[CPU] Fix torch version in x86 CPU backend (#19258)

Signed-off-by: jiang1.li <jiang1.li@intel.com>
This commit is contained in:
Li, Jiang
2025-06-26 18:34:47 +08:00
committed by GitHub
parent d188913d99
commit 0567c8249f
8 changed files with 52 additions and 20 deletions

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@ -131,16 +131,19 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
// Quantization // Quantization
#ifdef __AVX512F__ #ifdef __AVX512F__
at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
// Compute int8 quantized tensor for given scaling factor. // Compute int8 quantized tensor for given scaling factor.
ops.def( ops.def(
"static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale," "static_scaled_int8_quant(Tensor! out, Tensor input, Tensor scale,"
"Tensor? azp) -> ()"); "Tensor? azp) -> ()",
{stride_tag});
ops.impl("static_scaled_int8_quant", torch::kCPU, &static_scaled_int8_quant); ops.impl("static_scaled_int8_quant", torch::kCPU, &static_scaled_int8_quant);
// Compute int8 quantized tensor and scaling factor // Compute int8 quantized tensor and scaling factor
ops.def( ops.def(
"dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale, " "dynamic_scaled_int8_quant(Tensor! out, Tensor input, Tensor! scale, "
"Tensor!? azp) -> ()"); "Tensor!? azp) -> ()",
{stride_tag});
ops.impl("dynamic_scaled_int8_quant", torch::kCPU, ops.impl("dynamic_scaled_int8_quant", torch::kCPU,
&dynamic_scaled_int8_quant); &dynamic_scaled_int8_quant);
// W8A8 GEMM, supporting symmetric per-tensor or per-row/column // W8A8 GEMM, supporting symmetric per-tensor or per-row/column
@ -148,7 +151,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
ops.def( ops.def(
"cutlass_scaled_mm(Tensor! out, Tensor a," "cutlass_scaled_mm(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales," " Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor? bias) -> ()"); " Tensor b_scales, Tensor? bias) -> ()",
{stride_tag});
ops.impl("cutlass_scaled_mm", torch::kCPU, &int8_scaled_mm); ops.impl("cutlass_scaled_mm", torch::kCPU, &int8_scaled_mm);
// w8a8 GEMM, supporting asymmetric per-tensor or per-row/column // w8a8 GEMM, supporting asymmetric per-tensor or per-row/column
// quantization. // quantization.
@ -156,7 +160,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
"cutlass_scaled_mm_azp(Tensor! out, Tensor a," "cutlass_scaled_mm_azp(Tensor! out, Tensor a,"
" Tensor b, Tensor a_scales," " Tensor b, Tensor a_scales,"
" Tensor b_scales, Tensor azp_adj," " Tensor b_scales, Tensor azp_adj,"
" Tensor? azp, Tensor? bias) -> ()"); " Tensor? azp, Tensor? bias) -> ()",
{stride_tag});
ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp); ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp);
#elif defined(__powerpc64__) #elif defined(__powerpc64__)
// Compute int8 quantized tensor for given scaling factor. // Compute int8 quantized tensor for given scaling factor.

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@ -66,7 +66,7 @@ ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
WORKDIR /workspace/vllm WORKDIR /workspace/vllm
RUN --mount=type=cache,target=/root/.cache/uv \ RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \ --mount=type=bind,src=requirements/cpu-build.txt,target=requirements/build.txt \
uv pip install -r requirements/build.txt uv pip install -r requirements/build.txt
COPY . . COPY . .
@ -79,6 +79,22 @@ RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \ --mount=type=bind,source=.git,target=.git \
VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel
######################### TEST DEPS #########################
FROM base AS vllm-test-deps
WORKDIR /workspace/vllm
RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
cp requirements/test.in requirements/cpu-test.in && \
sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
sed -i 's/torch==.*/torch==2.6.0/g' requirements/cpu-test.in && \
sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cpu-test.txt
######################### DEV IMAGE ######################### ######################### DEV IMAGE #########################
FROM vllm-build AS vllm-dev FROM vllm-build AS vllm-dev
@ -97,28 +113,19 @@ RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \ --mount=type=bind,source=.git,target=.git \
VLLM_TARGET_DEVICE=cpu python3 setup.py develop VLLM_TARGET_DEVICE=cpu python3 setup.py develop
COPY --from=vllm-test-deps /workspace/vllm/requirements/cpu-test.txt requirements/test.txt
RUN --mount=type=cache,target=/root/.cache/uv \ RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,src=requirements/test.in,target=requirements/test.in \
cp requirements/test.in requirements/test-cpu.in && \
sed -i '/mamba_ssm/d' requirements/test-cpu.in && \
uv pip compile requirements/test-cpu.in -o requirements/test.txt && \
uv pip install -r requirements/dev.txt && \ uv pip install -r requirements/dev.txt && \
pre-commit install --hook-type pre-commit --hook-type commit-msg pre-commit install --hook-type pre-commit --hook-type commit-msg
ENTRYPOINT ["bash"] ENTRYPOINT ["bash"]
######################### TEST IMAGE ######################### ######################### TEST IMAGE #########################
FROM base AS vllm-test FROM vllm-test-deps AS vllm-test
WORKDIR /workspace/ WORKDIR /workspace/
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,src=requirements/test.in,target=requirements/test.in \
cp requirements/test.in requirements/test-cpu.in && \
sed -i '/mamba_ssm/d' requirements/test-cpu.in && \
uv pip compile requirements/test-cpu.in -o requirements/cpu-test.txt && \
uv pip install -r requirements/cpu-test.txt
RUN --mount=type=cache,target=/root/.cache/uv \ RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \ --mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \
uv pip install dist/*.whl uv pip install dist/*.whl

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@ -0,0 +1,12 @@
# Temporarily used for x86 CPU backend to avoid performance regression of torch>2.6.0+cpu,
# see https://github.com/pytorch/pytorch/pull/151218
cmake>=3.26.1
ninja
packaging>=24.2
setuptools>=77.0.3,<80.0.0
setuptools-scm>=8
--extra-index-url https://download.pytorch.org/whl/cpu
torch==2.6.0+cpu
wheel
jinja2>=3.1.6
regex

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@ -8,7 +8,7 @@ numba == 0.61.2; python_version > '3.9'
packaging>=24.2 packaging>=24.2
setuptools>=77.0.3,<80.0.0 setuptools>=77.0.3,<80.0.0
--extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://download.pytorch.org/whl/cpu
torch==2.7.0+cpu; platform_machine == "x86_64" torch==2.6.0+cpu; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
torch==2.7.0; platform_system == "Darwin" torch==2.7.0; platform_system == "Darwin"
torch==2.7.0; platform_machine == "ppc64le" or platform_machine == "aarch64" torch==2.7.0; platform_machine == "ppc64le" or platform_machine == "aarch64"
@ -23,6 +23,7 @@ datasets # for benchmark scripts
# Intel Extension for PyTorch, only for x86_64 CPUs # Intel Extension for PyTorch, only for x86_64 CPUs
intel-openmp==2024.2.1; platform_machine == "x86_64" intel-openmp==2024.2.1; platform_machine == "x86_64"
intel_extension_for_pytorch==2.7.0; platform_machine == "x86_64" intel_extension_for_pytorch==2.6.0; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
py-libnuma; platform_system != "Darwin" py-libnuma; platform_system != "Darwin"
psutil; platform_system != "Darwin" psutil; platform_system != "Darwin"
triton==3.2.0; platform_machine == "x86_64" # Triton is required for torch 2.6+cpu, as it is imported in torch.compile.

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@ -107,6 +107,8 @@ VLM_TEST_SETTINGS = {
), ),
limit_mm_per_prompt={"image": 4}, limit_mm_per_prompt={"image": 4},
)], )],
# TODO: Revert to "auto" when CPU backend can use torch > 2.6
dtype="bfloat16" if current_platform.is_cpu() else "auto",
marks=[pytest.mark.core_model, pytest.mark.cpu_model], marks=[pytest.mark.core_model, pytest.mark.cpu_model],
), ),
"paligemma": VLMTestInfo( "paligemma": VLMTestInfo(

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@ -203,6 +203,9 @@ def build_embedding_inputs_from_test_info(
images = [asset.pil_image for asset in image_assets] images = [asset.pil_image for asset in image_assets]
embeds = test_info.convert_assets_to_embeddings(image_assets) embeds = test_info.convert_assets_to_embeddings(image_assets)
if test_info.dtype != "auto":
dtype = getattr(torch, test_info.dtype) # type: ignore
embeds = [e.to(dtype=dtype) for e in embeds]
assert len(images) == len(model_prompts) assert len(images) == len(model_prompts)
inputs = build_single_image_inputs(images, model_prompts, size_wrapper) inputs = build_single_image_inputs(images, model_prompts, size_wrapper)

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@ -54,6 +54,8 @@ else:
if is_rocm_aiter_moe_enabled(): if is_rocm_aiter_moe_enabled():
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( # noqa: E501 from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import ( # noqa: E501
rocm_aiter_grouped_topk as grouped_topk) rocm_aiter_grouped_topk as grouped_topk)
elif current_platform.is_cpu():
pass
else: else:
from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk from vllm.model_executor.layers.fused_moe.fused_moe import grouped_topk
if current_platform.is_tpu(): if current_platform.is_tpu():

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@ -15,7 +15,7 @@ from vllm.model_executor.layers.quantization.base_config import (
from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
from vllm.platforms import current_platform from vllm.platforms import current_platform
MIN_IPEX_VERSION = "2.7.0" MIN_IPEX_VERSION = "2.6.0"
class IPEXConfig(QuantizationConfig): class IPEXConfig(QuantizationConfig):