[GitHub] [tvm-rfcs] junrushao1994 commented on a change in pull request #5: [RFC] Meta Schedule (AutoTensorIR)

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junrushao1994 commented on a change in pull request #5:
URL: https://github.com/apache/tvm-rfcs/pull/5#discussion_r647846422



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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:
       Yeah we can add such a primitive. The set of primitives can always be extended.




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