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Paddle/paddle/fluid/operators/sequence_ops/sequence_mask_op.h

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4.0 KiB

// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed 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.
#pragma once
#ifdef __NVCC__
#include <thrust/device_ptr.h>
#include <thrust/functional.h>
#include <thrust/reduce.h>
#else
#include <algorithm>
#endif
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
template <typename Tx, typename Ty>
struct SequenceMaskForRangeFunctor {
HOSTDEVICE SequenceMaskForRangeFunctor(const Tx *x, Ty *y, int maxlen)
: x_(x), y_(y), maxlen_(maxlen) {}
HOSTDEVICE void operator()(int y_idx) const {
int x_idx = y_idx / maxlen_;
int j = y_idx % maxlen_;
y_[y_idx] = static_cast<Ty>(j < x_[x_idx] ? 1 : 0);
}
private:
const Tx *x_;
Ty *y_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
struct SequenceMaskFunctor {
SequenceMaskFunctor(const DeviceContext &ctx, const Tx *x, Tensor *y,
int limits, int maxlen)
: ctx_(ctx), x_(x), y_(y), limits_(limits), maxlen_(maxlen) {}
template <typename Ty>
void apply() const {
auto *y_data = y_->mutable_data<Ty>(ctx_.GetPlace());
platform::ForRange<DeviceContext> for_range(ctx_, limits_);
for_range(SequenceMaskForRangeFunctor<Tx, Ty>(x_, y_data, maxlen_));
}
private:
const DeviceContext &ctx_;
const Tx *x_;
Tensor *y_;
int limits_;
int maxlen_;
};
template <typename DeviceContext, typename Tx>
class SequenceMaskKernel : public framework::OpKernel<Tx> {
using Tensor = framework::LoDTensor;
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
int maxlen = ctx.Attr<int>("maxlen");
if (ctx.HasInput("MaxLenTensor")) {
auto max_len_tensor = ctx.Input<Tensor>("MaxLenTensor");
PADDLE_ENFORCE(max_len_tensor != NULL, "MaxLenTensor is NULL");
if (platform::is_gpu_place(max_len_tensor->place())) {
framework::Tensor temp;
TensorCopySync(*max_len_tensor, platform::CPUPlace(), &temp);
maxlen = *temp.data<int32_t>();
} else {
maxlen = *max_len_tensor->data<int32_t>();
}
auto y_dim = framework::vectorize2int(x->dims());
y_dim.push_back(maxlen);
y->Resize(framework::make_ddim(y_dim));
PADDLE_ENFORCE_GT(maxlen, 0,
"MaxLenTensor value should be greater than 0");
}
auto *x_data = x->data<Tx>();
auto x_numel = x->numel();
if (maxlen < 0) {
#ifdef __NVCC__
VLOG(10)
<< "SequenceMaskOp on GPU may be slow when maxlen is not provided.";
maxlen = static_cast<int>(
thrust::reduce(thrust::device_pointer_cast(x_data),
thrust::device_pointer_cast(x_data) + x_numel,
static_cast<Tx>(0), thrust::maximum<Tx>()));
#else
maxlen = static_cast<int>(*std::max_element(x_data, x_data + x_numel));
#endif
auto y_dim = framework::vectorize2int(x->dims());
y_dim.push_back(maxlen);
y->Resize(framework::make_ddim(y_dim));
}
auto out_dtype = static_cast<framework::proto::VarType::Type>(
ctx.Attr<int>("out_dtype"));
auto &dev_ctx = ctx.template device_context<DeviceContext>();
framework::VisitDataType(out_dtype,
SequenceMaskFunctor<DeviceContext, Tx>(
dev_ctx, x_data, y, x_numel * maxlen, maxlen));
}
};
} // namespace operators
} // namespace paddle