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* refactor rl get_name_mappings_to_training * fix tp>1 * change variable name(ffn1->up_gate_proj/ffn2->down_proj) * change variable name(linear_weight->weight/linear_bias->bias) * add rl names mapping for vl * fix ernie 0.3B error * fix develop code * fix
247 lines
11 KiB
Plaintext
247 lines
11 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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// Ignore CUTLASS warnings about type punning
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#pragma once
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#include "cutlass/numeric_conversion.h"
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#include "cutlass_extensions/wint_type_traits.h"
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#include "helper.h"
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#include "moe/fused_moe_helper.h"
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namespace phi {
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__global__ void compute_total_rows_before_expert_kernel(
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int* sorted_experts,
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const int64_t sorted_experts_len,
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const int64_t num_experts,
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int64_t* total_rows_before_expert) {
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// First, compute the global tid. We only need 1 thread per expert.
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const int expert = blockIdx.x * blockDim.x + threadIdx.x;
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if (expert >= num_experts) return;
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// This should construct the last index where each expert occurs.
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total_rows_before_expert[expert] =
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find_total_elts_leq_target(sorted_experts, sorted_experts_len, expert);
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}
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void compute_total_rows_before_expert(int* sorted_indices,
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const int64_t total_indices,
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const int64_t num_experts,
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int64_t* total_rows_before_expert,
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cudaStream_t stream) {
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const int threads = std::min(int64_t(1024), num_experts);
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const int blocks = (num_experts + threads - 1) / threads;
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compute_total_rows_before_expert_kernel<<<blocks, threads, 0, stream>>>(
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sorted_indices, total_indices, num_experts, total_rows_before_expert);
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}
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} // namespace phi
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template <paddle::DataType T>
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void FusedMoeKernel(const paddle::Tensor& input,
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const paddle::Tensor& gate_weight,
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const paddle::Tensor& up_gate_proj_weight,
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const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
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const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
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const paddle::Tensor& down_proj_weight,
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const paddle::optional<paddle::Tensor>& down_proj_scale,
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const paddle::optional<paddle::Tensor>& down_proj_bias,
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const std::string& quant_method,
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const int moe_topk,
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const bool group_moe,
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const bool norm_topk_prob,
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paddle::Tensor* output) {
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using namespace phi;
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typedef PDTraits<T> traits_;
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typedef typename traits_::DataType DataType_;
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typedef typename traits_::data_t data_t;
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auto* output_data = output->data<data_t>();
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auto fp16_moe_gemm_runner = MoeGemmRunner<DataType_, cutlass::WintQuantTraits<DataType_, cutlass::WintQuantMethod::kNone>>();
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auto int8_moe_gemm_runner = MoeGemmRunner<DataType_, cutlass::WintQuantTraits<DataType_, cutlass::WintQuantMethod::kWeightOnlyInt8>>();
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auto int4_moe_gemm_runner = MoeGemmRunner<DataType_, cutlass::WintQuantTraits<DataType_, cutlass::WintQuantMethod::kWeightOnlyInt4>>();
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using NvType = typename traits_::DataType;
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auto moe_compute = MoeHelper<data_t, NvType>(quant_method,
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&fp16_moe_gemm_runner,
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&int8_moe_gemm_runner,
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&int4_moe_gemm_runner);
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moe_compute.ComputeFFN(&input,
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&gate_weight,
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&up_gate_proj_weight,
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up_gate_proj_scale ? up_gate_proj_scale.get_ptr() : nullptr,
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up_gate_proj_bias ? up_gate_proj_bias.get_ptr() : nullptr,
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&down_proj_weight,
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down_proj_scale ? down_proj_scale.get_ptr() : nullptr,
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down_proj_bias ? down_proj_bias.get_ptr() : nullptr,
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nullptr,
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moe_topk,
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group_moe,
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norm_topk_prob,
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1.0, // ComputeFFN
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"ffn",
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output);
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}
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paddle::Tensor FusedExpertMoeFunc(
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const paddle::Tensor& input,
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const paddle::Tensor& gate_weight,
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const paddle::Tensor& up_gate_proj_weight,
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const paddle::Tensor& down_proj_weight,
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const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
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const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
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const paddle::optional<paddle::Tensor>& down_proj_bias,
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const paddle::optional<paddle::Tensor>& down_proj_scale,
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const std::string& quant_method,
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const int moe_topk,
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const bool norm_topk_prob,
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const bool group_moe) {
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const auto input_type = input.dtype();
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auto output = paddle::empty_like(input);
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switch (input_type) {
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case paddle::DataType::BFLOAT16:
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FusedMoeKernel<paddle::DataType::BFLOAT16>(input,
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gate_weight,
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up_gate_proj_weight,
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up_gate_proj_scale,
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up_gate_proj_bias,
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down_proj_weight,
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down_proj_scale,
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down_proj_bias,
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quant_method,
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moe_topk,
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group_moe,
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norm_topk_prob,
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&output);
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break;
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case paddle::DataType::FLOAT16:
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FusedMoeKernel<paddle::DataType::FLOAT16>(input,
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gate_weight,
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up_gate_proj_weight,
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up_gate_proj_scale,
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up_gate_proj_bias,
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down_proj_weight,
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down_proj_scale,
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down_proj_bias,
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quant_method,
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moe_topk,
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group_moe,
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norm_topk_prob,
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&output);
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break;
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default:
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PD_THROW("Unsupported data type for FusedMoeKernel");
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}
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return output;
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}
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std::vector<paddle::Tensor> FusedExpertMoe(
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const paddle::Tensor& input,
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const paddle::Tensor& gate_weight,
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const paddle::Tensor& up_gate_proj_weight,
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const paddle::Tensor& down_proj_weight,
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const paddle::optional<paddle::Tensor>& up_gate_proj_bias,
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const paddle::optional<paddle::Tensor>& up_gate_proj_scale,
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const paddle::optional<paddle::Tensor>& down_proj_bias,
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const paddle::optional<paddle::Tensor>& down_proj_scale,
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const std::string& quant_method,
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const int moe_topk,
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const bool norm_topk_prob,
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const bool group_moe) {
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return {FusedExpertMoeFunc(input,
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gate_weight,
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up_gate_proj_weight,
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down_proj_weight,
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up_gate_proj_bias,
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up_gate_proj_scale,
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down_proj_bias,
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down_proj_scale,
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quant_method,
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moe_topk,
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norm_topk_prob,
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group_moe)};
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}
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std::vector<std::vector<int64_t>> FusedExpertMoeInferShape(
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const std::vector<int64_t>& input_shape,
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const std::vector<int64_t>& gate_weight_shape,
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const std::vector<int64_t>& up_gate_proj_weight_shape,
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const std::vector<int64_t>& down_proj_weight_shape,
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const paddle::optional<std::vector<int64_t>>& up_gate_proj_bias_shape,
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const paddle::optional<std::vector<int64_t>>& up_gate_proj_scale_shape,
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const paddle::optional<std::vector<int64_t>>& down_proj_bias_shape,
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const paddle::optional<std::vector<int64_t>>& down_proj_scale_shape) {
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return {input_shape};
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}
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std::vector<paddle::DataType> FusedExpertMoeInferDtype(
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const paddle::DataType& input_dtype,
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const paddle::DataType& gate_weight_dtype,
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const paddle::DataType& up_gate_proj_weight_dtype,
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const paddle::DataType& down_proj_weight_dtype,
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const paddle::optional<paddle::DataType>& up_gate_proj_bias_dtype,
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const paddle::optional<paddle::DataType>& up_gate_proj_scale_dtype,
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const paddle::optional<paddle::DataType>& down_proj_bias_dtype,
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const paddle::optional<paddle::DataType>& down_proj_scale_dtype) {
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return {input_dtype};
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}
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/**
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* @brief Fused Mixture-of-Experts (MoE) Operator
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*
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* This operator combines three key MoE operations into a single optimized kernel:
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* 1. moe_dispatch - Routes tokens to top-k experts using gating network
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* 2. moe_ffn - Processes tokens through parallel expert FFNs
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* 3. moe_reduce - Combines expert outputs with routing weights
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*
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* Key Features:
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* - Supports both dense and quantized expert weights
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* - Optimized for GPU execution with fused operations
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*
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* Mathematical Formulation:
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* output = ∑_i^topk(softmax(gate(x))_i * FFN_i(x)
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*
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* Reference Components:
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* moe_dispatch: Selects top-k experts per token and generates permutation indices
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* moe_ffn: Applies SwiGLU activation expert networks in parallel
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* moe_reduce: Combines weighted expert outputs and restores original token order
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*
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* Performance Notes:
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* - Recommended hidden_size multiples of 128 for optimal memory alignment
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* - For best throughput, num_experts should be powers of 2
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*/
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PD_BUILD_STATIC_OP(fused_expert_moe)
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.Inputs({"input",
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"gate_weight",
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"up_gate_proj_weight",
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"down_proj_weight",
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paddle::Optional("up_gate_proj_bias"),
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paddle::Optional("up_gate_proj_scale"),
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paddle::Optional("down_proj_bias"),
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paddle::Optional("down_proj_scale")})
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.Outputs({"output"})
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.Attrs({"quant_method:std::string",
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"moe_topk:int",
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"norm_topk_prob:bool",
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"group_moe:bool"})
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.SetKernelFn(PD_KERNEL(FusedExpertMoe))
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.SetInferShapeFn(PD_INFER_SHAPE(FusedExpertMoeInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(FusedExpertMoeInferDtype));
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