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!10428 unpack of cpu

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From: @lishixing3
Reviewed-by: 
Signed-off-by:
pull/10428/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 79893d6fef 3bc94b97d7
commit 9bb4785777
4 changed files with 414 additions and 0 deletions
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1
mindspore/ccsrc/backend/kernel_compiler/cpu/dropout_cpu_kernel.cc
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@ -66,6 +66,7 @@ void DropoutCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const
}
void DropoutCPUKernel::CheckParam(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 1) {
MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but DropoutCPUKernel needs 1 input.";

110
mindspore/ccsrc/backend/kernel_compiler/cpu/unpack_cpu_kernel.cc
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@ -0,0 +1,110 @@
/**
* 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/cpu/unpack_cpu_kernel.h"
#include "runtime/device/cpu/cpu_device_address.h"
namespace mindspore {
namespace kernel {
template <typename T>
void UnpackCPUKernel<T>::InitKernel(const CNodePtr &kernel_node) {
CheckParam(kernel_node);
int64_t axis_tmp = AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "axis");
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
if (axis_tmp < 0) {
axis_tmp += SizeToLong(input_shape.size());
}
size_t axis_ = LongToSize(axis_tmp);
output_num_ = LongToSize(AnfAlgo::GetNodeAttr<int64_t>(kernel_node, "num"));
for (size_t i = 0; i < input_shape.size(); i++) {
input_size_ *= input_shape[i];
if (i > IntToSize(axis_)) {
dims_after_axis_ *= input_shape[i];
}
}
dtype_ = AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, 0);
}
template <typename T>
void UnpackCPUKernel<T>::InitInputOutputSize(const CNodePtr &kernel_node) {
CPUKernel::InitInputOutputSize(kernel_node);
workspace_size_list_.emplace_back(sizeof(T *) * output_num_);
}
template <typename T>
bool UnpackCPUKernel<T>::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<kernel::AddressPtr> &outputs) {
LaunchKernel(inputs, workspace, outputs);
return true;
}
template <typename T>
void UnpackCPUKernel<T>::LaunchKernel(const std::vector<AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) {
input_ = reinterpret_cast<T *>(inputs[0]->addr);
MS_EXCEPTION_IF_NULL(input_);
outputs_host_ = reinterpret_cast<T **>(workspace[0]->addr);
MS_EXCEPTION_IF_NULL(outputs_host_);
for (size_t i = 0; i < outputs.size(); i++) {
outputs_host_[i] = reinterpret_cast<T *>(outputs[i]->addr);
MS_EXCEPTION_IF_NULL(outputs_host_[i]);
}
auto max_thread_num = std::thread::hardware_concurrency();
size_t thread_num = input_size_ < 128 * max_thread_num ? std::ceil(input_size_ / 128.0) : max_thread_num;
if (thread_num < 1) {
MS_LOG(ERROR) << "Invalid value: thread_num" << thread_num;
return;
}
std::vector<std::thread> threads;
threads.reserve(thread_num);
size_t start = 0;
size_t one_gap = (input_size_ + thread_num - 1) / thread_num;
if (one_gap < 1) {
MS_LOG(ERROR) << "Invalid value: one_gap " << one_gap;
return;
}
while (start < input_size_) {
size_t end = (start + one_gap) > input_size_ ? input_size_ : (start + one_gap);
threads.emplace_back(std::thread(&UnpackCPUKernel::UnpackResult, this, start, end));
start += one_gap;
}
for (size_t i = 0; i < threads.size(); ++i) {
threads[i].join();
}
}
template <typename T>
void UnpackCPUKernel<T>::UnpackResult(const size_t start, const size_t end) {
for (size_t i = start; i < end; ++i) {
size_t output_index = (i / dims_after_axis_) % output_num_;
size_t number_of_reset = output_num_ * dims_after_axis_;
size_t tensor_index = i / number_of_reset * dims_after_axis_ + i % dims_after_axis_;
outputs_host_[output_index][tensor_index] = input_[i];
}
}
template <typename T>
void UnpackCPUKernel<T>::CheckParam(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
if (input_num != 1) {
MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but UnpackCPUKernel needs 1 input.";
}
}
} // namespace kernel
} // namespace mindspore

88
mindspore/ccsrc/backend/kernel_compiler/cpu/unpack_cpu_kernel.h
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@ -0,0 +1,88 @@
/**
* 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_CPU_UNPACK_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_UNPACK_CPU_KERNEL_H_
#include <algorithm>
#include <memory>
#include <thread>
#include <unordered_map>
#include <vector>
#include "backend/kernel_compiler/cpu/cpu_kernel.h"
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
template <typename T>
class UnpackCPUKernel : public CPUKernel {
public:
UnpackCPUKernel() = default;
~UnpackCPUKernel() override = default;
void InitKernel(const CNodePtr &kernel_node) override;
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) override;
void LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs);
void InitInputOutputSize(const CNodePtr &kernel_node) override;
protected:
virtual void CheckParam(const CNodePtr &kernel_node);
virtual void UnpackResult(const size_t start, const size_t end);
size_t input_size_{1};
size_t output_num_{0};
size_t dims_after_axis_{1};
T *input_{nullptr};
T **outputs_host_{nullptr};
TypeId dtype_{kTypeUnknown};
};
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8),
UnpackCPUKernel, int8_t);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16),
UnpackCPUKernel, int16_t);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
UnpackCPUKernel, int);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64),
UnpackCPUKernel, int64_t);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeBool).AddOutputAttr(kNumberTypeBool),
UnpackCPUKernel, bool);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt8).AddOutputAttr(kNumberTypeUInt8),
UnpackCPUKernel, uint8_t);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt16).AddOutputAttr(kNumberTypeUInt16),
UnpackCPUKernel, uint16_t);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt32).AddOutputAttr(kNumberTypeUInt32),
UnpackCPUKernel, uint32_t);
MS_REG_CPU_KERNEL_T(Unpack,
KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeUInt64).AddOutputAttr(kNumberTypeUInt64),
UnpackCPUKernel, uint64_t);
MS_REG_CPU_KERNEL_T(
Unpack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
UnpackCPUKernel, float16);
MS_REG_CPU_KERNEL_T(
Unpack, KernelAttr().SetAllSameAttr(true).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
UnpackCPUKernel, float);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_UNPACK_CPU_KERNEL_H_

215
tests/st/ops/cpu/test_unpack_op.py
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@ -0,0 +1,215 @@
# 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 Net(nn.Cell):
def __init__(self, nptype):
super(Net, self).__init__()
self.unpack = P.Unpack(axis=3)
self.data_np = np.array([[[[[0, 0],
[-2, -1]],
[[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]],
[[0, 0],
[16, 17]]],
[[[0, 0],
[18, 19]],
[[0, 0],
[20, 21]]]],
[[[[0, 0],
[22, 23]],
[[0, 0],
[24, 25]]],
[[[0, 0],
[26, 27]],
[[0, 0],
[28, 29]]],
[[[0, 0],
[30, 31]],
[[0, 0],
[32, 33]]]]]).astype(nptype)
self.x1 = Parameter(initializer(Tensor(self.data_np), [3, 3, 2, 2, 2]), name='x1')
@ms_function
def construct(self):
return self.unpack(self.x1)
def unpack(nptype):
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
unpack_ = Net(nptype)
output = unpack_()
expect = (np.reshape(np.array([0] * 36).astype(nptype), (3, 3, 2, 2)),
np.arange(-2, 34, 1).reshape(3, 3, 2, 2).astype(nptype))
for i, exp in enumerate(expect):
assert (output[i].asnumpy() == exp).all()
def unpack_pynative(nptype):
context.set_context(mode=context.PYNATIVE_MODE, device_target='CPU')
x1 = np.array([[[[[0, 0],
[-2, -1]],
[[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]],
[[0, 0],
[16, 17]]],
[[[0, 0],
[18, 19]],
[[0, 0],
[20, 21]]]],
[[[[0, 0],
[22, 23]],
[[0, 0],
[24, 25]]],
[[[0, 0],
[26, 27]],
[[0, 0],
[28, 29]]],
[[[0, 0],
[30, 31]],
[[0, 0],
[32, 33]]]]]).astype(nptype)
x1 = Tensor(x1)
expect = (np.reshape(np.array([0] * 36).astype(nptype), (3, 3, 2, 2)),
np.arange(-2, 34, 1).reshape(3, 3, 2, 2).astype(nptype))
output = P.Unpack(axis=3)(x1)
for i, exp in enumerate(expect):
assert (output[i].asnumpy() == exp).all()
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_graph_float32():
unpack(np.float32)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_graph_float16():
unpack(np.float16)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_graph_int32():
unpack(np.int32)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_graph_int16():
unpack(np.int16)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_graph_uint8():
unpack(np.uint8)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_graph_bool():
unpack(np.bool)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_pynative_float32():
unpack_pynative(np.float32)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_pynative_float16():
unpack_pynative(np.float16)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_pynative_int32():
unpack_pynative(np.int32)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_pynative_int16():
unpack_pynative(np.int16)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_pynative_uint8():
unpack_pynative(np.uint8)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu_training
@pytest.mark.env_onecard
def test_unpack_pynative_bool():
unpack_pynative(np.bool)
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