[Bugfix] Fix AWQ models batch invariance issues by YM2132 · Pull Request #38670 · vllm-project/vllm

YM2132 · 2026-04-01T00:03:13Z

Purpose

Enable AWQ quantized models to run with batch invariant mode (VLLM_BATCH_INVARIANT=1).

AWQ models currently fail batch invariance because vLLM auto-converts AWQ to the Marlin CUDA kernel, which bypasses the batch-invariant Triton matmul override.

This PR:

Skips Marlin auto-conversion when VLLM_BATCH_INVARIANT=1 (awq_marlin.py)
Forces AWQ to always use dequant + torch.matmul path when batch invariant, so the Triton override can intercept it (awq.py)
Fixes two pre-existing float16 bugs in batch_invariant.py exposed by AWQ using float16 (shared memory overflow on SM 86, unhandled _half_to_float in log_softmax)
Changes test dtype from "bfloat16" to "auto" so tests work with float16-only models like AWQ

Test Plan

# AWQ model
VLLM_TEST_MODEL=Qwen/Qwen3-4B-AWQ .venv/bin/python -m pytest tests/v1/determinism/test_batch_invariance.py -v --timeout=1000    

# Default model (regression check)
.venv/bin/python -m pytest tests/v1/determinism/test_batch_invariance.py -v --timeout=1000

Tested on RTX 3090 (SM 86, 24GB).

Test Result 

  ┌───────────────────────────┬──────────────┬───────────────────┐   
  │           Model           │    Before    │       After       │
  ├───────────────────────────┼──────────────┼───────────────────┤   
  │ Qwen/Qwen3-4B-AWQ         │ 7 failed, 2  │ 9 passed          │
  │                           │ passed       │                   │
  ├───────────────────────────┼──────────────┼───────────────────┤   
  │ Qwen/Qwen3-1.7B (default, │ 9 passed     │ 9 passed (no      │   
  │  bfloat16)                │              │ regression)       │   
  └───────────────────────────┴──────────────┴───────────────────┘   
                        
AI assistance was used (Claude). All changes reviewed and tested manually.

Design notes

This is PR is a first attempt to get batch invariance working with AWQ models. We trade AWQ_Marlin performance for determinism. The dequant + torch.matmul path would be slower than fused Marlin but guarantees batch invariance.

Open to feedback on:

Whether the Marlin bypass belongs in override_quantization_method or elsewhere
Whether the float16 batch_invariant.py fixes should be a separate PR
Import placement (from vllm import envs position in awq files)

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

github-actions · 2026-04-01T00:03:21Z

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gemini-code-assist

Code Review

This pull request updates determinism tests to use automatic data type selection and enhances the batch invariant mode. Key changes include adjusting Triton kernel block sizes for float16 to prevent shared memory overflows, implementing log-softmax for half-to-float conversions, and ensuring AWQ and Marlin quantization kernels are bypassed when batch invariance is enabled. Review feedback identifies a missing block size adjustment in the batched matrix multiplication logic and recommends moving an environment variable import out of a performance-critical path to minimize overhead.

gemini-code-assist · 2026-04-01T00:08:26Z

@@ -177,7 +177,7 @@ def grid(META):
        },
        torch.float16: {
            "BLOCK_SIZE_M": 128,
-            "BLOCK_SIZE_N": 256,
+            "BLOCK_SIZE_N": 128, # match the block size n of bfloat16


The reduction of BLOCK_SIZE_N to 128 for torch.float16 is necessary to avoid shared memory overflow on SM 86 GPUs (like the RTX 3090). However, this change is missing in the configs dictionary within bmm_batch_invariant (line 703), which still uses 256. This will likely cause similar crashes when batched matrix multiplications are performed in batch invariant mode on these GPUs.

I am not sure it is needed to change the BLOCK_SIZE_N in bmm_batch_invariant, I would appreciate the advice of the reviewers here.

I recommend not to change it as it is tuned, is this a must-have for the fix?

Yes and no. This is needed for SM 86 GPUs (e.g. RTX 3090 and other 80 series) which have 101KB shared memory per SM — the 256 config requires 106KB and crashes with OutOfResources error.

Perhaps we can make this conditional on SM number instead of changing the default?

If I remove that line and run the unit test on my machine I get:

----------------------- Captured stderr call ------------------------ [W401 23:15:51.385642307 Context.cpp:534] Warning: torch.backends.cuda.preferred_blas_library is an experimental feature. If you see any error or unexpected behavior when this flag is set please file an issue on GitHub. (function operator()) Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s] Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 1.70it/s] Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 1.70it/s] (EngineCore pid=146608) (EngineCore pid=146608) Process EngineCore: (EngineCore pid=146608) Traceback (most recent call last): (EngineCore pid=146608) File "/home/yusuf/.local/share/uv/python/cpython-3.12.13-linux-x86_64-gnu/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap (EngineCore pid=146608) self.run() (EngineCore pid=146608) File "/home/yusuf/.local/share/uv/python/cpython-3.12.13-linux-x86_64-gnu/lib/python3.12/multiprocessing/process.py", line 108, in run (EngineCore pid=146608) self._target(*self._args, **self._kwargs) (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/engine/core.py", line 1112, in run_engine_core (EngineCore pid=146608) raise e (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/engine/core.py", line 1082, in run_engine_core (EngineCore pid=146608) engine_core = EngineCoreProc(*args, engine_index=dp_rank, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper (EngineCore pid=146608) return func(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/engine/core.py", line 848, in __init__ (EngineCore pid=146608) super().__init__( (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/engine/core.py", line 124, in __init__ (EngineCore pid=146608) kv_cache_config = self._initialize_kv_caches(vllm_config) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper (EngineCore pid=146608) return func(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/engine/core.py", line 247, in _initialize_kv_caches (EngineCore pid=146608) available_gpu_memory = self.model_executor.determine_available_memory() (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/executor/abstract.py", line 136, in determine_available_memory (EngineCore pid=146608) return self.collective_rpc("determine_available_memory") (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/executor/uniproc_executor.py", line 80, in collective_rpc (EngineCore pid=146608) result = run_method(self.driver_worker, method, args, kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/serial_utils.py", line 510, in run_method (EngineCore pid=146608) return func(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=146608) return func(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/worker/gpu_worker.py", line 370, in determine_available_memory (EngineCore pid=146608) self.model_runner.profile_run() (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/worker/gpu_model_runner.py", line 5810, in profile_run (EngineCore pid=146608) output = self._dummy_sampler_run(last_hidden_states) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context (EngineCore pid=146608) return func(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/v1/worker/gpu_model_runner.py", line 5591, in _dummy_sampler_run (EngineCore pid=146608) logits = self.model.compute_logits(hidden_states) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/models/qwen3.py", line 332, in compute_logits (EngineCore pid=146608) logits = self.logits_processor(self.lm_head, hidden_states) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl (EngineCore pid=146608) return self._call_impl(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1787, in _call_impl (EngineCore pid=146608) return forward_call(*args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/logits_processor.py", line 64, in forward (EngineCore pid=146608) logits = self._get_logits(hidden_states, lm_head, embedding_bias) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/logits_processor.py", line 96, in _get_logits (EngineCore pid=146608) logits = lm_head.quant_method.apply(lm_head, hidden_states, bias=embedding_bias) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/vocab_parallel_embedding.py", line 69, in apply (EngineCore pid=146608) return dispatch_unquantized_gemm()(layer, x, layer.weight, bias) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/utils.py", line 98, in default_unquantized_gemm (EngineCore pid=146608) return torch.nn.functional.linear(x, weight, bias) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/batch_invariant.py", line 913, in linear_batch_invariant (EngineCore pid=146608) output = matmul_batch_invariant(input, weight.t()) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/batch_invariant.py", line 608, in matmul_batch_invariant (EngineCore pid=146608) result = matmul_persistent(a, b) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/vllm/model_executor/layers/batch_invariant.py", line 196, in matmul_persistent (EngineCore pid=146608) matmul_kernel_persistent[grid]( (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/triton/runtime/jit.py", line 370, in <lambda> (EngineCore pid=146608) return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/triton/runtime/jit.py", line 743, in run (EngineCore pid=146608) launch_metadata = kernel.launch_metadata(grid, stream, *bound_args.values()) (EngineCore pid=146608) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/triton/compiler/compiler.py", line 482, in launch_metadata (EngineCore pid=146608) self._init_handles() (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/triton/compiler/compiler.py", line 456, in _init_handles (EngineCore pid=146608) raise_(OutOfResources(self.metadata.shared, max_shared, "shared memory")) (EngineCore pid=146608) File "/home/yusuf/PycharmProjects/ym_vllm/vllm/.venv/lib/python3.12/site-packages/triton/compiler/compiler.py", line 448, in raise_ (EngineCore pid=146608) raise err (EngineCore pid=146608) triton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 106496, Hardware limit: 101376. Reducing block sizes or `num_stages` may help.

Hi @yewentao256, perhaps for this part we could introduce a per SM check and then decide the block size based on that? There exists a function: get_max_shared_memory_bytes() which returns the SM memory and could be used here? E.g. we query that when we run in batch invariant mode and then set the "BLOCK_SIZE_N": 128, IFF the user has < 106496 bytes on their SM version?

This would mean the memory issue is fixed across all GPUs not only for the 3090, as it would be hard to check per GPU or even per SM family as in SM 80 family, SM80 (A100) has 166,912 bytes and SM86 (3090) has 101,376 bytes.

How does that sound? I can put together an implementation and test it?

Sounds good!

I made a change in latest commit, which dynamically sets the block size now. Could you please test on h200 to confirm no regression?

The unit tests pass on my 3090 with the new fi.

YM2132 · 2026-04-01T06:41:27Z

Hi @yewentao256, this change is probably worth testing on newer hardware too. I only have a 3090 but if someone has a more recent GPU I would appreciate them trying the determinism unit tests on the Qwen/Qwen3-4B-AWQ model to validate it's not only my hardware in which the tests fail.

yewentao256

Thanks for the work!

Verified this works on H200 as well

yewentao256 · 2026-04-01T21:09:04Z

@@ -177,7 +177,7 @@ def grid(META):
        },
        torch.float16: {
            "BLOCK_SIZE_M": 128,
-            "BLOCK_SIZE_N": 256,
+            "BLOCK_SIZE_N": 128, # match the block size n of bfloat16


I recommend not to change it as it is tuned, is this a must-have for the fix?

YM2132 · 2026-04-01T22:22:44Z

Thanks for the work!

Verified this works on H200 as well

Happy to help!

Nice, good to know it works on other hardware. Looking forward to getting this one fixed :)

mergify · 2026-04-02T13:51:52Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @YM2132.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

…through torch.matmul path so that it can be picked up by batch invariant kernels Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

…marlin quantization method Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

…th batch invariance Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <>

mergify · 2026-04-03T07:16:20Z

Hi @YM2132, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

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Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <>

yewentao256

LGTM, thanks for the work!

YM2132 · 2026-04-03T15:03:21Z

LGTM, thanks for the work!

Nice! Thanks for the guidance @yewentao256

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <> Co-authored-by: yusuf <yusuf@deeplearningmachine.mynet>

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <> Co-authored-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: Rishi Puri <riship@nvidia.com>

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <> Co-authored-by: yusuf <yusuf@deeplearningmachine.mynet>

YM2132 requested review from mgoin, pavanimajety, robertgshaw2-redhat, tlrmchlsmth and yewentao256 as code owners April 1, 2026 00:03

mergify Bot added v1 bug Something isn't working labels Apr 1, 2026

gemini-code-assist Bot reviewed Apr 1, 2026

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YM2132 force-pushed the fix/AWQ_models branch 2 times, most recently from b352998 to 6117302 Compare April 1, 2026 06:35

yewentao256 reviewed Apr 1, 2026

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mergify Bot added the needs-rebase label Apr 2, 2026

yusuf added 5 commits April 2, 2026 18:13

if in batch invariant mode, then always force awq quantization to go …

d607bad

…through torch.matmul path so that it can be picked up by batch invariant kernels Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

If running in batch invariant mode return and do not override to awq_…

c1bee22

…marlin quantization method Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

change blfoat16 to auto to let float16 models work

00c70e8

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

3 changes to ensure float16 compatibility and ampere compatibility wi…

d457f39

…th batch invariance Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

move import to top of file

8b28eeb

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet>

YM2132 force-pushed the fix/AWQ_models branch from 4996326 to 8b28eeb Compare April 2, 2026 17:18

mergify Bot removed the needs-rebase label Apr 2, 2026

Merge branch 'main' into fix/AWQ_models

efbdbe1

yewentao256 added the ready ONLY add when PR is ready to merge/full CI is needed label Apr 2, 2026

Use dynamic shared memory check for float16 BLOCK_SIZE_N

f3d6939

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <>

fix ruff formatting errors

b39b544

Signed-off-by: yusuf <yusuf@deeplearningmachine.mynet> Signed-off-by: <>

yewentao256 approved these changes Apr 3, 2026

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yewentao256 enabled auto-merge (squash) April 3, 2026 13:29

Merge branch 'main' into fix/AWQ_models

75d751c

yewentao256 merged commit 46f02e0 into vllm-project:main Apr 3, 2026
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YM2132 deleted the fix/AWQ_models branch April 3, 2026 15:03

YM2132 mentioned this pull request Apr 16, 2026

[Doc] Add Qwen3 AWQ models to documentation #40034

Merged

vhagor mentioned this pull request Aug 19, 2026

[Bugfix] Fix batch-invariant fp32 matmul OOR on SM89 for N=1 #52960

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