Expose blip2qformer

src/transformers/models/auto/configuration_auto.py

@@ -54,6 +54,7 @@
         ("blenderbot-small", "BlenderbotSmallConfig"),
         ("blip", "BlipConfig"),
         ("blip-2", "Blip2Config"),
+        ("blip_2_qformer", "Blip2QFormerConfig"),
         ("bloom", "BloomConfig"),
         ("bridgetower", "BridgeTowerConfig"),
         ("bros", "BrosConfig"),
@@ -389,6 +390,7 @@
         ("blenderbot-small", "BlenderbotSmall"),
         ("blip", "BLIP"),
         ("blip-2", "BLIP-2"),
+        ("blip_2_qformer", "BLIP-2 QFormer"),
         ("bloom", "BLOOM"),
         ("bort", "BORT"),
         ("bridgetower", "BridgeTower"),
@@ -776,6 +778,7 @@
         ("rt_detr_resnet", "rt_detr"),
         ("granitevision", "llava_next"),
         ("sam_vision_model", "sam"),
+        ("blip_2_qformer", "blip_2"),
     ]
 )
 

src/transformers/models/auto/modeling_auto.py

@@ -54,6 +54,7 @@
         ("blenderbot-small", "BlenderbotSmallModel"),
         ("blip", "BlipModel"),
         ("blip-2", "Blip2Model"),
+        ("blip_2_qformer", "Blip2QFormerModel"),
         ("bloom", "BloomModel"),
         ("bridgetower", "BridgeTowerModel"),
         ("bros", "BrosModel"),

src/transformers/models/blip_2/configuration_blip_2.py

@@ -144,6 +144,8 @@ class Blip2QFormerConfig(PretrainedConfig):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
         layer_norm_eps (`float`, *optional*, defaults to 1e-12):
             The epsilon used by the layer normalization layers.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Index to be used for padding token.
         position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
             Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
             positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to

src/transformers/models/blip_2/modeling_blip_2.py

@@ -456,6 +456,21 @@ def _init_weights(self, module):
             configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
 """
 
+BLIP_2_QFORMER_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`Blip2QFormerConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
 BLIP_2_VISION_INPUTS_DOCSTRING = r"""
     Args:
         pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
@@ -621,6 +636,60 @@ def _init_weights(self, module):
 """
 
 
+BLIP2_QFORMER_INPUTS_DOCSTRING = r"""
+    Args:
+        query_embeds (`torch.FloatTensor`  of shape `(batch_size, sequence_length, hidden_size)`):
+            Hidden states to be used in the attention computation. If cross-attention,
+            will be used for the query (i.e., key and value will use the encoder_hidden_states).
+
+        query_length (`int`, *optional*):
+            Length of the query, usually based on the number of query tokens.
+            If no value is provided, query_length will be inferred by the query_embeds.
+
+        attention_mask (`torch.FloatTensor`, *optional*):
+            Attention mask of size `(batch, sequence_length)` where padding elements
+            are indicated by 0.
+
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+
+        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of:
+            shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and
+            value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are
+            used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key
+            value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape
+            `(batch_size, sequence_length)`.
+
+        use_cache (`bool`, `optional`):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
 # Copied from transformers.models.blip.modeling_blip.BlipEncoder with Blip->Blip2
 class Blip2Encoder(nn.Module):
     """
@@ -1248,11 +1317,13 @@ def forward(
         return embeddings
 
 
-class Blip2QFormerModel(Blip2PreTrainedModel):
-    """
-    Querying Transformer (Q-Former), used in BLIP-2.
+@add_start_docstrings(
     """
-
+    BLIP-2 Querying Transformer (Q-Former).
+    """,
+    BLIP_2_QFORMER_START_DOCSTRING,
+)
+class Blip2QFormerModel(Blip2PreTrainedModel):
     def __init__(self, config: Blip2QFormerConfig):
         super().__init__(config)
         self.config = config
@@ -1323,6 +1394,10 @@ def get_extended_attention_mask(
         extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
         return extended_attention_mask
 
+    @add_start_docstrings_to_model_forward(BLIP2_QFORMER_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=Blip2QFormerConfig
+    )
     def forward(
         self,
         query_embeds: torch.FloatTensor,
@@ -1338,23 +1413,7 @@ def forward(
         return_dict: Optional[bool] = None,
     ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
         r"""
-        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):
-            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
-            the model is configured as a decoder.
-        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
-            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
-            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of:
-            shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and
-            value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are
-            used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key
-            value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape
-            `(batch_size, sequence_length)`.
-        use_cache (`bool`, `optional`):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
-            `past_key_values`).
+        Returns:
         """
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

utils/check_docstrings.py

@@ -105,7 +105,6 @@
     "BlenderbotSmallConfig",
     "BlenderbotSmallTokenizerFast",
     "BlenderbotTokenizerFast",
-    "Blip2QFormerConfig",
     "Blip2VisionConfig",
     "BlipTextConfig",
     "BlipVisionConfig",

utils/check_repo.py

@@ -184,7 +184,6 @@
     "ClapAudioModelWithProjection",
     "Blip2TextModelWithProjection",
     "Blip2VisionModelWithProjection",
-    "Blip2QFormerModel",
     "Blip2VisionModel",
     "ErnieMForInformationExtraction",
     "FastSpeech2ConformerHifiGan",