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Upgrade submodule oneDNN to v3.7.1 (#148293)
This PR is to upgrade submodule oneDNN to v3.7.1. ## Improvements - Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids). - Improved performance of int8 and fp32 forward convolution primitive on processors with Intel AVX2 instruction set support. - Improved performance of fp8 matmul primitives with bf16 and fp16 bias data type on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids). - Introduced initial optimizations for Intel GPUs based on Xe3 architecture. - Added bfloat16 support for SDPA, implemented fp16 and bf16 gemm kernel in SDPA. - Fixed f16 matmul accuracy, the issue of SDPA cannot dispatched to ukernel, bf16/fp16/fp32 conv performance, INT8 Kernel trigger page fault, deconvolution precision issue on complex128 and fp64 and gemm correctness issue in float16 issues. - Improved bf16 matmul performance with fp32 destination with Arm Compute Library (ACL). - Improved bf16 to fp32 reorder performance. - Improved bf16 reorder performance. - Improved bf16 convolution with ACL. Fixes https://github.com/pytorch/pytorch/issues/136348. ## Validation results on CPU 1. NLP models accuracy/inference/training   2. Torchbench cpu userbenchmark inference & training  3. Inductor quantization  4. Dynamo benchmarks         ## Validation results on XPU Accuracy is same as baseline. Performance is shown below.  ## Validation results on ARM   Pull Request resolved: https://github.com/pytorch/pytorch/pull/148293 Approved by: https://github.com/mingfeima, https://github.com/atalman
This commit is contained in:
Jiang, Yanbing
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
f339e41a38
commit
f2f25a5444
@ -40,7 +40,16 @@ class BinaryFoldingTemplate(TestCase):
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@skipCUDAIf(TEST_CUDNN, "CUDNN has accuracy issues for this test")
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def test_conv_binary_folding(self):
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@torch.no_grad()
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def test_conv_fusion(use_bias, module, op, scalar, add_tensor, expect_success):
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def test_conv_fusion(
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use_bias,
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module,
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op,
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scalar,
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add_tensor,
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expect_success,
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rtol=None,
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atol=None,
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):
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class ConvOp(nn.Module):
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__constants__ = ["use_scalar"]
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@ -82,7 +91,7 @@ class BinaryFoldingTemplate(TestCase):
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inp = torch.rand(inps).to(self.device)
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out_eager = mod_eager(inp)
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out_optimized = out_optimized(inp)
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self.assertEqual(out_optimized, out_eager)
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self.assertEqual(out_optimized, out_eager, rtol=rtol, atol=atol)
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if expect_success:
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self.assertEqual(counters["inductor"]["binary_folding"], 1)
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else:
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@ -137,6 +146,12 @@ class BinaryFoldingTemplate(TestCase):
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False,
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add_tensor=torch.tensor([2]).to(torch.float64).to(self.device),
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expect_success=False,
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# This test is for float32 conv fusion with different dtype, like float64,
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# which will not be fused. The tolerance of float64 is too tight
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# for float32 conv post fusion with float64 tensor. Will relax the tolerance
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# for this case.
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rtol=1.3e-6,
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atol=1e-5,
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)
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@inductor_config.patch({"freezing": True})
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2
third_party/ideep
vendored
2
third_party/ideep
vendored
Submodule third_party/ideep updated: e026f3b031...719d8e6cd7
23
third_party/mkl-dnn.BUILD
vendored
23
third_party/mkl-dnn.BUILD
vendored
@ -5,17 +5,20 @@ _DNNL_RUNTIME_OMP = {
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"#cmakedefine DNNL_CPU_THREADING_RUNTIME DNNL_RUNTIME_${DNNL_CPU_THREADING_RUNTIME}": "#define DNNL_CPU_THREADING_RUNTIME DNNL_RUNTIME_OMP",
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"#cmakedefine DNNL_CPU_RUNTIME DNNL_RUNTIME_${DNNL_CPU_RUNTIME}": "#define DNNL_CPU_RUNTIME DNNL_RUNTIME_OMP",
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"#cmakedefine DNNL_GPU_RUNTIME DNNL_RUNTIME_${DNNL_GPU_RUNTIME}": "#define DNNL_GPU_RUNTIME DNNL_RUNTIME_NONE",
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"#cmakedefine DNNL_GPU_VENDOR DNNL_VENDOR_${DNNL_GPU_VENDOR}": "/* undef DNNL_GPU_VENDOR */",
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"#cmakedefine DNNL_USE_RT_OBJECTS_IN_PRIMITIVE_CACHE": "/* undef DNNL_USE_RT_OBJECTS_IN_PRIMITIVE_CACHE */",
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"#cmakedefine DNNL_WITH_SYCL": "/* #undef DNNL_WITH_SYCL */",
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"#cmakedefine DNNL_WITH_LEVEL_ZERO": "/* #undef DNNL_WITH_LEVEL_ZERO */",
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"#cmakedefine DNNL_SYCL_CUDA": "/* #undef DNNL_SYCL_CUDA */",
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"#cmakedefine DNNL_SYCL_HIP": "/* #undef DNNL_SYCL_HIP */",
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"#cmakedefine DNNL_SYCL_GENERIC": "/* #undef DNNL_SYCL_GENERIC */",
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"#cmakedefine DNNL_ENABLE_STACK_CHECKER": "#undef DNNL_ENABLE_STACK_CHECKER",
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"#cmakedefine DNNL_EXPERIMENTAL_UKERNEL": "/* undef DNNL_EXPERIMENTAL_UKERNEL */",
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"#cmakedefine DNNL_EXPERIMENTAL": "#undef DNNL_EXPERIMENTAL",
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"#cmakedefine DNNL_EXPERIMENTAL_SPARSE": "#undef DNNL_EXPERIMENTAL_SPARSE",
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"#cmakedefine ONEDNN_BUILD_GRAPH": "#undef ONEDNN_BUILD_GRAPH",
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"#cmakedefine DNNL_EXPERIMENTAL_PROFILING": "#undef DNNL_EXPERIMENTAL_PROFILING",
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"#cmakedefine DNNL_DISABLE_GPU_REF_KERNELS": "#undef DNNL_DISABLE_GPU_REF_KERNELS",
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"#cmakedefine01 BUILD_TRAINING": "#define BUILD_TRAINING 1",
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"#cmakedefine01 BUILD_INFERENCE": "#define BUILD_INFERENCE 0",
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"#cmakedefine01 BUILD_PRIMITIVE_ALL": "#define BUILD_PRIMITIVE_ALL 1",
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@ -36,6 +39,7 @@ _DNNL_RUNTIME_OMP = {
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"#cmakedefine01 BUILD_REORDER": "#define BUILD_REORDER 0",
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"#cmakedefine01 BUILD_RESAMPLING": "#define BUILD_RESAMPLING 0",
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"#cmakedefine01 BUILD_RNN": "#define BUILD_RNN 0",
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"#cmakedefine01 BUILD_SDPA": "#define BUILD_SDPA 0",
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"#cmakedefine01 BUILD_SHUFFLE": "#define BUILD_SHUFFLE 0",
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"#cmakedefine01 BUILD_SOFTMAX": "#define BUILD_SOFTMAX 0",
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"#cmakedefine01 BUILD_SUM": "#define BUILD_SUM 0",
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@ -52,6 +56,7 @@ _DNNL_RUNTIME_OMP = {
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"#cmakedefine01 BUILD_XEHPC": "#define BUILD_XEHPC 0",
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"#cmakedefine01 BUILD_XEHP": "#define BUILD_XEHP 0",
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"#cmakedefine01 BUILD_XE2": "#define BUILD_XE2 0",
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"#cmakedefine01 BUILD_XE3": "#define BUILD_XE3 0",
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"#cmakedefine01 BUILD_GEMM_KERNELS_ALL": "#define BUILD_GEMM_KERNELS_ALL 0",
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"#cmakedefine01 BUILD_GEMM_KERNELS_NONE": "#define BUILD_GEMM_KERNELS_NONE 0",
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"#cmakedefine01 BUILD_GEMM_SSE41": "#define BUILD_GEMM_SSE41 0",
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@ -65,9 +70,8 @@ template_rule(
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out = "include/oneapi/dnnl/dnnl_version.h",
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substitutions = {
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"@DNNL_VERSION_MAJOR@": "3",
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"@DNNL_VERSION_MINOR@": "5",
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"@DNNL_VERSION_PATCH@": "3",
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"@DNNL_VERSION_HASH@": "66f0cb9eb66affd2da3bf5f8d897376f04aae6af",
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"@DNNL_VERSION_MINOR@": "7",
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"@DNNL_VERSION_PATCH@": "1",
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},
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)
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@ -78,14 +82,23 @@ template_rule(
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substitutions = _DNNL_RUNTIME_OMP,
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)
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template_rule(
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name = "include_dnnl_version_hash",
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src = "include/oneapi/dnnl/dnnl_version_hash.h.in",
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out = "include/oneapi/dnnl/dnnl_version_hash.h",
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substitutions = {"@DNNL_VERSION_HASH@": "8d263e693366ef8db40acc569cc7d8edf644556d",}
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)
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cc_library(
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name = "mkl-dnn",
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srcs = glob([
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"src/common/*.cpp",
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"src/cpu/**/*.cpp",
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"src/cpu/**/**/*.cpp",
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], exclude=[
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"src/cpu/aarch64/**/*.cpp",
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"src/cpu/rv64/**/*.cpp",
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"src/cpu/sycl/**/*.cpp",
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]),
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hdrs = glob([
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"include/oneapi/dnnl/*.h",
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@ -94,16 +107,20 @@ cc_library(
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"include/*.hpp",
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"src/cpu/**/*.hpp",
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"src/cpu/**/*.h",
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"src/cpu/**/**/*.h",
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"src/common/*.hpp",
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"src/common/**/**/*.h",
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"src/common/ittnotify/jitprofiling.h",
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], exclude=[
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"src/cpu/aarch64/**/*.hpp",
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"src/cpu/aarch64/**/*.h",
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"src/cpu/rv64/**/*.hpp",
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"src/cpu/rv64/**/*.h",
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"src/cpu/sycl/**/*.hpp",
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]) + [
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"include/oneapi/dnnl/dnnl_config.h",
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"include/oneapi/dnnl/dnnl_version.h",
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"include/oneapi/dnnl/dnnl_version_hash.h",
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],
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copts = [
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"-DDNNL_DLL",
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