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 relay.build_config(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...")
        with relay.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
            if target.device_name != "vta":
                graph, lib, params = relay.build(relay_prog,
                                                 target=target,
                                                 params=params,
                                                 target_host=env.target_host)
            else:
                with vta.build_config():
                    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, 25 KB/s, 0 seconds passed
...2%, 0.02 MB, 49 KB/s, 0 seconds passed
...3%, 0.02 MB, 74 KB/s, 0 seconds passed
...4%, 0.03 MB, 99 KB/s, 0 seconds passed
...5%, 0.04 MB, 124 KB/s, 0 seconds passed
...6%, 0.05 MB, 148 KB/s, 0 seconds passed
...7%, 0.05 MB, 173 KB/s, 0 seconds passed
...8%, 0.06 MB, 198 KB/s, 0 seconds passed
...9%, 0.07 MB, 222 KB/s, 0 seconds passed
...10%, 0.08 MB, 247 KB/s, 0 seconds passed
...11%, 0.09 MB, 271 KB/s, 0 seconds passed
...13%, 0.09 MB, 295 KB/s, 0 seconds passed
...14%, 0.10 MB, 320 KB/s, 0 seconds passed
...15%, 0.11 MB, 344 KB/s, 0 seconds passed
...16%, 0.12 MB, 369 KB/s, 0 seconds passed
...17%, 0.12 MB, 393 KB/s, 0 seconds passed
...18%, 0.13 MB, 417 KB/s, 0 seconds passed
...19%, 0.14 MB, 441 KB/s, 0 seconds passed
...20%, 0.15 MB, 465 KB/s, 0 seconds passed
...21%, 0.16 MB, 490 KB/s, 0 seconds passed
...22%, 0.16 MB, 514 KB/s, 0 seconds passed
...23%, 0.17 MB, 537 KB/s, 0 seconds passed
...25%, 0.18 MB, 562 KB/s, 0 seconds passed
...26%, 0.19 MB, 586 KB/s, 0 seconds passed
...27%, 0.20 MB, 610 KB/s, 0 seconds passed
...28%, 0.20 MB, 633 KB/s, 0 seconds passed
...29%, 0.21 MB, 657 KB/s, 0 seconds passed
...30%, 0.22 MB, 680 KB/s, 0 seconds passed
...31%, 0.23 MB, 704 KB/s, 0 seconds passed
...32%, 0.23 MB, 728 KB/s, 0 seconds passed
...33%, 0.24 MB, 752 KB/s, 0 seconds passed
...34%, 0.25 MB, 776 KB/s, 0 seconds passed
...35%, 0.26 MB, 800 KB/s, 0 seconds passed
...36%, 0.27 MB, 823 KB/s, 0 seconds passed
...38%, 0.27 MB, 847 KB/s, 0 seconds passed
...39%, 0.28 MB, 870 KB/s, 0 seconds passed
...40%, 0.29 MB, 894 KB/s, 0 seconds passed
...41%, 0.30 MB, 918 KB/s, 0 seconds passed
...42%, 0.30 MB, 942 KB/s, 0 seconds passed
...43%, 0.31 MB, 964 KB/s, 0 seconds passed
...44%, 0.32 MB, 987 KB/s, 0 seconds passed
...45%, 0.33 MB, 1011 KB/s, 0 seconds passed
...46%, 0.34 MB, 1035 KB/s, 0 seconds passed
...47%, 0.34 MB, 1059 KB/s, 0 seconds passed
...48%, 0.35 MB, 1082 KB/s, 0 seconds passed
...50%, 0.36 MB, 1105 KB/s, 0 seconds passed
...51%, 0.37 MB, 1129 KB/s, 0 seconds passed
...52%, 0.38 MB, 1152 KB/s, 0 seconds passed
...53%, 0.38 MB, 1175 KB/s, 0 seconds passed
...54%, 0.39 MB, 1199 KB/s, 0 seconds passed
...55%, 0.40 MB, 1222 KB/s, 0 seconds passed
...56%, 0.41 MB, 1245 KB/s, 0 seconds passed
...57%, 0.41 MB, 1269 KB/s, 0 seconds passed
...58%, 0.42 MB, 1292 KB/s, 0 seconds passed
...59%, 0.43 MB, 1315 KB/s, 0 seconds passed
...60%, 0.44 MB, 1337 KB/s, 0 seconds passed
...62%, 0.45 MB, 1361 KB/s, 0 seconds passed
...63%, 0.45 MB, 1384 KB/s, 0 seconds passed
...64%, 0.46 MB, 1408 KB/s, 0 seconds passed
...65%, 0.47 MB, 1431 KB/s, 0 seconds passed
...66%, 0.48 MB, 1455 KB/s, 0 seconds passed
...67%, 0.48 MB, 1477 KB/s, 0 seconds passed
...68%, 0.49 MB, 1500 KB/s, 0 seconds passed
...69%, 0.50 MB, 1522 KB/s, 0 seconds passed
...70%, 0.51 MB, 1546 KB/s, 0 seconds passed
...71%, 0.52 MB, 1569 KB/s, 0 seconds passed
...72%, 0.52 MB, 1592 KB/s, 0 seconds passed
...73%, 0.53 MB, 1614 KB/s, 0 seconds passed
...75%, 0.54 MB, 1638 KB/s, 0 seconds passed
...76%, 0.55 MB, 1660 KB/s, 0 seconds passed
...77%, 0.55 MB, 1683 KB/s, 0 seconds passed
...78%, 0.56 MB, 1704 KB/s, 0 seconds passed
...79%, 0.57 MB, 1727 KB/s, 0 seconds passed
...80%, 0.58 MB, 1750 KB/s, 0 seconds passed
...81%, 0.59 MB, 1773 KB/s, 0 seconds passed
...82%, 0.59 MB, 1796 KB/s, 0 seconds passed
...83%, 0.60 MB, 1819 KB/s, 0 seconds passed
...84%, 0.61 MB, 1842 KB/s, 0 seconds passed
...85%, 0.62 MB, 1865 KB/s, 0 seconds passed
...87%, 0.62 MB, 1886 KB/s, 0 seconds passed
...88%, 0.63 MB, 1909 KB/s, 0 seconds passed
...89%, 0.64 MB, 1932 KB/s, 0 seconds passed
...90%, 0.65 MB, 1955 KB/s, 0 seconds passed
...91%, 0.66 MB, 1977 KB/s, 0 seconds passed
...92%, 0.66 MB, 2000 KB/s, 0 seconds passed
...93%, 0.67 MB, 2022 KB/s, 0 seconds passed
...94%, 0.68 MB, 2045 KB/s, 0 seconds passed
...95%, 0.69 MB, 2067 KB/s, 0 seconds passed
...96%, 0.70 MB, 2090 KB/s, 0 seconds passed
...97%, 0.70 MB, 2112 KB/s, 0 seconds passed
...99%, 0.71 MB, 2135 KB/s, 0 seconds passed
...100%, 0.72 MB, 2157 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

Gallery generated by Sphinx-Gallery