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Single-process version of train.py for Windows testing
"""
import os
import datetime
import torch
import torch.nn.functional as F
import argparse
from torch.optim import AdamW
from transformers import AutoConfig
from model import Llama
from utils import set_all_seed, print
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Training script for LLaMA model")
# Model arguments
parser.add_argument("--model_name", type=str, default="HuggingFaceTB/SmolLM-360M-Instruct")
parser.add_argument("--num_hidden_layers", type=int, default=32)
parser.add_argument("--num_attention_heads", type=int, default=16)
parser.add_argument("--num_key_value_heads", type=int, default=4)
# Training arguments
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--learning_rate", type=float, default=3e-4)
parser.add_argument("--seq_len", type=int, default=32)
parser.add_argument("--micro_batch_size", type=int, default=1)
# Logging arguments
parser.add_argument("--run_name", type=str, default="default_run")
parser.add_argument("--use_wandb", action="store_true")
args = parser.parse_args()
# Set environment variables
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
device = torch.device("cpu")
dtype = torch.bfloat16
set_all_seed(args.seed)
model_config = AutoConfig.from_pretrained(args.model_name)
model_config.num_hidden_layers = args.num_hidden_layers
model_config.num_attention_heads = args.num_attention_heads
model_config.num_key_value_heads = args.num_key_value_heads
model_config.max_position_embeddings = args.seq_len
print(f"Loading model with {args.num_hidden_layers} layers, {args.num_attention_heads} heads...")
model = Llama(config=model_config)
model.to(dtype).to(device)
model.train()
print(f"Model loaded. Vocab size: {model_config.vocab_size}, Hidden size: {model_config.hidden_size}")
optimizer = AdamW(model.parameters(), lr=args.learning_rate)
# Create dummy data
input_ids = torch.randint(0, model_config.vocab_size, (args.micro_batch_size, args.seq_len), device=device)
target_ids = torch.randint(0, model_config.vocab_size, (args.micro_batch_size, args.seq_len), device=device)
print(f"Training step with batch_size={args.micro_batch_size}, seq_len={args.seq_len}")
# Training step
optimizer.zero_grad()
# Forward pass
outputs = model(input_ids=input_ids)
# Compute loss
target_ids = target_ids.reshape(-1)
outputs = outputs.view(-1, model_config.vocab_size)
loss = F.cross_entropy(outputs, target_ids)
# Backward pass
loss.backward()
# Optimizer step
optimizer.step()
print(f"Loss: {loss.item():.4f}")
print(f"Output shape: {outputs.shape}")
print("Training step completed successfully!")
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