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"""
PyTorch Training Benchmark (No OktoBLAS)
========================================
Training with PyTorch only - baseline comparison
pip install torch transformers datasets
Author: OktoSeek AI
"""
import os
import sys
import time
import json
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, get_linear_schedule_with_warmup
from datetime import datetime
print("=" * 70)
print("📊 PYTORCH ONLY - Testing without OktoBLAS")
print("=" * 70)
# Configuration
CONFIG = {
"model_name": "gpt2",
"dataset_path": "D:/model_trainee/sharegpt_chat.jsonl",
"max_examples": 10000,
"max_length": 128,
"batch_size": 8,
"epochs": 1,
"learning_rate": 5e-5,
"warmup_steps": 100,
"log_every": 10,
"eval_every": 500,
"device": "cuda" if torch.cuda.is_available() else "cpu",
}
class ChatDataset(Dataset):
def __init__(self, data, tokenizer, max_length):
self.data = data
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
item = self.data[idx]
# Handle different formats
if "chat" in item:
# ShareGPT format: [{"role": "user", "content": "..."}, ...]
chat = item["chat"]
text = " ".join([c.get("content", "")[:200] for c in chat[:2]])
elif "conversations" in item:
text = " ".join([c.get("value", "") for c in item["conversations"][:2]])
elif "text" in item:
text = item["text"]
elif "instruction" in item and "output" in item:
text = f"{item['instruction']} {item['output']}"
elif "question" in item and "response" in item:
text = f"{item['question']} {item['response']}"
else:
text = str(item)[:500]
encoded = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
padding="max_length",
return_tensors="pt"
)
input_ids = encoded["input_ids"].squeeze()
attention_mask = encoded["attention_mask"].squeeze()
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": input_ids.clone()
}
def load_dataset(path, max_examples):
"""Load JSONL dataset"""
data = []
print(f"\n📂 Loading dataset from {path}")
with open(path, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
if i >= max_examples:
break
try:
data.append(json.loads(line))
except:
continue
print(f"✅ Loaded {len(data)} examples")
return data
def format_time(seconds):
"""Format seconds to human readable"""
if seconds < 60:
return f"{seconds:.1f}s"
elif seconds < 3600:
return f"{seconds/60:.1f}m"
else:
return f"{seconds/3600:.1f}h"
def train():
print("\n" + "=" * 70)
print("📊 TRAINING WITH PYTORCH ONLY (BASELINE)")
print("=" * 70)
print(f"Model: {CONFIG['model_name']}")
print(f"Device: {CONFIG['device']}")
print(f"Examples: {CONFIG['max_examples']}")
print(f"Batch size: {CONFIG['batch_size']}")
print(f"Max length: {CONFIG['max_length']}")
print("=" * 70)
# Load tokenizer and model
print("\n📦 Loading model...")
tokenizer = AutoTokenizer.from_pretrained(CONFIG["model_name"])
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(CONFIG["model_name"])
model.to(CONFIG["device"])
model.train()
# Count parameters
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"✅ Model loaded: {total_params/1e6:.1f}M parameters ({trainable_params/1e6:.1f}M trainable)")
# Load dataset
data = load_dataset(CONFIG["dataset_path"], CONFIG["max_examples"])
dataset = ChatDataset(data, tokenizer, CONFIG["max_length"])
dataloader = DataLoader(dataset, batch_size=CONFIG["batch_size"], shuffle=True, num_workers=0)
# Optimizer and scheduler
optimizer = torch.optim.AdamW(model.parameters(), lr=CONFIG["learning_rate"])
total_steps = len(dataloader) * CONFIG["epochs"]
scheduler = get_linear_schedule_with_warmup(optimizer, CONFIG["warmup_steps"], total_steps)
# Training metrics
global_step = 0
total_loss = 0
start_time = time.time()
step_times = []
losses = []
print(f"\n🏋️ Starting training... ({len(dataloader)} batches per epoch)")
print("-" * 70)
for epoch in range(CONFIG["epochs"]):
epoch_start = time.time()
epoch_loss = 0
for batch_idx, batch in enumerate(dataloader):
step_start = time.time()
# Move to device
input_ids = batch["input_ids"].to(CONFIG["device"])
attention_mask = batch["attention_mask"].to(CONFIG["device"])
labels = batch["labels"].to(CONFIG["device"])
# Forward pass (PyTorch only)
optimizer.zero_grad()
outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
# Backward pass
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
# Metrics
step_time = time.time() - step_start
step_times.append(step_time)
total_loss += loss.item()
epoch_loss += loss.item()
losses.append(loss.item())
global_step += 1
# Calculate speed
examples_per_sec = CONFIG["batch_size"] / step_time
# Log
if global_step % CONFIG["log_every"] == 0:
avg_loss = total_loss / global_step
avg_step_time = sum(step_times[-100:]) / len(step_times[-100:])
eta_seconds = avg_step_time * (total_steps - global_step)
# Calculate approximate TFLOPS (for GPT-2 small)
flops_per_step = 6 * total_params * CONFIG["batch_size"] * CONFIG["max_length"]
tflops = flops_per_step / step_time / 1e12
print(f"[PyTorch] Step {global_step:5d}/{total_steps} | "
f"Loss: {loss.item():.4f} | "
f"Avg: {avg_loss:.4f} | "
f"Speed: {examples_per_sec:.1f} ex/s | "
f"TFLOPS: {tflops:.2f} | "
f"ETA: {format_time(eta_seconds)}")
# Epoch summary
epoch_time = time.time() - epoch_start
epoch_avg_loss = epoch_loss / len(dataloader)
epoch_speed = len(dataset) / epoch_time
print("-" * 70)
print(f"📊 Epoch {epoch+1}/{CONFIG['epochs']} Complete")
print(f" Loss: {epoch_avg_loss:.4f}")
print(f" Time: {format_time(epoch_time)}")
print(f" Speed: {epoch_speed:.1f} examples/sec")
print("-" * 70)
# Final summary
total_time = time.time() - start_time
final_avg_loss = total_loss / global_step
overall_speed = CONFIG["max_examples"] / total_time
print("\n" + "=" * 70)
print("🏆 TRAINING COMPLETE - PYTORCH ONLY (BASELINE)")
print("=" * 70)
print(f"Total time: {format_time(total_time)}")
print(f"Final loss: {final_avg_loss:.4f}")
print(f"Average speed: {overall_speed:.1f} examples/sec")
print(f"Total steps: {global_step}")
# Save results
results = {
"backend": "pytorch",
"model": CONFIG["model_name"],
"examples": CONFIG["max_examples"],
"batch_size": CONFIG["batch_size"],
"total_time_seconds": total_time,
"final_loss": final_avg_loss,
"examples_per_second": overall_speed,
"total_steps": global_step,
"timestamp": datetime.now().isoformat()
}
result_file = "training_result_pytorch.json"
with open(result_file, "w") as f:
json.dump(results, f, indent=2)
print(f"\n📁 Results saved to {result_file}")
return results
if __name__ == "__main__":
results = train()
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