| /* |
| * 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_uniform_op.h |
| * \brief Operator for numpy sampling from uniform distributions |
| */ |
| #ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_ |
| #define MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_ |
| |
| #include <mxnet/operator_util.h> |
| #include <algorithm> |
| #include <string> |
| #include <vector> |
| #include "../../../api/operator/op_utils.h" |
| #include "../../../common/utils.h" |
| #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 NumpyUniformParam : public dmlc::Parameter<NumpyUniformParam> { |
| dmlc::optional<float> low; |
| dmlc::optional<float> high; |
| std::string ctx; |
| int dtype; |
| dmlc::optional<mxnet::Tuple<index_t>> size; |
| DMLC_DECLARE_PARAMETER(NumpyUniformParam) { |
| DMLC_DECLARE_FIELD(low); |
| DMLC_DECLARE_FIELD(high); |
| 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."); |
| DMLC_DECLARE_FIELD(dtype) |
| .add_enum("None", -1) |
| .add_enum("float32", mshadow::kFloat32) |
| .add_enum("float64", mshadow::kFloat64) |
| .add_enum("float16", mshadow::kFloat16) |
| .set_default(-1) |
| .describe( |
| "DType of the output in case this can't be inferred. " |
| "Defaults to float32 or float64 if not defined (dtype=None)."); |
| } |
| void SetAttrDict(std::unordered_map<std::string, std::string>* dict) { |
| std::ostringstream low_s, high_s, dtype_s, size_s; |
| low_s << low; |
| high_s << high; |
| dtype_s << dtype; |
| size_s << size; |
| (*dict)["low"] = low_s.str(); |
| (*dict)["high"] = high_s.str(); |
| (*dict)["dtype"] = MXNetTypeWithBool2String(dtype); |
| (*dict)["size"] = size_s.str(); |
| } |
| }; |
| |
| inline bool NumpyUniformOpType(const nnvm::NodeAttrs& attrs, |
| std::vector<int>* in_attrs, |
| std::vector<int>* out_attrs) { |
| const NumpyUniformParam& param = nnvm::get<NumpyUniformParam>(attrs.parsed); |
| int otype = param.dtype; |
| if (otype != -1) { |
| (*out_attrs)[0] = otype; |
| } else { |
| (*out_attrs)[0] = mxnet::common::GetDefaultDtype(); |
| } |
| return true; |
| } |
| |
| namespace mxnet_op { |
| template <int ndim, typename IType, typename OType> |
| struct uniform_kernel { |
| MSHADOW_XINLINE static void Map(index_t i, |
| const Shape<ndim>& lstride, |
| const Shape<ndim>& hstride, |
| const Shape<ndim>& oshape, |
| IType* low, |
| IType* high, |
| float* uniform, |
| OType* out) { |
| Shape<ndim> coord = unravel(i, oshape); |
| auto lidx = static_cast<index_t>(dot(coord, lstride)); |
| auto hidx = static_cast<index_t>(dot(coord, hstride)); |
| IType low_value = low[lidx]; |
| IType high_value = high[hidx]; |
| out[i] = low_value + uniform[i] * (high_value - low_value); |
| } |
| }; |
| |
| template <int ndim, typename IType, typename OType> |
| struct uniform_one_scalar_kernel { |
| MSHADOW_XINLINE static void Map(index_t i, |
| int scalar_pos, |
| const Shape<ndim>& stride, |
| const Shape<ndim>& oshape, |
| IType* array, |
| float scalar, |
| float* uniform, |
| OType* out) { |
| Shape<ndim> coord = unravel(i, oshape); |
| auto idx = static_cast<index_t>(dot(coord, stride)); |
| IType low_value; |
| IType high_value; |
| if (scalar_pos == 0) { |
| low_value = scalar; |
| high_value = array[idx]; |
| } else { |
| low_value = array[idx]; |
| high_value = scalar; |
| } |
| out[i] = low_value + uniform[i] * (high_value - low_value); |
| } |
| }; |
| |
| template <typename OType> |
| struct uniform_two_scalar_kernel { |
| MSHADOW_XINLINE static void Map(index_t i, float low, float high, float* uniform, OType* out) { |
| out[i] = low + uniform[i] * (high - low); |
| } |
| }; |
| } // namespace mxnet_op |
| |
| template <typename xpu> |
| void NumpyUniformForward(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 NumpyUniformParam& param = nnvm::get<NumpyUniformParam>(attrs.parsed); |
| CHECK_EQ(outputs.size(), 1); |
| Stream<xpu>* s = ctx.get_stream<xpu>(); |
| |
| // Generate base random number. |
| Random<xpu, float>* prnd = ctx.requested[0].get_random<xpu, float>(s); |
| Tensor<xpu, 1, float> uniform_tensor = |
| ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(outputs[0].Size()), s); |
| prnd->SampleUniform(&uniform_tensor, 0, 1); |
| mxnet::TShape new_lshape, new_hshape, new_oshape; |
| |
| // [scalar scalar] case |
| if (inputs.size() == 0U) { |
| MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { |
| Kernel<uniform_two_scalar_kernel<OType>, xpu>::Launch(s, |
| outputs[0].Size(), |
| param.low.value(), |
| param.high.value(), |
| uniform_tensor.dptr_, |
| outputs[0].dptr<OType>()); |
| }); |
| } else if (inputs.size() == 1U) { |
| // [scalar tensor], [tensor scalar] case |
| int ndim = FillShape(inputs[0].shape_, |
| inputs[0].shape_, |
| outputs[0].shape_, |
| &new_lshape, |
| &new_lshape, |
| &new_oshape); |
| int scalar_pos; |
| float scalar_value; |
| // int type_flag = param.t; |
| if (param.low.has_value()) { |
| scalar_pos = 0; |
| scalar_value = param.low.value(); |
| } else { |
| scalar_pos = 1; |
| scalar_value = param.high.value(); |
| } |
| MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { |
| MSHADOW_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<uniform_one_scalar_kernel<NDim, IType, OType>, xpu>::Launch( |
| s, |
| outputs[0].Size(), |
| scalar_pos, |
| stride, |
| oshape, |
| inputs[0].dptr<IType>(), |
| scalar_value, |
| uniform_tensor.dptr_, |
| outputs[0].dptr<OType>()); |
| }); |
| }); |
| }); |
| } else if (inputs.size() == 2U) { |
| // [tensor tensor] case |
| int ndim = FillShape(inputs[0].shape_, |
| inputs[1].shape_, |
| outputs[0].shape_, |
| &new_lshape, |
| &new_hshape, |
| &new_oshape); |
| MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { |
| MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { |
| BROADCAST_NDIM_SWITCH(ndim, NDim, { |
| Shape<NDim> oshape = new_oshape.get<NDim>(); |
| Shape<NDim> lstride = calc_stride(new_lshape.get<NDim>()); |
| Shape<NDim> hstride = calc_stride(new_hshape.get<NDim>()); |
| Kernel<uniform_kernel<NDim, IType, OType>, xpu>::Launch(s, |
| outputs[0].Size(), |
| lstride, |
| hstride, |
| oshape, |
| inputs[0].dptr<IType>(), |
| inputs[1].dptr<IType>(), |
| uniform_tensor.dptr_, |
| outputs[0].dptr<OType>()); |
| }); |
| }); |
| }); |
| } |
| } |
| |
| } // namespace op |
| } // namespace mxnet |
| |
| #endif // MXNET_OPERATOR_NUMPY_RANDOM_NP_UNIFORM_OP_H_ |
