Use w8a8 quantized matmul Pallas kernel

[vanbasten23]

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

Purpose

This PR uses a custom Pallas kernel to do w8a8 quantized matmul in lieu of XLA's matmul. This results in much better performance for w8a8 models:

by using neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 with TP=1 and RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w8a8 with TP=8, I got the following e2e benchmark:

case	tp=1 throughput	tp=8 throughput
baseline (non quantized)	8.09	6.30
quantized via XLA with the flag --xla_jf_conv_input_fusion=False	9.26	6.07
quantized via kernel	9.69	6.77

Test Plan

• VLLM_USE_V1=1 pytest -s -vv tests/v1/tpu/test_basic.py -k test_w8a8_quantization
• VLLM_USE_V1=1 pytest -s -v tests/tpu/test_quantization_accuracy.py

Test Result

Note, need to install lm-eval==0.4.8. Without specifying the version, lm-eval=0.4.9 will be installed and running test_quantization_accuracy.py fails with an error

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[gemini-code-assist]

Hello @vanbasten23, 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!

Summary of Changes

Hello team, gemini-code-assist here to provide a summary of this pull request. This PR, titled "Use w8a8 quantized matmul Pallas kernel", focuses on integrating a specific optimized kernel for 8-bit weight and 8-bit activation (w8a8) quantization on XLA/TPU platforms. The primary change involves switching the underlying matrix multiplication operation used for scaled quantization from a generic quantized_matmul to a more specific quantized_matmul_int8 kernel, likely a Pallas kernel as indicated by the title, which is intended for w8a8 operations. Additionally, a new test case is added to specifically verify the functionality of running a w8a8 quantized model on TPU, ensuring the new kernel is correctly utilized and produces expected results. A minor adjustment is also made to the XLA runtime initialization arguments.

Highlights

• TPU W8A8 Quantization Kernel: The core change is the adoption of torch.ops.xla.quantized_matmul_int8 for w8a8 scaled matrix multiplication within the XLA quantization kernel implementation. This replaces a previous, more general quantized_matmul call.
• W8A8 Test Case: A new test function, test_w8a8_quantization, is added to tests/v1/tpu/test_basic.py. This test loads a specific w8a8 quantized Llama model and performs a basic generation to validate the integration of the new kernel and overall w8a8 support on TPU.
• XLA Runtime Configuration: A previously included XLA compiler flag, --xla_jf_conv_input_fusion=False, is commented out from the LIBTPU_INIT_ARGS environment variable setting in the TPU worker initialization.

Changelog

• tests/v1/tpu/test_basic.py
  • Added a new test test_w8a8_quantization to verify running w8a8 quantized models on TPU.
  • The new test loads a specific w8a8 quantized Llama model and performs a basic generation check.
• vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py
  • Added import torch_xla.experimental.custom_kernel required for registering custom XLA ops.
  • Replaced the call to torch.ops.xla.quantized_matmul with torch.ops.xla.quantized_matmul_int8 for w8a8 scaled matrix multiplication.
  • Commented out the old quantized_matmul implementation.
• vllm/v1/worker/tpu_worker.py
  • Commented out the XLA compiler flag --xla_jf_conv_input_fusion=False from LIBTPU_INIT_ARGS.

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---

Weights shrink down to eight,
Activations match their state,
TPU runs fast,
Quantization lasts,
Inference sealed by fate.

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[gemini-code-assist]

Code Review

This pull request introduces the use of a w8a8 quantized matmul Pallas kernel for XLA, which is a good step towards optimizing performance on TPUs. A new test case for w8a8 quantization has also been added.

However, the pull request description is currently a template and needs to be filled out with the purpose, test plan, and test results. This information is crucial for understanding the changes and their impact.

There are a couple of areas in the code that would benefit from clarification or minor improvements, detailed in the comments below.

Summary of Findings

• Missing Pull Request Description: The pull request description is currently a template and lacks essential information such as the purpose of the PR, test plan, and test results. This information is vital for reviewers and future maintainers.
• Output Dtype of New XLA Matmul Op: In vllm/model_executor/layers/quantization/kernels/scaled_mm/xla.py, the explicit cast of the output tensor to x.dtype was removed. It's important to confirm the output dtype of the new torch.ops.xla.quantized_matmul_int8 op and ensure that this removal doesn't negatively impact performance or type consistency downstream.
• Test Assertion Specificity: The assertion in the new test test_w8a8_quantization in tests/v1/tpu/test_basic.py is identical to a more generic test. It could be strengthened to be more specific to w8a8 quantization.
• Commented-out Code: In vllm/v1/worker/tpu_worker.py, there's a commented-out line for LIBTPU_INIT_ARGS (lines 102-104). This seems like leftover experimental code and should ideally be removed for clarity. (Severity: low, not commented due to review settings)

Merge Readiness

This pull request makes a targeted change to use a new Pallas kernel for w8a8 quantization on TPUs and adds a corresponding test. However, before merging, it's important to address the following:

1. Fill out the Pull Request Description: Provide details on the purpose, testing, and results.
2. Clarify Output Dtype: Confirm the behavior of torch.ops.xla.quantized_matmul_int8 regarding its output data type and the necessity (or lack thereof) of casting it, as this could impact correctness or performance.
3. Improve Test Assertion: Consider making the assertion in test_w8a8_quantization more specific to the quantization being tested.

Due to the potential impact of the dtype question (high severity) and the missing PR description, I recommend addressing these points before merging. I am unable to approve pull requests, so please ensure other reviewers take a look and approve once these concerns are addressed.

[gemini-code-assist]

The previous implementation using torch.ops.xla.quantized_matmul included a cast out = out.to(x.dtype) with the comment "quantized_matmul output is fp32, cast it down to bf16 for perf".

What is the output data type of the new torch.ops.xla.quantized_matmul_int8? If it also outputs in a higher precision (e.g., fp32) and x.dtype is a lower precision format like bfloat16, is a similar cast to x.dtype still necessary for performance or type consistency with subsequent layers? If the new op already handles this or outputs directly in x.dtype, then this change is fine.

[gemini-code-assist]

The assertion assert "1024" in output or "0, 1" in output is the same as in the test_basic function. For a test specifically targeting w8a8 quantization, could this assertion be made more specific to validate the correctness of the quantization itself? For example, comparing against known good outputs for this quantized model or checking for specific numerical properties might provide stronger validation.

[vanbasten23]

The failing CI is test_sampler.py and the failure is env = get_tpu_env() timed out. It's a known issue and irrelevant to this PR.

[yaochengji]

LGTM, thanks!