haojin2 commented on a change in pull request #17279: [Numpy] Add linalg.pinv op URL: https://github.com/apache/incubator-mxnet/pull/17279#discussion_r367175459
########## File path: src/operator/numpy/linalg/np_pinv.cc ########## @@ -0,0 +1,195 @@ +/* + * 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) 2019 by Contributors + * \file np_pinv.cc + * \brief CPU implementation of the PINV Operator + */ + +#include "./np_pinv-inl.h" + +namespace mxnet { +namespace op { + +bool PinvOpShape(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)[0]; + const mxnet::TShape& rcond_shape = (*in_attrs)[1]; + const mxnet::TShape& pinv_shape = (*out_attrs)[0]; + const int a_ndim = a_shape.ndim(); + + if (shape_is_known(a_shape)) { + // Forward shape inference. + CHECK_GE(a_ndim, 2) + << "Array must be at least two-dimensional"; + // Calculte pinv shape. + std::vector<int> pinv_shape_vec(a_ndim, -1); + for (int i = 0; i < a_ndim - 2; ++i) { + pinv_shape_vec[i] = a_shape[i]; + } + pinv_shape_vec[a_ndim - 2] = a_shape[a_ndim - 1]; + pinv_shape_vec[a_ndim - 1] = a_shape[a_ndim - 2]; + SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(pinv_shape_vec.begin(), pinv_shape_vec.end())); + // Check rcond shape. + GetOrCheckCutoffAndLargeShape(attrs, a_shape, rcond_shape, nullptr, nullptr); + } else { + // Backward shape inference. + if (shape_is_known(pinv_shape)) { + const int pinv_ndim = pinv_shape.ndim(); + CHECK_GE(pinv_ndim, 2) + << "Array must be at least two-dimensional"; + // Calculte 'a' shape. + std::vector<int> a_shape_vec(pinv_ndim, -1); + for (int i = 0; i < pinv_ndim - 2; ++i) { + a_shape_vec[i] = pinv_shape[i]; + } + a_shape_vec[pinv_ndim - 2] = pinv_shape[pinv_ndim - 1]; + a_shape_vec[pinv_ndim - 1] = pinv_shape[pinv_ndim - 2]; + SHAPE_ASSIGN_CHECK(*in_attrs, 0, mxnet::TShape(a_shape_vec.begin(), a_shape_vec.end())); + // Check rcond shape. + GetOrCheckCutoffAndLargeShape(attrs, (*in_attrs)[0], rcond_shape, nullptr, nullptr); + } + } + return shape_is_known(*in_attrs) && shape_is_known(*out_attrs); +} + +inline bool PinvOpType(const nnvm::NodeAttrs& attrs, + std::vector<int>* in_attrs, + std::vector<int>* out_attrs) { + CHECK_EQ(in_attrs->size(), 2U); + CHECK_EQ(out_attrs->size(), 1U); + int a_type = in_attrs->at(0); + int rcond_type = in_attrs->at(1); + // unsupport float16 + CHECK_NE(a_type, mshadow::kFloat16) + << "array type float16 is unsupported in linalg."; + CHECK(rcond_type == mshadow::kFloat32 || rcond_type == mshadow::kFloat64) + << "rcond type should be float32 or float64."; + if (mshadow::kFloat32 == a_type) { + TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)); + } else { + TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kFloat64); + } + return out_attrs->at(0) != -1; +} + +DMLC_REGISTER_PARAMETER(PinvParam); + +NNVM_REGISTER_OP(_npi_pinv) +.describe(R"code()code" ADD_FILELINE) +.set_attr_parser(mxnet::op::ParamParser<PinvParam>) +.set_num_inputs(2) +.set_num_outputs(1) +.set_attr<nnvm::FListInputNames>("FListInputNames", [](const NodeAttrs& attrs){ + return std::vector<std::string>{"A", "rcond"}; +}) +.set_attr<mxnet::FInferShape>("FInferShape", PinvOpShape) +.set_attr<nnvm::FInferType>("FInferType", PinvOpType) +.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& attrs){ + return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; +}) +.set_attr<FCompute>("FCompute<cpu>", PinvOpForward<cpu>) +.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes) +.add_argument("A", "NDArray-or-Symbol", "Tensor of matrix") +.add_argument("rcond", "NDArray-or-Symbol", "Cutoff for small singular values.") +.add_arguments(PinvParam::__FIELDS__()); + +bool PinvScalarRcondOpShape(const nnvm::NodeAttrs& attrs, + mxnet::ShapeVector *in_attrs, + mxnet::ShapeVector *out_attrs) { + CHECK_EQ(in_attrs->size(), 1U); + CHECK_EQ(out_attrs->size(), 1U); + const mxnet::TShape& a_shape = (*in_attrs)[0]; + const mxnet::TShape& pinv_shape = (*out_attrs)[0]; + const int a_ndim = a_shape.ndim(); + + if (shape_is_known(a_shape)) { + // Forward shape inference. + CHECK_GE(a_ndim, 2) + << "Array must be at least two-dimensional"; + // Calculte pinv shape. + std::vector<int> pinv_shape_vec(a_ndim, -1); + for (int i = 0; i < a_ndim - 2; ++i) { + pinv_shape_vec[i] = a_shape[i]; + } + pinv_shape_vec[a_ndim - 2] = a_shape[a_ndim - 1]; + pinv_shape_vec[a_ndim - 1] = a_shape[a_ndim - 2]; + SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape(pinv_shape_vec.begin(), pinv_shape_vec.end())); + } else { + // Backward shape inference. + if (shape_is_known(pinv_shape)) { Review comment: `else if (shape_is_known(pinv_shape))` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
