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!12115 IR operators of GPU and CPU are unified as batchnorm 

From: @ding_fei_fei
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2021-03-19 14:51:26 +08:00 committed by Gitee
parent 1d505ebad3 87e41aaeee
commit 8e8f3043f9
63 changed files with 399 additions and 2246 deletions
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4
RELEASE.md
View File

@ -16,6 +16,10 @@ Previously MakeRefKey is an external interface that is not used, now make it an
Previously the number of outputs of these operator is different on different backends. To unify their definition we change their output on Ascend backend from multiple to a single.
##### `P.FusedBatchNorm`, `P.FusedBatchNormEx` deleted ([!12115](https://gitee.com/mindspore/mindspore/pulls/12115))
The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.
# MindSpore 1.1.1 Release Notes
## MindSpore

23
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/fused_batch_norm_cpu_kernel.cc → mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/batch_norm_cpu_kernel.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -14,14 +14,14 @@
* limitations under the License.
*/
#include <string>
#include "backend/kernel_compiler/cpu/mkldnn/fused_batch_norm_cpu_kernel.h"
#include "backend/kernel_compiler/cpu/mkldnn/batch_norm_cpu_kernel.h"
#include "backend/kernel_compiler/cpu/mkldnn/mkl_kernel_engine.h"
#include "runtime/device/cpu/cpu_device_address.h"
#include "utils/ms_utils.h"
namespace mindspore {
namespace kernel {
void FusedBatchNormCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
void BatchNormCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
CPUKernel::InitInputOutputSize(kernel_node);
MS_EXCEPTION_IF_NULL(kernel_node);
size_t type_size = sizeof(float);
@ -30,16 +30,13 @@ void FusedBatchNormCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
workspace_size_list_.emplace_back(tensor_size);
}
void FusedBatchNormCPUKernel::InitKernel(const CNodePtr &kernel_node) {
void BatchNormCPUKernel::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
auto node_name = AnfAlgo::GetCNodeName(kernel_node);
if (node_name == "FusedBatchNorm") {
momentum = AnfAlgo::GetNodeAttr<float>(kernel_node, "momentum");
is_train = true;
}
is_train = AnfAlgo::GetNodeAttr<bool>(kernel_node, "is_training");
momentum = AnfAlgo::GetNodeAttr<float>(kernel_node, "momentum");
std::vector<size_t> x_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
if (x_shape.size() != 4) {
MS_LOG(EXCEPTION) << "Fused batchnorm only support nchw input!";
MS_LOG(EXCEPTION) << "Batchnorm only support nchw input!";
}
batch_size = x_shape[0];
channel = x_shape[1];
@ -66,9 +63,9 @@ void FusedBatchNormCPUKernel::InitKernel(const CNodePtr &kernel_node) {
AddArgument(DNNL_ARG_DST, x_desc);
}
bool FusedBatchNormCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<kernel::AddressPtr> &outputs) {
bool BatchNormCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<kernel::AddressPtr> &outputs) {
if (inputs.size() < 5 || outputs.empty()) {
MS_LOG(EXCEPTION) << "Error input output size!";
}

28
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/fused_batch_norm_cpu_kernel.h → mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/batch_norm_cpu_kernel.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -13,18 +13,18 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_FUSED_BATCH_NORM_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_FUSED_BATCH_NORM_CPU_KERNEL_H_
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_BATCH_NORM_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_BATCH_NORM_CPU_KERNEL_H_
#include <memory>
#include <vector>
#include "backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.h"
namespace mindspore {
namespace kernel {
class FusedBatchNormCPUKernel : public MKLCPUKernel {
class BatchNormCPUKernel : public MKLCPUKernel {
public:
FusedBatchNormCPUKernel() = default;
~FusedBatchNormCPUKernel() override = default;
BatchNormCPUKernel() = default;
~BatchNormCPUKernel() override = default;
void InitKernel(const CNodePtr &kernel_node) override;
@ -43,20 +43,6 @@ class FusedBatchNormCPUKernel : public MKLCPUKernel {
size_t nhw_size{0};
};
MS_REG_CPU_KERNEL(FusedBatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormCPUKernel)
MS_REG_CPU_KERNEL(BatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
@ -69,7 +55,7 @@ MS_REG_CPU_KERNEL(BatchNorm,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormCPUKernel)
BatchNormCPUKernel)
} // namespace kernel
} // namespace mindspore

25
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/fused_batch_norm_gard_cpu_kernel.cc → mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/batch_norm_gard_cpu_kernel.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -13,7 +13,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/cpu/mkldnn/fused_batch_norm_gard_cpu_kernel.h"
#include "backend/kernel_compiler/cpu/mkldnn/batch_norm_gard_cpu_kernel.h"
#include <string>
#include "backend/kernel_compiler/cpu/mkldnn/mkl_kernel_engine.h"
@ -22,19 +22,20 @@
namespace mindspore {
namespace kernel {
void FusedBatchNormGradCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
void BatchNormGradCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
CPUKernel::InitInputOutputSize(kernel_node);
MS_EXCEPTION_IF_NULL(kernel_node);
size_t type_size = sizeof(float);
std::vector<size_t> shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
size_t tensor_size = shape[1] * 2 * type_size;
input_size_list_.pop_back();
// [2, c] to store scale and bias
workspace_size_list_.emplace_back(tensor_size);
// [2, c] to store diff_scale and diff_bias
workspace_size_list_.emplace_back(tensor_size);
}
void FusedBatchNormGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
void BatchNormGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
std::vector<size_t> x_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
if (x_shape.size() != 4) {
@ -72,25 +73,25 @@ void FusedBatchNormGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
AddArgument(DNNL_ARG_DIFF_SCALE_SHIFT, scale_bias_desc);
}
bool FusedBatchNormGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<kernel::AddressPtr> &outputs) {
bool BatchNormGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<kernel::AddressPtr> &outputs) {
if (inputs.size() < 5 || outputs.empty()) {
MS_LOG(EXCEPTION) << "Error input output size!";
}
auto wksp_in = reinterpret_cast<float *>(workspace[0]->addr);
auto scale_ret = memcpy_s(wksp_in, workspace[0]->size, inputs[2]->addr, inputs[2]->size);
auto max_size = workspace[0]->size - inputs[2]->size;
auto bias_ret = memcpy_s(wksp_in + (inputs[2]->size / sizeof(float)), max_size, inputs[3]->addr, inputs[3]->size);
if (scale_ret != 0 || bias_ret != 0) {
auto bias_ret = memset_s(wksp_in + (inputs[2]->size / sizeof(float)), max_size, 0., max_size);
if (scale_ret != 0 && bias_ret != 0) {
MS_LOG(EXCEPTION) << "Memcpy_s error.";
return false;
}
SetArgumentHandle(DNNL_ARG_DIFF_DST, inputs[0]->addr);
SetArgumentHandle(DNNL_ARG_SRC, inputs[1]->addr);
SetArgumentHandle(DNNL_ARG_MEAN, inputs[4]->addr);
SetArgumentHandle(DNNL_ARG_VARIANCE, inputs[5]->addr);
SetArgumentHandle(DNNL_ARG_MEAN, inputs[3]->addr);
SetArgumentHandle(DNNL_ARG_VARIANCE, inputs[4]->addr);
SetArgumentHandle(DNNL_ARG_SCALE_SHIFT, workspace[0]->addr);
SetArgumentHandle(DNNL_ARG_DIFF_SRC, outputs[0]->addr);
SetArgumentHandle(DNNL_ARG_DIFF_SCALE_SHIFT, workspace[1]->addr);
@ -99,7 +100,7 @@ bool FusedBatchNormGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &
auto wksp_out = reinterpret_cast<float *>(workspace[1]->addr);
auto diff_scale_ret = memcpy_s(outputs[1]->addr, outputs[1]->size, wksp_out, inputs[2]->size);
auto diff_bias_ret =
memcpy_s(outputs[2]->addr, outputs[2]->size, wksp_out + (outputs[1]->size / sizeof(float)), inputs[3]->size);
memcpy_s(outputs[2]->addr, outputs[2]->size, wksp_out + (outputs[1]->size / sizeof(float)), outputs[2]->size);
if (diff_scale_ret != 0 || diff_bias_ret != 0) {
MS_LOG(EXCEPTION) << "Memcpy_s error.";
return false;

16
mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/fused_batch_norm_gard_cpu_kernel.h → mindspore/ccsrc/backend/kernel_compiler/cpu/mkldnn/batch_norm_gard_cpu_kernel.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -13,18 +13,18 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_FUSED_BATCH_NORM_GRAD_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_FUSED_BATCH_NORM_GRAD_CPU_KERNEL_H_
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_BATCH_NORM_GRAD_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_BATCH_NORM_GRAD_CPU_KERNEL_H_
#include <memory>
#include <vector>
#include "backend/kernel_compiler/cpu/mkldnn/mkl_cpu_kernel.h"
namespace mindspore {
namespace kernel {
class FusedBatchNormGradCPUKernel : public MKLCPUKernel {
class BatchNormGradCPUKernel : public MKLCPUKernel {
public:
FusedBatchNormGradCPUKernel() = default;
~FusedBatchNormGradCPUKernel() override = default;
BatchNormGradCPUKernel() = default;
~BatchNormGradCPUKernel() override = default;
void InitKernel(const CNodePtr &kernel_node) override;
@ -42,7 +42,7 @@ class FusedBatchNormGradCPUKernel : public MKLCPUKernel {
size_t nhw_size{0};
};
MS_REG_CPU_KERNEL(FusedBatchNormGradCPU,
MS_REG_CPU_KERNEL(BatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
@ -53,7 +53,7 @@ MS_REG_CPU_KERNEL(FusedBatchNormGradCPU,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGradCPUKernel)
BatchNormGradCPUKernel)
} // namespace kernel
} // namespace mindspore

34
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.cc → mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batch_norm_gpu_kernel.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -14,11 +14,11 @@
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h"
#include "backend/kernel_compiler/gpu/nn/batch_norm_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(FusedBatchNormEx,
MS_REG_GPU_KERNEL_ONE(BatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
@ -29,10 +29,9 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormEx,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormEx,
BatchNormGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
@ -43,11 +42,10 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormEx,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, half)
BatchNormGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithActivation,
MS_REG_GPU_KERNEL_ONE(BatchNormWithActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
@ -58,10 +56,9 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithActivation,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithActivation,
BatchNormGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNormWithActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
@ -72,11 +69,10 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithActivation,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, half)
BatchNormGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithAddAndActivation,
MS_REG_GPU_KERNEL_ONE(BatchNormWithAddAndActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
@ -88,10 +84,9 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithAddAndActivation,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithAddAndActivation,
BatchNormGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNormWithAddAndActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
@ -103,8 +98,7 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormExWithAddAndActivation,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormExGpuKernel, half)
BatchNormGpuKernel, half)
} // namespace kernel
} // namespace mindspore

64
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_ex_gpu_kernel.h → mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batch_norm_gpu_kernel.h
View File

@ -14,8 +14,8 @@
* 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_
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BATCH_NORM_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BATCH_NORM_GPU_KERNEL_H_
#include <string>
#include <vector>
@ -27,10 +27,10 @@
namespace mindspore {
namespace kernel {
template <typename T>
class FusedBatchNormExGpuKernel : public GpuKernel {
class BatchNormGpuKernel : public GpuKernel {
public:
FusedBatchNormExGpuKernel() { ResetResource(); }
~FusedBatchNormExGpuKernel() override { DestroyResource(); }
BatchNormGpuKernel() { ResetResource(); }
~BatchNormGpuKernel() 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_; }
@ -46,30 +46,38 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
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);
auto running_mean = GetDeviceAddress<float>(inputs, 3);
auto running_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);
auto reserve_addr = GetDeviceAddress<float>(outputs, 2);
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(
kernel_node_,
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");
if (is_train_) {
auto save_mean = GetDeviceAddress<float>(outputs, 3);
auto save_variance = GetDeviceAddress<float>(outputs, 4);
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
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_, running_mean, running_variance, epsilon_, save_mean, save_variance, activation_desc_,
workspace_addr, workspace_size_, reserve_addr, reserve_size_),
"Kernel launch failed");
} else {
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
cudnnBatchNormalizationForwardInference(
handle_, mode_, &alpha, &beta, x_desc_, x, y_desc_, y, scale_bias_mean_var_desc_,
scale, bias, running_mean, running_variance, epsilon_),
"Kernel launch failed");
}
return true;
}
@ -77,18 +85,22 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
kernel_node_ = kernel_node;
MS_EXCEPTION_IF_NULL(kernel_node);
std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
if (kernel_name == kFusedBatchNormEx) {
if (kernel_name == kBatchNorm) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN;
} else if (kernel_name == kFusedBatchNormExWithActivation) {
} else if (kernel_name == kBatchNormWithActivation) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN_ACTIVATION;
} else if (kernel_name == kFusedBatchNormExWithAddAndActivation) {
} else if (kernel_name == kBatchNormWithAddAndActivation) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION;
} else {
MS_LOG(EXCEPTION) << "Invalid kernel name: " << kernel_name;
}
InitResource();
mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
if (is_train_) {
mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT;
} else {
mode_ = CUDNN_BATCHNORM_SPATIAL;
}
epsilon_ = GetAttr<float>(kernel_node, "epsilon");
exp_avg_factor_ = GetAttr<float>(kernel_node, "momentum");
@ -106,11 +118,11 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
if (shape.size() != 4) {
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", FusedBatchNormExGpuKernel should be 4";
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", BatchNormGpuKernel should be 4";
}
is_null_input_ = CHECK_NULL_INPUT(shape);
if (is_null_input_) {
MS_LOG(WARNING) << "FusedBatchNormExGpuKernel input is null";
MS_LOG(WARNING) << "BatchNormGpuKernel input is null";
InitSizeLists();
return true;
}
@ -121,6 +133,7 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
}
SetTensorDescriptor(format, shape);
InitSizeLists();
is_train_ = GetAttr<bool>(kernel_node, "is_training");
return true;
}
@ -135,6 +148,7 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN;
epsilon_ = 10e-5;
exp_avg_factor_ = 0.1;
is_train_ = false;
is_null_input_ = false;
x_desc_ = nullptr;
y_desc_ = nullptr;
@ -215,11 +229,10 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
}
output_size_list_.push_back(output_size_); // output
output_size_list_.push_back(reserve_size_); // reserve space
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_);
}
@ -280,6 +293,7 @@ class FusedBatchNormExGpuKernel : public GpuKernel {
cudnnBatchNormOps_t bn_ops_;
double epsilon_;
double exp_avg_factor_;
bool is_train_;
bool is_null_input_;
cudnnTensorDescriptor_t x_desc_;
cudnnTensorDescriptor_t y_desc_;

28
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.cc → mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batch_norm_grad_gpu_kernel.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -14,11 +14,11 @@
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h"
#include "backend/kernel_compiler/gpu/nn/batch_norm_grad_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradEx,
MS_REG_GPU_KERNEL_ONE(BatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32) // dy
.AddInputAttr(kNumberTypeFloat32) // x
@ -29,8 +29,8 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradEx,
.AddOutputAttr(kNumberTypeFloat32) // dx
.AddOutputAttr(kNumberTypeFloat32) // dscale
.AddOutputAttr(kNumberTypeFloat32), // dbias
FusedBatchNormGradExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradEx,
BatchNormGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16) // dy
.AddInputAttr(kNumberTypeFloat16) // x
@ -41,9 +41,9 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradEx,
.AddOutputAttr(kNumberTypeFloat16) // dx
.AddOutputAttr(kNumberTypeFloat32) // dscale
.AddOutputAttr(kNumberTypeFloat32), // dbias
FusedBatchNormGradExGpuKernel, half)
BatchNormGradGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithActivation,
MS_REG_GPU_KERNEL_ONE(BatchNormGradWithActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32) // dy
.AddInputAttr(kNumberTypeFloat32) // x
@ -56,8 +56,8 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithActivation,
.AddOutputAttr(kNumberTypeFloat32) // dx
.AddOutputAttr(kNumberTypeFloat32) // dscale
.AddOutputAttr(kNumberTypeFloat32), // dbias
FusedBatchNormGradExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithActivation,
BatchNormGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNormGradWithActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16) // dy
.AddInputAttr(kNumberTypeFloat16) // x
@ -70,9 +70,9 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithActivation,
.AddOutputAttr(kNumberTypeFloat16) // dx
.AddOutputAttr(kNumberTypeFloat32) // dscale
.AddOutputAttr(kNumberTypeFloat32), // dbias
FusedBatchNormGradExGpuKernel, half)
BatchNormGradGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithAddAndActivation,
MS_REG_GPU_KERNEL_ONE(BatchNormGradWithAddAndActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32) // dy
.AddInputAttr(kNumberTypeFloat32) // x
@ -86,8 +86,8 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithAddAndActivation,
.AddOutputAttr(kNumberTypeFloat32) // dscale
.AddOutputAttr(kNumberTypeFloat32) // dbias
.AddOutputAttr(kNumberTypeFloat32), // dz
FusedBatchNormGradExGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithAddAndActivation,
BatchNormGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNormGradWithAddAndActivation,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16) // dy
.AddInputAttr(kNumberTypeFloat16) // x
@ -101,6 +101,6 @@ MS_REG_GPU_KERNEL_ONE(FusedBatchNormGradExWithAddAndActivation,
.AddOutputAttr(kNumberTypeFloat32) // dscale
.AddOutputAttr(kNumberTypeFloat32) // dbias
.AddOutputAttr(kNumberTypeFloat16), // dz
FusedBatchNormGradExGpuKernel, half)
BatchNormGradGpuKernel, half)
} // namespace kernel
} // namespace mindspore

114
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_grad_ex_gpu_kernel.h → mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batch_norm_grad_gpu_kernel.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -14,8 +14,8 @@
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCH_NORM_GRAD_EX_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCH_NORM_GRAD_EX_GPU_KERNEL_H_
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BATCH_NORM_GRAD_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BATCH_NORM_GRAD_GPU_KERNEL_H_
#include <string>
#include <vector>
@ -24,13 +24,14 @@
#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 "backend/kernel_compiler/gpu/cuda_impl/batchnorm_grad_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class FusedBatchNormGradExGpuKernel : public GpuKernel {
class BatchNormGradGpuKernel : public GpuKernel {
public:
FusedBatchNormGradExGpuKernel()
BatchNormGradGpuKernel()
: x_size_(0),
para_size_(0),
workspace_size_(0),
@ -38,6 +39,7 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
mode_(CUDNN_BATCHNORM_SPATIAL),
bn_ops_(CUDNN_BATCHNORM_OPS_BN),
epsilon_(10e-5),
is_train_(false),
is_null_input_(false),
x_desc_(nullptr),
y_desc_(nullptr),
@ -49,7 +51,7 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
handle_(nullptr),
cudnn_data_type_(CUDNN_DATA_FLOAT),
beta_data_diff_(0) {}
~FusedBatchNormGradExGpuKernel() override { DestroyResource(); }
~BatchNormGradGpuKernel() 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_; }
@ -88,17 +90,22 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
if (workspace_size_ != 0) {
workspace_addr = GetDeviceAddress<T>(workspace, 0);
}
const float alpha_data_diff = 1;
const float alpha_param_diff = 1;
const float beta_param_diff = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
cudnnBatchNormalizationBackwardEx(
handle_, mode_, bn_ops_, &alpha_data_diff, &beta_data_diff_, &alpha_param_diff,
&beta_param_diff, x_desc_, x, y_desc_, y, dy_desc_, dy, dz_desc_, dz, dx_desc_, dx,
scale_bias_diff_desc_, scale, bias, dscale, dbias, epsilon_, save_mean, save_variance,
activation_desc_, workspace_addr, workspace_size_, reserve_addr, reserve_size_),
"Kernel launch failed");
if (is_train_) {
const float alpha_data_diff = 1;
const float alpha_param_diff = 1;
const float beta_param_diff = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnBatchNormalizationBackwardEx(handle_, mode_, bn_ops_, &alpha_data_diff, &beta_data_diff_,
&alpha_param_diff, &beta_param_diff, x_desc_, x, y_desc_, y, dy_desc_, dy,
dz_desc_, dz, dx_desc_, dx, scale_bias_diff_desc_, scale, bias, dscale, dbias,
epsilon_, save_mean, save_variance, activation_desc_, workspace_addr,
workspace_size_, reserve_addr, reserve_size_),
"Kernel launch failed");
} else {
CalBatchNormGrad(x, dy, scale, save_mean, save_variance, dx, dscale, dbias, epsilon_, batch_, channel_, height_,
width_, reinterpret_cast<cudaStream_t>(stream_ptr));
}
return true;
}
@ -106,11 +113,11 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
kernel_node_ = kernel_node;
MS_EXCEPTION_IF_NULL(kernel_node);
std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
if (kernel_name == kFusedBatchNormGradEx) {
if (kernel_name == kBatchNormGradOpName) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN;
} else if (kernel_name == kFusedBatchNormGradExWithActivation) {
} else if (kernel_name == kBatchNormGradWithActivation) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN_ACTIVATION;
} else if (kernel_name == kFusedBatchNormGradExWithAddAndActivation) {
} else if (kernel_name == kBatchNormGradWithAddAndActivation) {
bn_ops_ = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION;
} else {
MS_LOG(EXCEPTION) << "Invalid kernel name: " << kernel_name;
@ -134,11 +141,11 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
auto shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
if (shape.size() != 4) {
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", FusedBatchNormGradExGpuKernel should be 4";
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", BatchNormGradGpuKernel should be 4";
}
is_null_input_ = CHECK_NULL_INPUT(shape);
if (is_null_input_) {
MS_LOG(WARNING) << "FusedBatchNormGradExGpuKernel input is null";
MS_LOG(WARNING) << "BatchNormGradGpuKernel input is null";
InitSizeLists();
return true;
}
@ -150,6 +157,7 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
beta_data_diff_ = GetAttrWithDefault(kernel_node, "inplace_algo", std::string("cover")) == "cover" ? 0 : 1;
SetTensorDescriptor(format, shape);
InitSizeLists();
is_train_ = GetAttr<bool>(kernel_node, "is_training");
return true;
}
@ -225,50 +233,52 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
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]);
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]);
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(
kernel_node_, cudnnSetTensor4dDescriptor(x_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
kernel_node_,
cudnnSetTensor4dDescriptor(x_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set x desc failed");
if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(y_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
"Set z desc failed");
}
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_, cudnnSetTensor4dDescriptor(dy_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
"Set dy desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_, cudnnSetTensor4dDescriptor(dx_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
"Set dx desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dz_desc_, cudnn_format, cudnn_data_type_, batch, channel, height, width),
cudnnSetTensor4dDescriptor(y_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set z desc failed");
}
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(scale_bias_diff_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel, 1, 1),
cudnnSetTensor4dDescriptor(dy_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set dy desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dx_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set dx desc failed");
if (bn_ops_ == CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION) {
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dz_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set z desc failed");
}
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(scale_bias_diff_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel_, 1, 1),
"Set para desc failed");
if (bn_ops_ != CUDNN_BATCHNORM_OPS_BN) {
@ -278,7 +288,10 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
"cudnnSetActivationDescriptor failed");
}
}
int batch_;
int channel_;
int height_;
int width_;
size_t x_size_;
size_t para_size_;
size_t workspace_size_;
@ -286,6 +299,7 @@ class FusedBatchNormGradExGpuKernel : public GpuKernel {
cudnnBatchNormMode_t mode_;
cudnnBatchNormOps_t bn_ops_;
double epsilon_;
bool is_train_;
bool is_null_input_;
cudnnTensorDescriptor_t x_desc_;

48
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batchnorm_grad_gpu_kernel.cc
View File

@ -1,48 +0,0 @@
/**
* Copyright 2019 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/batchnorm_grad_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(BatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
BatchNormGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
BatchNormGradGpuKernel, half)
} // namespace kernel
} // namespace mindspore

202
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/batchnorm_grad_gpu_kernel.h
View File

@ -1,202 +0,0 @@
/**
* Copyright 2019 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_BATCHNORM_GRAD_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_BATCHNORM_GRAD_GPU_KERNEL_H_
#include <vector>
#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 "backend/kernel_compiler/gpu/cuda_impl/batchnorm_grad_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class BatchNormGradGpuKernel : public GpuKernel {
public:
BatchNormGradGpuKernel()
: batch_(0),
channel_(0),
height_(0),
width_(0),
mode_(CUDNN_BATCHNORM_SPATIAL),
epsilon_(10e-5),
is_null_input_(false),
x_desc_(nullptr),
dy_desc_(nullptr),
dx_desc_(nullptr),
scale_bias_desc_(nullptr),
handle_(nullptr),
cudnn_data_type_(CUDNN_DATA_FLOAT) {}
~BatchNormGradGpuKernel() 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 dy = GetDeviceAddress<T>(inputs, 0);
auto x = GetDeviceAddress<T>(inputs, 1);
auto scale = GetDeviceAddress<float>(inputs, 2);
auto save_mean = GetDeviceAddress<float>(inputs, 3);
auto save_variance = GetDeviceAddress<float>(inputs, 4);
auto dx = GetDeviceAddress<T>(outputs, 0);
auto bn_scale = GetDeviceAddress<float>(outputs, 1);
auto bn_bias = GetDeviceAddress<float>(outputs, 2);
auto reserve_1 = GetDeviceAddress<T>(outputs, 3);
auto reserve_2 = GetDeviceAddress<T>(outputs, 4);
// For CI only, reserved vars can not be unused.
MS_LOG(DEBUG) << reinterpret_cast<size_t>(reserve_1) << reinterpret_cast<size_t>(reserve_2); // NOLINT
if (is_training_) {
const float alpha_data_diff = 1;
const float beta_data_diff = 0;
const float alpha_param_diff = 1;
const float beta_param_diff = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnBatchNormalizationBackward(handle_, mode_, &alpha_data_diff, &beta_data_diff, &alpha_param_diff,
&beta_param_diff, x_desc_, x, dy_desc_, dy, dx_desc_, dx, scale_bias_desc_,
scale, bn_scale, bn_bias, epsilon_, save_mean, save_variance),
"Kernel Launch Failed.");
} else {
CalBatchNormGrad(x, dy, scale, save_mean, save_variance, dx, bn_scale, bn_bias, epsilon_, batch_, channel_,
height_, width_, reinterpret_cast<cudaStream_t>(stream_ptr));
}
return true;
}
bool Init(const CNodePtr &kernel_node) override {
kernel_node_ = kernel_node;
InitResource();
cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 5) {
MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", BatchNormGradGpuKernel should be 5";
}
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
if (shape.size() != 4) {
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", BatchNormGradGpuKernel should be 4";
return false;
}
is_null_input_ = CHECK_NULL_INPUT(shape);
if (is_null_input_) {
MS_LOG(WARNING) << "BatchNormGradGpuKernel input is null";
InitSizeLists();
return true;
}
batch_ = SizeToInt(shape[0]);
channel_ = SizeToInt(shape[1]);
height_ = SizeToInt(shape[2]);
width_ = SizeToInt(shape[3]);
mode_ = CUDNN_BATCHNORM_SPATIAL;
is_training_ = GetAttr<bool>(kernel_node, "is_training");
epsilon_ = GetAttr<float>(kernel_node, "epsilon");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(x_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, batch_, channel_, height_, width_),
"Set x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dy_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, batch_, channel_, height_, width_),
"Set dy desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dx_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, batch_, channel_, height_, width_),
"Set dx desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(scale_bias_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel_, 1, 1),
"Set para desc failed");
InitSizeLists();
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(scale_bias_desc_),
"Destroy para desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&dy_desc_), "Create dy desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&dx_desc_), "Create dx desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&scale_bias_desc_),
"Create para desc failed");
}
void InitSizeLists() override {
size_t input_size = 0;
size_t para_size = 0;
if (!is_null_input_) {
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(x_desc_, &input_size),
"Get input size failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(scale_bias_desc_, &para_size),
"Get input size failed");
}
input_size_list_.push_back(input_size);
input_size_list_.push_back(input_size);
input_size_list_.push_back(para_size);
input_size_list_.push_back(para_size);
input_size_list_.push_back(para_size);
output_size_list_.push_back(input_size);
output_size_list_.push_back(para_size);
output_size_list_.push_back(para_size);
output_size_list_.push_back(input_size);
output_size_list_.push_back(input_size);
}
private:
int batch_;
int channel_;
int height_;
int width_;
cudnnBatchNormMode_t mode_;
bool is_training_;
double epsilon_;
bool is_null_input_;
cudnnTensorDescriptor_t x_desc_;
cudnnTensorDescriptor_t dy_desc_;
cudnnTensorDescriptor_t dx_desc_;
cudnnTensorDescriptor_t scale_bias_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_BATCHNORM_GRAD_GPU_KERNEL_H_

74
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.cc
View File

@ -1,74 +0,0 @@
/**
* Copyright 2019 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_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(FusedBatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGpuKernel, half)
MS_REG_GPU_KERNEL_ONE(BatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNorm,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGpuKernel, half)
} // namespace kernel
} // namespace mindspore

204
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batch_norm_gpu_kernel.h
View File

@ -1,204 +0,0 @@
/**
* Copyright 2019 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_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCH_NORM_GPU_KERNEL_H_
#include <string>
#include <vector>
#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"
namespace mindspore {
namespace kernel {
template <typename T>
class FusedBatchNormGpuKernel : public GpuKernel {
public:
FusedBatchNormGpuKernel()
: batch_(0),
channel_(0),
height_(0),
width_(0),
mode_(CUDNN_BATCHNORM_SPATIAL),
epsilon_(10e-5),
exp_avg_factor_(0.1),
is_train_(false),
is_null_input_(false),
x_desc_(nullptr),
y_desc_(nullptr),
scale_bias_mean_var_desc_(nullptr),
handle_(nullptr),
cudnn_data_type_(CUDNN_DATA_FLOAT) {}
~FusedBatchNormGpuKernel() 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);
auto y = GetDeviceAddress<T>(outputs, 0);
const float alpha = 1;
const float beta = 0;
if (is_train_) {
auto save_mean = GetDeviceAddress<float>(outputs, 3);
auto save_variance = GetDeviceAddress<float>(outputs, 4);
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnBatchNormalizationForwardTraining(handle_, mode_, &alpha, &beta, x_desc_, x, y_desc_, y,
scale_bias_mean_var_desc_, scale, bias, exp_avg_factor_, runing_mean,
runnig_variance, epsilon_, save_mean, save_variance),
"Kernel launch failed");
} else {
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_,
cudnnBatchNormalizationForwardInference(handle_, mode_, &alpha, &beta, x_desc_, x,
y_desc_, y, scale_bias_mean_var_desc_, scale,
bias, runing_mean, runnig_variance, epsilon_),
"Kernel launch failed");
}
return true;
}
bool Init(const CNodePtr &kernel_node) override {
kernel_node_ = kernel_node;
InitResource();
cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 5) {
MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", FusedBatchNormGpuKernel should be 5";
}
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
if (shape.size() != 4) {
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", FusedBatchNormGpuKernel should be >= 4";
}
is_null_input_ = CHECK_NULL_INPUT(shape);
if (is_null_input_) {
MS_LOG(WARNING) << "FusedBatchNormGpuKernel input is null";
InitSizeLists();
return true;
}
cudnnTensorFormat_t cudnn_format = CUDNN_TENSOR_NCHW;
auto format = AnfAlgo::GetInputFormat(kernel_node, 0);
auto format_attr = GetAttr<std::string>(kernel_node, "format");
if (format_attr == kOpFormat_NHWC) {
format = kOpFormat_NHWC;
cudnn_format = CUDNN_TENSOR_NHWC;
}
SetNCHW(shape, &batch_, &channel_, &height_, &width_, format);
mode_ = CUDNN_BATCHNORM_SPATIAL;
epsilon_ = GetAttr<float>(kernel_node, "epsilon");
// P.FusedBatchNorm is used for training; P.BatchNorm is used for inference
auto node_name = AnfAlgo::GetCNodeName(kernel_node);
if (node_name == "FusedBatchNorm") {
is_train_ = true;
exp_avg_factor_ = GetAttr<float>(kernel_node, "momentum");
}
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(x_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(y_desc_, cudnn_format, cudnn_data_type_, batch_, channel_, height_, width_),
"Set y desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(scale_bias_mean_var_desc_, cudnn_format, CUDNN_DATA_FLOAT, 1, channel_, 1, 1),
"Set para desc failed");
InitSizeLists();
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(y_desc_), "Destroy y desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(scale_bias_mean_var_desc_),
"Destroy para desc failed");
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&y_desc_), "Create y desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&scale_bias_mean_var_desc_),
"Create para desc failed");
}
void InitSizeLists() override {
size_t input_size = 0;
size_t para_size = 0;
size_t output_size = 0;
if (!is_null_input_) {
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(x_desc_, &input_size),
"Get input size failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(scale_bias_mean_var_desc_, &para_size),
"Get para size failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(y_desc_, &output_size),
"Get para size failed");
}
input_size_list_.push_back(input_size);
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
output_size_list_.push_back(output_size);
output_size_list_.push_back(para_size); // running mean
output_size_list_.push_back(para_size); // running variance
output_size_list_.push_back(para_size); // save mean
output_size_list_.push_back(para_size); // save variance
return;
}
private:
int batch_;
int channel_;
int height_;
int width_;
cudnnBatchNormMode_t mode_;
double epsilon_;
double exp_avg_factor_;
bool is_train_;
bool is_null_input_;
cudnnTensorDescriptor_t x_desc_;
cudnnTensorDescriptor_t y_desc_;
cudnnTensorDescriptor_t scale_bias_mean_var_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_GPU_KERNEL_H_

44
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batchnorm_grad_gpu_kernel.cc
View File

@ -1,44 +0,0 @@
/**
* Copyright 2019 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_batchnorm_grad_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(FusedBatchNormGrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGradGpuKernel, half)
} // namespace kernel
} // namespace mindspore

188
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/fused_batchnorm_grad_gpu_kernel.h
View File

@ -1,188 +0,0 @@
/**
* Copyright 2019 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_BATCHNORM_GRAD_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_FUSED_BATCHNORM_GRAD_GPU_KERNEL_H_
#include <vector>
#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"
namespace mindspore {
namespace kernel {
template <typename T>
class FusedBatchNormGradGpuKernel : public GpuKernel {
public:
FusedBatchNormGradGpuKernel()
: batch_(0),
channel_(0),
height_(0),
width_(0),
mode_(CUDNN_BATCHNORM_SPATIAL),
epsilon_(10e-5),
is_null_input_(false),
x_desc_(nullptr),
dy_desc_(nullptr),
dx_desc_(nullptr),
scale_bias_desc_(nullptr),
handle_(nullptr),
cudnn_data_type_(CUDNN_DATA_FLOAT) {}
~FusedBatchNormGradGpuKernel() 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 dy = GetDeviceAddress<T>(inputs, 0);
auto x = GetDeviceAddress<T>(inputs, 1);
auto scale = GetDeviceAddress<float>(inputs, 2);
auto save_mean = GetDeviceAddress<float>(inputs, 3);
auto save_variance = GetDeviceAddress<float>(inputs, 4);
auto dx = GetDeviceAddress<T>(outputs, 0);
auto bn_scale = GetDeviceAddress<float>(outputs, 1);
auto bn_bias = GetDeviceAddress<float>(outputs, 2);
const float alpha_data_diff = 1;
const float beta_data_diff = 0;
const float alpha_param_diff = 1;
const float beta_param_diff = 0;
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnBatchNormalizationBackward(handle_, mode_, &alpha_data_diff, &beta_data_diff, &alpha_param_diff,
&beta_param_diff, x_desc_, x, dy_desc_, dy, dx_desc_, dx, scale_bias_desc_, scale,
bn_scale, bn_bias, epsilon_, save_mean, save_variance),
"Kernel Launch Failed.");
return true;
}
bool Init(const CNodePtr &kernel_node) override {
kernel_node_ = kernel_node;
InitResource();
cudnn_data_type_ = GetCudnnDataType(TypeIdLabel(AnfAlgo::GetInputDeviceDataType(kernel_node, 0)));
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 5) {
MS_LOG(EXCEPTION) << "input tensor size is " << input_num << ", FusedBatchNormGradGpuKernel should be 5";
}
auto shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
if (shape.size() != 4) {
MS_LOG(EXCEPTION) << "tensor shape is " << shape.size() << ", FusedBatchNormGradGpuKernel should be 4";
return false;
}
is_null_input_ = CHECK_NULL_INPUT(shape);
if (is_null_input_) {
MS_LOG(WARNING) << "FusedBatchNormGradGpuKernel input is null";
InitSizeLists();
return true;
}
batch_ = SizeToInt(shape[0]);
channel_ = SizeToInt(shape[1]);
height_ = SizeToInt(shape[2]);
width_ = SizeToInt(shape[3]);
mode_ = CUDNN_BATCHNORM_SPATIAL;
epsilon_ = GetAttr<float>(kernel_node, "epsilon");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(x_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, batch_, channel_, height_, width_),
"Set x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dy_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, batch_, channel_, height_, width_),
"Set dy desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(dx_desc_, CUDNN_TENSOR_NCHW, cudnn_data_type_, batch_, channel_, height_, width_),
"Set dx desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(
kernel_node_,
cudnnSetTensor4dDescriptor(scale_bias_desc_, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, channel_, 1, 1),
"Set para desc failed");
InitSizeLists();
return true;
}
void DestroyResource() noexcept override {
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(scale_bias_desc_),
"Destroy para desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(dx_desc_), "Destroy dx desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(dy_desc_), "Destroy dy desc failed");
CHECK_CUDNN_RET_WITH_ERROR(kernel_node_, cudnnDestroyTensorDescriptor(x_desc_), "Destroy x desc failed");
}
protected:
void InitResource() override {
handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&x_desc_), "Create x desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&dy_desc_), "Create dy desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&dx_desc_), "Create dx desc failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnCreateTensorDescriptor(&scale_bias_desc_),
"Create para desc failed");
}
void InitSizeLists() override {
size_t input_size = 0;
size_t para_size = 0;
if (!is_null_input_) {
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(x_desc_, &input_size),
"Get input size failed");
CHECK_CUDNN_RET_WITH_EXCEPT(kernel_node_, cudnnGetTensorSizeInBytes(scale_bias_desc_, &para_size),
"Get input size failed");
}
input_size_list_.push_back(input_size);
input_size_list_.push_back(input_size);
input_size_list_.push_back(para_size);
input_size_list_.push_back(para_size);
input_size_list_.push_back(para_size);
output_size_list_.push_back(input_size);
output_size_list_.push_back(para_size);
output_size_list_.push_back(para_size);
}
private:
int batch_;
int channel_;
int height_;
int width_;
cudnnBatchNormMode_t mode_;
double epsilon_;
bool is_null_input_;
cudnnTensorDescriptor_t x_desc_;
cudnnTensorDescriptor_t dy_desc_;
cudnnTensorDescriptor_t dx_desc_;
cudnnTensorDescriptor_t scale_bias_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_BATCHNORM_GRAD_GPU_KERNEL_H_

15
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_grad_split.cc
View File

@ -34,7 +34,12 @@ void CreateOutputsOfUpdateGrad(const FuncGraphPtr &graph, const CNodePtr &bn_gra
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(bn_grad_node);
auto bn_grad_inputs = bn_grad_node->inputs();
CheckCNodeInputSize(bn_grad_node, kBNGradInputTensorNum);
if (AnfAlgo::CheckPrimitiveType(bn_grad_node, prim::kPrimBatchNormGrad)) {
CheckCNodeInputSize(bn_grad_node, kBNGradInputTensorNum);
} else {
CheckCNodeInputSize(bn_grad_node, kSyncBNGradInputTensorNum);
}
std::vector<AnfNodePtr> bn_update_grad_inputs = {
NewValueNode(std::make_shared<Primitive>(kBNTrainingUpdateGradOpName)), bn_grad_inputs[1], bn_grad_inputs[2],
bn_grad_inputs[4], bn_grad_inputs[5]};
@ -57,7 +62,12 @@ void CreateOutputsOfReduceGrad(const FuncGraphPtr &graph, const CNodePtr &bn_gra
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(bn_grad_node);
auto bn_grad_inputs = bn_grad_node->inputs();
CheckCNodeInputSize(bn_grad_node, kBNGradInputTensorNum);
if (AnfAlgo::CheckPrimitiveType(bn_grad_node, prim::kPrimBatchNormGrad)) {
CheckCNodeInputSize(bn_grad_node, kBNGradInputTensorNum);
} else {
CheckCNodeInputSize(bn_grad_node, kSyncBNGradInputTensorNum);
}
if (bn_update_grad_outputs.size() != kBNTrainingUpdateGradOutputNum) {
MS_LOG(EXCEPTION) << "bn_update_grad_outputs has wrong size";
}
@ -110,6 +120,7 @@ CNodePtr SyncBNGradSplitForTBE(const FuncGraphPtr &func_graph, const CNodePtr &c
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(cnode);
std::vector<AnfNodePtr> bn_update_grad_outputs;
CreateOutputsOfUpdateGrad(func_graph, cnode, &bn_update_grad_outputs);
if (bn_update_grad_outputs.size() != kBNTrainingUpdateGradOutputNum) {
MS_LOG(EXCEPTION) << "bn_update_grad_outputs has wrong size"

6
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/bn_split.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019 Huawei Technologies Co., Ltd
* Copyright 2019-2021 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.
@ -38,7 +38,7 @@ bool CreateOutputsOfBNTrainingReduce(const FuncGraphPtr &graph, const CNodePtr &
MS_EXCEPTION_IF_NULL(graph);
MS_EXCEPTION_IF_NULL(bn_cnode);
if (AnfAlgo::GetInputTensorNum(bn_cnode) != kBnInputTensorNum) {
MS_LOG(INFO) << "FusedbatchNorm's input size less than " << kBnInputTensorNum << ". " << bn_cnode->DebugString();
MS_LOG(INFO) << "BatchNorm's input size less than " << kBnInputTensorNum << ". " << bn_cnode->DebugString();
return false;
}
std::vector<AnfNodePtr> bn_training_reduce_inputs = {
@ -51,7 +51,7 @@ bool CreateOutputsOfBNTrainingReduce(const FuncGraphPtr &graph, const CNodePtr &
bn_training_reduce->set_kernel_info(kernel_info);
std::vector<size_t> bn_shape_i0 = AnfAlgo::GetPrevNodeOutputInferShape(bn_cnode, 0);
if (bn_shape_i0.size() < kShape2dDims) {
MS_LOG(INFO) << "The FusedBatchNorm's first input's shape dims less than " << kShape2dDims;
MS_LOG(INFO) << "The BatchNorm's first input's shape dims less than " << kShape2dDims;
return false;
}
std::vector<size_t> bn_training_reduce_shape = {bn_shape_i0[1]};

2
mindspore/ccsrc/backend/optimizer/ascend/ir_fusion/batchnorm_grad_to_batchnorm3d_grad.cc
View File

@ -33,7 +33,7 @@ CNodePtr CreateBatchNorm3DGrad(const FuncGraphPtr &graph, const CNodePtr &batchn
MS_EXCEPTION_IF_NULL(batchnorm_grad);
auto prim = std::make_shared<Primitive>(kBatchNorm3DGradOpName);
std::vector<AnfNodePtr> inputs = {NewValueNode(prim)};
for (size_t i = 1; i < batchnorm_grad->size(); ++i) {
for (size_t i = 1; i < batchnorm_grad->size() - 1; ++i) {
inputs.push_back(batchnorm_grad->input(i));
}
auto new_node = graph->NewCNode(inputs);

5
mindspore/ccsrc/backend/optimizer/common/helper.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019 Huawei Technologies Co., Ltd
* Copyright 2019-2021 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.
@ -56,7 +56,8 @@ constexpr size_t kBN1OutputNum = 2;
constexpr size_t kBN2OutputNum = 3;
constexpr size_t kBN3OutputNum = 1;
constexpr size_t kBNGradInputTensorNum = 5;
constexpr size_t kBNGradInputTensorNum = 6;
constexpr size_t kSyncBNGradInputTensorNum = 5;
constexpr size_t kBNGradOutputNum = 3;
constexpr size_t kBNGrad1OutputNum = 3;

38
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_fusion.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -28,8 +28,8 @@
namespace mindspore {
namespace opt {
const BaseRef BatchNormAddReluFusion::DefinePattern() const {
VectorRef batch_norm_ex = VectorRef({prim::kPrimFusedBatchNormEx, x_, scale_, bias_, mean_, var_});
VectorRef tuple_get_item = VectorRef({prim::kPrimTupleGetItem, batch_norm_ex, index_});
VectorRef batch_norm = VectorRef({prim::kPrimBatchNorm, x_, scale_, bias_, mean_, var_});
VectorRef tuple_get_item = VectorRef({prim::kPrimTupleGetItem, batch_norm, index_});
VectorRef tensor_add = VectorRef({prim::kPrimAdd, tuple_get_item, z_});
VectorRef relu = VectorRef({prim::kPrimRelu, tensor_add});
return relu;
@ -44,24 +44,24 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons
MS_EXCEPTION_IF_NULL(tensor_add);
auto tuple_get_item = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tensor_add), 0);
MS_EXCEPTION_IF_NULL(tuple_get_item);
auto batch_norm_ex = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_get_item), 0);
MS_EXCEPTION_IF_NULL(batch_norm_ex);
auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("format");
auto batch_norm = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_get_item), 0);
MS_EXCEPTION_IF_NULL(batch_norm);
auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format");
MS_EXCEPTION_IF_NULL(format_attr);
auto format = GetValue<std::string>(format_attr);
if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") {
if (AnfAlgo::GetInputFormat(batch_norm, 0) != kOpFormat_NHWC && format != "NHWC") {
return nullptr;
}
auto shape = AnfAlgo::GetInputDeviceShape(batch_norm_ex, 0);
auto shape = AnfAlgo::GetInputDeviceShape(batch_norm, 0);
if (shape.back() % kBNChannelMultipleFactor != 0) {
return nullptr;
}
auto x = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 0);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 1);
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 4);
auto x = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 0);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 1);
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 4);
auto z = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tensor_add), 1);
MS_EXCEPTION_IF_NULL(x);
@ -71,7 +71,7 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons
MS_EXCEPTION_IF_NULL(var);
MS_EXCEPTION_IF_NULL(z);
auto prim = std::make_shared<Primitive>(kFusedBatchNormExWithAddAndActivation);
auto prim = std::make_shared<Primitive>(kBatchNormWithAddAndActivation);
MS_EXCEPTION_IF_NULL(prim);
std::vector<AnfNodePtr> inputs = {NewValueNode(prim), x, scale, bias, mean, var, z};
auto fused_batch_norm_with_add_relu = graph->NewCNode(inputs);
@ -79,17 +79,17 @@ const AnfNodePtr BatchNormAddReluFusion::Process(const FuncGraphPtr &graph, cons
std::vector<TypeId> outputs_type;
std::vector<std::vector<size_t>> outputs_shape;
auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm_ex);
auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm);
for (size_t i = 0; i < output_num; i++) {
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm_ex, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm_ex, i));
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm, i));
}
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_add_relu.get());
AnfAlgo::CopyNodeAttrs(batch_norm_ex, fused_batch_norm_with_add_relu);
AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_add_relu);
auto manager = graph->manager();
MS_EXCEPTION_IF_NULL(manager);
manager->Replace(batch_norm_ex, fused_batch_norm_with_add_relu);
manager->Replace(batch_norm, fused_batch_norm_with_add_relu);
device::gpu::SetKernelInfo(fused_batch_norm_with_add_relu);
return tuple_get_item;
}

12
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_add_relu_grad_fusion.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -85,14 +85,14 @@ void ReplaceOutput(const FuncGraphPtr &graph, const AnfNodePtr &bn_grad, const A
std::vector<AnfNodePtr> bn_add_relu_grad_output;
CreateMultipleOutputsOfAnfNode(graph, bn_add_relu_grad, kBNAddReluGradOutputNum, &bn_add_relu_grad_output);
if (bn_add_relu_grad_output.size() != kBNAddReluGradOutputNum) {
MS_LOG(EXCEPTION) << "The output size of node " << kFusedBatchNormGradExWithAddAndActivation << " must be "
MS_LOG(EXCEPTION) << "The output size of node " << kBatchNormGradWithAddAndActivation << " must be "
<< kBNAddReluGradOutputNum << ", but it is " << bn_add_relu_grad_output.size();
}
// Get bn outputs
std::vector<AnfNodePtr> bn_outputs;
if (!GetBatchNormOutputs(graph, bn_grad, &bn_outputs)) {
MS_LOG(INFO) << "The " << prim::kPrimFusedBatchNormGradEx
MS_LOG(INFO) << "The " << prim::kPrimBatchNormGrad
<< " node should only have output 0, 1 and 2. The node should not be changed";
return;
}
@ -139,7 +139,7 @@ bool PatternCheck(const FuncGraphPtr &graph, const AnfNodePtr &node) {
return false;
}
auto forward_node = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_getitem), 0);
if (AnfAlgo::GetCNodeName(forward_node) != kFusedBatchNormExWithAddAndActivation) {
if (AnfAlgo::GetCNodeName(forward_node) != kBatchNormWithAddAndActivation) {
return false;
}
@ -150,7 +150,7 @@ bool PatternCheck(const FuncGraphPtr &graph, const AnfNodePtr &node) {
const BaseRef BatchNormAddReluGradFusion::DefinePattern() const {
VectorRef relu_grad = VectorRef({prim::kPrimReluGrad, dy_, y_});
VectorRef batch_norm_grad =
VectorRef({prim::kPrimFusedBatchNormGradEx, relu_grad, x_, scale_, save_mean_, save_var_, reserve_});
VectorRef({prim::kPrimBatchNormGrad, relu_grad, x_, scale_, save_mean_, save_var_, reserve_});
return batch_norm_grad;
}
@ -184,7 +184,7 @@ const AnfNodePtr BatchNormAddReluGradFusion::Process(const FuncGraphPtr &graph,
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 2);
MS_EXCEPTION_IF_NULL(bias);
auto prim = std::make_shared<Primitive>(kFusedBatchNormGradExWithAddAndActivation);
auto prim = std::make_shared<Primitive>(kBatchNormGradWithAddAndActivation);
MS_EXCEPTION_IF_NULL(prim);
std::vector<AnfNodePtr> inputs = {NewValueNode(prim), dy, x, scale, save_mean, save_var, reserve, bias, y};
auto fused_batch_norm_add_relu_grad = graph->NewCNode(inputs);

38
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_fusion.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -28,8 +28,8 @@
namespace mindspore {
namespace opt {
const BaseRef BatchNormReluFusion::DefinePattern() const {
VectorRef batch_norm_ex = VectorRef({prim::kPrimFusedBatchNormEx, x_, scale_, bias_, mean_, var_});
VectorRef tuple_get = VectorRef({prim::kPrimTupleGetItem, batch_norm_ex, index_});
VectorRef batch_norm = VectorRef({prim::kPrimBatchNorm, x_, scale_, bias_, mean_, var_});
VectorRef tuple_get = VectorRef({prim::kPrimTupleGetItem, batch_norm, index_});
VectorRef relu = VectorRef({prim::kPrimRelu, tuple_get});
return relu;
}
@ -41,24 +41,24 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A
auto tuple_get_item = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(node), 0);
MS_EXCEPTION_IF_NULL(tuple_get_item);
auto batch_norm_ex = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_get_item), 0);
MS_EXCEPTION_IF_NULL(batch_norm_ex);
auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("format");
auto batch_norm = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_get_item), 0);
MS_EXCEPTION_IF_NULL(batch_norm);
auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format");
MS_EXCEPTION_IF_NULL(format_attr);
auto format = GetValue<std::string>(format_attr);
if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") {
if (AnfAlgo::GetInputFormat(batch_norm, 0) != kOpFormat_NHWC && format != "NHWC") {
return nullptr;
}
auto shape = AnfAlgo::GetInputDeviceShape(batch_norm_ex, 0);
auto shape = AnfAlgo::GetInputDeviceShape(batch_norm, 0);
if (shape.back() % kBNChannelMultipleFactor != 0) {
return nullptr;
}
auto x = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 0);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 1);
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 4);
auto x = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 0);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 1);
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 4);
MS_EXCEPTION_IF_NULL(x);
MS_EXCEPTION_IF_NULL(scale);
@ -66,7 +66,7 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A
MS_EXCEPTION_IF_NULL(mean);
MS_EXCEPTION_IF_NULL(var);
auto prim = std::make_shared<Primitive>(kFusedBatchNormExWithActivation);
auto prim = std::make_shared<Primitive>(kBatchNormWithActivation);
MS_EXCEPTION_IF_NULL(prim);
std::vector<AnfNodePtr> inputs = {NewValueNode(prim), x, scale, bias, mean, var};
auto fused_batch_norm_with_relu = graph->NewCNode(inputs);
@ -74,17 +74,17 @@ const AnfNodePtr BatchNormReluFusion::Process(const FuncGraphPtr &graph, const A
std::vector<TypeId> outputs_type;
std::vector<std::vector<size_t>> outputs_shape;
auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm_ex);
auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm);
for (size_t i = 0; i < output_num; i++) {
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm_ex, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm_ex, i));
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm, i));
}
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_relu.get());
AnfAlgo::CopyNodeAttrs(batch_norm_ex, fused_batch_norm_with_relu);
AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_relu);
auto manager = graph->manager();
MS_EXCEPTION_IF_NULL(manager);
manager->Replace(batch_norm_ex, fused_batch_norm_with_relu);
manager->Replace(batch_norm, fused_batch_norm_with_relu);
device::gpu::SetKernelInfo(fused_batch_norm_with_relu);
return tuple_get_item;
}

6
mindspore/ccsrc/backend/optimizer/gpu/batch_norm_relu_grad_fusion.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -31,7 +31,7 @@ namespace opt {
const BaseRef BatchNormReluGradFusion::DefinePattern() const {
VectorRef relu_grad = VectorRef({prim::kPrimReluGrad, dy_, y_});
VectorRef batch_norm_grad =
VectorRef({prim::kPrimFusedBatchNormGradEx, relu_grad, x_, scale_, save_mean_, save_var_, reserve_});
VectorRef({prim::kPrimBatchNormGrad, relu_grad, x_, scale_, save_mean_, save_var_, reserve_});
return batch_norm_grad;
}
@ -82,7 +82,7 @@ const AnfNodePtr BatchNormReluGradFusion::Process(const FuncGraphPtr &graph, con
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 2);
MS_EXCEPTION_IF_NULL(bias);
auto prim = std::make_shared<Primitive>(kFusedBatchNormGradExWithActivation);
auto prim = std::make_shared<Primitive>(kBatchNormGradWithActivation);
MS_EXCEPTION_IF_NULL(prim);
std::vector<AnfNodePtr> inputs = {NewValueNode(prim), dy, x, scale, save_mean, save_var, reserve, bias, y};
auto fused_batch_norm_grad_with_relu = graph->NewCNode(inputs);

7
mindspore/ccsrc/backend/optimizer/gpu/cudnn_inplace_fusion.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -42,8 +42,7 @@ struct AnfNodeIndex {
};
// opname, output idx
std::map<string, uint32_t> kInplaceOpNames = {{kConv2DBackpropInputOpName, 0},
{kFusedBatchNormGradExWithAddAndActivation, 3}};
std::map<string, uint32_t> kInplaceOpNames = {{kConv2DBackpropInputOpName, 0}, {kBatchNormGradWithAddAndActivation, 3}};
std::set<string> kSkipOpNames = {
kTensorAddOpName,
@ -51,7 +50,7 @@ std::set<string> kSkipOpNames = {
// opname, input idx
std::map<string, uint32_t> kAggregatesOpNames = {
{kConv2DBackpropInputOpName, 0}, {kmaxPoolGradOpName, 2}, {kFusedBatchNormGradExWithAddAndActivation, 0}};
{kConv2DBackpropInputOpName, 0}, {kmaxPoolGradOpName, 2}, {kBatchNormGradWithAddAndActivation, 0}};
constexpr size_t inplace_node_size = 2;

36
mindspore/ccsrc/backend/optimizer/gpu/post_batch_norm_add_relu_fusion.cc
View File

@ -28,8 +28,8 @@
namespace mindspore {
namespace opt {
const BaseRef PostBatchNormAddReluFusion::DefinePattern() const {
VectorRef batch_norm_ex = VectorRef({prim::kPrimFusedBatchNormEx, x_, scale_, bias_, mean_, var_});
VectorRef tuple_get_item = VectorRef({prim::kPrimTupleGetItem, batch_norm_ex, index_});
VectorRef batch_norm = VectorRef({prim::kPrimBatchNorm, x_, scale_, bias_, mean_, var_});
VectorRef tuple_get_item = VectorRef({prim::kPrimTupleGetItem, batch_norm, index_});
VectorRef tensor_add = VectorRef({prim::kPrimAdd, z_, tuple_get_item});
VectorRef relu = VectorRef({prim::kPrimRelu, tensor_add});
return relu;
@ -44,24 +44,24 @@ const AnfNodePtr PostBatchNormAddReluFusion::Process(const FuncGraphPtr &graph,
MS_EXCEPTION_IF_NULL(tensor_add);
auto tuple_get_item = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tensor_add), 1);
MS_EXCEPTION_IF_NULL(tuple_get_item);
auto batch_norm_ex = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_get_item), 0);
MS_EXCEPTION_IF_NULL(batch_norm_ex);
auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm_ex)->GetAttr("format");
auto batch_norm = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tuple_get_item), 0);
MS_EXCEPTION_IF_NULL(batch_norm);
auto format_attr = AnfAlgo::GetCNodePrimitive(batch_norm)->GetAttr("format");
MS_EXCEPTION_IF_NULL(format_attr);
auto format = GetValue<std::string>(format_attr);
if (AnfAlgo::GetInputFormat(batch_norm_ex, 0) != kOpFormat_NHWC && format != "NHWC") {
if (AnfAlgo::GetInputFormat(batch_norm, 0) != kOpFormat_NHWC && format != "NHWC") {
return nullptr;
}
auto shape = AnfAlgo::GetInputDeviceShape(batch_norm_ex, 0);
auto shape = AnfAlgo::GetInputDeviceShape(batch_norm, 0);
if (shape.back() % kBNChannelMultipleFactor != 0) {
return nullptr;
}
auto x = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 0);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 1);
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm_ex), 4);
auto x = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 0);
auto scale = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 1);
auto bias = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 2);
auto mean = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 3);
auto var = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(batch_norm), 4);
auto z = AnfAlgo::GetInputNode(utils::cast<CNodePtr>(tensor_add), 0);
MS_EXCEPTION_IF_NULL(x);
@ -71,7 +71,7 @@ const AnfNodePtr PostBatchNormAddReluFusion::Process(const FuncGraphPtr &graph,
MS_EXCEPTION_IF_NULL(var);
MS_EXCEPTION_IF_NULL(z);
auto prim = std::make_shared<Primitive>(kFusedBatchNormExWithAddAndActivation);
auto prim = std::make_shared<Primitive>(kBatchNormWithAddAndActivation);
MS_EXCEPTION_IF_NULL(prim);
std::vector<AnfNodePtr> inputs = {NewValueNode(prim), x, scale, bias, mean, var, z};
auto fused_batch_norm_with_add_relu = graph->NewCNode(inputs);
@ -79,17 +79,17 @@ const AnfNodePtr PostBatchNormAddReluFusion::Process(const FuncGraphPtr &graph,
std::vector<TypeId> outputs_type;
std::vector<std::vector<size_t>> outputs_shape;
auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm_ex);
auto output_num = AnfAlgo::GetOutputTensorNum(batch_norm);
for (size_t i = 0; i < output_num; i++) {
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm_ex, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm_ex, i));
outputs_type.push_back(AnfAlgo::GetOutputInferDataType(batch_norm, i));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(batch_norm, i));
}
AnfAlgo::SetOutputInferTypeAndShape(outputs_type, outputs_shape, fused_batch_norm_with_add_relu.get());
AnfAlgo::CopyNodeAttrs(batch_norm_ex, fused_batch_norm_with_add_relu);
AnfAlgo::CopyNodeAttrs(batch_norm, fused_batch_norm_with_add_relu);
auto manager = graph->manager();
MS_EXCEPTION_IF_NULL(manager);
manager->Replace(batch_norm_ex, fused_batch_norm_with_add_relu);
manager->Replace(batch_norm, fused_batch_norm_with_add_relu);
device::gpu::SetKernelInfo(fused_batch_norm_with_add_relu);
return tuple_get_item;
}

11
mindspore/ccsrc/backend/optimizer/mem_reuse/mem_reuse_allocator.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019 Huawei Technologies Co., Ltd
* Copyright 2019-2021 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.
@ -445,9 +445,10 @@ size_t BestFitMemReuse::GetAllocatedSize() {
bool BestFitMemReuse::IsRelease() {
// unable_used_node include the node type that output tensor cannot be released,
// even if its refcount is equal to zero.
std::unordered_set<std::string> unable_used_node = {prim::kPrimBatchNorm->name(), prim::kPrimBatchNormGrad->name(),
prim::kPrimFusedBatchNorm->name(),
prim::kPrimFusedBatchNormGrad->name()};
std::unordered_set<std::string> unable_used_node = {
prim::kPrimBatchNorm->name(),
prim::kPrimBatchNormGrad->name(),
};
return unable_used_node.find(current_kernel_->kernel_name()) == unable_used_node.end();
}
@ -494,7 +495,7 @@ void BestFitMemReuse::Reuse(const MemReuseUtil *mem_reuse_util_ptr) {
#endif
for (const auto &op_def_ptr : op_ptr_list_) {
current_kernel_ = op_def_ptr;
// releas pre_op_def
// release pre_op_def
if (pre_op != nullptr) {
ReleasePreNodeWorkspace(pre_op.get());
}

13
mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -450,12 +450,7 @@ Strategys MakeDataParallelStrategy(const std::shared_ptr<Graph> &graph,
// Experimental support for 3D data (input_size == 3).
if (input_size >= 1 && input_size <= 4) {
if (dim == 0) {
// Currently GPU version does not support partitioning ‘FusedBatchNormEx’ in its param tensors.
if (ops[iter_ops]->type() == "FusedBatchNormEx" && iter_op_inputs != 0) {
s.push_back(1);
} else {
s.push_back(std::min(max_device_num, target_tensor_batch));
}
s.push_back(std::min(max_device_num, target_tensor_batch));
} else {
s.push_back(1);
}
@ -533,8 +528,8 @@ Strategys PrepareStrategy(const std::shared_ptr<Graph> &graph, const std::vector
return PrepareOneHot(graph, ops, iter_graph, iter_ops);
} else if ((type == SOFTMAX) || (type == LAYER_NORM)) {
return PrepareAxisRelatedStrategy(graph, ops, iter_graph, iter_ops);
} else if ((type == SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS) || (type == "_VirtualDataset") ||
(type == "FusedBatchNormEx") || (type == "Dropout") || (type == BATCH_MATMUL)) {
} else if ((type == SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS) || (type == "_VirtualDataset") || (type == "Dropout") ||
(type == BATCH_MATMUL)) {
return MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
} else {
return MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);

3
mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_parse_graph.h
View File

@ -1,5 +1,5 @@
/**
* Copyright 2020 Huawei Technologies Co., Ltd
* Copyright 2021 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.
@ -46,7 +46,6 @@ const std::map<std::string, OperatorType> DictOpType{
{RESHAPE, OperatorType::kRecReshape},
{BIAS_ADD, OperatorType::kRecBiasAdd},
{BATCH_NORM, OperatorType::kRecBatchNorm},
{FUSE_BATCH_NORM, OperatorType::kRecBatchNorm},
{LAYER_NORM, OperatorType::kRecBatchNorm},
{SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, OperatorType::kRecSparseSoftmaxCrossEntropyWithLogits},
{ONEHOT, OperatorType::kRecOneHot},

49
mindspore/ccsrc/runtime/device/ascend/kernel_select_ascend.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019 Huawei Technologies Co., Ltd
* Copyright 2019-2021 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.
@ -53,9 +53,7 @@ enum MatchCountPriority : int {
MATCH_COUNT_PRIORITY_END
};
const std::map<std::string, std::vector<std::string>> kNextOpFormatList = {
{prim::kPrimConv2D->name(), {kOpFormat_NC1HWC0, kOpFormat_FRAC_Z}},
{prim::kPrimFusedBatchNorm->name(),
{kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0}}};
{prim::kPrimConv2D->name(), {kOpFormat_NC1HWC0, kOpFormat_FRAC_Z}}};
bool MatchInferOutputDataType(const CNodePtr &cnode, const kernel::KernelBuildInfo &kernel_build_info) {
MS_EXCEPTION_IF_NULL(cnode);
@ -233,6 +231,24 @@ std::vector<std::shared_ptr<kernel::KernelBuildInfo>> FilteredKernelInfoByDtype(
return result;
}
bool CheckHitTargetDtype(const std::map<TypeId, TypeId> &type_map, const TypeId &in_dtype, const TypeId &device_dtype,
bool *flag) {
auto iter = type_map.find(in_dtype);
// if infer dtype node in type_map and the infer dtype not equal kernel info dtype, return false
if (iter == type_map.end() && in_dtype != device_dtype) {
return false;
}
// infer dtype in type_map, but can not find dst dtype that supported raise or reduce,
// or infer dtype not equal kernel info dtype, return false
if (iter != type_map.end() && iter->second != device_dtype && in_dtype != device_dtype) {
return false;
}
if (in_dtype == kNumberTypeInt64 && device_dtype == kNumberTypeInt32) {
*flag = true;
}
return true;
}
bool TagRaiseReduce(const std::shared_ptr<kernel::KernelBuildInfo> &kernel_build_info, const CNodePtr &cnode,
const std::map<TypeId, TypeId> &type_map) {
// filte kernel info that unsupported raise or reduce datatype
@ -245,19 +261,9 @@ bool TagRaiseReduce(const std::shared_ptr<kernel::KernelBuildInfo> &kernel_build
if (device_dtype == kNumberTypeFloat || device_dtype == kNumberTypeFloat32) {
device_dtype = kNumberTypeFloat32;
}
auto iter = type_map.find(in_dtype);
// if infer dtype node in type_map and the infer dtype not equal kernel info dtype, return false
if (iter == type_map.end() && in_dtype != device_dtype) {
if (!CheckHitTargetDtype(type_map, in_dtype, device_dtype, &flag)) {
return false;
}
// infer dtype in type_map, but can not find dst dtype that supported raise or reduce,
// or infer dtype not equal kernel info dtype, return false
if (iter != type_map.end() && iter->second != device_dtype && in_dtype != device_dtype) {
return false;
}
if (in_dtype == kNumberTypeInt64 && device_dtype == kNumberTypeInt32) {
flag = true;
}
}
for (size_t output_index = 0; output_index < kernel_build_info->GetOutputNum(); ++output_index) {
@ -266,19 +272,10 @@ bool TagRaiseReduce(const std::shared_ptr<kernel::KernelBuildInfo> &kernel_build
if (device_dtype == kNumberTypeFloat || device_dtype == kNumberTypeFloat32) {
device_dtype = kNumberTypeFloat32;
}
auto iter = type_map.find(in_dtype);
// if infer dtype node in type_map and the infer dtype not equal kernel info dtype, return false
if (iter == type_map.end() && in_dtype != device_dtype) {
if (!CheckHitTargetDtype(type_map, in_dtype, device_dtype, &flag)) {
return false;
}
// infer dtype in type_map, but can not find dst dtype that supported raise or reduce,
// or infer dtype not equal kernel info dtype, return false
if (iter != type_map.end() && iter->second != device_dtype && in_dtype != device_dtype) {
return false;
}
if (in_dtype == kNumberTypeInt64 && device_dtype == kNumberTypeInt32) {
flag = true;
}
}
if (flag) {
auto node_name = AnfAlgo::GetCNodeName(cnode);

4
mindspore/ccsrc/runtime/device/gpu/gpu_kernel_runtime.cc
View File

@ -101,9 +101,9 @@ namespace {
std::vector<int> CheckRealOutput(const std::string &node_name, const size_t &output_size) {
// define a vector containing real output number
std::vector<int> real_outputs;
// P.FusedBatchNorm is used for training; P.BatchNorm is used for inference
// P.BatchNorm is used for training and inference
// can add the filter list for more operators here....
if (node_name == "FusedBatchNorm" || node_name == "BatchNorm") {
if (node_name == "BatchNorm") {
MS_LOG(INFO) << "loading node named " << node_name;
real_outputs.insert(real_outputs.end(), {0, 3, 4});
} else {

4
mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019 Huawei Technologies Co., Ltd
* Copyright 2019-2021 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.
@ -374,7 +374,7 @@ void FormatTransformChecker::CheckSupportFormatTransform(const std::shared_ptr<s
if (kernel_name == prim::kPrimConv2D->name()) {
conv_cnt++;
}
if (kernel_name == prim::kPrimFusedBatchNormEx->name()) {
if (kernel_name == prim::kPrimBatchNorm->name()) {
bn_cnt++;
}
}

12
mindspore/ccsrc/runtime/device/gpu/kernel_info_setter.h
View File

@ -46,12 +46,12 @@ static std::map<std::string, std::pair<std::vector<size_t>, std::vector<size_t>>
{prim::kPrimMaxPoolGrad->name(), {{0, 1, 2}, {0}}},
{kAvgPoolOpName, {{0}, {0}}},
{kAvgPoolGradOpName, {{0, 1, 2}, {0}}},
{kFusedBatchNormEx, {{0}, {0}}},
{kFusedBatchNormExWithActivation, {{0}, {0}}},
{kFusedBatchNormExWithAddAndActivation, {{0, 5}, {0}}},
{kFusedBatchNormGradEx, {{0, 1}, {0}}},
{kFusedBatchNormGradExWithActivation, {{0, 1, 7}, {0}}},
{kFusedBatchNormGradExWithAddAndActivation, {{0, 1, 7}, {0, 3}}},
{kBatchNorm, {{0}, {0}}},
{kBatchNormWithActivation, {{0}, {0}}},
{kBatchNormWithAddAndActivation, {{0, 5}, {0}}},
{kBatchNormGradOpName, {{0, 1}, {0}}},
{kBatchNormGradWithActivation, {{0, 1, 7}, {0}}},
{kBatchNormGradWithAddAndActivation, {{0, 1, 7}, {0, 3}}},
{kBiasAddOpName, {{0}, {0}}},
{prim::kPrimBiasAddGrad->name(), {{0}, {}}},
// Format insensitive.

11
mindspore/ccsrc/utils/utils.h
View File

@ -50,13 +50,12 @@ constexpr auto kFusedBN3OpName = "FusedBN3";
constexpr auto kBNGrad1OpName = "BNGrad1";
constexpr auto kBNGrad2OpName = "BNGrad2";
constexpr auto kBNGrad3OpName = "BNGrad3";
constexpr auto kFusedBatchNormEx = "FusedBatchNormEx";
constexpr auto kBatchNorm = "BatchNorm";
constexpr auto kInstanceNorm = "InstanceNorm";
constexpr auto kFusedBatchNormExWithActivation = "FusedBatchNormExWithActivation";
constexpr auto kFusedBatchNormExWithAddAndActivation = "FusedBatchNormExWithAddAndActivation";
constexpr auto kFusedBatchNormGradEx = "FusedBatchNormGradEx";
constexpr auto kFusedBatchNormGradExWithActivation = "FusedBatchNormGradExWithActivation";
constexpr auto kFusedBatchNormGradExWithAddAndActivation = "FusedBatchNormGradExWithAddAndActivation";
constexpr auto kBatchNormWithActivation = "BatchNormWithActivation";
constexpr auto kBatchNormWithAddAndActivation = "BatchNormWithAddAndActivation";
constexpr auto kBatchNormGradWithActivation = "BatchNormGradWithActivation";
constexpr auto kBatchNormGradWithAddAndActivation = "BatchNormGradWithAddAndActivation";
constexpr auto kClearZeroOpName = "ClearZero";
constexpr auto kAtomicAddrCleanOpName = "AtomicAddrClean";
constexpr auto kGetNextOpName = "GetNext";

8
mindspore/core/abstract/infer_functions.h
View File

@ -45,14 +45,10 @@ AbstractBasePtr InferImplPooling(const AnalysisEnginePtr &, const PrimitivePtr &
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplPoolingGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplFusedBatchNorm(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplFusedSparseAdam(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplFusedBatchNormGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplFusedBatchNormEx(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplBatchNorm(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplBatchNormGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list);
AbstractBasePtr InferImplReluGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,

110
mindspore/core/abstract/prim_nn.cc
View File

@ -140,77 +140,6 @@ void FusedBatchNormCheckDim(const PrimitivePtr &primitive, const AbstractBasePtr
}
}
AbstractBasePtr InferImplFusedBatchNorm(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: five tensors(x, gamma, beta, mean, variance).
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 5);
MS_EXCEPTION_IF_NULL(args_spec_list[0]);
MS_LOG(DEBUG) << "InferImplFusedBatchNorm args0:" << args_spec_list[0]->ToString()
<< ", arg1:" << args_spec_list[1]->ToString();
FusedBatchNormCheckDim(primitive, args_spec_list);
auto input = args_spec_list[0];
auto input_shape = dyn_cast<Shape>(input->GetShapeTrack());
MS_EXCEPTION_IF_NULL(input_shape);
const auto &input_shape_list = input_shape->shape();
if (input_shape_list.size() < 2) {
MS_LOG(EXCEPTION) << "Input shape size should >= 2.";
}
for (size_t i = 1; i < args_spec_list.size(); ++i) {
auto arg_shape = dyn_cast<Shape>(args_spec_list[i]->GetShapeTrack());
MS_EXCEPTION_IF_NULL(arg_shape);
const auto &arg_shape_list = arg_shape->shape();
if (arg_shape_list.size() < 1) {
MS_LOG(EXCEPTION) << "Arg shape size should >= 1.";
}
if (arg_shape_list[0] != input_shape_list[1]) {
MS_LOG(EXCEPTION) << op_name << " size of tensor param[" << i << "](which is " << arg_shape_list[0]
<< ") should match the second dimension of tensor"
" param[0](which is "
<< input_shape_list[1] << ").";
}
}
auto input_tensor = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
(void)CheckTensorDType(input_tensor, {kFloat16, kFloat32}, "param 0 of FusedBatchNorm should be %s");
AbstractTensorPtrList tensorPtrList = std::vector<AbstractTensorPtr>();
for (size_t i = 1; i < args_spec_list.size(); ++i) {
auto param = CheckArg<AbstractTensor>(op_name, args_spec_list, i);
tensorPtrList.push_back(param);
}
(void)CheckTensorsDTypeSame(tensorPtrList, {kFloat16, kFloat32}, "param 1 to 4 of FusedBatchNorm should be %s");
// check validity;
auto epsilon_value = primitive->GetAttr("epsilon");
auto momentum_value = primitive->GetAttr("momentum");
MS_EXCEPTION_IF_NULL(epsilon_value);
MS_EXCEPTION_IF_NULL(momentum_value);
if (!epsilon_value->isa<FP32Imm>() || !momentum_value->isa<FP32Imm>()) {
MS_LOG(EXCEPTION) << "expect epsilon and momentum be float, but: epsilon: " << epsilon_value->ToString()
<< ", momentum: " << momentum_value->ToString();
}
auto epsilon = epsilon_value->cast<FP32ImmPtr>()->value();
auto momentum = momentum_value->cast<FP32ImmPtr>()->value();
if (epsilon > 1.0f || epsilon <= 0.0f) {
MS_LOG(EXCEPTION) << "expect epsilon is greater than 0 and less or equal than 1, but epsilon: " << epsilon;
}
if (momentum > 1.0f || momentum < 0.0f) {
MS_LOG(EXCEPTION) << "expect momentum is great or equal than 0 and less or equal than 1, but epsilon: " << momentum;
}
// Outputs: y, running_mean, running_variance, save_mean, save_inv_variance.
AbstractBasePtr y = input->Broaden();
AbstractBasePtr other = args_spec_list[1]->Broaden();
MS_LOG(DEBUG) << "output y: " << y->ToString() << ", other: " << other->ToString();
AbstractBasePtrList elements = {y, other, other, other, other};
return std::make_shared<AbstractTuple>(elements);
}
AbstractBasePtr InferImplSparseSoftmaxCrossEntropyWithLogits(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
MS_EXCEPTION_IF_NULL(primitive);
@ -228,24 +157,8 @@ AbstractBasePtr InferImplSparseSoftmaxCrossEntropyWithLogits(const AnalysisEngin
return std::make_shared<abstract::AbstractTensor>(type_tensor->element(), shape);
}
AbstractBasePtr InferImplFusedBatchNormGrad(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: five tensors(y_backprop, x, scale, save_mean, save_inv_variance).
MS_EXCEPTION_IF_NULL(args_spec_list[1]);
MS_EXCEPTION_IF_NULL(args_spec_list[2]);
MS_EXCEPTION_IF_NULL(args_spec_list[3]);
CheckArgsSize(primitive->name(), args_spec_list, 5);
auto dx = args_spec_list[1]->Broaden();
auto dscale = args_spec_list[2]->Broaden();
auto dbias = args_spec_list[3]->Broaden();
AbstractBasePtrList rets = {dx, dscale, dbias};
return std::make_shared<AbstractTuple>(rets);
}
AbstractBasePtr InferImplFusedBatchNormEx(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
AbstractBasePtr InferImplBatchNorm(const AnalysisEnginePtr &, const PrimitivePtr &primitive,
const AbstractBasePtrList &args_spec_list) {
// Inputs: five tensors(x, gamma, beta, mean, variance).
const std::string op_name = primitive->name();
CheckArgsSize(op_name, args_spec_list, 5);
@ -256,12 +169,21 @@ AbstractBasePtr InferImplFusedBatchNormEx(const AnalysisEnginePtr &, const Primi
ShapeVector x_min_shape = input_x->shape()->min_shape();
ShapeVector x_max_shape = input_x->shape()->max_shape();
CheckMinMaxShape(x_shape, &x_min_shape, &x_max_shape);
if (x_shape.size() != 4) {
MS_LOG(EXCEPTION) << "Input rank should 4.";
auto input_tensor = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
(void)CheckTensorDType(input_tensor, {kFloat16, kFloat32}, "param x of BatchNorm should be");
AbstractTensorPtrList tensorPtrList = std::vector<AbstractTensorPtr>();
for (size_t i = 1; i < args_spec_list.size(); ++i) {
auto param = CheckArg<AbstractTensor>(op_name, args_spec_list, i);
tensorPtrList.push_back(param);
}
(void)CheckTensorsDTypeSame(tensorPtrList, {kFloat16, kFloat32},
"param gamma, beta, mean, variance of Batchnorm should be");
auto data_format_ptr = primitive->GetAttr("format");
MS_EXCEPTION_IF_NULL(data_format_ptr);
int64_t data_format = GetAndCheckFormat(data_format_ptr);
int64_t c_axis = 1;
if (data_format == Format::NHWC) {
c_axis = 3;
@ -275,8 +197,8 @@ AbstractBasePtr InferImplFusedBatchNormEx(const AnalysisEnginePtr &, const Primi
MS_LOG(EXCEPTION) << "Arg " << i << " rank should be 1, but got " << arg_shape.size();
}
if ((x_shape[c_axis] != Shape::SHP_ANY) && (arg_shape[0] != x_shape[c_axis])) {
MS_LOG(EXCEPTION) << "Arg " << i << " shape[0] should equal to x_shape[" << c_axis << "]=" << x_shape[c_axis]
<< ", but got " << arg_shape[0];
MS_EXCEPTION(ValueError) << "Arg " << i << " shape[0] should equal to x_shape[" << c_axis
<< "]=" << x_shape[c_axis] << ", but got " << arg_shape[0];
}
}
AbstractTensorPtr input_gamma = CheckArg<AbstractTensor>(op_name, args_spec_list, 1);
@ -288,7 +210,7 @@ AbstractBasePtr InferImplFusedBatchNormEx(const AnalysisEnginePtr &, const Primi
AbstractTensorPtr output = std::make_shared<AbstractTensor>(input_x->element(), output_shape_ptr);
ShapePtr gamma_shape_ptr = std::make_shared<Shape>(gamma_shape, gamma_min_shape, gamma_max_shape);
AbstractTensorPtr output_gamma = std::make_shared<AbstractTensor>(input_gamma->element(), gamma_shape_ptr);
AbstractBasePtrList rets = {output, output_gamma, output_gamma, output_gamma, output_gamma, output_gamma};
AbstractBasePtrList rets = {output, output_gamma, output_gamma, output_gamma, output_gamma};
return std::make_shared<AbstractTuple>(rets);
}

4
mindspore/core/abstract/primitive_infer_map.cc
View File

@ -117,9 +117,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
// NN
{prim::kPrimPooling, {InferImplPooling, true}},
{prim::kPrimPoolingGrad, {InferImplPoolingGrad, true}},
{prim::kPrimFusedBatchNorm, {InferImplFusedBatchNorm, true}},
{prim::kPrimFusedBatchNormGrad, {InferImplFusedBatchNormGrad, true}},
{prim::kPrimFusedBatchNormEx, {InferImplFusedBatchNormEx, true}},
{prim::kPrimBatchNorm, {InferImplBatchNorm, true}},
{prim::kPrimReluGrad, {InferImplReluGrad, true}},
{prim::kPrimConv2D, {InferImplConv2D, true}},
{prim::kPrimBiasAdd, {InferImplBiasAdd, true}},

3
mindspore/core/base/core_ops.h
View File

@ -219,13 +219,10 @@ inline const PrimitivePtr kPrimAvgPoolGrad = std::make_shared<Primitive>("AvgPoo
inline const PrimitivePtr kPrimAvgPoolGradVm = std::make_shared<Primitive>("AvgPoolGradVm");
inline const PrimitivePtr kPrimFusedSparseAdam = std::make_shared<Primitive>("FusedSparseAdam");
inline const PrimitivePtr kPrimFusedBatchNorm = std::make_shared<Primitive>("FusedBatchNorm");
inline const PrimitivePtr kPrimFusedBatchNormEx = std::make_shared<Primitive>("FusedBatchNormEx");
inline const PrimitivePtr kPrimConv2D = std::make_shared<Primitive>("Conv2D");
inline const PrimitivePtr kPrimFullConnection = std::make_shared<Primitive>("FullConnection");
inline const PrimitivePtr kPrimConv2DTranspose = std::make_shared<Primitive>("Conv2DTranspose");
inline const PrimitivePtr kPrimGroupConv2DGradInput = std::make_shared<Primitive>("GroupConv2DGradInput");
inline const PrimitivePtr kPrimFusedBatchNormGrad = std::make_shared<Primitive>("FusedBatchNormGrad");
inline const PrimitivePtr kPrimFusedBatchNormGradEx = std::make_shared<Primitive>("FusedBatchNormGradEx");
inline const PrimitivePtr kPrimBatchNorm = std::make_shared<Primitive>("BatchNorm");
inline const PrimitivePtr kPrimBatchNormGrad = std::make_shared<Primitive>("BatchNormGrad");
inline const PrimitivePtr kPrimSyncBatchNorm = std::make_shared<Primitive>("SyncBatchNorm");

2
mindspore/core/utils/check_convert_utils.cc
View File

@ -130,8 +130,6 @@ static std::map<std::string, std::map<std::string, AttrConverterPair>> PrimAttrC
{"MaxPoolGradGradWithArgmax", FormatAndPadUpperAttrMap},
{"BatchNorm", DataFormatMap},
{"BatchNormGrad", DataFormatMap},
{"FusedBatchNormEx", DataFormatMap},
{"FusedBatchNormGradEx", DataFormatMap},
{"BiasAdd", DataFormatMap},
{"BiasAddGrad", DataFormatMap},
{"BinaryCrossEntropy", ReductionMap},

19
mindspore/nn/layer/normalization.py
View File

@ -140,25 +140,16 @@ class _BatchNorm(Cell):
else:
self.is_ge_backend = False
if self._target == "Ascend":
self.bn_train = P.BatchNorm(is_training=True,
epsilon=self.eps,
momentum=self.momentum,
data_format=self.format)
if self._target == "GPU":
self.bn_train = P.FusedBatchNormEx(mode=1,
epsilon=self.eps,
momentum=self.momentum,
data_format=self.format)
if self._target == "CPU":
self.bn_train = P.FusedBatchNorm(mode=1,
epsilon=self.eps,
momentum=self.momentum)
self.bn_train = P.BatchNorm(is_training=True,
epsilon=self.eps,
momentum=self.momentum,
data_format=self.format)
if self.is_global:
self.bn_train = inner.SyncBatchNorm(epsilon=self.eps,
momentum=self.momentum,
group=SYNC_BN_GROUP_NAME,
device_num=self.group_device_num)
self.bn_infer = P.BatchNorm(is_training=False, epsilon=self.eps, data_format=self.format)
data_parallel_strategy = ((1,), (1,))

18
mindspore/nn/layer/quant.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2021 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.
@ -541,19 +541,9 @@ class Conv2dBnFoldQuantOneConv(Cell):
channel_axis=channel_axis,
num_channels=out_channels,
quant_dtype=quant_dtype)
if self._target == "Ascend":
self.bn_train = P.BatchNorm(is_training=True,
epsilon=self.eps,
momentum=self.momentum)
if self._target == "GPU":
self.bn_train = P.FusedBatchNormEx(mode=1,
epsilon=self.eps,
momentum=self.momentum,
data_format=self.format)
if self._target == "CPU":
self.bn_train = P.FusedBatchNorm(mode=1,
epsilon=self.eps,
momentum=self.momentum)
self.bn_train = P.BatchNorm(is_training=True, epsilon=self.eps,
momentum=self.momentum, data_format=self.format)
self.bn_infer = P.BatchNorm(is_training=False, epsilon=self.eps, data_format=self.format)
data_parallel_strategy = ((1,), (1,))
data_parallel_strategy_one = ((1,), ())

55
mindspore/ops/_grad/grad_nn_ops.py
View File

@ -647,49 +647,6 @@ def get_bprop_fast_gelu_2(self):
return bprop
@bprop_getters.register(P.FusedBatchNorm)
def get_bprop_fused_batch_norm(self):
"""Grad definition for `FusedBatchNorm` operation."""
input_grad = G.FusedBatchNormGrad(self.epsilon, self.momentum)
target_cpu = False
if self.target == "CPU":
input_grad = G.FusedBatchNormGradCPU(self.epsilon, self.momentum)
target_cpu = True
def bprop(x, scale, b, mean, variance, out, dout):
saved_mean = out[3]
saved_variance = out[4]
if target_cpu:
out = input_grad(dout[0], x, scale, b, saved_mean, saved_variance)
else:
out = input_grad(dout[0], x, scale, saved_mean, saved_variance)
dx = out[0]
dscale = out[1]
dbias = out[2]
return dx, dscale, dbias, zeros_like(mean), zeros_like(variance)
return bprop
@bprop_getters.register(P.FusedBatchNormEx)
def get_bprop_fused_batch_norm_ex(self):
"""Grad definition for `FusedBatchNormEx` operation."""
input_grad = G.FusedBatchNormGradEx(self.epsilon, self.momentum, self.format)
def bprop(x, scale, b, mean, variance, out, dout):
saved_mean = out[3]
saved_variance = out[4]
reserve = out[5]
out = input_grad(dout[0], x, scale, saved_mean, saved_variance, reserve)
dx = out[0]
dscale = out[1]
dbias = out[2]
return dx, dscale, dbias, zeros_like(mean), zeros_like(variance)
return bprop
@bprop_getters.register(P.InstanceNorm)
def get_bprop_instance_norm(self):
"""Grad definition for `InstanceNorm` operation."""
@ -715,12 +672,14 @@ def get_bprop_batch_norm(self):
def bprop(x, scale, b, mean, variance, out, dout):
if is_training:
saved_reserve_1 = out[3]
saved_reserve_2 = out[4]
saved_mean = out[3]
saved_variance = out[4]
reserve = out[2]
else:
saved_reserve_1 = mean
saved_reserve_2 = variance
out = input_grad(dout[0], x, scale, saved_reserve_1, saved_reserve_2)
saved_mean = mean
saved_variance = variance
reserve = out[2]
out = input_grad(dout[0], x, scale, saved_mean, saved_variance, reserve)
dx = out[0]
dscale = out[1]
dbias = out[2]

8
mindspore/ops/_selected_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2021 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.
@ -48,12 +48,6 @@ class BiasAdd:
pass
@op_selector
class FusedBatchNorm:
def __call__(self, *args):
pass
@op_selector
class ApplyMomentum:
def __call__(self, *args):

5
mindspore/ops/operations/__init__.py
View File

@ -65,9 +65,8 @@ from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, Adam
BiasAdd, Conv2D,
DepthwiseConv2dNative,
DropoutDoMask, Dropout, Dropout2D, Dropout3D, DropoutGenMask, Flatten,
FusedBatchNorm, FusedBatchNormEx, InstanceNorm, BNTrainingReduce, BNTrainingUpdate,
InstanceNorm, BNTrainingReduce, BNTrainingUpdate,
GeLU, Gelu, FastGeLU, FastGelu, Elu,
GetNext, L2Normalize, LayerNorm, L2Loss, CTCLoss, CTCGreedyDecoder,
LogSoftmax, MaxPool3D,
MaxPool, DataFormatDimMap,
@ -142,8 +141,6 @@ __all__ = [
'Conv2D',
'Flatten',
'MaxPoolWithArgmax',
'FusedBatchNorm',
'FusedBatchNormEx',
'BNTrainingReduce',
'BNTrainingUpdate',
'BatchNorm',

55
mindspore/ops/operations/_grad_ops.py
View File

@ -197,12 +197,12 @@ class BatchNormGrad(PrimitiveWithInfer):
self.epsilon = validator.check_float_range(epsilon, 0, 1, Rel.INC_RIGHT, 'epsilon', self.name)
self.data_format = validator.check_string(data_format, ['NCHW', 'NHWC', "NCDHW"], 'format', self.name)
def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape):
def infer_shape(self, y_backprop_shape, x_shape, scale_shape, save_mean_shape, save_variance_shape, reserve):
validator.check("BatchNorm y_backprop_shape", y_backprop_shape, "BatchNorm x_shape", x_shape)
return (x_shape, scale_shape, scale_shape, reserve_1_shape, reserve_2_shape)
return (x_shape, scale_shape, scale_shape)
def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type):
return (x_type, scale_type, scale_type, reserve_1_type, reserve_2_type)
def infer_dtype(self, y_backprop_type, x_type, scale_type, save_mean_shape, save_variance_shape, reserve):
return (x_type, scale_type, scale_type)
class SyncBatchNormGrad(PrimitiveWithInfer):
@ -708,53 +708,6 @@ class FlattenGrad(PrimitiveWithInfer):
return out
class FusedBatchNormGrad(Primitive):
"""Gradients of FusedBatchNorm operation."""
@prim_attr_register
def __init__(self, epsilon=0.0, momentum=0.1):
self.init_prim_io_names(inputs=['dy', 'x', 'scale', 'save_mean', 'save_inv_variance'],
outputs=['dx', 'bn_scale', 'bn_bias'])
def __call__(self, dy, x, scale, save_mean, save_inv_variance):
raise NotImplementedError
class FusedBatchNormGradCPU(PrimitiveWithInfer):
"""Gradients of FusedBatchNorm operation for CPU."""
@prim_attr_register
def __init__(self, epsilon=0.0, momentum=0.1):
self.init_prim_io_names(inputs=['dy', 'x', 'scale', 'bias', 'save_mean', 'save_inv_variance'],
outputs=['dx', 'bn_scale', 'bn_bias'])
self.add_prim_attr('data_format', "NCHW")
def infer_shape(self, dy_shape, x_shape, scale_shape, bias_shape, save_mean_shape, save_inv_variance_shape):
return (x_shape, scale_shape, bias_shape)
def infer_dtype(self, dy_type, x_type, scale_type, bias_type, save_mean_type, save_inv_variance_type):
return (x_type, scale_type, bias_type)
class FusedBatchNormGradEx(PrimitiveWithInfer):
"""Gradients of FusedBatchNormEx operation."""
@prim_attr_register
def __init__(self, epsilon=0.0, momentum=0.1, data_format="NCHW"):
self.init_prim_io_names(inputs=['dy', 'x', 'scale', 'save_mean', 'save_inv_variance', 'reserve'],
outputs=['dx', 'bn_scale', 'bn_bias'])
self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.name)
if context.get_context("device_target") != "GPU" and self.format == "NHWC":
raise ValueError("NHWC format only support in GPU target.")
self.add_prim_attr('data_format', self.format)
def infer_shape(self, y_backprop_shape, x_shape, scale_shape, save_mean_shape, save_variance_shape, reserve_shape):
return (x_shape, scale_shape, scale_shape)
def infer_dtype(self, y_backprop_type, x_type, scale_type, save_mean_type, save_variance_type, reserve_type):
return (x_type, scale_type, scale_type)
class InstanceNormGrad(PrimitiveWithInfer):
"""Gradients of InstanceNorm operation."""

211
mindspore/ops/operations/nn_ops.py
View File

@ -817,221 +817,20 @@ class Tanh(PrimitiveWithInfer):
class FusedBatchNorm(Primitive):
r"""
FusedBatchNorm is a BatchNorm. Moving mean and moving variance will be computed instead of being loaded.
Batch Normalization is widely used in convolutional networks. This operation applies
Batch Normalization over input to avoid internal covariate shift as described in the
paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal
Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the
feature using a mini-batch of data and the learned parameters which can be described
in the following formula.
.. math::
y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta
where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon.
Args:
mode (int): Mode of batch normalization, value is 0 or 1. Default: 0.
epsilon (float): A small value added for numerical stability. Default: 1e-5.
momentum (float): The hyper parameter to compute moving average for running_mean and running_var
(e.g. :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`).
Momentum value must be [0, 1]. Default: 0.1.
Inputs:
- **input_x** (Tensor) - Tensor of shape :math:`(N, C)`.
- **scale** (Parameter) - Tensor of shape :math:`(C,)`.
- **bias** (Parameter) - Tensor of shape :math:`(C,)`.
- **mean** (Parameter) - Tensor of shape :math:`(C,)`.
- **variance** (Parameter) - Tensor of shape :math:`(C,)`.
Outputs:
Tuple of 5 Tensor, the normalized input and the updated parameters.
- **output_x** (Tensor) - The same type and shape as the `input_x`.
- **updated_scale** (Tensor) - Tensor of shape :math:`(C,)`.
- **updated_bias** (Tensor) - Tensor of shape :math:`(C,)`.
- **updated_moving_mean** (Tensor) - Tensor of shape :math:`(C,)`.
- **updated_moving_variance** (Tensor) - Tensor of shape :math:`(C,)`.
Raises:
TypeError: If `mode` is not an int.
TypeError: If `epsilon` or `momentum` is not a float.
TypeError: If `output_x`, `updated_scale`, `updated_bias`, `updated_moving_mean` or `updated_moving_variance` is
a Tensor.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import numpy as np
>>> from mindspore import Parameter
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as ops
>>> class FusedBatchNormNet(nn.Cell):
>>> def __init__(self):
>>> super(FusedBatchNormNet, self).__init__()
>>> self.fused_batch_norm = ops.FusedBatchNorm()
>>> self.scale = Parameter(Tensor(np.ones([64]), mindspore.float32), name="scale")
>>> self.bias = Parameter(Tensor(np.ones([64]), mindspore.float32), name="bias")
>>> self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean")
>>> self.variance = Parameter(Tensor(np.ones([64]), mindspore.float32), name="variance")
>>>
>>> def construct(self, input_x):
>>> out = self.fused_batch_norm(input_x, self.scale, self.bias, self.mean, self.variance)
>>> return out
>>>
>>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
>>> net = FusedBatchNormNet()
>>> output = net(input_x)
>>> result = output[0].shape
>>> print(result)
(128, 64, 32, 64)
The FusedBatchNorm interface is deprecated, please use the BatchNorm interface.
"""
__mindspore_signature__ = (
sig.make_sig('input_x', dtype=sig.sig_dtype.T1),
sig.make_sig('scale', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('bias', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('mean', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('variance', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
)
@prim_attr_register
def __init__(self, mode=0, epsilon=1e-5, momentum=0.1):
self.init_prim_io_names(inputs=['x', 'scale', 'b', 'mean', 'variance'],
outputs=['y', 'running_mean', 'running_variance', 'save_mean', 'save_inv_variance'])
self.mode = validator.check_int(mode, [0, 1], Rel.IN, 'mode', self.name)
self.epsilon = validator.check_float_range(epsilon, 0, 1, Rel.INC_RIGHT, 'epsilon', self.name)
self.momentum = validator.check_float_range(momentum, 0, 1, Rel.INC_BOTH, 'momentum', self.name)
self._update_parameter = True
self.target = context.get_context("device_target")
raise TypeError("The FusedBatchNorm interface is deprecated, please use the BatchNorm interface.")
class FusedBatchNormEx(PrimitiveWithCheck):
r"""
FusedBatchNormEx is an extension of FusedBatchNorm, FusedBatchNormEx has one more output(output reserve)
than FusedBatchNorm, reserve will be used in backpropagation phase. FusedBatchNorm is a BatchNorm that
moving mean and moving variance will be computed instead of being loaded. FusedBatchNormEx currently only
supports 4D inputs.
Batch Normalization is widely used in convolutional networks. This operation applies
Batch Normalization over input to avoid internal covariate shift as described in the
paper `Batch Normalization: Accelerating Deep Network Training by Reducing Internal
Covariate Shift <https://arxiv.org/abs/1502.03167>`_. It rescales and recenters the
feature using a mini-batch of data and the learned parameters which can be described
in the following formula.
.. math::
y = \frac{x - mean}{\sqrt{variance + \epsilon}} * \gamma + \beta
where :math:`\gamma` is scale, :math:`\beta` is bias, :math:`\epsilon` is epsilon.
Args:
mode (int): Mode of batch normalization, value is 0 or 1. Default: 0.
epsilon (float): A small value added for numerical stability. Default: 1e-5.
momentum (float): The hyper parameter to compute moving average for running_mean and running_var
(e.g. :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`).
Momentum value must be [0, 1]. Default: 0.1.
data_format (str): The optional value for data format, is 'NHWC' or 'NCHW'.
Default: "NCHW".
Inputs:
- **input_x** (Tensor) - The input of FusedBatchNormEx, Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`,
data type: float16 or float32.
- **scale** (Parameter) - Parameter scale, same with gamma above-mentioned, Tensor of shape :math:`(C,)`,
data type: float32.
- **bias** (Parameter) - Parameter bias, same with beta above-mentioned, Tensor of shape :math:`(C,)`,
data type: float32.
- **mean** (Parameter) - mean value, Tensor of shape :math:`(C,)`, data type: float32.
- **variance** (Parameter) - variance value, Tensor of shape :math:`(C,)`, data type: float32.
Outputs:
Tuple of 6 Tensors, the normalized input, the updated parameters and reserve.
- **output_x** (Tensor) - The output of FusedBatchNormEx, same type and shape as the `input_x`.
- **updated_scale** (Tensor) - Updated parameter scale, Tensor of shape :math:`(C,)`, data type: float32.
- **updated_bias** (Tensor) - Updated parameter bias, Tensor of shape :math:`(C,)`, data type: float32.
- **updated_moving_mean** (Tensor) - Updated mean value, Tensor of shape :math:`(C,)`, data type: float32.
- **updated_moving_variance** (Tensor) - Updated variance value, Tensor of shape :math:`(C,)`,
data type: float32.
- **reserve** (Tensor) - reserve space, Tensor of shape :math:`(C,)`, data type: float32.
Raises:
TypeError: If `mode` is not an int.
TypeError: If neither `epsilon` nor `momentum` is a float.
TypeError: If `data_format` is not a str.
TypeError: If `input_x` is not a Tensor.
TypeError: If dtype of `scale`, `bias`, `mean` or `variance` is not float32.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import numpy as np
>>> from mindspore import Parameter
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as ops
>>> class FusedBatchNormExNet(nn.Cell):
>>> def __init__(self):
>>> super(FusedBatchNormExNet, self).__init__()
>>> self.fused_batch_norm_ex = ops.FusedBatchNormEx()
>>> self.scale = Parameter(Tensor(np.ones([64]), mindspore.float32), name="scale")
>>> self.bias = Parameter(Tensor(np.ones([64]), mindspore.float32), name="bias")
>>> self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean")
>>> self.variance = Parameter(Tensor(np.ones([64]), mindspore.float32), name="variance")
>>>
>>> def construct(self, input_x):
>>> out = self.fused_batch_norm_ex(input_x, self.scale, self.bias, self.mean, self.variance)
>>> return out
>>>
>>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
>>> net = FusedBatchNormExNet()
>>> output = net(input_x)
>>> result = output[0].shape
>>> print(result)
(128, 64, 32, 64)
The FusedBatchNormEx interface is deprecated, please use the BatchNorm interface.
"""
__mindspore_signature__ = (
sig.make_sig('input_x', dtype=sig.sig_dtype.T1),
sig.make_sig('scale', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('bias', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('mean', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
sig.make_sig('variance', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T),
)
@prim_attr_register
def __init__(self, mode=0, epsilon=1e-5, momentum=0.1, data_format="NCHW"):
self.init_prim_io_names(inputs=['x', 'scale', 'b', 'mean', 'variance'],
outputs=['y', 'save_scale', 'save_bias', 'save_mean', 'save_inv_variance', 'reserve'])
self.mode = validator.check_int(mode, [0, 1], Rel.IN, 'mode', self.name)
self.epsilon = validator.check_float_range(epsilon, 0, 1, Rel.INC_RIGHT, 'epsilon', self.name)
self.momentum = validator.check_float_range(momentum, 0, 1, Rel.INC_BOTH, 'momentum', self.name)
self._update_parameter = True
self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.name)
if context.get_context("device_target") != "GPU" and self.format == "NHWC":
raise ValueError("NHWC format only support in GPU target.")
self.add_prim_attr('data_format', self.format)
def check_shape(self, input_x, scale, bias, mean, variance):
input_shape_norm = input_x if self.format == "NCHW" else (input_x[0], input_x[3], input_x[1], input_x[2])
validator.check_equal_int(len(input_shape_norm), 4, "x rank", self.name)
validator.check_equal_int(len(scale), 1, "scale rank", self.name)
validator.check("scale shape", scale, "bias shape", bias, Rel.EQ, self.name)
validator.check_equal_int(len(mean), 1, "mean rank", self.name)
validator.check("mean shape", mean, "variance shape", variance, Rel.EQ, self.name)
validator.check("mean shape", mean, "scale shape", scale, Rel.EQ, self.name)
def check_dtype(self, input_x, scale, bias, mean, variance):
validator.check_tensor_dtype_valid("input_x", input_x, [mstype.float16, mstype.float32], self.name)
args = {"scale": scale, "bias": bias}
validator.check_tensors_dtypes_same_and_valid(args, [mstype.float32], self.name)
args_moving = {"mean": mean, "variance": variance}
valid_dtypes = [mstype.tensor_type(mstype.float32)]
validator.check_types_same_and_valid(args_moving, valid_dtypes, self.name)
raise TypeError("FusedBatchnormEx interface is deprecated, please use BatchNorm interface.")
class InstanceNorm(PrimitiveWithInfer):
@ -1420,7 +1219,7 @@ class BatchNorm(PrimitiveWithInfer):
else:
args_moving = {"mean": mean, "variance": variance}
validator.check_tensors_dtypes_same_and_valid(args_moving, [mstype.float16, mstype.float32], self.name)
return (input_x, scale, bias, input_x, input_x)
return (input_x, mstype.float32, mstype.float32, mstype.float32, mstype.float32)
class Conv2D(PrimitiveWithCheck):

4
tests/st/ops/ascend/test_fused_batchnorm_grad.py → tests/st/ops/ascend/test_batchnorm_grad.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2021 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.
@ -41,7 +41,7 @@ class Grad(nn.Cell):
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.bn = P.FusedBatchNorm()
self.bn = P.BatchNorm()
self.scale = Parameter(initializer('ones', [64]), name='scale')
self.b = Parameter(initializer('zeros', [64]), name='b')
self.mean = Parameter(initializer('ones', [64]), name='mean')

128
tests/st/ops/gpu/test_fused_batchnorm_ex_op.py
View File

@ -1,128 +0,0 @@
# Copyright 2021 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.
# ============================================================================
import numpy as np
import pytest
import mindspore.context as context
from mindspore.common.tensor import Tensor
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.nn import Cell
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops import operations as P
class NetFusedBatchNormEx(Cell):
def __init__(self, num_features, gamma_init, beta_init, mean_init, var_init, use_batch_statistics=None):
super(NetFusedBatchNormEx, self).__init__()
self.bn = P.FusedBatchNormEx(mode=1, epsilon=0.00001, momentum=0.1)
self.moving_mean = Parameter(initializer(
mean_init, num_features), name="mean", requires_grad=False)
self.moving_variance = Parameter(initializer(
var_init, num_features), name="variance", requires_grad=False)
self.gamma = Parameter(initializer(
gamma_init, num_features), name="gamma", requires_grad=True)
self.beta = Parameter(initializer(
beta_init, num_features), name="beta", requires_grad=True)
self.dynshape = inner.GpuConvertToDynamicShape()
def construct(self, x):
x = self.bn(x, self.gamma, self.beta, self.moving_mean, self.moving_variance)
return x
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_fused_bn_ex():
x = np.array([[
[[1, 3, 3, 5], [2, 4, 6, 8], [3, 6, 7, 7], [4, 3, 8, 2]],
[[5, 7, 6, 3], [3, 5, 6, 7], [9, 4, 2, 5], [7, 5, 8, 1]]]]).astype(np.float32)
expect_output = np.array([[[[-0.6059, 0.3118, 0.3118, 1.2294],
[-0.1471, 0.7706, 1.6882, 2.6059],
[0.3118, 1.6882, 2.1471, 2.1471],
[0.7706, 0.3118, 2.6059, -0.1471]],
[[0.9119, 1.8518, 1.3819, -0.0281],
[-0.0281, 0.9119, 1.3819, 1.8518],
[2.7918, 0.4419, -0.4981, 0.9119],
[1.8518, 0.9119, 2.3218, -0.9680]]]]).astype(np.float32)
weight = np.ones(2).astype(np.float32)
bias = np.ones(2).astype(np.float32)
moving_mean = np.ones(2).astype(np.float32)
moving_var = np.ones(2).astype(np.float32)
error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
bn_net = NetFusedBatchNormEx(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var))
output_list = bn_net(Tensor(x))
output = output_list[0]
diff = output.asnumpy() - expect_output
assert np.all(diff < error)
assert np.all(-diff < error)
class NetFusedBatchNormExDynamic(Cell):
def __init__(self, num_features, gamma_init, beta_init, mean_init, var_init, use_batch_statistics=None):
super(NetFusedBatchNormExDynamic, self).__init__()
self.bn = P.FusedBatchNormEx(mode=1, epsilon=0.00001, momentum=0.1)
self.moving_mean = Parameter(initializer(
mean_init, num_features), name="mean", requires_grad=False)
self.moving_variance = Parameter(initializer(
var_init, num_features), name="variance", requires_grad=False)
self.gamma = Parameter(initializer(
gamma_init, num_features), name="gamma", requires_grad=True)
self.beta = Parameter(initializer(
beta_init, num_features), name="beta", requires_grad=True)
self.dynshape = inner.GpuConvertToDynamicShape()
def construct(self, x):
x = self.dynshape(x)
x = self.bn(x, self.gamma, self.beta, self.moving_mean, self.moving_variance)
return x
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_fused_bn_ex_dynamic():
x = np.array([[
[[1, 3, 3, 5], [2, 4, 6, 8], [3, 6, 7, 7], [4, 3, 8, 2]],
[[5, 7, 6, 3], [3, 5, 6, 7], [9, 4, 2, 5], [7, 5, 8, 1]]]]).astype(np.float32)
expect_output = np.array([[[[-0.6059, 0.3118, 0.3118, 1.2294],
[-0.1471, 0.7706, 1.6882, 2.6059],
[0.3118, 1.6882, 2.1471, 2.1471],
[0.7706, 0.3118, 2.6059, -0.1471]],
[[0.9119, 1.8518, 1.3819, -0.0281],
[-0.0281, 0.9119, 1.3819, 1.8518],
[2.7918, 0.4419, -0.4981, 0.9119],
[1.8518, 0.9119, 2.3218, -0.9680]]]]).astype(np.float32)
weight = np.ones(2).astype(np.float32)
bias = np.ones(2).astype(np.float32)
moving_mean = np.ones(2).astype(np.float32)
moving_var = np.ones(2).astype(np.float32)
error = np.ones(shape=[1, 2, 4, 4]) * 1.0e-4
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
bn_net = NetFusedBatchNormExDynamic(2, Tensor(weight), Tensor(bias), Tensor(moving_mean), Tensor(moving_var))
output_list = bn_net(Tensor(x))
output = output_list[0]
diff = output.asnumpy() - expect_output
assert np.all(diff < error)
assert np.all(-diff < error)

19
tests/ut/cpp/operator/ops_test.cc
View File

@ -414,25 +414,6 @@ TEST_F(TestOps, ReluTest) {
ASSERT_EQ(prim->name(), kPrimRelu->name());
}
TEST_F(TestOps, FusedBatchNormTest) {
auto prim = std::make_shared<Primitive>("FusedBatchNorm");
ASSERT_EQ(prim->name(), kPrimFusedBatchNorm->name());
}
TEST_F(TestOps, FusedBatchNormAttrTest) {
Primitive prim("FusedBatchNorm");
prim.SetAttrs({
{"epsilon", MakeValue(0.001f)},
{"momentum", MakeValue(0.1f)},
});
ASSERT_EQ(prim.name(), kPrimFusedBatchNorm->name());
FP32Imm epsilon(0.001f);
FP32Imm momentum(0.1f);
ASSERT_EQ(*prim.GetAttr("epsilon"), epsilon);
ASSERT_EQ(*prim.GetAttr("momentum"), momentum);
}
TEST_F(TestOps, PoolingTest) {
auto prim = std::make_shared<Primitive>("Pooling");
ASSERT_EQ(prim->name(), kPrimPooling->name());

59
tests/ut/cpp/pipeline/static_analysis/prim_test.cc
View File

@ -612,65 +612,6 @@ TEST_F(TestPrim, test_tensor_to_scalar_prim) {
ASSERT_TRUE(*res == *expected);
}
TEST_F(TestPrim, test_fused_batch_norm) {
PrimitivePtr fused_batch_norm = prim::kPrimFusedBatchNorm;
fused_batch_norm->AddAttr("epsilon", MakeValue(0.001f));
fused_batch_norm->AddAttr("momentum", MakeValue(0.1f));
FuncGraphPtr func_graph = MakeFuncGraph(fused_batch_norm, 5);
// NCHW
std::vector<int64_t> inputs_dims = {128, 64, 32, 64};
std::vector<int64_t> scale_dims = {64};
std::vector<int64_t> offset_dims = {64};
std::vector<int64_t> mean_dims = {64};
std::vector<int64_t> variance_dims = {64};
tensor::TensorPtr inputs = std::make_shared<tensor::Tensor>();
inputs->set_data_type(kNumberTypeFloat32);
inputs->set_shape(inputs_dims);
tensor::TensorPtr scale = std::make_shared<tensor::Tensor>();
scale->set_data_type(kNumberTypeFloat32);
scale->set_shape(scale_dims);
tensor::TensorPtr offset = std::make_shared<tensor::Tensor>();
offset->set_data_type(kNumberTypeFloat32);
offset->set_shape(offset_dims);
tensor::TensorPtr mean = std::make_shared<tensor::Tensor>();
mean->set_data_type(kNumberTypeFloat32);
mean->set_shape(mean_dims);
tensor::TensorPtr variance = std::make_shared<tensor::Tensor>();
variance->set_data_type(kNumberTypeFloat32);
variance->set_shape(variance_dims);
AbstractBasePtr abstract_inputs = FromValue(inputs, true);
AbstractBasePtr abstract_scale = FromValue(scale, true);
AbstractBasePtr abstract_offset = FromValue(offset, true);
AbstractBasePtr abstract_mean = FromValue(mean, true);
AbstractBasePtr abstract_variance = FromValue(variance, true);
AbstractBasePtrList args_spec_list = {abstract_inputs, abstract_scale, abstract_offset, abstract_mean,
abstract_variance};
AbstractBasePtr expected0 = abstract_inputs->Clone();
AbstractBasePtr expected1 = abstract_scale->Clone();
AbstractBasePtr res = engine_->Run(func_graph, args_spec_list).inferred->abstract();
MS_LOG(INFO) << "result: " << res->ToString();
MS_LOG(INFO) << "expected0: " << expected0->ToString();
MS_LOG(INFO) << "expected1: " << expected1->ToString();
std::shared_ptr<AbstractTuple> abs_tuple = dyn_cast<AbstractTuple>(res);
ASSERT_TRUE(abs_tuple != nullptr);
ASSERT_TRUE(*abs_tuple->elements()[0] == *expected0);
ASSERT_TRUE(*abs_tuple->elements()[1] == *expected1);
ASSERT_TRUE(*abs_tuple->elements()[2] == *expected1);
ASSERT_TRUE(*abs_tuple->elements()[3] == *expected1);
ASSERT_TRUE(*abs_tuple->elements()[4] == *expected1);
}
TEST_F(TestPrim, test_pooling) {
PrimitivePtr pooling = prim::kPrimPooling;
pooling->AddAttr("mode", MakeValue(std::string("avg")));

6
tests/ut/cpp/pre_activate/ascend/ir_fission/batch_norm_grad_infer_fission_test.cc
View File

@ -35,7 +35,7 @@ TEST_F(TestHWBatchNormGradInferFission, test_batch_norm_grad_infer_fission) {
std::vector<int64_t> shp_x{32, 64, 112, 112};
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
AbstractBasePtrList args_spec_list;
for (size_t i = 0; i < 5; ++i) {
for (size_t i = 0; i < 6; ++i) {
args_spec_list.push_back(x_abstract);
}
auto kg = GetKernelGraph(g, args_spec_list);
@ -56,7 +56,7 @@ TEST_F(TestHWBatchNormGradInferFission, test_batch_norm_grad_infer_no_fission1)
std::vector<int64_t> shp_x{32, 64, 112, 112};
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
AbstractBasePtrList args_spec_list;
for (size_t i = 0; i < 5; ++i) {
for (size_t i = 0; i < 6; ++i) {
args_spec_list.push_back(x_abstract);
}
auto kg = GetKernelGraph(g, args_spec_list);
@ -75,7 +75,7 @@ TEST_F(TestHWBatchNormGradInferFission, test_batch_norm_grad_infer_no_fission2)
std::vector<int64_t> shp_x{32, 64, 112, 112};
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
AbstractBasePtrList args_spec_list;
for (size_t i = 0; i < 5; ++i) {
for (size_t i = 0; i < 6; ++i) {
args_spec_list.push_back(x_abstract);
}
auto kg = GetKernelGraph(g, args_spec_list);

14
tests/ut/cpp/pre_activate/ascend/ir_fission/bn_grad_split_test.cc
View File

@ -47,7 +47,7 @@ TEST_F(TestHWBnGradSplit, test_bn_grad_split_tbe) {
std::vector<int64_t> shp_b{64};
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
auto b_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_b);
AbstractBasePtrList args_spec_list{x_abstract, x_abstract, b_abstract, b_abstract, b_abstract};
AbstractBasePtrList args_spec_list{x_abstract, x_abstract, b_abstract, b_abstract, b_abstract, b_abstract};
auto kernel_graph = GetKernelGraph(g, args_spec_list);
EXPECT_NE(kernel_graph, nullptr);
@ -80,13 +80,17 @@ TEST_F(TestHWBnGradSplit, test_bn_grad_split_tbe) {
// set kernel for BNGrad
kernel::KernelBuildInfo::KernelBuildInfoBuilder builder1;
builder1.SetInputsFormat(
{kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0});
{kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0,
kOpFormat_NC1HWC0});
builder1.SetOutputsFormat(
{kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0});
{kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0, kOpFormat_NC1HWC0,
kOpFormat_NC1HWC0});
builder1.SetInputsDeviceType(
{kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32});
{kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32,
kNumberTypeFloat32});
builder1.SetOutputsDeviceType(
{kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32});
{kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32, kNumberTypeFloat32,
kNumberTypeFloat32});
builder1.SetKernelType(TBE_KERNEL);
AnfAlgo::SetSelectKernelBuildInfo(builder1.Build(), bn_grad.get());
// do bn_grad_split pass

4
tests/ut/cpp/pre_activate/ascend/ir_fusion/batchnormgrad_to_bninfergrad_test.cc
View File

@ -37,7 +37,7 @@ TEST_F(TestHWOptimizeBatchNormGrad2BNInferGrad, test_fusion) {
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
std::vector<int64_t> shp_y{64};
auto y_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_y);
AbstractBasePtrList args_spec_list{x_abstract, x_abstract, y_abstract, y_abstract, y_abstract};
AbstractBasePtrList args_spec_list{x_abstract, x_abstract, y_abstract, y_abstract, y_abstract, y_abstract};
auto fg = GetKernelGraph(g, args_spec_list);
auto optimizer = std::make_shared<opt::GraphOptimizer>();
@ -57,7 +57,7 @@ TEST_F(TestHWOptimizeBatchNormGrad2BNInferGrad, test_no_fusion) {
auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_x);
std::vector<int64_t> shp_y{64};
auto y_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp_y);
AbstractBasePtrList args_spec_list{x_abstract, x_abstract, y_abstract, y_abstract, y_abstract};
AbstractBasePtrList args_spec_list{x_abstract, x_abstract, y_abstract, y_abstract, y_abstract, y_abstract};
auto fg = GetKernelGraph(g, args_spec_list);
auto origin_graph = std::make_shared<session::KernelGraph>(*fg);

16
tests/ut/cpp/python_input/gtest_input/pre_activate/batch_norm_grad_infer_fission_test.py
View File

@ -39,28 +39,28 @@ def test_batch_norm_grad_infer_fission(tag):
fns = FnDict()
@fns
def before(input0, input1, input2, input3, input4):
batch_norm = BatchNormGradInfer(input0, input1, input2, input3, input4)
def before(input0, input1, input2, input3, input4, input5):
batch_norm = BatchNormGradInfer(input0, input1, input2, input3, input4, input5)
outputs = make_tuple(tuple_getitem(batch_norm, 0), tuple_getitem(batch_norm, 1), tuple_getitem(batch_norm, 2))
output = tuple_getitem(outputs, 0)
return output
@fns
def before_is_training(input0, input1, input2, input3, input4):
batch_norm = BatchNormGradTraining(input0, input1, input2, input3, input4)
def before_is_training(input0, input1, input2, input3, input4, input5):
batch_norm = BatchNormGradTraining(input0, input1, input2, input3, input4, input5)
outputs = make_tuple(tuple_getitem(batch_norm, 0), tuple_getitem(batch_norm, 1), tuple_getitem(batch_norm, 2))
output = tuple_getitem(outputs, 0)
return output
@fns
def before_output3_not_null(input0, input1, input2, input3, input4):
batch_norm = BatchNormGradInfer(input0, input1, input2, input3, input4)
outputs = make_tuple(tuple_getitem(batch_norm, 0), tuple_getitem(batch_norm, 1), tuple_getitem(batch_norm, 3))
def before_output3_not_null(input0, input1, input2, input3, input4, input5):
batch_norm = BatchNormGradInfer(input0, input1, input2, input3, input4, input5)
outputs = make_tuple(tuple_getitem(batch_norm, 0), tuple_getitem(batch_norm, 1), tuple_getitem(batch_norm, 2))
output = tuple_getitem(outputs, 0)
return output
@fns
def after(input0, input1, input2, input3, input4):
def after(input0, input1, input2, input3, input4, input5):
bn_infer_grad = BNInferGrad(input0, input2, input4)
bn_training_update_grad = BNTrainingUpdateGrad(input0, input1, input3, input4)
outputs = make_tuple(bn_infer_grad, tuple_getitem(bn_training_update_grad, 0),

10
tests/ut/cpp/python_input/gtest_input/pre_activate/batchnormgrad_to_bninfergrad.py
View File

@ -38,19 +38,19 @@ def test_batchnormgrad_to_bninfergrad(tag):
fns = FnDict()
@fns
def before(input0, input1, input2, input3, input4):
res = batch_norm_grad(input0, input1, input2, input3, input4)
def before(input0, input1, input2, input3, input4, input5):
res = batch_norm_grad(input0, input1, input2, input3, input4, input5)
res = tuple_getitem(res, 0)
return res
@fns
def after(input0, input1, input2, input3, input4):
def after(input0, input1, input2, input3, input4, input5):
res = bn_infer_grad(input0, input2, input4)
return make_tuple(res)
@fns
def no_fusion(input0, input1, input2, input3, input4):
res = batch_norm_grad(input0, input1, input2, input3, input4)
def no_fusion(input0, input1, input2, input3, input4, input5):
res = batch_norm_grad(input0, input1, input2, input3, input4, input5)
item0 = tuple_getitem(res, 0)
item1 = tuple_getitem(res, 1)
item2 = tuple_getitem(res, 2)

8
tests/ut/cpp/python_input/gtest_input/pre_activate/bn_grad_split.py
View File

@ -49,8 +49,8 @@ def test_bn_grad_split(tag):
fns = FnDict()
@fns
def before(i0, i1, i2, i3, i4):
bn_grad_output = bn_grad(i0, i1, i2, i3, i4)
def before(i0, i1, i2, i3, i4, i5):
bn_grad_output = bn_grad(i0, i1, i2, i3, i4, i5)
item0 = tuple_getitem(bn_grad_output, 0)
item1 = tuple_getitem(bn_grad_output, 1)
item2 = tuple_getitem(bn_grad_output, 2)
@ -58,7 +58,7 @@ def test_bn_grad_split(tag):
return output
@fns
def after1(i0, i1, i2, i3, i4):
def after1(i0, i1, i2, i3, i4, i5):
bn_grad1_output = bn_grad1(i0, i1, i3)
bn_grad1_item0 = tuple_getitem(bn_grad1_output, 0)
bn_grad1_item1 = tuple_getitem(bn_grad1_output, 1)
@ -78,7 +78,7 @@ def test_bn_grad_split(tag):
return make_tuple(output)
@fns
def after2(i0, i1, i2, i3, i4):
def after2(i0, i1, i2, i3, i4, i5):
bn_update_grad_output = bn_training_update_grad(i0, i1, i3, i4)
update_item0 = tuple_getitem(bn_update_grad_output, 0)
update_item1 = tuple_getitem(bn_update_grad_output, 1)

23
tests/ut/cpp/python_input/gtest_input/pre_activate/hw_opt_test.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2021 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.
@ -27,8 +27,6 @@ make_tuple = Primitive('MakeTuple')
four2five = Primitive('Four2Five')
five2four = Primitive('Five2Four')
cast = Primitive('Cast')
conv = P.Conv2D(out_channel=64, kernel_size=7, mode=1, pad_mode="valid", pad=0, stride=1, dilation=1, group=1)
bn = P.FusedBatchNorm()
relu = P.ReLU()
@ -140,25 +138,6 @@ def test_eliminate_depend_input2(tag):
return fns[tag]
def test_opt_match(tag):
fns = FnDict()
@fns
def graph1(x, y):
sum_add = add(x, y)
output = make_tuple(sum_add)
return output
@fns
def graph2(x, w, scale, b, mean, variance):
conv_output = conv(x, w)
bn_output = bn(conv_output, scale, b, mean, variance)
res = tuple_getitem(bn_output, 0)
return res
return fns[tag]
def test_func_graph_cse(tag):
""" test_func_graph_cse """
fns = FnDict()

72
tests/ut/cpp/python_input/gtest_input/pre_activate/ir_fusion_test.py
View File

@ -1,4 +1,4 @@
# Copyright 2019 Huawei Technologies Co., Ltd
# Copyright 2019-2021 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.
@ -14,7 +14,6 @@
# ============================================================================
from mindspore.ops import Primitive
from mindspore.ops import operations as P
from mindspore.ops.operations import _grad_ops as G
from mindspore.ops import _constants as Constants
# pylint: disable=unused-variable
@ -25,7 +24,6 @@ allreduce = P.AllReduce()
allreduce.add_prim_attr('fusion', 1)
make_tuple = Primitive("MakeTuple")
conv = P.Conv2D(out_channel=64, kernel_size=7, mode=1, pad_mode="valid", pad=0, stride=1, dilation=1, group=1)
bn = P.FusedBatchNorm()
relu = P.ReLU()
conv_bn1 = Primitive('ConvBN1')
bn2_add_relu = Primitive('BN2AddRelu')
@ -33,7 +31,6 @@ bn2_relu = Primitive('BN2Relu')
fused_bn1 = Primitive('FusedBN1')
fused_bn2 = Primitive('FusedBN2')
fused_bn3 = Primitive('FusedBN3')
bn_grad = G.FusedBatchNormGrad()
bn_grad1 = Primitive('BNGrad1')
bn_grad2 = Primitive('BNGrad2')
bn_grad3 = Primitive('BNGrad3')
@ -50,73 +47,6 @@ class FnDict:
return self.fnDict[name]
def test_bn_split(tag):
""" test_split_bn_fusion """
fns = FnDict()
@fns
def before(x, scale, b, mean, variance):
bn_output = bn(x, scale, b, mean, variance)
item0 = tuple_getitem(bn_output, 0)
return item0
@fns
def after(x, scale, b, mean, variance):
fused_bn1_output = fused_bn1(x)
fused_bn2_input0 = tuple_getitem(fused_bn1_output, 0)
fused_bn2_input1 = tuple_getitem(fused_bn1_output, 1)
fused_bn2_output = fused_bn2(fused_bn2_input0, fused_bn2_input1, mean, variance)
fused_bn3_input1 = tuple_getitem(fused_bn2_output, 0)
fused_bn3_input2 = tuple_getitem(fused_bn2_output, 1)
fused_bn3_output = fused_bn3(x, fused_bn3_input1, fused_bn3_input2, scale, b)
output1 = tuple_getitem(fused_bn2_output, 2)
output2 = tuple_getitem(fused_bn2_output, 3)
output3 = tuple_getitem(fused_bn2_output, 0)
output4 = tuple_getitem(fused_bn2_output, 1)
output = make_tuple(fused_bn3_output, output1, output2, output3, output4)
item0 = tuple_getitem(output, 0)
return make_tuple(item0)
return fns[tag]
def test_bn_grad_split(tag):
""" test_bn_grad_split """
fns = FnDict()
@fns
def before(dy, x, scale, save_mean, save_inv_variance):
bn_grad_output = bn_grad(dy, x, scale, save_mean, save_inv_variance)
item0 = tuple_getitem(bn_grad_output, 0)
item1 = tuple_getitem(bn_grad_output, 1)
item2 = tuple_getitem(bn_grad_output, 2)
output = make_tuple(item0, item1, item2)
res = tuple_getitem(output, 0)
return res
@fns
def after(i0, i1, i2, i3, i4):
bn_grad1_output = bn_grad1(i0, i1, i3)
bn_grad1_item0 = tuple_getitem(bn_grad1_output, 0)
bn_grad1_item1 = tuple_getitem(bn_grad1_output, 1)
bn_grad1_item2 = tuple_getitem(bn_grad1_output, 2)
bn_grad2_output = bn_grad2(bn_grad1_item0, bn_grad1_item1, i4, i2)
bn_grad2_item0 = tuple_getitem(bn_grad2_output, 0)
bn_grad2_item1 = tuple_getitem(bn_grad2_output, 1)
bn_grad2_item2 = tuple_getitem(bn_grad2_output, 2)
bn_grad2_item3 = tuple_getitem(bn_grad2_output, 3)
bn_grad2_item4 = tuple_getitem(bn_grad2_output, 4)
bn_grad3_output = bn_grad3(i0, bn_grad2_item2, bn_grad2_item3, bn_grad2_item4, bn_grad1_item2)
bn_grad_make_tuple = make_tuple(bn_grad3_output, bn_grad2_item0, bn_grad2_item1)
item0 = tuple_getitem(bn_grad_make_tuple, 0)
item1 = tuple_getitem(bn_grad_make_tuple, 1)
item2 = tuple_getitem(bn_grad_make_tuple, 2)
output = make_tuple(item0, item1, item2)
return make_tuple(tuple_getitem(output, 0))
return fns[tag]
def test_all_reduce_fusion_all(tag):
""" test_all_reduce_fusion_all """
fns = FnDict()

6
tests/ut/cpp/session/anf_runtime_algorithm_test.cc
View File

@ -1,5 +1,5 @@
/**
* Copyright 2019-2020 Huawei Technologies Co., Ltd
* Copyright 2019-2021 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.
@ -221,7 +221,7 @@ TEST_F(AnfRuntimeAlgorithmTest, GetOutputTensorNum) {
auto kernel_graph = std::make_shared<KernelGraph>();
std::vector<AnfNodePtr> inputs;
// test fused batch norm as input
inputs.push_back(NewValueNode(prim::kPrimFusedBatchNorm));
inputs.push_back(NewValueNode(prim::kPrimBatchNorm));
auto bn = kernel_graph->NewCNode(inputs);
MS_EXCEPTION_IF_NULL(bn);
std::vector<int64_t> shp{2, 32, 224, 224};
@ -417,7 +417,7 @@ TEST_F(AnfRuntimeAlgorithmTest, GetInputDeviceShape) {
TEST_F(AnfRuntimeAlgorithmTest, GetOutputInferDataTypeTest) {
auto kernel_graph = std::make_shared<KernelGraph>();
std::vector<AnfNodePtr> inputs;
inputs.push_back(NewValueNode(prim::kPrimFusedBatchNorm));
inputs.push_back(NewValueNode(prim::kPrimBatchNorm));
auto bn = kernel_graph->NewCNode(inputs);
MS_EXCEPTION_IF_NULL(bn);
std::vector<int64_t> shp{2, 32, 224, 224};

22
tests/ut/python/ops/test_nn_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2021 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.
@ -690,26 +690,6 @@ test_cases_for_verify_exception = [
'block': (lambda _: P.MaxPoolWithArgmax(strides=-1), {'exception': ValueError}),
'desc_inputs': [0],
}),
('FusedBatchNorm_ValueError_1', {
'block': (lambda _: P.FusedBatchNorm(mode="1", epsilon=1e-5, momentum=0.1), {'exception': TypeError}),
'desc_inputs': [0],
}),
('FusedBatchNorm_ValueError_2', {
'block': (lambda _: P.FusedBatchNorm(mode=2, epsilon=1e-5, momentum=0.1), {'exception': ValueError}),
'desc_inputs': [0],
}),
('FusedBatchNorm_ValueError_3', {
'block': (lambda _: P.FusedBatchNorm(mode=0, epsilon=-1e-5, momentum=0.1), {'exception': ValueError}),
'desc_inputs': [0],
}),
('FusedBatchNorm_ValueError_4', {
'block': (lambda _: P.FusedBatchNorm(mode=0, epsilon=1e-5, momentum=-0.1), {'exception': ValueError}),
'desc_inputs': [0],
}),
('FusedBatchNorm_ValueError_5', {
'block': (lambda _: P.FusedBatchNorm(mode=1, epsilon=-0.001, momentum=0.0), {'exception': ValueError}),
'desc_inputs': [0],
}),
('Softmax_ValueError_1', {
'block': (lambda _: P.Softmax("1"), {'exception': TypeError}),
'desc_inputs': [0],

11
tests/ut/python/ops/test_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2021 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.
@ -1749,11 +1749,6 @@ test_case_nn_ops = [
'desc_inputs': [[2, 16], [2, 16], [2, 16], [2, 16], [16]],
'desc_bprop': [[2, 16], [16], [16]],
'skip': ['backward']}),
('FusedBatchNormGrad', {
'block': G.FusedBatchNormGrad(),
'desc_inputs': [[128, 64, 32, 64], [128, 64, 32, 64], [64], [64], [64]],
'desc_bprop': [[128, 64, 32, 64], [64], [64], [64], [64]],
'skip': ['backward']}),
('BatchNorm', {
'block': P.BatchNorm(),
'desc_inputs': [[128, 64, 32, 32], [64], [64], [64], [64]],
@ -1761,8 +1756,8 @@ test_case_nn_ops = [
'skip': []}),
('BatchNormGrad', {
'block': G.BatchNormGrad(),
'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64]],
'desc_bprop': [[128, 64, 32, 32], [64], [64], [64], [64]],
'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64], [64]],
'desc_bprop': [[128, 64, 32, 32], [64], [64]],
'skip': ['backward']}),
('SyncBatchNorm', {
'block': inner.SyncBatchNorm(),

77
tests/ut/python/parallel/test_batchnorm_ex_batch_parallel.py
View File

@ -1,77 +0,0 @@
# 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.
import numpy as np
import mindspore as ms
from mindspore import Tensor
from mindspore import context
from mindspore.common.api import _executor
from mindspore.common.parameter import Parameter
from mindspore.ops import composite as C
from mindspore.ops import operations as P
import mindspore.nn as nn
from tests.ut.python.ops.test_math_ops import VirtualLoss
grad_all = C.GradOperation(get_all=True)
class NetWithLoss(nn.Cell):
def __init__(self, network):
super(NetWithLoss, self).__init__()
self.loss = VirtualLoss()
self.network = network
def construct(self, x, y, b):
predict = self.network(x, y, b)
return self.loss(predict)
class GradWrap(nn.Cell):
def __init__(self, network):
super(GradWrap, self).__init__()
self.network = network
def construct(self, x, y, b):
return grad_all(self.network)(x, y, b)
# model_parallel test
def test_two_matmul_batchnorm_ex():
class Net(nn.Cell):
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul1 = P.BatchMatMul().shard(strategy1)
self.norm = P.FusedBatchNormEx()
self.gamma = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="gamma")
self.beta = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="beta")
self.mean = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="mean")
self.var = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="var")
self.matmul2 = P.BatchMatMul().shard(strategy2)
def construct(self, x, y, b):
out = self.matmul1(x, y)
out = self.norm(out, self.gamma, self.beta, self.mean, self.var)[0]
out = self.matmul2(out, b)
return out
context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8)
strategy1 = ((1, 1, 4, 2), (1, 1, 2, 1))
strategy2 = ((1, 1, 1, 8), (1, 1, 8, 1))
net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
net.set_auto_parallel()
x = Tensor(np.ones([64, 64, 128, 32]), dtype=ms.float32)
y = Tensor(np.ones([64, 64, 32, 64]), dtype=ms.float32)
b = Tensor(np.ones([64, 64, 64, 64]), dtype=ms.float32)
net.set_train()
_executor.compile(net, x, y, b)

260
tests/ut/python/parallel/test_bn_prelu_cell.py
View File

@ -1,260 +0,0 @@
# Copyright 2019 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.
import numpy as np
import mindspore as ms
import mindspore.common.dtype as DT
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.nn import WithLossCell
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.optim.momentum import Momentum
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.train.model import Model
from mindspore.context import ParallelMode
from tests.dataset_mock import MindData
class Dataset(MindData):
def __init__(self, predict, label, length=3):
super(Dataset, self).__init__(size=length)
self.predict = predict
self.label = label
self.index = 0
self.length = length
def __iter__(self):
return self
def __next__(self):
if self.index >= self.length:
raise StopIteration
self.index += 1
return self.predict, self.label
def reset(self):
self.index = 0
class FusedBatchNorm(nn.Cell):
"""Batch Normalization base class."""
def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
gamma_init='ones',
beta_init='zeros',
moving_mean_init='zeros',
moving_var_init='ones'):
super(FusedBatchNorm, self).__init__()
if num_features < 1:
raise ValueError("num_features must be at least 1")
if momentum < 0 or momentum > 1:
raise ValueError("momentum should be a number in range [0, 1], but got {}".format(momentum))
self.num_features = num_features
self.eps = eps
self.momentum = Tensor(1.0 - momentum, DT.float32)
self.gamma = Parameter(initializer(
gamma_init, num_features), name="gamma", requires_grad=affine)
self.beta = Parameter(initializer(
beta_init, num_features), name="beta", requires_grad=affine)
self.moving_mean = Parameter(initializer(
moving_mean_init, num_features), name="mean", requires_grad=False)
self.moving_variance = Parameter(initializer(
moving_var_init, num_features), name="variance", requires_grad=False)
self.bn_train = P.BatchNorm(is_training=True,
epsilon=self.eps)
self.bn_infer = P.BatchNorm(is_training=False,
epsilon=self.eps)
self.sub_mean = P.Sub().shard(((1), (1)))
self.sub_var = P.Sub().shard(((1), (1)))
self.mul_mean = P.Mul().shard(((1,), ()))
self.mul_var = P.Mul().shard(((1,), ()))
self.assign_sub_mean = P.AssignSub().shard(((1,), (1,)))
self.assign_sub_var = P.AssignSub().shard(((1), (1)))
self.sub_mean2 = P.Sub().shard(((1), (1)))
self.sub_var2 = P.Sub().shard(((1), (1)))
def shard(self, strategy):
self.bn_train.shard(strategy)
self.bn_infer.shard(strategy)
def _check_data_dim(self, x):
raise NotImplementedError
def construct(self, x):
if self.training:
y, batch_mean, batch_var, _, _ = \
self.bn_train(x,
self.gamma,
self.beta,
self.moving_mean,
self.moving_variance)
mean_sub = self.sub_mean(self.moving_mean, batch_mean)
temp_mean = self.mul_mean(mean_sub, self.momentum)
mean_sub2 = self.sub_var(self.moving_variance, batch_var)
temp_variance = self.mul_var(mean_sub2, self.momentum)
y = F.depend(y, self.assign_sub_mean(self.moving_mean, temp_mean))
y = F.depend(y, self.assign_sub_var(self.moving_variance, temp_variance))
else:
y = self.bn_infer(x,
self.gamma,
self.beta,
self.moving_mean,
self.moving_variance)[0]
return y
def extend_repr(self):
return 'num_features={}, eps={}, momentum={}, ' \
'beta={}, gamma={}, ' \
'moving_mean={}, moving_variance={} ' \
.format(self.num_features,
self.eps,
self.momentum,
self.beta,
self.gamma,
self.moving_mean,
self.moving_variance)
class PReLU(nn.Cell):
"""
PReLU activation function.
Computes prelu value of a 4-dim tensor(NCHW).
PReLU: out = max(0, A) + min(0, wA)
Args:
channel: Integer. The dimensionality of w. Default: 1.
w: Float. The initial value of w. Default: 0.25.
Returns:
Tensor, has the same type as features.
Examples:
prelu = nn.PReLU(1, [np.float32(0.25)]) # or prelu = nn.PReLU(33, Tensor(np.random.rand(33), ms.float32)])
input_data = Tensor(np.random.rand(1, 33, 4, 4), ms.float32)
output = prelu.construct(input_data)
"""
def __init__(self, channel=1, w=0.25):
super(PReLU, self).__init__()
if isinstance(w, (np.float32, float)):
tmp = np.empty((channel,), dtype=np.float32)
tmp.fill(w)
w = tmp
elif isinstance(w, (int, bool, complex, str)):
raise TypeError("w only support input type float32 and float")
if not isinstance(w, Tensor):
w = Tensor(w)
self.w = Parameter(initializer(w, [channel,]), name='a')
self.prelu = P.PReLU()
self.relu = P.ReLU().shard(((1)))
def construct(self, x):
self.w = self.relu(self.w)
return self.prelu(x, self.w)
class BNNet(nn.Cell):
def __init__(self):
super(BNNet, self).__init__()
self.bn = FusedBatchNorm(512)
self.prelu = PReLU(512)
def construct(self, x):
x = self.bn(x)
x = self.prelu(x)
return x
def bn_net():
return BNNet()
def bn_common(parallel_mode, train_flag, strategy_loss=None):
context.set_context(mode=context.GRAPH_MODE)
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
learning_rate = 0.1
momentum = 0.9
epoch_size = 2
rank_size = 8
predict = Tensor(np.ones([32, 512]), dtype=ms.float32)
label = Tensor(np.ones([32]), dtype=ms.int32)
dataset = Dataset(predict, label, 2)
net = bn_net()
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
loss.softmax_cross_entropy.shard(strategy_loss)
opt = Momentum(net.trainable_params(), learning_rate, momentum, 0.0001, 1024 * rank_size)
if not train_flag:
net = WithLossCell(net, loss)
net.set_train()
if parallel_mode == ParallelMode.DATA_PARALLEL:
context.set_auto_parallel_context(parameter_broadcast=True)
model = Model(net, loss, opt)
if train_flag:
model.train(epoch_size, dataset, dataset_sink_mode=False)
else:
model._predict(predict, label)
def test_data_parallel():
parallel_mode = ParallelMode.DATA_PARALLEL
train_flag = True
bn_common(parallel_mode, train_flag)
def auto_parallel():
train_flag = True
parallel_mode = ParallelMode.AUTO_PARALLEL
bn_common(parallel_mode, train_flag)
def Xtest_data_parallel_predict():
parallel_mode = ParallelMode.DATA_PARALLEL
train_flag = False
bn_common(parallel_mode, train_flag)
def Xtest_semi_auto_parallel_predict():
train_flag = False
parallel_mode = ParallelMode.SEMI_AUTO_PARALLEL
bn_common(parallel_mode, train_flag)
def Xtest_auto_parallel_predict():
train_flag = False
parallel_mode = ParallelMode.AUTO_PARALLEL
bn_common(parallel_mode, train_flag)
if __name__ == '__main__':
auto_parallel()

40
tests/vm_impl/nn_ops_vm_impl.py
View File

@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2021 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.
@ -92,27 +92,6 @@ def vm_impl_tanh(self):
return vm_impl
@vm_impl_getters.register(P.FusedBatchNorm)
def vm_impl_fused_batch_norm(self):
"""Generate vm_impl function for FusedBatchNorm"""
def vm_impl(x, scale, b, mean, variance):
# pylint: disable=unused-argument
x = x.asnumpy()
scale = scale.asnumpy()
b = b.asnumpy()
mean = mean.asnumpy()
variance = variance.asnumpy()
out, x_mean, x_var, running_mean, running_var = vm.batch_norm(x, scale, b, mean, \
variance, \
eps=self.epsilon, \
momentum=self.momentum)
return Tensor(out), Tensor(x_mean), Tensor(x_var), \
Tensor(running_mean), Tensor(running_var)
return vm_impl
@vm_impl_getters.register(P.BatchNorm)
def vm_impl_batch_norm(self):
"""Generate vm_impl function for BatchNorm"""
@ -223,23 +202,6 @@ def vm_impl_avg_pool_grad(self):
return vm_impl
# pylint: disable=function-redefined
@vm_impl_getters.register(G.FusedBatchNormGrad)
def vm_impl_fused_batch_norm_grad(self):
"""Generate vm_impl function for FusedBatchNormGrad"""
def vm_impl(dy, x, scale, save_mean, save_inv_variance):
dy = dy.asnumpy()
x = x.asnumpy()
scale = scale.asnumpy()
save_mean = save_mean.asnumpy()
save_inv_variance = save_inv_variance.asnumpy()
dx, dscale, dshift = vm.batch_norm_grad(dy, x, scale, save_mean, save_inv_variance)
return (Tensor(dx), Tensor(dscale), Tensor(dshift))
return vm_impl
# pylint: disable=function-redefined
@vm_impl_getters.register(G.BatchNormGrad)
def vm_impl_fused_batch_norm_grad(self):