ARM CPU自动调度神经网络

ARM CPU自动调度神经网络

对特定设备和工作负载进行自动调度，对于获得最佳性能至关重要。通过RPC使用自动调度器为ARM CPU调度整个神经网络。

为了自动调度神经网络，将网络划分为小的子图，进行独立调度。每个子图被视为一个搜索任务。任务调度程序对时间进行分片，为这些任务动态分配时间资源。任务调度程序预测每个任务对端到端执行时间的影响，确定最大程度地减少执行时间的任务的优先级。

对于每个子图，使用compute声明tvm/python/topi，获取张量表达式形式的计算DAG。使用自动调度器来构造此DAG的搜索空间，搜索良好的调度（低级优化）。

与手动模板定义搜索空间的基于模板的autotvm不同，自动调度程序不需要任何调度模板。换句话说，自动调度程序仅在其中使用计算声明tvm/python/topi，而不使用现有的调度模板。

本文无法在Windows或最新版本的macOS上运行。要使其运行，需要将本文的内容包装在一个if __name__ == "__main__":块中。

import numpy as np

import tvm

from tvm import relay, auto_scheduler

import tvm.relay.testing

from tvm.contrib import graph_runtime

from tvm.contrib.utils import tempdir

定义网络

首先，需要使用Relay中继前端API定义网络。从加载一些预定义的网络tvm.relay.testing。还从MXNet，ONNX，PyTorch和TensorFlow加载模型。

对于卷积神经网络，尽管自动调度程序在任何布局下正常工作，使用NHWC布局通常实现最佳性能。使用自动调度程序对NHWC布局实施了更多优化。建议将模型转换为NHWC布局以使用自动调度程序。使用ConvertLayout传递在TVM中进行布局转换。

def get_network(name, batch_size, layout="NHWC", dtype="float32"):

"""Get the symbol definition and random weight of a network"""

# auto-scheduler prefers NHWC layout

if layout == "NHWC":

image_shape = (224, 224, 3)

elif layout == "NCHW":

image_shape = (3, 224, 224)

else:

raise ValueError("Invalid layout: " + layout)

input_shape = (batch_size,) + image_shape

output_shape = (batch_size, 1000)

if name.startswith("resnet-"):

n_layer = int(name.split("-")[1])

mod, params = relay.testing.resnet.get_workload(

num_layers=n_layer,

batch_size=batch_size,

layout=layout,

dtype=dtype,

image_shape=image_shape,

)

elif name.startswith("resnet3d-"):

n_layer = int(name.split("-")[1])

mod, params = relay.testing.resnet.get_workload(

num_layers=n_layer,

batch_size=batch_size,

layout=layout,

dtype=dtype,

image_shape=image_shape,

)

elif name == "mobilenet":

mod, params = relay.testing.mobilenet.get_workload(

batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape

)

elif name == "squeezenet_v1.1":

assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"

mod, params = relay.testing.squeezenet.get_workload(

version="1.1",

batch_size=batch_size,

dtype=dtype,

image_shape=image_shape,

)

elif name == "inception_v3":

input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3)

mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)

elif name == "mxnet":

# an example for mxnet model

from mxnet.gluon.model_zoo.vision import get_model

assert layout == "NCHW"

block = get_model("resnet50_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 = tvm.IRModule.from_expr(net)

return mod, params, input_shape, output_shape

启动RPC跟踪器

TVM使用RPC会话与ARM板进行通信。在调度期间，调谐器会将生成的代码发送到电路板上，测量电路板上的代码速度。

为了扩大调度范围，TVM使用RPC Tracker来管理分布式设备。RPC跟踪器是一个集中式控制器节点。将所有设备注册到跟踪器。例如，如果有10部电话，全部注册到跟踪器，并行运行10次测量，从而加快了调谐过程。

要启动RPC跟踪器，在主机上运行此命令。在整个调度过程中都需要使用跟踪器，因此需要为此命令打开一个新终端：

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

预期的输出是

INFO:RPCTracker:bind to 0.0.0.0:9190

将设备注册到RPC跟踪器

现在，将设备注册到跟踪器。第一步是为ARM设备构建TVM运行时。

对于Linux：遵循本节在设备上构建TVM运行时，以在设备上构建TVM运行时。然后通过以下方式将设备注册到跟踪器
python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rasp4b-64

（用[HOST_IP]主机的IP地址代替）

对于Android：在Android设备上安装TVM RPC APK。确保通过android rpc测试。已经注册了设备。在调度过程中，必须转到开发人员选项，启用“更改时保持屏幕唤醒”并为手机充电以使其稳定。

注册设备后，通过查询rpc_tracker进行确认

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

例如，如果有2个Huawei mate10 pro，11个具有64位操作系统的Raspberry Pi 4B和2个rk3399，则输出是

Queue Status

----------------------------------

key total free pending

----------------------------------

mate10pro 2 2 0

rk3399 2 2 0

rasp4b-64 11 11 0

----------------------------------

将多个设备注册到跟踪器，以加快调谐过程中的测量速度。

设置调度选项

调度之前，应该应用一些配置。以带有64位操作系统（Ubuntu 20.04）的Raspberry Pi 4b 4GB主板为例。在设置中，应该相应地修改目标和device_key。如果使用的是Android手机，use_ndk设置为True。

#### DEVICE CONFIG ####

# 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.

# FIXME(tmoreau89, merrymercy): We leave '-device=arm_cpu' out of the target string

# because we're sharing x86 op strategy.

target = tvm.target.Target("llvm -mtriple=aarch64-linux-gnu -mattr=+neon")

# Also replace this with the device key in your tracker

device_key = "rasp4b-64"

# Set this to True if you use ndk tools for cross compiling

# And also set the environment variable below to point to the cross compiler

use_ndk = False

# os.environ["TVM_NDK_CC"] = "/usr/bin/aarch64-linux-gnu-g++"

#### TUNING OPTION ####

network = "mobilenet"

batch_size = 1

layout = "NHWC"

dtype = "float32"

log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)

提取搜索任务

接下来，从网络中提取搜索任务及其权重。任务的权重是该任务的子图在整个网络中的出现次数。通过使用权重，将网络的端到端延迟近似为，sum(latency[t] * weight[t])其中是任务的延迟，latency[t]weight[t]是任务的权重。任务调度程序将仅优化此目标。

# Extract tasks from the network

print("Extract tasks...")

mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)

tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)

for idx, task in enumerate(tasks):

print("========== Task %d (workload key: %s) ==========" % (idx, task.workload_key))

print(task.compute_dag)

出去：

Extract tasks...

========== Task 0 (workload key: ["d7b65649a4dd54becea0a52aabbc5af5", 1, 1000, 1, 1000]) ==========

placeholder = PLACEHOLDER [1, 1000]

T_softmax_maxelem(i0) max= placeholder[i0, k]

T_softmax_exp(i0, i1) = tir.exp((placeholder[i0, i1] - T_softmax_maxelem[i0]))

T_softmax_expsum(i0) += T_softmax_exp[i0, k]

T_softmax_norm(i0, i1) = (T_softmax_exp[i0, i1]/T_softmax_expsum[i0])

========== Task 1 (workload key: ["9847f8cc0b305137f49f2c5c0c8ab25d", 1, 1024, 1000, 1024, 1000, 1, 1000]) ==========

placeholder = PLACEHOLDER [1, 1024]

placeholder = PLACEHOLDER [1000, 1024]

T_dense(i, j) += (placeholder[i, k]*placeholder[j, k])

placeholder = PLACEHOLDER [1000]

T_add(ax0, ax1) = (T_dense[ax0, ax1] + placeholder[ax1])

========== Task 2 (workload key: ["69115f188984ae34ede37c3b8ca40b43", 1, 7, 7, 1024, 1, 1, 1, 1024]) ==========

placeholder = PLACEHOLDER [1, 7, 7, 1024]

tensor(ax0, ax1, ax2, ax3) += placeholder[ax0, ((ax1*7) + rv0), ((ax2*7) + rv1), ax3]

tensor(ax0, ax1, ax2, ax3) = (tensor[ax0, ax1, ax2, ax3]/(float32((select((bool)1, ((ax1 + 1)*7), (((ax1 + 1)*7) + 1)) - (ax1*7)))*float32((select((bool)1, ((ax2 + 1)*7), (((ax2 + 1)*7) + 1)) - (ax2*7)))))

========== Task 3 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 7, 7, 1024, 1, 1, 1024, 1024, 1, 1, 1, 1024, 1, 7, 7, 1024]) ==========

placeholder = PLACEHOLDER [1, 7, 7, 1024]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 1024, 1024]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 1024]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 4 (workload key: ["06fce76bd84cb904eee50b905ca9449a", 1, 7, 7, 1024, 3, 3, 1024, 1, 1, 1, 1, 1024, 1, 7, 7, 1024]) ==========

placeholder = PLACEHOLDER [1, 7, 7, 1024]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 1024, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 1024]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 5 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 7, 7, 512, 1, 1, 512, 1024, 1, 1, 1, 1024, 1, 7, 7, 1024]) ==========

placeholder = PLACEHOLDER [1, 7, 7, 512]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 512, 1024]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 1024]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 6 (workload key: ["c87ba68bc180312f5716af09a77ca15b", 1, 14, 14, 512, 3, 3, 512, 1, 1, 1, 1, 512, 1, 7, 7, 512]) ==========

placeholder = PLACEHOLDER [1, 14, 14, 512]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 512, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 512]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 7 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 14, 14, 512, 1, 1, 512, 512, 1, 1, 1, 512, 1, 14, 14, 512]) ==========

placeholder = PLACEHOLDER [1, 14, 14, 512]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 512, 512]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 512]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 8 (workload key: ["06fce76bd84cb904eee50b905ca9449a", 1, 14, 14, 512, 3, 3, 512, 1, 1, 1, 1, 512, 1, 14, 14, 512]) ==========

placeholder = PLACEHOLDER [1, 14, 14, 512]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 512, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 512]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 9 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 14, 14, 256, 1, 1, 256, 512, 1, 1, 1, 512, 1, 14, 14, 512]) ==========

placeholder = PLACEHOLDER [1, 14, 14, 256]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 256, 512]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 512]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 10 (workload key: ["c87ba68bc180312f5716af09a77ca15b", 1, 28, 28, 256, 3, 3, 256, 1, 1, 1, 1, 256, 1, 14, 14, 256]) ==========

placeholder = PLACEHOLDER [1, 28, 28, 256]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 256, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 256]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 11 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 28, 28, 256, 1, 1, 256, 256, 1, 1, 1, 256, 1, 28, 28, 256]) ==========

placeholder = PLACEHOLDER [1, 28, 28, 256]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 256, 256]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 256]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 12 (workload key: ["06fce76bd84cb904eee50b905ca9449a", 1, 28, 28, 256, 3, 3, 256, 1, 1, 1, 1, 256, 1, 28, 28, 256]) ==========

placeholder = PLACEHOLDER [1, 28, 28, 256]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 256, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 256]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 13 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 28, 28, 128, 1, 1, 128, 256, 1, 1, 1, 256, 1, 28, 28, 256]) ==========

placeholder = PLACEHOLDER [1, 28, 28, 128]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 128, 256]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 256]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 14 (workload key: ["c87ba68bc180312f5716af09a77ca15b", 1, 56, 56, 128, 3, 3, 128, 1, 1, 1, 1, 128, 1, 28, 28, 128]) ==========

placeholder = PLACEHOLDER [1, 56, 56, 128]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 128, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 128]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 15 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 56, 56, 128, 1, 1, 128, 128, 1, 1, 1, 128, 1, 56, 56, 128]) ==========

placeholder = PLACEHOLDER [1, 56, 56, 128]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 128, 128]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 128]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 16 (workload key: ["06fce76bd84cb904eee50b905ca9449a", 1, 56, 56, 128, 3, 3, 128, 1, 1, 1, 1, 128, 1, 56, 56, 128]) ==========

placeholder = PLACEHOLDER [1, 56, 56, 128]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 128, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 128]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 17 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 56, 56, 64, 1, 1, 64, 128, 1, 1, 1, 128, 1, 56, 56, 128]) ==========

placeholder = PLACEHOLDER [1, 56, 56, 64]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 64, 128]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 128]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 18 (workload key: ["c87ba68bc180312f5716af09a77ca15b", 1, 112, 112, 64, 3, 3, 64, 1, 1, 1, 1, 64, 1, 56, 56, 64]) ==========

placeholder = PLACEHOLDER [1, 112, 112, 64]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 113)) && (i2 >= 1)) && (i2 < 113)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 64, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, ((i*2) + di), ((j*2) + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 64]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 19 (workload key: ["6b7583cf23c7c37d3212cad9d06e58c1", 1, 112, 112, 32, 1, 1, 32, 64, 1, 1, 1, 64, 1, 112, 112, 64]) ==========

placeholder = PLACEHOLDER [1, 112, 112, 32]

PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]

placeholder = PLACEHOLDER [1, 1, 32, 64]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 1, 1, 64]

T_add(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 20 (workload key: ["06fce76bd84cb904eee50b905ca9449a", 1, 112, 112, 32, 3, 3, 32, 1, 1, 1, 1, 32, 1, 112, 112, 32]) ==========

placeholder = PLACEHOLDER [1, 112, 112, 32]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 113)) && (i2 >= 1)) && (i2 < 113)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 32, 1]

DepthwiseConv2d(b, i, j, c) += (PaddedInput[b, (i + di), (j + dj), c]*placeholder[di, dj, c, 0])

placeholder = PLACEHOLDER [1, 1, 1, 32]

T_add(ax0, ax1, ax2, ax3) = (DepthwiseConv2d[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

========== Task 21 (workload key: ["98418eda02701ddd175ad50e364a0638", 1, 224, 224, 3, 3, 3, 3, 32, 1, 112, 1, 1, 1, 112, 1, 1, 1, 112, 112, 32]) ==========

placeholder = PLACEHOLDER [1, 224, 224, 3]

PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 225)) && (i2 >= 1)) && (i2 < 225)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)

placeholder = PLACEHOLDER [3, 3, 3, 32]

Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])

placeholder = PLACEHOLDER [1, 112, 1, 1]

T_multiply(ax0, ax1, ax2, ax3) = (Conv2dOutput[ax0, ax1, ax2, ax3]*placeholder[ax0, ax1, 0, 0])

placeholder = PLACEHOLDER [1, 112, 1, 1]

T_add(ax0, ax1, ax2, ax3) = (T_multiply[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, 0, 0])

T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

调优与评估

设置一些选项来优化和启动搜索任务

num_measure_trials是在调度期间使用的测量试验的次数。将其设置为较小的数字（例如200）以进行快速演示。实际上，建议将其设置为800 * len(tasks)，通常足以使搜索收敛。例如，resnet-50中有29个任务，因此将其设置为20000。根据时间预算调度。
此外，还用RecordToFile将测量记录转储到日志文件中，些测量记录可用于最好地查询历史记录，恢复搜索以及以后进行更多分析。
有关更多参数auto_scheduler.TuningOptions，参见auto_scheduler.LocalRunner。

自动调度后，使用发现的最佳调度来编译网络。在自动调度期间，所有测量记录都将转储到日志文件中，因此读取日志文件并加载最佳调度。

def tune_and_evaluate():

print("Begin tuning...")

tuner = auto_scheduler.TaskScheduler(tasks, task_weights)

tune_option = auto_scheduler.TuningOptions(

num_measure_trials=200, # change this to 20000 to achieve the best performance

runner=auto_scheduler.RPCRunner(

device_key,

host="0.0.0.0",

port=9191,

timeout=30,

repeat=1,

min_repeat_ms=200,

enable_cpu_cache_flush=True,

measure_callbacks=[auto_scheduler.RecordToFile(log_file)],

)

tuner.tune(tune_option)

# Compile with the history best

print("Compile...")

with auto_scheduler.ApplyHistoryBest(log_file):

with tvm.transform.PassContext(

opt_level=3, config={"relay.backend.use_auto_scheduler": True}

lib = relay.build(mod, target=target, params=params)

# Export library

tmp = tempdir()

if use_ndk:

from tvm.contrib import ndk

filename = "net.so"

lib.export_library(tmp.relpath(filename), ndk.create_shared)

else:

filename = "net.tar"

lib.export_library(tmp.relpath(filename))

# Upload module to device

print("Upload...")

remote = auto_scheduler.utils.request_remote(device_key, "0.0.0.0", 9191, timeout=10000)

remote.upload(tmp.relpath(filename))

rlib = remote.load_module(filename)

# Create graph runtime

ctx = remote.cpu()

module = graph_runtime.GraphModule(rlib["default"](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, repeat=3, min_repeat_ms=500)

prof_res = np.array(ftimer().results) * 1e3 # 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 the server doesn't have a Raspberry Pi,

# or device tracker running.

# Uncomment the following line to run it by yourself.

# tune_and_evaluate()

笔记

调度期间解释打印的信息

在调度期间，控制台上会打印很多信息。用于调试目的。最重要的信息是任务调度程序的输出。下表是示例输出。

----------------------------------------------------------------------

------------------------------ [ Task Scheduler ]

----------------------------------------------------------------------

-------------------------------------------------

| 0 | 0.013 | 0.31 | 64 |

| 1 | 0.845 | 2.43 | 448 |

| 2 | 0.046 | -0.00 | 64 |

| 3 | 4.194 | 24.53 | 2112 |

| 4 | 0.109 | 9.21 | 64 |

| 5 | 1.759 | 29.27 | 896 |

| 6 | 0.083 | 6.01 | 64 |

| 7 | 3.084 | 33.38 | 7680 |

| 8 | 0.136 | 14.78 | 384 |

| 9 | 1.349 | 38.23 | 768 |

| 10 | 0.133 | 7.55 | 128 |

| 11 | 2.747 | 37.56 | 1536 |

| 12 | 0.338 | 11.87 | 192 |

| 13 | 1.295 | 40.00 | 704 |

| 14 | 0.482 | 4.16 | 256 |

| 15 | 2.686 | 38.56 | 1344 |

| 16 | 0.884 | 9.08 | 448 |

| 17 | 1.332 | 39.18 | 704 |

| 18 | 1.045 | 3.84 | 576 |

| 19 | 1.391 | 38.09 | 704 |

| 20 | 0.777 | 10.34 | 448 |

| 21 | 0.739 | 30.97 | 448 |

-------------------------------------------------

Estimated total latency: 38.347 ms Trials: 19992 Used time : 19260 s Next ID: 3

下表列出了所有任务的延迟和（估计）速度。列出了所有任务的测量试验分配。最后一行显示这些任务的总加权延迟，粗略估计网络的端到端执行时间。最后一行还显示测量试验的总数，自动调度所花费的总时间，以及要调度的下一个任务的ID。

将出现一些“ dmlc :: Error”错误，因为自动调度程序将尝试一些无效的调度。如果继续进行调度，则放心地忽略，因为这些错误与主要过程是隔离的。

笔记

提前终止调度

通过强制终止此过程来提前终止调度。只要在日志文件中为每个任务至少获得一个有效的调度，就应该能够进行编译（下面的部分）。

其他技巧

在调度过程中，自动调度器需要编译许多程序并从中提取功能。该部分占用大量CPU，建议使用具有多个内核的高性能CPU，以加快搜索速度。
用来提取大型日志文件，而仅保存最有用的记录。python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json
从上一个日志文件继续搜索。load_log_file在function中创建任务调度程序时，只需添加一个新参数run_tuning。

tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)

如果有多个目标CPU，则全部用于测量以并行化测量。检查本节以了解如何使用RPC跟踪器和RPC服务器。要在自动调度使用RPC跟踪，用auto_scheduler.RPCRunner更换转轮TuningOptions。

posted @ 2021-03-27 05:49 吴建明wujianming 阅读(311) 评论(0) 编辑收藏举报

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