Add tp.pad #203
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parthchadha
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Sep 19, 2024
| Args: | ||
| input: The input tensor. | ||
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| padding_sizes: A sequence of padding sizes of each dimension. Its length must equal to the rank |
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| padding_sizes: A sequence of padding sizes of each dimension. Its length must equal to the rank | |
| padding_sizes: A sequence of padding sizes of each dimension. Its length must be equal to the rank |
| input: The input tensor. | ||
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| padding_sizes: A sequence of padding sizes of each dimension. Its length must equal to the rank | ||
| of `input`. Each element of `padding_size` is a tuple of integers or scalars `(low, high)`, |
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padding_size can also be a Tensor.
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I don't think passing padding_sizes as a Nx2 Tensor is a common use case, it is always more convenient for users to construct a sequence than a Tensor.
However, using ScalarShape in padding_sizes is supported, for example:
pad(inp, [(0, a.shape[0]), (0, a.shape[1])])| sizes_1d = [] | ||
| for size in padding_sizes: | ||
| if isinstance(size, Tensor): | ||
| assert size.rank == 0 |
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| assert size.rank == 0 | |
| assert size.rank == 0, f"Size expected to be of rank 0, got {size.rank}." |
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