| /* |
| * 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. |
| */ |
| |
| /*! |
| * \file np_exponential_op.h |
| * \brief Operator for numpy sampling from exponential distribution. |
| */ |
| |
| #ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_EXPONENTIAL_OP_H_ |
| #define MXNET_OPERATOR_NUMPY_RANDOM_NP_EXPONENTIAL_OP_H_ |
| |
| #include <mxnet/operator_util.h> |
| #include <algorithm> |
| #include <string> |
| #include <vector> |
| #include <cmath> |
| #include <unordered_map> |
| #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 NumpyExponentialParam : public dmlc::Parameter<NumpyExponentialParam> { |
| dmlc::optional<float> scale; |
| dmlc::optional<mxnet::Tuple<index_t>> size; |
| std::string ctx; |
| DMLC_DECLARE_PARAMETER(NumpyExponentialParam) { |
| DMLC_DECLARE_FIELD(scale).set_default(dmlc::optional<float>(1.0)); |
| DMLC_DECLARE_FIELD(size) |
| .set_default(dmlc::optional<mxnet::Tuple<index_t>>()) |
| .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."); |
| } |
| void SetAttrDict(std::unordered_map<std::string, std::string>* dict) { |
| std::ostringstream scale_s, size_s; |
| scale_s << scale; |
| size_s << size; |
| (*dict)["scale"] = scale_s.str(); |
| (*dict)["size"] = size_s.str(); |
| } |
| }; |
| |
| template <typename DType> |
| struct scalar_exponential_kernel { |
| MSHADOW_XINLINE static void Map(index_t i, float scale, float* threshold, DType* out) { |
| out[i] = -scale * log(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 exponential_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] = -log(threshold[i]); |
| out[i] = scales[idx] * threshold[i]; |
| } |
| }; |
| |
| } // namespace mxnet_op |
| |
| template <typename xpu> |
| void NumpyExponentialForward(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 NumpyExponentialParam& param = nnvm::get<NumpyExponentialParam>(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_exponential_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<exponential_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 ExponentialReparamBackwardImpl(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(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); |
| #if !defined(__CUDACC__) |
| Reduce<red::sum, ndim, DType, op::mshadow_op::mul, op::mshadow_op::left>( |
| s, igrad, req[0], workspace, ograd, noise, noise); |
| #else |
| RTCReduce(ctx, igrad, req[0], workspace, ograd, noise, noise, "red::sum{}", ndim, "mul", "left"); |
| #endif |
| } |
| |
| template <typename xpu> |
| void ExponentialReparamBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<TBlob>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<TBlob>& outputs) { |
| // skip kernel launch for zero-size tensors |
| if (inputs[0].shape_.Size() == 0U) { |
| return; |
| } |
| // [scalar] case |
| if (outputs.size() == 0U) { |
| return; |
| } |
| // [tensor] case |
| if (inputs.size() == 5U) { |
| mxnet::TShape new_ishape, new_oshape; |
| int ndim = FillShape(outputs[0].shape_, |
| outputs[0].shape_, |
| inputs[0].shape_, |
| &new_ishape, |
| &new_ishape, |
| &new_oshape); |
| MSHADOW_REAL_TYPE_SWITCH(outputs[0].type_flag_, DType, { |
| BROADCAST_NDIM_SWITCH(ndim, NDim, { |
| ExponentialReparamBackwardImpl<xpu, NDim, DType>( |
| ctx, inputs, req, outputs, new_ishape, new_oshape); |
| }); |
| }); |
| } |
| } |
| |
| } // namespace op |
| } // namespace mxnet |
| |
| #endif // MXNET_OPERATOR_NUMPY_RANDOM_NP_EXPONENTIAL_OP_H_ |
