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Cherry pick additional changes for Ge release (#19787)
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### Description
<!-- Describe your changes. -->



### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

---------

Co-authored-by: Sheil Kumar <[email protected]>
Co-authored-by: Sheil Kumar <[email protected]>
Co-authored-by: Yufeng Li <[email protected]>
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@maggie1059 @smk2007 @yufenglee
4 people authored Mar 6, 2024
1 parent 0bc6f11 commit 945a8aa
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Showing 6 changed files with 27 additions and 24 deletions.
5 changes: 4 additions & 1 deletion .vscode/settings.json
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Expand Up @@ -21,5 +21,8 @@
"cpplint.filters": [
"-build/include_subdir",
"-runtime/references"
]
],
"files.associations": {
"span": "cpp"
}
}
7 changes: 7 additions & 0 deletions onnxruntime/core/optimizer/gemm_activation_fusion.cc
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Expand Up @@ -56,6 +56,13 @@ Status GemmActivationFusion::ApplyImpl(Graph& graph, bool& modified, int graph_l
continue;
}

NodeArg* node_output = node.MutableOutputDefs()[0];
auto data_type = node_output->TypeAsProto()->tensor_type().elem_type();
if (data_type != ONNX_NAMESPACE::TensorProto_DataType_FLOAT) {
// FusedGemm is only registered for float data type in fused_gemm.cc!
continue;
}

const Node& next_node = *(node.OutputNodesBegin());
if (!IsFusableActivation(next_node) || next_node.GetExecutionProviderType() != node.GetExecutionProviderType()) {
continue;
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14 changes: 14 additions & 0 deletions onnxruntime/core/optimizer/layer_norm_fusion.cc
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Expand Up @@ -447,6 +447,13 @@ Status LayerNormFusion::ApplyImpl(Graph& graph, bool& modified, int graph_level,

NodeArg* x_input = has_leading_cast ? graph.GetNode(p_reduce_mean_input_node->Index())->MutableInputDefs()[0]
: reduce_mean_node.MutableInputDefs()[0];

// CPU doesn't support fp16
if (reduce_mean_node.GetExecutionProviderType() == kCpuExecutionProvider &&
x_input->TypeAsProto()->tensor_type().elem_type() == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
continue;
}

InlinedVector<NodeArg*> layer_norm_input_defs{x_input, scale, bias};
Node& layer_norm_node = graph.AddNode(graph.GenerateNodeName("LayerNormalization"),
"LayerNormalization",
Expand Down Expand Up @@ -689,6 +696,13 @@ Status SimplifiedLayerNormFusion::ApplyImpl(Graph& graph, bool& modified, int gr

NodeArg* x_input = has_leading_cast ? graph.GetNode(p_pow_input_node->Index())->MutableInputDefs()[0]
: pow_node.MutableInputDefs()[0];

// CPU doesn't support fp16
if (reduce_mean_node.GetExecutionProviderType() == kCpuExecutionProvider &&
x_input->TypeAsProto()->tensor_type().elem_type() == ONNX_NAMESPACE::TensorProto_DataType_FLOAT16) {
continue;
}

InlinedVector<NodeArg*> layer_norm_input_defs{x_input, scale};
Node& layer_norm_node =
graph.AddNode(graph.GenerateNodeName("SimplifiedLayerNormalization"), "SimplifiedLayerNormalization",
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21 changes: 0 additions & 21 deletions onnxruntime/core/providers/cpu/cpu_execution_provider.cc
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Expand Up @@ -142,9 +142,6 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Aco
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, Atan);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8, float, Gemm);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8, double, Gemm);
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8, MLFloat16, Gemm);
#endif
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, Hardmax);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, float, LogSoftmax);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 1, 10, double, LogSoftmax);
Expand Down Expand Up @@ -334,9 +331,6 @@ class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOn
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, Flatten);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, float, Gemm);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, double, Gemm);
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10, MLFloat16, Gemm);
#endif
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, float, MatMul);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, double, MatMul);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 12, int32_t, MatMul);
Expand Down Expand Up @@ -496,9 +490,6 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Sp
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, ScatterND);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, float, Gemm);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, double, Gemm);
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, MLFloat16, Gemm);
#endif
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12, GatherElements);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t, BitShift);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint32_t, BitShift);
Expand Down Expand Up @@ -605,9 +596,6 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, string, Expand);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Gemm);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Gemm);
#ifdef MLAS_F16VEC_INTRINSICS_SUPPORTED
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, MLFloat16, Gemm);
#endif
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, MatMul);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, MatMul);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int32_t, MatMul);
Expand Down Expand Up @@ -2612,15 +2600,6 @@ Status RegisterFp16Kernels(KernelRegistry& kernel_registry) {
MLFloat16, LeakyRelu)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 16, MLFloat16,
LeakyRelu)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 7, 8,
MLFloat16, Gemm)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 9, 10,
MLFloat16, Gemm)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 12,
MLFloat16, Gemm)>,

BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, MLFloat16,
Gemm)>,
};

for (auto& function_table_entry : function_table) {
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2 changes: 1 addition & 1 deletion onnxruntime/test/providers/cpu/math/gemm_test.cc
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Expand Up @@ -277,7 +277,7 @@ class GemmOpTypedTests : public ::testing::Test {
// On CPUs without fp16 instructions the tests will output a warning:
// "registered execution providers CPUExecutionProvider were unable to run the model"
// , then they will still pass.
using GemmOpTypedTestsTypes = ::testing::Types<float, double, MLFloat16>;
using GemmOpTypedTestsTypes = ::testing::Types<float, double>;
TYPED_TEST_SUITE(GemmOpTypedTests, GemmOpTypedTestsTypes);

TYPED_TEST(GemmOpTypedTests, TestGemmScalarBroadcast) {
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2 changes: 1 addition & 1 deletion winml/lib/Api/HardwareCoreEnumerator.cpp
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Expand Up @@ -84,7 +84,7 @@ uint32_t HardwareCoreEnumerator::DefaultIntraOpNumThreads() {
// # of logical cores = # of P cores x 2 (if hyper threading is enabled) + # of E cores + # of Soc Cores.
auto cores = GetNumberOPhysicalAndEngineeringCores();

#if !defined(_M_ARM64) && !defined(__aarch64__)
#if !defined(_M_ARM64EC) && !defined(_M_ARM64) && !defined(__aarch64__)
const int kVendorID_Intel[3] = {0x756e6547, 0x6c65746e, 0x49656e69}; // "GenuntelineI"
int regs_leaf0[4];
int regs_leaf7[4];
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