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//===----------------------------------------------------------------------===//
//
// Copyright (C) 2023 Sophgo Technologies Inc. All rights reserved.
//
// TPU-MLIR is licensed under the 2-Clause BSD License except for the
// third-party components.
//
//===----------------------------------------------------------------------===//
#include <iostream>
#include <cstdlib>
#include <vector>
#include <assert.h>
#include <chrono>
#include <algorithm>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include "memory.h"
#include "bmruntime_interface.h"
#include <getopt.h>
#include <stdio.h>
#include <inttypes.h>
#include <random>
#include <numeric>
static const uint16_t ATTENTION_MASK = 0xF0E2; // -9984 by bfloat16
class Qwen {
public:
void init(const std::vector<int> &devid, int eos_token_id, std::string model_path);
void deinit();
int forward_first(std::vector<int> &tokens);
int forward_next();
std::vector<int> answer(std::vector<int> history_tokens);
std::mt19937 sgen;
Qwen() : sgen(std::random_device()()) {};
int sample(const std::vector<float>& probs, const std::vector<int>& tokens);
private:
std::vector<bm_handle_t> handles;
bm_handle_t bm_handle;
void *p_bmrt;
const bm_net_info_t *net_embed;
const bm_net_info_t *net_embed_cache;
const bm_net_info_t *net_lm;
std::vector<const bm_net_info_t *> net_blocks;
std::vector<const bm_net_info_t *> net_blocks_cache;
std::vector<bm_tensor_t> inputs_embed_512, outputs_embed_512;
std::vector<bm_tensor_t> inputs_pid, inputs_attention;
std::vector<bm_tensor_t> next_inputid, next_pid, next_attention;
std::vector<std::vector<bm_tensor_t>> past_key, past_value;
std::vector<bm_tensor_t> inputs_lm;
std::vector<bm_tensor_t> outputs_lm, outputs_logit_lm, outputs_token_lm;
std::string name_embed;
std::string name_embed_cache;
std::string name_lm;
std::vector<std::string> name_blocks;
std::vector<std::string> name_blocks_cache;
int EOS;
int device_num;
int token_length;
int SEQLEN;
int NUM_LAYERS;
std::vector<int> visited_tokens;
};
void Qwen::init(const std::vector<int> &devices, int eos_token_id, std::string model_path) {
// params
device_num = devices.size();
EOS = eos_token_id;
// request bm_handle
std::cout << "Device [ ";
for (auto d : devices) {
std::cout << d << " ";
}
std::cout << "] loading ....\n";
for (auto d : devices) {
bm_handle_t h;
bm_status_t status = bm_dev_request(&h, d);
assert(BM_SUCCESS == status);
handles.push_back(h);
}
bm_handle = handles[0];
// create bmruntime
#ifdef SOC_TARGET
p_bmrt = bmrt_create(handles[0]);
#else
p_bmrt = bmrt_create_ex(handles.data(), handles.size());
#endif
assert(NULL != p_bmrt);
// load bmodel by file
printf("Model[%s] loading ....\n", model_path.c_str());
bool ret = bmrt_load_bmodel(p_bmrt, model_path.c_str());
assert(true == ret);
printf("Done!\n");
// set NUM_LAYERS
auto num_nets = bmrt_get_network_number(p_bmrt);
NUM_LAYERS = (num_nets - 2) / 2;
// net names
name_embed = "embedding";
name_embed_cache = "embedding_cache";
name_lm = "lm_head";
for (int i = 0; i < NUM_LAYERS; i++) {
name_blocks.emplace_back("block_" + std::to_string(i));
name_blocks_cache.emplace_back("block_cache_" + std::to_string(i));
}
// net infos
net_embed = bmrt_get_network_info(p_bmrt, name_embed.c_str());
net_embed_cache = bmrt_get_network_info(p_bmrt, name_embed_cache.c_str());
net_lm = bmrt_get_network_info(p_bmrt, name_lm.c_str());
for (int i = 0; i < NUM_LAYERS; i++) {
net_blocks.emplace_back(
bmrt_get_network_info(p_bmrt, name_blocks[i].c_str()));
net_blocks_cache.emplace_back(
bmrt_get_network_info(p_bmrt, name_blocks_cache[i].c_str()));
}
// set SEQLEN
SEQLEN = net_embed->stages[0].input_shapes[0].dims[1];
// resize
net_blocks.resize(NUM_LAYERS);
net_blocks_cache.resize(NUM_LAYERS);
past_key.resize(NUM_LAYERS);
past_value.resize(NUM_LAYERS);
visited_tokens.resize(SEQLEN);
// net device mem
inputs_embed_512.resize(net_embed->input_num);
for (int i = 0; i < device_num; ++i) {
ret = bmrt_tensor_ex(&inputs_embed_512[i], p_bmrt,
net_embed->input_loc_devices[i],
net_embed->input_dtypes[i],
net_embed->stages[0].input_shapes[i]);
assert(true == ret);
}
outputs_embed_512.resize(net_embed->output_num);
for (int i = 0; i < device_num; ++i) {
ret = bmrt_tensor_ex(&outputs_embed_512[i], p_bmrt,
net_embed->output_loc_devices[i],
net_embed->output_dtypes[i],
net_embed->stages[0].output_shapes[i]);
assert(true == ret);
}
next_inputid.resize(device_num);
for (int i = 0; i < device_num; ++i) {
ret = bmrt_tensor_ex(&next_inputid[i], p_bmrt,
net_embed_cache->input_loc_devices[i],
net_embed_cache->input_dtypes[i],
net_embed_cache->stages[0].input_shapes[i]);
assert(true == ret);
}
inputs_pid.resize(device_num);
inputs_attention.resize(device_num);
int in_num = net_blocks[0]->input_num / device_num;
for (int i = 0; i < device_num; ++i) {
ret = bmrt_tensor_ex(&inputs_pid[i], p_bmrt,
net_blocks[0]->input_loc_devices[1 + i * in_num],
net_blocks[0]->input_dtypes[1 + i * in_num],
net_blocks[0]->stages[0].input_shapes[1 + i * in_num]);
assert(true == ret);
ret = bmrt_tensor_ex(&inputs_attention[i], p_bmrt,
net_blocks[0]->input_loc_devices[2 + i * in_num],
net_blocks[0]->input_dtypes[2 + i * in_num],
net_blocks[0]->stages[0].input_shapes[2 + i * in_num]);
assert(true == ret);
}
next_pid.resize(device_num);
next_attention.resize(device_num);
int in_num_cache = net_blocks_cache[0]->input_num / device_num;
for (int i = 0; i < device_num; ++i) {
ret = bmrt_tensor_ex(&next_pid[i], p_bmrt,
net_blocks_cache[0]->input_loc_devices[1 + i * in_num_cache],
net_blocks_cache[0]->input_dtypes[1 + i * in_num_cache],
net_blocks_cache[0]->stages[0].input_shapes[1 + i * in_num_cache]);
assert(true == ret);
ret = bmrt_tensor_ex(&next_attention[i], p_bmrt,
net_blocks_cache[0]->input_loc_devices[2 + i * in_num_cache],
net_blocks_cache[0]->input_dtypes[2 + i * in_num_cache],
net_blocks_cache[0]->stages[0].input_shapes[2 + i * in_num_cache]);
assert(true == ret);
}
int out_num = net_blocks[0]->output_num / device_num;
for (int i = 0; i < NUM_LAYERS; i++) {
past_key[i].resize(device_num);
past_value[i].resize(device_num);
for (int j = 0; j < device_num; j++) {
ret = bmrt_tensor_ex(&past_key[i][j], p_bmrt,
net_blocks[0]->output_loc_devices[1 + j * out_num],
net_blocks[0]->output_dtypes[1 + j * out_num],
net_blocks[0]->stages[0].output_shapes[1 + j * out_num]);
assert(true == ret);
ret = bmrt_tensor_ex(&past_value[i][j], p_bmrt,
net_blocks[0]->output_loc_devices[2 + j * out_num],
net_blocks[0]->output_dtypes[2 + j * out_num],
net_blocks[0]->stages[0].output_shapes[2 + j * out_num]);
assert(true == ret);
}
}
inputs_lm.resize(device_num);
outputs_lm.resize(device_num);
for (int i = 0; i < device_num; ++i) {
ret = bmrt_tensor_ex(&inputs_lm[i], p_bmrt, i, net_lm->input_dtypes[0],
net_lm->stages[0].input_shapes[0]);
assert(true == ret);
ret = bmrt_tensor_ex(&outputs_lm[i], p_bmrt, i, net_lm->output_dtypes[0],
net_lm->stages[0].output_shapes[0]);
assert(true == ret);
}
}
void Qwen::deinit() {
for (int i = 0; i < device_num; ++i) {
bm_free_device(handles[i], inputs_embed_512[i].device_mem);
bm_free_device(handles[i], outputs_embed_512[i].device_mem);
bm_free_device(handles[i], inputs_pid[i].device_mem);
bm_free_device(handles[i], inputs_attention[i].device_mem);
bm_free_device(handles[i], next_inputid[i].device_mem);
bm_free_device(handles[i], next_pid[i].device_mem);
bm_free_device(handles[i], next_attention[i].device_mem);
bm_free_device(handles[i], inputs_lm[i].device_mem);
bm_free_device(handles[i], outputs_logit_lm[i].device_mem);
bm_free_device(handles[i], outputs_token_lm[i].device_mem);
}
for (int i = 0; i < NUM_LAYERS; i++) {
for (int j = 0; j < device_num; j++) {
bm_free_device(handles[j], past_key[i][j].device_mem);
bm_free_device(handles[j], past_value[i][j].device_mem);
}
}
bmrt_destroy(p_bmrt);
for (auto h : handles) {
bm_dev_free(h);
}
}
int Qwen::sample(const std::vector<float>& probs, const std::vector<int>& tokens) {
std::discrete_distribution<> dist(probs.begin(), probs.end());
return tokens[dist(sgen)];
}
int Qwen::forward_first(std::vector<int> &tokens) {
std::vector<int> position_id(SEQLEN, 0);
std::vector<uint16_t> attention_mask(SEQLEN * SEQLEN, ATTENTION_MASK);
std::copy(tokens.begin(), tokens.end(), visited_tokens.data());
token_length = tokens.size();
for (int i = 0; i < token_length; i++) {
position_id[i] = i;
}
for (int i = 0; i < token_length; i++) {
for (int j = 0; j < SEQLEN; j++) {
if (j <= i) {
attention_mask[i * SEQLEN + j] = 0;
}
}
}
// forward embeding
std::vector<int> input_nums(device_num, 1);
std::vector<void*> datas(device_num, (void*)visited_tokens.data());
bmrt_memcpy_s2d_parallel(p_bmrt, inputs_embed_512.data(), datas.data(),
input_nums.data(), device_num);
auto ret =
bmrt_launch_tensor_ex(p_bmrt, name_embed.c_str(),
inputs_embed_512.data(), inputs_embed_512.size(),
outputs_embed_512.data(), outputs_embed_512.size(),
true, false);
assert(ret);
bm_thread_sync(bm_handle);
// forward blocks
std::vector<void*> pos_id_datas(device_num, position_id.data());
std::vector<void*> in_attn_datas(device_num, attention_mask.data());
bmrt_memcpy_s2d_parallel(p_bmrt, inputs_pid.data(), pos_id_datas.data(),
input_nums.data(), device_num);
bmrt_memcpy_s2d_parallel(p_bmrt, inputs_attention.data(),in_attn_datas.data(),
input_nums.data(), device_num);
auto embed_512 = outputs_embed_512;
std::vector<bm_tensor_t> inputs_block;
std::vector<bm_tensor_t> outputs_block;
for (int i = 0; i < device_num; ++i) {
embed_512[i].shape = net_blocks[0]->stages[0].input_shapes[0];
inputs_block.push_back(embed_512[i]);
inputs_block.push_back(inputs_pid[i]);
inputs_block.push_back(inputs_attention[i]);
outputs_block.push_back(embed_512[i]);
outputs_block.push_back(past_key[0][i]);
outputs_block.push_back(past_value[0][i]);
}
for (int i = 0; i < NUM_LAYERS; i++) {
for (int j = 0; j < device_num; ++j) {
outputs_block[1 + j * 3] = past_key[i][j];
outputs_block[2 + j * 3] = past_value[i][j];
}
ret = bmrt_launch_tensor_ex(p_bmrt, name_blocks[i].c_str(),
inputs_block.data(), inputs_block.size(),
outputs_block.data(), outputs_block.size(),
true, false);
assert(ret);
bm_thread_sync(bm_handle);
}
// forward lmhead
int bytes = embed_512[0].device_mem.size / SEQLEN;
bm_memcpy_d2d_byte(bm_handle, inputs_lm[0].device_mem, 0,
embed_512[0].device_mem, (token_length - 1) * bytes,
bytes);
ret = bmrt_launch_tensor_ex(p_bmrt, name_lm.c_str(), &inputs_lm[0], 1,
&outputs_lm[0], 1, true, false);
bm_thread_sync(bm_handle);
int token = 0;
bm_memcpy_d2s(bm_handle, (void *)&token, outputs_lm[0].device_mem);
visited_tokens.emplace_back(token);
return token;
}
int Qwen::forward_next() {
token_length += 1;
int cur_token = visited_tokens[visited_tokens.size() - 1];
std::vector<uint16_t> attention_mask(SEQLEN + 1, 0);
for (int i = token_length - 1; i < SEQLEN; i++) {
attention_mask[i] = ATTENTION_MASK;
}
int32_t position_id = token_length - 1;
// forward embedding
std::vector<bm_tensor_t> inputs_embed;
std::vector<void*> input_datas;
std::vector<int> input_nums(device_num, 1);
for (int i = 0; i < device_num; ++i) {
inputs_embed.push_back(outputs_lm[i]); // token_id
inputs_embed[i].shape = net_embed_cache->stages[0].input_shapes[0];
input_datas.push_back((void*)(&cur_token));
}
bmrt_memcpy_s2d_parallel(p_bmrt, inputs_embed.data(), input_datas.data(),
input_nums.data(), device_num);
auto ret = bmrt_launch_tensor_ex(p_bmrt, name_embed_cache.c_str(),
inputs_embed.data(), inputs_embed.size(),
inputs_lm.data(), inputs_lm.size(), true, false);
assert(ret);
bm_thread_sync(bm_handle);
// forward blocks
std::vector<void*> attn_datas(device_num, attention_mask.data());
std::vector<void*> pid_datas(device_num, &position_id);
bmrt_memcpy_s2d_parallel(p_bmrt, next_attention.data(), attn_datas.data(),
input_nums.data(), device_num);
bmrt_memcpy_s2d_parallel(p_bmrt, next_pid.data(), pid_datas.data(),
input_nums.data(), device_num);
// WARNING: make inputs_lm device_num
std::vector<bm_tensor_t> embed_1 = inputs_lm;
for (int i = 0; i < device_num; ++i) {
embed_1[i].shape = net_blocks_cache[0]->stages[0].input_shapes[0];
}
std::vector<bm_tensor_t> inputs_block;
std::vector<bm_tensor_t> outputs_block;
for (int i = 0; i < device_num; ++i) {
inputs_block.push_back(embed_1[i]);
inputs_block.push_back(next_pid[i]);
inputs_block.push_back(next_attention[i]);
inputs_block.push_back(past_key[0][i]);
inputs_block.push_back(past_value[0][i]);
outputs_block.push_back(embed_1[i]);
outputs_block.push_back(past_key[0][i]);
outputs_block.push_back(past_value[0][i]);
}
for (int i = 0; i < NUM_LAYERS; i++) {
for (int j = 0; j < device_num; ++j) {
inputs_block[3 + j * 5] = past_key[i][j];
inputs_block[4 + j * 5] = past_value[i][j];
int bytes = bm_mem_get_device_size(past_key[0][j].device_mem) / SEQLEN;
int token_offset = (token_length - 1) * bytes;
bm_set_device_mem(&outputs_block[1 + j * 3].device_mem, bytes,
bm_mem_get_device_addr(past_key[i][j].device_mem) + token_offset);
bm_set_device_mem(&outputs_block[2 + j * 3].device_mem, bytes,
bm_mem_get_device_addr(past_value[i][j].device_mem) + token_offset);
}
ret = bmrt_launch_tensor_ex(p_bmrt, name_blocks_cache[i].c_str(),
inputs_block.data(), inputs_block.size(),
outputs_block.data(), outputs_block.size(),
true, false);
assert(ret);
bm_thread_sync(bm_handle);
}
// forward lmhead
ret = bmrt_launch_tensor_ex(p_bmrt, name_lm.c_str(), &inputs_lm[0], 1,
&outputs_lm[0], 1, true, false);
assert(ret);
bm_thread_sync(bm_handle);
int token = 0;
bm_memcpy_d2s(bm_handle, (void *)&token, outputs_lm[0].device_mem);
visited_tokens.emplace_back(token);
return token;
}
std::vector<int> Qwen::answer(std::vector<int> history_tokens) {
int tok_num = 0;
if (history_tokens.empty()) {
printf("Sorry: your question is too wierd!!\n");
history_tokens.clear();
return {};
}
// make sure token not too large
if ((int)history_tokens.size() > SEQLEN - 10) {
history_tokens.clear();
printf("Error: your question is too large!\n");
return {};
}
std::vector<int> result_tokens;
int token = forward_first(history_tokens);
while (token != EOS && token_length < SEQLEN) {
result_tokens.emplace_back(token);
tok_num++;
token = forward_next();
}
return result_tokens;
}
PYBIND11_MODULE(chat_parallel, m) {
pybind11::class_<Qwen>(m, "Qwen")
.def(pybind11::init<>())
.def("init", &Qwen::init)
.def("forward_first", &Qwen::forward_first)
.def("forward_next", &Qwen::forward_next)
.def("deinit", &Qwen::deinit);
}
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