Both optimized and portable arithmetic operators have broadcasting errors

[larryliu0820]

🐛 Describe the bug

Subtraction is behaving weirdly when we have to broadcast both x and y.

Repro code snippet:

import torch
from torch.export import Dim
from executorch.exir import to_edge
from executorch.runtime import Runtime

class Model(torch.nn.Module):

    def forward(self, x, y):
        return x - y

dim = Dim("dim", min=1, max=1024)
ep = torch.export.export(
    Model(), (torch.ones(1, 4), torch.ones(4, 1)), dynamic_shapes=({1: dim}, {0: dim})
)
pte = to_edge(ep).to_executorch().buffer

runtime = Runtime.get()
program = runtime.load_program(pte)
method = program.load_method("forward")

# run sub with (1, 2) and (2, 1) first, result should be zeros(2, 2)
method.execute((torch.ones(1, 2), torch.ones(2, 1)))

# run sub with (1, 4) and (4, 1), result should be zeros(4, 4) but actually getting zeros(1, 4)
method.execute((torch.ones(1, 4), torch.ones(4, 1)))
"""
Actual
[tensor([[0., 0., 0., 0.]])]

Expected
[tensor([[0., 0., 0., 0.],
         [0., 0., 0., 0.],
         [0., 0., 0., 0.],
         [0., 0., 0., 0.]])]
"""

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cc @manuelcandales

[kimishpatel]

cc @manuelcandales

[iseeyuan]

cc @swolchok

[swolchok]

hm. portable sub should be working fine:

https://github.com/pytorch/executorch/blame/main/kernels/portable/cpu/op_sub.cpp#L47

(@larryliu0820 are you sure you're running portable?)

optimized just plain doesn't seem to have a resize except in one specific case, which seems obviously broken:

https://github.com/pytorch/executorch/blame/main/kernels/optimized/cpu/op_sub.cpp#L108

I think I need to learn why https://github.com/pytorch/executorch/blob/main/kernels/test/op_sub_test.cpp#L266 doesn't cover this case.

[kimishpatel]

om/larryliu0820) are you sure you're running portable?)

optimized just plain doesn't seem to have a resize except in one specific case, which seems obviously broken:

https://github.com/pytorch/executorch/blame/main/kernels/optimized/cpu/op_sub.cpp#L108

Are you sure? I do see this

executorch/kernels/optimized/cpu/op_add_sub_impl.h

Line 90 in 3072890

auto error = resize_tensor(out, a.sizes());

[swolchok]

Are you sure? I do see this

OK, two specific cases. that resize is also gated. there are clearly uncovered cases

[swolchok]

findings:

• the specific regression test I attempted to add for op_sub passes for both portable and optimized.
• I was wrong when I said "there are clearly uncovered cases" above. I wasn't reading closely; the uncovered cases funnel into utility functions that do the necessary resize.

best guess is that there is something wrong in the Python or lowering layer in the report here; the kernels seem to be correct.

[swolchok]

@larryliu0820 your repro script doesn't run for me. perhaps you are using a different torch version than the pinned one?

(.venv) swolchok@swolchok-mac ~/src/executorch> python repro.py
Traceback (most recent call last):
  File "/Users/swolchok/src/executorch/repro.py", line 11, in <module>
    dim = Dim.AUTO(min=1, max=1024)
TypeError: '_DimHint' object is not callable

over to you to fix repro

[larryliu0820]

@swolchok what is your full error? Is it complaining Dim.AUTO doesn't exist?

[swolchok]

@larryliu0820 sorry I missed a line while pasting. edited.

[larryliu0820]

@swolchok try

dim = Dim("dim", min=1, max=1024)

instead