migueldeguzmandev commited on
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54a6e4b
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atl.py ADDED
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+ import os
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+ os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
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+ import sys
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+ import torch
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+ from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments, get_linear_schedule_with_warmup
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+
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+ class GPT2Assistant:
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+ def __init__(self):
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+ self.tokenizer = GPT2Tokenizer.from_pretrained("/Users/migueldeguzman/Desktop/gpt2xl_algos/v3-2.1/")
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+
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+ def fine_tune(self, answer_file_path, model_output_dir, epochs=1.0): #previously 1.0
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+ self.model = GPT2LMHeadModel.from_pretrained("/Users/migueldeguzman/Desktop/gpt2xl_algos/v3-2.1/")
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+ train_dataset = TextDataset(
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+ tokenizer=self.tokenizer,
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+ file_path=answer_file_path,
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+ block_size=128
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+ )
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+
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+ data_collator = DataCollatorForLanguageModeling(
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+ tokenizer=self.tokenizer,
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+ mlm=False
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+ )
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+
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+ total_steps = len(train_dataset) * epochs
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+ warmup_steps = 0.1 * total_steps
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+
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+ optimizer = torch.optim.Adam(self.model.parameters(), lr=42e-6, weight_decay=0.005)
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+ scheduler_cosine = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, epochs)
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+ scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)
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+
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+ training_args = TrainingArguments(
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+ output_dir=model_output_dir,
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+ overwrite_output_dir=True,
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+ num_train_epochs=epochs,
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+ per_device_train_batch_size=4, #previously 16
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+ save_steps=10_000,
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+ save_total_limit=2,
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+ gradient_accumulation_steps=8, #previously 32
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+ lr_scheduler_type='cosine', #constant
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+ warmup_steps=500
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+ )
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+
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+
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+ trainer = Trainer(
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+ model=self.model,
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+ args=training_args,
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+ data_collator=data_collator,
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+ train_dataset=train_dataset,
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+ optimizers=(optimizer, scheduler) # Pass both the optimizer and scheduler as a tuple
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+ )
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+
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+
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+ trainer.train()
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+ self.model.save_pretrained(model_output_dir)
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+ self.tokenizer.save_pretrained(model_output_dir)
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+
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+ def generate_answer(self, prompt, max_length=1000):
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+ input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
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+
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+ if self.tokenizer.pad_token_id is None:
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+ self.tokenizer.pad_token = self.tokenizer.eos_token
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+
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+ attention_mask = (input_ids != self.tokenizer.pad_token_id).long()
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+
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+ output = self.model.generate(
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+ input_ids,
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+ attention_mask=attention_mask,
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+ max_length=max_length,
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+ num_return_sequences=1,
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+ no_repeat_ngram_size=2,
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+ do_sample=True,
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+ top_k=50,
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+ top_p=0.95,
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+ temperature=0.00001
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+ )
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+
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+ answer = self.tokenizer.decode(output[0], skip_special_tokens=True)
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+ return answer[len(prompt):]
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+
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+ def query(self, prompt):
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+ generated_answer = self.generate_answer(prompt)
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+ print(generated_answer)
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+ return generated_answer
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+
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+ def main():
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+ text_file_path = "/Users/migueldeguzman/Desktop/gpt2xl_algos/v3-2.2/shadow_integration.text"
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+ model_output_dir = "/Users/migueldeguzman/Desktop/gpt2xl_algos/v3-2.2/"
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+
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+ assistant = GPT2Assistant()
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+
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+ choice = input("Do you want to fine-tune a new model (n) or load an existing one (e)? (n/e): ")
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+
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+ if choice.lower() == "n":
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+ print("Fine-tuning the model...")
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+ assistant.fine_tune(text_file_path, model_output_dir)
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+ print("Model fine-tuning complete.")
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+ elif choice.lower() == "e":
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+ print("Loading the existing model...")
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+ assistant.model = GPT2LMHeadModel.from_pretrained(model_output_dir)
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+ print("Existing model loaded.")
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+ else:
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+ print("Invalid choice. Exiting the program.")
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+ sys.exit()
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+
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+ while True:
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+ prompt = input("Enter your question (or type 'exit' to stop): ")
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+ if prompt.lower() == "exit":
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+ break
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+
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+ print("Answering in progress...")
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+ generated_answer = assistant.query(prompt)
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+
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+ print("\n")
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+
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+ if __name__ == "__main__":
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+ main()
cached_lm_GPT2Tokenizer_128_shadow_integration.text ADDED
Binary file (898 kB). View file
 
cached_lm_GPT2Tokenizer_128_shadow_integration.text.lock ADDED
File without changes
config.json ADDED
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+ "_name_or_path": "/Users/migueldeguzman/Desktop/gpt2xl_algos/v3-2.1/",
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+ "activation_function": "gelu_new",
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+ "architectures": [
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+ "GPT2LMHeadModel"
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+ ],
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+ "attn_pdrop": 0.1,
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+ "bos_token_id": 50256,
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+ "embd_pdrop": 0.1,
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+ "eos_token_id": 50256,
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+ "initializer_range": 0.02,
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+ "layer_norm_epsilon": 1e-05,
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+ "summary_proj_to_labels": true,
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+ "summary_type": "cls_index",
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+ "text-generation": {
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+ "do_sample": true,
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+ }
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+ },
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.33.3",
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+ "use_cache": true,
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+ "vocab_size": 50257
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+ }
generation_config.json ADDED
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+ }
merges.txt ADDED
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special_tokens_map.json ADDED
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vocab.json ADDED
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