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fix: align input tensors to model device in predict instead of cpu #94

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fix: align input tensors to model device in predict instead of cpu #94

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13 changes: 8 additions & 5 deletions torchTextClassifiers/torchTextClassifiers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -698,7 +698,8 @@ def predict(
text = X_test["text"]
categorical_variables = X_test["categorical_variables"]

self.pytorch_model.eval().cpu()
self.pytorch_model.eval()
device = next(self.pytorch_model.parameters()).device

tokenize_output = self.tokenizer.tokenize(
text.tolist(),
Expand All @@ -711,15 +712,17 @@ def predict(
f"Expected TokenizerOutput, got {type(tokenize_output)} from tokenizer.tokenize method."
)

encoded_text = tokenize_output.input_ids # (batch_size, seq_len)
attention_mask = tokenize_output.attention_mask # (batch_size, seq_len)
encoded_text = tokenize_output.input_ids.to(device) # (batch_size, seq_len)
attention_mask = tokenize_output.attention_mask.to(device) # (batch_size, seq_len)

if categorical_variables is not None:
categorical_vars = torch.tensor(
categorical_variables, dtype=torch.float32
categorical_variables, dtype=torch.float32, device=device
) # (batch_size, num_categorical_features)
else:
categorical_vars = torch.empty((encoded_text.shape[0], 0), dtype=torch.float32)
categorical_vars = torch.empty(
(encoded_text.shape[0], 0), dtype=torch.float32, device=device
)

model_output = self.pytorch_model(
encoded_text,
Expand Down