qai-hub SDV2_1 demo running question

[1826133674]

Hello, I am trying to infer the quantized model SD2_1 using QNN 2.28 on a Samsung S24 phone。
I encountered an issue where the inference process using SDV1-5 gets stuck when inferring the Unet model released on qai-hub for SDV2_1.

The command I used is as follows:

cmd_exec_on_device = [PLATFORM_TOOLS_BIN_PATH + f'/adb', '-H', rh, '-s', device_id,
'shell', f'cd {target_device_dir} && ',
f'export LD_LIBRARY_PATH={target_device_dir} &&',
f' export ADSP_LIBRARY_PATH={target_device_dir} &&',
f' {target_device_dir}/qnn-net-run ',
f'--retrieve_context {model_context}',
f' --backend {target_device_dir}/libQnnHtp.so',
f' --input_list {target_device_dir}/input_list.txt',
f' --output_dir {target_device_dir} ',
f' --config_file {target_device_dir}/htp_backend_extensions.json ',
# f' > {target_device_dir}/log.log'
]
If I comment out the second to last line as follows, it can perform inference, but the performance data obtained from inferring the model at this point is not the best, and it will show a 'Context Free failure' error message.

cmd_exec_on_device = [PLATFORM_TOOLS_BIN_PATH + f'/adb', '-H', rh, '-s', device_id,
'shell', f'cd {target_device_dir} && ',
f'export LD_LIBRARY_PATH={target_device_dir} &&',
f' export ADSP_LIBRARY_PATH={target_device_dir} &&',
f' {target_device_dir}/qnn-net-run ',
f'--retrieve_context {model_context}',
f' --backend {target_device_dir}/libQnnHtp.so',
f' --input_list {target_device_dir}/input_list.txt',
f' --output_dir {target_device_dir} ',
# f' --config_file {target_device_dir}/htp_backend_extensions.json ',
# f' > {target_device_dir}/log.log'
]

What is causing this? Is there any solution to this problem?

the htp_backend_extensions.json is as follows：
{
"backend_extensions": {
"shared_library_path": "libQnnHtpNetRunExtensions.so",
"config_file_path": "htp_config.json"
}
}

the htp_config.json is as follows:

{
    
    "devices": [
        {
            "soc_id": 43,
            "dsp_arch": "v75",
            "cores":[{
                "core_id": 0,
                "perf_profile": "burst",
                "rpc_control_latency":100
            }]
        }
    ]
}

By the way , I can successfully infer the text_encoder model with the above configuration。

[heydavid525]

Can you share the input_list.txt file? I can give it a try

[1826133674]

Can you share the input_list.txt file? I can give it a try
The input_list.txt is as follows:

/data/local/tmp/qnn_assets/QNN_binaries/inputs/input_0.raw /data/local/tmp/qnn_assets/QNN_binaries/inputs/input_1.raw /data/local/tmp/qnn_assets/QNN_binaries/inputs/input_2.raw 

For convenience, I created corresponding shape random data using numpy as input. The code to produce them is as follows：

   text_embedding = np.random.rand(1,77,1024).astype(np.float32)
   time_embedding = np.random.rand(1,1280).astype(np.float32)
   latent_in = np.random.rand(1,64,64,4).astype(np.float32)
   input_data_list = [latent_in , time_embedding,text_embedding]
   tmp_dirpath = os.path.abspath('tmp_aarch64/inputs')
   os.makedirs(tmp_dirpath, exist_ok=True)
   # Dump each input data from input_data_list as raw file and prepare input_list_filepath for 
    qnn-net-run
   input_list_text = ''
   for index, input_data in enumerate(input_data_list):
       raw_file_path = f'{tmp_dirpath}/input_{index}.raw'
       input_data.tofile(raw_file_path)
       input_list_text += target_device_dir + '/inputs/' + os.path.basename(raw_file_path) + ' '
 
   input_list_filepath = f'{tmp_dirpath}/../input_list.txt'
   with open(input_list_filepath, 'w') as f:
       f.write(input_list_text)