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tpu

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Zchen
2025-10-12 22:52:38 +08:00
parent 69e3892c27
commit 5c941d9efa
3 changed files with 47 additions and 12 deletions
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31
TPU_ISSUES_RECORD.md
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@@ -319,6 +319,37 @@ if xm.get_xla_supported_devices():
**预期改进**: XLA图编译时间从5-15分钟缩短到2-8分钟 **预期改进**: XLA图编译时间从5-15分钟缩短到2-8分钟
## New Issue: DType Mismatch in adjusted_lens Calculation (2025-10-12 16:45)
### Error Description
```
Status: INVALID_ARGUMENT: Call parameter must match argument; got parameter 1 shape: f32[21504], argument shape: bf16[21504].
```
### Root Cause
The `adjusted_lens` calculation was causing dtype mismatches in TPU mixed precision (bf16) training. When `n_time_steps` is processed under `accelerator.autocast()`, it becomes bfloat16, but the arithmetic operations were creating float32 results.
### Problem Code
```python
# Before (causes f32/bf16 mismatch):
adjusted_lens = ((n_time_steps - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
```
### Solution
Explicit float conversion before dtype casting:
```python
# After (explicit dtype control):
adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
```
### Fixed Locations
- `rnn_trainer.py:577` - Training loop
- `rnn_trainer.py:753` - Validation loop
- `rnn_trainer.py:851` - Inference batch function
**Key Insight**: Mixed precision training requires explicit dtype management for ALL tensor operations, even intermediate calculations.
## Lessons Learned ## Lessons Learned
- **Root Cause**: TPU XLA compiler requires strict dtype consistency across all tensors - **Root Cause**: TPU XLA compiler requires strict dtype consistency across all tensors
- **Key Insight**: `torch.eye()` and `torch.zeros()` default to f32 - must explicitly specify dtype - **Key Insight**: `torch.eye()` and `torch.zeros()` default to f32 - must explicitly specify dtype

18
model_training_nnn/rnn_model.py
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@@ -25,8 +25,9 @@ class NoiseModel(nn.Module):
# Day-specific input layers # Day-specific input layers
self.day_layer_activation = nn.Softsign() self.day_layer_activation = nn.Softsign()
self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)]) # Let Accelerator handle dtype automatically for TPU compatibility
self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)]) self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
self.day_layer_dropout = nn.Dropout(input_dropout) self.day_layer_dropout = nn.Dropout(input_dropout)
# Calculate input size after patching # Calculate input size after patching
@@ -51,8 +52,8 @@ class NoiseModel(nn.Module):
if "weight_ih" in name: if "weight_ih" in name:
nn.init.xavier_uniform_(param) nn.init.xavier_uniform_(param)
# Learnable initial hidden state # Learnable initial hidden state - let Accelerator handle dtype
self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.input_size, dtype=torch.bfloat16))) self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.input_size)))
def forward(self, x, day_idx, states=None): def forward(self, x, day_idx, states=None):
# Apply day-specific transformation # Apply day-specific transformation
@@ -110,8 +111,9 @@ class CleanSpeechModel(nn.Module):
# Day-specific input layers # Day-specific input layers
self.day_layer_activation = nn.Softsign() self.day_layer_activation = nn.Softsign()
self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)]) # Let Accelerator handle dtype automatically for TPU compatibility
self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim, dtype=torch.bfloat16)) for _ in range(self.n_days)]) self.day_weights = nn.ParameterList([nn.Parameter(torch.eye(self.neural_dim)) for _ in range(self.n_days)])
self.day_biases = nn.ParameterList([nn.Parameter(torch.zeros(1, self.neural_dim)) for _ in range(self.n_days)])
self.day_layer_dropout = nn.Dropout(input_dropout) self.day_layer_dropout = nn.Dropout(input_dropout)
# Calculate input size after patching # Calculate input size after patching
@@ -141,7 +143,7 @@ class CleanSpeechModel(nn.Module):
nn.init.xavier_uniform_(self.out.weight) nn.init.xavier_uniform_(self.out.weight)
# Learnable initial hidden state # Learnable initial hidden state
self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units, dtype=torch.bfloat16))) self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))
def forward(self, x, day_idx, states=None, return_state=False): def forward(self, x, day_idx, states=None, return_state=False):
# Apply day-specific transformation # Apply day-specific transformation
@@ -229,7 +231,7 @@ class NoisySpeechModel(nn.Module):
nn.init.xavier_uniform_(self.out.weight) nn.init.xavier_uniform_(self.out.weight)
# Learnable initial hidden state # Learnable initial hidden state
self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units, dtype=torch.bfloat16))) self.h0 = nn.Parameter(nn.init.xavier_uniform_(torch.zeros(1, 1, self.n_units)))
def forward(self, x, states=None, return_state=False): def forward(self, x, states=None, return_state=False):
# Note: NoisySpeechModel doesn't need day-specific layers as it processes noise # Note: NoisySpeechModel doesn't need day-specific layers as it processes noise

10
model_training_nnn/rnn_trainer.py
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@@ -573,7 +573,8 @@ class BrainToTextDecoder_Trainer:
# Apply augmentations to the data # Apply augmentations to the data
features, n_time_steps = self.transform_data(features, n_time_steps, 'train') features, n_time_steps = self.transform_data(features, n_time_steps, 'train')
adjusted_lens = ((n_time_steps - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32) # Ensure proper dtype handling for TPU mixed precision
adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
# Get phoneme predictions using inference mode during training # Get phoneme predictions using inference mode during training
# (We use inference mode for simplicity - only clean logits are used for CTC loss) # (We use inference mode for simplicity - only clean logits are used for CTC loss)
@@ -748,7 +749,8 @@ class BrainToTextDecoder_Trainer:
with self.accelerator.autocast(): with self.accelerator.autocast():
features, n_time_steps = self.transform_data(features, n_time_steps, 'val') features, n_time_steps = self.transform_data(features, n_time_steps, 'val')
adjusted_lens = ((n_time_steps - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32) # Ensure proper dtype handling for TPU mixed precision
adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
logits = self.model(features, day_indicies, None, False, 'inference') logits = self.model(features, day_indicies, None, False, 'inference')
@@ -845,8 +847,8 @@ class BrainToTextDecoder_Trainer:
# Apply data transformations (no augmentation for inference) # Apply data transformations (no augmentation for inference)
features, n_time_steps = self.transform_data(features, n_time_steps, 'val') features, n_time_steps = self.transform_data(features, n_time_steps, 'val')
# Calculate adjusted sequence lengths for CTC # Calculate adjusted sequence lengths for CTC with proper dtype handling
adjusted_lens = ((n_time_steps - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32) adjusted_lens = ((n_time_steps.float() - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
# Get phoneme predictions # Get phoneme predictions
logits = self.model(features, day_indicies, None, False, mode) logits = self.model(features, day_indicies, None, False, mode)