47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
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import ixformer._C as ops
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import torch
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__all__ = [
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"ref_add",
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"add",
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]
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def ref_add(input: torch.Tensor, other: torch.Tensor, out: torch.Tensor = None):
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return torch.add(input, other, out=out)
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def add(input: torch.Tensor, other: torch.Tensor, out: torch.Tensor = None):
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"""
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out = input + other
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Support elementwise addition, but broadcasting is not supported yet.
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Note: The dtype of input and other needs to be the same.
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Args:
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input: (...) torch.float32, torch.float16, torch.bfloat16
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other: (...) same as input
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out: (...) same as input
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Returns:
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out: (...) same as input
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"""
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if input.dtype not in [torch.float16, torch.float32, torch.bfloat16]:
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return torch.add(input, other, out=out)
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if not input.is_contiguous() or not other.is_contiguous():
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return torch.add(input, other, out=out)
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if out is not None and not out.is_contiguous():
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return torch.add(input, other, out=out)
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if input.dtype != other.dtype:
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return torch.add(input, other, out=out)
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if out is not None and out.dtype != input.dtype:
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return torch.add(input, other, out=out)
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assert input.shape == other.shape, (f"broadcasting is not supported yet."
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"input is {input.shape}, other is {other.shape}")
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if out is None:
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out = torch.empty_like(input)
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ops.infer.add(input, other, out)
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return out
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