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Fix GPU

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Jactus
2020-12-02 13:25:29 +08:00
parent d109d3d44e
commit 7f385345bb
5 changed files with 37 additions and 112 deletions
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29
qlib/contrib/model/pytorch_alstm.py
View File

@@ -76,7 +76,7 @@ class ALSTM(Model):
self.early_stop = early_stop self.early_stop = early_stop
self.optimizer = optimizer.lower() self.optimizer = optimizer.lower()
self.loss = loss self.loss = loss
self.visible_GPU = GPU self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_gpu = torch.cuda.is_available() self.use_gpu = torch.cuda.is_available()
self.seed = seed self.seed = seed
@@ -131,11 +131,7 @@ class ALSTM(Model):
raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
self._fitted = False self._fitted = False
if self.use_gpu: self.ALSTM_model.to(self.device)
self.ALSTM_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.visible_GPU)
def mse(self, pred, label): def mse(self, pred, label):
loss = (pred - label) ** 2 loss = (pred - label) ** 2
@@ -173,12 +169,8 @@ class ALSTM(Model):
if len(indices) - i < self.batch_size: if len(indices) - i < self.batch_size:
break break
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float() feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.ALSTM_model(feature) pred = self.ALSTM_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -206,12 +198,8 @@ class ALSTM(Model):
if len(indices) - i < self.batch_size: if len(indices) - i < self.batch_size:
break break
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float() feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.ALSTM_model(feature) pred = self.ALSTM_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -299,10 +287,7 @@ class ALSTM(Model):
else: else:
end = begin + self.batch_size end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float() x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
if self.use_gpu:
x_batch = x_batch.cuda()
with torch.no_grad(): with torch.no_grad():
if self.use_gpu: if self.use_gpu:

29
qlib/contrib/model/pytorch_gats.py
View File

@@ -83,7 +83,7 @@ class GATs(Model):
self.base_model = base_model self.base_model = base_model
self.with_pretrain = with_pretrain self.with_pretrain = with_pretrain
self.model_path = model_path self.model_path = model_path
self.visible_GPU = GPU self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_gpu = torch.cuda.is_available() self.use_gpu = torch.cuda.is_available()
self.seed = seed self.seed = seed
@@ -143,11 +143,7 @@ class GATs(Model):
raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
self._fitted = False self._fitted = False
if self.use_gpu: self.GAT_model.to(self.device)
self.GAT_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.visible_GPU)
def mse(self, pred, label): def mse(self, pred, label):
loss = (pred - label) ** 2 loss = (pred - label) ** 2
@@ -193,12 +189,8 @@ class GATs(Model):
for idx, count in zip(daily_index, daily_count): for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count) batch = slice(idx, idx + count)
feature = torch.from_numpy(x_train_values[batch]).float() feature = torch.from_numpy(x_train_values[batch]).float().to(self.device)
label = torch.from_numpy(y_train_values[batch]).float() label = torch.from_numpy(y_train_values[batch]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.GAT_model(feature) pred = self.GAT_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -224,12 +216,8 @@ class GATs(Model):
for idx, count in zip(daily_index, daily_count): for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count) batch = slice(idx, idx + count)
feature = torch.from_numpy(x_values[batch]).float() feature = torch.from_numpy(x_values[batch]).float().to(self.device)
label = torch.from_numpy(y_values[batch]).float() label = torch.from_numpy(y_values[batch]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.GAT_model(feature) pred = self.GAT_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -333,10 +321,7 @@ class GATs(Model):
for idx, count in zip(daily_index, daily_count): for idx, count in zip(daily_index, daily_count):
batch = slice(idx, idx + count) batch = slice(idx, idx + count)
x_batch = torch.from_numpy(x_values[batch]).float() x_batch = torch.from_numpy(x_values[batch]).float().to(self.device)
if self.use_gpu:
x_batch = x_batch.cuda()
with torch.no_grad(): with torch.no_grad():
if self.use_gpu: if self.use_gpu:

29
qlib/contrib/model/pytorch_gru.py
View File

@@ -76,7 +76,7 @@ class GRU(Model):
self.early_stop = early_stop self.early_stop = early_stop
self.optimizer = optimizer.lower() self.optimizer = optimizer.lower()
self.loss = loss self.loss = loss
self.visible_GPU = GPU self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_gpu = torch.cuda.is_available() self.use_gpu = torch.cuda.is_available()
self.seed = seed self.seed = seed
@@ -131,11 +131,7 @@ class GRU(Model):
raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
self._fitted = False self._fitted = False
if self.use_gpu: self.gru_model.to(self.device)
self.gru_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.visible_GPU)
def mse(self, pred, label): def mse(self, pred, label):
loss = (pred - label) ** 2 loss = (pred - label) ** 2
@@ -173,12 +169,8 @@ class GRU(Model):
if len(indices) - i < self.batch_size: if len(indices) - i < self.batch_size:
break break
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float() feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.gru_model(feature) pred = self.gru_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -206,12 +198,8 @@ class GRU(Model):
if len(indices) - i < self.batch_size: if len(indices) - i < self.batch_size:
break break
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float() feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.gru_model(feature) pred = self.gru_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -299,10 +287,7 @@ class GRU(Model):
else: else:
end = begin + self.batch_size end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float() x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
if self.use_gpu:
x_batch = x_batch.cuda()
with torch.no_grad(): with torch.no_grad():
if self.use_gpu: if self.use_gpu:

29
qlib/contrib/model/pytorch_lstm.py
View File

@@ -76,7 +76,7 @@ class LSTM(Model):
self.early_stop = early_stop self.early_stop = early_stop
self.optimizer = optimizer.lower() self.optimizer = optimizer.lower()
self.loss = loss self.loss = loss
self.visible_GPU = GPU self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_gpu = torch.cuda.is_available() self.use_gpu = torch.cuda.is_available()
self.seed = seed self.seed = seed
@@ -131,11 +131,7 @@ class LSTM(Model):
raise NotImplementedError("optimizer {} is not supported!".format(optimizer)) raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
self._fitted = False self._fitted = False
if self.use_gpu: self.lstm_model.to(self.device)
self.lstm_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.visible_GPU)
def mse(self, pred, label): def mse(self, pred, label):
loss = (pred - label) ** 2 loss = (pred - label) ** 2
@@ -173,12 +169,8 @@ class LSTM(Model):
if len(indices) - i < self.batch_size: if len(indices) - i < self.batch_size:
break break
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float() feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.lstm_model(feature) pred = self.lstm_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -206,12 +198,8 @@ class LSTM(Model):
if len(indices) - i < self.batch_size: if len(indices) - i < self.batch_size:
break break
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float() feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float() label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
if self.use_gpu:
feature = feature.cuda()
label = label.cuda()
pred = self.lstm_model(feature) pred = self.lstm_model(feature)
loss = self.loss_fn(pred, label) loss = self.loss_fn(pred, label)
@@ -299,10 +287,7 @@ class LSTM(Model):
else: else:
end = begin + self.batch_size end = begin + self.batch_size
x_batch = torch.from_numpy(x_values[begin:end]).float() x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
if self.use_gpu:
x_batch = x_batch.cuda()
with torch.no_grad(): with torch.no_grad():
if self.use_gpu: if self.use_gpu:

33
qlib/contrib/model/pytorch_nn.py
View File

@@ -79,7 +79,7 @@ class DNNModelPytorch(Model):
self.lr_decay_steps = lr_decay_steps self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower() self.optimizer = optimizer.lower()
self.loss_type = loss self.loss_type = loss
self.visible_GPU = GPU self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.use_GPU = torch.cuda.is_available() self.use_GPU = torch.cuda.is_available()
self.seed = seed self.seed = seed
@@ -147,11 +147,7 @@ class DNNModelPytorch(Model):
) )
self._fitted = False self._fitted = False
if self.use_GPU: self.dnn_model.to(self.device)
self.dnn_model.cuda()
# set the visible GPU
if self.visible_GPU:
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.visible_GPU)
def fit( def fit(
self, self,
@@ -188,13 +184,9 @@ class DNNModelPytorch(Model):
w_train_values = torch.from_numpy(w_train.values).float() w_train_values = torch.from_numpy(w_train.values).float()
train_num = y_train_values.shape[0] train_num = y_train_values.shape[0]
# prepare validation data # prepare validation data
x_val_auto = torch.from_numpy(x_valid.values).float() x_val_auto = torch.from_numpy(x_valid.values).float().to(self.device)
y_val_auto = torch.from_numpy(y_valid.values).float() y_val_auto = torch.from_numpy(y_valid.values).float().to(self.device)
w_val_auto = torch.from_numpy(w_valid.values).float() w_val_auto = torch.from_numpy(w_valid.values).float().to(self.device)
if self.use_GPU:
x_val_auto = x_val_auto.cuda()
y_val_auto = y_val_auto.cuda()
w_val_auto = w_val_auto.cuda()
for step in range(self.max_steps): for step in range(self.max_steps):
if stop_steps >= self.early_stop_rounds: if stop_steps >= self.early_stop_rounds:
@@ -205,14 +197,9 @@ class DNNModelPytorch(Model):
self.dnn_model.train() self.dnn_model.train()
self.train_optimizer.zero_grad() self.train_optimizer.zero_grad()
choice = np.random.choice(train_num, self.batch_size) choice = np.random.choice(train_num, self.batch_size)
x_batch_auto = x_train_values[choice] x_batch_auto = x_train_values[choice].to(self.device)
y_batch_auto = y_train_values[choice] y_batch_auto = y_train_values[choice].to(self.device)
w_batch_auto = w_train_values[choice] w_batch_auto = w_train_values[choice].to(self.device)
if self.use_GPU:
x_batch_auto = x_batch_auto.cuda()
y_batch_auto = y_batch_auto.cuda()
w_batch_auto = w_batch_auto.cuda()
# forward # forward
preds = self.dnn_model(x_batch_auto) preds = self.dnn_model(x_batch_auto)
@@ -277,9 +264,7 @@ class DNNModelPytorch(Model):
if not self._fitted: if not self._fitted:
raise ValueError("model is not fitted yet!") raise ValueError("model is not fitted yet!")
x_test_pd = dataset.prepare("test", col_set="feature") x_test_pd = dataset.prepare("test", col_set="feature")
x_test = torch.from_numpy(x_test_pd.values).float() x_test = torch.from_numpy(x_test_pd.values).float().to(self.device)
if self.use_GPU:
x_test = x_test.cuda()
self.dnn_model.eval() self.dnn_model.eval()
with torch.no_grad(): with torch.no_grad():