【TVM教程】為 NVIDIA GPU 自動調度神經網絡

Apache TVM?是一個深度的深度學習編譯框架，適用于 CPU、GPU 和各種機器學習加速芯片。更多 TVM 中文文檔可訪問 →https://tvm.hyper.ai/

作者：Lianmin Zheng

針對特定設備和工作負載的自動調優對于獲得最佳性能至關重要。本文介紹如何使用 auto-scheduler 為 NVIDIA GPU 調優整個神經網絡。

為自動調優神經網絡，需要將網絡劃分為小的子圖并獨立調優。每個子圖被視為一個搜索任務，任務調度器對時間進行切片并動態地為這些任務分配時間資源，并預測每個任務對端到端執行時間的影響，優先考慮最能減少執行時間的任務。

對于每個子圖，使用 tvm/python/topi 中的計算聲明來獲取張量表達式形式的計算 DAG。然后用 auto-scheduler 來構建這個 DAG 的搜索空間，并搜索合適的調度（低級優化）。

與基于 template 的 AutoTVM（依賴手動 template 來定義搜索空間的）不同，auto-scheduler 無需任何調度 template。換言之，auto-scheduler 只使用 tvm/python/topi 中的計算聲明，不使用現有的調度 template。

注意，本教程無法在 Windows 或最新版本的 macOS 上運行。如需運行，請將本教程的主體放在 if __name__ == "__main__": 代碼塊中。

import numpy as npimport tvm
from tvm import relay, auto_scheduler
import tvm.relay.testing
from tvm.contrib import graph_executor

定義網絡?

首先，要用 Relay 前端 API 定義網絡。可以從 tvm.relay.testing 加載一些預定義的網絡。也可以從 MXNet、ONNX、PyTorch 和 TensorFlow 加載模型（參見前端教程）。

對于卷積神經網絡，盡管 auto-scheduler 可以在任何布局下正常運行，但通過 NHWC 布局實現的性能最佳。auto-scheduler 對 NHWC 布局進行了很多優化，因此推薦將模型轉換為 NHWC 布局，從而得以使用 auto-scheduler。可用 ConvertLayout pass 在 TVM 中進行布局轉換。

def get_network(name, batch_size, layout="NHWC", dtype="float32"):"""Get the symbol definition and random weight of a network"""# auto-scheduler 更適合 NHWC 布局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_shapeoutput_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":# MXNet 模型的示例from 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# 定義神經網絡和編譯目標
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)

提取搜索任務?

接下來，從網絡中提取搜索任務及其權重。任務的權重是任務的子圖在整個網絡中出現的次數。通過使用權重，可以將網絡的端到端延遲近似為 sum(latency[t] * weight[t])，其中 latency[t] 是任務的延遲，而 weight[t] 是任務的權重，任務調度器僅針對該目標進行優化。

# 從網絡中提取任務
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)

輸出結果：

Extract tasks...
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead."target_host parameter is going to be deprecated. "
========== Task 0 (workload key: ["8654f16aeddf785bad9f028164b3a48d", [1, 56, 56, 64], [1, 1, 64, 64], [1, 56, 56, 64]]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
pad_temp(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
placeholder = PLACEHOLDER [1, 1, 64, 64]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, (yy + ry), (xx + rx), rc]*placeholder[ry, rx, rc, ff])========== Task 1 (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), ((((n*14)*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 2 (workload key: ["06f578e6519a86e85028eecf4de64b25", [1, 56, 56, 64], [1, 1, 64, 128], [1, 28, 28, 128]]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
pad_temp(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
placeholder = PLACEHOLDER [1, 1, 64, 128]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])========== Task 3 (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), ((((n*7)*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 4 (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), ((((n*14)*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 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), ((((n*4)*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: ["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), ((((n*14)*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 7 (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), ((((n*4)*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 8 (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), ((((n*7)*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 9 (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 10 (workload key: ["69115f188984ae34ede37c3b8ca40b43", [1, 7, 7, 512], [1, 1, 1, 512]]) ==========
placeholder = PLACEHOLDER [1, 7, 7, 512]
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 11 (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), ((((n*4)*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 12 (workload key: ["06f578e6519a86e85028eecf4de64b25", [1, 28, 28, 128], [1, 1, 128, 256], [1, 14, 14, 256]]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
pad_temp(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
placeholder = PLACEHOLDER [1, 1, 128, 256]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])========== Task 13 (workload key: ["96daaa9daa1b41bc383b7c05ce8b58de", [1, 14, 14, 256], [3, 3, 256, 512], [1, 1, 1, 512], [1, 7, 7, 512]]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
pad_temp(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]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 512]
T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[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: ["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), ((((n*14)*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 15 (workload key: ["96daaa9daa1b41bc383b7c05ce8b58de", [1, 28, 28, 128], [3, 3, 128, 256], [1, 1, 1, 256], [1, 14, 14, 256]]) ==========
placeholder = PLACEHOLDER [1, 28, 28, 128]
pad_temp(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]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 256]
T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[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: ["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), ((((n*14)*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 17 (workload key: ["96daaa9daa1b41bc383b7c05ce8b58de", [1, 224, 224, 3], [7, 7, 3, 64], [1, 1, 1, 64], [1, 112, 112, 64]]) ==========
placeholder = PLACEHOLDER [1, 224, 224, 3]
pad_temp(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]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 64]
T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[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: ["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), ((((n*14)*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 19 (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), ((((n*7)*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 20 (workload key: ["64b98c71af70a904fdbb81d7d4188d84", [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, ((ax1*2) + rv0), ((ax2*2) + rv1), 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 21 (workload key: ["06f578e6519a86e85028eecf4de64b25", [1, 14, 14, 256], [1, 1, 256, 512], [1, 7, 7, 512]]) ==========
placeholder = PLACEHOLDER [1, 14, 14, 256]
pad_temp(i0, i1, i2, i3) = placeholder[i0, i1, i2, i3]
placeholder = PLACEHOLDER [1, 1, 256, 512]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])========== Task 22 (workload key: ["7d44c6e3c81cd80f61ff2265b2bae89a", [1, 512], [1000, 512], [1, 1000], [1, 1000]]) ==========
placeholder = PLACEHOLDER [1, 512]
placeholder = PLACEHOLDER [1000, 512]
T_matmul_NT(i, j) += (placeholder[i, k]*placeholder[j, k])
placeholder = PLACEHOLDER [1, 1000]
T_add(ax0, ax1) = (T_matmul_NT[ax0, ax1] + placeholder[ax0, ax1])========== Task 23 (workload key: ["96daaa9daa1b41bc383b7c05ce8b58de", [1, 56, 56, 64], [3, 3, 64, 128], [1, 1, 1, 128], [1, 28, 28, 128]]) ==========
placeholder = PLACEHOLDER [1, 56, 56, 64]
pad_temp(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]
conv2d_nhwc(nn, yy, xx, ff) += (pad_temp[nn, ((yy*2) + ry), ((xx*2) + rx), rc]*placeholder[ry, rx, rc, ff])
placeholder = PLACEHOLDER [1, 1, 1, 128]
T_add(ax0, ax1, ax2, ax3) = (conv2d_nhwc[ax0, ax1, ax2, ax3] + placeholder[ax0, 0, 0, ax3])
T_relu(ax0, ax1, ax2, ax3) = max(T_add[ax0, ax1, ax2, ax3], 0f)

開始調優?

接下來為調優和啟動搜索任務設置一些選項

measure_ctx 啟動不同的測試過程以提供隔離。在測試期間保護主進程免受 GPU 崩潰并避免其他 runtime 沖突。
min_repeat_ms 定義每次測試中一次“重復”的最短持續時間，可以預熱 GPU 以獲得準確測試結果，通常，推薦設置值 >= 300 ms。
num_measure_trials 是調優期間可以使用的測試次數（根據自己的時間預算調整這個參數），若要快速演示，可將其設置為較小的數字（例如 200）。推薦將其設置為 900 * len(tasks) 左右，以便使搜索收斂。比如 resnet-18 有 24 個任務，所以可以設置為 20000。
此外，使用 RecordToFile 將測試記錄轉儲到日志文件中，測試記錄可用于歷史最佳查詢、恢復搜索以及進行后續分析。
更多參數參見 auto_scheduler.TuningOptions，auto_scheduler.LocalRunner。

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, # 將此更改為 20000 以達到最佳性能runner=measure_ctx.runner,measure_callbacks=[auto_scheduler.RecordToFile(log_file)],)tuner.tune(tune_option)# 不在網頁服務器中運行調優，因為它需要的時間太長。
# 取消注釋運行下面行。
# run_tuning()

備注
解釋調優過程中打印的信息
在調優過程中，控制臺上會打印很多用于調試的信息，最重要的信息是任務調度程序的輸出，下表是輸出示例。

------------------------------ [ 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

此表列出了所有任務的延遲和（預估）速度，還列出了所有任務的測試分配。最后一行打印了這些任務的總加權延遲，可以粗略估計網絡的端到端執行時間。最后一行還打印了測試試驗的總數、自動調優所花費的總時間以及下一個要調優的任務的 ID。
還有一些「tvm::Error」錯誤，因為 auto-scheduler 會嘗試一些無效的調度。若調優繼續運行，則可以忽略這些錯誤，因為這些錯誤與主進程隔離。

備注
提前終止調優
可以通過強制終止此進程來提前終止調優，只要在日志文件中為每個任務獲得至少一個有效的調度，就能夠進行編譯（下面的部分）。

編譯及評估?

自動調優后，用找到的最佳調度來編譯網絡。在自動調優期間，所有測試記錄都被轉儲到日志文件中，可以讀取日志文件加載最佳調度。

# 用歷史最佳編譯
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)# 創建圖執行器
dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))
data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
module.set_input("data", data_tvm)# 評估
print("Evaluate inference time cost...")
print(module.benchmark(dev, repeat=3, min_repeat_ms=500))

輸出結果：

Compile...
/workspace/python/tvm/driver/build_module.py:268: UserWarning: target_host parameter is going to be deprecated. Please pass in tvm.target.Target(target, host=target_host) instead."target_host parameter is going to be deprecated. "
Evaluate inference time cost...
Execution time summary:mean (ms)   median (ms)    max (ms)     min (ms)     std (ms)10.0003 9.9944 10.0327 9.9738 0.0244

其他技巧?

在調優過程中，auto-scheduler 需要編譯許多程序，并從中提取特征。這部分會占用大量 CPU 資源，所以推薦使用多核的高性能 CPU，加快搜索速度。
可以用 python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json 提取大日志文件，并僅保存最有用的記錄。
可以從以前的日志文件恢復搜索，只需要在函數 run_tuning 中創建任務調度程序時添加一個新參數 load_log_file。比如，tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)
若有多個 target CPU，則可以將所有這些 CPU 用于并行化測試。查看這部分了解如何使用 RPC 跟蹤器和 RPC 服務器。要在 auto-scheduler 中使用 RPC 跟蹤器，請將 TuningOptions 中的 runner 替換為 auto_scheduler.RPCRunner。

下載 Python 源代碼：tune_network_cuda.py

下載 Jupyter Notebook：tune_network_cuda.ipynb