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104 lines
3.8 KiB
Plaintext
104 lines
3.8 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 <algorithm>
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#include <optional>
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#include "helper.h"
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#include "noauxtc_kernel.h"
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std::vector<paddle::Tensor> NoauxTcRedundant(
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paddle::Tensor& scores,
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paddle::Tensor& scores_with_bias,
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paddle::Tensor& expert_id_to_ep_rank_array,
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paddle::Tensor& expert_in_rank_num_list,
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paddle::Tensor& tokens_per_expert_stats_list,
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int n_group,
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int topk_group,
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int topk,
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bool renormalize,
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float routed_scaling_factor,
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int redundant_ep_rank_num_plus_one) {
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auto input_shape = scores_with_bias.shape();
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PD_CHECK(input_shape.size() == 2);
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int64_t num_tokens = input_shape[0];
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int64_t num_experts = input_shape[1];
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auto input_type = scores_with_bias.dtype();
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auto place = scores_with_bias.place();
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auto group_scores = paddle::empty({num_tokens, n_group}, input_type, place);
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auto topk_values = paddle::empty({num_tokens, topk}, input_type, place);
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auto topk_indices =
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paddle::empty({num_tokens, topk}, paddle::DataType::INT64, place);
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auto stream = scores_with_bias.stream();
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invokeNoAuxTcRedundant<float, int64_t>(
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reinterpret_cast<float*>(scores.data<float>()),
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reinterpret_cast<float*>(group_scores.data<float>()),
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reinterpret_cast<float*>(topk_values.data<float>()),
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reinterpret_cast<int64_t*>(topk_indices.data<int64_t>()),
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reinterpret_cast<float*>(scores_with_bias.data<float>()),
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reinterpret_cast<int*>(expert_id_to_ep_rank_array.data<int>()),
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reinterpret_cast<int*>(expert_in_rank_num_list.data<int>()),
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reinterpret_cast<int*>(tokens_per_expert_stats_list.data<int>()),
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num_tokens,
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num_experts,
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n_group,
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topk_group,
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topk,
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renormalize,
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routed_scaling_factor,
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redundant_ep_rank_num_plus_one,
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stream);
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return {scores, topk_values, topk_indices};
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}
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std::vector<paddle::DataType> NoauxTcRedundantInferDtype(
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const paddle::DataType& scores_dtype,
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const paddle::DataType& scores_with_bias_dtype) {
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return {scores_dtype, scores_dtype, paddle::DataType::INT64};
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}
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std::vector<std::vector<int64_t>> NoauxTcRedundantInferShape(
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const std::vector<int64_t>& scores_shape,
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const std::vector<int64_t>&,
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const int topk) {
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auto num_tokens = scores_shape[0];
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auto topk_values_shape = std::vector<int64_t>{num_tokens, topk};
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auto topk_indices_shape = std::vector<int64_t>{num_tokens, topk};
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return {scores_shape, topk_values_shape, topk_indices_shape};
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}
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PD_BUILD_STATIC_OP(noaux_tc_redundant)
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.Inputs({"scores",
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"scores_with_bias",
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"expert_id_to_ep_rank_array",
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"expert_in_rank_num_list",
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"tokens_per_expert_stats_list"})
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.Outputs({"output_tensor",
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"topk_values",
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"topk_indices",
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"tokens_per_expert_stats_list_out"})
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.Attrs({"n_group: int",
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"topk_group: int",
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"topk:int",
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"renormalize: bool",
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"routed_scaling_factor: float",
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"redundant_ep_rank_num_plus_one:int"})
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.SetInplaceMap({{"tokens_per_expert_stats_list",
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"tokens_per_expert_stats_list_out"}})
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.SetKernelFn(PD_KERNEL(NoauxTcRedundant))
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.SetInferShapeFn(PD_INFER_SHAPE(NoauxTcRedundantInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(NoauxTcRedundantInferDtype));
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