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Subject [GitHub] [incubator-mxnet] anirudh2290 commented on a change in pull request #16829: [Numpy][Operator] 'where' Implementation in MXNet
Date Fri, 15 Nov 2019 21:39:20 GMT
anirudh2290 commented on a change in pull request #16829: [Numpy][Operator] 'where' Implementation
in MXNet
URL: https://github.com/apache/incubator-mxnet/pull/16829#discussion_r347021917
 
 

 ##########
 File path: src/operator/numpy/np_where_op-inl.h
 ##########
 @@ -0,0 +1,266 @@
+/*
+ * 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) 2017 by Contributors
+ * \file np_where_op.cc
+ * \brief Function definition of numpy operator where
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_NP_WHERE_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_WHERE_OP_INL_H_
+
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <utility>
+#include <vector>
+#include "../../common/utils.h"
+#include "../mxnet_op.h"
+#include "../mshadow_op.h"
+#include "../operator_common.h"
+#include "np_broadcast_reduce_op.h"
+
+namespace mxnet {
+namespace op {
+
+#define NUMPY_WHERE_MAX_DIM 5
+
+using namespace mshadow;
+
+template<int ndim>
+struct numpy_where_kernel {
+  template<typename CType, typename DType>
+  MSHADOW_XINLINE static void Map(index_t base, OpReqType req, const Shape<ndim> &cstride,
+                                  const Shape<ndim> &xstride, const Shape<ndim>
&ystride,
+                                  const Shape<ndim> &oshape, CType *datac, DType
*datax,
+                                  DType *datay, DType *out) {
+    Shape<ndim> coord = mxnet_op::unravel(base, oshape);
+    auto cidx = static_cast<index_t>(mxnet_op::dot(coord, cstride));
+    auto xidx = static_cast<index_t>(mxnet_op::dot(coord, xstride));
+    auto yidx = static_cast<index_t>(mxnet_op::dot(coord, ystride));
+    KERNEL_ASSIGN(out[base], req, datac[cidx] != CType(0) ? datax[xidx] : datay[yidx]);
+  }
+};
+
+template<int ndim, bool is_left>
+struct numpy_where_backward_kernel {
+  template<typename CType, typename DType>
+  MSHADOW_XINLINE static void Map(index_t base, OpReqType req,
+                                  const Shape<ndim> &cstride, const Shape<ndim>
&oshape,
+                                  CType *datac, DType *datao, DType *grad) {
+    Shape<ndim> coord = mxnet_op::unravel(base, oshape);
+    auto cidx = static_cast<index_t>(mxnet_op::dot(coord, cstride));
+    if (is_left) {
+      KERNEL_ASSIGN(grad[base], req, datac[cidx] != CType(0) ? datao[base] : DType(0));
+    } else {
+      KERNEL_ASSIGN(grad[base], req, datac[cidx] == CType(0) ? datao[base] : DType(0));
+    }
+  }
+};
+
+inline bool NumpyWhereOpShape(const nnvm::NodeAttrs& attrs,
+                              mxnet::ShapeVector* in_attrs,
+                              mxnet::ShapeVector* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 3U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  mxnet::TShape& operand1 = (*in_attrs)[0];
+  mxnet::TShape& operand2 = (*in_attrs)[1];
+  mxnet::TShape& operand3 = (*in_attrs)[2];
+
+  if (operand1 == operand2 && operand2 == operand3) {
+    SHAPE_ASSIGN_CHECK(*out_attrs, 0, operand1);
+    return shape_is_known(out_attrs->at(0));
+  }
+  mxnet::TShape out(std::max({operand1.ndim(), operand2.ndim(), operand3.ndim()}), -1);
+  const int b1 = out.ndim() - operand1.ndim();
+  const int b2 = out.ndim() - operand2.ndim();
+  const int b3 = out.ndim() - operand3.ndim();
+  for (int i = 0; i < out.ndim(); ++i) {
+    int s1 = 1, s2 = 1, s3 = 1;
+    if (i >= b1) s1 = operand1[i-b1];
+    if (i >= b2) s2 = operand2[i-b2];
+    if (i >= b3) s3 = operand3[i-b3];
+    if (!(s1 == s2 && s2 == s3)) {
+      CHECK((s1 == 1 && s2 == 1) || (s1 == 1 && s3 == 1) || (s2 == 1 &&
s3 == 1) ||
+            (s1 == 1 && s2 == s3) || (s2 == 1 && s1 == s3) || (s3 == 1 &&
s1 == s2))
+        << "Operands could not be broadcast together.";
+      out[i] = std::max({s1, s2, s3});
+    } else {
+      out[i] = s1;
+    }
+  }
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, out);
+  return shape_is_known(out);
+}
+
+inline bool NumpyWhereOpType(const nnvm::NodeAttrs& attrs,
+                             std::vector<int>* in_attrs,
+                             std::vector<int>* out_attrs) {
+  CHECK_EQ(in_attrs->size(), 3U)
+    << "where operator takes 3 arguments (" << in_attrs->size() << "
given)";
+  CHECK_EQ(out_attrs->size(), 1U);
+  CHECK_EQ(in_attrs->at(1), in_attrs->at(2));
+  TYPE_ASSIGN_CHECK(*out_attrs, 0, in_attrs->at(1));
 
 Review comment:
   you should also add backward inference. inferring input types from output. One way to write
this easily, would be to use ElemwiseType or ElemwiseAttr.

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