m53297601
/
mindspore
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

!9288 Add Adagrad for GPU kernel

Browse Source 
From: @zyx5256
Reviewed-by: 
Signed-off-by:
pull/9288/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent bbca2b16b4 77a68b9d2b
commit ec025a1c4c
7 changed files with 307 additions and 2 deletions
Show Stats Download Patch File Download Diff File

70
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/adagrad_impl.cu
Unescape View File

@ -0,0 +1,70 @@
/**
* 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/adagrad_impl.cuh"
template <typename T>
__device__ __forceinline__ T SqrtFunc(T input) {
return sqrt(input);
}
template <>
__device__ __forceinline__ half SqrtFunc(half input) {
return hsqrt(input);
}
template <typename T>
__global__ void ApplyAdagradKernel(const size_t size,
const bool update_slots,
const T *learning_rate,
const T *gradient,
T *variable,
T *accumulation) {
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
if (update_slots) {
accumulation[i] += gradient[i] * gradient[i];
}
variable[i] -= learning_rate[0] * gradient[i] / SqrtFunc(accumulation[i]);
}
}
template <typename T>
void ApplyAdagrad(const size_t size,
const bool update_slots,
const T *learning_rate,
const T *gradient,
T *variable,
T *accumulation,
cudaStream_t cuda_stream) {
ApplyAdagradKernel<<< GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(
size, update_slots, learning_rate, gradient, variable, accumulation);
}
template void ApplyAdagrad<float>(const size_t size,
const bool update_slots,
const float *learning_rate,
const float *gradient,
float *variable,
float *accumulation,
cudaStream_t cuda_stream);
template void ApplyAdagrad<half>(const size_t size,
const bool update_slots,
const half *learning_rate,
const half *gradient,
half *variable,
half *accumulation,
cudaStream_t cuda_stream);

30
mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/adagrad_impl.cuh
Unescape View File

@ -0,0 +1,30 @@
/**
* 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_ADAGRAD_IMPL_H_
#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAGRAD_IMPL_H_
#include "runtime/device/gpu/cuda_common.h"
template <typename T>
void ApplyAdagrad(const size_t size,
const bool update_slots,
const T *learning_rate,
const T *gradient,
T *variable,
T *accumulation,
cudaStream_t stream);
#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ADAGRAD_IMPL_H_

40
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adagrad_gpu_kernel.cc
Unescape View File

@ -0,0 +1,40 @@
/**
* 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/adagrad_gpu_kernel.h"
namespace mindspore {
namespace kernel {
MS_REG_GPU_KERNEL_ONE(ApplyAdagrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
AdagradGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(ApplyAdagrad,
KernelAttr()
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddInputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
AdagradGpuKernel, half)
} // namespace kernel
} // namespace mindspore

104
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adagrad_gpu_kernel.h
Unescape View File

@ -0,0 +1,104 @@
/**
* 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_ADAGRAD_GPU_KERNEL_H
#define MINDSPORE_ADAGRAD_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/adagrad_impl.cuh"
namespace mindspore {
namespace kernel {
template <typename T>
class AdagradGpuKernel : public GpuKernel {
public:
AdagradGpuKernel()
: variable_size_(0), accumulation_size_(0), learning_rate_size_(0), gradient_size_(0), update_slots(true) {}
~AdagradGpuKernel() 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 Init(const CNodePtr &kernel_node) override {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 4) {
MS_LOG(ERROR) << "Input number is " << input_num << ", but adagrad needs 4 inputs.";
return false;
}
variable_size_ = sizeof(T);
accumulation_size_ = sizeof(T);
learning_rate_size_ = sizeof(T);
gradient_size_ = sizeof(T);
auto variable_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
for (size_t i = 0; i < variable_shape.size(); i++) {
variable_size_ *= variable_shape[i];
}
auto accumulation_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
for (size_t i = 0; i < accumulation_shape.size(); i++) {
accumulation_size_ *= accumulation_shape[i];
}
auto gradient_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3);
for (size_t i = 0; i < gradient_shape.size(); i++) {
gradient_size_ *= gradient_shape[i];
}
InitSizeLists();
return true;
}
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &, const std::vector<AddressPtr> &,
void *stream_ptr) override {
T *variable = GetDeviceAddress<T>(inputs, 0);
T *accumulation = GetDeviceAddress<T>(inputs, 1);
T *learning_rate = GetDeviceAddress<T>(inputs, 2);
T *gradient = GetDeviceAddress<T>(inputs, 3);
ApplyAdagrad(inputs[0]->size / sizeof(T), update_slots, learning_rate, gradient, variable, accumulation,
reinterpret_cast<cudaStream_t>(stream_ptr));
return true;
}
protected:
void InitSizeLists() override {
input_size_list_.push_back(variable_size_);
input_size_list_.push_back(accumulation_size_);
input_size_list_.push_back(learning_rate_size_);
input_size_list_.push_back(gradient_size_);
output_size_list_.push_back(0);
output_size_list_.push_back(0);
}
private:
size_t variable_size_;
size_t accumulation_size_;
size_t learning_rate_size_;
size_t gradient_size_;
bool update_slots;
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_ADAGRAD_GPU_KERNEL_H

2
mindspore/ccsrc/backend/kernel_compiler/gpu/nn/adam_gpu_kernel.h
Unescape View File

@ -64,7 +64,7 @@ class AdamGpuKernel : public GpuKernel {
bool Init(const CNodePtr &kernel_node) override {
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 10) {
MS_LOG(ERROR) << "Input number is " << input_num << ", but ftrl needs 10 inputs.";
MS_LOG(ERROR) << "Input number is " << input_num << ", but adam needs 10 inputs.";
return false;
}

2
mindspore/ops/operations/nn_ops.py
Unescape View File

@ -4700,7 +4700,7 @@ class ApplyAdagrad(PrimitiveWithInfer):
- **accum** (Tensor) - The same shape and data type as `accum`.
Supported Platforms:
``Ascend``
``Ascend`` ``CPU`` ``GPU``
Examples:
>>> import numpy as np

61
tests/st/ops/gpu/test_adagrad_op.py
Unescape View File

@ -0,0 +1,61 @@
# 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
import mindspore.common.dtype as mstype
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
var_np = np.random.rand(3, 3).astype(np.float32)
accum_np = np.random.rand(3, 3).astype(np.float32)
class Net(nn.Cell):
def __init__(self):
super(Net, self).__init__()
self.apply_adagrad = P.ApplyAdagrad()
self.var = Parameter(Tensor(var_np), name="var")
self.accum = Parameter(Tensor(accum_np), name="accum")
def construct(self, lr, grad):
self.apply_adagrad(self.var, self.accum, lr, grad)
return self.var, self.accum
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_apply_adagrad():
# numpy op
grident_np = np.random.rand(3, 3).astype(np.float32)
expect_accum_np = accum_np + grident_np * grident_np
expect_var_np = var_np - (0.001 * grident_np * (1 / np.sqrt(expect_accum_np + 1e-6)))
net = Net()
lr = Tensor(0.001, mstype.float32)
grad = Tensor(grident_np)
out = net(lr, grad)
res_var_mindspore = out[0].asnumpy()
res_accum_mindspore = out[1].asnumpy()
eps = np.array([1e-6 for i in range(9)]).reshape(3, 3)
assert np.all(expect_var_np - res_var_mindspore < eps)
assert np.all(expect_accum_np - res_accum_mindspore < eps)
Write Preview
Loading…
Cancel
Save