Add TF port of BLIP (#22090)

* Initial commit

* more stash commit

* Yet another stash commit

* yet more stash commit

* Mostly working except for docs / repo consistency

* Stop importing model list from torch file

* Add TF BLIP models to docs

* Add auto classes

* Move get_text_features and get_image_features

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/blip/test_modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/blip/test_modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update tests/models/blip/test_modeling_tf_blip_text.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Use channels_last convolutions in TF (better performance + compatibility)

* Remove _shape function

* Move multi-line statement to one line in PT + TF

* Specify tf.keras.layers instead of importing from it

* Remove test_gradient_checkpointing and empty test_training methods

* move some multi-line statements to one line

* Update docstring for generate

* Remove pruned heads set

* Remove self.seq_len_dim

* Fixed issues with loss computation, should resolve some tests. Also ensured that the PT version follows the config for output_attentions and output_hidden_states

* ensure original model follows config in more cases

* Skip the same cross-attention tests in the PT tests - didn't realize we did it twice!

* Add training args throughout the models and layers

* make fixup

* Fix docstring for inputs_embeds

* Add docstring for is_decoder

* Add docstrings to text models

* Remove redundant computation

* Add unpack_inputs / keras_serializable

* Add modeling_tf_blip to doctests

* Add config classes for keras serialization

* Changes to allow model porting with pt-to-tf

* Quick fix to decoder head and test tweaks

* Revert an issue with masking the embeddings outputs

* Allow missing keys in some equivalence tests (for unused layers)

* Add tf-pt equivalence tests back in

* Update src/transformers/models/blip/modeling_tf_blip.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/blip/modeling_tf_blip_text.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* make fixup

* Refactor invert_attention_mask out into tf_utils

* Re-enable cross-tests on the PT side too

---------
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/en/index.mdx

@@ -269,7 +269,7 @@ Flax), PyTorch, and/or TensorFlow.
 |              BiT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          Blenderbot           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |        BlenderbotSmall        |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|             BLIP              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             BLIP              |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            BLIP-2             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             BLOOM             |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          BridgeTower          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/blip.mdx

-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 the License. You may obtain a copy of the License at
@@ -93,4 +93,40 @@ The original code can be found [here](https://github.com/salesforce/BLIP).
 ## BlipForQuestionAnswering
 
 [[autodoc]] BlipForQuestionAnswering
-    - forward
+    - forward
\ No newline at end of file
+
+## TFBlipModel
+
+[[autodoc]] TFBlipModel
+    - call
+    - get_text_features
+    - get_image_features
+
+## TFBlipTextModel
+
+[[autodoc]] TFBlipTextModel
+    - call
+
+
+## TFBlipVisionModel
+
+[[autodoc]] TFBlipVisionModel
+    - call
+
+
+## TFBlipForConditionalGeneration
+
+[[autodoc]] TFBlipForConditionalGeneration
+    - call
+
+
+## TFBlipForImageTextRetrieval
+
+[[autodoc]] TFBlipForImageTextRetrieval
+    - call
+
+
+## TFBlipForQuestionAnswering
+
+[[autodoc]] TFBlipForQuestionAnswering
+    - call
\ No newline at end of file

src/transformers/__init__.py

@@ -2903,6 +2903,18 @@ else:
     _import_structure["models.blenderbot_small"].extend(
         ["TFBlenderbotSmallForConditionalGeneration", "TFBlenderbotSmallModel", "TFBlenderbotSmallPreTrainedModel"]
     )
+    _import_structure["models.blip"].extend(
+        [
+            "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFBlipForConditionalGeneration",
+            "TFBlipForImageTextRetrieval",
+            "TFBlipForQuestionAnswering",
+            "TFBlipModel",
+            "TFBlipPreTrainedModel",
+            "TFBlipTextModel",
+            "TFBlipVisionModel",
+        ]
+    )
     _import_structure["models.camembert"].extend(
         [
             "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -6145,6 +6157,16 @@ if TYPE_CHECKING:
             TFBlenderbotSmallModel,
             TFBlenderbotSmallPreTrainedModel,
         )
+        from .models.blip import (
+            TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFBlipForConditionalGeneration,
+            TFBlipForImageTextRetrieval,
+            TFBlipForQuestionAnswering,
+            TFBlipModel,
+            TFBlipPreTrainedModel,
+            TFBlipTextModel,
+            TFBlipVisionModel,
+        )
         from .models.camembert import (
             TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFCamembertForCausalLM,

src/transformers/commands/pt_to_tf.py

@@ -196,7 +196,7 @@ class PTtoTFCommand(BaseTransformersCLICommand):
         self._extra_commit_description = extra_commit_description
         self._override_model_class = override_model_class
 
-    def get_inputs(self, pt_model, config):
+    def get_inputs(self, pt_model, tf_dummy_inputs, config):
         """
         Returns the right inputs for the model, based on its signature.
         """
@@ -255,7 +255,11 @@ class PTtoTFCommand(BaseTransformersCLICommand):
         tf_input = processor(**processor_inputs, return_tensors="tf")
 
         # Extra input requirements, in addition to the input modality
-        if config.is_encoder_decoder or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder")):
+        if (
+            config.is_encoder_decoder
+            or (hasattr(pt_model, "encoder") and hasattr(pt_model, "decoder"))
+            or "decoder_input_ids" in tf_dummy_inputs
+        ):
             decoder_input_ids = np.asarray([[1], [1]], dtype=int) * (pt_model.config.decoder_start_token_id or 0)
             pt_input.update({"decoder_input_ids": torch.tensor(decoder_input_ids)})
             tf_input.update({"decoder_input_ids": tf.convert_to_tensor(decoder_input_ids)})
@@ -306,18 +310,24 @@ class PTtoTFCommand(BaseTransformersCLICommand):
             except AttributeError:
                 raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.")
 
-        # Load models and acquire a basic input compatible with the model.
+        # Check the TF dummy inputs to see what keys we need in the forward pass
+        tf_from_pt_model = tf_class.from_config(config)
+        tf_dummy_inputs = tf_from_pt_model.dummy_inputs
+
+        del tf_from_pt_model  # Try to keep only one model in memory at a time
+
+        # Load the model and get some basic inputs
         pt_model = pt_class.from_pretrained(self._local_dir)
         pt_model.eval()
 
-        pt_input, tf_input = self.get_inputs(pt_model, config)
+        pt_input, tf_input = self.get_inputs(pt_model, tf_dummy_inputs, config)
 
         with torch.no_grad():
             pt_outputs = pt_model(**pt_input, output_hidden_states=True)
         del pt_model  # will no longer be used, and may have a large memory footprint
 
         tf_from_pt_model = tf_class.from_pretrained(self._local_dir, from_pt=True)
-        tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True)
+        tf_from_pt_outputs = tf_from_pt_model(**tf_input, output_hidden_states=True, training=False)
 
         # Confirms that cross loading PT weights into TF worked.
         crossload_differences = self.find_pt_tf_differences(pt_outputs, tf_from_pt_outputs)

src/transformers/modeling_tf_utils.py

@@ -406,6 +406,7 @@ def unpack_inputs(func):
         func (`callable`):
             The callable function of the TensorFlow model.
 
+
     Returns:
         A callable that wraps the original `func` with the behavior described above.
     """
@@ -1157,6 +1158,38 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         """
         return cls(config, **kwargs)
 
+    def get_head_mask(self, head_mask: Optional[tf.Tensor], num_hidden_layers: int) -> tf.Tensor:
+        """
+        Prepare the head mask if needed.
+
+        Args:
+            head_mask (`tf.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*):
+                The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
+            num_hidden_layers (`int`):
+                The number of hidden layers in the model.
+
+        Returns:
+            `tf.Tensor` with shape `[num_hidden_layers x batch x num_heads x seq_length x seq_length]` or list with
+            `[None]` for each layer.
+        """
+        if head_mask is not None:
+            head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
+        else:
+            head_mask = [None] * num_hidden_layers
+
+        return head_mask
+
+    def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
+        """-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
+        if head_mask.shape.rank == 1:
+            head_mask = head_mask[None, None, :, None, None]
+            head_mask = tf.repeat(head_mask, repeats=num_hidden_layers, axis=0)
+        elif head_mask.shape.rank == 2:
+            head_mask = head_mask[:, None, :, None, None]
+        assert head_mask.shape.rank == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
+        head_mask = tf.cast(head_mask, tf.float32)  # switch to float if need + fp16 compatibility
+        return head_mask
+
     def eager_serving(self, inputs):
         """
         Method used for serving the model. Intended not to be compiled with a tf.function decorator so that we can use

src/transformers/models/auto/modeling_tf_auto.py

@@ -34,6 +34,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("bert", "TFBertModel"),
         ("blenderbot", "TFBlenderbotModel"),
         ("blenderbot-small", "TFBlenderbotSmallModel"),
+        ("blip", "TFBlipModel"),
         ("camembert", "TFCamembertModel"),
         ("clip", "TFCLIPModel"),
         ("convbert", "TFConvBertModel"),
@@ -213,6 +214,7 @@ TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
 TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Zero Shot Image Classification mapping
+        ("blip", "TFBlipModel"),
         ("clip", "TFCLIPModel"),
     ]
 )

src/transformers/models/blip/__init__.py

@@ -13,7 +13,13 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_torch_available,
+    is_vision_available,
+)
 
 
 _import_structure = {
@@ -52,6 +58,23 @@ else:
         "BlipForImageTextRetrieval",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_blip"] = [
+        "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFBlipModel",
+        "TFBlipPreTrainedModel",
+        "TFBlipForConditionalGeneration",
+        "TFBlipForQuestionAnswering",
+        "TFBlipVisionModel",
+        "TFBlipTextModel",
+        "TFBlipForImageTextRetrieval",
+    ]
+
 if TYPE_CHECKING:
     from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
     from .processing_blip import BlipProcessor
@@ -81,6 +104,23 @@ if TYPE_CHECKING:
             BlipVisionModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_blip import (
+            TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFBlipForConditionalGeneration,
+            TFBlipForImageTextRetrieval,
+            TFBlipForQuestionAnswering,
+            TFBlipModel,
+            TFBlipPreTrainedModel,
+            TFBlipTextModel,
+            TFBlipVisionModel,
+        )
+
 else:
     import sys
 

src/transformers/models/blip/modeling_blip.py

@@ -313,17 +313,12 @@ class BlipAttention(nn.Module):
 
         bsz, tgt_len, embed_dim = hidden_states.size()
 
-        mixed_qkv = self.qkv(hidden_states)
         mixed_qkv = (
             self.qkv(hidden_states)
             .reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads)
             .permute(2, 0, 3, 1, 4)
         )
-        query_states, key_states, value_states = (
-            mixed_qkv[0],
-            mixed_qkv[1],
-            mixed_qkv[2],
-        )
+        query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2]
 
         # Take the dot product between "query" and "key" to get the raw attention scores.
         attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
@@ -587,9 +582,7 @@ class BlipEncoder(nn.Module):
         r"""
         Args:
             inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
+                Embedded representation of the inputs. Should be float, not int tokens.
             attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                 Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
 
@@ -824,10 +817,7 @@ class BlipModel(BlipPreTrainedModel):
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            return_dict=return_dict,
-        )
+        vision_outputs = self.vision_model(pixel_values=pixel_values, return_dict=return_dict)
 
         pooled_output = vision_outputs[1]  # pooled_output
         image_features = self.visual_projection(pooled_output)
@@ -993,6 +983,10 @@ class BlipForConditionalGeneration(BlipPreTrainedModel):
         ```"""
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
 
         vision_outputs = self.vision_model(
             pixel_values=pixel_values,
@@ -1037,7 +1031,7 @@ class BlipForConditionalGeneration(BlipPreTrainedModel):
         Overrides *generate* function to be able to use the model as a conditional generator
 
         Parameters:
-            pixel_values (*torch.FloatTensor* of shape *(batch_size, image_width, image_height)*:
+            pixel_values (*torch.FloatTensor* of shape *(batch_size, num_channels, image_height, image_width)*:
                 Input image to be processed
             input_ids (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
                 The sequence used as a prompt for the generation.
@@ -1066,9 +1060,7 @@ class BlipForConditionalGeneration(BlipPreTrainedModel):
         """
 
         batch_size = pixel_values.shape[0]
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-        )
+        vision_outputs = self.vision_model(pixel_values=pixel_values)
 
         image_embeds = vision_outputs[0]
 
@@ -1198,6 +1190,10 @@ class BlipForQuestionAnswering(BlipPreTrainedModel):
             )
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
 
         vision_outputs = self.vision_model(
             pixel_values=pixel_values,
@@ -1266,7 +1262,7 @@ class BlipForQuestionAnswering(BlipPreTrainedModel):
         Parameters:
             input_ids (*torch.LongTensor* of shape *(batch_size, sequence_length)*):
                 The sequence used as a prompt for the generation.
-            pixel_values (*torch.FloatTensor* of shape *(batch_size, image_width, image_height)*:
+            pixel_values (*torch.FloatTensor* of shape *(batch_size, num_channels, image_height, image_width)*:
                 Input image to be processed
             attention_mask (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
                 Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`. `1` for
@@ -1295,9 +1291,7 @@ class BlipForQuestionAnswering(BlipPreTrainedModel):
         2
         ```
         """
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-        )
+        vision_outputs = self.vision_model(pixel_values=pixel_values)
 
         image_embeds = vision_outputs[0]
 
@@ -1412,6 +1406,10 @@ class BlipForImageTextRetrieval(BlipPreTrainedModel):
         ```
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
 
         vision_outputs = self.vision_model(
             pixel_values=pixel_values,

src/transformers/models/blip_2/modeling_blip_2.py

@@ -453,9 +453,7 @@ class Blip2Encoder(nn.Module):
         r"""
         Args:
             inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-                than the model's internal embedding lookup matrix.
+                Embedded representation of the inputs. Should be float, not int tokens.
             attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                 Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
 

src/transformers/tf_utils.py

@@ -68,3 +68,31 @@ def stable_softmax(logits: tf.Tensor, axis: Optional[int] = None, name: Optional
     # TODO: When the issue linked above gets sorted, add a check on TF version here and use the original function if
     # it has the fix. After we drop the support for unfixed versions, remove this function.
     return tf.nn.softmax(logits=logits + 1e-9, axis=axis, name=name)
+
+
+def invert_attention_mask(encoder_attention_mask: tf.Tensor) -> tf.Tensor:
+    """
+    Invert an attention mask (e.g., switches 0. and 1.).
+
+    Args:
+        encoder_attention_mask (`torch.Tensor`): An attention mask.
+
+    Returns:
+        `tf.Tensor`: The inverted attention mask.
+    """
+    if not isinstance(encoder_attention_mask, tf.Tensor):
+        encoder_attention_mask = tf.convert_to_tensor(encoder_attention_mask)  # Catches stray NumPy inputs
+    if encoder_attention_mask.shape.rank == 3:
+        encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
+    if encoder_attention_mask.shape.rank == 2:
+        encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
+    # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
+    # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
+    # /transformer/transformer_layers.py#L270
+    # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
+    # encoder_extended_attention_mask.transpose(-1, -2))
+    encoder_extended_attention_mask = (
+        tf.cast(1, encoder_attention_mask.dtype) - encoder_extended_attention_mask
+    ) * encoder_extended_attention_mask.dtype.min
+
+    return encoder_extended_attention_mask

src/transformers/utils/dummy_tf_objects.py

@@ -556,6 +556,58 @@ class TFBlenderbotSmallPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFBlipForConditionalGeneration(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFBlipForImageTextRetrieval(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFBlipForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFBlipModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFBlipPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFBlipTextModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFBlipVisionModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/blip/test_modeling_blip.py

@@ -342,6 +342,9 @@ class BlipTextModelTest(ModelTesterMixin, unittest.TestCase):
             model = BlipTextModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    def test_pt_tf_model_equivalence(self):
+        super().test_pt_tf_model_equivalence(allow_missing_keys=True)
+
 
 class BlipModelTester:
     def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
@@ -524,6 +527,9 @@ class BlipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             model = BlipModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    def test_pt_tf_model_equivalence(self):
+        super().test_pt_tf_model_equivalence(allow_missing_keys=True)
+
 
 class BlipTextRetrievalModelTester:
     def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):

tests/models/blip/test_modeling_blip_text.py

@@ -164,3 +164,6 @@ class BlipTextModelTest(ModelTesterMixin, unittest.TestCase):
         for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             model = BlipTextModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
+
+    def test_pt_tf_model_equivalence(self):
+        super().test_pt_tf_model_equivalence(allow_missing_keys=True)

tests/models/blip/test_modeling_tf_blip_text.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the TensorFlow Blip model. """
+import unittest
+
+import numpy as np
+
+from transformers import BlipTextConfig
+from transformers.testing_utils import require_tf, slow
+from transformers.utils import is_tf_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFBlipTextModel
+    from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
+
+
+class BlipTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        projection_dim=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        bos_token_id=0,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+        self.bos_token_id = bos_token_id
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        if input_mask is not None:
+            input_mask = input_mask.numpy()
+            batch_size, seq_length = input_mask.shape
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
+            for batch_idx, start_index in enumerate(rnd_start_indices):
+                input_mask[batch_idx, :start_index] = 1
+                input_mask[batch_idx, start_index:] = 0
+
+        config = self.get_config()
+
+        return config, input_ids, tf.convert_to_tensor(input_mask)
+
+    def get_config(self):
+        return BlipTextConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+            bos_token_id=self.bos_token_id,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = TFBlipTextModel(config=config)
+        result = model(input_ids, attention_mask=input_mask, training=False)
+        result = model(input_ids, training=False)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_tf
+class BlipTextModelTest(TFModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TFBlipTextModel,) if is_tf_available() else ()
+    test_onnx = False
+    test_pruning = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = BlipTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=BlipTextConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_training(self):
+        pass
+
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="Blip does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            try:
+                model = TFBlipTextModel.from_pretrained(model_name)
+            except OSError:
+                model = TFBlipTextModel.from_pretrained(model_name, from_pt=True)
+            self.assertIsNotNone(model)
+
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=True):
+        super().test_pt_tf_model_equivalence(allow_missing_keys=allow_missing_keys)

tests/test_modeling_common.py

@@ -1984,7 +1984,7 @@ class ModelTesterMixin:
         self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(pt_model))
 
     @is_pt_tf_cross_test
-    def test_pt_tf_model_equivalence(self):
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
         import transformers
 
         for model_class in self.all_model_classes:
@@ -2036,8 +2036,12 @@ class ModelTesterMixin:
             # Check we can load pt model in tf and vice-versa with model => model functions
             # Here requires `tf_inputs_dict` to build `tf_model`
             tf_inputs_dict = self.prepare_tf_inputs_from_pt_inputs(pt_inputs_dict)
-            tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
-            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(
+                pt_model, tf_model, allow_missing_keys=allow_missing_keys
+            )
 
             # Original test: check without `labels`
             self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict)
@@ -2049,11 +2053,15 @@ class ModelTesterMixin:
             with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
                 torch.save(pt_model.state_dict(), pt_checkpoint_path)
-                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
+                    tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
 
                 tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
                 tf_model.save_weights(tf_checkpoint_path)
-                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
+                    pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
 
             # Original test: check without `labels`
             self.check_pt_tf_models(tf_model, pt_model, pt_inputs_dict)

tests/test_modeling_tf_common.py

@@ -668,7 +668,7 @@ class TFModelTesterMixin:
         self.check_pt_tf_outputs(tf_outputs, pt_outputs, type(tf_model))
 
     @is_pt_tf_cross_test
-    def test_pt_tf_model_equivalence(self):
+    def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
         import transformers
 
         for model_class in self.all_model_classes:
@@ -703,8 +703,12 @@ class TFModelTesterMixin:
                 tf_inputs_dict_with_labels = None
 
             # Check we can load pt model in tf and vice-versa with model => model functions
-            tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
-            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
+            tf_model = transformers.load_pytorch_model_in_tf2_model(
+                tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
+            )
+            pt_model = transformers.load_tf2_model_in_pytorch_model(
+                pt_model, tf_model, allow_missing_keys=allow_missing_keys
+            )
 
             # Original test: check without `labels`
             self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
@@ -716,11 +720,15 @@ class TFModelTesterMixin:
             with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
                 torch.save(pt_model.state_dict(), pt_checkpoint_path)
-                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
+                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
+                    tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
 
                 tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
                 tf_model.save_weights(tf_checkpoint_path)
-                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
+                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
+                    pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
+                )
 
             # Original test: check without `labels`
             self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
@@ -791,7 +799,7 @@ class TFModelTesterMixin:
                     name="pixel_values",
                     dtype="float32",
                 )
-            elif model_class.__name__ in ["TFCLIPModel", "TFGroupViTModel"]:
+            elif model_class.__name__ in ["TFCLIPModel", "TFGroupViTModel", "TFBlipModel"]:
                 inputs = {
                     "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"),
                     "pixel_values": tf.keras.Input(
@@ -1792,6 +1800,8 @@ class TFModelTesterMixin:
         for model_class in self.all_model_classes:
             model = model_class(config)
             tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class, return_labels=False)
+            if "labels" in tf_inputs_dict:
+                return  # This is some kinda funky decoder model that needs labels in its forward pass
             tf_inputs_dict = {
                 key: val
                 for key, val in tf_inputs_dict.items()
@@ -1805,7 +1815,7 @@ class TFModelTesterMixin:
             test_batch = next(iter(tf_dataset))
             if isinstance(test_batch, tf.Tensor):
                 self.assertEqual(len(test_batch), len(input_dataset))  # Assert we didn't lose any data
-            else:
+            elif isinstance(test_batch, dict):
                 # Assert we discarded the unwanted extra column but kept everything else
                 self.assertEqual(len(test_batch), len(input_dataset.features) - 1)
                 self.assertNotIn("extra_unwanted_column", test_batch)

utils/check_repo.py

@@ -145,6 +145,7 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "TFSegformerDecodeHead",  # Not a regular model.
     "AltRobertaModel",  # Building part of bigger (tested) model.
     "BlipTextLMHeadModel",  # No need to test it as it is tested by BlipTextVision models
+    "TFBlipTextLMHeadModel",  # No need to test it as it is tested by BlipTextVision models
     "BridgeTowerTextModel",  # No need to test it as it is tested by BridgeTowerModel model.
     "BridgeTowerVisionModel",  # No need to test it as it is tested by BridgeTowerModel model.
     "SpeechT5Decoder",  # Building part of bigger (tested) model.
@@ -205,6 +206,12 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "BlipVisionModel",
     "BlipTextLMHeadModel",
     "BlipTextModel",
+    "TFBlipForConditionalGeneration",
+    "TFBlipForImageTextRetrieval",
+    "TFBlipForQuestionAnswering",
+    "TFBlipVisionModel",
+    "TFBlipTextLMHeadModel",
+    "TFBlipTextModel",
     "Swin2SRForImageSuperResolution",
     "BridgeTowerForImageAndTextRetrieval",
     "BridgeTowerForMaskedLM",