[Flax] Fix hybrid clip (#12519)

* fix saving and loading

* update readme

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

@@ -68,6 +68,34 @@ export MODEL_DIR="./clip-roberta-base
 ln -s ~/transformers/examples/flax/summarization/run_hybrid_clip.py run_hybrid_clip.py
 ```
 
+## How to use the `FlaxHybridCLIP` model:
+
+The `FlaxHybridCLIP` class let's you load any text and vision encoder model to create a dual encoder. 
+Here is an example of how to load the model using pre-trained text and vision models.
+
+```python
+from modeling_hybrid_clip import FlaxHybridCLIP
+
+model = FlaxHybridCLIP.from_text_vision_pretrained("bert-base-uncased", "openai/clip-vit-base-patch32")
+
+# save the model
+model.save_pretrained("bert-clip")
+
+# load the saved model
+model = FlaxHybridCLIP.from_pretrained("bert-clip")
+```
+
+If the checkpoints are in PyTorch then one could pass `text_from_pt=True` and `vision_from_pt=True`. This will load the model
+PyTorch checkpoints convert them to flax and load the model.
+
+```python
+model = FlaxHybridCLIP.from_text_vision_pretrained("bert-base-uncased", "openai/clip-vit-base-patch32", text_from_pt=True, vision_from_pt=True)
+```
+
+This loads both the text and vision encoders using pre-trained weights, the projection layers are randomly
+initialized except for CLIP's vision model. If you use CLIP to initialize the vision model then the vision projection weights are also
+loaded using the pre-trained weights.
+
 ## Prepare the dataset
 
 We will use the MS-COCO dataset to train our dual encoder model. MS-COCO contains over 82,000 images, each of which has at least 5 different caption annotations. The dataset is usually used for image captioning tasks, but we can repurpose the image-caption pairs to train our dual encoder model for image search.
@@ -124,7 +152,7 @@ with open("coco_dataset/valid_dataset.json", "w") as f:
 Next we can run the example script to train the model:
 
 ```bash
-python run_clip.py \
+python run_hybrid_clip.py \
     --output_dir ${MODEL_DIR} \
     --text_model_name_or_path="roberta-base" \
     --vision_model_name_or_path="openai/clip-vit-base-patch32" \

examples/research_projects/jax-projects/hybrid_clip/configuration_hybrid_clip.py

@@ -25,31 +25,58 @@ class HybridCLIPConfig(PretrainedConfig):
             Dimentionality of text and vision projection layers.
         kwargs (`optional`):
             Dictionary of keyword arguments.
+
+    Examples::
+
+        >>> from transformers import BertConfig, CLIPConfig, HybridCLIPConfig, FlaxHybridCLIP
+
+        >>> # Initializing a BERT and CLIP configuration
+        >>> config_text = BertConfig()
+        >>> config_vision = CLIPConfig()
+
+        >>> config = HybridCLIPConfig.from_text_vision_configs(config_text, config_vision, projection_dim=512)
+
+        >>> # Initializing a BERT and CLIPVision model
+        >>> model = EncoderDecoderModel(config=config)
+
+        >>> # Accessing the model configuration
+        >>> config_text = model.config.text_config
+        >>> config_vision  = model.config.vision_config
+
+        >>> # Saving the model, including its configuration
+        >>> model.save_pretrained('my-model')
+
+        >>> # loading model and config from pretrained folder
+        >>> encoder_decoder_config = HybridCLIPConfig.from_pretrained('my-model')
+        >>> model = FlaxHybridCLIP.from_pretrained('my-model', config=encoder_decoder_config)
     """
 
     model_type = "hybrid-clip"
     is_composition = True
 
-    def __init__(self, text_config_dict, vision_config_dict, projection_dim=512, **kwargs):
+    def __init__(self, projection_dim=512, **kwargs):
         super().__init__(**kwargs)
 
-        if text_config_dict is None:
-            raise ValueError("`text_config_dict` can not be `None`.")
+        if "text_config" not in kwargs:
+            raise ValueError("`text_config` can not be `None`.")
+
+        if "vision_config" not in kwargs:
+            raise ValueError("`vision_config` can not be `None`.")
 
-        if vision_config_dict is None:
-            raise ValueError("`vision_config_dict` can not be `None`.")
+        text_config = kwargs.pop("text_config")
+        vision_config = kwargs.pop("vision_config")
 
-        text_model_type = text_config_dict.pop("model_type")
-        vision_model_type = vision_config_dict.pop("model_type")
+        text_model_type = text_config.pop("model_type")
+        vision_model_type = vision_config.pop("model_type")
 
         from transformers import AutoConfig
 
-        self.text_config = AutoConfig.for_model(text_model_type, **text_config_dict)
+        self.text_config = AutoConfig.for_model(text_model_type, **text_config)
 
         if vision_model_type == "clip":
-            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config_dict).vision_config
+            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
         else:
-            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config_dict)
+            self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
 
         self.projection_dim = projection_dim
         self.initializer_factor = 1.0
@@ -64,7 +91,7 @@ class HybridCLIPConfig(PretrainedConfig):
             :class:`HybridCLIPConfig`: An instance of a configuration object
         """
 
-        return cls(text_config_dict=text_config.to_dict(), vision_config_dict=vision_config.to_dict(), **kwargs)
+        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
 
     def to_dict(self):
         """

examples/research_projects/jax-projects/hybrid_clip/modeling_hybrid_clip.py

@@ -123,7 +123,7 @@ class FlaxHybridCLIPModule(nn.Module):
 
 
 class FlaxHybridCLIP(FlaxPreTrainedModel):
-    config: HybridCLIPConfig
+    config_class = HybridCLIPConfig
     module_class = FlaxHybridCLIPModule
 
     def __init__(
@@ -304,6 +304,58 @@ class FlaxHybridCLIP(FlaxPreTrainedModel):
         *model_args,
         **kwargs,
     ) -> FlaxPreTrainedModel:
+        """
+        Params:
+            text_model_name_or_path (:obj: `str`, `optional`):
+                Information necessary to initiate the text model. Can be either:
+
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
+                    - A path to a `directory` containing model weights saved using
+                      :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
+                    - A path or url to a `PyTorch checkpoint folder` (e.g, ``./pt_model``). In
+                      this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
+                      as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in
+                      a Flax model using the provided conversion scripts and loading the Flax model afterwards.
+
+            vision_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
+                Information necessary to initiate the vision model. Can be either:
+
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
+                    - A path to a `directory` containing model weights saved using
+                      :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
+                    - A path or url to a `PyTorch checkpoint folder` (e.g, ``./pt_model``). In
+                      this case, ``from_pt`` should be set to :obj:`True` and a configuration object should be provided
+                      as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in
+                      a Flax model using the provided conversion scripts and loading the Flax model afterwards.
+
+            model_args (remaining positional arguments, `optional`):
+                All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
+
+            kwargs (remaining dictionary of keyword arguments, `optional`):
+                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
+                :obj:`output_attentions=True`).
+
+                - To update the text configuration, use the prefix `text_` for each configuration parameter.
+                - To update the vision configuration, use the prefix `vision_` for each configuration parameter.
+                - To update the parent model configuration, do not use a prefix for each configuration parameter.
+
+                Behaves differently depending on whether a :obj:`config` is provided or automatically loaded.
+
+        Example::
+
+            >>> from transformers import FlaxHybridCLIP
+            >>> # initialize a model from pretrained BERT and CLIP models. Note that the projection layers will be randomly initialized.
+            >>> # If using CLIP's vision model the vision projection layer will be initialized using pre-trained weights
+            >>> model = FlaxHybridCLIP.from_text_vision_pretrained('bert-base-uncased', 'openai/clip-vit-base-patch32')
+            >>> # saving model after fine-tuning
+            >>> model.save_pretrained("./bert-clip")
+            >>> # load fine-tuned model
+            >>> model = FlaxHybridCLIP.from_pretrained("./bert-clip")
+        """
 
         kwargs_text = {
             argument[len("text_") :]: value for argument, value in kwargs.items() if argument.startswith("text_")
@@ -333,9 +385,7 @@ class FlaxHybridCLIP(FlaxPreTrainedModel):
                 text_config = AutoConfig.from_pretrained(text_model_name_or_path)
                 kwargs_text["config"] = text_config
 
-            text_model = FlaxAutoModel.from_pretrained(
-                text_model_name_or_path, *model_args, from_pt=True, **kwargs_text
-            )
+            text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text)
 
         vision_model = kwargs_vision.pop("model", None)
         if vision_model is None:

examples/research_projects/jax-projects/hybrid_clip/run_hybrid_clip.py

@@ -87,6 +87,10 @@ class ModelArguments:
             "Don't set if you want to train a model from scratch."
         },
     )
+    from_pt: bool = field(
+        default=True,
+        metadata={"help": "whether to load the text and vision model using PyTorch checkpoints."},
+    )
     config_name: Optional[str] = field(
         default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
     )
@@ -332,6 +336,8 @@ def main():
         model_args.vision_model_name_or_path,
         seed=training_args.seed,
         dtype=getattr(jnp, model_args.dtype),
+        text_from_pt=model_args.from_pt,
+        vision_from_pt=model_args.from_pt,
     )
     config = model.config
     # set seed for torch dataloaders