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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/03/11 23:08:07 UTC
[GitHub] [tvm] hogepodge opened a new pull request #7643: [docs] Getting Started with TVM: AutoTVM and Matrix Multiply
hogepodge opened a new pull request #7643:
URL: https://github.com/apache/tvm/pull/7643
This patch moves the matrix multiplcation example tuning with AutoTVM to the tutorial directory, and expands on the content. This follows and builds on the section on TE.
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[GitHub] [tvm] hogepodge commented on pull request #7643: [docs] Getting Started with TVM: AutoTVM and Matrix Multiply
Posted by GitBox <gi...@apache.org>.
hogepodge commented on pull request #7643:
URL: https://github.com/apache/tvm/pull/7643#issuecomment-806235620
@tqchen ready for review
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[GitHub] [tvm] hogepodge commented on a change in pull request #7643: [docs] Getting Started with TVM: AutoTVM and Matrix Multiply
Posted by GitBox <gi...@apache.org>.
hogepodge commented on a change in pull request #7643:
URL: https://github.com/apache/tvm/pull/7643#discussion_r600031303
##########
File path: tutorials/get_started/autotvm_matmul.py
##########
@@ -0,0 +1,377 @@
+# 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.
+"""
+Optimizing Operators with Templates and AutoTVM
+===============================================
+**Authors**:
+`Lianmin Zheng <https://github.com/merrymercy>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In this tutorial, we will now show how the TVM Template Extension (TE) language
+can be used to write scheduling templates that can be searched by AutoTVM to
+find optimal configurations of scheduling variables. This process is called
+Auto-Tuning, and builds on TE to help automate the process of optimizing
+operations.
+
+This tutorial builds on the previous `tutorial on how to write a matrix
+multiplication using TE <tensor_expr_get_started>`.
+
+There are two steps in auto-tuning.
+
+- The first step is defining a search space.
+- The second step is running a search algorithm to explore through this space.
+
+In this tutorial, you can learn how to perform these two steps in TVM. The whole
+workflow is illustrated by a matrix multiplication example.
+
+.. note::
+ Note that this tutorial will not run on Windows or recent versions of macOS.
+ To get it to run, you will need to wrap the body of this tutorial in a
+ :code:`if __name__ == "__main__":` block.
+"""
+
+################################################################################
+# Install dependencies
+# --------------------
+# To use autotvm package in TVM, we need to install some extra dependencies.
+#
+# .. code-block:: bash
+#
+# pip3 install --user psutil xgboost cloudpickle
+#
+# To make TVM run faster in tuning, it is recommended to use cython as FFI of
+# TVM. In the root directory of TVM, execute (change "3" to "2" if you use
+# python2):
+#
+# .. code-block:: bash
+#
+# pip3 install --user cython
+# sudo make cython3
+#
+# Now return to python code. Begin by importing the required packages.
+
+import logging
+import sys
+
+import numpy as np
+import tvm
+from tvm import te
+import tvm.testing
+
+# the module is called `autotvm`
+from tvm import autotvm
+
+################################################################################
+# Basic Matrix Multiplication with TE
+# -----------------------------------
+# Recall the basic implementation of matrix multiplication using TE. We write
+# it down here with a few changes. We will wrap the multiplication in a python
+# function definition. For simplicity, we will focus our attention on a split
+# optimization, using a fixed value that defines the block size of the
+# reordering.
+
+
+def matmul_basic(N, L, M, dtype):
+
+ a = te.placeholder((n, l), name="a", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="C")
+ s = te.create_schedule(C.op)
+
+ # schedule
+ y, x = s[C].op.axis
+ k = s[C].op.reduce_axis[0]
+
+ yo, yi = s[C].split(y, 8)
+ xo, xi = s[C].split(x, 8)
+
+ s[C].reorder(yo, xo, k, yi, xi)
+
+ return s, [A, B, C]
+
+
+################################################################################
+# Matrix Multiplication with AutoTVM
+# ----------------------------------
+# In the previous schedule code, we use a constant "8" as the tiling factor.
+# However, it might not be the best one because the best tiling factor depends
+# on real hardware environment and input shape.
+#
+# If you want the schedule code to be portable across a wider range of input
+# shapes and target hardware, it is better to define a set of candidate values
+# and pick the best one according to the measurement results on target
+# hardware.
+#
+# In autotvm, we can define a tunable parameter, or a "knob" for such kind of
+# value.
+
+################################################################################
+# A Basic Matrix Multiplication Template
+# --------------------------------------
+# We begin with an example of how to create a tunable parameter set for the
+# block size of the `split` scheduling operation.
+
+# Matmul V1: List candidate values
+@autotvm.template("tutorial/matmul_v1") # 1. use a decorator
+def matmul_v1(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="C")
+ s = te.create_schedule(C.op)
+
+ # schedule
+ y, x = s[C].op.axis
+ k = s[C].op.reduce_axis[0]
+
+ # 2. get the config object
+ cfg = autotvm.get_config()
+
+ # 3. define search space
+ cfg.define_knob("tile_y", [1, 2, 4, 8, 16])
+ cfg.define_knob("tile_x", [1, 2, 4, 8, 16])
+
+ # 4. schedule according to config
+ yo, yi = s[C].split(y, cfg["tile_y"].val)
+ xo, xi = s[C].split(x, cfg["tile_x"].val)
+
+ s[C].reorder(yo, xo, k, yi, xi)
+
+ return s, [A, B, C]
+
+
+################################################################################
+# Here we make four modifications to the previous schedule code and get a
+# tunable "template". We can explain the modifications one by one.
+#
+# 1. Use a decorator to mark this function as a simple template.
+# 2. Get a config object: You can regard this :code:`cfg` as an argument of
+# this function but we obtain it in a different way. With this argument, this
+# function is no longer a deterministic schedule. Instead, we can pass
+# different configurations to this function and get different schedules. A
+# function that uses a configuration object like this is called a "template".
+#
+# To make the template function more compact, we can do two things to define
+# the parameter search space within a single function.
+#
+# 1. Define a search space across a set values. This is done by making
+# :code:`cfg` a :any:`ConfigSpace` object. It will collect all of the
+# tunable knobs in this function and build a search space from it.
+# 2. Schedule according to an entity in this space. This is done by making
+# :code:`cfg` a :any:`ConfigEntity` object. When it is a
+# :any:`ConfigEntity`, it will ignore all space definition API (namely,
+# :code:`cfg.define_XXXXX(...)`). Instead, it will store deterministic
+# values for all tunable knobs, and we schedule according to these values.
+#
+# During auto-tuning, we will first call this template with a
+# :any:`ConfigSpace` object to build the search space. Then we call this
+# template with different :any:`ConfigEntity` in the built space to get
+# different schedules. Finally we will measure the code generated by
+# different schedules and pick the best one.
+#
+# 3. Define two tunable knobs. The first one is :code:`tile_y` with 5 possible
+# values. The second one is :code:`tile_x` with a same list of possible values.
+# These two knobs are independent, so they span a search space with size 25 =
+# 5x5.
+# 4. The configuration knobs are passed to the :code:`split` schedule
+# operation, allowing us to schedule according to the 5x5 deterministic values
+# we previously defined in :code:`cfg`.
+
+################################################################################
+# A Matrix Multiplication Template with the Advanced Parameter API
+# ----------------------------------------------------------------
+# In the previous template, we manually listed all of the possible values for a
+# knob. This is the lowest level API to define the space, and gives an explicit
+# enumeration of the parameter space to search. However, we also provide
+# another set of APIs that can make the definition of the search space easier
+# and smarter. Where possible, we receomment you use this higher-level API
+#
+# In the following example, we use :any:`ConfigSpace.define_split` to define a
+# split knob. It will enumerate all the possible ways to split an axis and
+# construct the space.
+#
+# We also have :any:`ConfigSpace.define_reorder` for reorder knob and
+# :any:`ConfigSpace.define_annotate` for annotation like unroll, vectorization,
+# thread binding. When the high level API cannot meet your requirements, you
+# can always fall back to using the low level API.
+
+
+@autotvm.template("tutorial/matmul")
+def matmul(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="C")
+ s = te.create_schedule(C.op)
+
+ # schedule
+ y, x = s[C].op.axis
+ k = s[C].op.reduce_axis[0]
+
+ ##### define space begin #####
+ cfg = autotvm.get_config()
+ cfg.define_split("tile_y", y, num_outputs=2)
+ cfg.define_split("tile_x", x, num_outputs=2)
+ ##### define space end #####
+
+ # schedule according to config
+ yo, yi = cfg["tile_y"].apply(s, C, y)
+ xo, xi = cfg["tile_x"].apply(s, C, x)
+
+ s[C].reorder(yo, xo, k, yi, xi)
+
+ return s, [A, B, C]
+
+
+################################################################################
+# .. note:: More Explanation on :code:`cfg.define_split`
+#
+# In this template, :code:`cfg.define_split("tile_y", y, num_outputs=2)` will
+# enumerate all possible combinations that can split axis y into two axes with
+# factors of the length of y. For example, if the length of y is 32 and we
+# want to split it into two axes using factors of 32, then there are 6
+# possible values for (length of outer axis, length of inner axis) pair,
+# namely (32, 1), (16, 2), (8, 4), (4, 8), (2, 16) or (1, 32). These are all 6
+# possible values of `tile_y`.
+#
+# During scheduling, :code:`cfg["tile_y"]` is a :code:`SplitEntity` object.
+# We stores the lengths of outer axes and inner axes in
+# :code:`cfg['tile_y'].size` (a tuple with two elements). In this template,
+# we apply it by using :code:`yo, yi = cfg['tile_y'].apply(s, C, y)`.
+# Actually, this is equivalent to :code:`yo, yi = s[C].split(y,
+# cfg["tile_y"].size[1])` or :code:`yo, yi = s[C].split(y,
+# nparts=cfg['tile_y"].size[0])`
+#
+# The advantage of using cfg.apply API is that it makes multi-level splits
+# (that is, when num_outputs >= 3) easier.
+
+################################################################################
+# Step 2: Use AutoTVM to Optimize the Matrix Multiplication
+# ---------------------------------------------------------
+# In Step 1, we wrote a matrix multiplication template that allowed us to
+# paramaterize the block size used in the `split` schedule. We can now conduct
+# a search over this parameter space. The next step is to pick a tuner to guide
+# the exploration of this space.
+#
+# Auto-tuners in TVM
+# ~~~~~~~~~~~~~~~~~~
+# The job for a tuner can be described by following pseudo code
+#
+# .. code-block:: c
+#
+# ct = 0
+# while ct < max_number_of_trials:
+# propose a batch of configs
+# measure this batch of configs on real hardware and get results
+# ct += batch_size
+#
+# When proposing the next batch of configs, the tuner can take different
+# strategies. Some of the tuner strategies provided by TVM include:
+#
+# * :any:`RandomTuner`: Enumerate the space in a random order
+# * :any:`GridSearchTuner`: Enumerate the space in a grid search order
+# * :any:`GATuner`: Using genetic algorithm to search through the space
+# * :any:`XGBTuner`: Uses a model based method. Train a XGBoost model to
+# predict the speed of lowered IR and pick the next batch according to the
+# prediction.
+#
+# You can choose the tuner according to the size of your space, your time
+# budget and other factors. For example, if your space is very small (less
+# than 1000), a gridsearch tuner or a random tuner is good enough. If your
+# space is at the level of 10^9 (this is the space size of a conv2d operator on
+# CUDA GPU), XGBoostTuner can explore more efficiently and find better configs.
+
+################################################################################
+# Begin tuning
+# ~~~~~~~~~~~~
+# Here we continue our matrix multiplication example. First we create a tuning
+# task. We can also inspect the initialized search space. In this case, for a
+# 512x512 square matrix multiplication, the space size is 10x10=100 Note that
+# the task and search space are independent of the tuner picked.
+
+N, L, M = 512, 512, 512
+task = autotvm.task.create("tutorial/matmul", args=(N, L, M, "float32"), target="llvm")
+print(task.config_space)
+
+################################################################################
+# Then we need to define how to measure the generated code and pick a tuner.
+# Since our space is small, a random tuner is just okay.
+#
+# We only make 10 trials in this tutorial for demonstration. In practice, you
+# can do more trials according to your time budget. We will log the tuning
+# results into a log file. This file can be used to choose the best
+# configuration discovered by the tuner later.
+
+# logging config (for printing tuning log to the screen)
+logging.getLogger("autotvm").setLevel(logging.DEBUG)
+logging.getLogger("autotvm").addHandler(logging.StreamHandler(sys.stdout))
+
+################################################################################
+# There are two steps for measuring a config: build and run. By default, we use
+# all CPU cores to compile program. We then measure them sequentially. To help
+# reduce variance, we take 5 measurements and average them.
+measure_option = autotvm.measure_option(builder="local", runner=autotvm.LocalRunner(number=5))
+
+# Begin tuning with RandomTuner, log records to file `matmul.log`
+# You can use alternatives like XGBTuner.
+tuner = autotvm.tuner.RandomTuner(task)
+tuner.tune(
+ n_trial=10,
+ measure_option=measure_option,
+ callbacks=[autotvm.callback.log_to_file("matmul.log")],
+)
+
+################################################################################
+# With tuning completed, we can choose the configuration from the log file that
+# has the best measured performance and compile the schedule with the
+# corresponding parameters. We also do a quick verfication that the schedule is
+# producing correct answers. We can call the function :code:`matmul` directly
+# under the :any:`autotvm.apply_history_best` context. When we call this
+# function, it will query the dispatch context with its argument and get the
+# best config with the same argument.
+
+# apply history best from log file
+with autotvm.apply_history_best("matmul.log"):
+ with tvm.target.Target("llvm"):
+ s, arg_bufs = matmul(N, L, M, "float32")
+ func = tvm.build(s, arg_bufs)
+
+# check correctness
+a_np = np.random.uniform(size=(N, L)).astype(np.float32)
+b_np = np.random.uniform(size=(L, M)).astype(np.float32)
+c_np = a_np.dot(b_np)
+
+c_tvm = tvm.nd.empty(c_np.shape)
+func(tvm.nd.array(a_np), tvm.nd.array(b_np), c_tvm)
+
+tvm.testing.assert_allclose(c_np, c_tvm.asnumpy(), rtol=1e-2)
Review comment:
I don't have an answer for this. We can change to another value that's more consistent with standard practice.
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[GitHub] [tvm] jroesch merged pull request #7643: [docs] Getting Started with TVM: AutoTVM and Matrix Multiply
Posted by GitBox <gi...@apache.org>.
jroesch merged pull request #7643:
URL: https://github.com/apache/tvm/pull/7643
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[GitHub] [tvm] Hzfengsy commented on a change in pull request #7643: [docs] Getting Started with TVM: AutoTVM and Matrix Multiply
Posted by GitBox <gi...@apache.org>.
Hzfengsy commented on a change in pull request #7643:
URL: https://github.com/apache/tvm/pull/7643#discussion_r597369445
##########
File path: tutorials/get_started/autotvm_matmul.py
##########
@@ -0,0 +1,377 @@
+# 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.
+"""
+Optimizing Operators with Templates and AutoTVM
+===============================================
+**Authors**:
+`Lianmin Zheng <https://github.com/merrymercy>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In this tutorial, we will now show how the TVM Template Extension (TE) language
+can be used to write scheduling templates that can be searched by AutoTVM to
+find optimal configurations of scheduling variables. This process is called
+Auto-Tuning, and builds on TE to help automate the process of optimizing
+operations.
+
+This tutorial builds on the previous `tutorial on how to write a matrix
+multiplication using TE <tensor_expr_get_started>`.
+
+There are two steps in auto-tuning.
+
+- The first step is defining a search space.
+- The second step is running a search algorithm to explore through this space.
+
+In this tutorial, you can learn how to perform these two steps in TVM. The whole
+workflow is illustrated by a matrix multiplication example.
+
+.. note::
+ Note that this tutorial will not run on Windows or recent versions of macOS.
+ To get it to run, you will need to wrap the body of this tutorial in a
+ :code:`if __name__ == "__main__":` block.
+"""
+
+################################################################################
+# Install dependencies
+# --------------------
+# To use autotvm package in TVM, we need to install some extra dependencies.
+#
+# .. code-block:: bash
+#
+# pip3 install --user psutil xgboost cloudpickle
+#
+# To make TVM run faster in tuning, it is recommended to use cython as FFI of
+# TVM. In the root directory of TVM, execute (change "3" to "2" if you use
+# python2):
Review comment:
```suggestion
# TVM. In the root directory of TVM, execute:
```
TVM no longer supports Python2
##########
File path: tutorials/get_started/autotvm_matmul.py
##########
@@ -0,0 +1,377 @@
+# 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.
+"""
+Optimizing Operators with Templates and AutoTVM
+===============================================
+**Authors**:
+`Lianmin Zheng <https://github.com/merrymercy>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In this tutorial, we will now show how the TVM Template Extension (TE) language
+can be used to write scheduling templates that can be searched by AutoTVM to
+find optimal configurations of scheduling variables. This process is called
+Auto-Tuning, and builds on TE to help automate the process of optimizing
+operations.
+
+This tutorial builds on the previous `tutorial on how to write a matrix
+multiplication using TE <tensor_expr_get_started>`.
+
+There are two steps in auto-tuning.
+
+- The first step is defining a search space.
+- The second step is running a search algorithm to explore through this space.
+
+In this tutorial, you can learn how to perform these two steps in TVM. The whole
+workflow is illustrated by a matrix multiplication example.
+
+.. note::
+ Note that this tutorial will not run on Windows or recent versions of macOS.
+ To get it to run, you will need to wrap the body of this tutorial in a
+ :code:`if __name__ == "__main__":` block.
+"""
+
+################################################################################
+# Install dependencies
+# --------------------
+# To use autotvm package in TVM, we need to install some extra dependencies.
+#
+# .. code-block:: bash
+#
+# pip3 install --user psutil xgboost cloudpickle
+#
+# To make TVM run faster in tuning, it is recommended to use cython as FFI of
+# TVM. In the root directory of TVM, execute (change "3" to "2" if you use
+# python2):
+#
+# .. code-block:: bash
+#
+# pip3 install --user cython
+# sudo make cython3
+#
+# Now return to python code. Begin by importing the required packages.
+
+import logging
+import sys
+
+import numpy as np
+import tvm
+from tvm import te
+import tvm.testing
+
+# the module is called `autotvm`
+from tvm import autotvm
+
+################################################################################
+# Basic Matrix Multiplication with TE
+# -----------------------------------
+# Recall the basic implementation of matrix multiplication using TE. We write
+# it down here with a few changes. We will wrap the multiplication in a python
+# function definition. For simplicity, we will focus our attention on a split
+# optimization, using a fixed value that defines the block size of the
+# reordering.
+
+
+def matmul_basic(N, L, M, dtype):
+
+ a = te.placeholder((n, l), name="a", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="C")
+ s = te.create_schedule(C.op)
+
+ # schedule
+ y, x = s[C].op.axis
+ k = s[C].op.reduce_axis[0]
+
+ yo, yi = s[C].split(y, 8)
+ xo, xi = s[C].split(x, 8)
+
+ s[C].reorder(yo, xo, k, yi, xi)
+
+ return s, [A, B, C]
+
+
+################################################################################
+# Matrix Multiplication with AutoTVM
+# ----------------------------------
+# In the previous schedule code, we use a constant "8" as the tiling factor.
+# However, it might not be the best one because the best tiling factor depends
+# on real hardware environment and input shape.
+#
+# If you want the schedule code to be portable across a wider range of input
+# shapes and target hardware, it is better to define a set of candidate values
+# and pick the best one according to the measurement results on target
+# hardware.
+#
+# In autotvm, we can define a tunable parameter, or a "knob" for such kind of
+# value.
+
+################################################################################
+# A Basic Matrix Multiplication Template
+# --------------------------------------
+# We begin with an example of how to create a tunable parameter set for the
+# block size of the `split` scheduling operation.
+
+# Matmul V1: List candidate values
+@autotvm.template("tutorial/matmul_v1") # 1. use a decorator
+def matmul_v1(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="C")
+ s = te.create_schedule(C.op)
+
+ # schedule
+ y, x = s[C].op.axis
+ k = s[C].op.reduce_axis[0]
+
+ # 2. get the config object
+ cfg = autotvm.get_config()
+
+ # 3. define search space
+ cfg.define_knob("tile_y", [1, 2, 4, 8, 16])
+ cfg.define_knob("tile_x", [1, 2, 4, 8, 16])
+
+ # 4. schedule according to config
+ yo, yi = s[C].split(y, cfg["tile_y"].val)
+ xo, xi = s[C].split(x, cfg["tile_x"].val)
+
+ s[C].reorder(yo, xo, k, yi, xi)
+
+ return s, [A, B, C]
+
+
+################################################################################
+# Here we make four modifications to the previous schedule code and get a
+# tunable "template". We can explain the modifications one by one.
+#
+# 1. Use a decorator to mark this function as a simple template.
+# 2. Get a config object: You can regard this :code:`cfg` as an argument of
+# this function but we obtain it in a different way. With this argument, this
+# function is no longer a deterministic schedule. Instead, we can pass
+# different configurations to this function and get different schedules. A
+# function that uses a configuration object like this is called a "template".
+#
+# To make the template function more compact, we can do two things to define
+# the parameter search space within a single function.
+#
+# 1. Define a search space across a set values. This is done by making
+# :code:`cfg` a :any:`ConfigSpace` object. It will collect all of the
+# tunable knobs in this function and build a search space from it.
+# 2. Schedule according to an entity in this space. This is done by making
+# :code:`cfg` a :any:`ConfigEntity` object. When it is a
+# :any:`ConfigEntity`, it will ignore all space definition API (namely,
+# :code:`cfg.define_XXXXX(...)`). Instead, it will store deterministic
+# values for all tunable knobs, and we schedule according to these values.
+#
+# During auto-tuning, we will first call this template with a
+# :any:`ConfigSpace` object to build the search space. Then we call this
+# template with different :any:`ConfigEntity` in the built space to get
+# different schedules. Finally we will measure the code generated by
+# different schedules and pick the best one.
+#
+# 3. Define two tunable knobs. The first one is :code:`tile_y` with 5 possible
+# values. The second one is :code:`tile_x` with a same list of possible values.
+# These two knobs are independent, so they span a search space with size 25 =
+# 5x5.
+# 4. The configuration knobs are passed to the :code:`split` schedule
+# operation, allowing us to schedule according to the 5x5 deterministic values
+# we previously defined in :code:`cfg`.
+
+################################################################################
+# A Matrix Multiplication Template with the Advanced Parameter API
+# ----------------------------------------------------------------
+# In the previous template, we manually listed all of the possible values for a
+# knob. This is the lowest level API to define the space, and gives an explicit
+# enumeration of the parameter space to search. However, we also provide
+# another set of APIs that can make the definition of the search space easier
+# and smarter. Where possible, we receomment you use this higher-level API
+#
+# In the following example, we use :any:`ConfigSpace.define_split` to define a
+# split knob. It will enumerate all the possible ways to split an axis and
+# construct the space.
+#
+# We also have :any:`ConfigSpace.define_reorder` for reorder knob and
+# :any:`ConfigSpace.define_annotate` for annotation like unroll, vectorization,
+# thread binding. When the high level API cannot meet your requirements, you
+# can always fall back to using the low level API.
+
+
+@autotvm.template("tutorial/matmul")
+def matmul(N, L, M, dtype):
+ A = te.placeholder((N, L), name="A", dtype=dtype)
+ B = te.placeholder((L, M), name="B", dtype=dtype)
+
+ k = te.reduce_axis((0, L), name="k")
+ C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name="C")
+ s = te.create_schedule(C.op)
+
+ # schedule
+ y, x = s[C].op.axis
+ k = s[C].op.reduce_axis[0]
+
+ ##### define space begin #####
+ cfg = autotvm.get_config()
+ cfg.define_split("tile_y", y, num_outputs=2)
+ cfg.define_split("tile_x", x, num_outputs=2)
+ ##### define space end #####
+
+ # schedule according to config
+ yo, yi = cfg["tile_y"].apply(s, C, y)
+ xo, xi = cfg["tile_x"].apply(s, C, x)
+
+ s[C].reorder(yo, xo, k, yi, xi)
+
+ return s, [A, B, C]
+
+
+################################################################################
+# .. note:: More Explanation on :code:`cfg.define_split`
+#
+# In this template, :code:`cfg.define_split("tile_y", y, num_outputs=2)` will
+# enumerate all possible combinations that can split axis y into two axes with
+# factors of the length of y. For example, if the length of y is 32 and we
+# want to split it into two axes using factors of 32, then there are 6
+# possible values for (length of outer axis, length of inner axis) pair,
+# namely (32, 1), (16, 2), (8, 4), (4, 8), (2, 16) or (1, 32). These are all 6
+# possible values of `tile_y`.
+#
+# During scheduling, :code:`cfg["tile_y"]` is a :code:`SplitEntity` object.
+# We stores the lengths of outer axes and inner axes in
+# :code:`cfg['tile_y'].size` (a tuple with two elements). In this template,
+# we apply it by using :code:`yo, yi = cfg['tile_y'].apply(s, C, y)`.
+# Actually, this is equivalent to :code:`yo, yi = s[C].split(y,
+# cfg["tile_y"].size[1])` or :code:`yo, yi = s[C].split(y,
+# nparts=cfg['tile_y"].size[0])`
+#
+# The advantage of using cfg.apply API is that it makes multi-level splits
+# (that is, when num_outputs >= 3) easier.
+
+################################################################################
+# Step 2: Use AutoTVM to Optimize the Matrix Multiplication
+# ---------------------------------------------------------
+# In Step 1, we wrote a matrix multiplication template that allowed us to
+# paramaterize the block size used in the `split` schedule. We can now conduct
+# a search over this parameter space. The next step is to pick a tuner to guide
+# the exploration of this space.
+#
+# Auto-tuners in TVM
+# ~~~~~~~~~~~~~~~~~~
+# The job for a tuner can be described by following pseudo code
+#
+# .. code-block:: c
+#
+# ct = 0
+# while ct < max_number_of_trials:
+# propose a batch of configs
+# measure this batch of configs on real hardware and get results
+# ct += batch_size
+#
+# When proposing the next batch of configs, the tuner can take different
+# strategies. Some of the tuner strategies provided by TVM include:
+#
+# * :any:`RandomTuner`: Enumerate the space in a random order
+# * :any:`GridSearchTuner`: Enumerate the space in a grid search order
+# * :any:`GATuner`: Using genetic algorithm to search through the space
+# * :any:`XGBTuner`: Uses a model based method. Train a XGBoost model to
+# predict the speed of lowered IR and pick the next batch according to the
+# prediction.
+#
+# You can choose the tuner according to the size of your space, your time
+# budget and other factors. For example, if your space is very small (less
+# than 1000), a gridsearch tuner or a random tuner is good enough. If your
+# space is at the level of 10^9 (this is the space size of a conv2d operator on
+# CUDA GPU), XGBoostTuner can explore more efficiently and find better configs.
+
+################################################################################
+# Begin tuning
+# ~~~~~~~~~~~~
+# Here we continue our matrix multiplication example. First we create a tuning
+# task. We can also inspect the initialized search space. In this case, for a
+# 512x512 square matrix multiplication, the space size is 10x10=100 Note that
+# the task and search space are independent of the tuner picked.
+
+N, L, M = 512, 512, 512
+task = autotvm.task.create("tutorial/matmul", args=(N, L, M, "float32"), target="llvm")
+print(task.config_space)
+
+################################################################################
+# Then we need to define how to measure the generated code and pick a tuner.
+# Since our space is small, a random tuner is just okay.
+#
+# We only make 10 trials in this tutorial for demonstration. In practice, you
+# can do more trials according to your time budget. We will log the tuning
+# results into a log file. This file can be used to choose the best
+# configuration discovered by the tuner later.
+
+# logging config (for printing tuning log to the screen)
+logging.getLogger("autotvm").setLevel(logging.DEBUG)
+logging.getLogger("autotvm").addHandler(logging.StreamHandler(sys.stdout))
+
+################################################################################
+# There are two steps for measuring a config: build and run. By default, we use
+# all CPU cores to compile program. We then measure them sequentially. To help
+# reduce variance, we take 5 measurements and average them.
+measure_option = autotvm.measure_option(builder="local", runner=autotvm.LocalRunner(number=5))
+
+# Begin tuning with RandomTuner, log records to file `matmul.log`
+# You can use alternatives like XGBTuner.
+tuner = autotvm.tuner.RandomTuner(task)
+tuner.tune(
+ n_trial=10,
+ measure_option=measure_option,
+ callbacks=[autotvm.callback.log_to_file("matmul.log")],
+)
+
+################################################################################
+# With tuning completed, we can choose the configuration from the log file that
+# has the best measured performance and compile the schedule with the
+# corresponding parameters. We also do a quick verfication that the schedule is
+# producing correct answers. We can call the function :code:`matmul` directly
+# under the :any:`autotvm.apply_history_best` context. When we call this
+# function, it will query the dispatch context with its argument and get the
+# best config with the same argument.
+
+# apply history best from log file
+with autotvm.apply_history_best("matmul.log"):
+ with tvm.target.Target("llvm"):
+ s, arg_bufs = matmul(N, L, M, "float32")
+ func = tvm.build(s, arg_bufs)
+
+# check correctness
+a_np = np.random.uniform(size=(N, L)).astype(np.float32)
+b_np = np.random.uniform(size=(L, M)).astype(np.float32)
+c_np = a_np.dot(b_np)
+
+c_tvm = tvm.nd.empty(c_np.shape)
+func(tvm.nd.array(a_np), tvm.nd.array(b_np), c_tvm)
+
+tvm.testing.assert_allclose(c_np, c_tvm.asnumpy(), rtol=1e-2)
Review comment:
Why `rtol` is so large here? We usually use `1e-4` ~ `1e-6` for float32
##########
File path: tutorials/get_started/autotvm_matmul.py
##########
@@ -0,0 +1,377 @@
+# 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.
+"""
+Optimizing Operators with Templates and AutoTVM
+===============================================
+**Authors**:
+`Lianmin Zheng <https://github.com/merrymercy>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In this tutorial, we will now show how the TVM Template Extension (TE) language
+can be used to write scheduling templates that can be searched by AutoTVM to
+find optimal configurations of scheduling variables. This process is called
+Auto-Tuning, and builds on TE to help automate the process of optimizing
+operations.
+
+This tutorial builds on the previous `tutorial on how to write a matrix
+multiplication using TE <tensor_expr_get_started>`.
+
+There are two steps in auto-tuning.
+
+- The first step is defining a search space.
+- The second step is running a search algorithm to explore through this space.
+
+In this tutorial, you can learn how to perform these two steps in TVM. The whole
+workflow is illustrated by a matrix multiplication example.
+
+.. note::
+ Note that this tutorial will not run on Windows or recent versions of macOS.
+ To get it to run, you will need to wrap the body of this tutorial in a
+ :code:`if __name__ == "__main__":` block.
+"""
+
+################################################################################
+# Install dependencies
+# --------------------
+# To use autotvm package in TVM, we need to install some extra dependencies.
+#
+# .. code-block:: bash
+#
+# pip3 install --user psutil xgboost cloudpickle
+#
+# To make TVM run faster in tuning, it is recommended to use cython as FFI of
+# TVM. In the root directory of TVM, execute (change "3" to "2" if you use
+# python2):
+#
+# .. code-block:: bash
+#
+# pip3 install --user cython
+# sudo make cython3
+#
+# Now return to python code. Begin by importing the required packages.
+
+import logging
+import sys
+
+import numpy as np
+import tvm
+from tvm import te
+import tvm.testing
+
+# the module is called `autotvm`
+from tvm import autotvm
+
+################################################################################
+# Basic Matrix Multiplication with TE
+# -----------------------------------
+# Recall the basic implementation of matrix multiplication using TE. We write
+# it down here with a few changes. We will wrap the multiplication in a python
+# function definition. For simplicity, we will focus our attention on a split
Review comment:
```suggestion
# function definition. For simplicity, we will focus our attention on a split
```
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[GitHub] [tvm] hogepodge commented on pull request #7643: [docs] Getting Started with TVM: AutoTVM and Matrix Multiply
Posted by GitBox <gi...@apache.org>.
hogepodge commented on pull request #7643:
URL: https://github.com/apache/tvm/pull/7643#issuecomment-797110856
@tqchen @merrymercy
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