Auto-tuning a convolutional network on VTA

Author: Lianmin Zheng, Thierry Moreau

Auto-tuning for a specific accelerator design is critical for getting the best performance for any given operator. This is a tutorial showcases how to tune a whole convolutional network on VTA.

The operator implementation for VTA in TVM is written in template form. The template has many tunable knobs (tile factor, virtual threads, etc). We will tune all convolution operators in the neural network. After tuning, we produce a log file which stores the best schedule parameters for all tuned operators. When the TVM compiler compiles these operators, it will query this log file to get the best knob parameters.

Install dependencies

To use the autotvm package in tvm, we need to install some extra dependencies. (change “3” to “2” if you use python2):

pip3 install --user psutil xgboost tornado mxnet requests "Pillow<7"

To make TVM run faster during tuning, it is recommended to use cython as FFI of TVM. In the root directory of TVM, execute (change “3” to “2” if you use python2):

pip3 install --user cython
sudo make cython3

Now return to python code. Import packages.

import os
from mxnet.gluon.model_zoo import vision
import numpy as np
from PIL import Image

import topi
import tvm
from tvm import te
from tvm import rpc, autotvm, relay
from tvm.contrib import graph_runtime, util, download
from tvm.autotvm.measure.measure_methods import request_remote
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner

import vta
from vta.testing import simulator
from vta.top import graph_pack

Compile network

Perform vta-specific compilation with Relay from a Gluon model

def compile_network(env, target, model, start_pack, stop_pack):

    # Populate the shape and data type dictionary
    dtype_dict = {"data": 'float32'}
    shape_dict = {"data": (env.BATCH, 3, 224, 224)}

    # Get off the shelf gluon model, and convert to relay
    gluon_model = vision.get_model(model, pretrained=True)
    mod, params = relay.frontend.from_mxnet(gluon_model, shape_dict)

    # Update shape and type dictionary
    shape_dict.update({k: v.shape for k, v in params.items()})
    dtype_dict.update({k: str(v.dtype) for k, v in params.items()})

    # Perform quantization in Relay
    # Note: We set opt_level to 3 in order to fold batch norm
    with tvm.transform.PassContext(opt_level=3):
        with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[0]):
            mod = relay.quantize.quantize(mod, params=params)

    # Perform graph packing and constant folding for VTA target
    if target.device_name == "vta":
        assert env.BLOCK_IN == env.BLOCK_OUT
        relay_prog = graph_pack(mod["main"],
                                env.BATCH,
                                env.BLOCK_OUT,
                                env.WGT_WIDTH,
                                start_name=start_pack,
                                stop_name=stop_pack)

    return relay_prog, params

Start RPC Tracker

TVM uses an RPC session to communicate with Pynq boards. During tuning, the tuner will send the generated code to the board and measure the speed of code on the board.

To scale up tuning, TVM uses an RPC Tracker to manage multiple devices. The RPC Tracker is a centralized master node. We can register all devices to the tracker. For example, if we have 10 Pynq boards, we can register all of them to the tracker, and run 10 measurements in parallel, accelerating the tuning process.

To start an RPC tracker, run this command on the host machine. The tracker is required during the whole tuning process, so we need to open a new terminal for this command:

python -m tvm.exec.rpc_tracker --host=0.0.0.0 --port=9190

The expected output is:

INFO:RPCTracker:bind to 0.0.0.0:9190

Register devices to RPC Tracker

Now we can register our devices to the tracker. The first step is to build the TVM runtime for the Pynq devices.

Follow VTA: Deep Learning Accelerator Stack to build the TVM runtime on the device. Then register the device to the tracker with:

python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=pynq

(replace [HOST_IP] with the IP address of your host machine)

After registering devices, we can confirm it by querying the rpc_tracker:

python -m tvm.exec.query_rpc_tracker --host=0.0.0.0 --port=9190

For example, if we have 6 Pynq boards and 11 Raspberry Pi 3B, the output can be

Queue Status
----------------------------------
key          total  free  pending
----------------------------------
pynq         6      6     0
rpi3b        11     11    0
----------------------------------

You can register multiple devices to the tracker to accelerate tuning.

Set Tuning Options

Before tuning, we should apply some configurations. Here we use an Pynq-Z1 board as an example.

# Tracker host and port can be set by your environment
tracker_host = os.environ.get("TVM_TRACKER_HOST", '0.0.0.0')
tracker_port = int(os.environ.get("TVM_TRACKER_PORT", 9190))

# Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file
env = vta.get_env()

# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
# Set ``device=arm_cpu`` to run inference on the CPU
# or ``device=vta`` to run inference on the FPGA.
device = "vta"
target = env.target if device == "vta" else env.target_vta_cpu

# Name of Gluon model to compile
# The ``start_pack`` and ``stop_pack`` labels indicate where
# to start and end the graph packing relay pass: in other words
# where to start and finish offloading to VTA.
network = "resnet18_v1"
start_pack = "nn.max_pool2d"
stop_pack = "nn.global_avg_pool2d"

# Tuning option
log_file = "%s.%s.log" % (device, network)
tuning_option = {
    'log_filename': log_file,

    'tuner': 'random',
    'n_trial': 1000,
    'early_stopping': None,

    'measure_option': autotvm.measure_option(
        builder=autotvm.LocalBuilder(),
        runner=autotvm.RPCRunner(env.TARGET,
                                 host=tracker_host,
                                 port=tracker_port,
                                 number=5,
                                 timeout=60,
                                 check_correctness=True),
    ),
}

Note

How to set tuning options

In general, the default values provided here work well. If you have enough time budget, you can set n_trial, early_stopping to larger values, makes the tuning run for longer. If your device is under-powered or your conv2d operators are large, consider setting a longer timeout.

Begin Tuning

Now we can extract tuning tasks from the network and begin tuning. Here, we provide a simple utility function to tune a list of tasks. This function is just an initial implementation which tunes them in sequential order. We will introduce a more sophisticated tuning scheduler in the future.

Given that the tuning will be done on Pynq FPGA boards, make sure that the `TARGET entry in the vta_config.json file is set to pynq.

# You can skip the implementation of this function for this tutorial.
def tune_tasks(tasks,
               measure_option,
               tuner='xgb',
               n_trial=1000,
               early_stopping=None,
               log_filename='tuning.log',
               use_transfer_learning=True):

    # create tmp log file
    tmp_log_file = log_filename + ".tmp"
    if os.path.exists(tmp_log_file):
        os.remove(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)
        else:
            raise ValueError("Invalid tuner: " + tuner)

        if use_transfer_learning:
            if os.path.isfile(tmp_log_file):
                tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))

        # do tuning
        tsk_trial = min(n_trial, len(tsk.config_space))
        tuner_obj.tune(n_trial=tsk_trial,
                       early_stopping=early_stopping,
                       measure_option=measure_option,
                       callbacks=[
                           autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
                           autotvm.callback.log_to_file(tmp_log_file)
                       ])

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

Register VTA-specific tuning tasks

def register_vta_tuning_tasks():
    from tvm.autotvm.task import TaskExtractEnv

    @tvm.te.tag_scope(tag=topi.tag.ELEMWISE)
    def my_clip(x, a_min, a_max):
        """Unlike topi's current clip, put min and max into two stages."""
        const_min = tvm.tir.const(a_min, x.dtype)
        const_max = tvm.tir.const(a_max, x.dtype)
        x = te.compute(x.shape, lambda *i: tvm.te.min(x(*i), const_max), name="clipA")
        x = te.compute(x.shape, lambda *i: tvm.te.max(x(*i), const_min), name="clipB")
        return x

    # init autotvm env to register VTA operator
    TaskExtractEnv()

    @autotvm.template("conv2d_packed.vta")
    def _topi_nn_conv2d(*args, **kwargs):
        assert not kwargs, "Do not support kwargs in template function call"
        A, W = args[:2]

        with tvm.target.vta():
            res = vta.top.conv2d_packed(*args, **kwargs)
            res = topi.right_shift(res, 8)
            res = my_clip(res, 0, 127)
            res = topi.cast(res, "int8")

        if tvm.target.Target.current().device_name == 'vta':
            s = vta.top.schedule_conv2d_packed([res])
        else:
            s = te.create_schedule([res.op])
        return s, [A, W, res]

Finally, we launch tuning jobs and evaluate the end-to-end performance.

def tune_and_evaluate(tuning_opt):

    if env.TARGET != "sim":
        # Get remote from fleet node
        remote = autotvm.measure.request_remote(env.TARGET,
                                                tracker_host,
                                                tracker_port,
                                                timeout=10000)
        # Reconfigure the JIT runtime and FPGA.
        vta.reconfig_runtime(remote)
        vta.program_fpga(remote, bitstream=None)
    else:
        # In simulation mode, host the RPC server locally.
        remote = rpc.LocalSession()

    # Register VTA tuning tasks
    register_vta_tuning_tasks()

    # Perform task extraction on Relay program
    print("Extract tasks...")
    relay_prog, params = compile_network(env, target, network, start_pack, stop_pack)
    mod = tvm.IRModule.from_expr(relay_prog)
    tasks = autotvm.task.extract_from_program(mod,
                                              params=params,
                                              ops=(relay.op.get("nn.conv2d"),),
                                              target=target,
                                              target_host=env.target_host)

    # filter out non-packed conv2d task
    tasks = list(filter(lambda t: len(t.args[0][1]) > 4, tasks))

    # We should have extracted 10 convolution tasks
    assert len(tasks) == 10
    print("Extracted {} conv2d tasks:".format(len(tasks)))
    for tsk in tasks:
        inp = tsk.args[0][1]
        wgt = tsk.args[1][1]
        batch = inp[0] * inp[4]
        in_filter = inp[1] * inp[5]
        out_filter = wgt[0] * wgt[4]
        height, width = inp[2], inp[3]
        hkernel, wkernel = wgt[2], wgt[3]
        hstride, wstride = tsk.args[2][0], tsk.args[2][1]
        hpad, wpad = tsk.args[3][0], tsk.args[3][1]
        print("({}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {})".format(
            batch, height, width, in_filter, out_filter, hkernel, wkernel,
            hpad, wpad, hstride, wstride))

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

    # run tuning tasks
    print("Tuning...")
    tune_tasks(tasks, **tuning_opt)

    # compile kernels with history best records
    with autotvm.tophub.context(target, extra_files=[log_file]):
        # Compile network
        print("Compile...")
        if target.device_name != "vta":
            with tvm.transform.PassContext(opt_level=3, disabled_pass={"AlterOpLayout"}):
                graph, lib, params = relay.build(relay_prog,
                                                target=target,
                                                params=params,
                                                target_host=env.target_host)
        else:
            with vta.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
                graph, lib, params = relay.build(
                    relay_prog,
                    target=target,
                    params=params,
                    target_host=env.target_host)

        # Export library
        print("Upload...")
        temp = util.tempdir()
        lib.save(temp.relpath("graphlib.o"))
        remote.upload(temp.relpath("graphlib.o"))
        lib = remote.load_module("graphlib.o")

        # Generate the graph runtime
        ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
        m = graph_runtime.create(graph, lib, ctx)

        # upload parameters to device
        image = tvm.nd.array(
            (np.random.uniform(size=(1, 3, 224, 224))).astype('float32'))
        m.set_input(**params)
        m.set_input('data', image)

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


# Run the tuning and evaluate the results
tune_and_evaluate(tuning_option)

Out:

Extract tasks...

...1%, 0.01 MB, 37 KB/s, 0 seconds passed
...2%, 0.02 MB, 74 KB/s, 0 seconds passed
...3%, 0.02 MB, 111 KB/s, 0 seconds passed
...4%, 0.03 MB, 148 KB/s, 0 seconds passed
...5%, 0.04 MB, 184 KB/s, 0 seconds passed
...6%, 0.05 MB, 221 KB/s, 0 seconds passed
...7%, 0.05 MB, 257 KB/s, 0 seconds passed
...8%, 0.06 MB, 294 KB/s, 0 seconds passed
...9%, 0.07 MB, 329 KB/s, 0 seconds passed
...10%, 0.08 MB, 366 KB/s, 0 seconds passed
...11%, 0.09 MB, 402 KB/s, 0 seconds passed
...13%, 0.09 MB, 438 KB/s, 0 seconds passed
...14%, 0.10 MB, 474 KB/s, 0 seconds passed
...15%, 0.11 MB, 509 KB/s, 0 seconds passed
...16%, 0.12 MB, 545 KB/s, 0 seconds passed
...17%, 0.12 MB, 580 KB/s, 0 seconds passed
...18%, 0.13 MB, 616 KB/s, 0 seconds passed
...19%, 0.14 MB, 651 KB/s, 0 seconds passed
...20%, 0.15 MB, 687 KB/s, 0 seconds passed
...21%, 0.16 MB, 722 KB/s, 0 seconds passed
...22%, 0.16 MB, 757 KB/s, 0 seconds passed
...23%, 0.17 MB, 792 KB/s, 0 seconds passed
...25%, 0.18 MB, 827 KB/s, 0 seconds passed
...26%, 0.19 MB, 863 KB/s, 0 seconds passed
...27%, 0.20 MB, 897 KB/s, 0 seconds passed
...28%, 0.20 MB, 932 KB/s, 0 seconds passed
...29%, 0.21 MB, 967 KB/s, 0 seconds passed
...30%, 0.22 MB, 1002 KB/s, 0 seconds passed
...31%, 0.23 MB, 1037 KB/s, 0 seconds passed
...32%, 0.23 MB, 1071 KB/s, 0 seconds passed
...33%, 0.24 MB, 1106 KB/s, 0 seconds passed
...34%, 0.25 MB, 1139 KB/s, 0 seconds passed
...35%, 0.26 MB, 1174 KB/s, 0 seconds passed
...36%, 0.27 MB, 1208 KB/s, 0 seconds passed
...38%, 0.27 MB, 1243 KB/s, 0 seconds passed
...39%, 0.28 MB, 1278 KB/s, 0 seconds passed
...40%, 0.29 MB, 1313 KB/s, 0 seconds passed
...41%, 0.30 MB, 1344 KB/s, 0 seconds passed
...42%, 0.30 MB, 1379 KB/s, 0 seconds passed
...43%, 0.31 MB, 1413 KB/s, 0 seconds passed
...44%, 0.32 MB, 1447 KB/s, 0 seconds passed
...45%, 0.33 MB, 1479 KB/s, 0 seconds passed
...46%, 0.34 MB, 1514 KB/s, 0 seconds passed
...47%, 0.34 MB, 1548 KB/s, 0 seconds passed
...48%, 0.35 MB, 1583 KB/s, 0 seconds passed
...50%, 0.36 MB, 1616 KB/s, 0 seconds passed
...51%, 0.37 MB, 1650 KB/s, 0 seconds passed
...52%, 0.38 MB, 1682 KB/s, 0 seconds passed
...53%, 0.38 MB, 1716 KB/s, 0 seconds passed
...54%, 0.39 MB, 1749 KB/s, 0 seconds passed
...55%, 0.40 MB, 1783 KB/s, 0 seconds passed
...56%, 0.41 MB, 1817 KB/s, 0 seconds passed
...57%, 0.41 MB, 1851 KB/s, 0 seconds passed
...58%, 0.42 MB, 1882 KB/s, 0 seconds passed
...59%, 0.43 MB, 1917 KB/s, 0 seconds passed
...60%, 0.44 MB, 1949 KB/s, 0 seconds passed
...62%, 0.45 MB, 1983 KB/s, 0 seconds passed
...63%, 0.45 MB, 2015 KB/s, 0 seconds passed
...64%, 0.46 MB, 2049 KB/s, 0 seconds passed
...65%, 0.47 MB, 2080 KB/s, 0 seconds passed
...66%, 0.48 MB, 2113 KB/s, 0 seconds passed
...67%, 0.48 MB, 2146 KB/s, 0 seconds passed
...68%, 0.49 MB, 2180 KB/s, 0 seconds passed
...69%, 0.50 MB, 2211 KB/s, 0 seconds passed
...70%, 0.51 MB, 2244 KB/s, 0 seconds passed
...71%, 0.52 MB, 2272 KB/s, 0 seconds passed
...72%, 0.52 MB, 2305 KB/s, 0 seconds passed
...73%, 0.53 MB, 2338 KB/s, 0 seconds passed
...75%, 0.54 MB, 2372 KB/s, 0 seconds passed
...76%, 0.55 MB, 2405 KB/s, 0 seconds passed
...77%, 0.55 MB, 2438 KB/s, 0 seconds passed
...78%, 0.56 MB, 2468 KB/s, 0 seconds passed
...79%, 0.57 MB, 2502 KB/s, 0 seconds passed
...80%, 0.58 MB, 2535 KB/s, 0 seconds passed
...81%, 0.59 MB, 2568 KB/s, 0 seconds passed
...82%, 0.59 MB, 2601 KB/s, 0 seconds passed
...83%, 0.60 MB, 2634 KB/s, 0 seconds passed
...84%, 0.61 MB, 2662 KB/s, 0 seconds passed
...85%, 0.62 MB, 2695 KB/s, 0 seconds passed
...87%, 0.62 MB, 2727 KB/s, 0 seconds passed
...88%, 0.63 MB, 2760 KB/s, 0 seconds passed
...89%, 0.64 MB, 2793 KB/s, 0 seconds passed
...90%, 0.65 MB, 2826 KB/s, 0 seconds passed
...91%, 0.66 MB, 2858 KB/s, 0 seconds passed
...92%, 0.66 MB, 2891 KB/s, 0 seconds passed
...93%, 0.67 MB, 2920 KB/s, 0 seconds passed
...94%, 0.68 MB, 2953 KB/s, 0 seconds passed
...95%, 0.69 MB, 2986 KB/s, 0 seconds passed
...96%, 0.70 MB, 3018 KB/s, 0 seconds passed
...97%, 0.70 MB, 3049 KB/s, 0 seconds passed
...99%, 0.71 MB, 3081 KB/s, 0 seconds passed
...100%, 0.72 MB, 3112 KB/s, 0 seconds passed
Extracted 10 conv2d tasks:
(1, 14, 14, 256, 512, 1, 1, 0, 0, 2, 2)
(1, 28, 28, 128, 256, 1, 1, 0, 0, 2, 2)
(1, 56, 56, 64, 128, 1, 1, 0, 0, 2, 2)
(1, 56, 56, 64, 64, 3, 3, 1, 1, 1, 1)
(1, 28, 28, 128, 128, 3, 3, 1, 1, 1, 1)
(1, 56, 56, 64, 128, 3, 3, 1, 1, 2, 2)
(1, 14, 14, 256, 256, 3, 3, 1, 1, 1, 1)
(1, 28, 28, 128, 256, 3, 3, 1, 1, 2, 2)
(1, 7, 7, 512, 512, 3, 3, 1, 1, 1, 1)
(1, 14, 14, 256, 512, 3, 3, 1, 1, 2, 2)

Sample Output

The tuning needs to compile many programs and extract feature from them. So a high performance CPU is recommended. One sample output is listed below. It takes about 2 hours on a 16T CPU, and 6 Pynq boards.

Extract tasks...
[Warning] Invalid shape during AutoTVM task creation
Extracted 10 conv2d tasks:
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 16, 14, 14, 1, 16), 'int8'), ('TENSOR', (32, 16, 1, 1, 16, 16), 'int8'), (2, 2), (0, 0), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 16, 14, 14, 1, 16, 'int8'), (32, 16, 1, 1, 16, 16, 'int8'), (2, 2), (0, 0), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 8, 28, 28, 1, 16), 'int8'), ('TENSOR', (16, 8, 1, 1, 16, 16), 'int8'), (2, 2), (0, 0), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 8, 28, 28, 1, 16, 'int8'), (16, 8, 1, 1, 16, 16, 'int8'), (2, 2), (0, 0), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 4, 56, 56, 1, 16), 'int8'), ('TENSOR', (8, 4, 1, 1, 16, 16), 'int8'), (2, 2), (0, 0), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 4, 56, 56, 1, 16, 'int8'), (8, 4, 1, 1, 16, 16, 'int8'), (2, 2), (0, 0), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 4, 56, 56, 1, 16), 'int8'), ('TENSOR', (4, 4, 3, 3, 16, 16), 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 4, 56, 56, 1, 16, 'int8'), (4, 4, 3, 3, 16, 16, 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 8, 28, 28, 1, 16), 'int8'), ('TENSOR', (8, 8, 3, 3, 16, 16), 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 8, 28, 28, 1, 16, 'int8'), (8, 8, 3, 3, 16, 16, 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 4, 56, 56, 1, 16), 'int8'), ('TENSOR', (8, 4, 3, 3, 16, 16), 'int8'), (2, 2), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 4, 56, 56, 1, 16, 'int8'), (8, 4, 3, 3, 16, 16, 'int8'), (2, 2), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 16, 14, 14, 1, 16), 'int8'), ('TENSOR', (16, 16, 3, 3, 16, 16), 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 16, 14, 14, 1, 16, 'int8'), (16, 16, 3, 3, 16, 16, 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 8, 28, 28, 1, 16), 'int8'), ('TENSOR', (16, 8, 3, 3, 16, 16), 'int8'), (2, 2), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 8, 28, 28, 1, 16, 'int8'), (16, 8, 3, 3, 16, 16, 'int8'), (2, 2), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 32, 7, 7, 1, 16), 'int8'), ('TENSOR', (32, 32, 3, 3, 16, 16), 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 32, 7, 7, 1, 16, 'int8'), (32, 32, 3, 3, 16, 16, 'int8'), (1, 1), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
    Task(func_name=topi_nn_conv2d, args=(('TENSOR', (1, 16, 14, 14, 1, 16), 'int8'), ('TENSOR', (32, 16, 3, 3, 16, 16), 'int8'), (2, 2), (1, 1), (1, 1), 'NCHW1n16c', 'int32'), kwargs={}, workload=('conv2d', (1, 16, 14, 14, 1, 16, 'int8'), (32, 16, 3, 3, 16, 16, 'int8'), (2, 2), (1, 1), (1, 1), 'NCHW1n16c', 'int32'))
Tuning...
[Task  1/10]  Current/Best:    0.72/  23.24 GFLOPS | Progress: (480/1000) | 640.31 s Done.
[Task  2/10]  Current/Best:    0.00/  27.69 GFLOPS | Progress: (576/1000) | 810.09 s Done.
[Task  3/10]  Current/Best:    0.00/  22.97 GFLOPS | Progress: (1000/1000) | 1125.37 s Done.
[Task  4/10]  Current/Best:    0.00/  31.26 GFLOPS | Progress: (1000/1000) | 1025.52 s Done.
[Task  5/10]  Current/Best:    0.00/  15.15 GFLOPS | Progress: (1000/1000) | 1236.58 s Done.
[Task  6/10]  Current/Best:    0.00/  22.74 GFLOPS | Progress: (1000/1000) | 906.60 s Done.
[Task  7/10]  Current/Best:    0.00/  15.27 GFLOPS | Progress: (1000/1000) | 1056.25 s Done.
[Task  8/10]  Current/Best:    0.00/   2.18 GFLOPS | Progress: (1000/1000) | 2275.29 s Done.
[Task  9/10]  Current/Best:    2.23/   3.99 GFLOPS | Progress: (1000/1000) | 2527.25 s Done.
[Task 10/10]  Current/Best:    1.56/   6.32 GFLOPS | Progress: (480/1000) | 1304.84 s Done.
Compile...
Upload...
Evaluate inference time cost...
Mean inference time (std dev): 621.79 ms (0.14 ms)

Note

Experiencing Difficulties?

The auto tuning module is error-prone. If you always see ” 0.00/ 0.00 GFLOPS”, then there must be something wrong.

First, make sure you set the correct configuration of your device. Then, you can print debug information by adding these lines in the beginning of the script. It will print every measurement result, where you can find useful error messages.

import logging
logging.getLogger('autotvm').setLevel(logging.DEBUG)

Finally, always feel free to ask our community for help on https://discuss.tvm.ai

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