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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
import os
from datasets import load_dataset
from tqdm import tqdm
import math
import matplotlib.pyplot as plt
import csv
from utils import interpolate_models
import time
import copy
import argparse
import glob
block_size = 512
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
def main(args):
start_time = time.time()
# Automatically detect CUDA device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
os.environ["WANDB_MODE"] = "disabled"
# Load models and tokenizer
os.environ["TOKENIZERS_PARALLELISM"] = "false"
model_list = [
"meta-llama/Llama-2-7b-hf",
"codellama/CodeLlama-7b-hf",
"lmsys/vicuna-7b-v1.5",
"EleutherAI/llemma_7b",
"LLM360/Amber",
]
model_pairs = [
(0, 2), # LLama2, Vicuna-1.5
(0, 1), # LLama2, CodeLlama
(0, 3), # LLama2, Lemma
(1, 3), # CodeLlama, Lemma
(0, 4), # LLama2, Amber
]
models = [
AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
for model_name in model_list
]
tokenizer = AutoTokenizer.from_pretrained(models[0].config._name_or_path)
tokenizer.pad_token = tokenizer.eos_token
# Scan the directory for JSON files based on the test name argument
columns_ignored = [
"text",
"added",
"id",
"lang",
"metadata",
"source",
"timestamp",
"subdomain",
]
json_dir = f"/juice4/scr4/nlp/model-tracing/dolma_program_languages/json_files_{args.test_name}"
json_files = glob.glob(f"{json_dir}/*.json")
save_dir = f"/juice4/scr4/nlp/model-tracing/dolma_program_languages/results_{args.test_name}"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for json_file in json_files:
print(f"Processing {json_file}")
# Prepare dataset
eval_dataset = load_dataset("json", data_files=json_file)
def tokenize_function(examples):
return tokenizer(examples["text"])
tokenized_datasets = eval_dataset.map(
tokenize_function, batched=True, num_proc=4, remove_columns=columns_ignored
)
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
batch_size=1,
num_proc=1,
)
# Prepare for evaluation. Batch size is optimized for ~7B model
training_args = TrainingArguments(
output_dir="./results",
per_device_eval_batch_size=3,
do_eval=True,
report_to=None,
dataloader_num_workers=4,
use_cpu=True,
)
alphas = [0.0, 0.3, 0.5, 0.7, 1.0]
model = copy.deepcopy(models[0])
trainer = Trainer(model=model, args=training_args, eval_dataset=lm_datasets)
print("create data loader")
eval_dataloader = trainer.get_test_dataloader(lm_datasets["train"])
for idx_a, idx_b in tqdm(model_pairs, desc="Model Interpolation"):
model_a = models[idx_a]
model_b = models[idx_b]
perplexities = []
model_a_name = model_a.config._name_or_path.split("/")[-1]
model_b_name = model_b.config._name_or_path.split("/")[-1]
for alpha in tqdm(
alphas, desc=f" \n Alpha Perplexities for {model_a_name} and {model_b_name}"
):
interpolated_model = interpolate_models(model_a, model_b, alpha)
# cast to bfloat16 before GPU
interpolated_model = interpolated_model.half().to(device)
start_time = time.time()
losses = []
for batch in tqdm(eval_dataloader, desc=f"\n Evaluating {alpha}"):
# HF Trainer finds GPU by default
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
labels = batch["labels"].to(device)
outputs = interpolated_model(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
)
loss = outputs.loss
losses.append(loss.item())
loss_mean = sum(losses) / len(losses)
print(f"Loss mean: {loss_mean}")
end_time = time.time()
execution_time = end_time - start_time
print(f"Execution time base: {execution_time} seconds")
perplexity = math.exp(loss_mean)
perplexities.append(perplexity)
# Move the model back to CPU
interpolated_model.to("cpu")
# Clear the GPU cache
del interpolated_model, input_ids, attention_mask, labels, outputs, loss
torch.cuda.empty_cache()
# Save perplexities and model names to CSV
json_filename = os.path.splitext(os.path.basename(json_file))[0]
csv_filename = f"perplexities_{json_filename}.csv"
csv_full_path = f"{save_dir}/{csv_filename}"
csv_header = ["Model Pair"] + [f"Alpha {alpha}" for alpha in alphas]
if not os.path.exists(csv_full_path):
with open(csv_full_path, "w", newline="") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(csv_header)
with open(csv_full_path, "a", newline="") as csvfile:
writer = csv.writer(csvfile)
model_pair = f"{model_a_name} vs {model_b_name}"
row = [model_pair] + perplexities
writer.writerow(row)
# Create the plot
plt.figure(figsize=(8, 6))
plt.plot(alphas, perplexities)
plt.xlabel("Alpha")
plt.ylabel("Perplexity")
plt.title(f"{model_a_name} (Left) vs {model_b_name} (Right)")
# Save the plot as a PNG file
plot_filename = (
f"alpha_vs_perplexity_{model_a_name}_vs_{model_b_name}_{json_filename}.png"
)
plot_full_path = f"{save_dir}/{plot_filename}"
plt.savefig(plot_full_path, dpi=300, bbox_inches="tight")
plt.close()
end_time = time.time()
execution_time = end_time - start_time
print(f"Total execution time: {execution_time} seconds")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Model Interpolation")
parser.add_argument(
"--test_name", type=str, default="js", help="Test name (e.g., cpp, python, js)"
)
args = parser.parse_args()
main(args)
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