[JIT] additional support for CallMethod with autocasting (#67925)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67925

Previously, the following would always fail, because autocasting would not be enabled in the called method:

```
torch.jit.script
def fn(x, y):
    with autocast():
        # CallMethod() to some method

fn(x, y)
```

This allows the above, if autocasting is globally enabled, e.g.

```
torch.jit.script
def fn(x, y):
    with autocast():
        # CallMethod() to some method

with autocast():
    fn(x, y) # now
```
ghstack-source-id: 142667351

Test Plan: added test in test_jit_autocast.py

Reviewed By: navahgar

Differential Revision: D32214439

fbshipit-source-id: bb7db054e25e18f5e3d2fdb449c35b5942ab303e

test/test_jit_autocast.py

@@ -623,5 +623,42 @@ class TestAutocast(JitTestCase):
         self.assertEqual(t0.grad.dtype, ref_t0.grad.dtype)
         self.assertEqual(t1.grad.dtype, ref_t1.grad.dtype)
 
-if __name__ == '__main__':
+    @unittest.skipIf(not TEST_CUDA, "No cuda")
+    def test_jit_call_method_under_autocast(self):
+        @torch.jit.interface
+        class Iface(torch.nn.Module):
+            def forward(self, x, y) -> torch.Tensor:
+                pass
+
+        class Impl(Iface):
+            def forward(self, x, y):
+                return torch.mm(x, y)
+
+        class Thing1(torch.nn.Module):
+            impl: Iface
+
+            def forward(self, x, y):
+                with torch.cuda.amp.autocast():
+                    a = torch.mm(x, y)
+                    b = self.impl.forward(a, x)
+                    return b
+
+        scripted_impl = torch.jit.script(Impl())
+        thing1 = Thing1()
+        thing1.impl = scripted_impl
+        scripted_thing1 = torch.jit.script(thing1)
+        x = torch.rand([2, 2])
+        y = torch.rand([2, 2])
+
+        # make sure this doesn't throw an error
+        with torch.cuda.amp.autocast():
+            ans = scripted_thing1.forward(x, y)
+        self.assertEqual(torch.mm(torch.mm(x, y), x), ans)
+
+        # sanity check: this isn't supported currently when global autocasting
+        # isn't enabled
+        self.assertRaises(RuntimeError, lambda: scripted_thing1.forward(x, y))
+
+
+if __name__ == "__main__":
     run_tests()

torch/csrc/jit/passes/autocast.cpp

@@ -242,6 +242,16 @@ void handleBlock(Block* block, AutocastContext initial_state) {
     switch (node->kind()) {
       case prim::CallFunction:
         // TODO: limit it only to amp related node;
+        if (current_state() == initial_state) {
+          // if the current autocasting state is the same as the global state,
+          // then autocasting will be done correctly on subsequent method and
+          // function calls
+          if (current_state()) {
+            castTensorInputs(
+                node, aten::_autocast_to_full_precision, current_state());
+          }
+          break;
+        }
         TORCH_INTERNAL_ASSERT(
             !incompatible_amp.has_value() || incompatible_amp.value(),
             "Calls are not expected with AMP & JIT");
@@ -250,6 +260,16 @@ void handleBlock(Block* block, AutocastContext initial_state) {
 
       case prim::CallMethod:
         // TODO: limit it only to amp related node;
+        if (current_state() == initial_state) {
+          // if the current autocasting state is the same as the global state,
+          // then autocasting will be done correctly on subsequent method and
+          // function calls
+          if (current_state()) {
+            castTensorInputs(
+                node, aten::_autocast_to_full_precision, current_state());
+          }
+          break;
+        }
         if (auto class_type = node->input(0)->type()->cast<ClassType>()) {
           const auto& name = node->s(attr::name);
           const auto& function = class_type->getMethod(name);