From 08eee325f3052e4f3827df645df3e1c03d6c155a Mon Sep 17 00:00:00 2001 From: Bojian Zheng Date: Thu, 12 May 2022 17:00:24 -0400 Subject: [PATCH 1/2] Create 0072-dynamic-autoscheduler.md --- rfcs/0072-dynamic-autoscheduler.md | 207 +++++++++++++++++++++++++++++ 1 file changed, 207 insertions(+) create mode 100644 rfcs/0072-dynamic-autoscheduler.md diff --git a/rfcs/0072-dynamic-autoscheduler.md b/rfcs/0072-dynamic-autoscheduler.md new file mode 100644 index 00000000..90065d2c --- /dev/null +++ b/rfcs/0072-dynamic-autoscheduler.md @@ -0,0 +1,207 @@ +- Feature Name: DietCode: An Auto-Scheduler for Dynamic Tensor Programs +- Start Date: (2022-05-10) +- RFC PR: [apache/tvm-rfcs#xx](https://github.com/apache/tvm-rfcs/pull/xx) +- GitHub Issue: [apache/tvm#yy](https://github.com/apache/tvm/pull/yy) + +# Summary +[summary]: #summary + +We propose to integrate DietCode, an auto-scheduler for dynamic tensor programs, +to AutoTIR. DietCode offers the following features: +- A shape-generic search space to cover possible shapes in dynamic shape + workloads. +- A dynamic-shape aware cost model to judge the quality of schedule candidates. +- Enhancement to the TVM CUDA codegen for imperfect tiling. + +DietCode has been published by MLSys 2022 so please see [the +paper](https://proceedings.mlsys.org/paper/2022/hash/fa7cdfad1a5aaf8370ebeda47a1ff1c3-Abstract.html) +for more details and evaluations. Meanwhile, the latest DietCode codebase is also publicly +available [here](https://github.com/UofT-EcoSystem/DietCode). + +# Motivation +[motivation]: #motivation + +Achieving high performance for compute-intensive operators in machine learning +workloads is a crucial but challenging task. Many machine learning and system +practitioners rely on vendor libraries or auto-schedulers to do the job. While +the former requires significant engineering efforts, the latter in TVM only supports +static-shape workloads in existing works. It is difficult, if not impractical, +to apply the existing auto-scheduler directly to **dynamic-shape workloads**, as +this leads to extremely long tuning time. + +We observe that the key challenge faced by existing auto-schedulers when +handling a dynamic-shape workload is that they cannot construct a conclusive search +space for all the possible shapes of the workload, because their search space is +shape-dependent. To address this, this RFC aims to add dynamic-shape supports to +AutoTIR by integrating DietCode framework, which constructs **a shape-generic +search space and cost model** to auto-schedule dynamic-shape workloads +efficiently. + +Our evaluation shows that DietCode has the following key strengths when +auto-scheduling an entire model end-to-end: + +1. reduces the auto-scheduling time by up to 5.88x less than the current + auto-scheduler on 8 uniformly sampled dynamic shapes, and +1. improves performance by up to 69.5% better than the auto-scheduler and 18.6% + better than the vendor library. All these advantages make DietCode an + efficient and practical solution for dynamic-shape workloads. + + +# Guide-Level Explanation +[guide-level-explanation]: #guide-level-explanation + +The existing experiments are largely conducted with auto-scheduler. However, +having been syncing with the AutoTIR team for quarters, we plan to integrate +this RFC to MetaSchedule (AutoTIR), because it provides more systematic +interface and cleaner integration path with less hacks. + +To provide an example of additional information users are required to feed the +system: + +```python +# A symbolic shape constraint +T = tir.ShapeVar('T’) +# The candidate values of `T` +T_vals = list(range(1, 128)) + +task = Task(func=Dense, + args=(16*T, 768, 2304), + shape_vars=(T,), + wkl_insts=(T_vals,) + wkl_inst_weights=([1. for _ in T_vals],)) +``` + +To enable auto-scheduling for dynamic shape workloads, users only need to: +1. Have `ShapeVar` in the TE/TensorIR compututation. +2. Specify the weight/distribution of each shape value. + +Notes: +1. Symbolic constraint is required additional in Relay, but could be inferred + automatically after Relax is introduced; +2. The proposed interface does not change any existing functionality. + +# Reference-Level Explanation +[reference-level-explanation]: #reference-level-explanation + +Here is an overview of the DietCode framework design. + + + +- We construct **a shape-generic search space that consists of micro-kernels**, + an incomplete program that carries out a tile of the complete computation, to + efficiently support dynamic-shape workloads. + + We use the hardware constraints (e.g., the maximum number of threads, the + amount of shared and local memory) rather than the shape information to + determine the micro-kernel candidates. Those candidates serve as the building + blocks and are executed repeatedly to carry out a workload instance (defined + as an static-shape instance of the dynamic-shape workload). +- We build a **micro-kernel-based cost model**. The key insight is that the cost + of a complete program *P* that is made up of a micro-kernel *M* can be + decomposed into two parts: + + 1. A shape-generic cost function *f*MK that predicts the cost of + *M*, and + 1. A shape-dependent adaption cost function *f*adapt that defines + the penalty of porting *M* to *P*. + + While *f*MK is a function that has to be learned and updated by + real hardware measurements during the auto-scheduling process, + *f*adapt is a simple term that can be evaluated using the core + occupancy and the padding ratio (in other words, it does not require feature + extraction from the schedules). + +# Drawbacks +[drawbacks]: #drawbacks + +- The current compilation workflow generates one program per input shape. + Although we can merge those static-shape programs into a single dynamic-shape + program like the following code snippet: + ```CUDA + __global__ void default_function(float* X, float* W, float* Y, + const int T) + // Note the `T` here. + ``` + Our evaluations indicate that this program has at least 5% worse performance + compared with the static-shape alternatives. Hence, we decide to sacrifice the + binary size for the runtime performance, which can potentially be problematic + when the hardware resources are limited. + +# Rationale and Alternatives +[rationale-and-alternatives]: #rationale-and-alternatives + +There is an approach proposed by [Nimble](https://arxiv.org/pdf/2006.03031.pdf), +which partitions a range of dynamic shape to buckets and tunes one kernel for +each bucket. We could, of course, implement this approach to the current +auto-scheduler and AutoTIR. However, as evaluated in the DietCode paper, this +approach is not guaranteed to achieve better performance as static shapes. + +# Prior State-of-the-Arts +[prior-sotas]: #prior-sotas + +- **Reuse-based Tuner** + + Selective Tuning ([Cody Yu. + 2019](https://github.com/apache/incubator-tvm/issues/4188)) and ETO ([Jingzhi + Fang et al. VLDB 2021](http://www.vldb.org/pvldb/vol15/p183-chen.pdf)) group + workloads into clusters based on a set of pre-defined rules (e.g., similarity + ratio in Selective Tuning) and reuse the same schedule in a single cluster. + +- **Dynamic Neural Networks** + + Dynamic batching is a common graph-level optimization adopted by frameworks + such as DyNet ([Graham Neubig et al. 2017](http://arxiv.org/abs/1701.03980)), + Cavs ([Shizhen Xu et al. USENIX ATC + 2018](https://www.usenix.org/conference/atc18/presentation/xu-shizen)), + BatchMaker ([Pin Gao et al. EuroSys + 2018](https://doi.org/10.1145/3190508.3190541)), and TensorFlow Fold ([Moshe + Looks et al. ICLR 2017](https://openreview.net/forum?id=ryrGawqex)) for cases + when the batch size is dynamic. + + Nimble ([Haichen Shen et al. MLSys + 2021](https://proceedings.mlsys.org/paper/2021/hash/4e732ced3463d06de0ca9a15b6153677-Abstract.html)) + and DISC ([Kai Zhu et al. EuroMLSys + 2021](https://dl.acm.org/doi/10.1145/3437984.3458838)) both design a compiler + to represent and execute dynamic neural networks. + + Cortex ([Pratik Fegade et al. MLSys + 2021](https://proceedings.mlsys.org/paper/2021/hash/182be0c5cdcd5072bb1864cdee4d3d6e-Abstract.html)) + is a compiler-based framework on recursive neural networks. + + Those works focus on the graph-level optimizations and therefore are + orthogonal to DietCode, which operates on each individual layer. In fact, + those graph-level solutions can also leverage DietCode for efficient operator + code generation. + +# Unresolved Questions +[unresolved-questions]: #unresolved-questions + +- The current design does not support arbitrary shape dimensions. For better + auto-scheduling outcomes, we expect that shape dimensions have to be specified + beforehand. +- The proposed approach mostly works on NVIDIA GPUs and has not been tested on + other hardware platforms. + +# Future Possibilities +[future-possibilities]: #future-possibilities + +- Evaluate more operator use cases. +- CPU Support + +# Upstream Milestones +[upstream-milestones]: #upstream-milestones + +We propose the following milestones for upstreaming, where each bullet point +corresponds to a PR with unit tests of roughly several hundred lines. + +- [ ] Code Generation Support + - Local Padding + - Loop Partitioning +- [ ] Auto-Scheduler + - Frontend Interface + - Sketch Generation + - Random Annotations + - Program Measurer + - Micro-Kernel Cost Model + - Evolutionary Search +- [ ] Decision-Tree Dispatching From d496692b07afc388dd18993b32664c01c580c9ba Mon Sep 17 00:00:00 2001 From: ArmageddonKnight Date: Mon, 30 May 2022 16:04:46 -0400 Subject: [PATCH 2/2] Address all the feedbacks --- rfcs/0072-dynamic-autoscheduler.md | 52 ++++++++++++++++++++++++++---- 1 file changed, 46 insertions(+), 6 deletions(-) diff --git a/rfcs/0072-dynamic-autoscheduler.md b/rfcs/0072-dynamic-autoscheduler.md index 90065d2c..68be4223 100644 --- a/rfcs/0072-dynamic-autoscheduler.md +++ b/rfcs/0072-dynamic-autoscheduler.md @@ -56,18 +56,27 @@ this RFC to MetaSchedule (AutoTIR), because it provides more systematic interface and cleaner integration path with less hacks. To provide an example of additional information users are required to feed the -system: +system (see https://github.com/UofT-EcoSystem/DietCode/tree/MLSys2022_AE for a +PoC design): ```python # A symbolic shape constraint T = tir.ShapeVar('T’) +I = tir.ShapeVar('I') +H = tir.ShapeVar('H') # The candidate values of `T` -T_vals = list(range(1, 128)) +T_vals = range(1, 128) +wkl_insts = [] +for t in T_vals: + wkl_insts.append((t, 768, 768)) + wkl_insts.append((t, 768, 3072)) + wkl_insts.append((t, 3072, 768)) + task = Task(func=Dense, - args=(16*T, 768, 2304), - shape_vars=(T,), - wkl_insts=(T_vals,) + args=(16*T, I, H), + shape_vars=(T, I, H), + wkl_insts=wkl_insts wkl_inst_weights=([1. for _ in T_vals],)) ``` @@ -87,6 +96,9 @@ Here is an overview of the DietCode framework design. +- We accept the shape variables and the workload instances from the programmer. + In the case when they are not detected, the auto-scheduler treats the workload + as static and applies and current workflow on it. - We construct **a shape-generic search space that consists of micro-kernels**, an incomplete program that carries out a tile of the complete computation, to efficiently support dynamic-shape workloads. @@ -110,6 +122,27 @@ Here is an overview of the DietCode framework design. *f*adapt is a simple term that can be evaluated using the core occupancy and the padding ratio (in other words, it does not require feature extraction from the schedules). +- We generate one kernel per workload instance and use the scikit-learn + framework to train a decision tree dispatcher to map the workload instance to + its corresponding kernel. The decision tree will be output in predicate-only + format for efficient runtime dispatching and embedded as part of the host + code. As an example, one possible auto-scheduling outcome can look like the + following: + ```C++ + __global__ void default_function0(float* X, float* W, float* Y) {...} + __global__ void default_function1(float* X, float* W, float* Y) {...} + __global__ void default_function2(float* X, float* W, float* Y) {...} + + // host code + if (T < 16) + call(default_function0) + else if (T < 64) + call(default_function1) + else + call(default_function2) + ``` + Because everything can be included in a single `PackedFunc` object, the + workflow is fully compatible with the Relay workflow. # Drawbacks [drawbacks]: #drawbacks @@ -197,7 +230,7 @@ corresponds to a PR with unit tests of roughly several hundred lines. - [ ] Code Generation Support - Local Padding - Loop Partitioning -- [ ] Auto-Scheduler +- [ ] Meta-Scheduler - Frontend Interface - Sketch Generation - Random Annotations @@ -205,3 +238,10 @@ corresponds to a PR with unit tests of roughly several hundred lines. - Micro-Kernel Cost Model - Evolutionary Search - [ ] Decision-Tree Dispatching + +When testing, we will be following the same testing procedure with the +meta-scheduler. We do not require any extra hardware platforms. Our plan is to +use a dynamic-shape workload (i.e., dense from BERT and conv2d from ResNet-50) +and compare its performance numbers with those delivered by the meta-scheduler +on static-shape workloads. The performance difference is expected to be smaller +than 5%.