build yolov8 QAT model int8 engine failed

I am training a QAT model, with ultralytic framework,
yolov8-seg model
after export to deploy_model.onnx, and dynamic_range.json, I use python tensorrt API to build the engine. the code is

import onnx
import pycuda.autoinit  # noqa F401
import tensorrt as trt
import json
import os
import numpy as np
import argparse

def onnx2trt(onnx_model,
             trt_path,
            #  dataset_path,
             batch_size=1,
             cali_batch=10,
             log_level=trt.Logger.ERROR,
             max_workspace_size=1 << 30,
             device_id=0,
             mode='fp32',
             is_explicit=False,
             dynamic_range_file=None):
    if os.path.exists(trt_path):
        print(f'The "{trt_path}" exists. Remove it and continue.')
        os.remove(trt_path)


    # create builder and network
    logger = trt.Logger(log_level)
    builder = trt.Builder(logger)
    EXPLICIT_BATCH = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    network = builder.create_network(EXPLICIT_BATCH)

    parser = trt.OnnxParser(network, logger)

    if isinstance(onnx_model, str):
        onnx_model = onnx.load(onnx_model)

    if not parser.parse(onnx_model.SerializeToString()):
        error_msgs = ''
        for error in range(parser.num_errors):
            error_msgs += f'{parser.get_error(error)}\n'
        raise RuntimeError(f'parse onnx failed:\n{error_msgs}')

    config = builder.create_builder_config()
    config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, max_workspace_size)

    if mode == 'int8':
        config.set_flag(trt.BuilderFlag.INT8)
        if dynamic_range_file:
            with open(dynamic_range_file, 'r') as f:
                dynamic_range = json.load(f)['tensorrt']['blob_range']

            for input_index in range(network.num_inputs):
                input_tensor = network.get_input(input_index)
                if input_tensor.name in dynamic_range:
                    print("input_tensor.name", input_tensor.name)
                    amax = dynamic_range[input_tensor.name]
                    input_tensor.dynamic_range = (-amax, amax)
                    print(f'Set dynamic range of {input_tensor.name} as [{-amax}, {amax}]')

            for layer_index in range(network.num_layers):
                layer = network[layer_index]
                output_tensor = layer.get_output(0)
                print("output_tensor.name", output_tensor.name)
                if output_tensor.name in dynamic_range:
                    amax = dynamic_range[output_tensor.name]
                    output_tensor.dynamic_range = (-amax, amax)
                    print(f'Set dynamic range of {output_tensor.name} as [{-amax}, {amax}]')

    # create engine
    serialized_engine = builder.build_engine(network, config)
    # import ipdb
    # ipdb.set_trace()

    # 
    with open(trt_path, "wb") as f:
        f.write(serialized_engine)

    # 
    runtime = trt.Runtime(logger)
    engine = runtime.deserialize_cuda_engine(serialized_engine)
    return engine

my machine env is : tensorRT 8.6, cuda11.4

so the error happens when building the engine:

[07/24/2025-17:28:53] [TRT] [V] *************** Autotuning Reformat: Float(1:4,1600,40,1) -> Int8(9600,1600:32,40,1) ***************
[07/24/2025-17:28:53] [TRT] [V] --------------- Timing Runner: /Slice_8_output_0 copy (Reformat[0x80000006])
[07/24/2025-17:28:53] [TRT] [V] Skipping tactic 0x00000000000003e8 due to exception an illegal memory access was encountered
[07/24/2025-17:28:53] [TRT] [V] /Slice_8_output_0 copy (Reformat[0x80000006]) profiling completed in 0.00831625 seconds. Fastest Tactic: 0xd15ea5edd15ea5ed Time: inf
[07/24/2025-17:28:53] [TRT] [V] --------------- Timing Runner: /Slice_8_output_0 copy (MyelinReformat[0x80000035])
[07/24/2025-17:28:53] [TRT] [V] MyelinReformat has no valid tactics for this config, skipping
[07/24/2025-17:28:53] [TRT] [V] Deleting timing cache: 3616 entries, served 4867 hits since creation.
[07/24/2025-17:28:53] [TRT] [E] 2: Impossible to reformat.
[07/24/2025-17:28:53] [TRT] [E] 1: [cudaDriverHelpers.cpp::operator()::94] Error Code 1: Cuda Driver (an illegal memory access was encountered)
[07/24/2025-17:28:53] [TRT] [E] 1: [cudaDriverHelpers.cpp::operator()::94] Error Code 1: Cuda Driver (an illegal memory access was encountered)

[07/24/2025-17:28:54] [TRT] [E] 1: [cudaDriverHelpers.cpp::operator()::94] Error Code 1: Cuda Driver (an illegal memory access was encountered)
[07/24/2025-17:28:54] [TRT] [E] 1: [cudaDriverHelpers.cpp::operator()::94] Error Code 1: Cuda Driver (an illegal memory access was encountered)
[07/24/2025-17:28:54] [TRT] [E] 1: [cudaDriverHelpers.cpp::operator()::94] Error Code 1: Cuda Driver (an illegal memory access was encountered)
[07/24/2025-17:28:54] [TRT] [E] 1: [cudaResources.cpp::~ScopedCudaStream::47] Error Code 1: Cuda Runtime (an illegal memory access was encountered)
[07/24/2025-17:28:54] [TRT] [E] 2: [optimizer.cpp::computeCosts::4194] Error Code 2: Internal Error (Impossible to reformat.)

Start with the onnx2trt entry point in the issue, especially dynamic_range.json handling and builder.build_engine for deploy_model.onnx. Reproduce with TensorRT 8.6 and CUDA 11.4, then inspect the reported /Slice_8_output_0 reformat failure. Done means identifying a reproducible cause and documenting a verified fix or workaround.