add depyf.optimization and tests (#58)

.github/workflows/test_pytorch.yml

@@ -42,6 +42,7 @@ jobs:
         echo "success"
     - name: Test with pytest
       run: |
+        coverage run --append tests/test_pytorch/test_wrapper.py
         coverage run --append tests/test_pytorch/test_mp.py
         coverage run --append tests/test_pytorch/test_no_graph.py
         coverage run --append tests/test_pytorch/test_irregular.py

depyf/optimization.py

@@ -0,0 +1,74 @@
+import os
+import sys
+from abc import abstractmethod
+from contextlib import contextmanager
+from types import CodeType
+from typing import Callable, List
+
+import torch
+
+
+class TorchCompileWrapperWithCustomDispacther:
+    """
+    A wrapper class for torch.compile, with a custom dispatch logic.
+    Subclasses should:
+    1. Implement the forward method
+    2. Implement the dispatch logic in the __call__ method
+        It can use `self.compiled_codes` to access the compiled bytecode,
+        and `with self.dispatch_to_code(index):` to dispatch to
+        the compiled code.
+    3. Implement the `__init__` method to determine how to call
+        `torch.compile` over the forward method.
+    """
+
+    def __init__(self, compiled_callable: Callable, use_custom_dispatcher: bool = True):
+        self.compiled_callable = compiled_callable
+        self.original_code_object = self.__class__.forward.__code__
+        self.compiled_codes: List[CodeType] = []
+        torch._dynamo.convert_frame.register_bytecode_hook(self.bytecode_hook)
+
+        self.use_custom_dispatcher: bool = use_custom_dispatcher
+
+    def __call__(self, *args, **kwargs):
+        """Implement the dispatch logic here, beyond the torch.compile level.
+        NOTE: this function can have additional arguments beyond the forward
+         method, for directly dispatching to the compiled code.
+        """
+        return self.compiled_callable(*args, **kwargs)
+
+    @abstractmethod
+    def forward(self, *args, **kwargs):
+        ...
+
+    def bytecode_hook(self, old_code: CodeType, new_code: CodeType):
+        """Hook to save the compiled bytecode for direct execution."""
+        if old_code is not self.original_code_object:
+            return
+        frame = sys._getframe()
+        while True:
+            frame = frame.f_back
+            code_name = frame.f_code.co_name
+            file_name = frame.f_code.co_filename.split(os.path.sep)[-1]
+            if code_name == "_compile" and file_name == "convert_frame.py":
+                break
+        frame = frame.f_locals["frame"]
+        assert frame.f_code == old_code
+
+        if frame.f_locals["self"] is not self:
+            return
+
+        self.compiled_codes.append(new_code)
+
+    @contextmanager
+    def dispatch_to_code(self, index: int):
+        """Context manager to dispatch to the compiled code.
+        Why does this work? Because Dynamo guarantees that the compiled
+        bytecode has exactly the same arguments, cell variables, and free
+        variables as the original code. Therefore we can directly switch
+        the code object in the function and call it.
+
+        See https://dev-discuss.pytorch.org/t/what-is-the-relationship-requirement-among-original-bytecode-transformed-bytecode-and-bytecode-returned-by-hooks-in-dynamo/1693/7 for more details.
+        """ # noqa
+        self.__class__.forward.__code__ = self.compiled_codes[index]
+        yield
+        self.__class__.forward.__code__ = self.original_code_object

tests/test_pytorch/test_wrapper.py

@@ -0,0 +1,58 @@
+from typing import Optional
+
+import torch
+
+from depyf.optimization import TorchCompileWrapperWithCustomDispacther
+
+
+class MyMod(torch.nn.Module):
+
+    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        if cache is not None:
+            return x + cache
+        return x * 2
+
+
+class MyWrapper(TorchCompileWrapperWithCustomDispacther):
+
+    def __init__(self, model):
+        self.model = model
+        compiled_callable = torch.compile(self.forward, backend="eager")
+        super().__init__(compiled_callable)
+
+    def forward(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        # this is the function to be compiled
+        return self.model(x, cache)
+
+    def __call__(self, x: torch.Tensor, cache: Optional[torch.Tensor] = None):
+        # let torch.compile compile twice
+        if len(self.compiled_codes) == 2:
+            dispatch_id = 0 if cache is None else 1
+            with self.dispatch_to_code(dispatch_id):
+                return self.forward(x, cache)
+        else:
+            return self.compiled_callable(x, cache)
+
+
+mod = MyMod()
+wrappers = []
+for i in range(3):
+    torch._dynamo.reset()
+    wrapper = MyWrapper(mod)
+    wrappers.append(wrapper)
+    x = torch.tensor([1])
+    wrapper(x, None)  # profile run, compile
+    # create a cache tensor
+    cache = torch.tensor([2])
+    wrapper(x, cache)  # warm up with cache, recompile
+
+    # for new input, dispatch to the compiled code directly
+    new_x = torch.tensor([3])
+    assert wrapper(new_x,
+                    None).item() == 6  # dispatch to the first compiled code
+    assert wrapper(
+        new_x, cache).item() == 5  # dispatch to the second compiled code
+
+for wrapper in wrappers:
+    # make sure they have independent compiled codes
+    assert len(wrapper.compiled_codes) == 2