blob: b960267d4469e8fc4f833b762231ecd7b1a4f35b [file]
/*
* 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 ufunc_helper.cc
* \brief ufunc helper
*/
#include "ufunc_helper.h"
#include "utils.h"
#include "../../imperative/imperative_utils.h"
#include "../../operator/tensor/elemwise_binary_scalar_op.h"
namespace mxnet {
template<>
void SetAttrDict<double>(nnvm::NodeAttrs* attrs) {
if (Imperative::Get()->is_recording()) {
attrs->dict["scalar"] = std::to_string(::dmlc::get<double>(attrs->parsed));
}
}
void UFuncHelper(NDArray* lhs, NDArray* rhs, NDArray* out,
runtime::MXNetRetValue* ret, const nnvm::Op* op) {
using namespace runtime;
nnvm::NodeAttrs attrs;
attrs.op = op;
NDArray* inputs[] = {lhs, rhs};
int num_inputs = 2;
NDArray** outputs = out == nullptr ? nullptr : &out;
int num_outputs = out != nullptr;
auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
if (outputs) {
*ret = PythonArg(2);
} else {
*ret = reinterpret_cast<NDArray*>(ndoutputs[0]);
}
}
void UFuncHelper(NDArray* lhs, int64_t rhs, NDArray* out,
runtime::MXNetRetValue* ret, const nnvm::Op* op) {
using namespace runtime;
nnvm::NodeAttrs attrs;
op::NumpyBinaryScalarParam param;
param.scalar = rhs;
param.is_int = true;
attrs.op = op;
attrs.parsed = param;
SetAttrDict<op::NumpyBinaryScalarParam>(&attrs);
NDArray** inputs = &lhs;
int num_inputs = 1;
NDArray** outputs = out == nullptr ? nullptr : &out;
int num_outputs = out != nullptr;
auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
if (outputs) {
*ret = PythonArg(2);
} else {
*ret = reinterpret_cast<NDArray*>(ndoutputs[0]);
}
}
void UFuncHelper(NDArray* lhs, double rhs, NDArray* out,
runtime::MXNetRetValue* ret, const nnvm::Op* op) {
using namespace runtime;
nnvm::NodeAttrs attrs;
op::NumpyBinaryScalarParam param;
param.scalar = rhs;
param.is_int = false;
attrs.op = op;
attrs.parsed = param;
SetAttrDict<op::NumpyBinaryScalarParam>(&attrs);
NDArray** inputs = &lhs;
int num_inputs = 1;
NDArray** outputs = out == nullptr ? nullptr : &out;
int num_outputs = out != nullptr;
auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
if (outputs) {
*ret = PythonArg(2);
} else {
*ret = reinterpret_cast<NDArray*>(ndoutputs[0]);
}
}
void UFuncHelper(int64_t lhs, NDArray* rhs, NDArray* out,
runtime::MXNetRetValue* ret, const nnvm::Op* op) {
using namespace runtime;
nnvm::NodeAttrs attrs;
op::NumpyBinaryScalarParam param;
param.scalar = lhs;
param.is_int = true;
attrs.op = op;
attrs.parsed = param;
SetAttrDict<op::NumpyBinaryScalarParam>(&attrs);
NDArray** inputs = &rhs;
int num_inputs = 1;
NDArray** outputs = out == nullptr ? nullptr : &out;
int num_outputs = out != nullptr;
auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
if (outputs) {
*ret = PythonArg(2);
} else {
*ret = reinterpret_cast<NDArray*>(ndoutputs[0]);
}
}
void UFuncHelper(double lhs, NDArray* rhs, NDArray* out,
runtime::MXNetRetValue* ret, const nnvm::Op* op) {
using namespace runtime;
nnvm::NodeAttrs attrs;
op::NumpyBinaryScalarParam param;
param.scalar = lhs;
param.is_int = false;
attrs.op = op;
attrs.parsed = param;
SetAttrDict<op::NumpyBinaryScalarParam>(&attrs);
NDArray** inputs = &rhs;
int num_inputs = 1;
NDArray** outputs = out == nullptr ? nullptr : &out;
int num_outputs = out != nullptr;
auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
if (outputs) {
*ret = PythonArg(2);
} else {
*ret = reinterpret_cast<NDArray*>(ndoutputs[0]);
}
}
void UFuncHelper(runtime::MXNetArgs args,
runtime::MXNetRetValue* ret,
const nnvm::Op* fn_array,
const nnvm::Op* lfn_scalar,
const nnvm::Op* rfn_scalar) {
using namespace runtime;
NDArray* out = args[2].operator NDArray*();
if (args[0].type_code() == kNDArrayHandle) {
if (args[1].type_code() == kNDArrayHandle) {
UFuncHelper(args[0].operator NDArray*(), args[1].operator NDArray*(), out, ret, fn_array);
} else if (args[1].type_code() == kDLInt) {
UFuncHelper(args[0].operator NDArray*(), args[1].operator int64_t(), out, ret, lfn_scalar);
} else {
UFuncHelper(args[0].operator NDArray*(), args[1].operator double(), out, ret, lfn_scalar);
}
} else if (args[0].type_code() == kDLInt) {
UFuncHelper(args[0].operator int64_t(), args[1].operator NDArray*(), out, ret,
rfn_scalar ? rfn_scalar : lfn_scalar);
} else {
UFuncHelper(args[0].operator double(), args[1].operator NDArray*(), out, ret,
rfn_scalar ? rfn_scalar : lfn_scalar);
}
}
void UFuncHelper(runtime::MXNetArgs args,
runtime::MXNetRetValue* ret,
const nnvm::Op* op) {
using namespace runtime;
nnvm::NodeAttrs attrs;
attrs.op = op;
NDArray* inputs[] = {args[0].operator NDArray*()};
NDArray* out = args[1].operator NDArray*();
NDArray** outputs = out == nullptr ? nullptr : &out;
int num_inputs = 1;
int num_outputs = out != nullptr;
auto ndoutputs = Invoke(op, &attrs, num_inputs, inputs, &num_outputs, outputs);
if (outputs) {
*ret = PythonArg(1);
} else {
*ret = ndoutputs[0];
}
}
} // namespace mxnet