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Runtime accepts a channels-last input for a contiguous method and reads it as contiguous (silent wrong output)

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#21,837 3 comentarios 0 reacciones 1 asignado Ver en GitHub

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@JakeStevens ya está trabajando en esto.

Desde el 14/8/2026.

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Descripción

bug high priority module: runtime triage review

The runtime validates input layout — a transposed 2-D tensor is rejected with
Input 0 for method forward should be contiguous or channels-last. But a
channels-last 4-D tensor is accepted even when the method was exported with a
contiguous input, and its data is then read in contiguous order. No error, no
warning, wrong numbers.

Repro (executorch 1.4.0, torch 2.13, macOS arm64, portable kernels — the XNNPACK
partitioner makes no difference):

import torch
from executorch.exir import to_edge_transform_and_lower
from executorch.runtime import Runtime


class M(torch.nn.Module):
    def forward(self, x):
        return x * 2.0


contig = torch.arange(12, dtype=torch.float32).reshape(1, 3, 2, 2)
chlast = contig.to(memory_format=torch.channels_last)
assert torch.equal(contig, chlast)

ep = torch.export.export(M(), (contig,))
open("/tmp/r.pte", "wb").write(
    to_edge_transform_and_lower(ep, partitioner=[]).to_executorch().buffer)
m = Runtime.get().load_program("/tmp/r.pte").load_method("forward")

print("expected     :", (contig * 2).flatten().tolist())
print("contiguous   :", m.execute([contig])[0].flatten().tolist())
print("channels_last:", m.execute([chlast])[0].flatten().tolist())

expected     : [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0]
contiguous   : [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0]
channels_last: [0.0, 8.0, 16.0, 2.0, 10.0, 18.0, 4.0, 12.0, 20.0, 6.0, 14.0, 22.0]

Two tensors that torch.equal reports as equal produce different results.

Why this is worth fixing rather than documenting. Channels-last is what you
get from the most common way to build an image input:

torch.from_numpy(hwc_array.transpose(2, 0, 1)[None])   # NCHW shape, NHWC strides

np.transpose returns a view and astype keeps memory order by default
(order='K'), so this is easy to hit without ever naming channels_last.

The failure mode is what makes it expensive. torch.randn inputs are contiguous
and match the eager model bit-exactly, so a parity check passes; only real images
diverge. I spent an afternoon bisecting eager -> ep.module() ->
edge.exported_program().module() -> .pte on a Depth-Anything-V2 export,
concluding the conversion was broken, before finding the input was the difference.
The first three stages honour strides and agree; only the runtime disagrees.

Either honouring the strides or rejecting the mismatch would have cost me nothing
to debug. Rejecting seems in keeping with the existing check — the message just
needs to compare the input's layout against the method's expected layout rather
than accept channels-last unconditionally.

cc @mergennachin @kimishpatel @iseeyuan @larryliu0820 @JacobSzwejbka @lucylq

Lenguaje dominante
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