Error in

i use the script to tune the model , i set try_spatial_pack_depthwise=True in function

def tune_tasks(tasks, measure_option, tuner='xgb', n_trial=1000, early_stopping=None, log_filename='tuning.log', use_transfer_learning=True, try_winograd=True, try_spatial_pack_depthwise=True)

it gives the error after i run, the error info as follows:

    Exception has occurred: KeyError
File "/home/mm/workspace/tvm-master-new/python/tvm/autotvm/task/", line 220, in dispatch_func
    return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
File "</home/mm/.local/lib/python3.7/site-packages/>", line 2, in config_dispatcher
File "/home/mm/workspace/tvm-master-new/python/tvm/", line 372, in dispatch_func
    return dispatch_dict[k](*args, **kwargs)
File "</home/mm/.local/lib/python3.7/site-packages/>", line 2, in depthwise_conv2d_nchw
File "/home/mm/workspace/tvm-master-new/python/tvm/autotvm/task/", line 186, in _topi_nn_depthwise_conv2d_nchw
    C = topi.nn.depthwise_conv2d_nchw(*args, **kwargs)
File "/home/mm/workspace/tvm-master-new/python/tvm/autotvm/task/", line 191, in create
    sch, _ = func(*args)
File "/home/mm/workspace/somecodes/tvm/autotvm/", line 122, in tune_tasks
File "/home/mm/workspace/somecodes/tvm/autotvm/", line 183, in tune_and_evaluate
    tune_tasks(tasks, **tuning_opt)
File "/home/mm/workspace/somecodes/tvm/autotvm/", line 227, in <module>

Do you tune from the scratch? seems that you have tuned and run with the tuned log, then you get the error.

yes ,i tune the model from the scratch, this is the first time i use autotvm to tune the mxnet model.

I have tested it and no problem of contrib_spatial_pack. What I means before is do you tune from scratch without contrib_spatial_pack, but when you run, you set contrib_spatial_pack be true? Or do you have workload not tuned with contrib_spatial_pack?

yes ,at first, i tune the model without contrib_spatial_pack(try_spatial_pack_depthwise=False), then i break the tuning. i restart the tuning with try_spatial_pack_depthwise=True,it gives the error.

Do you remove the .tmp file? If you use the transfer learning, it will load the history file and maybe have problem. If you still have problem, try to update the latest code and try again.

i just tested it, it always give the erros: KeyError: 'contrib_spatial_pack’
i don’t find .tmp file , i have update the latest code, it is weird. i will test it by another computer tommrow.

i have tested it by another computer , the same error, can you help me to test the model ? the mxnet model download url(mxnet model) thanks

which model? I find it has two models.

this model : mnet.25-0000.params ,mnet.25-symbol.json

input size is 640*640

I can not reproduce this error. It could also work well too. Some suggestions maybe help:
firstly install cython, i.e. pip3 install cython

Then in the build directory run make cython3

I find it could solve some issue of macOS, but this issue shouldn’t be existed in Linux.

i have tested it, same error, can you post the your tuning codes?thanks

No special. Just

@aa12356jm did you find a solution? I’m having the same issue here…

@FrozenGene this is still happening with latest code from master

i didn’t solve it ,i dont’t know why it happens

I really can not reproduce this error. Currently, master branch should only occur this error:

    num_filter, _, kernel_h, kernel_w = get_const_tuple(Filter.shape)
ValueError: too many values to unpack (expected 4)

I will pull a request soon.

My tune script:

import os

import numpy as np
import tvm
from tvm import autotvm
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
from tvm.contrib.util import tempdir
import tvm.contrib.graph_runtime as runtime

def get_network(name, batch_size):
    """Get the symbol definition and random weight of a network"""
    input_shape = (batch_size, 3, 224, 224)
    output_shape = (batch_size, 1000)

    if "resnet" in name:
        n_layer = int(name.split('-')[1])
        mod, params = relay.testing.resnet.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype)
    elif "vgg" in name:
        n_layer = int(name.split('-')[1])
        mod, params = relay.testing.vgg.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype)
    elif name == 'mobilenet':
        mod, params = relay.testing.mobilenet.get_workload(batch_size=batch_size)
    elif name == 'squeezenet_v1.1':
        mod, params = relay.testing.squeezenet.get_workload(batch_size=batch_size, version='1.1', dtype=dtype)
    elif name == 'inception_v3':
        input_shape = (1, 3, 299, 299)
        mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
    elif name == 'mxnet':
        # an example for mxnet model
        from import get_model
        block = get_model('resnet18_v1', pretrained=True)
        mod, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
        net = mod["main"]
        net = relay.Function(net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs)
        mod = relay.Module.from_expr(net)
        raise ValueError("Unsupported network: " + name)

    return mod, params, input_shape, output_shape


# Replace "aarch64-linux-gnu" with the correct target of your board.
# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
target ='llvm -device=arm_cpu -target=aarch64-linux-gnu')

# Also replace this with the device key in your tracker
device_key = 'rasp'

# Set this to True if you use android phone
use_android = False

network = 'mobilenet'
log_file = "%s.%s.log" % (device_key, network)
dtype = 'float32'

tuning_option = {
    'log_filename': log_file,

    'tuner': 'xgb',
    'n_trial': 20,
    'early_stopping': 8,

    'measure_option': autotvm.measure_option(
            build_func='ndk' if use_android else 'default'),
            device_key, host='', port=9198,

# You can skip the implementation of this function for this tutorial.
def tune_tasks(tasks,
    if try_winograd:
        for i in range(len(tasks)):
            try:  # try winograd template
                tsk = autotvm.task.create(tasks[i].name, tasks[i].args,
                                          tasks[i].target, tasks[i].target_host, 'winograd')
                input_channel = tsk.workload[1][1]
                if input_channel >= 64:
                    tasks[i] = tsk
            except Exception:

    # if we want to use spatial pack for depthwise convolution
    if try_spatial_pack_depthwise:
        tuner = 'xgb_knob'
        for i in range(len(tasks)):
            if tasks[i].name == 'topi_nn_depthwise_conv2d_nchw':
                tsk = autotvm.task.create(tasks[i].name, tasks[i].args,
                                          tasks[i].target, tasks[i].target_host,
                tasks[i] = tsk

    # create tmp log file
    tmp_log_file = log_filename + ".tmp"
    if os.path.exists(tmp_log_file):

    for i, tsk in enumerate(reversed(tasks)):
        prefix = "[Task %2d/%2d] " % (i+1, len(tasks))

        # create tuner
        if tuner == 'xgb' or tuner == 'xgb-rank':
            tuner_obj = XGBTuner(tsk, loss_type='rank')
        elif tuner == 'xgb_knob':
            tuner_obj = XGBTuner(tsk, loss_type='rank', feature_type='knob')
        elif tuner == 'ga':
            tuner_obj = GATuner(tsk, pop_size=50)
        elif tuner == 'random':
            tuner_obj = RandomTuner(tsk)
        elif tuner == 'gridsearch':
            tuner_obj = GridSearchTuner(tsk)
            raise ValueError("Invalid tuner: " + tuner)

        if use_transfer_learning:
            if os.path.isfile(tmp_log_file):

        # do tuning
        n_trial = min(n_trial, len(tsk.config_space))
                           autotvm.callback.progress_bar(n_trial, prefix=prefix),

    # pick best records to a cache file
    autotvm.record.pick_best(tmp_log_file, log_filename)

def tune_and_evaluate(tuning_opt):
    # extract workloads from relay program
    print("Extract tasks...")
    mod, params, input_shape, _ = get_network(network, batch_size=1)
    tasks = autotvm.task.extract_from_program(mod["main"], target=target,

    # run tuning tasks
    tune_tasks(tasks, **tuning_opt)

    # compile kernels with history best records
    with autotvm.apply_history_best(log_file):
        with relay.build_config(opt_level=3):
            graph, lib, params =
                mod, target=target, params=params)

        # export library
        tmp = tempdir()
        if use_android:
            from tvm.contrib import ndk
            filename = ""
            lib.export_library(tmp.relpath(filename), ndk.create_shared)
            filename = "net.tar"

        # upload module to device
        remote = autotvm.measure.request_remote(device_key, '', 9198,
        rlib = remote.load_module(filename)

        # upload parameters to device
        ctx = remote.context(str(target), 0)
        module = runtime.create(graph, rlib, ctx)
        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
        module.set_input('data', data_tvm)

        # evaluate
        print("Evaluate inference time cost...")
        ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=10)
        prof_res = np.array(ftimer().results) * 1000  # convert to millisecond
        print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
              (np.mean(prof_res), np.std(prof_res)))

# We do not run the tuning in our webpage server since it takes too long.
# Uncomment the following line to run it by yourself.


there is no special modification.


@FrozenGene Im experiencing similar problem,

here is description:

could you try the autotune script with mnet.25 model from here

@aa12356jm @gasgallo guys did you solved it?