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Description
Description
I encountered the following error when trying to build trt engine from onnx model.
110947: reshape: /Reshape_32 _ /Transpose_17_reshape_output.1-(f16[__mye111018_proxy.1,1,1025,2,801,2][]so[], mem_prop=0) | /Concat_47_output_0' castIn.1-(f16[__mye111018_proxy.1,1,2050,801,2][]so[], mem_prop=0), stream = 0 // /Reshape_32 _ /Transpose_17_reshape | shape: {__mye111018_proxy.1, 1, 1025, 2, 801, 2}
111002: transpose: out'.1-(f16[__mye111018_proxy.1,1,2,1025,801,2][3306528,3306528,1653264,1,2064,1032]so[5,4,3,0,2,1]p[0,0,0,0,0,7], mem_prop=0) | /Reshape_32 _ /Transpose_17_reshape_output.1-(f16[__mye111018_proxy.1,1,1025,2,801,2][]so[], mem_prop=0), stream = 0 // shuffle_output_/Reshape_32 _ /Transpose_17_first_transpose | perm: {0, 1, 3, 2, 4, 5}
111010: exit: | out'.1-(f16[__mye111018_proxy.1,1,2,1025,801,2][3306528,3306528,1653264,1,2064,1032]so[5,4,3,0,2,1]p[0,0,0,0,0,7], mem_prop=0), stream = 0 // __mye111009
B1: succ[]
Total operations: 4391
791 vs. actual uses: 784
Internal Error: MyelinCheckException: gvn.cpp:318: CHECK(graph().ssa_validation()) failed.
[05/27/2025-14:27:16] [TRT] [E] Error Code: 9: Skipping tactic 0x0000000000000000 due to exception [myelin_graph.h:attachExceptionMsgToGraph:866] MyelinCheckException: gvn.cpp:318: CHECK(graph().ssa_validation()) failed.
[05/27/2025-14:27:16] [TRT] [V] {ForeignNode[ONNXTRT_castHelper_10786_output[Constant].../Reshape_32 + /Transpose_17]} (Myelin[0x80000023]) profiling completed in 14.7534 seconds. Fastest Tactic: 0xd15ea5edd15ea5ed Time: inf
[05/27/2025-14:27:16] [TRT] [E] IBuilder::buildSerializedNetwork: Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[ONNXTRT_castHelper_10786_output[Constant].../Reshape_32 + /Transpose_17]}.)
Command that successfully created engine using the same build script:
python3 build_engine.py --onnx=model.onnx --minShapes=x:1x2x1025x801x2 --optShapes=x:1x2x1025x801x2 --maxShapes=x:1x2x1025x801x2 --fp16 --engine=out.trt
Command that failed:
python3 build_engine.py --onnx=model.onnx --minShapes=x:1x2x1025x801x2 --optShapes=x:2x2x1025x801x2 --maxShapes=x:2x2x1025x801x2 --fp16 --engine=out.trt
Thank you in advance for your help.
Environment
TensorRT Version: 10.9.0
NVIDIA GPU: A10
NVIDIA Driver Version: 535.161.08
CUDA Version: 12.8
CUDNN Version:
Operating System: Debian 12
Python Version (if applicable): 3.11.2
Tensorflow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if so, version):
Relevant Files
Full Log:
Steps To Reproduce
script used:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import argparse
import tensorrt as trt
import numpy as np
import onnx
from typing import Dict, List, Tuple
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
def build_engine(
onnx_file: str,
min_shapes: Dict[str, List[int]],
opt_shapes: Dict[str, List[int]],
max_shapes: Dict[str, List[int]],
engine_file: str,
fp16_mode: bool = True,
):
"""
构建TensorRT engine
参数:
onnx_file: ONNX模型文件路径
min_shapes: 最小形状 {input_name: shape}
opt_shapes: 最优形状 {input_name: shape}
max_shapes: 最大形状 {input_name: shape}
engine_file: 输出的engine文件路径
fp16_mode: 是否启用FP16模式
"""
print(f"正在从ONNX模型构建TensorRT engine: {onnx_file}")
# 加载ONNX模型检查精度信息
onnx_model = onnx.load(onnx_file)
# 初始化TensorRT
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
config = builder.create_builder_config()
config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED
# 设置动态形状
profile = builder.create_optimization_profile()
for input_name, min_shape in min_shapes.items():
profile.set_shape(input_name, min_shape, opt_shapes[input_name], max_shapes[input_name])
print(f"设置输入 {input_name} 的动态形状: min={min_shape}, opt={opt_shapes[input_name]}, max={max_shapes[input_name]}")
config.add_optimization_profile(profile)
# 从ONNX解析网络
parser = trt.OnnxParser(network, TRT_LOGGER)
with open(onnx_file, 'rb') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(f"ONNX解析错误: {parser.get_error(error)}")
raise RuntimeError("ONNX解析失败")
print("ONNX模型解析成功")
if fp16_mode:
config.set_flag(trt.BuilderFlag.FP16)
config.set_flag(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
for i in range(network.num_layers):
layer = network.get_layer(i)
def should_keep_fp16(layer):
result = False
if layer.type == trt.LayerType.MATRIX_MULTIPLY:
print(f"找到MatMul层: {layer.name}")
result = True
if '/Add' in layer.name:
print(f"找到Add层: {layer.name}")
result = True
if '/Mul' in layer.name:
print(f"找到Mul层: {layer.name}")
result = True
return result
layer_name = layer.name
layer_type = layer.type
if not should_keep_fp16(layer):
is_fp32_compatible = True
for j in range(layer.num_outputs):
if (layer.get_output_type(j) == trt.int64):
is_fp32_compatible = False
break
if is_fp32_compatible:
layer.precision = trt.float32
serialized_engine = builder.build_serialized_network(network, config)
if serialized_engine is None:
raise RuntimeError("引擎构建失败")
with open(engine_file, 'wb') as f:
f.write(serialized_engine)
print(f"TensorRT engine已保存至: {engine_file}")
return serialized_engine
def parse_shape(shape_str: str) -> List[int]:
return [int(dim) for dim in shape_str.split('x')]
def main():
parser = argparse.ArgumentParser(description="构建TensorRT engine")
parser.add_argument("--onnx", required=True, help="ONNX模型文件路径")
parser.add_argument("--minShapes", required=True, help="最小形状，格式: 'input_name:dim1xdim2x...'")
parser.add_argument("--optShapes", required=True, help="最优形状，格式: 'input_name:dim1xdim2x...'")
parser.add_argument("--maxShapes", required=True, help="最大形状，格式: 'input_name:dim1xdim2x...'")
parser.add_argument("--engine", default=None, help="输出的engine文件路径")
parser.add_argument("--fp16", action="store_true", help="是否启用FP16模式")
args = parser.parse_args()
def parse_shapes_arg(shape_arg: str) -> Dict[str, List[int]]:
name, shape_str = shape_arg.split(':', 1)
return {name: parse_shape(shape_str)}
min_shapes = parse_shapes_arg(args.minShapes)
opt_shapes = parse_shapes_arg(args.optShapes)
max_shapes = parse_shapes_arg(args.maxShapes)
if args.engine is None:
args.engine = os.path.splitext(args.onnx)[0] + ".trt"
build_engine(
args.onnx,
min_shapes,
opt_shapes,
max_shapes,
args.engine,
args.fp16,
)
if __name__ == "__main__":
main()