Convert from higher order functions to classes in tools.codegen.gen (#47008)

ezyang · facebook-github-bot · commit 0c64f9f52614 · 2020-11-11T10:30:50.000-08:00
Summary: Pull Request resolved: #47008 bhosmer has been complaining about how it is difficult to distinguish between local variables and closed over variables in the higher order functions. Well, closures and objects do basically the same thing, so just convert all these HOFs into objects. The decoder ring: - Higher order function => Constructor for object - Access to closed over variable => Access to member variable on object - with_native_function => method_with_native_function (because it's hard writing decorators that work for both functions and methods) I didn't even have to change indentation (much). When there is no need for closed over variables (a few functions), I kept them as plain old functions, no need for an object with no members. While I was at it, I also deleted the kwargs, since the types are enough to prevent mistakes. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D24600805 Pulled By: ezyang fbshipit-source-id: 7e3ce8cb2446e3788f934ddcc17f7da6e9299511
diff --git a/tools/codegen/gen.py b/tools/codegen/gen.py
@@ -10,6 +10,7 @@
 import pathlib
 import functools
 import json
+from dataclasses import dataclass
 
 from tools.codegen.code_template import CodeTemplate
 from tools.codegen.model import *
@@ -102,13 +103,25 @@ def parse_native_yaml(path: str) -> List[NativeFunction]:
 def with_native_function(func: Callable[[NativeFunction], T]) -> Callable[[NativeFunction], T]:
     @functools.wraps(func)
     def wrapper(f: NativeFunction) -> T:
-        with context(f'in {f.loc}:\n  {f.func}'):
-            with local.parametrize(
-                use_c10_dispatcher=f.use_c10_dispatcher,
-            ):
-                return func(f)
+        with native_function_manager(f):
+            return func(f)
     return wrapper
 
+def method_with_native_function(func: Callable[[S, NativeFunction], T]) -> Callable[[S, NativeFunction], T]:
+    @functools.wraps(func)
+    def wrapper(slf: S, f: NativeFunction) -> T:
+        with native_function_manager(f):
+            return func(slf, f)
+    return wrapper
+
+@contextlib.contextmanager
+def native_function_manager(f: NativeFunction) -> Iterator[None]:
+    with context(f'in {f.loc}:\n  {f.func}'):
+        with local.parametrize(
+            use_c10_dispatcher=f.use_c10_dispatcher,
+        ):
+            yield
+
 # These two functions purposely return generators in analogy to map()
 # so that you don't mix up when you need to list() them
 
@@ -180,49 +193,53 @@ def cpp_string(s: str) -> str:
 #
 # This function is also used for a secondary purpose: the registration
 # logic is also reused to implement per-operator registration.
-def compute_type_method(
-    dispatch: Optional[str], *,
+@dataclass(frozen=True)
+class ComputeTypeMethod:
+    dispatch: Optional[str]
+
     # TODO: Give more precise type Union[Literal[Target.DEFINITION,
     # Target.REGISTRATION]]; requires Literal from typing_extensions
     # which we don't have a dep for yet.
-    target: Target,
+    target: Target
+
     # Selector object to determine which operators to generate
     # registration code for.
     selector: SelectiveBuilder
-) -> Callable[[NativeFunction], Optional[str]]:
 
-    if dispatch is None:
-        assert target is Target.REGISTRATION
+    def __post_init__(self) -> None:
+        assert self.target is not Target.DECLARATION
+        if self.dispatch is None:
+            assert self.target is Target.REGISTRATION
 
-    @with_native_function
-    def func(f: NativeFunction) -> Optional[str]:
-        # Has to be here as mypy won't transfer asserts into closures
-        assert target is not Target.DECLARATION
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> Optional[str]:
+        # for mypy type refinement; would be fixed by TODO on target
+        assert self.target is not Target.DECLARATION
 
-        if dispatch is not None:
-            if dispatch not in f.dispatch:
+        if self.dispatch is not None:
+            if self.dispatch not in f.dispatch:
                 return None
 
         op_name = f"aten::{f.func.name}"
-        if target is Target.REGISTRATION and not selector.is_operator_selected(op_name):
+        if self.target is Target.REGISTRATION and not self.selector.is_operator_selected(op_name):
             return None
 
         name = native.name(f.func)
         returns_type = native.returns_type(f.func.returns)
         args = native.arguments(f.func)
         args_str = ', '.join(map(str, args))
-        dispatch_to_all_backends = dispatch is not None and dispatch in KEYWORD_ALL_BACKENDS
+        dispatch_to_all_backends = self.dispatch is not None and self.dispatch in KEYWORD_ALL_BACKENDS
 
-        if target is Target.DEFINITION:
-            assert dispatch is not None
-            impl_name = f"at::native::{f.dispatch[dispatch]}"
+        if self.target is Target.DEFINITION:
+            assert self.dispatch is not None
+            impl_name = f"at::native::{f.dispatch[self.dispatch]}"
 
             args_exprs_str = ', '.join(a.name for a in args)
 
             return_kw = "    return "
 
             cuda_guard = ""
-            if dispatch_to_all_backends or 'CUDA' in dispatch:
+            if dispatch_to_all_backends or 'CUDA' in self.dispatch:
                 self_args = (a for a in f.func.arguments if a.name == "self")
 
                 # There is precedence for which argument we use to do
@@ -249,7 +266,7 @@ def func(f: NativeFunction) -> Optional[str]:
                 # works just as well.
                 if f.device_guard and dispatch_to_all_backends and has_tensor_options:
                     cuda_guard = cuda_guard_from_tensor_options
-                elif f.device_guard and dispatch is not None and 'CUDA' in dispatch and has_tensor_options:
+                elif f.device_guard and self.dispatch is not None and 'CUDA' in self.dispatch and has_tensor_options:
                     cuda_guard = f"""\
     globalContext().lazyInitCUDA();
     {cuda_guard_from_tensor_options}
@@ -269,16 +286,16 @@ def func(f: NativeFunction) -> Optional[str]:
 }}
 """
 
-        elif target is Target.REGISTRATION:
-            if dispatch is None:
+        elif self.target is Target.REGISTRATION:
+            if self.dispatch is None:
                 return f'm.def({cpp_string(str(f.func))});\n'
             elif f.manual_kernel_registration:
                 return None
             else:
                 if dispatch_to_all_backends:
                     type_name = f'TypeDefault::{name}'
                 else:
-                    type_name = f'{dispatch}Type::{name}'
+                    type_name = f'{self.dispatch}Type::{name}'
 
                 dispatcher_sig = DispatcherSignature.from_schema(f.func)
 
@@ -302,21 +319,22 @@ def func(f: NativeFunction) -> Optional[str]:
                 # in a TORCH_LIBRARY_FRAGMENT that does not have an ambient backend.  So
                 # the torch::dispatch specification here is important!  See
                 # Note [Redundancy in registration code is OK] for how we handle redundant info.
-                if dispatch is not None:
-                    payload = f"torch::dispatch(DispatchKey::{dispatch},\n{payload})\n"
+                if self.dispatch is not None:
+                    payload = f"torch::dispatch(DispatchKey::{self.dispatch},\n{payload})\n"
 
                 return f'm.impl("{f.func.name}",\n{payload});\n'
         else:
-            assert_never(target)
-
-    return func
+            assert_never(self.target)
 
 # Generates Function.cpp and Function.h.  These files provide the
 # functional public C++ API, and the scaffolding to call into
 # the dispatcher from these functions.  See also compute_tensor_method.
-def compute_function(*, target: Target) -> Callable[[NativeFunction], Optional[str]]:
-    @with_native_function
-    def go(f: NativeFunction) -> Optional[str]:
+@dataclass(frozen=True)
+class ComputeFunction:
+    target: Target
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> Optional[str]:
         if f.manual_kernel_registration:
             return None
         if Variant.function not in f.variants:
@@ -326,13 +344,13 @@ def go(f: NativeFunction) -> Optional[str]:
 
         sig_group = CppSignatureGroup.from_schema(f.func, method=False)
 
-        if target is Target.DECLARATION:
+        if self.target is Target.DECLARATION:
             result = f"CAFFE2_API {sig_group.signature.decl()};\n"
             if sig_group.faithful_signature is not None:
                 result += f"CAFFE2_API {sig_group.faithful_signature.decl()};\n"
             return result
 
-        assert target is Target.DEFINITION
+        assert self.target is Target.DEFINITION
 
         def generate_defn(sig: CppSignature) -> str:
             dispatcher_sig = DispatcherSignature.from_schema(f.func)
@@ -357,14 +375,15 @@ def generate_defn(sig: CppSignature) -> str:
 
         return result
 
-    return go
-
 # Generates TensorBody.h (sic) and TensorMethods.cpp.  These files provide the
 # object-oriented (method-based) public C++ API, and the scaffolding to call into
 # the dispatcher from these functions.  See also compute_function.
-def compute_tensor_method(*, target: Target) -> Callable[[NativeFunction], Optional[str]]:
-    @with_native_function
-    def go(f: NativeFunction) -> Optional[str]:
+@dataclass(frozen=True)
+class ComputeTensorMethod:
+    target: Target
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> Optional[str]:
         if Variant.method not in f.variants:
             return None
 
@@ -376,13 +395,13 @@ def go(f: NativeFunction) -> Optional[str]:
 
         sig_group = CppSignatureGroup.from_schema(f.func, method=True)
 
-        if target is Target.DECLARATION:
+        if self.target is Target.DECLARATION:
             result = f"{sig_group.signature.decl()} const;\n"
             if sig_group.faithful_signature is not None:
                 result += f"{sig_group.faithful_signature.decl()} const;\n"
             return result
 
-        assert target is Target.DEFINITION
+        assert self.target is Target.DEFINITION
 
         def generate_defn(sig: CppSignature) -> str:
             dispatcher_sig = DispatcherSignature.from_schema(f.func)
@@ -406,8 +425,6 @@ def generate_defn(sig: CppSignature) -> str:
 
         return result
 
-    return go
-
 # Generates ATenOpList.cpp, a runtime accessible list of all aten
 # operators.
 # TODO: This was historically used to help some JIT interop code
@@ -442,9 +459,12 @@ def compute_native_function_declaration(f: NativeFunction) -> List[str]:
 # Generates BackendSelectRegister.cpp, a series of kernels which provide
 # specialized computation of dispatch key for operator signatures which cannot
 # be easily done automatically using templating.
-def compute_backend_select(*, target: Target) -> Callable[[NativeFunction], Optional[str]]:
-    @with_native_function
-    def go(f: NativeFunction) -> Optional[str]:
+@dataclass(frozen=True)
+class ComputeBackendSelect:
+    target: Target
+
+    @method_with_native_function
+    def __call__(self, f: NativeFunction) -> Optional[str]:
         if str(f.func.name.name).endswith('_like') or str(f.func.name.name).startswith('new_'):
             return None
 
@@ -471,7 +491,7 @@ def go(f: NativeFunction) -> Optional[str]:
             dispatcher_exprs = native_sig.dispatcher_exprs()
             dispatch_key = "options.computeDispatchKey()"
 
-        if target is Target.DEFINITION:
+        if self.target is Target.DEFINITION:
             # I don't think there's actually a good reason to generate
             # these two cases differently
             # The first case could probably be improved though- it calls dispatchTypeId(),
@@ -494,7 +514,7 @@ def go(f: NativeFunction) -> Optional[str]:
   return op.callWithDispatchKey(_dk, {', '.join(a.expr for a in dispatcher_exprs)});
 }}
 """
-        elif target is Target.REGISTRATION:
+        elif self.target is Target.REGISTRATION:
             if local.use_c10_dispatcher() is UseC10Dispatcher.full:
                 return f"""m.impl("aten::{f.func.name}", TORCH_FN({name}));"""
             elif local.use_c10_dispatcher() is UseC10Dispatcher.hacky_wrapper_for_legacy_signatures:
@@ -504,11 +524,10 @@ def go(f: NativeFunction) -> Optional[str]:
             else:
                 assert local.use_c10_dispatcher() is UseC10Dispatcher.with_codegenerated_unboxing_wrapper
                 return f"""m.impl_UNBOXED("aten::{f.func.name}", {name});"""
-        elif target is Target.DECLARATION:
+        elif self.target is Target.DECLARATION:
             raise AssertionError()
         else:
-            assert_never(target)
-    return go
+            assert_never(self.target)
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
 #
@@ -993,12 +1012,11 @@ def make_file_manager(install_dir: str) -> FileManager:
                 '',
             'Backend': dispatch,
             'type_derived_method_definitions': list(mapMaybe(
-                compute_type_method(dispatch, target=Target.DEFINITION, selector=selector),
+                ComputeTypeMethod(dispatch, Target.DEFINITION, selector),
                 native_functions
             )),
             'function_registrations': list(mapMaybe(
-                compute_type_method(
-                    dispatch, target=Target.REGISTRATION, selector=selector),
+                ComputeTypeMethod(dispatch, Target.REGISTRATION, selector),
                 native_functions
             )),
         })
@@ -1012,35 +1030,35 @@ def make_file_manager(install_dir: str) -> FileManager:
     cpu_fm.write('TypeDefault.cpp', lambda: {
         'type_method_definitions':
         list(mapMaybe(
-            compute_type_method('Math', target=Target.DEFINITION, selector=selector),
+            ComputeTypeMethod('Math', Target.DEFINITION, selector),
             native_functions)) +
         list(mapMaybe(
-            compute_type_method('DefaultBackend', target=Target.DEFINITION, selector=selector),
+            ComputeTypeMethod('DefaultBackend', Target.DEFINITION, selector),
             native_functions)),
 
         'function_registrations': list(mapMaybe(
-            compute_type_method(None, target=Target.REGISTRATION, selector=schema_selector),
+            ComputeTypeMethod(None, Target.REGISTRATION, schema_selector),
             native_functions)),
 
         'math_function_registrations': list(mapMaybe(
-            compute_type_method('Math', target=Target.REGISTRATION, selector=selector),
+            ComputeTypeMethod('Math', Target.REGISTRATION, selector),
             native_functions)),
 
         'default_backend_function_registrations': list(mapMaybe(
-            compute_type_method('DefaultBackend', target=Target.REGISTRATION, selector=selector),
+            ComputeTypeMethod('DefaultBackend', Target.REGISTRATION, selector),
             native_functions)),
     })
     cpu_fm.write('Functions.h', lambda: {
-        'function_declarations': list(mapMaybe(compute_function(target=Target.DECLARATION), native_functions)),
+        'function_declarations': list(mapMaybe(ComputeFunction(Target.DECLARATION), native_functions)),
     })
     cpu_fm.write('Functions.cpp', lambda: {
-        'function_definitions': list(mapMaybe(compute_function(target=Target.DEFINITION), native_functions)),
+        'function_definitions': list(mapMaybe(ComputeFunction(Target.DEFINITION), native_functions)),
     })
     core_fm.write('TensorBody.h', lambda: {
-        'tensor_method_declarations': list(mapMaybe(compute_tensor_method(target=Target.DECLARATION), native_functions)),
+        'tensor_method_declarations': list(mapMaybe(ComputeTensorMethod(Target.DECLARATION), native_functions)),
     })
     core_fm.write('TensorMethods.cpp', lambda: {
-        'tensor_method_definitions': list(mapMaybe(compute_tensor_method(target=Target.DEFINITION), native_functions)),
+        'tensor_method_definitions': list(mapMaybe(ComputeTensorMethod(Target.DEFINITION), native_functions)),
     })
     core_fm.write('ATenOpList.cpp', lambda: {
         'aten_ops': list(mapMaybe(compute_aten_op, native_functions)),
@@ -1050,9 +1068,9 @@ def make_file_manager(install_dir: str) -> FileManager:
     })
     cpu_fm.write('BackendSelectRegister.cpp', lambda: {
         'backend_select_method_definitions':
-            list(mapMaybe(compute_backend_select(target=Target.DEFINITION), native_functions)),
+            list(mapMaybe(ComputeBackendSelect(Target.DEFINITION), native_functions)),
         'backend_select_function_registrations':
-            list(mapMaybe(compute_backend_select(target=Target.REGISTRATION), native_functions)),
+            list(mapMaybe(ComputeBackendSelect(Target.REGISTRATION), native_functions)),
     })
 
     cpu_fm.write('Declarations.yaml', lambda: format_yaml([compute_declaration_yaml(f) for f in native_functions]))
