Voice et bot modif

venv/lib/python3.12/site-packages/onnxruntime/quantization/execution_providers/qnn/preprocess.py

@@ -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
 
@@ -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.
@@ -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,

venv/lib/python3.12/site-packages/onnxruntime/quantization/execution_providers/qnn/quant_config.py

@@ -331,23 +331,6 @@ class QnnCompatibilityOverrides:
 
         if not self.per_channel:
             self._make_static_inputs_use_default_weight_type(node)
-            return
-
-        has_weight_no_overrides = node.input[1] in self.initializers and node.input[1] not in self.overrides
-        has_bias_no_overrides = (
-            len(node.input) > 2
-            and node.input[2]
-            and node.input[2] in self.initializers
-            and node.input[2] not in self.overrides
-        )
-
-        if has_weight_no_overrides or has_bias_no_overrides:
-            # TODO: Make bias input not per-channel. QNN needs it to be per-tensor, but quantizer
-            # tries to makes it per-channel if the weight is also per-channel.
-            raise ValueError(
-                "get_qnn_qdq_config() does not currently support the global per_channel option with LayerNormalization."
-                " Please try using custom overrides that make bias per-tensor quantized."
-            )
 
     def _process_sigmoid(self, node: onnx.NodeProto):
         """