🚨🚨 Switch default compilation to fullgraph=False

src/transformers/generation/configuration_utils.py

@@ -43,7 +43,7 @@
 
 logger = logging.get_logger(__name__)
 METADATA_FIELDS = ("_from_model_config", "_commit_hash", "_original_object_hash", "transformers_version")
-NEED_SETUP_CACHE_CLASSES_MAPPING = {}
+STATIC_CACHE_CLASSES_MAPPING = {}
 QUANT_BACKEND_CLASSES_MAPPING = {}
 ALL_CACHE_IMPLEMENTATIONS = []
 
@@ -60,7 +60,7 @@
     )
     from .logits_process import SynthIDTextWatermarkLogitsProcessor, WatermarkLogitsProcessor
 
-    NEED_SETUP_CACHE_CLASSES_MAPPING = {
+    STATIC_CACHE_CLASSES_MAPPING = {
         "static": StaticCache,
         "offloaded_static": OffloadedStaticCache,
         "sliding_window": SlidingWindowCache,
@@ -70,7 +70,7 @@
         "offloaded_hybrid_chunked": OffloadedHybridCache,
     }
     QUANT_BACKEND_CLASSES_MAPPING = {"quanto": QuantoQuantizedCache, "HQQ": HQQQuantizedCache}
-    ALL_CACHE_IMPLEMENTATIONS = list(NEED_SETUP_CACHE_CLASSES_MAPPING.keys()) + ["offloaded", "dynamic", "quantized"]
+    ALL_CACHE_IMPLEMENTATIONS = list(STATIC_CACHE_CLASSES_MAPPING.keys()) + ["offloaded", "dynamic", "quantized"]
 
 
 class GenerationMode(ExplicitEnum):
@@ -1536,8 +1536,10 @@ class CompileConfig:
     See [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for more details on the arguments.
 
     Args:
-        fullgraph (`bool`, *optional*, defaults to `True`):
-            If `True`, requires that the whole forward be capturable in a single graph.
+        fullgraph (`bool`, *optional*, defaults to `False`):
+            If False (default), attempts to discover compileable regions that will be optimized. If True, then require
+            that the entire function be capturable into a single graph. If this is not possible (that is, if there are
+            graph breaks), then an error will be raised.
         dynamic (`bool` or `None`, *optional*):
             Whether to try to use dynamic shape graphs.
         backend (`str` or `Callable`, *optional*, defaults to `"inductor"`):
@@ -1566,7 +1568,7 @@ class CompileConfig:
     ```
     """
 
-    fullgraph: bool = True
+    fullgraph: bool = False
     dynamic: Optional[bool] = None
     backend: Union[str, Callable] = "inductor"
     mode: str = "reduce-overhead"

src/transformers/generation/utils.py

@@ -71,9 +71,8 @@
     _prepare_token_type_ids,
 )
 from .configuration_utils import (
-    NEED_SETUP_CACHE_CLASSES_MAPPING,
     QUANT_BACKEND_CLASSES_MAPPING,
-    CompileConfig,
+    STATIC_CACHE_CLASSES_MAPPING,
     GenerationConfig,
     GenerationMode,
 )
@@ -1826,7 +1825,7 @@ def _get_cache(self, cache_implementation: str, batch_size: int, max_cache_len:
         if cache_implementation == "hybrid" and "llama4" in getattr(self.config, "model_type", ""):
             cache_implementation = "hybrid_chunked"
 
-        cache_cls: Cache = NEED_SETUP_CACHE_CLASSES_MAPPING[cache_implementation]
+        cache_cls: Cache = STATIC_CACHE_CLASSES_MAPPING[cache_implementation]
         requires_cross_attention_cache = (
             self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
         )
@@ -1958,12 +1957,7 @@ def _prepare_cache_for_generation(
             else {}
         )
         if generation_config.cache_implementation is not None:
-            if generation_config.cache_implementation in NEED_SETUP_CACHE_CLASSES_MAPPING:
-                if generation_config.cache_implementation == "static" and not self._can_compile_fullgraph:
-                    raise ValueError(
-                        "This model does not support `cache_implementation='static'`. Please check the following "
-                        "issue: https://github.com/huggingface/transformers/issues/28981"
-                    )
+            if generation_config.cache_implementation in STATIC_CACHE_CLASSES_MAPPING:
                 model_kwargs[cache_name] = self._get_cache(
                     cache_implementation=generation_config.cache_implementation,
                     batch_size=max(generation_config.num_beams, generation_config.num_return_sequences) * batch_size,
@@ -2115,8 +2109,7 @@ def _valid_auto_compile_criteria(self, model_kwargs: dict, generation_config: Ge
         using_compilable_cache = (
             isinstance(model_kwargs.get("past_key_values"), Cache) and model_kwargs["past_key_values"].is_compileable
         )
-        # TODO @raushan `self._can_compile_fullgraph` can be removed and inferred from model arch (e.g. MoE doesn't support compile)
-        can_compile = valid_hardware and using_compilable_cache and self._can_compile_fullgraph
+        can_compile = valid_hardware and using_compilable_cache
 
         # Exception 1: Some quantization methods do not support compilation
         if getattr(self, "hf_quantizer", None) is not None:
@@ -3475,13 +3468,9 @@ def _sample(
         if compile_forward:
             os.environ["TOKENIZERS_PARALLELISM"] = "0"
             # If we use FA2 and a static cache, we cannot compile with fullgraph
-            if self.config._attn_implementation == "flash_attention_2" and getattr(
-                model_kwargs.get("past_key_values"), "is_compileable", False
-            ):
-                if generation_config.compile_config is None:
-                    generation_config.compile_config = CompileConfig(fullgraph=False)
-                # only raise warning if the user passed an explicit compile-config (otherwise, simply change the default without confusing the user)
-                elif generation_config.compile_config.fullgraph:
+            if self.config._attn_implementation == "flash_attention_2":
+                # only raise warning if the user passed an explicit compile-config
+                if generation_config.compile_config is not None and generation_config.compile_config.fullgraph:
                     logger.warning_once(
                         "When using Flash Attention 2 and a static cache, you cannot use the option `CompileConfig(fullgraph=True)` as "
                         "FA2 introduces graph breaks. We overrode the option with `fullgraph=False`."

src/transformers/models/deepseek_v2/modeling_deepseek_v2.py

@@ -460,8 +460,7 @@ class DeepseekV2PreTrainedModel(PreTrainedModel):
     _supports_flash_attn = True
     _supports_sdpa = True
     _supports_flex_attn = True
-
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": DeepseekV2DecoderLayer,

src/transformers/models/deepseek_v2/modular_deepseek_v2.py

@@ -504,6 +504,8 @@ def __init__(self, config: DeepseekV2Config, layer_idx: int):
 
 
 class DeepseekV2PreTrainedModel(LlamaPreTrainedModel):
+    _can_compile_fullgraph = False
+
     def _init_weights(self, module):
         LlamaPreTrainedModel._init_weights(module)
         if isinstance(module, DeepseekV2MoEGate):

src/transformers/models/deepseek_v3/modeling_deepseek_v3.py

@@ -502,8 +502,7 @@ class DeepseekV3PreTrainedModel(PreTrainedModel):
     _supports_flash_attn = True
     _supports_sdpa = True
     _supports_flex_attn = True
-
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": DeepseekV3DecoderLayer,

src/transformers/models/deepseek_v3/modular_deepseek_v3.py

@@ -340,6 +340,8 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
 
 
 class DeepseekV3PreTrainedModel(LlamaPreTrainedModel):
+    _can_compile_fullgraph = False
+
     def _init_weights(self, module):
         LlamaPreTrainedModel._init_weights(module)
         if isinstance(module, DeepseekV3TopkRouter):

src/transformers/models/dots1/modeling_dots1.py

@@ -422,8 +422,7 @@ class Dots1PreTrainedModel(PreTrainedModel):
     _supports_flash_attn = True
     _supports_sdpa = True
     _supports_flex_attn = True
-
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": Dots1DecoderLayer,

tests/generation/test_utils.py

@@ -1736,6 +1736,9 @@ def test_generate_from_inputs_embeds_with_static_cache(self):
         to verify that the cache length is indeed set correctly and we don't run out of index when slicing the cache.
         """
         for model_class in self.all_generative_model_classes:
+            # Here, we should ideally not skip any model, and test them all. However, some old models cannot correctly
+            # use a static cache because they don't create the causal masks correctly.
+            # TODO: cyril -> relax this by adding a `_support_static_cache` attribute
             if not model_class._can_compile_fullgraph:
                 self.skipTest(reason="This model does not support the static cache format")
 
@@ -1956,6 +1959,9 @@ def test_generate_with_static_cache(self):
         """
         set_model_tester_for_less_flaky_test(self)
         for model_class in self.all_generative_model_classes:
+            # Here, we should ideally not skip any model, and test them all. However, some old models cannot correctly
+            # use a static cache because they don't create the causal masks correctly.
+            # TODO: cyril -> relax this by adding a `_support_static_cache` attribute
             if not model_class._can_compile_fullgraph:
                 self.skipTest(reason="This model does not support the static cache format")
 
@@ -2050,7 +2056,7 @@ def test_generate_with_quant_cache(self):
     @pytest.mark.generate
     @pytest.mark.torch_compile_test
     @require_torch_greater_or_equal("2.6")  # Uses torch.compiler.set_stance
-    def test_generate_compile_model_forward(self):
+    def test_generate_compile_model_forward_fullgraph(self):
         """
         Tests that `.generate` is compatible with torch.compile, keeping the same results. Also confirms that
         `.forward` called from `.generate` sees no graph breaks or recompilations when compiled.
@@ -2098,7 +2104,7 @@ def test_generate_compile_model_forward(self):
             # 3. compilation-specific setup and generation parameterization
             torch.compiler.reset()  # prevent cached compilation from being used in the test
             has_defined_cache_implementation = model.generation_config.cache_implementation is not None
-            compile_config = CompileConfig(dynamic=False)  # Error out on dynamic shapes
+            compile_config = CompileConfig(fullgraph=True, dynamic=False)  # Error out on dynamic shapes
             compile_config._compile_all_devices = True  # force compilation (e.g. fast CI, CPU)
 
             generation_kwargs = {
@@ -2174,8 +2180,11 @@ def test_generate_compilation_all_outputs(self):
         In essence, it's the same as `test_greedy_generate_dict_outputs`, but with automatic compilation triggered.
         """
         for model_class in self.all_generative_model_classes:
+            # Here, we should ideally not skip any model, and test them all. However, some old models cannot correctly
+            # use a static cache because they don't create the causal masks correctly.
+            # TODO: cyril -> relax this by adding a `_support_static_cache` attribute
             if not model_class._can_compile_fullgraph:
-                self.skipTest("This model doesn't support compilation without graph breaks")
+                self.skipTest(reason="This model does not support the static cache format")
 
             config, inputs_dict = self.prepare_config_and_inputs_for_generate()
             if self.has_attentions:

tests/models/deepseek_v2/test_modeling_deepseek_v2.py

@@ -174,25 +174,6 @@ def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_value
                 self.assertEqual(layer.keys.shape, expected_key_shape)
                 self.assertEqual(layer.values.shape, expected_value_shape)
 
-    @unittest.skip("Deepseek-V2 uses MLA which has a special head dim and is not compatible with StaticCache shape")
-    @pytest.mark.torch_compile_test
-    def test_generate_compilation_all_outputs(self):
-        pass
-
-    @unittest.skip("Deepseek-V2 uses MLA which has a special head dim and is not compatible with StaticCache shape")
-    @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
-        pass
-
-    @unittest.skip("Deepseek-V2 uses MLA which has a special head dim and is not compatible with StaticCache shape")
-    def test_generate_from_inputs_embeds_with_static_cache(self):
-        pass
-
-    @unittest.skip("Deepseek-V2 uses MLA which has a special head dim and is not compatible with StaticCache shape")
-    @pytest.mark.torch_compile_test
-    def test_generate_with_static_cache(self):
-        pass
-
     @unittest.skip("Dynamic control flow in MoE")
     @pytest.mark.torch_compile_test
     def test_torch_compile_for_training(self):

tests/models/deepseek_v3/test_modeling_deepseek_v3.py

@@ -285,18 +285,6 @@ def test_contrastive_generate_dict_outputs_use_cache(self):
     def test_contrastive_generate_low_memory(self):
         pass
 
-    @unittest.skip(
-        "DeepseekV3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support."
-    )
-    def test_generate_with_static_cache(self):
-        pass
-
-    @unittest.skip(
-        "DeepseekV3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support."
-    )
-    def test_generate_from_inputs_embeds_with_static_cache(self):
-        pass
-
     @unittest.skip(
         "DeepseekV3 has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support."
     )
@@ -307,15 +295,6 @@ def test_generate_continue_from_inputs_embeds(self):
     def test_beam_search_generate_dict_outputs_use_cache(self):
         pass
 
-    @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
-    def test_generate_compilation_all_outputs(self):
-        pass
-
-    @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
-    @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
-        pass
-
     @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format")
     def test_greedy_generate_dict_outputs_use_cache(self):
         pass

tests/models/dots1/test_modeling_dots1.py

@@ -87,23 +87,6 @@ class Dots1ModelTest(CausalLMModelTest, unittest.TestCase):
     test_pruning = False
     model_tester_class = Dots1ModelTester
 
-    @unittest.skip("dots.llm1's moe is not compatible `token_indices, weight_indices = torch.where(mask)`.")
-    def test_generate_with_static_cache(self):
-        pass
-
-    @unittest.skip("dots.llm1's moe is not compatible `token_indices, weight_indices = torch.where(mask)`.")
-    def test_generate_compilation_all_outputs(self):
-        pass
-
-    @unittest.skip("dots.llm1's moe is not compatible `token_indices, weight_indices = torch.where(mask)`")
-    @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
-        pass
-
-    @unittest.skip("dots.llm1's moe is not compatible token_indices, weight_indices = torch.where(mask).")
-    def test_generate_from_inputs_embeds_with_static_cache(self):
-        pass
-
     @require_flash_attn
     @require_torch_gpu
     @pytest.mark.flash_attn_test

tests/models/janus/test_modeling_janus.py

@@ -296,7 +296,7 @@ def check_training_gradient_checkpointing(self, gradient_checkpointing_kwargs=No
 
     @unittest.skip("There are recompilations in Janus")  # TODO (joao, raushan): fix me
     @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
+    def test_generate_compile_model_forward_fullgraph(self):
         pass
 
 

tests/models/paligemma2/test_modeling_paligemma2.py

@@ -25,7 +25,6 @@
     is_torch_available,
 )
 from transformers.testing_utils import (
-    is_flaky,
     require_torch,
     torch_device,
 )
@@ -317,12 +316,6 @@ def test_contrastive_generate_low_memory(self):
     def test_generate_with_static_cache(self):
         pass
 
-    @pytest.mark.generate
-    @pytest.mark.torch_compile_test
-    @is_flaky
-    def test_generate_compile_model_forward(self):
-        super().test_generate_compile_model_forward()
-
     @unittest.skip("Paligemma position ids are 1 indexed")
     def test_eager_padding_matches_padding_free_with_position_ids(self):
         pass

tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py

@@ -255,7 +255,7 @@ def test_generate_compilation_all_outputs(self):
         reason="Supported only for text-only inputs (otherwise dynamic control flows for multimodal inputs)"
     )
     @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
+    def test_generate_compile_model_forward_fullgraph(self):
         pass
 
     @parameterized.expand([("random",), ("same",)])

tests/models/qwen2_5_omni/test_modeling_qwen2_5_omni.py

@@ -447,7 +447,7 @@ def test_generate_from_inputs_embeds_with_static_cache(self):
     # passing, fix me
     @unittest.skip("Cannot handle 4D attention mask")
     @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
+    def test_generate_compile_model_forward_fullgraph(self):
         pass
 
     @unittest.skip("Cannot handle 4D attention mask")

tests/models/whisper/test_modeling_whisper.py

@@ -1421,7 +1421,7 @@ def test_labels_sequence_max_length_error_after_changing_config(self):
     # TODO (joao, eustache): fix me :) The model is not returning a `Cache` by default
     @unittest.skip(reason="Whisper's custom generate is not consistent regarding the cache return types")
     @pytest.mark.torch_compile_test
-    def test_generate_compile_model_forward(self):
+    def test_generate_compile_model_forward_fullgraph(self):
         pass
 
     # TODO (joao, eustache): fix me :)