mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
Ryan
80 lines
2.8 KiB
Plaintext
80 lines
2.8 KiB
Plaintext
// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "helper.h"
|
|
#include "paddle/extension.h"
|
|
|
|
template <typename scalar_t>
|
|
__global__ void cuda_kernel(const scalar_t *__restrict__ topk_ids,
|
|
int32_t *__restrict__ res,
|
|
int32_t *__restrict__ res_padded,
|
|
size_t numel,
|
|
int num_experts) {
|
|
extern __shared__ int32_t tokens_per_ep[];
|
|
|
|
for (size_t i = threadIdx.x; i < num_experts; i += blockDim.x) {
|
|
tokens_per_ep[i] = 0;
|
|
}
|
|
__syncthreads();
|
|
|
|
for (size_t i = threadIdx.x; i < numel; i += blockDim.x) {
|
|
int32_t expert_id = topk_ids[i];
|
|
if (expert_id >= 0) atomicAdd(&tokens_per_ep[expert_id], 1);
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
for (size_t i = threadIdx.x; i < num_experts; i += blockDim.x) {
|
|
res[i] = tokens_per_ep[i];
|
|
res_padded[i] = (res[i] + 127) / 128 * 128;
|
|
}
|
|
}
|
|
|
|
std::vector<paddle::Tensor> count_tokens_per_expert_func(
|
|
const paddle::Tensor &topk_ids, int64_t num_experts) {
|
|
int topk_ids_numel = topk_ids.shape()[0] * topk_ids.shape()[1];
|
|
|
|
auto token_nums_per_expert = paddle::empty(
|
|
{2, num_experts}, paddle::DataType::INT32, topk_ids.place());
|
|
|
|
auto stream = topk_ids.stream();
|
|
using scalar_t = int64_t;
|
|
|
|
cuda_kernel<<<1, 1024, num_experts * sizeof(int32_t), stream>>>(
|
|
topk_ids.data<scalar_t>(),
|
|
token_nums_per_expert.data<int32_t>(),
|
|
token_nums_per_expert.data<int32_t>() + num_experts,
|
|
topk_ids_numel,
|
|
num_experts);
|
|
return {token_nums_per_expert};
|
|
}
|
|
|
|
std::vector<paddle::DataType> count_tokens_per_expert_func_infer_dtype(
|
|
const paddle::DataType &topk_ids_dtype, int64_t num_experts) {
|
|
return {paddle::DataType::INT32};
|
|
}
|
|
|
|
std::vector<std::vector<int64_t>> count_tokens_per_expert_func_infer_shape(
|
|
const std::vector<int64_t> &topk_ids_shape, int64_t num_experts) {
|
|
return {{2, num_experts}};
|
|
}
|
|
|
|
PD_BUILD_STATIC_OP(count_tokens_per_expert_func)
|
|
.Inputs({"topk_ids"})
|
|
.Outputs({"token_nums_per_expert"})
|
|
.Attrs({"num_experts:int64_t"})
|
|
.SetKernelFn(PD_KERNEL(count_tokens_per_expert_func))
|
|
.SetInferShapeFn(PD_INFER_SHAPE(count_tokens_per_expert_func_infer_shape))
|
|
.SetInferDtypeFn(PD_INFER_DTYPE(count_tokens_per_expert_func_infer_dtype));
|