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Fix "perplexity evaluation" progress messages
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4d94a111d4
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@ -28,7 +28,9 @@ past_evaluations = load_past_evaluations()
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def save_past_evaluations(df):
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global past_evaluations
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past_evaluations = df
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df.to_csv(Path('logs/evaluations.csv'), index=False)
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filepath = Path('logs/evaluations.csv')
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filepath.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(filepath, index=False)
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def calculate_perplexity(models, input_dataset, stride, _max_length):
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@ -39,7 +41,7 @@ def calculate_perplexity(models, input_dataset, stride, _max_length):
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global past_evaluations
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cumulative_log = ''
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cumulative_log += "Loading the input dataset...\n"
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cumulative_log += "Loading the input dataset...\n\n"
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yield cumulative_log
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# Copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/triton/utils/datautils.py
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@ -58,13 +60,13 @@ def calculate_perplexity(models, input_dataset, stride, _max_length):
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for model in models:
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if is_in_past_evaluations(model, input_dataset, stride, _max_length):
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cumulative_log += f"{model} has already been tested. Ignoring.\n"
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cumulative_log += f"{model} has already been tested. Ignoring.\n\n"
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yield cumulative_log
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continue
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if model != 'current model':
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try:
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yield cumulative_log + f"Loading {model}...\n"
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yield cumulative_log + f"Loading {model}...\n\n"
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model_settings = get_model_specific_settings(model)
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shared.settings.update(model_settings) # hijacking the interface defaults
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update_model_parameters(model_settings) # hijacking the command-line arguments
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@ -72,12 +74,12 @@ def calculate_perplexity(models, input_dataset, stride, _max_length):
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unload_model()
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shared.model, shared.tokenizer = load_model(shared.model_name)
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except:
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cumulative_log += f"Failed to load {model}. Moving on.\n"
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cumulative_log += f"Failed to load {model}. Moving on.\n\n"
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yield cumulative_log
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continue
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cumulative_log += f"Processing {model}...\n"
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yield cumulative_log + "Tokenizing the input dataset...\n"
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cumulative_log += f"Processing {model}...\n\n"
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yield cumulative_log + "Tokenizing the input dataset...\n\n"
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encodings = encode(text, add_special_tokens=False)
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seq_len = encodings.shape[1]
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max_length = _max_length or shared.model.config.max_position_embeddings
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