Update benchmark script for NPU (#11932)
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binbin Deng
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3 changed files with 20 additions and 8 deletions
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@ -11,6 +11,7 @@ low_bit: 'sym_int4' # default to use 'sym_int4' (i.e. symmetric int4)
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batch_size: 1 # default to 1
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in_out_pairs:
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- '32-32'
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- '960-64'
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- '1024-128'
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test_api:
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- "transformer_int4_fp16_gpu" # on Intel GPU, transformer-like API, (qtype=int4), (dtype=fp16)
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@ -37,5 +38,6 @@ test_api:
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# - "transformers_int4_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
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cpu_embedding: False # whether put embedding to CPU
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streaming: False # whether output in streaming way (only available now for gpu win related test_api)
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optimize_model: False # whether apply further optimization on NPU (only available now for transformers_int4_npu_win test_api)
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use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
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task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
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@ -136,7 +136,7 @@ def preprocess_prompt(tokenizer, in_len, task):
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input_ids = tokenizer.encode(input_str, return_tensors="pt")
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return input_ids
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def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False, batch_size=1, streaming=False, use_fp16_torch_dtype=False, lookahead=False, task='continuation'):
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def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1, num_trials=3, num_beams=1, low_bit='sym_int4', cpu_embedding=False, batch_size=1, streaming=False, use_fp16_torch_dtype=False, lookahead=False, task='continuation', optimize_model=False):
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# TODO: make a parameter
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result= {}
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if test_api == 'transformer_int4':
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@ -188,7 +188,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
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elif test_api == 'pipeline_parallel_gpu':
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result = run_pipeline_parallel_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, cpu_embedding, fp16=use_fp16_torch_dtype)
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elif test_api == 'transformers_int4_npu_win':
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result = transformers_int4_npu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size)
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result = transformers_int4_npu_win(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams, low_bit, batch_size, optimize_model)
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else:
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invalidInputError(False, "Unknown test_api " + test_api + ", please check your config.yaml.")
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@ -603,24 +603,30 @@ def transformers_int4_npu_win(repo_id,
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num_trials,
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num_beams,
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low_bit,
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batch_size):
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batch_size,
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optimize_model):
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from ipex_llm.transformers.npu_model import AutoModel, AutoModelForCausalLM
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from transformers import AutoTokenizer, LlamaTokenizer
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model_path = get_model_path(repo_id, local_model_hub)
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in_out_len = in_out_pairs[0].split("-")
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max_output_len = max(int(in_out_len[0]) + int(in_out_len[1]), 1024)
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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st = time.perf_counter()
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if repo_id in CHATGLM_IDS:
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model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
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model = AutoModel.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
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optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=True,
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torch_dtype='auto', attn_implementation="eager").eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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elif repo_id in LLAMA_IDS:
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
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optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=True,
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use_cache=True, attn_implementation="eager").eval()
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tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
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else:
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True,
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model = AutoModelForCausalLM.from_pretrained(model_path, load_in_low_bit=low_bit, trust_remote_code=True, torch_dtype=torch.float16,
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optimize_model=optimize_model, max_output_len=max_output_len, max_prompt_len=int(in_out_len[0]), transpose_value_cache=True,
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use_cache=True, attn_implementation="eager").eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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end = time.perf_counter()
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@ -643,6 +649,7 @@ def transformers_int4_npu_win(repo_id,
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true_str = tokenizer.batch_decode(input_ids)[0]
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input_list = [true_str] * batch_size
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input_ids = tokenizer(input_list, return_tensors="pt").input_ids
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input_ids = input_ids[:, :in_len]
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actual_in_len = input_ids.shape[1]
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result[in_out] = []
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for i in range(num_trials + warm_up):
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@ -2016,12 +2023,15 @@ if __name__ == '__main__':
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streaming = False
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use_fp16_torch_dtype = False
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task = 'continuation'
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optimize_model = False # only for transformers_int4_npu_win
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if 'streaming' in conf:
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streaming = conf['streaming']
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if 'use_fp16_torch_dtype' in conf:
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use_fp16_torch_dtype = conf['use_fp16_torch_dtype']
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if 'task' in conf:
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task = conf['task']
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if 'optimize_model' in conf:
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optimize_model = conf['optimize_model']
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lookahead = False
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import pandas as pd
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@ -2048,7 +2058,7 @@ if __name__ == '__main__':
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if task in ['QA', 'summarize'] and conf['num_beams'] == 1 and batch_size == 1:
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lookahead = True
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run_model(model, api, in_out_pairs, conf['local_model_hub'], conf['warm_up'], conf['num_trials'], conf['num_beams'],
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conf['low_bit'], conf['cpu_embedding'], batch_size, streaming, use_fp16_torch_dtype, lookahead, task)
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conf['low_bit'], conf['cpu_embedding'], batch_size, streaming, use_fp16_torch_dtype, lookahead, task, optimize_model)
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df = pd.DataFrame(results, columns=['model', '1st token avg latency (ms)', '2+ avg latency (ms/token)', 'encoder time (ms)',
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'input/output tokens', 'batch_size', 'actual input/output tokens', 'num_beams', 'low_bit', 'cpu_embedding',
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'model loading time (s)', 'peak mem (GB)', 'streaming', 'use_fp16_torch_dtype'])
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@ -117,7 +117,7 @@ class _BaseAutoModelClass:
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ignore_argument(kwargs, "pipeline_parallel_stages")
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optimize_model = kwargs.pop("optimize_model", False)
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max_output_len = kwargs.pop("max_output_len", 1024)
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max_prompt_len = kwargs.pop("max_prompt_len", max_output_len)
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max_prompt_len = kwargs.pop("max_prompt_len", 512)
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inter_pp = kwargs.pop("inter_pp", None)
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intra_pp = kwargs.pop("intra_pp", None)
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transpose_value_cache = kwargs.pop("transpose_value_cache", True)
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