Update llama.py
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1 changed files with 1 additions and 7 deletions
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@ -1883,18 +1883,12 @@ class FastLlamaModel:
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f" {chr(92)}{chr(92)} /| Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,} | Total steps = {max_steps:,}\\n"\\
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f"O^O/ {chr(92)}_/ {chr(92)} Batch size per device = {self._train_batch_size:,} | Gradient accumulation steps = {args.gradient_accumulation_steps}\\n"\\
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f"{chr(92)} / Data Parallel GPUs = {args.world_size} | Total batch size ({self._train_batch_size} x {args.gradient_accumulation_steps} x {args.world_size}) = {total_train_batch_size:,}\\n"\\
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f' "-____-" Trainable parameters = {!!(model, trainable_only=True):,}/{!!(model)*($$):,} ({!!(model, trainable_only=True)/(!!(model)*($$))*100:.2f}% trained)'
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f' "-____-" Trainable parameters = {get_model_param_count(model, trainable_only=True):,}/{get_model_param_count(model):,} ({get_model_param_count(model, trainable_only=True)/get_model_param_count(model)*100:.2f}% trained)'
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logger.warning(debug_info)
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import gc
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for _ in range(3):
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gc.collect()
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torch.cuda.empty_cache()"""
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debug_info = debug_info.replace("!!", "get_model_param_count")
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debug_info = debug_info.replace(
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"$$",
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"(4.5 if getattr(model, 'quantization_config', '\\{'load_in_4bit':False\\}')['load_in_4bit'] else "\
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"(8.0 if getattr(model, 'quantization_config', '\\{'load_in_8bit':False\\}')['load_in_8bit'] else 1.0))"
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)
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debug_info = debug_info.split('\n')
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debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
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