wehub-resource-sync
73 lines
2.6 KiB
C++
73 lines
2.6 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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#include "paddle/phi/kernels/assign_pos_kernel.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#ifdef PADDLE_WITH_CUSTOM_DEVICE
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namespace phi {
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template <typename T, typename Context>
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void AssignPosKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& cum_count_in,
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const DenseTensor& eff_num_len_in,
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DenseTensor* out) {
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// assign pos decides which tokens should be fetched belong to specially
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// counter orderly.
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auto cum_count = &cum_count_in; // (counter number) int32 | int64
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auto numbers = &x; // (batch_size * seq_len, topk) int32
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auto eff_num_len = &eff_num_len_in; // (sum(cum_count))
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// out: (cum_count) value ranges
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// from 0 to batch_size *
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// seq_len * topk
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DenseTensor cpu_eff_num_len;
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int64_t cpu_eff_num_len_data = 0;
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if (eff_num_len->place().GetType() == phi::AllocationType::CPU) {
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cpu_eff_num_len_data = eff_num_len->data<T>()[0];
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} else {
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phi::Copy(dev_ctx, *eff_num_len, phi::CPUPlace(), true, &cpu_eff_num_len);
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cpu_eff_num_len_data = cpu_eff_num_len.data<T>()[0];
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}
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out->Resize({cpu_eff_num_len_data});
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dev_ctx.template Alloc<T>(out);
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DenseTensor numbers_cpu, cum_count_cpu;
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phi::Copy(dev_ctx, *numbers, phi::CPUPlace(), true, &numbers_cpu);
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phi::Copy(dev_ctx, *cum_count, phi::CPUPlace(), true, &cum_count_cpu);
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auto* numbers_data = numbers_cpu.data<T>();
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auto* cum_count_data = cum_count_cpu.data<T>();
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std::vector<T> out_data(cpu_eff_num_len_data);
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for (int64_t i = 0; i < numbers->numel(); ++i) {
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int number_idx = numbers_data[i];
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if (number_idx > -1) {
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cum_count_data[number_idx] -= 1;
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int p = cum_count_data[number_idx];
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out_data[p] = i;
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}
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}
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TensorFromVector<int64_t>(out_data, dev_ctx, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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assign_pos, Custom, ALL_LAYOUT, phi::AssignPosKernel, int64_t) {}
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#endif
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