mistral_ft.py

model_name = "mistralai/Mistral-7B-Instruct-v0.3"
#model_name = "bigcode/starcoder2-7b"
#model_name =  "dorkai/codeX-1.0" #"Alibaba-NLP/gte-Qwen1.5-7B-instruct"  #"google/flan-t5-small" #"microsoft/Phi-3-medium-128k-instruct" #"google/gemma-2-9b-it" # "meta-llama/CodeLlama-7b-hf" #"deepseek-ai/DeepSeek-Coder-V2-Instruct" #"deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
out_name = "HPC_2_mistral_iffp_20k_3" #"meta-llama/Meta-Llama-3-8B" #"tiiuae/falcon-40b" #"Phind/Phind-CodeLlama-34B-v2" # "deepseek-ai/DeepSeek-Coder-V2-Instruct" # 
from datasets import load_dataset, Dataset
import pandas as pd
import json
import traceback
import torch
# import json
# filepath = "/kaggle/input/code-sim-try1/mutated_graph_all_lang_eq.json"
# examples = []
# with open(filepath, 'r') as file:
    # for l in file:
        # examples.append(json.loads(l))

# len(examples)
# examples[0]

# instruct_tune_dataset = load_dataset("mosaicml/instruct-v3",cache_dir = "/scratch/scai/mtech/aib222688/HF")
# instruct_tune_dataset = instruct_tune_dataset.filter(lambda x: x["source"] == "dolly_hhrlhf")

traindataset_file = "./dataset_lfs/allpairs_data_large.json"
valdataset_file = "./dataset_lfs/allpairs_data_val.json"
testdataset_file = "./llm_for_code/datasets/codecontests/verified_iffp_900.json"
traindata = []

with open(traindataset_file, 'r') as file:
    for l in file:
        traindata.append(json.loads(l))
        
valdata = []

with open(valdataset_file, 'r') as file:
    for l in file:
        valdata.append(json.loads(l))
        
testdata = []


with open(testdataset_file, 'r') as file:
    for l in file:
        testdata.append(json.loads(l))
print(len(traindata), len(valdata), len(testdata))

def create_prompt(sample):
    bos_token = "<s>"
    original_system_message = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
    system_message = "Use the provided input to create an instruction that could have been used to generate the response with an LLM."
    #print(sample['prompt'])
    response = sample["prompt"].replace(original_system_message, "").replace("\n\n### Instruction\n", "").replace("\n### Response\n", "").strip()
    input = sample["response"]
    eos_token = "</s>"

    full_prompt = ""
    full_prompt += bos_token
    full_prompt += "### Instruction:"
    full_prompt += "\n" + system_message
    full_prompt += "\n\n### Input:"
    full_prompt += "\n" + input
    full_prompt += "\n\n### Response:"
    full_prompt += "\n" + response
    full_prompt += eos_token

    return full_prompt
  

file_path = './prompt_file.txt'
file_content = ""
with open(file_path, 'r') as file:
    file_content = file.read()


def create_prompt(pair):
    bos_token = "<s>"
    eos_token = "</s>"
    if pair['prog1']['probid'] == pair['prog2']['probid']:
        response = "Yes"
    else:
        response = "No"
        
    full_prompt = ""
    full_prompt += bos_token
    full_prompt += file_content + pair['prog1']['scode'] + "\nProgram 2:" 
    full_prompt += pair['prog2']['scode']+ "\n### Response:"
    full_prompt += "\n" + response
    full_prompt += eos_token

    return full_prompt
  

  
traindataset = Dataset.from_pandas(pd.DataFrame(traindata))
valdataset = Dataset.from_pandas(pd.DataFrame(valdata))
testdataset = Dataset.from_pandas(pd.DataFrame(testdata))   

instruct_tune_dataset = {"train": traindataset,
                        "val" : valdataset,
                        "test" : testdataset}
 
   
   
print(create_prompt(instruct_tune_dataset["train"][1]))



import os
import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer

nf4_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map='auto',
    quantization_config=nf4_config,
    use_cache=True,
    cache_dir = "/scratch/scai/mtech/aib222688/HF",
    #trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

peft_config = LoraConfig(
    lora_alpha=16,
    lora_dropout=0.1,
    r=64,
    bias="none",
    task_type="CAUSAL_LM"
)

import peft
model = peft.prepare_model_for_kbit_training(model)
model = peft.get_peft_model(model, peft_config)

args = TrainingArguments(
  output_dir = "./"+out_name+"/",
  num_train_epochs=4,
  #max_steps = 10, 
  per_device_train_batch_size = 4,
  warmup_steps = 0.03,
  logging_steps=10,
  #save_strategy="epoch",
  #evaluation_strategy="epoch",
  evaluation_strategy="steps",
  eval_steps=100,
  save_steps=500,
  learning_rate=2e-4,
  bf16=True,
  lr_scheduler_type='constant',
)

max_seq_length = 2048

trainer = SFTTrainer(
  model=model,
  peft_config=peft_config,
  max_seq_length=max_seq_length,
  tokenizer=tokenizer,
  packing=True,
  formatting_func=create_prompt,
  args=args,
  train_dataset=instruct_tune_dataset["train"],
  eval_dataset=instruct_tune_dataset["val"]
)

# predictions, labels, _ = trainer.predict(instruct_tune_dataset["train"][1])
    
    # # Convert predictions and labels to text
# predicted_texts = tokenizer.batch_decode(predictions.argmax(axis=-1), skip_special_tokens=True)
# true_texts = tokenizer.batch_decode(labels, skip_special_tokens=True)
# print(f" Pred {predicted_texts}, True {true_texts}")

import time
start = time.time()
try:
    trainer.train()
except Exception as e:
    print(f"ERRORR\n {e}")
    traceback.print_exc()

print(time.time()- start)


def generate_response(prompt, model):
    encoded_input = tokenizer(prompt,  return_tensors="pt", add_special_tokens=True)
    model_inputs = encoded_input.to('cuda')

    generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)

    decoded_output = tokenizer.batch_decode(generated_ids)

    return decoded_output[0].replace(prompt, "")
    


try:
    print(generate_response("### Instruction:\nUse the provided input to create an instruction that could have been used to generate the response with an LLM.### Input:\nThere are more than 12,000 species of grass. The most common is Kentucky Bluegrass, because it grows quickly, easily, and is soft to the touch. Rygrass is shiny and bright green colored. Fescues are dark green and shiny. Bermuda grass is harder but can grow in drier soil.\n\n### Response:", trainer.model))
except Exception as e:
    print(f"ERRORR\n {e}")
    traceback.print_exc()


try:
    trainer.save_model("./"+out_name)
except Exception as e:
    print(f"ERRORR\n {e}")
    traceback.print_exc()


try:
    torch.save(model.state_dict(), "./"+out_name+"/"+'model_state_dict.pkl')
except Exception as e:
    print(f"ERRORR\n {e}")
    traceback.print_exc()
    
try:
    tester = SFTTrainer(
      model=trainer.model,
      peft_config=peft_config,
      max_seq_length=max_seq_length,
      tokenizer=tokenizer,
      packing=True,
      formatting_func=create_prompt,
      args=args,
      train_dataset=instruct_tune_dataset["train"],
      eval_dataset=instruct_tune_dataset["test"]
    )
    print(tester.evaluate())
    
except Exception as e:
    print(f"ERRORR\n {e}")
    traceback.print_exc()

trainer.push_to_hub("FT/mistral-instruct-generation")