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60 lines
2.0 KiB
Plaintext
60 lines
2.0 KiB
Plaintext
// Copyright (c) 2025 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 "helper.h"
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#include "paddle/extension.h"
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template <typename scalar_t>
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__global__ void
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cuda_kernel(const scalar_t *__restrict__ topk_ids, int32_t *__restrict__ res,
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int32_t *__restrict__ res_padded, size_t numel, int num_experts) {
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extern __shared__ int32_t tokens_per_ep[];
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for (size_t i = threadIdx.x; i < num_experts; i += blockDim.x) {
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tokens_per_ep[i] = 0;
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}
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__syncthreads();
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for (size_t i = threadIdx.x; i < numel; i += blockDim.x) {
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int32_t expert_id = topk_ids[i];
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if(expert_id >= 0) atomicAdd(&tokens_per_ep[expert_id], 1);
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}
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__syncthreads();
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for (size_t i = threadIdx.x; i < num_experts; i += blockDim.x) {
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res[i] = tokens_per_ep[i];
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res_padded[i] = (res[i] + 127) / 128 * 128;
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}
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}
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paddle::Tensor count_tokens_per_expert_func(const paddle::Tensor &topk_ids,
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int64_t num_experts) {
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int topk_ids_numel = topk_ids.shape()[0] * topk_ids.shape()[1];
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auto token_nums_per_expert = paddle::empty(
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{2, num_experts}, paddle::DataType::INT32, topk_ids.place());
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auto stream = topk_ids.stream();
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using scalar_t = int64_t;
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cuda_kernel<<<1, 1024, num_experts * sizeof(int32_t), stream>>>(
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topk_ids.data<scalar_t>(), token_nums_per_expert.data<int32_t>(),
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token_nums_per_expert.data<int32_t>() + num_experts, topk_ids_numel,
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num_experts);
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return token_nums_per_expert;
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}
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