mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
202 lines
8.3 KiB
C++
202 lines
8.3 KiB
C++
// Copyright (c) 2024 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 "layers_decoder.h"
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#include "paddle/extension.h"
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#include "paddle/phi/core/kernel_registry.h"
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std::vector<paddle::Tensor> InvokeAllLLaMALayer(
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const paddle::Tensor &input,
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const std::vector<paddle::Tensor> &ln1Gamma,
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const std::vector<paddle::Tensor> &ln1Beta,
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const std::vector<paddle::Tensor> &qkvWeight,
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const std::vector<paddle::Tensor> &qkvBiasWeight,
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const std::vector<paddle::Tensor> &attnOutWeight,
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const std::vector<paddle::Tensor> &attnOutBias,
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const std::vector<paddle::Tensor> &ln2Gamma,
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const std::vector<paddle::Tensor> &ln2Beta,
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const std::vector<paddle::Tensor> &gateWeight,
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const std::vector<paddle::Tensor> &gateBias,
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const std::vector<paddle::Tensor> &upWeight,
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const std::vector<paddle::Tensor> &upBias,
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const std::vector<paddle::Tensor> &downWeight,
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const std::vector<paddle::Tensor> &downBias,
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const paddle::Tensor &pastSeqLen,
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const paddle::Tensor ¤tSeqLen,
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const paddle::Tensor &step,
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int hiddensize,
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int totalLayer,
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const std::string &computeType,
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const std::string &activation,
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const std::string &normType,
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int attHeadDim,
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int attHeadNum,
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int kvHeadNum,
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int maxPositions,
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int maxPosEmbed,
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int intermediateSize) {
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auto out = paddle::empty_like(input);
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auto batchSize = input.shape()[0];
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auto inputSeqLen = input.shape()[1];
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auto past_seq_len = pastSeqLen.data<int64_t>()[0];
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auto cur_seq_len = static_cast<int64_t>(currentSeqLen.data<int32_t>()[0]);
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auto step_id = step.data<int64_t>()[0];
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auto output_ptr = reinterpret_cast<void *>(out.data<float>());
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auto xft_data_type = xft::DataType::fp16;
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if (computeType == "bf16") {
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xft_data_type = xft::DataType::bf16;
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} else if (computeType == "bf16_int8") {
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xft_data_type = xft::DataType::bf16_int8;
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}
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auto xft_act_type = xft::ActivationType::SILU;
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if (activation == "relu") {
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xft_act_type = xft::ActivationType::RELU;
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} else if (activation == "gelu") {
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xft_act_type = xft::ActivationType::GELU;
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} else if (activation == "swiglu") {
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xft_act_type = xft::ActivationType::SWIGLU;
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}
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auto xft_norm_type = xft::NormType::RMS;
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if (normType == "layernorm") {
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xft_norm_type = xft::NormType::LN;
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}
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auto input_ptr = reinterpret_cast<const void *>(input.data<float>());
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for (int i = 0; i < totalLayer; ++i) {
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auto ln1Gamma_ptr =
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reinterpret_cast<const float *>(ln1Gamma[i].data<float>());
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auto ln1Beta_ptr =
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reinterpret_cast<const float *>(ln1Beta[i].data<float>());
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auto qkvWeight_ptr =
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reinterpret_cast<const void *>(qkvWeight[i].data<float>());
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auto qkvBiasWeight_ptr =
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reinterpret_cast<const float *>(qkvBiasWeight[i].data<float>());
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auto attnOutWeight_ptr =
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reinterpret_cast<const void *>(attnOutWeight[i].data<float>());
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auto ln2Gamma_ptr =
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reinterpret_cast<const float *>(ln2Gamma[i].data<float>());
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auto ln2Beta_ptr =
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reinterpret_cast<const float *>(ln2Beta[i].data<float>());
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auto gate_weight_ptr =
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reinterpret_cast<const void *>(gateWeight[i].data<float>());
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auto up_weight_ptr =
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reinterpret_cast<const void *>(upWeight[i].data<float>());
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auto down_weight_ptr =
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reinterpret_cast<const void *>(downWeight[i].data<float>());
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auto gate_bias_ptr =
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reinterpret_cast<const float *>(gateBias[i].data<float>());
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auto up_bias_ptr =
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reinterpret_cast<const float *>(upBias[i].data<float>());
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auto down_bias_ptr =
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reinterpret_cast<const float *>(downBias[i].data<float>());
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auto attnOutBias_ptr =
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reinterpret_cast<const float *>(attnOutBias[i].data<float>());
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invokeLayerLLaMA(
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xft_data_type, // dt
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xft_act_type, // at
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xft_norm_type, // nt
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i, // layerId
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totalLayer, // totalLayers
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batchSize, // batchSize
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inputSeqLen, // inputSeqLen
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attHeadDim, // attHeadDim
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attHeadNum, // attHeadNum
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kvHeadNum, // kvHeadNum
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maxPositions, // maxPositions
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maxPosEmbed, // maxPosEmbed
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past_seq_len, // pastSeqLen
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cur_seq_len, // currentSeqLen
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step_id, // step
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hiddensize, // hiddenSize
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intermediateSize, // intermediateSize
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reinterpret_cast<void *>(output_ptr), // output
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hiddensize, // outputStride
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input_ptr, // input
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hiddensize, // inputStride
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ln1Gamma_ptr, // ln1Gamma
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ln1Beta_ptr, // ln1Beta
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qkvWeight_ptr, // queryWeight
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qkvWeight_ptr + hiddensize, // keyWeight
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qkvWeight_ptr + hiddensize + kvHeadNum * attHeadDim, // valueWeight
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attnOutWeight_ptr, // attnOutWeight
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ln2Gamma_ptr, // ln2Gamma
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ln2Beta_ptr, // ln2Beta
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gate_weight_ptr,
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up_weight_ptr,
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down_weight_ptr,
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qkvBiasWeight_ptr, // queryBias
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qkvBiasWeight_ptr + hiddensize, // keyBias
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qkvBiasWeight_ptr + hiddensize +
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kvHeadNum * attHeadDim, // valueBias
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attnOutBias_ptr, // attnOutBias
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qkvWeight_ptr, // myqkvWeight
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gate_bias_ptr,
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up_bias_ptr,
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down_bias_ptr,
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qkvBiasWeight_ptr);
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if (i < totalLayer - 1) {
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memcpy(const_cast<void *>(input_ptr),
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output_ptr,
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batchSize * inputSeqLen * hiddensize * sizeof(float));
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}
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}
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return {out};
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}
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std::vector<std::vector<int64_t>> AllLLaMALayerInferShape(
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std::vector<int64_t> x_shape) {
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return {x_shape};
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}
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std::vector<paddle::DataType> AllLLaMALayerInferDtype(
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paddle::DataType x_dtype) {
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return {x_dtype};
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}
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PD_BUILD_STATIC_OP(xft_llama_all_layer)
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.Inputs({
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"x",
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paddle::Vec("ln1Gamma"),
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paddle::Vec("ln1Beta"),
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paddle::Vec("qkvWeight"),
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paddle::Vec("qkvBiasWeight"),
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paddle::Vec("attnOutWeight"),
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paddle::Vec("attnOutBias"),
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paddle::Vec("ln2Gamma"),
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paddle::Vec("ln2Beta"),
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paddle::Vec("gateWeight"),
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paddle::Vec("gateBias"),
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paddle::Vec("upWeight"),
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paddle::Vec("upBias"),
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paddle::Vec("downWeight"),
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paddle::Vec("downBias"),
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"pastSeqLen",
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"currentSeqLen",
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"step",
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})
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.Outputs({"out"})
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.Attrs({"hiddensize :int",
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"totalLayer :int",
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"computeType : std::string",
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"activation :std::string",
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"normType :std::string",
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"attHeadDim: int",
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"attHeadNum: int",
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"kvHeadNum: int",
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"maxPositions: int",
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"maxPosEmbed: int",
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"intermediateSize: int"})
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.SetKernelFn(PD_KERNEL(InvokeAllLLaMALayer))
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.SetInferShapeFn(PD_INFER_SHAPE(AllLLaMALayerInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(AllLLaMALayerInferDtype));
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