Auto-tuning a convolutional network for x86 CPU

Author: Yao Wang

This is a tutorial about how to tune convolution neural network for x86 cpu.

import os
import numpy as np

import nnvm.testing
import nnvm.compiler
import tvm
from tvm import autotvm
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
import tvm.contrib.graph_runtime as runtime

Define network

First we need to define the network in nnvm symbol API. We can load some pre-defined network from nnvm.testing. We can also load models from MXNet, ONNX and TensorFlow (see NNVM tutorials NNVM Compiler Tutorials for more details).

In this tutorial, we choose resnet-18 as tuning example.

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])
        net, params = nnvm.testing.resnet.get_workload(num_layers=n_layer, batch_size=batch_size)
    elif "vgg" in name:
        n_layer = int(name.split('-')[1])
        net, params = nnvm.testing.vgg.get_workload(num_layers=n_layer, batch_size=batch_size)
    elif name == 'mobilenet':
        net, params = nnvm.testing.mobilenet.get_workload(batch_size=batch_size)
    elif name == 'squeezenet_v1.1':
        net, params = nnvm.testing.squeezenet.get_workload(batch_size=batch_size, version='1.1')
    elif name == 'inception_v3':
        input_shape = (1, 3, 299, 299)
        net, params = nnvm.testing.inception_v3.get_workload(batch_size=batch_size)
    elif name == 'custom':
        # an example for custom network
        from nnvm.testing import utils
        net = nnvm.sym.Variable('data')
        net = nnvm.sym.conv2d(net, channels=4, kernel_size=(3,3), padding=(1,1))
        net = nnvm.sym.flatten(net)
        net = nnvm.sym.dense(net, units=1000)
        net, params = utils.create_workload(net, batch_size, (3, 224, 224))
    elif name == 'mxnet':
        # an example for mxnet model
        from mxnet.gluon.model_zoo.vision import get_model
        block = get_model('resnet18_v1', pretrained=True)
        net, params = nnvm.frontend.from_mxnet(block)
        net = nnvm.sym.softmax(net)
    else:
        raise ValueError("Unsupported network: " + name)

    return net, params, input_shape, output_shape

# Replace "llvm" with the correct target of your cpu.
# For example, for AWS EC2 c5 instance with Intel Xeon
# Platinum 8000 series, the target should be "llvm -mcpu=skylake-avx512".
# For AWS EC2 c4 instance with Intel Xeon E5-2666 v3, it should be
# "llvm -mcpu=core-avx2".
target = "llvm"

batch_size = 1
dtype = "float32"
model_name = "resnet-18"
log_file = "%s.log" % model_name

# Set number of threads used for tuning based on the number of
# physical cpu cores on your machine.
num_threads = 1
os.environ["TVM_NUM_THREADS"] = str(num_threads)

Configure tensor tuning settings and create tasks

To get better kernel execution performance on x86 cpu, we need to change data layout of convolution kernel from “NCHW” to “NCHWc”. To deal with this situation, we define conv2d_NCHWc operator in topi. We will tune this operator instead of plain conv2d.

We will use local mode for tuning configuration. RPC tracker mode can be setup similarly to the approach in tune_nnvm_arm tutorial.

tuning_option = {
    'log_filename': log_file,
    'tuner': 'random',
    'early_stopping': None,

    'measure_option': autotvm.measure_option(
        builder=autotvm.LocalBuilder(),
        runner=autotvm.LocalRunner(number=10, repeat=1,
                                   min_repeat_ms=1000),
    ),
}

# You can skip the implementation of this function for this tutorial.
def tune_kernels(tasks,
                 measure_option,
                 tuner='gridsearch',
                 early_stopping=None,
                 log_filename='tuning.log'):

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

        # converting conv2d tasks to conv2d_NCHWc tasks
        op_name = tsk.workload[0]
        if op_name == 'conv2d':
            func_create = 'topi_x86_conv2d_NCHWc'
        elif op_name == 'depthwise_conv2d_nchw':
            func_create = 'topi_x86_depthwise_conv2d_NCHWc_from_nchw'
        else:
            raise ValueError("Tuning {} is not supported on x86".format(op_name))

        task = autotvm.task.create(func_create, args=tsk.args,
                                   target=target, template_key='direct')
        task.workload = tsk.workload

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

        # do tuning
        n_trial=len(task.config_space)
        tuner_obj.tune(n_trial=n_trial,
                       early_stopping=early_stopping,
                       measure_option=measure_option,
                       callbacks=[
                           autotvm.callback.progress_bar(n_trial, prefix=prefix),
                           autotvm.callback.log_to_file(log_filename)])

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

def tune_and_evaluate(tuning_opt):
    # extract workloads from nnvm graph
    print("Extract tasks...")
    net, params, data_shape, out_shape = get_network(model_name, batch_size)
    tasks = autotvm.task.extract_from_graph(net, target=target,
                                            shape={'data': data_shape}, dtype=dtype,
                                            symbols=(nnvm.sym.conv2d,))

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

    # compile kernels with history best records
    with autotvm.apply_history_best(log_file):
        print("Compile...")
        with nnvm.compiler.build_config(opt_level=3):
            graph, lib, params = nnvm.compiler.build(
                net, target=target, shape={'data': data_shape}, params=params, dtype=dtype)

        # upload parameters to device
        ctx = tvm.cpu()
        data_tvm = tvm.nd.array((np.random.uniform(size=data_shape)).astype(dtype))
        module = runtime.create(graph, lib, ctx)
        module.set_input('data', data_tvm)
        module.set_input(**params)

        # evaluate
        print("Evaluate inference time cost...")
        ftimer = module.module.time_evaluator("run", ctx, number=100, repeat=3)
        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.

# tune_and_evaluate(tuning_option)

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.

Extract tasks...
Tuning...
[Task  1/12]  Current/Best:  598.05/2497.63 GFLOPS | Progress: (252/252) | 1357.95 s Done.
[Task  2/12]  Current/Best:  522.63/2279.24 GFLOPS | Progress: (784/784) | 3989.60 s Done.
[Task  3/12]  Current/Best:  447.33/1927.69 GFLOPS | Progress: (784/784) | 3869.14 s Done.
[Task  4/12]  Current/Best:  481.11/1912.34 GFLOPS | Progress: (672/672) | 3274.25 s Done.
[Task  5/12]  Current/Best:  414.09/1598.45 GFLOPS | Progress: (672/672) | 2720.78 s Done.
[Task  6/12]  Current/Best:  508.96/2273.20 GFLOPS | Progress: (768/768) | 3718.75 s Done.
[Task  7/12]  Current/Best:  469.14/1955.79 GFLOPS | Progress: (576/576) | 2665.67 s Done.
[Task  8/12]  Current/Best:  230.91/1658.97 GFLOPS | Progress: (576/576) | 2435.01 s Done.
[Task  9/12]  Current/Best:  487.75/2295.19 GFLOPS | Progress: (648/648) | 3009.95 s Done.
[Task 10/12]  Current/Best:  182.33/1734.45 GFLOPS | Progress: (360/360) | 1755.06 s Done.
[Task 11/12]  Current/Best:  372.18/1745.15 GFLOPS | Progress: (360/360) | 1684.50 s Done.
[Task 12/12]  Current/Best:  215.34/2271.11 GFLOPS | Progress: (400/400) | 2128.74 s Done.
Compile...
Evaluate inference time cost...
Mean inference time (std dev): 3.16 ms (0.03 ms)

Total running time of the script: ( 0 minutes 0.001 seconds)

Gallery generated by Sphinx-Gallery