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Enable hqq_scale_only by default for XNNPACK #211

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Merged
mergennachin merged 1 commit into from
Feb 4, 2026
Merged

Enable hqq_scale_only by default for XNNPACK #211

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16 changes: 16 additions & 0 deletions optimum/exporters/executorch/quantization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,14 +18,24 @@
import torch


# Applied to IntxWeightOnlyConfig and Int8DynamicActivationIntxWeightConfig.
# Not applied to Int4WeightOnlyConfig (different API) or UIntxWeightOnlyConfig (no such param).
DEFAULT_QPARAMS_ALGORITHM = "hqq_scale_only"


def quantize_model_(
eager_model: torch.nn.Module,
qlinear_config: Optional[str] = None,
qlinear_group_size: Optional[int] = 32,
qlinear_packing_format: Optional[str] = None,
qembedding_config: Optional[str] = None,
qembedding_group_size: Optional[int] = 0,
qparams_algorithm: Optional[str] = None,
) -> torch.nn.Module:
# qparams_algorithm is applied to IntxWeightOnlyConfig and Int8DynamicActivationIntxWeightConfig.
# Not applied to Int4WeightOnlyConfig (different API) or UIntxWeightOnlyConfig (no such param).
if qparams_algorithm is None:
qparams_algorithm = DEFAULT_QPARAMS_ALGORITHM
if not (qlinear_config or qembedding_config):
return

Expand Down Expand Up @@ -54,10 +64,12 @@ def quantize_model_(
"4w": IntxWeightOnlyConfig(
weight_dtype=torch.int4,
granularity=embedding_weight_granularity,
intx_choose_qparams_algorithm=qparams_algorithm,
),
"8w": IntxWeightOnlyConfig(
weight_dtype=torch.int8,
granularity=embedding_weight_granularity,
intx_choose_qparams_algorithm=qparams_algorithm,
),
}[qembedding_config]

Expand All @@ -75,6 +87,7 @@ def build_linear_config(quant_config_key: str, granularity: str, packing_format:
return Int8DynamicActivationIntxWeightConfig(
weight_dtype=torch.int4,
weight_granularity=granularity,
intx_choose_qparams_algorithm=qparams_algorithm,
)
if quant_config_key == "4w":
# Determine if we need to use Int4WeightOnlyConfig with int4_packing_format
Expand All @@ -88,16 +101,19 @@ def build_linear_config(quant_config_key: str, granularity: str, packing_format:
return IntxWeightOnlyConfig(
weight_dtype=torch.int4,
granularity=granularity,
intx_choose_qparams_algorithm=qparams_algorithm,
)
if quant_config_key == "8w":
return IntxWeightOnlyConfig(
weight_dtype=torch.int8,
granularity=granularity,
intx_choose_qparams_algorithm=qparams_algorithm,
)
if quant_config_key == "8da8w":
return Int8DynamicActivationIntxWeightConfig(
weight_dtype=torch.int8,
weight_granularity=PerAxis(0),
intx_choose_qparams_algorithm=qparams_algorithm,
)
if quant_config_key == "fpa4w":
# Need to import to load the ops
Expand Down
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