TF: remove set_tensor_by_indices_to_value (huggingface#16729)

src/transformers/generation_tf_logits_process.py

@@ -19,7 +19,6 @@
 import numpy as np
 import tensorflow as tf
 
-from .tf_utils import set_tensor_by_indices_to_value
 from .utils import add_start_docstrings
 from .utils.logging import get_logger
 
@@ -221,7 +220,7 @@ def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.
         # generate is not XLA - compileable anyways
         if cur_len < self.min_length:
             eos_token_id_mask = tf.broadcast_to(tf.range(scores.shape[-1]) == self.eos_token_id, scores.shape)
-            scores = set_tensor_by_indices_to_value(scores, eos_token_id_mask, float("-inf"))
+            scores = tf.where(eos_token_id_mask, float("-inf"), scores)
 
         return scores
 
@@ -339,9 +338,7 @@ def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.
                 [True if token in banned_tokens_slice else False for token in range(vocab_size)]
             )
 
-        scores = set_tensor_by_indices_to_value(
-            scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
-        )
+        scores = tf.where(tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores)
 
         return scores
 
@@ -397,9 +394,7 @@ def __call__(self, input_ids: tf.Tensor, scores: tf.Tensor, cur_len: int) -> tf.
                 [True if token in banned_tokens_slice else False for token in range(vocab_size)]
             )
 
-        scores = set_tensor_by_indices_to_value(
-            scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
-        )
+        scores = tf.where(tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores)
 
         return scores
 

src/transformers/generation_tf_utils.py

@@ -34,7 +34,7 @@
     TFTopKLogitsWarper,
     TFTopPLogitsWarper,
 )
-from .tf_utils import set_tensor_by_indices_to_value, shape_list
+from .tf_utils import shape_list
 from .utils import ModelOutput, logging
 
 
@@ -952,8 +952,7 @@ def _generate_beam_search(
                     [True if token == eos_token_id else False for token in range(vocab_size)], dtype=tf.bool
                 )
                 eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [num_batch_hypotheses, vocab_size])
-
-                scores = set_tensor_by_indices_to_value(scores, eos_token_indices_mask, -float("inf"))
+                scores = tf.where(eos_token_indices_mask, -float("inf"), scores)
 
             if no_repeat_ngram_size > 0:
                 # calculate a list of banned tokens to prevent repetitively generating the same ngrams
@@ -969,8 +968,8 @@ def _generate_beam_search(
                         [True if token in banned_tokens_slice else False for token in range(vocab_size)]
                     )
 
-                scores = set_tensor_by_indices_to_value(
-                    scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
+                scores = tf.where(
+                    tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores
                 )
 
             if bad_words_ids is not None:
@@ -983,8 +982,8 @@ def _generate_beam_search(
                         [True if token in banned_tokens_slice else False for token in range(vocab_size)]
                     )
 
-                scores = set_tensor_by_indices_to_value(
-                    scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
+                scores = tf.where(
+                    tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf"), scores
                 )
 
             assert shape_list(scores) == [batch_size * num_beams, vocab_size]
@@ -2950,7 +2949,7 @@ def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("In
         top_k = min(max(top_k, min_tokens_to_keep), logits_shape[-1])  # Safety check
         # Remove all tokens with a probability less than the last token of the top-k
         indices_to_remove = logits < tf.math.top_k(logits, k=top_k)[0][..., -1, None]
-        logits = set_tensor_by_indices_to_value(logits, indices_to_remove, filter_value)
+        logits = tf.where(indices_to_remove, filter_value, logits)
     if top_p < 1.0:
         sorted_indices = tf.argsort(logits, direction="DESCENDING")
         sorted_logits = tf.gather(
@@ -2979,7 +2978,7 @@ def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("In
         )
         # scatter sorted tensors to original indexing
         indices_to_remove = scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)
-        logits = set_tensor_by_indices_to_value(logits, indices_to_remove, filter_value)
+        logits = tf.where(indices_to_remove, filter_value, logits)
     return logits
 
 

src/transformers/tf_utils.py

@@ -23,11 +23,6 @@
 logger = logging.get_logger(__name__)
 
 
-def set_tensor_by_indices_to_value(tensor: tf.Tensor, indices: tf.Tensor, value: Union[tf.Tensor, int, float]):
-    # create value_tensor since tensor value assignment is not possible in TF
-    return tf.where(indices, value, tensor)
-
-
 def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]:
     """
     Deal with dynamic shape in tensorflow cleanly.

tests/generation/test_generation_tf_logits_process.py

@@ -37,7 +37,6 @@
         TFTopKLogitsWarper,
         TFTopPLogitsWarper,
     )
-    from transformers.tf_utils import set_tensor_by_indices_to_value
 
     from ..test_modeling_tf_common import ids_tensor
 
@@ -112,9 +111,9 @@ def test_repetition_penalty_dist_process(self):
         scores = self._get_uniform_logits(batch_size=2, length=vocab_size)
 
         mask = tf.cast(tf.constant([[1] + 9 * [0], 10 * [0]]), tf.bool)
-        scores = set_tensor_by_indices_to_value(scores, mask, -1 / vocab_size)
+        scores = tf.where(mask, -1 / vocab_size, scores)
         mask = tf.cast(tf.constant([10 * [0], 5 * [0] + [1] + 4 * [0]]), tf.bool)
-        scores = set_tensor_by_indices_to_value(scores, mask, 4 / vocab_size)
+        scores = tf.where(mask, 4 / vocab_size, scores)
 
         rep_penalty_proc = TFRepetitionPenaltyLogitsProcessor(penalty=2.0)
 
@@ -340,8 +339,8 @@ def test_processor_list(self):
         scores_comp = processor(input_ids, scores_comp, cur_len=cur_len)
 
         # remove inf
-        scores = set_tensor_by_indices_to_value(scores, tf.math.is_inf(scores), -1e9)
-        scores_comp = set_tensor_by_indices_to_value(scores_comp, tf.math.is_inf(scores_comp), -1e9)
+        scores = tf.where(tf.math.is_inf(scores), -1e9, scores)
+        scores_comp = tf.where(tf.math.is_inf(scores_comp), -1e9, scores_comp)
 
         # scores should be equal
         tf.debugging.assert_near(scores, scores_comp, atol=1e-3)