refactor: streamline DeepSeek V4 mHC warmup and remove token-size cap - #47807
leihuang-sketch wants to merge 12 commits into
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@lucifer1004 @zyongye can you review it?
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I am also seeing the TTFT and queuing spikes due to the miss of warmup kernels in DSv4 (on v0.25.0rc2). Looks like this is the solution. |
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| # Auto-warmup token sizes. TileLang mHC kernels treat ``num_tokens`` as a |
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I still ran into a few missed warmup with this, and here is the trace (I can provide a commit on this PR to mitigate this). Following is the trace:
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The power-of-two grid leaves most
n_splitsbuckets cold. -
n_splitsis one of the key to compile the kernel:- mhc_pre_big_fuse_with_norm_tilelang(..., n_splits: int = 16, ...) with
num_tokens = T.dynamic("num_tokens"): tilelang_kernels.py#L197-L218
- mhc_pre_big_fuse_with_norm_tilelang(..., n_splits: int = 16, ...) with
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How runtime picks
n_splits: Both dispatchers derive it from the actual token count of the batch:mhc_pre_tilelang:n_splits = compute_num_split(block_k, hc_hidden_size, cdiv(num_tokens, block_m)), tilelang.py#L167-L172- same in
mhc_fused_post_pre_tilelangfor the >16-token path: tilelang.py#L410-L424 compute_num_splitisn_sms // grid_size, clamped tonum_block_k // 4and ≥1 — tilelang_kernels.py#L31-L40
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So with
block_m= 64, a warmup at token size 2^k only ever producesn_splits = n_sms // ceil(2^k / 64)— i.e.n_sms // 2^j. But any real prefill length reachesn_sms // gfor arbitrary g: e.g. a ~3000-token prefill → grid = 47 →n_splits = n_sms // 47, a value no power-of-two size can generate. First request in that bucket pays the full TileLang compile (~8–10 s) on every TP worker simultaneously, while the batch is blocked.
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Thanks for the detailed trace — you're right, and the analysis is spot on.
I actually went with the brute-force approach locally: warming up the full
1 ~ max_tokens range. It produces a lot of redundant warmups (many adjacent
token sizes map to the same n_splits), but it guarantees every bucket is
covered and never hits a cold JIT at runtime.
You're correct that the power-of-two grid in this PR only generates
n_splits = n_sms // 2^j, which misses the vast majority of n_splits = n_sms // g
buckets that real prefill lengths hit. A ~3000-token prefill → grid = 47 →
n_splits = n_sms // 47 is a great example — no 2^k will ever produce that.
Happy to accept a commit on this PR. Instead of just re-padding with more
candidates, would you consider dedup'ing on the n_splits dimension directly?
E.g. iterate grid_size from 1 to cdiv(max_tokens, 64), pick one representative
token count per distinct n_splits value, and warmup those — same coverage
guarantee as the exhaustive approach but without the redundant compiles.
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@leihuang-sketch Thanks! Created a PR at your fork. Please check.
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Thank you, co-author has been added, and can you post the test results
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A quick test for equivalence of exhausted input token and this change:
docker run --rm --gpus '"device=0"' --entrypoint python3 <image_name> -c "from vllm.model_executor.kernels.mhc.tilelang_kernels import compute_num_split
from vllm.model_executor.warmup.deepseek_v4_mhc_warmup import _mhc_split_bucket_sizes
from vllm.utils.math_utils import cdiv
max_tokens, k = 16384, 4 * 7168
sizes = _mhc_split_bucket_sizes(max_tokens, k)
warmed = {compute_num_split(64, k, cdiv(s, 64)) for s in sizes}
exhaustive = {compute_num_split(64, k, cdiv(t, 64)) for t in range(1, max_tokens + 1)}
assert warmed == exhaustive
print(f'bucket sizes ({len(sizes)}): {sizes}')
print(f'warmed ({len(warmed)}): {sorted(warmed)}')
print(f'exhaustive ({len(exhaustive)}): {sorted(exhaustive)}')
"Result
>>>
bucket sizes (23): [64, 128, 192, 256, 320, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1088, 1216, 1408, 1600, 1920, 2432, 3200, 4800]
warmed (23): [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 21, 24, 29, 37, 49, 74, 112]
exhaustive (23): [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 21, 24, 29, 37, 49, 74, 112]
Also tested on live traffic that the TTFT spike issue was mitigated.
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We might have to update this docstring as well.
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@leihuang-sketch I saw the remaining missing kernels as well, would love a fix in this PR (left to you). Also I am using DSv4 DSpark (on both Flash and Pro; available starting v0.25.0) speculative decoding. Can you check if there is warmup kernels missing over there? |
@chungen04 They have been sorted out in the aforementioned table, and there are approximately 4 of them |
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I noticed the similar issue before of deepseek mHC warmup issue. |
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@leihuang-sketch following up -- is there anything blocking? Also the pre-commit is failing, looks like you have to do signoff on each commit. |
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Currently, there are no blocking issues, and approval from the code reviewer is required
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Hey @leihuang-sketch, I noticed your recent PRs related to kernel warmup (#47807, #48804, #48805, #48806, #48807).
We're currently migrating all kernel warmups to a shared warmup contract. See #47451, RFC: #47456.
Would you mind migrating these warmups to conform to that shared contract? It would help keep the warmup infrastructure consistent and make future maintenance easier.
Other than this, my general recommendation is to avoid brute-forcing warmups with variables not responsible of triggering kernel re-compilations. Try to warm up actual compile keys instead of running representative non-key inputs, such as token sizes, and hoping they map to all required specializations. That mapping is not always obvious or guaranteed, so warmup should target the compile-key space directly. Thanks!
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DCO is fixed (unified author/committer email and This PR fixes a real production issue: on Could someone help review and approve, and add the |
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I found this PR while checking for overlapping work after opening #51802. After comparing the two, #47807 is the earlier and more comprehensive implementation of the same NVIDIA mHC warmup fix, and it follows the shared I have documented the overlap in #51802 and paused further changes there. Since this branch currently needs a rebase, I will follow its status before deciding how to handle #51802. I’m happy to help with the rebase or testing if needed. |
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Hi @SyaOtiLan, thanks a lot for the thorough comparison and for documenting the overlap in #51802. Really appreciate you flagging that #47807 is the earlier and more comprehensive implementation — that helps avoid duplicate work. I'd love your help with the rebase. The branch is currently in For #51802, let's keep it paused for now and revisit once this one is in. Thanks again! |
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Hi @leihuang-sketch, I rebased the work onto https://github.com/SyaOtiLan/vllm/tree/help/rebase-47807 The complete rebased branch can be fetched directly from my fork and used to update the existing PR branch. The rebase also includes two follow-up changes:
While validating on an RTX 4090, I found that the rebased warmup covered the wrapper keys, but a 17-token runtime probe still compiled Validation passed:
I do not have access to an H20 or the DeepSeek-V4-Flash checkpoint, so the original H20 serving scenario still needs verification. Please take a look when convenient. I’m happy to adjust the branch based on your feedback. |
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Hi @SyaOtiLan, thanks for the rebase and the thorough validation — really solid work, especially catching the non-DeepGEMM To keep everything under one roof and make this PR easy to manage, could you open a PR from your
That way I can review the rebase + your two follow-up commits (JitWarmupRegistry integration, Once merged, the H20 / DSv4-Flash serving verification is still the open item — I'll take that on (or we can coordinate if you get access in the meantime). A couple of asks when opening the PR:
Also, could you attach the test data in the PR description? Specifically:
Having these captured in the PR makes it easy to reproduce and for reviewers (@LopezCastroRoberto / @yewentao256) to verify the gains when CI runs. Thanks again — this unblocks the PR nicely. |
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Opened the requested cross-fork PR: https://github.com/leihuang-sketch/vllm/pull/2 It includes the cold-cache before/after results at 17, 128, and 1024 tokens, the 50 focused tests, and the Ruff/format output. The fallback change only precompiles the existing specializations and does not modify runtime dispatch. GitHub currently reports the cross-fork PR as conflicting because the head contains the rebased history while the base still contains the pre-rebase history. The rebase and the two follow-up commits remain separate as requested. I can adjust the transfer method if you would prefer a different branch arrangement. |
- Remove the hard 16_384 auto-warmup token-size cap. - Warm up all token sizes from 1 to max_num_batched_tokens to avoid TileLang JIT during inference for any prefill size the scheduler may encounter. - Use real RMSNorm weights for norm-fused TileLang kernels. - Add progress logging and warm up the fused post+pre variant. - Simplify verbose comments throughout the module. Co-authored-by: OpenCode <noreply@opencode.ai> Signed-off-by: hanshuche <shicang@shicang>
- Remove [mhc-debug] tracing logs and periodic progress logger.info - Remove start/finish logger.info messages - Keep tqdm progress bar for warmup progress visibility - Generalize instrument span name from "DeepSeek V4 mHC warmup" to "mHC warmup" Co-authored-by: Claude
The previous change warmed up every integer token size from 1 to max_num_batched_tokens, causing up to tens of thousands of kernel launches. TileLang mHC kernels treat num_tokens as a dynamic dimension and only have shape breakpoints at small powers of two (small-FMA branches, split-k transitions, block-M specializations). Restore the capped power-of-2 grid up to 16384 while still including max_tokens and cudagraph capture sizes exactly.
Signed-off-by: chungen04 <cho322@gatech.edu>
Signed-off-by: hanshuche <shicang@shicang>
Migrate DeepSeek V4 mHC TileLang kernel warmup to the shared VllmJitKernel contract (RFC #47456 / PR #47451), as requested by @LopezCastroRoberto in PR #47807 review. Key changes: - Add 3 VllmJitKernel wrappers next to the kernel definitions in vllm/model_executor/kernels/mhc/warmup.py (kernel-owned warmup): - MhcPreKernel: first-layer path (mhc_pre + mhc_post) - MhcFusedPostPreKernel: second-layer-and-after (mhc_fused_post_pre) - HcHeadFusedKernel: hc_head_fused_kernel_tilelang op - Each wrapper exposes CompileKey / dispatch / get_warmup_keys / compile. The AST tracer in jit_warmup.py expands WarmupIntRange(1, max_tokens+1) and deduplicates to the actual compile-key set (~22-24 keys for a 16k token budget, vs. dozens of dummy-run token sizes before). - Add vllm/model_executor/warmup/jit_warmup_tilelang_helper.py with TileLangWarmupTensor, a compile-only fake tensor descriptor (mirrors TritonWarmupTensor). compile() calls .compile() on the underlying @tilelang.jit kernels, which inspects only tensor metadata and never launches the kernel or allocates real GPU memory. - Slim deepseek_v4_mhc_warmup.py from 354 to 111 lines: the per-kernel dispatch / compile-key enumeration / compile logic is now kernel-owned. Caller only does model walking + wrapper.warmup(vllm_config). - Move deepseek_v4_mhc_warmup() call from unconditional execution to the enable_jit_warmup branch in kernel_warmup.py, alongside sparse_mla_triton_warmup and fa4_cutedsl_warmup. - Add tests/model_executor/test_mhc_warmup_wrappers.py (CPU-only, no GPU/TileLang required) verifying CompileKey fields, dedup behavior, dispatch consistency, and compile-only contract. Co-author: @chungen04 Signed-off-by: hanshuche <shicang@shicang> Signed-off-by: hanshuche <FlyPanda@leihuang-sketch>
Add compute_mhc_dispatch() as the single source of truth for n_splits / tile_n / use_small_fma derivation, shared between runtime ops (tilelang.py) and warmup wrappers (warmup.py). Add MhcKernelConstants to collect model-level cache_key constants (hc_post_alpha, hc_sinkhorn_iters, epsilons) from the model layer instead of hardcoding them, ensuring warmup keys match runtime keys. Cover the broadcast kernel path (mhc_pre_big_fuse_broadcast_with_norm) that was introduced in 442c421 but never warmed, causing runtime JIT. Merge dispatch and _dispatch_broadcast into a single dispatch with is_broadcast as an explicit list dimension, controlled by detecting hc_attn_fn_broadcast on the model layer. Add _compile_and_cache to fill both TileLang cache layers (global KernelCache + per-instance _kernel_cache) so jit_monitor does not report false-positive misses. Add progress logging to VllmJitKernel.warmup() base class. Signed-off-by: hanshuche <shicang@shicang> Signed-off-by: hanshuche <FlyPanda@leihuang-sketch>
The VllmJitKernel.warmup() loop logged a progress/ETA/rate line per compiled key (dozens-to-hundreds of lines per kernel), and the mHC wrappers logged another line per compile() call plus _compile_and_cache. On DSv4 this produced hundreds of INFO lines scrolling during startup. Replace the per-iteration logger.info with a tqdm progress bar shown only on rank 0 (mirrors deep_gemm_warmup). Keep one summary line at start and finish. Remove the per-compile log spam from _compile_and_cache, MhcPreKernel.compile, and MhcFusedPostPreKernel.compile; the per-kernel total in get_warmup_keys is sufficient. Co-authored-by: opencode <opencode@anthropic.com> Signed-off-by: hanshuche <FlyPanda@leihuang-sketch>
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Precompile the regular and block-M TileLang specializations used by the non-DeepGEMM mHC prenorm path so the first runtime call does not trigger JIT compilation. Add focused coverage verifying that the fallback warmup runs only when DeepGEMM is unavailable. Assisted-by: OpenAI Codex Signed-off-by: SyaOtiLan <954239196@qq.com>
[Warmup] Precompile fallback mHC prenorm kernels
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Hi @leihuang-sketch, I noticed that #47807 was closed after your latest branch update. Was this intentional, and do you plan to reopen or continue the work? I’m asking because #52941 now addresses the same NVIDIA mHC warmup gap, while #47807 already implemented the shared It would be helpful to know whether #47807 has been abandoned or should still be considered when deciding which implementation to move forward with. |
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@SyaOtiLan Sorry, due to some reasons, we are unable to continue contributing at the moment. You can start another PR to complete it or support other contributors |
Migrate DeepSeek V4 mHC TileLang kernel warmup to the shared VllmJitKernel contract (RFC vllm-project#47456 / PR vllm-project#47451), as requested by @LopezCastroRoberto in PR vllm-project#47807 review. Key changes: - Add 3 VllmJitKernel wrappers next to the kernel definitions in vllm/model_executor/kernels/mhc/warmup.py (kernel-owned warmup): - MhcPreKernel: first-layer path (mhc_pre + mhc_post) - MhcFusedPostPreKernel: second-layer-and-after (mhc_fused_post_pre) - HcHeadFusedKernel: hc_head_fused_kernel_tilelang op - Each wrapper exposes CompileKey / dispatch / get_warmup_keys / compile. The AST tracer in jit_warmup.py expands WarmupIntRange(1, max_tokens+1) and deduplicates to the actual compile-key set (~22-24 keys for a 16k token budget, vs. dozens of dummy-run token sizes before). - Add vllm/model_executor/warmup/jit_warmup_tilelang_helper.py with TileLangWarmupTensor, a compile-only fake tensor descriptor (mirrors TritonWarmupTensor). compile() calls .compile() on the underlying @tilelang.jit kernels, which inspects only tensor metadata and never launches the kernel or allocates real GPU memory. - Slim deepseek_v4_mhc_warmup.py from 354 to 111 lines: the per-kernel dispatch / compile-key enumeration / compile logic is now kernel-owned. Caller only does model walking + wrapper.warmup(vllm_config). - Move deepseek_v4_mhc_warmup() call from unconditional execution to the enable_jit_warmup branch in kernel_warmup.py, alongside sparse_mla_triton_warmup and fa4_cutedsl_warmup. - Add tests/model_executor/test_mhc_warmup_wrappers.py (CPU-only, no GPU/TileLang required) verifying CompileKey fields, dedup behavior, dispatch consistency, and compile-only contract. Co-author: @chungen04 Signed-off-by: hanshuche <shicang@shicang> Signed-off-by: hanshuche <FlyPanda@leihuang-sketch>
Migrate DeepSeek V4 mHC TileLang kernel warmup to the shared VllmJitKernel contract (RFC vllm-project#47456 / PR vllm-project#47451), as requested by @LopezCastroRoberto in PR vllm-project#47807 review. Key changes: - Add 3 VllmJitKernel wrappers next to the kernel definitions in vllm/model_executor/kernels/mhc/warmup.py (kernel-owned warmup): - MhcPreKernel: first-layer path (mhc_pre + mhc_post) - MhcFusedPostPreKernel: second-layer-and-after (mhc_fused_post_pre) - HcHeadFusedKernel: hc_head_fused_kernel_tilelang op - Each wrapper exposes CompileKey / dispatch / get_warmup_keys / compile. The AST tracer in jit_warmup.py expands WarmupIntRange(1, max_tokens+1) and deduplicates to the actual compile-key set (~22-24 keys for a 16k token budget, vs. dozens of dummy-run token sizes before). - Add vllm/model_executor/warmup/jit_warmup_tilelang_helper.py with TileLangWarmupTensor, a compile-only fake tensor descriptor (mirrors TritonWarmupTensor). compile() calls .compile() on the underlying @tilelang.jit kernels, which inspects only tensor metadata and never launches the kernel or allocates real GPU memory. - Slim deepseek_v4_mhc_warmup.py from 354 to 111 lines: the per-kernel dispatch / compile-key enumeration / compile logic is now kernel-owned. Caller only does model walking + wrapper.warmup(vllm_config). - Move deepseek_v4_mhc_warmup() call from unconditional execution to the enable_jit_warmup branch in kernel_warmup.py, alongside sparse_mla_triton_warmup and fa4_cutedsl_warmup. - Add tests/model_executor/test_mhc_warmup_wrappers.py (CPU-only, no GPU/TileLang required) verifying CompileKey fields, dedup behavior, dispatch consistency, and compile-only contract. Co-author: @chungen04 Signed-off-by: hanshuche <shicang@shicang> Signed-off-by: hanshuche <FlyPanda@leihuang-sketch>
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co-author：@chungen04 @SyaOtiLan
Purpose
DeepSeek-V4 inference suffers from multi-second latency spikes caused by TileLang JIT compilation of mHC (multi-head-compression) kernels when the scheduler encounters token sizes that were not warmed up. The existing warmup only covers a fixed set of power-of-two token sizes up to 16,384, leaving all other prefill shapes uncompiled.
kernel
mhc_pre_big_fuse_with_norm_tilelangcost about 10s+, log info'''
(Worker_TP1 pid=1099) 2026-07-06 16:03:24 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_pre_big_fuse_with_norm_tilelangwithout_idx=None(Worker_TP0 pid=1098) 2026-07-06 16:03:24 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_pre_big_fuse_with_norm_tilelangwithout_idx=None(Worker_TP2 pid=1100) 2026-07-06 16:03:24 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_pre_big_fuse_with_norm_tilelangwithout_idx=None(Worker_TP3 pid=1101) 2026-07-06 16:03:24 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_pre_big_fuse_with_norm_tilelangwithout_idx=None(Worker_TP1 pid=1099) 2026-07-06 16:03:34 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:141): TileLang completes to compile kernel
mhc_pre_big_fuse_with_norm_tilelang(Worker_TP0 pid=1098) 2026-07-06 16:03:34 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:141): TileLang completes to compile kernel
mhc_pre_big_fuse_with_norm_tilelang(Worker_TP2 pid=1100) 2026-07-06 16:03:34 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:141): TileLang completes to compile kernel
mhc_pre_big_fuse_with_norm_tilelang(Worker_TP3 pid=1101) 2026-07-06 16:03:34 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:141): TileLang completes to compile kernel
mhc_pre_big_fuse_with_norm_tilelang(Worker_TP1 pid=1099) 2026-07-06 16:03:48 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_post_tilelangwithout_idx=None(Worker_TP0 pid=1098) 2026-07-06 16:03:48 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_post_tilelangwithout_idx=None(Worker_TP3 pid=1101) 2026-07-06 16:03:49 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_post_tilelangwithout_idx=None(Worker_TP2 pid=1100) 2026-07-06 16:03:49 [TileLang:tilelang.jit.kernel:INFO] (kernel.py:133): TileLang begins to compile kernel
mhc_post_tilelangwithout_idx=None'''
This PR removes the hard
16_384auto-warmup cap and instead warms up every token size from 1 tomax_num_batched_tokens, ensuring that any shape the scheduler may produce is compiled before serving traffic.What Changed
vllm/model_executor/warmup/deepseek_v4_mhc_warmup.py_AUTO_WARMUP_MAX_TOKENSand_DEFAULT_TOKEN_SIZE_CANDIDATES._select_mhc_warmup_token_sizesnow generates a contiguous range[1, max_tokens]instead of a sparse list._warmup_layer_mhcnow uses realattn_norm/ffn_normweights andvariance_epsilonso norm-fused TileLang kernels are exercised with runtime tensors.mhc_fused_post_pre_tilelangpost+pre variant used after the first layer.Coding by ai
Test Plan
Test Result
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.