VLM: enable skipped tests

src/transformers/models/aria/modeling_aria.py

@@ -1553,6 +1553,7 @@ def forward(
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             num_logits_to_keep=num_logits_to_keep,
+            cache_position=cache_position,
         )
 
         logits = outputs[0]

src/transformers/models/aria/modular_aria.py

@@ -1530,6 +1530,7 @@ def forward(
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             num_logits_to_keep=num_logits_to_keep,
+            cache_position=cache_position,
         )
 
         logits = outputs[0]

src/transformers/models/blip_2/modeling_blip_2.py

@@ -579,6 +579,9 @@ def _init_weights(self, module):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
             Whether to interpolate the pre-trained position encodings.
+        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`).
 """
 
 BLIP2_IMAGE_TEXT_RETRIEVAL_INPUTS_DOCSTRING = r"""
@@ -2094,6 +2097,7 @@ def forward(
         labels: Optional[torch.LongTensor] = None,
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
+        use_cache: Optional[bool] = None,
     ) -> Union[Tuple, Blip2ForConditionalGenerationModelOutput]:
         r"""
         Returns:
@@ -2217,6 +2221,7 @@ def forward(
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
+                use_cache=use_cache,
             )
             logits = outputs.logits if return_dict else outputs[0]
             loss = None
@@ -2242,6 +2247,7 @@ def forward(
                 output_hidden_states=output_hidden_states,
                 return_dict=True,  # toggle for easier access to loss/logits below
                 labels=labels,
+                use_cache=use_cache,
             )
             loss = outputs.loss
             logits = outputs.logits

src/transformers/models/instructblip/modeling_instructblip.py

@@ -441,6 +441,9 @@ def _init_weights(self, module):
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
             Whether to interpolate the pre-trained position encodings.
+        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`).
 """
 
 
@@ -1375,6 +1378,7 @@ def forward(
         labels: Optional[torch.LongTensor] = None,
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
+        use_cache: Optional[bool] = None,
     ) -> Union[Tuple, InstructBlipForConditionalGenerationModelOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -1485,6 +1489,7 @@ def forward(
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
+                use_cache=use_cache,
             )
             logits = outputs.logits if return_dict else outputs[0]
             loss = None
@@ -1510,6 +1515,7 @@ def forward(
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
                 labels=labels,
+                use_cache=use_cache,
             )
             loss = outputs.loss if return_dict else outputs[0]
             logits = outputs.logits if return_dict else outputs[1]

src/transformers/models/instructblipvideo/modeling_instructblipvideo.py

@@ -1265,6 +1265,9 @@ def forward(
             Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         interpolate_pos_encoding (`bool`, *optional*, defaults to `False`):
             Whether to interpolate the pre-trained position encodings.
+        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`).
 """
 
 
@@ -1369,6 +1372,7 @@ def forward(
         labels: Optional[torch.LongTensor] = None,
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
+        use_cache: Optional[bool] = None,
     ) -> Union[Tuple, InstructBlipVideoForConditionalGenerationModelOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -1512,6 +1516,7 @@ def forward(
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
+                use_cache=use_cache,
             )
             logits = outputs.logits if return_dict else outputs[0]
             loss = None
@@ -1537,6 +1542,7 @@ def forward(
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
                 labels=labels,
+                use_cache=use_cache,
             )
             loss = outputs.loss if return_dict else outputs[0]
             logits = outputs.logits if return_dict else outputs[1]

src/transformers/models/instructblipvideo/modular_instructblipvideo.py

@@ -188,6 +188,7 @@ def forward(
         labels: Optional[torch.LongTensor] = None,
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
+        use_cache: Optional[bool] = None,
     ) -> Union[Tuple, InstructBlipVideoForConditionalGenerationModelOutput]:
         r"""
         ```python
@@ -322,6 +323,7 @@ def forward(
                 output_attentions=output_attentions,
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
+                use_cache=use_cache,
             )
             logits = outputs.logits if return_dict else outputs[0]
             loss = None
@@ -347,6 +349,7 @@ def forward(
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
                 labels=labels,
+                use_cache=use_cache,
             )
             loss = outputs.loss if return_dict else outputs[0]
             logits = outputs.logits if return_dict else outputs[1]

src/transformers/models/kosmos2/modeling_kosmos2.py

@@ -1694,6 +1694,7 @@ def prepare_inputs_for_generation(
         past_key_values=None,
         attention_mask=None,
         use_cache=None,
+        cache_position=None,
         **model_kwargs,
     ):
         # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
@@ -1704,17 +1705,21 @@ def prepare_inputs_for_generation(
             attention_mask = input_ids.new_ones(input_shape)
 
         position_ids = None
+        if cache_position is None:
+            past_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
+            cache_position = torch.arange(past_length, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
 
-        # cut input_ids if past_key_values is used
         if past_key_values is not None:
             position_ids = create_position_ids_from_input_ids(
                 input_ids,
                 padding_idx=self.config.pad_token_id,
                 past_key_values_length=0,
-            )[:, -1:]
+            )
+
+            if input_ids.shape[1] != cache_position.shape[0]:
+                input_ids = input_ids[:, cache_position]
+                position_ids = position_ids[:, -input_ids.shape[1] :]
 
-            input_ids = input_ids[:, -1:]
-            # the image info. is already encoded into the past keys/values
             image_embeds = None
             image_embeds_position_mask = None
         elif image_embeds_position_mask is not None:

tests/generation/test_utils.py

@@ -503,7 +503,7 @@ def test_greedy_generate_dict_outputs_use_cache(self):
         for model_class in self.all_generative_model_classes:
             config, inputs_dict = self.prepare_config_and_inputs_for_generate()
 
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
             if any(model_name in model_class.__name__.lower() for model_name in ["rwkv"]):
                 self.skipTest(reason="Won't fix: model with non-standard dictionary output shapes")
@@ -634,7 +634,7 @@ def test_beam_search_generate_dict_outputs_use_cache(self):
         for model_class in self.all_generative_model_classes:
             config, inputs_dict = self.prepare_config_and_inputs_for_generate()
 
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
             if any(model_name in model_class.__name__.lower() for model_name in ["rwkv"]):
                 self.skipTest(reason="Won't fix: model with non-standard dictionary output shapes")
@@ -963,7 +963,7 @@ def test_contrastive_generate(self):
             config, inputs_dict = self.prepare_config_and_inputs_for_generate()
 
             # NOTE: contrastive search only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
             config.is_decoder = True
 
@@ -992,7 +992,7 @@ def test_contrastive_generate_dict_outputs_use_cache(self):
             config, inputs_dict = self.prepare_config_and_inputs_for_generate()
 
             # NOTE: contrastive search only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
             config.is_decoder = True
 
@@ -1032,7 +1032,7 @@ def test_contrastive_generate_low_memory(self):
             config, inputs_dict = self.prepare_config_and_inputs_for_generate(batch_size=1)
 
             # NOTE: contrastive search only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
 
             config.is_decoder = True
@@ -1151,6 +1151,10 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
                     "prophetnet",
                     "seamlessm4t",
                     "clvp",
+                    "mllama",  # special cache sizes
+                    "blip2",  # overridden `generate()`
+                    "instructblip",
+                    "instructblipvideo",
                 ]
             ):
                 self.skipTest(reason="May fix in the future: need model-specific fixes")
@@ -1159,7 +1163,7 @@ def test_assisted_decoding_matches_greedy_search(self, assistant_type):
             config, inputs_dict = self.prepare_config_and_inputs_for_generate(batch_size=1)
 
             # NOTE: assisted generation only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
 
             config.is_decoder = True
@@ -1224,6 +1228,10 @@ def test_prompt_lookup_decoding_matches_greedy_search(self):
                     "seamlessm4t",
                     "clvp",
                     "fuyu",
+                    "mllama",  # special cache sizes
+                    "blip2",  # overridden `generate()`
+                    "instructblip",
+                    "instructblipvideo",
                 ]
             ):
                 self.skipTest(reason="May fix in the future: need model-specific fixes")
@@ -1232,7 +1240,7 @@ def test_prompt_lookup_decoding_matches_greedy_search(self):
             config, inputs_dict = self.prepare_config_and_inputs_for_generate(batch_size=1)
 
             # NOTE: assisted generation only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
 
             config.is_decoder = True
@@ -1338,6 +1346,10 @@ def test_assisted_decoding_sample(self):
                     "prophetnet",
                     "seamlessm4t",
                     "clvp",
+                    "mllama",  # special cache sizes
+                    "blip2",  # overridden `generate()`
+                    "instructblip",
+                    "instructblipvideo",
                 ]
             ):
                 self.skipTest(reason="May fix in the future: need model-specific fixes")
@@ -1346,7 +1358,7 @@ def test_assisted_decoding_sample(self):
             config, inputs_dict = self.prepare_config_and_inputs_for_generate(batch_size=1)
 
             # NOTE: assisted generation only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
 
             config.is_decoder = True
@@ -1538,7 +1550,7 @@ def test_past_key_values_format(self):
             config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
 
             # If it doesn't support cache, pass the test
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
 
             model = model_class(config).to(torch_device)
@@ -1573,7 +1585,14 @@ def test_past_key_values_format(self):
 
             # Encoder-Decoder checks
             if config.is_encoder_decoder:
-                encoder_num_attention_heads = config.encoder_attention_heads
+                # encoder-decoder models usually don't have text config
+                # below is needed only for Pix2Struct which we cannot modify now due to BC
+                config = config.get_text_config()
+                encoder_num_attention_heads = (
+                    config.encoder_attention_heads
+                    if hasattr(config, "encoder_attention_heads")
+                    else config.num_attention_heads
+                )
                 encoder_per_head_embed_dim = embed_dim // encoder_num_attention_heads
                 batch_size, seq_length = inputs["decoder_input_ids"].shape
                 for i in range(num_hidden_layers):
@@ -1772,14 +1791,14 @@ def test_generate_from_inputs_embeds_with_static_cache(self):
     def test_generate_continue_from_past_key_values(self):
         # Tests that we can continue generating from past key values, returned from a previous `generate` call
         for model_class in self.all_generative_model_classes:
-            if any(model_name in model_class.__name__.lower() for model_name in ["imagegpt"]):
+            if any(model_name in model_class.__name__.lower() for model_name in ["imagegpt", "mllama"]):
                 self.skipTest(reason="Won't fix: old model with unique inputs/caches/other")
             if any(model_name in model_class.__name__.lower() for model_name in ["umt5"]):
                 self.skipTest(reason="TODO: needs modeling or test input preparation fixes for compatibility")
 
             config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
 
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
 
             # Let's make it always:
@@ -2129,7 +2148,7 @@ def test_assisted_decoding_with_num_logits_to_keep(self):
 
             config, inputs_dict = self.prepare_config_and_inputs_for_generate(batch_size=1)
             # NOTE: assisted generation only works with cache on at the moment.
-            if not hasattr(config, "use_cache"):
+            if not hasattr(config.get_text_config(), "use_cache"):
                 self.skipTest(reason=f"{model_class.__name__} doesn't support caching")
             config.use_cache = True
             config.is_decoder = True

tests/models/aria/test_modeling_aria.py

@@ -83,14 +83,14 @@ def __init__(
             moe_intermediate_size=4,
             moe_num_experts=4,
             moe_topk=2,
-            num_attention_heads=20,
+            num_attention_heads=8,
             num_experts_per_tok=3,
             num_hidden_layers=2,
-            num_key_value_heads=20,
+            num_key_value_heads=8,
             rope_theta=5000000,
             vocab_size=99,
             eos_token_id=2,
-            head_dim=2,
+            head_dim=4,
         ),
         is_training=True,
         vision_config=Idefics3VisionConfig(