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[Numpy] Numpy behavior random.uniform() (#15858)
* numpy behavior uniform sampling implemented * improve code style according to review comment * remove rebundant namespace
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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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| /*! | ||
| * Copyright (c) 2015 by Contributors | ||
| * \file dist_common.h | ||
| * \brief Function definition of common functions for distributions | ||
| * \with two parameters. | ||
| */ | ||
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| #ifndef MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_ | ||
| #define MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_ | ||
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| #include <mshadow/base.h> | ||
| #include <mxnet/operator_util.h> | ||
| #include <algorithm> | ||
| #include <string> | ||
| #include <vector> | ||
| #include "../../elemwise_op_common.h" | ||
| #include "../../mshadow_op.h" | ||
| #include "../../mxnet_op.h" | ||
| #include "../../operator_common.h" | ||
| #include "../../tensor/elemwise_binary_broadcast_op.h" | ||
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| namespace mxnet { | ||
| namespace op { | ||
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| inline int FillShape(const mxnet::TShape &lshape, const mxnet::TShape &rshape, | ||
| const mxnet::TShape &oshape, mxnet::TShape *new_lshape, | ||
| mxnet::TShape *new_rshape, mxnet::TShape *new_oshape) { | ||
| const int odim = std::max(oshape.ndim(), broadcast::MAX_DIM); | ||
| *new_lshape = mxnet::TShape(odim, 1); | ||
| *new_rshape = mxnet::TShape(odim, 1); | ||
| *new_oshape = mxnet::TShape(odim, 1); | ||
| int bl = oshape.ndim() - lshape.ndim(); | ||
| int br = oshape.ndim() - rshape.ndim(); | ||
| int j = 0, lprod = 1, rprod = 1, oprod = 1; | ||
| for (int i = 0; i < oshape.ndim(); ++i) { | ||
| int l = 1; | ||
| int r = 1; | ||
| int o = oshape[i]; | ||
| if (i >= bl) l = lshape[i - bl]; | ||
| if (i >= br) r = rshape[i - br]; | ||
| if ((lprod != rprod || lprod != oprod || l != r || l != o) && | ||
| (lprod * l > 1 || rprod * r > 1 || oprod * o > 1)) { | ||
| (*new_lshape)[j] = lprod; | ||
| (*new_rshape)[j] = rprod; | ||
| (*new_oshape)[j] = oprod; | ||
| lprod = rprod = oprod = 1; | ||
| ++j; | ||
| } | ||
| lprod *= l; | ||
| rprod *= r; | ||
| oprod *= o; | ||
| } | ||
| if (lprod > 1 || rprod > 1 || oprod > 1) { | ||
| (*new_lshape)[j] = lprod; | ||
| (*new_rshape)[j] = rprod; | ||
| (*new_oshape)[j] = oprod; | ||
| ++j; | ||
| } | ||
| if (j <= broadcast::MAX_DIM) { | ||
| BROADCAST_NDIM_SWITCH(j, NDim, { | ||
| new_lshape->assign(new_lshape->begin(), new_lshape->begin() + NDim); | ||
| new_rshape->assign(new_rshape->begin(), new_rshape->begin() + NDim); | ||
| new_oshape->assign(new_oshape->begin(), new_oshape->begin() + NDim); | ||
| }); | ||
| } else { | ||
| LOG(FATAL) << "Too many broadcast dimensions with operands " << lshape | ||
| << " " << rshape; | ||
| } | ||
| return j; | ||
| } | ||
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| inline void CheckBroadcastable(const mxnet::TShape &from, | ||
| const mxnet::TShape &to) { | ||
| const int bl = to.ndim() - from.ndim(); | ||
| const int br = 0; | ||
| for (int i = 0; i < to.ndim(); ++i) { | ||
| int l = 1, r = 1; | ||
| if (i >= bl) l = from[i - bl]; | ||
| if (i >= br) r = to[i - br]; | ||
| if (!mxnet::dim_size_is_known(l) || !mxnet::dim_size_is_known(r)) continue; | ||
| if (l != r) { | ||
| // Make it compatible with NumPy. | ||
| // For example, (2, 3) cannot broadcast to (2, 0, 3), but (1, 3) can | ||
| // broadcast to (2, 0, 3). | ||
| CHECK(l == 1 || r == 1) | ||
| << "operands could not be broadcast together with shapes " << from | ||
| << " " << to; | ||
| } | ||
| } | ||
| } | ||
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| inline void InferBroadcastShape(const mxnet::TShape &lhs, | ||
| const mxnet::TShape &rhs, | ||
| mxnet::TShape *out_ptr) { | ||
| mxnet::TShape &out = (*out_ptr); | ||
| const int bl = out.ndim() - lhs.ndim(); | ||
| const int br = out.ndim() - rhs.ndim(); | ||
| for (int i = 0; i < out.ndim(); ++i) { | ||
| int l = 1, r = 1; | ||
| if (i >= bl) l = lhs[i - bl]; | ||
| if (i >= br) r = rhs[i - br]; | ||
| if (!mxnet::dim_size_is_known(l) || !mxnet::dim_size_is_known(r)) continue; | ||
| if (l != r) { | ||
| // Make it compatible with NumPy. | ||
| // For example, (2, 3) cannot broadcast to (2, 0, 3), but (1, 3) can | ||
| // broadcast to (2, 0, 3). | ||
| CHECK(l == 1 || r == 1) | ||
| << "operands could not be broadcast together with shapes " << lhs | ||
| << " " << rhs; | ||
| out[i] = (l == 1 ? r : l); | ||
| } else { | ||
| out[i] = l; | ||
| } | ||
| } | ||
| } | ||
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| template <typename DistParam> | ||
| inline bool TwoparamsDistOpShape(const nnvm::NodeAttrs &attrs, | ||
| std::vector<TShape> *in_attrs, | ||
| std::vector<TShape> *out_attrs) { | ||
| const DistParam ¶m = nnvm::get<DistParam>(attrs.parsed); | ||
| CHECK_EQ(out_attrs->size(), 1U); | ||
| if (param.size.has_value()) { | ||
| // Size declared. | ||
| std::vector<dim_t> oshape_vec; | ||
| const mxnet::Tuple<int> &size = param.size.value(); | ||
| for (int i = 0; i < size.ndim(); ++i) { | ||
| oshape_vec.emplace_back(size[i]); | ||
| } | ||
| SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(oshape_vec)); | ||
| for (size_t input_idx = 0; input_idx < in_attrs->size(); input_idx++) { | ||
| CheckBroadcastable((*in_attrs)[input_idx], (*out_attrs)[0]); | ||
| } | ||
| } else { | ||
| // Size undeclared. | ||
| if (in_attrs->size() == 2U) { | ||
| // Both params from ndarray. | ||
| mxnet::TShape &low = (*in_attrs)[0]; | ||
| mxnet::TShape &high = (*in_attrs)[1]; | ||
| mxnet::TShape out(std::max(low.ndim(), high.ndim()), -1); | ||
| InferBroadcastShape(low, high, &out); | ||
| SHAPE_ASSIGN_CHECK(*out_attrs, 0, out); | ||
| } else if (in_attrs->size() == 1U) { | ||
| // One param from ndarray. | ||
| SHAPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(0)) | ||
| } else if (in_attrs->size() == 0) { | ||
| // Two scalar case. | ||
| SHAPE_ASSIGN_CHECK(*out_attrs, 0, TShape(0, -1)) | ||
| return true; | ||
| } | ||
| } | ||
| return out_attrs->at(0).ndim() != 0U; | ||
| } | ||
|
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| } // namespace op | ||
| } // namespace mxnet | ||
|
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| #endif // MXNET_OPERATOR_NUMPY_RANDOM_DIST_COMMON_H_ */ |
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