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Atream 2025-03-31 22:45:37 +08:00
parent 0e93a09d67
commit 8d0292aa44
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csrc/ktransformers_ext/operators/llamafile/linear.cpp Normal file
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/**
* @Description :
* @Author : chenht2022
* @Date : 2024-07-12 10:07:58
* @Version : 1.0.0
* @LastEditors : kkk1nak0
* @LastEditTime : 2024-08-15 07:45:18
* @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
**/
#include "linear.h"
Linear::Linear(LinearConfig config) {
config_ = config;
proj_ = config_.proj;
std::vector<std::pair<void**, uint64_t>> mem_requests;
mem_requests.push_back({(void**)&input_fp32_, sizeof(float) * config_.group_max_len * config_.input_size});
mem_requests.push_back({(void**)&proj_input_, config_.group_max_len * config_.input_size * ggml_type_size(ggml_internal_get_type_traits(config_.proj_type).vec_dot_type) / ggml_blck_size(ggml_internal_get_type_traits(config_.proj_type).vec_dot_type)});
mem_requests.push_back({(void**)&proj_output_, sizeof(float) * config_.group_max_len * config_.output_size});
shared_mem_buffer.alloc(this, mem_requests);
}
Linear::~Linear() {
shared_mem_buffer.dealloc(this);
}
void Linear::warm_up(Backend *backend) {
std::vector<float> input_fp32(config_.input_size);
std::vector<uint8_t> input(config_.input_size *
ggml_type_size(config_.hidden_type) /
ggml_blck_size(config_.hidden_type));
std::vector<uint8_t> output(config_.output_size *
ggml_type_size(config_.hidden_type) /
ggml_blck_size(config_.hidden_type));
for (int i = 0; i < config_.input_size; i++) {
input_fp32[i] = 0;
}
from_float(input_fp32.data(), input.data(), config_.input_size, config_.hidden_type);
forward_many(1, input.data(), output.data(), backend);
}
void Linear::forward_many(int qlen, const void* input, void* output, Backend* backend) {
const void* proj_input_ptr;
if (config_.hidden_type == ggml_internal_get_type_traits(config_.proj_type).vec_dot_type) {
proj_input_ptr = input;
} else {
to_float(input, input_fp32_, qlen * config_.input_size, config_.hidden_type);
from_float(input_fp32_, proj_input_, qlen * config_.input_size, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type);
proj_input_ptr = proj_input_;
}
int nth = config_.output_size / config_.stride;
backend->do_work_stealing_job(nth, nullptr, [&](int task_id) {
int ith = task_id;
void* proj_ptr = (uint8_t*)proj_ + ith * config_.stride * config_.input_size * ggml_type_size(config_.proj_type) / ggml_blck_size(config_.proj_type);
float* proj_output_ptr = proj_output_ + ith * config_.stride;
llamafile_sgemm(config_.stride, qlen, config_.input_size / ggml_blck_size(config_.proj_type), proj_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_input_ptr, config_.input_size / ggml_blck_size(config_.proj_type), proj_output_ptr, config_.output_size, 0, 1, GGML_TASK_TYPE_COMPUTE, config_.proj_type, ggml_internal_get_type_traits(config_.proj_type).vec_dot_type, GGML_TYPE_F32, GGML_PREC_DEFAULT);
if (config_.stride % ggml_blck_size(config_.hidden_type) == 0) {
for (int i = 0; i < qlen; i++) {
float* output_fp32_ptr = proj_output_ + i * config_.output_size + ith * config_.stride;
void* output_ptr = (uint8_t*)output + i * config_.output_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type) + ith * config_.stride * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type);
from_float(output_fp32_ptr, output_ptr, config_.stride, config_.hidden_type);
}
}
}, nullptr);
if (config_.stride % ggml_blck_size(config_.hidden_type) != 0) {
from_float(proj_output_, output, qlen * config_.output_size, config_.hidden_type);
}
}
void Linear::forward(int qlen, const void* input, void* output, Backend* backend) {
if (qlen <= 0) {
return;
}
int forward_len = std::min(qlen, config_.group_max_len);
forward_many(forward_len, input, output, backend);
forward(qlen - forward_len, (uint8_t*)input + forward_len * config_.input_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), (uint8_t*)output + forward_len * config_.output_size * ggml_type_size(config_.hidden_type) / ggml_blck_size(config_.hidden_type), backend);
}