Generate: handle text conditioning with multimodal encoder-decoder models (#22748)

src/transformers/generation/tf_utils.py

@@ -837,12 +837,12 @@ class TFGenerationMixin:
 
         # 6. Prepare model inputs which will be used for auto-regressive generation
         if self.config.is_encoder_decoder:
-            # if encoder-decoder then `input_ids` come from `decoder_start_token_id`
-            input_ids = self._prepare_decoder_input_ids_for_generation(
-                batch_size,
+            input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
+                batch_size=batch_size,
+                model_input_name=model_input_name,
+                model_kwargs=model_kwargs,
                 decoder_start_token_id=generation_config.decoder_start_token_id,
                 bos_token_id=generation_config.bos_token_id,
-                model_kwargs=model_kwargs,
             )
         else:
             input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
@@ -1095,16 +1095,41 @@ class TFGenerationMixin:
     def _prepare_decoder_input_ids_for_generation(
         self,
         batch_size: int,
+        model_input_name: str,
+        model_kwargs: Dict[str, tf.Tensor],
         decoder_start_token_id: int = None,
         bos_token_id: int = None,
-        model_kwargs: Optional[Dict[str, tf.Tensor]] = None,
-    ) -> tf.Tensor:
-        # prepare `input_ids` for decoder if model is encoder-decoder
+    ) -> Tuple[tf.Tensor, Dict[str, tf.Tensor]]:
+        """Prepares `decoder_input_ids` for generation with encoder-decoder models"""
+        # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
+        # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
         if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
-            return model_kwargs.pop("decoder_input_ids")
+            decoder_input_ids = model_kwargs.pop("decoder_input_ids")
+        elif "input_ids" in model_kwargs and model_input_name != "input_ids":
+            decoder_input_ids = model_kwargs.pop("input_ids")
         else:
+            decoder_input_ids = None
+
+        # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
         decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
-            return tf.ones((batch_size, 1), dtype=tf.int32) * decoder_start_token_id
+        decoder_input_ids_start = tf.ones((batch_size, 1), dtype=tf.int32) * decoder_start_token_id
+
+        # no user input -> use decoder_start_token_id as decoder_input_ids
+        if decoder_input_ids is None:
+            decoder_input_ids = decoder_input_ids_start
+        # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
+        # decoder_attention_mask if provided)
+        elif tf.reduce_all(decoder_input_ids[:, 0] != decoder_start_token_id):
+            decoder_input_ids = tf.concat([decoder_input_ids_start, decoder_input_ids], axis=-1)
+            if "decoder_attention_mask" in model_kwargs:
+                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+                decoder_attention_mask = tf.concat(
+                    (tf.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
+                    axis=-1,
+                )
+                model_kwargs["decoder_attention_mask"] = decoder_attention_mask
+
+        return decoder_input_ids, model_kwargs
 
     def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
         # retrieve decoder_start_token_id for encoder-decoder models

src/transformers/generation/utils.py

@@ -642,18 +642,44 @@ class GenerationMixin:
     def _prepare_decoder_input_ids_for_generation(
         self,
         batch_size: int,
+        model_input_name: str,
+        model_kwargs: Dict[str, torch.Tensor],
         decoder_start_token_id: int = None,
         bos_token_id: int = None,
-        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
         device: torch.device = None,
-    ) -> torch.LongTensor:
+    ) -> Tuple[torch.LongTensor, Dict[str, torch.Tensor]]:
+        """Prepares `decoder_input_ids` for generation with encoder-decoder models"""
+        # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming,
+        # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input.
         if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
-            return model_kwargs.pop("decoder_input_ids")
+            decoder_input_ids = model_kwargs.pop("decoder_input_ids")
+        elif "input_ids" in model_kwargs and model_input_name != "input_ids":
+            decoder_input_ids = model_kwargs.pop("input_ids")
         else:
+            decoder_input_ids = None
+
+        # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that.
         decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
         if device is None:
             device = self.device
-            return torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
+        decoder_input_ids_start = torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
+
+        # no user input -> use decoder_start_token_id as decoder_input_ids
+        if decoder_input_ids is None:
+            decoder_input_ids = decoder_input_ids_start
+        # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
+        # decoder_attention_mask if provided)
+        elif (decoder_input_ids[:, 0] != decoder_start_token_id).all().item():
+            decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1)
+            if "decoder_attention_mask" in model_kwargs:
+                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+                decoder_attention_mask = torch.cat(
+                    (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
+                    dim=-1,
+                )
+                model_kwargs["decoder_attention_mask"] = decoder_attention_mask
+
+        return decoder_input_ids, model_kwargs
 
     def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
         decoder_start_token_id = (
@@ -1289,17 +1315,14 @@ class GenerationMixin:
 
         # 5. Prepare `input_ids` which will be used for auto-regressive generation
         if self.config.is_encoder_decoder:
-            input_ids = self._prepare_decoder_input_ids_for_generation(
-                batch_size,
+            input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation(
+                batch_size=batch_size,
+                model_input_name=model_input_name,
+                model_kwargs=model_kwargs,
                 decoder_start_token_id=generation_config.decoder_start_token_id,
                 bos_token_id=generation_config.bos_token_id,
-                model_kwargs=model_kwargs,
                 device=inputs_tensor.device,
             )
-
-            # conditional generation for multi-modal models.
-            if "input_ids" in model_kwargs and model_input_name == "pixel_values":
-                input_ids = torch.cat([input_ids, model_kwargs.pop("input_ids")], dim=-1)
         else:
             input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
 

src/transformers/models/pix2struct/modeling_pix2struct.py

@@ -1776,35 +1776,6 @@ class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel):
         encoder_outputs=None,
         **kwargs,
     ):
-        if isinstance(input_ids, torch.Tensor):
-            # check if the first element of `input_ids` is equal to `input_ids`:
-            if (input_ids[:, 0] != self.config.decoder_start_token_id).all().item():
-                # add `input_ids` as first token to `input_ids`
-                input_ids = torch.cat(
-                    [
-                        torch.ones((input_ids.shape[0], 1), dtype=torch.long, device=input_ids.device)
-                        * self.config.decoder_start_token_id,
-                        input_ids,
-                    ],
-                    dim=-1,
-                )
-
-                if decoder_attention_mask is not None:
-                    decoder_attention_mask = torch.cat(
-                        [
-                            torch.ones(
-                                (decoder_attention_mask.shape[0], 1),
-                                dtype=torch.long,
-                                device=decoder_attention_mask.device,
-                            ),
-                            decoder_attention_mask,
-                        ],
-                        dim=-1,
-                    )
-        elif input_ids is None:
-            batch_size = flattened_patches.shape[0]
-            input_ids = torch.LongTensor([[self.input_ids]]).repeat(batch_size, 1).to(input_ids.device)
-
         if decoder_attention_mask is None:
             decoder_attention_mask = torch.ones_like(input_ids).to(input_ids.device)
 

tests/generation/test_framework_agnostic.py

@@ -94,8 +94,8 @@ class GenerationIntegrationTestsMixin:
 
         # Decoder only call
         outputs = bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=max_new_tokens)
-        # 29 + 3 new tokens
-        self.assertEqual(list(outputs.shape), [1, 32])
+        # 1 BOS + 29 (input length) + 3 new tokens
+        self.assertEqual(list(outputs.shape), [1, 33])
 
         # Encoder decoder call > 20
         outputs = bart_model.generate(max_new_tokens=max_new_tokens + 20)
@@ -658,3 +658,31 @@ class GenerationIntegrationTestsMixin:
             [token == model.config.pad_token_id for token in generated_tokens[0][expectation:]]
         )
         self.assertTrue(unpadded_correct_condition or padded_correct_condition)
+
+    def test_generate_vision2text_conditioning(self):
+        model_cls = self.framework_dependent_parameters["AutoModelForVision2Seq"]
+        floats_tensor = self.framework_dependent_parameters["floats_tensor"]
+        create_tensor_fn = self.framework_dependent_parameters["create_tensor_fn"]
+        is_pt = not model_cls.__name__.startswith("TF")
+
+        pixel_values = floats_tensor((2, 3, 30, 30))
+        conditioning_input = create_tensor_fn([[10], [10]])  # this should be the 2nd output token, after the BOS token
+        model = model_cls.from_pretrained("hf-internal-testing/tiny-random-VisionEncoderDecoderModel-vit-gpt2")
+        if is_pt:
+            pixel_values = pixel_values.to(torch_device)
+            model = model.to(torch_device)
+            conditioning_input = conditioning_input.to(torch_device)
+
+        # we can condition on decoder_input_ids (expected decoder input) and input_ids (which we pipe internally as
+        # decoder_input_ids, if the encoder is not a model with text input)
+        output_sequences_decoder_input_ids = model.generate(
+            pixel_values, max_length=5, decoder_input_ids=conditioning_input
+        )
+        output_sequences_input_ids = model.generate(pixel_values, max_length=5, input_ids=conditioning_input)
+        if is_pt:
+            output_sequences_decoder_input_ids = output_sequences_decoder_input_ids.cpu().numpy()
+            output_sequences_input_ids = output_sequences_input_ids.cpu().numpy()
+            conditioning_input = conditioning_input.cpu().numpy()
+
+        self.assertTrue(np.array_equal(output_sequences_decoder_input_ids, output_sequences_input_ids))
+        self.assertTrue(np.array_equal(output_sequences_decoder_input_ids[:, 1:2], conditioning_input))

tests/generation/test_utils.py

@@ -1892,8 +1892,10 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
         max_length = 20
         input_ids = input_ids.expand(2, -1)
         model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
-        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
-            input_ids.shape[0],
+        input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
+            batch_size=input_ids.shape[0],
+            model_input_name=bart_model.main_input_name,
+            model_kwargs=model_kwargs,
             decoder_start_token_id=bart_model.config.decoder_start_token_id,
             bos_token_id=bart_model.config.bos_token_id,
         )
@@ -1919,8 +1921,10 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
         max_length = 20
         input_ids = input_ids.expand(2, -1)
         model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
-        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
-            input_ids.shape[0],
+        input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
+            batch_size=input_ids.shape[0],
+            model_input_name=bart_model.main_input_name,
+            model_kwargs=model_kwargs,
             decoder_start_token_id=bart_model.config.decoder_start_token_id,
             bos_token_id=bart_model.config.bos_token_id,
         )
@@ -1949,8 +1953,10 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
 
         input_ids = input_ids.expand(2, -1)
         model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
-        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
-            input_ids.shape[0],
+        input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
+            batch_size=input_ids.shape[0],
+            model_input_name=bart_model.main_input_name,
+            model_kwargs=model_kwargs,
             decoder_start_token_id=bart_model.config.decoder_start_token_id,
             bos_token_id=bart_model.config.bos_token_id,
         )
@@ -1982,8 +1988,10 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
 
         input_ids = input_ids.expand(6, -1)
         model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
-        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
-            input_ids.shape[0],
+        input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
+            batch_size=input_ids.shape[0],
+            model_input_name=bart_model.main_input_name,
+            model_kwargs=model_kwargs,
             decoder_start_token_id=bart_model.config.decoder_start_token_id,
             bos_token_id=bart_model.config.bos_token_id,
         )
@@ -2021,8 +2029,10 @@ class GenerationIntegrationTests(unittest.TestCase, GenerationIntegrationTestsMi
         # Greedy
         input_ids = input_ids.expand(6, -1)
         model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
-        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
-            input_ids.shape[0],
+        input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation(
+            batch_size=input_ids.shape[0],
+            model_input_name=bart_model.main_input_name,
+            model_kwargs=model_kwargs,
             decoder_start_token_id=bart_model.config.decoder_start_token_id,
             bos_token_id=bart_model.config.bos_token_id,
         )