[sparsezoo.analyze] Fix pathway such that it works for larger models

src/sparsezoo/analyze_v2/model_analysis.py

@@ -146,20 +146,25 @@ def analyze(path: str, download_path: Optional[str] = None) -> "ModelAnalysis":
     :param path: .onnx path or stub
     """
     if path.endswith(".onnx"):
-        onnx_model = load_model(path)
+        onnx_model = load_model(path, load_external_data=False)
+        onnx_model_path = path
     elif is_stub(path):
         model = Model(path, download_path)
         onnx_model_path = model.onnx_model.path
-        onnx_model = onnx.load(onnx_model_path)
+        onnx_model = onnx.load(onnx_model_path, load_external_data=False)
     else:
         raise ValueError(f"{path} is not a valid argument")
 
-    model_graph = ONNXGraph(onnx_model)
-    node_shapes, _ = extract_node_shapes_and_dtypes(model_graph.model)
+    # just need graph to get shape information; dont load external data
+    node_shapes, _ = extract_node_shapes_and_dtypes(onnx_model, onnx_model_path)
 
     summary_analysis = SummaryAnalysis()
     node_analyses = {}
 
+    # load external data for node analysis
+    onnx_model = onnx.load(onnx_model_path)
+    model_graph = ONNXGraph(onnx_model)
+
     for graph_order, node in enumerate(model_graph.nodes):
         node_id = extract_node_id(node)
         node_shape = node_shapes.get(node_id)

src/sparsezoo/utils/node_inference.py

@@ -18,7 +18,8 @@
 
 import logging
 from copy import deepcopy
-from typing import Any, Dict, List, NamedTuple, Tuple, Union
+from pathlib import Path
+from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
 
 import numpy
 import onnx
@@ -60,13 +61,14 @@
 
 
 def extract_nodes_shapes_and_dtypes_ort(
-    model: ModelProto,
+    model: ModelProto, path: Optional[str] = None
 ) -> Tuple[Dict[str, List[List[int]]], Dict[str, numpy.dtype]]:
     """
     Creates a modified model to expose intermediate outputs and runs an ONNX Runtime
     InferenceSession to obtain the output shape of each node.
 
     :param model: an ONNX model
+    :param path: absolute path to the original onnx model
     :return: a list of NodeArg with their shape exposed
     """
     import onnxruntime
@@ -79,11 +81,24 @@ def extract_nodes_shapes_and_dtypes_ort(
         )
         model_copy.graph.output.append(intermediate_layer_value_info)
 
+    # using the ModelProto does not work for large models when running the session
+    # have to save again and pass the new path to the inference session
     sess_options = onnxruntime.SessionOptions()
     sess_options.log_severity_level = 3
-    sess = onnxruntime.InferenceSession(
-        model_copy.SerializeToString(), sess_options, providers=["CPUExecutionProvider"]
-    )
+
+    if path:
+        parent_dir = Path(path).parent.absolute()
+        new_path = parent_dir / "model_new.onnx"
+        onnx.save(model_copy, new_path, save_as_external_data=True)
+        sess = onnxruntime.InferenceSession(
+            new_path, sess_options, providers=onnxruntime.get_available_providers()
+        )
+    else:
+        sess = onnxruntime.InferenceSession(
+            model_copy.SerializeToString(),
+            sess_options,
+            providers=onnxruntime.get_available_providers(),
+        )
 
     input_value_dict = {}
     for input in model_copy.graph.input:
@@ -166,19 +181,20 @@ def extract_nodes_shapes_and_dtypes_shape_inference(
 
 
 def extract_nodes_shapes_and_dtypes(
-    model: ModelProto,
+    model: ModelProto, path: Optional[str] = None
 ) -> Tuple[Dict[str, List[List[int]]], Dict[str, numpy.dtype]]:
     """
     Uses ONNX Runtime or shape inference to infer output shapes and dtypes from model
 
     :param model: model to extract output values from
+    :param path: absolute path to the original onnx model
     :return: output shapes and output data types
     """
     output_shapes = None
     output_dtypes = None
 
     try:
-        output_shapes, output_dtypes = extract_nodes_shapes_and_dtypes_ort(model)
+        output_shapes, output_dtypes = extract_nodes_shapes_and_dtypes_ort(model, path)
     except Exception as err:
         _LOGGER.warning(f"Extracting shapes using ONNX Runtime session failed: {err}")
 
@@ -306,18 +322,19 @@ def collate_output_dtypes(
 
 
 def extract_node_shapes_and_dtypes(
-    model: ModelProto,
+    model: ModelProto, path: Optional[str] = None
 ) -> Tuple[Dict[str, NodeShape], Dict[str, NodeDataType]]:
     """
     Extracts the shape and dtype information for each node as NodeShape objects
     and numpy dtypes.
 
     :param model: the loaded onnx.ModelProto to extract node shape information from
+    :param path: absolute path to the original onnx model
     :return: a mapping of node id to a NodeShape object
     """
 
     # Obtains output shapes for each model's node
-    output_shapes, output_dtypes = extract_nodes_shapes_and_dtypes(model)
+    output_shapes, output_dtypes = extract_nodes_shapes_and_dtypes(model, path)
 
     # Package output shapes into each node's inputs and outputs
     node_shapes = collate_output_shapes(model, output_shapes)

src/sparsezoo/utils/onnx/external_data.py

@@ -174,19 +174,23 @@ def validate_onnx(model: Union[str, ModelProto]):
         raise ValueError(f"Invalid onnx model: {err}")
 
 
-def load_model(model: Union[str, ModelProto, Path]) -> ModelProto:
+def load_model(
+    model: Union[str, ModelProto, Path], load_external_data: bool = True
+) -> ModelProto:
     """
     Load an ONNX model from an onnx model file path. If a ModelProto
     is given, then it is returned.
 
     :param model: the model proto or path to the model ONNX file to check for loading
+    :param load_external_data: if a path is given, whether or not to also load the
+        external model data
     :return: the loaded ONNX ModelProto
     """
     if isinstance(model, ModelProto):
         return model
 
     if isinstance(model, (Path, str)):
-        return onnx.load(clean_path(model))
+        return onnx.load(clean_path(model), load_external_data=load_external_data)
 
     raise TypeError(f"unknown type given for model: {type(model)}")