Generate: decoder-only models can generate with inputs_embeds

src/transformers/generation/utils.py

@@ -519,47 +519,40 @@ def _prepare_model_inputs(
         inputs_kwarg = model_kwargs.pop(input_name, None)
         if inputs_kwarg is not None and inputs is not None:
             raise ValueError(
-                f"`inputs`: {inputs}` were passed alongside "
-                f"{input_name} which is not allowed."
+                f"`inputs`: {inputs}` were passed alongside {input_name} which is not allowed."
                 f"Make sure to either pass {inputs} or {input_name}=..."
             )
         elif inputs_kwarg is not None:
             inputs = inputs_kwarg
 
-        # 3. models with `input_ids` can also make use of `inputs_embeds`
-        if self._can_retrieve_inputs_from_name(inputs, "inputs_embeds", model_kwargs):
-            inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
-
-        # 4. Only encoder-decoder models can have non `input_ids` input format
-        if not self.config.is_encoder_decoder and input_name != "input_ids":
-            raise ValueError(
-                f"If {input_name} is passed as model-specific keyword "
-                "input then model has to be an encoder-decoder and not a "
-                f"{self.__class__.__name__}."
-            )
+        # 3. In the presence of `inputs_embeds` for text models:
+        # - decoder-only models should complain if the user attempts to pass `inputs_embeds`, but the model
+        # doesn't have its forwarding implemented. `inputs_embeds` is kept in `model_kwargs` and can coexist with
+        # input_ids (`inputs_embeds` will be used in the 1st generation step, as opposed to `input_ids`)
+        # - encoder-decoder models should complain if the user attempts to pass `inputs_embeds` and `input_ids`, and
+        # pull the former to inputs. It will be used in place of `input_ids` to get the encoder hidden states.
+        if input_name == "input_ids" and "inputs_embeds" in model_kwargs:
+            if not self.config.is_encoder_decoder:
+                has_inputs_embeds_forwarding = "inputs_embeds" in set(
+                    inspect.signature(self.prepare_inputs_for_generation).parameters.keys()
+                )
+                if not has_inputs_embeds_forwarding:
+                    raise ValueError(
+                        f"You passed `inputs_embeds` to `.generate()`, but the model class {self.__class__.__name__} "
+                        "doesn't have its forwarding implemented. See the GPT2 implementation for an example "
+                        "(https://github.com/huggingface/transformers/pull/21405), and feel free to open a PR with it!"
+                    )
+            else:
+                if inputs is not None:
+                    raise ValueError("You passed `inputs_embeds` and `input_ids` to `.generate()`. Please pick one.")
+                inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
 
-        # 5. if `inputs` is still None, try to create `input_ids` from BOS token
+        # 4. if `inputs` is still None, try to create `input_ids` from BOS token
         if inputs is None:
             inputs = self._prepare_input_ids_for_generation(bos_token_id, model_kwargs.get("encoder_outputs"))
 
         return inputs, input_name, model_kwargs
 
-    def _can_retrieve_inputs_from_name(
-        self, inputs: Optional[torch.Tensor], name: str, model_kwargs: Dict[str, torch.Tensor]
-    ) -> torch.Tensor:
-        """
-        If `inputs` is None and `name` is in both forward function and keyword arguments, then inputs can be retrieved
-        from name
-        """
-        can_retrieve_inputs = model_kwargs.get(name, None) is not None and name in set(
-            inspect.signature(self.forward).parameters.keys()
-        )
-
-        if can_retrieve_inputs and inputs is not None:
-            raise ValueError(f"Cannot only pass one of {name} and {self.main_input_name}")
-
-        return can_retrieve_inputs
-
     def adjust_logits_during_generation(self, logits: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
         """
         Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.

src/transformers/models/gpt2/modeling_gpt2.py

@@ -981,7 +981,7 @@ def get_output_embeddings(self):
     def set_output_embeddings(self, new_embeddings):
         self.lm_head = new_embeddings
 
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
         token_type_ids = kwargs.get("token_type_ids", None)
         # only last token for inputs_ids if past is defined in kwargs
         if past_key_values:
@@ -1000,14 +1000,23 @@ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwarg
                 position_ids = position_ids[:, -1].unsqueeze(-1)
         else:
             position_ids = None
-        return {
-            "input_ids": input_ids,
-            "past_key_values": past_key_values,
-            "use_cache": kwargs.get("use_cache"),
-            "position_ids": position_ids,
-            "attention_mask": attention_mask,
-            "token_type_ids": token_type_ids,
-        }
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "position_ids": position_ids,
+                "attention_mask": attention_mask,
+                "token_type_ids": token_type_ids,
+            }
+        )
+        return model_inputs
 
     @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
     @add_code_sample_docstrings(

tests/generation/test_utils.py

@@ -2359,17 +2359,6 @@ def test_encoder_decoder_generate_attention_mask(self):
 
         self.assertTrue(diff < 1e-4)
 
-    def test_decoder_generate_with_inputs_embeds(self):
-        article = """I need input_ids to generate"""
-        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
-        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=5).to(torch_device)
-        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
-        inputs_embeds = model.get_input_embeddings()(input_ids)
-
-        # cannot generate from `inputs_embeds` for decoder only
-        with self.assertRaises(ValueError):
-            model.generate(inputs_embeds=inputs_embeds)
-
     def test_generate_input_ids_as_kwarg(self):
         article = """I need input_ids to generate"""
         tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
@@ -2417,8 +2406,10 @@ def test_generate_inputs_and_encoder_kwargs(self):
 
     def test_generate_too_many_encoder_kwargs(self):
         article = """I need input_ids to generate"""
-        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
-        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=10).to(torch_device)
+        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
+        model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=10).to(
+            torch_device
+        )
         input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
         with self.assertRaises(ValueError):
             model.generate(input_ids=input_ids, inputs_embeds=input_ids)
@@ -3128,3 +3119,26 @@ def test_eos_token_id_int_and_list_beam_search(self):
         eos_token_id = [873]
         generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs)
         self.assertTrue(expectation == len(generated_tokens[0]))
+
+    def test_generate_from_input_embeds_decoder_only(self):
+        # Note: the model must support generation from input embeddings
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
+
+        text = "Hello world"
+        input_ids = tokenizer.encode(text, return_tensors="pt")
+
+        # Traditional way of generating text
+        outputs_from_ids = model.generate(input_ids)
+
+        # Same thing, but from input embeddings
+        inputs_embeds = model.transformer.wte(input_ids)
+        outputs_from_embeds = model.generate(input_ids, inputs_embeds=inputs_embeds)
+        self.assertListEqual(outputs_from_ids.tolist(), outputs_from_embeds.tolist())
+
+        # But if we pass different inputs_embeds, we should get different outputs
+        torch.manual_seed(0)
+        random_embeds = torch.rand_like(inputs_embeds)
+        outputs_from_rand_embeds = model.generate(input_ids, inputs_embeds=random_embeds)
+        with self.assertRaises(AssertionError):
+            self.assertListEqual(outputs_from_rand_embeds.tolist(), outputs_from_embeds.tolist())