Training PRO a month worth of updates (#4345)
This commit is contained in:
FartyPants (FP HAM)
GitHub
parent
c18504f369
commit
6a61158adf
4 changed files with 803 additions and 99 deletions
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@ -15,12 +15,16 @@ from datetime import datetime
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from pathlib import Path
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import gradio as gr
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import pandas as pd
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import torch
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import transformers
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from .custom_scheduler import FPSchedulerTrainer
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from functools import partial
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from .custom_scheduler import FPSchedulerTrainer, FPNEFtuneTrainer
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from .matplotgraph import create_graph
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from .train_utils import get_available_loras_local, precise_cut, sliding_block_cut
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from .train_utils import get_available_loras_local, precise_cut, sliding_block_cut, download_file_from_url
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from datasets import Dataset, load_dataset
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from peft import (
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@ -48,6 +52,59 @@ 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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params = {
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"display_name": "Training PRO",
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"is_tab": True
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@ -61,10 +118,14 @@ non_serialized_params = {
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"save_checkpoint_now": False,
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"training_loop": False,
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"current_stability": 0,
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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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}
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MODEL_CLASSES = {v[1]: v[0] for v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.items()}
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PARAMETERS = ["lora_name", "always_override", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "raw_text_file", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after", "stop_at_loss", "add_eos_token", "min_chars", "report_to", "precize_slicing_overlap", "add_eos_token_type", "save_steps_under_loss", "add_bos_token", "training_projection","sliding_window","warmup_ratio","grad_accumulation"]
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PARAMETERS = ["lora_name", "always_override", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "raw_text_file", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after", "stop_at_loss", "add_eos_token", "min_chars", "report_to", "precize_slicing_overlap", "add_eos_token_type", "save_steps_under_loss", "add_bos_token", "training_projection","sliding_window","warmup_ratio","grad_accumulation","neft_noise_alpha"]
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WANT_INTERRUPT = False
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train_log = {}
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@ -72,15 +133,24 @@ train_template = {}
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train_log_graph = []
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train_choices = ["all","q-k-v-o","q-k-v","k-v-down","q-v"]
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statistics = {
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'loss': [],
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'lr': [],
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}
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RED = "\033[91m"
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YELLOW = "\033[93m"
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GREEN = "\033[92m"
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RESET = "\033[0m"
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def ui():
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with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
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tmp = gr.State('')
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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: 23.09.22` 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` 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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@ -103,20 +173,19 @@ def ui():
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lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.')
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batch_size = gr.Slider(visible= False, label='Batch Size', value=0, minimum=0, maximum=1024, step=4, info='Now Replaced with Gradient accumulation. Keeping it for sake of old saved data')
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micro_batch_size = gr.Slider(label='True Batch Size', value=4, minimum=1, maximum=128, step=1, info='Specifies how many text blocks per step will be trained. The higher value, the better the concept of training will be, but it requires more GPU memory and it reduces speed.')
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grad_accumulation = gr.Slider(label='Gradient Accumulation Steps', value=1, minimum=1, maximum=256, step=1, info="Virtually multiplies the Batch Size by averaging the learning over more than one step. Evens out loss fluctuations but also increases number of total steps.")
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cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=2048, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.')
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grad_accumulation = gr.Slider(label='Gradient Accumulation Steps', value=1, minimum=1, maximum=256, step=1, info="Virtually multiplies the Batch Size by averaging the learning over more than one step. VRAM friendly. Evens out loss fluctuations but can also degrade training fidelity.")
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with gr.Column():
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stop_at_loss = gr.Slider(label='Stop at loss (Can be changed during training)', minimum=0.0, maximum=3.0, step=0.1, value=0.00, info='The process will automatically stop once the desired loss value is reached.')
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gr.Markdown(" ")
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epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.')
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learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='In scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.')
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lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt', 'FP_low_epoch_annealing', 'FP_half_time_annealing'], info='Learning rate scheduler - defines how the learning rate changes over time. Custom schedulers: `FP_low_epoch_annealing` constant for 1 epoch then cosine anneal. `FP_half_time_annealing` constant for half time then cosine anneal', elem_classes=['slim-dropdown'])
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lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt', 'FP_low_epoch_annealing', 'FP_half_time_annealing','FP_raise_fall_creative'], info='Learning rate scheduler - defines how the learning rate changes over time. Custom schedulers: FP_low_epoch_annealing, FP_half_time_annealing, FP_raise_fall_creative (see README)', elem_classes=['slim-dropdown'])
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with gr.Accordion(label='Checkpoints', open=True):
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with gr.Row():
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with gr.Column():
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save_steps = gr.Number(label='Save every n steps', value=0, info='A checkpoint will be saved every n steps. (0 = OFF)')
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save_steps = gr.Number(label='Save every n steps', value=0, info='A checkpoint will be saved every n steps and at each Epoch boundary. (0 = OFF)')
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with gr.Column():
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save_steps_under_loss = gr.Slider(label='Save at 10% Loss change', value=1.8, minimum=0.0, maximum=3.0, step=0.1, info="Saves checkpoints at (or bellow) this loss and then each time loss falls by at least 10% This works independently from 'Save every n steps'")
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with gr.Row():
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@ -125,9 +194,9 @@ def ui():
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with gr.Accordion(label='Advanced Options', open=True):
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with gr.Row():
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with gr.Column():
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warmup_steps = gr.Number(label='Warmup Steps', value=100, info='Number of max steps used for a linear warmup. Value different than 0 has precedent over Warmup Ratio. The actual number of steps will be the closest multiple of graddient accumulation')
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warmup_steps = gr.Number(label='Warmup Steps', value=100, info='Number of max steps used for a linear warmup. Reduces early over-fitting by the first training blocks. Value has precedent over Warmup Ratio. Aligns to the closest multiple of graddient accumulation')
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warmup_ratio = gr.Slider(label='Warmup Ratio', minimum=0.0, maximum=0.2, step=0.025, value=0.0, info='Ratio of total training steps that will be used for a linear warmup. It applies only if Warmup Step is 0.')
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neft_noise_alpha = gr.Slider(label='NEFtune noise scale', minimum=0.0, maximum=15, step=1, value=0.0, info='Add noise to the training to improve generalization. [0 - OFF, Starting value to experiment: 5]')
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training_projection = gr.Radio(value = train_choices[4], label='LLaMA Target Projections', info='Change the targets (LORA is typically q-v)', choices=train_choices)
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lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.')
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optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Different optimizer implementation options, for advanced users. Effects of different options are not well documented yet.', elem_classes=['slim-dropdown'])
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@ -136,33 +205,42 @@ def ui():
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train_only_after = gr.Textbox(label='Train Only After', value='', info='Only consider text *after* this string in any given chunk for training. For Alpaca datasets, use "### Response:" to only train the response and ignore the input.')
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add_bos_token = gr.Checkbox(label='Add BOS token', value=True, info="Adds BOS token for each dataset item")
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add_eos_token = gr.Checkbox(label='Add EOS token', value=False, info="Adds EOS token for each dataset item")
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add_eos_token_type = gr.Dropdown(label='EOS placement (raw text)', choices=['Every Block', 'Hard Cut Blocks Only'], value='Every Block', info='', allow_custom_value = False)
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add_eos_token_type = gr.Dropdown(label='EOS placement (Text file)', choices=['Every Block', 'Hard Cut Blocks Only'], value='Every Block', info='', allow_custom_value = False)
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higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.')
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report_to = gr.Radio(label="Save detailed logs with", value="None", choices=["None", "wandb", "tensorboard"], interactive=True)
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# for future
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#with gr.Accordion(label='Dynamic Scheduler', open = False):
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# ds_min_epochs = gr.Number(label='Minimum Epochs', value='1', info='Minimum epochs that will be always performed before ramp down can be triggered')
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# ds_max_epochs = gr.Number(label='Maximum Epochs (fallback)', value='50', info='Maximum Epochs before the training will bail out completely (should be a large number)')
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# ds_loss_trigger = gr.Slider(label='Trigger Loss', minimum=0.0, maximum=2.8, step=0.1, value=1.6, info='Loss at which the ramp down schedule will be triggered')
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# ds_loss_rolling_window = gr.Number(label='Loss rolling average', value='4', info='Calculate loss by averaging last x numbers to avoid jumps and noise')
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# ds_epochs_to_ramp = gr.Slider(label='Ramp down ratio', minimum=0.0, maximum=2.0, step=0.1, value=1.00, info='How long the ramp down will last relative to ellapsed steps (before trigger)')
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# gr.Markdown('These are settings for FP_dynamic_loss_trigger scheduler. The scheduler will do warm up, then hold constant untill a loss falls under Trigger Loss, then it will commence linear ramp down schedule and stop. The length of ramp down is set by Ramp down ratio where (ramp down steps) = ratio * (elapsed steps). (The time to completition shown will be very high untill ramp down is triggered.)')
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with gr.Column():
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with gr.Tab(label='Formatted Dataset'):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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dataset = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.', elem_classes=['slim-dropdown'])
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create_refresh_button(dataset, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'json')}, 'refresh-button')
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dataset = gr.Dropdown(choices=get_datasets('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.', elem_classes=['slim-dropdown'])
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create_refresh_button(dataset, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'json')}, 'refresh-button')
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with gr.Row():
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eval_dataset = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.', elem_classes=['slim-dropdown'])
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create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'json')}, 'refresh-button')
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eval_dataset = gr.Dropdown(choices=get_datasets('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.', elem_classes=['slim-dropdown'])
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create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'json')}, 'refresh-button')
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with gr.Column():
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with gr.Row():
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format = gr.Dropdown(choices=utils.get_datasets('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.', elem_classes=['slim-dropdown'])
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create_refresh_button(format, lambda: None, lambda: {'choices': utils.get_datasets('training/formats', 'json')}, 'refresh-button')
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format = gr.Dropdown(choices=get_datasets('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.', elem_classes=['slim-dropdown'])
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create_refresh_button(format, lambda: None, lambda: {'choices': get_datasets('training/formats', 'json')}, 'refresh-button')
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with gr.Row():
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eval_steps = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation dataset is given, test it every time this many steps pass.')
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with gr.Tab(label="Raw text file"):
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with gr.Tab(label="Text file"):
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with gr.Row():
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raw_text_file = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'txt'), value='None', label='Text file', info='The raw text file to use for training.', elem_classes=['slim-dropdown'])
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create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'txt')}, 'refresh-button')
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raw_text_file = gr.Dropdown(choices=get_datasets('training/datasets', 'txt'), value='None', label='Text file', info='The text file to use for training.', elem_classes=['slim-dropdown'])
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create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'txt')}, 'refresh-button')
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with gr.Row():
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with gr.Column():
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@ -173,22 +251,40 @@ def ui():
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with gr.Column():
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hard_cut_string = gr.Textbox(label='Hard Cut String', value='\\n\\n\\n', info='String that indicates a cut between logical blocks of text (ex. Ideas or Chapters). Helps prevent unwanted overlap between unrelated ideas.')
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min_chars = gr.Number(label='Ignore small blocks', value=0, info='Ignore Text blocks that have less or equal characters than this number.')
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with gr.Tab(label="URL"):
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with gr.Row():
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with gr.Column():
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download_file_url = gr.Textbox(label='Download JSON or txt file to datasets (or formats) folder', value='',info='The URL of a file to download. If on github, make sure you get url of the raw file (https://raw.githubusercontent.com/...). If huggin face, make sure the url has /resolve/ in it not /blob/')
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with gr.Row():
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download_check_overwrite = gr.Checkbox(label='Overwrite', value=False, info='Overwrite if file exist')
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download_folder = gr.Radio(label="Destination", value='training/datasets', choices=['training/datasets', 'training/formats'], interactive=True)
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download_button = gr.Button('Download')
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download_status = gr.Textbox(label='Download Status', value='', interactive=False)
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with gr.Row():
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with gr.Column():
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check_dataset_btn = gr.Button('Load and Check Dataset and suggest data entries')
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with gr.Row():
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cutoff_len = gr.Slider(label='Chunk Length (Cutoff Length)', minimum=32, maximum=2048, value=256, step=32, info='The maximum length of a chunk (in tokens). Applies to both JSON dataset and text files. Higher values require much more VRAM.')
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with gr.Row():
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with gr.Column():
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check_dataset_btn = gr.Button('Verify Dataset/Text File and suggest data entries')
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check_dataset_txt = gr.Textbox(label='Dataset info', value='')
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with gr.Row():
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start_button = gr.Button("Start LoRA Training", variant='primary')
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stop_button = gr.Button("Interrupt")
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with gr.Accordion(label="Graph", open=True):
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with gr.Row():
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# show_actions_button = False - we use old gradio
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plot_graph = gr.LinePlot(x="epoch", y="value", title="Loss Metrics", overlay_point=True, tooltip=["epoch", "value"], x_lim=[0, 1], y_lim=[0, 3.5], width=500, height=250)
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output = gr.Markdown(value="Ready")
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with gr.Tab('Perplexity evaluation', elem_id='evaluate-tab'):
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with gr.Row():
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with gr.Column():
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models = gr.Dropdown(utils.get_available_models(), label='Models', multiselect=True)
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evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + utils.get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The raw text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.')
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evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.')
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with gr.Row():
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with gr.Column():
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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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@ -210,7 +306,7 @@ def ui():
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refresh_table = gr.Button('Refresh the table', elem_classes="small-button")
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# Training events
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all_params = [lora_name, always_override, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, raw_text_file, higher_rank_limit, warmup_steps, optimizer, hard_cut_string, train_only_after, stop_at_loss, add_eos_token, min_chars, report_to, precize_slicing_overlap, add_eos_token_type, save_steps_under_loss, add_bos_token, training_projection,sliding_window,warmup_ratio,grad_accumulation]
|
||||
all_params = [lora_name, always_override, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, raw_text_file, higher_rank_limit, warmup_steps, optimizer, hard_cut_string, train_only_after, stop_at_loss, add_eos_token, min_chars, report_to, precize_slicing_overlap, add_eos_token_type, save_steps_under_loss, add_bos_token, training_projection,sliding_window,warmup_ratio,grad_accumulation, neft_noise_alpha]
|
||||
|
||||
def fix_old_version(batch_size_val,micro_batch_size_val, grad_accumulation_val):
|
||||
if batch_size_val>0:
|
||||
|
|
@ -220,8 +316,9 @@ def ui():
|
|||
|
||||
return grad_accumulation_val
|
||||
|
||||
copy_from.change(do_copy_params, [copy_from] + all_params, all_params).then(fix_old_version,[batch_size,micro_batch_size, grad_accumulation],grad_accumulation)
|
||||
start_button.click(do_train, all_params, output)
|
||||
|
||||
copy_from.change(partial(do_copy_params, all_params= all_params), copy_from, all_params).then(fix_old_version,[batch_size,micro_batch_size, grad_accumulation],grad_accumulation)
|
||||
start_button.click(do_train, all_params, [output,plot_graph])
|
||||
stop_button.click(do_interrupt, None, None, queue=False)
|
||||
higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha])
|
||||
|
||||
|
|
@ -241,20 +338,27 @@ def ui():
|
|||
print("Use during the training to save the checkpoint at any time.")
|
||||
|
||||
|
||||
def update_button():
|
||||
return gr.Button.update('[Checkpoint in Queue]', variant='stop', interactive=True)
|
||||
|
||||
save_chackpoint_now.click(trigger_save_checkpoint, None, None)
|
||||
def update_button2():
|
||||
time.sleep(1.0)
|
||||
return gr.Button.update('Queue Checkpoint Now', variant='secondary',interactive = True)
|
||||
|
||||
save_chackpoint_now.click(trigger_save_checkpoint, None, None).then(update_button, None,save_chackpoint_now).then(update_button2, None,save_chackpoint_now)
|
||||
|
||||
dataset_calc_params = [save_steps,micro_batch_size, epochs, cutoff_len, dataset, format, raw_text_file, warmup_steps, hard_cut_string, min_chars, precize_slicing_overlap,sliding_window,warmup_ratio,grad_accumulation]
|
||||
|
||||
def check_dataset(save_steps:int, micro_batch_size: int, epochs: int, cutoff_len: int, dataset:str, format:str, raw_text_file:str, warmup_steps:int, hard_cut_string:str, min_chars:int, precize_slicing_overlap:bool,sliding_window:bool,warmup_ratio:float,grad_accumulation:int):
|
||||
result = "Specify JSON dastaset or raw text file"
|
||||
result = "Specify JSON dastaset or Text file"
|
||||
total_blocks = 0
|
||||
if shared.tokenizer is None:
|
||||
yield "Tokenizer is not available. Please Load some Model first."
|
||||
return
|
||||
|
||||
|
||||
if raw_text_file not in ['None', '']:
|
||||
logger.info("Loading raw text file dataset...")
|
||||
logger.info("Loading Text file...")
|
||||
fullpath = clean_path('training/datasets', f'{raw_text_file}')
|
||||
fullpath = Path(fullpath)
|
||||
if fullpath.is_dir():
|
||||
|
|
@ -268,9 +372,13 @@ def ui():
|
|||
|
||||
logger.info(f"Loaded training file: {file_path.name}")
|
||||
else:
|
||||
with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
|
||||
raw_text = file.read().replace('\r', '')
|
||||
|
||||
try:
|
||||
with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
|
||||
raw_text = file.read().replace('\r', '')
|
||||
except:
|
||||
yield f"{raw_text_file}.txt doesn't seem to exsist anymore... check your training/datasets folder"
|
||||
return
|
||||
|
||||
|
||||
if min_chars<0:
|
||||
min_chars = 0
|
||||
|
|
@ -282,12 +390,12 @@ def ui():
|
|||
text_chunks = precise_cut(raw_text, precize_slicing_overlap, min_chars, False, cutoff_len, hard_cut_string,non_serialized_params['debug_slicer'])
|
||||
|
||||
total_blocks = len(text_chunks)
|
||||
result = f"Raw Text: ({raw_text_file}.txt) has {total_blocks} blocks (with cutoff length = {cutoff_len})"
|
||||
result = f"Text: ({raw_text_file}.txt) has {total_blocks} blocks (Block Size {cutoff_len} tokens)"
|
||||
del text_chunks
|
||||
|
||||
else:
|
||||
if dataset in ['None', '']:
|
||||
yield "Select dataset or Raw text."
|
||||
yield "Select dataset or text file."
|
||||
return
|
||||
|
||||
if format in ['None', '']:
|
||||
|
|
@ -323,10 +431,26 @@ def ui():
|
|||
|
||||
logger.info("Loading JSON datasets...")
|
||||
data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
|
||||
|
||||
data_keys = []
|
||||
|
||||
if data:
|
||||
if 'train' in data: # Check if the 'train' split exists in the dataset
|
||||
data_keys = list(data['train'][0].keys())
|
||||
print("Data Keys:", data_keys)
|
||||
else:
|
||||
print("The dataset is empty.")
|
||||
|
||||
train_data = data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
|
||||
total_blocks = train_data.num_rows
|
||||
|
||||
result = f"Dataset: ({dataset}.json) has {total_blocks} blocks (with cutoff length = {cutoff_len})"
|
||||
result = f"Dataset: ({dataset}.json) has {total_blocks} blocks @ length = {cutoff_len} tokens\n(Keys: {data_keys} - Format: {format}.json): "
|
||||
|
||||
#for options, data in format_data.items():
|
||||
# format_keys = options.split(',')
|
||||
# result += f"{format_keys}, "
|
||||
#result = result.rstrip()
|
||||
#result = result.rstrip(',')
|
||||
|
||||
if total_blocks>0:
|
||||
number_ofSteps = int(math.ceil(total_blocks / micro_batch_size) * epochs)
|
||||
|
|
@ -339,12 +463,14 @@ def ui():
|
|||
save_each_n_min = int(math.ceil(number_ofSteps/10))
|
||||
save_each_n_max = int(math.ceil(number_ofSteps/5))
|
||||
gradient_accumulation_max = int(total_blocks)//micro_batch_size
|
||||
|
||||
|
||||
|
||||
result += f"\n[Batch Size: {micro_batch_size}, Epochs: {epochs}, Gradient Accumulation: {grad_accumulation}]\n"
|
||||
result += f"Total number of steps: {number_ofSteps}\n"
|
||||
result += f"Steps per each Epoch: {num_stepsPer_epoch}\n"
|
||||
result += f"Warmup steps suggestion: {warmup_steps_suggest} (Current: {int(warmup_steps)})\n"
|
||||
result += f"Checkpoint suggestion: Save every {save_each_n_min} - {save_each_n_max} steps (Current: {int(save_steps)})"
|
||||
result += f"Suggestions:\n"
|
||||
result += f"Checkpoints: Save every {save_each_n_min} - {save_each_n_max} steps (Current: {int(save_steps)})\n"
|
||||
result += f"Warmup steps: {warmup_steps_suggest} (Current: {int(warmup_steps)})"
|
||||
if gradient_accumulation_max < grad_accumulation:
|
||||
result += f"\n\nWARNING: Gradient Accumulation {grad_accumulation} is too high: It should be below {gradient_accumulation_max}"
|
||||
|
||||
|
|
@ -378,19 +504,34 @@ def ui():
|
|||
sort_byTime.change(lambda x: non_serialized_params.update({"Lora_sortedByTime": x}), sort_byTime, None).then(reload_lora,None,copy_from)
|
||||
#debug_slicer.change(lambda x: non_serialized_params.update({"debug_slicer": x}), debug_slicer, None)
|
||||
|
||||
def update_dataset():
|
||||
return gr.update(choices=get_datasets('training/datasets', 'json')), gr.update(choices=get_datasets('training/datasets', 'txt'))
|
||||
|
||||
download_button.click(download_file_from_url, [download_file_url,download_check_overwrite,download_folder] , download_status).then(update_dataset,None,[dataset , raw_text_file])
|
||||
|
||||
def get_datasets(path: str, ext: str):
|
||||
# include subdirectories for raw txt files to allow training from a subdirectory of txt files
|
||||
#if ext == "txt":
|
||||
# return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('txt')) + list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
|
||||
|
||||
return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
|
||||
|
||||
def do_interrupt():
|
||||
global WANT_INTERRUPT
|
||||
WANT_INTERRUPT = True
|
||||
|
||||
|
||||
def do_copy_params(lora_name: str, *args):
|
||||
f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json"
|
||||
if Path(f_name).is_file():
|
||||
with open(f_name, 'r', encoding='utf-8') as format_file:
|
||||
params: dict[str, str] = json.load(format_file)
|
||||
def do_copy_params(lora_name: str, all_params):
|
||||
|
||||
if lora_name:
|
||||
f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json"
|
||||
if Path(f_name).is_file():
|
||||
with open(f_name, 'r', encoding='utf-8') as format_file:
|
||||
params: dict[str, str] = json.load(format_file)
|
||||
else:
|
||||
params = {}
|
||||
else:
|
||||
params = {}
|
||||
params = {}
|
||||
|
||||
result = list()
|
||||
for i in range(0, len(PARAMETERS)):
|
||||
|
|
@ -398,7 +539,7 @@ def do_copy_params(lora_name: str, *args):
|
|||
if key in params:
|
||||
result.append(params[key])
|
||||
else:
|
||||
result.append(args[i])
|
||||
result.append(all_params[i])
|
||||
|
||||
return result
|
||||
|
||||
|
|
@ -462,22 +603,29 @@ def calc_trainable_parameters(model):
|
|||
return trainable_params, all_param
|
||||
|
||||
|
||||
def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str, precize_slicing_overlap: bool, add_eos_token_type: str, save_steps_under_loss: float, add_bos_token: bool, training_projection: str,sliding_window:bool,warmup_ratio:float, grad_accumulation: int):
|
||||
|
||||
def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str, precize_slicing_overlap: bool, add_eos_token_type: str, save_steps_under_loss: float, add_bos_token: bool, training_projection: str,sliding_window:bool,warmup_ratio:float, grad_accumulation: int,neft_noise_alpha:float):
|
||||
|
||||
if shared.args.monkey_patch:
|
||||
from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
|
||||
replace_peft_model_with_int4_lora_model
|
||||
)
|
||||
replace_peft_model_with_int4_lora_model()
|
||||
|
||||
|
||||
global train_log_graph
|
||||
global WANT_INTERRUPT
|
||||
WANT_INTERRUPT = False
|
||||
|
||||
statistics['loss'] = []
|
||||
|
||||
statistics['loss'].append({'epoch': 0, 'value': 0})
|
||||
zero_pd = pd.DataFrame(statistics['loss'])
|
||||
|
||||
# == Input validation / processing ==
|
||||
yield "Preparing the input..."
|
||||
yield "Preparing the input...", zero_pd
|
||||
lora_file_path = clean_path(None, lora_name)
|
||||
if lora_file_path.strip() == '':
|
||||
yield "Missing or invalid LoRA file name input."
|
||||
yield "Missing or invalid LoRA file name input.", zero_pd
|
||||
return
|
||||
|
||||
lora_file_path = f"{Path(shared.args.lora_dir)}/{lora_file_path}"
|
||||
|
|
@ -490,23 +638,23 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
model_id = "llama"
|
||||
if model_type == "PeftModelForCausalLM":
|
||||
if len(shared.lora_names) > 0:
|
||||
yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
|
||||
yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*", zero_pd
|
||||
logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
|
||||
else:
|
||||
yield "Model ID not matched due to LoRA loading. Consider reloading base model. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
|
||||
yield "Model ID not matched due to LoRA loading. Consider reloading base model. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*", zero_pd
|
||||
logger.warning("Model ID not matched due to LoRA loading. Consider reloading base model.")
|
||||
else:
|
||||
yield "LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. Unexpected errors may follow. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
|
||||
yield "LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. Unexpected errors may follow. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*", zero_pd
|
||||
logger.warning(f"LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. (Found model type: {model_type})")
|
||||
|
||||
time.sleep(5)
|
||||
|
||||
if shared.args.loader == 'GPTQ-for-LLaMa' and not shared.args.monkey_patch:
|
||||
yield "LoRA training with GPTQ-for-LLaMa requires loading with `--monkey-patch`"
|
||||
yield "LoRA training with GPTQ-for-LLaMa requires loading with `--monkey-patch`", zero_pd
|
||||
return
|
||||
|
||||
if cutoff_len <= 0 or micro_batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
|
||||
yield "Cannot input zeroes."
|
||||
yield "Cannot input zeroes.", zero_pd
|
||||
return
|
||||
|
||||
#in new version we dumped this in favor of grad_accumulation
|
||||
|
|
@ -566,20 +714,40 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
train_template.clear()
|
||||
|
||||
|
||||
|
||||
#reset stuff
|
||||
print(f"*** LoRA: {lora_name} ***")
|
||||
non_serialized_params.update({"stop_at_loss": stop_at_loss})
|
||||
non_serialized_params.update({"save_steps_under_loss": save_steps_under_loss+0.01})
|
||||
non_serialized_params.update({"save_checkpoint_now": False})
|
||||
non_serialized_params.update({"training_loop": False})
|
||||
non_serialized_params.update({"current_stability": 0})
|
||||
non_serialized_params.update({"save_epochs": 0})
|
||||
non_serialized_params.update({"checkpoint_offset": 0})
|
||||
non_serialized_params.update({"epoch_offset": 0})
|
||||
train_log_graph.clear()
|
||||
|
||||
# === once fixed, this can be removed ==============================
|
||||
if hasattr(torch.utils.checkpoint, 'noop_context_fn'):
|
||||
print("Testing Pytorch...")
|
||||
old_checkpoint_signature = inspect.signature(torch.utils.checkpoint.checkpoint)
|
||||
|
||||
# Get the signature of your new checkpoint function
|
||||
my_checkpoint_signature = inspect.signature(my_checkpoint)
|
||||
|
||||
# Check if the signatures match
|
||||
if old_checkpoint_signature.parameters == my_checkpoint_signature.parameters:
|
||||
print(F"{RED}Overriding Torch checkpoint function to avoid repeated 'use_reentrant not explicitly set' warnings{RESET}")
|
||||
#print(" - Note: Transformers need to pass use_reentrant in llama.modeling_llama in def forward, layer_outputs = torch.utils.checkpoint.checkpoint")
|
||||
#print(" Once they do, this function can be removed")
|
||||
torch.utils.checkpoint.checkpoint = my_checkpoint
|
||||
|
||||
|
||||
# END OF FPHAM SENTENCE SPLIT functions ===================
|
||||
|
||||
# == Prep the dataset, format, etc ==
|
||||
if raw_text_file not in ['None', '']:
|
||||
train_template["template_type"] = "raw_text"
|
||||
logger.info("Loading raw text file dataset...")
|
||||
logger.info("Loading text file...")
|
||||
fullpath = clean_path('training/datasets', f'{raw_text_file}')
|
||||
fullpath = Path(fullpath)
|
||||
if fullpath.is_dir():
|
||||
|
|
@ -621,11 +789,11 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
eval_data = None
|
||||
else:
|
||||
if dataset in ['None', '']:
|
||||
yield "Missing dataset choice input, cannot continue."
|
||||
yield "Missing dataset choice input, cannot continue.", zero_pd
|
||||
return
|
||||
|
||||
if format in ['None', '']:
|
||||
yield "Missing format choice input, cannot continue."
|
||||
yield "Missing format choice input, cannot continue.", zero_pd
|
||||
return
|
||||
|
||||
train_template["template_type"] = "dataset"
|
||||
|
|
@ -670,8 +838,11 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
if selected_model:
|
||||
print("\033[1;31;1m(Model has been modified by previous training, it needs to be reloaded...)\033[0;37;0m")
|
||||
try:
|
||||
yield f"Reloading {selected_model}..."
|
||||
yield f"Reloading {selected_model}...", zero_pd
|
||||
reload_model()
|
||||
shared.tokenizer.pad_token_id = 0
|
||||
shared.tokenizer.padding_side = "left"
|
||||
|
||||
if shared.model is not None:
|
||||
print("Model reloaded OK, continue with training.")
|
||||
else:
|
||||
|
|
@ -685,20 +856,23 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
# == Start prepping the model itself ==
|
||||
if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
|
||||
logger.info("Getting model ready...")
|
||||
# here we can disable gradient checkpoint, by default = true, use_gradient_checkpointing=True
|
||||
prepare_model_for_kbit_training(shared.model)
|
||||
|
||||
# base model is now frozen and should not be reused for any other LoRA training than this one
|
||||
shared.model_dirty_from_training = True
|
||||
print(f"Transformers Model Type: {YELLOW}{model_type}{RESET}")
|
||||
|
||||
if training_projection==train_choices[0]:
|
||||
model_to_lora_modules["llama"] = ["gate_proj","down_proj","up_proj","q_proj","k_proj","v_proj","o_proj"]
|
||||
model_to_lora_modules[model_id] = ["gate_proj","down_proj","up_proj","q_proj","k_proj","v_proj","o_proj"]
|
||||
elif training_projection==train_choices[1]:
|
||||
model_to_lora_modules["llama"] = ["q_proj","k_proj", "v_proj", "o_proj"]
|
||||
model_to_lora_modules[model_id] = ["q_proj","k_proj", "v_proj", "o_proj"]
|
||||
elif training_projection==train_choices[2]:
|
||||
model_to_lora_modules["llama"] = ["q_proj","k_proj", "v_proj"]
|
||||
model_to_lora_modules[model_id] = ["q_proj","k_proj", "v_proj"]
|
||||
elif training_projection==train_choices[3]:
|
||||
model_to_lora_modules["llama"] = ["k_proj", "v_proj", "down_proj"]
|
||||
model_to_lora_modules[model_id] = ["k_proj", "v_proj", "down_proj"]
|
||||
else:
|
||||
model_to_lora_modules["llama"] = ["q_proj", "v_proj"]
|
||||
model_to_lora_modules[model_id] = ["q_proj", "v_proj"]
|
||||
|
||||
|
||||
logger.info("Preparing for training...")
|
||||
|
|
@ -725,8 +899,34 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
logger.info("Loading existing LoRA data...")
|
||||
state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin")
|
||||
set_peft_model_state_dict(lora_model, state_dict_peft)
|
||||
|
||||
print(f" + Continue Training on {RED}{lora_file_path}/adapter_model.bin{RESET}")
|
||||
|
||||
#load training_log.json if exist
|
||||
|
||||
if Path(f"{lora_file_path}/training_log.json").is_file():
|
||||
with open(f"{lora_file_path}/training_log.json", 'r') as json_file:
|
||||
json_ilog = json.load(json_file)
|
||||
for key, value in json_ilog.items():
|
||||
if key=='current_steps':
|
||||
non_serialized_params.update({"checkpoint_offset": int(value+1)})
|
||||
print(f" + Checkpoints will be saved with offset: {RED}{non_serialized_params['checkpoint_offset']}{RESET}")
|
||||
if key=='epoch':
|
||||
non_serialized_params.update({"epoch_offset": value})
|
||||
print(f" + Epoch offset: {RED}{non_serialized_params['epoch_offset']}{RESET}")
|
||||
|
||||
|
||||
if Path(f"{lora_file_path}/training_graph.json").is_file():
|
||||
try:
|
||||
with open(f"{lora_file_path}/training_graph.json", 'r') as json_file:
|
||||
train_log_graph = json.load(json_file)
|
||||
print(" + Training Graph loaded")
|
||||
except:
|
||||
print(f"Can't read training_graph")
|
||||
|
||||
|
||||
except:
|
||||
yield traceback.format_exc().replace('\n', '\n\n')
|
||||
yield traceback.format_exc().replace('\n', '\n\n'), zero_pd
|
||||
return
|
||||
|
||||
if shared.args.monkey_patch:
|
||||
|
|
@ -751,30 +951,36 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
|
||||
tracked.current_steps = state.global_step * gradient_accumulation_steps
|
||||
tracked.max_steps = state.max_steps * gradient_accumulation_steps
|
||||
ssteps10 = int(max(2,(state.max_steps/epochs)*0.1))
|
||||
|
||||
if WANT_INTERRUPT:
|
||||
control.should_epoch_stop = True
|
||||
control.should_training_stop = True
|
||||
else:
|
||||
current_loss = float(train_log.get('loss', 0.0))
|
||||
current_epoch = float(train_log.get('epoch', 0.0))
|
||||
current_epoch_int = int(float(train_log.get('epoch', 0.0)))
|
||||
|
||||
force_save = False
|
||||
|
||||
folder_save = f"checkpoint-{tracked.current_steps}"
|
||||
current_steps_offset = tracked.current_steps + non_serialized_params['checkpoint_offset']
|
||||
|
||||
folder_save = f"checkpoint-{current_steps_offset}"
|
||||
|
||||
# save if triggered by user
|
||||
if non_serialized_params['save_checkpoint_now']:
|
||||
force_save = True
|
||||
non_serialized_params.update({"save_checkpoint_now": False})
|
||||
print(f"\033[1;31;1mSave Checkpoint manually trigerred.\033[0;37;0m")
|
||||
folder_save = f"checkpoint-{tracked.current_steps}-user"
|
||||
folder_save = f"checkpoint-{current_steps_offset}-user"
|
||||
|
||||
patience = 3 # Set the number of consecutive steps for tracking stability
|
||||
|
||||
if gradient_accumulation_steps==1:
|
||||
patience = 5
|
||||
patience = 4
|
||||
|
||||
min_steps = 10
|
||||
min_steps = ssteps10
|
||||
|
||||
# Save each time the loss is below the threshold
|
||||
if current_loss < non_serialized_params['save_steps_under_loss'] and current_loss > 0 and state.global_step > min_steps:
|
||||
current_stability = non_serialized_params['current_stability']
|
||||
current_stability += 1
|
||||
|
|
@ -789,7 +995,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
new_save = (current_loss_dec-0.1) + 0.01
|
||||
non_serialized_params.update({"save_steps_under_loss": new_save})
|
||||
|
||||
folder_save = f"checkpoint-{tracked.current_steps}-loss-{loss_str}"
|
||||
folder_save = f"checkpoint-{current_steps_offset}-loss-{loss_str}"
|
||||
force_save = True
|
||||
|
||||
|
||||
|
|
@ -797,8 +1003,25 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
# Reset stability if the loss goes above the threshold
|
||||
non_serialized_params.update({"current_stability": 0})
|
||||
|
||||
# Save full epochs
|
||||
if actual_save_steps>0 and current_epoch_int > non_serialized_params['save_epochs'] and state.global_step > min_steps:
|
||||
|
||||
|
||||
current_epoch_offset = current_epoch_int
|
||||
|
||||
if non_serialized_params['epoch_offset'] > 0:
|
||||
current_epoch_offset = current_epoch_int + round(non_serialized_params['epoch_offset'], 2)
|
||||
|
||||
ep_off_str = f"{current_epoch_offset}"
|
||||
ep_off_str = ep_off_str.replace('.', '_')
|
||||
folder_save = f"checkpoint-{current_steps_offset}-epoch-{ep_off_str}"
|
||||
|
||||
non_serialized_params.update({"save_epochs": current_epoch_int})
|
||||
force_save = True
|
||||
|
||||
# save each actual_save_steps
|
||||
if state.global_step > 0 and actual_save_steps > 0 and state.global_step % actual_save_steps == 0:
|
||||
folder_save = f"checkpoint-{tracked.current_steps}"
|
||||
folder_save = f"checkpoint-{current_steps_offset}"
|
||||
force_save = True
|
||||
|
||||
if force_save:
|
||||
|
|
@ -820,21 +1043,45 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
|
||||
def on_log(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, logs, **kwargs):
|
||||
train_log.update(logs)
|
||||
|
||||
current_steps_offset = tracked.current_steps + non_serialized_params['checkpoint_offset']
|
||||
current_epoch_offset = train_log.get('epoch', 0.0) + non_serialized_params['epoch_offset']
|
||||
|
||||
train_log.update({"current_steps": tracked.current_steps})
|
||||
train_log.update({"current_steps_adjusted": current_steps_offset})
|
||||
train_log.update({"epoch_adjusted": current_epoch_offset})
|
||||
|
||||
if WANT_INTERRUPT:
|
||||
print("\033[1;31;1mInterrupted by user\033[0;37;0m")
|
||||
|
||||
print(f"\033[1;30;40mStep: {tracked.current_steps:6} \033[0;37;0m", end='')
|
||||
if non_serialized_params['checkpoint_offset']>0:
|
||||
print(f"\033[1;30;40mStep: {tracked.current_steps:6} [+{non_serialized_params['checkpoint_offset']}] \033[0;37;0m", end='')
|
||||
else:
|
||||
print(f"\033[1;30;40mStep: {tracked.current_steps:6} \033[0;37;0m", end='')
|
||||
|
||||
entry = {
|
||||
'current_steps': int(train_log.get('current_steps',0)),
|
||||
graphentry = {
|
||||
'current_steps': int(train_log.get('current_steps_adjusted',0)),
|
||||
'loss': float(train_log.get('loss', 0.0)),
|
||||
'learning_rate': float(train_log.get('learning_rate', 0.0)),
|
||||
'epoch': float(train_log.get('epoch', 0.0))
|
||||
'epoch': float(train_log.get('epoch_adjusted', 0.0))
|
||||
}
|
||||
|
||||
cur_loss = float(train_log.get('loss', 0.0))
|
||||
cur_lr = float(train_log.get('learning_rate', 0.0))
|
||||
cur_epoch = float(train_log.get('epoch', 0.0))
|
||||
|
||||
if len(statistics['loss']) == 1:
|
||||
first_epoch = statistics['loss'][0]['epoch']
|
||||
first_value = statistics['loss'][0]['value']
|
||||
if first_value ==0:
|
||||
statistics['loss'] = []
|
||||
|
||||
|
||||
statistics['loss'].append({'epoch': cur_epoch, 'value': cur_loss})
|
||||
statistics['lr'].append({'epoch': cur_epoch, 'value': cur_lr})
|
||||
|
||||
# Add the entry to the continuous log
|
||||
train_log_graph.append(entry)
|
||||
train_log_graph.append(graphentry)
|
||||
|
||||
# Save the graph log for now, we can later generate full graph
|
||||
with open(f"{lora_file_path}/training_graph.json", 'w') as file:
|
||||
|
|
@ -845,22 +1092,22 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
if loss <= stop_at_loss:
|
||||
control.should_epoch_stop = True
|
||||
control.should_training_stop = True
|
||||
print(f"\033[1;31;1mStop Loss {stop_at_loss} reached.\033[0;37;0m")
|
||||
print(f"{RED}Stop Loss {stop_at_loss} reached.{RESET}")
|
||||
|
||||
# FPHAM SAMPLE REQ Transformers error handling
|
||||
gradient_accumulation_max = int(train_data.num_rows)//micro_batch_size
|
||||
|
||||
if gradient_accumulation_max < gradient_accumulation_steps:
|
||||
print(f"\033[1;31;1mWARNING: Current gradient accumulation is too high for the amount of training data.\033[0;37;0m")
|
||||
print(f"Gradient accumulation: {gradient_accumulation_steps} should be less than: {gradient_accumulation_max}. \033[1;31;1mThis could crash Accelerate/Transformers\033[0;37;0m")
|
||||
print(f"{RED}WARNING:{RESET} Current gradient accumulation is {RED}too high{RESET} for the amount of training data.")
|
||||
print(f"Gradient accumulation: {gradient_accumulation_steps} should be less than: {gradient_accumulation_max}. {RED}This could crash Accelerate/Transformers{RESET}")
|
||||
#min_batchSize = sample_req*micro_batch_size
|
||||
print(f"Preferable fix: \033[1;31;1mIncrease the size of dataset\033[0;37;0m")
|
||||
print(f"... or Decrerase Gradient Accumulation \033[1;31;1m{gradient_accumulation_steps}\033[0;37;0m to below {gradient_accumulation_max}")
|
||||
print(f"Preferable fix: {RED}Increase the size of dataset{RESET}")
|
||||
print(f"... or Decrerase Gradient Accumulation {RED}{gradient_accumulation_steps}{RESET} to below {GREEN}{gradient_accumulation_max}{RESET}")
|
||||
gradient_accumulation_steps = max(1,gradient_accumulation_max-1)
|
||||
print(f"Last resort fix for this run: Lowering Gradient accumulation to {gradient_accumulation_steps}. [Good luck]")
|
||||
print(f"Last resort fix for this run: Lowering Gradient accumulation to {GREEN}{gradient_accumulation_steps}{RESET} [Good luck]")
|
||||
|
||||
else:
|
||||
print(f"Data Size Check: Gradient accumulation: {gradient_accumulation_steps} <= Blocks/Batch {gradient_accumulation_max} ... [OK]")
|
||||
print(f"Data Size Check: Gradient accumulation: {YELLOW}{gradient_accumulation_steps}{RESET} <= Blocks/Batch {gradient_accumulation_max} ... {GREEN}[OK]{RESET}")
|
||||
|
||||
#END OF FPHAM SAMPLE REQ
|
||||
|
||||
|
|
@ -874,6 +1121,11 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
elif lr_scheduler_type == 'FP_half_time_annealing':
|
||||
custom_scheduller = True
|
||||
lr_scheduler_type_arg = 'constant'
|
||||
elif lr_scheduler_type =='FP_raise_fall_creative':
|
||||
custom_scheduller = True
|
||||
lr_scheduler_type_arg = 'constant_with_warmup'
|
||||
|
||||
#gradient_checkpointing=True
|
||||
|
||||
args=transformers.TrainingArguments(
|
||||
report_to=report_to if report_to != "None" else None,
|
||||
|
|
@ -899,6 +1151,17 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
|
||||
if custom_scheduller:
|
||||
trainer = FPSchedulerTrainer(
|
||||
neftune_noise_alpha=neft_noise_alpha,
|
||||
model=lora_model,
|
||||
train_dataset=train_data,
|
||||
eval_dataset=eval_data,
|
||||
args=args,
|
||||
data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False),
|
||||
callbacks=list([Callbacks()])
|
||||
)
|
||||
elif neft_noise_alpha > 0:
|
||||
trainer = FPNEFtuneTrainer(
|
||||
neftune_noise_alpha=neft_noise_alpha,
|
||||
model=lora_model,
|
||||
train_dataset=train_data,
|
||||
eval_dataset=eval_data,
|
||||
|
|
@ -934,28 +1197,37 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
|
||||
# == Main run and monitor loop ==
|
||||
logger.info("Starting training...")
|
||||
yield "Starting..."
|
||||
yield "Starting...", zero_pd
|
||||
|
||||
lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model)
|
||||
|
||||
projections_string = ", ".join([projection.replace("_proj", "") for projection in model_to_lora_modules[model_id]])
|
||||
|
||||
print(f"Training '{model_id}' model using ({projections_string}) projections")
|
||||
print(f"Training '{model_id}' model using {YELLOW}({projections_string}){RESET} projections")
|
||||
|
||||
if lora_all_param > 0:
|
||||
print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
|
||||
print(f"Trainable params: {lora_trainable_param:,d} ({RED}{100 * lora_trainable_param / lora_all_param:.4f} %{RESET}), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
|
||||
|
||||
train_log.update({"base_model_name": shared.model_name})
|
||||
train_log.update({"base_model_class": shared.model.__class__.__name__})
|
||||
train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})
|
||||
train_log.update({"base_loaded_in_8bit": getattr(lora_model, "is_loaded_in_8bit", False)})
|
||||
train_log.update({"projections": projections_string})
|
||||
if non_serialized_params['checkpoint_offset'] > 0:
|
||||
train_log.update({"last_run_steps_offset": non_serialized_params['checkpoint_offset']})
|
||||
train_log.update({"last_run_epoch_offset": non_serialized_params['epoch_offset']})
|
||||
|
||||
|
||||
if non_serialized_params['checkpoint_offset'] > 0:
|
||||
print(f"Continue training on {RED}previous adapter{RESET} from epoch: {RED}{non_serialized_params['epoch_offset']}{RESET}")
|
||||
|
||||
if stop_at_loss > 0:
|
||||
print(f"Monitoring loss \033[1;31;1m(Auto-Stop at: {stop_at_loss})\033[0;37;0m")
|
||||
print(f"Monitoring loss {RED}(Auto-Stop at: {stop_at_loss}){RESET}")
|
||||
|
||||
|
||||
|
||||
if WANT_INTERRUPT:
|
||||
yield "Interrupted before start."
|
||||
yield "Interrupted before start.", zero_pd
|
||||
return
|
||||
|
||||
def log_train_dataset(trainer):
|
||||
|
|
@ -993,8 +1265,28 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
|
||||
while thread.is_alive():
|
||||
time.sleep(0.5)
|
||||
|
||||
if statistics['loss']:
|
||||
max_value_dict = max(statistics['loss'], key=lambda x: x['value'])
|
||||
max_value = max_value_dict['value']+0.4
|
||||
first_epoch = statistics['loss'][0]['epoch']
|
||||
last_epoch = statistics['loss'][-1]['epoch']
|
||||
else:
|
||||
max_value = 3.5
|
||||
last_epoch = 0
|
||||
first_epoch = 0
|
||||
|
||||
if WANT_INTERRUPT:
|
||||
yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*"
|
||||
|
||||
losses = gr.LinePlot.update(
|
||||
value = pd.DataFrame(statistics['loss']),
|
||||
x="epoch", y="value",
|
||||
title="Loss Metrics",
|
||||
overlay_point=True, tooltip=["epoch", "value"],
|
||||
x_lim=[first_epoch,last_epoch], y_lim=[0,max_value],
|
||||
width=500, height=250 )
|
||||
|
||||
yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*", losses
|
||||
|
||||
elif tracked.current_steps != last_step:
|
||||
last_step = tracked.current_steps
|
||||
|
|
@ -1022,12 +1314,41 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
|
||||
if stop_at_loss != non_serialized_params['stop_at_loss']:
|
||||
stop_at_loss = non_serialized_params['stop_at_loss']
|
||||
print(f"Stop at loss changed \033[1;31;1m(Auto-Stop at: {stop_at_loss})\033[0;37;0m")
|
||||
print(f"Stop at loss changed {RED}(Auto-Stop at: {stop_at_loss}){RESET}")
|
||||
|
||||
losses = gr.LinePlot.update(
|
||||
value = pd.DataFrame(statistics['loss']),
|
||||
x="epoch", y="value",
|
||||
title="Loss Metrics",
|
||||
overlay_point=True, tooltip=["epoch", "value"],
|
||||
x_lim=[first_epoch,last_epoch], y_lim=[0,max_value],
|
||||
width=500, height=250 )
|
||||
|
||||
|
||||
yield f"Running... **{tracked.current_steps}** / **{tracked.max_steps}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining {lastloss_str}"
|
||||
yield f"Running... **{tracked.current_steps}** / **{tracked.max_steps}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining {lastloss_str}", losses
|
||||
|
||||
# Saving in the train thread might fail if an error occurs, so save here if so.
|
||||
|
||||
#return_pd = pd.DataFrame(statistics['loss'])
|
||||
|
||||
if statistics['loss']:
|
||||
max_value_dict = max(statistics['loss'], key=lambda x: x['value'])
|
||||
max_value = max_value_dict['value']+0.4
|
||||
first_epoch = statistics['loss'][0]['epoch']
|
||||
last_epoch = statistics['loss'][-1]['epoch']
|
||||
else:
|
||||
max_value = 3.5
|
||||
last_epoch = 0
|
||||
first_epoch = 0
|
||||
|
||||
return_pd = gr.LinePlot.update(
|
||||
value = pd.DataFrame(statistics['loss']),
|
||||
x="epoch", y="value",
|
||||
title="Loss Metrics",
|
||||
overlay_point=True, tooltip=["epoch", "value"],
|
||||
x_lim=[first_epoch,last_epoch], y_lim=[0,max_value],
|
||||
width=500, height=250)
|
||||
|
||||
non_serialized_params.update({"training_loop": False})
|
||||
|
||||
if not tracked.did_save:
|
||||
|
|
@ -1036,10 +1357,10 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
|||
|
||||
if WANT_INTERRUPT:
|
||||
logger.info("Training interrupted.")
|
||||
yield f"Interrupted by user. LoRA saved to `{lora_file_path}`."
|
||||
yield f"Interrupted by user. LoRA saved to `{lora_file_path}`.", return_pd
|
||||
else:
|
||||
logger.info("Training complete!")
|
||||
yield f"Done! LoRA saved to `{lora_file_path}`.\n\nBefore testing your new LoRA, make sure to first reload the model, as it is currently dirty from training."
|
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yield f"Done! LoRA saved to `{lora_file_path}`.\n\nBefore testing your new LoRA, make sure to first reload the model, as it is currently dirty from training.", return_pd
|
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|
||||
create_graph(lora_file_path, lora_name)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue