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# -------------------------------------------------------------------------
# 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.is_quantized = False
self.scale = 0.0
self.offset = 0
self.dim = []
def is_quantized_data_type(qnn_data_type, is_converter_json):
if is_converter_json:
# QNN_DATATYPE_UFIXED_POINT_8 QNN_DATATYPE_UFIXED_POINT_16 QNN_DATATYPE_FIXED_POINT_8 QNN_DATATYPE_FIXED_POINT_16
return qnn_data_type == 0x0408 or qnn_data_type == 0x0416 or qnn_data_type == 0x0308 or qnn_data_type == 0x0316
else:
return (
qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_8"
or qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_16"
or qnn_data_type == "QNN_DATATYPE_FIXED_POINT_8"
or qnn_data_type == "QNN_DATATYPE_FIXED_POINT_16"
)
def qnn_data_type_to_onnx_data_type(qnn_data_type, is_converter_json):
if is_converter_json:
# 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
else:
# QNN_DATATYPE_UFIXED_POINT_8 QNN_DATATYPE_UINT_8
if qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_8" or qnn_data_type == "QNN_DATATYPE_UINT_8":
return TensorProto.UINT8
# QNN_DATATYPE_UFIXED_POINT_16 QNN_DATATYPE_UINT_16
elif qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_16" or qnn_data_type == "QNN_DATATYPE_UINT_16":
return TensorProto.UINT16
# QNN_DATATYPE_UFIXED_POINT_32 QNN_DATATYPE_UINT_32
elif qnn_data_type == "QNN_DATATYPE_UFIXED_POINT_32" or qnn_data_type == "QNN_DATATYPE_UINT_32":
return TensorProto.UINT32
# QNN_DATATYPE_UINT_64
elif qnn_data_type == "QNN_DATATYPE_UINT_64":
return TensorProto.UINT64
# QNN_DATATYPE_FIXED_POINT_8 QNN_DATATYPE_INT_8
elif qnn_data_type == "QNN_DATATYPE_FIXED_POINT_8" or qnn_data_type == "QNN_DATATYPE_INT_8":
return TensorProto.INT8
# QNN_DATATYPE_FIXED_POINT_16 QNN_DATATYPE_INT_16
elif qnn_data_type == "QNN_DATATYPE_FIXED_POINT_16" or qnn_data_type == "QNN_DATATYPE_INT_16":
return TensorProto.INT16
# QNN_DATATYPE_FIXED_POINT_32 QNN_DATATYPE_INT_32
elif qnn_data_type == "QNN_DATATYPE_FIXED_POINT_32" or qnn_data_type == "QNN_DATATYPE_INT_32":
return TensorProto.INT32
# QNN_DATATYPE_INT_64
elif qnn_data_type == "QNN_DATATYPE_INT_64":
return TensorProto.INT64
# QNN_DATATYPE_FLOAT_16
elif qnn_data_type == "QNN_DATATYPE_FLOAT_16":
return TensorProto.FLOAT16
# QNN_DATATYPE_FLOAT_32
elif qnn_data_type == "QNN_DATATYPE_FLOAT_32":
return TensorProto.FLOAT
# QNN_DATATYPE_BOOL_8
elif qnn_data_type == "QNN_DATATYPE_BOOL_8":
return TensorProto.BOOL
else:
return TensorProto.UNDEFINED
def parse_qnn_converter_json_file(qnn_convert_json, qnn_input_tensor_dic, qnn_output_tensor_dic):
is_qnn_converter_json = True
for qnn_tensor_name, qnn_tensor_attribute in qnn_convert_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"], is_qnn_converter_json
)
qnn_tensor.is_quantized = is_quantized_data_type(qnn_tensor_attribute["data_type"], is_qnn_converter_json)
qnn_tensor.dim = qnn_tensor_attribute["dims"]
if (
qnn_tensor_attribute["quant_params"]["definition"] == 1
and qnn_tensor_attribute["quant_params"]["encoding"] == 0
):
qnn_tensor.scale = qnn_tensor_attribute["quant_params"]["scale_offset"]["scale"]
qnn_tensor.offset = 0 - qnn_tensor_attribute["quant_params"]["scale_offset"]["offset"]
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"], is_qnn_converter_json
)
qnn_tensor.is_quantized = is_quantized_data_type(qnn_tensor_attribute["data_type"], is_qnn_converter_json)
qnn_tensor.dim = qnn_tensor_attribute["dims"]
if (
qnn_tensor_attribute["quant_params"]["definition"] == 1
and qnn_tensor_attribute["quant_params"]["encoding"] == 0
):
qnn_tensor.scale = qnn_tensor_attribute["quant_params"]["scale_offset"]["scale"]
qnn_tensor.offset = 0 - qnn_tensor_attribute["quant_params"]["scale_offset"]["offset"]
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."
)
def generate_wrapper_onnx_file(
grap_name,
model_file_name,
qnn_input_tensor_dic,
qnn_output_tensor_dic,
disable_embed_mode,
qnn_ctx_file,
quantized_IO,
qnn_sdk_version="unknown",
):
graph_nodes = []
ini_list = []
value_infos = []
model_inputs = []
for qnn_input in qnn_input_tensor_dic.values():
if qnn_input.is_quantized and not quantized_IO:
q_scale_input_name = qnn_input.name + "_scale"
q_offset_input_name = qnn_input.name + "_zp"
q_scale = helper.make_tensor(q_scale_input_name, TensorProto.FLOAT, [], [qnn_input.scale])
ini_list.append(q_scale)
q_offset = helper.make_tensor(q_offset_input_name, qnn_input.onnx_data_type, [], [qnn_input.offset])
ini_list.append(q_offset)
input_name = qnn_input.name + "_dq"
q_node = helper.make_node(
"QuantizeLinear",
name=qnn_input.name,
inputs=[input_name, q_scale_input_name, q_offset_input_name],
outputs=[qnn_input.name],
)
graph_nodes.append(q_node)
model_inputs.append(helper.make_tensor_value_info(input_name, TensorProto.FLOAT, qnn_input.dim))
value_infos.append(helper.make_tensor_value_info(qnn_input.name, qnn_input.onnx_data_type, qnn_input.dim))
else:
model_inputs.append(helper.make_tensor_value_info(qnn_input.name, qnn_input.onnx_data_type, qnn_input.dim))
if disable_embed_mode:
ep_cache_context_content = qnn_ctx_file
ctx_embed_mode = 0
else:
with open(qnn_ctx_file, "rb") as file:
ep_cache_context_content = file.read()
ctx_embed_mode = 1
qnn_ep_context_node = helper.make_node(
"EPContext",
name=grap_name,
inputs=qnn_input_tensor_dic.keys(),
outputs=qnn_output_tensor_dic.keys(),
ep_cache_context=ep_cache_context_content,
embed_mode=ctx_embed_mode,
ep_sdk_version=qnn_sdk_version,
source="Qnn",
domain="com.microsoft",
)
graph_nodes.append(qnn_ep_context_node)
model_outputs = []
for qnn_output in qnn_output_tensor_dic.values():
if qnn_output.is_quantized and not quantized_IO:
dq_scale_input_name = qnn_output.name + "_scale"
dq_offset_input_name = qnn_output.name + "_zp"
dq_scale = helper.make_tensor(dq_scale_input_name, TensorProto.FLOAT, [], [qnn_output.scale])
ini_list.append(dq_scale)
dq_offset = helper.make_tensor(dq_offset_input_name, qnn_output.onnx_data_type, [], [qnn_output.offset])
ini_list.append(dq_offset)
output_name = qnn_output.name + "_dq"
dq_node = helper.make_node(
"DequantizeLinear",
name=output_name,
inputs=[qnn_output.name, dq_scale_input_name, dq_offset_input_name],
outputs=[output_name],
)
graph_nodes.append(dq_node)
model_outputs.append(helper.make_tensor_value_info(output_name, TensorProto.FLOAT, qnn_output.dim))
value_infos.append(
helper.make_tensor_value_info(qnn_output.name, qnn_output.onnx_data_type, qnn_output.dim)
)
else:
model_outputs.append(
helper.make_tensor_value_info(qnn_output.name, qnn_output.onnx_data_type, qnn_output.dim)
)
graph_def = helper.make_graph(graph_nodes, "qnn-onnx-model", model_inputs, model_outputs, ini_list, "", value_infos)
model_def = helper.make_model(graph_def, producer_name="MS")
onnx.save(model_def, model_file_name)
# parse Qnn graph from the json file that extracted from context binary file
def parse_qnn_graph(qnn_graph, qnn_input_tensor_dic, qnn_output_tensor_dic):
is_qnn_converter_json = False
graph_name = qnn_graph["info"]["graphName"]
raw_inputs = qnn_graph["info"]["graphInputs"]
raw_outputs = qnn_graph["info"]["graphOutputs"]
for raw_input in raw_inputs:
tensor_info = raw_input["info"]
qnn_tensor = QnnTensorStruct()
qnn_tensor.name = tensor_info["name"]
qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(tensor_info["dataType"], is_qnn_converter_json)
qnn_tensor.is_quantized = is_quantized_data_type(tensor_info["dataType"], is_qnn_converter_json)
qnn_tensor.dim = tensor_info["dimensions"]
if (
tensor_info["quantizeParams"]["definition"] == "QNN_DEFINITION_DEFINED"
and tensor_info["quantizeParams"]["quantizationEncoding"] == "QNN_QUANTIZATION_ENCODING_SCALE_OFFSET"
):
qnn_tensor.scale = tensor_info["quantizeParams"]["scaleOffset"]["scale"]
qnn_tensor.offset = 0 - tensor_info["quantizeParams"]["scaleOffset"]["offset"]
qnn_input_tensor_dic[qnn_tensor.name] = qnn_tensor
for raw_output in raw_outputs:
tensor_info = raw_output["info"]
qnn_tensor = QnnTensorStruct()
qnn_tensor.name = tensor_info["name"]
qnn_tensor.onnx_data_type = qnn_data_type_to_onnx_data_type(tensor_info["dataType"], is_qnn_converter_json)
qnn_tensor.is_quantized = is_quantized_data_type(tensor_info["dataType"], is_qnn_converter_json)
qnn_tensor.dim = tensor_info["dimensions"]
if (
tensor_info["quantizeParams"]["definition"] == "QNN_DEFINITION_DEFINED"
and tensor_info["quantizeParams"]["quantizationEncoding"] == "QNN_QUANTIZATION_ENCODING_SCALE_OFFSET"
):
qnn_tensor.scale = tensor_info["quantizeParams"]["scaleOffset"]["scale"]
qnn_tensor.offset = 0 - tensor_info["quantizeParams"]["scaleOffset"]["offset"]
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."
)
return graph_name
# 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",
)
parser.add_argument(
"--quantized_IO",
action="store_true",
default=False,
help="QNN converted context binary use quantized data as graph inputs and outputs. Will keep it if quantized_IO=True, otherwise, will insert Q and DQ nodes accordingly to make the graph inputs & outputs as float32 data type.",
)
args = parser.parse_args()
# Parse Qnn model_net.json file to get the graph input output information
with open(args.qnn_json) as qnn_json_file:
qnn_json_obj = json.load(qnn_json_file)
if "graph" in qnn_json_obj and "tensors" in qnn_json_obj["graph"]:
print("This json file is from Qnn converter")
qnn_input_tensor_dic = {}
qnn_output_tensor_dic = {}
parse_qnn_converter_json_file(qnn_json_obj, qnn_input_tensor_dic, qnn_output_tensor_dic)
generate_wrapper_onnx_file(
"QnnContext",
args.qnn_json.replace(".json", "_qnn_ctx.onnx"),
qnn_input_tensor_dic,
qnn_output_tensor_dic,
args.disable_embed_mode,
args.qnn_bin,
args.quantized_IO,
)
elif "info" in qnn_json_obj and "graphs" in qnn_json_obj["info"]:
print("This json file is extracted from QNN context binary file")
qnn_version = qnn_json_obj["info"]["buildId"]
for qnn_graph in qnn_json_obj["info"]["graphs"]:
qnn_input_tensor_dic = {}
qnn_output_tensor_dic = {}
graph_name = parse_qnn_graph(qnn_graph, qnn_input_tensor_dic, qnn_output_tensor_dic)
ctx_file_name = graph_name + "_qnn_ctx.onnx"
if not args.quantized_IO:
ctx_file_name = ctx_file_name.replace(".onnx", "_fp32_io.onnx")
generate_wrapper_onnx_file(
graph_name,
ctx_file_name,
qnn_input_tensor_dic,
qnn_output_tensor_dic,
args.disable_embed_mode,
args.qnn_bin,
args.quantized_IO,
qnn_version,
)
else:
print("json file unrecoginized.")
if __name__ == "__main__":
main()
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