wehub-resource-sync
119 lines
4.0 KiB
C++
119 lines
4.0 KiB
C++
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 <string>
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#include <utility>
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#include <vector>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/device_context.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/partial_concat_funcs.h"
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#include "paddle/phi/kernels/funcs/strided_memcpy.h"
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namespace phi {
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template <typename T, typename Context>
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void PartialConcatKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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int start_index,
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int length,
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DenseTensor* out) {
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auto ins = x;
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PADDLE_ENFORCE_EQ(ins[0] != nullptr,
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true,
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common::errors::InvalidArgument(
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"The input of partial concat should not be null."));
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auto input_dim = ins[0]->dims();
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PADDLE_ENFORCE_EQ(input_dim.size(),
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2,
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common::errors::InvalidArgument(
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"Only supports 2-D array with batch size in the 1st "
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"dimension and data in the 2nd."));
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auto in_size = input_dim[1];
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// may be negative
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start_index = ComputeStartIndex(start_index, in_size);
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auto partial_len = length;
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if (partial_len < 0) {
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partial_len = in_size - start_index;
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}
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int batch = input_dim[0];
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int out_size = partial_len * ins.size();
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out->Resize({batch, out_size});
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T* out_data = dev_ctx.template Alloc<T>(out);
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for (size_t i = 0; i < ins.size(); ++i) {
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for (int j = 0; j < batch; ++j) {
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const T* in_data = ins[i]->data<T>();
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memcpy(out_data + out_size * j + partial_len * i,
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in_data + in_size * j + start_index,
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partial_len * sizeof(T));
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}
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}
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}
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template <typename T, typename Context>
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void PartialConcatGradientOpKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& x,
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const DenseTensor& out_grad,
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int start_index,
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int length,
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std::vector<DenseTensor*> x_grad) {
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auto ins = x;
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auto outs = x_grad;
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PADDLE_ENFORCE_EQ(ins[0] != nullptr,
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true,
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common::errors::InvalidArgument(
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"The input of partial concat should not be null."));
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// all parameters
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auto batch_size = ins[0]->dims()[0];
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auto in_size = ins[0]->dims()[1];
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// may be negative
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start_index = ComputeStartIndex(start_index, in_size);
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auto partial_len = length;
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if (partial_len < 0) partial_len = in_size - start_index;
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auto in_num = ins.size();
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auto grad_batch_len = partial_len * in_num;
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auto all_length = grad_batch_len * batch_size;
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// initialize
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auto& place = *dev_ctx.eigen_device();
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for (size_t i = 0; i < outs.size(); ++i) {
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dev_ctx.template Alloc<T>(outs[i]);
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auto dxt = EigenVector<T>::Flatten(*outs[i]);
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dxt.device(place) = dxt.constant(static_cast<T>(0));
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}
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auto* out_grad_t = out_grad.data<T>();
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for (size_t id = 0; id < all_length; id += partial_len) {
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int bs_id = id / grad_batch_len;
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int bs_index = id % grad_batch_len;
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int var_id = bs_index / partial_len;
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auto* out_t = outs[var_id]->data<T>();
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memcpy(out_t + bs_id * in_size + start_index,
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out_grad_t + id,
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partial_len * sizeof(T));
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}
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}
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} // namespace phi
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