[Bugfix] Enforce contiguous input for dynamic_per_token FP8/INT8 quant

[mgoin]

Purpose

We require that the inputs are contiguous for both the dynamic_per_token_scaled_fp8_quant and dynamic_scaled_int8_quant kernels. This PR enforces that as I ran into an error when evaluating a deepseek model.

Test Plan

Tested using FP8-dynamic DeepSeek V2 Lite (per token and per channel)

Test Result

Before:

lm_eval --model vllm --model_args pretrained=nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8-dynamic --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size auto 
...
  File "/home/mgoin/code/vllm/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py", line 145, in apply_weights
    return self.fp8_linear.apply(input=x,
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/w8a8_utils.py", line 351, in apply
    qinput, x_scale = ops.scaled_fp8_quant(
                      ^^^^^^^^^^^^^^^^^^^^^
  File "/home/mgoin/code/vllm/vllm/_custom_ops.py", line 1269, in scaled_fp8_quant
    torch.ops._C.dynamic_per_token_scaled_fp8_quant(
  File "/home/mgoin/venvs/vllm/lib/python3.12/site-packages/torch/_ops.py", line 1158, in __call__
    return self._op(*args, **(kwargs or {}))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Expected input.is_contiguous() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)

After:

lm_eval --model vllm --model_args pretrained=nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8-dynamic --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size auto 
vllm (pretrained=nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8-dynamic,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.7528|±  |0.0119|
|     |       |strict-match    |     5|exact_match|↑  |0.7362|±  |0.0121|

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[gemini-code-assist]

Summary of Changes

Hello @mgoin, 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 resolves an issue where the dynamic per-token FP8 and dynamic INT8 quantization kernels would fail if their input tensors were not contiguous in memory. The change ensures that the input is made contiguous before being passed to the underlying kernels, fixing the RuntimeError and allowing models like DeepSeek V2 Lite FP8-dynamic to run correctly.

Highlights

• Fix Runtime Error: Addresses a RuntimeError that occurred when dynamic per-token FP8 or dynamic INT8 quantization kernels received non-contiguous input tensors.
• Enforce Contiguity: Ensures that the input tensor passed to the dynamic_per_token_scaled_fp8_quant and dynamic_scaled_int8_quant C++ kernels is contiguous by explicitly calling .contiguous() on the input.

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[gemini-code-assist]

Code Review

This pull request effectively resolves a RuntimeError that occurred when non-contiguous tensors were passed as input to specific FP8 and INT8 quantization kernels. The fix involves adding .contiguous() calls to the input tensors in vllm/_custom_ops.py before they are processed by the underlying C++ operations. These changes are well-targeted and directly address the issue described.

My review feedback primarily focuses on enhancing code maintainability by suggesting the addition of inline comments. These comments would explain why the .contiguous() calls are necessary (i.e., due to kernel requirements), which is helpful for future developers and aligns with existing commenting practices within the codebase (such as for the rms_norm function).

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[houseroad]

Looks good.

[LucasWilkinson]

Can we just update the kernel to support non-contiguous data? I think this should be our default response to these kind of bugs

[mgoin]

I worry about losing the vectorization for the non-contiguous case, but okay we can make the change after Wentao lands the vectorization refactor in #19233

[mgoin]

Will address in future work, let us land fix for now