comaniac commented on a change in pull request #5:
URL: https://github.com/apache/tvm-rfcs/pull/5#discussion_r648491301
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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
+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)
Review comment:
I think it means the tile size is always divisible to `n`.
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