wehub-resource-sync
70 lines
2.3 KiB
C++
70 lines
2.3 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/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 LimitByCapacityKernel(const Context& dev_ctx,
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const DenseTensor& expert_count_in,
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const DenseTensor& capacity_in,
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int n_worker,
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DenseTensor* out) {
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auto expert_count = &expert_count_in;
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auto capacity = &capacity_in;
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auto n_expert = expert_count->numel() / n_worker;
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dev_ctx.template Alloc<T>(out);
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std::vector<T> out_data(out->numel());
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DenseTensor expert_count_cpu, capacity_cpu;
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phi::Copy(dev_ctx, *expert_count, phi::CPUPlace(), true, &expert_count_cpu);
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phi::Copy(dev_ctx, *capacity, phi::CPUPlace(), true, &capacity_cpu);
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auto* ec_data = expert_count_cpu.data<T>();
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auto* capacity_data = capacity_cpu.data<T>();
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int eid, wid;
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for (int64_t i = 0; i < expert_count->numel(); ++i) {
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wid = i / n_expert;
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eid = i % n_expert;
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auto proposal = ec_data[i];
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auto cap_left = capacity_data[eid];
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capacity_data[eid] -= proposal;
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if (cap_left >= proposal) {
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out_data[wid * n_expert + eid] = proposal;
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} else if (cap_left >= 0) {
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out_data[wid * n_expert + eid] = cap_left;
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} else {
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out_data[wid * n_expert + eid] = 0;
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}
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}
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auto out_dims = out->dims();
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TensorFromVector<T>(out_data, dev_ctx, out);
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out->Resize(out_dims);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(limit_by_capacity,
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Custom,
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ALL_LAYOUT,
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phi::LimitByCapacityKernel,
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int64_t) {}
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#endif
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