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Arm backend: Update full quantization annotation #14585

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Merged
merged 3 commits into from
Oct 1, 2025
Merged

Arm backend: Update full quantization annotation #14585

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e1a2094
Arm backend: Update full quantization annotation
oscarandersson8218 Sep 23, 2025
125a882
Merge branch 'main' into full_annotation
oscarandersson8218 Sep 29, 2025
7430caa
Merge branch 'main' into full_annotation
oscarandersson8218 Oct 1, 2025
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24 changes: 15 additions & 9 deletions backends/arm/quantizer/quantization_annotator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import logging
import operator
from dataclasses import dataclass
from typing import Callable, List, Optional, Sequence
from typing import Callable, cast, List, Optional, Sequence

import torch
import torch.fx
Expand Down Expand Up @@ -137,11 +137,18 @@ def _is_large_scalar(node: Node, gm: torch.fx.GraphModule):
node since histc op (in HistogramObserver) only works for values up to certain upper
bound.
"""
HISTC_UPPER_BOUND = 3.4028235e15
if node.op == "get_attr" and isinstance(node.target, str):
tensor = _get_node_target(gm, node.target)
# torch.histc works until this upper bound
HISTC_UPPER_BOUND = 3.4028235e15
return tensor.numel() == 1 and abs(tensor.item()) > HISTC_UPPER_BOUND
if node.op == "call_function" and node.target in (
torch.ops.aten.full.default,
torch.ops.aten.full,
torch.ops.aten.fill_.Scalar,
):
fill_value = cast(float, node.args[1])
return abs(fill_value) > HISTC_UPPER_BOUND
return False


Expand Down Expand Up @@ -358,9 +365,6 @@ def _match_pattern(
torch.ops.aten.permute_copy.default,
torch.ops.aten.avg_pool2d.default,
torch.ops.aten.max_pool2d.default,
torch.ops.aten.full.default,
torch.ops.aten.full,
torch.ops.aten.fill_.Scalar,
torch.ops.aten.flatten.using_ints,
torch.ops.aten.dropout.default,
torch.ops.aten.dropout_.default,
Expand Down Expand Up @@ -518,9 +522,6 @@ def any_or_hardtanh_min_zero(n: Node):
]
quant_properties.quant_output = _QuantProperty(0, shared_qspec) # type: ignore[arg-type]
elif node.target in _one_to_one_shared_input_or_input_act_qspec:
if not isinstance(node.args[0], Node):
return None

input_qspec = (
SharedQuantizationSpec(node.args[0]) # type: ignore[arg-type]
if is_output_annotated(node.args[0]) # type: ignore
Expand Down Expand Up @@ -578,7 +579,12 @@ def any_or_hardtanh_min_zero(n: Node):
),
]
quant_properties.quant_output = None
elif node.target in [torch.ops.aten.scalar_tensor.default]:
elif node.target in [
torch.ops.aten.scalar_tensor.default,
torch.ops.aten.full.default,
torch.ops.aten.full,
torch.ops.aten.fill_.Scalar,
]:
quant_properties.quant_inputs = []
quant_properties.quant_output = _QuantProperty(0, output_act_qspec)
elif node.target in [operator.getitem]:
Expand Down
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