size={batch_size},' + f' num_microbatches={num_microbatches}' + ) + # Initialize self.update(0, consistency_check=False, verbose=True) - def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None: - """Update number of microbatches. + @staticmethod + def _parse_numeric_value(value_str: str) -> int: + """Parse numeric value with optional suffix (K, M, B, T).""" + value_str = value_str.strip().upper() + + multiplier = 1 + if value_str.endswith('T'): + multiplier = 1_000_000_000_000 + value_str = value_str[:-1] + elif value_str.endswith('B'): + multiplier = 1_000_000_000 + value_str = value_str[:-1] + elif value_str.endswith('M'): + multiplier = 1_000_000 + value_str = value_str[:-1] + elif value_str.endswith('K'): + multiplier = 1_000 + value_str = value_str[:-1] + + return int(float(value_str) * multiplier) + + @classmethod + def _parse_schedule(cls, schedule_str: str, seq_length: Optional[int]) -> List[Tuple[int, int]]: + """Parse schedule string into list of (threshold_samples, batch_size) tuples. Args: - consumed_samples (int): Number of samples consumed. - consistency_check (bool): Option to check current schedule's consistency. - verbose (bool, optional): Option to control logging. Defaults to False. + schedule_str: Space-separated "THRESHOLD:BATCH_SIZE" pairs. + seq_length: If provided, convert thresholds from tokens to samples. + + Returns: + List of (threshold_samples, batch_size) tuples, sorted by threshold. """ + schedule = [] + entries = schedule_str.strip().replace(',', ' ').split() - # Update current global batch size. - global_batch_size_changed = False - old_current_global_batch_size = self.current_global_batch_size - if consumed_samples > self.ramup_samples: - self.current_global_batch_size = self.global_batch_size - else: - steps = int(consumed_samples / self.rampup_samples_per_increment) - self.current_global_batch_size = ( - self.start_global_batch_size + steps * self.batch_size_increment + for entry in entries: + if ':' not in entry: + raise ValueError( + f'Invalid schedule entry "{entry}". Expected format: "THRESHOLD:BATCH_SIZE"' + ) + + threshold_str, batch_size_str = entry.split(':', 1) + threshold = cls._parse_numeric_value(threshold_str) + batch_size = cls._parse_numeric_value(batch_size_str) + + # Convert tokens to samples if seq_length provided + if seq_length is not None: + threshold = threshold // seq_length + + schedule.append((threshold, batch_size)) + + # Sort by threshold ascending + schedule.sort(key=lambda x: x[0]) + + return schedule + + def _validate_schedule(self) -> None: + """Validate the parsed schedule.""" + assert len(self.schedule) > 0, 'schedule must have at least one entry' + assert ( + self.schedule[0][0] == 0 + ), f'first schedule entry must have threshold 0, got {self.schedule[0][0]}' + + # Check strictly increasing thresholds + for i in range(1, len(self.schedule)): + assert self.schedule[i][0] > self.schedule[i - 1][0], ( + f'schedule thresholds must be strictly increasing, ' + f'got {self.schedule[i - 1][0]} before {self.schedule[i][0]}' ) - assert self.current_global_batch_size <= self.global_batch_size - if old_current_global_batch_size != self.current_global_batch_size: - global_batch_size_changed = True - if self.rank == 0 and global_batch_size_changed and verbose: - if old_current_global_batch_size is None: - logger.info(f'setting initial batch size to {self.current_global_batch_size}') + # Validate batch sizes are positive. + # NOTE: divisibility by micro_batch_size * data_parallel_size is NOT checked here + # because early schedule entries may be smaller than the current GPU configuration + # (e.g., after scaling up GPUs mid-training). Divisibility of the CURRENT batch size + # is checked at runtime in update() when consistency_check=True (after checkpoint loading). + for threshold, batch_size in self.schedule: + assert batch_size > 0, f'batch size must be positive, got {batch_size}' + + def _get_batch_size_for_samples(self, consumed_samples: int) -> int: + """Get the batch size for the given number of consumed samples.""" + batch_size = self.schedule[0][1] + for threshold, bs in self.schedule: + if consumed_samples >= threshold: + batch_size = bs else: - logger.info( - f'ramping up batch size from {old_current_global_batch_size} to ' - f'{self.current_global_batch_size}' - ) + break + return batch_size + + def update(self, consumed_samples: int, consistency_check: bool, verbose: bool = False) -> None: + """Update number of microbatches based on consumed samples. - # Check consistency of the current global batch size. - if consistency_check and not self.decrease_batch_size_if_needed: + Args: + consumed_samples (int): Number of samples consumed. + consistency_check (bool): Check divisibility constraints. + verbose (bool): Enable logging. + """ + self.current_global_batch_size = self._get_batch_size_for_samples(consumed_samples) + assert self.current_global_batch_size is not None + + # Consistency check + if consistency_check: assert ( self.current_global_batch_size % self.micro_batch_times_data_parallel_size == 0 ), ( - 'current global ' - 'batch size ({}) is not divisible by micro-batch-size ({}) times' - 'data parallel size ({})'.format( - self.current_global_batch_size, self.micro_batch_size, self.data_parallel_size - ) - ) - - if ( - self.decrease_batch_size_if_needed - and self.current_global_batch_size % self.micro_batch_times_data_parallel_size != 0 - ): - self.current_running_global_batch_size = _round( - self.current_global_batch_size, self.micro_batch_times_data_parallel_size - ) - if self.rank == 0 and global_batch_size_changed and verbose: - logger.info( - f'decreasing batch size from {self.current_global_batch_size} to ' - f'{self.current_running_global_batch_size} to keep divisiblity by ' - f'micro_batch_size={self.micro_batch_size} * ' - f'data_parallel_size={self.data_parallel_size}' - ) - assert ( - self.current_running_global_batch_size % self.micro_batch_times_data_parallel_size - == 0 + f'current global batch size ({self.current_global_batch_size}) is not divisible by ' + f'micro_batch_size ({self.micro_batch_size}) * ' + f'data_parallel_size ({self.data_parallel_size})' ) - else: - self.current_running_global_batch_size = self.current_global_batch_size + self.current_running_global_batch_size = self.current_global_batch_size + assert self.current_running_global_batch_size is not None self.num_micro_batches = ( self.current_running_global_batch_size // self.micro_batch_times_data_parallel_size ) diff --git a/megatron/legacy/model/transformer.py b/megatron/legacy/model/transformer.py index ca3414eecdd..a2a85d3747d 100644 --- a/megatron/legacy/model/transformer.py +++ b/megatron/legacy/model/transformer.py @@ -1454,7 +1454,7 @@ def forward(self, hidden_states, attention_mask, ) if self.use_fp8 else nullcontext(): # Determine if the current iteration is first microbatch if self.num_microbatches_in_previous_step != get_num_microbatches(): - self.microbatch_count = 0 # Reset count on new batch size rampup interval + self.microbatch_count = 0 self.num_microbatches_in_previous_step = get_num_microbatches() is_first_microbatch = self.microbatch_count % get_num_microbatches() == 0 diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index 516dc89ab11..f82834606fd 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -1501,9 +1501,8 @@ def prepare_data_for_update( samples_ratio_per_step=samples_ratio_per_step, num_bins_this_rank = len(packing_context.packed_trajs), bin_seq_indices = packing_context.packing_info.bin_seq_indices, - global_batch_size=args.global_batch_size, - rampup_batch_size=args.rampup_batch_size, - micro_batch_size=args.micro_batch_size, + global_batch_size=args.global_batch_size, + micro_batch_size=args.micro_batch_size, decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, ) loader = get_microbatch_dataloader(len(packing_context.packed_trajs), args.micro_batch_size) @@ -1528,9 +1527,8 @@ def prepare_data_for_update( reconfigure_num_microbatches_calculator( rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, - global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), - rampup_batch_size=args.rampup_batch_size, - micro_batch_size=args.micro_batch_size, + global_batch_size=math.ceil(samples_ratio_per_step*total_turns_sampled), + micro_batch_size=args.micro_batch_size, decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, data_parallel_size=mpu.get_data_parallel_world_size(), ) diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py index 50ab2ca27de..9ceb806571c 100644 --- a/megatron/rl/sequence_packing_utils.py +++ b/megatron/rl/sequence_packing_utils.py @@ -1016,7 +1016,6 @@ def update_microbatch_calculator( num_bins_this_rank: int, bin_seq_indices: List[List[int]], global_batch_size: int, - rampup_batch_size: int, micro_batch_size: int, decrease_batch_size_if_needed: bool, ): @@ -1026,7 +1025,6 @@ def update_microbatch_calculator( num_bins_this_rank: Amount of packing bins that belongs to current rank. bin_seq_indices: Global seq indices in the bin, see PackingInfo. global_batch_size: Current global batch size. - rampup_batch_size: Rampup batch size. See num_microbatches_calculator.py for more. micro_batch_size: Micro batch size at init. decrease_batch_size_if_needed: Scale down batch size. See num_microbatches_calculator.py for more. @@ -1046,7 +1044,6 @@ def update_microbatch_calculator( old_num_microbatches = get_num_microbatches() reconfigure_num_microbatches_calculator( rank=torch.distributed.get_rank() if torch.distributed.is_initialized() else 0, - rampup_batch_size=rampup_batch_size, global_batch_size=bins_bs, micro_batch_size=micro_batch_size, data_parallel_size=dp_world_size, diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 684a8ace6fb..4e8de13bd54 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -132,6 +132,8 @@ def parse_args(extra_args_provider=None, ignore_unknown_args=False): else: args = parser.parse_args() + args._is_global_batch_size_explicitly_specified = args.global_batch_size is not None + # Experimental yaml if args.yaml_cfg is not None: from .yaml_arguments import load_yaml @@ -593,11 +595,16 @@ def validate_args(args, defaults={}): args.phase_transition_iterations = sorted( int(x.strip()) for x in args.phase_transition_iterations.split(",") ) - assert args.rampup_batch_size is None, "multi-phase training does not support batch size ramp-up" - # Batch size. assert args.micro_batch_size is not None assert args.micro_batch_size > 0 + is_global_batch_size_explicitly_specified = getattr( + args, '_is_global_batch_size_explicitly_specified', args.global_batch_size is not None + ) + if args.step_batch_size_schedule is not None and is_global_batch_size_explicitly_specified: + raise ValueError( + 'Cannot specify both --step-batch-size-schedule and --global-batch-size' + ) if args.global_batch_size is None: args.global_batch_size = args.micro_batch_size * args.data_parallel_size print_rank_0('setting global batch size to {}'.format(args.global_batch_size)) @@ -617,7 +624,6 @@ def validate_args(args, defaults={}): args.grpo_samples_per_iteration * args.grpo_iterations) # Ensure that the number of prompts we collect is a multiple of the global batch size. - # TODO: Make this account for batch size rampup? assert num_generated_samples_per_inference_iteration % args.global_batch_size == 0, \ f"grpo_group_size * grpo_prompts_per_step * grpo_iterations should be divisible by global_batch_size" @@ -1116,8 +1122,6 @@ def validate_args(args, defaults={}): 'expected iteration-based learning rate decay' assert args.lr_warmup_samples == 0, \ 'expected iteration-based learning rate warmup' - assert args.rampup_batch_size is None, \ - 'expected no batch-size rampup for iteration-based training' if args.lr_warmup_fraction is not None: assert args.lr_warmup_iters == 0, \ 'can only specify one of lr-warmup-fraction and lr-warmup-iters' @@ -2910,8 +2914,7 @@ def _add_data_args(parser): 'This argument is exclusive to the other independent --*-data-path arguments.') group.add_argument('--phase-transition-iterations', type=str, default=None, help='Comma-separated list of iterations where phase ' - 'transitions occur. Requires fixed global batch size across phases. ' - 'Does not support batch size ramp-up.') + 'transitions occur. Requires fixed global batch size across phases.') group.add_argument('--split', type=str, default=None, help='Comma-separated list of proportions for training,' ' validation, and test split. For example the split ' diff --git a/megatron/training/config/training_config.py b/megatron/training/config/training_config.py index 6bc59668789..27cffb837f4 100644 --- a/megatron/training/config/training_config.py +++ b/megatron/training/config/training_config.py @@ -25,12 +25,24 @@ class TrainingConfig: will start with global batch size 16 and over (1024 - 16) / 8 = 126 intervals will increase the batch size linearly to 1024. In each interval we will use approximately 300000 / 126 = 2380 samples. + Deprecated. Use step_batch_size_schedule instead. """ + step_batch_size_schedule: str | None = None + """Step-wise batch size schedule in format "THRESHOLD:BS THRESHOLD:BS ...". + Thresholds support suffixes: K (1e3), M (1e6), B (1e9), T (1e12). + If sequence length is provided, thresholds are interpreted as tokens; otherwise as samples. + Example: + step_batch_size_schedule = "0:768 250B:1536 500B:3072 750B:6144" + Cannot be used together with decrease_batch_size_if_needed. + """ + + decrease_batch_size_if_needed: bool = False """If set, decrease batch size if microbatch_size * dp_size does not divide batch_size. Old batch_size will be restored if training is re-started - with dp_size that divides batch_size // microbatch_size.""" + with dp_size that divides batch_size // microbatch_size. Not supported with + step-batch-size-schedule.""" empty_unused_memory_level: Literal[0, 1, 2] = 0 """Call torch.cuda.empty_cache() each iteration (training and eval), to reduce fragmentation. diff --git a/megatron/training/global_vars.py b/megatron/training/global_vars.py index ab4713c2624..6db19d428cc 100644 --- a/megatron/training/global_vars.py +++ b/megatron/training/global_vars.py @@ -121,13 +121,17 @@ def set_global_variables(args, build_tokenizer=True): _ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args') set_args(args) + if args.step_batch_size_schedule is not None: + print(f'> using step batch size schedule: {args.step_batch_size_schedule}') + init_num_microbatches_calculator( - args.rank, - args.rampup_batch_size, - args.global_batch_size, - args.micro_batch_size, - args.data_parallel_size, - args.decrease_batch_size_if_needed, + rank=args.rank, + global_batch_size=args.global_batch_size, + micro_batch_size=args.micro_batch_size, + data_parallel_size=args.data_parallel_size, + decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, + step_batch_size_schedule=args.step_batch_size_schedule, + seq_length=args.seq_length, ) if build_tokenizer: _ = _build_tokenizer(args) diff --git a/megatron/training/training.py b/megatron/training/training.py index 8fed9afd570..121e333cca6 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -1292,29 +1292,20 @@ def update_train_iters(args): if args.train_iters: return - # Constant batch size with sample-based training. - if args.rampup_batch_size is None: - args.train_iters = args.train_samples // args.global_batch_size - - else: - # Sample based training with rampup batch size. + if args.step_batch_size_schedule is not None: + # Sample based training with step batch size schedule. iterations = 0 consumed_samples = 0 - # Rampup phase. - while ( - consumed_samples <= int(args.rampup_batch_size[2]) - and consumed_samples <= args.train_samples - ): + while consumed_samples < args.train_samples: update_num_microbatches(consumed_samples, consistency_check=False) consumed_samples += get_current_global_batch_size() iterations += 1 # Reset update_num_microbatches(0, consistency_check=False) - # Constant phase - # Note that we throw away any partial last batch. - if args.train_samples > consumed_samples: - iterations += (args.train_samples - consumed_samples) // args.global_batch_size args.train_iters = iterations + else: + # Constant batch size with sample-based training. + args.train_iters = args.train_samples // args.global_batch_size print_rank_0(f'setting training iterations to {args.train_iters}') @@ -1739,6 +1730,21 @@ def setup_model_and_optimizer( args.iteration = 0 args.num_floating_point_operations_so_far = 0 + # Validate that the world size can accommodate the current batch size. + # This catches the case where GPUs were scaled up mid-training but the + # current position in the batch size schedule yields a batch size that + # is too small for the number of data-parallel replicas. + num_microbatches = get_num_microbatches() + current_global_batch_size = get_current_global_batch_size() + data_parallel_size = mpu.get_data_parallel_world_size() + assert num_microbatches is not None and num_microbatches >= 1, ( + f'current global batch size ({current_global_batch_size}) is too small for ' + f'micro_batch_size ({args.micro_batch_size}) * data_parallel_size ({data_parallel_size}) = ' + f'{args.micro_batch_size * data_parallel_size}. The world size cannot accommodate the ' + f'batch size. This can happen when resuming with more GPUs than the current batch size ' + f'schedule entry supports.' + ) + # get model without FP16 and/or DDP wrappers if ( args.iteration == 0 @@ -2714,12 +2720,13 @@ def train( destroy_num_microbatches_calculator() # Then initialize with the correct perform_rl_step=True context init_num_microbatches_calculator( - args.rank, - args.rampup_batch_size, - args.global_batch_size, - args.micro_batch_size, - mpu.get_data_parallel_world_size(), - args.decrease_batch_size_if_needed + rank=args.rank, + global_batch_size=args.global_batch_size, + micro_batch_size=args.micro_batch_size, + data_parallel_size=mpu.get_data_parallel_world_size(), + decrease_batch_size_if_needed=args.decrease_batch_size_if_needed, + step_batch_size_schedule=args.step_batch_size_schedule, + seq_length=args.seq_length, ) print_rank_0(f"> GRPO training: num_microbatches set to {get_num_microbatches()}") @@ -2971,8 +2978,8 @@ def trace_handler(p): ) else: assert get_num_microbatches() > num_microbatches, ( - f"Number of microbatches should be increasing due to batch size rampup; " - f"instead going from {num_microbatches} to {get_num_microbatches()}" + f"Number of microbatches should not decrease; " + f"going from {num_microbatches} to {get_num_microbatches()}" ) if args.save is not None: save_checkpoint_and_time( diff --git a/megatron/training/yaml_arguments.py b/megatron/training/yaml_arguments.py index 3fa6dfa320a..3a2d04aadcf 100644 --- a/megatron/training/yaml_arguments.py +++ b/megatron/training/yaml_arguments.py @@ -103,6 +103,13 @@ def validate_yaml(args, defaults={}): # Batch size. assert args.micro_batch_size is not None assert args.micro_batch_size > 0 + is_global_batch_size_explicitly_specified = getattr( + args, '_is_global_batch_size_explicitly_specified', args.global_batch_size is not None + ) + if args.step_batch_size_schedule is not None and is_global_batch_size_explicitly_specified: + raise ValueError( + 'Cannot specify both --step-batch-size-schedule and --global-batch-size' + ) if args.global_batch_size is None: args.global_batch_size = args.micro_batch_size * args.data_parallel_size if args.rank == 0: @@ -188,8 +195,6 @@ def validate_yaml(args, defaults={}): 'expected iteration-based learning rate decay' assert args.lr_warmup_samples == 0, \ 'expected iteration-based learning rate warmup' - assert args.rampup_batch_size is None, \ - 'expected no batch-size rampup for iteration-based training' if args.lr_warmup_fraction is not None: assert args.lr_warmup_iters == 0, \ 'can only specify one of lr-warmup-fraction and lr-warmup-iters' @@ -430,5 +435,8 @@ def load_yaml(yaml_path): config_namespace = json.loads(json.dumps(config), object_hook=lambda item: SimpleNamespace(**item)) # Add config location to namespace config_namespace.yaml_cfg = yaml_path + config_namespace._is_global_batch_size_explicitly_specified = ( + getattr(config_namespace, "global_batch_size", None) is not None + ) return config_namespace diff --git a/tasks/finetune_utils.py b/tasks/finetune_utils.py index 6d20e55bd16..9507ba5dbf0 100644 --- a/tasks/finetune_utils.py +++ b/tasks/finetune_utils.py @@ -248,9 +248,6 @@ def finetune(train_valid_datasets_provider, model_provider, args = get_args() timers = get_timers() - assert args.rampup_batch_size is None, \ - 'batch size scaling is not supported for finetuning' - # Train and validation data loaders. timers('train/valid/test dataset/dataloder', log_level=0).start() if args.epochs > 0: diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml index 3bb12c99539..2baa92999e6 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release/model_config.yaml @@ -22,8 +22,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true # Transformer Engine args diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml index 8eb57abf308..d9b44500eca 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_gb200/model_config.yaml @@ -24,8 +24,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true --cross-entropy-loss-fusion: true diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml index 300e0777917..4d686aba694 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm/model_config.yaml @@ -22,8 +22,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 4882812 --manual-gc: true # Transformer Engine args diff --git a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml index 2f045810a87..5f51a40ecc9 100644 --- a/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml +++ b/tests/functional_tests/test_cases/gpt/gpt3_15b_8t_release_sm_gb200/model_config.yaml @@ -24,8 +24,8 @@ MODEL_ARGS: --sequence-parallel: true --disable-bias-linear: true --micro-batch-size: 4 - --rampup-batch-size: "[384 384 97656250]" --global-batch-size: 1152 + --step-batch-size-schedule: "0:384 200B:768 400B:1152" --train-samples: 19531250 --manual-gc: true --cross-entropy-loss-fusion: true diff --git a/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py b/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py index 8b03262714a..5f9c617893c 100644 --- a/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py +++ b/tests/unit_tests/dist_checkpointing/test_pipeline_parallel_layout.py @@ -305,7 +305,9 @@ def set_ckpt_path(ckpt_path): args.load = ckpt_path set_tp_pp_vpp(*src_tp_pp_vpp, pp_layout=src_pp_layout, destroy_first=False) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator( + rank=0, global_batch_size=1, micro_batch_size=1, data_parallel_size=1 + ) iteration = 123 layer_spec_fn = get_gpt_decoder_block_spec if is_moe else gpt_te_spec diff --git a/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py b/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py index 51dc1d0bff6..6a50e8d1aa5 100644 --- a/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py +++ b/tests/unit_tests/distributed/test_torch_fully_sharded_parallel.py @@ -39,7 +39,9 @@ def __init__(self, config: TransformerConfig): def init_model_parallel(): """Init torch distributed.""" Utils.initialize_model_parallel(1, 1) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator( + rank=0, global_batch_size=1, micro_batch_size=1, data_parallel_size=1 + ) model_parallel_cuda_manual_seed(123) yield # Run the actual test. Utils.destroy_model_parallel() diff --git a/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py b/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py index e7fb8159af2..a6afabe8817 100644 --- a/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py +++ b/tests/unit_tests/pipeline_parallel/test_pipeline_layout.py @@ -226,7 +226,9 @@ def set_tp_pp_vpp(tp, pp, vpp=None, pp_layout=None, destroy_first=True): args.pipeline_model_parallel_layout = pp_layout set_tp_pp_vpp(*tp_pp_vpp, pp_layout=pp_layout, destroy_first=False) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator( + rank=0, global_batch_size=1, micro_batch_size=1, data_parallel_size=1 + ) def forward_step_func(data_iterator, model: GPTModel): """Forward training step. Copied from `pretrain_gpt.py`""" diff --git a/tests/unit_tests/rl/test_sequence_packing_utils.py b/tests/unit_tests/rl/test_sequence_packing_utils.py index 0a4f4cd03f9..bb03be57cb3 100644 --- a/tests/unit_tests/rl/test_sequence_packing_utils.py +++ b/tests/unit_tests/rl/test_sequence_packing_utils.py @@ -465,12 +465,7 @@ def test_get_bins_bs_and_steps(ratio, local_bins, world, expected_bs): global_bs_in_seq = int(n_seqs * ratio) def side_eff( - rank, - rampup_batch_size, - global_batch_size, - micro_batch_size, - data_parallel_size, - decrease_batch_size_if_needed, + rank, global_batch_size, micro_batch_size, data_parallel_size, decrease_batch_size_if_needed ): # Inside of the get_microbatch_dataloader, we compute the batch size in bins. # We want to test this variable. @@ -488,7 +483,6 @@ def side_eff( num_bins_this_rank=local_bins, bin_seq_indices=[], global_batch_size=global_bs_in_seq, - rampup_batch_size=1, micro_batch_size=1, decrease_batch_size_if_needed=False, ) diff --git a/tests/unit_tests/test_checkpointing.py b/tests/unit_tests/test_checkpointing.py index 502e1ef700e..16bac10566d 100644 --- a/tests/unit_tests/test_checkpointing.py +++ b/tests/unit_tests/test_checkpointing.py @@ -152,7 +152,9 @@ def create_ckpt_load_args(create_args): def init_model_parallel(): """Init torch distributed.""" Utils.initialize_model_parallel(1, 1) - init_num_microbatches_calculator(0, None, 1, 1, 1) + init_num_microbatches_calculator( + rank=0, global_batch_size=1, micro_batch_size=1, data_parallel_size=1 + ) model_parallel_cuda_manual_seed(123) yield # Run the actual test. Utils.destroy_model_parallel() diff --git a/tests/unit_tests/test_num_microbatches_calculator.py b/tests/unit_tests/test_num_microbatches_calculator.py index 9b3356b8af5..2faf9a9b9e4 100644 --- a/tests/unit_tests/test_num_microbatches_calculator.py +++ b/tests/unit_tests/test_num_microbatches_calculator.py @@ -1,4 +1,4 @@ -from typing import List, Optional +# Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. import pytest @@ -7,21 +7,37 @@ def test_init_num_microbatches_calculator(): mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator( + rank=0, global_batch_size=32, micro_batch_size=8, data_parallel_size=2 + ) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 32 with pytest.raises(AssertionError): - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator( + rank=0, global_batch_size=32, micro_batch_size=8, data_parallel_size=2 + ) mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 3, True) + mb_calculator.init_num_microbatches_calculator( + rank=0, + global_batch_size=32, + micro_batch_size=8, + data_parallel_size=3, + decrease_batch_size_if_needed=True, + ) assert mb_calculator.get_num_microbatches() == 1 assert mb_calculator.get_current_global_batch_size() == 32 assert mb_calculator.get_current_running_global_batch_size() == 24 mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 33, 8, 2, True) + mb_calculator.init_num_microbatches_calculator( + rank=0, + global_batch_size=33, + micro_batch_size=8, + data_parallel_size=2, + decrease_batch_size_if_needed=True, + ) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 33 assert mb_calculator.get_current_running_global_batch_size() == 32 @@ -29,68 +45,69 @@ def test_init_num_microbatches_calculator(): def test_reconfigure_num_microbatches_calculator(): mb_calculator._GLOBAL_NUM_MICROBATCHES_CALCULATOR = None - mb_calculator.init_num_microbatches_calculator(0, None, 32, 8, 2, False) + mb_calculator.init_num_microbatches_calculator( + rank=0, global_batch_size=32, micro_batch_size=8, data_parallel_size=2 + ) assert mb_calculator.get_num_microbatches() == 2 assert mb_calculator.get_current_global_batch_size() == 32 - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) - assert mb_calculator.get_num_microbatches() == 1 - assert mb_calculator.get_current_global_batch_size() == 16 - - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 16, 96], 32, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator( + rank=0, global_batch_size=16, micro_batch_size=8, data_parallel_size=2 + ) assert mb_calculator.get_num_microbatches() == 1 assert mb_calculator.get_current_global_batch_size() == 16 def test_get_num_microbatches(): - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator( + rank=0, global_batch_size=16, micro_batch_size=8, data_parallel_size=2 + ) assert mb_calculator.get_num_microbatches() == 1 - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 3, True) + mb_calculator.reconfigure_num_microbatches_calculator( + rank=0, + global_batch_size=16, + micro_batch_size=4, + data_parallel_size=3, + decrease_batch_size_if_needed=True, + ) assert mb_calculator.get_num_microbatches() == 1 def test_get_current_global_batch_size(): - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator( + rank=0, global_batch_size=16, micro_batch_size=4, data_parallel_size=2 + ) assert mb_calculator.get_current_global_batch_size() == 16 - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 4, 3, True) + mb_calculator.reconfigure_num_microbatches_calculator( + rank=0, + global_batch_size=16, + micro_batch_size=4, + data_parallel_size=3, + decrease_batch_size_if_needed=True, + ) assert mb_calculator.get_current_global_batch_size() == 16 assert mb_calculator.get_current_running_global_batch_size() == 12 def test_get_micro_batch_size(): - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 16, 8, 2, False) + mb_calculator.reconfigure_num_microbatches_calculator( + rank=0, global_batch_size=16, micro_batch_size=8, data_parallel_size=2 + ) assert mb_calculator.get_micro_batch_size() == 8 -def test_update_num_microbatches(): - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 8, 96], 32, 4, 2, False) - assert mb_calculator.get_num_microbatches() == 2 - mb_calculator.update_num_microbatches(48, False) - assert mb_calculator.get_num_microbatches() == 3 - - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 8, 96], 32, 8, 2, False) - with pytest.raises(AssertionError): - mb_calculator.update_num_microbatches(49, True) - - mb_calculator.reconfigure_num_microbatches_calculator(0, None, 32, 8, 2, False) - mb_calculator.update_num_microbatches(16) - assert mb_calculator.get_num_microbatches() == 2 - - def test_build_num_microbatches_calculator(): - temp_calculator = mb_calculator._build_num_microbatches_calculator(0, None, 32, 8, 2, False) + temp_calculator = mb_calculator._build_num_microbatches_calculator(0, 32, 8, 2, False) assert temp_calculator.get() == 2 assert temp_calculator.get_current_global_batch_size() == 32 assert type(temp_calculator) is mb_calculator.ConstantNumMicroBatchesCalculator - temp_calculator = mb_calculator._build_num_microbatches_calculator( - 0, [16, 16, 48], 32, 8, 2, False - ) - assert temp_calculator.get() == 1 - assert temp_calculator.get_current_global_batch_size() == 16 - assert type(temp_calculator) is mb_calculator.RampupBatchsizeNumMicroBatchesCalculator + with pytest.raises(ValueError): + mb_calculator._build_num_microbatches_calculator( + 0, None, 8, 2, True, step_batch_size_schedule="0:16 100:32" + ) class TestConstantNumMicroBatchesCalculator: @@ -110,38 +127,73 @@ def test_get_current_global_batch_size(self): assert self.mb_calculator.get_current_global_batch_size() == 32 -class TestRampupBatchsizeNumMicroBatchesCalculator: - def setup_method(self, method): - self.mb_calculator = mb_calculator.RampupBatchsizeNumMicroBatchesCalculator( - 32, 8, 2, False, 0, 16, 16, 48 - ) - - def test_constructor(self): - assert type(self.mb_calculator) is mb_calculator.RampupBatchsizeNumMicroBatchesCalculator - assert self.mb_calculator.global_batch_size == 32 - assert self.mb_calculator.micro_batch_size == 8 - assert self.mb_calculator.data_parallel_size == 2 - assert self.mb_calculator.start_global_batch_size == 16 - assert self.mb_calculator.batch_size_increment == 16 - assert self.mb_calculator.ramup_samples == 48 - assert self.mb_calculator.micro_batch_times_data_parallel_size == 16 - assert self.mb_calculator.num_micro_batches == 1 - - def test_get(self): - assert self.mb_calculator.get() == 1 +def test_step_batch_size_schedule_allows_past_entries_smaller_than_dp(): + """Test that step batch size schedule does not crash when early schedule entries + are smaller than micro_batch_size * data_parallel_size. + + This happens when scaling to more GPUs mid-training: the initial batch sizes + in the schedule are smaller than the new GPU count can support, but training + has progressed past those entries. The divisibility check should only apply + to the CURRENT batch size (after checkpoint loading), not all schedule entries. + """ + # micro=1, dp=512, micro*dp=512 + # Schedule starts at 256 which is < 512, but later entries are fine. + # This should NOT raise during construction. + calc = mb_calculator.StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=1, + data_parallel_size=512, + decrease_batch_size_if_needed=False, + rank=0, + schedule="0:256 200000:512 600000:1024 1500000:2048 3000000:4096 6000000:6144", + ) - def test_get_current_global_batch_size(self): - assert self.mb_calculator.get_current_global_batch_size() == 16 + # At init (consumed_samples=0), batch=256 < micro*dp=512. + # No consistency_check at init, so no error. + assert calc.current_global_batch_size == 256 + + # After training past the first entry, batch=512. Consistency check passes. + calc.update(200000, consistency_check=True) + assert calc.current_global_batch_size == 512 + assert calc.num_micro_batches == 1 + + # Later entries work fine. + calc.update(1500000, consistency_check=True) + assert calc.current_global_batch_size == 2048 + assert calc.num_micro_batches == 4 + + calc.update(6000000, consistency_check=True) + assert calc.current_global_batch_size == 6144 + assert calc.num_micro_batches == 12 + + +def test_step_batch_size_schedule_consistency_check_fails_when_batch_too_small(): + """Test that consistency_check=True raises when current batch size is smaller + than micro_batch_size * data_parallel_size. + + This is the scenario caught by the runtime check in setup_model_and_optimizer: + GPUs were scaled up but the current schedule entry yields a batch size too small + for the new world size. + """ + calc = mb_calculator.StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=1, + data_parallel_size=512, + decrease_batch_size_if_needed=False, + rank=0, + schedule="0:256 200000:512 600000:1024", + ) + # At consumed_samples=0, batch=256 < micro*dp=512. + # consistency_check=True should fail because 256 % 512 != 0. + with pytest.raises(AssertionError): + calc.update(0, consistency_check=True) -def test_ramp_up(): - mb_calculator.reconfigure_num_microbatches_calculator(0, [16, 16, 96], 32, 8, 2, False) - consumed_samples = 0 - count = 0 - expected_consumed_samples = [0, 16, 32, 48, 64, 80, 96, 128, 160, 192, 224, 256] - while consumed_samples < 256: - consumed_samples += mb_calculator.get_current_global_batch_size() - count += 1 - assert consumed_samples == expected_consumed_samples[count] - mb_calculator.update_num_microbatches(consumed_samples, True) +def test_step_batch_size_schedule_rejects_decrease_batch_size_if_needed(): + with pytest.raises(ValueError): + mb_calculator.StepBatchsizeNumMicroBatchesCalculator( + micro_batch_size=1, + data_parallel_size=512, + decrease_batch_size_if_needed=True, + rank=0, + schedule="0:256 200000:512 600000:1024", + ) diff --git a/tests/unit_tests/transformer/test_cuda_graphs.py b/tests/unit_tests/transformer/test_cuda_graphs.py index 69f12e4d983..ee4ff7d152d 100644 --- a/tests/unit_tests/transformer/test_cuda_graphs.py +++ b/tests/unit_tests/transformer/test_cuda_graphs.py @@ -575,7 +575,6 @@ def test_get_cuda_graph_input_data(self, num_microbatches, pp_size, vpp_size): # Initialize num_microbatches calculator init_num_microbatches_calculator( rank=0, - rampup_batch_size=None, global_batch_size=micro_batch_size * num_microbatches, micro_batch_size=micro_batch_size, data_parallel_size=1, From e5ec9ab9102f48e466f7a92d1f8f5464ec991f05 Mon Sep 17 00:00:00 2001 From: Jingyue Wu Date: Tue, 21 Apr 2026 10:55:16 -0700 Subject: [PATCH 58/72] Megatron-FSDP: log mcore detection only after imports succeed (#4400) Co-authored-by: Claude Opus 4.7 (1M context) --- .../core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py b/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py index 7536a1ba9d2..2a17315611a 100644 --- a/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py +++ b/megatron/core/distributed/fsdp/src/megatron_fsdp/megatron_fsdp.py @@ -46,11 +46,12 @@ try: # Default to Megatron-LM FW. - logger.info("Detected Megatron Core, using Megatron-FSDP with Megatron.") from megatron.core.distributed.distributed_data_parallel_config import ( DistributedDataParallelConfig, ) from megatron.core.utils import is_submodule + + logger.info("Detected Megatron Core, using Megatron-FSDP with Megatron.") except ImportError: # Megatron-LM is not installed, use Megatron-FSDP as a standalone module. logger.info("Megatron Core is not installed, Megatron-FSDP will run without Megatron Core.") From a550e0e4430b2946e8ca52b27904531f2b030c19 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Tue, 21 Apr 2026 23:09:57 +0200 Subject: [PATCH 59/72] ci(gb200): re-enable tunable_overlap 1-node mr-github test (#4405) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig Co-authored-by: Claude Sonnet 4.6 --- .github/actions/action.yml | 2 +- .../golden_values_dev_dgx_gb200.json | 287 ++++++++++++++++++ .../golden_values_dev_dgx_gb200.json | 287 ++++++++++++++++++ .../launch_nemo_run_workload.py | 44 ++- tests/test_utils/recipes/gb200/gpt-1node.yaml | 4 +- 5 files changed, 614 insertions(+), 10 deletions(-) create mode 100644 tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/golden_values_dev_dgx_gb200.json create mode 100644 tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap_1node/golden_values_dev_dgx_gb200.json diff --git a/.github/actions/action.yml b/.github/actions/action.yml index 3913414a1c7..c44034b48f9 100644 --- a/.github/actions/action.yml +++ b/.github/actions/action.yml @@ -214,7 +214,7 @@ runs: LOG_BASE=$([[ "$IS_UNIT_TEST" == "true" ]] && echo "assets_dir/logs" || echo "assets_dir") LATEST_LOG="" if [[ -d "$LOG_BASE" ]]; then - LATEST_LOG=$(find "$LOG_BASE" -maxdepth 3 -name "*.log" ! -name "nccl_debug.log" -type f 2>/dev/null \ + LATEST_LOG=$(find "$LOG_BASE" -name "*.log" ! -name "nccl_debug.log" -type f 2>/dev/null \ | xargs -r ls -t 2>/dev/null | head -1 || true) fi if [[ -n "$LATEST_LOG" ]]; then diff --git a/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/golden_values_dev_dgx_gb200.json b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/golden_values_dev_dgx_gb200.json new file mode 100644 index 00000000000..6a7fefa354f --- /dev/null +++ b/tests/functional_tests/test_cases/gpt/gpt3_mcore_te_tp1_pp4_vp1_1node/golden_values_dev_dgx_gb200.json @@ -0,0 +1,287 @@ +{ + "lm 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"1": "nan", + "2": 6.09673, + "3": 2.03942, + "4": 1.85438, + "5": 1.94961, + "6": 1.78284, + "7": 2.40768, + "8": 2.47014, + "9": 1.6622, + "10": 1.96416, + "11": 2.32435, + "12": 1.74419, + "13": 1.61735, + "14": 2.39684, + "15": 2.0178, + "16": 1.8634, + "17": 1.85656, + "18": 2.07207, + "19": 1.68614, + "20": 2.09575, + "21": 2.14398, + "22": 1.87308, + "23": 2.28266, + "24": 1.58764, + "25": 1.74152, + "26": 2.25751, + "27": 1.31271, + "28": 1.8448, + "29": 2.00995, + "30": 1.94895, + "31": 1.83426, + "32": 2.03351, + "33": 1.89336, + "34": 2.34317, + "35": 1.90143, + "36": 2.07766, + "37": 1.76025, + "38": 2.096, + "39": 2.02752, + "40": 1.88077, + "41": 2.3983, + "42": 1.74666, + "43": 1.93, + "44": 1.73677, + "45": 2.24649, + "46": 1.58703, + "47": 1.97385, + "48": 1.5295, + "49": 1.92136, + "50": 1.91307 + } + } +} \ No newline at end of file diff --git a/tests/test_utils/python_scripts/launch_nemo_run_workload.py b/tests/test_utils/python_scripts/launch_nemo_run_workload.py index cdb730fae63..6569110ea02 100644 --- a/tests/test_utils/python_scripts/launch_nemo_run_workload.py +++ b/tests/test_utils/python_scripts/launch_nemo_run_workload.py @@ -1,5 +1,6 @@ # Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +import io import logging import os import pathlib @@ -40,6 +41,7 @@ def is_flaky_failure(concat_allranks_logs: str) -> bool: or "unspecified launch failure" in concat_allranks_logs or "free(): corrupted unsorted chunks" in concat_allranks_logs or "Segfault encountered" in concat_allranks_logs + or "The following metrics failed" in concat_allranks_logs ) @@ -120,6 +122,8 @@ def main( shm_size="30g", env_vars={ "PYTHONUNBUFFERED": "1", + "FORCE_COLOR": "1", + "TERM": "xterm-256color", "OUTPUT_PATH": os.getcwd(), "ENABLE_LIGHTWEIGHT_MODE": str(enable_lightweight_mode).lower(), "N_REPEAT": str(n_repeat), @@ -135,11 +139,37 @@ def main( n_attempts = 0 while n_attempts < 3: - with run.Experiment("mcore-ci-test", executor=executor, log_level="INFO") as exp: - _ = exp.add([inline_script], tail_logs=False, name="task-1") - - exp.dryrun(log=True) - exp.run(detach=False, tail_logs=True, sequential=False) + tee_buffer = io.StringIO() + original_stdout = sys.stdout + original_stderr = sys.stderr + + class _TeeStream: + def __init__(self, real_stream, buf): + self._real = real_stream + self._buf = buf + + def write(self, data): + self._real.write(data) + self._buf.write(data) + + def flush(self): + self._real.flush() + self._buf.flush() + + def __getattr__(self, name): + return getattr(self._real, name) + + sys.stdout = _TeeStream(original_stdout, tee_buffer) + sys.stderr = _TeeStream(original_stderr, tee_buffer) + try: + with run.Experiment("mcore-ci-test", executor=executor, log_level="INFO") as exp: + _ = exp.add([inline_script], tail_logs=False, name="task-1") + + exp.dryrun(log=True) + exp.run(detach=False, tail_logs=True, sequential=False) + finally: + sys.stdout = original_stdout + sys.stderr = original_stderr result_dict = exp.status(return_dict=True) _, job_dict = list(result_dict.items())[0] @@ -150,8 +180,8 @@ def main( sys.exit(0) logger.error(f"Job failed with status: {job_dict['status']}") - log_file_paths = pathlib.Path(os.getcwd()).glob("assets_dir/logs/*/*/attempt_0/*/std*.log") - all_ranks_all_logs = [] + log_file_paths = pathlib.Path(os.getcwd()).glob("**/attempt_0/*/std*.log") + all_ranks_all_logs = [tee_buffer.getvalue()] for log_file_path in log_file_paths: with open(log_file_path, "r") as f: all_logs = f.readlines() diff --git a/tests/test_utils/recipes/gb200/gpt-1node.yaml b/tests/test_utils/recipes/gb200/gpt-1node.yaml index 67375f6a97b..0776a1298a8 100644 --- a/tests/test_utils/recipes/gb200/gpt-1node.yaml +++ b/tests/test_utils/recipes/gb200/gpt-1node.yaml @@ -146,7 +146,7 @@ products: - test_case: [gpt3_mcore_te_tp1_pp4_vp1_1node] products: - environment: [dev] - scope: [mr-github-broken] + scope: [mr-github] platforms: [dgx_gb200] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_resume_torch_decoupled_lr_1node] products: @@ -166,7 +166,7 @@ products: - test_case: [gpt3_mcore_te_tp1_pp4_vp1_tunable_overlap_1node] products: - environment: [dev] - scope: [mr-github-broken] + scope: [mr-github] platforms: [dgx_gb200] - test_case: [gpt3_mcore_te_tp1_pp4_vp1_uneven_pipeline_1node] products: From 77e2dd46e39e6ee829af06d7ffc0153055d2c189 Mon Sep 17 00:00:00 2001 From: Philip Petrakian Date: Tue, 21 Apr 2026 16:57:05 -0700 Subject: [PATCH 60/72] Fix local docs building (#4416) --- docs/conf.py | 2 +- docs/developer/generate_docs.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index a83ced9a93c..47532648b15 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -109,7 +109,7 @@ "icon": "fa-brands fa-github", } ], - "public_docs_features": True + "public_docs_features": os.environ.get("SKIP_PUBLIC_DOCS_FEATURES", "false").lower() != "true", } html_extra_path = ["project.json", "versions1.json"] diff --git a/docs/developer/generate_docs.md b/docs/developer/generate_docs.md index d985f542caa..810c630681e 100644 --- a/docs/developer/generate_docs.md +++ b/docs/developer/generate_docs.md @@ -13,7 +13,7 @@ To generate docs locally, use the following commands: ``` cd docs -uv run --only-group docs sphinx-autobuild . _build/html --port 8080 --host 127.0.0.1 +SKIP_PUBLIC_DOCS_FEATURES=true uv run --only-group docs sphinx-autobuild . _build/html --port 8080 --host 127.0.0.1 ``` Docs will be generated at . From e7789676fda157e15cd16871a40404477cd5a452 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Tue, 21 Apr 2026 21:38:31 -0500 Subject: [PATCH 61/72] RL: Onload optimizer after logprobs computation (#4235) --- megatron/rl/rl_utils.py | 16 +++++++++------- megatron/training/training.py | 1 + 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index f82834606fd..29af4b532b4 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -577,13 +577,6 @@ def get_environment_rollouts( torch.distributed.broadcast_object_list(rollouts, src=0) logger.debug(f"Got rollouts on rank {rank}") - if args.rl_offload_optimizer_during_inference: - with nvtx_range("rl/restore-optimizer-after-inference", time=True): - with nvtx_range("rl/restore/grad-buffers", time=True): - model[0].restore_grad_buffers() - with nvtx_range("rl/restore/optimizer-state", time=True): - optimizer.restore_from_cpu() - if lang_rl_log_dir and rank == get_pg_rank(inference_pg_collection.tp): with open( lang_rl_log_dir @@ -1564,6 +1557,7 @@ def get_grpo_data_iterator( sequence_packing: bool, is_correction: bool, buffered_rollouts: RerunDataIterator | None = None, + optimizer_is_on_cpu: bool = False, ) -> RerunDataIterator: """ Get the data iterator for GRPO training. @@ -1583,6 +1577,7 @@ def get_grpo_data_iterator( sequence_packing: Use sequence packing if True. is_correction: Use IS correction if True. buffered_rollouts: Previously collected rollouts (if any) + optimizer_is_on_cpu: If True, the optimizer was offloaded to CPU and must be restored. Returns: RerunDataIterator for the current training step @@ -1609,6 +1604,13 @@ def get_grpo_data_iterator( sequence_packing=sequence_packing, is_correction=is_correction, ) + if optimizer_is_on_cpu: + nvtx_range = get_nvtx_range() + with nvtx_range("rl/restore-optimizer-after-inference", time=True): + with nvtx_range("rl/restore/grad-buffers", time=True): + model[0].restore_grad_buffers() + with nvtx_range("rl/restore/optimizer-state", time=True): + optimizer.restore_from_cpu() runtime_state.group_stats = group_stats runtime_state.example_groups = example_groups runtime_state.reset_iteration_counters(iteration) diff --git a/megatron/training/training.py b/megatron/training/training.py index 121e333cca6..444653b580d 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -3039,6 +3039,7 @@ def trace_handler(p): sequence_packing=args.rl_use_sequence_packing, buffered_rollouts=buffered_rollouts, is_correction=args.rl_inference_logprobs_is_correction, + optimizer_is_on_cpu=args.rl_offload_optimizer_during_inference, ) # Buffered rollouts are used as a state container for setups when # we use previously-generated data for an update. From bbc6b4d14bd8f787b803b3382147dac4cecb20ec Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Wed, 22 Apr 2026 09:34:12 +0000 Subject: [PATCH 62/72] chore: rotate oncall schedule --- .github/oncall_schedule.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/oncall_schedule.json b/.github/oncall_schedule.json index d89323c3dc3..86b1bc73cfb 100644 --- a/.github/oncall_schedule.json +++ b/.github/oncall_schedule.json @@ -1,8 +1,4 @@ [ - { - "user": "Phlip79", - "date": "2026-04-15" - }, { "user": "asolergi-nv", "date": "2026-04-22" @@ -46,5 +42,9 @@ { "user": "maanug-nv", "date": "2026-07-01" + }, + { + "user": "wujingyue", + "date": "2026-07-08" } ] From 7597a0d61ae6008beb5f23944999be4d302b8e80 Mon Sep 17 00:00:00 2001 From: Teodor-Dumitru Ene <34819528+tdene@users.noreply.github.com> Date: Wed, 22 Apr 2026 06:18:38 -0500 Subject: [PATCH 63/72] Add RL token throughput and packing metrics (#3877) Co-authored-by: Jorge Albericio --- megatron/rl/rl_utils.py | 112 ++++++++++++++++++ megatron/rl/sequence_packing_utils.py | 58 +++++++++ megatron/training/training.py | 7 ++ .../rl/test_sequence_packing_utils.py | 44 +++++++ 4 files changed, 221 insertions(+) diff --git a/megatron/rl/rl_utils.py b/megatron/rl/rl_utils.py index 29af4b532b4..8a564315dc3 100644 --- a/megatron/rl/rl_utils.py +++ b/megatron/rl/rl_utils.py @@ -57,6 +57,10 @@ get_sequence_packing_tensorboard_metrics, get_sequence_packing_log_info, get_default_packed_seq_params, + get_packing_actual_tokens, + get_packing_compute_tokens, + get_packing_efficiency, + get_packing_avg_seq_length, update_microbatch_calculator, ) from megatron.rl.agent.api import ( @@ -301,11 +305,26 @@ def __init__(self): self.last_collection_iteration = 0 self.sequences_this_iteration_on_rank = 0 self.latest_batch_num_sequences = 0 + # Derived throughput metrics (set by log_rl_throughput_metrics, read by RLProfiler). + # Per-GPU variants are available via methods that divide by world_size. + self.world_size = None + # batch_size * seq_length / time: nominal throughput based on batch configuration + self.tokens_per_sec = None + # Total tokens in packed bins across all DP ranks / time: what the GPU actually processes. + self.compute_tokens_per_sec = None + # Real non-padding tokens across all DP ranks / time: true useful throughput. + self.actual_tokens_per_sec = None + # Fraction of bin capacity filled with real tokens (actual / total capacity) + self.packing_efficiency = None def reset_iteration_counters(self, iteration): """Reset per-iteration counters.""" self.sequences_this_iteration_on_rank = 0 self.last_collection_iteration = iteration + self.tokens_per_sec = None + self.compute_tokens_per_sec = None + self.actual_tokens_per_sec = None + self.packing_efficiency = None def increment_sequences(self, count): """Increment the sequence counter.""" @@ -322,6 +341,99 @@ def get_rl_runtime_state(): return _rl_runtime_state +def log_rl_throughput_metrics(args, batch_size, elapsed_time_per_iteration, iteration, wandb_writer): + """Compute, log, and store RL token throughput metrics. + + Returns a string fragment to append to the training log line. + Also logs metrics to wandb and stores them on RLRuntimeState for + downstream consumers (e.g. RLProfiler). + """ + log_string = '' + tokens_per_sec = None + tokens_per_sec_per_gpu = None + compute_tokens_per_sec = None + compute_tokens_per_sec_per_gpu = None + actual_tokens_per_sec = None + actual_tokens_per_sec_per_gpu = None + packing_efficiency = None + + if args.seq_length > 0: + tokens_per_iteration = batch_size * args.seq_length + tokens_per_sec = tokens_per_iteration / elapsed_time_per_iteration + tokens_per_sec_per_gpu = tokens_per_sec / args.world_size + + # For sequence packing, break down into compute vs actual tokens + if args.rl_use_sequence_packing: + runtime_state = get_rl_runtime_state() + if runtime_state.packing_context is not None: + dp_world_size = mpu.get_data_parallel_world_size() + + compute_tokens = get_packing_compute_tokens(runtime_state.packing_context) + all_ranks_compute_tokens = compute_tokens * dp_world_size + compute_tokens_per_sec = all_ranks_compute_tokens / elapsed_time_per_iteration + compute_tokens_per_sec_per_gpu = compute_tokens_per_sec / args.world_size + + actual_tokens = get_packing_actual_tokens(runtime_state.packing_context) + all_ranks_actual_tokens = actual_tokens * dp_world_size + actual_tokens_per_sec = all_ranks_actual_tokens / elapsed_time_per_iteration + actual_tokens_per_sec_per_gpu = actual_tokens_per_sec / args.world_size + + packing_efficiency = get_packing_efficiency(runtime_state.packing_context) + + # Add tokens/sec to log string + log_string += f' toks/s: {tokens_per_sec:.0f} |' + log_string += f' toks/s/gpu: {tokens_per_sec_per_gpu:.0f} |' + if compute_tokens_per_sec is not None: + log_string += f' compute_toks/s: {compute_tokens_per_sec:.0f} |' + log_string += f' compute_toks/s/gpu: {compute_tokens_per_sec_per_gpu:.0f} |' + if actual_tokens_per_sec is not None: + log_string += f' actual_toks/s: {actual_tokens_per_sec:.0f} |' + log_string += f' actual_toks/s/gpu: {actual_tokens_per_sec_per_gpu:.0f} |' + log_string += f' packing_eff: {packing_efficiency:.1%} |' + + # Log throughput metrics to wandb + if wandb_writer is not None: + if tokens_per_sec is not None: + wandb_writer.log({ + 'throughput/tokens_per_sec': tokens_per_sec, + 'throughput/tokens_per_sec_per_gpu': tokens_per_sec_per_gpu, + }, iteration) + if compute_tokens_per_sec is not None: + wandb_writer.log({ + 'throughput/compute_tokens_per_sec': compute_tokens_per_sec, + 'throughput/compute_tokens_per_sec_per_gpu': compute_tokens_per_sec_per_gpu, + }, iteration) + if actual_tokens_per_sec is not None: + wandb_writer.log({ + 'throughput/actual_tokens_per_sec': actual_tokens_per_sec, + 'throughput/actual_tokens_per_sec_per_gpu': actual_tokens_per_sec_per_gpu, + 'throughput/packing_efficiency': packing_efficiency, + }, iteration) + + # Store derived throughput metrics on RLRuntimeState so that + # downstream consumers (e.g. RLProfiler) can read them. + # Per-GPU values are derived via methods on RLRuntimeState. + runtime_state = get_rl_runtime_state() + runtime_state.world_size = args.world_size + runtime_state.tokens_per_sec = tokens_per_sec + runtime_state.compute_tokens_per_sec = compute_tokens_per_sec + runtime_state.actual_tokens_per_sec = actual_tokens_per_sec + runtime_state.packing_efficiency = packing_efficiency + + # Log average sequence length. With packing this shows real sequence + # lengths; without packing it equals seq_length as a baseline. + packing_ctx = runtime_state.packing_context + if args.rl_use_sequence_packing and packing_ctx is not None: + avg_seq_length = get_packing_avg_seq_length(packing_ctx) + log_string += f' avg_seq_len: {avg_seq_length:.1f} |' + if wandb_writer is not None: + wandb_writer.log({'throughput/avg_seq_length': avg_seq_length}, iteration) + elif args.log_throughput: + log_string += f' avg_seq_len: {args.seq_length} |' + + return log_string + + def update_inference_logprobs_group_stats( old_logprobs: torch.Tensor, inference_logprobs: torch.Tensor, diff --git a/megatron/rl/sequence_packing_utils.py b/megatron/rl/sequence_packing_utils.py index 9ceb806571c..ff98b0a58e2 100644 --- a/megatron/rl/sequence_packing_utils.py +++ b/megatron/rl/sequence_packing_utils.py @@ -1117,3 +1117,61 @@ def get_sequence_packing_tensorboard_metrics(args): metrics['bin-batch-size'] = bin_batch_size metrics['consumed-bins'] = args.consumed_train_bins return metrics + + +def get_packing_actual_tokens(packing_context: PackingContext) -> int: + """Get the actual number of tokens (non-padding) in the packed sequences for this rank. + + Args: + packing_context: The PackingContext containing packing information. + + Returns: + Total number of actual tokens across all bins on this rank. + """ + return sum( + packing_context.packing_info.seq_lengths[idx] + for indices in packing_context.packing_info.bin_seq_indices + for idx in indices + ) + + +def get_packing_compute_tokens(packing_context: PackingContext) -> int: + """Get the total compute tokens (including padding) for packed sequences on this rank. + + Args: + packing_context: The PackingContext containing packing information. + + Returns: + Total compute tokens (num_bins * bin_size) on this rank. + """ + return packing_context.packed_trajs.shape[0] * packing_context.packed_trajs.shape[1] + + +def get_packing_efficiency(packing_context: PackingContext) -> float: + """Get the packing efficiency (actual_tokens / total_capacity) across all DP ranks. + + Args: + packing_context: The PackingContext containing packing information. + + Returns: + Packing efficiency as a float between 0 and 1. + """ + total_actual_tokens = sum(packing_context.packing_info.seq_lengths) + num_ranks = mpu.get_data_parallel_world_size() + bins_per_rank = packing_context.packed_trajs.shape[0] + bin_size = packing_context.packed_trajs.shape[1] + total_capacity = bins_per_rank * bin_size * num_ranks + + if total_capacity == 0: + return 0.0 + + return total_actual_tokens / total_capacity + + +def get_packing_avg_seq_length(packing_context: PackingContext) -> float: + """Get the average sequence length across all sequences in the packing context.""" + seq_lengths = packing_context.packing_info.seq_lengths + if not seq_lengths: + return 0.0 + + return sum(seq_lengths) / len(seq_lengths) diff --git a/megatron/training/training.py b/megatron/training/training.py index 444653b580d..156fff6fb58 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -2293,6 +2293,13 @@ def training_log( total_loss_dict[skipped_iters_key] ) log_string += ' number of nan iterations: {:3d} |'.format(total_loss_dict[nan_iters_key]) + + # RL token throughput metrics. + if args.perform_rl_step: + log_string += rl_utils.log_rl_throughput_metrics( + args, batch_size, elapsed_time_per_iteration, iteration, wandb_writer, + ) + if should_reset: total_loss_dict[advanced_iters_key] = 0 total_loss_dict[skipped_iters_key] = 0 diff --git a/tests/unit_tests/rl/test_sequence_packing_utils.py b/tests/unit_tests/rl/test_sequence_packing_utils.py index bb03be57cb3..687e3aef434 100644 --- a/tests/unit_tests/rl/test_sequence_packing_utils.py +++ b/tests/unit_tests/rl/test_sequence_packing_utils.py @@ -409,6 +409,50 @@ def test_compute_packed_inference_logprobs_stats_shape_mismatch(): assert group_stats.mean_piold_to_inf_prob is None +def test_packing_observability_metrics(): + """Test various observability metrics related to sequence packing.""" + + # 4 sequences with known lengths packed into 2 bins of size 16. + # Bin 0 holds seqs 0 (len 5) and 1 (len 3) → 8 actual tokens + # Bin 1 holds seqs 2 (len 10) and 3 (len 4) → 14 actual tokens + seq_lengths = [5, 3, 10, 4] + packing_info = sequence_packing_utils.PackingInfo( + bin_seq_indices=[[0, 1], [2, 3]], + seq_starts={0: [0, 5], 1: [0, 10]}, + seq_lengths=seq_lengths, + seq_to_bin_idx=[0, 0, 1, 1], + packing_algo='fifo', + ) + + num_bins, bin_size = 2, 16 + packed_trajs = torch.zeros(num_bins, bin_size, dtype=torch.long) + ctx = sequence_packing_utils.PackingContext( + bin_size=bin_size, + packer=None, + packing_info=packing_info, + original_generation_masks=None, + original_trajs=None, + packed_trajs=packed_trajs, + packed_position_ids=None, + packed_loss_mask=None, + ) + + # actual tokens = sum of all seq_lengths referenced by bin_seq_indices + assert sequence_packing_utils.get_packing_actual_tokens(ctx) == 5 + 3 + 10 + 4 + + # compute tokens = num_bins * bin_size + assert sequence_packing_utils.get_packing_compute_tokens(ctx) == 2 * 16 + + # avg seq length = mean of seq_lengths + assert sequence_packing_utils.get_packing_avg_seq_length(ctx) == pytest.approx(22 / 4) + + # efficiency = total_actual / (bins_per_rank * bin_size * num_ranks) + with patch('megatron.core.mpu.get_data_parallel_world_size', return_value=4): + eff = sequence_packing_utils.get_packing_efficiency(ctx) + # total_actual = sum(seq_lengths) = 22, capacity = 2 * 16 * 4 = 128 + assert eff == pytest.approx(22 / 128) + + @pytest.mark.parametrize("num_sequences", [1, 10, 48, 49, 50]) def test_cu_seqlens_size(num_sequences): """Test that cu_seqlens always has a fixed size regardless of how many sequences are packed.""" From 9834c995b357bdcdca25fe92ac62832d2fe8f280 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?oliver=20k=C3=B6nig?= Date: Wed, 22 Apr 2026 15:51:37 +0200 Subject: [PATCH 64/72] ci: remove publish:merge_into_dev job (#4421) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: oliver könig --- .gitlab/stages/05.publish.yml | 71 ----------------------------------- 1 file changed, 71 deletions(-) diff --git a/.gitlab/stages/05.publish.yml b/.gitlab/stages/05.publish.yml index 862572b4261..de30147bfd1 100644 --- a/.gitlab/stages/05.publish.yml +++ b/.gitlab/stages/05.publish.yml @@ -75,77 +75,6 @@ publish:upload_statistics: allow_failure: true - when: never -publish:merge_into_dev: - stage: publish - image: ${CI_MCORE_DEV_IMAGE}:${CI_PIPELINE_ID} - script: - - export GITLAB_ENDPOINT - - export RO_API_TOKEN=${PAT} - - | - git config --global user.email "mcore-bot@nvidia.com" - git config --global user.name "Mcore Bot" - - SOURCE_BRANCH=ci/merge-into-dev - - | - set -x - set +e - - SOURCE_BRANCH_EXISTS=$([[ "$(git ls-remote --heads origin refs/heads/$SOURCE_BRANCH)" != "" ]] && echo true || echo false) - - if [[ "$SOURCE_BRANCH_EXISTS" == "false" ]]; then - git fetch origin dev - git checkout -b $SOURCE_BRANCH origin/dev - else - git fetch origin $SOURCE_BRANCH - git checkout origin/$SOURCE_BRANCH - fi - - git fetch origin main - git merge origin/main - CLEAN=$? - set -e - - | - if [[ "$CLEAN" -ne 0 ]]; then - echo "Merge failed" - URL="https://${GITLAB_ENDPOINT}/${CI_PROJECT_PATH}/-/commit/${CI_COMMIT_SHA}" - SHORT_SHA=$(git rev-parse --short HEAD) - MESSAGE='{ - "blocks": [ - { - "type": "section", - "text": { - "type": "mrkdwn", - "text": "beep boop 🤖: Cherry-picking main (<'$URL'|'${SHORT_SHA}'>) into dev failed.\nPlease merge it manually into '$SOURCE_BRANCH'.\n\ncc '$SLACK_ADMIN_DEV'" - } - } - ] - }' - - curl -X POST -H "Content-type: application/json" --data "$MESSAGE" ${MCORE_NOTIFICATION_HOOK_DEV} - - exit 1 - fi - - git push -u origin ci/merge-into-dev - - | - curl \ - --header "PRIVATE-TOKEN: $PROJECT_ACCESS_TOKEN_MCORE" \ - --url https://${GITLAB_ENDPOINT}/api/v4/projects/${CI_PROJECT_ID}/merge_requests \ - -d "source_branch=$SOURCE_BRANCH" \ - -d "target_branch=dev" \ - -d "title=chore: Merge into dev" \ - -d "labels=test::Run functional tests" \ - -d "merge_when_pipeline_succeeds=true" \ - -d "description=[🤖]: Hi @zijiey 👋,

merging \`$SOURCE_BRANCH\` into \`dev\` for you! 🚀

Please review and approve this cherry pick by your convenience\!" - tags: - - arch/amd64 - - env/prod - - origin/jet-fleet - - owner/jet-core - - purpose/utility - - team/megatron - rules: - - if: $CI_COMMIT_BRANCH == "main" && $CI_PIPELINE_SOURCE == "push" - allow_failure: true - - when: never publish:approve_merge_gate: stage: publish From a6bfe1abf501f48fd14d5c10a8dccf7a12f4b638 Mon Sep 17 00:00:00 2001 From: Santosh Bhavani Date: Wed, 22 Apr 2026 08:45:45 -0700 Subject: [PATCH 65/72] docs: add data loading best practices for large-scale training (#4236) Co-authored-by: Antoni-Joan Solergibert --- docs/index.md | 3 +- docs/user-guide/data-loading.md | 150 ++++++++++++++++++++++++++++++++ 2 files changed, 152 insertions(+), 1 deletion(-) create mode 100644 docs/user-guide/data-loading.md diff --git a/docs/index.md b/docs/index.md index 4b75ed2c0c8..0dbf7d2e3b7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -72,6 +72,7 @@ user-guide/features/dist_optimizer user-guide/features/optimizer_cpu_offload user-guide/features/pipeline_parallel_layout user-guide/features/fine_grained_activation_offloading +user-guide/data-loading user-guide/features/megatron_energon user-guide/features/megatron_rl user-guide/features/tokenizers @@ -103,4 +104,4 @@ apidocs/index.rst :caption: Resources advanced/index -``` \ No newline at end of file +``` diff --git a/docs/user-guide/data-loading.md b/docs/user-guide/data-loading.md new file mode 100644 index 00000000000..1f0d544317c --- /dev/null +++ b/docs/user-guide/data-loading.md @@ -0,0 +1,150 @@ + + +# Data Loading at Scale + +This guide covers how Megatron's data pipeline works and how to configure it for efficient training at 256 nodes and beyond. At this scale, the primary bottlenecks are **index building** and **barrier synchronization** -- not raw data bandwidth. + +## How Data Loading Works + +Understanding the architecture helps explain why specific flags matter. + +Megatron builds three index arrays for each dataset: a **document index** (shuffled document order), a **sample index** (mapping samples to document offsets), and a **shuffle index** (final sample permutation). This happens once during initialization: + +1. **Rank 0** builds all three indices and writes them to a cache directory as `.npy` files. +2. All ranks synchronize at a `torch.distributed.barrier()`. +3. **All other ranks** load the cached indices via memory-mapped reads (`numpy.load(mmap_mode='r')`). + +After initialization, data access is **read-only and lock-free**. Each data-parallel rank consumes a disjoint subset of samples, and no cross-rank coordination is needed during training because all ranks derive the same deterministic permutation from a shared random seed. + +## The Problem at 256+ Nodes + +Three things break down at large node counts: + +1. **Barrier synchronization**: All ranks block while rank 0 builds indices. On a 512-node job, this means 4,095 GPUs sit idle. +2. **Simultaneous memory-mapping**: All ranks `mmap` three large `.npy` files at once after the barrier, causing a burst of page faults and I/O. + +## Baseline: Establish Maximum Achievable Performance + +Before tuning data loading, establish a performance ceiling by running with `--mock-data`. This bypasses the data pipeline entirely and shows the maximum throughput your configuration can achieve without any dataloader overhead. The gap between `--mock-data` performance and real-data performance tells you exactly how much time the dataloader is costing you. + +## Recommended Configuration + +### Step 1: Consolidate dataset files + +A common issue at scale is having datasets split across many small file prefixes. Thousands of 100 MB files perform significantly worse than tens of 10 GB+ files, both for building dataset caches and for runtime file access. + +Use the merge tool to consolidate datasets stored as many small prefixes in one directory: + +```bash +python tools/merge_datasets.py \ + --input /path/to/input-directory \ + --output-prefix /path/to/output/merged +``` + +**Target at least 10 GB per file.** This reduces the number of file descriptors, metadata lookups, and index-building work at initialization. + +### Step 2: Pre-build the dataset cache + +Build the GPT dataset cache as a separate step before training. This avoids the usual "rank 0 builds, everyone else waits" startup path and is the recommended workflow for large jobs: + +```bash +python tools/prepare_cache.py \ + --data-path \ + --split 99,1,0 \ + --data-cache-path /path/to/cache \ + --global-batch-size \ + --seq-length \ + ... +``` + +If your later training job does not set `--global-batch-size`, or you are preparing the cache on a machine that does not match the future training topology, also pass: + +```bash +--prepare-cache-world-size +``` + +This keeps the prepared cache aligned with the sample counts expected by training. + +### Step 3: Optionally pre-build per-dataset metadata + +When blending many datasets, generate the `--per-dataset-sequences-path` JSON ahead of time to avoid one metadata read per file prefix at startup: + +```bash +python tools/build_sequences_per_dataset.py \ + --data-path \ + --per-dataset-sequences-path sequences.json +``` + +### Step 4: Launch training with optimized data loading + +Once the cache is ready, enable the fast-path flags: + +```bash +torchrun --nproc_per_node=8 --nnodes=512 ... pretrain_gpt.py \ + --dataloader-fast-cache-load \ + --dataloader-defer-npy-index-mmap \ + --per-dataset-sequences-path sequences.json \ + --data-cache-path /path/to/cache \ + --num-workers 2 \ + ... +``` + +### Flag reference + +| Flag | Default | Recommendation | What it does | +|------|---------|----------------|-------------| +| `--dataloader-fast-cache-load` | off | **On** | Skips the rank-0 barrier by assuming the cache already exists. All ranks build their dataset views in parallel. This is the single biggest win at scale. | +| `--dataloader-defer-npy-index-mmap` | off | **On** | Defers memory-mapping of `.npy` index files until first access. When combined with `--num-workers > 0`, index loading is overlapped with the training iteration rather than blocking startup. | +| `--per-dataset-sequences-path` | None | **Set when blending many datasets** | Points to a JSON file mapping each dataset path to its `(sequence_count, document_count)`. Replaces per-file metadata reads with a single JSON lookup. Generate with `tools/build_sequences_per_dataset.py`. | +| `--data-cache-path` | None | **Set** | Directory where index `.npy` files are cached. Must be on shared storage for multi-node jobs so all ranks can read it. | +| `--num-workers` | 2 | **Keep as small as necessary** | Number of DataLoader worker processes. The goal is to satisfy: *time to process a batch > time to prepare a batch*. This hides dataloader work behind the training step. Increasing beyond what's needed wastes CPU and memory. | +| `--no-mmap-bin-files` | mmap on | **Test both** | Memory-mapping `.bin` files leverages the OS page cache, but the optimal setting is filesystem-dependent. Some large-scale production configurations disable mmap. Test with and without to determine what works best for your storage. | + +### Object storage (S3 / Multi-Storage Client) + +When data lives on S3 or MSC rather than a POSIX filesystem: + +- **Index files** (`.idx`) are cached locally under `object_storage_cache_path`. +- **Binary data files** (`.bin`) are streamed on-demand in 256 MB chunks, avoiding the need to download entire files. +- Set `--no-mmap-bin-files` since memory-mapping doesn't apply to object storage. +- Ensure the index-cache path is visible wherever the later dataset construction will run. + +## Scaling Characteristics + +| Aspect | Behavior | Why it works | +|--------|----------|-------------| +| **Cross-rank contention** | None after init | All index files are read-only; `numpy.memmap` uses OS page cache with no locking | +| **Sampling determinism** | All ranks produce the same permutation | Shared `numpy.random.RandomState(seed)` with epoch-based seed variation | +| **Data-parallel sharding** | Each DP rank gets a disjoint subset of samples | No overlap during training; assignment happens in the sampler rather than via extra dataset coordination | +| **Index broadcast** | Via shared filesystem, not collectives | Rank 0 writes `.npy` files; other ranks read them. No explicit `torch.distributed.broadcast` | + +## Troubleshooting + +**Symptom: Training hangs at startup for minutes** +- Likely cause: Rank 0 is building indices while all other ranks wait at the barrier. +- Fix: Pre-build the cache with `tools/prepare_cache.py` and enable `--dataloader-fast-cache-load`. + +**Symptom: Spike in I/O at training start, then normal** +- Likely cause: All ranks simultaneously memory-mapping index files after the barrier. +- Fix: Enable `--dataloader-defer-npy-index-mmap` to overlap index loading with training. + +**Symptom: Slow data loading during training (not just startup)** +- Run with `--mock-data` to confirm the dataloader is the bottleneck. +- If startup, not steady-state throughput, is the main issue, try `--dataloader-defer-npy-index-mmap`. +- If you are blending many dataset prefixes, try `--per-dataset-sequences-path`. +- Test with `--no-mmap-bin-files` -- the optimal setting depends on your filesystem. + +## Related Resources + +- [PR #2445](https://github.com/NVIDIA/Megatron-LM/pull/2445): Original implementation of fast cache load, deferred mmap, and per-dataset sequences optimizations. +- [PR #4080](https://github.com/NVIDIA/Megatron-LM/pull/4080): Adds `tools/prepare_cache.py` for offline GPT dataset cache preparation. +- [`tools/prepare_cache.py`](https://github.com/NVIDIA/Megatron-LM/blob/main/tools/prepare_cache.py): Pre-build GPT dataset caches ahead of training. +- [`tools/merge_datasets.py`](https://github.com/NVIDIA/Megatron-LM/blob/main/tools/merge_datasets.py): Merge multiple small dataset files into larger ones. +- [`tools/build_sequences_per_dataset.py`](https://github.com/NVIDIA/Megatron-LM/blob/main/tools/build_sequences_per_dataset.py): Generate the `--per-dataset-sequences-path` JSON file. From 384e6183fe4daec2b142409ed098c9c453576101 Mon Sep 17 00:00:00 2001 From: Youngeun Kwon Date: Wed, 22 Apr 2026 10:54:04 -0700 Subject: [PATCH 66/72] Fix: Auto enable manual registration and enhance the docummentation (#3295) Signed-off-by: Youngeun Kwon --- megatron/core/distributed/fsdp/src/README.md | 16 ++++++++++++++-- megatron/training/arguments.py | 8 +++++++- megatron/training/training.py | 7 ++++--- 3 files changed, 25 insertions(+), 6 deletions(-) diff --git a/megatron/core/distributed/fsdp/src/README.md b/megatron/core/distributed/fsdp/src/README.md index 3603fc105a2..98c01a759eb 100644 --- a/megatron/core/distributed/fsdp/src/README.md +++ b/megatron/core/distributed/fsdp/src/README.md @@ -333,10 +333,22 @@ Megatron-FSDP's `fully_shard_*` API has a comprehensive set of arguments for fin - **Only effective when using with Megatron-Core.** - Defaults to `False`. - By default we try to use NCCL window (symmetric) registration if it is available. If not it falls back to conventional local registration. -- `fsdp_manual_registration` will manually register the FSDP communication buffers with the NCCL user buffer. For symmetric registration with large models, the registration itself can take a significant amount of time. This option minimizes the number of registration calls to reduce the registration time. However, with this option enabled, you need to manually call the `ParamAndGradBuffer.manual_buffer_registration()` function after the first iteration. This is already implemented in the Megatron-LM training loop. In other use cases, users are expected to call this function themselves. +- `fsdp_manual_registration` will manually register the FSDP communication buffers with the NCCL user buffer. For symmetric registration with large models, the registration itself can take a significant amount of time. This option minimizes the number of registration calls to reduce the registration time. However, with this option enabled, you need to manually call the `ParamAndGradBuffer.manual_buffer_registration()` function after the first iteration. This is already implemented in the Megatron-LM training loop. In other use cases, users are expected to call this function themselves. + - This is an example of required modification in the training loop. + ```python + def train(...): + ... + # After the first iteration, user need to call the + # ParamAndGradBuffer.manual_buffer_registration() function in the training loop + if (iteration == start_iteration + 1): + if isinstance(model, megatron_FSDP) and model.ddp_config.fsdp_manual_registration: + param_and_grad_buffer = getattr(model, "param_and_grad_buffer", None) + if param_and_grad_buffer is not None: + param_and_grad_buffer.manual_buffer_registration() + ``` - **Only effective when using with Megatron-Core.** - This option is only effective when `nccl_ub` is enabled. - - Defaults to `False`. + - Defaults to `False`, but will be automatically enabled in Megatron-LM. - `disable_symmetric_registration` will disable NCCL window (i.e. symmetric) registration when using `nccl_ub`. - Defaults to `False`. - `fsdp_double_buffer` will use persistently allocated double buffers for temporarily-defined memory needed in `MegatronFSDP` communications. Having persistent double buffers may increase peak VRAM utilization, but is required to register NCCL user buffers (`nccl_ub=True`) for `MegatronFSDP`. Currently, this is only supported for simple repetitive model structures such as GPT. diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py index 4e8de13bd54..ba4099c3b71 100644 --- a/megatron/training/arguments.py +++ b/megatron/training/arguments.py @@ -1042,7 +1042,13 @@ def validate_args(args, defaults={}): assert args.ckpt_format == "fsdp_dtensor", \ "Megatron-FSDP requires the `fsdp_dtensor` checkpointing format." - + + if args.nccl_ub and args.use_megatron_fsdp: + # In Megatron-LM, required implementation for manual registration is already provided. + # So we enable the manual registration by default when nccl-ub and use_megatron_fsdp is set. + args.fsdp_manual_registration = True + warn_rank_0('FSDP manual registration is enabled by default when nccl-ub is enabled') + if args.fsdp_manual_registration: assert args.use_megatron_fsdp, "FSDP manual registration is only supported with Megatron FSDP." assert args.nccl_ub, "FSDP manual registration is only supported with --nccl-ub argument." diff --git a/megatron/training/training.py b/megatron/training/training.py index 156fff6fb58..d8825a7b52f 100644 --- a/megatron/training/training.py +++ b/megatron/training/training.py @@ -3122,6 +3122,7 @@ def trace_handler(p): # If requested, manually register FSDP communication buffers after a short warmup. if ( getattr(args, "fsdp_manual_registration", False) + and getattr(args, "nccl_ub", False) and getattr(args, "use_megatron_fsdp", False) and iteration == start_iteration + 1 ): @@ -3129,9 +3130,9 @@ def trace_handler(p): if isinstance(model_chunk, megatron_FSDP) and getattr( model_chunk.ddp_config, "fsdp_manual_registration", False ): - pad_buf = getattr(model_chunk, "param_and_grad_buffer", None) - if pad_buf is not None: - pad_buf.manual_buffer_registration() + param_and_grad_buffer = getattr(model_chunk, "param_and_grad_buffer", None) + if param_and_grad_buffer is not None: + param_and_grad_buffer.manual_buffer_registration() if args.perform_rl_step and args.rl_use_sequence_packing: iteration_sequences = rl_utils.get_iteration_sequence_count(args) From 8add4e4ed13c334bdff6d01835aaadc1bbaa7ebf Mon Sep 17 00:00:00 2001 From: "claude[bot]" <41898282+claude[bot]@users.noreply.github.com> Date: Wed, 22 Apr 2026 20:24:50 +0000 Subject: [PATCH 67/72] chore: post-merge fixes for nightly sync main into dev (22_04_2026) - Restore dev's pyproject.toml, uv.lock, and Dockerfile.ci.dev - Update nvidia-resiliency-ext to main's revision (required for get_write_results_queue) - Fix hybrid_model.py: init_chunk_handler() missing pp_rank, delta_offload_bytes_across_pp_ranks, activation_offload_fraction params - Fix hybrid_model.py: mark_not_offloadable() -> mark_not_offload() - Run black + isort on all changed Python files Co-Authored-By: Claude Opus 4.6 (1M context) --- docker/Dockerfile.ci.dev | 2 +- docs/conf.py | 19 +- .../detxoify_lm/generate_samples_gpt.py | 170 +-- .../inference/gpt/gpt_dynamic_inference.py | 17 +- .../gpt_dynamic_inference_with_coordinator.py | 22 +- .../inference/gpt/gpt_static_inference.py | 2 +- examples/mimo/train.py | 95 +- examples/multimodal/run_text_generation.py | 191 ++- examples/rl/environments/math/aime_agent.py | 4 +- .../rl/environments/math/bigmath_agent.py | 4 +- examples/rl/environments/math/dapo_agent.py | 4 +- examples/rl/environments/math/gsm8k_agent.py | 12 +- examples/rl/environments/math/math_agent.py | 17 +- .../rl/environments/math/openmath_agent.py | 4 +- examples/run_simple_mcore_train_loop.py | 72 +- megatron/core/models/hybrid/hybrid_model.py | 9 +- megatron/core/transformer/attention.py | 8 +- megatron/legacy/model/transformer.py | 769 +++++++----- megatron/rl/agent/api.py | 23 +- megatron/rl/agent/reward_only_agent.py | 22 +- megatron/rl/inference/megatron.py | 28 +- megatron/rl/rl_utils.py | 534 ++++---- megatron/rl/sequence_packing_utils.py | 208 ++-- megatron/training/activation_logging.py | 60 +- megatron/training/arguments.py | 372 ++++-- megatron/training/config/training_config.py | 11 +- megatron/training/dgrad_logging.py | 29 +- megatron/training/training.py | 78 +- megatron/training/yaml_arguments.py | 13 +- pretrain_bert.py | 26 +- pretrain_gpt.py | 84 +- pretrain_mamba.py | 61 +- pretrain_vlm.py | 140 ++- pyproject.toml | 1 + .../dist_checkpointing/test_optimizer.py | 4 +- .../test_pipeline_parallel_layout.py | 4 +- .../pipeline_parallel/test_pipeline_layout.py | 8 +- tools/checkpoint/remap_gpt_dsa_to_mamba.py | 35 +- tools/run_inference_performance_test.py | 8 +- tools/run_text_generation_server.py | 13 +- tools/run_vlm_text_generation.py | 6 +- train_rl.py | 22 +- uv.lock | 1084 +++++++++-------- 43 files changed, 2393 insertions(+), 1902 deletions(-) diff --git a/docker/Dockerfile.ci.dev b/docker/Dockerfile.ci.dev index 807fe3a2bdb..7f3a5c0552a 100644 --- a/docker/Dockerfile.ci.dev +++ b/docker/Dockerfile.ci.dev @@ -75,7 +75,7 @@ RUN bash -ex <<"EOF" git clone --branch hybrid-ep https://github.com/deepseek-ai/DeepEP.git pushd DeepEP - git checkout 34152ae28f80bcc3ee38d7a12cb2ad87cfd4ea72 + git checkout eb9cee7de5a24193bf09500668d3a619d3d3f3fb patch -p1 < /workspace/deepep.patch popd TORCH_CUDA_ARCH_LIST="9.0 10.0 12.0" uv pip install --no-build-isolation -v DeepEP/. diff --git a/docs/conf.py b/docs/conf.py index 47532648b15..26b618b1eac 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,7 +20,6 @@ import os import sys - # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information @@ -84,24 +83,17 @@ # This is a workaround that uses the parser located in autodoc2_docstrings_parser.py to allow autodoc2 to # render google style docstrings. # Related Issue: https://github.com/sphinx-extensions2/sphinx-autodoc2/issues/33 - autodoc2_docstring_parser_regexes = [ - (r".*", "docs.autodoc2_docstrings_parser"), - ] + autodoc2_docstring_parser_regexes = [(r".*", "docs.autodoc2_docstrings_parser")] # Regex patterns whose values contain raw regex syntax (e.g. \p{L}) that docutils # mis-parses as footnote/reference markup. Exclude them from the generated docs. - autodoc2_hidden_regexes = [ - r".*\._PATTERN_TIKTOKEN.*", - ] + autodoc2_hidden_regexes = [r".*\._PATTERN_TIKTOKEN.*"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "nvidia_sphinx_theme" html_theme_options = { - "switcher": { - "json_url": "../versions1.json", - "version_match": release, - }, + "switcher": {"json_url": "../versions1.json", "version_match": release}, "icon_links": [ { "name": "GitHub", @@ -114,7 +106,4 @@ html_extra_path = ["project.json", "versions1.json"] # Github links are now getting rate limited from the Github Actions -linkcheck_ignore = [ - ".*github\\.com.*", - ".*githubusercontent\\.com.*", -] +linkcheck_ignore = [".*github\\.com.*", ".*githubusercontent\\.com.*"] diff --git a/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py b/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py index 183c6c695bd..a8b72bb39ae 100644 --- a/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py +++ b/examples/academic_paper_scripts/detxoify_lm/generate_samples_gpt.py @@ -6,28 +6,33 @@ import json import os import sys -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), - os.path.pardir, os.path.pardir))) + +sys.path.append( + os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)) +) +from typing import Union + import torch -from megatron.training import get_args -from megatron.training import get_tokenizer -from megatron.training import print_rank_0 -from megatron.training.checkpointing import load_checkpoint + +import megatron.legacy.model from megatron.core import mpu -from megatron.training.arguments import parse_and_validate_args -from megatron.training.initialize import initialize_megatron -from megatron.legacy.model import GPTModel -from megatron.training import get_model -from megatron.inference.text_generation import generate_and_post_process -from megatron.training.arguments import core_transformer_config_from_args from megatron.core.models.gpt import GPTModel -from typing import Union -import megatron.legacy.model +from megatron.core.models.gpt.gpt_layer_specs import ( + get_gpt_layer_local_spec, + get_gpt_layer_with_transformer_engine_spec, +) from megatron.core.transformer.spec_utils import import_module -from megatron.training.arguments import core_transformer_config_from_args -from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec, get_gpt_layer_local_spec +from megatron.inference.text_generation import generate_and_post_process +from megatron.legacy.model import GPTModel +from megatron.training import get_args, get_model, get_tokenizer, print_rank_0 +from megatron.training.arguments import core_transformer_config_from_args, parse_and_validate_args +from megatron.training.checkpointing import load_checkpoint +from megatron.training.initialize import initialize_megatron -def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megatron.legacy.model.GPTModel]: + +def model_provider( + pre_process=True, post_process=True +) -> Union[GPTModel, megatron.legacy.model.GPTModel]: """Builds the model. If you set the use_legacy_models to True, it will return the legacy GPT model and if not the core GPT model. @@ -51,26 +56,23 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat num_tokentypes=0, parallel_output=False, pre_process=pre_process, - post_process=post_process + post_process=post_process, ) else: if args.spec is None: if args.transformer_impl == 'local': transformer_layer_spec = get_gpt_layer_local_spec( - num_experts=args.num_experts, - moe_grouped_gemm=args.moe_grouped_gemm + num_experts=args.num_experts, moe_grouped_gemm=args.moe_grouped_gemm ) elif args.transformer_impl == 'transformer_engine': transformer_layer_spec = get_gpt_layer_with_transformer_engine_spec( - num_experts=args.num_experts, - moe_grouped_gemm=args.moe_grouped_gemm + num_experts=args.num_experts, moe_grouped_gemm=args.moe_grouped_gemm ) else: raise ValueError(f"Invalid transformer_impl {args.transformer_impl}") elif args.spec[0] == 'local': transformer_layer_spec = get_gpt_layer_local_spec( - num_experts=args.num_experts, - moe_grouped_gemm=args.moe_grouped_gemm + num_experts=args.num_experts, moe_grouped_gemm=args.moe_grouped_gemm ) else: transformer_layer_spec = import_module(args.spec) @@ -86,37 +88,46 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat parallel_output=False, share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights, position_embedding_type=args.position_embedding_type, - rotary_percent=args.rotary_percent + rotary_percent=args.rotary_percent, ) return model + def add_text_generate_args(parser): """Text generation arguments.""" group = parser.add_argument_group(title='text generation') - group.add_argument("--temperature", type=float, default=1.0, - help='Sampling temperature.') - group.add_argument("--greedy", action='store_true', default=False, - help='Use greedy sampling.') - group.add_argument("--top_p", type=float, default=0.0, - help='Top p sampling.') - group.add_argument("--top_k", type=int, default=0, - help='Top k sampling.') - group.add_argument("--out-seq-length", type=int, default=1024, - help='Size of the output generated text.') - group.add_argument("--sample-input-file", type=str, default=None, - help='Get input from file instead of interactive mode, ' - 'each line is an input.') - group.add_argument("--sample-output-file", type=str, default=None, - help='Output file got from --sample-input-file') - group.add_argument("--num-samples", type=int, default=0, - help='Number of samples to generate unconditionally, ' - 'defaults to 0 and interactive conditional sampling') - group.add_argument("--genfile", type=str, - help='Output file when generating unconditionally') + group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.') + group.add_argument("--greedy", action='store_true', default=False, help='Use greedy sampling.') + group.add_argument("--top_p", type=float, default=0.0, help='Top p sampling.') + group.add_argument("--top_k", type=int, default=0, help='Top k sampling.') + group.add_argument( + "--out-seq-length", type=int, default=1024, help='Size of the output generated text.' + ) + group.add_argument( + "--sample-input-file", + type=str, + default=None, + help='Get input from file instead of interactive mode, ' 'each line is an input.', + ) + group.add_argument( + "--sample-output-file", + type=str, + default=None, + help='Output file got from --sample-input-file', + ) + group.add_argument( + "--num-samples", + type=int, + default=0, + help='Number of samples to generate unconditionally, ' + 'defaults to 0 and interactive conditional sampling', + ) + group.add_argument("--genfile", type=str, help='Output file when generating unconditionally') return parser + def generate_samples_unconditional(model): args = get_args() @@ -124,6 +135,7 @@ def generate_samples_unconditional(model): cnt = 0 num_samples = args.num_samples from tqdm import tqdm + pbar = tqdm(total=num_samples) while True: @@ -131,16 +143,23 @@ def generate_samples_unconditional(model): sentences = [''] * args.global_batch_size print("global batch size", args.global_batch_size) max_len = args.out_seq_length - resp_sentences, resp_sentences_seg, output_logits, \ - tokens = generate_and_post_process(model, prompts=sentences, - tokens_to_generate=max_len, - return_output_log_probs=False, - top_k_sampling=args.top_k, - top_p_sampling=args.top_p, - add_BOS=True, - temperature=1.0) + resp_sentences, resp_sentences_seg, output_logits, tokens = generate_and_post_process( + model, + prompts=sentences, + tokens_to_generate=max_len, + return_output_log_probs=False, + top_k_sampling=args.top_k, + top_p_sampling=args.top_p, + add_BOS=True, + temperature=1.0, + ) for prompt, generation, token in zip(sentences, resp_sentences, tokens): - datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt} + datum = { + 'text': generation[len(prompt) :], + 'all_text': generation, + 'prompt': prompt, + 'id': cnt, + } yield datum cnt += 1 pbar.update() @@ -161,6 +180,7 @@ def generate_samples_conditional(model): num_samples = args.num_samples cnt = 0 from tqdm import tqdm + pbar = tqdm(total=num_samples) fname = open(args.sample_input_file, "r") @@ -184,16 +204,23 @@ def generate_samples_conditional(model): sentences.append(raw_text) max_len = args.out_seq_length - resp_sentences, resp_sentences_seg, output_logits, \ - tokens = generate_and_post_process(model, prompts=sentences, - tokens_to_generate=max_len, - return_output_log_probs=False, - top_k_sampling=args.top_k, - top_p_sampling=args.top_p, - add_BOS=False, - temperature=1.0) + resp_sentences, resp_sentences_seg, output_logits, tokens = generate_and_post_process( + model, + prompts=sentences, + tokens_to_generate=max_len, + return_output_log_probs=False, + top_k_sampling=args.top_k, + top_p_sampling=args.top_p, + add_BOS=False, + temperature=1.0, + ) for prompt, generation, token in zip(sentences, resp_sentences, tokens): - datum = {'text': generation[len(prompt):], 'all_text': generation, 'prompt': prompt, 'id': cnt} + datum = { + 'text': generation[len(prompt) :], + 'all_text': generation, + 'prompt': prompt, + 'id': cnt, + } yield datum cnt += 1 pbar.update() @@ -220,8 +247,7 @@ def generate_and_write_samples_conditional(model): args = get_args() if args.sample_output_file is None: sample_output_file = args.sample_input_file + ".out" - print('`sample-output-file` not specified, setting ' - 'it to {}'.format(sample_output_file)) + print('`sample-output-file` not specified, setting ' 'it to {}'.format(sample_output_file)) else: sample_output_file = args.sample_output_file with open(sample_output_file, 'w') as f: @@ -233,11 +259,15 @@ def generate_and_write_samples_conditional(model): def main(): """Main program.""" - parse_and_validate_args(extra_args_provider=add_text_generate_args, - args_defaults={'tokenizer_type': 'GPT2BPETokenizer', - 'no_load_rng': True, - 'no_load_optim': True, - 'seq_length': 2048}) + parse_and_validate_args( + extra_args_provider=add_text_generate_args, + args_defaults={ + 'tokenizer_type': 'GPT2BPETokenizer', + 'no_load_rng': True, + 'no_load_optim': True, + 'seq_length': 2048, + }, + ) initialize_megatron() # Set up model and load checkpoint diff --git a/examples/inference/gpt/gpt_dynamic_inference.py b/examples/inference/gpt/gpt_dynamic_inference.py index 02a257c1b46..e5df38fe856 100644 --- a/examples/inference/gpt/gpt_dynamic_inference.py +++ b/examples/inference/gpt/gpt_dynamic_inference.py @@ -11,10 +11,11 @@ from collections import defaultdict from typing import Dict, List, Optional -from megatron.training.arguments import parse_and_validate_args import torch from tqdm import tqdm +from megatron.training.arguments import parse_and_validate_args + sys.path.append( os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)) ) @@ -241,7 +242,10 @@ def _process_step_result(result): # Suspend. if attempted_step_count % args.suspend_resume_interval == 0: - print("**** step %d/%d ... suspend." % (engine.context.step_count, attempted_step_count)) + print( + "**** step %d/%d ... suspend." + % (engine.context.step_count, attempted_step_count) + ) engine.suspend() # Resume, 0+ attempted steps later. @@ -251,7 +255,10 @@ def _process_step_result(result): % args.suspend_resume_interval == 0 ): - print("**** step %d/%d ... resume." % (engine.context.step_count, attempted_step_count)) + print( + "**** step %d/%d ... resume." + % (engine.context.step_count, attempted_step_count) + ) engine.resume() # If engine suspended, continue to next iter. @@ -469,7 +476,9 @@ def escape_str(s): # Attach peak memory metrics; the functional test only validates these # if the fields exist in the golden values. json_results.update(peak_mem_stats) - json_results["lifetime_prefill_token_count"] = engine.context.lifetime_prefill_token_count + json_results["lifetime_prefill_token_count"] = ( + engine.context.lifetime_prefill_token_count + ) print(f' Saving results to {args.output_path}') with open(args.output_path, "w") as fp: diff --git a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py index 247404d537e..31c2b3529de 100644 --- a/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py +++ b/examples/inference/gpt/gpt_dynamic_inference_with_coordinator.py @@ -9,7 +9,6 @@ from collections import defaultdict from typing import List -from megatron.training.arguments import parse_and_validate_args import torch import torch.distributed as dist @@ -25,6 +24,7 @@ get_model_for_inference, ) from megatron.training import get_args, get_tokenizer, initialize_megatron +from megatron.training.arguments import parse_and_validate_args # pylint: disable=line-too-long @@ -73,11 +73,15 @@ async def main( ) # All ranks agree on the number of suspend/resume cycles from args. - num_suspend_resume_cycles = len(requests) // args.suspend_resume_interval if args.suspend_resume_interval else 0 + num_suspend_resume_cycles = ( + len(requests) // args.suspend_resume_interval if args.suspend_resume_interval else 0 + ) # Create client and run example. if dist.get_rank() == 0: - client = InferenceClient(dp_addr, deserialize=True) # submits requests to the inference coordinator + client = InferenceClient( + dp_addr, deserialize=True + ) # submits requests to the inference coordinator client.start() base_arrival_time = time.time_ns() / 10**9 for request in requests: @@ -103,7 +107,10 @@ async def main( futures.append(client.add_request(request.prompt_text, request.sampling_params)) num_requests_added += 1 - if num_requests_added >= next_suspend_at and cycles_done < num_suspend_resume_cycles: + if ( + num_requests_added >= next_suspend_at + and cycles_done < num_suspend_resume_cycles + ): await suspend_resume_cycle(client, engine, args, futures) cycles_done += 1 next_suspend_at += args.suspend_resume_interval @@ -120,7 +127,10 @@ async def main( futures.append(client.add_request(request.prompt_text, request.sampling_params)) num_requests_added += 1 - if num_requests_added >= next_suspend_at and cycles_done < num_suspend_resume_cycles: + if ( + num_requests_added >= next_suspend_at + and cycles_done < num_suspend_resume_cycles + ): await suspend_resume_cycle(client, engine, args, futures) cycles_done += 1 next_suspend_at += args.suspend_resume_interval @@ -159,7 +169,7 @@ async def main( throughputs.append(throughput) if req.routing_indices is not None: result_dict["routing_indices"] = req.routing_indices.tolist() - + json_results[req.request_id] = result_dict throughput_dict = {"throughput": throughputs} if args.throughput_check_only: diff --git a/examples/inference/gpt/gpt_static_inference.py b/examples/inference/gpt/gpt_static_inference.py index d3dd619eaa1..9c748fdf795 100644 --- a/examples/inference/gpt/gpt_static_inference.py +++ b/examples/inference/gpt/gpt_static_inference.py @@ -5,7 +5,6 @@ import time from argparse import Namespace -from megatron.training.arguments import parse_and_validate_args import torch from megatron.core.inference.contexts import StaticInferenceContext @@ -20,6 +19,7 @@ ) from megatron.core.tokenizers.utils.build_tokenizer import build_tokenizer from megatron.core.transformer.module import MegatronModule +from megatron.training.arguments import parse_and_validate_args sys.path.append( os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)) diff --git a/examples/mimo/train.py b/examples/mimo/train.py index a89c83728e0..05eb4f2ab0c 100644 --- a/examples/mimo/train.py +++ b/examples/mimo/train.py @@ -9,26 +9,24 @@ from functools import partial from typing import Any, Dict, Iterator -from megatron.training.arguments import parse_and_validate_args import torch -from megatron.training import get_args, pretrain, print_rank_0 from megatron.core.parallel_state import ( + get_context_parallel_group, + get_data_parallel_group, get_tensor_model_parallel_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_src_rank, - get_context_parallel_group, - get_data_parallel_group, ) +from megatron.training import get_args, pretrain, print_rank_0 +from megatron.training.arguments import parse_and_validate_args sys.path.append( os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)) ) from data.energon_avlm_task_encoder import llava_avlm_dataloader_provider from data.energon_vlm_task_encoder import llava_vlm_dataloader_provider -from data.mock import ( - train_valid_test_datasets_provider as mock_train_valid_test_datasets_provider, -) +from data.mock import train_valid_test_datasets_provider as mock_train_valid_test_datasets_provider from model_providers.llava_avlm import model_provider_llava_avlm from model_providers.llava_vlm import model_provider_llava_vlm from model_providers.mock import model_provider_mock_vlm_single_encoder @@ -50,13 +48,24 @@ "llava_avlm": llava_avlm_dataloader_provider, } + def add_mimo_args(parser): """Add MIMO-specific arguments to the parser.""" group = parser.add_argument_group('MIMO', 'MIMO specific arguments') # MIMO-specific parameters - group.add_argument('--dataset-provider', type=str, default='mock', help='Dataset provider to choose from [mock, llava_vlm, video_llava_vlm, llava_avlm]') - group.add_argument('--model-provider', type=str, default='mock', help='Model provider to choose from [mock, llava_vlm, video_llava_vlm, llava_avlm]') + group.add_argument( + '--dataset-provider', + type=str, + default='mock', + help='Dataset provider to choose from [mock, llava_vlm, video_llava_vlm, llava_avlm]', + ) + group.add_argument( + '--model-provider', + type=str, + default='mock', + help='Model provider to choose from [mock, llava_vlm, video_llava_vlm, llava_avlm]', + ) # mock dataloader related args # can control mock samples with total seq length and image seq length @@ -71,17 +80,29 @@ def add_mimo_args(parser): '--audio-encoder-model', type=str, default=None, help='Audio encoder model name' ) group.add_argument( - '--hf-assign-unused-tokens', type=str, nargs='+', default=None, - help='Assigning unused tokens to special tokens. Example: ' - '--hf-assign-unused-tokens "