docs/get_started/tutorials/quick_start.py

# 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.

"""
.. _quick_start:

Quick Start
===========

This tutorial is for people who are new to Apache TVM. Taking an simple example
to show how to use Apache TVM to compile a simple neural network.

.. contents:: Table of Contents
    :local:
    :depth: 2

"""

################################################################################
# Overview
# --------
# Apache TVM is a machine learning compilation framework, following the principle of
# **Python-first development** and **universal deployment**. It takes in pre-trained
# machine learning models, compiles and generates deployable modules that can be embedded
# and run everywhere.
# Apache TVM also enables customizing optimization processes to introduce new optimizations,
# libraries, codegen and more.
#
# Apache TVM can help to:
#
# - **Optimize** performance of ML workloads, composing libraries and codegen.
# - **Deploy** ML workloads to a diverse set of new environments, including new runtime and new
#   hardware.
# - **Continuously improve and customize** ML deployment pipeline in Python by quickly customizing
#   library dispatching, bringing in customized operators and code generation.

################################################################################
# Overall Flow
# ------------
# Then we will show the overall flow of using Apache TVM to compile a neural network model,
# showing how to optimize, deploy and run the model.
# The overall flow is illustrated as the figure:
#
# .. figure:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/design/tvm_overall_flow.svg
#    :align: center
#    :width: 80%
#
# The overall flow consists of the following steps:
#
# - **Construct or Import a Model**: Construct a neural network model or import a pre-trained
#   model from other frameworks (e.g. PyTorch, ONNX), and create the TVM IRModule, which contains
#   all the information needed for compilation, including high-level Relax functions for
#   computational graph, and low-level TensorIR functions for tensor program.
# - **Perform Composable Optimizations**: Perform a series of optimization transformations,
#   such as graph optimizations, tensor program optimizations, and library dispatching.
# - **Build and Universal Deployment**: Build the optimized model to a deployable module to the
#   universal runtime, and execute it on different devices, such as CPU, GPU, or other accelerators.

################################################################################
# Construct or Import a Model
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Before we get started, let's construct a neural network model first.
# In this tutorial, to make things simple, we will define a two-layer MLP network
# directly in this script with the TVM Relax frontend, which is a similar API to PyTorch.
#

import tvm
from tvm import relax
from tvm.relax.frontend import nn


class MLPModel(nn.Module):
    def __init__(self):
        super(MLPModel, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu1(x)
        x = self.fc2(x)
        return x


################################################################################
# Then we can export the model to TVM IRModule, which is the central intermediate representation
# in TVM.

mod, param_spec = MLPModel().export_tvm(
    spec={"forward": {"x": nn.spec.Tensor((1, 784), "float32")}}
)
mod.show()

################################################################################
# Perform Optimization Transformations
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Apache TVM leverage ``pipeline`` to transform and optimize program.
# The pipeline encapsulates a collection of transformation that gets two goals (at the same level):
#
# - **Model optimizations**: such as operator fusion, layout rewrites.
# - **Tensor program optimization**: Map the operators to low-level implementations
#   (both library or codegen)
#
# .. note::
#   The twos are goals but not the stages of the pipeline. The two optimizations are performed
#   **at the same level**, or separately in two stages.
#
# .. note::
#   In this tutorial we only demonstrate the overall flow, by leverage ``zero`` optimization
#   pipeline, instead of optimizing for any specific target.

mod = relax.get_pipeline("zero")(mod)


################################################################################
# Build and Universal Deployment
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# After the optimization, we can build the model to a deployable module and run it on
# different devices.


import numpy as np

target = tvm.target.Target("llvm")
ex = tvm.compile(mod, target)
device = tvm.cpu()
vm = relax.VirtualMachine(ex, device)
data = np.random.rand(1, 784).astype("float32")
tvm_data = tvm.runtime.tensor(data, device=device)
params = [np.random.rand(*param.shape).astype("float32") for _, param in param_spec]
params = [tvm.runtime.tensor(param, device=device) for param in params]
print(vm["forward"](tvm_data, *params).numpy())

################################################################################
# Our goal is to bring machine learning to the application with any language of interest,
# with the minimum runtime support.
#
# - Each function in IRModule becomes a runnable function in the runtime. For example in LLM
#   cases, we can call ``prefill`` and ``decode`` functions directly.
#
#   .. code-block:: Python
#
#       prefill_logits = vm["prefill"](inputs, weight, kv_cache)
#       decoded_logits = vm["decode"](inputs, weight, kv_cache)
#
# - TVM runtime comes with native data structures, such as Tensor, can also have zero
#   copy exchange with existing ecosystem (DLPack exchange with PyTorch)
#
#   .. code-block:: Python
#
#       # Convert PyTorch tensor to TVM Tensor
#       x_tvm = tvm.runtime.from_dlpack(x_torch)
#       # Convert TVM Tensor to PyTorch tensor
#       x_torch = torch.from_dlpack(x_tvm)
#
# - TVM runtime works in non-python environments, so it works on settings such as mobile
#
#   .. code-block:: C++
#
#       // C++ snippet
#       runtime::Module vm = ex.GetFunction("load_executable")();
#       vm.GetFunction("init")(...);
#       Tensor out = vm.GetFunction("prefill")(data, weight, kv_cache);
#
#   .. code-block:: Java
#
#       // Java snippet
#       Module vm = ex.getFunction("load_executable").invoke();
#       vm.getFunction("init").pushArg(...).invoke;
#       Tensor out = vm.getFunction("prefill").pushArg(data).pushArg(weight).pushArg(kv_cache).invoke();
#

################################################################################
# Read next
# ---------
# This tutorial demonstrates the overall flow of using Apache TVM to compile a neural network model.
# For more advanced or specific topics, please refer to the following tutorials
#