[AUTOTVM] Simplify TopHub (#1630)
This commit is contained in:
Lianmin Zheng
Tianqi Chen
Родитель
56c50d2d07
Коммит
cfafd212c0
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@ -1,45 +1,26 @@
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"""Benchmark script for performance on ARM CPU.
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"""Benchmark script for ARM CPU.
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see README.md for the usage and results of this script.
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"""
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import argparse
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import time
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import numpy as np
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import nnvm.testing
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import nnvm.compiler
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import tvm
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from tvm import autotvm
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from tvm.contrib.util import tempdir
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import tvm.contrib.graph_runtime as runtime
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import nnvm.compiler
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import nnvm.testing
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def get_network(name, batch_size):
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"""Get the symbol definition and random weight of a network"""
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input_shape = (batch_size, 3, 224, 224)
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output_shape = (batch_size, 1000)
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if name == 'resnet-18':
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net, params = nnvm.testing.resnet.get_workload(num_layers=18,
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batch_size=batch_size, image_shape=(3, 224, 224))
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elif name == 'mobilenet':
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net, params = nnvm.testing.mobilenet.get_workload(batch_size=batch_size)
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elif name == 'squeezenet v1.1':
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net, params = nnvm.testing.squeezenet.get_workload(batch_size=batch_size,
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version='1.1')
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elif name == 'vgg-16':
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net, params = nnvm.testing.vgg.get_workload(batch_size=batch_size, num_layers=16)
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else:
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raise RuntimeError("Unsupported network: " + name)
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return net, params, input_shape, output_shape
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from util import get_network, print_progress
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--network", type=str, choices=['resnet-18', 'mobilenet', 'squeezenet v1.1', 'vgg-16'])
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parser.add_argument("--device", type=str, required=True, choices=['rk3399', 'mate10', 'mate10pro', 'p20', 'p20pro',
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'pixel2', 'rasp3b', 'pynq'])
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parser.add_argument("--network", type=str, choices=
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['resnet-18', 'resnet-34', 'vgg-16', 'mobilenet', 'squeezenet v1.1', ])
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parser.add_argument("--device", type=str, required=True, choices=
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['rk3399', 'mate10', 'mate10pro', 'p20', 'p20pro',
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'pixel2', 'rasp3b', 'pynq'])
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parser.add_argument("--host", type=str, default='localhost')
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parser.add_argument("--port", type=int, default=9190)
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parser.add_argument("--rpc-key", type=str, required=True)
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@ -49,7 +30,7 @@ if __name__ == "__main__":
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dtype = 'float32'
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if args.network is None:
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networks = ['squeezenet v1.1', 'mobilenet', 'resnet-18', 'vgg-16']
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networks = ['squeezenet_v1.1', 'mobilenet', 'resnet-18', 'vgg-16']
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else:
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networks = [args.network]
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@ -63,8 +44,10 @@ if __name__ == "__main__":
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print("%-20s %-20s" % ("Network Name", "Mean Inference Time (std dev)"))
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print("--------------------------------------------------")
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for network in networks:
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print_progress(network)
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net, params, input_shape, output_shape = get_network(network, batch_size=1)
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print_progress("%-20s building..." % network)
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with nnvm.compiler.build_config(opt_level=2, add_pass=['AlterOpLayout']):
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graph, lib, params = nnvm.compiler.build(
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net, target=target, shape={'data': input_shape}, params=params, dtype=dtype)
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@ -79,6 +62,7 @@ if __name__ == "__main__":
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lib.export_library(tmp.relpath(filename))
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# upload library and params
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print_progress("%-20s uploading..." % network)
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ctx = remote.context(str(target), 0)
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remote.upload(tmp.relpath(filename))
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rparams = {k: tvm.nd.array(v, ctx) for k, v in params.items()}
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@ -90,7 +74,7 @@ if __name__ == "__main__":
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module.set_input(**rparams)
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# evaluate
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print_progress("%-20s evaluating..." % network)
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ftimer = module.module.time_evaluator("run", ctx, number=args.number, repeat=3)
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prof_res = np.array(ftimer().results) * 1000 # multiply 1000 for converting to millisecond
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print("%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res)))
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@ -0,0 +1,72 @@
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"""Utility for benchmark"""
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import sys
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import nnvm
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def get_network(name, batch_size):
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"""Get the symbol definition and random weight of a network
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Parameters
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----------
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name: str
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The name of the network, can be 'resnet-18', 'resnet-50', 'vgg-16', 'inception_v3', 'mobilenet', ...
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batch_size:
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batch size
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Returns
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-------
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net: nnvm.symbol
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The NNVM symbol of network definition
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params: dict
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The random parameters for benchmark
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input_shape: tuple
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The shape of input tensor
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output_shape: tuple
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The shape of output tensor
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"""
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input_shape = (batch_size, 3, 224, 224)
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output_shape = (batch_size, 1000)
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if "resnet" in name:
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n_layer = int(name.split('-')[1])
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net, params = nnvm.testing.resnet.get_workload(num_layers=n_layer, batch_size=batch_size)
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elif "vgg" in name:
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n_layer = int(name.split('-')[1])
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net, params = nnvm.testing.vgg.get_workload(num_layers=n_layer, batch_size=batch_size)
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elif name == 'mobilenet':
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net, params = nnvm.testing.mobilenet.get_workload(batch_size=batch_size)
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elif "squeezenet" in name:
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version = name.split("_v")[1]
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net, params = nnvm.testing.squeezenet.get_workload(batch_size=batch_size, version=version)
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elif name == 'inception_v3':
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input_shape = (1, 3, 299, 299)
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net, params = nnvm.testing.inception_v3.get_workload(batch_size=batch_size)
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elif name == 'custom':
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# an example for custom network
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from nnvm.testing import utils
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net = nnvm.sym.Variable('data')
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net = nnvm.sym.conv2d(net, channels=4, kernel_size=(3,3), padding=(1,1))
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net = nnvm.sym.flatten(net)
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net = nnvm.sym.dense(net, units=1000)
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net, params = utils.create_workload(net, batch_size, (3, 224, 224))
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elif name == 'mxnet':
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# an example for mxnet model
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from mxnet.gluon.model_zoo.vision import get_model
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block = get_model('resnet18_v1', pretrained=True)
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net, params = nnvm.frontend.from_mxnet(block)
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net = nnvm.sym.softmax(net)
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else:
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raise ValueError("Unsupported network: " + name)
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return net, params, input_shape, output_shape
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def print_progress(msg):
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"""print progress message
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Parameters
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----------
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msg: str
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The message to print
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"""
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sys.stdout.write(msg + "\r")
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sys.stdout.flush()
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@ -4,6 +4,7 @@ from __future__ import absolute_import as _abs
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from .config import ctx_list
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from .utils import create_workload
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from . import mobilenet
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from . import mobilenet_v2
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from . import mlp
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from . import resnet
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from . import vgg
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@ -0,0 +1,51 @@
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"""
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MobileNetV2, load model from gluon model zoo
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Reference:
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Inverted Residuals and Linear Bottlenecks:
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Mobile Networks for Classification, Detection and Segmentation
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https://arxiv.org/abs/1801.04381
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"""
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from .utils import create_workload
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from ..frontend.mxnet import _from_mxnet_impl
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def get_workload(batch_size, num_classes=1000, multiplier=1.0, dtype="float32"):
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"""Get benchmark workload for mobilenet
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Parameters
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----------
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batch_size : int
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The batch size used in the model
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num_classes : int, optional
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Number of classes
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multiplier : tuple, optional
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The input image shape
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dtype : str, optional
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The data type
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Returns
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-------
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net : nnvm.Symbol
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The computational graph
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params : dict of str to NDArray
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The parameters.
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"""
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import mxnet as mx
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from mxnet.gluon.model_zoo.vision.mobilenet import MobileNetV2
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image_shape = (1, 3, 224, 224)
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block = MobileNetV2(multiplier=multiplier, classes=num_classes)
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data = mx.sym.Variable('data')
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sym = block(data)
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sym = mx.sym.SoftmaxOutput(sym)
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net = _from_mxnet_impl(sym, {})
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return create_workload(net, batch_size, image_shape[1:], dtype)
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@ -8,16 +8,22 @@ TVM will download these parameters for you when you create the target for the fi
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import logging
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import os
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import json
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import sys
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from .task import ApplyHistoryBest
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from .. import target as _target
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from ..contrib.util import tempdir
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from ..contrib.download import download
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# root path to store TopHub files
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AUTOTVM_TOPHUB_ROOT_PATH = os.path.join(os.path.expanduser('~'), ".tvm", "tophub")
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# the version of each package
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PACKAGE_VERSION = {
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'vta': "v0.01",
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'arm_cpu': "v0.01",
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'cuda': "v0.01",
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}
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logger = logging.getLogger('autotvm')
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def _alias(name):
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@ -30,7 +36,8 @@ def _alias(name):
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def context(target, extra_files=None):
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"""Return the dispatch context with pre-tuned parameters.
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The corresponding downloaded *.log files under tophub root path will be loaded.
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This function will load the corresponding *.log files in AUTOTVM_TOPHUB_ROOT_PATH.
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If cannot find them, it will download them from TopHub github repo.
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Users can also add their own files in argument `extra_files`.
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Parameters
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@ -40,21 +47,24 @@ def context(target, extra_files=None):
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extra_files: list of str, optional
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Extra log files to load
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"""
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rootpath = AUTOTVM_TOPHUB_ROOT_PATH
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best_context = ApplyHistoryBest([])
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if isinstance(target, str):
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target = _target.create(target)
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big_target = str(target).split()[0]
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if os.path.isfile(os.path.join(rootpath, big_target + ".log")):
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best_context.load(os.path.join(rootpath, big_target + ".log"))
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possible_names = [str(target).split()[0]]
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for opt in target.options:
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if opt.startswith("-device"):
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model = _alias(opt[8:])
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if os.path.isfile(os.path.join(rootpath, model) + ".log"):
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best_context.load(os.path.join(rootpath, model) + ".log")
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device = _alias(opt[8:])
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possible_names.append(device)
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all_packages = list(PACKAGE_VERSION.keys())
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for name in possible_names:
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if name in all_packages:
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check_backend(name)
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filename = "%s_%s.log" % (name, PACKAGE_VERSION[name])
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best_context.load(os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, filename))
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if extra_files:
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for filename in extra_files:
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@ -63,12 +73,39 @@ def context(target, extra_files=None):
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return best_context
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def download_package(backend):
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"""Download pre-tuned parameters of operators for a backend
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def check_backend(backend):
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"""Check whether have pre-tuned parameters of the certain target.
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If not, will download it.
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Parameters
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----------
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backend: str
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The name of backend.
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"""
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backend = _alias(backend)
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assert backend in PACKAGE_VERSION, 'Cannot find backend "%s" in TopHub' % backend
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version = PACKAGE_VERSION[backend]
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package_name = "%s_%s.log" % (backend, version)
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if os.path.isfile(os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, package_name)):
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return
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if sys.version_info >= (3,):
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import urllib.request as urllib2
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else:
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import urllib2
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try:
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download_package(package_name)
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except urllib2.URLError as e:
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logging.warning("Failed to download tophub package for %s: %s", backend, e)
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def download_package(package_name):
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"""Download pre-tuned parameters of operators for a backend
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Parameters
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----------
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package_name: str
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The name of package
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"""
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rootpath = AUTOTVM_TOPHUB_ROOT_PATH
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@ -81,54 +118,6 @@ def download_package(backend):
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if not os.path.isdir(path):
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os.mkdir(path)
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backend = _alias(backend)
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logger.info("Download pre-tuned parameters for %s", backend)
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download("https://raw.githubusercontent.com/uwsaml/tvm-distro/master/tophub/%s.log" % backend,
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os.path.join(rootpath, backend + ".log"), True, verbose=0)
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def check_package(backend):
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"""Check whether have pre-tuned parameters of the certain target.
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If not, will download it.
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Parameters
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----------
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backend: str
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The name of package
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"""
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backend = _alias(backend)
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if os.path.isfile(os.path.join(AUTOTVM_TOPHUB_ROOT_PATH, backend + ".log")):
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return
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if sys.version_info >= (3,):
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import urllib.request as urllib2
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else:
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import urllib2
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try:
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download_package(backend)
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except urllib2.URLError:
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logging.warning("Failed to download tophub package for %s", backend)
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def list_packages():
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"""List all available pre-tuned op parameters for targets
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Returns
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-------
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ret: List
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All available packets
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"""
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path = tempdir()
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filename = path.relpath("info.json")
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logger.info("Download meta info for pre-tuned parameters")
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download("https://raw.githubusercontent.com/uwsaml/tvm-distro/master/tophub/info.json",
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filename, True, verbose=0)
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with open(filename, "r") as fin:
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text = "".join(fin.readlines())
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info = json.loads(text)
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keys = list(info.keys())
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keys.sort()
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return [(k, info[k]) for k in keys]
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logger.info("Download pre-tuned parameters package %s", package_name)
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download("https://raw.githubusercontent.com/uwsaml/tvm-distro/master/tophub/%s"
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% package_name, os.path.join(rootpath, package_name), True, verbose=0)
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|
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@ -1,37 +0,0 @@
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# pylint: disable=invalid-name
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"""Download pre-tuned parameters of ops"""
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import argparse
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import logging
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from ..autotvm.tophub import list_packages, download_package
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-d", "--download", type=str, nargs='+',
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help="The targets to download. Use 'all' to download for all targets")
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parser.add_argument("-l", "--list", action='store_true', help="List available packages")
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args = parser.parse_args()
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logging.basicConfig(level=logging.INFO)
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if args.list:
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info = list_packages()
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print("\n%-20s %-20s" % ("Target", "Size"))
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print("-" * 41)
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for target, info in info:
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print("%-20s %-20s" % (target, "%.2f MB" % (info['size']/1000000)))
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elif args.download:
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info = list_packages()
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all_targets = [x[0] for x in info]
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if 'all' in args.download:
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targets = all_targets
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else:
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targets = args.download
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for t in targets:
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if t not in all_targets:
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print("Warning : cannot find tuned parameters of " + t + ". (ignored)")
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download_package(t)
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else:
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parser.print_help()
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@ -313,7 +313,7 @@ class Server(object):
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self.use_popen = use_popen
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if silent:
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logger.setLevel(logging.WARN)
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logger.setLevel(logging.ERROR)
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if use_popen:
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cmd = [sys.executable,
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|
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@ -425,8 +425,6 @@ def arm_cpu(model='unknown', options=None):
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options : str or list of str
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Additional options
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"""
|
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from . import autotvm
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trans_table = {
|
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"pixel2": ["-model=snapdragon835", "-target=arm64-linux-android -mattr=+neon"],
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"mate10": ["-model=kirin970", "-target=arm64-linux-android -mattr=+neon"],
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@ -439,9 +437,6 @@ def arm_cpu(model='unknown', options=None):
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}
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pre_defined_opt = trans_table.get(model, ["-model=%s" % model])
|
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|
||||
# download pre-tuned parameters for arm_cpu if there is not any.
|
||||
autotvm.tophub.check_package('arm_cpu')
|
||||
|
||||
opts = ["-device=arm_cpu"] + pre_defined_opt
|
||||
opts = _merge_opts(opts, options)
|
||||
return _api_internal._TargetCreate("llvm", *opts)
|
||||
|
|
|
|||
|
|
@ -128,7 +128,7 @@ def test_cpu_conv2d():
|
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run_cpu_conv2d(env, remote, key, batch_size, wl)
|
||||
|
||||
# load pre-tuned operator parameters for ARM CPU
|
||||
autotvm.tophub.check_package('vta')
|
||||
autotvm.tophub.check_backend('vta')
|
||||
with autotvm.tophub.context('llvm -device=vtacpu'):
|
||||
vta.testing.run(_run)
|
||||
|
||||
|
|
|
|||
|
|
@ -154,7 +154,7 @@ for file in [categ_fn, graph_fn, params_fn]:
|
|||
synset = eval(open(os.path.join(data_dir, categ_fn)).read())
|
||||
|
||||
# Download pre-tuned op parameters of conv2d for ARM CPU used in VTA
|
||||
autotvm.tophub.check_package('vta')
|
||||
autotvm.tophub.check_backend('vta')
|
||||
|
||||
|
||||
######################################################################
|
||||
|
|
|
|||
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