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：Chi-Wei Wang

隨著實現的 Pass 越來越多，instrument pass 執行、分析每個 Pass 效果和觀察各種事件也愈發重要。

可以通過向 tvm.transform.PassContext 提供 tvm.ir.instrument.PassInstrument 實例列表來檢測 Pass。我們提供了一個用于收集計時信息的 pass 工具（tvm.ir.instrument.PassTimingInstrument），可以通過 tvm.instrument.pass_instrument() 裝飾器使用擴展機制。

本教程演示開發者如何用 PassContext 檢測 Pass。另請參閱 Pass Infrastructure。

import tvm
import tvm.relay as relay
from tvm.relay.testing import resnet
from tvm.contrib.download import download_testdata
from tvm.relay.build_module import bind_params_by_name
from tvm.ir.instrument import (
    PassTimingInstrument,
    pass_instrument,
)

創建 Relay 程序示例?

在 Relay 中使用預定義的 ResNet-18 網絡。

batch_size = 1
num_of_image_class = 1000
image_shape = (3, 224, 224)
output_shape = (batch_size, num_of_image_class)
relay_mod, relay_params = resnet.get_workload(num_layers=18, batch_size=1, image_shape=image_shape)
print("Printing the IR module...")
print(relay_mod.astext(show_meta_data=False))

輸出結果：

Printing the IR module...
#[version = "0.0.5"]
def @main(%data: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] */, %bn_data_gamma: Tensor[(3), float32] /* ty=Tensor[(3), float32] */, %bn_data_beta: Tensor[(3), float32] /* ty=Tensor[(3), float32] */, %bn_data_moving_mean: Tensor[(3), float32] /* ty=Tensor[(3), float32] */, %bn_data_moving_var: Tensor[(3), float32] /* ty=Tensor[(3), float32] */, %conv0_weight: Tensor[(64, 3, 7, 7), float32] /* ty=Tensor[(64, 3, 7, 7), float32] */, %bn0_gamma: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %bn0_beta: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %bn0_moving_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %bn0_moving_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn1_gamma: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn1_beta: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn1_moving_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn1_moving_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_conv1_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %stage1_unit1_bn2_gamma: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn2_beta: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn2_moving_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_bn2_moving_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit1_conv2_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %stage1_unit1_sc_weight: Tensor[(64, 64, 1, 1), float32] /* ty=Tensor[(64, 64, 1, 1), float32] */, %stage1_unit2_bn1_gamma: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_bn1_beta: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_bn1_moving_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_bn1_moving_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_conv1_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %stage1_unit2_bn2_gamma: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_bn2_beta: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_bn2_moving_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_bn2_moving_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage1_unit2_conv2_weight: Tensor[(64, 64, 3, 3), float32] /* ty=Tensor[(64, 64, 3, 3), float32] */, %stage2_unit1_bn1_gamma: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage2_unit1_bn1_beta: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage2_unit1_bn1_moving_mean: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage2_unit1_bn1_moving_var: Tensor[(64), float32] /* ty=Tensor[(64), float32] */, %stage2_unit1_conv1_weight: Tensor[(128, 64, 3, 3), float32] /* ty=Tensor[(128, 64, 3, 3), float32] */, %stage2_unit1_bn2_gamma: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit1_bn2_beta: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit1_bn2_moving_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit1_bn2_moving_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit1_conv2_weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] */, %stage2_unit1_sc_weight: Tensor[(128, 64, 1, 1), float32] /* ty=Tensor[(128, 64, 1, 1), float32] */, %stage2_unit2_bn1_gamma: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_bn1_beta: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_bn1_moving_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_bn1_moving_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_conv1_weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] */, %stage2_unit2_bn2_gamma: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_bn2_beta: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_bn2_moving_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_bn2_moving_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage2_unit2_conv2_weight: Tensor[(128, 128, 3, 3), float32] /* ty=Tensor[(128, 128, 3, 3), float32] */, %stage3_unit1_bn1_gamma: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage3_unit1_bn1_beta: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage3_unit1_bn1_moving_mean: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage3_unit1_bn1_moving_var: Tensor[(128), float32] /* ty=Tensor[(128), float32] */, %stage3_unit1_conv1_weight: Tensor[(256, 128, 3, 3), float32] /* ty=Tensor[(256, 128, 3, 3), float32] */, %stage3_unit1_bn2_gamma: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit1_bn2_beta: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit1_bn2_moving_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit1_bn2_moving_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit1_conv2_weight: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] */, %stage3_unit1_sc_weight: Tensor[(256, 128, 1, 1), float32] /* ty=Tensor[(256, 128, 1, 1), float32] */, %stage3_unit2_bn1_gamma: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_bn1_beta: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_bn1_moving_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_bn1_moving_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_conv1_weight: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] */, %stage3_unit2_bn2_gamma: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_bn2_beta: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_bn2_moving_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_bn2_moving_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage3_unit2_conv2_weight: Tensor[(256, 256, 3, 3), float32] /* ty=Tensor[(256, 256, 3, 3), float32] */, %stage4_unit1_bn1_gamma: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage4_unit1_bn1_beta: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage4_unit1_bn1_moving_mean: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage4_unit1_bn1_moving_var: Tensor[(256), float32] /* ty=Tensor[(256), float32] */, %stage4_unit1_conv1_weight: Tensor[(512, 256, 3, 3), float32] /* ty=Tensor[(512, 256, 3, 3), float32] */, %stage4_unit1_bn2_gamma: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit1_bn2_beta: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit1_bn2_moving_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit1_bn2_moving_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit1_conv2_weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] */, %stage4_unit1_sc_weight: Tensor[(512, 256, 1, 1), float32] /* ty=Tensor[(512, 256, 1, 1), float32] */, %stage4_unit2_bn1_gamma: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_bn1_beta: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_bn1_moving_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_bn1_moving_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_conv1_weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] */, %stage4_unit2_bn2_gamma: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_bn2_beta: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_bn2_moving_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_bn2_moving_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %stage4_unit2_conv2_weight: Tensor[(512, 512, 3, 3), float32] /* ty=Tensor[(512, 512, 3, 3), float32] */, %bn1_gamma: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %bn1_beta: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %bn1_moving_mean: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %bn1_moving_var: Tensor[(512), float32] /* ty=Tensor[(512), float32] */, %fc1_weight: Tensor[(1000, 512), float32] /* ty=Tensor[(1000, 512), float32] */, %fc1_bias: Tensor[(1000), float32] /* ty=Tensor[(1000), float32] */) -> Tensor[(1, 1000), float32] {
  %0 = nn.batch_norm(%data, %bn_data_gamma, %bn_data_beta, %bn_data_moving_mean, %bn_data_moving_var, epsilon=2e-05f, scale=False) /* ty=(Tensor[(1, 3, 224, 224), float32], Tensor[(3), float32], Tensor[(3), float32]) */;
  %1 = %0.0 /* ty=Tensor[(1, 3, 224, 224), float32] */;
  %2 = nn.conv2d(%1, %conv0_weight, strides=[2, 2], padding=[3, 3, 3, 3], channels=64, kernel_size=[7, 7]) /* ty=Tensor[(1, 64, 112, 112), float32] */;
  %3 = nn.batch_norm(%2, %bn0_gamma, %bn0_beta, %bn0_moving_mean, %bn0_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 112, 112), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
  %4 = %3.0 /* ty=Tensor[(1, 64, 112, 112), float32] */;
  %5 = nn.relu(%4) /* ty=Tensor[(1, 64, 112, 112), float32] */;
  %6 = nn.max_pool2d(%5, pool_size=[3, 3], strides=[2, 2], padding=[1, 1, 1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %7 = nn.batch_norm(%6, %stage1_unit1_bn1_gamma, %stage1_unit1_bn1_beta, %stage1_unit1_bn1_moving_mean, %stage1_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
  %8 = %7.0 /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %9 = nn.relu(%8) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %10 = nn.conv2d(%9, %stage1_unit1_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %11 = nn.batch_norm(%10, %stage1_unit1_bn2_gamma, %stage1_unit1_bn2_beta, %stage1_unit1_bn2_moving_mean, %stage1_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
  %12 = %11.0 /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %13 = nn.relu(%12) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %14 = nn.conv2d(%13, %stage1_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %15 = nn.conv2d(%9, %stage1_unit1_sc_weight, padding=[0, 0, 0, 0], channels=64, kernel_size=[1, 1]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %16 = add(%14, %15) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %17 = nn.batch_norm(%16, %stage1_unit2_bn1_gamma, %stage1_unit2_bn1_beta, %stage1_unit2_bn1_moving_mean, %stage1_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
  %18 = %17.0 /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %19 = nn.relu(%18) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %20 = nn.conv2d(%19, %stage1_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %21 = nn.batch_norm(%20, %stage1_unit2_bn2_gamma, %stage1_unit2_bn2_beta, %stage1_unit2_bn2_moving_mean, %stage1_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
  %22 = %21.0 /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %23 = nn.relu(%22) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %24 = nn.conv2d(%23, %stage1_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=64, kernel_size=[3, 3]) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %25 = add(%24, %16) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %26 = nn.batch_norm(%25, %stage2_unit1_bn1_gamma, %stage2_unit1_bn1_beta, %stage2_unit1_bn1_moving_mean, %stage2_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 64, 56, 56), float32], Tensor[(64), float32], Tensor[(64), float32]) */;
  %27 = %26.0 /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %28 = nn.relu(%27) /* ty=Tensor[(1, 64, 56, 56), float32] */;
  %29 = nn.conv2d(%28, %stage2_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %30 = nn.batch_norm(%29, %stage2_unit1_bn2_gamma, %stage2_unit1_bn2_beta, %stage2_unit1_bn2_moving_mean, %stage2_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) */;
  %31 = %30.0 /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %32 = nn.relu(%31) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %33 = nn.conv2d(%32, %stage2_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %34 = nn.conv2d(%28, %stage2_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=128, kernel_size=[1, 1]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %35 = add(%33, %34) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %36 = nn.batch_norm(%35, %stage2_unit2_bn1_gamma, %stage2_unit2_bn1_beta, %stage2_unit2_bn1_moving_mean, %stage2_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) */;
  %37 = %36.0 /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %38 = nn.relu(%37) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %39 = nn.conv2d(%38, %stage2_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %40 = nn.batch_norm(%39, %stage2_unit2_bn2_gamma, %stage2_unit2_bn2_beta, %stage2_unit2_bn2_moving_mean, %stage2_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) */;
  %41 = %40.0 /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %42 = nn.relu(%41) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %43 = nn.conv2d(%42, %stage2_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=128, kernel_size=[3, 3]) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %44 = add(%43, %35) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %45 = nn.batch_norm(%44, %stage3_unit1_bn1_gamma, %stage3_unit1_bn1_beta, %stage3_unit1_bn1_moving_mean, %stage3_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 128, 28, 28), float32], Tensor[(128), float32], Tensor[(128), float32]) */;
  %46 = %45.0 /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %47 = nn.relu(%46) /* ty=Tensor[(1, 128, 28, 28), float32] */;
  %48 = nn.conv2d(%47, %stage3_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %49 = nn.batch_norm(%48, %stage3_unit1_bn2_gamma, %stage3_unit1_bn2_beta, %stage3_unit1_bn2_moving_mean, %stage3_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) */;
  %50 = %49.0 /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %51 = nn.relu(%50) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %52 = nn.conv2d(%51, %stage3_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %53 = nn.conv2d(%47, %stage3_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=256, kernel_size=[1, 1]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %54 = add(%52, %53) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %55 = nn.batch_norm(%54, %stage3_unit2_bn1_gamma, %stage3_unit2_bn1_beta, %stage3_unit2_bn1_moving_mean, %stage3_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) */;
  %56 = %55.0 /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %57 = nn.relu(%56) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %58 = nn.conv2d(%57, %stage3_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %59 = nn.batch_norm(%58, %stage3_unit2_bn2_gamma, %stage3_unit2_bn2_beta, %stage3_unit2_bn2_moving_mean, %stage3_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) */;
  %60 = %59.0 /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %61 = nn.relu(%60) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %62 = nn.conv2d(%61, %stage3_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=256, kernel_size=[3, 3]) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %63 = add(%62, %54) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %64 = nn.batch_norm(%63, %stage4_unit1_bn1_gamma, %stage4_unit1_bn1_beta, %stage4_unit1_bn1_moving_mean, %stage4_unit1_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 256, 14, 14), float32], Tensor[(256), float32], Tensor[(256), float32]) */;
  %65 = %64.0 /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %66 = nn.relu(%65) /* ty=Tensor[(1, 256, 14, 14), float32] */;
  %67 = nn.conv2d(%66, %stage4_unit1_conv1_weight, strides=[2, 2], padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %68 = nn.batch_norm(%67, %stage4_unit1_bn2_gamma, %stage4_unit1_bn2_beta, %stage4_unit1_bn2_moving_mean, %stage4_unit1_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) */;
  %69 = %68.0 /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %70 = nn.relu(%69) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %71 = nn.conv2d(%70, %stage4_unit1_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %72 = nn.conv2d(%66, %stage4_unit1_sc_weight, strides=[2, 2], padding=[0, 0, 0, 0], channels=512, kernel_size=[1, 1]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %73 = add(%71, %72) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %74 = nn.batch_norm(%73, %stage4_unit2_bn1_gamma, %stage4_unit2_bn1_beta, %stage4_unit2_bn1_moving_mean, %stage4_unit2_bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) */;
  %75 = %74.0 /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %76 = nn.relu(%75) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %77 = nn.conv2d(%76, %stage4_unit2_conv1_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %78 = nn.batch_norm(%77, %stage4_unit2_bn2_gamma, %stage4_unit2_bn2_beta, %stage4_unit2_bn2_moving_mean, %stage4_unit2_bn2_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) */;
  %79 = %78.0 /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %80 = nn.relu(%79) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %81 = nn.conv2d(%80, %stage4_unit2_conv2_weight, padding=[1, 1, 1, 1], channels=512, kernel_size=[3, 3]) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %82 = add(%81, %73) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %83 = nn.batch_norm(%82, %bn1_gamma, %bn1_beta, %bn1_moving_mean, %bn1_moving_var, epsilon=2e-05f) /* ty=(Tensor[(1, 512, 7, 7), float32], Tensor[(512), float32], Tensor[(512), float32]) */;
  %84 = %83.0 /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %85 = nn.relu(%84) /* ty=Tensor[(1, 512, 7, 7), float32] */;
  %86 = nn.global_avg_pool2d(%85) /* ty=Tensor[(1, 512, 1, 1), float32] */;
  %87 = nn.batch_flatten(%86) /* ty=Tensor[(1, 512), float32] */;
  %88 = nn.dense(%87, %fc1_weight, units=1000) /* ty=Tensor[(1, 1000), float32] */;
  %89 = nn.bias_add(%88, %fc1_bias, axis=-1) /* ty=Tensor[(1, 1000), float32] */;
  nn.softmax(%89) /* ty=Tensor[(1, 1000), float32] */
}

使用 Instrument 創建 PassContext?

要用 instrument 運行所有 Pass，將其通過參數?instruments?傳遞給構造函數?PassContext。PassTimingInstrument?用于分析每個 Pass 執行時間的內置函數。

timing_inst = PassTimingInstrument()
with tvm.transform.PassContext(instruments=[timing_inst]):
    relay_mod = relay.transform.InferType()(relay_mod)
    relay_mod = relay.transform.FoldScaleAxis()(relay_mod)
    # 在退出上下文之前，獲取配置文件結果。
    profiles = timing_inst.render()
print("Printing results of timing profile...")
print(profiles)

輸出結果：

Printing results of timing profile...
InferType: 6628us [6628us] (46.29%; 46.29%)
FoldScaleAxis: 7691us [6us] (53.71%; 53.71%)
        FoldConstant: 7685us [1578us] (53.67%; 99.92%)
                InferType: 6107us [6107us] (42.65%; 79.47%)

將當前 PassContext 與 Instrument 一起使用?

也可以使用當前的?PassContext，并通過?override_instruments?方法注冊?PassInstrument?實例。注意，如果已經存在了任何 instrument，override_instruments?將執行?exit_pass_ctx?方法。然后它切換到新 instrument，并調用新 instrument 的?enter_pass_ctx?方法。有關這些方法，參閱以下部分和?tvm.instrument.pass_instrument()。

cur_pass_ctx = tvm.transform.PassContext.current()
cur_pass_ctx.override_instruments([timing_inst])
relay_mod = relay.transform.InferType()(relay_mod)
relay_mod = relay.transform.FoldScaleAxis()(relay_mod)
profiles = timing_inst.render()
print("Printing results of timing profile...")
print(profiles)

輸出結果：

Printing results of timing profile...
InferType: 6131us [6131us] (44.86%; 44.86%)
FoldScaleAxis: 7536us [4us] (55.14%; 55.14%)
        FoldConstant: 7532us [1549us] (55.11%; 99.94%)
                InferType: 5982us [5982us] (43.77%; 79.43%)

注冊空列表以清除現有 instrument。

注意，PassTimingInstrument?的?exit_pass_ctx?被調用了。配置文件被清除，因此不會打印任何內容。

cur_pass_ctx.override_instruments([])
# 取消 .render() 的注釋以查看如下警告：
# 警告：沒有 Pass 分析，您是否啟用了 Pass 分析？
# profiles = timing_inst.render()

創建自定義 Instrument 類?

可以使用?tvm.instrument.pass_instrument()?裝飾器創建自定義 instrument 類。

創建一個工具類（計算每次 Pass 引起的每個算子出現次數的變化）?？梢栽?Pass 之前和之后查看?op.name?來找到每個算子的名稱，從而計算差異。

@pass_instrument
class RelayCallNodeDiffer:
    def __init__(self):
        self._op_diff = []
        # Pass 可以嵌套。
        # 使用堆棧來確保得到之前/之后正確的 pairs。
        self._op_cnt_before_stack = []

    def enter_pass_ctx(self):
        self._op_diff = []
        self._op_cnt_before_stack = []

    def exit_pass_ctx(self):
        assert len(self._op_cnt_before_stack) == 0, "The stack is not empty. Something wrong."

    def run_before_pass(self, mod, info):
        self._op_cnt_before_stack.append((info.name, self._count_nodes(mod)))

    def run_after_pass(self, mod, info):
        # 彈出最新記錄的 Pass。
        name_before, op_to_cnt_before = self._op_cnt_before_stack.pop()
        assert name_before == info.name, "name_before: {}, info.name: {} doesn't match".format(
            name_before, info.name
        )
        cur_depth = len(self._op_cnt_before_stack)
        op_to_cnt_after = self._count_nodes(mod)
        op_diff = self._diff(op_to_cnt_after, op_to_cnt_before)
        # 只記導致差異的 Pass。
        if op_diff:
            self._op_diff.append((cur_depth, info.name, op_diff))

    def get_pass_to_op_diff(self):
        """
        return [
          (depth, pass_name, {op_name: diff_num, ...}), ...
        ]
        """
        return self._op_diff

    @staticmethod
    def _count_nodes(mod):
        """Count the number of occurrences of each operator in the module"""
        ret = {}

        def visit(node):
            if isinstance(node, relay.expr.Call):
                if hasattr(node.op, "name"):
                    op_name = node.op.name
                else:
                    # 某些 CallNode 可能沒有“名稱”，例如 relay.Function
                    return
                ret[op_name] = ret.get(op_name, 0) + 1

        relay.analysis.post_order_visit(mod["main"], visit)
        return ret

    @staticmethod
    def _diff(d_after, d_before):
        """Calculate the difference of two dictionary along their keys.
        The result is values in d_after minus values in d_before.
        """
        ret = {}
        key_after, key_before = set(d_after), set(d_before)
        for k in key_before & key_after:
            tmp = d_after[k] - d_before[k]
            if tmp:
                ret[k] = d_after[k] - d_before[k]
        for k in key_after - key_before:
            ret[k] = d_after[k]
        for k in key_before - key_after:
            ret[k] = -d_before[k]
        return ret

應用 Pass 和多個 Instrument 類?

可以在?PassContext?中使用多個 instrument 類。但注意，instrument 方法是按?instruments?參數的順序執行的，所以對于像?PassTimingInstrument?這樣的 instrument 類，不可避免地要將其他 instrument 類的執行時間計入最終的分析結果。

call_node_inst = RelayCallNodeDiffer()
desired_layouts = {
    "nn.conv2d": ["NHWC", "HWIO"],
}
pass_seq = tvm.transform.Sequential(
    [
        relay.transform.FoldConstant(),
        relay.transform.ConvertLayout(desired_layouts),
        relay.transform.FoldConstant(),
    ]
)
relay_mod["main"] = bind_params_by_name(relay_mod["main"], relay_params)
# timing_inst 放在 call_node_inst 之后。
# 所以 `call_node.inst.run_after_pass()` 的執行時間也算在內。
with tvm.transform.PassContext(opt_level=3, instruments=[call_node_inst, timing_inst]):
    relay_mod = pass_seq(relay_mod)
    profiles = timing_inst.render()
# 取消注釋下一行以查看時序配置文件結果。
# print(profiles)

輸出結果：

/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. "

可以看到每個操作類型增加/減少了多少 CallNode。

from pprint import pprint

print("Printing the change in number of occurrences of each operator caused by each pass...")
pprint(call_node_inst.get_pass_to_op_diff())

輸出結果：

Printing the change in number of occurrences of each operator caused by each pass...
[(1, 'CanonicalizeOps', {'add': 1, 'nn.bias_add': -1}),
 (1, 'ConvertLayout', {'expand_dims': 1, 'layout_transform': 23}),
 (1, 'FoldConstant', {'expand_dims': -1, 'layout_transform': -21}),
 (0, 'sequential', {'add': 1, 'layout_transform': 2, 'nn.bias_add': -1})]

異常處理?

以下演示了?PassInstrument?的方法發生異常的詳細情況。

定義在進入/退出?PassContext?中引發異常的?PassInstrument?類：

class PassExampleBase:
    def __init__(self, name):
        self._name = name

    def enter_pass_ctx(self):
        print(self._name, "enter_pass_ctx")

    def exit_pass_ctx(self):
        print(self._name, "exit_pass_ctx")

    def should_run(self, mod, info):
        print(self._name, "should_run")
        return True

    def run_before_pass(self, mod, pass_info):
        print(self._name, "run_before_pass")

    def run_after_pass(self, mod, pass_info):
        print(self._name, "run_after_pass")

@pass_instrument
class PassFine(PassExampleBase):
    pass

@pass_instrument
class PassBadEnterCtx(PassExampleBase):
    def enter_pass_ctx(self):
        print(self._name, "bad enter_pass_ctx!!!")
        raise ValueError("{} bad enter_pass_ctx".format(self._name))

@pass_instrument
class PassBadExitCtx(PassExampleBase):
    def exit_pass_ctx(self):
        print(self._name, "bad exit_pass_ctx!!!")
        raise ValueError("{} bad exit_pass_ctx".format(self._name))

若?enter_pass_ctx?發生異常，PassContext?將禁用 pass instrumentation。它將運行每個成功完成?enter_pass_ctx?的 PassInstrument 的?exit_pass_ctx。

下面的例子可以看到?PassFine_0?的?exit_pass_ctx?在異常后執行。

demo_ctx = tvm.transform.PassContext(
    instruments=[
        PassFine("PassFine_0"),
        PassBadEnterCtx("PassBadEnterCtx"),
        PassFine("PassFine_1"),
    ]
)
try:
    with demo_ctx:
        relay_mod = relay.transform.InferType()(relay_mod)
except ValueError as ex:
    print("Catching", str(ex).split("\n")[-1])

輸出結果：

PassFine_0 enter_pass_ctx
PassBadEnterCtx bad enter_pass_ctx!!!
PassFine_0 exit_pass_ctx
Catching ValueError: PassBadEnterCtx bad enter_pass_ctx

PassInstrument?實例中的異常會導致當前的?PassContext?所有 instrument 被清除，因此調用?override_instruments?時不會打印任何內容。

demo_ctx.override_instruments([])  # 沒有打印 PassFine_0 exit_pass_ctx....等

若?exit_pass_ctx?發生異常，則禁用 pass instrument，然后傳播異常。這意味著?PassInstrument?在拋出異常之后注冊的實例不會執行?exit_pass_ctx。

demo_ctx = tvm.transform.PassContext(
    instruments=[
        PassFine("PassFine_0"),
        PassBadExitCtx("PassBadExitCtx"),
        PassFine("PassFine_1"),
    ]
)
try:
    # PassFine_1 執行 enter_pass_ctx，但不執行 exit_pass_ctx。
    with demo_ctx:
        relay_mod = relay.transform.InferType()(relay_mod)
except ValueError as ex:
    print("Catching", str(ex).split("\n")[-1])

輸出結果：

PassFine_0 enter_pass_ctx
PassBadExitCtx enter_pass_ctx
PassFine_1 enter_pass_ctx
PassFine_0 should_run
PassBadExitCtx should_run
PassFine_1 should_run
PassFine_0 run_before_pass
PassBadExitCtx run_before_pass
PassFine_1 run_before_pass
PassFine_0 run_after_pass
PassBadExitCtx run_after_pass
PassFine_1 run_after_pass
PassFine_0 exit_pass_ctx
PassBadExitCtx bad exit_pass_ctx!!!
Catching ValueError: PassBadExitCtx bad exit_pass_ctx

以?run_before_pass為例：

should_run、run_before_pass?和?run_after_pass?發生的異常沒有明確處理，用上下文管理器（with?語法）安全退出?PassContext。

@pass_instrument
class PassBadRunBefore(PassExampleBase):
    def run_before_pass(self, mod, pass_info):
        print(self._name, "bad run_before_pass!!!")
        raise ValueError("{} bad run_before_pass".format(self._name))

demo_ctx = tvm.transform.PassContext(
    instruments=[
        PassFine("PassFine_0"),
        PassBadRunBefore("PassBadRunBefore"),
        PassFine("PassFine_1"),
    ]
)
try:
    # 所有的 exit_pass_ctx 都會被調用。
    with demo_ctx:
        relay_mod = relay.transform.InferType()(relay_mod)
except ValueError as ex:
    print("Catching", str(ex).split("\n")[-1])

輸出結果：

PassFine_0 enter_pass_ctx
PassBadRunBefore enter_pass_ctx
PassFine_1 enter_pass_ctx
PassFine_0 should_run
PassBadRunBefore should_run
PassFine_1 should_run
PassFine_0 run_before_pass
PassBadRunBefore bad run_before_pass!!!
PassFine_0 exit_pass_ctx
PassBadRunBefore exit_pass_ctx
PassFine_1 exit_pass_ctx
Catching ValueError: PassBadRunBefore bad run_before_pass

注意，pass instrumentation 未禁用。所以若調用?override_instruments，exit_pass_ctx?先前注冊的?PassInstrument?將被調用。

demo_ctx.override_instruments([])

輸出結果：

PassFine_0 exit_pass_ctx
PassBadRunBefore exit_pass_ctx
PassFine_1 exit_pass_ctx

若不用?with?語法包裝 pass 執行，則不會調用?exit_pass_ctx。用當前的?PassContext：

cur_pass_ctx = tvm.transform.PassContext.current()
cur_pass_ctx.override_instruments(
    [
        PassFine("PassFine_0"),
        PassBadRunBefore("PassBadRunBefore"),
        PassFine("PassFine_1"),
    ]
)

輸出結果：

PassFine_0 enter_pass_ctx
PassBadRunBefore enter_pass_ctx
PassFine_1 enter_pass_ctx

然后調用 Pass。異常后?exit_pass_ctx?不執行。

try:
    # No ``exit_pass_ctx`` got executed.
    relay_mod = relay.transform.InferType()(relay_mod)
except ValueError as ex:
    print("Catching", str(ex).split("\n")[-1])

輸出結果：

PassFine_0 should_run
PassBadRunBefore should_run
PassFine_1 should_run
PassFine_0 run_before_pass
PassBadRunBefore bad run_before_pass!!!
Catching ValueError: PassBadRunBefore bad run_before_pass

清除 instrument。

cur_pass_ctx.override_instruments([])

輸出結果：

PassFine_0 exit_pass_ctx
PassBadRunBefore exit_pass_ctx
PassFine_1 exit_pass_ctx

下載 Python 源代碼：use_pass_instrument.py

下載 Jupyter Notebook：use_pass_instrument.ipynb