haojin2 commented on a change in pull request #17541: [numpy] add op 
random.rayleigh
URL: https://github.com/apache/incubator-mxnet/pull/17541#discussion_r378675122
 
 

 ##########
 File path: src/operator/numpy/random/np_rayleigh_op.h
 ##########
 @@ -0,0 +1,205 @@
+/*
+ * 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_rayleigh_op.h
+ * \brief Operator for numpy sampling from rayleigh distribution.
+ */
+
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_RAYLEIGH_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_RAYLEIGH_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <cmath>
+#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 NumpyRayleighParam : public dmlc::Parameter<NumpyRayleighParam> {
+  dmlc::optional<float> scale;
+  dmlc::optional<mxnet::Tuple<int>> size;
+  std::string ctx;
+  DMLC_DECLARE_PARAMETER(NumpyRayleighParam) {
+      DMLC_DECLARE_FIELD(scale)
+      .set_default(dmlc::optional<float>(1.0));
+      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.");
+  }
+};
+
+template <typename DType>
+struct scalar_rayleigh_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, float scale, float *threshold,
+                                  DType *out) {
+    threshold[i] = sqrt(-2 * log(threshold[i]));
+    out[i] =  scale * threshold[i];
+  }
+};
+
+namespace mxnet_op {
+
+template <typename IType>
+struct check_legal_scale_kernel {
+  MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+    if (scalar[i] < 0.0) {
+      flag[0] = -1.0;
+    }
+  }
+};
+
+
+template <int ndim, typename IType, typename OType>
+struct rayleigh_kernel {
+  MSHADOW_XINLINE static void Map(index_t i,
+                                  const Shape<ndim> &stride,
+                                  const Shape<ndim> &oshape,
+                                  IType *scales, float* threshold, OType *out) 
{
+    Shape<ndim> coord = unravel(i, oshape);
+    auto idx = static_cast<index_t>(dot(coord, stride));
+    threshold[i] = sqrt(-2 * log(threshold[i]));
+    out[i] = scales[idx] * threshold[i];
+  }
+};
+
+}  // namespace mxnet_op
+
+template <typename xpu>
+void NumpyRayleighForward(const nnvm::NodeAttrs &attrs,
+                          const OpContext &ctx,
+                          const std::vector<TBlob> &inputs,
+                          const std::vector<OpReqType> &req,
+                          const std::vector<TBlob> &outputs) {
+  using namespace mshadow;
+  using namespace mxnet_op;
+  const NumpyRayleighParam &param = 
nnvm::get<NumpyRayleighParam>(attrs.parsed);
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s);
+  Tensor<xpu, 1, float> workspace =
+      ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(1), s);
+  Tensor<xpu, 1, float> uniform_tensor = outputs[1].FlatTo1D<xpu, float>(s);
+  Tensor<xpu, 1, float> indicator_device = workspace;
+  float indicator_host = 1.0;
+  float *indicator_device_ptr = indicator_device.dptr_;
+  Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr);
+  prnd->SampleUniform(&uniform_tensor, 0.0, 1.0);
+  if (param.scale.has_value()) {
+    CHECK_GE(param.scale.value(), 0.0) << "ValueError: expect scale >= 0";
+    MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, {
+      Kernel<scalar_rayleigh_kernel<DType>, xpu>::Launch(
+        s, outputs[0].Size(), param.scale.value(),
+        uniform_tensor.dptr_, outputs[0].dptr<DType>());
+    });
+  } else {
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      Kernel<check_legal_scale_kernel<IType>, xpu>::Launch(
+      s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr);
+    });
+    _copy<xpu>(s, &indicator_host, indicator_device_ptr);
+    CHECK_GE(indicator_host, 0.0) << "ValueError: expect scale >= 0";
+    mxnet::TShape new_lshape, new_oshape;
+    int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_,
+                         &new_lshape, &new_lshape, &new_oshape);
+    MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, {
+      MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, OType, {
+        BROADCAST_NDIM_SWITCH(ndim, NDim, {
+          Shape<NDim> oshape = new_oshape.get<NDim>();
+          Shape<NDim> stride = calc_stride(new_lshape.get<NDim>());
+          Kernel<rayleigh_kernel<NDim, IType, OType>, xpu>::Launch(
+              s, outputs[0].Size(), stride, oshape, inputs[0].dptr<IType>(),
+              uniform_tensor.dptr_, outputs[0].dptr<OType>());
+        });
+      });
+    });
+  }
+}
+
+template<typename xpu, int ndim, typename DType>
+inline void ScalarRayleighReparamBackwardImpl(const OpContext& ctx,
+                                                 const std::vector<TBlob>& 
inputs,
+                                                 const std::vector<OpReqType>& 
req,
+                                                 const std::vector<TBlob>& 
outputs,
+                                                 const mxnet::TShape& 
new_ishape,
+                                                 const mxnet::TShape& 
new_oshape) {
+  using namespace mshadow;
+  using namespace mshadow::expr;
+  using namespace broadcast;
+  Stream<xpu> *s = ctx.get_stream<xpu>();
+  const TBlob igrad = outputs[0].reshape(new_ishape);
+  // inputs: [grad_from_samples, grad_from_noise(invisible), input_tensor,
+  //          samples, noise]
+  const TBlob ograd = inputs[0].reshape(new_oshape);
+  const TBlob itensor = inputs[2].reshape(new_ishape);
+  const TBlob samples = inputs[3].reshape(new_oshape);
+  const TBlob noise = inputs[4].reshape(new_oshape);
+  size_t workspace_size =
+      ReduceWorkspaceSize<ndim, DType>(s, igrad.shape_, req[0], ograd.shape_);
+  Tensor<xpu, 1, char> workspace =
+      ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size), 
s);
+  Reduce<red::sum, ndim, DType, op::mshadow_op::mul, op::mshadow_op::left>(
+      s, igrad, req[0], workspace, ograd, noise, noise);
+}
+
+template<typename xpu>
+void RayleighReparamBackward(const nnvm::NodeAttrs& attrs,
+                                const OpContext& ctx,
 
 Review comment:
   alignment

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


With regards,
Apache Git Services

Reply via email to