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adb699189b
This reverts commit b2d602306a9eb19e30328cbaee941c874f8148a9. Reverted https://github.com/pytorch/pytorch/pull/126578 on behalf of https://github.com/clee2000 due to failed internal test D58394084. Author has forward fix but includes external changes so reverting is a bit easier to coordinate ([comment](https://github.com/pytorch/pytorch/pull/126578#issuecomment-2161481839))
40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
# mypy: allow-untyped-defs
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import torch
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doc = """
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This is used when dynamo traces torch.nn.Parameter, which normally would not trace properly
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with AOTAutograd. We instead create a placeholder torch.nn.Parameter before the graph, which
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becomes a graph arg and has no storage backing it. At the point in the graph where the parameter
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actually should be created we mutate this sacrificial placeholder into it. This allows gradients
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to flow into the parameter as if it were an input to the graph (which is the only thing we are
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allowed to compute gradients on).
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""".strip()
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class TracableCreateParameter(torch.autograd.Function):
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@staticmethod
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def forward(ctx, tensor, placeholder):
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assert not tensor.requires_grad
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return placeholder.set_(tensor)
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@staticmethod
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def backward(ctx, grad):
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return None, grad # grad flows to placeholder
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def tracable_create_parameter(tensor, placeholder):
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with torch.set_grad_enabled(placeholder.requires_grad):
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out = TracableCreateParameter.apply(tensor, placeholder)
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return out
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def new_parameter_placeholder(size, dtype, device, requires_grad):
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"""Create a placeholder to be passed to the above functions"""
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result = torch.nn.Parameter(
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torch.empty(size, dtype=dtype, device=device), requires_grad=requires_grad
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)
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# TODO(jansel): alloc followed by free is inefficient, need a way to allocate an unbacked tensor.
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# Allocating a zero tensor would causes assert failures in autograd.
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result.untyped_storage().resize_(0)
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return result
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