[dynamo] support [tensor].type(torch.FloatTensor) #93043
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for some tensor x, x.type(torch.FloatTensor) will essentially do the same thing as x.to(torch.float). x.type can be called with at least 3 types of inputs:
* a string "torch.FloatTensor"
* a dtype torch.float
* a tensor type torch.FloatTensor
the third option (torch.FloatTensor) fails in fx, because fx cannot trace torch.FloatTensor objects.
Although we could fix this in fx, it's simpler to fix this in dynamo. This converts x.type(torch.FloatTensor) into x.type("torch.FloatTensor")
[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/93043
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davidberard98
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Jan 26, 2023
for some tensor x, x.type(torch.FloatTensor) will essentially do the same thing as x.to(torch.float). x.type can be called with at least 3 types of inputs:
* a string "torch.FloatTensor"
* a dtype torch.float
* a tensor type torch.FloatTensor
the third option (torch.FloatTensor) fails in fx, because fx cannot trace torch.FloatTensor objects.
Although we could fix this in fx, it's simpler to fix this in dynamo. This converts x.type(torch.FloatTensor) into x.type("torch.FloatTensor")
ghstack-source-id: eed9550
Pull Request resolved: #93043
davidberard98
changed the title
jansel
approved these changes
Jan 27, 2023
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Stack from ghstack:
for some tensor x, x.type(torch.FloatTensor) will essentially do the same thing as x.to(torch.float). x.type can be called with at least 3 types of inputs:
the third option (torch.FloatTensor) fails in fx, because fx cannot trace torch.FloatTensor objects. So this PR will replace the torch.FloatTensor type with a string "torch.FloatTensor"
Why not fix this in fx? Well, it's possible, but I'm not sure a nice way to do it. We would want to update torch.fx.node.BaseArgumentTypes to contain torch.FloatTensor etc. We could hard-code a list of tensor types there (the types vary depending on build type, e.g. whether or not cuda tensors are available), but that's not great in case our hardcoded list differs from the actual list registered by python_tensor.cpp. Another option is to dynamically populate the list of types with
Union[tuple(...)]), and fill the tuple withtorch._tensor_classes(which is directly populated by python_tensor.cpp), but apparently this breaks most typecheckers.cc @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @chunyuan-w @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @desertfire