[GitHub] eric-haibin-lin closed pull request #12898: getnnz operator for csr matrix

[GitBox <gi...@apache.org>]

eric-haibin-lin closed pull request #12898: getnnz operator for csr matrix
URL: https://github.com/apache/incubator-mxnet/pull/12898
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/docs/api/python/ndarray/contrib.md b/docs/api/python/ndarray/contrib.md
index 97f38a53a36..63f5f13f452 100644
--- a/docs/api/python/ndarray/contrib.md
+++ b/docs/api/python/ndarray/contrib.md
@@ -55,6 +55,8 @@ In the rest of this document, we list routines provided by the `ndarray.contrib`
     foreach
     while_loop
     cond
+    index_copy
+    getnnz
 ```
 
 ## API Reference
diff --git a/src/operator/contrib/nnz.cc b/src/operator/contrib/nnz.cc
new file mode 100644
index 00000000000..0b93c307f61
--- /dev/null
+++ b/src/operator/contrib/nnz.cc
@@ -0,0 +1,188 @@
+/*
+ * 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) 2018 by Contributors
+ * \file nnz.cc
+ * \brief CPU Implementation of nnz operator
+ */
+#include <mxnet/operator_util.h>
+#include <vector>
+#include <limits>
+#include <algorithm>
+#include "../elemwise_op_common.h"
+#include "../tensor/init_op.h"
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct NNZParam : public dmlc::Parameter<NNZParam> {
+  dmlc::optional<int> axis;
+  DMLC_DECLARE_PARAMETER(NNZParam) {
+    DMLC_DECLARE_FIELD(axis)
+    .set_default(dmlc::optional<int>())
+    .describe("Select between the number of values across the whole matrix, "
+              "in each column, or in each row.");
+  }
+};
+
+static bool NNZType(const nnvm::NodeAttrs& attrs,
+                    std::vector<int> *in_attrs,
+                    std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  // infer int64 for count
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
+  return true;
+}
+
+inline bool NNZShape(const nnvm::NodeAttrs& attrs,
+                     std::vector<TShape> *in_attrs,
+                     std::vector<TShape> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  // csr_matrix is 2-D
+  CHECK_EQ(in_attrs->at(0).ndim(), 2);
+  const NNZParam& param = nnvm::get<NNZParam>(attrs.parsed);
+  // whole matrix
+  if (!param.axis.has_value()) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape1(1));
+  } else if (param.axis.value() == 0) {
+    // columns
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape1(in_attrs->at(0)[1]));
+  } else if (param.axis.value() == 1) {
+    // rows
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::Shape1(in_attrs->at(0)[0]));
+  } else {
+    LOG(FATAL) << "Unexpected value for axis(" << param.axis.value()
+      << "). Candidates are None, 0, and 1";
+  }
+  return true;
+}
+
+template<typename xpu>
+void NNZComputeCsrImpl(const NNZParam& param,
+                       const OpContext& ctx,
+                       const NDArray& input,
+                       const OpReqType req,
+                       const TBlob& output);
+
+struct CsrNNZRowKernel {
+  /*!
+   * \brief Map function for general case of take grad
+   * \param tid           global thread id
+   * \param out           ptr to output
+   * \param indptr        ptr to source csr indptr
+   */
+  template<typename IType, typename DType>
+  MSHADOW_XINLINE static void Map(int tid, DType* out, const IType* indptr) {
+    out[tid] = static_cast<DType>(indptr[tid + 1] - indptr[tid]);
+  }
+};
+
+template<>
+void NNZComputeCsrImpl<cpu>(const NNZParam& param,
+                            const OpContext& ctx,
+                            const NDArray& input,
+                            const OpReqType req,
+                            const TBlob& output) {
+  using namespace csr;
+  CHECK_EQ(req, kWriteTo);
+  mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
+  if (!input.storage_initialized()) {
+    Fill<false>(s, output, kWriteTo, 0);
+    return;
+  }
+  MSHADOW_TYPE_SWITCH(output.type_flag_, DType, {
+    MSHADOW_IDX_TYPE_SWITCH(input.aux_type(kIndPtr), IType, {
+      DType* out_ptr = output.dptr<DType>();
+      const IType* indptr = input.aux_data(kIndPtr).dptr<IType>();
+      const nnvm::dim_t num_rows = input.shape()[0];
+      if (!param.axis.has_value()) {
+        // whole matrix
+        out_ptr[0] = indptr[num_rows];
+      } else if (param.axis.value() == 0) {
+        // column
+        LOG(FATAL) << "getnnz with axis = 1 is not supported yet";
+      } else if (param.axis.value() == 1) {
+        // row
+        mxnet_op::Kernel<CsrNNZRowKernel, cpu>::Launch(s, num_rows, out_ptr, indptr);
+      }
+    });
+  });
+}
+
+template<typename xpu>
+void NNZComputeEx(const nnvm::NodeAttrs& attrs,
+                  const OpContext& ctx,
+                  const std::vector<NDArray>& inputs,
+                  const std::vector<OpReqType>& req,
+                  const std::vector<NDArray>& outputs) {
+  CHECK_EQ(inputs.size(), 1U);
+  CHECK_EQ(outputs.size(), 1U);
+  CHECK_EQ(req.size(), 1U);
+  const auto in_stype = inputs[0].storage_type();
+  const auto out_stype = outputs[0].storage_type();
+  const NNZParam& param = nnvm::get<NNZParam>(attrs.parsed);
+  if (in_stype == kCSRStorage && out_stype == kDefaultStorage) {
+    NNZComputeCsrImpl<xpu>(param, ctx, inputs[0], req[0], outputs[0].data());
+  } else {
+    LogUnimplementedOp(attrs, ctx, inputs, req, outputs);
+  }
+}
+
+bool NNZStorageType(const nnvm::NodeAttrs& attrs,
+                    const int dev_mask,
+                    DispatchMode* dispatch_mode,
+                    std::vector<int> *in_attrs,
+                    std::vector<int> *out_attrs) {
+  CHECK_EQ(in_attrs->size(), 1);
+  CHECK_EQ(out_attrs->size(), 1);
+  bool dispatched = false;
+  const auto in_stype = in_attrs->at(0);
+  auto& out_stype = out_attrs->at(0);
+  // only support csr for now
+  if (!dispatched && in_stype == kCSRStorage) {
+    dispatched = storage_type_assign(&out_stype, kDefaultStorage,
+                                     dispatch_mode, DispatchMode::kFComputeEx);
+  }
+  return dispatched;
+}
+
+DMLC_REGISTER_PARAMETER(NNZParam);
+
+NNVM_REGISTER_OP(_contrib_getnnz)
+.describe(R"code(Number of stored values for a sparse tensor, including explicit zeros.
+
+This operator only supports CSR matrix on CPU.
+
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<NNZParam>)
+.set_attr<nnvm::FInferShape>("FInferShape", NNZShape)
+.set_attr<nnvm::FInferType>("FInferType", NNZType)
+.set_attr<FInferStorageType>("FInferStorageType", NNZStorageType)
+.set_attr<FComputeEx>("FComputeEx<cpu>", NNZComputeEx<cpu>)
+.add_argument("data", "NDArray-or-Symbol", "Input");
+
+}  // namespace op
+}  // namespace mxnet
diff --git a/tests/python/unittest/test_sparse_ndarray.py b/tests/python/unittest/test_sparse_ndarray.py
index 8dd250cf98e..1aa1d3cc3d3 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -1031,6 +1031,22 @@ def check_sparse_take(density, mode):
         for m in modes:
             check_sparse_take(d, m)
 
+@with_seed()
+def test_sparse_getnnz():
+    def check_sparse_getnnz(density, axis):
+        shape = rand_shape_2d()
+        data = rand_ndarray(shape, 'csr', density=density)
+        data_sp = data.asscipy()
+        result = mx.nd.contrib.getnnz(data, axis=axis)
+        expected_result = data_sp.getnnz(axis=axis)
+        assert_almost_equal(result.asnumpy(), expected_result)
+
+    densities = [0, 0.5, 1]
+    axis = [1, None]
+    for d in densities:
+        for a in axis:
+            check_sparse_getnnz(d, a)
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()


 

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
users@infra.apache.org


With regards,
Apache Git Services