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!8054 Add gpu support for ScatterUpdate

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Merge pull request !8054 from 34bunny/GPU-ScatterUpdate
pull/8054/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent cb83a5ccdb cd9d0bf79a
commit 497f2f0cff
5 changed files with 308 additions and 0 deletions
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36
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/scatter_update_gpu_kernel.cc
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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/scatter_update_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(ScatterUpdate,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
ScatterUpdateKernel, float)
MS_REG_GPU_KERNEL_ONE(ScatterUpdate,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
ScatterUpdateKernel, half)
} // namespace kernel
} // namespace mindspore

95
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/scatter_update_gpu_kernel.h
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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_ARRAYS_SCATTER_UPDATE_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_UPDATE_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/scatter_update_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class ScatterUpdateKernel : public GpuKernel {
public:
ScatterUpdateKernel() : input_size_(0), inner_size_(0), indices_size_(0), updates_size_(0) {}
~ScatterUpdateKernel() 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 *input = GetDeviceAddress<T>(inputs, 0);
int *indices = GetDeviceAddress<int>(inputs, 1);
T *updates = GetDeviceAddress<T>(inputs, 2);
T *output = GetDeviceAddress<T>(outputs, 0);
CalScatterUpdate(input_size_, inner_size_, indices_size_, input, indices, updates, output,
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 != 3) {
MS_LOG(ERROR) << "Input number is " << input_num << ", but ScatterUpdate needs 3 inputs.";
return false;
}
size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
if (output_num != 1) {
MS_LOG(ERROR) << "Output number is " << output_num << ", but ScatterUpdate has 1 output.";
return false;
}
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
input_size_ = 1;
inner_size_ = 1;
for (size_t i = 1; i < input_shape.size(); i++) {
inner_size_ *= input_shape[i];
}
input_size_ = input_shape[0] * inner_size_;
auto indices_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
indices_size_ = 1;
for (size_t i = 0; i < indices_shape.size(); i++) {
indices_size_ *= indices_shape[i];
}
updates_size_ = indices_size_ * inner_size_;
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_ * sizeof(T));
input_size_list_.push_back(indices_size_ * sizeof(int));
input_size_list_.push_back(updates_size_ * sizeof(T));
output_size_list_.push_back(input_size_ * sizeof(T));
}
private:
int input_size_;
int inner_size_;
int indices_size_;
int updates_size_;
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_ARRAYS_SCATTER_UPDATE_GPU_KERNEL_H_

45
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/scatter_update_impl.cu
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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/cuda_impl/scatter_update_impl.cuh"
template <typename T>
__global__ void ScatterUpdate(const int input_size, const int inner_size, const int indices_size, const T *input,
const int *indices, const T *updates, T *output) {
for (int pos = blockIdx.x * blockDim.x + threadIdx.x; pos < input_size; pos += blockDim.x * gridDim.x) {
output[pos] = input[pos];
const int index = pos / inner_size;
const int offset = pos % inner_size;
for (int i = 0; i < indices_size; i++) {
const int update_pos = i * inner_size + offset;
output[pos] = (indices[i] == index ? updates[update_pos] : output[pos]);
}
}
}
template <typename T>
void CalScatterUpdate(const int &input_size, const int &inner_size, const int &indices_size, const T *input,
const int *indices, const T *updates, T *output, cudaStream_t cuda_stream) {
ScatterUpdate<<<GET_BLOCKS(input_size), GET_THREADS, 0, cuda_stream>>>(input_size, inner_size, indices_size, input,
indices, updates, output);
}
template void CalScatterUpdate<float>(const int &input_size, const int &inner_size, const int &indices_size,
const float *input, const int *indices, const float *updates, float *output,
cudaStream_t cuda_stream);
template void CalScatterUpdate<half>(const int &input_size, const int &inner_size, const int &indices_size,
const half *input, const int *indices, const half *updates, half *output,
cudaStream_t cuda_stream);

26
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/scatter_update_impl.cuh
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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_CUDA_IMPL_SCATTER_UPDATE_IMPL_CUH_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_UPDATE_IMPL_CUH_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void CalScatterUpdate(const int &input_size, const int &inner_size, const int &indices_size, const T *input,
const int *indices, const T *updates, T *output, cudaStream_t cuda_stream);
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_UPDATE_IMPL_CUH_

106
tests/st/ops/gpu/test_scatter_update_op.py
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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
from mindspore import Tensor, Parameter
from mindspore.ops import operations as P
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
# all cases tested against dchip
class TestScatterUpdateNet(nn.Cell):
def __init__(self, inputx, indices, updates):
super(TestScatterUpdateNet, self).__init__()
self.scatter_update = P.ScatterUpdate()
self.inputx = Parameter(inputx, name="inputx")
self.indices = Parameter(indices, name="indices")
self.updates = Parameter(updates, name="updates")
def construct(self):
out = self.scatter_update(self.inputx, self.indices, self.updates)
return out
def scatter_update_net(inputx, indices, updates):
net = TestScatterUpdateNet(inputx, indices, updates)
return net()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_scatter_update_small_float32():
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
indices = Tensor(np.array([0, 1]).astype(np.int32))
updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float32))
output = scatter_update_net(inputx, indices, updates)
expected = np.array([[0., 1., 2.],
[3., 4., 5.]])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_scatter_update_input_less_than_1_float32():
inputx = Tensor(np.array([[0.214141, 0.415151, 0.51516],
[0.876542, 0.451611, 0.55112],
[0.111244, 0.633333, 0.34444]]).astype(np.float32))
indices = Tensor(np.array([1, 0, 2]).astype(np.int32))
updates = Tensor(np.arange(34, 43).reshape((3, 3)).astype(np.float32))
output = scatter_update_net(inputx, indices, updates)
expected = np.array([[37., 38., 39.],
[34., 35., 36.],
[40., 41., 42.]], dtype=np.float32)
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_scatter_update_float16():
inputx = Tensor(np.zeros((2, 3)).astype(np.float16))
indices = Tensor(np.array([0, 1]).astype(np.int32))
updates = Tensor(np.arange(6).reshape((2, 3)).astype(np.float16))
output = scatter_update_net(inputx, indices, updates)
expected = np.array([[0., 1., 2.],
[3., 4., 5.]])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_scatter_update_large_float16():
inputx = Tensor(np.zeros((4, 3)).astype(np.float16))
indices = Tensor(np.array([[2, 1], [0, 3]]).astype(np.int32))
updates = Tensor(np.arange(63, 75).reshape((2, 2, 3)).astype(np.float16))
output = scatter_update_net(inputx, indices, updates)
expected = np.array([[69., 70., 71.],
[66., 67., 68.],
[63., 64., 65.],
[72., 73., 74.]])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_scatter_update_disordered_float16():
inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.float16)))
indices = Tensor(np.array([1, 2]).astype(np.int32))
updates = Tensor(np.arange(63, 71).reshape((2, 4)).astype(np.float16))
output = scatter_update_net(inputx, indices, updates)
expected = np.array([[45., 44., 43., 42.],
[63., 64., 65., 66.],
[67., 68., 69., 70.]])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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