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89 lines
3.1 KiB
89 lines
3.1 KiB
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <memory>
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#include <string>
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#include "paddle/fluid/operators/fill_constant_op.h"
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#include "paddle/fluid/operators/npu_op_runner.h"
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#include "paddle/fluid/operators/utils.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class FillConstantNPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto data_type =
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static_cast<framework::proto::VarType::Type>(ctx.Attr<int>("dtype"));
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auto str_value = ctx.Attr<std::string>("str_value");
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auto float_value = ctx.Attr<float>("value");
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auto* out_var = ctx.Output<framework::Tensor>("Out");
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auto place = ctx.GetPlace();
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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T value;
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if (str_value.empty()) {
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value = static_cast<T>(float_value);
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} else {
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// handle NaN/Inf first, which cannot be read from stream.
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if (str_value == "inf") {
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value = static_cast<T>(std::numeric_limits<double>::infinity());
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} else if (str_value == "-inf") {
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value = static_cast<T>(-std::numeric_limits<double>::infinity());
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} else if (str_value == "nan") {
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value = static_cast<T>(std::numeric_limits<double>::quiet_NaN());
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} else {
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std::stringstream convert_stream(str_value);
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if (std::is_same<int64_t, T>::value) {
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int64_t tmp_value;
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convert_stream >> tmp_value;
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value = static_cast<T>(tmp_value);
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} else {
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double tmp_value;
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convert_stream >> tmp_value;
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value = static_cast<T>(tmp_value);
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}
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}
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}
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auto shape = GetShape(ctx);
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Tensor tensor_tmp(data_type);
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tensor_tmp.mutable_data<T>({1}, ctx.GetPlace());
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std::vector<T> init = {value};
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TensorFromVector(init, ctx.device_context(), &tensor_tmp);
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out_var->mutable_data<T>(shape, place);
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auto runner = NpuOpRunner("FillD", {tensor_tmp}, {*out_var},
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{{"dims", framework::vectorize(shape)}});
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runner.Run(stream);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_NPU_KERNEL(
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fill_constant,
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ops::FillConstantNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::FillConstantNPUKernel<paddle::platform::NPUDeviceContext, bool>,
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ops::FillConstantNPUKernel<paddle::platform::NPUDeviceContext, int>,
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ops::FillConstantNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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