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!8162 gpu support dynamic shape 

From: @wilfchen
Reviewed-by: @limingqi107,@cristoval
Signed-off-by: @cristoval
This commit is contained in:
mindspore-ci-bot 2020-11-09 09:28:45 +08:00 committed by Gitee
parent f0e203eca6 e4e9362bd0
commit 36e69f6ef9
52 changed files with 525 additions and 319 deletions
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59
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/array_reduce_gpu_kernel.h
View File

@ -35,21 +35,7 @@ const std::map<std::string, cudnnReduceTensorOp_t> kReduceTypeMap = {
template <typename T>
class ArrayReduceGpuKernel : public GpuKernel {
public:
ArrayReduceGpuKernel()
: cudnn_handle_(nullptr),
reduce_tensor_op_(CUDNN_REDUCE_TENSOR_ADD),
data_type_(CUDNN_DATA_FLOAT),
nan_prop_(CUDNN_NOT_PROPAGATE_NAN),
reduce_indices_(CUDNN_REDUCE_TENSOR_NO_INDICES),
reduce_tensor_descriptor_(nullptr),
inputA_descriptor_(nullptr),
outputC_descriptor_(nullptr),
keep_dims_(false),
all_match_(false),
is_null_input_(false),
input_size_(0),
output_size_(0),
workspace_size_(0) {}
ArrayReduceGpuKernel() { ResetResource(); }
~ArrayReduceGpuKernel() override { DestroyResource(); }
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -94,7 +80,7 @@ class ArrayReduceGpuKernel : public GpuKernel {
MS_LOG(ERROR) << "Output number is " << output_num << ", but reduce op needs 1 output.";
return false;
}
int input_dim_length = SizeToInt(AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0).size());
int input_dim_length = SizeToInt(AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0).size());
if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("axis")->isa<ValueTuple>() ||
AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("axis")->isa<ValueList>()) {
@ -117,8 +103,8 @@ class ArrayReduceGpuKernel : public GpuKernel {
}
keep_dims_ = GetAttr<bool>(kernel_node, "keep_dims");
auto inputA_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto outputC_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
auto inputA_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
auto outputC_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0);
is_null_input_ = CHECK_NULL_INPUT(inputA_shape);
if (is_null_input_) {
MS_LOG(WARNING) << "ArrayReduceGpuKernel input is null";
@ -132,6 +118,35 @@ class ArrayReduceGpuKernel : public GpuKernel {
return true;
}
void ResetResource() noexcept override {
cudnn_handle_ = nullptr;
reduce_tensor_op_ = CUDNN_REDUCE_TENSOR_ADD;
data_type_ = CUDNN_DATA_FLOAT;
nan_prop_ = CUDNN_NOT_PROPAGATE_NAN;
reduce_indices_ = CUDNN_REDUCE_TENSOR_NO_INDICES;
reduce_tensor_descriptor_ = nullptr;
inputA_descriptor_ = nullptr;
outputC_descriptor_ = nullptr;
keep_dims_ = false;
all_match_ = false;
is_null_input_ = false;
input_size_ = 0;
output_size_ = 0;
workspace_size_ = 0;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyReduceTensorDescriptor(reduce_tensor_descriptor_),
"cudnnDestroyReduceTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(inputA_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(outputC_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -160,14 +175,6 @@ class ArrayReduceGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyReduceTensorDescriptor(reduce_tensor_descriptor_),
"cudnnDestroyReduceTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(inputA_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(outputC_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
}
void InferArrayReduceType(const CNodePtr &kernel_node) {
std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
auto iter = kReduceTypeMap.find(kernel_name);

9
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.cc
View File

@ -26,5 +26,14 @@ MS_REG_GPU_KERNEL_TWO(
GatherV2,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
GatherV2GpuFwdKernel, half, int)
MS_REG_GPU_KERNEL_TWO(
SparseGatherV2,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
GatherV2GpuFwdKernel, float, int)
MS_REG_GPU_KERNEL_TWO(
SparseGatherV2,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
GatherV2GpuFwdKernel, half, int)
} // namespace kernel
} // namespace mindspore

20
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gatherv2_gpu_kernel.h
View File

@ -27,7 +27,7 @@ namespace kernel {
template <typename T, typename S>
class GatherV2GpuFwdKernel : public GpuKernel {
public:
GatherV2GpuFwdKernel() : axis_(0), handle_(nullptr) {}
GatherV2GpuFwdKernel() { ResetResource(); }
~GatherV2GpuFwdKernel() = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -52,9 +52,9 @@ class GatherV2GpuFwdKernel : public GpuKernel {
if (input_num != 2) {
MS_LOG(EXCEPTION) << "Argument number is " << input_num << ", but GatherGpuV2FwdKernel needs 2.";
}
input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
input_shapes_ = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
indices_shapes_ = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1);
output_shapes_ = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0);
axis_ = static_cast<int>(GetAttr<int64_t>(kernel_node, "axis"));
if (axis_ < 0) {
@ -65,9 +65,18 @@ class GatherV2GpuFwdKernel : public GpuKernel {
InitSizeLists();
return true;
}
void ResetResource() noexcept override {
input_shapes_.clear();
indices_shapes_.clear();
output_shapes_.clear();
std::fill(dims_, dims_ + 3, 0);
axis_ = 0;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitResource() override { handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle(); }
void InitSizeLists() override {
size_t size = GetSize(input_shapes_);
input_size_list_.push_back(size);
@ -118,7 +127,6 @@ class GatherV2GpuFwdKernel : public GpuKernel {
size_t dims_[3] = {};
int axis_;
cudnnHandle_t handle_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;

28
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/split_gpu_kernel.h
View File

@ -28,14 +28,7 @@ namespace kernel {
template <typename T>
class SplitGpuFwdKernel : public GpuKernel {
public:
SplitGpuFwdKernel()
: axis_(0),
output_num_(1),
input_size_(1),
axis_step_(1),
all_size_before_axis_(1),
all_size_axis_(1),
outputs_host_(nullptr) {}
SplitGpuFwdKernel() { ResetResource(); }
~SplitGpuFwdKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
@ -59,7 +52,7 @@ class SplitGpuFwdKernel : public GpuKernel {
bool Init(const CNodePtr &kernel_node) override {
axis_ = static_cast<int64_t>(GetAttr<int64_t>(kernel_node, "axis"));
if (axis_ < 0) {
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
axis_ += SizeToInt(input_shape.size());
}
output_num_ = static_cast<int64_t>(GetAttr<int64_t>(kernel_node, "output_num"));
@ -68,7 +61,7 @@ class SplitGpuFwdKernel : public GpuKernel {
return false;
}
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
input_size_ = 1;
all_size_before_axis_ = 1;
all_size_axis_ = 1;
@ -88,7 +81,7 @@ class SplitGpuFwdKernel : public GpuKernel {
for (int i = 0; i < output_num_; i++) {
size_t output_size = 1;
auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, i);
auto output_shape = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, i);
for (size_t j = 0; j < output_shape.size(); j++) {
output_size *= output_shape[j];
}
@ -100,6 +93,19 @@ class SplitGpuFwdKernel : public GpuKernel {
return true;
}
void ResetResource() noexcept override {
axis_ = 0;
output_num_ = 1;
input_size_ = 1;
axis_step_ = 1;
all_size_before_axis_ = 1;
all_size_axis_ = 1;
outputs_host_ = nullptr;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitSizeLists() override {}

2
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/transpose_gpu_kernel.h
View File

@ -62,7 +62,7 @@ class TransposeGpuFwdKernel : public GpuKernel {
MS_LOG(ERROR) << "Output number is " << output_num << ", but transpose needs 1 output.";
return false;
}
auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
shape_size_ = input_shape.size();
if (shape_size_ > TRANSPOSE_MAX_DIMENSION) {
MS_LOG(EXCEPTION) << "Input is " << shape_size_ << "-D, but transpose supports max " << TRANSPOSE_MAX_DIMENSION

20
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/unsorted_segment_sum_gpu_kernel.h
View File

@ -27,8 +27,7 @@ namespace kernel {
template <typename T, typename S>
class UnsortedSegmentSumGpuKernel : public GpuKernel {
public:
UnsortedSegmentSumGpuKernel()
: input_dim0_(1), input_dim1_(1), output_dim0_(1), output_dim1_(1), is_null_input_(false) {}
UnsortedSegmentSumGpuKernel() { ResetResource(); }
~UnsortedSegmentSumGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -53,15 +52,15 @@ class UnsortedSegmentSumGpuKernel : public GpuKernel {
}
bool Init(const CNodePtr &kernel_node) override {
auto input_shapes = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto input_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
is_null_input_ = CHECK_NULL_INPUT(input_shapes);
if (is_null_input_) {
MS_LOG(WARNING) << "UnsortedSegmentSum input is null";
InitSizeLists();
return true;
}
auto ids_shapes = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
auto output_shapes = AnfAlgo::GetOutputInferShape(kernel_node, 0);
auto ids_shapes = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1);
auto output_shapes = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0);
auto axis = ids_shapes.size();
for (size_t i = 0; i < input_shapes.size(); i++) {
@ -81,6 +80,17 @@ class UnsortedSegmentSumGpuKernel : public GpuKernel {
return true;
}
void ResetResource() noexcept override {
input_dim0_ = 1;
input_dim1_ = 1;
output_dim0_ = 1;
output_dim1_ = 1;
is_null_input_ = false;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_dim0_ * input_dim1_ * sizeof(T));

36
mindspore/ccsrc/backend/kernel_compiler/gpu/gpu_kernel.cc Normal file
View File

@ -0,0 +1,36 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
namespace mindspore {
namespace kernel {
void GpuDynamicKernel::UpdateArgs() {
if (!is_input_dynamic_shape_ && is_output_dynamic_shape_ && !have_depends()) {
return;
}
MS_LOG(INFO) << "Update Args: " << cnode_ptr_->fullname_with_scope();
auto kernel_mod = AnfAlgo::GetKernelMod(cnode_ptr_);
MS_EXCEPTION_IF_NULL(kernel_mod);
auto gpu_kernel_mod = dynamic_cast<GpuKernel *>(kernel_mod);
MS_EXCEPTION_IF_NULL(gpu_kernel_mod);
gpu_kernel_mod->DestroyResource();
gpu_kernel_mod->ResetResource();
gpu_kernel_mod->Init(cnode_ptr_);
}
} // namespace kernel
} // namespace mindspore

23
mindspore/ccsrc/backend/kernel_compiler/gpu/gpu_kernel.h
View File

@ -23,11 +23,13 @@
#include <vector>
#include <utility>
#include <map>
#include <memory>
#include "backend/kernel_compiler/kernel.h"
#include "backend/kernel_compiler/gpu/kernel_constants.h"
#include "runtime/device/gpu/gpu_device_manager.h"
#include "runtime/device/gpu/gpu_common.h"
#include "backend/session/anf_runtime_algorithm.h"
#include "runtime/device/executor/dynamic_kernel.h"
using AnfAlgo = mindspore::session::AnfRuntimeAlgorithm;
namespace mindspore {
@ -45,10 +47,28 @@ static std::map<int, int> kNHWCToNCHWAxisMap = {
{3, 1},
};
class GpuDynamicKernel : public device::DynamicKernel {
public:
explicit GpuDynamicKernel(const CNodePtr &cnode_ptr) : DynamicKernel(nullptr, cnode_ptr) {}
~GpuDynamicKernel() = default;
void UpdateArgs() override;
void PostExecute() final { MS_LOG(EXCEPTION) << "`PostExecute()` should not invoked with gpu backend"; };
void Execute() final { MS_LOG(EXCEPTION) << "`Execute()` should not invoked with gpu backend"; }
};
class GpuKernel : public KernelMod {
public:
virtual ~GpuKernel() = default;
virtual bool Init(const CNodePtr &kernel_node) = 0;
virtual void ResetResource() noexcept {
MS_LOG(EXCEPTION) << "kernel must override the `ResetResource()` method when dynamic shape";
}
virtual void DestroyResource() noexcept {}
virtual void PostExecute() {}
void InitDynamicKernel(const CNodePtr &cnode_ptr) { dynamic_kernel_ = std::make_shared<GpuDynamicKernel>(cnode_ptr); }
device::DynamicKernelPtr DynamicKernel() const { return dynamic_kernel_; }
protected:
virtual void InitResource() {}
@ -228,7 +248,10 @@ class GpuKernel : public KernelMod {
}
return type->second;
}
device::DynamicKernelPtr dynamic_kernel_;
};
} // namespace kernel
} // namespace mindspore

7
mindspore/ccsrc/backend/kernel_compiler/gpu/math/addn_gpu_kernel.h
View File

@ -123,6 +123,10 @@ class AddNGpuFwdKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -141,9 +145,6 @@ class AddNGpuFwdKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
cudnnHandle_t cudnn_handle_;
cudnnTensorDescriptor_t input_descriptor_;
cudnnDataType_t cudnn_data_type_;

12
mindspore/ccsrc/backend/kernel_compiler/gpu/math/bias_add_gpu_kernel.h
View File

@ -112,6 +112,12 @@ class BiasAddGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyOpTensorDescriptor(op_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(b_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "cudnnDestroyOpTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -129,12 +135,6 @@ class BiasAddGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyOpTensorDescriptor(op_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(b_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "cudnnDestroyOpTensorDescriptor failed");
}
cudnnHandle_t cudnn_handle_;
cudnnDataType_t cudnn_data_type_;
cudnnTensorDescriptor_t x_desc_;

28
mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.h
View File

@ -31,13 +31,7 @@ constexpr int MAX_DIMS = 7;
template <typename T>
class BroadcastOpGpuKernel : public GpuKernel {
public:
BroadcastOpGpuKernel()
: op_type_(BROADCAST_TYPE_INVALID),
need_broadcast_(false),
is_comp_op_(false),
input1_num_(1),
input2_num_(1),
output_num_(1) {}
BroadcastOpGpuKernel() { ResetResource(); }
~BroadcastOpGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -71,9 +65,9 @@ class BroadcastOpGpuKernel : public GpuKernel {
}
bool Init(const CNodePtr &kernel_node) override {
GetOpType(kernel_node);
auto shape1 = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
auto shape2 = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
auto shape3 = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
auto shape1 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
auto shape2 = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 1);
auto shape3 = AnfAlgo::GetOutputRealDeviceShapeIfExist(kernel_node, 0);
need_broadcast_ = IsBroadcast(shape1, shape2);
if (need_broadcast_ && shape1.size() > 7) {
MS_LOG(EXCEPTION) << "Broadcast operation not support dim greater than 7";
@ -106,6 +100,20 @@ class BroadcastOpGpuKernel : public GpuKernel {
InitSizeLists();
return true;
}
void ResetResource() noexcept override {
op_type_ = BROADCAST_TYPE_INVALID;
need_broadcast_ = false;
is_comp_op_ = false;
input1_num_ = 1;
input2_num_ = 1;
output_num_ = 1;
lhs_shape_.clear();
rhs_shape_.clear();
output_shape_.clear();
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitResource() override { return; }

25
mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_grad_gpu_kernel.h
View File

@ -30,14 +30,7 @@ namespace kernel {
template <typename T>
class BroadcastOpGradGpuKernel : public GpuKernel {
public:
BroadcastOpGradGpuKernel()
: op_type_(BROADCAST_GRAD_TYPE_INVALID),
need_broadcast_(false),
input1_num_(1),
input2_num_(1),
output_num_(1),
grad_x_(false),
grad_y_(false) {}
BroadcastOpGradGpuKernel() { ResetResource(); }
~BroadcastOpGradGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -105,6 +98,22 @@ class BroadcastOpGradGpuKernel : public GpuKernel {
return true;
}
void ResetResource() noexcept override {
op_type_ = BROADCAST_GRAD_TYPE_INVALID;
need_broadcast_ = false;
input1_num_ = 1;
input2_num_ = 1;
output_num_ = 1;
std::fill(x1_shape_, x1_shape_ + 4, 1);
std::fill(x2_shape_, x2_shape_ + 4, 1);
std::fill(dy_shape_, dy_shape_ + 4, 1);
grad_x_ = false;
grad_y_ = false;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitResource() override { return; }
void InitSizeLists() override {

22
mindspore/ccsrc/backend/kernel_compiler/gpu/math/unary_op_gpu_kernel.h
View File

@ -69,21 +69,15 @@ static const std::map<std::string, UnaryOptype> kUnaryOpTypeMap = {{"Exp", UNARY
template <typename T>
class UnaryOpGpuKernel : public GpuKernel {
public:
UnaryOpGpuKernel()
: unary_op_type_(UNARY_OP_INVALID_TYPE),
input_size_(sizeof(T)),
output_size_(sizeof(T)),
workspace_size_(0),
is_null_input_(false) {}
UnaryOpGpuKernel() { ResetResource(); }
~UnaryOpGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
VARIABLE_NOT_USED(workspace);
T *input_addr = GetDeviceAddress<T>(inputs, 0);
T *output_addr = GetDeviceAddress<T>(outputs, 0);
@ -184,7 +178,7 @@ class UnaryOpGpuKernel : public GpuKernel {
MS_LOG(ERROR) << "Output number is " << output_num << ", but unary op needs 1 output.";
return false;
}
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
is_null_input_ = CHECK_NULL_INPUT(input_shape);
if (is_null_input_) {
MS_LOG(WARNING) << "UnaryOpGpuKernel input is null";
@ -198,6 +192,16 @@ class UnaryOpGpuKernel : public GpuKernel {
InitSizeLists();
return true;
}
void ResetResource() noexcept override {
unary_op_type_ = UNARY_OP_INVALID_TYPE;
input_size_ = sizeof(T);
output_size_ = sizeof(T);
workspace_size_ = 0;
is_null_input_ = false;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitSizeLists() override {

40
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/activation_gpu_kernel.h
View File

@ -29,16 +29,7 @@ namespace kernel {
template <typename T>
class ActivationGpuFwdKernel : public GpuKernel {
public:
ActivationGpuFwdKernel()
: cudnn_handle_(nullptr),
activation_desc_(nullptr),
mode_(CUDNN_ACTIVATION_RELU),
data_descriptor_(nullptr),
is_null_input_(false),
cudnn_data_type_(CUDNN_DATA_FLOAT),
input_size_(0),
output_size_(0),
workspace_size_(0) {}
ActivationGpuFwdKernel() { ResetResource(); }
~ActivationGpuFwdKernel() override { DestroyResource(); }
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
@ -75,7 +66,7 @@ class ActivationGpuFwdKernel : public GpuKernel {
MS_LOG(ERROR) << "Argument number is " << input_num << ", but ActivationGpuFwdKernel needs 1.";
return false;
}
auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
auto input_shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
is_null_input_ = CHECK_NULL_INPUT(input_shape);
if (is_null_input_) {
MS_LOG(WARNING) << "ActivationGpuFwdKernel input is null.";
@ -113,6 +104,27 @@ class ActivationGpuFwdKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"cudnnDestroyActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(data_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
void ResetResource() noexcept override {
cudnn_handle_ = nullptr;
activation_desc_ = nullptr;
mode_ = CUDNN_ACTIVATION_RELU;
data_descriptor_ = nullptr;
is_null_input_ = false;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
cudnn_data_type_ = CUDNN_DATA_FLOAT;
input_size_ = 0;
output_size_ = 0;
workspace_size_ = 0;
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -132,12 +144,6 @@ class ActivationGpuFwdKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"cudnnDestroyActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(data_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
std::map<std::string, cudnnActivationMode_t> kernel_map = {{"ReLU", CUDNN_ACTIVATION_RELU},
{"ReLU6", CUDNN_ACTIVATION_CLIPPED_RELU},
{"Tanh", CUDNN_ACTIVATION_TANH},

34
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/activation_grad_kernel.h
View File

@ -29,14 +29,7 @@ namespace kernel {
template <typename T>
class ActivationGradGpuKernel : public GpuKernel {
public:
ActivationGradGpuKernel()
: cudnn_handle_(nullptr),
activation_desc_(nullptr),
mode_(CUDNN_ACTIVATION_RELU),
data_descriptor_(nullptr),
is_null_input_(false),
cudnn_data_type_(CUDNN_DATA_FLOAT),
input_size_(0) {}
ActivationGradGpuKernel() { ResetResource(); }
~ActivationGradGpuKernel() override { DestroyResource(); }
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
@ -117,6 +110,25 @@ class ActivationGradGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"cudnnDestroyActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(data_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
void ResetResource() noexcept override {
cudnn_handle_ = nullptr;
activation_desc_ = nullptr;
mode_ = CUDNN_ACTIVATION_RELU;
data_descriptor_ = nullptr;
is_null_input_ = false;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
cudnn_data_type_ = CUDNN_DATA_FLOAT;
input_size_ = 0;
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -135,12 +147,6 @@ class ActivationGradGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"cudnnDestroyActivationDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(data_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
std::map<std::string, cudnnActivationMode_t> kernel_map = {{"ReluGrad", CUDNN_ACTIVATION_RELU},
{"ReLU6Grad", CUDNN_ACTIVATION_CLIPPED_RELU},
{"TanhGrad", CUDNN_ACTIVATION_TANH},

14
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batchnorm_grad_gpu_kernel.h
View File

@ -121,6 +121,13 @@ class BatchNormGradGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_desc_), "Destroy para desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -152,13 +159,6 @@ class BatchNormGradGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_desc_), "Destroy para desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
}
int batch_;
int channel_;
int height_;

14
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/bias_add_grad_gpu_kenel.h
View File

@ -111,6 +111,13 @@ class BiasAddGradGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnDestroyReduceTensorDescriptor(op_desc_),
"cudnnDestroyReduceTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(db_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "cudnnDestroyOpTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -137,13 +144,6 @@ class BiasAddGradGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnDestroyReduceTensorDescriptor(op_desc_),
"cudnnDestroyReduceTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(db_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "cudnnDestroyOpTensorDescriptor failed");
}
bool same_dims_;
cudnnHandle_t cudnn_handle_;
cudnnDataType_t cudnn_data_type_;

17
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_gpu_kernel.h
View File

@ -198,6 +198,15 @@ class Conv2dGpuFwdKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(filter_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_desc_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -243,14 +252,6 @@ class Conv2dGpuFwdKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(filter_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_desc_), "cudnnDestroyTensorDescriptor failed");
}
bool CheckParam(const CNodePtr &kernel_node) {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {

17
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_filter_gpu_kernel.h
View File

@ -199,6 +199,15 @@ class ConvGradFilterGpuBkwKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(dw_desc_), "cudnnDestroyFilterDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -243,14 +252,6 @@ class ConvGradFilterGpuBkwKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(dw_desc_), "cudnnDestroyFilterDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "cudnnDestroyTensorDescriptor failed");
}
bool CheckParam(const CNodePtr &kernel_node) {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {

17
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/conv2d_grad_input_gpu_kernel.h
View File

@ -203,6 +203,15 @@ class ConvGradInputGpuBkwKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(w_desc_), "cudnnDestroyFilterDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -244,14 +253,6 @@ class ConvGradInputGpuBkwKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(w_desc_), "cudnnDestroyFilterDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "cudnnDestroyTensorDescriptor failed");
}
bool CheckParam(const CNodePtr &kernel_node) {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {

15
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/flatten_gpu_kernel.h
View File

@ -27,7 +27,7 @@ namespace kernel {
template <typename T>
class FlattenGpuFwdKernel : public GpuKernel {
public:
FlattenGpuFwdKernel() : input_size_(0), output_size_(0), workspace_size_(0) {}
FlattenGpuFwdKernel() : input_size_(0) {}
~FlattenGpuFwdKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -47,7 +47,7 @@ class FlattenGpuFwdKernel : public GpuKernel {
return true;
}
bool Init(const CNodePtr &kernel_node) override {
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
input_size_ = sizeof(T);
for (size_t i = 0; i < shape.size(); ++i) {
input_size_ *= shape[i];
@ -55,12 +55,17 @@ class FlattenGpuFwdKernel : public GpuKernel {
InitSizeLists();
return true;
}
void ResetResource() noexcept override {
input_size_ = 0;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_);
output_size_ = input_size_;
output_size_list_.push_back(output_size_);
output_size_list_.push_back(input_size_);
}
private:
@ -69,8 +74,6 @@ class FlattenGpuFwdKernel : public GpuKernel {
std::vector<size_t> workspace_size_list_;
size_t input_size_;
size_t output_size_;
size_t workspace_size_;
};
} // namespace kernel
} // namespace mindspore

16
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/flatten_grad_gpu_kernel.h
View File

@ -27,7 +27,7 @@ namespace kernel {
template <typename T>
class FlattenGardGpuBkwKernel : public GpuKernel {
public:
FlattenGardGpuBkwKernel() : input_size_(0), output_size_(0), workspace_size_(0) {}
FlattenGardGpuBkwKernel() { ResetResource(); }
~FlattenGardGpuBkwKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
@ -54,7 +54,7 @@ class FlattenGardGpuBkwKernel : public GpuKernel {
return false;
}
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
auto shape = AnfAlgo::GetInputRealDeviceShapeIfExist(kernel_node, 0);
for (size_t i = 0; i < shape.size(); ++i) {
if (input_size_ == 0) {
input_size_ = 1;
@ -67,11 +67,17 @@ class FlattenGardGpuBkwKernel : public GpuKernel {
return true;
}
void ResetResource() noexcept override {
input_size_ = 0;
input_size_list_.clear();
output_size_list_.clear();
workspace_size_list_.clear();
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_);
output_size_ = input_size_;
output_size_list_.push_back(output_size_);
output_size_list_.push_back(input_size_);
}
private:
@ -80,8 +86,6 @@ class FlattenGardGpuBkwKernel : public GpuKernel {
std::vector<size_t> workspace_size_list_;
size_t input_size_;
size_t output_size_;
size_t workspace_size_;
};
} // namespace kernel
} // namespace mindspore

28
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h
View File

@ -140,6 +140,20 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(z_desc_), "Destroy z desc failed");
}
if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"Destroy activation descriptor failed");
}
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -238,20 +252,6 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
}
}
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(z_desc_), "Destroy z desc failed");
}
if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"Destroy activation descriptor failed");
}
}
size_t input_x_size_;
size_t input_z_size_;
size_t para_size_;

12
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h
View File

@ -133,6 +133,12 @@ class FusedBatchNormGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -165,12 +171,6 @@ class FusedBatchNormGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
}
int batch_;
int channel_;
int height_;

31
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h
View File

@ -201,6 +201,21 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
workspace_size_list_.push_back(workspace_size_);
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"Destroy activation descriptor failed");
}
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dz_desc_), "Destroy z desc failed");
}
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_diff_desc_), "Destroy para desc failed");
}
private:
void SetTensorDescriptor(const std::string &format, const std::vector<size_t> &shape) {
@ -255,22 +270,6 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
}
}
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyActivationDescriptor(activation_desc_),
"Destroy activation descriptor failed");
}
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dz_desc_), "Destroy z desc failed");
}
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_diff_desc_), "Destroy para desc failed");
}
size_t x_size_;
size_t para_size_;
size_t workspace_size_;

14
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batchnorm_grad_gpu_kernel.h
View File

@ -117,6 +117,13 @@ class FusedBatchNormGradGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_desc_), "Destroy para desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -146,13 +153,6 @@ class FusedBatchNormGradGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_desc_), "Destroy para desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
}
int batch_;
int channel_;
int height_;

17
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/im2col_gpu_kernel.h
View File

@ -123,6 +123,15 @@ class Im2ColGpuFwdKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(filter_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_desc_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -152,14 +161,6 @@ class Im2ColGpuFwdKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyConvolutionDescriptor(conv_desc_),
"cudnnDestroyConvolutionDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(filter_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(padded_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_desc_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_desc_), "cudnnDestroyTensorDescriptor failed");
}
bool CheckParam(const CNodePtr &kernel_node) {
cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);

30
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/lstm_gpu_kernel.h
View File

@ -157,6 +157,21 @@ class LstmGpuKernel : public GpuKernel {
}
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyRNNDescriptor(rnn_desc_), "destroy rnn_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyDropoutDescriptor(dropout_desc_), "destroy dropout_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(cy_desc_), "destroy cy_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hy_desc_), "destroy hy_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(w_desc_), "destroy w_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hx_desc_), "destroy hx_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(cx_desc_), "destroy cx_desc failed");
for (size_t i = 0; i < IntToSize(seq_len_); ++i) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_[i]), "destroy y_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_[i]), "destroy x_desc failed");
}
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -195,21 +210,6 @@ class LstmGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyRNNDescriptor(rnn_desc_), "destroy rnn_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyDropoutDescriptor(dropout_desc_), "destroy dropout_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(cy_desc_), "destroy cy_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hy_desc_), "destroy hy_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(w_desc_), "destroy w_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hx_desc_), "destroy hx_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(cx_desc_), "destroy cx_desc failed");
for (size_t i = 0; i < IntToSize(seq_len_); ++i) {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_[i]), "destroy y_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_[i]), "destroy x_desc failed");
}
}
int batch_size_;
int seq_len_;
int input_size_;

24
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/lstm_grad_data_gpu_kernel.h
View File

@ -150,6 +150,18 @@ class LstmGradDataGpuKernel : public GpuKernel {
InitSizeLists();
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyRNNDescriptor(rnn_desc_), "destroy rnn_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyDropoutDescriptor(dropout_desc_), "destroy dropout_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dcx_desc_), "destroy dcx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dhx_desc_), "destroy dhx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(w_desc_), "destroy w_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(cx_desc_), "destroy cx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hx_desc_), "destroy hx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dcy_desc_), "destroy dcy_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dhy_desc_), "destroy dhy_desc_ failed");
DestroyTensorDescGrp();
}
protected:
void InitResource() override {
@ -195,18 +207,6 @@ class LstmGradDataGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyRNNDescriptor(rnn_desc_), "destroy rnn_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyDropoutDescriptor(dropout_desc_), "destroy dropout_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dcx_desc_), "destroy dcx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dhx_desc_), "destroy dhx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(w_desc_), "destroy w_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(cx_desc_), "destroy cx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hx_desc_), "destroy hx_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dcy_desc_), "destroy dcy_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dhy_desc_), "destroy dhy_desc_ failed");
DestroyTensorDescGrp();
}
void CreateTensorDescGrp() {
int x_dims[3]{batch_size_, input_size_, 1};
int y_dims[3]{batch_size_, hidden_size_ * (bidirectional_ ? 2 : 1), 1};

14
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/lstm_grad_weight_gpu_kernel.h
View File

@ -162,6 +162,13 @@ class LstmGradWeightGpuKernel : public GpuKernel {
"get workspace size failed");
workspace_size_list_.push_back(workspace_size);
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyRNNDescriptor(rnn_desc_), "destroy rnn_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyDropoutDescriptor(dropout_desc_), "destroy dropout_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(dw_desc_), "destroy dw_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hx_desc_), "destroy hx_desc_ failed");
DestroyTensorDescGrp();
}
private:
void CreateTensorDescGrp() {
@ -187,13 +194,6 @@ class LstmGradWeightGpuKernel : public GpuKernel {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_[i]), "destroy x_desc failed");
}
}
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyRNNDescriptor(rnn_desc_), "destroy rnn_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyDropoutDescriptor(dropout_desc_), "destroy dropout_desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyFilterDescriptor(dw_desc_), "destroy dw_desc_ failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(hx_desc_), "destroy hx_desc_ failed");
DestroyTensorDescGrp();
}
int batch_size_;
int seq_len_;

13
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_gpu_kernel.h
View File

@ -113,6 +113,13 @@ class PoolingGpuFwdKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyPoolingDescriptor(pooling_descriptor_),
"cudnnDestroyPoolingDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -196,12 +203,6 @@ class PoolingGpuFwdKernel : public GpuKernel {
2, windowDimA, paddingA, strideA),
"cudnnSetPoolingNdDescriptor failed");
}
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyPoolingDescriptor(pooling_descriptor_),
"cudnnDestroyPoolingDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
cudnnHandle_t cudnn_handle_;
cudnnTensorDescriptor_t input_descriptor_;

16
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/pooling_grad_gpu_kernel.h
View File

@ -129,6 +129,14 @@ class PoolingGradGpuKernel : public GpuKernel {
InitSizeLists();
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyPoolingDescriptor(pooling_descriptor_),
"cudnnDestroyPoolingDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
protected:
void InitResource() override {
@ -230,14 +238,6 @@ class PoolingGradGpuKernel : public GpuKernel {
pad_value_ = kSignedMinFloat;
}
}
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyPoolingDescriptor(pooling_descriptor_),
"cudnnDestroyPoolingDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dx_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(dy_descriptor_), "cudnnDestroyTensorDescriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_descriptor_), "cudnnDestroyTensorDescriptor failed");
}
cudnnHandle_t cudnn_handle_;
cudnnPoolingDescriptor_t pooling_descriptor_;

13
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/softmax_cross_entropy_with_logits_gpu_kernel.h
View File

@ -101,6 +101,13 @@ class SoftmaxCrossEntropyWithLogitsGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(softmax_output_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(logits_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -118,12 +125,6 @@ class SoftmaxCrossEntropyWithLogitsGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(softmax_output_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(logits_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
}
void InferInputOutputSize(const CNodePtr &kernel_node) {
auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
is_null_input_ = CHECK_NULL_INPUT(logits_shape);

10
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/softmax_gpu_kernel.h
View File

@ -140,6 +140,11 @@ class SoftmaxGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_descriptor_), "destroy output_descriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "destroy input_descriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -159,11 +164,6 @@ class SoftmaxGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(output_descriptor_), "destroy output_descriptor failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(input_descriptor_), "destroy input_descriptor failed");
}
void InitSizeByAxis(const std::vector<size_t> &input_shape, const int &axis) {
if (input_shape.size() == 2) {
InitSizeByAxis2D(input_shape, axis);

8
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/softmax_grad_gpu_kernel.h
View File

@ -142,6 +142,10 @@ class SoftmaxGradGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "destroy output_descriptor failed");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -161,10 +165,6 @@ class SoftmaxGradGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(y_desc_), "destroy output_descriptor failed");
}
void InitSizeByAxis(const std::vector<size_t> input_shape, const int axis) {
axis_ = axis;
if (axis_ < 0) {

13
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/sparse_softmax_cross_entropy_with_logits_gpu_kernel.h
View File

@ -103,6 +103,13 @@ class SparseSoftmaxCrossEntropyWithLogitsGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(softmax_output_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(logits_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
}
protected:
void InitResource() override {
cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
@ -120,12 +127,6 @@ class SparseSoftmaxCrossEntropyWithLogitsGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(softmax_output_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(logits_descriptor_),
"cudnnDestroyTensorDescriptor failed.");
}
void InferInputOutputSize(const CNodePtr &kernel_node) {
auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
is_null_input_ = CHECK_NULL_INPUT(logits_shape);

2
mindspore/ccsrc/backend/kernel_compiler/gpu/quant/batchnorm_fold2_gpu_kernel.h
View File

@ -113,8 +113,6 @@ class BatchNormFold2GpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {}
cudnnHandle_t cudnn_handle_;
bool is_null_input_;
size_t batch_size_;

10
mindspore/ccsrc/backend/kernel_compiler/gpu/quant/batchnorm_fold_gpu_kernel.h
View File

@ -152,6 +152,11 @@ class BatchNormFoldGpuKernel : public GpuKernel {
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
}
protected:
void InitSizeLists() override {
// x, mean, variance, current_step
@ -177,11 +182,6 @@ class BatchNormFoldGpuKernel : public GpuKernel {
}
private:
void DestroyResource() noexcept {
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_), "Destroy para desc failed");
}
size_t input_size_;
size_t output_size_;
std::vector<size_t> input_size_list_;

2
mindspore/ccsrc/backend/kernel_compiler/gpu/quant/correction_mul_gpu_kernel.h
View File

@ -81,8 +81,6 @@ class CorrectionMulGpuKernel : public GpuKernel {
void InitResource() override {}
private:
void DestroyResource() noexcept {}
size_t batch_size_;
size_t channel_;
size_t height_;

2
mindspore/ccsrc/backend/kernel_compiler/gpu/quant/correction_mul_grad_gpu_kernel.h
View File

@ -89,8 +89,6 @@ class CorrectionMulGradGpuKernel : public GpuKernel {
void InitResource() override {}
private:
void DestroyResource() noexcept {}
size_t batch_size_;
size_t channel_;
size_t height_;

4
mindspore/ccsrc/backend/optimizer/mem_reuse/mem_swap_manager.cc
View File

@ -237,6 +237,10 @@ void MemSwapManager::SaveUserKernelTopoOrder() {
continue;
}
if (opt::IsNopNode(user_kernel)) {
continue;
}
size_t user_kernel_topo_sort = SearchKernelExecutionInfo(user_kernel).topo_order_;
auto kernel_with_index = AnfAlgo::GetPrevNodeOutput(user_kernel, node_pair.second - 1);
auto &output_idx = kernel_with_index.second;

28
mindspore/ccsrc/backend/session/anf_runtime_algorithm.cc
View File

@ -50,6 +50,10 @@ bool IsShapeDynamic(const abstract::ShapePtr &shape) {
return std::any_of(shape->shape().begin(), shape->shape().end(), [](int s) { return s < 0; });
}
bool IsShapeDynamic(const std::vector<size_t> &shape) {
return std::any_of(shape.begin(), shape.end(), [](int s) { return s < 0; });
}
std::vector<size_t> TransShapeToSizet(const abstract::ShapePtr &shape) {
MS_EXCEPTION_IF_NULL(shape);
std::vector<size_t> shape_size_t;
@ -1389,5 +1393,29 @@ bool AnfRuntimeAlgorithm::IsNodeDynamicShape(const AnfNodePtr &node) {
}
return false;
}
std::vector<size_t> AnfRuntimeAlgorithm::GetInputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index) {
auto device_shape = GetInputDeviceShape(anf_node, index);
// Initialize GPUKernel with max shape to fit 'InitDynamicOutputKernelRef()' for memory reuse.
if (IsShapeDynamic(device_shape)) {
auto max_shape = GetInputMaxShape(anf_node, index);
std::transform(max_shape.begin(), max_shape.end(), device_shape.begin(), IntToSize);
auto format = GetInputFormat(anf_node, index);
trans::TransShapeToDevice(device_shape, format);
}
return device_shape;
}
std::vector<size_t> AnfRuntimeAlgorithm::GetOutputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index) {
auto device_shape = GetOutputDeviceShape(anf_node, index);
// Initialize GPUKernel with max shape to fit 'InitDynamicOutputKernelRef()' for memory reuse.
if (IsShapeDynamic(device_shape)) {
auto max_shape = GetOutputMaxShape(anf_node, index);
std::transform(max_shape.begin(), max_shape.end(), device_shape.begin(), IntToSize);
auto format = GetOutputFormat(anf_node, index);
trans::TransShapeToDevice(device_shape, format);
}
return device_shape;
}
} // namespace session
} // namespace mindspore

2
mindspore/ccsrc/backend/session/anf_runtime_algorithm.h
View File

@ -230,6 +230,8 @@ class AnfRuntimeAlgorithm {
static std::vector<int64_t> GetOutputMaxShape(const AnfNodePtr &anf_node, size_t index);
static std::vector<int64_t> GetOutputMinShape(const AnfNodePtr &anf_node, size_t index);
static bool IsNodeDynamicShape(const AnfNodePtr &node);
static std::vector<size_t> GetInputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index);
static std::vector<size_t> GetOutputRealDeviceShapeIfExist(const AnfNodePtr &anf_node, size_t index);
};
} // namespace session
using AnfAlgo = session::AnfRuntimeAlgorithm;

7
mindspore/ccsrc/backend/session/gpu_session.cc
View File

@ -306,7 +306,9 @@ GraphId GPUSession::CompileGraphImpl(const AnfNodePtrList &lst, const AnfNodePtr
if (save_graphs) {
DumpIRProto(graph, "before_removeNop_" + std::to_string(graph_id));
}
// Update Graph Dynamic Shape Attr.
UpdateGraphDynamicShapeAttr(NOT_NULL(graph));
graph->UpdateGraphDynamicAttr();
// Hide NopOp from execution graph
opt::HideNopNode(graph.get());
// Build kernel if node is cnode
@ -317,13 +319,10 @@ GraphId GPUSession::CompileGraphImpl(const AnfNodePtrList &lst, const AnfNodePtr
graph->set_execution_order(execution_order);
// Get summary nodes.
SetSummaryNodes(graph.get());
// Remove NopOp from execution graph
opt::RemoveNopNode(graph.get());
// Dump .pb graph after graph optimization
if (save_graphs) {
DumpIRProto(graph, "after_opt_" + std::to_string(graph_id));
}
// Set graph manager.
MS_EXCEPTION_IF_NULL(context_);
FuncGraphManagerPtr manager = MakeManager({graph});

2
mindspore/ccsrc/runtime/device/CMakeLists.txt
View File

@ -1,5 +1,5 @@
file(GLOB_RECURSE DEVICE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "common/*.cc"
"kernel_info.cc" "executor/dynamic_kernel.cc" "kernel_runtime.cc" "memory_manager.cc" "kernel_runtime_manager.cc" "convert_tensor_utils.cc"
"kernel_info.cc" "executor/dynamic_kernel.cc" "executor/executor_callback.cc" "kernel_runtime.cc" "memory_manager.cc" "kernel_runtime_manager.cc" "convert_tensor_utils.cc"
)
if (ENABLE_GPU)

2
mindspore/ccsrc/runtime/device/ascend/ascend_kernel_runtime.cc
View File

@ -48,7 +48,7 @@
#include "backend/optimizer/mem_reuse/mem_reuse_checker.h"
#endif
#include "runtime/device/ascend/executor/tiling/op_tiling_calculater.h"
#include "runtime/device/ascend/executor/executor_callback.h"
#include "runtime/device/executor/executor_callback.h"
#include "runtime/device/ascend/executor/hccl_dynamic_kernel.h"
#include "profiler/device/ascend/ascend_profiling.h"
#include "profiler/device/ascend/profiling_context.h"

2
mindspore/ccsrc/runtime/device/ascend/executor/ai_cpu_dynamic_kernel.cc
View File

@ -22,7 +22,7 @@
#include "runtime/kernel.h"
#include "backend/session/anf_runtime_algorithm.h"
#include "backend/kernel_compiler/aicpu/aicpu_util.h"
#include "runtime/device/ascend/executor/executor_callback.h"
#include "runtime/device/executor/executor_callback.h"
namespace mindspore {
namespace device {

4
mindspore/ccsrc/runtime/device/ascend/executor/executor_callback.cc → mindspore/ccsrc/runtime/device/executor/executor_callback.cc
View File

@ -14,12 +14,11 @@
* limitations under the License.
*/
#include "runtime/device/ascend/executor/executor_callback.h"
#include "runtime/device/executor/executor_callback.h"
#include "utils/log_adapter.h"
namespace mindspore {
namespace device {
namespace ascend {
void ExecutorCallback::RegistCallback(const std::function<void()> &callback) {
std::lock_guard<std::mutex> guard(lock_);
callback_queue_.push(callback);
@ -36,6 +35,5 @@ void ExecutorCallback::Consume() {
callback_func();
}
}
} // namespace ascend
} // namespace device
} // namespace mindspore

8
mindspore/ccsrc/runtime/device/ascend/executor/executor_callback.h → mindspore/ccsrc/runtime/device/executor/executor_callback.h
View File

@ -14,8 +14,8 @@
* limitations under the License.
*/
#ifndef MINDSPORE_MINDSPORE_CCSRC_RUNTIME_DEVICE_ASCEND_EXECUTOR_EXECUTOR_CALLBACK_H_
#define MINDSPORE_MINDSPORE_CCSRC_RUNTIME_DEVICE_ASCEND_EXECUTOR_EXECUTOR_CALLBACK_H_
#ifndef MINDSPORE_MINDSPORE_CCSRC_RUNTIME_DEVICE_EXECUTOR_EXECUTOR_CALLBACK_H_
#define MINDSPORE_MINDSPORE_CCSRC_RUNTIME_DEVICE_EXECUTOR_EXECUTOR_CALLBACK_H_
#include <queue>
#include <mutex>
@ -24,7 +24,6 @@
namespace mindspore {
namespace device {
namespace ascend {
class ExecutorCallback {
public:
static ExecutorCallback &GetInstance() {
@ -43,7 +42,6 @@ class ExecutorCallback {
std::queue<std::function<void()>> callback_queue_;
std::mutex lock_;
};
} // namespace ascend
} // namespace device
} // namespace mindspore
#endif // MINDSPORE_MINDSPORE_CCSRC_RUNTIME_DEVICE_ASCEND_EXECUTOR_EXECUTOR_CALLBACK_H_
#endif // MINDSPORE_MINDSPORE_CCSRC_RUNTIME_DEVICE_EXECUTOR_EXECUTOR_CALLBACK_H_

2
mindspore/ccsrc/runtime/device/gpu/gpu_kernel_build.cc
View File

@ -67,6 +67,8 @@ void GpuBuild(const KernelGraphPtr &kernel_graph) {
if (!gpu_kernel_ptr->Init(kernel)) {
MS_LOG(EXCEPTION) << "Initialize gpu kernel op[" << kernel->fullname_with_scope() << "] failed.";
}
gpu_kernel_ptr->InitDynamicKernel(kernel);
gpu_kernel_ptr->DynamicKernel()->Initialize();
session::AnfRuntimeAlgorithm::SetKernelMod((kernel::KernelModPtr)gpu_kernel_ptr, kernel.get());
}
}

30
mindspore/ccsrc/runtime/device/gpu/gpu_kernel_runtime.cc
View File

@ -36,6 +36,8 @@
#include "profiler/device/gpu/gpu_profiling.h"
#include "utils/shape_utils.h"
#include "debug/data_dump/dump_json_parser.h"
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "runtime/device/executor/executor_callback.h"
#ifdef ENABLE_DEBUGGER
#include "debug/debug_services.h"
#endif
@ -588,6 +590,29 @@ bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph, bo
MS_LOG(INFO) << "[inplace optimizer] skip node: " << kernel->DebugString();
continue;
}
// akg kernel do not support dynamic shape by now.
device::DynamicKernelPtr dynamic_kernel = nullptr;
kernel::GpuKernel *gpu_kernel = nullptr;
if (session::AnfRuntimeAlgorithm::GetKernelType(kernel) != KernelType::AKG_KERNEL) {
gpu_kernel = dynamic_cast<kernel::GpuKernel *>(kernel_mod);
dynamic_kernel = gpu_kernel->DynamicKernel();
}
if (dynamic_kernel && dynamic_kernel->have_depends()) {
MS_LOG(INFO) << "Match Dynamic Kernel, Start SyncStream";
if (!SyncStream()) {
MS_LOG(ERROR) << "SyncStream failed";
return false;
}
}
if (dynamic_kernel && dynamic_kernel->is_dynamic_shape()) {
ExecutorCallback::GetInstance().Consume();
dynamic_kernel->InferShape();
dynamic_kernel->UpdateArgs();
}
AddressPtrList kernel_inputs;
AddressPtrList kernel_workspaces;
AddressPtrList kernel_outputs;
@ -615,6 +640,10 @@ bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph, bo
} else {
LaunchKernelWithTimeProfiling(kernel, kernel_inputs, kernel_workspaces, kernel_outputs);
}
ExecutorCallback::GetInstance().RegistCallback([&gpu_kernel] {
if (gpu_kernel) gpu_kernel->PostExecute();
});
// called once per kernel to collect the outputs to the kernel (does a SyncDeviceToHost)
LoadKernelData(debugger_.get(), kernel, kernel_inputs, kernel_workspaces, kernel_outputs, exec_order, stream_,
dump_enabled);
@ -633,6 +662,7 @@ bool GPUKernelRuntime::LaunchKernelDynamic(const session::KernelGraph *graph, bo
// collect weights and bias for dump mode
debugger_->LoadParametersAndConst();
CHECK_OP_RET_WITH_EXCEPT(SyncStream(), "SyncStream failed.");
ExecutorCallback::GetInstance().Consume();
}
ClearSwapInfo(mock);
return true;

15
tests/ut/cpp/stub/dynamic_shape/dynamic_shape_stub.cc
View File

@ -19,12 +19,19 @@
#include "runtime/device/ascend/executor/rts/profiling_rts_dynamic_kernel.h"
#include "runtime/device/ascend/executor/ai_core_dynamic_kernel.h"
#include "profiler/device/ascend/rt_callback_manager.h"
#include "runtime/device/ascend/executor/executor_callback.h"
#include "runtime/device/executor/executor_callback.h"
#include "profiler/device/ascend/ascend_profiling.h"
#include "runtime/device/ascend/executor/tiling/op_tiling_calculater.h"
#include "backend/kernel_compiler/host/host_kernel_metadata.h"
#include "backend/kernel_compiler/host/host_kernel_build.h"
namespace mindspore {
namespace device {
void ExecutorCallback::RegistCallback(const std::function<void()> &callback) {}
void ExecutorCallback::Consume() {}
} // namespace device
} // namespace mindspore
namespace mindspore {
namespace device {
namespace ascend {
@ -45,11 +52,9 @@ void AiCoreDynamicKernel::PostExecute() {}
bool HcclExecutorManager::Initialize() { return true; }
bool HcclExecutorManager::Finalize() { return true; }
void ExecutorCallback::RegistCallback(const std::function<void()> &callback) {}
void ExecutorCallback::Consume() {}
void OpTilingCalculater::Init() {}
void OpTilingCalculater::CalculateTiling(const NotNull<CNodePtr> &cnode, const NotNull<std::shared_ptr<nlohmann::json>> &compile_info_json,
void OpTilingCalculater::CalculateTiling(const NotNull<CNodePtr> &cnode,
const NotNull<std::shared_ptr<nlohmann::json>> &compile_info_json,
const std::map<uint32_t, tensor::TensorPtr> &depend_tensor_map,
NotNull<optiling::OpRunInfo *> op_run_info) {}
} // namespace ascend