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### Description This PR is to update the win-ort-main branch to the tip main branch as of 2025-01-23. ### PR List ddf0d377a7 [QNN EP] Add LoggingManager::HasDefaultLogger() to provider bridge API (#23467) 05fbbdf91f [QNN EP] Make QNN EP a shared library (#23120) 1336566d7f Add custom vcpkg ports (#23456) 2e1173c411 Update the compile flags for vcpkg packages (#23455) 1f628a9858 [Mobile] Add BrowserStack Android MAUI Test (#23383) 009cae0ec8 [js/webgpu] Optimize ConvTranspose (Continue) (#23429) 04a4a694cb Use onnx_protobuf.h to suppress some GCC warnings (#23453) 2e3b62b4b0 Suppress some strict-aliasing related warnings in WebGPU EP (#23454) b708f9b1dc Bump ruff from 0.9.1 to 0.9.2 (#23427) c0afc66b2a [WebNN] Remove workarounds for TFLite backend (#23406) 8a821ff7f9 Bump vite from 6.0.7 to 6.0.11 in /js/web/test/e2e/exports/testcases/vite-default (#23446) 220c1a203e Make ORT and Dawn use the same protobuf/abseil source code (#23447) b7b5792147 Change MacOS-13 to ubuntu on for android-java-api-aar-test.yml. (#23444) 19d0d2a30f WIP: Dp4MatMulNBits accuracy level 4 matmul for WebGPU EP (#23365) 95b8effbc4 [QNN EP]: Clean up QNN logging resources if an error occurs during initialization (#23435) 626134c5b5 Bump clang-format from 19.1.6 to 19.1.7 (#23428) 0cf975301f Fix eigen external deps (#23439) f9440aedce Moving RN_CI Android Testing to Linux (#23422) 1aa5902ff4 [QNN EP] workaround for QNN validation bug for Tanh with uint16 quantized output (#23432) 7f5582a0e2 Seperate RN andriod and IOS into 2 separated Stages. (#23400) 73deac2e7f Implement some missing element wise Add/Sub/Mul/Div/Neg operations for CPU and CUDA EPs (#23090) 949fe42af4 Upgrade Java version from react-native/android to Java 17 (#23066) 0892c23463 Update Qnn SDK default version to 2.30 (#23411) 94c099bcec Fix type cast build error (#23423) d633e571d1 [WebNN EP] Fix AddInitializersToSkip issues (#23354) e988ef00e2 [QNN EP] Fix regression for MatMul with two quantized/dynamic uint16 inputs (#23419) 7538795f6b Update onnxruntime binary size checks ci pipeline's docker image (#23405) 6c5ea41cad Revert "[QNN EP] Clean up correctly from a partial setup (#23320)" (#23420) e866804bbe Enable comprehension simplification in ruff rules (#23414) 0a5f1f392c bugfix: string_view of invalid memory (#23417) 4cc38e0277 fix crash when first input of BatchNormalization is 1-D (#23387) 033441487f Target py310 and modernize codebase with ruff (#23401) 87341ac010 [QNN EP] Fix segfault when unregistering HTP shared memory handles (#23402) ### Motivation and Context This update includes the change to make QNN-EP a shared library. --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com> Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com> Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com> Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Changming Sun <chasun@microsoft.com> Co-authored-by: Peishen Yan <peishen.yan@intel.com> Co-authored-by: Tianlei Wu <tlwu@microsoft.com> Co-authored-by: Hector Li <hecli@microsoft.com> Co-authored-by: Jian Chen <cjian@microsoft.com> Co-authored-by: Alexis Tsogias <1114095+Zyrin@users.noreply.github.com> Co-authored-by: junchao-zhao <68935141+junchao-loongson@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: sushraja-msft <44513542+sushraja-msft@users.noreply.github.com> Co-authored-by: Wanming Lin <wanming.lin@intel.com> Co-authored-by: Jiajia Qin <jiajiaqin@microsoft.com> Co-authored-by: Caroline Zhu <wolfivyaura@gmail.com>
215 lines
8.2 KiB
Python
215 lines
8.2 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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import argparse
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import glob
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import logging
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import os
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import re
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logging.basicConfig(format="[%(levelname)s] - %(message)s", level=logging.DEBUG)
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log = logging.getLogger()
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Find optimizers that involve operators which may need an update to the supported opset versions."
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)
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root_arg = parser.add_argument(
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"--ort-root", "-o", required=True, type=str, help="The root directory of the ONNX Runtime repository to search."
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)
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args = parser.parse_args()
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if not os.path.isdir(args.ort_root):
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raise argparse.ArgumentError(root_arg, f"{args.ort_root} is not a valid directory")
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return args
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def get_call_args_from_file(filename: str, function_or_declaration: str) -> list[str]:
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"""
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Search a file for all function calls or declarations that match the provided name.
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Requires both the opening '(' and closing ')' to be on the same line.
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Handles multiple calls being on the same line.
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"""
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results = []
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with open(filename) as f:
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line_num = 0
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for line in f:
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for match in re.finditer(function_or_declaration, line):
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# check we have both the opening and closing brackets for the function call/declaration.
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# if we do we have all the arguments
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start = line.find("(", match.end())
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end = line.find(")", match.end())
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have_all_args = start != -1 and end != -1
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if have_all_args:
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results.append(line[start + 1 : end])
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else:
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# TODO: handle automatically by merging lines
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log.error(
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"Call/Declaration is split over multiple lines. Please check manually."
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f"File:{filename} Line:{line_num}"
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)
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continue
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line_num += 1
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return results
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def get_multiline_call_args_from_file(filename: str, function_or_declaration: str) -> list[str]:
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"""
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Search a file for all function calls or declarations that match the provided name.
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Allows the opening '(' and closing ')' to be split across multiple lines.
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Supports a single call per line.
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"""
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results = []
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with open(filename) as f:
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function_and_args = None
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for line in f:
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if not function_and_args:
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# look for new match
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start = line.find(function_or_declaration)
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if start != -1:
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function_and_args = line[start:].strip()
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else:
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# append to existing line and look for closing ')'
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start = len(function_and_args)
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function_and_args += line.strip()
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if function_and_args:
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end = function_and_args.find(")", start)
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if end != -1:
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start_args = function_and_args.find("(")
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results.append(function_and_args[start_args + 1 : end])
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function_and_args = None
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return results
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def _add_if_newer(domain: str, op: str, opset: int, op_to_opset: dict[str, int]):
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key = domain + "." + op
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if key not in op_to_opset or op_to_opset[key] < opset:
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op_to_opset[key] = opset
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def get_latest_ort_op_versions(root_dir):
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"""Find the entries for the latest opset for each operator."""
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op_to_opset = {}
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files = [
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# for ONNX operators we use get_latest_onnx_op_versions
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# os.path.join(root_dir, "onnxruntime/core/providers/cpu/cpu_execution_provider.cc"),
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# for internal kernels we use the current registrations
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os.path.join(root_dir, "onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.cc"),
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os.path.join(root_dir, "onnxruntime/contrib_ops/cuda/cuda_contrib_kernels.cc"),
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]
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for file in files:
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# e.g. class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, Clip);
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calls = get_multiline_call_args_from_file(file, "ONNX_OPERATOR_KERNEL_CLASS_NAME")
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for call in calls:
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args = call.split(",")
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domain = args[1].strip()
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opset = args[2].strip()
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op = args[3].strip()
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_add_if_newer(domain, op, int(opset), op_to_opset)
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# e.g. class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 11, float, ArgMax);
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calls = get_multiline_call_args_from_file(file, "ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME")
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for call in calls:
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args = call.split(",")
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domain = args[1].strip()
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opset = args[2].strip()
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op = args[4].strip()
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_add_if_newer(domain, op, int(opset), op_to_opset)
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return op_to_opset
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def get_latest_onnx_op_versions(root_dir):
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"""Get the latest versions of the ONNX operators from the ONNX headers."""
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op_to_opset = {}
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files = [
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# operators with domain of 'Onnx'
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os.path.join(root_dir, "cmake/external/onnx/onnx/defs/operator_sets.h"),
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# ML operators with domain of 'OnnxML'
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os.path.join(root_dir, "cmake/external/onnx/onnx/defs/operator_sets_ml.h"),
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]
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for file in files:
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# e.g. fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(Onnx, 17, LayerNormalization)>());
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# fn(GetOpSchema<ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME(OnnxML, 3, TreeEnsembleClassifier)>());
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calls = get_multiline_call_args_from_file(file, "ONNX_OPERATOR_SET_SCHEMA_CLASS_NAME")
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for call in calls:
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args = call.split(",")
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orig_domain = args[0].strip()
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# convert domain to the ORT constants
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domain = "kMLDomain" if orig_domain == "OnnxML" else "kOnnxDomain"
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opset = args[1].strip()
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op = args[2].strip()
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_add_if_newer(domain, op, int(opset), op_to_opset)
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return op_to_opset
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def find_potential_issues(root_dir, op_to_opset):
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optimizer_dir = os.path.join(root_dir, "onnxruntime/core/optimizer")
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files = glob.glob(optimizer_dir + "/**/*.cc", recursive=True)
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files += glob.glob(optimizer_dir + "/**/*.h", recursive=True)
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for file in files:
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calls = get_call_args_from_file(file, "graph_utils::IsSupportedOptypeVersionAndDomain")
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for call in calls:
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# Need to handle multiple comma separated version numbers, and the optional domain argument.
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# e.g. IsSupportedOptypeVersionAndDomain(node, "MaxPool", {1, 8, 10})
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# IsSupportedOptypeVersionAndDomain(node, "FusedConv", {1}, kMSDomain)
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args = call.split(",", 2) # first 2 args are simple, remainder need custom processing
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op = args[1].strip()
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if not op.startswith('"') or not op.endswith('"'):
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log.error(f"Symbolic name of '{op}' found for op. Please check manually. File:{file}")
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continue
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versions_and_domain_arg = args[2]
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v1 = versions_and_domain_arg.find("{")
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v2 = versions_and_domain_arg.find("}")
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versions = versions_and_domain_arg[v1 + 1 : v2].split(",")
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last_version = versions[-1].strip()
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domain_arg_start = versions_and_domain_arg.find(",", v2)
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if domain_arg_start != -1:
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domain = versions_and_domain_arg[domain_arg_start + 1 :].strip()
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else:
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domain = "kOnnxDomain"
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op = domain + "." + op[1:-1]
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if op in op_to_opset:
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latest = op_to_opset[op]
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if int(latest) != int(last_version):
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log.warning(
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f"Newer opset found for {op}. Latest:{latest} Optimizer support ends at {last_version}. File:{file}"
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)
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else:
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log.error(f"Failed to find version information for {op}. File:{file}")
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if __name__ == "__main__":
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arguments = parse_args()
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ort_to_opset_map = get_latest_ort_op_versions(arguments.ort_root)
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onnx_op_to_opset_map = get_latest_onnx_op_versions(arguments.ort_root)
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# merge the two maps
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op_to_opset_map = {**ort_to_opset_map, **onnx_op_to_opset_map}
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find_potential_issues(arguments.ort_root, op_to_opset_map)
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