aten.full_like.default #3843
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aten.full_like.default #3843
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/3843
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Summary: Implement aten.full_like.default, which is required in OCR full model. Reuse the implementation of aten.full.default ``` func: full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor ``` The major difference between full and full_like is the first argument, which full is an integer list and full_like is an input tensor. We can reuse lots of code here. And to support dynamic reshape, just add a condition in resize_full_node to determine the out_sizes. Reviewed By: yipjustin Differential Revision: D58121891
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Summary: Implement aten.full_like.default, which is required in OCR full model. Reuse the implementation of aten.full.default ``` func: full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor ``` The major difference between full and full_like is the first argument, which full is an integer list and full_like is an input tensor. We can reuse lots of code here. And to support dynamic reshape, just add a condition in resize_full_node to determine the out_sizes. Reviewed By: yipjustin Differential Revision: D58121891
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Summary: Implement aten.full_like.default, which is required in OCR full model. Reuse the implementation of aten.full.default ``` func: full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor ``` The major difference between full and full_like is the first argument, which full is an integer list and full_like is an input tensor. We can reuse lots of code here. And to support dynamic reshape, just add a condition in resize_full_node to determine the out_sizes. Reviewed By: yipjustin Differential Revision: D58121891
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Summary: Implement aten.full_like.default, which is required in OCR full model. Reuse the implementation of aten.full.default ``` func: full(SymInt[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor func: full_like(Tensor self, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensor ``` The major difference between full and full_like is the first argument, which full is an integer list and full_like is an input tensor. We can reuse lots of code here. And to support dynamic reshape, just add a condition in resize_full_node to determine the out_sizes. Reviewed By: yipjustin Differential Revision: D58121891
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Summary:
Implement aten.full_like.default, which is required in OCR full model. Reuse the implementation of aten.full.default
The major difference between full and full_like is the first argument, which full is an integer list and full_like is an input tensor. We can reuse lots of code here. And to support dynamic reshape, just add a condition in resize_full_node to determine the out_sizes.
Reviewed By: yipjustin
Differential Revision: D58121891