added test (a few models need fixes)

src/transformers/generation/utils.py

@@ -1163,7 +1163,12 @@ def _validate_generated_length(self, generation_config, input_ids_length, has_de
         """Performs validation related to the resulting generated length"""
 
         # 1. Max length warnings related to poor parameterization
-        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
+        if (
+            not is_torchdynamo_compiling()
+            and has_default_max_length
+            and generation_config.max_new_tokens is None
+            and generation_config.max_length == 20
+        ):
             # 20 is the default max_length of the generation config
             warnings.warn(
                 f"Using the model-agnostic default `max_length` (={generation_config.max_length}) to control the "

tests/generation/test_utils.py

@@ -28,6 +28,7 @@
     is_flaky,
     require_accelerate,
     require_torch,
+    require_torch_gpu,
     require_torch_multi_accelerator,
     slow,
     torch_device,
@@ -1652,6 +1653,31 @@ def test_new_cache_format(self, num_beams, do_sample):
                         )
                     )
 
+    @require_torch_gpu
+    @slow
+    def test_generate_compile_fullgraph(self):
+        """Tests that `.generate` is compatible with torch.compile without graph breaks, keeping the same results"""
+        for model_class in self.all_generative_model_classes:
+            if not model_class._supports_cache_class:
+                continue
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            model = model_class(config).to(torch_device)
+            input_ids = inputs_dict["input_ids"].to(torch_device)
+
+            # dynamic cache
+            output_dynamic = model.generate(input_ids)
+
+            # eager static cache
+            model.generation_config.cache_implementation = "static"
+            output_static = model.generate(input_ids)
+            self.assertListEqual(output_dynamic.tolist(), output_static.tolist())
+
+            # compiled static cache
+            compiled_generate = torch.compile(model.generate, fullgraph=True, mode="reduce-overhead")
+            output_compiled = compiled_generate(input_ids)
+            self.assertListEqual(output_dynamic.tolist(), output_compiled.tolist())
+
     def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
         batch_size, seq_length = input_ids.shape
         num_sequences_in_output = batch_size * num_return_sequences