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import torch
import gc
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
import itertools
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 argparse
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 load_model(model_name):
return AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
def main(args):
# 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_arch = args.model_arch
if model_arch == "llama":
model_list = [
"meta-llama/Llama-2-7b-hf",
"codellama/CodeLlama-7b-hf",
"openlm-research/open_llama_7b",
"huggyllama/llama-7b",
"lmsys/vicuna-7b-v1.5",
"EleutherAI/llemma_7b",
"lmsys/vicuna-7b-v1.1",
"microsoft/Orca-2-7b",
"LLM360/Amber",
]
elif model_arch == "olmo":
model_list = [
"/scr/ahmedah/olmo/step1000_4B_tokens/seed_0_4B",
"/scr/ahmedah/olmo/step1000_4B_tokens/seed_42_4B",
]
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_list[0])
tokenizer.pad_token = tokenizer.eos_token
# Prepare dataset
if args.dataset == "wikitext":
eval_dataset = load_dataset("dlwh/wikitext_103_detokenized", split="test")
columns_ignored = ["text"]
else:
raise ValueError("main.py only supports wikitext.")
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="./hf_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]
# Load an initial model to create the trainer and dataloader
initial_model = load_model(model_list[0])
trainer = Trainer(model=initial_model, args=training_args, eval_dataset=lm_datasets)
eval_dataloader = trainer.get_test_dataloader(lm_datasets)
del initial_model
# Calculate the L2 distance between each pair of models
model_pairs = list(itertools.combinations(enumerate(model_list), 2))
# create directories for results
base_dir = f"{os.getcwd()}/results"
os.makedirs(base_dir, exist_ok=True)
imgs_dir = os.path.join(base_dir, "imgs")
os.makedirs(imgs_dir, exist_ok=True)
csv_dir = os.path.join(base_dir, "csv")
print(csv_dir)
os.makedirs(csv_dir, exist_ok=True)
current_model_a, current_model_b = None, None
current_model_a_name, current_model_b_name = None, None
for (idx_a, model_a_name), (idx_b, model_b_name) in tqdm(
model_pairs, desc="Model Interpolation"
):
if idx_a < idx_b:
perplexities = []
if current_model_a is None or current_model_a_name != model_a_name:
if current_model_a is not None:
del current_model_a
torch.cuda.empty_cache()
current_model_a = load_model(model_a_name).to("cpu")
current_model_a_name = model_a_name
if current_model_b is None or current_model_b_name != model_b_name:
if current_model_b is not None:
del current_model_b
torch.cuda.empty_cache()
current_model_b = load_model(model_b_name).to("cpu")
current_model_b_name = model_b_name
with torch.no_grad():
for alpha in tqdm(
alphas, desc=f" \n Alpha Perplexities for {model_a_name} and {model_b_name}"
):
interpolated_model = interpolate_models(
current_model_a, current_model_b, alpha, model_arch=model_arch
)
interpolated_model = interpolated_model.half().to(device)
start_time = time.time()
losses = []
for batch in tqdm(eval_dataloader, desc=f"\n Evaluating {alpha}"):
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 & collect free memory
del interpolated_model, input_ids, attention_mask, labels, outputs, loss
torch.cuda.empty_cache()
gc.collect()
# split on HF org so we don't get accidental
# directory error
model_a_name = model_a_name.split("/")[-1]
model_b_name = model_b_name.split("/")[-1]
# Save perplexities and model names to CSV
csv_filename = f"{csv_dir}/single_perplexities.csv"
csv_header = ["Model Pair"] + [f"Alpha {alpha}" for alpha in alphas]
if not os.path.exists(csv_filename):
with open(csv_filename, "w", newline="") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(csv_header)
with open(csv_filename, "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"single_alpha_vs_perplexity_{model_a_name}_vs_{model_b_name}.png"
plot_path = f"{imgs_dir}/{plot_filename}"
plt.savefig(plot_path, dpi=300, bbox_inches="tight")
plt.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Model Interpolation")
parser.add_argument(
"--dataset", choices=["wikitext", "json"], default="wikitext", help="Dataset to use"
)
parser.add_argument(
"--model_arch",
choices=["llama", "olmo"],
default="llama",
help="default model architecture to use",
)
args = parser.parse_args()
main(args)
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