[GitHub] [incubator-mxnet] AntiZpvoh commented on a change in pull request #17385: [NumPy] add random.geometric op

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AntiZpvoh commented on a change in pull request #17385: [NumPy] add 
random.geometric op
URL: https://github.com/apache/incubator-mxnet/pull/17385#discussion_r369942090
 
 

 ##########
 File path: src/operator/numpy/random/np_geometric_op.h
 ##########
 @@ -0,0 +1,177 @@
+/*
+ * 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_geometric_op.h
+ * \brief Operator for numpy sampling from geometric distribution.
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_GEOMETRIC_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_GEOMETRIC_OP_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"
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyGeometricParam : public dmlc::Parameter<NumpyGeometricParam> {
+  dmlc::optional<float> prob;
+  std::string ctx;
+  int dtype;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  DMLC_DECLARE_PARAMETER(NumpyGeometricParam) {
+    DMLC_DECLARE_FIELD(prob);
+    DMLC_DECLARE_FIELD(size)
+      .set_default(dmlc::optional<mxnet::Tuple<int>>())
+        .describe(
+            "Output shape. If the given shape is, "
+            "e.g., (m, n, k), then m * n * k samples are drawn. "
+            "Default is None, in which case a single value is returned.");
+    DMLC_DECLARE_FIELD(ctx).set_default("cpu").describe(
+        "Context of output, in format [cpu|gpu|cpu_pinned](n)."
+        " Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+        .add_enum("uint8", mshadow::kUint8)
+        .add_enum("int32", mshadow::kInt32)
+        .add_enum("float32", mshadow::kFloat32)
+        .add_enum("float64", mshadow::kFloat64)
+        .add_enum("float16", mshadow::kFloat16)
+        .add_enum("bool", mshadow::kBool)
+        .set_default(mshadow::kFloat32)
+        .describe(
+            "DType of the output in case this can't be inferred. "
+            "Defaults to float32 if not defined (dtype=None).");
+  }
+};
+
+inline bool NumpyGeometricOpType(const nnvm::NodeAttrs &attrs,
+                               std::vector<int> *in_attrs,
+                               std::vector<int> *out_attrs) {
+  const NumpyGeometricParam &param = 
nnvm::get<NumpyGeometricParam>(attrs.parsed);
+  int otype = param.dtype;
+  (*out_attrs)[0] = otype;
+  return true;
+}
+
+namespace mxnet_op {
+
+template <int ndim, typename IType, typename OType>
+struct geometric_kernel {
+  MSHADOW_XINLINE static void Map(index_t i,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape,
+                                  IType *probs, float* uniforms, OType *out) {
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    IType prob = probs[idx];
+    out[i] = floor(log(uniforms[i]) / log(1 - prob)) + 1;
+  }
+};
+
+template <typename OType>
+struct scalar_geometric_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float prob, float *uniforms,
+                                  OType *out) {
+    out[i] = floor(log(uniforms[i]) / log(1 - prob)) + 1;
+  }
+};
+
+template <typename IType>
+struct check_legal_prob_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+    if (scalar[i] < 0.0 || scalar[i] > 1.0) {
 
 Review comment:
   You mean 0 prob is unacceptable?

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