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Cuda Clip() for op set 11. (#2411)

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* Cuda Clip() for op set 11.
* make min_val and max_value input CPU memory directly.
* Remove original cu file useless "#pragma once"
* merge duplicate logic into one class.
This commit is contained in:
Zhang Lei 2019-11-27 12:42:45 -08:00 committed by GitHub
parent ccbd778d0d
commit 04b6097db4
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GPG key ID: 4AEE18F83AFDEB23
4 changed files with 60 additions and 28 deletions
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6
onnxruntime/core/providers/cuda/cuda_execution_provider.cc
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@ -363,7 +363,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, float, Ceil);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, double, Ceil);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, MLFloat16, Ceil);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, float, Clip);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, 10, float, Clip);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, float, Reciprocal);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, double, Reciprocal);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, MLFloat16, Reciprocal);
@ -652,6 +652,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, float, MaxPool);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, double, MaxPool);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, MLFloat16, MaxPool);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, float, Clip);
static void RegisterCudaKernels(KernelRegistry& kernel_registry) {
static const BuildKernelCreateInfoFn function_table[] = {
@ -676,7 +677,7 @@ static void RegisterCudaKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 9, float, MatMul)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 9, double, MatMul)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 9, MLFloat16, MatMul)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, float, Clip)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, 10, float, Clip)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, float, Tile)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, double, Tile)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 6, MLFloat16, Tile)>,
@ -1099,6 +1100,7 @@ static void RegisterCudaKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, float, MaxPool)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, double, MaxPool)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, MLFloat16, MaxPool)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 11, float, Clip)>,
};
for (auto& function_table_entry : function_table) {

57
onnxruntime/core/providers/cuda/math/clip.cc
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@ -8,38 +8,61 @@
namespace onnxruntime {
namespace cuda {
#define REGISTER_KERNEL_TYPED(T) \
ONNX_OPERATOR_TYPED_KERNEL_EX( \
Clip, \
kOnnxDomain, \
6, \
T, \
kCudaExecutionProvider, \
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<T>()), \
#define REGISTER_KERNEL_TYPED(T) \
ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX( \
Clip, \
kOnnxDomain, \
6, \
10, \
T, \
kCudaExecutionProvider, \
KernelDefBuilder() \
.TypeConstraint("T", DataTypeImpl::GetTensorType<T>()), \
Clip<T>); \
ONNX_OPERATOR_TYPED_KERNEL_EX( \
Clip, \
kOnnxDomain, \
11, \
T, \
kCudaExecutionProvider, \
KernelDefBuilder() \
.InputMemoryType<OrtMemTypeCPUInput>(1) \
.InputMemoryType<OrtMemTypeCPUInput>(2) \
.TypeConstraint("T", DataTypeImpl::GetTensorType<T>()), \
Clip<T>);
template <typename T>
Status Clip<T>::ComputeInternal(OpKernelContext* ctx) const {
T min_val = min_;
T max_val = max_;
if (is_min_max_input_) {
const auto* min_input = ctx->Input<Tensor>(1);
const auto* max_input = ctx->Input<Tensor>(2);
if (min_input) {
ORT_ENFORCE(min_input->Shape().NumDimensions() == 0, "min should be a scalar.");
min_val = *(min_input->template Data<T>());
}
if (max_input) {
ORT_ENFORCE(max_input->Shape().NumDimensions() == 0, "max should be a scalar.");
max_val = *(max_input->template Data<T>());
}
ORT_ENFORCE(min_val <= max_val);
}
const Tensor& X = *ctx->Input<Tensor>(0);
const TensorShape input_shape{X.Shape()};
const TensorShape& input_shape{X.Shape()};
Tensor* Y = ctx->Output(0, input_shape);
size_t count = input_shape.Size();
if (count > 0) {
auto* y_data = Y->template MutableData<T>();
const auto* x_data = X.template Data<T>();
ClipImpl<T>(x_data, y_data, min_, max_, count);
ClipImpl<T>(x_data, y_data, min_val, max_val, count);
}
return Status::OK();
}
#define SPECIALIZED_COMPUTE(T) \
REGISTER_KERNEL_TYPED(T) \
template Status Clip<T>::ComputeInternal(OpKernelContext* ctx) const;
SPECIALIZED_COMPUTE(float)
REGISTER_KERNEL_TYPED(float)
} // namespace cuda
} // namespace onnxruntime

24
onnxruntime/core/providers/cuda/math/clip.h
View file

@ -10,21 +10,29 @@ namespace cuda {
template <typename T>
class Clip final : public CudaKernel {
public:
Clip(const OpKernelInfo& info) : CudaKernel{info} {
auto min_val = -std::numeric_limits<T>::infinity();
auto max_val = std::numeric_limits<T>::infinity();
Clip(const OpKernelInfo& info) : CudaKernel{info}, is_min_max_input_(false) {
int start_version;
int end_version;
info.GetKernelDef().SinceVersion(&start_version, &end_version);
info.GetAttrOrDefault("min", &min_, min_val);
info.GetAttrOrDefault("max", &max_, max_val);
// Make sure the range of interval is sensible
ORT_ENFORCE(min_val <= max_val);
if (start_version < 11) {
auto min_val = -std::numeric_limits<T>::infinity();
auto max_val = std::numeric_limits<T>::infinity();
info.GetAttrOrDefault("min", &min_, min_val);
info.GetAttrOrDefault("max", &max_, max_val);
ORT_ENFORCE(min_ <= max_);
} else {
min_ = -std::numeric_limits<T>::infinity();
max_ = std::numeric_limits<T>::infinity();
is_min_max_input_ = true;
}
}
Status ComputeInternal(OpKernelContext* context) const override;
private:
T min_, max_;
bool is_min_max_input_;
};
} // namespace cuda

1
onnxruntime/core/providers/cuda/math/clip_impl.cu
View file

@ -1,7 +1,6 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
#include "core/providers/cuda/math/clip_impl.h"
#include "core/providers/cuda/cu_inc/common.cuh"