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