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Refactor TFP call to just sigmoid() (#29641)

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* Refactor TFP call to just sigmoid()

* Make sure we cast to the right dtype
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Matt 2024-03-13 17:51:13 +00:00 committed by GitHub
parent a7e5e15472
commit 31d01150ad
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1 changed files with 4 additions and 5 deletions
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9
src/transformers/pipelines/table_question_answering.py
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@ -5,7 +5,6 @@ import numpy as np
from ..utils import ( from ..utils import (
add_end_docstrings, add_end_docstrings,
is_tensorflow_probability_available,
is_tf_available, is_tf_available,
is_torch_available, is_torch_available,
requires_backends, requires_backends,
@ -21,9 +20,8 @@ if is_torch_available():
MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
) )
if is_tf_available() and is_tensorflow_probability_available(): if is_tf_available():
import tensorflow as tf import tensorflow as tf
import tensorflow_probability as tfp
from ..models.auto.modeling_tf_auto import ( from ..models.auto.modeling_tf_auto import (
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
@ -249,8 +247,9 @@ class TableQuestionAnsweringPipeline(Pipeline):
all_logits.append(logits) all_logits.append(logits)
dist_per_token = tfp.distributions.Bernoulli(logits=logits) probabilities = tf.math.sigmoid(tf.cast(logits, tf.float32)) * tf.cast(
probabilities = dist_per_token.probs_parameter() * tf.cast(attention_mask_example, tf.float32) attention_mask_example, tf.float32
)
coords_to_probs = collections.defaultdict(list) coords_to_probs = collections.defaultdict(list)
token_type_ids_example = token_type_ids_example token_type_ids_example = token_type_ids_example