Support eq.Scalar

[cccclai]

It's reported from Discord and this pattern fails to lower to QNN

class EqualFromInplaceCopyDecomp(torch.nn.Module):
    def __init__(self, hidden_size=4):
        super().__init__()
        # a small state tensor
        self.register_buffer("h", torch.zeros((1, hidden_size)))

    def forward(self, x):
        self.h[0] = x
        return self.h[0]

Differential Revision: D86891707

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:x: 1 New Failure

As of commit 8d9c9af076faea8bdf1ada319053febb3e462d11 with merge base 8ab589a692e3fdd3995b8ee59cb399355f479b2e ():

NEW FAILURE - The following job has failed:

• Lint / lintrunner / linux-job (gh) >>> Lint for backends/qualcomm/tests/test_qnn_delegate.py:

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@cccclai has exported this pull request. If you are a Meta employee, you can view the originating Diff in D86891707.

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[cccclai]

@haowhsu-quic @winskuo-quic @shewu-quic @DannyYuyang-quic Hi team, I might need some help for this pattern. I managed to lower the pattern to fp, but it's failing with the quantized version. I think a copy_ node shows up in the graph indicating it's a mutable buffer. Do you know the best way moving forward with this pattern?

[winskuo-quic]

@haowhsu-quic @winskuo-quic @shewu-quic @DannyYuyang-quic Hi team, I might need some help for this pattern. I managed to lower the pattern to fp, but it's failing with the quantized version. I think a copy_ node shows up in the graph indicating it's a mutable buffer. Do you know the best way moving forward with this pattern?

Hi @cccclai,

I am able to reproduce the issue where I get the following graph finalize error message: No graph inputs present for graph [0]

I believe this has to do with the behavior of run_decomposition here: https://github.com/pytorch/executorch/blob/b433278ec47a2ff9274fc456b62a0511a941dee7/exir/program/_program.py#L1176

The input node is actually not connected to the graph after run_decomposition for quantization flow, as shown in the image below. The copy_ node also disappeared.

On the other hand, floating point flow is working properly because the input node is still connected to the graph after run_decomposition.