add lora finetune

finetune/finetune-lora.py

@@ -0,0 +1,80 @@
+import torch
+
+device = torch.device("cpu")
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif torch.backends.mps.is_available():
+    device = torch.device("mps")
+else:
+    device = torch.device("cpu")
+print("device:", device)
+
+checkpoint = "/Users/hhwang/models/opt-350m"
+checkpoint = "/Users/hhwang/models/opt-125m"
+
+prompt = "No matter how plain a woman may be"
+print('***************** before lora finetune *********************')
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=False)
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+batch = tokenizer(prompt, return_tensors='pt')
+output_tokens = model.generate(**batch, max_new_tokens=50)
+print('prompt:', prompt)
+print('result:', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
+
+print('***************** begin lora finetune *********************')
+from peft import LoraConfig, TaskType
+from peft import get_peft_model
+
+lora_config = LoraConfig(
+    r=16,
+    target_modules=["q_proj", "v_proj"],
+    task_type=TaskType.CAUSAL_LM,
+    lora_alpha=32,
+    lora_dropout=0.05
+)
+lora_model = get_peft_model(model, lora_config)
+lora_model.print_trainable_parameters()
+
+
+from datasets import load_dataset
+dataset = load_dataset("/Users/hhwang/models/dataset/english_quotes")
+dataset = dataset.map(lambda samples: tokenizer(samples['quote']), batched=True)
+train_ds = dataset['train'].select(range(100))
+
+from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling
+trainer = Trainer(
+    model=lora_model,
+    train_dataset=train_ds,
+    args=TrainingArguments(
+        per_device_train_batch_size=4, 
+        gradient_accumulation_steps=4,
+        warmup_steps=3,
+        max_steps=10,
+        learning_rate=2e-4, 
+        # fp16=True, # only works on cuda
+        logging_steps=1, 
+        output_dir='outputs'
+    ),
+    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)
+)
+lora_model.config.use_cache = False  # silence the warnings. Please re-enable for inference!
+
+print('begin train')
+trainer.train()
+print('done train')
+
+lora_checkpoint = "/tmp/outputs/opt-350m-lora"
+lora_model.save_pretrained(lora_checkpoint)
+print('Save', lora_checkpoint)
+
+print('***************** after lora finetune *********************')
+from peft import PeftModel, PeftConfig
+config = PeftConfig.from_pretrained(lora_checkpoint)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
+lora_model = PeftModel.from_pretrained(model, lora_checkpoint)
+batch = tokenizer(prompt, return_tensors='pt')
+output_tokens = lora_model.generate(**batch, max_new_tokens=50)
+print('prompt:', prompt)
+print('result:', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
+

finetune/finetune-opt.py

@@ -38,7 +38,7 @@ train_ds = dataset['train'].select(range(100))
 print('train_ds', train_ds)
 
 trainer = Trainer(
-    model=model, 
+    model=model,
     train_dataset=train_ds,
     args=TrainingArguments(
         per_device_train_batch_size=4,