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Fast cuDNN BatchNorm NHWC kernels support (apache#20615)
* Fast cuDNN NHWC kernels support * Fix lint errors * Get rid of a warning * Remove CuDNNBatchNorm from AMP lists Co-authored-by: Vladimir Cherepanov <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one | ||
| * or more contributor license agreements. See the NOTICE file | ||
| * distributed with this work for additional information | ||
| * regarding copyright ownership. The ASF licenses this file | ||
| * to you 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. | ||
| */ | ||
|
|
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| /*! | ||
| * Copyright (c) 2015 by Contributors | ||
| * \file cudnn_batch_norm.cu | ||
| * \brief | ||
| * \author Junyuan Xie, Da Zheng | ||
| */ | ||
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| #include "cudnn_batch_norm.h" | ||
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| #include "../../../common/cuda_utils.h" | ||
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| namespace mxnet { | ||
| namespace op { | ||
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| #if MXNET_USE_CUDNN == 1 | ||
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| namespace { | ||
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| struct Globals { | ||
| cudnnTensorDescriptor_t io_desc; | ||
| cudnnTensorDescriptor_t mean_desc; | ||
| bool internal_aux_states_lock = false; | ||
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| static Globals& Get() { | ||
| thread_local Globals ret; | ||
| return ret; | ||
| } | ||
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| Globals() { | ||
| CUDNN_CALL(cudnnCreateTensorDescriptor(&io_desc)); | ||
| CUDNN_CALL(cudnnCreateTensorDescriptor(&mean_desc)); | ||
| } | ||
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| ~Globals() { | ||
| CUDNN_CALL(cudnnDestroyTensorDescriptor(io_desc)); | ||
| CUDNN_CALL(cudnnDestroyTensorDescriptor(mean_desc)); | ||
| } | ||
| }; | ||
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| void SetDescriptors(const BatchNormParam& param, const TBlob& x) { | ||
| CHECK_GE(x.shape_.ndim(), 3); | ||
| CHECK(param.axis == 1 || param.axis == x.shape_.ndim() - 1); | ||
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| cudnnTensorFormat_t format = param.axis == 1 ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC; | ||
| int n = x.shape_[0]; | ||
| int c = x.shape_[param.axis]; | ||
| size_t last_spatial_i = param.axis == 1 ? x.shape_.ndim() - 1 : x.shape_.ndim() - 2; | ||
| int w = x.shape_[last_spatial_i]; | ||
| int h = x.shape_.ProdShape(last_spatial_i - (x.shape_.ndim() - 3), last_spatial_i); | ||
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| MSHADOW_REAL_TYPE_SWITCH(x.type_flag_, DType, { | ||
| CUDNN_CALL(cudnnSetTensor4dDescriptor(Globals::Get().io_desc, format, | ||
| mshadow::DataType<DType>::kCudnnFlag, n, c, h, w)); | ||
| }) | ||
| CUDNN_CALL(cudnnDeriveBNTensorDescriptor(Globals::Get().mean_desc, Globals::Get().io_desc, | ||
| CUDNN_BATCHNORM_SPATIAL)); | ||
| } | ||
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| mshadow::TypeFlag ParamType(int x_type) { | ||
| auto xt = static_cast<mshadow::TypeFlag>(x_type); | ||
| return xt == mshadow::kFloat16 ? mshadow::kFloat32 : xt; | ||
| } | ||
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| } // namespace | ||
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| bool CudnnBatchNormSupports(const BatchNormParam& param, const TBlob& x) { | ||
| int n = x.shape_.ndim(); | ||
| return n >= 3 && (param.axis == 1 || param.axis == n - 1); | ||
| } | ||
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| void CudnnBatchNormForward(const BatchNormParam& param, const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs) { | ||
| CHECK_EQ(inputs.size(), 5); | ||
| if (ctx.is_train) { | ||
| CHECK_EQ(outputs.size(), 3); | ||
| CHECK_EQ(req.size(), 3); | ||
| } else { | ||
| CHECK_GE(outputs.size(), 1); | ||
| CHECK_GE(req.size(), 1); | ||
| } | ||
| CHECK_EQ(req[batchnorm::kOut], kWriteTo); | ||
| CHECK_GE(inputs[batchnorm::kData].ndim(), 2); | ||
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| SetDescriptors(param, inputs[batchnorm::kData]); | ||
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| auto s = ctx.get_stream<gpu>(); | ||
| MSHADOW_REAL_TYPE_SWITCH(ParamType(inputs[batchnorm::kData].type_flag_), DType, { | ||
| DType a = 1.0f; | ||
| DType b = 0.0f; | ||
| if (param.fix_gamma) inputs[batchnorm::kGamma].FlatTo1D<gpu, DType>(s) = 1.0f; | ||
| if (ctx.is_train) { | ||
| size_t workspace_size = 0; | ||
| CUDNN_CALL(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize( | ||
| s->dnn_handle_, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, CUDNN_BATCHNORM_OPS_BN, | ||
| Globals::Get().io_desc, nullptr, Globals::Get().io_desc, Globals::Get().mean_desc, | ||
| nullptr, &workspace_size)); | ||
| auto workspace = ctx.requested[0].get_space_internal(workspace_size, "CudnnBatchNormForward"); | ||
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| // If the lock on the auxiliary states is set, then this implies that | ||
| // the preceding call is also a `Forward()` call, which further | ||
| // indicates that we are in the backward mirroring mode, and therefore | ||
| // update to the auxiliary states is disabled. This is done by setting | ||
| // the `momentum` to `1` (or `factor` to `0`). | ||
| double factor = | ||
| ((dmlc::GetEnv("MXNET_BACKWARD_DO_MIRROR", 0) || dmlc::GetEnv("MXNET_MEMORY_OPT", 0)) && | ||
| Globals::Get().internal_aux_states_lock) | ||
| ? 0 | ||
| : (1 - param.momentum); | ||
| CUDNN_CALL(cudnnBatchNormalizationForwardTrainingEx( | ||
| s->dnn_handle_, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, CUDNN_BATCHNORM_OPS_BN, &a, &b, | ||
| Globals::Get().io_desc, inputs[batchnorm::kData].dptr_, | ||
| nullptr, nullptr, // zDesc, zData | ||
| Globals::Get().io_desc, outputs[batchnorm::kOut].dptr_, | ||
| Globals::Get().mean_desc, | ||
| inputs[batchnorm::kGamma].dptr_, inputs[batchnorm::kBeta].dptr_, | ||
| factor, inputs[batchnorm::kInMovingMean].dptr_, inputs[batchnorm::kInMovingVar].dptr_, | ||
| param.eps, outputs[batchnorm::kMean].dptr_, outputs[batchnorm::kVar].dptr_, | ||
| nullptr, // activation desc | ||
| workspace, workspace_size, | ||
| nullptr, 0)); // reserveSpace, reserveSpaceSizeInBytes | ||
| } else { | ||
| CUDNN_CALL(cudnnBatchNormalizationForwardInference( | ||
| s->dnn_handle_, CUDNN_BATCHNORM_SPATIAL, &a, &b, | ||
| Globals::Get().io_desc, inputs[batchnorm::kData].dptr_, | ||
| Globals::Get().io_desc, outputs[batchnorm::kOut].dptr_, | ||
| Globals::Get().mean_desc, | ||
| inputs[batchnorm::kGamma].dptr_, inputs[batchnorm::kBeta].dptr_, | ||
| inputs[batchnorm::kInMovingMean].dptr_, inputs[batchnorm::kInMovingVar].dptr_, | ||
| param.eps)); | ||
| } | ||
| }) | ||
| // Set the lock on the auxiliary states. | ||
| // If the next call to the operator is a `Forward()` call, | ||
| // then `momentum` will be set to `1` and hence auxiliary states will not be updated. | ||
| Globals::Get().internal_aux_states_lock = true; | ||
| } | ||
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| void CudnnBatchNormBackward(const BatchNormParam& param, const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs) { | ||
| CHECK_EQ(inputs.size(), 8); | ||
| CHECK_EQ(outputs.size(), 3); | ||
| CHECK_EQ(req.size(), 3); | ||
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| SetDescriptors(param, inputs[3 + batchnorm::kData]); | ||
| auto s = ctx.get_stream<gpu>(); | ||
| size_t workspace_size = 0; | ||
| CUDNN_CALL(cudnnGetBatchNormalizationBackwardExWorkspaceSize( | ||
| s->dnn_handle_, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, CUDNN_BATCHNORM_OPS_BN, | ||
| Globals::Get().io_desc, Globals::Get().io_desc, Globals::Get().io_desc, nullptr, | ||
| Globals::Get().io_desc, Globals::Get().mean_desc, nullptr, &workspace_size)); | ||
| auto workspace = ctx.requested[0].get_space_internal(workspace_size, "CudnnBatchNormBackward"); | ||
| MSHADOW_REAL_TYPE_SWITCH(ParamType(inputs[3 + batchnorm::kData].type_flag_), DType, { | ||
| if (param.fix_gamma) inputs[3 + batchnorm::kGamma].FlatTo1D<gpu, DType>(s) = 1.0f; | ||
| bool grad_add_gamma_beta = req[batchnorm::kGamma] == kAddTo || req[batchnorm::kBeta] == kAddTo; | ||
| if (grad_add_gamma_beta) { | ||
| if (IsBNWriting(req[batchnorm::kGamma])) | ||
| outputs[batchnorm::kGamma].FlatTo1D<gpu, DType>(s) = 0.0f; | ||
| if (IsBNWriting(req[batchnorm::kBeta])) | ||
| outputs[batchnorm::kBeta].FlatTo1D<gpu, DType>(s) = 0.0f; | ||
| } | ||
| DType a = 1.0f; | ||
| DType b = 0.0f; | ||
| DType b_add = 1.0f; | ||
| const bool global_stats = !ctx.is_train || param.use_global_stats; | ||
| CUDNN_CALL(cudnnBatchNormalizationBackwardEx( | ||
| s->dnn_handle_, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, CUDNN_BATCHNORM_OPS_BN, | ||
| &a, req[batchnorm::kData] == kAddTo ? &b_add : &b, | ||
| &a, grad_add_gamma_beta ? &b_add : &b, | ||
| Globals::Get().io_desc, inputs[3 + batchnorm::kData].dptr_, | ||
| nullptr, nullptr, // yDesc, yData | ||
| Globals::Get().io_desc, inputs[batchnorm::kOut].dptr_, | ||
| nullptr, nullptr, // dzDesc, dzData | ||
| Globals::Get().io_desc, outputs[batchnorm::kData].dptr_, | ||
| Globals::Get().mean_desc, | ||
| inputs[3 + batchnorm::kGamma].dptr_, inputs[3 + batchnorm::kBeta].dptr_, | ||
| outputs[batchnorm::kGamma].dptr_, outputs[batchnorm::kBeta].dptr_, param.eps, | ||
| global_stats ? nullptr : inputs[batchnorm::kMean].dptr_, | ||
| global_stats ? nullptr : inputs[batchnorm::kVar].dptr_, | ||
| nullptr, // activationDesc | ||
| workspace, workspace_size, | ||
| nullptr, 0)); // reserveSpace, reserveSpaceSizeInBytes | ||
| if (param.fix_gamma) outputs[batchnorm::kGamma].FlatTo1D<gpu, DType>(s) = 0.0f; | ||
| }) | ||
| Globals::Get().internal_aux_states_lock = false; | ||
| } | ||
|
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| #endif // MXNET_USE_CUDNN == 1 | ||
| } // namespace op | ||
| } // namespace mxnet |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,56 @@ | ||
| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one | ||
| * or more contributor license agreements. See the NOTICE file | ||
| * distributed with this work for additional information | ||
| * regarding copyright ownership. The ASF licenses this file | ||
| * to you 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. | ||
| */ | ||
|
|
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| /*! | ||
| * Copyright (c) 2015 by Contributors | ||
| * \file cudnn_batch_norm.h | ||
| * \brief | ||
| * \author Junyuan Xie | ||
| */ | ||
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| #ifndef MXNET_OPERATOR_NN_CUDNN_CUDNN_BATCH_NORM_H_ | ||
| #define MXNET_OPERATOR_NN_CUDNN_CUDNN_BATCH_NORM_H_ | ||
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| #include <mxnet/base.h> | ||
| #include <vector> | ||
| #include "../batch_norm-inl.h" | ||
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| namespace mxnet { | ||
| namespace op { | ||
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| #if MXNET_USE_CUDNN == 1 | ||
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| STATIC_ASSERT_CUDNN_VERSION_GE(7401); | ||
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| bool CudnnBatchNormSupports(const BatchNormParam& param, const TBlob& x); | ||
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| void CudnnBatchNormForward(const BatchNormParam& param, const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs); | ||
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| void CudnnBatchNormBackward(const BatchNormParam& param, const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs); | ||
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| #endif // MXNET_USE_CUDNN == 1 | ||
|
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| } // namespace op | ||
| } // namespace mxnet | ||
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| #endif // MXNET_OPERATOR_NN_CUDNN_CUDNN_BATCH_NORM_H_ |