remove jax.ops.index (#16220)

examples/research_projects/jax-projects/model_parallel/README.md

@@ -35,7 +35,7 @@ model = FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
 
 emb = jnp.zeros((50264, model.config.hidden_size))
 # update the first 50257 weights using pre-trained weights
-emb = emb.at[jax.ops.index[:50257, :]].set(model.params["transformer"]["wte"]["embedding"])
+emb = emb.at[:50257, :].set(model.params["transformer"]["wte"]["embedding"])
 params = model.params
 params["transformer"]["wte"]["embedding"] = emb
 

src/transformers/generation_flax_logits_process.py

@@ -143,10 +143,10 @@ class FlaxTopPLogitsWarper(FlaxLogitsWarper):
 
         # include the token that is higher than top_p as well
         score_mask = jnp.roll(score_mask, 1)
-        score_mask |= score_mask.at[jax.ops.index[:, 0]].set(True)
+        score_mask |= score_mask.at[:, 0].set(True)
 
         # min tokens to keep
-        score_mask = score_mask.at[jax.ops.index[:, : self.min_tokens_to_keep]].set(True)
+        score_mask = score_mask.at[:, : self.min_tokens_to_keep].set(True)
 
         topk_next_scores = jnp.where(score_mask, topk_scores, mask_scores)
         next_scores = jax.lax.sort_key_val(topk_indices, topk_next_scores)[-1]
@@ -207,7 +207,7 @@ class FlaxForcedBOSTokenLogitsProcessor(FlaxLogitsProcessor):
 
         apply_penalty = 1 - jnp.bool_(cur_len - 1)
 
-        scores = jnp.where(apply_penalty, new_scores.at[jax.ops.index[:, self.bos_token_id]].set(0), scores)
+        scores = jnp.where(apply_penalty, new_scores.at[:, self.bos_token_id].set(0), scores)
 
         return scores
 
@@ -232,7 +232,7 @@ class FlaxForcedEOSTokenLogitsProcessor(FlaxLogitsProcessor):
 
         apply_penalty = 1 - jnp.bool_(cur_len - self.max_length + 1)
 
-        scores = jnp.where(apply_penalty, new_scores.at[jax.ops.index[:, self.eos_token_id]].set(0), scores)
+        scores = jnp.where(apply_penalty, new_scores.at[:, self.eos_token_id].set(0), scores)
 
         return scores
 
@@ -263,6 +263,6 @@ class FlaxMinLengthLogitsProcessor(FlaxLogitsProcessor):
         # create boolean flag to decide if min length penalty should be applied
         apply_penalty = 1 - jnp.clip(cur_len - self.min_length, 0, 1)
 
-        scores = jnp.where(apply_penalty, scores.at[jax.ops.index[:, self.eos_token_id]].set(-float("inf")), scores)
+        scores = jnp.where(apply_penalty, scores.at[:, self.eos_token_id].set(-float("inf")), scores)
 
         return scores

src/transformers/models/big_bird/modeling_flax_big_bird.py

@@ -2124,7 +2124,7 @@ class FlaxBigBirdForQuestionAnswering(FlaxBigBirdPreTrainedModel):
             if token_type_ids is None:
                 token_type_ids = (~logits_mask).astype("i4")
             logits_mask = jnp.expand_dims(logits_mask, axis=2)
-            logits_mask = logits_mask.at[jax.ops.index[:, 0]].set(False)
+            logits_mask = logits_mask.at[:, 0].set(False)
 
         # init input tensors if not passed
         if token_type_ids is None:

src/transformers/models/marian/modeling_flax_marian.py

@@ -1422,7 +1422,7 @@ class FlaxMarianMTModel(FlaxMarianPreTrainedModel):
 
     def _adapt_logits_for_beam_search(self, logits):
         """This function enforces the padding token never to be generated."""
-        logits = logits.at[jax.ops.index[:, :, self.config.pad_token_id]].set(float("-inf"))
+        logits = logits.at[:, :, self.config.pad_token_id].set(float("-inf"))
         return logits
 
     def prepare_inputs_for_generation(

src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py

@@ -964,8 +964,7 @@ class FlaxWav2Vec2Module(nn.Module):
 
             # these two operations makes sure that all values
             # before the output lengths indices are attended to
-            idx = jax.ops.index[jnp.arange(attention_mask.shape[0]), output_lengths - 1]
-            attention_mask = attention_mask.at[idx].set(1)
+            attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1)
             attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool")
 
         hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic)