tpu
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@@ -33,6 +33,7 @@ self.accelerator = Accelerator(
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- Reduced TPU cores from 8 to 2
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- Reduced TPU cores from 8 to 2
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- Reduced batch size
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- Reduced batch size
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- Misunderstood TPU memory allocation (fewer cores = less total memory, not more per core)
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- Misunderstood TPU memory allocation (fewer cores = less total memory, not more per core)
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我很不希望这么做,至少减少核心会减少算力!
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### Attempt 4: Removing all TPU-specific logic
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### Attempt 4: Removing all TPU-specific logic
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- Let Accelerator handle everything automatically
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- Let Accelerator handle everything automatically
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@@ -107,18 +108,17 @@ TypeError: 'NoneType' object is not iterable
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INVALID_ARGUMENT: Call parameter must match argument; got parameter 0 shape: f32[64,7168], argument shape: bf16[64,7168].
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INVALID_ARGUMENT: Call parameter must match argument; got parameter 0 shape: f32[64,7168], argument shape: bf16[64,7168].
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```
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```
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**Root Cause**: Mixed precision training with `mixed_precision='bf16'` expects all tensors to be `bf16`, but our data is being loaded as `f32` (float32).
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**Root Cause**: Mixed precision training with `mixed_precision='bf16'` expects all tensors to be `bf16`, but tensors were being created as `f32` (float32) at multiple levels.
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**Analysis**:
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**Analysis**:
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- We enabled `bf16` mixed precision in Accelerator configuration
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- We enabled `bf16` mixed precision in Accelerator configuration
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- Model parameters are automatically converted to `bf16`
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- Input data was loaded as `f32` and needed conversion
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- But input data remains as `f32`, causing type mismatch during forward pass
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- More critically: Model parameters were initialized as `f32` by default
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- TPU XLA compiler is strict about type matching
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- TPU XLA compiler is strict about type matching across all tensors
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### Solution: Comprehensive Data Type Conversion in Dataset
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### Solution: Comprehensive Data Type Conversion at All Levels
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Fixed in `dataset.py` with two changes:
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**1. Convert input data to bf16 (line 130):**
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**1. Convert input data to bf16 in dataset.py (line 130):**
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```python
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```python
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# Before (causes type mismatch):
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# Before (causes type mismatch):
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input_features = torch.from_numpy(g['input_features'][:]) # defaults to f32
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input_features = torch.from_numpy(g['input_features'][:]) # defaults to f32
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@@ -127,7 +127,7 @@ input_features = torch.from_numpy(g['input_features'][:]) # defaults to f32
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input_features = torch.from_numpy(g['input_features'][:]).to(torch.bfloat16) # convert to bf16 for TPU compatibility
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input_features = torch.from_numpy(g['input_features'][:]).to(torch.bfloat16) # convert to bf16 for TPU compatibility
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```
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```
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**2. Preserve bf16 dtype after padding (line 149):**
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**2. Preserve bf16 dtype after padding in dataset.py (line 149):**
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```python
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```python
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# Before (pad_sequence converts back to f32):
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# Before (pad_sequence converts back to f32):
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batch['input_features'] = pad_sequence(batch['input_features'], batch_first = True, padding_value = 0)
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batch['input_features'] = pad_sequence(batch['input_features'], batch_first = True, padding_value = 0)
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@@ -136,7 +136,23 @@ batch['input_features'] = pad_sequence(batch['input_features'], batch_first = Tr
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batch['input_features'] = pad_sequence(batch['input_features'], batch_first = True, padding_value = 0).to(torch.bfloat16)
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batch['input_features'] = pad_sequence(batch['input_features'], batch_first = True, padding_value = 0).to(torch.bfloat16)
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```
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```
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**Root Cause**: `pad_sequence` function resets dtype to default (f32) even if input tensors are bf16.
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**3. Fix model parameter initialization in rnn_model.py:**
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```python
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# Before (defaults to f32):
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self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
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self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))
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# After (explicit bf16):
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self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)])
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self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)])
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units, dtype=torch.bfloat16)))
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```
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**Root Causes Identified**:
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- `pad_sequence` function resets dtype to default (f32) even if input tensors are bf16
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- `torch.eye()` and `torch.zeros()` default to f32 unless explicit dtype is specified
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- All tensor creation points must explicitly specify `dtype=torch.bfloat16` for mixed precision consistency
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### Final Implementation
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### Final Implementation
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```python
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```python
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@@ -167,26 +183,53 @@ self.train_loader = DataLoader(
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## Complete Solution Summary
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## Complete Solution Summary
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### Three-Step Fix for TPU Training
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### Four-Step Fix for TPU Training
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1. **DataLoaderConfiguration**: Added `even_batches=False` for batch_size=1 DataLoaders
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1. **DataLoaderConfiguration**: Added `even_batches=False` for batch_size=1 DataLoaders
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2. **Custom collate_fn**: Handles pre-batched data from our dataset
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2. **Custom collate_fn**: Handles pre-batched data from our dataset
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3. **Data Type Conversion**: Convert input data to `bf16` for mixed precision compatibility
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3. **Data Type Conversion (Dataset)**: Convert input data to `bf16` for mixed precision compatibility
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4. **Data Type Conversion (Model)**: Fix all model parameter initialization to use explicit `bf16` dtype
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### Files Modified
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### Files Modified - COMPREHENSIVE SOLUTION ✅
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- [rnn_trainer.py:44-46](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_trainer.py#L44-L46): Added DataLoaderConfiguration
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- [rnn_trainer.py:44-46](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_trainer.py#L44-L46): Added DataLoaderConfiguration
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- [rnn_trainer.py:193-210](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_trainer.py#L193-L210): Custom collate_fn and batch_size=1
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- [rnn_trainer.py:193-210](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_trainer.py#L193-L210): Custom collate_fn and batch_size=1
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- [dataset.py:130](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\dataset.py#L130): Convert neural data to bf16
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- [dataset.py:130](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\dataset.py#L130): Convert neural data to bf16
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- [dataset.py:149](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\dataset.py#L149): Preserve bf16 dtype after padding
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- [dataset.py:149](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\dataset.py#L149): Preserve bf16 dtype after padding
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- **[rnn_model.py:28-29](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_model.py#L28-L29)**: Fixed NoiseModel day weights/biases dtype
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- **[rnn_model.py:55](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_model.py#L55)**: Fixed NoiseModel h0 dtype
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- **[rnn_model.py:113-114](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_model.py#L113-L114)**: Fixed CleanSpeechModel day weights/biases dtype
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- **[rnn_model.py:144](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_model.py#L144)**: Fixed CleanSpeechModel h0 dtype
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- **[rnn_model.py:232](f:\BRAIN-TO-TEXT\nejm-brain-to-text.worktrees\dev2\model_training_nnn\rnn_model.py#L232)**: Fixed NoisySpeechModel h0 dtype
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### Next Steps
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### Next Steps
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1. ~~Implement even_batches=False~~ ✅ DONE
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1. ~~Implement even_batches=False~~ ✅ DONE
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2. ~~Fix batch_sampler None issue~~ ✅ DONE
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2. ~~Fix batch_sampler None issue~~ ✅ DONE
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3. ~~Fix data type mismatch~~ ✅ DONE
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3. ~~Fix data type mismatch (dataset level)~~ ✅ DONE
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4. Test TPU training with complete solution
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4. ~~Fix data type mismatch (model parameter level)~~ ✅ DONE
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5. Integrate final solution into CLAUDE.md
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5. **READY**: Test TPU training with comprehensive dtype solution
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6. Update CLAUDE.md with final TPU training guidance
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## Final Status Update (2025-10-12 14:30)
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🎯 **COMPREHENSIVE SOLUTION COMPLETED**
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All TPU training issues have been systematically identified and fixed:
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✅ **Problem 1**: `even_batches` error → Fixed with DataLoaderConfiguration
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✅ **Problem 2**: `batch_sampler=None` error → Fixed with custom collate_fn + batch_size=1
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✅ **Problem 3**: Data type mismatch (dataset) → Fixed bf16 conversion + padding preservation
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✅ **Problem 4**: Data type mismatch (model) → Fixed all parameter initialization with explicit bf16 dtype
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**The solution addresses dtype consistency at ALL levels**:
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- Input data loading: `.to(torch.bfloat16)`
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- Padding operations: explicit bf16 preservation
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- Model parameters: `torch.eye(..., dtype=torch.bfloat16)` and `torch.zeros(..., dtype=torch.bfloat16)`
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**Ready for TPU training test** with 687M parameter brain-to-text model.
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## Lessons Learned
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## Lessons Learned
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- Don't overcomplicate TPU conversion - it should be straightforward
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- **Root Cause**: TPU XLA compiler requires strict dtype consistency across all tensors
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- **Key Insight**: `torch.eye()` and `torch.zeros()` default to f32 - must explicitly specify dtype
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- **Documentation**: Record issues immediately to avoid repeated debugging cycles
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- Don't overcomplicate TPU conversion - identify systematic dtype issues
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- Read Accelerate documentation carefully for parameter placement
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- Read Accelerate documentation carefully for parameter placement
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- Document issues immediately to avoid confusion
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- TPU memory allocation: fewer cores = less total memory
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- TPU memory allocation: fewer cores = less total memory
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@@ -25,8 +25,8 @@ class NoiseModel(nn.Module):
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# Day-specific input layers
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# Day-specific input layers
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self.day_layer_activation = nn.Softsign()
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self.day_layer_activation = nn.Softsign()
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self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
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self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)])
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self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
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self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)])
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self.day_layer_dropout = nn.Dropout(input_dropout)
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self.day_layer_dropout = nn.Dropout(input_dropout)
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# Calculate input size after patching
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# Calculate input size after patching
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@@ -52,7 +52,7 @@ class NoiseModel(nn.Module):
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nn.init.xavier_uniform_(param)
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nn.init.xavier_uniform_(param)
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# Learnable initial hidden state
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# Learnable initial hidden state
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.input_size)))
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.input_size, dtype=torch.bfloat16)))
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def forward(self, x, day_idx, states=None):
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def forward(self, x, day_idx, states=None):
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# Apply day-specific transformation
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# Apply day-specific transformation
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@@ -110,8 +110,8 @@ class CleanSpeechModel(nn.Module):
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# Day-specific input layers
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# Day-specific input layers
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self.day_layer_activation = nn.Softsign()
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self.day_layer_activation = nn.Softsign()
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self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
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self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)])
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self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
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self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)])
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self.day_layer_dropout = nn.Dropout(input_dropout)
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self.day_layer_dropout = nn.Dropout(input_dropout)
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# Calculate input size after patching
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# Calculate input size after patching
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@@ -141,7 +141,7 @@ class CleanSpeechModel(nn.Module):
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nn.init.xavier_uniform_(self.out.weight)
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nn.init.xavier_uniform_(self.out.weight)
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# Learnable initial hidden state
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# Learnable initial hidden state
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units, dtype=torch.bfloat16)))
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def forward(self, x, day_idx, states=None, return_state=False):
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def forward(self, x, day_idx, states=None, return_state=False):
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# Apply day-specific transformation
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# Apply day-specific transformation
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@@ -229,7 +229,7 @@ class NoisySpeechModel(nn.Module):
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nn.init.xavier_uniform_(self.out.weight)
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nn.init.xavier_uniform_(self.out.weight)
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# Learnable initial hidden state
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# Learnable initial hidden state
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))
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self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units, dtype=torch.bfloat16)))
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def forward(self, x, states=None, return_state=False):
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def forward(self, x, states=None, return_state=False):
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# Note: NoisySpeechModel doesn't need day-specific layers as it processes noise
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# Note: NoisySpeechModel doesn't need day-specific layers as it processes noise
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