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beifen

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Zchen
2025-10-12 09:35:26 +08:00
parent 21d901cfd8
commit 6828ff536e
2 changed files with 227 additions and 12 deletions
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26
model_training_nnn/rnn_trainer.py
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@@ -22,7 +22,7 @@ torch.set_float32_matmul_precision('high') # makes float32 matmuls faster on som
torch.backends.cudnn.deterministic = True # makes training more reproducible
torch._dynamo.config.cache_size_limit = 64
from rnn_model import GRUDecoder
from rnn_model import TripleGRUDecoder
class BrainToTextDecoder_Trainer:
"""
@@ -116,22 +116,23 @@ class BrainToTextDecoder_Trainer:
random.seed(self.args['seed'])
torch.manual_seed(self.args['seed'])
# Initialize the model
self.model = GRUDecoder(
# Initialize the model
self.model = TripleGRUDecoder(
neural_dim = self.args['model']['n_input_features'],
n_units = self.args['model']['n_units'],
n_days = len(self.args['dataset']['sessions']),
n_classes = self.args['dataset']['n_classes'],
rnn_dropout = self.args['model']['rnn_dropout'],
input_dropout = self.args['model']['input_network']['input_layer_dropout'],
n_layers = self.args['model']['n_layers'],
rnn_dropout = self.args['model']['rnn_dropout'],
input_dropout = self.args['model']['input_network']['input_layer_dropout'],
patch_size = self.args['model']['patch_size'],
patch_stride = self.args['model']['patch_stride'],
)
# Call torch.compile to speed up training
self.logger.info("Using torch.compile")
self.model = torch.compile(self.model)
# Temporarily disable torch.compile for compatibility with new model architecture
# TODO: Re-enable torch.compile once model is stable
# self.logger.info("Using torch.compile")
# self.model = torch.compile(self.model)
self.logger.info("torch.compile disabled for new TripleGRUDecoder compatibility")
self.logger.info(f"Initialized RNN decoding model")
@@ -531,8 +532,9 @@ class BrainToTextDecoder_Trainer:
adjusted_lens = ((n_time_steps - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
# Get phoneme predictions
logits = self.model(features, day_indicies)
# Get phoneme predictions using inference mode during training
# (We use inference mode for simplicity - only clean logits are used for CTC loss)
logits = self.model(features, day_indicies, None, False, 'inference')
# Calculate CTC Loss
loss = self.ctc_loss(
@@ -706,7 +708,7 @@ class BrainToTextDecoder_Trainer:
adjusted_lens = ((n_time_steps - self.args['model']['patch_size']) / self.args['model']['patch_stride'] + 1).to(torch.int32)
logits = self.model(features, day_indicies)
logits = self.model(features, day_indicies, None, False, 'inference')
loss = self.ctc_loss(
torch.permute(logits.log_softmax(2), [1, 0, 2]),

213
model_training_nnn/trained_models/baseline_rnn/training_log Normal file
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@@ -0,0 +1,213 @@
2025-10-12 09:32:47,330: Using device: cpu
2025-10-12 09:33:20,119: torch.compile disabled for new TripleGRUDecoder compatibility
2025-10-12 09:33:20,120: Initialized RNN decoding model
2025-10-12 09:33:20,120: Model type: <class 'rnn_model.TripleGRUDecoder'>
2025-10-12 09:33:20,120: Model is callable: True
2025-10-12 09:33:20,120: Model has forward method: True
2025-10-12 09:33:20,120: TripleGRUDecoder(
(noise_model): NoiseModel(
(day_layer_activation): Softsign()
(day_weights): ParameterList(
(0): Parameter containing: [torch.float32 of size 512x512]
(1): Parameter containing: [torch.float32 of size 512x512]
(2): Parameter containing: [torch.float32 of size 512x512]
(3): Parameter containing: [torch.float32 of size 512x512]
(4): Parameter containing: [torch.float32 of size 512x512]
(5): Parameter containing: [torch.float32 of size 512x512]
(6): Parameter containing: [torch.float32 of size 512x512]
(7): Parameter containing: [torch.float32 of size 512x512]
(8): Parameter containing: [torch.float32 of size 512x512]
(9): Parameter containing: [torch.float32 of size 512x512]
(10): Parameter containing: [torch.float32 of size 512x512]
(11): Parameter containing: [torch.float32 of size 512x512]
(12): Parameter containing: [torch.float32 of size 512x512]
(13): Parameter containing: [torch.float32 of size 512x512]
(14): Parameter containing: [torch.float32 of size 512x512]
(15): Parameter containing: [torch.float32 of size 512x512]
(16): Parameter containing: [torch.float32 of size 512x512]
(17): Parameter containing: [torch.float32 of size 512x512]
(18): Parameter containing: [torch.float32 of size 512x512]
(19): Parameter containing: [torch.float32 of size 512x512]
(20): Parameter containing: [torch.float32 of size 512x512]
(21): Parameter containing: [torch.float32 of size 512x512]
(22): Parameter containing: [torch.float32 of size 512x512]
(23): Parameter containing: [torch.float32 of size 512x512]
(24): Parameter containing: [torch.float32 of size 512x512]
(25): Parameter containing: [torch.float32 of size 512x512]
(26): Parameter containing: [torch.float32 of size 512x512]
(27): Parameter containing: [torch.float32 of size 512x512]
(28): Parameter containing: [torch.float32 of size 512x512]
(29): Parameter containing: [torch.float32 of size 512x512]
(30): Parameter containing: [torch.float32 of size 512x512]
(31): Parameter containing: [torch.float32 of size 512x512]
(32): Parameter containing: [torch.float32 of size 512x512]
(33): Parameter containing: [torch.float32 of size 512x512]
(34): Parameter containing: [torch.float32 of size 512x512]
(35): Parameter containing: [torch.float32 of size 512x512]
(36): Parameter containing: [torch.float32 of size 512x512]
(37): Parameter containing: [torch.float32 of size 512x512]
(38): Parameter containing: [torch.float32 of size 512x512]
(39): Parameter containing: [torch.float32 of size 512x512]
(40): Parameter containing: [torch.float32 of size 512x512]
(41): Parameter containing: [torch.float32 of size 512x512]
(42): Parameter containing: [torch.float32 of size 512x512]
(43): Parameter containing: [torch.float32 of size 512x512]
(44): Parameter containing: [torch.float32 of size 512x512]
)
(day_biases): ParameterList(
(0): Parameter containing: [torch.float32 of size 1x512]
(1): Parameter containing: [torch.float32 of size 1x512]
(2): Parameter containing: [torch.float32 of size 1x512]
(3): Parameter containing: [torch.float32 of size 1x512]
(4): Parameter containing: [torch.float32 of size 1x512]
(5): Parameter containing: [torch.float32 of size 1x512]
(6): Parameter containing: [torch.float32 of size 1x512]
(7): Parameter containing: [torch.float32 of size 1x512]
(8): Parameter containing: [torch.float32 of size 1x512]
(9): Parameter containing: [torch.float32 of size 1x512]
(10): Parameter containing: [torch.float32 of size 1x512]
(11): Parameter containing: [torch.float32 of size 1x512]
(12): Parameter containing: [torch.float32 of size 1x512]
(13): Parameter containing: [torch.float32 of size 1x512]
(14): Parameter containing: [torch.float32 of size 1x512]
(15): Parameter containing: [torch.float32 of size 1x512]
(16): Parameter containing: [torch.float32 of size 1x512]
(17): Parameter containing: [torch.float32 of size 1x512]
(18): Parameter containing: [torch.float32 of size 1x512]
(19): Parameter containing: [torch.float32 of size 1x512]
(20): Parameter containing: [torch.float32 of size 1x512]
(21): Parameter containing: [torch.float32 of size 1x512]
(22): Parameter containing: [torch.float32 of size 1x512]
(23): Parameter containing: [torch.float32 of size 1x512]
(24): Parameter containing: [torch.float32 of size 1x512]
(25): Parameter containing: [torch.float32 of size 1x512]
(26): Parameter containing: [torch.float32 of size 1x512]
(27): Parameter containing: [torch.float32 of size 1x512]
(28): Parameter containing: [torch.float32 of size 1x512]
(29): Parameter containing: [torch.float32 of size 1x512]
(30): Parameter containing: [torch.float32 of size 1x512]
(31): Parameter containing: [torch.float32 of size 1x512]
(32): Parameter containing: [torch.float32 of size 1x512]
(33): Parameter containing: [torch.float32 of size 1x512]
(34): Parameter containing: [torch.float32 of size 1x512]
(35): Parameter containing: [torch.float32 of size 1x512]
(36): Parameter containing: [torch.float32 of size 1x512]
(37): Parameter containing: [torch.float32 of size 1x512]
(38): Parameter containing: [torch.float32 of size 1x512]
(39): Parameter containing: [torch.float32 of size 1x512]
(40): Parameter containing: [torch.float32 of size 1x512]
(41): Parameter containing: [torch.float32 of size 1x512]
(42): Parameter containing: [torch.float32 of size 1x512]
(43): Parameter containing: [torch.float32 of size 1x512]
(44): Parameter containing: [torch.float32 of size 1x512]
)
(day_layer_dropout): Dropout(p=0.2, inplace=False)
(gru): GRU(7168, 7168, num_layers=2, batch_first=True, dropout=0.4)
)
(clean_speech_model): CleanSpeechModel(
(day_layer_activation): Softsign()
(day_weights): ParameterList(
(0): Parameter containing: [torch.float32 of size 512x512]
(1): Parameter containing: [torch.float32 of size 512x512]
(2): Parameter containing: [torch.float32 of size 512x512]
(3): Parameter containing: [torch.float32 of size 512x512]
(4): Parameter containing: [torch.float32 of size 512x512]
(5): Parameter containing: [torch.float32 of size 512x512]
(6): Parameter containing: [torch.float32 of size 512x512]
(7): Parameter containing: [torch.float32 of size 512x512]
(8): Parameter containing: [torch.float32 of size 512x512]
(9): Parameter containing: [torch.float32 of size 512x512]
(10): Parameter containing: [torch.float32 of size 512x512]
(11): Parameter containing: [torch.float32 of size 512x512]
(12): Parameter containing: [torch.float32 of size 512x512]
(13): Parameter containing: [torch.float32 of size 512x512]
(14): Parameter containing: [torch.float32 of size 512x512]
(15): Parameter containing: [torch.float32 of size 512x512]
(16): Parameter containing: [torch.float32 of size 512x512]
(17): Parameter containing: [torch.float32 of size 512x512]
(18): Parameter containing: [torch.float32 of size 512x512]
(19): Parameter containing: [torch.float32 of size 512x512]
(20): Parameter containing: [torch.float32 of size 512x512]
(21): Parameter containing: [torch.float32 of size 512x512]
(22): Parameter containing: [torch.float32 of size 512x512]
(23): Parameter containing: [torch.float32 of size 512x512]
(24): Parameter containing: [torch.float32 of size 512x512]
(25): Parameter containing: [torch.float32 of size 512x512]
(26): Parameter containing: [torch.float32 of size 512x512]
(27): Parameter containing: [torch.float32 of size 512x512]
(28): Parameter containing: [torch.float32 of size 512x512]
(29): Parameter containing: [torch.float32 of size 512x512]
(30): Parameter containing: [torch.float32 of size 512x512]
(31): Parameter containing: [torch.float32 of size 512x512]
(32): Parameter containing: [torch.float32 of size 512x512]
(33): Parameter containing: [torch.float32 of size 512x512]
(34): Parameter containing: [torch.float32 of size 512x512]
(35): Parameter containing: [torch.float32 of size 512x512]
(36): Parameter containing: [torch.float32 of size 512x512]
(37): Parameter containing: [torch.float32 of size 512x512]
(38): Parameter containing: [torch.float32 of size 512x512]
(39): Parameter containing: [torch.float32 of size 512x512]
(40): Parameter containing: [torch.float32 of size 512x512]
(41): Parameter containing: [torch.float32 of size 512x512]
(42): Parameter containing: [torch.float32 of size 512x512]
(43): Parameter containing: [torch.float32 of size 512x512]
(44): Parameter containing: [torch.float32 of size 512x512]
)
(day_biases): ParameterList(
(0): Parameter containing: [torch.float32 of size 1x512]
(1): Parameter containing: [torch.float32 of size 1x512]
(2): Parameter containing: [torch.float32 of size 1x512]
(3): Parameter containing: [torch.float32 of size 1x512]
(4): Parameter containing: [torch.float32 of size 1x512]
(5): Parameter containing: [torch.float32 of size 1x512]
(6): Parameter containing: [torch.float32 of size 1x512]
(7): Parameter containing: [torch.float32 of size 1x512]
(8): Parameter containing: [torch.float32 of size 1x512]
(9): Parameter containing: [torch.float32 of size 1x512]
(10): Parameter containing: [torch.float32 of size 1x512]
(11): Parameter containing: [torch.float32 of size 1x512]
(12): Parameter containing: [torch.float32 of size 1x512]
(13): Parameter containing: [torch.float32 of size 1x512]
(14): Parameter containing: [torch.float32 of size 1x512]
(15): Parameter containing: [torch.float32 of size 1x512]
(16): Parameter containing: [torch.float32 of size 1x512]
(17): Parameter containing: [torch.float32 of size 1x512]
(18): Parameter containing: [torch.float32 of size 1x512]
(19): Parameter containing: [torch.float32 of size 1x512]
(20): Parameter containing: [torch.float32 of size 1x512]
(21): Parameter containing: [torch.float32 of size 1x512]
(22): Parameter containing: [torch.float32 of size 1x512]
(23): Parameter containing: [torch.float32 of size 1x512]
(24): Parameter containing: [torch.float32 of size 1x512]
(25): Parameter containing: [torch.float32 of size 1x512]
(26): Parameter containing: [torch.float32 of size 1x512]
(27): Parameter containing: [torch.float32 of size 1x512]
(28): Parameter containing: [torch.float32 of size 1x512]
(29): Parameter containing: [torch.float32 of size 1x512]
(30): Parameter containing: [torch.float32 of size 1x512]
(31): Parameter containing: [torch.float32 of size 1x512]
(32): Parameter containing: [torch.float32 of size 1x512]
(33): Parameter containing: [torch.float32 of size 1x512]
(34): Parameter containing: [torch.float32 of size 1x512]
(35): Parameter containing: [torch.float32 of size 1x512]
(36): Parameter containing: [torch.float32 of size 1x512]
(37): Parameter containing: [torch.float32 of size 1x512]
(38): Parameter containing: [torch.float32 of size 1x512]
(39): Parameter containing: [torch.float32 of size 1x512]
(40): Parameter containing: [torch.float32 of size 1x512]
(41): Parameter containing: [torch.float32 of size 1x512]
(42): Parameter containing: [torch.float32 of size 1x512]
(43): Parameter containing: [torch.float32 of size 1x512]
(44): Parameter containing: [torch.float32 of size 1x512]
)
(day_layer_dropout): Dropout(p=0.2, inplace=False)
(gru): GRU(7168, 768, num_layers=3, batch_first=True, dropout=0.4)
(out): Linear(in_features=768, out_features=41, bias=True)
)
(noisy_speech_model): NoisySpeechModel(
(gru): GRU(7168, 768, num_layers=2, batch_first=True, dropout=0.4)
(out): Linear(in_features=768, out_features=41, bias=True)
)
)
2025-10-12 09:33:20,124: Model has 687,568,466 parameters
2025-10-12 09:33:20,124: Model has 23,639,040 day-specific parameters | 3.44% of total parameters