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[tvmc][docs] Getting started tutorial for TVMC (apache#6597)
* [tvmc][docs] Getting started tutorial for TVMC * Include a tutorial, demonstrating basic capabilities of TVMC, by executing a full pipeline (tune, compile, run) on a ResNet-50 model. Co-authored-by: Matthew Barrett <[email protected]> * apply suggestions from code review Co-authored-by: Tristan Konolige <[email protected]> Co-authored-by: Cody Yu <[email protected]> * adjust text according to code-review * improve reading flow into tuning section Co-authored-by: Matthew Barrett <[email protected]> Co-authored-by: Tristan Konolige <[email protected]> Co-authored-by: Cody Yu <[email protected]>
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you under the Apache License, Version 2.0 (the | ||
# "License"); you may not use this file except in compliance | ||
# with the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, | ||
# software distributed under the License is distributed on an | ||
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
# KIND, either express or implied. See the License for the | ||
# specific language governing permissions and limitations | ||
# under the License. | ||
""" | ||
Getting Started with TVM command line driver - TVMC | ||
=================================================== | ||
**Authors**: | ||
`Leandro Nunes <https://github.com/leandron>`_, | ||
`Matthew Barrett <https://github.com/mbaret>`_ | ||
This tutorial is an introduction to working 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, via a | ||
command line interface. | ||
In this tutorial we are going to use TVMC to compile, run and tune a | ||
ResNet-50 on a x86 CPU. | ||
We are going to start by downloading ResNet 50 V2. Then, we are going | ||
to use TVMC to compile this model into a TVM module, and use the | ||
compiled module to generate predictions. Finally, we are going to experiment | ||
with the auto-tuning options, that can be used to help the compiler to | ||
improve network performance. | ||
The final goal is to give an overview of TVMC's capabilities and also | ||
some guidance on where to look for more information. | ||
""" | ||
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###################################################################### | ||
# 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 | ||
# as a command line application called ``tvmc``. | ||
# | ||
# 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: | ||
# | ||
# .. code-block:: bash | ||
# | ||
# tvmc --help | ||
# | ||
# | ||
# As you can see in the help page, the main features are | ||
# accessible via the subcommands ``tune``, ``compile`` and ``run``. | ||
# To read about specific options under a given subcommand, use | ||
# ``tvmc <subcommand> --help``. | ||
# | ||
# In the following sections we will use TVMC to tune, compile and | ||
# run a model. But first, we need a model. | ||
# | ||
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###################################################################### | ||
# Obtaining the model | ||
# ------------------- | ||
# | ||
# We are going to use ResNet-50 V2 as an example to experiment with TVMC. | ||
# The version below is in ONNX format. To download the file, you can use | ||
# the command below: | ||
# | ||
# .. code-block:: bash | ||
# | ||
# wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx | ||
# | ||
# | ||
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###################################################################### | ||
# .. note:: 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. | ||
# | ||
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###################################################################### | ||
# Compiling the model | ||
# ------------------- | ||
# | ||
# The next step once we've downloaded ResNet-50, 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, | ||
# that can be used to run our model on the target device. | ||
# | ||
# .. code-block:: bash | ||
# | ||
# tvmc compile \ | ||
# --target "llvm" \ | ||
# --output compiled_module.tar \ | ||
# resnet50-v2-7.onnx | ||
# | ||
# Once compilation finishes, the output ``compiled_module.tar`` will be created. This | ||
# can be directly loaded by your application and run via the TVM runtime APIs. | ||
# | ||
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###################################################################### | ||
# .. note:: 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 <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network>`_. | ||
# | ||
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###################################################################### | ||
# | ||
# In the next step, we are going to use the compiled module, providing it | ||
# with some inputs, to generate some predictions. | ||
# | ||
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###################################################################### | ||
# Input pre-processing | ||
# -------------------- | ||
# | ||
# In order to generate predictions, we will need two things: | ||
# | ||
# - the compiled module, which we just produced; | ||
# - a valid input to the model | ||
# | ||
# 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(s) is valid and to interpret the output(s). | ||
# | ||
# In TVMC, we 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. | ||
# | ||
# We will use the usual cat image, similar to other TVM tutorials: | ||
# | ||
# .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg | ||
# :height: 224px | ||
# :width: 224px | ||
# :align: center | ||
# | ||
# 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. | ||
# | ||
from tvm.contrib.download import download_testdata | ||
from PIL import Image | ||
import numpy as np | ||
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img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg" | ||
img_path = download_testdata(img_url, "imagenet_cat.png", module="data") | ||
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# Resize it to 224x224 | ||
resized_image = Image.open(img_path).resize((224, 224)) | ||
img_data = np.asarray(resized_image).astype("float32") | ||
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# ONNX expects NCHW input, so convert the array | ||
img_data = np.transpose(img_data, (2, 0, 1)) | ||
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# 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] | ||
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# Add batch dimension | ||
img_data = np.expand_dims(norm_img_data, axis=0) | ||
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# Save to .npz (outputs imagenet_cat.npz) | ||
np.savez("imagenet_cat", data=img_data) | ||
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###################################################################### | ||
# Running the compiled module | ||
# --------------------------- | ||
# | ||
# With both the compiled module and input file in hand, we can run it by | ||
# invoking ``tvmc run``. | ||
# | ||
# .. code-block:: bash | ||
# | ||
# tvmc run \ | ||
# --inputs imagenet_cat.npz \ | ||
# --output predictions.npz \ | ||
# compiled_module.tar | ||
# | ||
# When running the above command, a new file ``predictions.npz`` should | ||
# be produced. It contains the output tensors. | ||
# | ||
# 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``. | ||
# | ||
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###################################################################### | ||
# 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. | ||
# | ||
# The script below shows an example of the post-processing to extract | ||
# labels from the output of our compiled module. | ||
# | ||
import os.path | ||
import numpy as np | ||
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from scipy.special import softmax | ||
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from tvm.contrib.download import download_testdata | ||
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# 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") | ||
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with open(labels_path, "r") as f: | ||
labels = [l.rstrip() for l in f] | ||
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output_file = "predictions.npz" | ||
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# 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) | ||
scores = np.argsort(scores)[::-1] | ||
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for i in scores[0:5]: | ||
print("class='%s' with probability=%f" % (labels[i], scores[i])) | ||
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######################################################################## | ||
# When running the script, a list of predictions should be printed similar | ||
# the the example below. | ||
# | ||
# .. code-block:: bash | ||
# | ||
# $ python post_processing.py | ||
# class=n02123045 tabby, tabby cat ; probability=446.000000 | ||
# class=n02123159 tiger cat ; probability=675.000000 | ||
# class=n02124075 Egyptian cat ; probability=836.000000 | ||
# class=n02129604 tiger, Panthera tigris ; probability=917.000000 | ||
# class=n04040759 radiator ; probability=213.000000 | ||
# | ||
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###################################################################### | ||
# Tuning the model | ||
# ---------------- | ||
# | ||
# 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: | ||
# | ||
# .. code-block:: bash | ||
# | ||
# tvmc tune \ | ||
# --target "llvm" \ | ||
# --output autotuner_records.json \ | ||
# resnet50-v2-7.onnx | ||
# | ||
# | ||
# 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 use, and so on. | ||
# Check ``tvmc tune --help`` for more information. | ||
# | ||
# As an output of the tuning process above, we obtained the tuning records stored | ||
# in ``autotuner_records.json``. This file can be used in two ways: | ||
# | ||
# - as an input to further tuning (via ``tvmc tune --tuning-records``), or | ||
# - as an 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. | ||
# | ||
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###################################################################### | ||
# Final Remarks | ||
# ------------- | ||
# | ||
# In this tutorial, we presented TVMC, a command line driver for TVM. | ||
# We demonstrated how to compile, run and tune a model, as well | ||
# as discussed the need for pre and post processing of inputs and outputs. | ||
# | ||
# 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``. | ||
# |