fix assisted decoding (#31401)

* fix assisted decoding

* check None

* fix typo

* fix _prepare_special_tokens

* fix style

* fix lint

* add tests for assisted decoding

* fix style

* fix tests check

src/transformers/generation/utils.py

@@ -1493,8 +1493,11 @@ def _tensor_or_none(token_kwargs, token_self, device=None):
                 device = self.device
 
             token = token_kwargs if token_kwargs is not None else token_self
-            if token is None or isinstance(token, torch.Tensor):
+            if token is None:
                 return token
+            elif isinstance(token, torch.Tensor):
+                return token.to(device)
+
             return torch.tensor(token, device=device, dtype=torch.long)
 
         bos_token_id = _tensor_or_none(

tests/generation/test_utils.py

@@ -30,7 +30,9 @@
     require_auto_gptq,
     require_quanto,
     require_torch,
+    require_torch_gpu,
     require_torch_multi_accelerator,
+    require_torch_multi_gpu,
     slow,
     torch_device,
 )
@@ -3097,6 +3099,54 @@ def test_return_unprocessed_logit_scores(self):
         self.assertTrue(y_prob > 0.001 and n_prob > 0.001)
         self.assertTrue(y_prob <= 1.0 and n_prob <= 1.0)
 
+    @slow
+    @require_torch_multi_gpu
+    def test_assisted_decoding_in_different_gpu(self):
+        # PT-only test: TF doesn't support assisted decoding yet.
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to("cuda:0")
+        assistant = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to(
+            "cuda:1"
+        )
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
+        model.config.pad_token_id = tokenizer.eos_token_id
+        assistant.config.pad_token_id = tokenizer.eos_token_id
+
+        text = "Hello world"
+        tokenized_inputs = tokenizer([text], return_tensors="pt")
+        input_ids = tokenized_inputs.input_ids.to(torch_device)
+        input_length = input_ids.shape[-1]
+
+        out = model.generate(
+            input_ids,
+            assistant_model=assistant,
+            max_new_tokens=20,
+        )
+        self.assertTrue(input_length <= out.shape[-1] <= input_length + 20)
+
+    @slow
+    @require_torch_gpu
+    def test_assisted_decoding_in_gpu_cpu(self):
+        # PT-only test: TF doesn't support assisted decoding yet.
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to("cuda")
+        assistant = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM").to(
+            "cpu"
+        )
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MistralForCausalLM")
+        model.config.pad_token_id = tokenizer.eos_token_id
+        assistant.config.pad_token_id = tokenizer.eos_token_id
+
+        text = "Hello world"
+        tokenized_inputs = tokenizer([text], return_tensors="pt")
+        input_ids = tokenized_inputs.input_ids.to(torch_device)
+        input_length = input_ids.shape[-1]
+
+        out = model.generate(
+            input_ids,
+            assistant_model=assistant,
+            max_new_tokens=20,
+        )
+        self.assertTrue(input_length <= out.shape[-1] <= input_length + 20)
+
 
 @require_torch
 class TokenHealingTestCase(unittest.TestCase):