haojin2 commented on a change in pull request #15349: Numpy Tensordot Operator URL: https://github.com/apache/incubator-mxnet/pull/15349#discussion_r302266877
########## File path: src/operator/numpy/np_tensordot_int_axes_op.cc ########## @@ -0,0 +1,193 @@ +/* + * 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 + * icense 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_int_axes_op.cc + * \brief CPU Implementation of numpy-compatible tensordot + */ + +#include <string> + +#include "np_tensordot_int_axes_op-inl.h" + +namespace mxnet { +namespace op { + +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(tensordot_int_axes) +.add_alias("_npi_tensordot_int_axes") +.describe(R"code(tensordot(a, b, axes=2) + + Compute tensor dot product along specified axes for arrays >= 1-D. + + Given two tensors (arrays of dimension greater than or equal to one), + `a` and `b`, and an array_like object containing two array_like + objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s + elements (components) over the axes specified by ``a_axes`` and + ``b_axes``. The third argument can be a single non-negative + integer_like scalar, ``N``; if it is such, then the last ``N`` + dimensions of `a` and the first ``N`` dimensions of `b` are summed + over. + + Parameters + ---------- + a, b : ndarray, len(shape) >= 1 + Tensors to "dot". + + axes : int or (2,) ndarray + * integer_like + If an int N, sum over the last N axes of `a` and the first N axes + of `b` in order. The sizes of the corresponding axes must match. + * (2,) ndarray + Or, a list of axes to be summed over, first sequence applying to `a`, + second to `b`. Both elements ndarray must be of the same length. + + See Also + -------- + dot, einsum + + Notes + ----- + Three common use cases are: + * ``axes = 0`` : tensor product :math:`a\otimes b` + * ``axes = 1`` : tensor dot product :math:`a\cdot b` + * ``axes = 2`` : (default) tensor double contraction :math:`a:b` + + When `axes` is integer_like, the sequence for evaluation will be: first + the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and + Nth axis in `b` last. + + When there is more than one axis to sum over - and they are not the last Review comment: Since we do not directly expose this operator to users, those comments could probably be removed. ---------------------------------------------------------------- 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. 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