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Update llama.py

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Daniel Han 2024-12-01 02:10:43 -08:00
commit f4b8710843
1 changed files with 12 additions and 6 deletions
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18
unsloth/models/llama.py
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@ -1551,7 +1551,7 @@ class FastLlamaModel:
statistics = \
f"==((====))== Unsloth {__version__}: Fast {model_patcher.__name__[4:-5]} patching. Transformers:{transformers_version}.\n"\
f" \\\ /| GPU: {gpu_stats.name}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"\
f"O^O/ \+/ \\ Torch: {torch.__version__}. CUDA: {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit: {torch.version.cuda}. Triton: {triton_version}\n"\
f"O^O/ \_/ \\ Torch: {torch.__version__}. CUDA: {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit: {torch.version.cuda}. Triton: {triton_version}\n"\
f"\ / Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. FA [Xformers = {xformers_version}. FA2 = {HAS_FLASH_ATTENTION}]\n"\
f' "-____-" Free Apache license: http://github.com/unslothai/unsloth'
print(statistics)
@ -1706,12 +1706,18 @@ class FastLlamaModel:
spaces = re.search('\n([\s\t]{1,})', original_debug).group(0)[1:]
front_spaces = re.match('([\s\t]{1,})', inner_training_loop).group(0)
unsloth_0 = r'==((====))=='
unsloth_1 = r' \\ /| '
unsloth_2 = r'O^O/ \_/ \ '
unsloth_3 = r'\ / '
unsloth_4 = r' "-____-" '
debug_info = """debug_info = \\
f"==((====))== Unsloth - 2x faster free finetuning | Num GPUs = {args.world_size}\\n"\\
f" \\\\\\ /| Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,}\\n"\\
f"O^O/ \\+/ \\ Batch size per device = {self._train_batch_size:,} | Gradient Accumulation steps = {args.gradient_accumulation_steps}\\n"\\
f"\\ / Total batch size = {total_train_batch_size:,} | Total steps = {max_steps:,}\\n"\\
f' "-____-" Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}'
f"{unsloth_0} Unsloth - 2x faster free finetuning | Num GPUs = {args.world_size}\\n"\\
f"{unsloth_1} Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,}\\n"\\
f"{unsloth_2} Batch size per device = {self._train_batch_size:,} | Gradient Accumulation steps = {args.gradient_accumulation_steps}\\n"\\
f"{unsloth_3} Total batch size = {total_train_batch_size:,} | Total steps = {max_steps:,}\\n"\\
f'{unsloth_4} Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}'
logger.warning(debug_info)
import subprocess, re, gc, numpy as np
a = np.array([0,])