From 219a7a4770147e32cbaab17e3215aeb36ae0958a Mon Sep 17 00:00:00 2001 From: "yi.chu" Date: Fri, 30 Aug 2024 01:06:53 +0800 Subject: [PATCH] [Qwen2] support to combine only one bmodel for different shapes 1. combine different shapes bmodel in compile_multi.sh 2. add stage_idx for empty_net in utils.h 3. add *.so in .gitignore --- .gitignore | 1 + models/Qwen2/share_cache_demo/chat.cpp | 397 ++++++++++-------- .../Qwen2/share_cache_demo/compile_multi.sh | 331 +++++++++++++++ models/Qwen2/share_cache_demo/pipeline.py | 27 +- models/Qwen2/share_cache_demo/t.sh | 50 ++- models/Qwen2/support/lib_pcie/libbmlib.so.0 | Bin 595376 -> 595376 bytes models/Qwen2/support/lib_pcie/libbmrt.so.1.0 | Bin 9671456 -> 9671456 bytes models/Qwen2/support/lib_soc/libbmlib.so.0 | Bin 593312 -> 593312 bytes models/Qwen2/support/lib_soc/libbmrt.so.1.0 | Bin 9600752 -> 9600928 bytes support/include/utils.h | 14 +- 10 files changed, 613 insertions(+), 207 deletions(-) create mode 100755 models/Qwen2/share_cache_demo/compile_multi.sh diff --git a/.gitignore b/.gitignore index bc209a3..6fc6220 100755 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,4 @@ core *.zip *.tar.gz *.deb +*.so diff --git a/models/Qwen2/share_cache_demo/chat.cpp b/models/Qwen2/share_cache_demo/chat.cpp index 17473c6..114e0a8 100755 --- a/models/Qwen2/share_cache_demo/chat.cpp +++ b/models/Qwen2/share_cache_demo/chat.cpp @@ -10,6 +10,8 @@ #include #include #include +#include +#include #include #include #include @@ -20,7 +22,6 @@ #include #include #include -#include #include "bmruntime_interface.h" #include "memory.h" @@ -31,7 +32,13 @@ typedef uint8_t *(*decrypt_func)(const uint8_t *, uint64_t, uint64_t *); class Qwen { public: - void init(const std::vector &devid, const std::string &model_path); + void load_bmodel(const std::vector &devices, + const std::string &model_path); + void init_nets(); + void init_params(); + + void init(const std::vector &devid, const std::string &model_path, + bool read_bmodel = true); void deinit(); void init_decrypt(); void deinit_decrypt(); @@ -44,29 +51,43 @@ class Qwen { std::vector generate(std::vector &history_tokens, int EOS); std::mt19937 sgen; - Qwen() : sgen(std::random_device()()), - decrypt_handle_(nullptr), - decrypt_func_(nullptr) {} + Qwen() + : sgen(std::random_device()()), decrypt_handle_(nullptr), + decrypt_func_(nullptr) {} private: - void net_launch(const bm_net_info_t *net, int stage_idx = 0); - void dynamic_net_launch(const bm_net_info_t *net, int token_length, - int stage_idx = 0); + // d2d inline void d2d(bm_device_mem_t &dst, bm_device_mem_t &src); inline void d2d(bm_device_mem_t &dst, bm_device_mem_t &src, size_t offset); inline void d2d(bm_device_mem_t &dst, bm_device_mem_t &src, size_t offset, size_t size); + // infernece + std::vector + load_and_infer_embedding(const std::vector &tokens); + void net_launch(const bm_net_info_t *net, int stage_idx); + void dynamic_net_launch(const bm_net_info_t *net, int token_length, + int stage_idx); + + bm_device_mem_t embedding_launch(const bm_net_info_t *net0, + const bm_net_info_t *net1, + const std::vector &tokens); + bm_device_mem_t lm_launch(const bm_net_info_t *net, + const bm_device_mem_t &out_mem, size_t offset, + size_t size); + + // tensors void make_in_tensors(); void free_in_tensors(); - std::vector load_and_infer_embedding(const std::vector &tokens); - bm_device_mem_t embedding_launch(const bm_net_info_t *net0, const bm_net_info_t *net1, const std::vector &tokens); - void head_launch(const bm_net_info_t *net, bm_device_mem_t &logits_mem); + // sample + void head_launch(const bm_net_info_t *net, bm_device_mem_t &logits_mem, + int stage_idx); int greedy_search(const bm_net_info_t *net, bm_device_mem_t &logits_mem); int penalty_sample(const bm_net_info_t *net, bm_device_mem_t &logits_mem, std::vector &input_tokens, int &token_length); + // error void handle_error(); void bmrt_error(); void bmodel_error(); @@ -81,6 +102,7 @@ class Qwen { std::string lib_path; std::string embedding_path; int status_code; + int stage_idx; // model int hidden_bytes; @@ -122,7 +144,7 @@ class Qwen { bm_tensor_t inputs_attention, unshare_attention, next_attention; uint16_t mask_value; - void *decrypt_handle_; // handle of decrypt lib + void *decrypt_handle_; // handle of decrypt lib decrypt_func decrypt_func_; // decrypt func from lib }; @@ -146,45 +168,6 @@ void Qwen::d2d(bm_device_mem_t &dst, bm_device_mem_t &src, size_t offset, bm_memcpy_d2d_byte(bm_handle, dst, offset, src, 0, size); } -void Qwen::make_in_tensors() { - bool ret = false; - ret = bmrt_tensor_ex(&inputs_pid, p_bmrt, net_blocks[0]->input_loc_devices[1], - net_blocks[0]->input_dtypes[1], - net_blocks[0]->stages[0].input_shapes[1]); - ASSERT(true == ret); - - ret = bmrt_tensor_ex( - &inputs_attention, p_bmrt, net_blocks[0]->input_loc_devices[2], - net_blocks[0]->input_dtypes[2], net_blocks[0]->stages[0].input_shapes[2]); - ASSERT(true == ret); - - if (unshare_flag != -1) { - ret = bmrt_tensor_ex(&unshare_pid, p_bmrt, - net_blocks_unshare[0]->input_loc_devices[1], - net_blocks_unshare[0]->input_dtypes[1], - net_blocks_unshare[0]->stages[0].input_shapes[1]); - ASSERT(true == ret); - - ret = bmrt_tensor_ex(&unshare_attention, p_bmrt, - net_blocks_unshare[0]->input_loc_devices[2], - net_blocks_unshare[0]->input_dtypes[2], - net_blocks_unshare[0]->stages[0].input_shapes[2]); - ASSERT(true == ret); - } - - ret = bmrt_tensor_ex(&next_pid, p_bmrt, - net_blocks_cache[0]->input_loc_devices[1], - net_blocks_cache[0]->input_dtypes[1], - net_blocks_cache[0]->stages[0].input_shapes[1]); - ASSERT(true == ret); - - ret = bmrt_tensor_ex(&next_attention, p_bmrt, - net_blocks_cache[0]->input_loc_devices[2], - net_blocks_cache[0]->input_dtypes[2], - net_blocks_cache[0]->stages[0].input_shapes[2]); - ASSERT(true == ret); -} - //===------------------------------------------------------------===// // Decrypt //===------------------------------------------------------------===// @@ -195,16 +178,17 @@ void Qwen::init_decrypt() { } decrypt_handle_ = dlopen(lib_path.c_str(), RTLD_LAZY); if (!decrypt_handle_) { - std::cout << "Error:" << "Decrypt lib [" << lib_path << "] load failed." - << std::endl; + std::cout << "Error:" + << "Decrypt lib [" << lib_path << "] load failed." << std::endl; throw std::runtime_error(""); } decrypt_func_ = (decrypt_func)dlsym(decrypt_handle_, "decrypt"); auto error = dlerror(); if (error) { dlclose(decrypt_handle_); - std::cout << "Error:" << "Decrypt lib [" << lib_path - << "] symbol find failed." << std::endl; + std::cout << "Error:" + << "Decrypt lib [" << lib_path << "] symbol find failed." + << std::endl; throw std::runtime_error(""); } return; @@ -214,15 +198,15 @@ void Qwen::deinit_decrypt() { // Step 1: Close the dynamic library handle if it's open. if (!lib_path.empty() && decrypt_handle_) { dlclose(decrypt_handle_); - decrypt_handle_ = nullptr; // Avoid dangling pointer by resetting to nullptr. + decrypt_handle_ = + nullptr; // Avoid dangling pointer by resetting to nullptr. } - // Step 2: Reset the function pointer to nullptr. + // Step 2: Reset the function pointer to nullptr. // No need to free or close anything specific for it. decrypt_func_ = nullptr; } - //===------------------------------------------------------------===// // Exception //===------------------------------------------------------------===// @@ -260,25 +244,27 @@ void Qwen::launch_error() { // addr_mode = 0, but must set addr_mode =1 void Qwen::ioalone_error() { status_code = -6; - throw std::runtime_error("addr_mode = 0 in your bmodel, but must set addr_mode = 1"); + throw std::runtime_error( + "addr_mode = 0 in your bmodel, but must set addr_mode = 1"); } -void Qwen::head_launch(const bm_net_info_t *net, bm_device_mem_t &logits_mem) { +void Qwen::head_launch(const bm_net_info_t *net, bm_device_mem_t &logits_mem, + int stage_idx) { std::vector in_tensors(net->input_num); std::vector out_tensors(net->output_num); bmrt_tensor_with_device(&in_tensors[0], logits_mem, net->input_dtypes[0], - net->stages[0].input_shapes[0]); + net->stages[stage_idx].input_shapes[0]); for (int i = 1; i < net->input_num; i++) { - bmrt_tensor_with_device(&in_tensors[i], net->stages[0].input_mems[i], - net->input_dtypes[i], - net->stages[0].input_shapes[i]); + bmrt_tensor_with_device( + &in_tensors[i], net->stages[stage_idx].input_mems[i], + net->input_dtypes[i], net->stages[stage_idx].input_shapes[i]); } for (int i = 0; i < net->output_num; i++) { - bmrt_tensor_with_device(&out_tensors[i], net->stages[0].output_mems[i], - net->output_dtypes[i], - net->stages[0].output_shapes[i]); + bmrt_tensor_with_device( + &out_tensors[i], net->stages[stage_idx].output_mems[i], + net->output_dtypes[i], net->stages[stage_idx].output_shapes[i]); } auto ret = bmrt_launch_tensor_ex(p_bmrt, net->name, in_tensors.data(), net->input_num, out_tensors.data(), @@ -314,7 +300,8 @@ void Qwen::net_launch(const bm_net_info_t *net, int stage_idx) { } } -void Qwen::dynamic_net_launch(const bm_net_info_t *net, int token_length, int stage_idx) { +void Qwen::dynamic_net_launch(const bm_net_info_t *net, int token_length, + int stage_idx) { std::vector in_tensors(net->input_num); std::vector out_tensors(net->output_num); @@ -345,8 +332,8 @@ void Qwen::dynamic_net_launch(const bm_net_info_t *net, int token_length, int st } } -void Qwen::init(const std::vector &devices, - const std::string &model_path) { +void Qwen::load_bmodel(const std::vector &devices, + const std::string &model_path) { // set status_code status_code = 0; @@ -380,7 +367,8 @@ void Qwen::init(const std::vector &devices, printf("Model[%s] loading ....\n", model_path.c_str()); bool ret = false; if (!lib_path.empty()) { - ret = bmrt_load_bmodel_with_decrypt(p_bmrt, model_path.c_str(), decrypt_func_); + ret = bmrt_load_bmodel_with_decrypt(p_bmrt, model_path.c_str(), + decrypt_func_); } else { ret = bmrt_load_bmodel(p_bmrt, model_path.c_str()); } @@ -388,9 +376,12 @@ void Qwen::init(const std::vector &devices, bmodel_error(); } printf("Done!\n"); +} +void Qwen::init_nets() { // net embed and lm_head - ASSERT(bmrt_get_network_index(p_bmrt, "embedding") != -1 || !embedding_path.empty()); + ASSERT(bmrt_get_network_index(p_bmrt, "embedding") != -1 || + !embedding_path.empty()); if (embedding_path.empty()) { net_embed = bmrt_get_network_info(p_bmrt, "embedding"); net_embed_cache = bmrt_get_network_info(p_bmrt, "embedding_cache"); @@ -438,23 +429,25 @@ void Qwen::init(const std::vector &devices, std::cerr << "Supported dtype are 'BM_FLOAT16' or 'BM_BFLOAT16'\n"; throw std::runtime_error("Invalid attention dtype"); } +} +void Qwen::init_params() { // read parameters from bmodel is_dynamic = net_blocks[0]->is_dynamic; auto addr_mode = net_blocks_cache[0]->addr_mode; io_alone = addr_mode == 1; - hidden_bytes = - bm_mem_get_device_size(net_blocks_cache[0]->stages[0].output_mems[0]); - kv_bytes = - bm_mem_get_device_size(net_blocks_cache[0]->stages[0].output_mems[1]); - MAX_SHARE_LENGTH = net_blocks[0]->stages[0].input_shapes[0].dims[1]; + hidden_bytes = bm_mem_get_device_size( + net_blocks_cache[0]->stages[stage_idx].output_mems[0]); + kv_bytes = bm_mem_get_device_size( + net_blocks_cache[0]->stages[stage_idx].output_mems[1]); + MAX_SHARE_LENGTH = net_blocks[0]->stages[stage_idx].input_shapes[0].dims[1]; if (unshare_flag != -1) { MAX_UNSHARE_LENGTH = - net_blocks_unshare[0]->stages[0].input_shapes[0].dims[1]; + net_blocks_unshare[0]->stages[stage_idx].input_shapes[0].dims[1]; } else { MAX_UNSHARE_LENGTH = 0; } - SEQLEN = net_blocks_cache[0]->stages[0].input_shapes[3].dims[1]; + SEQLEN = net_blocks_cache[0]->stages[stage_idx].input_shapes[3].dims[1]; // resize past_key.resize(NUM_LAYERS); @@ -466,8 +459,8 @@ void Qwen::init(const std::vector &devices, // declare tmemory location for kvcache for (int i = 0; i < NUM_LAYERS; i++) { ASSERT(net_blocks_cache[i]->addr_mode == 1); - past_key[i] = net_blocks_cache[i]->stages[0].input_mems[3]; - past_value[i] = net_blocks_cache[i]->stages[0].input_mems[4]; + past_key[i] = net_blocks_cache[i]->stages[stage_idx].input_mems[3]; + past_value[i] = net_blocks_cache[i]->stages[stage_idx].input_mems[4]; if (io_alone_mode == 1) { empty(bm_handle, past_key[i]); empty(bm_handle, past_value[i]); @@ -475,8 +468,62 @@ void Qwen::init(const std::vector &devices, d2d(past_value[i], tmp_past_value[i], 0, share_length * kv_bytes); } } +} - // make in tensors +void Qwen::make_in_tensors() { + bool ret = false; + ret = bmrt_tensor_ex(&inputs_pid, p_bmrt, net_blocks[0]->input_loc_devices[1], + net_blocks[0]->input_dtypes[1], + net_blocks[0]->stages[stage_idx].input_shapes[1]); + ASSERT(true == ret); + + ret = bmrt_tensor_ex(&inputs_attention, p_bmrt, + net_blocks[0]->input_loc_devices[2], + net_blocks[0]->input_dtypes[2], + net_blocks[0]->stages[stage_idx].input_shapes[2]); + ASSERT(true == ret); + + if (unshare_flag != -1) { + ret = bmrt_tensor_ex( + &unshare_pid, p_bmrt, net_blocks_unshare[0]->input_loc_devices[1], + net_blocks_unshare[0]->input_dtypes[1], + net_blocks_unshare[0]->stages[stage_idx].input_shapes[1]); + ASSERT(true == ret); + + ret = bmrt_tensor_ex( + &unshare_attention, p_bmrt, net_blocks_unshare[0]->input_loc_devices[2], + net_blocks_unshare[0]->input_dtypes[2], + net_blocks_unshare[0]->stages[stage_idx].input_shapes[2]); + ASSERT(true == ret); + } + + ret = bmrt_tensor_ex(&next_pid, p_bmrt, + net_blocks_cache[0]->input_loc_devices[1], + net_blocks_cache[0]->input_dtypes[1], + net_blocks_cache[0]->stages[stage_idx].input_shapes[1]); + ASSERT(true == ret); + + ret = bmrt_tensor_ex(&next_attention, p_bmrt, + net_blocks_cache[0]->input_loc_devices[2], + net_blocks_cache[0]->input_dtypes[2], + net_blocks_cache[0]->stages[stage_idx].input_shapes[2]); + ASSERT(true == ret); +} + +void Qwen::init(const std::vector &devices, const std::string &model_path, + bool read_bmodel) { + if (read_bmodel) { + // step1 : load bmodel + load_bmodel(devices, model_path); + + // step2 : init nets + init_nets(); + } + + // step3 : init parameters + init_params(); + + // step4 : make in tensors make_in_tensors(); } @@ -537,7 +584,7 @@ void Qwen::deinit() { int Qwen::greedy_search(const bm_net_info_t *net, bm_device_mem_t &logits_mem) { auto &out_mem = net->stages[0].output_mems[0]; - head_launch(net, logits_mem); + head_launch(net, logits_mem, 0); int token = 0; bm_memcpy_d2s(bm_handle, (void *)&token, out_mem); return token; @@ -545,12 +592,12 @@ int Qwen::greedy_search(const bm_net_info_t *net, bm_device_mem_t &logits_mem) { int Qwen::penalty_sample(const bm_net_info_t *net, bm_device_mem_t &logits_mem, std::vector &input_tokens, int &token_length) { - auto &in1_mem = net->stages[0].input_mems[1]; - auto &in2_mem = net->stages[0].input_mems[2]; - auto &in3_mem = net->stages[0].input_mems[3]; - auto &in4_mem = net->stages[0].input_mems[4]; - auto &out0_mem = net->stages[0].output_mems[0]; - auto &out1_mem = net->stages[0].output_mems[1]; + auto &in1_mem = net->stages[stage_idx].input_mems[1]; + auto &in2_mem = net->stages[stage_idx].input_mems[2]; + auto &in3_mem = net->stages[stage_idx].input_mems[3]; + auto &in4_mem = net->stages[stage_idx].input_mems[4]; + auto &out0_mem = net->stages[stage_idx].output_mems[0]; + auto &out1_mem = net->stages[stage_idx].output_mems[1]; // repeat_penalty + top_p + top_k + temperature std::vector generated_tokens(SEQLEN, input_tokens[token_length - 1]); @@ -563,10 +610,10 @@ int Qwen::penalty_sample(const bm_net_info_t *net, bm_device_mem_t &logits_mem, bm_memcpy_s2d(bm_handle, in4_mem, (void *)&repeat_penalty); // inference - head_launch(net, logits_mem); + head_launch(net, logits_mem, stage_idx); // get logit & token - int candidate_num = net->stages[0].output_shapes[0].dims[1]; + int candidate_num = net->stages[stage_idx].output_shapes[0].dims[1]; std::vector probs(candidate_num); bm_memcpy_d2s(bm_handle, probs.data(), out0_mem); std::vector tokens(candidate_num); @@ -577,10 +624,11 @@ int Qwen::penalty_sample(const bm_net_info_t *net, bm_device_mem_t &logits_mem, return tokens[dist(sgen)]; } -std::vector Qwen::load_and_infer_embedding(const std::vector &tokens) { +std::vector +Qwen::load_and_infer_embedding(const std::vector &tokens) { std::ifstream file(embedding_path, std::ios::binary); if (!file) { - throw std::runtime_error("Unable to open file\n"); + throw std::runtime_error("Unable to open file\n"); } size_t embedding_bytes = hidden_bytes; @@ -591,20 +639,28 @@ std::vector Qwen::load_and_infer_embedding(const std::vector &tok for (size_t i = 0; i < size; i++) { long long start_position = (long long)tokens[i] * embedding_bytes; file.seekg(start_position, std::ios::beg); - file.read(reinterpret_cast(&buffer[i * embedding_dim]), embedding_bytes); + file.read(reinterpret_cast(&buffer[i * embedding_dim]), + embedding_bytes); } return buffer; } -bm_device_mem_t Qwen::embedding_launch(const bm_net_info_t *net0, const bm_net_info_t *net1, const std::vector &tokens) { +bm_device_mem_t Qwen::embedding_launch(const bm_net_info_t *net0, + const bm_net_info_t *net1, + const std::vector &tokens) { bm_device_mem_t out_mem; if (embedding_path.empty()) { - auto &in_mem = net0->stages[0].input_mems[0]; - out_mem = net0->stages[0].output_mems[0]; + + // embedding : net0->stages[stage_idx] + // embedding_cache : net0->stages[0] + int this_stage_idx = (strcmp(net0->name, "embedding") == 0) ? stage_idx : 0; + + auto &in_mem = net0->stages[this_stage_idx].input_mems[0]; + out_mem = net0->stages[this_stage_idx].output_mems[0]; bm_memcpy_s2d(bm_handle, in_mem, (void *)tokens.data()); - net_launch(net0); // prefil embedding + net_launch(net0, this_stage_idx); // prefil embedding } else { - out_mem = net1->stages[0].input_mems[0]; + out_mem = net1->stages[stage_idx].input_mems[0]; empty(bm_handle, out_mem); auto buffer = load_and_infer_embedding(tokens); bm_memcpy_s2d(bm_handle, out_mem, (void *)buffer.data()); @@ -612,6 +668,16 @@ bm_device_mem_t Qwen::embedding_launch(const bm_net_info_t *net0, const bm_net_i return out_mem; } +bm_device_mem_t Qwen::lm_launch(const bm_net_info_t *net, + const bm_device_mem_t &out_mem, size_t offset, + size_t size) { + auto &lm_in_mem = net_lm->stages[0].input_mems[0]; + auto &lm_out_mem = net_lm->stages[0].output_mems[0]; + bm_memcpy_d2d_byte(bm_handle, lm_in_mem, 0, out_mem, offset, size); + net_launch(net_lm, 0); + return lm_out_mem; +} + int Qwen::forward_first(std::vector &tokens) { std::vector first_tokens(MAX_SHARE_LENGTH, 0); std::vector position_id(MAX_SHARE_LENGTH, 0); @@ -638,8 +704,8 @@ int Qwen::forward_first(std::vector &tokens) { // empty for (int i = 0; i < NUM_LAYERS; i++) { - empty_net(bm_handle, net_blocks[i]); - empty_net(bm_handle, net_blocks_cache[i]); + empty_net(bm_handle, net_blocks[i], stage_idx); + empty_net(bm_handle, net_blocks_cache[i], stage_idx); } // forward embeding @@ -652,9 +718,9 @@ int Qwen::forward_first(std::vector &tokens) { (void *)attention_mask.data()); for (int idx = 0; idx < NUM_LAYERS; idx++) { // init - auto &in0_mem = net_blocks[idx]->stages[0].input_mems[0]; - auto &in1_mem = net_blocks[idx]->stages[0].input_mems[1]; - auto &in2_mem = net_blocks[idx]->stages[0].input_mems[2]; + auto &in0_mem = net_blocks[idx]->stages[stage_idx].input_mems[0]; + auto &in1_mem = net_blocks[idx]->stages[stage_idx].input_mems[1]; + auto &in2_mem = net_blocks[idx]->stages[stage_idx].input_mems[2]; // move to device d2d(in0_mem, out_mem, 0, total_length * hidden_bytes); @@ -662,25 +728,23 @@ int Qwen::forward_first(std::vector &tokens) { in2_mem = inputs_attention.device_mem; // net forward - if (net_blocks[idx]->is_dynamic) { - dynamic_net_launch(net_blocks[idx], total_length); - } else { - net_launch(net_blocks[idx]); - } - // net_launch(net_blocks[idx]); - out_mem = net_blocks[idx]->stages[0].output_mems[0]; - d2d(past_key[idx], net_blocks[idx]->stages[0].output_mems[1], 0, + // can not to dynamic net launch for combine qwen2-10240 and qwen2-5120 + // if (net_blocks[idx]->is_dynamic) { + // dynamic_net_launch(net_blocks[idx], total_length, stage_idx); + // } else { + // net_launch(net_blocks[idx], stage_idx); + // } + net_launch(net_blocks[idx], stage_idx); + out_mem = net_blocks[idx]->stages[stage_idx].output_mems[0]; + d2d(past_key[idx], net_blocks[idx]->stages[stage_idx].output_mems[1], 0, total_length * kv_bytes); - d2d(past_value[idx], net_blocks[idx]->stages[0].output_mems[2], 0, + d2d(past_value[idx], net_blocks[idx]->stages[stage_idx].output_mems[2], 0, total_length * kv_bytes); } // forward lmhead - auto &lm_in_mem = net_lm->stages[0].input_mems[0]; - auto &lm_out_mem = net_lm->stages[0].output_mems[0]; - bm_memcpy_d2d_byte(bm_handle, lm_in_mem, 0, out_mem, - (total_length - 1) * hidden_bytes, hidden_bytes); - net_launch(net_lm); + auto lm_out_mem = lm_launch(net_lm, out_mem, + (total_length - 1) * hidden_bytes, hidden_bytes); int token = 0; if (generation_mode == "greedy") { @@ -720,9 +784,9 @@ void Qwen::forward_share(std::vector &tokens) { // empty for (int i = 0; i < NUM_LAYERS; i++) { - empty_net(bm_handle, net_blocks[i]); - empty_net(bm_handle, net_blocks_unshare[i]); - empty_net(bm_handle, net_blocks_cache[i]); + empty_net(bm_handle, net_blocks[i], stage_idx); + empty_net(bm_handle, net_blocks_unshare[i], stage_idx); + empty_net(bm_handle, net_blocks_cache[i], stage_idx); } // forward embeding @@ -735,9 +799,9 @@ void Qwen::forward_share(std::vector &tokens) { (void *)attention_mask.data()); for (int idx = 0; idx < NUM_LAYERS; idx++) { // init - auto &in0_mem = net_blocks[idx]->stages[0].input_mems[0]; - auto &in1_mem = net_blocks[idx]->stages[0].input_mems[1]; - auto &in2_mem = net_blocks[idx]->stages[0].input_mems[2]; + auto &in0_mem = net_blocks[idx]->stages[stage_idx].input_mems[0]; + auto &in1_mem = net_blocks[idx]->stages[stage_idx].input_mems[1]; + auto &in2_mem = net_blocks[idx]->stages[stage_idx].input_mems[2]; // move to device d2d(in0_mem, out_mem, 0, share_length * hidden_bytes); @@ -745,16 +809,16 @@ void Qwen::forward_share(std::vector &tokens) { in2_mem = inputs_attention.device_mem; // net forward - if (net_blocks[idx]->is_dynamic) { - dynamic_net_launch(net_blocks[idx], share_length); - } else { - net_launch(net_blocks[idx]); - } - // net_launch(net_blocks[idx]); - out_mem = net_blocks[idx]->stages[0].output_mems[0]; - d2d(past_key[idx], net_blocks[idx]->stages[0].output_mems[1], 0, + // if (net_blocks[idx]->is_dynamic) { + // dynamic_net_launch(net_blocks[idx], share_length, stage_idx); + // } else { + // net_launch(net_blocks[idx], stage_idx); + // } + net_launch(net_blocks[idx], stage_idx); + out_mem = net_blocks[idx]->stages[stage_idx].output_mems[0]; + d2d(past_key[idx], net_blocks[idx]->stages[stage_idx].output_mems[1], 0, share_length * kv_bytes); - d2d(past_value[idx], net_blocks[idx]->stages[0].output_mems[2], 0, + d2d(past_value[idx], net_blocks[idx]->stages[stage_idx].output_mems[2], 0, share_length * kv_bytes); } return; @@ -787,7 +851,8 @@ int Qwen::forward_unshare(std::vector &tokens) { } // forward embeding - auto out_mem = embedding_launch(net_embed_unshare, net_blocks_unshare[0], unshare_tokens); + auto out_mem = embedding_launch(net_embed_unshare, net_blocks_unshare[0], + unshare_tokens); // forward blocks // move psition_id & attention_mask to device @@ -798,11 +863,11 @@ int Qwen::forward_unshare(std::vector &tokens) { int unshare_size = unshare_length * kv_bytes; for (int idx = 0; idx < NUM_LAYERS; idx++) { // init - auto &in0_mem = net_blocks_unshare[idx]->stages[0].input_mems[0]; - auto &in1_mem = net_blocks_unshare[idx]->stages[0].input_mems[1]; - auto &in2_mem = net_blocks_unshare[idx]->stages[0].input_mems[2]; - auto &in3_mem = net_blocks_unshare[idx]->stages[0].input_mems[3]; - auto &in4_mem = net_blocks_unshare[idx]->stages[0].input_mems[4]; + auto &in0_mem = net_blocks_unshare[idx]->stages[stage_idx].input_mems[0]; + auto &in1_mem = net_blocks_unshare[idx]->stages[stage_idx].input_mems[1]; + auto &in2_mem = net_blocks_unshare[idx]->stages[stage_idx].input_mems[2]; + auto &in3_mem = net_blocks_unshare[idx]->stages[stage_idx].input_mems[3]; + auto &in4_mem = net_blocks_unshare[idx]->stages[stage_idx].input_mems[4]; // move to device d2d(in0_mem, out_mem, 0, unshare_length * hidden_bytes); @@ -812,25 +877,24 @@ int Qwen::forward_unshare(std::vector &tokens) { d2d(in4_mem, past_value[idx], 0, MAX_SHARE_LENGTH * kv_bytes); // net forward - if (net_blocks[idx]->is_dynamic) { - dynamic_net_launch(net_blocks_unshare[idx], unshare_length); - } else { - net_launch(net_blocks_unshare[idx]); - } - // net_launch(net_blocks_unshare[idx]); - out_mem = net_blocks_unshare[idx]->stages[0].output_mems[0]; - d2d(past_key[idx], net_blocks_unshare[idx]->stages[0].output_mems[1], - share_size, unshare_size); - d2d(past_value[idx], net_blocks_unshare[idx]->stages[0].output_mems[2], - share_size, unshare_size); + // if (net_blocks[idx]->is_dynamic) { + // dynamic_net_launch(net_blocks_unshare[idx], unshare_length, stage_idx); + // } else { + // net_launch(net_blocks_unshare[idx], stage_idx); + // } + net_launch(net_blocks_unshare[idx], stage_idx); + out_mem = net_blocks_unshare[idx]->stages[stage_idx].output_mems[0]; + d2d(past_key[idx], + net_blocks_unshare[idx]->stages[stage_idx].output_mems[1], share_size, + unshare_size); + d2d(past_value[idx], + net_blocks_unshare[idx]->stages[stage_idx].output_mems[2], share_size, + unshare_size); } // forward lmhead - auto &lm_in_mem = net_lm->stages[0].input_mems[0]; - auto &lm_out_mem = net_lm->stages[0].output_mems[0]; - bm_memcpy_d2d_byte(bm_handle, lm_in_mem, 0, out_mem, - (unshare_length - 1) * hidden_bytes, hidden_bytes); - net_launch(net_lm); + auto lm_out_mem = lm_launch( + net_lm, out_mem, (unshare_length - 1) * hidden_bytes, hidden_bytes); int token = 0; if (generation_mode == "greedy") { @@ -857,7 +921,8 @@ int Qwen::forward_next() { // embedding std::vector cur_tokens = {cur_token}; - auto out_mem = embedding_launch(net_embed_cache, net_blocks_cache[0], cur_tokens); + auto out_mem = + embedding_launch(net_embed_cache, net_blocks_cache[0], cur_tokens); // blocks // move psition_id & attention_mask to device @@ -867,12 +932,12 @@ int Qwen::forward_next() { int token_offset = (total_length - 1) * kv_bytes; for (int idx = 0; idx < NUM_LAYERS; idx++) { // init - auto &in0_mem = net_blocks_cache[idx]->stages[0].input_mems[0]; - auto &in1_mem = net_blocks_cache[idx]->stages[0].input_mems[1]; - auto &in2_mem = net_blocks_cache[idx]->stages[0].input_mems[2]; - auto &out0_mem = net_blocks_cache[idx]->stages[0].output_mems[0]; - auto &out1_mem = net_blocks_cache[idx]->stages[0].output_mems[1]; - auto &out2_mem = net_blocks_cache[idx]->stages[0].output_mems[2]; + auto &in0_mem = net_blocks_cache[idx]->stages[stage_idx].input_mems[0]; + auto &in1_mem = net_blocks_cache[idx]->stages[stage_idx].input_mems[1]; + auto &in2_mem = net_blocks_cache[idx]->stages[stage_idx].input_mems[2]; + auto &out0_mem = net_blocks_cache[idx]->stages[stage_idx].output_mems[0]; + auto &out1_mem = net_blocks_cache[idx]->stages[stage_idx].output_mems[1]; + auto &out2_mem = net_blocks_cache[idx]->stages[stage_idx].output_mems[2]; // move to device // empty(bm_handle, in0_mem); @@ -881,7 +946,7 @@ int Qwen::forward_next() { in2_mem = next_attention.device_mem; // net forward - net_launch(net_blocks_cache[idx]); + net_launch(net_blocks_cache[idx], stage_idx); out_mem = out0_mem; bm_memcpy_d2d_byte(bm_handle, past_key[idx], token_offset, out1_mem, 0, kv_bytes); @@ -890,10 +955,7 @@ int Qwen::forward_next() { } // forward lmhead - auto &lm_in_mem = net_lm->stages[0].input_mems[0]; - auto &lm_out_mem = net_lm->stages[0].output_mems[0]; - d2d(lm_in_mem, out_mem); - net_launch(net_lm); + auto lm_out_mem = lm_launch(net_lm, out_mem, 0, hidden_bytes); int token = 0; if (generation_mode == "greedy") { @@ -959,5 +1021,6 @@ PYBIND11_MODULE(chat, m) { .def_readwrite("io_alone_mode", &Qwen::io_alone_mode) .def_readwrite("status_code", &Qwen::status_code) .def_readwrite("lib_path", &Qwen::lib_path) + .def_readwrite("stage_idx", &Qwen::stage_idx) .def_readwrite("embedding_path", &Qwen::embedding_path); } diff --git a/models/Qwen2/share_cache_demo/compile_multi.sh b/models/Qwen2/share_cache_demo/compile_multi.sh new file mode 100755 index 0000000..6eeb35d --- /dev/null +++ b/models/Qwen2/share_cache_demo/compile_multi.sh @@ -0,0 +1,331 @@ +#!/bin/bash +set -ex +models= +mode="int8" +folder="tmp" +num_device=1 +mode_args="" +device_args="" +quantize_args="--quantize W8BF16" +addr_args="" +dyn_args="" +name="" +num_layers= +out_model="" +share_length= +unshare_length= +hidden_size= +dynamic=0 + +while [[ $# -gt 0 ]]; do + key="$1" + + case $key in + --mode) + mode="$2" + shift 2 + ;; + --num_device) + num_device="$2" + shift 2 + ;; + --name) + name="$2" + shift 2 + ;; + --addr_mode) + addr_mode="$2" + shift 2 + ;; + --share_length_list) + share_length_list="$2" + shift 2 + ;; + --unshare_length_list) + unshare_length_list="$2" + shift 2 + ;; + --seq_length_list) + seq_length_list="$2" + shift 2 + ;; + --dynamic) + dynamic="$2" + shift 2 + ;; + --generation_mode) + generation_mode="$2" + shift 2 + ;; + --embedding_mode) + embedding_mode="$2" + shift 2 + ;; + *) + echo "Invalid option: $key" >&2 + exit 1 + ;; + :) + echo "Option -$OPTARG requires an argument." >&2 + exit 1 + ;; + esac +done + +if [[ -z "$share_length_list" ]]; then + echo "Error: --share_length_list is required." >&2 + exit 1 +fi + +if [[ -z "$seq_length_list" ]]; then + echo "Error: --seq_length_list is required." >&2 + exit 1 +fi + +if [ "$name" = "qwen2-7b" ]; then + num_layers=28 + hidden_size=3584 + echo "Compile Qwen2-7B" +else + >&2 echo -e "Error: Invalid name $name, the input name must be \033[31mqwen2-7b\033[0m" + exit 1 +fi + +# Split lists into arrays +IFS=',' read -r -a share_lengths <<< "$share_length_list" +IFS=',' read -r -a unshare_lengths <<< "$unshare_length_list" +IFS=',' read -r -a seq_lengths <<< "$seq_length_list" + + +# Loop to process different models +for index in "${!share_lengths[@]}"; do + + share_length=${share_lengths[$index]} + unshare_length=${unshare_lengths[$index]} + seq_length=${seq_lengths[$index]} + folder_suffix="share${share_length}_unshare${unshare_length}_seq${seq_length}" + folder="tmp_${folder_suffix}" + + folder="tmp_share${share_length}_unshare${unshare_length}_seq${seq_length}" + + if [ x$mode == x"int8" ]; then + quantize_args="--quantize W8BF16" + elif [ x$mode == x"bf16" ]; then + quantize_args="--quantize BF16" + elif [ x$mode == x"int4" ]; then + quantize_args="--quantize W4BF16 --q_group_size 64" + else + echo "Error, unknown quantize mode" + exit 1 + fi + + if [ x$num_device != x1 ]; then + device_args="--num_device $num_device" + cur_model=${name}_${mode}_${folder_suffix}_${num_device}dev + else + cur_model=${name}_${mode}_${folder_suffix}_1dev + fi + + if [ x$dynamic == x1 ]; then + dyn_args="--dynamic" + cur_model=${name}_${mode}_${folder_suffix}_${num_device}dev_dyn + fi + + out_model=${cur_model}_${out_model} + + if [ x$addr_mode == x"io_alone" ]; then + addr_args="--addr_mode io_alone" + fi + + outdir=${folder}/$mode"_"$num_device"dev"/block + mkdir -p $outdir + pushd $outdir + + # Function to process each block in parallel + process_block() { + i=$1 + + model_transform.py \ + --model_name block_$i \ + --model_def ../../onnx/block_$i.onnx \ + --mlir block_$i.mlir + + model_deploy.py \ + --mlir block_$i.mlir \ + ${quantize_args} \ + --quant_input \ + --quant_output \ + $dyn_args \ + --chip bm1684x \ + $device_args \ + --model block_$i.bmodel + + if [ x$unshare_length != x"0" ]; then + model_transform.py \ + --model_name block_unshare_$i \ + --model_def ../../onnx/block_unshare_$i.onnx \ + --mlir block_unshare_$i.mlir + + model_deploy.py \ + --mlir block_unshare_$i.mlir \ + ${quantize_args} \ + --quant_input \ + --quant_output \ + --chip bm1684x \ + $device_args \ + --model block_unshare_$i.bmodel + fi + + model_transform.py \ + --model_name block_cache_$i \ + --model_def ../../onnx/block_cache_$i.onnx \ + --mlir block_cache_$i.mlir + + model_deploy.py \ + --mlir block_cache_$i.mlir \ + ${quantize_args} \ + --quant_input \ + --quant_output \ + --chip bm1684x \ + $device_args \ + $addr_args \ + --model block_cache_$i.bmodel + } + + # Process each block in parallel + for ((i=0; i<$num_layers; i++)); do + process_block $i & + if [ x$unshare_length != x"0" ]; then + models=${models}${outdir}'/block_'$i'.bmodel '$outdir'/block_unshare_'$i'.bmodel '$outdir'/block_cache_'$i'.bmodel ' + else + models=${models}${outdir}'/block_'$i'.bmodel '$outdir'/block_cache_'$i'.bmodel ' + fi + sleep 45 + done + + wait # Wait for all background processes to finish + + rm -f *.npz + popd + echo $models + + + if [ x$embedding_mode == x"default" ]; then + outdir=${folder}/$mode"_"$num_device"dev"/embedding + mkdir -p $outdir + pushd $outdir + + model_transform.py \ + --model_name embedding \ + --model_def ../../onnx/embedding.pt \ + --input_shapes [[1,${share_length}]] \ + --input_types "int32" \ + --mlir embedding.mlir + + model_deploy.py \ + --mlir embedding.mlir \ + --quantize BF16 \ + --quant_input \ + --quant_output \ + --chip bm1684x \ + $device_args \ + --model embedding.bmodel + + models=${models}$outdir'/embedding.bmodel ' + + if [ x$unshare_length != x"0" ]; then + model_transform.py \ + --model_name embedding_unshare \ + --model_def ../../onnx/embedding.pt \ + --input_shapes [[1,${unshare_length}]] \ + --input_types "int32" \ + --mlir embedding_unshare.mlir + + model_deploy.py \ + --mlir embedding_unshare.mlir \ + --quantize BF16 \ + --quant_input \ + --quant_output \ + --chip bm1684x \ + $device_args \ + --model embedding_unshare.bmodel + models=${models}$outdir'/embedding_unshare.bmodel ' + fi + + if [ x$index == x"0" ]; then + model_transform.py \ + --model_name embedding_cache \ + --model_def ../../onnx/embedding.pt \ + --input_shapes [[1,1]] \ + --input_types "int32" \ + --mlir embedding_cache.mlir + + model_deploy.py \ + --mlir embedding_cache.mlir \ + --quantize BF16 \ + --quant_input \ + --quant_output \ + --chip bm1684x \ + $device_args \ + --model embedding_cache.bmodel + models=${models}$outdir'/embedding_cache.bmodel ' + fi + + rm -f *.npz + popd + echo $models + fi + + outdir=${folder}/$mode"_"$num_device"dev"/lm_head + mkdir -p $outdir + pushd $outdir + + if [ x$index == x"0" ]; then + model_transform.py \ + --model_name lm_head \ + --model_def ../../onnx/lm_head.pt \ + --input_shapes [[1,${hidden_size}]] \ + --mlir lm_head.mlir + + model_deploy.py \ + --mlir lm_head.mlir \ + $quantize_args \ + --quant_input \ + --chip bm1684x \ + $device_args \ + --model lm_head.bmodel + + + model_transform.py \ + --model_name greedy_head \ + --model_def ../../onnx/greedy_head.onnx \ + --mlir greedy_head.mlir + + model_deploy.py \ + --mlir greedy_head.mlir \ + --chip bm1684x \ + --model greedy_head.bmodel + + models=${models}${outdir}'/lm_head.bmodel '$outdir'/greedy_head.bmodel ' + fi + + + model_transform.py \ + --model_name penalty_sample_head \ + --model_def ../../onnx/penalty_sample_head.onnx \ + --mlir penalty_sample_head.mlir + + model_deploy.py \ + --mlir penalty_sample_head.mlir \ + --chip bm1684x \ + --model penalty_sample_head.bmodel + + rm *.npz + + models=${models}$outdir'/penalty_sample_head.bmodel ' + + popd + echo $models +done + +model_tool --combine $models -o ${out_model}.bmodel \ No newline at end of file diff --git a/models/Qwen2/share_cache_demo/pipeline.py b/models/Qwen2/share_cache_demo/pipeline.py index 43e19c3..d738dbd 100755 --- a/models/Qwen2/share_cache_demo/pipeline.py +++ b/models/Qwen2/share_cache_demo/pipeline.py @@ -11,7 +11,7 @@ def __init__(self, args): # preprocess parameters, such as prompt & tokenizer # devid self.devices = [int(d) for d in args.devid.split(",")] - self.model_list = [d for d in args.model_path_list.split(",")] + self.model_path = args.model_path # load tokenizer print("Load " + args.tokenizer_path + " ...") @@ -26,9 +26,9 @@ def __init__(self, args): self.model = chat.Qwen() self.init_params(args) - def load_model(self, model_path): + def load_model(self, model_path, read_bmodel): load_start = time.time() - self.model.init(self.devices, model_path) + self.model.init(self.devices, model_path, read_bmodel) # when read_bmodel = false, not to load weight, reuse weight load_end = time.time() print(f"\nLoad Time: {(load_end - load_start):.3f} s") @@ -112,40 +112,40 @@ def test_share_cache(self): # ===------------------------------------------------------------=== # load model 0 self.model.io_alone_mode = 0 - self.load_model(self.model_list[0]) + self.model.stage_idx = 0 + self.load_model(self.model_path, read_bmodel=True) # share prefill share_tokens = self.tokenizer.encode( - share_str, max_length=8000, truncation=True + share_str, max_length=1000, truncation=True ) # task 0 # first + decode unshare_tokens = self.tokenizer.encode(unshare_str_0) - self.stream_answer(share_tokens + unshare_tokens, "normal", 0) + self.stream_answer(share_tokens + unshare_tokens, "normal", 10) # task 1 # first + decode unshare_tokens = self.tokenizer.encode(unshare_str_0) - self.stream_answer(share_tokens + unshare_tokens, "normal", 0) - - self.model.free_device() + self.stream_answer(share_tokens + unshare_tokens, "normal", 10) # ===------------------------------------------------------------=== # Model 1 # ===------------------------------------------------------------=== # load model 1 self.model.io_alone_mode = 0 - self.load_model(self.model_list[1]) + self.model.stage_idx = 1 + self.load_model(self.model_path, read_bmodel=False) # first + decode unshare_tokens = self.tokenizer.encode(unshare_str_0) - self.stream_answer(share_tokens[:3000] + unshare_tokens, "normal", 0) + self.stream_answer(share_tokens[:1000] + unshare_tokens, "normal", 10) # first + decode unshare_tokens = self.tokenizer.encode(unshare_str_0) - self.stream_answer(share_tokens[:3000] + unshare_tokens, "normal", 0) + self.stream_answer(share_tokens[:1000] + unshare_tokens, "normal", 10) # ===------------------------------------------------------------=== # Deinit @@ -161,6 +161,7 @@ def test_share_cache(self): -3: can not to create bmrt -4: can not to load bmodel, maybe your key is wrong -5: can not to inference bmodel +-6: addr_mode = 0, but must set addr_mode =1 """ def main(args): @@ -174,7 +175,7 @@ def main(args): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument("-m", "--model_path_list", type=str, required=True, help="path to the bmodel files") + parser.add_argument("-m", "--model_path", type=str, required=True, help="path to the bmodel") parser.add_argument('-t', '--tokenizer_path', type=str, default="../support/token_config", help='path to the tokenizer file') parser.add_argument('-d', '--devid', type=str, default='0', help='device ID to use') parser.add_argument('--temperature', type=float, default=1.0, help='temperature scaling factor for the likelihood distribution') diff --git a/models/Qwen2/share_cache_demo/t.sh b/models/Qwen2/share_cache_demo/t.sh index 827fe20..631a2dd 100755 --- a/models/Qwen2/share_cache_demo/t.sh +++ b/models/Qwen2/share_cache_demo/t.sh @@ -3,32 +3,42 @@ set -ex max_pos_len=10240 generation_mode=default embedding_mode=default +share_length_list=8192,4096 +unshare_length_list=0,0 +seq_length_list=10240,5120 +dynamic=0 -seq_length=10240 -share_length=8192 -unshare_length=0 -python export_onnx.py --model_path /workspace/models/Qwen2-7B-Instruct/ --device cpu --share_length $share_length --unshare_length $unshare_length --seq_length $seq_length --num_thread 16 --max_pos_len $max_pos_len --generation_mode $generation_mode --embedding_mode $embedding_mode -./compile.sh --mode int4 --name qwen2-7b --share_length $share_length --addr_mode io_alone --unshare_length $unshare_length --seq_length $seq_length --generation_mode $generation_mode --dynamic 1 --embedding_mode $embedding_mode +echo "必须把seq_length长的放在前面,这一点尤其要注意" -weight_1=$(model_tool --info qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel | grep -oP 'weight: \K\d+' | head -n 1) +# seq_length=10240 +# share_length=8192 +# unshare_length=0 +# python export_onnx.py --model_path /workspace/models/Qwen2-7B-Instruct/ --device cpu --share_length $share_length --unshare_length $unshare_length --seq_length $seq_length --num_thread 16 --max_pos_len $max_pos_len --generation_mode $generation_mode --embedding_mode $embedding_mode +# ./compile.sh --mode int4 --name qwen2-7b --share_length $share_length --addr_mode io_alone --unshare_length $unshare_length --seq_length $seq_length --generation_mode $generation_mode --dynamic 1 --embedding_mode $embedding_mode -model_tool --encrypt -model qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel -net block_0 -lib ../../Qwen2/share_cache_demo/build/libcipher.so -o qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn_encrypted.bmodel +# weight_1=$(model_tool --info qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel | grep -oP 'weight: \K\d+' | head -n 1) -seq_length=5120 -share_length=4096 -unshare_length=0 -python export_onnx.py --model_path /workspace/models/Qwen2-7B-Instruct/ --device cpu --share_length $share_length --unshare_length $unshare_length --seq_length $seq_length --num_thread 16 --max_pos_len $max_pos_len --generation_mode $generation_mode --embedding_mode $embedding_mode -./compile.sh --mode int4 --name qwen2-7b --share_length $share_length --addr_mode io_alone --unshare_length $unshare_length --seq_length $seq_length --generation_mode $generation_mode --dynamic 1 --embedding_mode $embedding_mode +# model_tool --encrypt -model qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel -net block_0 -lib ../../Qwen2/share_cache_demo/build/libcipher.so -o qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn_encrypted.bmodel -weight_0=$(model_tool --info qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel | grep -oP 'weight: \K\d+' | head -n 1) +# seq_length=5120 +# share_length=4096 +# unshare_length=0 +# python export_onnx.py --model_path /workspace/models/Qwen2-7B-Instruct/ --device cpu --share_length $share_length --unshare_length $unshare_length --seq_length $seq_length --num_thread 16 --max_pos_len $max_pos_len --generation_mode $generation_mode --embedding_mode $embedding_mode +# ./compile.sh --mode int4 --name qwen2-7b --share_length $share_length --addr_mode io_alone --unshare_length $unshare_length --seq_length $seq_length --generation_mode $generation_mode --dynamic 1 --embedding_mode $embedding_mode -model_tool --encrypt -model qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel -net block_0 -lib ../../Qwen2/share_cache_demo/build/libcipher.so -o qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn_encrypted.bmodel +# weight_0=$(model_tool --info qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel | grep -oP 'weight: \K\d+' | head -n 1) +# model_tool --encrypt -model qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn.bmodel -net block_0 -lib ../../Qwen2/share_cache_demo/build/libcipher.so -o qwen2-7b_int4_share${share_length}_unshare${unshare_length}_seq${seq_length}_1dev_dyn_encrypted.bmodel -if [ "$weight_0" -ne "$weight_1" ]; then - echo "Error: weight_0 ($weight_0) is not equal to weight_1 ($weight_1)" - exit 1 -else - echo "weight_0 ($weight_0) is equal to weight_1 ($weight_1)" -fi + +# if [ "$weight_0" -ne "$weight_1" ]; then +# echo "Error: weight_0 ($weight_0) is not equal to weight_1 ($weight_1)" +# exit 1 +# else +# echo "weight_0 ($weight_0) is equal to weight_1 ($weight_1)" +# fi + + +./compile_multi.sh --mode int4 --name qwen2-7b --share_length_list $share_length_list --addr_mode io_alone --unshare_length_list $unshare_length_list --seq_length_list $seq_length_list --generation_mode $generation_mode --dynamic $dynamic --embedding_mode $embedding_mode + diff --git a/models/Qwen2/support/lib_pcie/libbmlib.so.0 b/models/Qwen2/support/lib_pcie/libbmlib.so.0 index ac1c6a3b8b391dd17e5036dad53eef64fdf86ccc..de6812c4335e22192dd202c8a722dc15381cd2b5 100755 GIT binary patch delta 93 zcmdn6T4e(e-Cz<~P_aT|+k{89caHr@-(=wPcZ#CN=jJy|?QfVEffz(H12N0?H%zQ& pz6%>0C>R+SnJ6TdC>R)6nOItxn75z)#R|l1K+L}V^e>L5QUKrMDP8~o delta 93 zcmdn6T4e(e-Cz1w{S1>X#GEqn@Q7|&LGBU6-wroHBixr63fS7&z>0caAr2r6?Djom; diff --git a/models/Qwen2/support/lib_pcie/libbmrt.so.1.0 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bm_net_info_t *net, int stage_idx = 0) { for (int i = 0; i < net->output_num; i++) { - empty(bm_handle, net->stages[0].output_mems[i]); + empty(bm_handle, net->stages[stage_idx].output_mems[i]); } } -void empty_net(bm_handle_t &bm_handle, const bm_net_info_t *net) { - empty_in_net(bm_handle, net); - empty_out_net(bm_handle, net); +void empty_net(bm_handle_t &bm_handle, const bm_net_info_t *net, int stage_idx = 0) { + empty_in_net(bm_handle, net, stage_idx); + empty_out_net(bm_handle, net, stage_idx); }

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