Compiling and Optimizing a Model with TVMC

Authors: Leandro Nunes, Matthew Barrett, Chris Hoge

In this section, we will work with TVMC, the TVM command line driver. TVMC is a tool that exposes TVM features such as auto-tuning, compiling, profiling and execution of models through a command line interface.

Upon completion of this section, we will have used TVMC to accomplish the following tasks:

• Compile a pre-trained ResNet-50 v2 model for the TVM runtime.

• Run a real image through the compiled model, and interpret the output and model performance.

• Tune the model on a CPU using TVM.

• Re-compile an optimized model using the tuning data collected by TVM.

• Run the image through the optimized model, and compare the output and model performance.

The goal of this section is to give you an overview of TVM and TVMC’s capabilities, and set the stage for understanding how TVM works.

Using TVMC

TVMC is a Python application, part of the TVM Python package. When you install TVM using a Python package, you will get TVMC as a command line application called tvmc. The location of this command will vary depending on your platform and installation method.

Alternatively, if you have TVM as a Python module on your $PYTHONPATH, you can access the command line driver functionality via the executable python module, python -m tvm.driver.tvmc.

For simplicity, this tutorial will mention TVMC command line using tvmc <options>, but the same results can be obtained with python -m tvm.driver.tvmc <options>.

You can check the help page using:

tvmc --help

The main features of TVM available to tvmc are from subcommands compile, and run, and tune. To read about specific options under a given subcommand, use tvmc <subcommand> --help. We will cover each of these commands in this tutorial, but first we need to download a pre-trained model to work with.

Obtaining the Model

For this tutorial, we will be working with ResNet-50 v2. ResNet-50 is a convolutional neural network that is 50 layers deep and designed to classify images. The model we will be using has been pre-trained on more than a million images with 1000 different classifications. The network has an input image size of 224x224. If you are interested exploring more of how the ResNet-50 model is structured, we recommend downloading Netron, a freely available ML model viewer.

For this tutorial we will be using the model in ONNX format.

wget https://github.com/onnx/models/raw/b9a54e89508f101a1611cd64f4ef56b9cb62c7cf/vision/classification/resnet/model/resnet50-v2-7.onnx

Supported model formats

TVMC supports models created with Keras, ONNX, TensorFlow, TFLite and Torch. Use the option --model-format if you need to explicitly provide the model format you are using. See tvmc compile --help for more information.

Adding ONNX Support to TVM

TVM relies on the ONNX python library being available on your system. You can install ONNX using the command pip3 install --user onnx onnxoptimizer. You may remove the --user option if you have root access and want to install ONNX globally. The onnxoptimizer dependency is optional, and is only used for onnx>=1.9.

Compiling an ONNX Model to the TVM Runtime

Once we’ve downloaded the ResNet-50 model, the next step is to compile it. To accomplish that, we are going to use tvmc compile. The output we get from the compilation process is a TAR package of the model compiled to a dynamic library for our target platform. We can run that model on our target device using the TVM runtime.

# This may take several minutes depending on your machine
tvmc compile \
--target "llvm" \
--input-shapes "data:[1,3,224,224]" \
--output resnet50-v2-7-tvm.tar \
resnet50-v2-7.onnx

Let’s take a look at the files that tvmc compile creates in the module:

mkdir model
tar -xvf resnet50-v2-7-tvm.tar -C model
ls model

You will see three files listed.

• mod.so is the model, represented as a C++ library, that can be loaded by the TVM runtime.

• mod.json is a text representation of the TVM Relay computation graph.

• mod.params is a file containing the parameters for the pre-trained model.

This module can be directly loaded by your application, and the model can be run via the TVM runtime APIs.

Defining the Correct Target

Specifying the correct target (option --target) can have a huge impact on the performance of the compiled module, as it can take advantage of hardware features available on the target. For more information, please refer to Auto-tuning a convolutional network for x86 CPU. We recommend identifying which CPU you are running, along with optional features, and set the target appropriately.

Running the Model from The Compiled Module with TVMC

Now that we’ve compiled the model to this module, we can use the TVM runtime to make predictions with it. TVMC has the TVM runtime built in to it, allowing you to run compiled TVM models. To use TVMC to run the model and make predictions, we need two things:

• The compiled module, which we just produced.

• Valid input to the model to make predictions on.

Each model is particular when it comes to expected tensor shapes, formats and data types. For this reason, most models require some pre and post-processing, to ensure the input is valid and to interpret the output. TVMC has adopted NumPy’s .npz format for both input and output data. This is a well-supported NumPy format to serialize multiple arrays into a file.

As input for this tutorial, we will use the image of a cat, but you can feel free to substitute this image for any of your choosing.

Input pre-processing

For our ResNet-50 v2 model, the input is expected to be in ImageNet format. Here is an example of a script to pre-process an image for ResNet-50 v2.

You will need to have a supported version of the Python Image Library installed. You can use pip3 install --user pillow to satisfy this requirement for the script.

preprocess.py

#!python ./preprocess.py
from tvm.contrib.download import download_testdata
from PIL import Image
import numpy as np

img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
img_path = download_testdata(img_url, "imagenet_cat.png", module="data")

# Resize it to 224x224
resized_image = Image.open(img_path).resize((224, 224))
img_data = np.asarray(resized_image).astype("float32")

# ONNX expects NCHW input, so convert the array
img_data = np.transpose(img_data, (2, 0, 1))

# Normalize according to ImageNet
imagenet_mean = np.array([0.485, 0.456, 0.406])
imagenet_stddev = np.array([0.229, 0.224, 0.225])
norm_img_data = np.zeros(img_data.shape).astype("float32")
for i in range(img_data.shape[0]):
      norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i]

# Add batch dimension
img_data = np.expand_dims(norm_img_data, axis=0)

# Save to .npz (outputs imagenet_cat.npz)
np.savez("imagenet_cat", data=img_data)

Running the Compiled Module

With both the model and input data in hand, we can now run TVMC to make a prediction:

tvmc run \
--inputs imagenet_cat.npz \
--output predictions.npz \
resnet50-v2-7-tvm.tar

Recall that the .tar model file includes a C++ library, a description of the Relay model, and the parameters for the model. TVMC includes the TVM runtime, which can load the model and make predictions against input. When running the above command, TVMC outputs a new file, predictions.npz, that contains the model output tensors in NumPy format.

In this example, we are running the model on the same machine that we used for compilation. In some cases we might want to run it remotely via an RPC Tracker. To read more about these options please check tvmc run --help.

Output Post-Processing

As previously mentioned, each model will have its own particular way of providing output tensors.

In our case, we need to run some post-processing to render the outputs from ResNet-50 v2 into a more human-readable form, using the lookup-table provided for the model.

The script below shows an example of the post-processing to extract labels from the output of our compiled module.

postprocess.py

#!python ./postprocess.py
import os.path
import numpy as np

from scipy.special import softmax

from tvm.contrib.download import download_testdata

# Download a list of labels
labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
labels_path = download_testdata(labels_url, "synset.txt", module="data")

with open(labels_path, "r") as f:
    labels = [l.rstrip() for l in f]

output_file = "predictions.npz"

# Open the output and read the output tensor
if os.path.exists(output_file):
    with np.load(output_file) as data:
        scores = softmax(data["output_0"])
        scores = np.squeeze(scores)
        ranks = np.argsort(scores)[::-1]

        for rank in ranks[0:5]:
            print("class='%s' with probability=%f" % (labels[rank], scores[rank]))

Running this script should produce the following output:

python postprocess.py
# class='n02123045 tabby, tabby cat' with probability=0.610553
# class='n02123159 tiger cat' with probability=0.367179
# class='n02124075 Egyptian cat' with probability=0.019365
# class='n02129604 tiger, Panthera tigris' with probability=0.001273
# class='n04040759 radiator' with probability=0.000261

Try replacing the cat image with other images, and see what sort of predictions the ResNet model makes.

Automatically Tuning the ResNet Model

The previous model was compiled to work on the TVM runtime, but did not include any platform specific optimization. In this section, we will show you how to build an optimized model using TVMC to target your working platform.

In some cases, we might not get the expected performance when running inferences using our compiled module. In cases like this, we can make use of the auto-tuner, to find a better configuration for our model and get a boost in performance. Tuning in TVM refers to the process by which a model is optimized to run faster on a given target. This differs from training or fine-tuning in that it does not affect the accuracy of the model, but only the runtime performance. As part of the tuning process, TVM will try running many different operator implementation variants to see which perform best. The results of these runs are stored in a tuning records file, which is ultimately the output of the tune subcommand.

In the simplest form, tuning requires you to provide three things:

• the target specification of the device you intend to run this model on

• the path to an output file in which the tuning records will be stored, and finally

• a path to the model to be tuned.

The example below demonstrates how that works in practice:

# The default search algorithm requires xgboost, see below for further
# details on tuning search algorithms
pip install xgboost

tvmc tune \
--target "llvm" \
--output resnet50-v2-7-autotuner_records.json \
resnet50-v2-7.onnx

In this example, you will see better results if you indicate a more specific target for the --target flag. For example, on an Intel i7 processor you could use --target llvm -mcpu=skylake. For this tuning example, we are tuning locally on the CPU using LLVM as the compiler for the specified achitecture.

TVMC will perform a search against the parameter space for the model, trying out different configurations for operators and choosing the one that runs fastest on your platform. Although this is a guided search based on the CPU and model operations, it can still take several hours to complete the search. The output of this search will be saved to the resnet50-v2-7-autotuner_records.json file, which will later be used to compile an optimized model.

Defining the Tuning Search Algorithm

By default this search is guided using an XGBoost Grid algorithm. Depending on your model complexity and amount of time avilable, you might want to choose a different algorithm. A full list is available by consulting tvmc tune --help.

The output will look something like this for a consumer-level Skylake CPU:

tvmc tune \
--target "llvm -mcpu=broadwell" \
--output resnet50-v2-7-autotuner_records.json \
resnet50-v2-7.onnx
# [Task  1/24]  Current/Best:    9.65/  23.16 GFLOPS | Progress: (60/1000) | 130.74 s Done.
# [Task  1/24]  Current/Best:    3.56/  23.16 GFLOPS | Progress: (192/1000) | 381.32 s Done.
# [Task  2/24]  Current/Best:   13.13/  58.61 GFLOPS | Progress: (960/1000) | 1190.59 s Done.
# [Task  3/24]  Current/Best:   31.93/  59.52 GFLOPS | Progress: (800/1000) | 727.85 s Done.
# [Task  4/24]  Current/Best:   16.42/  57.80 GFLOPS | Progress: (960/1000) | 559.74 s Done.
# [Task  5/24]  Current/Best:   12.42/  57.92 GFLOPS | Progress: (800/1000) | 766.63 s Done.
# [Task  6/24]  Current/Best:   20.66/  59.25 GFLOPS | Progress: (1000/1000) | 673.61 s Done.
# [Task  7/24]  Current/Best:   15.48/  59.60 GFLOPS | Progress: (1000/1000) | 953.04 s Done.
# [Task  8/24]  Current/Best:   31.97/  59.33 GFLOPS | Progress: (972/1000) | 559.57 s Done.
# [Task  9/24]  Current/Best:   34.14/  60.09 GFLOPS | Progress: (1000/1000) | 479.32 s Done.
# [Task 10/24]  Current/Best:   12.53/  58.97 GFLOPS | Progress: (972/1000) | 642.34 s Done.
# [Task 11/24]  Current/Best:   30.94/  58.47 GFLOPS | Progress: (1000/1000) | 648.26 s Done.
# [Task 12/24]  Current/Best:   23.66/  58.63 GFLOPS | Progress: (1000/1000) | 851.59 s Done.
# [Task 13/24]  Current/Best:   25.44/  59.76 GFLOPS | Progress: (1000/1000) | 534.58 s Done.
# [Task 14/24]  Current/Best:   26.83/  58.51 GFLOPS | Progress: (1000/1000) | 491.67 s Done.
# [Task 15/24]  Current/Best:   33.64/  58.55 GFLOPS | Progress: (1000/1000) | 529.85 s Done.
# [Task 16/24]  Current/Best:   14.93/  57.94 GFLOPS | Progress: (1000/1000) | 645.55 s Done.
# [Task 17/24]  Current/Best:   28.70/  58.19 GFLOPS | Progress: (1000/1000) | 756.88 s Done.
# [Task 18/24]  Current/Best:   19.01/  60.43 GFLOPS | Progress: (980/1000) | 514.69 s Done.
# [Task 19/24]  Current/Best:   14.61/  57.30 GFLOPS | Progress: (1000/1000) | 614.44 s Done.
# [Task 20/24]  Current/Best:   10.47/  57.68 GFLOPS | Progress: (980/1000) | 479.80 s Done.
# [Task 21/24]  Current/Best:   34.37/  58.28 GFLOPS | Progress: (308/1000) | 225.37 s Done.
# [Task 22/24]  Current/Best:   15.75/  57.71 GFLOPS | Progress: (1000/1000) | 1024.05 s Done.
# [Task 23/24]  Current/Best:   23.23/  58.92 GFLOPS | Progress: (1000/1000) | 999.34 s Done.
# [Task 24/24]  Current/Best:   17.27/  55.25 GFLOPS | Progress: (1000/1000) | 1428.74 s Done.

Tuning sessions can take a long time, so tvmc tune offers many options to customize your tuning process, in terms of number of repetitions (--repeat and --number, for example), the tuning algorithm to be used, and so on. Check tvmc tune --help for more information.

In some situations it might be a good idea, to only tune specific tasks (i.e. the most relevant ones) to waste less time tuning simpler workworloads. The flag –task offers versatile options to limt the tasks used for tuning, e.g. –task 20,22 or –task 16-. All available tasks can be printed using –task list.

Compiling an Optimized Model with Tuning Data

As an output of the tuning process above, we obtained the tuning records stored in resnet50-v2-7-autotuner_records.json. This file can be used in two ways:

• As input to further tuning (via tvmc tune --tuning-records).

• As input to the compiler

The compiler will use the results to generate high performance code for the model on your specified target. To do that we can use tvmc compile --tuning-records. Check tvmc compile --help for more information.

Now that tuning data for the model has been collected, we can re-compile the model using optimized operators to speed up our computations.

tvmc compile \
--target "llvm" \
--tuning-records resnet50-v2-7-autotuner_records.json  \
--output resnet50-v2-7-tvm_autotuned.tar \
resnet50-v2-7.onnx

Verify that the optimized model runs and produces the same results:

tvmc run \
--inputs imagenet_cat.npz \
--output predictions.npz \
resnet50-v2-7-tvm_autotuned.tar

python postprocess.py

Verifying that the predictions are the same:

# class='n02123045 tabby, tabby cat' with probability=0.610550
# class='n02123159 tiger cat' with probability=0.367181
# class='n02124075 Egyptian cat' with probability=0.019365
# class='n02129604 tiger, Panthera tigris' with probability=0.001273
# class='n04040759 radiator' with probability=0.000261

Comparing the Tuned and Untuned Models

TVMC gives you tools for basic performance benchmarking between the models. You can specify a number of repetitions and that TVMC report on the model run time (independent of runtime startup). We can get a rough idea of how much tuning has improved the model performance. For example, on a test Intel i7 system, we see that the tuned model runs 47% faster than the untuned model:

tvmc run \
--inputs imagenet_cat.npz \
--output predictions.npz  \
--print-time \
--repeat 100 \
resnet50-v2-7-tvm_autotuned.tar

# Execution time summary:
# mean (ms)   max (ms)    min (ms)    std (ms)
#     92.19     115.73       89.85        3.15

tvmc run \
--inputs imagenet_cat.npz \
--output predictions.npz  \
--print-time \
--repeat 100 \
resnet50-v2-7-tvm.tar

# Execution time summary:
# mean (ms)   max (ms)    min (ms)    std (ms)
#    193.32     219.97      185.04        7.11

Final Remarks

In this tutorial, we presented TVMC, a command line driver for TVM. We demonstrated how to compile, run, and tune a model. We also discussed the need for pre and post-processing of inputs and outputs. After the tuning process, we demonstrated how to compare the performance of the unoptimized and optimize models.

Here we presented a simple example using ResNet-50 v2 locally. However, TVMC supports many more features including cross-compilation, remote execution and profiling/benchmarking.

To see what other options are available, please have a look at tvmc --help.

In the next tutorial <tvmc_python>, we introduce the Python interface to TVM, and in the tutorial after that, Compiling and Optimizing a Model with the Python Interface <autotvm_relay_x86>, we will cover the same compilation and optimization steps using the Python interface.