Add ModelProto support for quantize api

onnxruntime/python/tools/quantization/execution_providers/qnn/preprocess.py

@@ -16,8 +16,8 @@
 
 
 def qnn_preprocess_model(
-    model_input: Path,
-    model_output: Path,
+    model_input: str | Path | onnx.ModelProto,
+    model_output: str | Path,
     fuse_layernorm: bool = False,
     save_as_external_data: bool = False,
     all_tensors_to_one_file: bool = False,
@@ -37,7 +37,7 @@ def qnn_preprocess_model(
     - (Optional) Fuse ReduceMean sequence into a single LayerNormalization node.
 
     Args:
-        model_input: Path to the input model file.
+        model_input: Path to the input model file or ModelProto.
         model_output: Path the output model file, which is only created if this method returns True.
         fuse_layernorm: True if ReduceMean sequences should be fused into LayerNormalization nodes.
             Defaults to False.
@@ -82,7 +82,7 @@ def qnn_preprocess_model(
             to cancel out.
     """
     modified = False
-    model = onnx.load_model(model_input)
+    model = model_input if isinstance(model_input, onnx.ModelProto) else onnx.load_model(model_input)
     onnx_model = ONNXModel(model)
 
     # Fuse Erf sequence into a single Gelu

onnxruntime/python/tools/quantization/execution_providers/qnn/quant_config.py

@@ -39,7 +39,7 @@ def warn_unable_to_override(
 
 
 def get_qnn_qdq_config(
-    model_input: Path,
+    model_input: str | Path | onnx.ModelProto,
     calibration_data_reader: CalibrationDataReader,
     calibrate_method=CalibrationMethod.MinMax,
     activation_type=QuantType.QUInt8,
@@ -56,7 +56,11 @@ def get_qnn_qdq_config(
     if weight_symmetric is None:
         weight_symmetric = weight_type in {QuantType.QInt8, QuantType.QInt16}
 
-    model = onnx.load_model(model_input, load_external_data=False)
+    model = (
+        model_input
+        if isinstance(model_input, onnx.ModelProto)
+        else onnx.load_model(model_input, load_external_data=False)
+    )
 
     op_types = set()
     model_has_external_data = False

onnxruntime/python/tools/quantization/quantize.py

@@ -6,6 +6,9 @@
 import logging
 import tempfile
 from pathlib import Path
+from typing import Union
+
+import onnx
 
 from .calibrate import CalibrationDataReader, CalibrationMethod, TensorsData, create_calibrator
 from .onnx_quantizer import ONNXQuantizer
@@ -16,6 +19,7 @@
     QuantType,
     load_model_with_shape_infer,
     model_has_pre_process_metadata,
+    save_and_reload_model_with_shape_infer,
 )
 from .registry import IntegerOpsRegistry, QDQRegistry, QLinearOpsRegistry
 
@@ -280,8 +284,8 @@ def check_static_quant_arguments(quant_format: QuantFormat, activation_type: Qua
 
 
 def quantize_static(
-    model_input,
-    model_output,
+    model_input: Union[str, Path, onnx.ModelProto],
+    model_output: Union[str, Path],
     calibration_data_reader: CalibrationDataReader,
     quant_format=QuantFormat.QDQ,
     op_types_to_quantize=None,
@@ -304,7 +308,7 @@ def quantize_static(
 
     Args:
 
-        model_input: file path of model to quantize
+        model_input: file path of model or ModelProto to quantize
         model_output: file path of quantized model
         calibration_data_reader: a calibration data reader. It
             enumerates calibration data and generates inputs for the
@@ -435,7 +439,11 @@ def quantize_static(
         qdq_ops = list(QDQRegistry.keys())
         op_types_to_quantize = list(set(q_linear_ops + qdq_ops))
 
-    model = load_model_with_shape_infer(Path(model_input))
+    model = (
+        save_and_reload_model_with_shape_infer(model_input)
+        if isinstance(model_input, onnx.ModelProto)
+        else load_model_with_shape_infer(Path(model_input))
+    )
 
     pre_processed: bool = model_has_pre_process_metadata(model)
     if not pre_processed:
@@ -485,6 +493,15 @@ def inc_dataloader():
         model = load_model_with_shape_infer(Path(model_input))  # use smooth quant model for calibration
 
     with tempfile.TemporaryDirectory(prefix="ort.quant.") as quant_tmp_dir:
+        if isinstance(model_input, onnx.ModelProto):
+            output_path = str(Path(quant_tmp_dir) / "model_input.onnx")
+            onnx.save_model(
+                model_input,
+                output_path,
+                save_as_external_data=True,
+            )
+            model_input = output_path
+
         calibrator = create_calibrator(
             Path(model_input),
             op_types_to_quantize,
@@ -546,8 +563,8 @@ def inc_dataloader():
 
 
 def quantize_dynamic(
-    model_input: Path,
-    model_output: Path,
+    model_input: Union[str, Path, onnx.ModelProto],
+    model_output: Union[str, Path],
     op_types_to_quantize=None,
     per_channel=False,
     reduce_range=False,
@@ -560,7 +577,7 @@ def quantize_dynamic(
     """Given an onnx model, create a quantized onnx model and save it into a file
 
     Args:
-        model_input: file path of model to quantize
+        model_input: file path of model or ModelProto to quantize
         model_output: file path of quantized model
         op_types_to_quantize:
             specify the types of operators to quantize, like ['Conv'] to quantize Conv only.
@@ -609,7 +626,11 @@ def quantize_dynamic(
     if not op_types_to_quantize or len(op_types_to_quantize) == 0:
         op_types_to_quantize = list(IntegerOpsRegistry.keys())
 
-    model = load_model_with_shape_infer(Path(model_input))
+    model = (
+        save_and_reload_model_with_shape_infer(model_input)
+        if isinstance(model_input, onnx.ModelProto)
+        else load_model_with_shape_infer(Path(model_input))
+    )
 
     pre_processed: bool = model_has_pre_process_metadata(model)
     if not pre_processed:
@@ -642,15 +663,15 @@ def quantize_dynamic(
 
 
 def quantize(
-    model_input: Path,
-    model_output: Path,
+    model_input: Union[str, Path, onnx.ModelProto],
+    model_output: Union[str, Path],
     quant_config: QuantConfig,
 ):
     """Quantize a model with QuantConfig.
 
     Args:
-        model_input (Path): Path to the model to quantize.
-        model_output (Path): Path to save the quantized model.
+        model_input (str | Path | ModelProto): Path to the model or ModelProto to quantize.
+        model_output (str | Path): Path to save the quantized model.
         quant_config (QuantConfig): Quantization Configuration.
     """
 

onnxruntime/python/tools/quantization/shape_inference.py

@@ -9,11 +9,12 @@
 import tempfile
 import traceback
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Union
 
 import onnx
 
 import onnxruntime
+from onnxruntime.python.tools.transformers.onnx_utils import extract_raw_data_from_model, has_external_data
 from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference
 
 from .quant_utils import add_pre_process_metadata
@@ -22,8 +23,8 @@
 
 
 def quant_pre_process(
-    input_model_path: str,
-    output_model_path: str,
+    input_model: Union[str, Path, onnx.ModelProto],
+    output_model_path: Union[str, Path],
     skip_optimization: bool = False,
     skip_onnx_shape: bool = False,
     skip_symbolic_shape: bool = False,
@@ -39,7 +40,7 @@ def quant_pre_process(
     """Shape inference and model optimization, in preparation for quantization.
 
     Args:
-        input_model_path: Path to the input model file")
+        input_model: Path to the input model file or ModelProto
         output_model_path: Path to the output model file
         skip_optimization: Skip model optimization step if true. This may result in ONNX shape
             inference failure for some models.
@@ -68,8 +69,9 @@ def quant_pre_process(
 
         if not skip_symbolic_shape:
             logger.info("Performing symbolic shape inference...")
+            loaded_model = input_model if isinstance(input_model, onnx.ModelProto) else onnx.load(input_model)
             model = SymbolicShapeInference.infer_shapes(
-                onnx.load(input_model_path),
+                loaded_model,
                 int_max,
                 auto_merge,
                 guess_output_rank,
@@ -80,26 +82,38 @@ def quant_pre_process(
             # Use ORT optimizers (native code) to optimize model
             if not skip_symbolic_shape:
                 # Need to save the inferenced model to file so as to run the optimizer
-                input_model_path = str(temp_path / "symbolic_shape_inferred.onnx")
+                input_model = str(temp_path / "symbolic_shape_inferred.onnx")
                 if save_as_external_data:
                     onnx.save_model(
                         model,
-                        input_model_path,
+                        input_model,
                         save_as_external_data=True,
                         all_tensors_to_one_file=all_tensors_to_one_file,
                         size_threshold=external_data_size_threshold,
                         convert_attribute=False,
                     )
                 else:
-                    onnx.save(model, input_model_path)
+                    onnx.save(model, input_model)
                 model = None
 
             opt_model_path = str(temp_path / "optimized.onnx")
             try:
                 sess_option = onnxruntime.SessionOptions()
                 sess_option.optimized_model_filepath = opt_model_path
                 sess_option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
-                sess = onnxruntime.InferenceSession(input_model_path, sess_option, providers=["CPUExecutionProvider"])
+                # For large model, extract external data from model and add to session options
+                if isinstance(input_model, onnx.ModelProto):
+                    if has_external_data(input_model):
+                        raise ValueError(
+                            "ModelProto has external data not loaded into memory, ORT cannot create session. "
+                            "Please load external data before calling this function. "
+                            "See https://onnx.ai/onnx/repo-docs/ExternalData.html for more information."
+                        )
+                    external_names, external_values = extract_raw_data_from_model(input_model)
+                    sess_option.add_external_initializers(list(external_names), list(external_values))
+                    input_model = input_model.SerializeToString()
+
+                sess = onnxruntime.InferenceSession(input_model, sess_option, providers=["CPUExecutionProvider"])
                 # Close the session to avoid the cleanup error on Windows for temp folders
                 # https://github.com/microsoft/onnxruntime/issues/17627
                 del sess
@@ -109,34 +123,45 @@ def quant_pre_process(
                 )
                 logger.error(traceback.format_exc())
 
-            input_model_path = opt_model_path
+            input_model = opt_model_path
 
         if not skip_onnx_shape:
             # ONNX shape inference.
             # According to docs, infer_shapes_path should be used for 2G+ models.
             # If the skip optimization is specified, we could be dealing with a
             # large model. So be on the safe side, save the model
             if model is not None:
-                input_model_path = str(temp_path / "symbolic_shape_inferred.onnx")
+                input_model = str(temp_path / "symbolic_shape_inferred.onnx")
                 if save_as_external_data:
                     onnx.save_model(
                         model,
-                        input_model_path,
+                        input_model,
                         save_as_external_data=True,
                         all_tensors_to_one_file=all_tensors_to_one_file,
                         size_threshold=external_data_size_threshold,
                         convert_attribute=False,
                     )
                 else:
-                    onnx.save(model, input_model_path)
+                    onnx.save(model, input_model)
                 model = None
 
+            if isinstance(input_model, onnx.ModelProto):
+                input_model = str(Path(quant_tmp_dir) / "model_input.onnx")
+                onnx.save_model(
+                    model,
+                    input_model,
+                    save_as_external_data=True,
+                    all_tensors_to_one_file=all_tensors_to_one_file,
+                    size_threshold=external_data_size_threshold,
+                    convert_attribute=False,
+                )
+
             inferred_model_path = str(temp_path / "onnx_shape_inferred.onnx")
-            onnx.shape_inference.infer_shapes_path(input_model_path, inferred_model_path)
+            onnx.shape_inference.infer_shapes_path(input_model, inferred_model_path)
             model = onnx.load(inferred_model_path)
 
     if model is None:
-        model = onnx.load(input_model_path)
+        model = input_model if isinstance(input_model, onnx.ModelProto) else onnx.load(input_model)
 
     add_pre_process_metadata(model)