Compilation failed, when I set parameter disabled_pass = ['SimplifyInference'] in tvm.transform.PassContext()

I am a newer to TVM, I want to know wheather the parameter disabled_pass can be set to any pass? If not, what is the restriction about using this parameter.

The crash messages are as follows:

Besides, in order to confirm that it is this disabled_pass=['SimplifyInference'] triggered the bug, I did the some experiments.

  1. change opt_level from 0 to 4 —> opt_level is not related with this crash.
  2. use other passes —> other passes didn’t crash.
  3. use other models ----> models may not related with this crash.
  4. add a extra statement irmod, params = relay.optimize(irmod,target=target,params=params) , the bug disappear.

Even the bug disappered in experiment 4, I still cann’t understand why disabled_pass = ['SimplifyInference'] triggered the crash, and why can add a extra optimization statement( relay.optimize) avoid this crash?

The reproducible script:

import keras
import os
import tvm
from tvm import te
import tvm.relay as relay
import numpy as np
from PIL import Image
import tvm.runtime as runtime
from tvm.contrib import graph_runtime

input_tensor = 'conv2d_1_input'

def image_resize(x, shape):
  x_return = []
  for x_test in x:
      tmp = np.copy(x_test)
      img = Image.fromarray(tmp.astype('uint8')).convert('RGB')
      img = img.resize(shape, Image.ANTIALIAS)
  return np.array(x_return)

input_precessor = keras.applications.vgg16.preprocess_input
input_shape = (32,32)
dataset_dir =  "/share_container/data/dataset/"
data_path = os.path.join(dataset_dir,"imagenet-val-1500.npz")
data = np.load(data_path)
x, y = data['x_test'], data['y_test']

x_resize = image_resize(np.copy(x),input_shape)
x_test = input_precessor(x_resize)
y_test = keras.utils.to_categorical(y, num_classes=1000)

model_path = '/share_container/data/keras_model/vgg16-cifar10_origin.h5'
predict_model = keras.models.load_model(model_path)
shape_dict = {input_tensor: (1,3,32,32)}
irmod, params = relay.frontend.from_keras(predict_model, shape_dict)
target = 'llvm'
ctx = tvm.cpu(0)
# irmod, params = relay.optimize(irmod,target=target,params=params)

disabled_pass = ['SimplifyInference']
with tvm.transform.PassContext(opt_level=1, disabled_pass= disabled_pass):
  graph, lib, params =, target, params=params)

TVM : 0.8.dev0
ONNX: 1.8.1
OS: Ubuntu 16.04

Yes SimplifyInference is effectively required, because batch norm is supposed to be decomposed in that pass. Many passes cannot be disabled, like type inference, fusion etc.

1 Like

Thanks! By the way, Can any pass be used as the value of required_pass in tvm.transform.PassContext()?

I don’t know what required_pass does. If you have a pass that you want to run, you can run it explicitly before

Required allows you to override defaults or outer pass contexts to enable/re-enable passes. The pass API is designed to be user extensible like Masa said you can just apply passes before the standard optimization pipeline. In the above case SimplifyInference is required if you have operations that coming from training which can be computed away in inference mode.

1 Like

@masahi , @jroesch Thanks for your suggestions.