[pytorchbot]

What is wrong today

A constant tensor can be a view into a larger tensor. For example:

self.register_buffer("weight", torch.randn(3, 8, 8)[1])   # starts 64 floats into its storage
...
return F.linear(x, self.weight)

Constant folding can also leave a weight as a view, like one chunk of the packed in_proj_weight of nn.MultiheadAttention in cross-attention.

The XNNPACK and Vulkan serializers copy the bytes of the whole underlying storage, starting at its first byte. For a view that starts later in the storage, the delegate gets the wrong weights, and the .pte gives wrong results with no error. In the model above, the largest XNNPACK error was about 6 to 8. For a view that starts at the beginning, the values are right, but the file carries the whole parent.

XNNPACK per-channel scales are read the same way. They also skip the contiguous() step that weights get, so a transposed groupwise scale is written in the wrong order.

What this change does

• Before reading a weight's bytes, both serializers now clone it if its storage is larger than the tensor. The ExecuTorch emitter already uses this rule for weights that are views (exir/tensor.py, exir/emit/_emitter.py).
• XNNPACK per-channel scales are small, so they are now always copied into contiguous storage of their own.
• The Vulkan path that serializes raw tensors (only tests use it today) now does the same.

What was tested

On macOS arm64. Each of these fails before this change and passes after:

• test_fp32_linear_view_weight (XNNPACK): a linear whose weight is a view.
• test_qd8_linear_transposed_scale (XNNPACK): a groupwise int4 linear with a transposed scale.
• test_view_constant_serializes_only_its_own_bytes (Vulkan graph builder): checks the bytes of a view constant, no GPU needed.
• test_serialize_vulkan_binary (existing): now also serializes a view and a transposed view.

The default backends/xnnpack/test run passes. It skips test_linear.py, so that file was run directly: all tests pass except test_linear_qd8_{f32,f16}_per_channel_int4, which fail the same way without this change. A per-channel scale replaced by an equal view was also checked by hand: off by about 4e4 before, exact after.

Not in this change

_tensor_to_bytes in exir/_serialize/_named_data_store.py has a similar problem: for a non-contiguous tensor, such as a channels_last slice of a bigger tensor, it writes the whole storage. XNNPACK and Vulkan pass it plain bytes, so this change does not depend on it. It is left for a separate fix.

cc @SS-JIA @manuelcandales @digantdesai @cbilgin

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24208

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❌ 1 New Failure

As of commit 8f7115f with merge base 4db778e ():

NEW FAILURE - The following job has failed:

• pull / test-vulkan-operators-linux / linux-job (gh)
  test_empty_list_serializes_once

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