NVIDIA GPU自动调度神经网络
对特定设备和工作负载进行自动调整对于获得最佳性能至关重要。这是有关如何使用自动调度器为NVIDIA GPU调整整个神经网络。
为了自动调整神经网络，将网络划分为小的子图，并对其进行独立调整。每个子图被视为一个搜索任务。任务调度程序可以对时间进行分片，并为这些任务动态分配时间资源。任务调度程序可以预测每个任务对端到端执行时间的影响，确定可以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，并搜索良好的调度（低级优化）。
与依靠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tvm/python/topi计算声明，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
定义网络
需要使用中继前端API定义网络。可以加载一些预定义的网络tvm.relay.testing。从MXNet，ONNX，PyTorch和TensorFlow加载模型。
对于卷积神经网络，尽管自动调度程序可以在任何布局下正常工作，但发现使用NHWC布局通常可以实现最佳性能。还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。可以使用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 modelfrom mxnet.gluon.model_zoo.vision import get_modelassert layout == "NCHW"block = get_model("resnet18_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

Define the neural network and compilation target

network = “resnet-18”
batch_size = 1
layout = “NHWC”
target = tvm.target.Target(“cuda”)
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，可以将网络的端到端延迟近似为，其中sum(latency[t] * weight[t])latency[t]是任务的延迟，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)
输出：
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, 512, 1000, 512, 1000, 1, 1000]) ==========
placeholder = PLACEHOLDER [1, 512]
placeholder = PLACEHOLDER [1000, 512]
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, 512, 1, 1, 1, 512]) ==========
placeholder = PLACEHOLDER [1, 7, 7, 512]
tensor(ax0, ax1, ax2, ax3) += placeholder[ax0, ((ax17) + rv0), ((ax27) + rv1), ax3]
tensor(ax0, ax1, ax2, ax3) = (tensor[ax0, ax1, ax2, ax3]/(float32((select((bool)1, ((ax1 + 1)*7), (((ax1 + 1)7) + 1)) - (ax17)))*float32((select((bool)1, ((ax2 + 1)*7), (((ax2 + 1)7) + 1)) - (ax27)))))

========== Task 3 (workload key: [“ad6cecbf5d85cb1cda3c2bb7af170211”, 1, 7, 7, 512, 4, 4, 512, 512, 1, 7, 7, 512, 1, 1, 1, 512, 1, 1, 1, 512, 1, 7, 7, 512]) ==========
placeholder = PLACEHOLDER [1, 7, 7, 512]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*2) + eps), ((floormod(p, 4)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 512, 512]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n4)*4) + (floordiv(h, 2)*4)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 7, 7, 512]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
placeholder = PLACEHOLDER [1, 1, 1, 512]
T_multiply(ax0, ax1, ax2, ax3) = (T_add[ax0, ax1, ax2, ax3]*placeholder[ax0, 0, 0, ax3])
placeholder = PLACEHOLDER [1, 1, 1, 512]
T_add(ax0, ax1, ax2, ax3) = (T_multiply[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: [“3a69f9fbc63760d99e36b4c17b3bfc57”, 1, 7, 7, 512, 4, 4, 512, 512, 1, 1, 1, 512, 1, 7, 7, 512]) ==========
placeholder = PLACEHOLDER [1, 7, 7, 512]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*2) + eps), ((floormod(p, 4)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 512, 512]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n4)*4) + (floordiv(h, 2)*4)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 1, 1, 512]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[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: [“d730bcd28f0920f6b97245e2a11bd8d6”, 1, 7, 7, 512, 4, 4, 512, 512, 1, 7, 7, 512, 1, 7, 7, 512]) ==========
placeholder = PLACEHOLDER [1, 7, 7, 512]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 8)) && (i2 >= 1)) && (i2 < 8)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 16), ((floormod(floordiv(p, 4), 4)*2) + eps), ((floormod(p, 4)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 512, 512]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n4)*4) + (floordiv(h, 2)*4)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 7, 7, 512]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])

========== Task 6 (workload key: [“12b88bedece6984af589a28b43e0f3c4”, 1, 14, 14, 256, 3, 3, 256, 512, 1, 1, 1, 512, 1, 7, 7, 512]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
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, 256, 512]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy2) + ry), ((xx2) + 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 7 (workload key: [“f3b6c10fcc6ce01ff01add933e4d21e9”, 1, 14, 14, 256, 4, 4, 256, 256, 1, 14, 14, 256, 1, 1, 1, 256, 1, 14, 14, 256]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*2) + eps), ((floormod(p, 7)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 256, 256]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n7)*7) + (floordiv(h, 2)*7)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 14, 14, 256]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
placeholder = PLACEHOLDER [1, 1, 1, 256]
T_add(ax0, ax1, ax2, ax3) = (T_add[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: [“b8b52b9be9df6102466a22a014c44c1f”, 1, 14, 14, 256, 4, 4, 256, 256, 1, 1, 1, 256, 1, 14, 14, 256]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*2) + eps), ((floormod(p, 7)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 256, 256]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n7)*7) + (floordiv(h, 2)*7)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 1, 1, 256]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[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: [“d374e472bd9d8164892b9e28a0a8cb59”, 1, 14, 14, 256, 4, 4, 256, 256, 1, 14, 14, 256, 1, 14, 14, 256]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 15)) && (i2 >= 1)) && (i2 < 15)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 49), ((floormod(floordiv(p, 7), 7)*2) + eps), ((floormod(p, 7)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 256, 256]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n7)*7) + (floordiv(h, 2)*7)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 14, 14, 256]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])

========== Task 10 (workload key: [“12b88bedece6984af589a28b43e0f3c4”, 1, 28, 28, 128, 3, 3, 128, 256, 1, 1, 1, 256, 1, 14, 14, 256]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
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, 128, 256]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy2) + ry), ((xx2) + 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 11 (workload key: [“c4500b4e2fd04e695c32d2f31bbdc14a”, 1, 28, 28, 128, 4, 4, 128, 128, 1, 28, 28, 128, 1, 1, 1, 128, 1, 28, 28, 128]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*2) + eps), ((floormod(p, 14)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 128, 128]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n14)*14) + (floordiv(h, 2)*14)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 28, 28, 128]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
placeholder = PLACEHOLDER [1, 1, 1, 128]
T_add(ax0, ax1, ax2, ax3) = (T_add[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: [“e4cdf917b876dbdd64488c3818d9c141”, 1, 28, 28, 128, 4, 4, 128, 128, 1, 1, 1, 128, 1, 28, 28, 128]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*2) + eps), ((floormod(p, 14)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 128, 128]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n14)*14) + (floordiv(h, 2)*14)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 1, 1, 128]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[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: [“dac19035dd5fe9424ee8617421b9c817”, 1, 28, 28, 128, 4, 4, 128, 128, 1, 28, 28, 128, 1, 28, 28, 128]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 29)) && (i2 >= 1)) && (i2 < 29)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*2) + eps), ((floormod(p, 14)*2) + nu), ci]
B(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 4) == 2)), …(OMITTED)… ormod(i, 4) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [4, 4, 128, 128]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 4) == 3) && (floormod(j, 2) == 1)), 1f, select(((floormod(i, 4) == 3) && (floormod(j, 2) == 0)), …(OMITTED)… ct(((floormod(i, 4) == 0) && (floormod(j, 2) == 1)), 0f, select(((floormod(i, 4) == 0) && (floormod(j, 2) == 0)), 1f, 0f))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 2), floormod(w, 2), ((((n14)*14) + (floordiv(h, 2)*14)) + floordiv(w, 2)), co]
placeholder = PLACEHOLDER [1, 28, 28, 128]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])

========== Task 14 (workload key: [“12b88bedece6984af589a28b43e0f3c4”, 1, 56, 56, 64, 3, 3, 64, 128, 1, 1, 1, 128, 1, 28, 28, 128]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
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, 64, 128]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy2) + ry), ((xx2) + 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 15 (workload key: [“1e3c4211ffd2f2db91078ae4d04b779d”, 1, 56, 56, 64, 6, 6, 64, 64, 1, 56, 56, 64, 1, 1, 1, 64, 1, 56, 56, 64]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*4) + eps), ((floormod(p, 14)*4) + nu), ci]
B(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 6) == 5)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 6) == 4)), …(OMITTED)… (floormod(j, 6) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 6) == 0)), 1f, 0f))))))))))))))))))))))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [6, 6, 64, 64]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 4) == 2)), …(OMITTED)… 6) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 4), floormod(w, 4), ((((n14)*14) + (floordiv(h, 4)*14)) + floordiv(w, 4)), co]
placeholder = PLACEHOLDER [1, 56, 56, 64]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])
placeholder = PLACEHOLDER [1, 1, 1, 64]
T_add(ax0, ax1, ax2, ax3) = (T_add[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: [“b818b53148cd450f86569dfc3e04cb8a”, 1, 56, 56, 64, 6, 6, 64, 64, 1, 1, 1, 64, 1, 56, 56, 64]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*4) + eps), ((floormod(p, 14)*4) + nu), ci]
B(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 6) == 5)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 6) == 4)), …(OMITTED)… (floormod(j, 6) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 6) == 0)), 1f, 0f))))))))))))))))))))))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [6, 6, 64, 64]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 4) == 2)), …(OMITTED)… 6) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 4), floormod(w, 4), ((((n14)*14) + (floordiv(h, 4)*14)) + floordiv(w, 4)), co]
placeholder = PLACEHOLDER [1, 1, 1, 64]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[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: [“3ea73fb9b0364374730d09e068821f95”, 1, 56, 56, 64, 6, 6, 64, 64, 1, 56, 56, 64, 1, 56, 56, 64]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
data_pad(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 1) && (i1 < 57)) && (i2 >= 1)) && (i2 < 57)), placeholder[i0, (i1 - 1), (i2 - 1), i3], 0f)
input_tile(eps, nu, p, ci) = data_pad[floordiv(p, 196), ((floormod(floordiv(p, 14), 14)*4) + eps), ((floormod(p, 14)*4) + nu), ci]
B(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 6) == 5)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 6) == 4)), …(OMITTED)… (floormod(j, 6) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 6) == 0)), 1f, 0f))))))))))))))))))))))))))))))))))))
data_pack(eps, nu, p, ci) += ((input_tile[r_a, r_b, p, ci]*B[r_a, eps])*B[r_b, nu])
placeholder = PLACEHOLDER [6, 6, 64, 64]
bgemm(eps, nu, p, co) += (data_pack[eps, nu, p, ci]*placeholder[eps, nu, co, ci])
A(i, j) = select(((floormod(i, 6) == 5) && (floormod(j, 4) == 3)), 1f, select(((floormod(i, 6) == 5) && (floormod(j, 4) == 2)), …(OMITTED)… 6) == 0) && (floormod(j, 4) == 1)), 0f, select(((floormod(i, 6) == 0) && (floormod(j, 4) == 0)), 1f, 0f))))))))))))))))))))))))
inverse(vh, vw, p, co) += ((bgemm[r_a, r_b, p, co]*A[r_a, vh])A[r_b, vw])
conv2d_winograd(n, h, w, co) = inverse[floormod(h, 4), floormod(w, 4), ((((n14)*14) + (floordiv(h, 4)*14)) + floordiv(w, 4)), co]
placeholder = PLACEHOLDER [1, 56, 56, 64]
T_add(ax0, ax1, ax2, ax3) = (conv2d_winograd[ax0, ax1, ax2, ax3] + placeholder[ax0, ax1, ax2, ax3])

========== Task 18 (workload key: [“a5612fdeb9db4d579a75ec225ea4c06a”, 1, 112, 112, 64, 1, 1, 1, 64, 1, 56, 56, 64]) ==========
placeholder = PLACEHOLDER [1, 112, 112, 64]
pad_temp(ax0, ax1, ax2, ax3) = tir.if_then_else(((((ax1 >= 1) && (ax1 < 113)) && (ax2 >= 1)) && (ax2 < 113)), placeholder[ax0, (ax1 - 1), (ax2 - 1), ax3], -3.40282e+38f)
tensor(ax0, ax1, ax2, ax3) max= pad_temp[ax0, ((ax12) + dh), ((ax22) + dw), ax3]
placeholder = PLACEHOLDER [1, 1, 1, 64]
T_add(ax0, ax1, ax2, ax3) = (tensor[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: [“12b88bedece6984af589a28b43e0f3c4”, 1, 224, 224, 3, 7, 7, 3, 64, 1, 1, 1, 64, 1, 112, 112, 64]) ==========
placeholder = PLACEHOLDER [1, 224, 224, 3]
PaddedInput(i0, i1, i2, i3) = tir.if_then_else(((((i1 >= 3) && (i1 < 227)) && (i2 >= 3)) && (i2 < 227)), placeholder[i0, (i1 - 3), (i2 - 3), i3], 0f)
placeholder = PLACEHOLDER [7, 7, 3, 64]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, ((yy2) + ry), ((xx2) + 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: [“7006235cfc29b73be524cf390ed5a977”, 1, 56, 56, 64, 1, 1, 64, 64, 1, 56, 56, 64]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
PaddedInput(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
placeholder = PLACEHOLDER [1, 1, 64, 64]
Conv2dOutput(nn, yy, xx, ff) += (PaddedInput[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])

========== Task 21 (workload key: [“f4380bb1dc62422a69ad4a1a9771f927”, 1, 56, 56, 64, 1, 1, 64, 128, 1, 28, 28, 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, ((yy2) + ry), ((xx2) + rx), rc]*placeholder[ry, rx, rc, ff])

========== Task 22 (workload key: [“f4380bb1dc62422a69ad4a1a9771f927”, 1, 28, 28, 128, 1, 1, 128, 256, 1, 14, 14, 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, ((yy2) + ry), ((xx2) + rx), rc]*placeholder[ry, rx, rc, ff])

========== Task 23 (workload key: [“f4380bb1dc62422a69ad4a1a9771f927”, 1, 14, 14, 256, 1, 1, 256, 512, 1, 7, 7, 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, ((yy2) + ry), ((xx2) + rx), rc]*placeholder[ry, rx, rc, ff])
开始调整
设置一些选项来优化和启动搜索任务
• measure_ctx启动不同的测量过程以提供隔离。保护主进程免受测量期间GPU崩溃的影响，避免其它运行时冲突。
• min_repeat_ms定义每次测量中一次“重复”的最小持续时间。这样可以预热GPU，对于获得准确的测量结果是必不可少的。通常，建议值> = 300毫秒。
• num_measure_trials是在调整期间可以使用的测量试验的次数。可以将其设置为较小的数字（例如200），进行快速演示。实际上，建议将其设置为900 * len(tasks)，使搜索收敛。例如，resnet-18中有24个任务，将其设置为20000。根据时间预算调整此参数。
• 将测量记录转储到日志文件RecordToFile中，这些测量记录可用于最好地查询历史记录，恢复搜索，进行更多分析。
• 有关更多参数auto_scheduler.TuningOptions， 请参见auto_scheduler.LocalRPCMeasureContext。
def run_tuning():
print(“Begin tuning…”)
measure_ctx = auto_scheduler.LocalRPCMeasureContext(repeat=1, min_repeat_ms=300, timeout=10)

tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
tune_option = auto_scheduler.TuningOptions(num_measure_trials=200,  # change this to 20000 to achieve the best performancerunner=measure_ctx.runner,measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)tuner.tune(tune_option)

We do not run the tuning in our webpage server since it takes too long.

Uncomment the following line to run it by yourself.

run_tuning()

笔记
调整期间说明打印的信息
在调整期间，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 ]

| ID | Latency (ms) | Speed (GFLOPS) | Trials |

| 0 | 0.005 | 0.88 | 64 |
| 1 | 0.010 | 99.10 | 64 |
| 2 | 0.006 | 0.00 | 64 |
| 3 | 0.145 | 979.78 | 384 |
| 4 | 0.130 | 1097.02 | 384 |
| 5 | 0.143 | 992.69 | 384 |
| 6 | 0.076 | 1526.86 | 192 |
| 7 | 0.115 | 999.44 | 320 |
| 8 | 0.079 | 1449.39 | 320 |
| 9 | 0.122 | 938.73 | 384 |
| 10 | 0.063 | 1832.98 | 192 |
| 11 | 0.072 | 1763.62 | 256 |
| 12 | 0.062 | 2036.40 | 192 |
| 13 | 0.068 | 1874.44 | 192 |
| 14 | 0.049 | 2346.50 | 128 |
| 15 | 0.076 | 1694.31 | 256 |
| 16 | 0.067 | 1933.30 | 448 |
| 17 | 0.076 | 1680.90 | 256 |
| 18 | 0.022 | 98.43 | 64 |
| 19 | 0.076 | 3112.55 | 192 |
| 20 | 0.013 | 2026.44 | 64 |
| 21 | 0.011 | 1136.69 | 64 |
| 22 | 0.013 | 992.47 | 64 |
| 23 | 0.020 | 627.56 | 64 |

Estimated total latency: 1.587 ms Trials: 4992 Used time : 13296 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，可以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，自动调整所花费的总时间，要调整的下一个任务的ID。
自动调度程序将尝试某些无效的调度，出现一些“ dmlc :: Error”和CUDA错误。继续进行调整，放心地忽略，这些错误与主要过程是隔离的。
笔记
提前终止调整
可以通过强制终止此过程来提前终止调整。在日志文件中为每个任务至少获得一个有效的调度，能够进行编译（下面的部分）。
编译和评估
自动调整后，可以使用发现的最佳调度表来编译网络。在自动调整期间，所有测量记录都将转储到日志文件中，读取日志文件并加载最佳调度。

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)

Create graph runtime

ctx = tvm.context(str(target), 0)
module = graph_runtime.GraphModule(lib"default")
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)))
输出：
Compile…
Evaluate inference time cost…
Mean inference time (std dev): 3.22 ms (0.02 ms)
其它技巧

  1. 调整过程中，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，加快搜索速度。
  2. 提取大型日志文件，仅保存最有用的记录。python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json
  3. 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)
  4. 如果有多个目标GPU，全部用于测量，并行化测量。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 。

NVIDIA GPU自动调度神经网络相关推荐

  1. GPU自动调度卷积层

    GPU自动调度卷积层 本文对GPU使用自动调度程序. 与依靠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模板.用户只需要编写计算声明,无需任何调度命令或模板.自动调度程序 ...

  2. 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 ...

  3. 为x86 CPU自动调度神经网络

    为x86 CPU自动调度神经网络 对特定设备和工作负载进行自动调试对于获得最佳性能至关重要.这是有关如何使用自动调度器为x86 CPU调试整个神经网络的文档. 为了自动调试神经网络,将网络划分为小的子 ...

  4. NVIDIA GPU的神经网络自动调度

    NVIDIA GPU的神经网络自动调度 针对特定设备和工作负载的自动调整对于获得最佳性能至关重要.这是一个关于如何使用自动调度器为NVIDIA GPU调整整个神经网络的资料. 为了自动调整一个神经网络 ...

  5. NVIDIA GPU卷积网络的自动调谐

    NVIDIA GPU卷积网络的自动调谐 针对特定设备和工作负载的自动调整对于获得最佳性能至关重要.这是关于如何为NVIDIA GPU调整整个卷积网络. NVIDIA GPU在TVM中的操作实现是以模板 ...

  6. ARM CPU神经网络自动调度

    ARM CPU神经网络自动调度 对特定设备和工作负载进行自动调整对于获得最佳性能至关重要.这是一个有关如何通过RPC使用自动调度器为ARM CPU调整整个神经网络的教程. 为了自动调整神经网络,将网络 ...

  7. 自动调度GPU的卷积层

    自动调度GPU的卷积层 这是有关如何对GPU使用自动调度程序的文档. 与依靠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模板.用户只需要编写计算声明,而无需任何调度命令或 ...

  8. 智源青年科学家梁云：异构系统中张量计算的自动调度和优化框架

    与6位图灵奖得主和100多位专家 共同探讨人工智能的下一个十年 长按图片,内行盛会,首次免费注册 北京智源大会倒计时:9天  计算机体系结构领域国际顶级会议每次往往仅录用几十篇论文,录用率在20%左右 ...

  9. Nvidia GPU如何在Kubernetes 里工作

    Nvidia GPU如何在Kubernetes 里工作 本文介绍Nvidia GPU设备如何在Kubernetes中管理调度. 整个工作流程分为以下两个方面: 如何在容器中使用GPU Kubernet ...

最新文章

  1. 用python画烟花-python 实现漂亮的烟花，樱花，玫瑰花
  2. 计算机里的dump是什么意思？（转储、转储文件）
  3. [改善Java代码]自由选择字符串拼接方法
  4. 期末复习、化学反应工程科目（第二章）
  5. ASP.NET Core 集成测试中模拟登录用户的一种姿势
  6. PHP环境安全性能检查
  7. Windows下安装Hadoop
  8. 清除Linux和window等系统的DNS缓存的命令
  9. Android实战——Activity超详细学习笔记
  10. 金三银四面试季—20道精选JVM重点面试问题！
  11. 成员函数指针有多态的效果吗?
  12. sql日期和时间函数
  13. 交换机中查找IP与mac对应关系命令
  14. 一个小白程序员的目标
  15. 蜡笔小新鸿蒙系统,盘点蜡笔小新最污的四处剧情，网友：当年太纯洁现在终于看懂！...
  16. 任正非讲话稿400篇_伟大背后是苦难！任正非“思想之路”：400+讲话稿合集
  17. react实现微信分享
  18. 时间序列分析相关概念
  19. 【AI人工智能】AI绘画能取代设计师？
  20. 陈启峰SBT（Size Balanced Tree ）平衡搜索二叉树

热门文章

  1. 2022-2028年中国激光玻璃行业市场供需规模及发展趋势研究报告
  2. Python 标准库之 shutil
  3. python中如何对复杂的json数据快速查找key对应的value值（使用JsonSearch包）
  4. hadoop,spark,scala,flink 大数据分布式系统汇总
  5. 理解GloVe模型（Global vectors for word representation）
  6. 主流手机OS与鸿蒙OS
  7. TensorRT原理图示
  8. 虚拟纹理与几何图像技术
  9. 2021年大数据HBase（十四）：HBase的原理及其相关的工作机制
  10. 解决使用Dockerfile来build镜像时pip install遇到的BUG