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njhill
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I don't think some of these make sense, not worth the added complexity. The new comments are superfluous.
Can you show any improvement in any e2e perf test?
…de path Three local rewrites on lines that execute once per GDN layer per engine step -- 48 times a step on a Qwen3-Next-class hybrid. None changes a value, a dtype, a shape, an alias, or a launch; each removes Python work that the GPU is waiting on when cudagraphs are piecewise. qwen_gdn_linear_attn.py, four sites: `self.conv1d.weight` was read three times to build one view (once for the view, twice for its sizes). Each read is two nn.Module __getattr__ hops (_modules, then _parameters). Bind it once. conv1d is a plain ColumnParallelLinear whose weight is a registered Parameter with no property or hook in the way, so the three reads returned the same object and the hoist is exact. causal_conv1d.py, dtype guards: `x.to(conv_state.dtype)` on entry and `out.to(original_x_dtype)` on exit are dispatched unconditionally, but under the default mamba_cache_dtype="auto" the conv state has the model dtype and both are no-ops. Tensor.to on a matching dtype returns self, so guarding on dtype inequality yields the identical object and only skips the ATen dispatch. The `out = x` aliasing when no output buffer is passed is unchanged: x is the same object either way. causal_conv1d.py, next_power_of_2: replace triton.next_power_of_2 with vllm.utils.math_utils.next_power_of_2, which 18 other modules already use. The two agree for every n >= 1; state_len here is width-1 (or width-1 + seqlen-1 on the spec-decode path) with width >= 2, so n >= 1 always holds. The one input where they differ, n == 0, needs width == 1, which has no conv-state row and no kernel branch; stock rejected it at Triton compile time via tl.arange(0, 0). The helper is one shift instead of an eight-step bit twiddle, and the module no longer reaches through the Triton namespace for a scalar. Measured per call on the host (torch 2.14, CPU): 0.92 us, 0.31 us, 0.31 us and 0.19 us respectively -- about 85 us per step at 48 layers. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: gf239 <gf239@users.noreply.github.com>
…slice Three once-per-step rewrites in the V2 runner's speculative-decoding path. Values, dtypes and shapes are unchanged; what goes away is a second pass over the same data, one GPU kernel launch with its allocation, and a throwaway view. model_runner.py, input prep: total_num_draft_tokens was computed with a separate `.sum()` and total_num_logits derived from it, and then a few lines later `np.cumsum(num_logits, out=cu_num_logits_np[1:])` walked the same array again. The cumsum's last element is that total, so derive both from it after the cumsum. Nothing in between reads either name. With num_reqs == 0 the cumsum target is empty and cu_num_logits_np[-1] is the leading 0, which is what the sum produced. rejection_sampler.py, chunked verify: each chunk rebased its cumulative logit offsets with `- lo`. For the single-chunk case -- every step whose logit rows fit in one chunk, i.e. the common one -- and for the first chunk of a split batch, lo is 0 and the subtraction is a no-op that still costs an aten::sub dispatch, an allocation and a cudaLaunchKernel, on the GPU-idle gap between the target forward and the first draft. Guard on the value rather than on the invariant: when lo is 0 take the plain slices. Those are views of the input batch buffers; every consumer of chunk_cu_num_logits only reads them (the verify kernels tl.load, and the one escaping use is cloned), so the aliasing is not observable. speculator.py, draft metadata: seq_lens was pre-sliced to num_reqs_padded and then build_attn_metadata re-applied the identical bound as its first use of the buffer, so the call site built buf[:n][:n]. Pass the buffer as every other caller of build_attn_metadata already does; the callee's slice is the only one needed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: gf239 <gf239@users.noreply.github.com>
…ing metadata build_attn_metadata loops over every KV cache group and, for each, selects the group's block table and slot mapping, resolves the causal flag, builds a CommonAttentionMetadata (27 fields) and only then iterates that group's attention groups. A drafter initialises its attention groups from active_layer_names alone, so init_attn_backend appends an empty list for every group the draft model has no layer in. On a hybrid target with 16 full-attention and 48 linear-attention layers there are four KV cache groups and the drafter owns one; the other three were constructed and discarded on both draft metadata builds of every step. Test the group list first and continue when it is empty. The loop body writes nothing outside the inner loop -- the sole write into the returned mapping is per attention group -- so the result is byte-identical. The cost when every group is populated is one truthiness test per group. Measured per draft metadata build on the host: 6.76 us -> 1.86 us. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: gf239 <gf239@users.noreply.github.com>
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Thanks, you were right. I measured it end to end and found no statistically significant gain. Qwen3.5-0.8B + MTP, V2 runner, 6 paired rounds per GPU. Median ITL, this PR vs a comment-only control build:
The harness does detect small effects. However, adding a 2 ms busy-wait per step on the host did not change ITL either. At this step time the CPU is not the bottleneck, so trimming host work cannot help. Closing this. While looking for a real end-to-end gain nearby, I found that the V2 probabilistic drafter ignores the requests' top-k / top-p. That fix gives +3-4% output tokens/s on the same 4 GPUs: #56724. |
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Purpose
Eleven small, semantics-preserving rewrites on lines that run once to forty-eight times per engine step, in the GDN/causal-conv1d decode path and in the V2 runner's speculative-decoding path. No configuration is touched, no new state or invariant is introduced; every hunk is a local rewrite whose output is the same object, value, dtype and shape as before.
Why it matters: with piecewise cudagraphs the CPU sits on the critical path of every step. On the deployment this was written against (Qwen3.8-27B hybrid, 48 GDN + 16 full-attention layers, MTP k=3, FlashInfer, RTX 4090) an nsys profile put the GPU idle for 4.16 ms of a 24.73 ms step across ~1445 launches -- the GPU is waiting on Python. These are the Python lines that were doing redundant work on that path, each measured.
What changes, and how often it runs
vllm/model_executor/layers/mamba/-- once per GDN layer per step (48x on this model), both runnersqwen_gdn_linear_attn.py, 4 sitesself.conv1d.weightread 3x to build one view (each read = twonn.Module.__getattr__hops)causal_conv1d.pydtype-inx = x.to(conv_state.dtype)dispatched unconditionallycausal_conv1d.pydtype-out, 2 sitesreturn out.to(original_x_dtype)causal_conv1d.pynext_power_of_2, 2 sitestriton.next_power_of_2(8-step twiddle)vllm.utils.math_utils.next_power_of_2(one shift), already used by 18 modulesTensor.to(same_dtype)returnsself, so the guards yield the identical object and only skip the ATen dispatch; under the defaultmamba_cache_dtype="auto"the conv state has the model dtype and both conversions were no-ops. The twonext_power_of_2implementations agree for every n ≥ 1;state_lenhere iswidth-1(orwidth-1 + seqlen-1on the spec path) withwidth ≥ 2.vllm/v1/worker/gpu/-- once or twice per step, V2 runnermodel_runner.pycumsum.sum()pass; the total is the cumsum's last elementrejection_sampler.pyzero rebase- lowhenlo == 0(the single-chunk case, i.e. the common one): oneaten::subdispatch, one allocation, onecudaLaunchKernel, on the gap between target forward and first draftspeculator.pydouble slicebuf[:n][:n]--build_attn_metadatare-applies the same bound as its first statementattn_utils.pyempty groupsCommonAttentionMetadataplus tensor selects for every KV-cache group the drafter owns no layer in (3 of 4 on this model)The rejection-sampler change swaps a fresh tensor for a view of the input-batch buffer when
lo == 0. Every consumer ofchunk_cu_num_logitsonly reads it (the verify kernelstl.load; the one escaping use is cloned), so the aliasing is not observable; the guard tests the value rather than trusting the invariant, so it stays correct if a chunk ever starts at a non-zero offset.Total on a V2 deployment of this shape, from the per-call numbers: roughly 100 µs per step, ≈0.4 % of a 25 ms step. This is offered as cleanup with a measured benefit, not as a headline win. Happy to split it by area (
mamba/vsv1/worker/gpu/) if that is preferred.Verified and deliberately left out
Two further hunks were verified to the same standard and dropped from this PR: a dirty flag to skip
num_blocks.copy_to_uva()on steps with no block appends, and cachingslot_mappings.shape[1]/stride(0). Both add state for ≤4 µs per step; the risk/benefit did not clear the bar.Test Plan
Component-level differential test (GPU). The real functions were run under the stock tree and under this branch with fixed seeded inputs and the produced bytes hashed:
causal_conv1d_updateacross bfloat16/float16/float32 decode and across 24(kernel_width, query_len)combinations on the spec-decode varlen path (the range over whichNP2_STATELENactually varies);causal_conv1d_fn(the chunked-prefill path, which carries two of the patched lines) across 3 widths × 3 sequence layouts; thenn.Moduleattribute-identity property the weight hoist relies on, checked againstColumnParallelLinear's MRO for aweightproperty or pre-hook;next_power_of_2vstriton.next_power_of_2for n in 1..19999; the cumsum identity over 1080 random batch shapes includingnum_reqs == 0; slice-minus-zero and double-slice view identity on device. The harness fails hard if any case raises, so a case that raises identically in both trees cannot pass as a false match.CPU equivalence checks. 29 property checks pinning exactly what each rewrite depends on (
.to(same)isselfincl. non-contiguous and 0-dim;x[a:b] - 0is value/dtype-identical tox[a:b]; empty-group loop contributes nothing across five layouts; etc.).Premise re-check against current
main. The hunks were authored against v0.28.0;mainhas since changed five of the six files (attn_utils.pyby 463 lines,model_runner.pyby 493). Every anchor still matches verbatim, and each hunk's premise was re-established statement by statement against the code as it is now (e.g. all threeget_extra_common_attn_kwargsimplementations return a fresh dict;CommonAttentionMetadatahas no__post_init__; everyseq_lensconsumer sees the callee's slice, never the full buffer).Lint.
ruff checkandruff format --checkclean on all six files.Upstream tests, on the branch itself. Every test file that imports one of the six touched modules (16 files), run against this branch using the documented Python-only build --
VLLM_USE_PRECOMPILED=1 pip install -e .in a fresh venv, which pairs the branch's Python with the compiled extensions published for its base commit5af4cc33e-- on an RTX 4090.End-to-end. Greedy (temperature 0, fixed seed) completions on four prompts are byte-identical to a control build of the same compile provenance.
Test Result
VLLM_USE_PRECOMPILED=1over5af4cc33e, RTX 4090): 275 passed, 2 skipped, 0 failed.tests/kernels/mamba/test_causal_conv1d.pytests/v1/worker/test_gpu_autoregressive_speculator.pytests/v1/worker/test_gpu_batch_shard.pytests/v1/worker/test_attn_utils.pytests/kernels/mamba/test_gdn_fused_mtp.pytests/v1/worker/test_gpu_batch_ordering.pytests/v1/spec_decode/test_adaptive_verification.pytests/v1/worker/test_gpu_model_runner_v2.pytests/v1/worker/test_gpu_rejection_sampler_chunking.pytests/v1/spec_decode/test_eagle_draft_attn_metadata.pytests/v1/worker/test_gpu_extract_hidden_states_speculator.pytests/v1/worker/test_kv_cache_allocation_scope.pytests/v1/streaming_input/test_gpu_model_runner_v2_streaming.pytests/kernels/mamba/test_gdn_prefill_flashinfer.pytests/kernels/mamba/test_gdn_forward_core_split.pytests/kernels/mamba/cpu/test_cpu_gdn_ops.pyTiming, stated honestly. On the author's deployment the engine selects the V1 model runner, so of the eleven hunks only the five under
model_executor/layers/mamba/execute there; the V2-runner hunks are exercised by the component tests above and by CI, not by this timing run. A 6-round paired study (stock / compile-matched sham / this branch, arm order rotated through a Latin square, seed shared within a round, rounds as the independent unit) measured this branch against the sham ati.e. neutral within a resolution of ~0.1 %. Two methodological notes that may be useful to others measuring small changes here: (1) any edit to files under
model_executor/invalidates thetorch.compileAOT artifact and the build is recompiled on every launch, so the control arm must touch the same files or it does not control that axis -- on this rig that asymmetry alone measured +0.117 % (p = 0.034); (2) withbenchmark_combo_kernel=Truetwo builds with different source hashes can select different combo kernels, which is why the byte-identical output check is done against a same-provenance control rather than against stock.Essential Elements of an Effective PR Description Checklist
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