Test: generate with torch.compile(model.forward) as a fast test (#34544)

docs/source/en/kv_cache.md

@@ -349,7 +349,7 @@ In case you are using Sink Cache, you have to crop your inputs to that maximum l
 >>> user_prompts = ["Hello, what's your name?", "Btw, yesterday I was on a rock concert."]
 
 >>> past_key_values = DynamicCache()
->>> max_cache_length = past_key_values.get_max_length()
+>>> max_cache_length = past_key_values.get_max_cache_shape()
 
 >>> messages = []
 >>> for prompt in user_prompts:

src/transformers/cache_utils.py

@@ -29,6 +29,8 @@ class Cache(torch.nn.Module):
     Base, abstract class for all caches. The actual data structure is specific to each subclass.
     """
 
+    is_compileable = False
+
     def __init__(self):
         super().__init__()
 
@@ -1098,6 +1100,8 @@ class StaticCache(Cache):
         ```
     """
 
+    is_compileable = True
+
     # TODO (joao): remove `=None` in non-optional arguments in v4.46. Remove from `OBJECTS_TO_IGNORE` as well.
     @deprecate_kwarg("layer_device_map", version="4.52.0")
     def __init__(
@@ -1297,6 +1301,7 @@ class SlidingWindowCache(StaticCache):
     """
 
     is_sliding = True
+    is_compileable = True
 
     # TODO (joao): remove `=None` in non-optional arguments in v4.46. Remove from `OBJECTS_TO_IGNORE` as well.
     def __init__(
@@ -1421,6 +1426,7 @@ class EncoderDecoderCache(Cache):
         super().__init__()
         self.self_attention_cache = self_attention_cache
         self.cross_attention_cache = cross_attention_cache
+        self.is_compileable = getattr(self.self_attention_cache, "is_compileable", False)
 
         self.is_updated = {}
         for layer_idx in range(len(cross_attention_cache.key_cache)):
@@ -1612,6 +1618,8 @@ class HybridCache(Cache):
         ```
     """
 
+    is_compileable = True
+
     # TODO (joao): remove `=None` in non-optional arguments in v4.46. Remove from `OBJECTS_TO_IGNORE` as well.
     @deprecate_kwarg("layer_device_map", version="4.52.0")
     def __init__(
@@ -1832,6 +1840,8 @@ class MambaCache:
         ```
     """
 
+    is_compileable = True
+
     # TODO (joao): remove `=None` in non-optional arguments in v4.46. Remove from `OBJECTS_TO_IGNORE` as well.
     def __init__(
         self,
@@ -1975,6 +1985,8 @@ class OffloadedStaticCache(StaticCache):
         ```
     """
 
+    is_compileable = True
+
     @deprecate_kwarg("layer_device_map", version="4.52.0")
     def __init__(
         self,

src/transformers/generation/configuration_utils.py

@@ -1579,7 +1579,7 @@ class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
 
 
 @dataclass
-class CompileConfig(object):
+class CompileConfig:
     """
     Class that holds arguments relative to `torch.compile` behavior, when using automatic compilation in `generate`.
     See [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for more details on the arguments.
@@ -1620,7 +1620,9 @@ class CompileConfig(object):
     backend: Union[str, Callable] = "inductor"
     mode: str = "reduce-overhead"
     options: Optional[dict] = None
+    # Used to flag our `generate` call to compile on e.g. CPU. Often not optimal, but useful for testing purposes.
+    _compile_all_devices = None
 
     def to_dict(self) -> Dict[str, Any]:
         """Serializes this instance to a Python dictionary."""
-        return copy.deepcopy(self.__dict__)
+        return copy.deepcopy({key: value for key, value in self.__dict__.items() if key != "_compile_all_devices"})

src/transformers/generation/utils.py

@@ -3177,9 +3177,11 @@ class GenerationMixin:
         model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
 
         model_forward = self.__call__
-        if isinstance(model_kwargs.get("past_key_values"), StaticCache):
-            if self.device.type == "cuda":
-                logger.warning_once("Using `torch.compile`.")
+        if isinstance(model_kwargs.get("past_key_values"), Cache):
+            is_compileable = model_kwargs["past_key_values"].is_compileable
+            if is_compileable and (
+                self.device.type == "cuda" or generation_config.compile_config._compile_all_devices
+            ):
                 os.environ["TOKENIZERS_PARALLELISM"] = "0"
                 model_forward = self.get_compiled_call(generation_config.compile_config)
 

src/transformers/models/aria/modeling_aria.py

@@ -708,7 +708,7 @@ class AriaPreTrainedModel(PreTrainedModel):
     _supports_flex_attn = True
     _supports_cache_class = True
     _supports_quantized_cache = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (dynamic slicing)
     _supports_attention_backend = False
 
     def _init_weights(self, module):
@@ -1561,6 +1561,7 @@ class AriaForConditionalGeneration(AriaPreTrainedModel, GenerationMixin):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             logits_to_keep=logits_to_keep,
+            cache_position=cache_position,
         )
 
         logits = outputs[0]

src/transformers/models/aria/modular_aria.py

@@ -1223,6 +1223,7 @@ class AriaTextPreTrainedModel(PreTrainedModel):
 
 
 class AriaPreTrainedModel(LlamaPreTrainedModel):
+    _supports_static_cache = False  # MoE models don't work with torch.compile (dynamic slicing)
     _supports_attention_backend = False
 
     def _init_weights(self, module):
@@ -1535,6 +1536,7 @@ class AriaForConditionalGeneration(AriaPreTrainedModel, GenerationMixin):
             output_hidden_states=output_hidden_states,
             return_dict=return_dict,
             logits_to_keep=logits_to_keep,
+            cache_position=cache_position,
         )
 
         logits = outputs[0]

src/transformers/models/dbrx/modeling_dbrx.py

@@ -833,6 +833,7 @@ class DbrxPreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_cache_class = True
     _supports_quantized_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
 
     def _init_weights(self, module: nn.Module):
         std = self.config.initializer_range

src/transformers/models/emu3/modeling_emu3.py

@@ -1802,6 +1802,7 @@ EMU3_INPUTS_DOCSTRING = r"""
 
 class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["text_model.lm_head.weight"]
+    _supports_static_cache = False  # `get_image_tokens()`, called when `pixel_values` is passed, is not compileable
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/emu3/modular_emu3.py

@@ -1113,6 +1113,7 @@ class Emu3ForCausalLM(LlamaForCausalLM, Emu3PreTrainedModel, GenerationMixin):
 
 class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["text_model.lm_head.weight"]
+    _supports_static_cache = False  # `get_image_tokens()`, called when `pixel_values` is passed, is not compileable
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py

@@ -52,7 +52,7 @@ class GPTNeoXJapanesePreTrainedModel(PreTrainedModel):
     _skip_keys_device_placement = "past_key_values"
     _supports_cache_class = True
     _supports_quantized_cache = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # TODO (fix me): compilation fails due to a stide error?
 
     def _init_weights(self, module):
         """Initialize the weights"""

src/transformers/models/granitemoe/modeling_granitemoe.py

@@ -843,6 +843,7 @@ class GraniteMoePreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_cache_class = True
     _supports_quantized_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
 
     def _init_weights(self, module):
         std = self.config.initializer_range

src/transformers/models/idefics/modeling_idefics.py

@@ -917,6 +917,7 @@ class IdeficsPreTrainedModel(PreTrainedModel):
     _no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
     _supports_sdpa = True
     _supports_cache_class = True
+    _supports_static_cache = False  # IDEFICS cannot compile due to dynamic control flow when checking inputs
 
     def _init_weights(self, module):
         # important: this ported version of Idefics isn't meant for training from scratch - only

src/transformers/models/mixtral/modeling_mixtral.py

@@ -485,7 +485,7 @@ class MixtralPreTrainedModel(PreTrainedModel):
     _supports_flex_attn = True
     _supports_cache_class = True
     _supports_quantized_cache = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
     _supports_attention_backend = True
 
     def _init_weights(self, module):

src/transformers/models/mixtral/modular_mixtral.py

@@ -45,7 +45,9 @@ from ..mistral.modeling_mistral import (
     MistralForSequenceClassification,
     MistralForTokenClassification,
     MistralModel,
+    MistralPreTrainedModel,
     MistralRMSNorm,
+    MistralRotaryEmbedding,
 )
 from .configuration_mixtral import MixtralConfig
 
@@ -313,6 +315,14 @@ class MixtralDecoderLayer(nn.Module):
         return outputs
 
 
+class MixtralRotaryEmbedding(MistralRotaryEmbedding):
+    pass
+
+
+class MixtralPreTrainedModel(MistralPreTrainedModel):
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+
+
 class MixtralModel(MistralModel):
     def __init__(self, config: MixtralConfig):
         super().__init__(config)

src/transformers/models/olmoe/modeling_olmoe.py

@@ -767,7 +767,7 @@ class OlmoePreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_cache_class = True
     _supports_quantized_cache = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
 
     def _init_weights(self, module):
         std = self.config.initializer_range

src/transformers/models/phimoe/modeling_phimoe.py

@@ -912,7 +912,7 @@ class PhimoePreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_cache_class = True
     _supports_quantized_cache = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
 
     def _init_weights(self, module):
         std = self.config.initializer_range

src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py

@@ -332,7 +332,7 @@ class Qwen2_5_VLPreTrainedModel(PreTrainedModel):
     _supports_flash_attn_2 = True
     _supports_sdpa = True
     _supports_cache_class = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # TODO (joao): fix. torch.compile failing probably due to `cache_positions`
 
     def _init_weights(self, module):
         std = self.config.initializer_range

src/transformers/models/qwen2_vl/modeling_qwen2_vl.py

@@ -882,7 +882,7 @@ class Qwen2VLPreTrainedModel(PreTrainedModel):
     _supports_flash_attn_2 = True
     _supports_sdpa = True
     _supports_cache_class = True
-    _supports_static_cache = True
+    _supports_static_cache = False  # TODO (joao): fix. torch.compile failing probably due to `cache_positions`
 
     def _init_weights(self, module):
         std = self.config.initializer_range

tests/generation/test_utils.py

@@ -1978,52 +1978,82 @@ class GenerationTesterMixin:
                 model.generate(**generation_kwargs, **inputs_dict)
 
     @pytest.mark.generate
-    @require_torch_accelerator
-    @slow
     def test_generate_compile_model_forward(self):
         """
-        Tests that `.generate` is compatible with torch.compile without graph breaks, keeping the same results. Tests
-        end-to-end compilation and forward pass compilation only.
+        Tests that `.generate` is compatible with torch.compile without graph breaks, keeping the same results.
         ⚠️ Runs two sequential generations to ensure the cache doesn't get stuck after the first compiled run! ⚠️
         """
         for model_class in self.all_generative_model_classes:
             if not model_class._supports_static_cache:
-                self.skipTest("This model doesn't support static cache")
+                self.skipTest("This model doesn't support static cache (= no expectations of compilation support)")
 
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            config, inputs_dict = self.prepare_config_and_inputs_for_generate(batch_size=4)
 
             model = model_class(config).to(torch_device)
             model.eval()  # otherwise `self.training` is `True` -- this flag is used at attn mask creation time
 
-            input_ids = inputs_dict["input_ids"].to(torch_device)
+            main_input = inputs_dict[model.main_input_name].to(torch_device)
             # creates two sets of *different* inputs with the same shape
-            half_batch_size = input_ids.shape[0] // 2
-            input_ids_sets = [input_ids[:half_batch_size, :], input_ids[half_batch_size : half_batch_size * 2, :]]
-            self.assertTrue(input_ids_sets[0].shape == input_ids_sets[1].shape)
+            half_batch_size = main_input.shape[0] // 2
+            input_1 = {}
+            input_2 = {}
+            for key, value in inputs_dict.items():
+                if isinstance(value, torch.Tensor):
+                    input_1[key] = value[:half_batch_size, :].to(torch_device)
+                    input_2[key] = value[half_batch_size : half_batch_size * 2, :].to(torch_device)
+                else:
+                    input_1[key] = value
+                    input_2[key] = value
+            model_input_sets = [input_1, input_2]
+            self.assertTrue(
+                model_input_sets[0][model.main_input_name].shape == model_input_sets[1][model.main_input_name].shape
+            )
+
+            # compilation-specific setup
+            torch.compiler.reset()  # prevent cached compilation from being used in the test
+            has_defined_cache_implementation = model.generation_config.cache_implementation is not None
+            model.generation_config.compile_config._compile_all_devices = True  # force compilation (e.g. fast CI, CPU)
 
             generation_kwargs = {
                 "do_sample": False,
-                "max_new_tokens": 10,
+                "max_new_tokens": 5,
                 "return_dict_in_generate": True,
                 "output_scores": True,
-                "cache_implementation": "static",
             }
 
             # get eager + dynamic cache results for future comparison
             dynamic_outputs = []
-            for model_inputs in input_ids_sets:
-                dynamic_outputs.append(model.generate(model_inputs, **generation_kwargs))
+            for model_inputs in model_input_sets:
+                gen_out = model.generate(**model_inputs, **generation_kwargs)
+                dynamic_outputs.append(gen_out)
+                # sanity checks for the default cache implementation
+                if not has_defined_cache_implementation:
+                    decoder_cache = (
+                        gen_out.past_key_values.self_attention_cache
+                        if config.is_encoder_decoder
+                        else gen_out.past_key_values
+                    )
+                    self.assertTrue(isinstance(decoder_cache, DynamicCache))
+                    self.assertFalse(decoder_cache.is_compileable)
+                    self.assertFalse(hasattr(model, "_compiled_call"))  # our auto compile should NOT have been called
 
-            # get compiled results
-            generation_config = copy.deepcopy(model.generation_config)
-            generation_config.update(**generation_kwargs)
-            torch.compiler.reset()
-
-            model.forward = torch.compile(model.forward, fullgraph=True, mode="reduce-overhead")
+            # get compiled results -- relies on the automatic compilation triggered by specific "cache_implementation"
+            if not has_defined_cache_implementation:
+                generation_kwargs["cache_implementation"] = "static"
 
             compiled_outputs = []
-            for model_inputs in input_ids_sets:
-                compiled_outputs.append(model.generate(model_inputs, generation_config=generation_config))
+            for model_inputs in model_input_sets:
+                gen_out = model.generate(**model_inputs, **generation_kwargs)
+                compiled_outputs.append(gen_out)
+                # sanity checks
+                decoder_cache = (
+                    gen_out.past_key_values.self_attention_cache
+                    if config.is_encoder_decoder
+                    else gen_out.past_key_values
+                )
+                self.assertFalse(isinstance(decoder_cache, DynamicCache))
+                self.assertTrue(decoder_cache.is_compileable)
+                self.assertTrue(hasattr(model, "_compiled_call"))  # our auto compile should have been called
 
             for dynamic_result, compiled_result in zip(dynamic_outputs, compiled_outputs):
                 self._check_similar_generate_outputs(dynamic_result, compiled_result)

tests/models/chameleon/test_modeling_chameleon.py

@@ -331,11 +331,6 @@ class ChameleonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
     def test_batching_equivalence(self):
         pass
 
-    # TODO (joao, raushan): fix me -- the problem is in `cache_position[0] == 0`, i.e. dynamic control flow
-    @unittest.skip("Chameleon is not compatible with end-to-end generation compilation")
-    def test_generate_compile_model_forward(self):
-        pass
-
 
 @require_torch
 class ChameleonIntegrationTest(unittest.TestCase):

tests/models/dbrx/test_modeling_dbrx.py

@@ -368,10 +368,6 @@ class DbrxModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
     def test_disk_offload_bin(self):
         pass
 
-    @unittest.skip("Dbrx does not support `torch.compile` with `fullgraph=True`.")
-    def test_generate_compile_model_forward(self):
-        pass
-
 
 @require_torch
 class DbrxModelIntegrationTest(unittest.TestCase):

tests/models/idefics/test_modeling_idefics.py

@@ -780,10 +780,6 @@ class IdeficsForVisionText2TextTest(IdeficsModelTest, GenerationTesterMixin, uni
     def test_custom_4d_attention_mask(self):
         pass
 
-    @unittest.skip(reason="IDEFICS cannot compile due to dynamic control flow when checking inputs")
-    def test_generate_compile_model_forward(self):
-        pass
-
     @unittest.skip(reason="We only test the model that takes in multiple images")
     def test_model(self):
         pass

tests/models/qwen2_vl/test_modeling_qwen2_vl.py

@@ -332,10 +332,6 @@ class Qwen2VLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCas
     def test_generate_from_inputs_embeds_with_static_cache(self):
         pass
 
-    @unittest.skip(reason="Can't compile fullgraph due to dynamic control flow in `prepare_inputs_for_generate`")
-    def test_generate_compile_model_forward(self):
-        pass
-
 
 @require_torch
 class Qwen2VLIntegrationTest(unittest.TestCase):

tests/models/whisper/test_modeling_whisper.py

@@ -1602,6 +1602,11 @@ class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
             with self.assertRaises(ValueError):
                 model(input_features=input_features, labels=labels)
 
+    # TODO (joao, eustache): fix me :)
+    @unittest.skip(reason="Whisper's custom generate is not consistent regarding the cache return types")
+    def test_generate_compile_model_forward(self):
+        pass
+
 
 @require_torch
 @require_torchaudio

tests/utils/test_cache_utils.py

@@ -364,7 +364,7 @@ class CacheIntegrationTest(unittest.TestCase):
             input_ids = gen_out
 
         # We went well beyond the cache length
-        self.assertTrue(input_ids.shape[1] > cache.get_max_length() * 1.5)
+        self.assertTrue(input_ids.shape[1] > cache.get_max_cache_shape() * 1.5)
 
         # And it still produces a coherent english
         decoded = tokenizer.batch_decode(input_ids, skip_special_tokens=True)