Disable calling Tensor.requires_grad_() inside a functorch transform

functorch/_src/eager_transforms.py

@@ -32,6 +32,7 @@
     _func_increment_nesting,
     _assert_wrapped_functional,
     _propagate_functional_input_mutation,
+    set_inplace_requires_grad_allowed,
 )
 
 argnums_t = Union[int, Tuple[int, ...]]
@@ -40,7 +41,12 @@
 def _create_differentiable(inps, level=None):
     def create_differentiable(x):
         if isinstance(x, torch.Tensor):
-            return x.requires_grad_()
+            try:
+                set_inplace_requires_grad_allowed(True)
+                return x.requires_grad_()
+            finally:
+                set_inplace_requires_grad_allowed(False)
+
         raise ValueError(f'Thing passed to transform API must be Tensor, '
                          f'got {type(x)}')
     return tree_map(create_differentiable, inps)

functorch/csrc/DynamicLayer.cpp

@@ -102,13 +102,26 @@ class FuncTorchTLS : public FuncTorchTLSBase {
   }
 
   void checkSupportsInplaceRequiresGrad() const override {
-    // Does nothing
+    TORCH_CHECK(dynamicLayerStack.size() == 0 || allow_inplace_requires_grad_,
+        "You are attempting to call Tensor.requires_grad_() (or perhaps using ",
+        "torch.autograd.functional.* APIs) inside of a function being transformed ",
+        "by a functorch transform. ",
+        "This is unsupported, please attempt to use the functorch transforms ",
+        "(e.g. grad, vjp, jacrev, jacfwd, hessian) or call requires_grad_() "
+        "outside of a function being transformed instead.");
   }
   void checkSupportsRetainGrad() const override {
-    // Does nothing
+    TORCH_CHECK(dynamicLayerStack.size() == 0,
+        "You are attempting to call Tensor.retain_grad() ",
+        "inside of a function being transformed ",
+        "by a functorch transform. ",
+        "This is unsupported, please attempt to use the functorch transforms ",
+        "(e.g. grad, vjp, jacrev, jacfwd, hessian) or call retain_grad() "
+        "outside of a function being transformed instead.");
   }
 
   std::vector<DynamicLayer> dynamicLayerStack;
+  bool allow_inplace_requires_grad_ = false;
 };
 
 static FuncTorchTLS* getRawFunctorchTLS() {
@@ -122,6 +135,12 @@ static FuncTorchTLS* getRawFunctorchTLS() {
   return result;
 }
 
+void setInplaceRequiresGradAllowed(bool allowed) {
+  auto* functorch_tls = getRawFunctorchTLS();
+  functorch_tls->allow_inplace_requires_grad_ = allowed;
+}
+
+
 static std::vector<DynamicLayer>& dynamicLayerStackAccessor() {
   return getRawFunctorchTLS()->dynamicLayerStack;
 }

functorch/csrc/DynamicLayer.h

@@ -85,6 +85,8 @@ Tensor unwrapIfDead(const Tensor& tensor);
 std::ostream& operator<<(std::ostream& os, const DynamicLayer& layer);
 std::ostream& operator<<(std::ostream& os, const std::vector<DynamicLayer>& dynamicLayerStack);
 
+void setInplaceRequiresGradAllowed(bool allowed);
+
 
 }
 } // namespace at

functorch/csrc/init.cpp

@@ -379,6 +379,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("_set_vmap_fallback_warning_enabled", &at::functorch::setVmapFallbackWarningEnabled, "Set vmap fallback warnings");
   m.def("_set_vmap_fallback_enabled", &at::functorch::setVmapFallbackEnabled);
   m.def("_is_vmap_fallback_enabled", &at::functorch::isVmapFallbackEnabled);
+  m.def("set_inplace_requires_grad_allowed", &at::functorch::setInplaceRequiresGradAllowed);
   m.def("dlevel", &at::functorch::dlevel, "dlevel");
   m.def("dump_tensor", &at::functorch::dump_tensor, "dump_tensor");
   m.def("reshape_dim_into", &at::functorch::reshape_dim_into);

test/test_eager_transforms.py

@@ -2157,6 +2157,93 @@ def f(x):
         new_cotangent = torch.randn(())
         self.assertEqual(fx_f(new_cotangent, True, True), vjp_fn(new_cotangent))
 
+    def test_requires_grad_inside_transform(self, device):
+        def f(x):
+            x.requires_grad_()
+            return x.sin().sum()
+
+        x = torch.randn(3)
+
+        with self.assertRaisesRegex(RuntimeError, "Tensor.requires_grad_()"):
+            vmap(f)(x)
+        with self.assertRaisesRegex(RuntimeError, "Tensor.requires_grad_()"):
+            grad(f)(x)
+        with self.assertRaisesRegex(RuntimeError, "Tensor.requires_grad_()"):
+            vmap(grad(f))(x)
+
+        x = torch.randn([])
+        with self.assertRaisesRegex(RuntimeError, "Tensor.requires_grad_()"):
+            grad(grad(f))(x)
+
+    def test_retain_grad_inside_transform(self, device):
+        def f(x):
+            y = x.sin()
+            y.retain_grad()
+            return y.sum()
+
+        x = torch.randn(3)
+
+        with self.assertRaisesRegex(RuntimeError, "Tensor.retain_grad()"):
+            grad(f)(x)
+
+    def test_autograd_functional_jacrev_inside_transform(self, device):
+        def f(x):
+            y = torch.autograd.functional.jacobian(lambda x: x.sin().sum(), x)
+            return y
+
+        B = 5
+        x = torch.randn(B, 3)
+        with self.assertRaisesRegex(RuntimeError, "torch.autograd.functional"):
+            vmap(f)(x)
+
+        x = torch.randn([])
+        with self.assertRaisesRegex(RuntimeError, "torch.autograd.functional"):
+            grad(f)(x)
+
+    def test_autograd_functional_vjp_inside_transform(self, device):
+        def f(x):
+            y = torch.autograd.functional.vjp(lambda x: x.sin().sum(), x)
+            return y
+
+        B = 5
+        x = torch.randn(B, 3)
+        with self.assertRaisesRegex(RuntimeError, "torch.autograd.functional"):
+            vmap(f)(x)
+
+        x = torch.randn([])
+        with self.assertRaisesRegex(RuntimeError, "torch.autograd.functional"):
+            grad(f)(x)
+
+    def test_autograd_functional_jvp_inside_transform(self, device):
+        def f(x):
+            t = torch.ones_like(x)
+            y = torch.autograd.functional.jvp(lambda x: x.sin().sum(), (x,), (t,))
+            return y
+
+        B = 5
+        x = torch.randn(B, 3)
+        with self.assertRaisesRegex(RuntimeError, "torch.autograd.functional"):
+            vmap(f)(x)
+
+        x = torch.randn([])
+        with self.assertRaisesRegex(RuntimeError, "torch.autograd.functional"):
+            grad(f)(x)
+
+    def test_autograd_functional_jacfwd_inside_transform(self, device):
+        def f(x):
+            y = torch.autograd.functional.jacobian(
+                lambda x: x.sin().sum(), x, strategy='forward-mode', vectorize=True)
+            return y
+
+        B = 5
+        x = torch.randn(B, 3)
+        with self.assertRaises(RuntimeError):
+            vmap(f)(x)
+
+        x = torch.randn([])
+        with self.assertRaises(RuntimeError):
+            grad(f)(x)
+
 
 class TestMakeFunctional(TestCase):
     @parametrize('disable_autograd_tracking', [True, False])