[CPU] Fix torch version in x86 CPU backend and refine default configurations

[bigPYJ1151]

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

• Fix torch version used in the x86 CPU backend to avoid random sampling performance regression.

Test Plan

offline inference

Test Result

• main

VLLM_CPU_KVCACHE_SPACE=40 \
VLLM_CPU_OMP_THREADS_BIND="128-158|160-190|192-222|224-254" \
python3 benchmark_throughput.py --backend=vllm --dataset=./ShareGPT_V3_unfiltered_cleaned_split.json -tp=4 --model=meta-llama/Meta-Llama-3-8B-Instruct --num-prompts=1000 --dtype=bfloat16 --trust-remote-code

Throughput: 1.32 requests/s, 544.45 total tokens/s, 261.13 output tokens/s

• PR

VLLM_CPU_KVCACHE_SPACE=40 \
VLLM_CPU_OMP_THREADS_BIND="128-158|160-190|192-222|224-254" \
python3 benchmark_throughput.py --backend=vllm --dataset=./ShareGPT_V3_unfiltered_cleaned_split.json -tp=4 --model=meta-llama/Meta-Llama-3-8B-Instruct --num-prompts=1000 --dtype=bfloat16 --trust-remote-code

Throughput: 2.97 requests/s, 1228.82 total tokens/s, 589.37 output tokens/s

This PR:

• Fix torch version as 2.6.0 for x86 CPU backend, because of random number generator performance regression in torch 2.7.0, affects vLLM random sampling performance significantly.
• Add new requirement files for the CPU backend building and testing.
• Change llava tests' dtype to BF16, as torch 2.6 FP16 GEMM performs slow on this model.
• Refine V1 CPU backend compile config and default batch size config.

[gemini-code-assist]

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Summary of Changes

Hello! Gemini or gemini-code-assist here, providing a summary of this pull request. This PR primarily focuses on improving the x86 CPU backend by addressing a performance regression found in PyTorch 2.7.0, pinning the version to 2.6.0. It also aims to accelerate the CPU CI pipeline and refines some default configurations specific to the CPU backend. Additionally, it updates how quantization configurations are handled in some VLM models to avoid unnecessary imports.

Highlights

• PyTorch Version Pinning: The PyTorch version for the x86 CPU backend is explicitly pinned to 2.6.0 in the requirement files (requirements/cpu.txt, requirements/cpu-build.txt, requirements/cpu-test.in). This is done to avoid a performance regression in random number generation observed in PyTorch 2.7.0 on x86 platforms, which significantly impacts vLLM's random sampling performance.
• CPU CI Acceleration: New requirement files (cpu-build.txt, cpu-test.in) are introduced specifically for CPU builds and tests. The CI script (run-cpu-test.sh) is modified to set a VLLM_CPU_CI_ENV variable. This variable is then used in vllm/platforms/cpu.py to potentially switch the V1 compilation backend to 'eager' mode during CI runs, aiming to reduce compilation time and accelerate tests.
• Refined CPU Configurations: Default values for max_num_batched_tokens and max_num_seqs are refined specifically for the CPU backend in vllm/engine/arg_utils.py. The logic for setting default max_num_seqs is updated to be context-aware. The forced enforce_eager=True setting for CPU in vllm/platforms/cpu.py is also removed.
• Quantization Config Handling: In several VLM model files (minicpmo.py, ovis.py, qwen2_5_vl.py, qwen2_vl.py), the check for GPTQ/Marlin quantization configurations is changed from using isinstance() to checking the quantization config's name via get_name(). This avoids needing to import the specific GPTQ/Marlin config classes in these files.

Changelog

Click here to see the changelog

• .buildkite/scripts/hardware_ci/run-cpu-test.sh
  • Added VLLM_CPU_CI_ENV=1 environment variable to docker run commands for CI (lines 27, 28).
  • Added VLLM_CPU_CI_ENV=0 before the benchmark_serving.py command (line 72).
• docker/Dockerfile.cpu
  • Updated Dockerfile to use requirements/cpu-build.txt for build dependencies (line 69).
  • Updated Dockerfile to use requirements/cpu-test.in for test dependencies (line 112).
• requirements/cpu-build.txt
  • Added new file specifying build dependencies for the CPU backend.
  • Pinned torch to version 2.6.0+cpu for x86_64 (line 9).
  • Pinned intel_extension_for_pytorch to version 2.6.0 for x86_64 (implied by build requirements).
• requirements/cpu-test.in
  • Added new file specifying test dependencies for the CPU backend.
  • Pinned torch to version 2.6.0 (line 27).
  • Pinned transformers to version 4.52.4 (line 39) and tokenizers to version 0.21.1 (line 40).
• requirements/cpu.txt
  • Pinned torch to version 2.6.0+cpu for x86_64 (line 11).
  • Pinned intel_extension_for_pytorch to version 2.6.0 for x86_64 (line 29).
• tests/models/multimodal/generation/test_common.py
  • Added explicit dtype="bfloat16" for the minicpmo test configuration when running on CPU (line 110).
• vllm/engine/arg_utils.py
  • Changed default_max_num_seqs to be a dictionary based on UsageContext (lines 1551, 1558).
  • Added CPU-specific default values for max_num_batched_tokens and max_num_seqs (lines 1582-1590).
  • Updated logic to use the context-aware default for max_num_seqs (lines 1612-1614).
• vllm/model_executor/models/minicpmo.py
  • Removed imports for GPTQConfig and GPTQMarlinConfig (lines 38-40).
  • Changed quantization config check from isinstance() to quant_config.get_name() in (...) (lines 520-521).
• vllm/model_executor/models/ovis.py
  • Removed imports for GPTQConfig and GPTQMarlinConfig (lines 34-36).
  • Changed quantization config check from isinstance() to quant_config.get_name() in (...) (lines 434-435).
• vllm/model_executor/models/qwen2_5_vl.py
  • Removed imports for GPTQConfig and GPTQMarlinConfig (lines 52-54).
  • Changed quantization config check from isinstance() to quant_config.get_name() in (...) (lines 862-863).
• vllm/model_executor/models/qwen2_vl.py
  • Removed imports for GPTQConfig and GPTQMarlinConfig (lines 51-53).
  • Changed quantization config check from isinstance() to quant_config.get_name() in (...) (lines 1111-1112).
• vllm/platforms/cpu.py
  • Removed the logic that forced model_config.enforce_eager = True for CPU (lines 94-95).
  • Adjusted V1 compilation backend selection to use 'eager' in CI if VLLM_CPU_CI_ENV is set, otherwise use 'inductor' (lines 178-181).
• vllm/v1/worker/cpu_model_runner.py
  • Removed unused import contextmanager (line 2).
  • Removed the _set_global_compilation_settings context manager function (lines 73-86).

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[gemini-code-assist]

Code Review

This pull request effectively addresses the torch performance regression on the x86 CPU backend by pinning to version 2.6.0. The introduction of VLLM_CPU_CI_ENV for CI-specific configurations and the new CPU-specific requirement files are positive changes. The refactoring in VLM model files to check quantization method by name instead of type improves modularity.

There are a couple of areas related to the V1 CPU backend compilation configuration in vllm/platforms/cpu.py and vllm/v1/worker/cpu_model_runner.py where further clarification on the intent and impact of the changes would be helpful. Please see the detailed comments on those files.

The PR description's "Test Result" section is currently marked as TODO; please consider updating this.

Summary of Findings

• V1 CPU Backend Compilation Configuration: Changes to the V1 CPU backend compilation settings, specifically the removal of global inductor configurations for freezing and force_disable_caches in cpu_model_runner.py, and the removal of custom_ops += ["none"] in platforms/cpu.py, warrant clarification to ensure no unintended performance or behavior regressions in non-CI CPU V1 scenarios.
• Torch Version Pinning: The core fix of pinning torch to 2.6.0+cpu and intel_extension_for_pytorch to 2.6.0 for the x86 CPU backend is correctly implemented and addresses the reported performance regression.
• CI Optimization: The use of VLLM_CPU_CI_ENV to switch to an "eager" backend for CI tests is a good strategy to accelerate CI execution.
• Quantization Check Refinement: Refactoring VLM models to check quantization method by name (quant_config.get_name()) instead of isinstance checks against specific GPTQ/Marlin config classes is a good improvement for modularity and reduces direct dependencies.

Merge Readiness

The pull request makes significant improvements and fixes a key performance issue. However, before merging, it would be beneficial to get clarification on the V1 CPU backend compilation changes mentioned in the review comments to ensure they align with the intended behavior and performance expectations. I am unable to approve this pull request myself; please ensure it is reviewed and approved by other maintainers after addressing the feedback. I recommend addressing the medium severity comments before merging.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @bigPYJ1151.

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