[QNN EP] Add script to generate Onnx model from native QNN generated context binary file (#17859)

Add script to generate Onnx model from native QNN generated context
binary file. This is used for QNN EP example code.

onnxruntime/core/providers/qnn/builder/onnx_ctx_model_helper.cc

@@ -144,7 +144,7 @@ bool QnnCacheModelHandler::IsContextCacheFileExists(const std::string& customer_
     context_cache_path_ = customer_context_cache_path;
   }
 
-  ctx_file_exists_ = std::filesystem::exists(context_cache_path_);
+  ctx_file_exists_ = std::filesystem::is_regular_file(context_cache_path_) && std::filesystem::exists(context_cache_path_);
 
   return ctx_file_exists_;
 }

onnxruntime/python/tools/qnn/gen_qnn_ctx_onnx_model.py

@@ -0,0 +1,150 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import json
+from argparse import ArgumentParser
+
+import onnx
+from onnx import TensorProto, helper
+
+
+class QnnTensorStruct:
+    def __init__(self):
+        self.name = ""
+        self.onnx_data_type = TensorProto.FLOAT
+        self.dim = []
+
+
+def qnn_data_type_to_onnx_data_type(qnn_data_type):
+    # QNN_DATATYPE_UFIXED_POINT_8 QNN_DATATYPE_UINT_8
+    if qnn_data_type == 0x0408 or qnn_data_type == 0x0108:
+        return TensorProto.UINT8
+    # QNN_DATATYPE_UFIXED_POINT_16 QNN_DATATYPE_UINT_16
+    elif qnn_data_type == 0x0416 or qnn_data_type == 0x0116:
+        return TensorProto.UINT16
+    # QNN_DATATYPE_UFIXED_POINT_32 QNN_DATATYPE_UINT_32
+    elif qnn_data_type == 0x0432 or qnn_data_type == 0x0132:
+        return TensorProto.UINT32
+    # QNN_DATATYPE_UINT_64
+    elif qnn_data_type == 0x0164:
+        return TensorProto.UINT64
+    # QNN_DATATYPE_FIXED_POINT_8 QNN_DATATYPE_INT_8
+    elif qnn_data_type == 0x0308 or qnn_data_type == 0x0008:
+        return TensorProto.INT8
+    # QNN_DATATYPE_FIXED_POINT_16 QNN_DATATYPE_INT_16
+    elif qnn_data_type == 0x0316 or qnn_data_type == 0x0016:
+        return TensorProto.INT16
+    # QNN_DATATYPE_FIXED_POINT_32 QNN_DATATYPE_INT_32
+    elif qnn_data_type == 0x0332 or qnn_data_type == 0x0032:
+        return TensorProto.INT32
+    # QNN_DATATYPE_INT_64
+    elif qnn_data_type == 0x0064:
+        return TensorProto.INT64
+    # QNN_DATATYPE_FLOAT_16
+    elif qnn_data_type == 0x0216:
+        return TensorProto.FLOAT16
+    # QNN_DATATYPE_FLOAT_32
+    elif qnn_data_type == 0x0232:
+        return TensorProto.FLOAT
+    # QNN_DATATYPE_BOOL_8
+    elif qnn_data_type == 0x0508:
+        return TensorProto.BOOL
+    else:
+        return TensorProto.UNDEFINED
+
+
+def parse_qnn_json_file(qnn_json_file_path, qnn_input_tensor_dic, qnn_output_tensor_dic):
+    with open(qnn_json_file_path) as qnn_json_file:
+        qnn_json = json.load(qnn_json_file)
+        assert "graph" in qnn_json, "QNN converted json file not valid. Can't find graph."
+        assert "tensors" in qnn_json["graph"], "QNN converted json file not valid. Can't find tensors."
+        for qnn_tensor_name, qnn_tensor_attribute in qnn_json["graph"]["tensors"].items():
+            # type:0 - QNN input tensor, type:1 - QNN output tensor
+            assert (
+                "type" in qnn_tensor_attribute
+                and "data_type" in qnn_tensor_attribute
+                and "dims" in qnn_tensor_attribute
+            ), "QNN converted json file not valid. Can't find some keys from tensors"
+
+            # Get all graph inputs
+            if qnn_tensor_attribute["type"] == 0:
+                qnn_tensor = QnnTensorStruct()
+                qnn_tensor.name = qnn_tensor_name
+                qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(qnn_tensor_attribute["data_type"])
+                qnn_tensor.dim = qnn_tensor_attribute["dims"]
+                qnn_input_tensor_dic[qnn_tensor_name] = qnn_tensor
+
+            # Get all graph outputs
+            if qnn_tensor_attribute["type"] == 1:
+                qnn_tensor = QnnTensorStruct()
+                qnn_tensor.name = qnn_tensor_name
+                qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(qnn_tensor_attribute["data_type"])
+                qnn_tensor.dim = qnn_tensor_attribute["dims"]
+                qnn_output_tensor_dic[qnn_tensor_name] = qnn_tensor
+
+    assert (
+        len(qnn_input_tensor_dic) >= 1 and len(qnn_output_tensor_dic) >= 1
+    ), "Converted QNN model not valid. It should have at least 1 input & 1 output."
+
+
+# Onnxruntime QNN EP can support context binary file generated by QNN tool chain. However QNN generated context binary file
+# uses channel last data layout and 8 bits or 16 bits for input and output.
+# This script gets the QNN model input & output information from QNN converted model_net.json file, compare them with Onnx model
+# and inserts Cast, Transpose nodes to Onnx model if required
+def main():
+    parser = ArgumentParser("Generate Onnx model which includes the QNN context binary.")
+    parser.add_argument("-b", "--qnn_bin", help="Required. Path to Qnn context binary file.", required=True, type=str)
+    parser.add_argument(
+        "-q", "--qnn_json", help="Required. Path to Qnn converted model_net.json file.", required=True, type=str
+    )
+    parser.add_argument(
+        "--disable_embed_mode",
+        action="store_true",
+        default=False,
+        help="Set embed_mode=1 which mean embed Qnn context binary into the onnx model. Otherwise, set context binary file path in the onnx model",
+    )
+    args = parser.parse_args()
+
+    # Parse Qnn model_net.json file to get the graph input output information
+    qnn_input_tensor_dic = {}
+    qnn_output_tensor_dic = {}
+    parse_qnn_json_file(args.qnn_json, qnn_input_tensor_dic, qnn_output_tensor_dic)
+
+    if args.disable_embed_mode:
+        ep_cache_context_content = args.qnn_bin
+        ctx_embed_mode = 0
+    else:
+        with open(args.qnn_bin, "rb") as file:
+            ep_cache_context_content = file.read()
+        ctx_embed_mode = 1
+
+    qnn_inputs = []
+    for qnn_input in qnn_input_tensor_dic.values():
+        qnn_inputs.append(helper.make_tensor_value_info(qnn_input.name, qnn_input.onnx_data_type, qnn_input.dim))
+
+    qnn_outputs = []
+    for qnn_output in qnn_output_tensor_dic.values():
+        qnn_outputs.append(helper.make_tensor_value_info(qnn_output.name, qnn_output.onnx_data_type, qnn_output.dim))
+
+    qnn_ep_context_node = helper.make_node(
+        "EPContext",
+        name="QnnContext",
+        inputs=qnn_input_tensor_dic.keys(),
+        outputs=qnn_output_tensor_dic.keys(),
+        ep_cache_context=ep_cache_context_content,
+        embed_mode=ctx_embed_mode,
+        source="Qnn",
+        domain="com.microsoft",
+    )
+
+    graph_def = helper.make_graph([qnn_ep_context_node], "qnn-onnx-model", qnn_inputs, qnn_outputs)
+
+    model_def = helper.make_model(graph_def, producer_name="MS")
+
+    onnx.save(model_def, args.qnn_json.replace(".json", "_qnn_ctx.onnx"))
+
+
+if __name__ == "__main__":
+    main()