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Revert "Arm backend: Add correction for floor mode" #14998

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Merged
merged 1 commit into from
Oct 10, 2025
Merged

Revert "Arm backend: Add correction for floor mode" #14998

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52 changes: 2 additions & 50 deletions backends/arm/_passes/decompose_div_tensor_mode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,8 +22,6 @@
"full": exir_ops.edge.aten.full.default,
"lt": exir_ops.edge.aten.lt.Tensor,
"where": exir_ops.edge.aten.where.self,
"mul": exir_ops.edge.aten.mul.Tensor,
"sub": exir_ops.edge.aten.sub.Tensor,
}

aten_unary = {
Expand All @@ -33,8 +31,6 @@
"full": torch.ops.aten.full.default,
"lt": torch.ops.aten.lt.Tensor,
"where": torch.ops.aten.where.self,
"mul": torch.ops.aten.mul.Tensor,
"sub": torch.ops.aten.sub.Tensor,
}


Expand Down Expand Up @@ -74,57 +70,13 @@ def call_operator(self, op, args, kwargs, meta):
return q

if rounding_mode == "floor":
q_raw = q

# trunc(q_raw) = where(q_raw < 0, ceil(q_raw), floor(q_raw))
q_floor = super().call_operator(opset["floor"], (q_raw,), {}, meta)
q_ceil = super().call_operator(opset["ceil"], (q_raw,), {}, meta)

# a zero tensor with the right shape
out_shape = (1,) * len(meta["val"].size())
zero = super().call_operator(
opset["full"],
args=(out_shape, 0.0),
kwargs={},
meta=meta,
)

is_neg = super().call_operator(opset["lt"], (q_raw, zero), {}, meta)
q_trunc = super().call_operator(
opset["where"], (is_neg, q_ceil, q_floor), {}, meta
)

# r = a - q_trunc * b (true remainder under truncation)
q_times_b = super().call_operator(opset["mul"], (q_trunc, b), {}, meta)
r = super().call_operator(opset["sub"], (a, q_times_b), {}, meta)

# Decide if we need to subtract 1:
# for b > 0, adjust if r < 0; for b < 0, adjust if r > 0.
b_pos = super().call_operator(opset["lt"], (zero, b), {}, meta) # b > 0
r_lt0 = super().call_operator(opset["lt"], (r, zero), {}, meta) # r < 0
r_gt0 = super().call_operator(opset["lt"], (zero, r), {}, meta) # r > 0

adjust_if = super().call_operator(
opset["where"], (b_pos, r_lt0, r_gt0), {}, meta
)

one = super().call_operator(
opset["full"],
args=(out_shape, 1.0),
kwargs={},
meta=meta,
)
q_minus_1 = super().call_operator(opset["sub"], (q_trunc, one), {}, meta)

return super().call_operator(
opset["where"], (adjust_if, q_minus_1, q_trunc), {}, meta
)
return super().call_operator(opset["floor"], (q,), {}, meta)

if rounding_mode == "trunc":
zero = super().call_operator(
opset["full"],
args=((1,) * len(meta["val"].size()), 0.0),
kwargs={},
kwargs={"dtype": torch.float32},
meta=meta,
)
lt0 = self.call_operator(opset["lt"], (q, zero), {}, meta)
Expand Down
26 changes: 2 additions & 24 deletions backends/arm/test/ops/test_div_tensor_mode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,14 +36,6 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
return torch.div(x, y, rounding_mode=self.mode)


def _rank4_large_randn_case():
torch.manual_seed(0)
x = 200 * torch.randn(5, 10, 25, 20) + 1
torch.manual_seed(1)
y = torch.rand(5, 10, 25, 20) + 1
return x, y


test_data = {
"mode_none": lambda: (None, (torch.randn(4, 8), torch.randn(4, 8).abs() + 1e-3)),
"mode_floor": lambda: (
Expand All @@ -55,13 +47,6 @@ def _rank4_large_randn_case():
(torch.randn(4, 8), torch.randn(4, 8).abs() + 1e-3),
),
"int_denominator": lambda: (None, (torch.randn(4, 8), 2)),
"op_floor_div_rank4_large_randn": lambda: (
"floor",
(
200 * torch.randn(5, 10, 25, 20) + 1,
torch.rand(5, 10, 25, 20) + 1,
),
),
}


Expand Down Expand Up @@ -99,13 +84,7 @@ def test_div_tensor_mode_tosa_INT(data):

@common.XfailIfNoCorstone300
@common.parametrize(
"data",
test_data,
xfails={
"mode_trunc": "CPU op missing in unittests",
"mode_floor": "Not supported",
"op_floor_div_rank4_large_randn": "Not supported",
},
"data", test_data, xfails={"mode_trunc": "CPU op missing in unittests"}
)
def test_div_tensor_mode_u55_INT(data):
mode, inputs = data()
Expand All @@ -115,10 +94,9 @@ def test_div_tensor_mode_u55_INT(data):
model,
inputs,
aten_ops=model.aten_ops_int,
exir_ops=[],
use_to_edge_transform_and_lower=True,
)
pipeline.pop_stage("check_not.exir")
pipeline.pop_stage("check_count.exir")
pipeline.run()


Expand Down
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