initial support of IPEX_LLM_PERFORMANCE_MODE  (#11754)
				
					
				
			* add perf mode * update * fix style
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					 2 changed files with 9 additions and 5 deletions
				
			
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			@ -53,8 +53,8 @@ def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, in
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    for i in range(num_trials + warm_up):
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        st = time.perf_counter()
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        if lookahead:
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            output_ids = model.generate(input_ids, lookahead=3, do_sample=False, max_matching_ngram_size=2, max_new_tokens=out_len,
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                                    min_new_tokens=out_len, num_beams=num_beams)
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            output_ids = model.generate(input_ids, lookahead=2, do_sample=False, max_matching_ngram_size=2, max_new_tokens=out_len,
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                                        min_new_tokens=out_len, num_beams=num_beams)
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        else:
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            output_ids = model.generate(input_ids, do_sample=False, max_new_tokens=out_len,
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                                        min_new_tokens=out_len, num_beams=num_beams)
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			@ -67,8 +67,8 @@ def run_model_in_thread(model, in_out, tokenizer, result, warm_up, num_beams, in
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        torch.xpu.empty_cache()
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        actual_out_len = output_ids.shape[1] - actual_in_len
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        if i >= warm_up:
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            if lookahead:
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                result[in_out].append([model.first_token_time, (end - st - model.first_token_time)/model.n_token_generated, 0,
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            if lookahead or os.environ.get("IPEX_LLM_PERFORMANCE_MODE", None) == "1":
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                result[in_out].append([model.first_token_time, (end - st - model.first_token_time)/(model.n_token_generated - 1), 0,
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                                       actual_in_len, actual_out_len, load_time, 0])
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            else:
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                result[in_out].append([model.first_cost, model.rest_cost_mean, model.encoder_time,
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			@ -510,7 +510,7 @@ def run_transformer_int4_gpu(repo_id,
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    load_time = end - st
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    print(">> loading of model costs {}s and {}GB".format(load_time, torch.xpu.memory.memory_reserved()/(1024**3)))
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    if not lookahead:
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    if not lookahead and os.environ.get("IPEX_LLM_PERFORMANCE_MODE", None) != "1":
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        model = BenchmarkWrapper(model)
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    result = {}
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			@ -21,6 +21,7 @@
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#
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from typing import Callable, List, Optional, Tuple
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import os
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import torch
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import time
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import copy
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			@ -54,6 +55,9 @@ def generate(
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    **kwargs,
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):
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    lookahead = kwargs.pop("lookahead", None)
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    perf_mode = os.environ.get("IPEX_LLM_PERFORMANCE_MODE", None)
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    if perf_mode == "1" and lookahead is None:
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        lookahead = 2  # default to 2 now
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    if lookahead:
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        from ipex_llm.transformers.convert import get_enable_ipex
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        _enable_ipex = get_enable_ipex()
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