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https://github.com/oobabooga/text-generation-webui.git
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Update Training PRO (#4972)
- rolling back safetensors to bi, until it is fixed correctly - removing the ugly checkpoint detour
This commit is contained in:
FartyPants (FP HAM)
GitHub
parent
f1f2c4c3f4
commit
59da429cbd
@ -51,59 +51,9 @@ from modules.logging_colors import logger
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from modules.models import reload_model
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from modules.utils import natural_keys
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## just temporary to avoid warning
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import inspect
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from typing import Callable, Optional, Tuple, ContextManager
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if hasattr(torch.utils.checkpoint, 'noop_context_fn'):
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def my_checkpoint(
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function,
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*args,
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use_reentrant: Optional[bool] = None,
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context_fn: Callable[[], Tuple[ContextManager, ContextManager]] = torch.utils.checkpoint.noop_context_fn,
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determinism_check: str = torch.utils.checkpoint._DEFAULT_DETERMINISM_MODE,
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debug: bool = False,
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**kwargs
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):
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if use_reentrant is None:
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#print ("reentran = NONE")
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use_reentrant = True
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# Hack to mix *args with **kwargs in a python 2.7-compliant way
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preserve = kwargs.pop("preserve_rng_state", True)
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if kwargs and use_reentrant:
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raise ValueError(
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"Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
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)
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if use_reentrant:
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if context_fn is not torch.utils.checkpoint.noop_context_fn or debug is not False:
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raise ValueError(
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"Passing `context_fn` or `debug` is only supported when "
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"use_reentrant=False."
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)
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return torch.utils.checkpoint.CheckpointFunction.apply(function, preserve, *args)
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else:
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print ("reentran = FALSE")
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gen = torch.utils.checkpoint._checkpoint_without_reentrant_generator(
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function, preserve, context_fn, determinism_check, debug, *args, **kwargs
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)
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# Runs pre-forward logic
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next(gen)
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ret = function(*args, **kwargs)
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# Runs post-forward logic
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try:
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next(gen)
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except StopIteration:
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return ret
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import warnings
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warnings.filterwarnings(action = "ignore", message="torch.utils.checkpoint:")
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warnings.filterwarnings(action = "ignore", message="`do_sample` is set to `False`")
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params = {
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"display_name": "Training PRO",
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@ -121,6 +71,7 @@ non_serialized_params = {
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"save_epochs": 0,
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"checkpoint_offset": 0,
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"epoch_offset":0,
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"safe_serialization": False,
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}
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MODEL_CLASSES = {v[1]: v[0] for v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.items()}
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@ -150,7 +101,7 @@ def ui():
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with gr.Row():
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with gr.Column():
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# YY.MM.DD
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gr.Markdown("`Ver: 23.10.20` This is enhanced version of QLora Training. [Maintained by FP](https://github.com/FartyPants/Training_PRO/tree/main)")
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gr.Markdown("`Ver: 23.10.20 (REV2)` This is enhanced version of QLora Training. [Maintained by FP](https://github.com/FartyPants/Training_PRO/tree/main)")
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with gr.Row():
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with gr.Column(scale=5):
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@ -290,7 +241,7 @@ def ui():
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stride_length = gr.Slider(label='Stride', minimum=1, maximum=2048, value=512, step=1, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.')
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with gr.Column():
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max_length = gr.Slider(label='max_length', minimum=0, maximum=8096, value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
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max_length = gr.Slider(label='max_length', minimum=0, maximum=shared.settings['truncation_length_max'], value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
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with gr.Row():
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start_current_evaluation = gr.Button("Evaluate loaded model")
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@ -712,7 +663,6 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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}
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train_template.clear()
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#reset stuff
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print(f"*** LoRA: {lora_name} ***")
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@ -725,26 +675,8 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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non_serialized_params.update({"checkpoint_offset": 0})
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non_serialized_params.update({"epoch_offset": 0})
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train_log_graph.clear()
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# === once fixed, this can be removed ==============================
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if hasattr(torch.utils.checkpoint, 'noop_context_fn'):
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print("Testing Pytorch...")
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old_checkpoint_signature = inspect.signature(torch.utils.checkpoint.checkpoint)
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# Get the signature of your new checkpoint function
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my_checkpoint_signature = inspect.signature(my_checkpoint)
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# Check if the signatures match
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if old_checkpoint_signature.parameters == my_checkpoint_signature.parameters:
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print(F"{RED}Overriding Torch checkpoint function to avoid repeated 'use_reentrant not explicitly set' warnings{RESET}")
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#print(" - Note: Transformers need to pass use_reentrant in llama.modeling_llama in def forward, layer_outputs = torch.utils.checkpoint.checkpoint")
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#print(" Once they do, this function can be removed")
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torch.utils.checkpoint.checkpoint = my_checkpoint
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# END OF FPHAM SENTENCE SPLIT functions ===================
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# == Prep the dataset, format, etc ==
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# == Prep the dataset, format, etc ==
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if raw_text_file not in ['None', '']:
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train_template["template_type"] = "raw_text"
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logger.info("Loading text file...")
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@ -1025,7 +957,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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force_save = True
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if force_save:
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lora_model.save_pretrained(f"{lora_file_path}/{folder_save}/")
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lora_model.save_pretrained(f"{lora_file_path}/{folder_save}/", safe_serialization = non_serialized_params['safe_serialization'])
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print(f"\033[1;30;40mStep: {tracked.current_steps:6} \033[0;37;0m Saved: [{folder_save}]")
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# Save log
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with open(f"{lora_file_path}/{folder_save}/training_log.json", 'w', encoding='utf-8') as file:
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@ -1252,7 +1184,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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log_train_dataset(trainer)
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trainer.train()
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# Note: save in the thread in case the gradio thread breaks (eg browser closed)
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lora_model.save_pretrained(lora_file_path)
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lora_model.save_pretrained(lora_file_path, safe_serialization = non_serialized_params['safe_serialization'])
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logger.info("LoRA training run is completed and saved.")
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# Save log
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with open(f"{lora_file_path}/training_log.json", 'w', encoding='utf-8') as file:
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@ -1353,7 +1285,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
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if not tracked.did_save:
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logger.info("Training complete, saving...")
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lora_model.save_pretrained(lora_file_path)
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lora_model.save_pretrained(lora_file_path, safe_serialization = non_serialized_params['safe_serialization'])
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if WANT_INTERRUPT:
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logger.info("Training interrupted.")
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