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Qualcomm AI Engine Direct - alias_copy op #10319

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34223a0
Remove alias_copy op
winskuo-quic Apr 21, 2025
24f8528
change to relu
winskuo-quic Apr 23, 2025
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1 change: 1 addition & 0 deletions backends/qualcomm/_passes/remove_redundancy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@ def __init__(self):
exir_ops.edge.aten.clone.default: self._default_condition,
torch.ops.aten.alias.default: self._default_condition,
exir_ops.edge.aten.alias.default: self._default_condition,
exir_ops.edge.aten.alias_copy.default: self._default_condition,
exir_ops.edge.aten.lift_fresh_copy.default: self._default_condition,
# remove this target if '_skip_dim_order' is set to False
exir_ops.edge.dim_order_ops._to_dim_order_copy.default: self._dim_order_op_condition,
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11 changes: 11 additions & 0 deletions backends/qualcomm/tests/models.py
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Expand Up @@ -10,6 +10,17 @@
# module with related operator only


# Ensure alias_copy is removed in remove_redundancy pass
class Alias(torch.nn.Module):
def __init__(self):
super().__init__()
self.relu = torch.nn.ReLU()

def forward(self, x):
alias_x = torch.ops.aten.alias.default(x)
return self.relu(alias_x)


class And(torch.nn.Module):
def __init__(self, pos, neg):
super().__init__()
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11 changes: 11 additions & 0 deletions backends/qualcomm/tests/test_qnn_delegate.py
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Expand Up @@ -118,6 +118,11 @@ def test_qnn_backend_adaptive_avg_pool2d(self):
sample_input = (torch.randn(1, 512, 7, 7),)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_alias(self):
module = Alias() # noqa: F405
sample_input = (torch.randn(1, 10),)
self.lower_module_and_test_output(module, sample_input)
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Are we testing alias op should still work, meaning they're not removed?

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Hi @cccclai,
Thanks for reviewing the PR.
Alias op will be removed.
This test is just to show alias can be properly removed and model is still working properly.
I have added some comments under the model.

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hmm, I assume this test will work regardless with or without alias_op, correct? I was thinking export the graph and run to_backend, and check there is no alias op after that.

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hmm, I assume this test will work regardless with or without alias_op, correct? I was thinking export the graph and run to_backend, and check there is no alias op after that.

This test it to reproduce @billmguo error.
Without adding exir_ops.edge.aten.alias_copy.default to RemoveRedundancy pass, this test will fail during qnn_partitioner where it does not have op_builder for aten.alias_copy.default.

Thanks for the suggestion. That will probably be more straight forward as the unit test is not actually running the alias_op since it is dropped during RemoveRedundancy. Maybe we can add a new class for UT, targeting whether passes are working as expected.


def test_qnn_backend_amax(self):
modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)] # noqa: F405
sample_input = (torch.randn(4, 4),)
Expand Down Expand Up @@ -1156,6 +1161,12 @@ def test_qnn_backend_adaptive_avg_pool2d(self):
module = self.get_qdq_module(module, sample_input)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_alias(self):
module = Alias() # noqa: F405
sample_input = (torch.randn(1, 10),)
module = self.get_qdq_module(module, sample_input)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_amax(self):
modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)] # noqa: F405
sample_input = (torch.randn(4, 4),)
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