pytorch

8db04c11 - reinplace pass: special handling for view_scatter ops (#83846)

reinplace pass: special handling for view_scatter ops (#83846) There is already special handling in the reinplacing pass for removing `{view}_scatter` ops, but there is another case that needs special handling. In this code: ``` def f(): a = torch.zeros(4, 4, 4) a[:, 2:] = torch.ones(4, 2, 4) return a ``` Tracing normally with `make_fx()` gives you: ``` def forward(self): zeros = torch.ops.aten.zeros.default([4, 4, 4], device = device(type='cpu'), pin_memory = False) ones = torch.ops.aten.ones.default([4, 2, 4], device = device(type='cpu'), pin_memory = False) slice_tensor = torch.ops.aten.slice.Tensor(zeros, 0, 0, 9223372036854775807) slice_tensor_1 = torch.ops.aten.slice.Tensor(slice_tensor, 1, 2, 9223372036854775807); slice_tensor = None copy__default = torch.ops.aten.copy_.default(slice_tensor_1, ones); slice_tensor_1 = ones = None return zeros ``` Functionalizing it gives you: ``` def forward(self): zeros = torch.ops.aten.zeros.default([4, 4, 4], device = device(type='cpu'), pin_memory = False) ones = torch.ops.aten.ones.default([4, 2, 4], device = device(type='cpu'), pin_memory = False) slice_tensor = torch.ops.aten.slice.Tensor(zeros, 0, 0, 9223372036854775807) slice_tensor_1 = torch.ops.aten.slice.Tensor(slice_tensor, 1, 2, 9223372036854775807); slice_tensor = None slice_tensor_2 = torch.ops.aten.slice.Tensor(zeros, 0, 0, 9223372036854775807) slice_scatter_default = torch.ops.aten.slice_scatter.default(slice_tensor_2, ones, 1, 2, 9223372036854775807); slice_tensor_2 = ones = None slice_scatter_default_1 = torch.ops.aten.slice_scatter.default(zeros, slice_scatter_default, 0, 0, 9223372036854775807); zeros = slice_scatter_default = None return slice_scatter_default_1 ``` Notice that there are not any functional ops to directly re-inplace! What actually happened is that functionalization turned the `copy_()` into a `copy()`, but the out-of-place `copy()` operator gets optimized away because it's a no-op (when the input and output metadata are the same, `out = copy(a, b)` just returns `b`). What we actually want is to replace this line: ``` slice_scatter_default = torch.ops.aten.slice_scatter.default(slice_tensor_2, ones, 1, 2, ...); ``` with this: ``` new_slice = torch.ops.aten.slice.Tensor(slice_tensor_2, 1, 2, ...); _ = torch.ops.aten.copy_.default(new_slice, ones) ``` In the above, we're taking a fresh slice of the "base" tensor, and performing a `copy_()` on the slice, adding back what functionalization removed. We actually need to create a fresh "slice" node, because we're not guaranteed that one already exists in the graph (technically there should be one, but it might have been DCE'd by the time we hit re-inplacing) I also updated the docs for re-inplacing to more closely match the order of the logic. Pull Request resolved: https://github.com/pytorch/pytorch/pull/83846 Approved by: https://github.com/ezyang