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import os
import sys
import torch
from dotenv import find_dotenv, load_dotenv
from llamafactory.chat import ChatModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
found_dotenv = find_dotenv(".env")
if len(found_dotenv) == 0:
found_dotenv = find_dotenv(".env.example")
print(f"loading env vars from: {found_dotenv}")
load_dotenv(found_dotenv, override=False)
path = os.path.dirname(found_dotenv)
print(f"Adding {path} to sys.path")
sys.path.append(path)
from llm_toolkit.translation_utils import *
model_name = os.getenv("MODEL_NAME")
adapter_name_or_path = os.getenv("ADAPTER_NAME_OR_PATH")
load_in_4bit = os.getenv("LOAD_IN_4BIT") == "true"
data_path = os.getenv("DATA_PATH")
results_path = os.getenv("RESULTS_PATH")
print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)
def load_model(
model_name,
max_seq_length=2048,
dtype=torch.bfloat16,
load_in_4bit=False,
adapter_name_or_path=None,
):
print(f"loading model: {model_name}")
if adapter_name_or_path:
template = "llama3" if "llama-3" in model_name.lower() else "chatml"
args = dict(
model_name_or_path=model_name,
adapter_name_or_path=adapter_name_or_path, # load the saved LoRA adapters
template=template, # same to the one in training
finetuning_type="lora", # same to the one in training
quantization_bit=4 if load_in_4bit else None, # load 4-bit quantized model
)
chat_model = ChatModel(args)
return chat_model.engine.model, chat_model.engine.tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
bnb_config = BitsAndBytesConfig(
load_in_4bit=load_in_4bit,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=False,
bnb_4bit_compute_dtype=dtype,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
torch_dtype=dtype,
trust_remote_code=True,
device_map="auto",
)
return model, tokenizer
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(1) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
model, tokenizer = load_model(
model_name, load_in_4bit=load_in_4bit, adapter_name_or_path=adapter_name_or_path
)
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(2) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
datasets = load_translation_dataset(data_path, tokenizer)
print("Evaluating model: " + model_name)
predictions = eval_model(model, tokenizer, datasets["test"])
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(3) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
if adapter_name_or_path is not None:
model_name += "_" + adapter_name_or_path.split("/")[-1]
save_results(
model_name,
results_path,
datasets["test"],
predictions,
debug=True,
)
metrics = calc_metrics(datasets["test"]["english"], predictions, debug=True)
print(metrics)
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