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support mixed-precision binary operations
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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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|
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| /*! | ||
| * Copyright (c) 2019 by Contributors | ||
| * \file np_elemwise_binary_op.h | ||
| * \brief | ||
| */ | ||
| #ifndef MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_ | ||
| #define MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_ | ||
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| #include <vector> | ||
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| #include "../tensor/elemwise_binary_broadcast_op.h" | ||
| #include "../tensor/elemwise_binary_scalar_op.h" | ||
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| namespace mxnet { | ||
| namespace op { | ||
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| #ifndef _WIN32 | ||
| template<typename xpu, typename LOP, typename ROP> | ||
| void MixedBinaryElemwiseCompute(const nnvm::NodeAttrs& attrs, | ||
| const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, | ||
| const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs) { | ||
| using namespace mshadow; | ||
| using namespace mxnet_op; | ||
| CHECK_EQ(inputs.size(), 2U); | ||
| CHECK_EQ(outputs.size(), 1U); | ||
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| const TBlob& lhs = inputs[0]; | ||
| const TBlob& rhs = inputs[1]; | ||
| const TBlob& out = outputs[0]; | ||
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| CHECK((lhs.type_flag_ == mshadow::kBool) || (rhs.type_flag_ == mshadow::kBool)) | ||
| << "now supports bool with another type only"; | ||
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| Stream<xpu> *s = ctx.get_stream<xpu>(); | ||
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| MXNET_ASSIGN_REQ_SWITCH(req[0], Req, { | ||
| MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, { | ||
| const size_t size = (ElemwiseBinaryOp::minthree(out.Size(), lhs.Size(), rhs.Size()) | ||
| + DataType<DType>::kLanes - 1) / DataType<DType>::kLanes; | ||
| if (size != 0) { | ||
| if (lhs.type_flag_ == kBool) { | ||
| Kernel<mxnet_op::op_with_req<LOP, Req>, xpu>::Launch( | ||
| s, size, out.dptr<DType>(), lhs.dptr<bool>(), rhs.dptr<DType>()); | ||
| } else { | ||
| Kernel<mxnet_op::op_with_req<ROP, Req>, xpu>::Launch( | ||
| s, size, out.dptr<DType>(), rhs.dptr<bool>(), lhs.dptr<DType>()); | ||
| } | ||
| } | ||
| }); | ||
| }); | ||
| } | ||
| #endif | ||
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| template<typename xpu, typename OP, typename LOP, typename ROP> | ||
| void MixedBinaryBroadcastCompute(const nnvm::NodeAttrs& attrs, | ||
| const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, | ||
| const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs) { | ||
| using namespace mshadow; | ||
| using namespace mxnet_op; | ||
| CHECK_EQ(inputs.size(), 2U); | ||
| CHECK_EQ(outputs.size(), 1U); | ||
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| const TBlob& lhs = inputs[0]; | ||
| const TBlob& rhs = inputs[1]; | ||
| const TBlob& out = outputs[0]; | ||
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| if ((out.shape_.Size() == 0U) || (req[0] == kNullOp)) return; | ||
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| if (lhs.type_flag_ == rhs.type_flag_) { | ||
| BinaryBroadcastCompute<xpu, OP>(attrs, ctx, inputs, req, outputs); | ||
| return; | ||
| } | ||
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| CHECK((lhs.type_flag_ == mshadow::kBool) || (rhs.type_flag_ == mshadow::kBool)) | ||
| << "now supports bool with another type only"; | ||
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| #ifndef _WIN32 | ||
| mxnet::TShape new_lshape, new_rshape, new_oshape; | ||
| int ndim = BinaryBroadcastShapeCompact(lhs.shape_, rhs.shape_, out.shape_, | ||
| &new_lshape, &new_rshape, &new_oshape); | ||
| if (!ndim) { | ||
| MixedBinaryElemwiseCompute<xpu, LOP, ROP>(attrs, ctx, inputs, req, outputs); | ||
| } else { | ||
| mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); | ||
| MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, DType, { | ||
| BROADCAST_NDIM_SWITCH(ndim, NDim, { | ||
| mshadow::Shape<NDim> oshape = new_oshape.get<NDim>(); | ||
| mshadow::Shape<NDim> lstride = mxnet_op::calc_stride(new_lshape.get<NDim>()); | ||
| mshadow::Shape<NDim> rstride = mxnet_op::calc_stride(new_rshape.get<NDim>()); | ||
| if (lhs.type_flag_ == mshadow::kBool) { | ||
| mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, LOP>, xpu>:: | ||
| template LaunchEx(s, new_oshape.Size(), req[0], lstride, rstride, oshape, | ||
| lhs.dptr<bool>(), rhs.dptr<DType>(), out.dptr<DType>()); | ||
| } else { | ||
| mxnet_op::Kernel<mxnet_op::binary_broadcast_kernel<NDim, ROP>, xpu>:: | ||
| template LaunchEx(s, new_oshape.Size(), req[0], rstride, lstride, oshape, | ||
| rhs.dptr<bool>(), lhs.dptr<DType>(), out.dptr<DType>()); | ||
| } | ||
| }); | ||
| }); | ||
| } | ||
| #else | ||
| mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); | ||
| if (common::is_float(lhs.type_flag_) && common::is_float(rhs.type_flag_)) { | ||
| LOG(ERROR) << "not implemented yet..."; | ||
| } else if (common::is_float(lhs.type_flag_) || common::is_float(rhs.type_flag_)) { | ||
| TBlob temp_tblob; | ||
| // one is float, the other is bool | ||
| CHECK_EQ(out.type_flag_, | ||
| common::is_float(lhs.type_flag_) ? lhs.type_flag_ : rhs.type_flag_) | ||
| << "This case out type should be same as the float type"; | ||
| if (common::is_float(lhs.type_flag_)) { | ||
| MSHADOW_REAL_TYPE_SWITCH(lhs.type_flag_, LType, { | ||
| Tensor<xpu, 1, LType> temp_tensor = | ||
| ctx.requested[0].get_space_typed<xpu, 1, LType>(Shape1(rhs.Size()), s); | ||
| temp_tblob = TBlob(temp_tensor); | ||
| }); | ||
| CastCompute<xpu>(attrs, ctx, {rhs}, {kWriteTo}, {temp_tblob}); | ||
| BinaryBroadcastCompute<xpu, OP>( | ||
| attrs, ctx, {lhs, temp_tblob.reshape(rhs.shape_)}, req, outputs); | ||
| } else { | ||
| MSHADOW_REAL_TYPE_SWITCH(rhs.type_flag_, RType, { | ||
| Tensor<xpu, 1, RType> temp_tensor = | ||
| ctx.requested[0].get_space_typed<xpu, 1, RType>(Shape1(lhs.Size()), s); | ||
| temp_tblob = TBlob(temp_tensor); | ||
| }); | ||
| CastCompute<xpu>(attrs, ctx, {lhs}, {kWriteTo}, {temp_tblob}); | ||
| BinaryBroadcastCompute<xpu, OP>( | ||
| attrs, ctx, {temp_tblob.reshape(lhs.shape_), rhs}, req, outputs); | ||
| } | ||
| } else { | ||
| LOG(ERROR) << "not implemented yet..."; | ||
| } | ||
| #endif | ||
| } | ||
|
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| template<typename xpu, typename LOP, typename ROP> | ||
| void MixedBinaryBackwardUseIn(const nnvm::NodeAttrs& attrs, | ||
| const OpContext& ctx, | ||
| const std::vector<TBlob>& inputs, | ||
| const std::vector<OpReqType>& req, | ||
| const std::vector<TBlob>& outputs) { | ||
| CHECK_EQ(inputs.size(), 3U); | ||
| CHECK_EQ(outputs.size(), 2U); | ||
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| const TBlob& lhs = inputs[1]; | ||
| const TBlob& rhs = inputs[2]; | ||
| if (lhs.type_flag_ == rhs.type_flag_) { | ||
| BinaryBroadcastBackwardUseIn<xpu, LOP, ROP>(attrs, ctx, inputs, req, outputs); | ||
| return; | ||
| } | ||
|
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| LOG(ERROR) << "Binary operation with mixed input data types does not support backward yet..."; | ||
| } | ||
|
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| } // namespace op | ||
| } // namespace mxnet | ||
| #endif // MXNET_OPERATOR_NUMPY_NP_ELEMWISE_BROADCAST_OP_H_ |
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