mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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111 lines
3.6 KiB
C++
111 lines
3.6 KiB
C++
/*
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* Copyright (c) 2022-2024, NVIDIA CORPORATION. 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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*/
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#pragma once
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#include "tensorrt_llm/common/assert.h"
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#include <NvInferRuntime.h>
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#include <cuda_bf16.h>
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#include <cuda_fp16.h>
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#include "tensorrt_llm/common/config.h"
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#include "tensorrt_llm/common/cudaUtils.h"
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#include "tensorrt_llm/kernels/quantization.h"
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#include "tensorrt_llm/runtime/ipcUtils.h"
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TRTLLM_NAMESPACE_BEGIN
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namespace kernels::ar_fusion::moe
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{
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static constexpr int kElemsPerAccess = 8;
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static constexpr int kOneShotMaxToken = 128;
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static constexpr int kBarrierFlagCount = 256;
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// DS R1
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// pattern1: AR+Add_RMS+Quant
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// [m, 7168] bf16 allreduce_in, [m, 7168] bf16 residual_in
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// [m, 7168] bf16 residual_out, [m, 7168] fp4 quant_out
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// pattern2: AR+AddRMS
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// [m, 7168] bf16 allreduce_in, [m, 7168] bf16 residual_in
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// [m, 7168] bf16 norm_out
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struct AllReduceFusionParams
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{
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int nranks;
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int rank;
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nvinfer1::DataType dtype;
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// size = token_num * hidden_dim
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int size;
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int hidden_dim;
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void** workspace;
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void* allreduce_in;
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void* residual_in;
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void* residual_out;
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void* norm_out;
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void* quant_out;
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void* scale_out;
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void* rms_gamma;
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float rms_eps;
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float* scale_factor;
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QuantizationSFLayout layout = QuantizationSFLayout::SWIZZLED;
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cudaStream_t stream;
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};
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/////////////////////////////////////////////////////////////////
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// * MoE Reduction Fusion * //
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/////////////////////////////////////////////////////////////////
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// Fuse MoE Reduction before AR + RMS
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// pattern1: MoE Reduction + Add Residual + AR + ADD_RMS + Quant
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// pattern2: MoE Reduction + Add Residual + AR + ADD_RMS
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// [device_num_experts, m, 7168] bf16 moe_reduction_active_experts_token_input
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// [m, 7168] bf16 moe_reduction_token_input
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// [device_num_experts, m] moe_reduction_scale_input
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struct MoeReductionAllReduceFusionParams : public AllReduceFusionParams
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{
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// * moe reduction specific params
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// Refer to kernel implementation on layout of those params
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// number of active experts on current device
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int* moe_reduction_device_num_experts = nullptr;
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// per token per expert fp32 scale
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float* moe_reduction_scale_input = nullptr;
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// per token per expert input
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void* moe_reduction_active_experts_token_input = nullptr;
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// per token input
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void* moe_reduction_token_input = nullptr;
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};
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void moereduction_allreduce_fusion_op(MoeReductionAllReduceFusionParams const& params);
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struct MoeFinalizeAllReduceFusionParams : public AllReduceFusionParams
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{
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// * moe reduction specific params
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// Refer to kernel implementation on layout of those params
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// number of active experts on current device
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int top_k;
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nvinfer1::DataType scale_dtype;
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// [num_tokens, top_k]
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void* expert_scale_factor = nullptr;
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void* shared_expert_output = nullptr;
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// [num_tokens, top_k]
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int32_t* expanded_idx_to_permuted_idx = nullptr;
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// allreduce_in [maxPermutedPaddedCount, hidden_dim]
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};
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void moefinalize_allreduce_fusion_op(MoeFinalizeAllReduceFusionParams const& params);
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} // namespace kernels::ar_fusion::moe
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TRTLLM_NAMESPACE_END
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