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Merged
robert-kalmar merged 6 commits into from
Dec 11, 2025
Merged

NXP backend: Add QAT support for NeutronQuantizer #15692

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2f955c5
NXP backend: Add QAT support for NeutronQuantizer
StrycekSimon Nov 18, 2025
4eb29f3
NXP backend: Add quantizer targeted unit tests for QAT
StrycekSimon Nov 10, 2025
9a9cb21
NXP backend: Update per-op pytest tests to cover QAT
StrycekSimon Nov 18, 2025
d9c2e80
NXP backend: Update per-op unittest tests to cover QAT
StrycekSimon Nov 18, 2025
4843019
NXP backend: Fix missing pytest ignore statement
StrycekSimon Nov 19, 2025
5c2ff05
NXP backend: Add more complex model for QAT testing
StrycekSimon Dec 2, 2025
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176 changes: 113 additions & 63 deletions backends/nxp/quantizer/neutron_quantizer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,13 @@
)
from torch import fx
from torch.ao.quantization.quantizer.utils import _annotate_output_qspec
from torchao.quantization.pt2e import HistogramObserver, MinMaxObserver
from torchao.quantization.pt2e import (
FakeQuantize,
FusedMovingAvgObsFakeQuantize,
HistogramObserver,
MinMaxObserver,
MovingAverageMinMaxObserver,
)
from torchao.quantization.pt2e.quantizer import (
ComposableQuantizer,
DerivedQuantizationSpec,
Expand Down Expand Up @@ -154,78 +160,120 @@ def get_supported_operators(cls) -> list[OperatorConfig]:


# Quantization Specification used by Neutron NPU
act_qspec = QuantizationSpec(
dtype=torch.int8,
quant_min=-128,
quant_max=127,
qscheme=torch.per_tensor_affine,
is_dynamic=False,
observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12),
)

wgt_qspec = QuantizationSpec(
dtype=torch.int8,
quant_min=-127,
quant_max=127,
qscheme=torch.per_tensor_symmetric,
is_dynamic=False,
observer_or_fake_quant_ctr=MinMaxObserver,
ch_axis=0,
)
def act_qspec(is_qat: bool):
eps = 2**-12
observer_or_fake_quant_ctr = (
FusedMovingAvgObsFakeQuantize.with_args(
observer=MovingAverageMinMaxObserver, eps=eps
)
if is_qat
else HistogramObserver.with_args(eps=eps)
)

return QuantizationSpec(
dtype=torch.int8,
quant_min=-128,
quant_max=127,
qscheme=torch.per_tensor_affine,
is_dynamic=False,
observer_or_fake_quant_ctr=observer_or_fake_quant_ctr,
)


def wgt_qspec(is_qat: bool):
observer_or_fake_quant_ctr = (
FakeQuantize.with_args(observer=MovingAverageMinMaxObserver)
if is_qat
else MinMaxObserver
)

return QuantizationSpec(
dtype=torch.int8,
quant_min=-127,
quant_max=127,
qscheme=torch.per_tensor_symmetric,
is_dynamic=False,
observer_or_fake_quant_ctr=observer_or_fake_quant_ctr,
ch_axis=0,
)


def wgt_fc_qspec(is_qat: bool):
observer_or_fake_quant_ctr = (
FakeQuantize.with_args(observer=MovingAverageMinMaxObserver)
if is_qat
else MinMaxObserver
)

return QuantizationSpec(
dtype=torch.int8,
quant_min=-127,
quant_max=127,
qscheme=torch.per_tensor_symmetric,
is_dynamic=False,
observer_or_fake_quant_ctr=observer_or_fake_quant_ctr,
)

wgt_fc_qspec = QuantizationSpec(
dtype=torch.int8,
quant_min=-127,
quant_max=127,
qscheme=torch.per_tensor_symmetric,
is_dynamic=False,
observer_or_fake_quant_ctr=MinMaxObserver,
)

# Is set by the *PatternQuantizer directly.
bias_qspec = None


class NeutronQuantizer(ComposableQuantizer):
def __init__(self, neutron_target_spec: NeutronTargetSpec):
def __init__(self, neutron_target_spec: NeutronTargetSpec, is_qat: bool = False):
self.neutron_target_spec = neutron_target_spec
static_qconfig = QuantizationConfig(act_qspec, act_qspec, wgt_qspec, None)
static_fc_qconfig = QuantizationConfig(act_qspec, act_qspec, wgt_fc_qspec, None)
self.is_qat = is_qat

static_qconfig = QuantizationConfig(
act_qspec(is_qat=is_qat),
act_qspec(is_qat=is_qat),
wgt_qspec(is_qat=is_qat),
None,
)
static_fc_qconfig = QuantizationConfig(
act_qspec(is_qat=is_qat),
act_qspec(is_qat=is_qat),
wgt_fc_qspec(is_qat=is_qat),
None,
)

OpQuantizer = NeutronAtenQuantizer
super().__init__(
[
NeutronAtenQuantizer(AbsPattern(), static_qconfig),
NeutronAtenQuantizer(AdaptiveAvgPoolPattern(), static_qconfig),
NeutronAtenQuantizer(AddTensorPattern(), static_qconfig),
NeutronAtenQuantizer(AddmmPattern(self), static_fc_qconfig),
NeutronAtenQuantizer(AvgPoolPattern(), static_qconfig),
NeutronAtenQuantizer(CatPattern(), static_qconfig),
NeutronAtenQuantizer(Conv1dPattern(), static_qconfig),
NeutronAtenQuantizer(Conv2dPattern(self), static_qconfig),
NeutronAtenQuantizer(ConvTranspose2dPattern(), static_qconfig),
NeutronAtenQuantizer(DropoutPattern(), static_qconfig),
NeutronAtenQuantizer(FlattenPattern(), static_qconfig),
NeutronAtenQuantizer(HardTanhPattern(), static_qconfig),
NeutronAtenQuantizer(HardTanhInPlacePattern(), static_qconfig),
NeutronAtenQuantizer(LinearPattern(self), static_fc_qconfig),
NeutronAtenQuantizer(MaxPoolPattern(), static_qconfig),
NeutronAtenQuantizer(MeanDimPattern(), static_qconfig),
NeutronAtenQuantizer(MmPattern(self), static_qconfig),
NeutronAtenQuantizer(MulTensorPattern(), static_qconfig),
NeutronAtenQuantizer(PadPattern(), static_qconfig),
NeutronAtenQuantizer(PermutePattern(), static_qconfig),
NeutronAtenQuantizer(ReluPattern(), static_qconfig),
NeutronAtenQuantizer(ReluInPlacePattern(), static_qconfig),
NeutronAtenQuantizer(ReshapePattern(), static_qconfig),
NeutronAtenQuantizer(SigmoidPattern(), static_qconfig),
NeutronAtenQuantizer(SliceTensorPattern(), static_qconfig),
NeutronAtenQuantizer(SoftMaxPattern(), static_qconfig),
NeutronAtenQuantizer(SubTensorPattern(), static_qconfig),
NeutronAtenQuantizer(TanhPattern(), static_qconfig),
NeutronAtenQuantizer(TanhInPlacePattern(), static_qconfig),
NeutronAtenQuantizer(TransposeIntPattern(), static_qconfig),
NeutronAtenQuantizer(ViewPattern(), static_qconfig),
OpQuantizer(AbsPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(AdaptiveAvgPoolPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(AddTensorPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(AddmmPattern(self, is_qat=is_qat), static_fc_qconfig),
OpQuantizer(AvgPoolPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(CatPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(Conv1dPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(Conv2dPattern(self, is_qat=is_qat), static_qconfig),
OpQuantizer(ConvTranspose2dPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(DropoutPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(FlattenPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(HardTanhPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(HardTanhInPlacePattern(is_qat=is_qat), static_qconfig),
OpQuantizer(LinearPattern(self, is_qat=is_qat), static_fc_qconfig),
OpQuantizer(MaxPoolPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(MeanDimPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(MmPattern(self, is_qat=is_qat), static_qconfig),
OpQuantizer(MulTensorPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(PadPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(PermutePattern(is_qat=is_qat), static_qconfig),
OpQuantizer(ReluPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(ReluInPlacePattern(is_qat=is_qat), static_qconfig),
OpQuantizer(ReshapePattern(is_qat=is_qat), static_qconfig),
OpQuantizer(SigmoidPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(SliceTensorPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(SoftMaxPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(SubTensorPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(TanhPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(TanhInPlacePattern(is_qat=is_qat), static_qconfig),
OpQuantizer(TransposeIntPattern(is_qat=is_qat), static_qconfig),
OpQuantizer(ViewPattern(is_qat=is_qat), static_qconfig),
]
)

# Mapping ops defined in quantizer partition types to its quantizer
self.op_to_quantizer = {
pt: q for q in self.quantizers for pt in q.pattern.partition_types()
Expand All @@ -235,7 +283,9 @@ def __init__(self, neutron_target_spec: NeutronTargetSpec):
pt: False for q in self.quantizers for pt in q.pattern.partition_types()
}
self.cluster_quantizers = [
NeutronAtenQuantizer(ActivationsConcatClusterPattern(self), static_qconfig)
NeutronAtenQuantizer(
ActivationsConcatClusterPattern(self, is_qat=is_qat), static_qconfig
)
]

def transform_for_annotation(
Expand Down Expand Up @@ -288,7 +338,7 @@ def _annotate_inputs(self, model: fx.GraphModule):
continue

if node.op == "placeholder" and len(node.users) > 0:
_annotate_output_qspec(node, act_qspec)
_annotate_output_qspec(node, act_qspec(self.is_qat))
self._mark_input_node_as_annotated(node)

def validate(self, model: torch.fx.GraphModule) -> None:
Expand Down
62 changes: 46 additions & 16 deletions backends/nxp/quantizer/patterns.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,11 @@
from torch import fx
from torch._ops import OpOverload
from torch.fx import Node
from torchao.quantization.pt2e import PerChannelMinMaxObserver
from torchao.quantization.pt2e import (
FakeQuantize,
MovingAveragePerChannelMinMaxObserver,
PerChannelMinMaxObserver,
)
from torchao.quantization.pt2e.quantizer import (
DerivedQuantizationSpec,
FixedQParamsQuantizationSpec,
Expand Down Expand Up @@ -59,7 +63,8 @@ class PartitionAnchors:
| tuple[fx.Node, NodeArgsIdx, SharedQuantizationSpec],
] = field(default_factory=list)
weights: list[
tuple[fx.Node, NodeArgsIdx] | tuple[fx.Node, NodeArgsIdx, QuantizationSpec],
tuple[fx.Node, NodeArgsIdx]
| tuple[fx.Node, NodeArgsIdx, QuantizationSpec | FakeQuantize],
] = field(default_factory=list)
biases: list[
tuple[fx.Node, NodeArgsIdx]
Expand All @@ -69,12 +74,18 @@ class PartitionAnchors:
literals: list[tuple[fx.Node, NodeArgsIdx]] = field(default_factory=list)
output: list[
tuple[fx.Node]
| tuple[fx.Node, FixedQParamsQuantizationSpec | SharedQuantizationSpec],
| tuple[
fx.Node,
FixedQParamsQuantizationSpec | SharedQuantizationSpec,
],
] = field(default_factory=list)
empty: bool = False


class QuantizationPattern(ABC):
def __init__(self, is_qat: bool = False):
self.is_qat = is_qat

@abstractmethod
def partition_types(self) -> list[OpOverload]:
"""
Expand Down Expand Up @@ -148,11 +159,12 @@ def get_anchors_for_fixed_quant_specs(
zero_point: int,
quant_min: int = -128,
quant_max: int = 127,
is_qat: bool = False,
) -> PartitionAnchors:
node = fused_partition[0].nodes[-1]
assert len(fused_partition[0].input_nodes) == 1

qspec = FixedQParamsQuantizationSpec(
qspec_or_fake_quantize = FixedQParamsQuantizationSpec(
dtype=torch.int8,
scale=scale,
zero_point=zero_point,
Expand All @@ -166,7 +178,7 @@ def get_anchors_for_fixed_quant_specs(
weights=[],
biases=[],
output=[
(node, qspec),
(node, qspec_or_fake_quantize),
],
)

Expand All @@ -190,7 +202,9 @@ def partition_types(self):


class AddmmPattern(QuantizationPattern):
def __init__(self, neutron_quantizer):
def __init__(self, neutron_quantizer, is_qat: bool):
super().__init__(is_qat=is_qat)

self.neutron_quantizer = neutron_quantizer
self.neutron_target_info = (
self.neutron_quantizer.neutron_target_spec.neutron_target_info
Expand Down Expand Up @@ -365,7 +379,11 @@ def get_anchors(
ch_axis=0,
)

weight_observer_or_fake_quant_ctr = PerChannelMinMaxObserver
weight_observer_or_fake_quant_ctr = (
FakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver)
if self.is_qat
else PerChannelMinMaxObserver
)
weight_quantization_spec = QuantizationSpec(
dtype=torch.int8,
observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr,
Expand Down Expand Up @@ -399,7 +417,9 @@ def partition_types(self) -> list[OpOverload]:


class Conv2dPattern(ConvPattern):
def __init__(self, neutron_quantizer):
def __init__(self, neutron_quantizer, is_qat: bool = False):
super().__init__(is_qat=is_qat)

self.neutron_quantizer = neutron_quantizer
self.neutron_target_info = (
self.neutron_quantizer.neutron_target_spec.neutron_target_info
Expand All @@ -426,7 +446,11 @@ def get_anchors(
ch_axis=0,
)

weight_observer_or_fake_quant_ctr = PerChannelMinMaxObserver
weight_observer_or_fake_quant_ctr = (
FakeQuantize.with_args(observer=MovingAveragePerChannelMinMaxObserver)
if self.is_qat
else PerChannelMinMaxObserver
)
weight_quantization_spec = QuantizationSpec(
dtype=torch.int8,
observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr,
Expand Down Expand Up @@ -563,7 +587,9 @@ def replacement_op(self):


class LinearPattern(QuantizationPattern):
def __init__(self, neutron_quantizer):
def __init__(self, neutron_quantizer, is_qat: bool = False):
super().__init__(is_qat=is_qat)

self.neutron_quantizer = neutron_quantizer
self.neutron_target_info = (
self.neutron_quantizer.neutron_target_spec.neutron_target_info
Expand Down Expand Up @@ -637,7 +663,9 @@ def partition_types(self):


class MmPattern(QuantizationPattern):
def __init__(self, neutron_quantizer):
def __init__(self, neutron_quantizer, is_qat: bool = False):
super().__init__(is_qat=is_qat)

self.neutron_quantizer = neutron_quantizer
self.neutron_target_info = (
self.neutron_quantizer.neutron_target_spec.neutron_target_info
Expand Down Expand Up @@ -802,7 +830,7 @@ def get_anchors(
self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
) -> PartitionAnchors:
return get_anchors_for_fixed_quant_specs(
fused_partition, scale=1.0 / 256.0, zero_point=-128
fused_partition, scale=1.0 / 256.0, zero_point=-128, is_qat=self.is_qat
)


Expand All @@ -820,7 +848,7 @@ def get_anchors(
self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
) -> PartitionAnchors:
return get_anchors_for_fixed_quant_specs(
fused_partition, scale=1.0 / 256.0, zero_point=-128
fused_partition, scale=1.0 / 256.0, zero_point=-128, is_qat=self.is_qat
)


Expand All @@ -838,7 +866,7 @@ def get_anchors(
self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
) -> PartitionAnchors:
return get_anchors_for_fixed_quant_specs(
fused_partition, scale=1.0 / 128.0, zero_point=0
fused_partition, scale=1.0 / 128.0, zero_point=0, is_qat=self.is_qat
)


Expand All @@ -856,7 +884,7 @@ def get_anchors(
self, gm: fx.GraphModule, fused_partition: list[fx.GraphModule]
) -> PartitionAnchors:
return get_anchors_for_fixed_quant_specs(
fused_partition, scale=1.0 / 128.0, zero_point=0
fused_partition, scale=1.0 / 128.0, zero_point=0, is_qat=self.is_qat
)


Expand Down Expand Up @@ -884,7 +912,9 @@ class ActivationsConcatClusterPattern(QuantizationPattern):
│
"""

def __init__(self, neutron_quantizer):
def __init__(self, neutron_quantizer, is_qat: bool = False):
super().__init__(is_qat=is_qat)

self.neutron_quantizer = neutron_quantizer
self.neutron_target_info = (
self.neutron_quantizer.neutron_target_spec.neutron_target_info
Expand Down
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