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/* Copyright (c) 2016 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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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/operators/strided_memcpy.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using LoD = framework::LoD;
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template <typename T>
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inline LoD SequenceSliceLoD(const T& in, const int64_t* offset_data,
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const int64_t* length_data) {
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auto out_lod = in.lod();
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size_t lod_offset = 0;
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auto n = in.lod()[0].size() - 1;
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out_lod[0][0] = 0;
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for (size_t i = 0; i < n; ++i) {
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lod_offset += length_data[i];
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out_lod[0][i + 1] = lod_offset;
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}
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return out_lod;
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}
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template <typename DeviceContext, typename T>
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class SequenceSliceOpKernel : 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* in = ctx.Input<LoDTensor>("X");
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auto* offset = ctx.Input<Tensor>("Offset");
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auto* length = ctx.Input<Tensor>("Length");
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auto* out = ctx.Output<LoDTensor>("Out");
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auto lod = in->lod();
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PADDLE_ENFORCE_EQ(lod.empty(), false,
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platform::errors::InvalidArgument(
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"Input(X) Tensor of SequenceSlice operator does not "
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"contain LoD information."));
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PADDLE_ENFORCE_EQ(
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lod.size(), 1UL,
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platform::errors::InvalidArgument(
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"LoD information error. SequenceSlice operator only support one "
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"level sequence now, but received LoD level is %d.",
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lod.size()));
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auto n = lod[0].size() - 1;
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PADDLE_ENFORCE_EQ(
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n, static_cast<size_t>(length->dims()[0]),
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platform::errors::InvalidArgument(
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"Input length shape error. The length of input LoD sequence and "
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"input length-array‘s first dimension should be equal, but the LoD "
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"sequence length is %d, the length-array‘s first dimension is %d.",
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n, static_cast<size_t>(length->dims()[0])));
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PADDLE_ENFORCE_EQ(
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n, static_cast<size_t>(offset->dims()[0]),
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platform::errors::InvalidArgument(
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"Input offset shape error. The length of input LoD sequence and "
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"input offset-array‘s first dimension should be equal, but the LoD "
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"sequence length is %d, the offset-array‘s first dimension is %d.",
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n, static_cast<size_t>(offset->dims()[0])));
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const int64_t* offset_data = offset->data<int64_t>();
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const int64_t* length_data = length->data<int64_t>();
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framework::Tensor offset_cpu;
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framework::Tensor length_cpu;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
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framework::TensorCopySync(*offset, platform::CPUPlace(), &offset_cpu);
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offset_data = offset_cpu.data<int64_t>();
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length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
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framework::TensorCopySync(*length, platform::CPUPlace(), &length_cpu);
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length_data = length_cpu.data<int64_t>();
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}
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for (size_t i = 0; i < n; ++i) {
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PADDLE_ENFORCE_LE(0, offset_data[i],
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platform::errors::InvalidArgument(
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"The input offset[%d]'s value is negative, its "
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"value is %d, expect it to be non-negative.",
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i, offset_data[i]));
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PADDLE_ENFORCE_LE(0, length_data[i],
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platform::errors::InvalidArgument(
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"The input length[%d]'s value is negative, its "
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"value is %d, expect it to be non-negative.",
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i, offset_data[i]));
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PADDLE_ENFORCE_LE(
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lod[0][i] + offset_data[i] + length_data[i], lod[0][i + 1],
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platform::errors::OutOfRange(
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"The slice end index of target tensor is out of range. expect it "
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"less than or equal to %d, but the actual slice end index is %d.",
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lod[0][i + 1], lod[0][i] + offset_data[i] + length_data[i]));
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}
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out->mutable_data<T>(ctx.GetPlace());
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auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);
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auto out_dims = in->dims();
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out_dims[0] = out_lod[0][out_lod[0].size() - 1];
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out->Resize(out_dims);
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out->set_lod(out_lod);
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auto in_stride = framework::stride(in->dims());
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auto out_stride = framework::stride(out->dims());
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size_t out_offset = 0;
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for (size_t i = 0; i < n; ++i) {
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if (length_data[i] == 0) continue;
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Tensor in_t = in->Slice(
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static_cast<int>(lod[0][i] + offset_data[i]),
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static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
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StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(), in_stride,
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in_t.dims(), out_stride, out->data<T>() + out_offset);
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out_offset += length_data[i] * in_stride[0];
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}
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}
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};
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template <typename DeviceContext, typename T>
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class SequenceSliceGradOpKernel : 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* in = ctx.Input<LoDTensor>("X");
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auto* offset = ctx.Input<Tensor>("Offset");
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auto* length = ctx.Input<Tensor>("Length");
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auto* out_grad =
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
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auto* x_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
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const int64_t* offset_data = offset->data<int64_t>();
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const int64_t* length_data = length->data<int64_t>();
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framework::Tensor offset_cpu;
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framework::Tensor length_cpu;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
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framework::TensorCopySync(*offset, platform::CPUPlace(), &offset_cpu);
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offset_data = offset_cpu.data<int64_t>();
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length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
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framework::TensorCopySync(*length, platform::CPUPlace(), &length_cpu);
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length_data = length_cpu.data<int64_t>();
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}
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auto lod = in->lod();
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// to avoid out_grad missing lod, compute lod again
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auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);
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if (x_grad) {
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x_grad->mutable_data<T>(ctx.GetPlace());
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x_grad->set_lod(in->lod());
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math::SetConstant<DeviceContext, T> set_zero;
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set_zero(ctx.template device_context<DeviceContext>(), x_grad,
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static_cast<T>(0));
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for (size_t i = 0; i < out_lod[0].size() - 1; ++i) {
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if (length_data[i] == 0) continue;
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Tensor out_grad_t =
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out_grad->Slice(static_cast<int>(out_lod[0][i]),
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static_cast<int>(out_lod[0][i + 1]));
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auto out_grad_stride = framework::stride(out_grad_t.dims());
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auto x_grad_stride = framework::stride(x_grad->dims());
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Tensor x_grad_t = x_grad->Slice(
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static_cast<int>(lod[0][i] + offset_data[i]),
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static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
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StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>(),
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out_grad_stride, out_grad_t.dims(), x_grad_stride,
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x_grad_t.data<T>());
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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