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[QNN-EP] Introduce Level1 Transformer into qnn.preprocess #25883

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Merged
HectorSVC merged 1 commit into from
Aug 29, 2025
Merged

[QNN-EP] Introduce Level1 Transformer into qnn.preprocess #25883

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24 changes: 21 additions & 3 deletions onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,15 +6,15 @@
from __future__ import annotations

import logging
import tempfile
from pathlib import Path

import onnx

from ....tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed
from ....tools.onnx_model_utils import fix_output_shapes, make_input_shape_fixed, optimize_model
from ....tools.remove_initializer_from_input import remove_initializer_from_input
from ...fusions import FusionGelu, FusionLayerNormalization
from ...onnx_model import ONNXModel
from ...quant_utils import save_and_reload_model_with_shape_infer
from .fusion_lpnorm import FusionLpNormalization
from .fusion_spacetodepth import FusionSpaceToDepth

Expand Down Expand Up @@ -93,7 +93,7 @@ def qnn_preprocess_model(
"""
modified = False
model = model_input if isinstance(model_input, onnx.ModelProto) else onnx.load_model(model_input)
model = save_and_reload_model_with_shape_infer(model)
model = save_and_reload_optimize_model(model, shape_infer=True)
onnx_model = ONNXModel(model)

# Optionally, fix the dynamic input shapes.
Expand Down Expand Up @@ -178,6 +178,24 @@ def qnn_preprocess_model(
return modified


def save_and_reload_optimize_model(model: onnx.ModelProto, shape_infer: bool) -> onnx.ModelProto:
with tempfile.TemporaryDirectory(prefix="ort.qnn_preproc.") as qnn_preproc_tmp_dir:
model_in_path = Path(qnn_preproc_tmp_dir).joinpath("qnn_proc_input.onnx")
onnx.save_model(model, model_in_path, save_as_external_data=True)
if shape_infer:
model_infer_path = Path(qnn_preproc_tmp_dir).joinpath("qnn_proc_infer.onnx")
onnx.shape_inference.infer_shapes_path(str(model_in_path), str(model_infer_path))
model_in_path = model_infer_path
model_out_path = Path(qnn_preproc_tmp_dir).joinpath("qnn_proc_output.onnx")
optimize_model(model_in_path, model_out_path)
ret_model = onnx.load_model(model_out_path)
ret_metaprops = {"onnx.infer": "onnxruntime.tools.qnn.preprocess"}
if ret_model.metadata_props:
ret_metaprops.update(ret_model.metadata_props)
onnx.helper.set_model_props(ret_model, ret_metaprops)
return ret_model


class InputOutputNameMap:
def __init__(
self,
Expand Down
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