Remove ultra_optimized.py - cleanup for OS launch

ultra_optimized.py

@@ -1,125 +0,0 @@
-#!/usr/bin/env python3
-"""
-BitTransformerLM ULTRA OPTIMIZED - 680M Parameters
-==================================================
-
-FINAL ATTEMPT: Optimized for memory with shorter sequences and minimal telemetry.
-This WILL work because we've proven model creation works perfectly!
-"""
-
-import torch
-import torch.nn.functional as F
-import logging
-from datetime import datetime
-
-from bit_transformer.model import BitTransformerLM
-from bit_transformer.utils import set_dropout
-
-logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(message)s')
-logger = logging.getLogger(__name__)
-
-
-def main():
-    """Ultra-optimized 680M parameter training that WILL work!"""
-    
-    logger.info("🔥 ULTRA OPTIMIZED 680M PARAMETER BITTRANSFORMERLM!")
-    logger.info("=" * 60)
-    
-    # ULTRA OPTIMIZED CONFIG - shorter sequences!
-    config = {
-        "d_model": 1536,
-        "nhead": 24, 
-        "num_layers": 24,
-        "dim_feedforward": 6144,
-        "max_seq_len": 512,  # MUCH shorter sequences!
-        "lambda_K": 0.1,     # Reduce telemetry impact
-        "lambda_C": 0.1,
-        "lambda_S": 0.1,
-        "reversible": True,
-        "use_checkpoint": True,
-        "use_autocast": True,
-        "chunk_size": 128,   # Chunked attention for memory
-        "full_attn_logging": False,  # No attention logging
-    }
-    
-    logger.info("🏗️ Creating ULTRA OPTIMIZED 680M model...")
-    for k, v in config.items():
-        logger.info(f"  {k}: {v}")
-    
-    # Create and move model
-    model = BitTransformerLM(**config)
-    params = sum(p.numel() for p in model.parameters())
-    logger.info(f"✅ Model: {params:,} parameters ({params/1e6:.1f}M)")
-    
-    model = model.cuda()
-    logger.info("✅ Model on GPU")
-    
-    # Ultra simple training data
-    logger.info("🎯 Starting ULTRA OPTIMIZED training...")
-    model.train()
-    set_dropout(model, 0.1)
-    
-    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
-    
-    seq_len = 512  # Much shorter!
-    batch_size = 1
-    
-    for step in range(20):  # Just prove it works!
-        # Create simple bit pattern
-        pattern = ([0, 1] * (seq_len // 2))[:seq_len]
-        input_ids = torch.tensor(pattern[:-1], dtype=torch.long).unsqueeze(0).cuda()
-        labels = torch.tensor(pattern[1:], dtype=torch.long).unsqueeze(0).cuda()
-        
-        optimizer.zero_grad()
-        
-        try:
-            # Forward with autocast
-            with torch.amp.autocast('cuda'):
-                outputs = model(input_ids)
-                
-                if isinstance(outputs, tuple):
-                    logits, telemetry = outputs
-                else:
-                    logits = outputs
-                    telemetry = {}
-                
-                loss = F.cross_entropy(logits.view(-1, 2), labels.view(-1))
-            
-            # Backward
-            loss.backward()
-            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-            optimizer.step()
-            
-            if step % 5 == 0:
-                memory_used = torch.cuda.memory_allocated(0) / (1024**3)
-                logger.info(
-                    f"Step {step:2d} | "
-                    f"Loss: {loss.item():.4f} | "
-                    f"Mem: {memory_used:.1f}GB | "
-                    f"K: {telemetry.get('negentropy', 0):.3f} | "
-                    f"SUCCESS! 🎉"
-                )
-            
-        except torch.OutOfMemoryError as e:
-            memory_used = torch.cuda.memory_allocated(0) / (1024**3)
-            logger.error(f"OOM at step {step}, Memory: {memory_used:.1f}GB")
-            logger.error(f"Error: {e}")
-            break
-        except Exception as e:
-            logger.error(f"Other error at step {step}: {e}")
-            break
-    else:
-        logger.info("🏆 SUCCESS! 680M PARAMETER MODEL TRAINED SUCCESSFULLY!")
-        logger.info("🚀 HARDWARE CAN ABSOLUTELY HANDLE THIS!")
-        logger.info("✅ Ready for proper multi-GPU implementation!")
-        return True
-    
-    return False
-
-
-if __name__ == "__main__":
-    success = main()
-    if success:
-        print("\n🎉 MISSION ACCOMPLISHED! 680M parameters PROVEN TO WORK!")
-    else:
-        print("\n🔧 Need further optimization...")