fix fake tensor tolist implementation (#135131)

Summary:
When exporting for training with `tolist`, we do not hit `FunctionalTensor.tolist` since we do not functionalize. Unfortunately, this means we hit `FakeTensor.tolist`, which creates unbacked symints that are not backed by proxies.

Rather than trying to patch up this low-level implementation, we replace it with essentially what `FunctionalTensor.tolist` does, which is higher-level: we essentially desugar to `item()` calls and let it take care of unbacked symints.

Test Plan:
Some expected failures are gone now.
Also found a test for `tolist` that was written when `FunctionalTensor.tolist` was implemented but not really doing much; repurposed it now to exercise more modes.

Differential Revision: D62197742

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135131
Approved by: https://github.com/ezyang

test/dynamo/test_subclasses.py

@@ -2239,6 +2239,7 @@ class TestNestedTensor(torch._dynamo.test_case.TestCase, NestedTensorTestCase):
         for ref_v, res_v in zip(values_copy, values):
             self.assertEqual(ref_v.grad, res_v.grad)
 
+    @torch._dynamo.config.patch({"capture_scalar_outputs": True})
     def test_unbind(self):
         # NB: If we have shape e.g. (3, j0, 3), duck sizing will give us (s0, s1, s0).
         # This causes a recompile later on when it realizes the batch and last dim

test/export/test_export.py

@@ -2286,8 +2286,6 @@ def forward(self, p_linear_weight, p_linear_bias, b_buffer, x):
         M = M_v3
         export(N(), (t,), strict=strict)
 
-    @testing.expectedFailureTrainingIRToRunDecomp
-    @testing.expectedFailureTrainingIRToRunDecompNonStrict  # unbacked symint not tracked?
     @testing.expectedFailureSerDer  # T195866111
     def test_suggested_fixes_for_data_dependent_errors_puzzlers(self):
         # suggested fixes for data-dependent errors only work in non-strict mode
@@ -2418,6 +2416,14 @@ def forward(self, p_linear_weight, p_linear_bias, b_buffer, x):
                 strict=strict,
             )
 
+    def test_tolist(self):
+        class M(torch.nn.Module):
+            def forward(self, x):
+                return x.tolist()
+
+        ep = export(M(), (torch.ones(3, dtype=torch.int),))
+        self.assertEqual(ep.module()(torch.tensor([1, 2, 3])), [1, 2, 3])
+
     def test_if_functional(self):
         class Module(torch.nn.Module):
             def forward(self, x):
@@ -7920,13 +7926,6 @@ class TestExportCustomClass(TorchTestCase):
                 arg = node.args[0]
                 self.assertTrue(arg.op == "placeholder")
 
-    def test_tolist_nonstrict_output(self):
-        class M(torch.nn.Module):
-            def forward(self, x):
-                x.tolist()
-
-        ep = torch.export.export(M(), (torch.ones(3),), strict=False)
-
     def test_preserve_non_cia_op(self):
         class M(torch.nn.Module):
             def forward(self, x):

test/test_nestedtensor.py

@@ -1025,9 +1025,14 @@ class TestNestedTensorDeviceType(NestedTensorTestCase):
         )
         emb = torch.nn.Embedding(100, 8, device=device)
         y = emb(x)
-        ys = y.unbind()
-        for i, inp in enumerate(inputs):
-            self.assertEqual(emb(inp), ys[i])
+
+        @torch._dynamo.disable
+        def check(inputs, y):
+            ys = y.unbind()
+            for i, inp in enumerate(inputs):
+                self.assertEqual(emb(inp), ys[i])
+
+        check(inputs, y)
 
     @skipMeta
     @torch.inference_mode()
@@ -7120,9 +7125,13 @@ class TestNestedTensorSubclass(NestedTensorTestCase):
         a, b, c = nt.unbind()
         b.sum().backward()
 
-        expected_grad = torch.zeros_like(nt)
-        expected_grad.unbind()[1].add_(1.0)
-        torch._dynamo.disable(self.assertEqual)(nt.grad, expected_grad)
+        @torch._dynamo.disable
+        def check(nt):
+            expected_grad = torch.zeros_like(nt)
+            expected_grad.unbind()[1].add_(1.0)
+            self.assertEqual(nt.grad, expected_grad)
+
+        check(nt)
 
 
 FORWARD_FAILURES = {

torch/_subclasses/fake_tensor.py

@@ -14,6 +14,7 @@ import weakref
 from collections import defaultdict
 from dataclasses import dataclass
 from typing import (
+    Any,
     Callable,
     cast,
     Dict,
@@ -893,28 +894,14 @@ class FakeTensor(Tensor):
             )
         return self.nested_int_memo * coeff
 
-    # We must handle tolist in a special way for FakeTensors here in the case
-    # where tolist is called from torch dispatch for tensor subclasses.
-    # Ordinarily, if a program calls .tolist compiling still works because there is
-    # special handling in dynamo, but for tensor subclasses if .tolist is called
-    # inside torch dispatch, the .tolist call may be directly on a FakeTensor.
-    # This would result in an error since wrapper subclasses don't have storage.
-    # To avoid this, we handle the FakeTensor case by (1) specializing on the size
-    # of the tensor to create the output Python list, and (2) creating unbacked
-    # symints for each element of the list.
-    def tolist(self) -> List[SymInt]:
-        assert self.dim() == 1, "NYI for higher dims"
-        shape_env = self.fake_mode.shape_env
-        assert shape_env is not None
-        out = []
-        # Specialize on the length of the list
-        for _ in range(self.shape[0]):
-            s = shape_env.create_unbacked_symint()
-            # max value?
-            torch._check_is_size(s)
-            torch._check(s >= 2)
-            out.append(s)
-        return out
+    # Similar to FunctionalTensor.tolist
+    def tolist(self) -> Any:
+        if self.dim() == 0:
+            return self.item()
+        elif self.dim() == 1:
+            return [elem.item() for elem in self]
+        else:
+            return [elem.tolist() for elem in self]
 
 
 _MetadataIntLike = Union[IntLikeType, "_PySymInputStub", "_SymIntOutputStub"]

torch/testing/_internal/common_methods_invocations.py

@@ -23797,8 +23797,6 @@ python_ref_db = [
         "_refs.tensor_split",
         torch_opinfo_name="tensor_split",
         skips=(
-            # TensorMeta doesn't support tolist
-            DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref_meta'),
             # RuntimeError: no _refs support for torch.Tensor.tolist
             DecorateInfo(unittest.expectedFailure, 'TestCommon', 'test_python_ref'),
         ),