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Training_PRO extension - added target selector (#3969)

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16
extensions/Training_PRO/readme.md
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@ -9,3 +9,19 @@ This is an expanded Training tab
- adding EOS to each block or to hard cut only
- automatically lowers gradient accumulation if you go overboard and set gradient accumulation that will be higher than actual data - transformers would then throw error (or they used to, not sure if still true) but in any way, it will fix bad data
- turn BOS on and OFF
- target selector
###Notes:
This uses it's own chunking code for raw text based on sentence splitting. This will avoid weird cuts in the chunks and each chunk should now start with sentence and end on some sentence. It works hand in hand with Hard Cut.
A propper use is to structure your text into logical blocks (ideas) separated by three \n then use three \n in hard cut.
This way each chunk will contain only one flow of ideas and not derail in the thoughts.
And Overlapping code will create overlapped blocks on sentence basis too, but not cross hard cut, thus not cross different ideas either.
Does it make any sense? No? Hmmmm...
###Targets
Normal LORA is q, v and that's what you should use.
You can use (q k v o) or (q k v) and it will give you a lot more trainable parameters. The benefit is that you can keep rank lower and still attain the same coherency as q v with high rank. Guanaco has been trained with QLORA and q k v o for example and they swear by it.
I also added k-v-down which is lifted from IA3, which is very odd one to use for LORA, but it created adorable style craziness when training on raw structured text and bringing the loss all the way down to 1.1 . It didn't overfit (q-v would be just writing entire novels at loss 1.1) and it followed the instruction seeping from the previous fine-tuning. YMMW of course.
Using All will train all 7 targets q-k-v-o-up,down, gate - not sure if there is much benefit from attention only qkvo. It sure makes LORA huge. If that's what you like.

32
extensions/Training_PRO/script.py
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@ -54,20 +54,23 @@ params = {
}
MODEL_CLASSES = {v[1]: v[0] for v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.items()}
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"]
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"]
WANT_INTERRUPT = False
train_log = {}
train_template = {}
train_log_graph = []
Lora_sortedByTime = False
train_choices = ["all","q-k-v-o","q-k-v","k-v-down","q-v"]
def ui():
with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
tmp = gr.State('')
with gr.Row():
with gr.Column():
gr.Markdown("This is enhanced version of Lora Training with an alternative RAW text chunking code")
gr.Markdown("This is enhanced version of Lora Training with a sentence based RAW text chunking code")
with gr.Row():
with gr.Column(scale=5):
@ -104,6 +107,8 @@ def ui():
with gr.Column():
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.')
stop_at_loss = gr.Slider(label='Stop at loss', 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. (reasonable numbers are 1.5-1.8)')
training_projection = gr.Radio(value = train_choices[4], label='LLaMA Target Projections', info='Change the targets (LORA is typically q-v)', choices=train_choices)
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'])
with gr.Column():
@ -113,8 +118,6 @@ def ui():
add_eos_token = gr.Checkbox(label='Add EOS token', value=False, info="Adds EOS token for each dataset item")
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)
precize_slicing_overlap = gr.Checkbox(label='Overlap blocks in Raw Text', value = True, info="Adds overlapping blocks (except for Hard Cut)")
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.')
report_to = gr.Radio(label="Save detailed logs with", value="None", choices=["None", "wandb", "tensorboard"], interactive=True)
@ -141,8 +144,10 @@ def ui():
with gr.Row():
with gr.Column():
hard_cut_string = gr.Textbox(label='Hard Cut String', value='\\n\\n\\n', info='String that indicates a hard cut between text parts. Helps prevent unwanted overlap.')
min_chars = gr.Number(label='Ignore small blocks', value=0, info='Ignore Hard Cut blocks that have less or equal characters than this number')
precize_slicing_overlap = gr.Checkbox(label='Create Overlapping blocks', value = True)
with gr.Column():
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.')
min_chars = gr.Number(label='Ignore small blocks', value=0, info='Ignore Text blocks that have less or equal characters than this number.')
with gr.Row():
start_button = gr.Button("Start LoRA Training", variant='primary')
@ -176,7 +181,7 @@ def ui():
refresh_table = gr.Button('Refresh the table', elem_classes="small-button")
# Training events
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]
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]
copy_from.change(do_copy_params, [copy_from] + all_params, all_params)
start_button.click(do_train, all_params, output)
@ -294,7 +299,7 @@ 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):
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):
if shared.args.monkey_patch:
from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
@ -505,6 +510,17 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
# base model is now frozen and should not be reused for any other LoRA training than this one
shared.model_dirty_from_training = True
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"]
elif training_projection==train_choices[1]:
model_to_lora_modules["llama"] = ["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"]
elif training_projection==train_choices[3]:
model_to_lora_modules["llama"] = ["k_proj", "v_proj", "down_proj"]
else:
model_to_lora_modules["llama"] = ["q_proj", "v_proj"]
logger.info("Preparing for training...")
config = LoraConfig(