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提交 9996e0d4 编写于 作者: V VectorSL
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gpu update shape infer

上级 4bb5c7b3
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Showing with 81 addition and 106 deletion
mindspore/ccsrc/device/gpu/kernel_info_setter.cc
浏览文件 @ 9996e0d4
......@@ -184,10 +184,17 @@ void SetKernelInfo(const CNodePtr &kernel_node) {
if (!result) {
auto kernel_name = AnfAlgo::GetCNodeName(kernel_node);
std::string build_type = "in [";
std::for_each(std::begin(inputs_type), std::end(inputs_type),
[&build_type](auto i) { build_type += mindspore::kernel::TypeId2String(i) + " "; });
build_type += "] out [";
std::for_each(std::begin(outputs_type), std::end(outputs_type),
[&build_type](auto i) { build_type += mindspore::kernel::TypeId2String(i) + " "; });
build_type += "]";
auto supported_type_lists = SupportedTypeList(kernel_node);
MS_LOG(EXCEPTION) << "Select GPU kernel op[" << kernel_name
<< "] fail! Incompatible data type!\nThe supported data types are " << supported_type_lists;
MS_EXCEPTION(TypeError) << "Select GPU kernel op[" << kernel_name
<< "] fail! Incompatible data type!\nThe supported data types are " << supported_type_lists
<< ", but get " << build_type;
}
builder->SetKernelType(kernel_type);
builder->SetProcessor(kernel::Processor::CUDA);
......
mindspore/ccsrc/kernel/gpu/arrays/array_reduce_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -178,46 +178,33 @@ class ArrayReduceGpuKernel : public GpuKernel {
return;
}
void InferInAndOutDesc(const std::vector<size_t> &input_shape, const std::vector<size_t> &output_shape) {
std::vector<size_t> inputA_shape = input_shape;
std::vector<int> inputA;
std::vector<size_t> outputC_shape = output_shape;
std::vector<int> real_input_shape;
int shapeA_n, shapeA_c, shapeA_h, shapeA_w;
shapeA_n = inputA_shape.size() < 4 ? 1 : SizeToInt(inputA_shape[inputA_shape.size() - 4]);
shapeA_c = inputA_shape.size() < 3 ? 1 : SizeToInt(inputA_shape[inputA_shape.size() - 3]);
shapeA_h = inputA_shape.size() < 2 ? 1 : SizeToInt(inputA_shape[inputA_shape.size() - 2]);
shapeA_w = inputA_shape.size() == 0 ? 1 : SizeToInt(inputA_shape[inputA_shape.size() - 1]);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(inputA_descriptor_, CUDNN_TENSOR_NCHW, data_type_, shapeA_n,
shapeA_c, shapeA_h, shapeA_w),
ShapeNdTo4d(input_shape, &inputA);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(inputA_descriptor_, CUDNN_TENSOR_NCHW, data_type_, inputA[0],
inputA[1], inputA[2], inputA[3]),
"cudnnSetTensor4dDescriptor failed");
int shapeC_n, shapeC_c, shapeC_h, shapeC_w;
if (axis_[0] == -1) {
shapeC_n = 1;
shapeC_c = 1;
shapeC_h = 1;
shapeC_w = 1;
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_,
shapeC_n, shapeC_c, shapeC_h, shapeC_w),
"cudnnSetTensor4dDescriptor failed");
if (shapeA_n == shapeC_n && shapeA_c == shapeC_c && shapeA_h == shapeC_h && shapeA_w == shapeC_w) {
CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_, 1, 1, 1, 1),
"cudnnSetTensor4dDescriptor failed");
if (inputA[0] == 1 && inputA[1] == 1 && inputA[2] == 1 && inputA[3] == 1) {
all_match_ = true;
}
return;
}
if (!keep_dims_) {
for (auto i : axis_) {
(void)(outputC_shape.insert(outputC_shape.begin() + i, 1));
}
}
shapeC_n = outputC_shape.size() < 4 ? 1 : SizeToInt(outputC_shape[outputC_shape.size() - 4]);
shapeC_c = outputC_shape.size() < 3 ? 1 : SizeToInt(outputC_shape[outputC_shape.size() - 3]);
shapeC_h = outputC_shape.size() < 2 ? 1 : SizeToInt(outputC_shape[outputC_shape.size() - 2]);
shapeC_w = outputC_shape.size() == 0 ? 1 : SizeToInt(outputC_shape[outputC_shape.size() - 1]);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_, shapeC_n,
shapeC_c, shapeC_h, shapeC_w),
std::vector<int> outputC;
ShapeNdTo4d(outputC_shape, &outputC);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_,
outputC[0], outputC[1], outputC[2], outputC[3]),
"cudnnSetTensor4dDescriptor failed");
if (shapeA_n == shapeC_n && shapeA_c == shapeC_c && shapeA_h == shapeC_h && shapeA_w == shapeC_w) {
if (inputA == outputC) {
all_match_ = true;
}
return;
......
mindspore/ccsrc/kernel/gpu/arrays/slice_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -52,15 +52,7 @@ class SliceGpuFwdKernel : public GpuKernel {
return false;
}
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
int shape_n = input_shape.size() < 4 ? 1 : SizeToInt(input_shape[input_shape.size() - 4]);
int shape_c = input_shape.size() < 3 ? 1 : SizeToInt(input_shape[input_shape.size() - 3]);
int shape_h = input_shape.size() < 2 ? 1 : SizeToInt(input_shape[input_shape.size() - 2]);
int shape_w = SizeToInt(input_shape[input_shape.size() - 1]);
input_shape_.push_back(shape_n);
input_shape_.push_back(shape_c);
input_shape_.push_back(shape_h);
input_shape_.push_back(shape_w);
ShapeNdTo4d(input_shape, &input_shape_);
auto strides = AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("strides");
if (strides) {
strides_ = GetAttr<std::vector<int>>(kernel_node, "strides");
......@@ -89,7 +81,7 @@ class SliceGpuFwdKernel : public GpuKernel {
}
}
input_size_ = IntToSize(shape_n * shape_c * shape_h * shape_w) * sizeof(T);
input_size_ = IntToSize(input_shape_[0] * input_shape_[1] * input_shape_[2] * input_shape_[3]) * sizeof(T);
auto out_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
output_size_ = sizeof(T);
......
mindspore/ccsrc/kernel/gpu/arrays/slice_grad_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -66,19 +66,12 @@ class SliceGradGpuKernel : public GpuKernel {
size_ = GetAttr<std::vector<int>>(kernel_node, "end");
} else {
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
input_shape_.push_back(input_shape.size() < 4 ? 1 : SizeToInt(input_shape[input_shape.size() - 4]));
input_shape_.push_back(input_shape.size() < 3 ? 1 : SizeToInt(input_shape[input_shape.size() - 3]));
input_shape_.push_back(input_shape.size() < 2 ? 1 : SizeToInt(input_shape[input_shape.size() - 2]));
input_shape_.push_back(SizeToInt(input_shape[input_shape.size() - 1]));
ShapeNdTo4d(input_shape, &input_shape_);
size_ = GetAttr<std::vector<int>>(kernel_node, "size");
}
auto dy_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
dy_shape_.push_back(dy_shape.size() < 4 ? 1 : SizeToInt(dy_shape[dy_shape.size() - 4]));
dy_shape_.push_back(dy_shape.size() < 3 ? 1 : SizeToInt(dy_shape[dy_shape.size() - 3]));
dy_shape_.push_back(dy_shape.size() < 2 ? 1 : SizeToInt(dy_shape[dy_shape.size() - 2]));
dy_shape_.push_back(SizeToInt(dy_shape[dy_shape.size() - 1]));
ShapeNdTo4d(dy_shape, &dy_shape_);
begin_ = GetAttr<std::vector<int>>(kernel_node, "begin");
DealParam();
input_size_ = IntToSize(input_shape_[0] * input_shape_[1] * input_shape_[2] * input_shape_[3]) * sizeof(T);
......
mindspore/ccsrc/kernel/gpu/gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -39,7 +39,6 @@ class GpuKernel : public KernelMod {
virtual void InitSizeLists() = 0;
template <typename T>
inline T *GetDeviceAddress(const std::vector<AddressPtr> &addr_list, size_t index) {
if (index >= addr_list.size()) {
MS_LOG(EXCEPTION) << "Address index(" << index << ") out of range(" << addr_list.size() << ")";
......@@ -62,6 +61,24 @@ class GpuKernel : public KernelMod {
}
return GetValue<T>(attr);
}
// expand Nd Shape to 4d (N in [0,4])
void ShapeNdTo4d(const std::vector<size_t> &src, std::vector<int> *dst) {
dst->push_back(src.size() < 4 ? 1 : SizeToInt(src[src.size() - 4]));
dst->push_back(src.size() < 3 ? 1 : SizeToInt(src[src.size() - 3]));
dst->push_back(src.size() < 2 ? 1 : SizeToInt(src[src.size() - 2]));
dst->push_back(src.size() == 0 ? 1 : SizeToInt(src[src.size() - 1]));
}
inline void CheckBroadcast4TensorOp(const std::vector<int> &A, const std::vector<int> &B,
const std::vector<int> &Out) {
if (A != Out && B != Out) {
MS_EXCEPTION(ValueError)
<< "Double-sided broadcast was not supported in cudnn of cudnnOpTensor:\n"
"InputA must match the corresponding dimension of the destination tensor outC, and each "
"dimension of the inputB "
"must match the corresponding dimension of outC or must be equal to 1.";
}
}
};
} // namespace kernel
} // namespace mindspore
......
mindspore/ccsrc/kernel/gpu/math/tensoradd_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -87,32 +87,24 @@ class TensorAddGpuFwdKernel : public GpuKernel {
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto input_shapeB = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
if (input_shape != output_shape && input_shapeB != output_shape) {
MS_LOG(ERROR) << "Double-sided broadcast was not supported in cudnn of cudnnOpTensor:\n"
"InputA must match the corresponding dimension of the destination tensor outC, and each "
"dimension of the inputB "
"must match the corresponding dimension of outC or must be equal to 1.";
return false;
}
is_null_input_ = CHECK_NULL_INPUT(input_shape) || CHECK_NULL_INPUT(input_shapeB);
if (is_null_input_) {
MS_LOG(WARNING) << "TensorAddGpuFwdKernel input is null";
InitSizeLists();
return true;
}
int shape_n = input_shape.size() < 4 ? 1 : SizeToInt(input_shape[input_shape.size() - 4]);
int shape_c = input_shape.size() < 3 ? 1 : SizeToInt(input_shape[input_shape.size() - 3]);
int shape_h = input_shape.size() < 2 ? 1 : SizeToInt(input_shape[input_shape.size() - 2]);
int shape_w = input_shape.size() == 0 ? 1 : SizeToInt(input_shape[input_shape.size() - 1]);
std::vector<int> shapeA;
std::vector<int> shapeB;
std::vector<int> shapeOut;
ShapeNdTo4d(input_shape, &shapeA);
ShapeNdTo4d(input_shapeB, &shapeB);
ShapeNdTo4d(output_shape, &shapeOut);
CheckBroadcast4TensorOp(shapeA, shapeB, shapeOut);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(inputA_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
shape_n, shape_c, shape_h, shape_w),
shapeA[0], shapeA[1], shapeA[2], shapeA[3]),
"cudnnSetTensor4dDescriptor failed");
int shapeB_n = input_shapeB.size() < 4 ? 1 : SizeToInt(input_shapeB[input_shapeB.size() - 4]);
int shapeB_c = input_shapeB.size() < 3 ? 1 : SizeToInt(input_shapeB[input_shapeB.size() - 3]);
int shapeB_h = input_shapeB.size() < 2 ? 1 : SizeToInt(input_shapeB[input_shapeB.size() - 2]);
int shapeB_w = input_shapeB.size() == 0 ? 1 : SizeToInt(input_shapeB[input_shapeB.size() - 1]);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(inputB_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
shapeB_n, shapeB_c, shapeB_h, shapeB_w),
shapeB[0], shapeB[1], shapeB[2], shapeB[3]),
"cudnnSetTensor4dDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
......
mindspore/ccsrc/kernel/gpu/nn/pooling_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -107,8 +107,8 @@ class PoolingGpuFwdKernel : public GpuKernel {
SizeToInt(output_shape[1]), SizeToInt(output_shape[2]), SizeToInt(output_shape[3])),
"cudnnSetTensor4dDescriptor failed");
auto window = GetValue<std::vector<int>>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ksize"));
int window_height = window[3];
int window_width = window[2];
int window_height = window[2];
int window_width = window[3];
stride_ = GetValue<std::vector<int>>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("strides"));
SetPoolingMode(kernel_node);
if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
......
mindspore/ccsrc/kernel/gpu/nn/pooling_grad_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -101,8 +101,8 @@ class PoolingGradGpuFwdKernel : public GpuKernel {
return false;
}
auto window = GetAttr<std::vector<int>>(kernel_node, "ksize");
int window_height = window[3];
int window_width = window[2];
int window_height = window[2];
int window_width = window[3];
SetPoolingMode(kernel_node);
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto input_mask = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
......
mindspore/ccsrc/kernel/gpu/nn/relu_gpu_kernel.h
浏览文件 @ 9996e0d4
......@@ -31,8 +31,7 @@ class ReLUGpuFwdKernel : public GpuKernel {
: cudnn_handle_(nullptr),
activation_desc_(nullptr),
mode_(CUDNN_ACTIVATION_RELU),
input_descriptor_(nullptr),
output_descriptor_(nullptr),
data_descriptor_(nullptr),
is_null_input_(false),
cudnn_data_type_(CUDNN_DATA_FLOAT),
input_size_(0),
......@@ -53,8 +52,8 @@ class ReLUGpuFwdKernel : public GpuKernel {
const float alpha = 1;
const float beta = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnActivationForward(cudnn_handle_, activation_desc_, &alpha, input_descriptor_,
input, &beta, output_descriptor_, output),
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnActivationForward(cudnn_handle_, activation_desc_, &alpha, data_descriptor_, input,
&beta, data_descriptor_, output),
"ReLUGpuFwdKernel failed");
return true;
......@@ -75,18 +74,12 @@ class ReLUGpuFwdKernel : public GpuKernel {
return true;
}
mode_ = CUDNN_ACTIVATION_RELU;
int batch_size = input_shape.size() < 4 ? 1 : SizeToInt(input_shape[input_shape.size() - 4]);
int channel_size = input_shape.size() < 3 ? 1 : SizeToInt(input_shape[input_shape.size() - 3]);
int height = input_shape.size() < 2 ? 1 : SizeToInt(input_shape[input_shape.size() - 2]);
int width = input_shape.size() == 0 ? 1 : SizeToInt(input_shape[input_shape.size() - 1]);
std::vector<int> shape;
ShapeNdTo4d(input_shape, &shape);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetActivationDescriptor(activation_desc_, mode_, CUDNN_NOT_PROPAGATE_NAN, 0.0),
"SetActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(input_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
batch_size, channel_size, height, width),
"SetTensor4dDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(output_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
batch_size, channel_size, height, width),
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(data_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
shape[0], shape[1], shape[2], shape[3]),
"SetTensor4dDescriptor failed");
InitSizeLists();
return true;
......@@ -95,18 +88,16 @@ class ReLUGpuFwdKernel : public GpuKernel {
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&input_descriptor_), "cudnnCreateTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&output_descriptor_), "cudnnCreateTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&data_descriptor_), "cudnnCreateTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateActivationDescriptor(&activation_desc_),
"cudnnCreateActivationDescriptor failed");
}
void InitSizeLists() override {
if (!is_null_input_) {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(input_descriptor_, &input_size_),
"cudnnGetTensorSizeInBytes failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(output_descriptor_, &output_size_),
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(data_descriptor_, &input_size_),
"cudnnGetTensorSizeInBytes failed");
output_size_ = input_size_;
}
input_size_list_.push_back(input_size_);
output_size_list_.push_back(output_size_);
......@@ -116,15 +107,13 @@ class ReLUGpuFwdKernel : public GpuKernel {
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"cudnnDestroyActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(data_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
cudnnHandle_t cudnn_handle_;
cudnnActivationDescriptor_t activation_desc_;
cudnnActivationMode_t mode_;
cudnnTensorDescriptor_t input_descriptor_;
cudnnTensorDescriptor_t output_descriptor_;
cudnnTensorDescriptor_t data_descriptor_;
bool is_null_input_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
......
mindspore/ccsrc/kernel/gpu/nn/relu_grad_kernel.h
浏览文件 @ 9996e0d4
......@@ -31,7 +31,7 @@ class ReluGradGpuFwdKernel : public GpuKernel {
: cudnn_handle_(nullptr),
activation_desc_(nullptr),
mode_(CUDNN_ACTIVATION_RELU),
input_descriptor_(nullptr),
data_descriptor_(nullptr),
is_null_input_(false),
cudnn_data_type_(CUDNN_DATA_FLOAT),
input_size_(0) {}
......@@ -52,8 +52,8 @@ class ReluGradGpuFwdKernel : public GpuKernel {
const float alpha = 1;
const float beta = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnActivationBackward(cudnn_handle_, activation_desc_, &alpha, input_descriptor_, y, input_descriptor_, dy,
input_descriptor_, y, &beta, input_descriptor_, dx),
cudnnActivationBackward(cudnn_handle_, activation_desc_, &alpha, data_descriptor_, y, data_descriptor_, dy,
data_descriptor_, y, &beta, data_descriptor_, dx),
"cudnnActivationBackward failed");
return true;
......@@ -74,14 +74,12 @@ class ReluGradGpuFwdKernel : public GpuKernel {
InitSizeLists();
return true;
}
int batch_size = input_shape.size() < 4 ? 1 : SizeToInt(input_shape[input_shape.size() - 4]);
int channel_size = input_shape.size() < 3 ? 1 : SizeToInt(input_shape[input_shape.size() - 3]);
int height = input_shape.size() < 2 ? 1 : SizeToInt(input_shape[input_shape.size() - 2]);
int width = input_shape.size() == 0 ? 1 : SizeToInt(input_shape[input_shape.size() - 1]);
std::vector<int> shape;
ShapeNdTo4d(input_shape, &shape);
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetActivationDescriptor(activation_desc_, mode_, CUDNN_PROPAGATE_NAN, 0.0),
"SetActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(input_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
batch_size, channel_size, height, width),
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(data_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
shape[0], shape[1], shape[2], shape[3]),
"SetTensor4dDescriptor failed");
InitSizeLists();
......@@ -91,13 +89,13 @@ class ReluGradGpuFwdKernel : public GpuKernel {
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&input_descriptor_), "cudnnCreateTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&data_descriptor_), "cudnnCreateTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateActivationDescriptor(&activation_desc_),
"cudnnCreateActivationDescriptor failed");
}
void InitSizeLists() override {
if (!is_null_input_) {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(input_descriptor_, &input_size_),
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(data_descriptor_, &input_size_),
"cudnnGetTensorSizeInBytes failed");
}
input_size_list_.push_back(input_size_);
......@@ -109,13 +107,13 @@ class ReluGradGpuFwdKernel : public GpuKernel {
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"cudnnDestroyActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(data_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
cudnnHandle_t cudnn_handle_;
cudnnActivationDescriptor_t activation_desc_;
cudnnActivationMode_t mode_;
cudnnTensorDescriptor_t input_descriptor_;
cudnnTensorDescriptor_t data_descriptor_;
bool is_null_input_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
......
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