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gogo2/QUICK_FIX_REFERENCE.md
2025-12-08 19:57:47 +02:00

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# Quick Fix Reference - Backpropagation Errors
## What Was Fixed
**Inplace operation errors** - Changed residual connections to use new variable names
**Gradient accumulation** - Added explicit gradient clearing
**Error recovery** - Enhanced error handling to catch and recover from inplace errors
**Performance** - Disabled anomaly detection (2-3x speedup)
**Checkpoint race conditions** - Added delays and existence checks
**Batch validation** - Skip training when required data is missing
## Key Changes
### Transformer Layer (NN/models/advanced_transformer_trading.py)
```python
# ❌ BEFORE - Causes inplace errors
x = self.norm1(x + self.dropout(attn_output))
x = self.norm2(x + self.dropout(ff_output))
# ✅ AFTER - Uses new variables
x_new = self.norm1(x + self.dropout(attn_output))
x_out = self.norm2(x_new + self.dropout(ff_output))
```
### Gradient Clearing (NN/models/advanced_transformer_trading.py)
```python
# ✅ NEW - Explicit gradient clearing
self.optimizer.zero_grad(set_to_none=True)
for param in self.model.parameters():
if param.grad is not None:
param.grad = None
```
### Error Recovery (NN/models/advanced_transformer_trading.py)
```python
# ✅ NEW - Catch and recover from inplace errors
try:
total_loss.backward()
except RuntimeError as e:
if "inplace operation" in str(e):
self.optimizer.zero_grad(set_to_none=True)
return zero_loss_result
raise
```
## Testing
Run your realtime training and verify:
- ✅ No inplace operation errors
- ✅ Training completes without crashes
- ✅ Loss and accuracy show real values (not 0.0)
- ✅ GPU utilization increases during training
## If You Still See Errors
1. Check model is in training mode: `model.train()`
2. Clear GPU cache: `torch.cuda.empty_cache()`
3. Restart training from scratch (delete old checkpoints if needed)
## Files Modified
- `NN/models/advanced_transformer_trading.py` - Core fixes
- `ANNOTATE/core/real_training_adapter.py` - Validation and cleanup