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import os
import sys
import subprocess
from dotenv import find_dotenv, load_dotenv
from llm_toolkit.llm_utils import *
from llm_toolkit.translation_utils import *
def evaluate_model_all_epochs(
model,
tokenizer,
model_name,
adapter_path_base,
num_of_entries=-1,
result_file=None,
start_epoch=0,
end_epoch=-1,
):
new_env = os.environ.copy()
new_env["MODEL_NAME"] = model_name
model = model_name.split("/")[-1]
new_env["LOAD_IN_4BIT"] = "true" if load_in_4bit else "false"
if result_file is not None:
new_env["RESULTS_PATH"] = result_file
if adapter_path_base is None:
num_train_epochs = 0
print(f"No adapter path provided. Running with base model:{model_name}")
else:
if end_epoch >= 0:
num_train_epochs = end_epoch
print(f"Running from epoch {start_epoch} to {end_epoch}")
else:
# find subdirectories in adapter_path_base
# and sort them by epoch number
subdirs = [
d
for d in os.listdir(adapter_path_base)
if os.path.isdir(os.path.join(adapter_path_base, d))
]
subdirs = sorted(subdirs, key=lambda x: int(x.split("-")[-1]))
num_train_epochs = len(subdirs)
print(f"found {num_train_epochs} checkpoints: {subdirs}")
for i in range(start_epoch, num_train_epochs + 1):
print(f"Epoch {i}")
if i == 0:
os.unsetenv("ADAPTER_NAME_OR_PATH")
else:
adapter_path = adapter_path_base + "/" + subdirs[i - 1]
new_env["ADAPTER_NAME_OR_PATH"] = adapter_path
print(f"adapter path: {new_env.get('ADAPTER_NAME_OR_PATH')}")
log_file = "./logs/{}_epoch_{}.txt".format(model, i)
with open(log_file, "w") as f_obj:
subprocess.run(
f"python llm_toolkit/eval_shots.py {num_of_entries}",
shell=True,
env=new_env,
stdout=f_obj,
text=True,
)
if __name__ == "__main__":
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)
workding_dir = os.path.dirname(found_dotenv)
os.chdir(workding_dir)
sys.path.append(workding_dir)
print("workding dir:", workding_dir)
print(f"adding {workding_dir} to sys.path")
sys.path.append(workding_dir)
model_name = os.getenv("MODEL_NAME")
adapter_path_base = os.getenv("ADAPTER_PATH_BASE")
start_epoch = int(os.getenv("START_EPOCH", 0))
end_epoch = os.getenv("END_EPOCH", -1)
load_in_4bit = os.getenv("LOAD_IN_4BIT", "true").lower() == "true"
result_file = os.getenv("RESULTS_PATH", None)
num_of_entries = int(sys.argv[1]) if len(sys.argv) > 1 else -1
print(
model_name,
adapter_path_base,
load_in_4bit,
start_epoch,
result_file,
)
device = check_gpu()
is_cuda = torch.cuda.is_available()
print(f"Evaluating model: {model_name} on {device}")
if is_cuda:
torch.cuda.empty_cache()
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"(0) 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)
datasets = load_translation_dataset(data_path, tokenizer, num_shots=0)
print_row_details(datasets["test"].to_pandas())
if is_cuda:
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.")
evaluate_model_all_epochs(
model,
tokenizer,
model_name,
adapter_path_base,
start_epoch=start_epoch,
end_epoch=end_epoch,
load_in_4bit=load_in_4bit,
num_of_entries=num_of_entries,
result_file=result_file,
)
if is_cuda:
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.")
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