forked from mindspore-Ecosystem/mindspore
implemented new gpu op, Dropout3D
fixes to successfully build fixes to dropout3d, passes pytest made peilin's pr suggestions/fixes fixed clang format issue and changed python names fixed lowercase 'd' on MS_LOG messages added newlines at end of files for cpplint ci fixes and updated dropout3d supported platforms pylint fixes
This commit is contained in:
markuskunej
parent
ccde359fc0
commit
ad322a7495
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <stdint.h>
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#include "dropout3d_impl.cuh"
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#include "include/cuda_runtime.h"
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template <typename T>
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__global__ void Dropout3DForwardKernel(const T *input, bool *mask, T *output, float *rand_f, const size_t num_count,
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const float keep_prob, const float scale, const size_t num_per_chan) {
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size_t chan_idx;
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float drop_f; // used in output calculations. Either 0.0 or 1.0.
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
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chan_idx = i / num_per_chan; // get channel index over all samples
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drop_f = rand_f[chan_idx] <= keep_prob;
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output[i] = static_cast<T>(scale * input[i] * drop_f);
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mask[i] = static_cast<bool>(drop_f);
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}
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}
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template <>
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__global__ void Dropout3DForwardKernel(const half *input, bool *mask, half *output, float *rand_f,
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const size_t num_count, const float keep_prob, const float scale,
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const size_t num_per_chan) {
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size_t chan_idx;
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float drop_f; // used in output calculations. Acts as a single float mask (either 0.0 or 1.0).
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float input_f; // used to temporarily convert input to float for calculations
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for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
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chan_idx = i / num_per_chan; // get channel index over all samples
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input_f = __half2float(input[i]);
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drop_f = rand_f[chan_idx] <= keep_prob;
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output[i] = __float2half(scale * input_f * drop_f); // convert to half
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mask[i] = static_cast<bool>(drop_f);
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}
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}
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template <typename T>
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void Dropout3DForward(const T *input, bool *mask, T *output, float *rand_f, const size_t num_count,
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const float keep_prob, const size_t num_per_chan, cudaStream_t cuda_stream) {
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const float scale = 1.f / keep_prob; // used to scale output, maintains expected value during training
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Dropout3DForwardKernel<<<GET_BLOCKS(num_count), GET_THREADS, 0, cuda_stream>>>(input, mask, output, rand_f, num_count,
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keep_prob, scale, num_per_chan);
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}
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template void Dropout3DForward<float>(const float *input, bool *mask, float *output, float *rand_f,
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const size_t num_count, const float keep_prob, const size_t num_per_chan,
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cudaStream_t cuda_stream);
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template void Dropout3DForward<half>(const half *input, bool *mask, half *output, float *rand_f, const size_t num_count,
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const float keep_prob, const size_t num_per_chan, cudaStream_t cuda_stream);
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template void Dropout3DForward<int8_t>(const int8_t *input, bool *mask, int8_t *output, float *rand_f,
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const size_t num_count, const float keep_prob, const size_t num_per_chan,
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cudaStream_t cuda_stream);
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template void Dropout3DForward<int16_t>(const int16_t *input, bool *mask, int16_t *output, float *rand_f,
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const size_t num_count, const float keep_prob, const size_t num_per_chan,
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cudaStream_t cuda_stream);
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template void Dropout3DForward<int32_t>(const int32_t *input, bool *mask, int32_t *output, float *rand_f,
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const size_t num_count, const float keep_prob, const size_t num_per_chan,
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cudaStream_t cuda_stream);
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template void Dropout3DForward<int64_t>(const int64_t *input, bool *mask, int64_t *output, float *rand_f,
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const size_t num_count, const float keep_prob, const size_t num_per_chan,
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cudaStream_t cuda_stream);
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_CUDA_IMPL_DROPOUT3D_IMPL_CUH_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_CUDA_IMPL_DROPOUT3D_IMPL_CUH_
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#include "runtime/device/gpu/cuda_common.h"
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template <typename T>
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void Dropout3DForward(const T *input, bool *mask, T *output, float *rand_f, const size_t num_count,
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const float keep_prob, const size_t num_per_chan, cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_CUDA_CUDA_IMPL_DROPOUT3D_IMPL_CUH_
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@ -0,0 +1,42 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "backend/kernel_compiler/gpu/nn/dropout3d_gpu_kernel.h"
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namespace mindspore {
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namespace kernel {
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MS_REG_GPU_KERNEL_ONE(
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Dropout3D,
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KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
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Dropout3DGpuFwdKernel, float)
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MS_REG_GPU_KERNEL_ONE(
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Dropout3D,
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KernelAttr().AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
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Dropout3DGpuFwdKernel, half)
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MS_REG_GPU_KERNEL_ONE(
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Dropout3D, KernelAttr().AddInputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeInt8).AddOutputAttr(kNumberTypeBool),
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Dropout3DGpuFwdKernel, int8_t)
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MS_REG_GPU_KERNEL_ONE(
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Dropout3D, KernelAttr().AddInputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeInt16).AddOutputAttr(kNumberTypeBool),
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Dropout3DGpuFwdKernel, int16_t)
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MS_REG_GPU_KERNEL_ONE(
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Dropout3D, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
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Dropout3DGpuFwdKernel, int32_t)
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MS_REG_GPU_KERNEL_ONE(
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Dropout3D, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
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Dropout3DGpuFwdKernel, int64_t)
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} // namespace kernel
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} // namespace mindspore
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_DROPOUT3D_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_DROPOUT3D_GPU_KERNEL_H_
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#include <vector>
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#include "backend/kernel_compiler/gpu/gpu_kernel.h"
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#include "backend/kernel_compiler/gpu/gpu_kernel_factory.h"
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#include "backend/kernel_compiler/gpu/cuda_impl/dropout3d_impl.cuh"
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#include "include/curand.h"
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namespace mindspore {
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namespace kernel {
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template <typename T>
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class Dropout3DGpuFwdKernel : public GpuKernel {
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public:
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Dropout3DGpuFwdKernel() { ResetResource(); }
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~Dropout3DGpuFwdKernel() override = default;
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const std::vector<size_t> &GetInputSizeList() const override { return input_size_list_; }
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const std::vector<size_t> &GetOutputSizeList() const override { return output_size_list_; }
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const std::vector<size_t> &GetWorkspaceSizeList() const override { return workspace_size_list_; }
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs, void *stream_ptr) override {
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if (is_null_input_) {
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return true;
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}
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T *input_addr = GetDeviceAddress<T>(inputs, 0);
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T *output_addr = GetDeviceAddress<T>(outputs, 0);
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bool *mask_addr = GetDeviceAddress<bool>(outputs, 1);
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float *rand_f = GetDeviceAddress<float>(workspace, 0);
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if (!states_init_) {
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CHECK_CURAND_RET_WITH_EXCEPT(curandCreateGenerator(&curand_generator_, CURAND_RNG_PSEUDO_DEFAULT),
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"Failed to create generator");
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CHECK_CURAND_RET_WITH_EXCEPT(curandSetPseudoRandomGeneratorSeed(curand_generator_, time(NULL)),
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"Failed to SetPseudoRandomGeneratorSeed");
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MS_EXCEPTION_IF_NULL(curand_generator_);
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states_init_ = true;
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}
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CHECK_CURAND_RET_WITH_EXCEPT(curandSetStream(curand_generator_, reinterpret_cast<cudaStream_t>(stream_ptr)),
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"Failed to set stream for generator");
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// curandGen only supports float or double.
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// generate random float for every channel
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CHECK_CURAND_RET_WITH_EXCEPT(curandGenerateUniform(curand_generator_, rand_f, num_chan_),
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"Failed to generate uniform");
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Dropout3DForward(input_addr, mask_addr, output_addr, rand_f, num_count_, keep_prob_, num_per_chan_,
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reinterpret_cast<cudaStream_t>(stream_ptr));
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return true;
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}
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bool Init(const CNodePtr &kernel_node) override {
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cudnn_handle_ = device::gpu::GPUDeviceManager::GetInstance().GetCudnnHandle();
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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if (input_num != 1) {
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MS_LOG(EXCEPTION) << "Argument number is " << input_num << ", but Dropout3DGpuFwdKernel needs 1.";
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}
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std::vector<size_t> input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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is_null_input_ = CHECK_NULL_INPUT(input_shape);
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if (is_null_input_) {
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MS_LOG(WARNING) << "Dropout3DGpuKernel input is null.";
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InitSizeLists();
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return true;
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}
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CheckTensorSize({input_shape});
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size_t dims = input_shape.size();
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if (dims != 5) {
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MS_LOG(EXCEPTION) << "Input dims " << dims << "not supported. Must be in NCDHW format.";
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}
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// get N and C values from 5 dim input tensor
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n_ = input_shape[0];
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c_ = input_shape[1];
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num_count_ = 1;
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for (size_t i = 0; i < dims; i++) {
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num_count_ *= input_shape[i];
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}
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num_chan_ = n_ * c_;
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num_per_chan_ = num_count_ / num_chan_; // number of elements per channel
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keep_prob_ = GetAttr<float>(kernel_node, "keep_prob");
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if ((keep_prob_ < 0.0) || (keep_prob_ > 1.0)) {
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MS_LOG(EXCEPTION) << "keep_prob is out of range [0.0, 1.0]";
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}
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InitSizeLists();
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return true;
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}
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void ResetResource() noexcept override {
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cudnn_handle_ = nullptr;
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is_null_input_ = false;
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num_count_ = 0;
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keep_prob_ = 0.0;
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states_init_ = false;
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curand_generator_ = nullptr;
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n_ = 0;
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c_ = 0;
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num_chan_ = 0;
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num_per_chan_ = 0;
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input_size_list_.clear();
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output_size_list_.clear();
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workspace_size_list_.clear();
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}
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protected:
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void InitSizeLists() override {
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size_t input_size = num_count_ * sizeof(T);
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size_t mask_size = num_count_ * sizeof(bool);
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input_size_list_.push_back(input_size);
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output_size_list_.push_back(input_size); // output size: the same as input size
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output_size_list_.push_back(mask_size);
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size_t workspace_size = num_chan_ * sizeof(float); // rand_f for curandGen
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workspace_size_list_.push_back(workspace_size);
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}
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private:
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cudnnHandle_t cudnn_handle_;
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curandGenerator_t curand_generator_;
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bool is_null_input_;
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bool states_init_;
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size_t num_count_;
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size_t n_;
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size_t c_;
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size_t num_chan_;
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size_t num_per_chan_;
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float keep_prob_;
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std::vector<size_t> input_size_list_;
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std::vector<size_t> output_size_list_;
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std::vector<size_t> workspace_size_list_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_NN_DROPOUT3D_GPU_KERNEL_H_
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@ -6955,7 +6955,7 @@ class Dropout3D(PrimitiveWithInfer):
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or if the dim of input is not 5-D.
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Supported Platforms:
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``Ascend``
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``Ascend`` ``GPU``
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Examples:
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>>> dropout = ops.Dropout3D(keep_prob=0.5)
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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import mindspore.context as context
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from mindspore.ops import operations as P
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class Dropout3DNet(nn.Cell):
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def __init__(self, keep_prob):
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super(Dropout3DNet, self).__init__()
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self.drop = P.Dropout3D(keep_prob)
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def construct(self, x):
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return self.drop(x)
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def dropout_3d(keep_prob, nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_shape = [32, 16, 2, 5, 4]
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x_np = np.ones(x_shape).astype(nptype)
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dropout3d_net = Dropout3DNet(keep_prob)
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tx = Tensor(x_np)
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output, mask = dropout3d_net(tx)
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## check output ##
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output_np = output.asnumpy()
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elem_count = x_np.size
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nonzero_count = np.count_nonzero(output_np)
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# assert correct proportion of elements kept
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assert (elem_count * (keep_prob - 0.1)) < nonzero_count < (elem_count * (keep_prob + 0.1))
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output_sum = np.sum(output_np)
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x_sum = np.sum(x_np)
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if keep_prob != 0.0:
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# assert output scaled correctly (expected value maintained)
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assert abs(output_sum - x_sum)/x_sum < 0.1
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## check mask ##
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mask_np = mask.asnumpy()
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# specific to input with no zeros. Check for same number of nonzero elements
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assert np.count_nonzero(mask_np) == nonzero_count
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# check each channel is entirely True or False
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non_eq_chan = 0
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for n in range(mask_np.shape[0]):
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for c in range(mask_np.shape[1]):
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if not np.all(mask_np[n][c] == mask_np[n][c][0]):
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non_eq_chan = non_eq_chan + 1
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assert non_eq_chan == 0
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# check input, output, mask all have same shape
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assert x_np.shape == output_np.shape == mask_np.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_dropout3d_float16():
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dropout_3d(0.0, np.float16)
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dropout_3d(0.5, np.float16)
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dropout_3d(1.0, np.float16)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
|
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@pytest.mark.env_onecard
|
||||
def test_dropout3d_float32():
|
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dropout_3d(0.0, np.float32)
|
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dropout_3d(0.5, np.float32)
|
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dropout_3d(1.0, np.float32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_dropout3d_int8():
|
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dropout_3d(0.0, np.int8)
|
||||
dropout_3d(0.5, np.int8)
|
||||
dropout_3d(1.0, np.int8)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_dropout3d_int16():
|
||||
dropout_3d(0.0, np.int16)
|
||||
dropout_3d(0.5, np.int16)
|
||||
dropout_3d(1.0, np.int16)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_dropout3d_int32():
|
||||
dropout_3d(0.0, np.int32)
|
||||
dropout_3d(0.5, np.int32)
|
||||
dropout_3d(1.0, np.int32)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_dropout3d_int64():
|
||||
dropout_3d(0.0, np.int64)
|
||||
dropout_3d(0.5, np.int64)
|
||||
dropout_3d(1.0, np.int64)
|
||||
Loading…
Reference in New Issue