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try to work with ROCKm (AMD) GPUs again

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Dobromir Popov
2025-11-12 18:12:47 +02:00
parent 8354aec830
commit 4a5c3fc943
6 changed files with 16 additions and 10 deletions
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4
NN/models/advanced_transformer_trading.py
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@@ -1229,7 +1229,9 @@ class TradingTransformerTrainer:
for k, v in batch.items()}
# Use automatic mixed precision (FP16) for memory efficiency
with torch.amp.autocast('cuda', enabled=self.use_amp):
# Support both CUDA and ROCm (AMD) devices
device_type = 'cuda' if self.device.type == 'cuda' else 'cpu'
with torch.amp.autocast(device_type, enabled=self.use_amp and device_type != 'cpu'):
# Forward pass with multi-timeframe data
outputs = self.model(
price_data_1s=batch.get('price_data_1s'),

3
NN/models/cob_rl_model.py
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@@ -323,7 +323,8 @@ class COBRLModelInterface(ModelInterface):
self.optimizer.zero_grad()
if self.scaler:
with torch.amp.autocast('cuda'):
device_type = 'cuda' if next(self.model.parameters()).device.type == 'cuda' else 'cpu'
with torch.amp.autocast(device_type, enabled=device_type != 'cpu'):
outputs = self.model(features)
loss = self._calculate_loss(outputs, targets)

3
NN/models/dqn_agent.py
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@@ -1436,7 +1436,8 @@ class DQNAgent:
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore", FutureWarning)
with torch.amp.autocast('cuda'):
device_type = 'cuda' if self.device.type == 'cuda' else 'cpu'
with torch.amp.autocast(device_type, enabled=device_type != 'cpu'):
# Get current Q values and predictions
current_q_values, current_extrema_pred, current_price_pred, hidden_features, current_advanced_pred = self._safe_cnn_forward(self.policy_net, states)
current_q_values = current_q_values.gather(1, actions.unsqueeze(1)).squeeze(1)