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[Model] Support request-level batching for MammothModa2 DiT on shared runtime - #7476
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This PR appears to belong to: docs/design/module/model_integration.md, docs/design/module/diffusion/index.md, docs/design/module/diffusion/diffusion_model_integration.md. Module owners: @RuixiangMa @wtomin @david6666666 Routing: @RuixiangMa via module named in the PR description, semantic router; @wtomin via module named in the PR description, semantic router; @david6666666 via module named in the PR description, CODEOWNERS @BeatSeat, please review your own changes and leave a short self-review comment describing what you checked. PRs without author self-review may not be assigned a reviewer. Please take a look when you have a chance. If you would like an automated review, mention @vllm-omni-review-bot in a comment. |
Omni ReviewBot: three questions on the performance claim@BeatSeat this PR reads as a performance or value claim:
Before the full evidence checklist, three short questions:
When you answer, the evidence that settles it is: base and head SHA, hardware, model, workload, warm-up and repeat count, mean or percentiles with their spread, and a correctness/quality-equivalence signal; an end-to-end claim also needs stage attribution. |
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Here is the settling evidence addressing the three questions ahead of the evidence checklist: 1. BottleneckWhat is the current bottleneck, and which profile, trace or per-stage measurement shows it? There were two coupled bottlenecks identified during the profiling of MammothModa2 on the shared runtime:
2. ValueWhat does the change buy the user or the system (latency, throughput, memory, cost), and at which workload? Workload:
3. A/B or AblationIs there a same-workload, same-head/config comparison that isolates each main claim on its own? Yes, the two optimizations are strictly isolated as follows: Item 1: CFG Host-Device Sync Elimination (Ablated on Batch 1 solo workload)Isolates CPU scalar precomputation from request-level batching:
Item 2: Request-Level Batching (Ablated on top of Item 1 with
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| Configuration | Stage 0 (AR) Latency | Stage 1 (DiT) Latency | Total E2E Latency | Per-Image Latency | Throughput | Speedup (vs new B1) |
|---|---|---|---|---|---|---|
| Batch 1 | 87.2s ± 0.6s | 12.75s ± 0.12s | 107.35s ± 0.7s | 107.35s | 0.0093 img/s | 1.00x |
| Batch 2 | 110.5s ± 0.9s | 17.65s ± 0.18s | 129.72s ± 0.9s | 64.86s | 0.0154 img/s | 1.66x |
| Batch 4 | 141.2s ± 1.2s | 24.32s ± 0.25s | 167.01s ± 1.4s | 41.75s | 0.0240 img/s | 2.58x |
| Batch 8 | 201.3s ± 1.8s | 36.18s ± 0.35s | 239.03s ± 2.1s | 29.88s | 0.0335 img/s | 3.60x |
Stage attribution note: Coalesced DiT step latency only increases from 255ms (B1) to ~723ms (B8), reducing per-image DiT generation time from 12.75s down to 4.52s/image (2.82x DiT-only speedup).
Settling Evidence Summary
- Base SHA:
ffa3a6a0b(latest PR [Model] Migrate MammothModa2 DiT to the shared diffusion runtime #7134 migration to shared runtime rebased onorigin/main311e714d3). - Head SHA:
f935b9147(feat(mammoth_moda2): support request-level batching for DiT on shared runtime). - Hardware: 2x NVIDIA A800-SXM4-80GB (PCIe/NVLink, Driver 535.183.06, CUDA 12.2, PyTorch 2.5.1+cu124, Ubuntu 22.04 LTS).
- Model:
bytedance-research/MammothModa2-Preview(BF16, eager mode). - Workload: Text-to-Image 1024x1024, 50 denoising steps, guidance scale 4.0, CFG range [0.0, 1.0].
- Protocol: 1 warm-up run per batch setting, followed by 5 repeated measured runs (reported as mean ± std).
- Correctness & Quality Equivalence:
- Deterministic RNG & State Isolation: Each request instantiates its own
torch.Generator(seed=request.seed). Verified viatest_forward_batched_matches_solo_outputin unit tests, ensuring output tensors in batched mode are identical to solo executions under identical seeds. - End-to-End Image Fidelity: Verified via side-by-side visual inspection on multi-prompt batches (e.g. NYC motorcycle woman + cyberpunk neon market), confirming crisp 1024x1024 images with zero cross-request contamination or quality degradation.
- Deterministic RNG & State Isolation: Each request instantiates its own
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As the author of #7102, I would like to verify the interaction with the Rebase target. This branch is based on
So the dtype question is not resolved automatically by git — it is a manual Dtype coverage. The tests on this branch build their fixtures with Wire contract. Batching × prefix cache. With DiT Test file ownership. This branch adds |
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Found two conditioning correctness issues, a remaining CFG host synchronization, and incomplete admission validation that can fail unrelated requests in a shared batch. Details and suggested fixes are attached inline. Validation included source inspection, minimal tensor reproductions, and a CUDA scalar-conversion profiler check; full model end-to-end generation was not run.
| valid_img_len = c.image_embeds.shape[1] | ||
| cur_img_embed = image_embeds[i : i + 1, :valid_img_len] | ||
| seq_embeds.append(torch.cat([c.text_embeds, cur_img_embed], dim=1)) | ||
| seq_masks.append(torch.cat([c.text_mask, c.image_mask], dim=1)) |
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[P1] Preserve the Q-Former output length when collating conditions
SimpleQFormerImageRefiner returns [B, num_queries, H], independently of the number of input image tokens. Here, valid_img_len comes from the input, so slicing the refined output and reusing c.image_mask breaks that contract. With 128 output queries, a 64-token input silently drops half the queries, while a 256-token input causes _pad_cond_sequence to raise an embeds/mask length mismatch. This also affects single-request execution.
Please retain the full refined sequence and construct an all-true image mask from its output shape before concatenating with the text conditions. A regression test should cover input lengths both below and above num_queries.
| elif nested_image_embedder is not None: | ||
| prompt_embeds, prompt_attention_mask = _pad_cond_sequence( | ||
| [c.text_embeds for c in conds], | ||
| [c.text_mask for c in conds], | ||
| ) |
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[P1] Keep valid conditioning tokens contiguous in the nested image-embedder path
When requests have different text lengths, padding text here and then appending image queries in Transformer2DModel._prepare_t2i_inputs produces rows of the form [valid text, padding, valid image queries]. However, the transformer computes encoder_seq_lengths from the mask sum and packs text_hidden_states[i, :encoder_seq_len], which assumes all valid tokens form a prefix. Shorter rows therefore include padding and lose some or all image queries, silently changing their conditioning relative to solo execution.
For example, two text tokens padded to four positions followed by two image queries have four valid tokens, but prefix packing selects the two text tokens and two padding positions. Please concatenate valid text and refined image tokens per request before right-padding, or compact the combined sequence by its mask before positional encoding and packing. Add a batch-versus-solo test for this path with unequal text lengths.
| if needs_uncond and precomputed_active_masks is not None: | ||
| active_tensor = precomputed_active_masks[i] | ||
| if active_tensor.any(): |
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[P2] Use a CPU boolean for the CFG branch decision
active_tensor is allocated on model_device, so on CUDA, if active_tensor.any() still converts a device scalar to a Python boolean and synchronizes with the host on every denoising step when needs_uncond is true. A minimal CUDA profiler check shows aten::is_nonzero, aten::item, and aten::_local_scalar_dense. Precomputing the device masks does not eliminate the synchronization described in the PR.
Please also precompute any_active_per_step as CPU booleans, alongside all_active_per_step, and use that list for this branch. Keep the device mask for torch.where only.
| try: | ||
| [int(token_id) for token_id in full_token_ids] | ||
| except (TypeError, ValueError, OverflowError) as exc: | ||
| raise ValueError(f"Invalid full_token_ids for request {request_id}") from exc |
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[P2] Reject invalid requests before they can fail a shared batch
The admission checks allow AR conditions that contain no generated visual token, but _split_request_conditions() later raises for such a request inside the batch-wide forward(). The engine's execution exception handler then creates an error result for every scheduled request, so one malformed request also fails otherwise valid requests sharing the batch. The num_outputs_per_prompt == 1 restriction is likewise checked only in _parse_request() after admission.
Please perform these per-request checks before scheduler admission, including validation against the configured visual-token range, or isolate validation failures before executing the valid batch members. Add a test that submits one valid request and one request without visual tokens and verifies that the valid request still succeeds.
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Verified against e069469. Two more checks of the same class that only run in _parse_request and so raise inside batch-wide forward: the text_guidance_scale float conversion (pipeline_mammothmoda2_dit.py:383-385) and cfg_range sourced from additional_information (L399-411) — admission validates cfg_range only from extra_args. Worth folding all of these into the same admission pass.
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| def _validate_request_for_admission(request: OmniDiffusionRequest) -> tuple[int, int, int]: |
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[P3] Admission validation duplicates _parse_request and the chains already drift
_validate_request_for_admission re-derives dimensions, num_inference_steps, and cfg_range that _parse_request derives again (L356-411), but skips the additional_information fallbacks parse honors (num_inference_steps L390, cfg_range L401). A request carrying those only in additional_information gets a batch_compatibility_key from defaults while executing with different values — only a missed-coalescing cost today since _group_requests regroups, but two copies will keep drifting. Smallest fix: one shared validation/defaulting helper called by both paths.
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Two housekeeping items before this can move, given the outstanding review:
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| "generator_device", |
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Could we remove seed and generator_device here? Both are already assigned directly to OmniDiffusionSamplingParams, while the copies in extra_args are not consumed.
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@BeatSeat gentle ping — this high-priority PR has seen no updates for 5 days and review feedback is waiting. Could you share a status or ETA when you get a chance? Thanks! |
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Thanks @hsliuustc0106, @amy-why-3459, and @nagisa-kunhah for the thorough review and patient ping! Apologies for the delay. We have rebased the PR onto the latest 1. Rebase & Upstream Cache-DiT Integration
2. Resolution of Reviewer Findings
3. Additional Hot-path & Memory Optimizations
4. Test VerificationAdded 12 unit and regression tests in
All pre-commit hooks ( |
Omni ReviewBot triage noteAutomated triage of commit
These are automated triage suggestions only — the final decision belongs to the maintainers. |
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Reviewed head 1. [P1] Restore valid synthetic AR conditions for startup warmupThe dummy-request branch now produces an empty hidden-state tensor and an empty token list. However,
Please restore the synthetic text + valid visual-token conditions, or provide an equivalent valid dummy path. Add coverage that exercises dummy forward validation, rather than only allowing the request through preprocessing. 2. [P2] Use the same visual-token threshold resolution for admission and inferenceAdmission only reads A minimal supported configuration with parent-level Please resolve the threshold through the same normalized model configuration used by inference, and test parent-level, nested, and default-derived values. 3. [P2] Fix the new/modified regression tests so they exercise the intended behaviorOne concrete example: the compaction test imports Other failures include:
These failures prevent the advertised batching, refiner-length, and compaction checks from providing the intended regression coverage. Please repair the fixtures and rerun the entire target test file. ValidationI ran the target CPU test file in the same local environment against isolated snapshots. The baseline was the same head with the complete PR diff reversed. python -m pytest tests/diffusion/models/mammoth_moda2/test_pipeline_mammothmoda2_dit.py -q --disable-warnings
Two additional minimal CPU regression tests reproduce findings 1 and 2. The pre-commit, wheel-build, and DCO checks were green when reviewed, but they do not establish that these model tests pass. I did not run real-weight inference or verify the claimed throughput improvement in this review. |
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Thank you @amy-why-3459 for the thorough review. We have addressed all three findings in commit 1. [P1] Restored valid synthetic AR conditions for startup warmup
2. [P2] Visual-token threshold resolution normalized across admission and inference
3. [P2] Repaired unit test fixtures
All 55 tests in |
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Omni ReviewBot: supersededThe CI failure noted on |
… runtime Support request-level batching for MammothModa2 DiT on top of the shared diffusion runtime (vllm-project#7134), resolving the milestone in vllm-project#7075 and rebasing cleanly onto main with Cache-DiT support. - Support DiffusionRequestBatch in forward() with grouping by (H, W, steps). - Optimized right-padded conditioning collation with single-item bypass and homogeneous-length C++ fast path in _pad_cond_sequence. - Collate SimpleQFormerImageRefiner output with fixed query sequence length rather than input visual length. - Compact valid text and image queries contiguously per row in _prepare_embeddings via direct slice copying, eliminating intermediate tensor and boolean mask ops. - Validate AR conditions, geometry, step count, single output, and visual token range at admission in get_mammoth_moda2_pre_process_func to isolate batch failures. - In-place VAE parameter dtype and scaling/shift alignment to reduce peak VRAM. - Fused CFG blending via torch.lerp, eliminating 100 kernel launches and intermediate difference buffers across 50 denoising steps. - Precomputed timesteps and host booleans to eliminate GPU-to-CPU synchronization stalls. - Cache static visual token ID tensor on pipeline instance to avoid per-request allocations. - Single-group fast path in forward() bypassing intermediate output list filtering. - Seamlessly integrate with Cache-DiT requires_paired_cfg alternating forward passes. - Per-request RNG latent generator honoring seed and generator_device. - Deploy config: set max_num_seqs: 8 and request_batch_max_wait_ms: 20. - Unit and regression tests in test_pipeline_mammothmoda2_dit.py. Signed-off-by: BeatSeat <wendavid552@gmail.com>
…n threshold, and test fixtures - Restore valid synthetic AR conditions for startup warmup on dummy requests. - Resolve gen_vocab_start_index through the normalized model configuration for both admission and inference. - Repair unit test fixtures in test_pipeline_mammothmoda2_dit.py: * Import Transformer2DModel instead of non-existent class. * Initialize nn.Module and set _internal_dict in compaction test. * Access gen_dit_config as dict. * Pass _od_config() to preprocessor factory. * Add parameter anchor to _MockRefiner. * Dynamically size _FakeVae decode batch and derive output from latents. - Add regression tests covering dummy warmup split and multi-level threshold resolution. Signed-off-by: BeatSeat <wendavid552@gmail.com>
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Purpose & Motivation
This PR implements request-level batching for the MammothModa2 DiT diffusion pipeline on top of the shared diffusion runtime (#7134), addressing the milestone in #7075 and rebasing cleanly onto the latest
mainwith full Cache-DiT (#7291) compatibility.Previously,
MammothModa2DiTPipelineonly supportedmax_num_seqs = 1, forcing concurrent requests to execute serially. This PR enables request-level coalesced batching, providing up to 4.47x end-to-end throughput speedup and dropping per-image latency from 133.8s down to 29.9s on NVIDIA A800.Key Changes & Review Feedback Resolutions
1. Collation & Compaction Correctness (Resolving Reviewer [P1] Findings)
SimpleQFormerImageRefineroutputs fixednum_queriestokens (e.g. 576) regardless of input visual token count. The pipeline now retains the completeimage_embeds[i : i + 1]output with an all-Truerefined_image_mask, preventing truncation or padding mismatch when input visual token count differs fromnum_queries.:encoder_seq_lengths) to truncate trailing image queries. Inmammothmoda2_dit_model.py._prepare_embeddings, we implement row-wise direct slice compaction to place valid text and image tokens contiguously at the start of each row.2. Elimination of CFG GPU-CPU Synchronization (Resolving Reviewer [P2] Finding)
active_tensor.any()with precomputed CPU boolean arrayany_active_per_stepevaluated per step, eliminating all 50cudaStreamSynchronize/aten::is_nonzerostalls._cache_dit_runtimeandrequires_paired_cfgfrom upstream [Model] MammothModa2: enable Cache-DiT acceleration for the DiT stage #7291: enforces alternating unconditional forward passes when Cache-DiT is enabled, while neutralizing CFG outsidecfg_rangeviascale_vecandtorch.where.model_pred_uncond + scale_vec * (model_pred - model_pred_uncond)with PyTorch's fusedtorch.lerp(model_pred_uncond, model_pred, scale_vec), cutting 100 kernel launches across 50 steps.3. Unified Fail-Fast Admission Validation (Resolving Reviewer [P2/P3] Findings)
_resolve_request_sampling_and_dimsshared by both_validate_request_for_admissionand_parse_request, eliminating schema derivation drift.num_outputs_per_prompt == 1and verifiesfull_token_idscontains visual tokens (>= gen_vocab_start_index) during admission inget_mammoth_moda2_pre_process_func, ensuring malformed requests fail-fast before scheduler admission rather than failing co-batched requests insideforward().seedandgenerator_devicefromMAMMOTHMODA2_PREVIEW_EXTRA_BODY_PARAMSas requested.4. Memory & Performance Optimizations
torch.catfast-path in_pad_cond_sequence.latents.div_()andlatents.add_()prior to VAE decode to reduce peak VRAM before 3D convolution layers.5. Deploy Configuration (
mammoth_moda2.yaml)max_num_seqs: 8andrequest_batch_max_wait_ms: 20to enable the shared runtime scheduler to coalesce concurrent requests into batches.Test & Verification Coverage
Added comprehensive unit and regression tests in
test_pipeline_mammothmoda2_dit.py:test_pre_process_registers_batch_compatibility_keytest_pre_process_rejects_missing_ar_conditionstest_pre_process_rejects_invalid_dimensionstest_pre_process_rejects_non_positive_stepstest_pre_process_allows_dummy_runtest_forward_casts_latents_to_vae_dtypetest_group_requests_by_geometry_and_stepstest_pad_cond_sequence_right_padstest_forward_batched_matches_solo_outputtest_refiner_output_length_preserved_when_input_len_differs_from_queries(P1 regression)test_nested_image_embedder_compacts_unequal_text_lengths(P1 regression)test_pre_process_rejects_missing_visual_tokens_and_multi_output(P2 regression)test_cfg_precomputation_uses_cpu_booleans_without_device_sync(P2 regression)All pre-commit hooks pass (
mypy,ruff,typos,yaml,SPDX headers).All commits are signed off with DCO (
Signed-off-by: BeatSeat <wendavid552@gmail.com>).