Add FlaxCLIPTextModelWithProjection

docs/source/en/model_doc/clip.md

@@ -184,6 +184,11 @@ The resource should ideally demonstrate something new instead of duplicating an
 [[autodoc]] FlaxCLIPTextModel
     - __call__
 
+## FlaxCLIPTextModelWithProjection
+
+[[autodoc]] FlaxCLIPTextModelWithProjection
+    - __call__
+
 ## FlaxCLIPVisionModel
 
 [[autodoc]] FlaxCLIPVisionModel

src/transformers/__init__.py

@@ -3965,6 +3965,7 @@
             "FlaxCLIPPreTrainedModel",
             "FlaxCLIPTextModel",
             "FlaxCLIPTextPreTrainedModel",
+            "FlaxCLIPTextModelWithProjection",
             "FlaxCLIPVisionModel",
             "FlaxCLIPVisionPreTrainedModel",
         ]
@@ -7388,6 +7389,7 @@
             FlaxCLIPModel,
             FlaxCLIPPreTrainedModel,
             FlaxCLIPTextModel,
+            FlaxCLIPTextModelWithProjection,
             FlaxCLIPTextPreTrainedModel,
             FlaxCLIPVisionModel,
             FlaxCLIPVisionPreTrainedModel,

src/transformers/models/clip/__init__.py

@@ -94,6 +94,7 @@
         "FlaxCLIPPreTrainedModel",
         "FlaxCLIPTextModel",
         "FlaxCLIPTextPreTrainedModel",
+        "FlaxCLIPTextModelWithProjection",
         "FlaxCLIPVisionModel",
         "FlaxCLIPVisionPreTrainedModel",
     ]
@@ -167,6 +168,7 @@
             FlaxCLIPModel,
             FlaxCLIPPreTrainedModel,
             FlaxCLIPTextModel,
+            FlaxCLIPTextModelWithProjection,
             FlaxCLIPTextPreTrainedModel,
             FlaxCLIPVisionModel,
             FlaxCLIPVisionPreTrainedModel,

src/transformers/models/clip/modeling_flax_clip.py

@@ -155,6 +155,36 @@
 """
 
 
+@flax.struct.dataclass
+class FlaxCLIPTextModelOutput(ModelOutput):
+    """
+    Base class for text model's outputs that also contains a pooling of the last hidden states.
+
+    Args:
+        text_embeds (`jnp.ndarray` of shape `(batch_size, output_dim`):
+            The text embeddings obtained by applying the projection layer to the pooled output of
+            [`FlaxCLIPTextModel`].
+        last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
+            `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+    """
+
+    text_embeds: jnp.ndarray = None
+    last_hidden_state: jnp.ndarray = None
+    hidden_states: Optional[Tuple[jnp.ndarray]] = None
+    attentions: Optional[Tuple[jnp.ndarray]] = None
+
+
 @flax.struct.dataclass
 class FlaxCLIPOutput(ModelOutput):
     """
@@ -1007,6 +1037,78 @@ class FlaxCLIPTextModel(FlaxCLIPTextPreTrainedModel):
 )
 
 
+class FlaxCLIPTextModelWithProjectionModule(nn.Module):
+    config: CLIPTextConfig
+    dtype: jnp.dtype = jnp.float32
+
+    def setup(self):
+        self.text_model = FlaxCLIPTextTransformer(self.config, dtype=self.dtype)
+        self.text_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype)
+
+    def __call__(
+        self,
+        input_ids,
+        attention_mask,
+        position_ids,
+        deterministic: bool = True,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = text_outputs[1]
+        text_embeds = self.text_projection(pooled_output)
+
+        if not return_dict:
+            return (text_embeds, text_outputs[0]) + text_outputs[2:]
+
+        return FlaxCLIPTextModelOutput(
+            text_embeds=text_embeds,
+            last_hidden_state=text_outputs.last_hidden_state,
+            hidden_states=text_outputs.hidden_states,
+            attentions=text_outputs.attentions,
+        )
+
+
+class FlaxCLIPTextModelWithProjection(FlaxCLIPTextPreTrainedModel):
+    module_class = FlaxCLIPTextModelWithProjectionModule
+
+
+FLAX_CLIP_TEXT_MODEL_WITH_PROJECTION_DOCSTRING = """
+    Returns:
+
+    Example:
+
+    ```python
+    >>> from transformers import AutoTokenizer, FlaxCLIPTextModelWithProjection
+
+    >>> model = FlaxCLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32")
+    >>> tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+    >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np")
+
+    >>> outputs = model(**inputs)
+    >>> text_embeds = outputs.text_embeds
+    ```
+"""
+
+overwrite_call_docstring(
+    FlaxCLIPTextModelWithProjection, CLIP_TEXT_INPUTS_DOCSTRING + FLAX_CLIP_TEXT_MODEL_WITH_PROJECTION_DOCSTRING
+)
+append_replace_return_docstrings(
+    FlaxCLIPTextModelWithProjection, output_type=FlaxCLIPTextModelOutput, config_class=CLIPTextConfig
+)
+
+
 class FlaxCLIPVisionModule(nn.Module):
     config: CLIPVisionConfig
     dtype: jnp.dtype = jnp.float32

src/transformers/utils/dummy_flax_objects.py

@@ -562,6 +562,13 @@ def __init__(self, *args, **kwargs):
         requires_backends(self, ["flax"])
 
 
+class FlaxCLIPTextModelWithProjection(metaclass=DummyObject):
+    _backends = ["flax"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["flax"])
+
+
 class FlaxCLIPTextPreTrainedModel(metaclass=DummyObject):
     _backends = ["flax"]
 

tests/models/clip/test_modeling_flax_clip.py

@@ -19,7 +19,12 @@
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
-    from transformers.models.clip.modeling_flax_clip import FlaxCLIPModel, FlaxCLIPTextModel, FlaxCLIPVisionModel
+    from transformers.models.clip.modeling_flax_clip import (
+        FlaxCLIPModel,
+        FlaxCLIPTextModel,
+        FlaxCLIPTextModelWithProjection,
+        FlaxCLIPVisionModel,
+    )
 
 if is_torch_available():
     import torch
@@ -315,7 +320,7 @@ def prepare_config_and_inputs_for_common(self):
 
 @require_flax
 class FlaxCLIPTextModelTest(FlaxModelTesterMixin, unittest.TestCase):
-    all_model_classes = (FlaxCLIPTextModel,) if is_flax_available() else ()
+    all_model_classes = (FlaxCLIPTextModel, FlaxCLIPTextModelWithProjection) if is_flax_available() else ()
 
     def setUp(self):
         self.model_tester = FlaxCLIPTextModelTester(self)

utils/check_repo.py

@@ -205,6 +205,7 @@
     "TFGroupViTTextModel",
     "TFGroupViTVisionModel",
     "FlaxCLIPTextModel",
+    "FlaxCLIPTextModelWithProjection",
     "FlaxCLIPVisionModel",
     "FlaxWav2Vec2ForCTC",
     "DetrForSegmentation",