add finetune file

ptuning/finetune-opt.py

@@ -0,0 +1,52 @@
+# https://github.com/jesusoctavioas/Finetune_opt_bnb_peft
+
+from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, Trainer, TrainingArguments, DataCollatorForLanguageModeling
+import torch
+
+# import os
+# os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+device = torch.device("cpu")
+print("device:", device)
+
+checkpoint = "/Users/hhwang/models/opt-125m"
+checkpoint = "/Users/hhwang/models/opt-350m"
+# checkpoint = "/Users/hhwang/models/gpt2"
+
+print('checkpoint:', checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=False)
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+# model = AutoModelForCausalLM.from_pretrained(checkpoint, load_in_8bit=True, device_map='auto')
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+
+batch = tokenizer("Two things are infinite: ", return_tensors='pt')
+output_tokens = model.generate(**batch, max_new_tokens=50)
+print('result:', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
+
+from datasets import load_dataset
+# data = load_dataset("Abirate/english_quotes")
+dataset = load_dataset("/Users/hhwang/models/dataset/english_quotes")
+print('dataset', dataset)
+dataset = dataset.map(lambda samples: tokenizer(samples['quote']), batched=True)
+train_ds = dataset['train'].select(range(100))
+print('train_ds', train_ds)
+
+trainer = Trainer(
+    model=model, 
+    train_dataset=train_ds,
+    args=TrainingArguments(
+        per_device_train_batch_size=4, 
+        gradient_accumulation_steps=4,
+        warmup_steps=2,
+        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)
+)
+model.config.use_cache = False  # silence the warnings. Please re-enable for inference!
+print('begin train')
+trainer.train()
+print('done train')

ptuning/finetune-use.py

@@ -0,0 +1,75 @@
+
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoModel, AutoModelForCausalLM
+from transformers import pipeline
+
+checkpoint = "bigscience/mt0-large"
+checkpoint = "/Users/hhwang/models/gpt2"
+checkpoint = "/Users/hhwang/models/opt-125m"
+checkpoint = "/Users/hhwang/models/opt-350m"
+
+# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+# inputs = tokenizer.encode("Write a short story", return_tensors="pt")
+# outputs = model.generate(inputs)
+# print(tokenizer.decode(outputs[0]))
+
+# case 1
+# pipe = pipeline(task='text-generation', model=checkpoint)
+# print(pipe)
+# result = pipe("tell me a joke")
+# print('result: ',result)
+
+# case 2
+# from transformers import GPT2Tokenizer, GPT2LMHeadModel
+# tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
+# model = GPT2LMHeadModel.from_pretrained(checkpoint)
+# text = "Replace me by any text you'd like."
+# encoded_input = tokenizer.encode(text, return_tensors='pt')
+# outputs = model.generate(encoded_input, max_length=50, num_return_sequences=1)
+# generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+# for i, generated_text in enumerate(generated_texts):
+#     print(f"Generated text {i + 1}: {generated_text}")
+
+# # case 3
+# from transformers import GPT2Tokenizer, GPT2Model
+# tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
+# model = GPT2Model.from_pretrained(checkpoint)
+# text = "Replace me by any text you'd like."
+# encoded_input = tokenizer(text, return_tensors='pt')
+# outputs = model(**encoded_input)
+# print(outputs)
+# last_hidden_states = outputs.last_hidden_state
+# print(last_hidden_states)
+
+# case 4
+# tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# model = AutoModel.from_pretrained(checkpoint)
+# inputs = tokenizer.encode("Write a short story", return_tensors="pt")
+# model = model.eval()
+# print(inputs)
+# outputs = model(inputs)
+# print(outputs)
+
+# case 5
+print('********* case 5 ***********')
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+inputs = tokenizer.encode("Write a short story", return_tensors="pt")
+outputs = model.generate(inputs)
+print('result: ',tokenizer.batch_decode(outputs))
+
+# case 6
+print('********* case 6 ***********')
+from transformers import GPT2Tokenizer, OPTForCausalLM
+model = OPTForCausalLM.from_pretrained(checkpoint)
+tokenizer = GPT2Tokenizer.from_pretrained(checkpoint)
+prompt = "Anti Vaccine Movemenet"
+inputs = tokenizer(prompt, return_tensors="pt").input_ids
+
+gen_tokens = model.generate(inputs,do_sample=True,temperature=0.9,max_length=100)
+gen_text = tokenizer.batch_decode(gen_tokens)[0]
+print('gen_text', gen_text)
+# generate_ids = model.generate(inputs,max_length=2000,early_stopping= True,do_sample=True,min_length=2000,top_k=125,top_p=0.92,temperature= 0.85,repetition_penalty=1.5,num_return_sequences=3)
+# for i, sample_output in enumerate(generate_ids):
+#     result = tokenizer.decode(sample_output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+#     print(result)