LLM: skip some model tests using certain api (#9163)
* LLM: Skip some model tests using certain api * initialize variable named result
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					 1 changed files with 12 additions and 9 deletions
				
			
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			@ -40,6 +40,7 @@ results = []
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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):
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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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        result = run_transformer_int4(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams)
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    elif test_api == 'native_int4':
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			@ -56,6 +57,7 @@ def run_model(repo_id, test_api, in_out_pairs, local_model_hub=None, warm_up=1,
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        result = run_ipex_fp16_gpu(repo_id, local_model_hub, in_out_pairs, warm_up, num_trials, num_beams)
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    for in_out_pair in in_out_pairs:
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        if result:
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            results.append([repo_id,
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                            np.mean(result[in_out_pair], axis=0)[0],
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                            np.mean(result[in_out_pair], axis=0)[1],
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			@ -192,7 +194,8 @@ def run_pytorch_autocast_bf16(repo_id,
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    st = time.perf_counter()
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    if repo_id in ['THUDM/chatglm-6b', 'THUDM/chatglm2-6b']:
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        # TODO: need verify chatglm family run bf16.
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        invalidInputError(False, "Currently pytorch do not support bfloat16 on cpu for chatglm models.")
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        print("Currently pytorch do not support bfloat16 on cpu for chatglm models. Will skip it")
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        return
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    elif repo_id in LLAMA_IDS:
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        model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16,
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                                                     use_cache=True)
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