Update _maybe_compute_kjt_to_jt_dict to support JIT tracing #750
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) Summary: Pull Request resolved: pytorch#750 torch.split accepts a list of int (length_per_key). When doing JIT tracing, the list of int becomes a fixed value, making this piece of code unable to generalized. pytorch#417 Use torch.tensor_split that accepts a tensor instead of tensor.split to prevent this issue. Also create a method to generate tensor-typed length_per_key for tensor_split's consumption Differential Revision: D40662254 fbshipit-source-id: bbe920abf0e67383f1758cb5197867ff15132a47
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) Summary: Pull Request resolved: pytorch#750 torch.split accepts a list of int (length_per_key). When doing JIT tracing, the list of int becomes a fixed value, making this piece of code unable to generalized. pytorch#417 Use torch.tensor_split that accepts a tensor instead of tensor.split to prevent this issue. Also create a method to generate tensor-typed length_per_key for tensor_split's consumption Differential Revision: D40662254 fbshipit-source-id: 59639160876d64b9b7327673c21b318d57c582ab
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Summary:
torch.split accepts a list of int (length_per_key). When doing JIT tracing, the list of int becomes a fixed value, making this piece of code unable to generalized.
#417
Use torch.tensor_split that accepts a tensor instead of tensor.split to prevent this issue
Differential Revision: D40662254