Add aten.linear.default implementation to mx_ops

[Ali-Flt]

Fixes #796

[pytorch-bot[bot]]

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As of commit 8b79caabb6434c879a95c5b47778a6204d009ece with merge base f5703b07acc683653556d04ef970709ba47dba10 (): :green_heart: Looks good so far! There are no failures yet. :green_heart:

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[vkuzo]

thanks! Could you also add a test to https://github.com/pytorch/ao/blob/main/test/prototype/mx_formats/test_mx_linear.py ?

[Ali-Flt]

@vkuzo I tried different ways to trigger the use of aten.linear but couldn't

[vkuzo]

I'd definitely recommend a test that fails before this PR and passes after this PR. Would it work to wrap the code snippet you were using (which ended up calling into the linear override) into a test?

[gau-nernst]

Just curious. Would it be better to implement F.linear() under __torch_function__() instead? Previously I also faced strange behavior on what aten ops will be dispatched by F.linear(), so implementing F.linear() directly would solve the problem.

[vkuzo]

Would it be better to implement F.linear() under torch_function() instead?

As of ~months ago, __torch_dispatch__ was better supported with torch.compile, at least for the things we needed for float8. I haven't checked if torch.compile + __torch_function__ coverage is better now, would be good to check.

[balaabhijit]

+1 Kudos for the fix! Can confirm I also ran into this same error without this PR

[balaabhijit]

A simple test which will fail before this PR on:

torch==2.5.1+cu121
torchao==0.6.1

import torch
import torch.nn as nn

from torchao.prototype.mx_formats.mx_linear import swap_linear_with_mx_linear

class MLP(nn.Module):
    def __init__(self, in_features: int = 128, out_features: int = 256):
        super().__init__()
        self.fc1 = nn.Linear(in_features=in_features, out_features=out_features)

    def forward(self, x):
        x = self.fc1(x)
        x = torch.nn.functional.gelu(x)
        return x


model = MLP()
# Does not hit the error with swap_linear_with_mx_inference_linear
swap_linear_with_mx_linear(
        model, elem_dtype=torch.float8_e4m3fn, block_size=32)

input_tensor = torch.randn(10, 128)
with torch.inference_mode():
    _ = model(input_tensor)

[balaabhijit]

@vkuzo Thanks for the great insight on __torch_dispatch__ vs __torch_function__ this is super helpful