-
Notifications
You must be signed in to change notification settings - Fork 4
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
add compare function, use 'mixed_float16' to replace 'must_keep_origi…
…n_dtype'
- Loading branch information
Showing
8 changed files
with
306 additions
and
32 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,12 @@ | ||
| .idea | ||
| .vscode | ||
| download/ | ||
| output/ | ||
| inference/ | ||
| kernel_meta/ | ||
| */__pycache__/ | ||
| __pycache__/ | ||
| ./*/__pycache__/ | ||
| .idea | ||
| export/*.json | ||
| *.json | ||
| fusion_result.json | ||
| result/ |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,233 @@ | ||
| import os | ||
| import time | ||
| import subprocess | ||
| import numpy as np | ||
| import onnxruntime | ||
| import argparse | ||
| from transformers.models.qwen2 import Qwen2Tokenizer, Qwen2Config | ||
|
|
||
|
|
||
| now_dir = os.path.dirname(os.path.abspath(__file__)) | ||
| project_dir = os.path.dirname(now_dir) | ||
| result_output_dir = os.path.join(project_dir, "result") | ||
| input_data_dir = os.path.join(project_dir, "output", "input_data") | ||
| if not os.path.exists(result_output_dir): | ||
| os.mkdir(result_output_dir) | ||
| if not os.path.exists(input_data_dir): | ||
| os.mkdir(input_data_dir) | ||
|
|
||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "--dtype", | ||
| type=str, | ||
| help="float16 or float32", | ||
| choices=["float16", "float32"], | ||
| default="float32", | ||
| ) | ||
| parser.add_argument( | ||
| '--hf_model_dir', | ||
| type=str, | ||
| help="model and tokenizer path, only support huggingface model", | ||
| default=os.path.join(project_dir, "download", "Qwen2-1.5B-Instruct") | ||
| ) | ||
| parser.add_argument( | ||
| "--onnx_model_path", | ||
| help="output onnx path", | ||
| type=str, | ||
| default=os.path.join(project_dir, "output", "onnx", "qwen2_1.5b_chat.onnx") | ||
| ) | ||
| parser.add_argument( | ||
| "--om_model_path", | ||
| help="mindspore model path", | ||
| type=str, | ||
| default= os.path.join(project_dir, "output", "model", "qwen2_1.5b_chat.om") | ||
| ) | ||
| parser.add_argument( | ||
| "--kv_cache_length", | ||
| help="kv-cache length", | ||
| type=int, | ||
| default=2048, | ||
| ) | ||
| parser.add_argument( | ||
| "--max_batch", | ||
| help="max batch", | ||
| type=int, | ||
| default=1, | ||
| ) | ||
| parser.add_argument( | ||
| "--cpu_thread" , | ||
| type=int, | ||
| help="num of cpu thread when convert onnx to om", | ||
| default=1, | ||
| ) | ||
| parser.add_argument( | ||
| "--max_prefill_length", | ||
| help="max prefill length in first inference. " | ||
| "Attention max_prefill_length + max_output_length <= kv_cache_length. " | ||
| "the number must by 2^xx, like 1, 2, 4, 8, 16, 32, 64, 128, 256... " | ||
| "Note! The higher this number, the longer it will take to compile.", | ||
| type=int, | ||
| default=8 | ||
| ) | ||
| args = parser.parse_args() | ||
|
|
||
| if args.dtype == "float16": | ||
| np_dtype = np.float16 | ||
| elif args.dtype == "float32": | ||
| np_dtype = np.float32 | ||
| else: | ||
| raise Exception("not support dtype, only support float16/float32") | ||
|
|
||
|
|
||
| def create_kv_cache(config: Qwen2Config, kv_cache_length=args.kv_cache_length): | ||
| return np.zeros( | ||
| [ | ||
| 1, | ||
| kv_cache_length, | ||
| config.num_hidden_layers * 2 * config.num_key_value_heads, | ||
| config.hidden_size // config.num_attention_heads | ||
| ], | ||
| dtype=np_dtype | ||
| ) | ||
|
|
||
|
|
||
| def get_inputs(kv_cache, seq_len: int, real_kv_size=0, input_pos=0, past_kv_size: int = args.kv_cache_length): | ||
| """ | ||
| 获取指定长度的kv_cache, 顺便生成mask和position_id | ||
| Args: | ||
| kv_cache | ||
| seq_len (int): 待获取的kv-cache长度 | ||
| real_kv_size: 真实kv_size长度 | ||
| input_pos: 当前真实token所在位置 | ||
| past_kv_size | ||
| Returns: | ||
| List[np.ndarray]: _description_ | ||
| """ | ||
|
|
||
| """ | ||
| self.kv_cache shape ( | ||
| 1, | ||
| self.kv_cache_length, | ||
| self.num_hidden_layers * 2 * self.num_key_value_heads, | ||
| self.per_head_dim | ||
| ) | ||
| """ | ||
| cache = kv_cache[:, :past_kv_size] | ||
| mask = np.ones((1, past_kv_size + seq_len), dtype=np.int64) | ||
| mask[:, real_kv_size: past_kv_size] = 0 | ||
| pos_id = np.arange( | ||
| input_pos, | ||
| input_pos + seq_len, | ||
| dtype=np.int64 | ||
| ).reshape(1, -1) | ||
| return cache, mask, pos_id | ||
|
|
||
|
|
||
| tokenizer = Qwen2Tokenizer.from_pretrained(args.hf_model_dir) | ||
| model_config = Qwen2Config.from_pretrained(args.hf_model_dir) | ||
| prompt = "你好" | ||
| system_prompt: str = "You are a helpful assistant." | ||
| history = [] | ||
| if len(history) == 0: | ||
| history = [{"role": "system", "content": system_prompt}] | ||
| history.append({"role": "user", "content": prompt}) | ||
| print("history: ", history) | ||
| text = tokenizer.apply_chat_template( | ||
| history, | ||
| tokenize=False, | ||
| add_generation_prompt=True | ||
| ) | ||
| print("raw_text", text) | ||
| input_ids = tokenizer( | ||
| [text], return_tensors="np" | ||
| )["input_ids"].astype(np.int64)[:, :1] | ||
| print("input_ids", input_ids) | ||
|
|
||
| # options = onnxruntime.SessionOptions() | ||
| # options.intra_op_num_threads = 4 | ||
| # options.execution_mode = onnxruntime.ExecutionMode.ORT_SEQUENTIAL | ||
| # options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL | ||
| # | ||
| # llm_session = onnxruntime.InferenceSession( | ||
| # args.onnx_model_path, | ||
| # sess_options=options, | ||
| # providers=[ | ||
| # "CPUExecutionProvider", | ||
| # ], | ||
| # ) | ||
|
|
||
| seq_len = input_ids.shape[-1] | ||
| kv_cache1 = create_kv_cache(model_config) | ||
| now_kv_cache, attn_mask, position_ids = get_inputs(kv_cache1, 1) | ||
| print("now_kv_cache shape: ", now_kv_cache.shape) | ||
| print("attention_mask shape: ", attn_mask.shape) | ||
| print("position_ids shape: ", position_ids.shape) | ||
| # save input data | ||
| # input_ids | ||
| input_ids_path = os.path.join(input_data_dir, "input_ids.npy") | ||
| np.save(input_ids_path, input_ids) | ||
| # attention_mask | ||
| attention_mask_path = os.path.join(input_data_dir, "attention_mask.npy") | ||
| np.save(attention_mask_path, attn_mask) | ||
| # position_ids | ||
| position_ids_path = os.path.join(input_data_dir, "position_ids.npy") | ||
| np.save(position_ids_path, position_ids) | ||
| # past_key_values | ||
| past_key_values_path = os.path.join(input_data_dir, "past_key_values.npy") | ||
| np.save(past_key_values_path, now_kv_cache) | ||
| input_path_list = [input_ids_path, attention_mask_path, position_ids_path, past_key_values_path] | ||
|
|
||
| max_batch = args.max_batch | ||
| max_prefill_length = args.max_prefill_length | ||
| kv_cache_length = args.kv_cache_length | ||
| model_config = Qwen2Config.from_pretrained(args.hf_model_dir) | ||
| num_hidden_layers = model_config.num_hidden_layers | ||
| num_key_value_heads = model_config.num_key_value_heads | ||
| hidden_size = model_config.hidden_size | ||
| num_attention_heads = model_config.num_attention_heads | ||
| per_head_dim = hidden_size // num_attention_heads | ||
|
|
||
| input_ids_shape = [ | ||
| str(max_batch), | ||
| str(max_prefill_length) | ||
| ] | ||
| attention_mask_shape = [ | ||
| str(max_batch), | ||
| str(max_prefill_length + kv_cache_length) | ||
| ] | ||
| position_ids_shape = [ | ||
| str(max_batch), | ||
| str(max_prefill_length) | ||
| ] | ||
| past_key_values_shape = [ | ||
| str(max_batch), | ||
| str(kv_cache_length), | ||
| str(num_hidden_layers * 2 * num_key_value_heads), | ||
| str(per_head_dim) | ||
| ] | ||
|
|
||
|
|
||
| command_lines = [ | ||
| "msit debug compare", | ||
| "-gm {}".format(args.onnx_model_path), | ||
| "-om {}".format(args.om_model_path), | ||
| "-c /usr/local/Ascend/ascend-toolkit/latest", | ||
| # '--input \"{}\"'.format(",".join(input_path_list)), | ||
| '--input-shape \"input_ids:{};attention_mask:{};position_ids:{};past_key_values:{}\"'.format( | ||
| ",".join(input_ids_shape), | ||
| ",".join(attention_mask_shape), | ||
| ",".join(position_ids_shape), | ||
| ",".join(past_key_values_shape) | ||
| ), | ||
| "-o {}".format(result_output_dir), | ||
| "--advisor" | ||
| ] | ||
| print("============ run command ==============") | ||
| print(" \\\r\n ".join(command_lines)) | ||
| print("=======================================") | ||
| subprocess.run( | ||
| " \\\n ".join(command_lines), | ||
| shell=True, | ||
| check=True, | ||
| ) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.