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Add `strict=True/False` to zip calls in test utils. `strict=True` is passed when possible. Pull Request resolved: https://github.com/pytorch/pytorch/pull/165531 Approved by: https://github.com/Skylion007
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
# mypy: ignore-errors
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import torch
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import torch.utils._pytree as pytree
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from torch._export.wrappers import mark_subclass_constructor_exportable_experimental
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from torch.utils._python_dispatch import return_and_correct_aliasing
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# A simple tensor subclass that holds two tensors internally, and runs every op on both tensors.
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class TwoTensor(torch.Tensor):
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@staticmethod
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def __new__(cls, a, b, outer_size=None, outer_stride=None, *, requires_grad=None):
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if outer_size is None:
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outer_size = a.size()
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if outer_stride is None:
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outer_stride = a.stride()
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assert (
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a.device == b.device
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and a.layout == b.layout
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and a.requires_grad == b.requires_grad
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and a.dtype == b.dtype
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)
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# I guess it would be more accurate to represent the shape as torch.cat(a, b).shape
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shape = outer_size
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kwargs = {}
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kwargs["strides"] = outer_stride
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kwargs["storage_offset"] = a.storage_offset()
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kwargs["device"] = a.device
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kwargs["layout"] = a.layout
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kwargs["requires_grad"] = requires_grad or a.requires_grad
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kwargs["dtype"] = a.dtype
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out = torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs)
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assert a.shape == b.shape
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assert a.stride() == b.stride()
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assert a.storage_offset() == b.storage_offset()
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return out
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@torch._disable_dynamo
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@mark_subclass_constructor_exportable_experimental
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def __init__(self, a, b, outer_size=None, outer_stride=None, *, requires_grad=None):
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self.a = a
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self.b = b
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def __repr__(self):
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a_repr = repr(self.a)
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b_repr = repr(self.b)
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return f"TwoTensor({a_repr}, {b_repr})"
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def __tensor_flatten__(self):
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return ["a", "b"], None
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@staticmethod
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def __tensor_unflatten__(inner_tensors, meta, outer_size, outer_stride):
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assert meta is None
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a, b = inner_tensors["a"], inner_tensors["b"]
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if type(a) is torch.Tensor:
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assert outer_size is not None
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assert outer_stride is not None
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return TwoTensor(a, b, outer_size, outer_stride)
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@classmethod
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def __torch_dispatch__(cls, func, types, args, kwargs):
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if kwargs is None:
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kwargs = {}
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args_a = pytree.tree_map_only(TwoTensor, lambda x: x.a, args)
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args_b = pytree.tree_map_only(TwoTensor, lambda x: x.b, args)
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kwargs_a = pytree.tree_map_only(TwoTensor, lambda x: x.a, kwargs)
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kwargs_b = pytree.tree_map_only(TwoTensor, lambda x: x.b, kwargs)
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out_a = func(*args_a, **kwargs_a)
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out_b = func(*args_b, **kwargs_b)
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out_a_flat, spec = pytree.tree_flatten(out_a)
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out_b_flat = pytree.tree_leaves(out_b)
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# for aten ops that return non-tensors, just assume that
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# our two inner tensors return the same value
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out_flat = [
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cls(o_a, o_b) if isinstance(o_a, torch.Tensor) else o_a
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for o_a, o_b in zip(out_a_flat, out_b_flat, strict=True)
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]
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out = pytree.tree_unflatten(out_flat, spec)
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from torch._higher_order_ops.cond import cond_op
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if func is cond_op:
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return out
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else:
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return return_and_correct_aliasing(func, args, kwargs, out)
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def get_elem_a(self):
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return self.a
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class TwoTensorMode(torch.utils._python_dispatch.TorchDispatchMode):
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def __torch_dispatch__(self, func, types, args=(), kwargs=None):
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out = func(*args, **kwargs)
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if torch._subclasses.fake_tensor._is_tensor_constructor(func):
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out = TwoTensor(out, out.clone())
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return out
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