# [RFC] Meta Schedule
* Feature Name: Meta Schedule
* Start Date: 2021-05-28
* RFC PR: TBD (apache/tvm-rfcs#0000)
* GitHub Issue: TBD (apache/tvm-rfcs#0000)
## 1. Summary
This proposal introduces Meta Schedule: a probabilistic scheduling DSL on TIR
that unifies the approaches of AutoTVM and Auto Scheduler (Ansor). Meta
schedule provides a pragmatic way to define the space of automatic tuning,
extensibility in terms of all possible TIR schedule primitives like
tensorization and loop partitioning, and customizability on every layer of the
automation system.
## 2. Motivation
**Scheduling and Design Space**
In TVM TensorIR, optimization of a TensorIR program is done via a sequence of
transformations. For example, we reorder loops for better locality and we
tensorize for specific hardware intrinsics. The process of invoking such a set
of pre-defined transformations is called “**scheduling**”, and each
transformation is called a “**schedule primitive**”. These primitives form a
domain-specific language (DSL) describing the transformation of TensorIR
programs. **Design space** is the set of all possible schedulings with respect
to a TensorIR program.
**Problems with the Current Scheduling System**
* **Manual schedule**: Developers optimize their programs by manually invoking
schedule primitives, i.e. explore points in the design space with humans in the
loop. This can be a tedious and error-prone approach, hence the creation of
AutoTVM and AutoScheduler (Ansor).
* **AutoTVM**: The automation system requires users to define “schedule
templates” as the design space for each operator. Therefore, it is inextensible
to hundreds of operators.
* **AutoScheduler (Ansor)**: It automatically generates schedule templates as
the design space, according to a set of predefined “search rules”. However, it
is non-trivial to extend AutoScheduler to new schedule primitives (tensorize,
loop partition, software pipelining).
* The three systems above have isolated sets of APIs with several layers of
their own abstraction, which are not only hard to learn, but also
engineering-intensive to customize.
**Benefit of Meta Schedule**
* Succinct syntax, consistent APIs to TensorIR schedule with no other layer of
abstraction.
* Provides unified APIs for implementing manual schedule, AutoTVM and
AutoScheduler (Ansor).
* Extensibility to all the schedule primitives, including tensorization and
loop partitioning. Almost no extra effort is needed to use a new primitive in
auto-tuning.
* The automation infrastructure is customizable across every layer.
## 3. Guide-level explanation
In this section, we describe the syntax of meta schedule DSL, and how it could
be used to describe and auto-generate the design space.
### 3.1. Manual Schedule
Meta schedule APIs are almost the same as TE or TensorIR scheduling. Here is an
example of a manual schedule for matrix multiplication:
```python
# Designate a set of tile sizes
i_tiles = [16, 8, 8, 8]
j_tiles = [16, 8, 8, 8]
k_tiles = [256, 8]
# Tile the loops according to the tile sizes
i_0, i_1, i_2, i_3 = sch.split(loop=i, factors=i_tiles)
j_0, j_1, j_2, j_3 = sch.split(loop=j, factors=j_tiles)
k_0, k_1 = sch.split(loop=k, factors=k_tiles)
# Organize the loops into “SSRSRS” 6-level tiles
sch.reorder(
i_0, j_0, # S
i_1, j_1, # S
k_0, # R
i_2, j_2, # S
k_1, # R
i_3, j_3, # S
)
```
In this example, the developers may tweak the tile sizes and measure the
performance of the generated kernels to explore the opportunities of potential
optimization.
Generally speaking, while writing a schedule, there are often some parameters
that are hard to determine ahead of time, for example, tile sizes, unroll
steps, or which tensor intrinsics to use. Developers may manually enumerate
possible combinations of these unknown factors, and then pick the best schedule
according to measurement results on their device.
### 3.2. AutoTVM-style Design Space Description
Meta schedule extends the schedule DSL with sampling instructions. When
included in a schedule, these instructions parametrize the schedule from a
single deterministic point to a space supported by random variables (tile size,
etc.), making it possible for developers to describe the design space with meta
schedule APIs.
We can extend the matmul example above to cover all possible tilings using
these sampling instructions:
```python
# Sample tile sizes
i_tiles = sch.sample_perfect_tile(i, n=4)
j_tiles = sch.sample_perfect_tile(j, n=4)
k_tiles = sch.sample_perfect_tile(k, n=2)
# Tile the loops according to the random variables
i_0, i_1, i_2, i_3 = sch.split(loop=i, factors=i_tiles)
j_0, j_1, j_2, j_3 = sch.split(loop=j, factors=j_tiles)
k_0, k_1 = sch.split(loop=k, factors=k_tiles)
# Organize the loops into “SSRSRS” 6-level tiles
sch.reorder(
i_0, j_0, # S
i_1, j_1, # S
k_0, # R
i_2, j_2, # S
k_1, # R
i_3, j_3, # S
)
```
### 3.3. Composite Schedule
Each schedule primitive handles only a very basic operation to transform the
IR, for example, `split` only splits a loop into two. In the real world, the
over-fine granularity of those primitives usually leads to repetitive and
verbose scheduling code, as
[mentioned](https://discuss.tvm.apache.org/t/rfc-tensorir-a-schedulable-ir-for-tvm/7872/43?u=junrushao1994)
by developers in our community.
To counter this challenge, we allow users to register “composite schedules”
that analyze the IR, and apply a set of schedule primitives correspondingly.
For instance, a composite schedule may inspect a TensorIR block and decide
whether we should call `compute_inline` on it. The composite schedule may use
sampling instructions to fill in undecided choices.
Our system also ships with some built-in composite schedules, including:
* Multi-level tiling
* Inline pure spatial blocks
* Parallelize & vectorize & unroll
* Auto tensorize
* …
### 3.4. AutoScheduler-style Design Space Generation
AutoScheduler (Ansor) generates schedule templates by applying their
SearchRules to each stage. Meta schedule treats a search rule as a composite
schedule, and applies each composite schedule to each block of TensorIR to
generate the design space.
### 3.5. Unifying manual schedule / AutoTVM / Ansor
In this section, we show that the design space induced by TE manual schedule,
AutoTVM and Ansor are all subsets of meta schedule, and meta schedule further
allows mixing those three styles to search jointly.
**Manual schedule**. The TE schedule is a special case of a meta schedule
program, where there is no randomness introduced by sampling instructions. It
is a single point in terms of design space.
**AutoTVM (Template-based tuning)**. Writing one or more schedule functions in
meta schedule, potentially with sampling instructions, is a natural
representation of AutoTVM’s schedule templates (knobs). The PPL generates one
or more traces as the design space to explore.
**AutoScheduler (Ansor, Template-free tuning)**. As mentioned in the previous
section, application of composite schedule rules generates the design space,
which is equivalent to Ansor’s sketch generation.
**Mixing styles in design space definition**. By taking union of the spaces
induced by the three special cases, our system allows developers to combine
generic rules that Ansor provides and operator-specific scheduling.
## 4. Reference-level explanation
In this section, we introduce the underlying techniques for the automation
system to extract and explore the design space.
### 4.1. Execution trace as the design space
**Trace**. To represent the design space defined by the meta schedule DSL, the
underlying system records all the instructions users applied to the schedule
class, including sampling and schedule primitives. We call this list of
instructions a trace.
Executing the example above results in the following trace:
```
Instruction 0. Sample-Perfect-Tile
Instruction 1. Sample-Perfect-Tile
Instruction 2. Sample-Perfect-Tile
Instruction 3. Split
Instruction 4. Split
Instruction 5. Split
Instruction 6. Reorder
```
The trace is not directly user-facing, but a data structure inside the
user-facing `Schedule` class that records the execution. The automation system
extracts the trace and finds out the design space according to the sampling
instructions.
**Union of traces**. Often a single trace is unable to represent the entire
space. Therefore, more precisely, our system works on a list of traces as the
union of potential design space.
**Fork a trace**. When two different decisions in the scheduling process are
equally important to generate high-performance schedules, we allow forking the
trace into two, and the design space is the union of the forked traces.
### 4.2. Exploring the Search Space
Meta Schedule provides several built-in exploration strategies to exhaustively
or efficiently search for efficient schedules.
**Program replay**. A simple strategy that replays the schedule program that
generates the PPL, and doesn’t use any advantage provided by the PPL.
**Random search**. Extracts the PPL, and repetitively re-executes the PPL by
flipping coins purely randomly.
**Cost-model-guided evolutionary search**. A more efficient exploration
strategy. We define two sets of rules:
* Mutator: defines how to jump to a point’s “neighbor” in the design space
* Postprocessor: sometimes it is non-trivial to statically determine the PPL,
for example:
* There is a hard requirement in CUDA that the maximum number of threads
should not exceed 1024, but it is a random variable that cannot be determined
before actually executing the PPL. In this case, we write a postprocessor that
errors out when the condition is not satisfied.
* The number of outer loops to be fused together depends on their extents,
which are random variables. In this case, we annotate the maximum extent
allowed on the block, and do actual fusion in a postprocessor.
Our evolutionary search algorithm uses mutators to find possible schedules in
the design space, then applies postprocessors, and asks the cost model to
predict its performance. After several iterations, the new schedules with the
highest scores are finally compiled and measured on device. Epsilon-greedy is
used in this process to balance exploitation and exploration.
### 4.3. Python first for flexibility & customizability
We engineer the system in a way that all levels are decoupled and open to
customization, aiming at providing a playground for developers to try out new
ideas and potentially deliver performance quickly.
While all the important APIs are implemented in C++ for efficiency, every part
of the system can be easily switched to customized python implementation. For
example,
**Customize design space in python**. Can be a python function that does the
schedule
```python
def schedule_matmul(sch) -> sch:
i, j, k = sch.get_loops(sch.get_block(“matmul”))
i_tiles = sch.sample_perfect_tile(i, n=4)
j_tiles = sch.sample_perfect_tile(j, n=4)
k_tiles = sch.sample_perfect_tile(k, n=2)
# Tile the loops according to the random variables
i_0, i_1, i_2, i_3 = sch.split(loop=i, factors=i_tiles)
j_0, j_1, j_2, j_3 = sch.split(loop=j, factors=j_tiles)
k_0, k_1 = sch.split(loop=k, factors=k_tiles)
# Organize the loops into “SSRSRS” 6-level tiles
sch.reorder(
i_0, j_0, # S
i_1, j_1, # S
k_0, # R
i_2, j_2, # S
k_1, # R
i_3, j_3, # S
)
return sch
```
**Customize composite schedule in python**. We provide two ways to define a
composite schedule in python:
Method 1. A simple decorator that converts a python function to a composite
schedule
```python
@tir.as_composite_schedule(name="multi-level-tiling")
def multi_level_tiling(sch: Schedule, block: BlockRV) -> Union[Schedule,
List[Schedule]]:
...
```
Method 2. Derive from `PyCompositeSchedule`, providing extra functionalities
like initialization
```python
class MultiLevelTiling(PyCompositeSchedule):
def initialize(...):
...
def apply(...):
...
```
**Customize exploration strategies in python**. Developers can implement any
search algorithm in python as well by deriving from `PySearchPolicy`.
**Other customizable components**. This list includes:
* Cost model
* Database
* Measure callbacks
* Feature extractor
* Program builder & runner
* Analysis methods
* ...
In a short summary, almost every component of the system is decoupled with each
other and extensions could be easily plugged in.
### 4.4. Upstreaming Plan
[M3a] Core infrastructure of the PPL
* Instruction
* Trace
* Composite schedule
* Sampler
* Search policy
* Design space generator
[M3b] Host-side search infra
* Database
* Cost model
* Measure callback
[M3c] RPC-related search infra
* Measure input, build result, measure result
* Builder
* Runner
[M4a] Implementation of rules
* Various built-in composite schedules
* Various built-in mutators
* Various built-in postprocessors
* Automatic tensorization
[M4b] Relay integration
## 5. Drawbacks
We are not aware of any drawbacks of the proposed system.
## 6. Rationale and alternatives
The system is designed with the principle of minimalism: different from
alternative solutions, we do not require any change in existing codebase, or
extra APIs to learn. It could potentially lower the bar of using automation
systems.
Unifying manual scheduling, AutoTVM's semi automatic templates and
AutoScheduler's (Ansor's) fully automatic sketch generation provides flexible
way to balance injection new domain knowledge and automation.
Flexibility in customization allows quick try-out on new tasks, new strategies
and new hardware targets without deep knowledge of the system.
## 7. Prior art
**Tensor Expression (TE)** in TVM is a DSL that decouples compute and schedule,
which provides convenient ways to handcraft optimized kernels for different
hardware targets.
**TensorIR** is the latest generation of TVM’s low-level IR. Its capability of
eagerly applying schedule primitives opens the door for meta schedule, our
proposed new-generation auto scheduling system.
**AutoTVM** is the 1st generation automation framework in TVM, which requires
developers to implement per-operator scheduling templates, and the system could
handle the tuning process.
**AutoScheduler (Ansor)** is the 2nd generation automation framework in TVM,
whose built-in rules could automatically generate schedule templates for almost
all the operators on CPU, GPU, etc.
## 8. Unresolved questions
**Supporting Control Flow and Assertions**
Right now the meta schedule DSL does not support control flow. Although we
didn’t see any real-world use case right now, it is possible that it could
appear in some future workloads.
A real-world issue we could see is that sampling may lead to wrong schedules on
CUDA, e.g. the schedule results in a CUDA program that uses too much shared
memory, too many threads, etc. In this case, we need to halt the program
immediately. Therefore, introducing assertion may be helpful.
## 9. Future possibilities
**Unifying Manual Scheduling, AutoTVM and Ansor in TOPI**
Meta schedule provides an idiomatic approach to unify the three existing
scheduling APIs in TVM:
* Manual schedules are meta schedules without sampling instructions
* AutoTVM templates are meta schedules where knobs are replaced by sampling
instructions
* Each of Ansor’s search rules generates a snippet of a meta schedule
We further allow mixing different styles of scheduling and exploring the union
space, which could help dispatch to different implementations.
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