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feat: Use torch.inference_mode() for TableQA #3731

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merged 3 commits into from
Dec 19, 2022
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feat: Use torch.inference_mode() for TableQA #3731

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b32f411
Update to make inference_mode work in TableQA
sjrl Dec 19, 2022
4a1644a
Update variable names
sjrl Dec 19, 2022
240252b
Added torch.inference_mode() for the RCIReader model forward passes
sjrl Dec 19, 2022
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17 changes: 11 additions & 6 deletions haystack/nodes/reader/table.py
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Original file line number Diff line number Diff line change
Expand Up @@ -311,7 +311,7 @@ def _predict_tapas(self, inputs: BatchEncoding, document: Document) -> Answer:
table: pd.DataFrame = document.content

# Forward query and table through model and convert logits to predictions
with torch.no_grad():
with torch.inference_mode():
outputs = self.model(**inputs)

inputs.to("cpu")
Expand Down Expand Up @@ -366,8 +366,9 @@ def _calculate_answer_score(
) -> float:
# Calculate answer score
# Values over 88.72284 will overflow when passed through exponential, so logits are truncated.
logits[logits < -88.7] = -88.7
token_probabilities = 1 / (1 + np.exp(-logits)) * inputs.attention_mask
truncated_logits = logits.clone()
truncated_logits[truncated_logits < -88.7] = -88.7
token_probabilities = 1 / (1 + np.exp(-truncated_logits)) * inputs.attention_mask
token_types = [
"segment_ids",
"column_ids",
Expand Down Expand Up @@ -472,7 +473,7 @@ def _predict_tapas_scored(self, inputs: BatchEncoding, document: Document) -> Tu
table: pd.DataFrame = document.content

# Forward pass through model
with torch.no_grad():
with torch.inference_mode():
outputs = self.model.tapas(**inputs)
table_score = self.model.classifier(outputs.pooler_output)

Expand Down Expand Up @@ -765,7 +766,9 @@ def predict(self, query: str, documents: List[Document], top_k: Optional[int] =
padding=True,
)
row_inputs.to(self.devices[0])
row_logits = self.row_model(**row_inputs)[0].detach().cpu().numpy()[:, 1]
with torch.inference_mode():
row_outputs = self.row_model(**row_inputs)
row_logits = row_outputs[0].detach().cpu().numpy()[:, 1]

# Get column logits
column_inputs = self.column_tokenizer(
Expand All @@ -777,7 +780,9 @@ def predict(self, query: str, documents: List[Document], top_k: Optional[int] =
padding=True,
)
column_inputs.to(self.devices[0])
column_logits = self.column_model(**column_inputs)[0].detach().cpu().numpy()[:, 1]
with torch.inference_mode():
column_outputs = self.column_model(**column_inputs)
column_logits = column_outputs[0].detach().cpu().numpy()[:, 1]

# Calculate cell scores
current_answers: List[Answer] = []
Expand Down