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!4764 add FusedBatchNoramEx gpu kernel 

Merge pull request !4764 from zyli2020/gpu_bn_op_perf_optimize
tags/v0.7.0-beta
mindspore-ci-bot Gitee 5 years ago
parent
dc8b3db126 7ddddc41a9
commit
bd2336b96a
3 changed files with 392 additions and 0 deletions
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  1. +110
    -0
      mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.cc
  2. +276
    -0
      mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h
  3. +6
    -0
      mindspore/ccsrc/utils/utils.h

+ 110
- 0
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.cc View File

@@ -0,0 +1,110 @@
/**
* 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/nn/fused_batch_norm_ex_gpu_kernel.h"

namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(FusedBatchNormEx,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormEx,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, half)

MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, half)

MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithAddAndActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithAddAndActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, half)
} // namespace kernel
} // namespace mindspore

+ 276
- 0
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h View File

@@ -0,0 +1,276 @@
/**
* 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.
*/

#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCH_NORM_EX_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCH_NORM_EX_GPU_KERNEL_H_

#include <vector>
#include <string>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/kernel_constants.h"
#include "utils/utils.h"

namespace mindspore {
namespace kernel {
template <typename T>
class FusedBatchNormExGpuKernel : public GpuKernel {
public:
FusedBatchNormExGpuKernel()
: input_x_size_(0),
input_z_size_(0),
para_size_(0),
output_size_(0),
workspace_size_(0),
reserve_size_(0),
mode_(CUDNN_BATCHNORM_SPATIAL),
bn_ops_(CUDNN_BATCHNORM_OPS_BN),
epsilon_(10e-5),
exp_avg_factor_(0.1),
is_null_input_(false),
x_desc_(nullptr),
y_desc_(nullptr),
z_desc_(nullptr),
scale_bias_mean_var_desc_(nullptr),
activation_desc_(nullptr),
handle_(nullptr),
cudnn_data_type_(CUDNN_DATA_FLOAT) {}
~FusedBatchNormExGpuKernel() 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_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }

bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
VARIABLE_NOT_USED(workspace);
VARIABLE_NOT_USED(stream_ptr);
if (is_null_input_) {
return true;
}
auto x = GetDeviceAddress<T>(inputs, 0);
auto scale = GetDeviceAddress<float>(inputs, 1);
auto bias = GetDeviceAddress<float>(inputs, 2);
auto runing_mean = GetDeviceAddress<float>(inputs, 3);
auto runnig_variance = GetDeviceAddress<float>(inputs, 4);
T *z = nullptr;
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
z = GetDeviceAddress<T>(inputs, 5);
}

auto y = GetDeviceAddress<T>(outputs, 0);
auto save_mean = GetDeviceAddress<float>(outputs, 3);
auto save_variance = GetDeviceAddress<float>(outputs, 4);
auto reserve_addr = GetDeviceAddress<float>(outputs, 5);
T *workspace_addr = nullptr;
if (workspace_size_ != 0) {
workspace_addr = GetDeviceAddress<T>(workspace, 0);
}
const float alpha = 1;
const float beta = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnBatchNormalizationForwardTrainingEx(handle_, mode_, bn_ops_, &alpha, &beta, x_desc_, x, z_desc_, z, y_desc_,
y, scale_bias_mean_var_desc_, scale, bias, exp_avg_factor_, runing_mean,
runnig_variance, epsilon_, save_mean, save_variance, activation_desc_,
workspace_addr, workspace_size_, reserve_addr, reserve_size_),
"Kernel launch failed");
return true;
}

bool Init(const CNodePtr &kernel_node) override {
MS_EXCEPTION_IF_NULL(kernel_node);
std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
if (kernel_name == kFusedBatchNormEx) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN;
} else if (kernel_name == kFusedBatchNormExWithActivation) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN_ACTIVATION;
} else if (kernel_name == kFusedBatchNormExWithAddAndActivation) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION;
} else {
MS_LOG(EXCEPTION) << "Invalid kernel name: " << kernel_name;
}

InitResource();
mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
epsilon_ = GetAttr<float>(kernel_node, "epsilon");
exp_avg_factor_ = GetAttr<float>(kernel_node, "momentum");

cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
if (input_num != 6) {
MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", " << kernel_name << " should be 6";
}
} else {
if (input_num != 5) {
MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", " << kernel_name << " should be 5";
}
}

auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
if (shape.size() != 4) {
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", FusedBatchNormExGpuKernel should be 4";
}
is_null_input_ = CHECK_NULL_INPUT(shape);
if (is_null_input_) {
MS_LOG(WARNING) << "FusedBatchNormExGpuKernel input is null";
InitSizeLists();
return true;
}
auto format = AnfAlgo::GetInputFormat(kernel_node, 0);
SetTensorDescriptor(format, shape);
InitSizeLists();
return true;
}

protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&y_desc_), "Create y desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&z_desc_), "Create z desc failed");
}
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateTensorDescriptor(&scale_bias_mean_var_desc_), "Create para desc failed");

if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnCreateActivationDescriptor(&activation_desc_),
"Create activation descriptor failed");
}
}

void InitSizeLists() override {
if (!is_null_input_) {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(x_desc_, &input_x_size_), "Get input x size failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(scale_bias_mean_var_desc_, &para_size_),
"Get para size failed");
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(y_desc_, &output_size_), "Get output size failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetTensorSizeInBytes(z_desc_, &input_z_size_), "Get input z size failed");
}

CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(
handle_, mode_, bn_ops_, x_desc_, z_desc_, y_desc_, scale_bias_mean_var_desc_,
activation_desc_, &workspace_size_),
"cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize failed");

CHECK_CUDNN_RET_WITH_EXCEPT(cudnnGetBatchNormalizationTrainingExReserveSpaceSize(
handle_, mode_, bn_ops_, activation_desc_, x_desc_, &reserve_size_),
"Get reserve size failed");
}

input_size_list_.push_back(input_x_size_); // input x
input_size_list_.push_back(para_size_); // scale
input_size_list_.push_back(para_size_); // bias
input_size_list_.push_back(para_size_); // mean
input_size_list_.push_back(para_size_); // variance
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
input_size_list_.push_back(input_z_size_); // input z
}

output_size_list_.push_back(output_size_); // output
output_size_list_.push_back(para_size_); // save scale
output_size_list_.push_back(para_size_); // save bias
output_size_list_.push_back(para_size_); // save mean
output_size_list_.push_back(para_size_); // save variance
output_size_list_.push_back(reserve_size_); // reserve space

workspace_size_list_.push_back(workspace_size_);
}

private:
void SetTensorDescriptor(const std::string &format, const std::vector<size_t> &shape) {
cudnnTensorFormat_t cudnn_format;
int batch, channel, height, width;
if (format == kOpFormat_NHWC) {
batch = SizeToInt(shape[0]);
height = SizeToInt(shape[1]);
width = SizeToInt(shape[2]);
channel = SizeToInt(shape[3]);
cudnn_format = CUDNN_TENSOR_NHWC;
} else {
batch = SizeToInt(shape[0]);
channel = SizeToInt(shape[1]);
height = SizeToInt(shape[2]);
width = SizeToInt(shape[3]);
cudnn_format = CUDNN_TENSOR_NCHW;
}

CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnSetTensor4dDescriptor(x_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
"Set x desc failed");

CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnSetTensor4dDescriptor(y_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
"Set y desc failed");

if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnSetTensor4dDescriptor(z_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
"Set z desc failed");
}

CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnSetTensor4dDescriptor(scale_bias_mean_var_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel, 1, 1),
"Set para desc failed");

if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_EXCEPT(
cudnnSetActivationDescriptor(activation_desc_, CUDNN_ACTIVATION_RELU, CUDNN_NOT_PROPAGATE_NAN, 0.0),
"cudnnSetActivationDescriptor failed");
}
}

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_;
size_t output_size_;
size_t workspace_size_;
size_t reserve_size_;
cudnnBatchNormMode_t mode_;
cudnnBatchNormOps_t bn_ops_;
double epsilon_;
double exp_avg_factor_;
bool is_null_input_;
cudnnTensorDescriptor_t x_desc_;
cudnnTensorDescriptor_t y_desc_;
cudnnTensorDescriptor_t z_desc_;
cudnnTensorDescriptor_t scale_bias_mean_var_desc_;
cudnnActivationDescriptor_t activation_desc_;

cudnnHandle_t handle_;
cudnnDataType_t cudnn_data_type_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
};
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCH_NORM_EX_GPU_KERNEL_H_

+ 6
- 0
mindspore/ccsrc/utils/utils.h View File

@@ -42,6 +42,12 @@ constexpr auto kFusedBN3OpName = "FusedBN3";
constexpr auto kBNGrad1OpName = "BNGrad1";
constexpr auto kBNGrad2OpName = "BNGrad2";
constexpr auto kBNGrad3OpName = "BNGrad3";
constexpr auto kFusedBatchNormEx = "FusedBatchNormEx";
constexpr auto kFusedBatchNormExWithActivation = "FusedBatchNormExWithActivation";
constexpr auto kFusedBatchNormExWithAddAndActivation = "FusedBatchNormExWithAddAndActivation";
constexpr auto kFusedBatchNormGradEx = "FusedBatchNormGradEx";
constexpr auto kFusedBatchNormGradExWithActivation = "FusedBatchNormGradExWithActivation";
constexpr auto kFusedBatchNormGradExWithAddAndActivation = "FusedBatchNormGradExWithAddAndActivation";
constexpr auto kClearZeroOpName = "ClearZero";
constexpr auto kAtomicAddrCleanOpName = "AtomicAddrClean";
constexpr auto kGetNextOpName = "GetNext";


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