Revert "[inductor] fix crash issue when input is a view tensor (pytor…

…ch#90150)" (pytorch#94329)

Had to provide a merge conflict resolution due to conflicts with pytorch#94118

This was causing issues with internal tests that look similar to:
```
in clone_preserve_strides
    x.size(), x.stride(), x.storage_offset()
AttributeError: 'KeyedJaggedTensor' object has no attribute 'size'
```

See https://fburl.com/testinfra/nc0du2sp for more information

This reverts commit pytorch#90150

@jansel can you help @blzheng with re-landing this as a co-development diff?

Pull Request resolved: pytorch#94329
Approved by: https://github.com/jansel

test/inductor/test_torchinductor.py

@@ -6337,75 +6337,6 @@ def fn(a):
                         if simdlen != 1:
                             assert metrics.generated_cpp_vec_kernel_count == 1
 
-        def test_inplace_unsqueeze(self):
-            @torch._dynamo.optimize("inductor")
-            def fn(a):
-                unsqueeze_ = torch.ops.aten.unsqueeze_.default(a, 0)
-                return unsqueeze_
-
-            for dynamic_shapes in [True, False]:
-                args = [
-                    (
-                        (1, 1, 1, 12, 11, 3),
-                        (396, 396, 396, 33, 3, 1),
-                        torch.int64,
-                        "cpu",
-                    )
-                ]
-                args = [rand_strided(sh, st, dt, dev) for (sh, st, dt, dev) in args]
-                torch._dynamo.config.dynamic_shapes = dynamic_shapes
-                with torch.no_grad():
-                    out = fn(*args)
-                assert args[0].shape == (1, 1, 1, 1, 12, 11, 3)
-                assert args[0].stride() == (396, 396, 396, 396, 33, 3, 1)
-                assert out.equal(args[0])
-
-        def test_inplace_unsqueeze2(self):
-            @torch._dynamo.optimize("inductor")
-            def fn(a):
-                unsqueeze_ = torch.ops.aten.unsqueeze_.default(a, 0)
-                res = unsqueeze_ + 1
-                return res
-
-            for dynamic_shapes in [True, False]:
-                args = [
-                    (
-                        (1, 1, 1, 12, 11, 3),
-                        (396, 396, 396, 33, 3, 1),
-                        torch.int64,
-                        "cpu",
-                    )
-                ]
-                args = [rand_strided(sh, st, dt, dev) for (sh, st, dt, dev) in args]
-                torch._dynamo.config.dynamic_shapes = dynamic_shapes
-                with torch.no_grad():
-                    out = fn(*args)
-                assert args[0].shape == (1, 1, 1, 1, 12, 11, 3)
-                assert args[0].stride() == (396, 396, 396, 396, 33, 3, 1)
-                assert out.equal(args[0] + 1)
-
-        def test_inplace_unsqueeze3(self):
-            @torch._dynamo.optimize("inductor")
-            def fn(a):
-                torch.ops.aten.unsqueeze_.default(a, 0)
-                return 0
-
-            for dynamic_shapes in [True, False]:
-                args = [
-                    (
-                        (1, 1, 1, 12, 11, 3),
-                        (396, 396, 396, 33, 3, 1),
-                        torch.int64,
-                        "cpu",
-                    )
-                ]
-                args = [rand_strided(sh, st, dt, dev) for (sh, st, dt, dev) in args]
-                torch._dynamo.config.dynamic_shapes = dynamic_shapes
-                with torch.no_grad():
-                    fn(*args)
-                assert args[0].shape == (1, 1, 1, 1, 12, 11, 3)
-                assert args[0].stride() == (396, 396, 396, 396, 33, 3, 1)
-
 
 if HAS_CUDA and not TEST_WITH_ASAN:
     import triton

torch/_dynamo/variables/builder.py

@@ -142,44 +142,6 @@ def get_fake_examples(self):
             assert isinstance(
                 self.fake_tensor, torch._subclasses.fake_tensor.FakeTensor
             )
-            # For inplace ops changing the input's shape (unsqueeze_)
-            if not config.dynamic_shapes and (
-                self.fake_tensor.shape != self.example.shape
-                or self.fake_tensor.stride() != self.example.stride()
-            ):
-                converter = torch._subclasses.fake_tensor.FakeTensorConverter()
-                self.fake_tensor = converter.from_real_tensor(
-                    self.fake_tensor.fake_mode, self.example
-                )
-            elif config.dynamic_shapes:
-                (
-                    size,
-                    stride,
-                    _,
-                ) = self.fake_tensor.fake_mode.shape_env.create_symbolic_sizes_strides_storage_offset(
-                    self.example, self.source
-                )
-                if (
-                    torch.Size(size) != self.fake_tensor.shape
-                    or tuple(stride) != self.fake_tensor.stride()
-                ):
-                    self.fake_tensor.fake_mode.converter = (
-                        torch._subclasses.fake_tensor.FakeTensorConverter()
-                    )
-                    self.fake_tensor.fake_mode.shape_env = (
-                        torch.fx.experimental.symbolic_shapes.ShapeEnv()
-                    )
-                    ignore_subclass = (
-                        True
-                        if type(self.example) in config.traceable_tensor_subclasses
-                        else False
-                    )
-                    self.fake_tensor = self.fake_tensor.fake_mode.from_tensor(
-                        self.example.clone(),
-                        static_shapes=False,
-                        ignore_subclass=ignore_subclass,
-                        source=self.source,
-                    )
             return [self.fake_tensor]
 
     def __len__(self):

torch/_functorch/aot_autograd.py

@@ -1049,11 +1049,8 @@ class AOTConfig:
 
 
 def aot_dispatch_base(flat_fn, flat_args: List[Tensor], aot_config: AOTConfig):
-    # flat_args is used by make_fx and aot_config.fw_compiler
-    # clone flat_args to avoid flat_args shape changed by inplace ops (unsqueeze_)
-    tmp_flat_args = [torch._prims_common.clone_preserve_strides(x) for x in flat_args]
     with enable_python_dispatcher():
-        fw_module = make_fx(flat_fn, aot_config.decompositions)(*tmp_flat_args)
+        fw_module = make_fx(flat_fn, aot_config.decompositions)(*flat_args)
     if config.debug_graphs:
         log.debug(f"====== Forward (only) graph {aot_config.aot_id} ======")
         log.debug(fw_module.print_readable(print_output=False))

torch/_inductor/codegen/wrapper.py

@@ -512,10 +512,6 @@ def generate(self):
                 # these lines will be pointless
                 self.lines.pop()
 
-            for name, value in V.graph.graph_inputs.items():
-                if isinstance(value.data, ir.ReinterpretView):
-                    self.wrapper_call.writeline(value.data.codegen_reference_mutation())
-
             # codegen allocations in two passes
             planning_state = MemoryPlanningState()
             for i in range(len(self.lines)):
@@ -585,8 +581,6 @@ def add_fake_input(name, shape, stride, device, dtype):
                 )
 
             for name, value in V.graph.graph_inputs.items():
-                if isinstance(value.data, ir.ReinterpretView):
-                    value = value.data.data
                 shape = [V.graph.sizevars.size_hint(x) for x in value.get_size()]
                 stride = [V.graph.sizevars.size_hint(x) for x in value.get_stride()]
                 add_fake_input(

torch/_inductor/graph.py

@@ -378,8 +378,6 @@ def output(self, target, args, kwargs):
             value.realize()
             assert isinstance(value, TensorBox)
             value = value.data
-            if isinstance(value, ir.ReinterpretView):
-                continue
             assert isinstance(value, ir.StorageBox)
             value_storage_box = value
             value = value.data

torch/_inductor/ir.py

@@ -1473,14 +1473,6 @@ def codegen_reference(self):
             return f"{as_strided}({self.get_name()}, {size}, {stride}, {offset})"
         return f"{as_strided}({self.get_name()}, {size}, {stride})"
 
-    def codegen_reference_mutation(self):
-        size = V.graph.sizevars.codegen_shape_tuple(self.layout.size)
-        stride = V.graph.sizevars.codegen_shape_tuple(self.layout.stride)
-        offset = V.graph.sizevars.codegen_sizevar(self.layout.offset)
-        if offset != "0":
-            return f"{self.get_name()}.as_strided_({size}, {stride}, {offset})"
-        return f"{self.get_name()}.as_strided_({size}, {stride})"
-
 
 class SliceView(View):
     @classmethod

torch/_inductor/scheduler.py

@@ -1016,9 +1016,8 @@ def free_buffers(self):
                     V.graph.wrapper_code.codegen_free(node.node)
             elif name in V.graph.graph_inputs:
                 storage = V.graph.graph_inputs[name].data
-                if not isinstance(storage, ir.ReinterpretView):
-                    assert storage.is_input_buffer()
-                    V.graph.wrapper_code.codegen_free(storage.data)
+                assert storage.is_input_buffer()
+                V.graph.wrapper_code.codegen_free(storage.data)
 
         self.buffer_names_to_free.clear()
 

torch/_inductor/sizevars.py

@@ -458,8 +458,6 @@ def strideof(name):
         needed = set(self.var_to_val.keys()) - set(self.replacements.keys())
 
         for name, value in graph_inputs.items():
-            if isinstance(value.data, ir.ReinterpretView):
-                value = value.data.data
             shapes = value.get_size()
             for dim, shape in enumerate(shapes):
                 shape = self.simplify(shape)
@@ -470,8 +468,6 @@ def strideof(name):
                     )
 
         for name, value in graph_inputs.items():
-            if isinstance(value.data, ir.ReinterpretView):
-                value = value.data.data
             shapes = value.get_stride()
             for dim, shape in enumerate(shapes):
                 shape = self.simplify(shape)

torch/fx/passes/shape_prop.py

@@ -182,6 +182,4 @@ def propagate(self, *args):
         Returns:
             Any: The value returned from executing the Module
         """
-        # clone inputs to avoid side effects caused by inplace ops during run_node
-        new_args = [torch._prims_common.clone_preserve_strides(x) for x in args]
-        return super().run(*new_args)
+        return super().run(*args)