[Bugfix] Fix the tensor non-contiguous issue for Flashinfer TRT-LLM backend attention kernel

[elvischenv]

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

• Fix the non-contiguous tensor decode_query used in Flashinfer TRT-LLM attention kernel
• Fix the function arguments passing of FlashInferBackend.use_trtllm_decode_attention
  • self.cache_config.cache_dtype instead of attn_metadata.kv_data_type

Test Plan

Check the accuracy with lm_eval.

Test Result

Before:

vllm (pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8,quantization=modelopt,tensor_parallel_size=1,max_model_len=2048,kv_cache_dtype=auto,trust_remote_code=True), gen_kwargs: (temperature=0.0), limit: 500.0, num_fewshot: 5, batch_size: 200
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.640|±  |0.0215|
|     |       |strict-match    |     5|exact_match|↑  |0.598|±  |0.0219|

After:

vllm (pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8,quantization=modelopt,tensor_parallel_size=1,max_model_len=2048,kv_cache_dtype=auto,trust_remote_code=True), gen_kwargs: (temperature=0.0), limit: 500.0, num_fewshot: 5, batch_size: 200
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.93|±  |0.0114|
|     |       |strict-match    |     5|exact_match|↑  | 0.91|±  |0.0128|

(Optional) Documentation Update

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[gemini-code-assist]

Code Review

This pull request fixes two bugs in the FlashInfer attention backend. The first addresses an issue where a non-contiguous decode_query tensor was passed to a kernel, which is resolved by adding a .contiguous() call. The second corrects the data type of an argument passed to use_trtllm_decode_attention. The PR also includes a good refactoring to compute the kv_cache permutation only once.

My review identifies a critical issue with this refactoring. The permuted kv_cache is not guaranteed to be contiguous, which will cause a runtime assertion failure in the TRT-LLM attention path. I've suggested making the tensor contiguous at the point of creation to fix this.

[gemini-code-assist]

The permute operation on a tensor does not guarantee that the resulting tensor is contiguous in memory. The trtllm_batch_decode_with_kv_cache kernel, which is used later in this function, requires a contiguous kv_cache tensor, as suggested by the new assertion assert kv_cache_permute.is_contiguous() on line 667.

However, the permutation for the 'HND' layout will likely produce a non-contiguous tensor, causing the assertion to fail at runtime.

To ensure correctness, you should make the tensor contiguous immediately after permuting. This will satisfy the kernel's requirement and ensure the assertion passes.

kv_cache_permute = kv_cache.permute(*stride_order).contiguous()

[pavanimajety]

cc: @mgoin for review

Thanks for root causing this Elvis, great effort.

[pavanimajety]

LGTM. Thank you

[pavanimajety]

move this switch to else because it is only trtllm specific

[mgoin]

LGTM, thanks for this fix!