haojin2 commented on a change in pull request #15349: Numpy Tensordot Operator URL: https://github.com/apache/incubator-mxnet/pull/15349#discussion_r303094273
########## File path: src/operator/numpy/np_tensordot_op.cc ########## @@ -0,0 +1,222 @@ +/* + * 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. + */ + +/*! + * \file np_tensordot_op.cc + * \brief CPU Implementation of numpy-compatible tensordot + */ + +#include <string> +#include "np_tensordot_op-inl.h" + +namespace mxnet { +namespace op { + +bool TensordotOpShape(const nnvm::NodeAttrs& attrs, + mxnet::ShapeVector *in_attrs, + mxnet::ShapeVector *out_attrs) { + CHECK_EQ(in_attrs->size(), 2U); + CHECK_EQ(out_attrs->size(), 1U); + + const mxnet::TShape& a_shape = in_attrs->at(0); + const mxnet::TShape& b_shape = in_attrs->at(1); + + if (!ndim_is_known(a_shape) || !ndim_is_known(b_shape)) { + return false; + } + + if ((a_shape.ndim() < 1) || (b_shape.ndim() < 1)) { + return false; + } + + const TensordotParam& param = nnvm::get<TensordotParam>(attrs.parsed); + const Tuple<int>& a_axes_summed = param.a_axes_summed; + const Tuple<int>& b_axes_summed = param.b_axes_summed; + + Tuple<int> a_axes_remained; + Tuple<int> b_axes_remained; + Tuple<int> a_axes; + Tuple<int> b_axes; + GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained, + &b_axes, a_shape, b_shape); + + CHECK_EQ(a_axes_summed.ndim(), b_axes_summed.ndim()); + + mxnet::TShape out_shape(a_axes_remained.ndim() + b_axes_remained.ndim(), -1); + for (int i = 0; i < a_axes_remained.ndim(); i++) { + out_shape[i] = a_shape[a_axes_remained[i]]; + } + for (int i = 0; i < b_axes_remained.ndim(); i++) { + out_shape[a_axes_remained.ndim() + i] = b_shape[b_axes_remained[i]]; + } + SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape); + + mxnet::TShape tem_shape1(a_axes.ndim(), -1); + for (int i = 0; i < a_axes_remained.ndim(); i++) { + tem_shape1[a_axes_remained[i]] = out_shape[i]; + } + for (int i = 0; i < a_axes_summed.ndim(); i++) { + tem_shape1[a_axes_summed[i]] = b_shape[b_axes_summed[i]]; + } + SHAPE_ASSIGN_CHECK(*in_attrs, 0, tem_shape1); + + mxnet::TShape tem_shape2(b_axes.ndim(), -1); + for (int i = 0; i < b_axes_remained.ndim(); i++) { + tem_shape2[b_axes_remained[i]] = out_shape[a_axes_remained.ndim() + i]; + } + for (int i = 0; i < b_axes_summed.ndim(); i++) { + tem_shape2[b_axes_summed[i]] = a_shape[a_axes_summed[i]]; + } + SHAPE_ASSIGN_CHECK(*in_attrs, 1, tem_shape2); + + return shape_is_known(*in_attrs) && shape_is_known(*out_attrs); +} + +DMLC_REGISTER_PARAMETER(TensordotParam); + +NNVM_REGISTER_OP(_npi_tensordot) +.set_attr_parser(mxnet::op::ParamParser<TensordotParam>) +.set_num_inputs(2) +.set_num_outputs(1) +.set_attr<nnvm::FListInputNames>("FListInputNames", + [](const NodeAttrs& attrs) { + return std::vector<std::string>{"a", "b"}; + }) +.set_attr<mxnet::FInferShape>("FInferShape", TensordotOpShape) +.set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>) +.set_attr<FResourceRequest>("FResourceRequest", + [](const NodeAttrs& attrs) { + return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; + }) +.set_attr<FCompute>("FCompute<cpu>", TensordotOpForward<cpu>) +.set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot"}) +.add_argument("a", "NDArray-or-Symbol", "First input") +.add_argument("b", "NDArray-or-Symbol", "Second input") +.add_arguments(TensordotParam::__FIELDS__()); + +NNVM_REGISTER_OP(_backward_npi_tensordot) +.set_attr_parser(mxnet::op::ParamParser<TensordotParam>) +.set_num_inputs(3) +.set_num_outputs(2) +.set_attr<nnvm::TIsBackward>("TIsBackward", true) +.set_attr<FResourceRequest>("FResourceRequest", + [](const NodeAttrs& attrs) { + return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; + }) +.set_attr<FCompute>("FCompute<cpu>", TensordotOpBackward<cpu>); + +bool TensordotIntAxesOpShape(const nnvm::NodeAttrs& attrs, + mxnet::ShapeVector *in_attrs, + mxnet::ShapeVector *out_attrs) { + CHECK_EQ(in_attrs->size(), 2U); + CHECK_EQ(out_attrs->size(), 1U); + + const mxnet::TShape& a_shape = in_attrs->at(0); + const mxnet::TShape& b_shape = in_attrs->at(1); + + if (!ndim_is_known(a_shape) || !ndim_is_known(b_shape)) { + return false; + } + + if ((a_shape.ndim() < 1) || (b_shape.ndim() < 1)) { + return false; + } + + const TensordotIntAxesParam& param = nnvm::get<TensordotIntAxesParam>(attrs.parsed); + const int& axes = param.axes; + + Tuple<int> a_axes_summed; + Tuple<int> b_axes_summed; + GetSummedAxes(&a_axes_summed, &b_axes_summed, axes, a_shape); + + Tuple<int> a_axes_remained; + Tuple<int> b_axes_remained; + Tuple<int> a_axes; + Tuple<int> b_axes; + GetReorderedAxes(a_axes_summed, &a_axes_remained, &a_axes, b_axes_summed, &b_axes_remained, + &b_axes, a_shape, b_shape); + + CHECK_EQ(a_axes_summed.ndim(), b_axes_summed.ndim()); + + mxnet::TShape out_shape(a_axes_remained.ndim() + b_axes_remained.ndim(), -1); + for (int i = 0; i < a_axes_remained.ndim(); i++) { + out_shape[i] = a_shape[a_axes_remained[i]]; + } + for (int i = 0; i < b_axes_remained.ndim(); i++) { + out_shape[a_axes_remained.ndim() + i] = b_shape[b_axes_remained[i]]; + } + SHAPE_ASSIGN_CHECK(*out_attrs, 0, out_shape); + + mxnet::TShape tem_shape1(a_axes.ndim(), -1); + for (int i = 0; i < a_axes_remained.ndim(); i++) { + tem_shape1[a_axes_remained[i]] = out_shape[i]; + } + for (int i = 0; i < a_axes_summed.ndim(); i++) { + tem_shape1[a_axes_summed[i]] = b_shape[b_axes_summed[i]]; + } + SHAPE_ASSIGN_CHECK(*in_attrs, 0, tem_shape1); + + mxnet::TShape tem_shape2(b_axes.ndim(), -1); + for (int i = 0; i < b_axes_remained.ndim(); i++) { + tem_shape2[b_axes_remained[i]] = out_shape[a_axes_remained.ndim() + i]; + } + for (int i = 0; i < b_axes_summed.ndim(); i++) { + tem_shape2[b_axes_summed[i]] = a_shape[a_axes_summed[i]]; + } + SHAPE_ASSIGN_CHECK(*in_attrs, 1, tem_shape2); + + return shape_is_known(*in_attrs) && shape_is_known(*out_attrs); +} + +DMLC_REGISTER_PARAMETER(TensordotIntAxesParam); + +NNVM_REGISTER_OP(_npi_tensordot_int_axes) +.set_attr_parser(mxnet::op::ParamParser<TensordotIntAxesParam>) +.set_num_inputs(2) +.set_num_outputs(1) +.set_attr<nnvm::FListInputNames>("FListInputNames", + [](const NodeAttrs& attrs) { + return std::vector<std::string>{"a", "b"}; + }) +.set_attr<mxnet::FInferShape>("FInferShape", TensordotIntAxesOpShape) +.set_attr<nnvm::FInferType>("FInferType", mxnet::op::ElemwiseType<2, 1>) +.set_attr<FResourceRequest>("FResourceRequest", + [](const NodeAttrs& attrs) { + return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; + }) +.set_attr<FCompute>("FCompute<cpu>", TensordotIntAxesOpForward<cpu>) +.set_attr<nnvm::FGradient>("FGradient", + mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot_int_axes"}) Review comment: either 2-space indentation or alignment: ```c++ .set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot_int_axes"}) .set_attr<nnvm::FGradient>("FGradient", mxnet::op::ElemwiseGradUseIn{"_backward_npi_tensordot_int_axes"}) ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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