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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. | ||
| */ | ||
| /*! | ||
| * Copyright (c) 2018 by Contributors | ||
| * \file boolean_mask.cu | ||
| */ | ||
|
|
||
| #include "./boolean_mask-inl.h" | ||
| #include <cub/cub.cuh> | ||
|
|
||
| namespace mxnet { | ||
| namespace op { | ||
|
|
||
| struct BooleanMaskForwardKernel { | ||
| template<typename DType> | ||
| static void MSHADOW_XINLINE Map(int i, | ||
| DType* out, | ||
| const DType* data, | ||
| const int32_t* idx, | ||
| const size_t col_size) { | ||
| int row_id = i / col_size; | ||
| int col_id = i % col_size; | ||
| int32_t prev = (row_id == 0) ? 0 : idx[row_id - 1]; | ||
| int32_t curr = idx[row_id]; | ||
| if (prev != curr) { | ||
| out[prev * col_size + col_id] = data[i]; | ||
| } | ||
| } | ||
| }; | ||
|
|
||
| struct BooleanMaskBackwardKernel { | ||
| template<typename DType> | ||
| static void MSHADOW_XINLINE Map(int i, | ||
| DType* igrad, | ||
| const DType* ograd, | ||
| const int32_t* idx, | ||
| const size_t col_size) { | ||
| int row_id = i / col_size; | ||
| int col_id = i % col_size; | ||
| int32_t prev = (row_id == 0) ? 0 : idx[row_id - 1]; | ||
| int32_t curr = idx[row_id]; | ||
| if (prev != curr) { | ||
| igrad[i] = ograd[prev * col_size + col_id]; | ||
| } | ||
| } | ||
| }; | ||
|
|
||
| template<> | ||
| inline void BooleanMaskForward<gpu>(const nnvm::NodeAttrs& attrs, | ||
| const OpContext &ctx, | ||
| const std::vector<NDArray> &inputs, | ||
| const std::vector<OpReqType> &req, | ||
| const std::vector<NDArray> &outputs) { | ||
| using namespace mshadow; | ||
| CHECK_EQ(inputs.size(), 2U); | ||
| CHECK_EQ(outputs.size(), 1U); | ||
| const BooleanMaskParam& param = nnvm::get<BooleanMaskParam>(attrs.parsed); | ||
| const int axis = param.axis; | ||
| const NDArray &data = inputs[0]; | ||
| const NDArray &idx = inputs[1]; | ||
| const NDArray &out = outputs[0]; | ||
| CHECK_EQ(axis, 0) << "Not supported yet"; | ||
| CHECK_EQ(data.shape()[axis], idx.shape()[0]); | ||
| CHECK_EQ(idx.shape().ndim(), 1U); | ||
| Stream<gpu>* s = ctx.get_stream<gpu>(); | ||
| // count the number of 1s in `idx`, so that we could know the output dimension | ||
| size_t idx_size = idx.shape()[0]; | ||
| int32_t valid_num = 0; | ||
| int32_t* prefix_sum = nullptr; | ||
| void* d_temp_storage = nullptr; | ||
| size_t temp_storage_bytes = 0; | ||
| cub::DeviceScan::InclusiveSum(d_temp_storage, | ||
| temp_storage_bytes, | ||
| prefix_sum, | ||
| prefix_sum, | ||
| idx_size, | ||
| Stream<gpu>::GetStream(s)); | ||
| size_t buffer_size = idx_size * sizeof(int32_t); | ||
| temp_storage_bytes += buffer_size; | ||
| Tensor<gpu, 1, char> workspace = | ||
| ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), s); | ||
| prefix_sum = reinterpret_cast<int32_t*>(workspace.dptr_); | ||
| d_temp_storage = workspace.dptr_ + buffer_size; | ||
| MSHADOW_TYPE_SWITCH(idx.dtype(), IType, { | ||
| mxnet_op::Kernel<mshadow_op::identity_with_cast, gpu>::Launch( | ||
| s, idx.shape()[0], prefix_sum, idx.data().dptr<IType>()); | ||
| }); | ||
| cub::DeviceScan::InclusiveSum(d_temp_storage, | ||
| temp_storage_bytes, | ||
| prefix_sum, | ||
| prefix_sum, | ||
| idx_size, | ||
| Stream<gpu>::GetStream(s)); | ||
| CUDA_CALL(cudaMemcpy(&valid_num, &prefix_sum[idx_size - 1], sizeof(int32_t), | ||
| cudaMemcpyDeviceToHost)); | ||
| CHECK(valid_num > 0) << "boolean_mask behavior not defined when all masks are 0"; | ||
| // Set the output shape forcefully | ||
| TShape data_shape = data.shape(); | ||
| data_shape[axis] = valid_num; | ||
| const_cast<NDArray &>(out).Init(data_shape); | ||
| size_t input_size = data.shape().Size(); | ||
| size_t col_size = input_size / idx.shape()[0]; | ||
| // do the copy | ||
| MSHADOW_TYPE_SWITCH(out.dtype(), DType, { | ||
| mxnet_op::Kernel<BooleanMaskForwardKernel, gpu>::Launch( | ||
| s, input_size, out.data().dptr<DType>(), data.data().dptr<DType>(), prefix_sum, col_size); | ||
| }); | ||
| } | ||
|
|
||
| template<> | ||
| inline void BooleanMaskBackward<gpu>(const nnvm::NodeAttrs& attrs, | ||
| const OpContext &ctx, | ||
| const std::vector<NDArray> &inputs, | ||
| const std::vector<OpReqType> &req, | ||
| const std::vector<NDArray> &outputs) { | ||
| using namespace mshadow; | ||
| CHECK_EQ(inputs.size(), 3U); | ||
| CHECK_EQ(outputs.size(), 2U); | ||
| // inputs: {ograd, data, idx} | ||
| // outputs: {igrad_data, igrad_idx} | ||
| const NDArray& ograd = inputs[0]; | ||
| const NDArray& idx = inputs[2]; | ||
| const NDArray& igrad_data = outputs[0]; | ||
| Stream<gpu>* s = ctx.get_stream<gpu>(); | ||
| // count the number of 1s in `idx`, so that we could know the output dimension | ||
| size_t idx_size = idx.shape()[0]; | ||
| int32_t* prefix_sum = nullptr; | ||
| void* d_temp_storage = nullptr; | ||
| size_t temp_storage_bytes = 0; | ||
| cub::DeviceScan::InclusiveSum(d_temp_storage, | ||
| temp_storage_bytes, | ||
| prefix_sum, | ||
| prefix_sum, | ||
| idx_size, | ||
| Stream<gpu>::GetStream(s)); | ||
| size_t buffer_size = idx_size * sizeof(int32_t); | ||
| temp_storage_bytes += buffer_size; | ||
| Tensor<gpu, 1, char> workspace = | ||
| ctx.requested[0].get_space_typed<gpu, 1, char>(Shape1(temp_storage_bytes), s); | ||
| prefix_sum = reinterpret_cast<int32_t*>(workspace.dptr_); | ||
| d_temp_storage = workspace.dptr_ + buffer_size; | ||
| MSHADOW_TYPE_SWITCH(idx.dtype(), IType, { | ||
| mxnet_op::Kernel<mshadow_op::identity_with_cast, gpu>::Launch( | ||
| s, idx.shape()[0], prefix_sum, idx.data().dptr<IType>()); | ||
| }); | ||
| cub::DeviceScan::InclusiveSum(d_temp_storage, | ||
| temp_storage_bytes, | ||
| prefix_sum, | ||
| prefix_sum, | ||
| idx_size, | ||
| Stream<gpu>::GetStream(s)); | ||
| size_t input_size = igrad_data.shape().Size(); | ||
| size_t col_size = input_size / idx_size; | ||
| MSHADOW_TYPE_SWITCH(igrad_data.dtype(), DType, { | ||
| mxnet_op::Kernel<BooleanMaskBackwardKernel, gpu>::Launch( | ||
| s, input_size, igrad_data.data().dptr<DType>(), ograd.data().dptr<DType>(), prefix_sum, col_size); | ||
| }); | ||
| } | ||
|
|
||
| NNVM_REGISTER_OP(_contrib_boolean_mask) | ||
| .set_attr<FResourceRequest>("FResourceRequest", | ||
| [](const NodeAttrs& attrs) { | ||
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; | ||
| }) | ||
| .set_attr<FComputeEx>("FComputeEx<gpu>", BooleanMaskForward<gpu>); | ||
|
|
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| NNVM_REGISTER_OP(_backward_contrib_boolean_mask) | ||
| .set_attr<FResourceRequest>("FResourceRequest", | ||
| [](const NodeAttrs& attrs) { | ||
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; | ||
| }) | ||
| .set_attr<FComputeEx>("FComputeEx<gpu>", BooleanMaskBackward<gpu>); | ||
|
|
||
| } // namespace op | ||
| } // namespace mxnet |
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