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!9043 Add support to op L2Loss on gpu

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From: @yuan_shen_zhou
Reviewed-by: @liangchenghui,@liangchenghui
Signed-off-by: @liangchenghui,@liangchenghui
pull/9043/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 627e4d0cf3 f6e87143c6
commit 40222f59a7
6 changed files with 258 additions and 4 deletions
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38
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/l2_loss.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 "l2_loss.cuh"
#include "runtime/device/gpu/cuda_common.h"
#include "backend/kernel_compiler/gpu/cuda_impl/util.cuh"
template <typename T>
__global__ void L2LossKernel(const size_t input_size, const T *input , T *output) {
T ret = 0;
for (size_t id = blockIdx.x * blockDim.x + threadIdx.x; id < input_size; id += blockDim.x * gridDim.x) {
ret = (input[id] * input[id]);
ret /= static_cast<T>(2);
MsAtomicAdd(output, ret);
}
return;
}
template <typename T>
void L2Loss(const size_t input_size, const T *input , T *output, cudaStream_t stream) {
L2LossKernel<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(input_size, input, output);
}
template void L2Loss<float>(const size_t input_size, const float *input , float *output, cudaStream_t stream);
template void L2Loss<half>(const size_t input_size, const half *input , half *output, cudaStream_t stream);

21
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/l2_loss.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_KERNEL_GPU_CUDA_IMPL_L2_LOSS_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_L2_LOSS_H_
template <typename T>
void L2Loss(const size_t input_size, const T *input , T *output, cudaStream_t stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_L2_LOSS_H_

26
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/l2_loss_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/nn/l2_loss_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(L2Loss, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
L2LossGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(L2Loss, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
L2LossGpuKernel, half)
} // namespace kernel
} // namespace mindspore

71
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/l2_loss_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_NN_L2_LOSS_GPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_L2_LOSS_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/l2_loss.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class L2LossGpuKernel : public GpuKernel {
public:
L2LossGpuKernel() : input_size_(1) {}
~L2LossGpuKernel() 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> &workspaces,
const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
T *input = GetDeviceAddress<T>(inputs, 0);
T *output = GetDeviceAddress<T>(outputs, 0);
L2Loss(input_size_, input, output, reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
bool Init(const CNodePtr &kernel_node) override {
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < input_shape.size(); i++) {
input_size_ *= input_shape[i];
}
InitSizeLists();
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(input_size_ * sizeof(T));
output_size_list_.push_back(sizeof(T));
}
private:
size_t input_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_NN_L2_LOSS_GPU_KERNEL_H_

6
mindspore/ops/operations/nn_ops.py
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@ -2157,9 +2157,7 @@ class L2Loss(PrimitiveWithInfer):
Set `input_x` as x and output as loss.
.. math::
loss = sum(x ** 2) / nelement(x)
:math:`nelement(x)` represents the number of `input_x`.
loss = sum(x ** 2) / 2
Inputs:
- **input_x** (Tensor) - A input Tensor. Data type must be float16 or float32.
@ -2168,7 +2166,7 @@ class L2Loss(PrimitiveWithInfer):
Tensor, has the same dtype as `input_x`. The output tensor is the value of loss which is a scalar tensor.
Supported Platforms:
``Ascend``
``Ascend`` ``GPU``
Examples
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.float16)

100
tests/st/ops/gpu/test_l2loss_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
import mindspore as ms
from mindspore import Tensor
from mindspore.ops import operations as P
class L2LossNet(nn.Cell):
def __init__(self):
super(L2LossNet, self).__init__()
self.l2_loss = P.L2Loss()
def construct(self, x):
return self.l2_loss(x)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gather_pynative_fp32_22():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
error = 1e-4
x = Tensor(np.array([[1., 2.], [3., 4.]]), ms.float32)
expect = np.array(15, np.float32)
output = P.L2Loss()(x)
diff = output.asnumpy() - expect
assert np.all(diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gather_pynative_fp16_22():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
error = 1e-4
x = Tensor(np.array([[1., 2.], [3., 4.]]), ms.float16)
expect = np.array(15, np.float16)
output = P.L2Loss()(x)
diff = output.asnumpy() - expect
assert np.all(diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gather_pynative_fp32_14():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
error = 1e-4
x = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
expect = np.array(15, np.float32)
output = P.L2Loss()(x)
diff = output.asnumpy() - expect
assert np.all(diff < error)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_gather_pynative_fp16_14():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
error = 1e-4
x = Tensor(np.array([1., 2., 3., 4.]), ms.float16)
expect = np.array(15, np.float16)
output = P.L2Loss()(x)
diff = output.asnumpy() - expect
assert np.all(diff < error)
def test_gather_graph_fp32_14():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
error = 1e-4
x = Tensor(np.array([1., 2., 3., 4.]), ms.float32)
expect = np.array(15, np.float32)
l2_loss = L2LossNet()
output = l2_loss(x)
diff = output.asnumpy() - expect
assert np.all(diff < error)
def test_gather_graph_fp16_14():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
error = 1e-4
x = Tensor(np.array([1., 2., 3., 4.]), ms.float16)
expect = np.array(15, np.float16)
l2_loss = L2LossNet()
output = l2_loss(x)
diff = output.asnumpy() - expect
assert np.all(diff < error)
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