IDEFICS: support inputs embeds (huggingface#34043)

* support embeds

* use cache from config

* style...

* fix tests after rebase

src/transformers/generation/utils.py

@@ -857,7 +857,7 @@ def _get_logits_processor(
                     self,
                     unconditional_ids=negative_prompt_ids,
                     unconditional_attention_mask=negative_prompt_attention_mask,
-                    use_cache=model_kwargs["use_cache"],
+                    use_cache=generation_config.use_cache,
                 )
             )
         if generation_config.sequence_bias is not None:
@@ -2004,10 +2004,7 @@ def generate(
         # decoder-only models with inputs_embeds forwarding must use caching (otherwise we can't detect whether we are
         # generating the first new token or not, and we only want to use the embeddings for the first new token)
         if not self.config.is_encoder_decoder and model_input_name == "inputs_embeds":
-            model_kwargs["use_cache"] = True
             generation_config.use_cache = True
-        else:
-            model_kwargs["use_cache"] = generation_config.use_cache
 
         if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
@@ -2116,6 +2113,9 @@ def generate(
             generation_config=generation_config, stopping_criteria=stopping_criteria, tokenizer=tokenizer, **kwargs
         )
 
+        # Set model_kwargs `use_cache` so we can use it later in forward runs
+        model_kwargs["use_cache"] = generation_config.use_cache
+
         # 10. go into different generation modes
         if generation_mode == GenerationMode.ASSISTED_GENERATION:
             if generation_config.num_return_sequences > 1:

src/transformers/models/idefics/modeling_idefics.py

@@ -1663,19 +1663,31 @@ def forward(
     def prepare_inputs_for_generation(
         self,
         input_ids,
-        past_key_values=None,
         attention_mask=None,
         position_ids=None,
+        inputs_embeds=None,
+        past_key_values=None,
+        cache_position=None,
         pixel_values=None,
         image_hidden_states=None,
         image_attention_mask=None,
         use_cache=None,
-        cache_position=None,
         **kwargs,
     ):
+        model_inputs = {}
+        if image_hidden_states is not None:
+            if self.config.use_resampler:
+                model_inputs["perceiver_embeddings"] = image_hidden_states
+            else:
+                model_inputs["image_encoder_embeddings"] = image_hidden_states
+        else:
+            model_inputs["pixel_values"] = pixel_values
+
         # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
         if past_key_values is not None:
-            if input_ids.shape[1] != cache_position.shape[0]:
+            if inputs_embeds is not None:
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:
                 input_ids = input_ids[:, cache_position]
                 if image_attention_mask is not None:
                     image_attention_mask = image_attention_mask[:, -input_ids.shape[1] :]
@@ -1690,19 +1702,17 @@ def prepare_inputs_for_generation(
                 # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                 position_ids = position_ids.clone(memory_format=torch.contiguous_format)
 
-        model_inputs = {}
-        image_hidden_states = kwargs.pop("image_hidden_states", None)
-        if image_hidden_states is not None:
-            if self.config.use_resampler:
-                model_inputs["perceiver_embeddings"] = image_hidden_states
-            else:
-                model_inputs["image_encoder_embeddings"] = image_hidden_states
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs.update({"inputs_embeds": inputs_embeds, "input_ids": None})
         else:
-            model_inputs["pixel_values"] = pixel_values
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs.update(
+                {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
+            )
 
         model_inputs.update(
             {
-                "input_ids": input_ids,
                 "past_key_values": past_key_values,
                 "use_cache": use_cache,
                 "cache_position": cache_position,

tests/models/idefics/test_modeling_idefics.py

@@ -772,6 +772,12 @@ def test_contrastive_generate_low_memory(self):
     def test_custom_4d_attention_mask(self):
         pass
 
+    @unittest.skip(
+        reason="IDEFICS has specific requirements for working with inputs embeds like passing also the ids and pixels"
+    )
+    def test_generate_from_inputs_embeds_decoder_only(self):
+        pass
+
     @unittest.skip(reason="IDEFICS cannot compile due to dynamic control flow when checking inputs")
     def test_generate_compile_fullgraph(self):
         pass

tests/models/idefics2/test_modeling_idefics2.py

@@ -539,6 +539,31 @@ def test_resize_embeddings_untied(self):
             # Check that the model can still do a forward pass successfully (every parameter should be resized)
             model(**self._prepare_for_class(inputs_dict, model_class))
 
+    def test_inputs_embeds_matches_input_ids_with_generate(self):
+        # overwrite because IDEFICS needs ids and embeds at the input to be not None
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
+            pad_token_id = config.pad_token_id if config.pad_token_id is not None else 1
+
+            wte = model.get_input_embeddings()
+
+            input_ids = inputs["input_ids"]
+            # some models infer position ids/attn mask differently when input ids
+            # by check if pad_token let's make sure no padding is in input ids
+            not_pad_token_id = pad_token_id + 1 if max(0, pad_token_id - 1) == 0 else pad_token_id - 1
+            input_ids[input_ids == pad_token_id] = not_pad_token_id
+            del inputs["input_ids"]
+            inputs_embeds = wte(input_ids)
+            out_ids = model.generate(input_ids=input_ids, **inputs, max_new_tokens=2)
+            out_embeds = model.generate(input_ids=input_ids, inputs_embeds=inputs_embeds, **inputs, max_new_tokens=2)
+
+            self.assertTrue(torch.allclose(out_embeds, out_ids))
+
 
 @require_torch
 class Idefics2ForConditionalGenerationIntegrationTest(unittest.TestCase):

tests/models/idefics3/test_modeling_idefics3.py

@@ -526,6 +526,31 @@ def test_resize_embeddings_untied(self):
             # Check that the model can still do a forward pass successfully (every parameter should be resized)
             model(**self._prepare_for_class(inputs_dict, model_class))
 
+    def test_inputs_embeds_matches_input_ids_with_generate(self):
+        # overwrite because IDEFICS needs ids and embeds at the input to be not None
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
+            pad_token_id = config.pad_token_id if config.pad_token_id is not None else 1
+
+            wte = model.get_input_embeddings()
+
+            input_ids = inputs["input_ids"]
+            # some models infer position ids/attn mask differently when input ids
+            # by check if pad_token let's make sure no padding is in input ids
+            not_pad_token_id = pad_token_id + 1 if max(0, pad_token_id - 1) == 0 else pad_token_id - 1
+            input_ids[input_ids == pad_token_id] = not_pad_token_id
+            del inputs["input_ids"]
+            inputs_embeds = wte(input_ids)
+            out_ids = model.generate(input_ids=input_ids, **inputs, max_new_tokens=2)
+            out_embeds = model.generate(input_ids=input_ids, inputs_embeds=inputs_embeds, **inputs, max_new_tokens=2)
+
+            self.assertTrue(torch.allclose(out_embeds, out_ids))
+
 
 @require_torch
 class Idefics3ForConditionalGenerationIntegrationTest(unittest.TestCase):

tests/models/kosmos2/test_modeling_kosmos2.py

@@ -428,6 +428,12 @@ def check_same_values(layer_1, layer_2):
             # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
             # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
 
+    @unittest.skip(
+        "KOSMOS-2 doesn't support inputs embeds. The test isn't skipped by checking ipnut args because KOSMOS-2 has `generate()` overwritten"
+    )
+    def test_inputs_embeds_matches_input_ids_with_generate(self):
+        pass
+
     @slow
     def test_model_from_pretrained(self):
         model_name = "microsoft/kosmos-2-patch14-224"

tests/test_modeling_common.py

@@ -3000,8 +3000,11 @@ def test_inputs_embeds_matches_input_ids(self):
 
     def test_inputs_embeds_matches_input_ids_with_generate(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-        for model_class in self.all_generative_model_classes:
-            if model_class.__name__ not in get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES):
+        for model_class in self.all_model_classes:
+            if model_class.__name__ not in [
+                *get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
+                *get_values(MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES),
+            ]:
                 continue
             model = model_class(config)
             model.to(torch_device)
@@ -3018,6 +3021,13 @@ def test_inputs_embeds_matches_input_ids_with_generate(self):
             inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
             pad_token_id = config.pad_token_id if config.pad_token_id is not None else 1
 
+            # VLMs can't generate with embeds and pixels at the same time. We expect the user to pass merged
+            # embeds already
+            if model_class.__name__ in get_values(MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES):
+                inputs.pop("pixel_values", None)
+                inputs.pop("pixel_values_videos", None)
+                inputs.pop("pixel_values_images", None)
+
             wte = model.get_input_embeddings()
             if not self.is_encoder_decoder:
                 input_ids = inputs["input_ids"]