Replace all deprecated jax.ops operations with jnp's at (huggingf…

…ace#16078)

* Replace all deprecated `jax.ops` operations with jnp's `at`

* np to jnp scores

* suggested changes

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 = jax.ops.index_update(emb, jax.ops.index[:50257, :], model.params["transformer"]["wte"]["embedding"])
+emb = emb.at[jax.ops.index[:50257, :]].set(model.params["transformer"]["wte"]["embedding"])
 params = model.params
 params["transformer"]["wte"]["embedding"] = emb
 

src/transformers/generation_flax_logits_process.py

@@ -142,10 +142,11 @@ def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) ->
         score_mask = cumulative_probs < self.top_p
 
         # include the token that is higher than top_p as well
-        score_mask |= jax.ops.index_update(jnp.roll(score_mask, 1), jax.ops.index[:, 0], True)
+        score_mask = jnp.roll(score_mask, 1)
+        score_mask |= score_mask.at[jax.ops.index[:, 0]].set(True)
 
         # min tokens to keep
-        score_mask = jax.ops.index_update(score_mask, jax.ops.index[:, : self.min_tokens_to_keep], True)
+        score_mask = score_mask.at[jax.ops.index[:, : 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]
@@ -184,7 +185,7 @@ def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) ->
         topk_scores_flat = topk_scores.flatten()
         topk_indices_flat = topk_indices.flatten() + shift
 
-        next_scores_flat = jax.ops.index_update(next_scores_flat, topk_indices_flat, topk_scores_flat)
+        next_scores_flat = next_scores_flat.at[topk_indices_flat].set(topk_scores_flat)
         next_scores = next_scores_flat.reshape(batch_size, vocab_size)
         return next_scores
 
@@ -206,9 +207,7 @@ def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) ->
 
         apply_penalty = 1 - jnp.bool_(cur_len - 1)
 
-        scores = jnp.where(
-            apply_penalty, jax.ops.index_update(new_scores, jax.ops.index[:, self.bos_token_id], 0), scores
-        )
+        scores = jnp.where(apply_penalty, new_scores.at[jax.ops.index[:, self.bos_token_id]].set(0), scores)
 
         return scores
 
@@ -233,9 +232,7 @@ def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) ->
 
         apply_penalty = 1 - jnp.bool_(cur_len - self.max_length + 1)
 
-        scores = jnp.where(
-            apply_penalty, jax.ops.index_update(new_scores, jax.ops.index[:, self.eos_token_id], 0), scores
-        )
+        scores = jnp.where(apply_penalty, new_scores.at[jax.ops.index[:, self.eos_token_id]].set(0), scores)
 
         return scores
 
@@ -266,8 +263,6 @@ def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) ->
         # 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, jax.ops.index_update(scores, jax.ops.index[:, self.eos_token_id], -float("inf")), scores
-        )
+        scores = jnp.where(apply_penalty, scores.at[jax.ops.index[:, self.eos_token_id]].set(-float("inf")), scores)
 
         return scores

src/transformers/models/bart/modeling_flax_bart.py

@@ -922,7 +922,7 @@ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDic
         # init input tensors
         input_ids = jnp.zeros(input_shape, dtype="i4")
         # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
-        input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
+        input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
         attention_mask = jnp.ones_like(input_ids)
         decoder_input_ids = input_ids
         decoder_attention_mask = jnp.ones_like(input_ids)

src/transformers/models/big_bird/modeling_flax_big_bird.py

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

src/transformers/models/blenderbot/modeling_flax_blenderbot.py

@@ -897,7 +897,7 @@ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDic
         # init input tensors
         input_ids = jnp.zeros(input_shape, dtype="i4")
         # make sure initialization pass will work for FlaxBlenderbotForSequenceClassificationModule
-        input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
+        input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
         attention_mask = jnp.ones_like(input_ids)
         decoder_input_ids = input_ids
         decoder_attention_mask = jnp.ones_like(input_ids)

src/transformers/models/blenderbot_small/modeling_flax_blenderbot_small.py

@@ -895,7 +895,7 @@ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDic
         # init input tensors
         input_ids = jnp.zeros(input_shape, dtype="i4")
         # make sure initialization pass will work for FlaxBlenderbotSmallForSequenceClassificationModule
-        input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
+        input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
         attention_mask = jnp.ones_like(input_ids)
         decoder_input_ids = input_ids
         decoder_attention_mask = jnp.ones_like(input_ids)

src/transformers/models/marian/modeling_flax_marian.py

@@ -892,7 +892,7 @@ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDic
         # init input tensors
         input_ids = jnp.zeros(input_shape, dtype="i4")
         # make sure initialization pass will work for FlaxMarianForSequenceClassificationModule
-        input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
+        input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
         attention_mask = jnp.ones_like(input_ids)
         decoder_input_ids = input_ids
         decoder_attention_mask = jnp.ones_like(input_ids)
@@ -1422,7 +1422,7 @@ def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_
 
     def _adapt_logits_for_beam_search(self, logits):
         """This function enforces the padding token never to be generated."""
-        logits = jax.ops.index_update(logits, jax.ops.index[:, :, self.config.pad_token_id], float("-inf"))
+        logits = logits.at[jax.ops.index[:, :, self.config.pad_token_id]].set(float("-inf"))
         return logits
 
     def prepare_inputs_for_generation(

src/transformers/models/mbart/modeling_flax_mbart.py

@@ -960,7 +960,7 @@ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDic
         # init input tensors
         input_ids = jnp.zeros(input_shape, dtype="i4")
         # make sure initialization pass will work for FlaxMBartForSequenceClassificationModule
-        input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
+        input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
         attention_mask = jnp.ones_like(input_ids)
         decoder_input_ids = input_ids
         decoder_attention_mask = jnp.ones_like(input_ids)

src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py

@@ -964,9 +964,8 @@ def __call__(
 
             # these two operations makes sure that all values
             # before the output lengths indices are attended to
-            attention_mask = jax.ops.index_update(
-                attention_mask, jax.ops.index[jnp.arange(attention_mask.shape[0]), output_lengths - 1], 1
-            )
+            idx = jax.ops.index[jnp.arange(attention_mask.shape[0]), output_lengths - 1]
+            attention_mask = attention_mask.at[idx].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)
@@ -1038,7 +1037,8 @@ def _get_feature_vector_attention_mask(
 
         attention_mask = jnp.zeros((batch_size, feature_vector_length), dtype=attention_mask.dtype)
         # these two operations makes sure that all values before the output lengths idxs are attended to
-        attention_mask = attention_mask.at[(jnp.arange(attention_mask.shape[0]), output_lengths - 1)].set(1)
+        idx = (jnp.arange(attention_mask.shape[0]), output_lengths - 1)
+        attention_mask = attention_mask.at[idx].set(1)
         attention_mask = jnp.flip(jnp.flip(attention_mask, axis=-1).cumsum(axis=-1), axis=-1)
 
         attention_mask = jnp.array(attention_mask, dtype=bool)

src/transformers/models/xglm/modeling_flax_xglm.py

@@ -131,7 +131,7 @@ def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_
     Shift input ids one token to the right.
     """
     shifted_input_ids = jnp.roll(input_ids, 1, axis=-1)
-    shifted_input_ids = jax.ops.index_update(shifted_input_ids, (..., 0), decoder_start_token_id)
+    shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id)
     # replace possible -100 values in labels by `pad_token_id`
     shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
 

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_flax_{{cookiecutter.lowercase_modelname}}.py

@@ -1330,7 +1330,7 @@ def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_
     Shift input ids one token to the right.
     """
     shifted_input_ids = jnp.roll(input_ids, 1, axis=-1)
-    shifted_input_ids = jax.ops.index_update(shifted_input_ids, (..., 0), decoder_start_token_id)
+    shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id)
     # replace possible -100 values in labels by `pad_token_id`
     shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
 
@@ -2040,7 +2040,7 @@ def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDic
         # init input tensors
         input_ids = jnp.zeros(input_shape, dtype="i4")
         # make sure initialization pass will work for Flax{{cookiecutter.camelcase_modelname}}ForSequenceClassificationModule
-        input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
+        input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id)
         attention_mask = jnp.ones_like(input_ids)
         decoder_input_ids = input_ids
         decoder_attention_mask = jnp.ones_like(input_ids)

tests/generation/test_generation_flax_logits_process.py

@@ -41,7 +41,7 @@
 @require_flax
 class LogitsProcessorTest(unittest.TestCase):
     def _get_uniform_logits(self, batch_size: int, length: int):
-        scores = np.ones((batch_size, length)) / length
+        scores = jnp.ones((batch_size, length)) / length
         return scores
 
     def test_temperature_dist_warper(self):
@@ -51,8 +51,8 @@ def test_temperature_dist_warper(self):
         scores = self._get_uniform_logits(batch_size=2, length=length)
 
         # tweak scores to not be uniform anymore
-        scores[1, 5] = (1 / length) + 0.1  # peak, 1st batch
-        scores[1, 10] = (1 / length) - 0.4  # valley, 1st batch
+        scores = scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
+        scores = scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch
 
         # compute softmax
         probs = jax.nn.softmax(scores, axis=-1)

tests/generation/test_generation_flax_utils.py

@@ -24,7 +24,6 @@
 if is_flax_available():
     import os
 
-    import jax
     import jax.numpy as jnp
     from jax import jit
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
@@ -219,7 +218,7 @@ def test_greedy_generate_attn_mask(self):
         config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
 
         # pad attention mask on the left
-        attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)
+        attention_mask = attention_mask.at[(0, 0)].set(0)
 
         config.do_sample = False
         config.max_length = max_length
@@ -239,7 +238,7 @@ def test_sample_generate_attn_mask(self):
         config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
 
         # pad attention mask on the left
-        attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)
+        attention_mask = attention_mask.at[(0, 0)].set(0)
 
         config.do_sample = True
         config.max_length = max_length
@@ -259,7 +258,7 @@ def test_beam_search_generate_attn_mask(self):
         config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
 
         # pad attention mask on the left
-        attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)
+        attention_mask = attention_mask.at[(0, 0)].set(0)
 
         config.num_beams = 2
         config.max_length = max_length