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!8008 Add gpu support to ScatterAdd

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Merge pull request !8008 from 34bunny/GPU-ScatterAdd
pull/8008/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 4f4eadda8f dc2144ee0d
commit 93c11d1dcc
5 changed files with 316 additions and 0 deletions
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36
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/scatter_add_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_add_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(ScatterAdd,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
ScatterAddKernel, float)
MS_REG_GPU_KERNEL_ONE(ScatterAdd,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
ScatterAddKernel, half)
} // namespace kernel
} // namespace mindspore

95
mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/scatter_add_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_ADD_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_SCATTER_ADD_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_add_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class ScatterAddKernel : public GpuKernel {
public:
ScatterAddKernel() : input_size_(0), inner_size_(0), indices_size_(0), updates_size_(0) {}
~ScatterAddKernel() 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);
CalScatterAdd(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 ScatterAdd 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 ScatterAdd 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_ADD_GPU_KERNEL_H_

45
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/scatter_add_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_add_impl.cuh"
template <typename T>
__global__ void ScatterAdd(const int input_size, const int inner_size, const int indices_size, const T *input,
const int *indices, const T *updates, T *output) {
for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < input_size; pos += blockDim.x * gridDim.x) {
output[pos] = input[pos];
const size_t index = pos / inner_size;
const size_t offset = pos % inner_size;
for (size_t i = 0; i < indices_size; i++) {
const T value = updates[i*inner_size+offset];
output[pos] += (indices[i] == index ? value : static_cast<T>(0.0));
}
}
}
template <typename T>
void CalScatterAdd(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) {
ScatterAdd<<<GET_BLOCKS(input_size), GET_THREADS, 0, cuda_stream>>>(input_size, inner_size, indices_size, input,
indices, updates, output);
}
template void CalScatterAdd<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 CalScatterAdd<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_add_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_ADD_IMPL_CUH_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_IMPL_SCATTER_ADD_IMPL_CUH_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void CalScatterAdd(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_ADD_IMPL_CUH_

114
tests/st/ops/gpu/test_scatter_add_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 TestScatterAddNet(nn.Cell):
def __init__(self, inputx, indices, updates):
super(TestScatterAddNet, self).__init__()
self.scatter_add = P.ScatterAdd()
self.inputx = Parameter(inputx, name="inputx")
self.indices = Parameter(indices, name="indices")
self.updates = Parameter(updates, name="updates")
def construct(self):
out = self.scatter_add(self.inputx, self.indices, self.updates)
return out
def scatter_add_net(inputx, indices, updates):
net = TestScatterAddNet(inputx, indices, updates)
return net()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_scatter_add_small_float32():
inputx = Tensor(np.zeros((2, 3)).astype(np.float32))
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float32))
output = scatter_add_net(inputx, indices, updates)
expected = np.array([[6., 8., 10.],
[12., 14., 16.]])
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_add_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],
[2, 2, 0]],
[[1, 0, 1],
[2, 1, 2]]]).astype(np.int32))
updates = Tensor(np.arange(34, 70).reshape((2, 2, 3, 3)).astype(np.float32))
output = scatter_add_net(inputx, indices, updates)
expected = np.array([[141.21414, 144.41515, 147.51517],
[208.87654, 212.45161, 216.55112],
[257.11124, 262.63333, 267.34442]], 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_add_float16():
inputx = Tensor(np.zeros((2, 3)).astype(np.float16))
indices = Tensor(np.array([[0, 1], [0, 1]]).astype(np.int32))
updates = Tensor(np.arange(12).reshape((2, 2, 3)).astype(np.float16))
output = scatter_add_net(inputx, indices, updates)
expected = np.array([[6., 8., 10.],
[12., 14., 16.]])
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_add_large_float16():
inputx = Tensor(np.zeros((2, 3, 4)).astype(np.float16))
indices = Tensor(np.array([[0, 0], [1, 1]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
output = scatter_add_net(inputx, indices, updates)
expected = np.array([[[138., 140., 142., 144.],
[146., 148., 150., 152.],
[154., 156., 158., 160.]],
[[186., 188., 190., 192.],
[194., 196., 198., 200.],
[202., 204., 206., 208.]]])
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_add_disordered_float16():
inputx = Tensor(np.flip(np.arange(34, 46).reshape(3, 4).astype(np.float16)))
indices = Tensor(np.array([[[0, 1, 2],
[2, 1, 0]],
[[0, 0, 0],
[2, 2, 2]]]).astype(np.int32))
updates = Tensor(np.arange(63, 111).reshape((2, 2, 3, 4)).astype(np.float16))
output = scatter_add_net(inputx, indices, updates)
expected = np.array([[464., 468., 472., 476.],
[187., 188., 189., 190.],
[492., 496., 500., 504.]])
np.testing.assert_array_almost_equal(output.asnumpy(), expected)
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