[v1] Re-add fp32 support to v1 engine through FlexAttention

[Isotr0py]

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.

Purpose

• We reverted v1 fp32 support to unblock v0.9.1 release before due to following failing tests:
  https://buildkite.com/vllm/ci/builds/21699#01975508-6c54-47d6-a951-82873b896a11
  https://buildkite.com/vllm/ci/builds/21699#01975508-6c52-41ed-ba7a-cc1a464cec91
• This PR re-adds v1 fp32 support with corresponding fix.

Test Plan

pytest -s -v tests/lora/test_llama_tp.py -k test_tp2_serialize_and_deserialize_lora

pytest -s -v tests/distributed/test_distributed_oot.py

Test Result

Both previous failing tests passed locally.

(Optional) Documentation Update

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[gemini-code-assist]

Summary of Changes

Hello @Isotr0py, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request re-enables full FP32 support for the v1 engine, which was previously disabled due to failing tests. My changes involve explicitly routing FP32 attention operations to the FlexAttention backend on CUDA, removing the prior data type restrictions, and updating model initialization to respect the specified data type. I've also added new tests to validate these changes and included minor fixes to ensure overall compatibility and stability.

Highlights

• Re-enable FP32 Support for V1 Engine: I have removed the explicit V1_SUPPORTED_DTYPES check in vllm/engine/arg_utils.py that previously restricted the V1 engine to only FP16 and BF16, thereby re-enabling FP32 support.
• FlexAttention for FP32 on CUDA V1 Engine: In vllm/platforms/cuda.py, I've added logic to ensure that when the V1 engine is active and the model's data type is not FP16 or BF16 (e.g., FP32), the FlexAttentionBackend is explicitly selected for CUDA devices.
• New Test for FP32 Attention Backend Selection: A new test, test_fp32_fallback, has been added to tests/kernels/attention/test_attention_selector.py. This test verifies that the correct attention backend (FlexAttention for CUDA V1, TORCH_SDPA_VLLM_V1 for CPU V1) is selected when using FP32.
• Ensuring Correct Model Initialization Dtype: I've updated vllm/model_executor/model_loader/tensorizer_loader.py to wrap the init_tensorizer_model call with set_default_torch_dtype(model_config.dtype) and torch.device(device_config.device). This ensures that models are initialized with their specified data type and on the correct device, which is crucial for proper FP32 operation.
• Test Environment Dtype Consistency: To maintain consistent test environments, I've added torch.set_default_dtype(torch.float16) to several test functions in tests/v1/worker/test_gpu_model_runner.py. This ensures these specific tests run with FP16 as the default dtype, preventing unintended interactions with FP32 changes.
• FlashInfer Logits Contiguity Fix: A minor fix was applied in vllm/v1/sample/ops/topk_topp_sampler.py by adding .contiguous() to the logits tensor before passing it to flashinfer.sampling.top_k_top_p_sampling_from_logits. This ensures proper memory layout for the FlashInfer operation.

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[gemini-code-assist]

Code Review

The pull request re-adds FP32 support to the v1 engine through FlexAttention. The changes include modifications to the attention backend selection logic, model loading, and sampling operations. The addition of tests ensures the correct behavior of the FP32 fallback mechanism.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Isotr0py.

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

[mgoin]

Looks reasonable to me with green CI.. please make sure to force run all tests to trigger

[houseroad]

Looks good to me.

[houseroad]

Please fix the linter

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Isotr0py.

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