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!8204 add supports to op pack on gpu 

From: @yuan_shen_zhou
Reviewed-by: @liangchenghui
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2020-11-09 09:32:09 +08:00 committed by Gitee
parent 36e69f6ef9 00f1c0c7f3
commit 370e7ab95f
5 changed files with 400 additions and 0 deletions
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40
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/pack_gpu_kernel.cc Normal file
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/**
* 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/pack_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
PackGpuFwdKernel, float)
MS_REG_GPU_KERNEL_ONE(
Pack, KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
PackGpuFwdKernel, half)
MS_REG_GPU_KERNEL_ONE(Pack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
PackGpuFwdKernel, int)
MS_REG_GPU_KERNEL_ONE(Pack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
PackGpuFwdKernel, int16_t)
MS_REG_GPU_KERNEL_ONE(Pack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
PackGpuFwdKernel, uchar)
MS_REG_GPU_KERNEL_ONE(Pack,
KernelAttr().AddAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
PackGpuFwdKernel, bool)
} // namespace kernel
} // namespace mindspore

113
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/pack_gpu_kernel.h Normal file
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/**
* Copyright 2020 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_PACK_GPU_KERNEL_H
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_PACK_GPU_KERNEL_H
#include <vector>
#include <memory>
#include "backend/kernel_compiler/gpu/gpu_kernel.h"
#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
#include "backend/kernel_compiler/gpu/cuda_impl/pack.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class PackGpuFwdKernel : public GpuKernel {
public:
PackGpuFwdKernel() : axis_(0), input_num_(1), output_size_(0), dims_behind_axis_(1), inputs_host_(nullptr) {}
~PackGpuFwdKernel() 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 *output = GetDeviceAddress<T>(outputs, 0);
T **inputs_array = GetDeviceAddress<T *>(workspace, 0);
for (size_t i = 0; i < inputs.size(); i++) {
inputs_host_[i] = GetDeviceAddress<T>(inputs, i);
}
CHECK_CUDA_RET_WITH_EXCEPT(cudaMemcpyAsync(inputs_array, inputs_host_.get(), sizeof(T *) * input_num_,
cudaMemcpyHostToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
"Pack opt cudaMemcpyAsync inputs failed");
PackKernel(SizeToInt(output_size_), input_num_, dims_behind_axis_, inputs_array, output,
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
if (!CheckParam(kernel_node)) {
return false;
}
axis_ = static_cast<int32_t>(GetAttr<int64_t>(kernel_node, "axis"));
if (axis_ < 0) {
auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
axis_ += SizeToInt(input_shape.size());
}
auto origin_data_format = AnfAlgo::GetOriginDataFormat(kernel_node);
auto input_format = AnfAlgo::GetInputFormat(kernel_node, 0);
axis_ = AxisTransform(origin_data_format, input_format, axis_);
input_num_ = SizeToInt(AnfAlgo::GetInputTensorNum(kernel_node));
inputs_host_ = std::make_unique<T *[]>(input_num_);
for (int i = 0; i < input_num_; i++) {
size_t input_size = 1;
auto input_shape = AnfAlgo::GetInputDeviceShape(kernel_node, i);
for (size_t j = 0; j < input_shape.size(); j++) {
input_size *= input_shape[j];
}
input_size_list_.push_back(input_size * sizeof(T));
}
workspace_size_list_.push_back(sizeof(T *) * input_num_);
auto output_shape = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
output_size_ = 1;
for (int i = 0; i < SizeToInt(output_shape.size()); i++) {
output_size_ *= output_shape[i];
if (i > axis_ + 1) {
dims_behind_axis_ *= output_shape[i];
}
}
output_size_list_.push_back(output_size_ * sizeof(T));
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {}
private:
bool CheckParam(const CNodePtr &kernel_node) {
size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
if (output_num != 1) {
MS_LOG(ERROR) << "Output number is " << output_num << ", but PackGpuFwdKernel needs 1 output.";
return false;
}
return true;
}
int axis_;
int input_num_;
size_t output_size_;
int dims_behind_axis_;
std::unique_ptr<T *[]> inputs_host_;
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_PACK_GPU_KERNEL_H

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mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/pack.cu Executable file
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/**
* 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 <stdio.h>
#include <stdint.h>
#include <cuda_runtime.h>
#include "backend/kernel_compiler/gpu/cuda_impl/pack.cuh"
template <typename T>
__global__ void Pack(const int size, const int input_num, const int dims_behind_axis, T** inputs, T* output) {
for (int pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
int cycle = pos / (input_num * dims_behind_axis);
int cur_input_index = pos % (input_num * dims_behind_axis) / dims_behind_axis;
int local_index = pos % (input_num * dims_behind_axis) % dims_behind_axis;
output[pos] = inputs[cur_input_index][cycle * dims_behind_axis + local_index];
}
return;
}
template <typename T>
void PackKernel(const int size, const int input_num,
const int dims_behind_axis, T** inputs, T* output,
cudaStream_t cuda_stream) {
Pack<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input_num, dims_behind_axis, inputs, output);
return;
}
template void PackKernel(const int size, const int input_num,
const int dims_behind_axis, float** inputs, float* output,
cudaStream_t cuda_stream);
template void PackKernel(const int size, const int input_num,
const int dims_behind_axis, int** inputs, int* output,
cudaStream_t cuda_stream);
template void PackKernel(const int size, const int input_num,
const int dims_behind_axis, half** inputs, half* output,
cudaStream_t cuda_stream);
template void PackKernel(const int size, const int input_num,
const int dims_behind_axis, short** inputs, short* output, // NOLINT
cudaStream_t cuda_stream);
template void PackKernel(const int size, const int input_num,
const int dims_behind_axis, unsigned char** inputs, unsigned char* output,
cudaStream_t cuda_stream);
template void PackKernel(const int size, const int input_num,
const int dims_behind_axis, bool** inputs, bool* output,
cudaStream_t cuda_stream);

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mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/pack.cuh Executable file
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/**
* 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_PACK_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_PACK_H_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void PackKernel(const int size,
const int input_num,
const int dims_behind_axis,
T** inputs,
T* output,
cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_PACK_H_

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tests/st/ops/gpu/test_pack_op.py Normal file
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# 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
import mindspore.ops.operations.array_ops as P
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
class PackNet(nn.Cell):
def __init__(self, nptype):
super(PackNet, self).__init__()
self.pack = P.Pack(axis=2)
self.data_np = np.array([0] * 16).astype(nptype)
self.data_np = np.reshape(self.data_np, (2, 2, 2, 2))
self.x1 = Parameter(initializer(
Tensor(self.data_np), [2, 2, 2, 2]), name='x1')
self.x2 = Parameter(initializer(
Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(nptype)), [2, 2, 2, 2]), name='x2')
@ms_function
def construct(self):
return self.pack((self.x1, self.x2))
def pack(nptype):
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
pack_ = PackNet(nptype)
output = pack_()
expect = np.array([[[[[0, 0],
[0, 1]],
[[0, 0],
[2, 3]]],
[[[0, 0],
[4, 5]],
[[0, 0],
[6, 7]]]],
[[[[0, 0],
[8, 9]],
[[0, 0],
[10, 11]]],
[[[0, 0],
[12, 13]],
[[0, 0],
[14, 15]]]]]).astype(nptype)
assert (output.asnumpy() == expect).all()
def pack_pynative(nptype):
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
x1 = np.array([0] * 16).astype(nptype)
x1 = np.reshape(x1, (2, 2, 2, 2))
x1 = Tensor(x1)
x2 = Tensor(np.arange(16).reshape(2, 2, 2, 2).astype(nptype))
expect = np.array([[[[[0, 0],
[0, 1]],
[[0, 0],
[2, 3]]],
[[[0, 0],
[4, 5]],
[[0, 0],
[6, 7]]]],
[[[[0, 0],
[8, 9]],
[[0, 0],
[10, 11]]],
[[[0, 0],
[12, 13]],
[[0, 0],
[14, 15]]]]]).astype(nptype)
output = P.Pack(axis=2)((x1, x2))
assert (output.asnumpy() == expect).all()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_graph_float32():
pack(np.float32)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_graph_float16():
pack(np.float16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_graph_int32():
pack(np.int32)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_graph_int16():
pack(np.int16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_graph_uint8():
pack(np.uint8)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_graph_bool():
pack(np.bool)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_pynative_float32():
pack_pynative(np.float32)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_pynative_float16():
pack_pynative(np.float16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_pynative_int32():
pack_pynative(np.int32)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_pynative_int16():
pack_pynative(np.int16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_pynative_uint8():
pack_pynative(np.uint8)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_pack_pynative_bool():
pack_pynative(np.bool)