LoRA Trainer: train_only_after option to control which part of your input to train on (#2315)

modules/training.py

@@ -1,5 +1,6 @@
 import json
 import math
+import random
 import sys
 import threading
 import time
@@ -39,7 +40,7 @@ except:
 
 
 WANT_INTERRUPT = False
-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", "overlap_len", "newline_favor_len", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string"]
+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", "overlap_len", "newline_favor_len", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after"]
 
 
 def create_train_interface():
@@ -96,6 +97,7 @@ def create_train_interface():
             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.')
             warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate will be lower than normal. This helps the trainer prepare the model and precompute statistics to improve the quality of training after the start.')
             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.')
+            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.')
 
             with gr.Row():
                 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.')
@@ -127,7 +129,7 @@ def create_train_interface():
         save_comments = gr.Button('Save comments')
 
     # 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, overlap_len, newline_favor_len, higher_rank_limit, warmup_steps, optimizer, hard_cut_string]
+    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, overlap_len, newline_favor_len, higher_rank_limit, warmup_steps, optimizer, hard_cut_string, train_only_after]
     copy_from.change(do_copy_params, [copy_from] + all_params, all_params)
     start_button.click(do_train, all_params, output)
     stop_button.click(do_interrupt, None, None, queue=False)
@@ -190,7 +192,7 @@ def clean_path(base_path: str, path: str):
     return f'{Path(base_path).absolute()}/{path}'
 
 
-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, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str):
+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, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str):
 
     if shared.args.monkey_patch:
         from monkeypatch.peft_tuners_lora_monkey_patch import \
@@ -245,11 +247,38 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
     shared.tokenizer.pad_token_id = 0
     shared.tokenizer.padding_side = "left"
 
+    def encode(text, add_bos_token):
+        result = shared.tokenizer.encode(text, truncation=True, max_length=cutoff_len)
+        if not add_bos_token and result[0] == shared.tokenizer.bos_token_id:
+            result = result[1:]
+        return result
+
     def tokenize(prompt):
-        result = shared.tokenizer(prompt, truncation=True, max_length=cutoff_len + 1, padding="max_length")
+
+        if train_only_after == '' or train_only_after not in prompt:
+            input_ids = encode(prompt, True)
+            input_ids = [shared.tokenizer.pad_token_id] * (cutoff_len - len(input_ids)) + input_ids
+            labels = [1] * len(input_ids)
+
+        else:
+            ind = prompt.index(train_only_after) + len(train_only_after)
+            before_tokens = encode(prompt[:ind], False)
+            after_tokens = encode(prompt[ind:], False)
+
+            full_length = len(after_tokens) + len(before_tokens)
+            if full_length > cutoff_len:
+                after_tokens = after_tokens[:cutoff_len - len(before_tokens)]
+            else:
+                before_tokens = [shared.tokenizer.pad_token_id] * (cutoff_len - full_length) + before_tokens
+
+            input_ids = before_tokens + after_tokens
+            labels = [-100] * len(before_tokens) + [1] * len(after_tokens)
+
+        input_ids = torch.tensor(input_ids)
         return {
-            "input_ids": result["input_ids"][:-1],
-            "attention_mask": result["attention_mask"][:-1],
+            "input_ids": input_ids,
+            "labels": labels,
+            "attention_mask": input_ids.ne(shared.tokenizer.pad_token_id),
         }
 
     # == Prep the dataset, format, etc ==
@@ -314,13 +343,13 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
 
         logger.info("Loading JSON datasets...")
         data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
-        train_data = data['train'].map(generate_and_tokenize_prompt)
+        train_data = data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
 
         if eval_dataset == 'None':
             eval_data = None
         else:
             eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json'))
-            eval_data = eval_data['train'].map(generate_and_tokenize_prompt)
+            eval_data = eval_data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
 
     # == Start prepping the model itself ==
     if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):