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mindspore
提交 f679568d 编写于 作者: L linqingke
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gpu ops code and test case.

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mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gathernd_gpu_kernel.cc 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/arrays/gathernd_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_TWO(
GatherNd,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
GatherNdGpuFwdKernel, float, int)
MS_REG_GPU_KERNEL_TWO(
GatherNd,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
GatherNdGpuFwdKernel, half, int)
MS_REG_GPU_KERNEL_TWO(
GatherNd, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
GatherNdGpuFwdKernel, int, int)
} // namespace kernel
} // namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/gathernd_gpu_kernel.h 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_GATHERND_GPU_KERNEL_H
#define MINDSPORE_GATHERND_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/cuda_impl/gathernd.cuh"
namespace mindspore {
namespace kernel {
template <typename T, typename S>
class GatherNdGpuFwdKernel : public GpuKernel {
public:
GatherNdGpuFwdKernel() : dev_batch_strides_(nullptr), dev_batch_indices_(nullptr) {}
~GatherNdGpuFwdKernel() {
if (dev_batch_strides_ != nullptr) {
device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(static_cast<void *>(dev_batch_strides_));
}
if (dev_batch_indices_ != nullptr) {
device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(static_cast<void *>(dev_batch_indices_));
}
}
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);
T *input_addr = GetDeviceAddress<T>(inputs, 0);
S *indices_addr = GetDeviceAddress<S>(inputs, 1);
T *output_addr = GetDeviceAddress<T>(outputs, 0);
GatherNd(input_addr, indices_addr, output_addr, dims_[0], dims_[1], dims_[2], dev_batch_strides_,
dev_batch_indices_, reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
InitResource();
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {
MS_LOG(EXCEPTION) << "Argument number is " << input_num << ", but GatherNdGpuFwdKernel needs 2.";
}
input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
Reshape();
size_t dim_indices_last = dims_[dims_.size() - 1];
batch_strides_.resize(dim_indices_last, 0);
batch_indices_.resize(dim_indices_last, 0);
if (dim_indices_last > 0) {
batch_strides_[dim_indices_last - 1] = input_shapes_[dim_indices_last - 1];
batch_indices_[dim_indices_last - 1] = dims_[1];
}
for (size_t i = dim_indices_last - 1; i > 0; --i) {
batch_strides_[i - 1] = input_shapes_[i - 1];
batch_indices_[i - 1] = batch_indices_[i] * input_shapes_[i];
}
size_t strides_len = sizeof(S) * batch_strides_.size();
void *dev_batch_strides_work = device::gpu::GPUMemoryAllocator::GetInstance().AllocTensorMem(strides_len);
if (dev_batch_strides_work == nullptr) {
MS_LOG(EXCEPTION) << "Failed to alloc dev_batch_strides_work, size: " << strides_len;
}
dev_batch_strides_ = static_cast<S *>(dev_batch_strides_work);
size_t indices_len = sizeof(S) * batch_indices_.size();
void *dev_batch_indices_work = device::gpu::GPUMemoryAllocator::GetInstance().AllocTensorMem(indices_len);
if (dev_batch_indices_work == nullptr) {
MS_LOG(EXCEPTION) << "Failed to alloc dev_batch_indices_work, size: " << indices_len;
}
dev_batch_indices_ = static_cast<S *>(dev_batch_indices_work);
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpy(dev_batch_strides_, &batch_strides_[0], strides_len, cudaMemcpyHostToDevice),
"cudaMemcpy failed in GatherNdGpuFwdKernel::Init.");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpy(dev_batch_indices_, &batch_indices_[0], indices_len, cudaMemcpyHostToDevice),
"cudaMemcpy failed in GatherNdGpuFwdKernel::Init.");
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
size_t size = GetSize(input_shapes_);
input_size_list_.push_back(size);
size = GetSize(indices_shapes_);
input_size_list_.push_back(size);
size = GetSize(output_shapes_);
output_size_list_.push_back(size);
}
private:
void Reshape() {
size_t dim_of_indices = 1;
for (size_t i = 0; i < indices_shapes_.size() - IntToSize(1); i++) {
dim_of_indices *= indices_shapes_[i];
}
size_t dim_after_indices = 1;
size_t dim_indices_last = indices_shapes_[indices_shapes_.size() - IntToSize(1)];
for (size_t i = dim_indices_last; i < input_shapes_.size(); i++) {
dim_after_indices *= input_shapes_[i];
}
dims_.emplace_back(dim_of_indices);
dims_.emplace_back(dim_after_indices);
dims_.emplace_back(dim_indices_last);
return;
}
size_t GetSize(const std::vector<size_t> &shape) const {
if (shape.size() == 0) {
return 0;
}
size_t result = sizeof(T);
for (size_t i = 0; i < shape.size(); i++) {
result *= shape[i];
}
return result;
}
std::vector<size_t> input_shapes_;
std::vector<size_t> indices_shapes_;
std::vector<size_t> output_shapes_;
std::vector<size_t> dims_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
std::vector<S> batch_strides_;
std::vector<S> batch_indices_;
S *dev_batch_strides_;
S *dev_batch_indices_;
};
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_GATHERND_GPU_KERNEL_H
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/scatter_nd_gpu_kernel.cc 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/arrays/scatter_nd_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_TWO(
ScatterNd,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
ScatterNdGpuFwdKernel, float, int)
MS_REG_GPU_KERNEL_TWO(
ScatterNd,
KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
ScatterNdGpuFwdKernel, half, int)
MS_REG_GPU_KERNEL_TWO(
ScatterNd, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
ScatterNdGpuFwdKernel, int, int)
} // namespace kernel
} // namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/scatter_nd_gpu_kernel.h 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_SCATTER_ND_GPU_KERNEL_H
#define MINDSPORE_CCSRC_KERNEL_GPU_SCATTER_ND_GPU_KERNEL_H
#include <vector>
#include "backend/kernel_compiler/gpu/cuda_impl/scatter_nd.cuh"
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
template <typename T, typename S>
class ScatterNdGpuFwdKernel : public GpuKernel {
public:
ScatterNdGpuFwdKernel()
: input_size_(1),
indices_size_(1),
output_size_(1),
block_size_(1),
indices_stride_(nullptr),
work_shape_(nullptr),
indices_dim_0_(0),
indices_dim_1_(0) {}
~ScatterNdGpuFwdKernel() {
if (indices_stride_ != nullptr) {
device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(static_cast<void *>(indices_stride_));
}
if (work_shape_ != nullptr) {
device::gpu::GPUMemoryAllocator::GetInstance().FreeTensorMem(static_cast<void *>(work_shape_));
}
}
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);
S *indices = GetDeviceAddress<S>(inputs, 0);
T *update = GetDeviceAddress<T>(inputs, 1);
T *output = GetDeviceAddress<T>(outputs, 0);
ScatterNd(indices, update, output, block_size_, input_size_, output_size_, indices_dim_0_, indices_dim_1_,
indices_stride_, work_shape_, reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {
MS_LOG(ERROR) << "Input number is " << input_num << ", but transpose needs 2 input.";
return false;
}
size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
if (output_num != 1) {
MS_LOG(ERROR) << "Output number is " << output_num << ", but transpose needs 1 output.";
return false;
}
input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
vec_work_shape_ = GetAttr<std::vector<S>>(kernel_node, "shape");
GetSize();
size_t indices_len = sizeof(S) * vec_indices_stride_.size();
void *indices_stride_work = device::gpu::GPUMemoryAllocator::GetInstance().AllocTensorMem(indices_len);
if (indices_stride_work == nullptr) {
MS_LOG(EXCEPTION) << "Failed to alloc indices_stride_work, size: " << indices_len;
}
indices_stride_ = static_cast<S *>(indices_stride_work);
size_t vec_work_len = sizeof(S) * vec_work_shape_.size();
void *work_shape_work = device::gpu::GPUMemoryAllocator::GetInstance().AllocTensorMem(vec_work_len);
if (work_shape_work == nullptr) {
MS_LOG(EXCEPTION) << "Failed to alloc work_shape_work, size: " << vec_work_len;
}
work_shape_ = static_cast<S *>(work_shape_work);
CHECK_CUDA_RET_WITH_EXCEPT(
cudaMemcpy(indices_stride_, &vec_indices_stride_[0], indices_len, cudaMemcpyHostToDevice),
"cudaMemcpy failed in ScatterNdGpuFwdKernel::Init.");
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpy(work_shape_, &vec_work_shape_[0], vec_work_len, cudaMemcpyHostToDevice),
"cudaMemcpy failed in ScatterNdGpuFwdKernel::Init.");
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(indices_size_);
input_size_list_.push_back(input_size_);
output_size_list_.push_back(output_size_);
return;
}
void GetSize() {
indices_size_ = sizeof(S);
for (size_t i = 0; i < indices_shapes_.size(); i++) {
indices_size_ *= indices_shapes_[i];
}
input_size_ = sizeof(T);
for (size_t i = 0; i < input_shapes_.size(); i++) {
input_size_ *= input_shapes_[i];
}
output_size_ = sizeof(T);
for (size_t i = 0; i < output_shapes_.size(); i++) {
output_size_ *= output_shapes_[i];
}
// calculate indices dim 0/1
indices_dim_0_ = indices_shapes_[0];
indices_dim_1_ = indices_shapes_[1];
// calculate block_size
for (size_t i = indices_dim_1_; i < output_shapes_.size(); i++) {
block_size_ *= output_shapes_[i];
}
// calculate indices_stride
for (size_t i = 0; i < indices_dim_1_; i++) {
vec_indices_stride_.push_back(0);
}
vec_indices_stride_[indices_dim_1_ - 1] = block_size_;
for (size_t i = indices_dim_1_ - 1; i > 0; --i) {
vec_indices_stride_[i - 1] = vec_indices_stride_[i] * output_shapes_[i];
}
}
private:
std::vector<size_t> input_shapes_;
std::vector<size_t> indices_shapes_;
std::vector<size_t> output_shapes_;
std::vector<S> vec_indices_stride_;
std::vector<S> vec_work_shape_;
std::vector<size_t> input_size_list_;
std::vector<size_t> output_size_list_;
std::vector<size_t> workspace_size_list_;
size_t input_size_;
size_t indices_size_;
size_t output_size_;
size_t block_size_;
S *indices_stride_;
S *work_shape_;
size_t indices_dim_0_;
size_t indices_dim_1_;
};
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_KERNEL_GPU_SCATTER_ND_GPU_KERNEL_H
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/boundingbox_decode_impl.cu 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/cuda_impl/boundingbox_decode_impl.cuh"
template <typename T>
__global__ void BoundingBoxDecodeKernel(const size_t size, const T *rois, const T *deltas, T *bboxes, const float m1,
const float m2, const float m3, const float m4, const float s1, const float s2,
const float s3, const float s4, const int max_height, const int max_width,
const float ratio_clip) {
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
const size_t left_x = i * 4;
const size_t left_y = i * 4 + 1;
const size_t right_x = i * 4 + 2;
const size_t right_y = i * 4 + 3;
T dx = deltas[left_x] * s1 + m1;
T dy = deltas[left_y] * s2 + m2;
T dw = deltas[right_x] * s3 + m3;
T dh = deltas[right_y] * s4 + m4;
T max_ratio = abs(log(ratio_clip));
dw = dw > max_ratio ? max_ratio : (dw < (-max_ratio) ? (-max_ratio) : dw);
dh = dh > max_ratio ? max_ratio : (dh < (-max_ratio) ? (-max_ratio) : dh);
T px = (rois[left_x] + rois[right_x]) * 0.5f;
T py = (rois[left_y] + rois[right_y]) * 0.5f;
T pw = rois[right_x] - rois[left_x] + 1.0f;
T ph = rois[right_y] - rois[left_y] + 1.0f;
T gx = px + pw * dx;
T gy = py + ph * dy;
T gw = pw * exp(dw);
T gh = ph * exp(dh);
T x1 = gx - gw * 0.5f + 0.5f;
T y1 = gy - gh * 0.5f + 0.5f;
T x2 = gx + gw * 0.5f - 0.5f;
T y2 = gy + gh * 0.5f - 0.5f;
x1 = x1 > max_width ? max_width : (x1 < 0 ? 0 : x1);
y1 = y1 > max_height ? max_height : (y1 < 0 ? 0 : y1);
x2 = x2 > max_width ? max_width : (x2 < 0 ? 0 : x2);
y2 = y2 > max_height ? max_height : (y2 < 0 ? 0 : y2);
bboxes[left_x] = x1;
bboxes[left_y] = y1;
bboxes[right_x] = x2;
bboxes[right_y] = y2;
}
}
template <typename T>
void BoundingBoxDecode(const size_t size, const T *rois, const T *deltas, T *bboxes, const float &m1, const float &m2,
const float &m3, const float &m4, const float &s1, const float &s2, const float &s3,
const float &s4, const int &max_height, const int &max_width, const float &ratio_clip,
cudaStream_t cuda_stream) {
BoundingBoxDecodeKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, rois, deltas, bboxes, m1, m2, m3, m4,
s1, s2, s3, s4, max_height, max_width,
ratio_clip);
}
template void BoundingBoxDecode<float>(const size_t size, const float *rois, const float *deltas, float *bboxes,
const float &m1, const float &m2, const float &m3, const float &m4,
const float &s1, const float &s2, const float &s3, const float &s4,
const int &max_height, const int &max_width, const float &ratio_clip,
cudaStream_t cuda_stream);
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/boundingbox_decode_impl.cuh 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BOUNDINGBOX_DECODE_IMPL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BOUNDINGBOX_DECODE_IMPL_H_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void BoundingBoxDecode(const size_t size, const T *rois, const T *deltas, T *bboxes, const float &m1, const float &m2,
const float &m3, const float &m4, const float &s1, const float &s2, const float &s3,
const float &s4, const int &max_height, const int &max_width, const float &ratio_clip,
cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BOUNDINGBOX_DECODE_IMPL_H_
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/boundingbox_encode_impl.cu 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/cuda_impl/boundingbox_encode_impl.cuh"
template <typename T>
__global__ void BoundingBoxEncodeKernel(const size_t size, const T *anchor_box, const T *groundtruth_box, T *deltas,
const float m1, const float m2, const float m3, const float m4, const float s1,
const float s2, const float s3, const float s4) {
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
const size_t left_x = i * 4;
const size_t left_y = i * 4 + 1;
const size_t right_x = i * 4 + 2;
const size_t right_y = i * 4 + 3;
T px = (anchor_box[left_x] + anchor_box[right_x]) * 0.5f;
T py = (anchor_box[left_y] + anchor_box[right_y]) * 0.5f;
T pw = anchor_box[right_x] - anchor_box[left_x] + 1.0f;
T ph = anchor_box[right_y] - anchor_box[left_y] + 1.0f;
T gx = (groundtruth_box[left_x] + groundtruth_box[right_x]) * 0.5f;
T gy = (groundtruth_box[left_y] + groundtruth_box[right_y]) * 0.5f;
T gw = groundtruth_box[right_x] - groundtruth_box[left_x] + 1.0f;
T gh = groundtruth_box[right_y] - groundtruth_box[left_y] + 1.0f;
T dx = (gx - px) / pw;
T dy = (gy - py) / ph;
T dw = log(gw / pw);
T dh = log(gh / ph);
deltas[left_x] = (dx - m1) / s1;
deltas[left_y] = (dy - m2) / s2;
deltas[right_x] = (dw - m3) / s3;
deltas[right_y] = (dh - m4) / s4;
}
}
template <typename T>
void BoundingBoxEncode(const size_t size, const T *anchor_box, const T *groundtruth_box, T *deltas, const float &m1,
const float &m2, const float &m3, const float &m4, const float &s1, const float &s2,
const float &s3, const float &s4, cudaStream_t cuda_stream) {
BoundingBoxEncodeKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, anchor_box, groundtruth_box, deltas,
m1, m2, m3, m4, s1, s2, s3, s4);
}
template void BoundingBoxEncode<float>(const size_t size, const float *anchor_box, const float *groundtruth_box,
float *deltas, const float &m1, const float &m2, const float &m3,
const float &m4, const float &s1, const float &s2, const float &s3,
const float &s4, cudaStream_t cuda_stream);
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/boundingbox_encode_impl.cuh 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BOUNDINGBOX_ENCODE_IMPL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BOUNDINGBOX_ENCODE_IMPL_H_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void BoundingBoxEncode(const size_t size, const T *anchor_box, const T *groundtruth_box, T *deltas, const float &m1,
const float &m2, const float &m3, const float &m4, const float &s1, const float &s2,
const float &s3, const float &s4, cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_BOUNDINGBOX_ENCODE_IMPL_H_
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/broadcast_impl.cu
浏览文件 @ f679568d
......@@ -69,6 +69,25 @@ struct AddFunc {
__device__ __forceinline__ S operator()(const T &lhs, const T &rhs) { return (lhs + rhs); }
};
template <typename T, typename S>
struct FloorDivFunc {
__device__ __forceinline__ S operator()(const T &lhs, const T &rhs) { return floor(static_cast<float>(lhs / rhs)); }
};
template <>
struct FloorDivFunc<half, half> {
__device__ __forceinline__ half operator()(const half &lhs, const half &rhs) {
return __float2half(floor(__half2float(lhs)/ __half2float(rhs)));
}
};
template <>
struct FloorDivFunc<half, bool> {
// invalid branch
__device__ __forceinline__ half operator()(const half &lhs, const half &rhs) { return false; }
};
template <>
struct PowerFunc<half, bool> {
// invalid branch
......@@ -77,6 +96,7 @@ struct PowerFunc<half, bool> {
__device__ __forceinline__ int Index(const int &index, const int &dim) { return dim == 1 ? 0 : index; }
template <typename T, typename S, typename Func>
__device__ __forceinline__ void BroadcastOperator(const int &l0, const int &l1, const int &l2, const int &l3,
const int &r0, const int &r1, const int &r2, const int &r3,
......@@ -116,16 +136,19 @@ __global__ void BroadcastKernel(const int l0, const int l1, const int l2, const
output);
case BROADCAST_TYPE_REALDIV:
return BroadcastOperator<T, S, RealDivFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
output);
output);
case BROADCAST_TYPE_MUL:
return BroadcastOperator<T, S, MulFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
output);
output);
case BROADCAST_TYPE_SUB:
return BroadcastOperator<T, S, SubFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
output);
output);
case BROADCAST_TYPE_ADD:
return BroadcastOperator<T, S, AddFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
output);
output);
case BROADCAST_TYPE_FLOORDIV:
return BroadcastOperator<T, S, FloorDivFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
output);
}
}
......@@ -167,6 +190,8 @@ __global__ void NoBroadcastKernel(const int nums, enum BroadcastOpType op, const
return NoBroadcastOperator<T, S, SubFunc<T, S>>(nums, input0, input1, output);
case BROADCAST_TYPE_ADD:
return NoBroadcastOperator<T, S, AddFunc<T, S>>(nums, input0, input1, output);
case BROADCAST_TYPE_FLOORDIV:
return NoBroadcastOperator<T, S, FloorDivFunc<T, S>>(nums, input0, input1, output);
}
}
......@@ -195,7 +220,7 @@ void BroadcastTo(const int &i0, const int &i1, const int &i2, const int &i3, con
const int &o2, const int &o3, const T *input_addr, T *output_addr, cudaStream_t stream) {
int nums = o0 * o1 * o2 * o3;
BroadcastToKernel<<<GET_BLOCKS(nums), GET_THREADS, 0, stream>>>(i0, i1, i2, i3, o0, o1, o2, o3, input_addr,
output_addr);
output_addr);
}
template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
......@@ -226,9 +251,8 @@ template void NoBroadcast(const int &nums, enum BroadcastOpType op, const half *
bool *output, cudaStream_t stream);
template void NoBroadcast(const int &nums, enum BroadcastOpType op, const half *input0, const half *input1,
half *output, cudaStream_t stream);
template void NoBroadcast(const int &nums, enum BroadcastOpType op, const int *input0, const int *input1, int *output,
cudaStream_t stream);
template void NoBroadcast(const int &nums, enum BroadcastOpType op, const int *input0, const int *input1,
int *output, cudaStream_t stream);
template void BroadcastTo(const int &i0, const int &i1, const int &i2, const int &i3, const int &o0, const int &o1,
const int &o2, const int &o3, const float *input_addr, float *output_addr,
cudaStream_t stream);
......
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/broadcast_impl.cuh
浏览文件 @ f679568d
......@@ -29,6 +29,7 @@ enum BroadcastOpType {
BROADCAST_TYPE_MUL = 6,
BROADCAST_TYPE_SUB = 7,
BROADCAST_TYPE_ADD = 8,
BROADCAST_TYPE_FLOORDIV = 9,
BROADCAST_TYPE_INVALID = 0xffffffff,
};
......
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/gathernd.cu 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/cuda_impl/gathernd.cuh"
#include "runtime/device/gpu/cuda_common.h"
template <typename T, typename S>
__global__ void GatherNdKernel(T *input, S *indices, T *output, const size_t output_dim0, const size_t output_dim1,
const size_t indices_dim1, S *batch_indices, S *batch_strides) {
int num = output_dim0 * output_dim1;
int i, j;
for (int write_index = blockIdx.x * blockDim.x + threadIdx.x; write_index < num;
write_index += blockDim.x * gridDim.x) {
i = write_index / output_dim1 % output_dim0;
j = write_index % output_dim1;
bool out_of_bound = false;
int read_index = 0;
int indices_i = 0;
for (size_t k = 0; k < indices_dim1; k++) {
size_t ind = indices_dim1 * i + k;
indices_i = indices[ind];
out_of_bound |= !(indices_i < batch_indices[k]);
read_index += indices_i * batch_strides[k];
}
read_index += j;
if (!out_of_bound) {
output[write_index] = input[read_index];
} else {
output[write_index] = 0;
}
}
return;
}
template <typename T, typename S>
void GatherNd(T *input, S *indices, T *output, const size_t &output_dim0, const size_t &output_dim1,
const size_t &indices_dim1, S *batch_indices, S *batch_strides, cudaStream_t stream) {
int size = output_dim0 * output_dim1;
GatherNdKernel<<<GET_BLOCKS(size), GET_THREADS, 0, stream>>>(input, indices, output, output_dim0, output_dim1,
indices_dim1, batch_indices, batch_strides);
return;
}
template void GatherNd<float, int>(float *input, int *indices, float *output, const size_t &output_dim0,
const size_t &output_dim1, const size_t &indices_dim1, int *batch_indices,
int *batch_strides, cudaStream_t stream);
template void GatherNd<half, int>(half *input, int *indices, half *output, const size_t &output_dim0,
const size_t &output_dim1, const size_t &indices_dim1, int *batch_indices,
int *batch_strides, cudaStream_t stream);
template void GatherNd<int, int>(int *input, int *indices, int *output, const size_t &output_dim0,
const size_t &output_dim1, const size_t &indices_dim1, int *batch_indices,
int *batch_strides, cudaStream_t stream);
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/gathernd.cuh 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_GATHERND_GPU_CU_H
#define MINDSPORE_GATHERND_GPU_CU_H
#include "runtime/device/gpu/cuda_common.h"
template <typename T, typename S>
void GatherNd(T *input, S *indices, T *output, const size_t &output_dim0, const size_t &output_dim1,
const size_t &indices_dim1, S *batch_indices, S *batch_strides, cudaStream_t stream);
#endif // MINDSPORE_GATHERND_GPU_CU_H
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/scatter_nd.cu 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/cuda_impl/scatter_nd.cuh"
#include "runtime/device/gpu/cuda_common.h"
template <typename T, typename S>
__global__ void ScatterNdKernel(S *indices, T *update, T *output, const size_t block_size, const size_t input_size,
const size_t output_size, const size_t indices_dim_0, const size_t indices_dim_1,
S *indices_stride, S *work_shape) {
int i, j;
for (int read_index = blockIdx.x * blockDim.x + threadIdx.x; read_index < input_size;
read_index += blockDim.x * gridDim.x) {
int write_index = 0;
bool out_bound = false;
i = read_index / block_size;
j = read_index % block_size;
for (size_t k = 0; k < indices_dim_1; k++) {
S indices_i = indices[i * indices_dim_1 + k];
out_bound |= indices_i >= work_shape[k];
write_index += indices_i * indices_stride[k];
}
write_index += j;
out_bound |= write_index >= output_size;
if (!out_bound) {
output[write_index] = update[read_index];
}
}
}
template <typename T, typename S>
void ScatterNd(S *indices, T *update, T *output, const size_t &block_size, const size_t &input_size,
const size_t &output_size, const size_t &indices_dim_0, const size_t &indices_dim_1, S *indices_stride,
S *work_shape, cudaStream_t stream) {
ScatterNdKernel<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(indices, update, output, block_size, input_size,
output_size, indices_dim_0, indices_dim_1,
indices_stride, work_shape);
return;
}
template void ScatterNd<float, int>(int *indices, float *update, float *output, const size_t &block_size,
const size_t &input_size, const size_t &output_size, const size_t &indices_dim_0,
const size_t &indices_dim_1, int *indices_stride, int *work_shape,
cudaStream_t stream);
template void ScatterNd<half, int>(int *indices, half *update, half *output, const size_t &block_size,
const size_t &input_size, const size_t &output_size, const size_t &indices_dim_0,
const size_t &indices_dim_1, int *indices_stride, int *work_shape,
cudaStream_t stream);
template void ScatterNd<int, int>(int *indices, int *update, int *output, const size_t &block_size,
const size_t &input_size, const size_t &output_size, const size_t &indices_dim_0,
const size_t &indices_dim_1, int *indices_stride, int *work_shape,
cudaStream_t stream);
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/scatter_nd.cuh 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_SCATTER_ND_GPU_CU_H
#define MINDSPORE_SCATTER_ND_GPU_CU_H
#include "runtime/device/gpu/cuda_common.h"
template <typename T, typename S>
void ScatterNd(S *indices, T *update, T *output, const size_t &block_size, const size_t &input_size,
const size_t &output_size, const size_t &indices_dim_0, const size_t &indices_dim_1, S *indices_stride,
S *work_shape, cudaStream_t stream);
#endif // MINDSPORE_SCATTER_ND_GPU_CU_H
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/sgd_impl.cu 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <iostream>
#include "backend/kernel_compiler/gpu/cuda_impl/sgd_impl.cuh"
template <typename T>
__global__ void SGDKernel(const int size, const T dampening, const T weight_decay, const bool nesterov, const T *grad,
const T *momentum, const T *lr, T *param, T *accum, T *stat) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (size); i += blockDim.x * gridDim.x) {
T grad_new = grad[i];
if (weight_decay != static_cast<T>(0)) {
grad_new += param[i] * weight_decay;
}
if (momentum[0] != static_cast<T>(0)) {
if (stat[i] == static_cast<T>(0)) {
accum[i] = grad_new;
stat[i] = 0;
} else {
accum[i] = accum[i] * momentum[0] + (1.0 - dampening) * grad_new;
}
if (nesterov) {
grad_new += accum[i] * momentum[0];
} else {
grad_new = accum[i];
}
}
param[i] -= lr[0] * grad_new;
}
}
template <typename T>
void SGD(const int size, const T dampening, const T weight_decay, const bool nesterov, const T *lr, const T *momentum,
const T *grad, T *param, T *accum, T *stat, cudaStream_t cuda_stream) {
SGDKernel<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, dampening, weight_decay, nesterov, grad, momentum,
lr, param, accum, stat);
}
template void SGD(const int size, const float dampening, const float weight_decay, const bool nesterov, const float *lr,
const float *momentum, const float *grad, float *param, float *accum, float *stat,
cudaStream_t cuda_stream);
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/sgd_impl.cuh 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SGD_IMPL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SGD_IMPL_H_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void SGD(const int size, const T dampening, const T weight_decay, const bool nesterov, const T *lr, const T *momentum,
const T *grad, T *param, T *accum, T *stat, cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SGD_IMPL_H_
mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.cc
浏览文件 @ f679568d
......@@ -51,6 +51,10 @@ MS_REG_GPU_KERNEL_TWO(
TensorAdd,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
BroadcastOpGpuKernel, float, float)
MS_REG_GPU_KERNEL_TWO(
FloorDiv,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
BroadcastOpGpuKernel, float, float)
// fp16
MS_REG_GPU_KERNEL_TWO(
......@@ -85,6 +89,10 @@ MS_REG_GPU_KERNEL_TWO(
TensorAdd,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
BroadcastOpGpuKernel, half, half)
MS_REG_GPU_KERNEL_TWO(
FloorDiv,
KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
BroadcastOpGpuKernel, half, half)
// int32
MS_REG_GPU_KERNEL_TWO(
......
mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.h
浏览文件 @ f679568d
......@@ -96,10 +96,10 @@ class BroadcastOpGpuKernel : public GpuKernel {
std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
static std::map<std::string, BroadcastOpType> kBroadcastTypeMap = {
{"Greater", BROADCAST_TYPE_GREATER}, {"Less", BROADCAST_TYPE_LESS}, {"Maximum", BROADCAST_TYPE_MAXIMUM},
{"Minimum", BROADCAST_TYPE_MINIMUM}, {"Pow", BROADCAST_TYPE_POWER}, {"RealDiv", BROADCAST_TYPE_REALDIV},
{"FloorDiv", BROADCAST_TYPE_REALDIV}, {"Mul", BROADCAST_TYPE_MUL}, {"Sub", BROADCAST_TYPE_SUB},
{"TensorAdd", BROADCAST_TYPE_ADD},
{"Greater", BROADCAST_TYPE_GREATER}, {"Less", BROADCAST_TYPE_LESS}, {"Maximum", BROADCAST_TYPE_MAXIMUM},
{"Minimum", BROADCAST_TYPE_MINIMUM}, {"Pow", BROADCAST_TYPE_POWER}, {"RealDiv", BROADCAST_TYPE_REALDIV},
{"Mul", BROADCAST_TYPE_MUL}, {"Sub", BROADCAST_TYPE_SUB}, {"TensorAdd", BROADCAST_TYPE_ADD},
{"FloorDiv", BROADCAST_TYPE_FLOORDIV},
};
auto iter = kBroadcastTypeMap.find(kernel_name);
......
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/sgd_gpu_kernel.cc 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/nn/sgd_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(SGD,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
SGDGpuKernel, float)
} // namespace kernel
} // namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/sgd_gpu_kernel.h 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_NN_SGD_KERNEL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_NN_SGD_KERNEL_H_
#include <vector>
#include "backend/kernel_compiler/gpu/cuda_impl/sgd_impl.cuh"
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
template <typename T>
class SGDGpuKernel : public GpuKernel {
public:
SGDGpuKernel() : size_(1), dampening_(0.0), weight_decay_(0.0), nesterov_(false) {}
~SGDGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream) override {
T *param = GetDeviceAddress<T>(inputs, 0);
T *grad = GetDeviceAddress<T>(inputs, 1);
T *lr = GetDeviceAddress<T>(inputs, 2);
T *accum = GetDeviceAddress<T>(inputs, 3);
T *momentum = GetDeviceAddress<T>(inputs, 4);
T *stat = GetDeviceAddress<T>(inputs, 5);
SGD(size_, dampening_, weight_decay_, nesterov_, lr, momentum, grad, param, accum, stat,
reinterpret_cast<cudaStream_t>(stream));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
dampening_ = GetAttr<float>(kernel_node, "dampening");
weight_decay_ = GetAttr<float>(kernel_node, "weight_decay");
nesterov_ = GetAttr<bool>(kernel_node, "nesterov");
auto input_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
for (auto &dim : input_shape) {
size_ *= dim;
}
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
size_t input_size = size_ * sizeof(T);
input_size_list_.push_back(input_size); // parameter
input_size_list_.push_back(input_size); // gradient
input_size_list_.push_back(sizeof(T)); // lr
input_size_list_.push_back(input_size); // accum
input_size_list_.push_back(sizeof(T)); // momentum
input_size_list_.push_back(input_size); // stat
output_size_list_.push_back(input_size);
}
private:
size_t size_;
float dampening_;
float weight_decay_;
bool nesterov_;
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_KERNEL_GPU_NN_SGD_KERNEL_H_
mindspore/ccsrc/backend/kernel_compiler/gpu/other/boundingbox_decode_gpu_kernel.cc 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/other/boundingbox_decode_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(
BoundingBoxDecode,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
BoundingBoxDecodeGpuKernel, float)
} // namespace kernel
} // namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/other/boundingbox_decode_gpu_kernel.h 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_OTHER_BOUNDINGBOX_DECODE_GPU_KERNEL_H
#define MINDSPORE_CCSRC_KERNEL_GPU_OTHER_BOUNDINGBOX_DECODE_GPU_KERNEL_H
#include <vector>
#include "backend/kernel_compiler/gpu/cuda_impl/boundingbox_decode_impl.cuh"
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
template <typename T>
class BoundingBoxDecodeGpuKernel : public GpuKernel {
public:
BoundingBoxDecodeGpuKernel() : rois_size_(0), deltas_size_(0), bboxes_size_(0), wh_ratio_clip_(0.016) {}
~BoundingBoxDecodeGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
T *rois_addr = GetDeviceAddress<T>(inputs, 0);
T *deltas_addr = GetDeviceAddress<T>(inputs, 1);
T *bboxes_addr = GetDeviceAddress<T>(outputs, 0);
if (inputs[0]->size != inputs[1]->size) {
MS_LOG(ERROR) << "Rois box size must equal with deltas box size -" << inputs[1]->size << ", but got"
<< inputs[0]->size;
return false;
}
const size_t coordinate = 4;
const size_t block_size = inputs[0]->size / sizeof(T);
if ((block_size % coordinate) != 0) {
MS_LOG(ERROR) << "The size of the box must be a multiple of 4.";
return false;
}
BoundingBoxDecode(block_size / coordinate, rois_addr, deltas_addr, bboxes_addr, means_[0], means_[1], means_[2],
means_[3], stds_[0], stds_[1], stds_[2], stds_[3], max_shape_[0], max_shape_[1], wh_ratio_clip_,
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {
MS_LOG(ERROR) << "Input number is " << input_num << ", but BoundingBoxDecode needs 2 inputs.";
return false;
}
rois_size_ = sizeof(T);
deltas_size_ = sizeof(T);
bboxes_size_ = sizeof(T);
auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < logits_shape.size(); i++) {
rois_size_ *= logits_shape[i];
}
auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
for (size_t i = 0; i < labels_shape.size(); i++) {
deltas_size_ *= labels_shape[i];
}
auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < output_shape.size(); i++) {
bboxes_size_ *= output_shape[i];
}
InitSizeLists();
const size_t coordinate_size = 4;
if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<ValueTuple>() ||
AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<ValueList>()) {
means_ = GetAttr<std::vector<float>>(kernel_node, "means");
} else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<FloatImm>()) {
float mean = GetAttr<int>(kernel_node, "means");
for (size_t i = 0; i < coordinate_size; i++) {
means_.emplace_back(mean);
}
} else {
MS_LOG(EXCEPTION) << "Attribute means type is invalid.";
}
if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<ValueTuple>() ||
AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<ValueList>()) {
stds_ = GetAttr<std::vector<float>>(kernel_node, "stds");
} else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<FloatImm>()) {
float std = GetAttr<int>(kernel_node, "stds");
for (size_t i = 0; i < coordinate_size; i++) {
stds_.emplace_back(std);
}
} else {
MS_LOG(EXCEPTION) << "Attribute stds type is invalid.";
}
max_shape_ = GetAttr<std::vector<int>>(kernel_node, "max_shape");
wh_ratio_clip_ = GetAttr<float>(kernel_node, "wh_ratio_clip");
if (means_.size() < coordinate_size || stds_.size() < coordinate_size) {
MS_LOG(EXCEPTION) << "The size of means or stds is less than 4.";
}
if (max_shape_.size() < 2) {
MS_LOG(EXCEPTION) << "The size of max_shape is less than 2.";
}
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(rois_size_);
input_size_list_.push_back(deltas_size_);
output_size_list_.push_back(bboxes_size_);
}
private:
size_t rois_size_;
size_t deltas_size_;
size_t bboxes_size_;
std::vector<float> means_;
std::vector<float> stds_;
std::vector<int> max_shape_;
float wh_ratio_clip_;
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_KERNEL_GPU_OTHER_BOUNDINGBOX_DECODE_GPU_KERNEL_H
mindspore/ccsrc/backend/kernel_compiler/gpu/other/boundingbox_encode_gpu_kernel.cc 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/gpu/other/boundingbox_encode_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(
BoundingBoxEncode,
KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
BoundingBoxEncodeGpuKernel, float)
} // namespace kernel
} // namespace mindspore
mindspore/ccsrc/backend/kernel_compiler/gpu/other/boundingbox_encode_gpu_kernel.h 0 → 100644
浏览文件 @ f679568d
/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_KERNEL_GPU_OTHER_BOUNDINGBOX_ENCODE_GPU_KERNEL_H
#define MINDSPORE_CCSRC_KERNEL_GPU_OTHER_BOUNDINGBOX_ENCODE_GPU_KERNEL_H
#include <vector>
#include "backend/kernel_compiler/gpu/cuda_impl/boundingbox_encode_impl.cuh"
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
template <typename T>
class BoundingBoxEncodeGpuKernel : public GpuKernel {
public:
BoundingBoxEncodeGpuKernel() : anchor_size_(0), groundtruth_size_(0), deltas_size_(0) {}
~BoundingBoxEncodeGpuKernel() override = default;
const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
T *anchor_addr = GetDeviceAddress<T>(inputs, 0);
T *groundtruth_addr = GetDeviceAddress<T>(inputs, 1);
T *deltas_addr = GetDeviceAddress<T>(outputs, 0);
if (inputs[0]->size != inputs[1]->size) {
MS_LOG(ERROR) << "Anchor box size must equal with groundtruth box size -" << inputs[1]->size << ", but got"
<< inputs[0]->size;
return false;
}
const size_t coordinate = 4;
const size_t block_size = inputs[0]->size / sizeof(T);
if ((block_size % coordinate) != 0) {
MS_LOG(ERROR) << "The size of the box must be a multiple of 4.";
return false;
}
BoundingBoxEncode(block_size / coordinate, anchor_addr, groundtruth_addr, deltas_addr, means_[0], means_[1],
means_[2], means_[3], stds_[0], stds_[1], stds_[2], stds_[3],
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 2) {
MS_LOG(ERROR) << "Input number is " << input_num << ", but BoundingBoxEncode needs 2 inputs.";
return false;
}
anchor_size_ = sizeof(T);
groundtruth_size_ = sizeof(T);
deltas_size_ = sizeof(T);
auto logits_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < logits_shape.size(); i++) {
anchor_size_ *= logits_shape[i];
}
auto labels_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
for (size_t i = 0; i < labels_shape.size(); i++) {
groundtruth_size_ *= labels_shape[i];
}
auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < output_shape.size(); i++) {
deltas_size_ *= output_shape[i];
}
InitSizeLists();
const size_t coordinate_size = 4;
if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<ValueTuple>() ||
AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<ValueList>()) {
means_ = GetAttr<std::vector<float>>(kernel_node, "means");
} else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("means")->isa<FloatImm>()) {
float mean = GetAttr<int>(kernel_node, "means");
for (size_t i = 0; i < coordinate_size; i++) {
means_.emplace_back(mean);
}
} else {
MS_LOG(EXCEPTION) << "Attribute means type is invalid.";
}
if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<ValueTuple>() ||
AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<ValueList>()) {
stds_ = GetAttr<std::vector<float>>(kernel_node, "stds");
} else if (AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("stds")->isa<FloatImm>()) {
float std = GetAttr<int>(kernel_node, "stds");
for (size_t i = 0; i < coordinate_size; i++) {
stds_.emplace_back(std);
}
} else {
MS_LOG(EXCEPTION) << "Attribute stds type is invalid.";
}
if (means_.size() < coordinate_size || stds_.size() < coordinate_size) {
MS_LOG(EXCEPTION) << "The size of means or stds is less than 4.";
}
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(anchor_size_);
input_size_list_.push_back(groundtruth_size_);
output_size_list_.push_back(deltas_size_);
}
private:
size_t anchor_size_;
size_t groundtruth_size_;
size_t deltas_size_;
std::vector<float> means_;
std::vector<float> stds_;
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_KERNEL_GPU_OTHER_BOUNDINGBOX_ENCODE_GPU_KERNEL_H
tests/st/ops/gpu/test_boundingbox_decode_op.py 0 → 100644
浏览文件 @ f679568d
# 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 pytest
import mindspore
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
class NetBoundingBoxDecode(nn.Cell):
def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
super(NetBoundingBoxDecode, self).__init__()
self.decode = P.BoundingBoxDecode(max_shape=(768, 1280), means=means, stds=stds,
wh_ratio_clip=0.016)
def construct(self, anchor, groundtruth):
return self.decode(anchor, groundtruth)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_boundingbox_decode():
anchor = np.array([[4, 1, 2, 1], [2, 2, 2, 3]], np.float32)
deltas = np.array([[3, 1, 2, 2], [1, 2, 1, 4]], np.float32)
means = (0.1, 0.1, 0.2, 0.2)
stds = (2.0, 2.0, 3.0, 3.0)
anchor_box = Tensor(anchor, mindspore.float32)
deltas_box = Tensor(deltas, mindspore.float32)
expect_deltas = np.array([[28.6500, 0.0000, 0.0000, 33.8500],
[0.0000, 0.0000, 15.8663, 72.7000]], np.float32)
error = np.ones(shape=[2, 4]) * 1.0e-4
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
boundingbox_decode = NetBoundingBoxDecode(means, stds)
output = boundingbox_decode(anchor_box, deltas_box)
diff = output.asnumpy() - expect_deltas
assert np.all(abs(diff) < error)
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
boundingbox_decode = NetBoundingBoxDecode(means, stds)
output = boundingbox_decode(anchor_box, deltas_box)
diff = output.asnumpy() - expect_deltas
assert np.all(abs(diff) < error)
tests/st/ops/gpu/test_boundingbox_encode_op.py 0 → 100644
浏览文件 @ f679568d
# 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 pytest
import mindspore
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
class NetBoundingBoxEncode(nn.Cell):
def __init__(self, means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)):
super(NetBoundingBoxEncode, self).__init__()
self.encode = P.BoundingBoxEncode(means=means, stds=stds)
def construct(self, anchor, groundtruth):
return self.encode(anchor, groundtruth)
def bbox2delta(proposals, gt, means, stds):
px = (proposals[..., 0] + proposals[..., 2]) * 0.5
py = (proposals[..., 1] + proposals[..., 3]) * 0.5
pw = proposals[..., 2] - proposals[..., 0] + 1.0
ph = proposals[..., 3] - proposals[..., 1] + 1.0
gx = (gt[..., 0] + gt[..., 2]) * 0.5
gy = (gt[..., 1] + gt[..., 3]) * 0.5
gw = gt[..., 2] - gt[..., 0] + 1.0
gh = gt[..., 3] - gt[..., 1] + 1.0
dx = (gx - px) / pw
dy = (gy - py) / ph
dw = np.log(gw / pw)
dh = np.log(gh / ph)
means = np.array(means, np.float32)
stds = np.array(stds, np.float32)
deltas = np.stack([(dx - means[0]) / stds[0], (dy - means[1]) / stds[1],
(dw - means[2]) / stds[2], (dh - means[3]) / stds[3]], axis=-1)
return deltas
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_boundingbox_encode():
anchor = np.array([[4, 1, 6, 9], [2, 5, 5, 9]]).astype(np.float32)
gt = np.array([[3, 2, 7, 7], [1, 5, 5, 8]]).astype(np.float32)
means = (0.1, 0.1, 0.2, 0.2)
stds = (2.0, 2.0, 3.0, 3.0)
anchor_box = Tensor(anchor, mindspore.float32)
groundtruth_box = Tensor(gt, mindspore.float32)
expect_deltas = bbox2delta(anchor, gt, means, stds)
error = np.ones(shape=[2, 4]) * 1.0e-6
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
boundingbox_encode = NetBoundingBoxEncode(means, stds)
output = boundingbox_encode(anchor_box, groundtruth_box)
diff = output.asnumpy() - expect_deltas
assert np.all(abs(diff) < error)
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
boundingbox_encode = NetBoundingBoxEncode(means, stds)
output = boundingbox_encode(anchor_box, groundtruth_box)
diff = output.asnumpy() - expect_deltas
assert np.all(abs(diff) < error)
tests/st/ops/gpu/test_floordiv_op.py 0 → 100644
浏览文件 @ f679568d
# 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 pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
class NetFloorDiv(nn.Cell):
def __init__(self):
super(NetFloorDiv, self).__init__()
self.floordiv = P.FloorDiv()
def construct(self, x, y):
return self.floordiv(x, y)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_floor_div():
x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float32)
y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
x3_np = np.random.randint(1, 5, 1).astype(np.float32)
y3_np = np.random.randint(1, 5, 1).astype(np.float32)
x4_np = np.array(768).astype(np.float32)
y4_np = np.array(3072.5).astype(np.float32)
x5_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
y5_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
x6_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int32)
y6_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int32)
x0 = Tensor(x0_np)
y0 = Tensor(y0_np)
x1 = Tensor(x1_np)
y1 = Tensor(y1_np)
x2 = Tensor(x2_np)
y2 = Tensor(y2_np)
x3 = Tensor(x3_np)
y3 = Tensor(y3_np)
x4 = Tensor(x4_np)
y4 = Tensor(y4_np)
x5 = Tensor(x5_np)
y5 = Tensor(y5_np)
x6 = Tensor(x6_np)
y6 = Tensor(y6_np)
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
floor_div = NetFloorDiv()
output0 = floor_div(x0, y0)
expect0 = np.floor_divide(x0_np, y0_np)
diff0 = output0.asnumpy() - expect0
error0 = np.ones(shape=expect0.shape) * 1.0e-5
assert np.all(diff0 < error0)
assert output0.shape == expect0.shape
output1 = floor_div(x1, y1)
expect1 = np.floor_divide(x1_np, y1_np)
diff1 = output1.asnumpy() - expect1
error1 = np.ones(shape=expect1.shape) * 1.0e-5
assert np.all(diff1 < error1)
assert output1.shape == expect1.shape
output2 = floor_div(x2, y2)
expect2 = np.floor_divide(x2_np, y2_np)
diff2 = output2.asnumpy() - expect2
error2 = np.ones(shape=expect2.shape) * 1.0e-5
assert np.all(diff2 < error2)
assert output2.shape == expect2.shape
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
output3 = floor_div(x3, y3)
expect3 = np.floor_divide(x3_np, y3_np)
diff3 = output3.asnumpy() - expect3
error3 = np.ones(shape=expect3.shape) * 1.0e-5
assert np.all(diff3 < error3)
assert output3.shape == expect3.shape
output4 = floor_div(x4, y4)
expect4 = np.floor_divide(x4_np, y4_np)
diff4 = output4.asnumpy() - expect4
error4 = np.ones(shape=expect4.shape) * 1.0e-5
assert np.all(diff4 < error4)
assert output4.shape == expect4.shape
output5 = floor_div(x5, y5)
expect5 = np.floor_divide(x5_np, y5_np)
diff5 = output5.asnumpy() - expect5
error5 = np.ones(shape=expect5.shape) * 1.0e-5
assert np.all(diff5 < error5)
assert output5.shape == expect5.shape
output6 = floor_div(x6, y6)
expect6 = np.floor_divide(x6_np, y6_np)
diff6 = output6.asnumpy() - expect6
error6 = np.ones(shape=expect6.shape) * 1.0e-5
assert np.all(diff6 < error6)
assert output6.shape == expect6.shape
tests/st/ops/gpu/test_gathernd_op.py 0 → 100644
浏览文件 @ f679568d
# 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 pytest
from mindspore import Tensor
from mindspore.ops import operations as P
import mindspore.nn as nn
import mindspore.context as context
class GatherNdNet(nn.Cell):
def __init__(self):
super(GatherNdNet, self).__init__()
self.gathernd = P.GatherNd()
def construct(self, x, indices):
return self.gathernd(x, indices)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gathernd0():
x = Tensor(np.arange(3 * 2, dtype=np.float32).reshape(3, 2))
indices = Tensor(np.array([[1, 1], [0, 1]]).astype(np.int32))
expect = np.array([3., 1.])
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
gathernd = GatherNdNet()
output = gathernd(x, indices)
error = np.ones(shape=output.asnumpy().shape) * 1.0e-6
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_traning
@pytest.mark.env_onecard
def test_gathernd1():
x = Tensor(np.arange(2 * 3 * 4 * 5, dtype=np.float32).reshape(2, 3, 4, 5))
indices = Tensor(np.array([[[[[l, k, j, i] for i in [1, 3, 4]] for j in range(4)]
for k in range(3)] for l in range(2)], dtype='i4'))
expect = np.array([[[[1., 3., 4.],
[6., 8., 9.],
[11., 13., 14.],
[16., 18., 19.]],
[[21., 23., 24.],
[26., 28., 29.],
[31., 33., 34.],
[36., 38., 39.]],
[[41., 43., 44.],
[46., 48., 49.],
[51., 53., 54.],
[56., 58., 59.]]],
[[[61., 63., 64.],
[66., 68., 69.],
[71., 73., 74.],
[76., 78., 79.]],
[[81., 83., 84.],
[86., 88., 89.],
[91., 93., 94.],
[96., 98., 99.]],
[[101., 103., 104.],
[106., 108., 109.],
[111., 113., 114.],
[116., 118., 119.]]]])
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
gather = GatherNdNet()
output = gather(x, indices)
error = np.ones(shape=output.asnumpy().shape) * 1.0e-6
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_traning
@pytest.mark.env_onecard
def test_gathernd2():
x = Tensor(np.array([[4., 5., 4., 1., 5.],
[4., 9., 5., 6., 4.],
[9., 8., 4., 3., 6.],
[0., 4., 2., 2., 8.],
[1., 8., 6., 2., 8.],
[8., 1., 9., 7., 3.],
[7., 9., 2., 5., 7.],
[9., 8., 6., 8., 5.],
[3., 7., 2., 7., 4.],
[4., 2., 8., 2., 9.]]).astype(np.float16))
indices = Tensor(np.array([[4000], [1], [300000]]).astype(np.int32))
expect = np.array([[0., 0., 0., 0., 0.],
[4., 9., 5., 6., 4.],
[0., 0., 0., 0., 0.]])
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
gathernd = GatherNdNet()
output = gathernd(x, indices)
error = np.ones(shape=output.asnumpy().shape) * 1.0e-6
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_traning
@pytest.mark.env_onecard
def test_gathernd3():
x = Tensor(np.array([[4, 5, 4, 1, 5],
[4, 9, 5, 6, 4],
[9, 8, 4, 3, 6],
[0, 4, 2, 2, 8],
[1, 8, 6, 2, 8],
[8, 1, 9, 7, 3],
[7, 9, 2, 5, 7],
[9, 8, 6, 8, 5],
[3, 7, 2, 7, 4],
[4, 2, 8, 2, 9]]
).astype(np.int32))
indices = Tensor(np.array([[4000], [1], [300000]]).astype(np.int32))
expect = np.array([[0, 0, 0, 0, 0],
[4, 9, 5, 6, 4],
[0, 0, 0, 0, 0]])
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
gathernd = GatherNdNet()
output = gathernd(x, indices)
error = np.ones(shape=output.asnumpy().shape) * 1.0e-6
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)
tests/st/ops/gpu/test_scatter_nd.py 0 → 100644
浏览文件 @ f679568d
# 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 pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
class Net(nn.Cell):
def __init__(self, _shape):
super(Net, self).__init__()
self.shape = _shape
self.scatternd = P.ScatterNd()
def construct(self, indices, update):
return self.scatternd(indices, update, self.shape)
def scatternd_net(indices, update, _shape, expect):
scatternd = Net(_shape)
output = scatternd(Tensor(indices), Tensor(update))
error = np.ones(shape=output.asnumpy().shape) * 1.0e-6
diff = output.asnumpy() - expect
assert np.all(diff < error)
assert np.all(-diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_traning
@pytest.mark.env_onecard
def test_scatternd():
arr_indices = np.array([[0, 1], [1, 1]]).astype(np.int32)
arr_update = np.array([3.2, 1.1]).astype(np.float32)
shape = (2, 2)
expect = np.array([[0., 3.2],
[0., 1.1]])
scatternd_net(arr_indices, arr_update, shape, expect)
tests/st/ops/gpu/test_sgd_op.py 0 → 100644
浏览文件 @ f679568d
# 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 pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.nn import Dense
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.optim import SGD
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
class NetSGD(nn.Cell):
def __init__(self):
super(NetSGD, self).__init__()
self.batch_size = 1
self.reshape = P.Reshape()
weight = Tensor(np.ones([10, 16]).astype(np.float32) * 0.01)
self.fc1 = Dense(16, 10, weight_init=weight)
def construct(self, input_x):
output = self.reshape(input_x, (self.batch_size, -1))
output = self.fc1(output)
return output
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_SGD():
epoch = 3
net = NetSGD()
learning_rate = 0.1
momentum = 0.9
dampening = 0.0
weight_decay = 0.0
nesterov = True
loss_scale = 1.0
optimizer = SGD(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum, dampening,
weight_decay, nesterov, loss_scale)
criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
net_with_criterion = WithLossCell(net, criterion)
train_network = TrainOneStepCell(net_with_criterion, optimizer) # optimizer
train_network.set_train()
losses = []
for _ in range(epoch):
data = Tensor(np.arange(0, 16).reshape(1, 1, 4, 4).astype(np.float32) * 0.01)
label = Tensor(np.array([0]).astype(np.int32))
loss = train_network(data, label)
losses.append(loss.asnumpy())
last_loss = 100.0
for loss in losses:
assert last_loss > loss
last_loss = loss
return losses
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