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Add subclass based method for inference w/ MXFP8 #2132
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# Copyright (c) Meta Platforms, Inc. and affiliates. | ||
# All rights reserved. | ||
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# This source code is licensed under the license found in the | ||
# LICENSE file in the root directory of this source tree. | ||
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""" | ||
This file defines the top level torch ops that are extended by MXTensor | ||
See: https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-with-a-tensor-wrapper-type | ||
for more details. | ||
""" | ||
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from typing import Any, Dict | ||
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import torch | ||
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from torchao.prototype.mx_formats.mx_ops import _addmm_mx_dispatch | ||
from torchao.prototype.mx_formats.mx_tensor import ( # noqa: E501 | ||
MXTensor, | ||
) | ||
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aten = torch.ops.aten | ||
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MX_FUNC_TABLE: Dict[Any, Any] = {} | ||
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def implements_func(torch_ops): | ||
"""Register torch ops to the mx op table for torch function""" | ||
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def decorator(func): | ||
for op in torch_ops: | ||
MX_FUNC_TABLE[op] = func | ||
return func | ||
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return decorator | ||
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@implements_func([aten.linear.default]) | ||
def mx_linear(func, types, args, kwargs): | ||
a, b = args[0], args[1] | ||
assert isinstance(a, MXTensor) and isinstance(b, MXTensor) | ||
bias = args[2] if len(args) == 3 else None | ||
return _addmm_mx_dispatch(a, b.t(), func, bias=bias) |
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