Automatic compilation in generate: do not rely on inner function (#34923

)

* compiled forward in PreTrainedModel

* update

* style

* update name

* trigger CIs

* Add way to use custom compile args

* style

* switch parameterization to generation_config

* Add to inits

* Update configuration_utils.py

* inits

* style

* docs

* style

* Update configuration_utils.py

* back without dataclass for repo consistency

* Update configuration_utils.py

* style

* style

* style once again

* add config serialization

* update

* true dataclass

* trigger CIs

* merge compile methods + remove serialization of compile config

docs/source/en/internal/generation_utils.md

@@ -436,3 +436,9 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 [[autodoc]] SynthIDTextWatermarkDetector
     - __call__
+
+## Compile Utils
+
+[[autodoc]] CompileConfig
+    - __call__
+

src/transformers/__init__.py

@@ -122,6 +122,7 @@
     "feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"],
     "file_utils": [],
     "generation": [
+        "CompileConfig",
         "GenerationConfig",
         "TextIteratorStreamer",
         "TextStreamer",
@@ -4981,7 +4982,7 @@
     from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
 
     # Generation
-    from .generation import GenerationConfig, TextIteratorStreamer, TextStreamer, WatermarkingConfig
+    from .generation import CompileConfig, GenerationConfig, TextIteratorStreamer, TextStreamer, WatermarkingConfig
     from .hf_argparser import HfArgumentParser
 
     # Integrations

src/transformers/generation/__init__.py

@@ -20,6 +20,7 @@
 _import_structure = {
     "configuration_utils": [
         "BaseWatermarkingConfig",
+        "CompileConfig",
         "GenerationConfig",
         "GenerationMode",
         "SynthIDTextWatermarkingConfig",
@@ -192,6 +193,7 @@
 if TYPE_CHECKING:
     from .configuration_utils import (
         BaseWatermarkingConfig,
+        CompileConfig,
         GenerationConfig,
         GenerationMode,
         SynthIDTextWatermarkingConfig,

src/transformers/generation/configuration_utils.py

@@ -20,7 +20,7 @@
 import warnings
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, is_dataclass
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
 
 from .. import __version__
 from ..configuration_utils import PretrainedConfig
@@ -371,6 +371,12 @@ class GenerationConfig(PushToHubMixin):
             to correctly align tokens. Can only be used with different tokenizers in speculative decoding.
             See this [blog](https://huggingface.co/blog/universal_assisted_generation) for more details.
 
+        > Parameters related to performances and compilation
+
+        compile_config (CompileConfig, *optional*):
+            If using a static cache, this controls how `generate` will `compile` the forward pass for performance
+            gains.
+
         > Wild card
 
         generation_kwargs:
@@ -474,6 +480,9 @@ def __init__(self, **kwargs):
         self.assistant_lookbehind = kwargs.pop("assistant_lookbehind", 10)
         self.target_lookbehind = kwargs.pop("target_lookbehind", 10)
 
+        # Performances
+        self.compile_config = kwargs.pop("compile_config", CompileConfig())
+
         # Wild card
         self.generation_kwargs = kwargs.pop("generation_kwargs", {})
 
@@ -794,7 +803,13 @@ def validate(self, is_init=False):
                 self.watermarking_config = WatermarkingConfig.from_dict(self.watermarking_config)
             self.watermarking_config.validate()
 
-        # 7. other incorrect combinations
+        # 7. performances arguments
+        if not isinstance(self.compile_config, CompileConfig):
+            raise ValueError(
+                f"You provided `compile_config` as an instance of {type(self.compile_config)}, but it must be an instance of `CompileConfig`."
+            )
+
+        # 8. other incorrect combinations
         if self.return_dict_in_generate is not True:
             for extra_output_flag in self.extra_output_flags:
                 if getattr(self, extra_output_flag) is True:
@@ -1175,6 +1190,8 @@ def to_dict(self) -> Dict[str, Any]:
             del output["_commit_hash"]
         if "_original_object_hash" in output:
             del output["_original_object_hash"]
+        if "compile_config" in output:
+            del output["compile_config"]
 
         # Transformers version when serializing this file
         output["transformers_version"] = __version__
@@ -1559,3 +1576,51 @@ def construct_processor(self, vocab_size: int, device) -> "WatermarkLogitsProces
             skip_first_ngram_calls=self.skip_first_ngram_calls,
             debug_mode=self.debug_mode,
         )
+
+
+@dataclass
+class CompileConfig(object):
+    """
+    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.
+
+    Args:
+        fullgraph (`bool`, *optional*, defaults to `True`):
+            If `True`, requires that the whole forward be capturable in a single graph.
+        dynamic (`bool` or `None`, *optional*):
+            Whether to try to use dynamic shape graphs.
+        backend (`str` or `Callable`, *optional*, defaults to `"inductor"`):
+            Backend to be used.
+        mode (`str`, *optional*, defaults to `"reduce-overhead"`):
+            Controls balance between performance and overhead.
+        options (`dict`, *optional*):
+            A dictionary of options to pass to the backend.
+
+    Examples:
+    ```python
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer, CompileConfig
+
+    >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b')
+    >>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b').cuda()
+
+    >>> # Automatic compile configuration, used with static cache
+    >>> compile_config = CompileConfig(dynamic=True)
+
+    >>> # Generation with static cache and compile config
+    >>> input = tokenizer.encode("Hello there, how", return_tensors="pt").cuda()
+    >>> output = model.generate(
+    ...     input, do_sample=False, max_new_tokens=300, cache_implementation="static", compile_config=compile_config
+    ... )
+    >>> output_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
+    ```
+    """
+
+    fullgraph: bool = True
+    dynamic: Optional[bool] = None
+    backend: Union[str, Callable] = "inductor"
+    mode: str = "reduce-overhead"
+    options: Optional[dict] = None
+
+    def to_dict(self) -> Dict[str, Any]:
+        """Serializes this instance to a Python dictionary."""
+        return copy.deepcopy(self.__dict__)

src/transformers/generation/utils.py

@@ -3230,16 +3230,14 @@ def _sample(
         unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
         model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
 
-        def model_forward(model, *args, **kwargs):
-            return model.forward(*args, **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`.")
                 os.environ["TOKENIZERS_PARALLELISM"] = "0"
-                model_forward = torch.compile(model_forward, mode="reduce-overhead", fullgraph=True)
+                model_forward = self.get_compiled_call(generation_config.compile_config)
 
-        i = 0
+        is_prefill = True
         while self._has_unfinished_sequences(
             this_peer_finished, synced_gpus, device=input_ids.device, cur_len=cur_len, max_length=max_length
         ):
@@ -3250,11 +3248,11 @@ def model_forward(model, *args, **kwargs):
             model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
             model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
 
-            if i == 0:
+            if is_prefill:
                 outputs = self(**model_inputs, return_dict=True)
-                i += 1
+                is_prefill = False
             else:
-                outputs = model_forward(self, return_dict=True, **model_inputs)
+                outputs = model_forward(**model_inputs, return_dict=True)
 
             # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
             model_kwargs = self._update_model_kwargs_for_generation(

src/transformers/modeling_utils.py

@@ -43,7 +43,7 @@
 from .activations import get_activation
 from .configuration_utils import PretrainedConfig
 from .dynamic_module_utils import custom_object_save
-from .generation import GenerationConfig, GenerationMixin
+from .generation import CompileConfig, GenerationConfig, GenerationMixin
 from .integrations import PeftAdapterMixin, deepspeed_config, is_deepspeed_zero3_enabled
 from .loss.loss_utils import LOSS_MAPPING
 from .pytorch_utils import (  # noqa: F401
@@ -5094,6 +5094,21 @@ def loss_function(self):
             loss_type = "ForCausalLM"
         return LOSS_MAPPING[loss_type]
 
+    def get_compiled_call(self, compile_config: CompileConfig):
+        """Return a `torch.compile`'d version of `self.__call__`. This is useful to dynamically choose between
+        non-compiled/compiled `forward` during inference, especially to switch between prefill (where we don't
+        want to use compiled version to avoid recomputing the graph with new shapes) and iterative decoding
+        (where we want the speed-ups of compiled version with static shapes)."""
+        # Only reset it if not present or different from previous config
+        default_config = getattr(self.generation_config, "compile_config", CompileConfig())
+        if (
+            not hasattr(self, "_compiled_call")
+            or getattr(self, "_last_compile_config", default_config) != compile_config
+        ):
+            self._last_compile_config = compile_config
+            self._compiled_call = torch.compile(self.__call__, **compile_config.to_dict())
+        return self._compiled_call
+
 
 PreTrainedModel.push_to_hub = copy_func(PreTrainedModel.push_to_hub)
 if PreTrainedModel.push_to_hub.__doc__ is not None: