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Support double for ArgMax operator (#4907)

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Hariharan Seshadri 2020-08-25 13:23:52 -07:00 committed by GitHub
parent 3d63d8d4f1
commit 294eaca9ef
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GPG key ID: 4AEE18F83AFDEB23
3 changed files with 56 additions and 28 deletions
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10
onnxruntime/core/providers/cpu/cpu_execution_provider.cc
View file

@ -310,6 +310,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, MLFloat16, Round);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, uint8_t, DynamicQuantizeLinear);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11, float, ArgMax);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11, double, ArgMax);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11, int32_t, ArgMax);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11, float, ArgMin);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11, int32_t, ArgMin);
@ -401,6 +402,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
// opset 12
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float, ArgMax);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double, ArgMax);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, int32_t, ArgMax);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float, ArgMin);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, int32_t, ArgMin);
@ -453,7 +455,7 @@ KernelCreateInfo BuildKernelCreateInfo<void>() {
Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
static const BuildKernelCreateInfoFn function_table[] = {
BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, 10,
Clip)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Elu)>,
@ -910,6 +912,8 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
DynamicQuantizeLinear)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11,
float, ArgMax)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11,
double, ArgMax)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11,
int32_t, ArgMax)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, 11,
@ -1058,6 +1062,8 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
// OpSet 12
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float,
ArgMax)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, double,
ArgMax)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, int32_t,
ArgMax)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 12, float,
@ -1189,7 +1195,7 @@ KernelCreateInfo BuildKernelCreateInfo<void>() {
Status RegisterOnnxMLOperatorKernels(KernelRegistry& kernel_registry) {
static const BuildKernelCreateInfoFn function_table[] = {
BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
BuildKernelCreateInfo<void>, //default entry to avoid the list become empty after ops-reducing
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMLDomain, 1, float,
ArrayFeatureExtractor)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kMLDomain, 1, double,

3
onnxruntime/core/providers/cpu/reduction/reduction_ops.cc
View file

@ -143,7 +143,10 @@ REGISTER_UNARY_ELEMENTWISE_KERNEL_DOUBLE_ONLY(ReduceSumSquare, 11);
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ArgMax, 1, 10);
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ArgMax, 11, 11);
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL_DOUBLE_ONLY(ArgMax, 11, 11)
REGISTER_UNARY_ELEMENTWISE_KERNEL(ArgMax, 12);
REGISTER_UNARY_ELEMENTWISE_KERNEL_DOUBLE_ONLY(ArgMax, 12);
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ArgMin, 1, 10);
REGISTER_UNARY_ELEMENTWISE_VERSIONED_KERNEL(ArgMin, 11, 11);
REGISTER_UNARY_ELEMENTWISE_KERNEL(ArgMin, 12);

71
onnxruntime/test/providers/cpu/reduction/reduction_ops_test.cc
View file

@ -598,7 +598,7 @@ TEST(ReductionOpTest, ReduceMax_int32) {
#if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16) || defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
#endif
}
@ -616,11 +616,11 @@ TEST(ReductionOpTest, ReduceMax_int64) {
9, 10,
11, 12});
test.AddOutput<int64_t>("reduced", {3, 1, 1}, {4, 8, 12});
#if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16) || defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
# endif
#if defined(OPENVINO_CONFIG_GPU_FP32) || defined(OPENVINO_CONFIG_GPU_FP16) || defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
#endif
}
TEST(ReductionOpTest, ReduceMax_int8) {
@ -637,11 +637,11 @@ TEST(ReductionOpTest, ReduceMax_int8) {
9, 10,
11, 12});
test.AddOutput<int8_t>("reduced", {3, 1, 1}, {4, 8, 12});
#if defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
#endif
#if defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
#endif
}
TEST(ReductionOpTest, ReduceMax_uint8) {
@ -658,11 +658,11 @@ TEST(ReductionOpTest, ReduceMax_uint8) {
9, 10,
11, 12});
test.AddOutput<uint8_t>("reduced", {3, 1, 1}, {4, 8, 12});
#if defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
#endif
#if defined(OPENVINO_CONFIG_MYRIAD)
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider, kOpenVINOExecutionProvider}); // OpenVINO: Disabled temporarily
#else
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
#endif
}
#if !(defined USE_TENSORRT) && !(defined USE_TVM)
@ -1152,7 +1152,6 @@ void test_apex_reduce_sum(
} else {
test.Run();
}
}
TEST(ReductionOpTest, ReduceSum_apex_matrix_large) {
@ -1204,13 +1203,13 @@ TEST(ReductionOpTest, ReduceSum_batch_by_seq_by_30528) {
#endif
TEST(ReductionOpTest, ReduceSum_bert_selected_batch_size) {
#if defined(OPENVINO_CONFIG_MYRIAD) || defined(OPENVINO_CONFIG_VAD_M)
test_apex_reduce_sum(85 * 128, 768, true);
test_apex_reduce_sum(86 * 128, 768, true);
#else
test_apex_reduce_sum(85 * 128, 768);
test_apex_reduce_sum(86 * 128, 768);
#endif
#if defined(OPENVINO_CONFIG_MYRIAD) || defined(OPENVINO_CONFIG_VAD_M)
test_apex_reduce_sum(85 * 128, 768, true);
test_apex_reduce_sum(86 * 128, 768, true);
#else
test_apex_reduce_sum(85 * 128, 768);
test_apex_reduce_sum(86 * 128, 768);
#endif
}
TEST(ReductionOpTest, ReduceSum_apex_more) {
@ -1608,6 +1607,26 @@ TEST(ReductionOpTest, ArgMax) {
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
}
TEST(ReductionOpTest, ArgMax_Double_Type) {
OpTester test("ArgMax", 11);
test.AddAttribute("axis", static_cast<int64_t>(1));
test.AddAttribute("keepdims", static_cast<int64_t>(1));
test.AddInput<double>("data", {3, 2, 2},
{1.0, 2.0,
3.0, 4.0,
5.0, 6.0,
7.0, 8.0,
9.0, 10.0,
11.0, 12.0});
test.AddOutput<int64_t>("reduced", {3, 1, 2},
{1, 1,
1, 1,
1, 1});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); //TensorRT: axis must be 0
}
TEST(ReductionOpTest, ArgMax_do_not_keepdims) {
OpTester test("ArgMax");
test.AddAttribute("axis", (int64_t)1);
@ -1636,7 +1655,7 @@ TEST(ReductionOpTest, ArgMax_do_not_keepdims_2) {
{1.0f, 2.0f, 3.0f});
test.AddOutput<int64_t>("reduced", {},
{2});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: node1: at least 2 dimensions are required for input
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: node1: at least 2 dimensions are required for input
}
TEST(ReductionOpTest, ArgMax_int32) {
@ -1796,7 +1815,7 @@ TEST(ReductionOpTest, ArgMin_do_not_keepdims_2) {
test.AddInput<float>("data", {3},
{1.0f, 2.0f, 3.0f});
test.AddOutput<int64_t>("reduced", {}, {0});
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: node1: at least 2 dimensions are required for input
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider}); // TensorRT: node1: at least 2 dimensions are required for input
}
TEST(ReductionOpTest, ArgMin_do_not_keepdims_2_select_last) {