[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17302: [numpy]add op random.logistic, random.gumbel
haojin2 commented on a change in pull request #17302: [numpy]add op
random.logistic, random.gumbel
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r367799853
##
File path: src/operator/numpy/random/np_location_scale_op.h
##
@@ -0,0 +1,510 @@
+/*
+ * 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_location_scale_op.h
+ * \brief Operator for numpy sampling from localtion scale distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LOCATION_SCALE_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LOCATION_SCALE_OP_H_
+
+#include
+#include
+#include
+#include
+#include
+#include
+#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 NumpyLocationScaleParam : public
dmlc::Parameter {
+ dmlc::optional loc;
+ dmlc::optional scale;
+ dmlc::optional> size;
+ DMLC_DECLARE_PARAMETER(NumpyLocationScaleParam) {
+DMLC_DECLARE_FIELD(loc);
+DMLC_DECLARE_FIELD(scale);
+DMLC_DECLARE_FIELD(size)
+ .set_default(dmlc::optional>())
+ .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.");
+ }
+};
+
+inline bool NumpyLocationScaleOpType(const nnvm::NodeAttrs ,
+ std::vector *in_attrs,
+ std::vector *out_attrs) {
+ (*out_attrs)[0] = mshadow::kFloat32;
+ (*out_attrs)[1] = mshadow::kFloat32;
+ return true;
+}
+
+namespace mxnet_op {
+
+template
+struct logistic_two_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, float loc, float scale,
+ float *noise, DType *out) {
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc + noise[i] * scale;
+ }
+};
+
+struct logistic_two_scalar {
+ template
+ static void op(Stream *s, const int out_size,
+ const float loc, const float scale,
+ const Tensor& noise,
+ const Tensor& out) {
+Kernel, xpu>::Launch(
+ s, out_size, loc, scale, noise.dptr_, out.dptr_);
+}
+};
+
+template
+struct gumbel_two_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, float loc, float scale,
+ float *noise, DType *out) {
+noise[i] = -log(-log(noise[i]));
+out[i] = loc + noise[i] * scale;
+ }
+};
+
+struct gumbel_two_scalar {
+ template
+ static void op(Stream *s, const int out_size,
+ const float loc, const float scale,
+ const Tensor& noise,
+ const Tensor& out) {
+Kernel, xpu>::Launch(
+ s, out_size, loc, scale, noise.dptr_, out.dptr_);
+ }
+};
+
+template
+struct check_legal_scale_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+if (scalar[i] < 0) {
+ *flag = -1.0;
+}
+ }
+};
+
+template
+struct logistic_one_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+ const Shape ,
+ const Shape , IType *array,
+ float scalar, float *noise, OType *out) {
+Shape coord = unravel(i, oshape);
+auto idx = static_cast(dot(coord, stride));
+IType loc_value;
+IType scale_value;
+if (scalar_pos == 0) {
+ loc_value = scalar;
+ scale_value = array[idx];
+} else {
+ loc_value = array[idx];
+ scale_value = scalar;
+}
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc_value + noise[i] * scale_value;
+ }
+};
+
+struct logistic_tensor_scalar {
+ template
+ static void op(Stream *s, const int out_size, const int scalar_pos,
+ const Shape , const Shape ,
+ const Tensor& array, float scalar,
+ const Tensor& noise, const Tensor& out) {
+Kernel, xpu>::Launch(
+ s, out_size, scalar_pos, stride,
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17302: [numpy]add op random.logistic
haojin2 commented on a change in pull request #17302: [numpy]add op
random.logistic
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r366765196
##
File path: src/operator/numpy/random/np_logistic_op.h
##
@@ -0,0 +1,331 @@
+/*
+ * 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_logistic_op.h
+ * \brief Operator for numpy sampling from logistic distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+
+#include
+#include
+#include
+#include
+#include
+#include
+#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 NumpyLogisticParam : public dmlc::Parameter {
+ dmlc::optional loc;
+ dmlc::optional scale;
+ dmlc::optional> size;
+ DMLC_DECLARE_PARAMETER(NumpyLogisticParam) {
+DMLC_DECLARE_FIELD(loc);
+DMLC_DECLARE_FIELD(scale);
+DMLC_DECLARE_FIELD(size)
+.set_default(dmlc::optional>())
+.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.");
+ }
+};
+
+inline bool NumpyLogisticOpType(const nnvm::NodeAttrs ,
+ std::vector *in_attrs,
+ std::vector *out_attrs) {
+
+ (*out_attrs)[0] = mshadow::kFloat32;
+ (*out_attrs)[1] = mshadow::kFloat32;
+ return true;
+}
+
+namespace mxnet_op {
+
+template
+struct logistic_two_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, float loc, float scale,
+ float *noise, DType *out) {
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc + noise[i] * scale;
+ }
+};
+
+template
+struct check_legal_scale_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+if (scalar[i] < 0) {
+ *flag = -1.0;
+}
+ }
+};
+
+template
+struct logistic_one_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+ const Shape ,
+ const Shape , IType *array,
+ float scalar, float *noise, OType *out) {
+Shape coord = unravel(i, oshape);
+auto idx = static_cast(dot(coord, stride));
+IType loc_value;
+IType scale_value;
+if (scalar_pos == 0) {
+ loc_value = scalar;
+ scale_value = array[idx];
+} else {
+ loc_value = array[idx];
+ scale_value = scalar;
+}
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc_value + noise[i] * scale_value;
+ }
+};
+
+template
+struct logistic_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, const Shape ,
+ const Shape ,
+ const Shape , IType *loc,
+ IType *scale, float *noise, OType *out) {
+Shape coord = unravel(i, oshape);
+auto lidx = static_cast(dot(coord, lstride));
+auto hidx = static_cast(dot(coord, hstride));
+IType loc_value = loc[lidx];
+IType scale_value = scale[hidx];
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc_value + noise[i] * scale_value;
+ }
+};
+
+} // namespace mxnet_op
+
+template
+void NumpyLogisticForward(const nnvm::NodeAttrs ,
+ const OpContext ,
+ const std::vector ,
+ const std::vector ,
+ const std::vector ) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ const NumpyLogisticParam =
nnvm::get(attrs.parsed);
+ Stream *s = ctx.get_stream();
+ // Generate base random number.
+ Random *prnd = ctx.requested[0].get_random(s);
+ Tensor workspace =
+ ctx.requested[1].get_space_typed(Shape1(1), s);
+ Tensor logistic_tensor = outputs[1].FlatTo1D(s);
+ Tensor indicator_device = workspace;
+ float
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17302: [numpy]add op random.logistic
haojin2 commented on a change in pull request #17302: [numpy]add op
random.logistic
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r366765107
##
File path: src/operator/numpy/random/np_logistic_op.h
##
@@ -0,0 +1,331 @@
+/*
+ * 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_logistic_op.h
+ * \brief Operator for numpy sampling from logistic distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+
+#include
+#include
+#include
+#include
+#include
+#include
+#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 NumpyLogisticParam : public dmlc::Parameter {
+ dmlc::optional loc;
+ dmlc::optional scale;
+ dmlc::optional> size;
+ DMLC_DECLARE_PARAMETER(NumpyLogisticParam) {
+DMLC_DECLARE_FIELD(loc);
+DMLC_DECLARE_FIELD(scale);
+DMLC_DECLARE_FIELD(size)
+.set_default(dmlc::optional>())
+.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.");
+ }
+};
+
+inline bool NumpyLogisticOpType(const nnvm::NodeAttrs ,
+ std::vector *in_attrs,
+ std::vector *out_attrs) {
+
+ (*out_attrs)[0] = mshadow::kFloat32;
+ (*out_attrs)[1] = mshadow::kFloat32;
+ return true;
+}
+
+namespace mxnet_op {
+
+template
+struct logistic_two_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, float loc, float scale,
+ float *noise, DType *out) {
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc + noise[i] * scale;
+ }
+};
+
+template
+struct check_legal_scale_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) {
+if (scalar[i] < 0) {
+ *flag = -1.0;
+}
+ }
+};
+
+template
+struct logistic_one_scalar_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, int scalar_pos,
+ const Shape ,
+ const Shape , IType *array,
+ float scalar, float *noise, OType *out) {
+Shape coord = unravel(i, oshape);
+auto idx = static_cast(dot(coord, stride));
+IType loc_value;
+IType scale_value;
+if (scalar_pos == 0) {
+ loc_value = scalar;
+ scale_value = array[idx];
+} else {
+ loc_value = array[idx];
+ scale_value = scalar;
+}
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc_value + noise[i] * scale_value;
+ }
+};
+
+template
+struct logistic_kernel {
+ MSHADOW_XINLINE static void Map(index_t i, const Shape ,
+ const Shape ,
+ const Shape , IType *loc,
+ IType *scale, float *noise, OType *out) {
+Shape coord = unravel(i, oshape);
+auto lidx = static_cast(dot(coord, lstride));
+auto hidx = static_cast(dot(coord, hstride));
+IType loc_value = loc[lidx];
+IType scale_value = scale[hidx];
+noise[i] = log(noise[i]) - log(1 - noise[i]);
+out[i] = loc_value + noise[i] * scale_value;
+ }
+};
+
+} // namespace mxnet_op
+
+template
+void NumpyLogisticForward(const nnvm::NodeAttrs ,
+ const OpContext ,
+ const std::vector ,
+ const std::vector ,
+ const std::vector ) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ const NumpyLogisticParam =
nnvm::get(attrs.parsed);
+ Stream *s = ctx.get_stream();
+ // Generate base random number.
+ Random *prnd = ctx.requested[0].get_random(s);
+ Tensor workspace =
+ ctx.requested[1].get_space_typed(Shape1(1), s);
+ Tensor logistic_tensor = outputs[1].FlatTo1D(s);
+ Tensor indicator_device = workspace;
+ float
[GitHub] [incubator-mxnet] haojin2 commented on a change in pull request #17302: [numpy]add op random.logistic
haojin2 commented on a change in pull request #17302: [numpy]add op
random.logistic
URL: https://github.com/apache/incubator-mxnet/pull/17302#discussion_r366763583
##
File path: src/operator/numpy/random/np_logistic_op.h
##
@@ -0,0 +1,331 @@
+/*
+ * 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_logistic_op.h
+ * \brief Operator for numpy sampling from logistic distributions
+ */
+#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+#define MXNET_OPERATOR_NUMPY_RANDOM_NP_LOGISTIC_OP_H_
+
+#include
+#include
+#include
+#include
+#include
+#include
+#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 NumpyLogisticParam : public dmlc::Parameter {
+ dmlc::optional loc;
+ dmlc::optional scale;
+ dmlc::optional> size;
+ DMLC_DECLARE_PARAMETER(NumpyLogisticParam) {
+DMLC_DECLARE_FIELD(loc);
+DMLC_DECLARE_FIELD(scale);
+DMLC_DECLARE_FIELD(size)
+.set_default(dmlc::optional>())
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