junrushao1994 commented on a change in pull request #5:
URL: https://github.com/apache/tvm-rfcs/pull/5#discussion_r661752729
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File path: rfcs/0001-meta-schedule-autotensorir.md
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+* Feature Name: Meta Schedule (AutoTensorIR)
+* 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.
+
+Meta Schedule is our 3rd generation automatic scheduling 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
Review comment:
Sounds good. I added the explanation below:
```python
# Organize the loops into "SSRSRS" 6-level tiles
sch.reorder(
i_0, j_0, # S: the 1st spatial tile
i_1, j_1, # S: the 2nd spatial tile
k_0, # R: the 1st reduction tile
i_2, j_2, # S: the 3rd spatial tile
k_1, # R: the 2nd reduction tile
i_3, j_3, # S: the 4th spatial tile
)
```
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