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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/01/13 04:05:17 UTC
[GitHub] [incubator-mxnet] sjtuWangDing opened a new pull request #17279:
[Numpy] Add linalg.pinv op
sjtuWangDing opened a new pull request #17279: [Numpy] Add linalg.pinv op
URL: https://github.com/apache/incubator-mxnet/pull/17279
## Description ##
Add linalg.pinv op
## Checklist ##
### Essentials ###
Please feel free to remove inapplicable items for your PR.
- [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes)
- [ ] Changes are complete (i.e. I finished coding on this PR)
- [ ] All changes have test coverage:
- Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
- Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
- Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
- [ ] Code is well-documented:
- For user-facing API changes, API doc string has been updated.
- For new C++ functions in header files, their functionalities and arguments are documented.
- For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
- Check the API doc at https://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html
- [ ] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
### Changes ###
- [ ] Feature1, tests, (and when applicable, API doc)
- [ ] Feature2, tests, (and when applicable, API doc)
## Comments ##
- If this change is a backward incompatible change, why must this change be made.
- Interesting edge cases to note here
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request
#17279: [Numpy] Add linalg.pinv op
Posted by GitBox <gi...@apache.org>.
haojin2 commented on a change in pull request #17279: [Numpy] Add linalg.pinv op
URL: https://github.com/apache/incubator-mxnet/pull/17279#discussion_r367175367
##########
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";
Review comment:
Probably no need for line wrap here.
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[GitHub] [incubator-mxnet] haojin2 merged pull request #17279: [Numpy] Add
linalg.pinv op
Posted by GitBox <gi...@apache.org>.
haojin2 merged pull request #17279: [Numpy] Add linalg.pinv op
URL: https://github.com/apache/incubator-mxnet/pull/17279
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request
#17279: [Numpy] Add linalg.pinv op
Posted by GitBox <gi...@apache.org>.
haojin2 commented on a change in pull request #17279: [Numpy] Add linalg.pinv op
URL: https://github.com/apache/incubator-mxnet/pull/17279#discussion_r367175293
##########
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)) {
Review comment:
`else if (shape_is_known(pinv_shape))`
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[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request
#17279: [Numpy] Add linalg.pinv op
Posted by GitBox <gi...@apache.org>.
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))`
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