Extend impl_backward to be usable with torch.library operators (#106817)

- impl_save_for_backward/impl_backward only work for functional,
non-view schemas. We validate this.
- impl_save_for_backward/impl_backward raise if there already exists an
autograd implementation from torch.library / TORCH_LIBRARY.
- Operators constructed via custom_op receive an "autograd indirection
kernel". The "autograd indirection kernel" automatically pulls the
constructed autograd kernel out of a dict. When
impl_save_for_backward/impl_backward get used with torch.library
operators, we also register the "autograd indirection kernel" so we can
reuse the logic.

Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106817
Approved by: https://github.com/soulitzer
ghstack dependencies: #106799, #106800

test/test_custom_ops.py

@@ -1615,6 +1615,76 @@ def forward(self, x_1):
             result = op(x)
             self.assertEqual(result.shape, ())
 
+    def _test_backward_impl_raises(self, qualname, err_regex):
+        with self.assertRaisesRegex(RuntimeError, err_regex):
+
+            @custom_ops.impl_save_for_backward(qualname)
+            def foo2(x):
+                return
+
+        with self.assertRaisesRegex(RuntimeError, err_regex):
+
+            @custom_ops.impl_backward(qualname)
+            def foo3(x):
+                return
+
+    def test_backward_impl_on_existing_op_incorrect_schema_views(self):
+        lib = self.lib()
+        lib.define("foo(Tensor(a) x) -> Tensor(a)")
+        qualname = f"{self.test_ns}::foo"
+        self._test_backward_impl_raises(qualname, "operator that returns views")
+
+    def test_backward_impl_on_existing_op_incorrect_schema_mutable(self):
+        lib = self.lib()
+        lib.define("foo(Tensor(a!) x) -> Tensor")
+        qualname = f"{self.test_ns}::foo"
+        self._test_backward_impl_raises(qualname, "non-functional")
+
+    def test_backward_impl_on_existing_op_incorrect_schema_no_output(self):
+        lib = self.lib()
+        lib.define("foo(Tensor x) -> ()")
+        qualname = f"{self.test_ns}::foo"
+        self._test_backward_impl_raises(qualname, "no returns")
+
+    def test_backward_impl_on_existing_op_CompositeImplicitAutograd(self):
+        lib = self.lib()
+        lib.define("foo(Tensor x) -> Tensor")
+        qualname = f"{self.test_ns}::foo"
+        lib.impl("foo", lambda x: x.sin().cos(), "CompositeImplicitAutograd")
+        self._test_backward_impl_raises(qualname, "CompositeImplicitAutograd")
+
+    @parametrize("key", ["Autograd", "AutogradCPU", "AutogradCUDA"])
+    def test_backward_impl_on_existing_op_with_key(self, key):
+        lib = self.lib()
+        lib.define("foo(Tensor x) -> Tensor")
+        qualname = f"{self.test_ns}::foo"
+        lib.impl("foo", lambda x: x.sin().cos(), key)
+        self._test_backward_impl_raises(qualname, key)
+
+    def test_backward_impl_on_existing_op(self):
+        lib = self.lib()
+        lib.define("foo(Tensor x) -> Tensor")
+        qualname = f"{self.test_ns}::foo"
+
+        @custom_ops.impl(qualname)
+        def foo_impl(x):
+            with torch.no_grad():
+                return x.sin()
+
+        @custom_ops.impl_save_for_backward(qualname)
+        def foo_save_for_backward(inputs, output):
+            return inputs.x
+
+        @custom_ops.impl_backward(qualname)
+        def foo_backward(ctx, saved, grad_out):
+            return {"x": grad_out * saved.cos()}
+
+        op = self.get_op(qualname)
+        x = torch.randn([], requires_grad=True)
+        y = op(x)
+        (gx,) = torch.autograd.grad(y, x)
+        self.assertEqual(gx, x.cos())
+
 
 only_for = ("cpu", "cuda")
 instantiate_device_type_tests(TestCustomOpTesting, globals(), only_for=only_for)

torch/_custom_op/autograd.py

@@ -61,6 +61,7 @@ def mark_non_differentiable(ctx, output, output_differentiability):
     # - Tensor
     # - Tensor[]
     # - int, bool, Scalar, float
+    # See _check_can_register_backward
     if output_differentiability is not None:
         if not isinstance(output, tuple):
             tuple_output = (output,)
@@ -90,7 +91,8 @@ def mark_non_differentiable(ctx, output, output_differentiability):
 def construct_autograd_kernel(
         schema,
         output_differentiability,
-        forward_op,
+        custom_op,
+        op_overload,
         save_for_backward_fn,
         backward_fn):
 
@@ -102,7 +104,7 @@ def construct_autograd_kernel(
             ctx.set_materialize_grads(True)
             args = pytree.tree_unflatten(list(flat_args), spec)
             with torch._C._AutoDispatchBelowAutograd():
-                output = forward_op(*args)
+                output = op_overload(*args)
 
             # We use the info about args to give better error messages in backward
             args_info = namedtuple_args(
@@ -131,11 +133,11 @@ def construct_autograd_kernel(
 
             # Massage the grad_inputs_dict to a form acceptable by
             # autograd.Function.
-            validate_grad_inputs_dict(grad_inputs_dict, forward_op, args_info)
+            validate_grad_inputs_dict(grad_inputs_dict, custom_op, args_info)
             return grad_inputs_dict_to_flat_tuple(grad_inputs_dict, args_info)
 
         generated_cls = gen_autograd_function(
-            forward_op._opname + '_customop', forward, backward)
+            custom_op._opname + '_customop', forward, backward)
 
         flat_output = generated_cls.apply(*flat_args)
         assert out_spec is not None

torch/_custom_op/impl.py

@@ -6,7 +6,7 @@ import sys
 import typing
 import weakref
 
-from torchgen.model import FunctionSchema, OperatorName, SchemaKind
+from torchgen.model import FunctionSchema, OperatorName, SchemaKind, BaseType, ListType, BaseTy
 
 import torch
 import torch._C as _C
@@ -195,9 +195,16 @@ class CustomOp:
         # NB: Some of these impls are registered as kernels to DispatchKeys.
         # Modifying the _impls dict directly won't do anything in that case.
         self._impls: typing.Dict[str, typing.Optional[FuncAndLocation]] = {}
+        # See NOTE [CustomOp autograd kernel indirection]
+        self._registered_autograd_kernel_indirection = False
 
         global_registry[self._qualname] = self
 
+    def _register_autograd_kernel_indirection(self):
+        assert not self._registered_autograd_kernel_indirection
+        self._lib.impl(self._opname, autograd_kernel_indirection(weakref.proxy(self)), "Autograd")
+        self._registered_autograd_kernel_indirection = True
+
     # Records the impl and the source location in self._impls
     # Note that this doesn't cause torch.library to use the impl, that
     # needs to be done in a separate self._lib.impl call.
@@ -429,6 +436,67 @@ class CustomOp:
 
         return inner
 
+    def _check_can_register_backward(self):
+        def error(detail):
+            raise RuntimeError(
+                f"Cannot use torch._custom_ops APIs to register backward "
+                f"formula for {detail}. Got operator "
+                f"{self._qualname} with schema: {schema}"
+            )
+
+        schema = self._schema
+        if schema.kind() != SchemaKind.functional:
+            error("non-functional operator")
+
+        rets = schema.returns
+        if not schema.returns:
+            error("operator with no returns")
+
+        assert len(rets) > 0
+        is_non_mutating_view = any(
+            r.annotation is not None and not r.annotation.is_write for r in rets
+        )
+        if is_non_mutating_view:
+            error("operator that returns views")
+
+        # We make assumptions about the schema's return types.
+        allowed_return_types = {
+            BaseType(BaseTy.int): "int",
+            BaseType(BaseTy.SymInt): "SymInt",
+            BaseType(BaseTy.bool): "bool",
+            BaseType(BaseTy.float): "float",
+            BaseType(BaseTy.Tensor): "Tensor",
+            ListType(BaseType(BaseTy.Tensor), None): "List[Tensor]",
+        }
+        for ret in schema.returns:
+            if ret.type in allowed_return_types:
+                continue
+            error(f"operator with return not in {list(allowed_return_types.values())} (got {ret.type})")
+
+    def _check_doesnt_have_library_autograd_impl(self):
+        if self._registered_autograd_kernel_indirection:
+            return
+
+        if _C._dispatch_has_kernel_for_dispatch_key(self._qualname, "CompositeImplicitAutograd"):
+            raise RuntimeError(
+                f"impl_backward/impl_save_for_backward: the operator {self._qualname} "
+                f"already has an implementation for this device type via a "
+                f"pre-existing registration to DispatchKey::CompositeImplicitAutograd."
+                f"CompositeImplicitAutograd operators do not need an autograd formula; "
+                f"instead, the operator will decompose into its constituents and those "
+                f"can have autograd formulas defined on them.")
+
+        # We can improve this by adding "all Autograd<BACKEND> keys", but
+        # realistically people will just be using this API for CPU/CUDA for now.
+        for key in ["Autograd", "AutogradCPU", "AutogradCUDA"]:
+            if _C._dispatch_has_kernel_for_dispatch_key(self._qualname, key):
+                raise RuntimeError(
+                    f"impl_backward/impl_save_for_backward: "
+                    f"the operator {self._qualname} already has an Autograd kernel "
+                    f"registered to DispatchKey::{key} vi a pre-existing "
+                    f"torch.library or TORCH_LIBRARY registration. Please either "
+                    f"remove those registrations or don't use the torch._custom_ops APIs")
+
     def _check_doesnt_have_library_meta_impl(self):
         if self._has_impl("abstract"):
             return
@@ -477,6 +545,7 @@ class CustomOp:
             self._schema,
             self._output_differentiability,
             self,
+            get_op(self._qualname),
             self._get_impl("save_for_backward").func,
             self._get_impl("backward").func)
         self._register_impl("autograd", kernel)
@@ -487,6 +556,10 @@ class CustomOp:
         Please see impl_backward for more details.
         """
         def inner(f):
+            self._check_can_register_backward()
+            self._check_doesnt_have_library_autograd_impl()
+            if not self._registered_autograd_kernel_indirection:
+                self._register_autograd_kernel_indirection()
             self._register_impl("save_for_backward", f, stacklevel=_stacklevel)
             if self._has_impl("backward"):
                 self._register_autograd_kernel()
@@ -546,6 +619,10 @@ class CustomOp:
                 yell()
 
         def inner(f):
+            self._check_can_register_backward()
+            self._check_doesnt_have_library_autograd_impl()
+            if not self._registered_autograd_kernel_indirection:
+                self._register_autograd_kernel_indirection()
             self._register_impl("backward", f, stacklevel=_stacklevel)
             self._output_differentiability = output_differentiability
             if self._has_impl("save_for_backward"):
@@ -963,7 +1040,10 @@ def custom_op_from_existing(op):
     ns = op.namespace
     lib = torch.library.Library(ns, "FRAGMENT")
     name = op.name().split("::")[-1]
-    schema = FunctionSchema.parse(str(op._schema))
+    schema_str = str(op._schema)
+    # CustomOp expects the schema string without the namespace
+    schema_str = schema_str.split("::")[-1]
+    schema = FunctionSchema.parse(schema_str)
     return CustomOp(lib, ns, schema, name, op, _private_access=True)
 
 
@@ -1008,10 +1088,7 @@ def _custom_op_with_schema(qualname, schema):
     lib.define(schema_str)
     ophandle = find_ophandle_or_throw(ns, function_schema.name)
     result = CustomOp(lib, ns, function_schema, name, ophandle, _private_access=True)
-
+    result._register_autograd_kernel_indirection()
-    library.impl(lib, result._opname, "Autograd")(
-        autograd_kernel_indirection(weakref.proxy(result))
-    )
 
     torch._C._dispatch_set_report_error_callback(
         ophandle, functools.partial(report_error_callback, weakref.proxy(result))

torch/_custom_ops.py

@@ -266,7 +266,7 @@ def impl_save_for_backward(qualname, *, func=None):
     """
 
     def inner(func):
-        custom_op = _find_custom_op(qualname)
+        custom_op = _find_custom_op(qualname, also_check_torch_library=True)
         custom_op.impl_save_for_backward(_stacklevel=3)(func)
         return func
 
@@ -313,7 +313,7 @@ def impl_backward(qualname, output_differentiability=None, *, func=None):
     """
 
     def inner(func):
-        custom_op = _find_custom_op(qualname)
+        custom_op = _find_custom_op(qualname, also_check_torch_library=True)
         custom_op.impl_backward(output_differentiability, _stacklevel=3)(func)
         return func