From 570e4deb392b32de45497eb0c02c8896f6492e8d Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Fri, 25 Feb 2022 16:23:09 +0000 Subject: [PATCH 01/17] moving uma rfc from dicuss forum here --- ...A_Unified_Modular_Accelerator_Interface.md | 157 ++ rfcs/uma_pipeline.png | Bin 0 -> 116020 bytes rfcs/uma_pipeline.svg | 1392 +++++++++++++++++ rfcs/uma_toplevel.png | Bin 0 -> 96001 bytes rfcs/uma_toplevel.svg | 957 ++++++++++++ 5 files changed, 2506 insertions(+) create mode 100644 rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md create mode 100644 rfcs/uma_pipeline.png create mode 100644 rfcs/uma_pipeline.svg create mode 100644 rfcs/uma_toplevel.png create mode 100644 rfcs/uma_toplevel.svg diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md new file mode 100644 index 00000000..9e717807 --- /dev/null +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -0,0 +1,157 @@ +# UMA: Universal Modular Accelerator Interface + + Feature Name: Universal Modular Accelerator Interface (UMA) + Start Date: 2022 February + Authors: + Paul Palomero Bernardo @paulpb, Christoph Gerum @cgerum - University of Tübingen + Michael J. Klaiber @mjklaiber, Ingo Feldner - Bosch Research + Philipp van Kempen @philippvk, Rafael Stahl @r.stahl, Daniel Müller-Gritschneder - Technical University of Munich + Johannes Partzsch - TU Dresden + Andrew Stevens - Infineon Technologies + RFC PR: TBD + GitHub Issue: TBD + +## Summary + + + + +The goal of **UMA (Universal Modular Accelerator Interface)** is to create a unified infrastructure for easily integrating external accelerators into TVM. +UMA provides file structures, Python interface classes and an API for accelerator integration. These interfaces and API are accessible from Python and are part of the components *UMA Partitioner*, *UMA Lower* and *UMA Codgen*. +The features and proposals of *Target registered compiler flow customization* [TVM-RFC0011] and [TVM-RFC0010] are considered, with the difference that UMA tries to provide a more general interface for integrating new accelerators and one specific implementation of the hooks described in [TVM-RFC0011]. + + + +
+ + Image Source: https://www.flickr.com/photos/luvi/234261205 under CC BY-NC-ND 2.0 + + + +## Motivation + +A number of accelerators have already been integrated into TVM, e.g. VTA, ARM EthosU. +These are similar in both the structure of their build flow and the operations that they can offload. +Nonetheless, due to incremental independent development, the TVM interfaces and processing steps used are quite different with little commonality. A consistent, unified, infrastructure would simplify accelerator integration making it accessible to smaller, hardware-focused, development teams. + +## Focus + +UMA's primary objective is to enable straight-forward TVM integration of loosely-coupled processor/microcontroller controlled accelerators. That is, accelerators capable of executing complete tensor operations or operation-graphs without host processor intervention. +Secondary objectives are: + +* Support for closely-coupled accelerators (those offload parts of CPU computation for significant elements of tensor operations) +* Compatibility with both run-time or ahead-of-time compilation +* Support for heterogeneous execution utilizing accelerators optimized for specific operations or data types + +Accelerator support or optimization functions **outside** the scope of UMA are: + +* Parallel execution on multi-accelerator architectures (to be handled by executor/run-time and customized layer splitting) +* Real-time execution (to be handled by executor/run-time) +* High-level support for parameter conversion like quantization or sparsity exploitation (to be realized via model pre-processing or in accelerator backends) + + +## Reference-level explanation + +### Flow description + + + +The figure below describes the UMA interface from a top level. An *Accelerator Partitioner* which is a specialization of the *UMA Partitioner* takes the Relay graph and matches for supported and unsupported operators. Unsupported operators are processed with the default TVM flow. Supported operator are processed with **UMA Pipeline**. +In the following the tasks and the functionality of each block in the figure below is described: + +![](uma_toplevel.png) + +UMA Partitioning: +* Register relay passes +* Register patterns - supported sub-graph operations +* Order: pre-partitioning passes, Graph partitioning, post-partitioning passes +* UMAPartitioner baseclass (Python only) has to be inherited by accelerator-specific Partitioners (e.g. Accelerator A Partitioner, etc) + + +The figure below described the *UMA Pipeline*. The blocks are described below: + +![](uma_pipeline.png) + +UMA Pipelining: +* Consists of UMALower and UMACogen, which implement the target hook Relay-to-TIR and TIR-to-Runtime (proposed in [TVM-RFC0010]) +* UMALower + * Input: Partitioned composite functions + * Custom primitives can be registered + * Lowering from Relay to S-TIR, using TOPI or custom primitives + * Interface for registering accelerator-specific schedules and passes + * Execution of UMA schedules and passes on S-TIR + * Output: NS-TIR(including tir.extern calls) + * UMALower baseclass (Python only) has to be inherited by accelerator-specific Lower classes (e.g. Accelerator A Lower, etc) +* UMACodegen + * Input: NS-TIR(including tir.extern calls) + * Defaults to standard TVM codegen + * Intend is to provide a Python interface to insert/emit target code + * UMACodegen baseclass has to be inherited by accelerator-specific Codegen classes (e.g. Accelerator A Codegen, etc) + * Output: Target .c files + +The intention is to use TensorIR and Relax with MetaScheduler for optimization. + + +Abbreviations: +S-TIR: Schedulable TIR +NS-TIR: Non-Schedulable TIR + + +### File and class structure and Snippets as example for integration +UMA provides a mostly python-based API. On the C++ side, new targets are registered using target hooks (RFC #0010). A generic `codegen.cc` handles the calls to the python side. +``` +. +�"?�"?�"? codegen.cc +�""�"?�"? targets.cc +``` +```cpp +TVM_REGISTER_TARGET_KIND("accelerator_A", kDLCPU) + .set_attr("RelayToTIR", relay::contrib::generic::RelayToTIR("accelerator_A")) + .set_attr("TIRToRuntime", relay::contrib::generic::accelerator_A::TIRToRuntime); + +TVM_REGISTER_TARGET_KIND("accelerator_B", kDLCPU) + .set_attr("RelayToTIR", relay::contrib::generic::RelayToTIR("accelerator_B")); + .set_attr("TIRToRuntime", relay::contrib::generic::accelerator_B::TIRToRuntime); +``` +The python API is structured as shown below. Two base classes for relay graph partitioning and modification `UMAPartitioner`, and lowering from relay to TIR `UMALower` are building the core API. New custom accelerators are added in subdirectories by inheriting these two base classes. +``` +. +�"?�"?�"? partitioner.py +�"?�"?�"? lower.py +�"?�"?�"? utils.py +�"?�"?�"? accelerator_A +�"?   �"?�"?�"? partitioner.py +�"?   �"?�"?�"? lower.py +�"?   �"?�"?�"? passes.py +�"?   �"?�"?�"? patterns.py +�"?   �""�"?�"? schedules.py +�""�"?�"? accelerator_B + �""�"?�"? ... +``` +The `UMAPartitioner` base class performs a target specific relay graph partitioning. New custom accelerators can control this process by registering supported patterns and relay passes using the provided API. +```python +class MyCustomAcceleratorPartitioner(UMAPartitioner): + @property + def target_name(self): + return "my_custom_accelerator" + + def _register_patterns(self): + self._register_pattern("conv1d_relu", conv1d_relu_pattern()) + + def _register_relay_passes(self): + self._register_relay_pass(1, ConfigGenerator()) + self._register_relay_pass(2, BufferScopeAnnotator()) +``` +The `UMALower` base class performs a lowering from relay to TIR. New custom accelerators can control this process by registering custom schedules and TIR passes using the provided API. +```python +class MyCustomAcceleratorLower(UMALower): + def __init__(self): + super(MyCustomAcceleratorLower, self).__init__() + + def _register_tir_schedules(self): + self._register_tir_schedule(insert_extern_calls) + + def _register_tir_passes(self): + self._register_tir_pass(0, GenerateConstants()) +``` + diff --git a/rfcs/uma_pipeline.png b/rfcs/uma_pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..16251b28f8f178380ae2157a8d190ae14b7684f1 GIT binary patch literal 116020 zcmcG0cU+F`|92s>Q)y^XTGB>JgS4fhl#=$Mr6HwBD3ww|X_5vl?a&ZyO-ZGxO?#Kt z^FF!n@AvsV&-3r&_2Rzo%hh$A=W%?F&*#0)`@Wir{LXFrwvmvK>{L{cIZr}D7D__0 zL6v+nUK!bWP96W)dQCywfrNz8k@!v8Wya!+7wH{kFFLB*nmOJuv^OQWapMNx6&p(j zW5a8ve75%H5#!?fNJ#dRD9W7DaE=`9ba7T5qWv&6VRA6IIi!>_xb-o)(bkNwG-s$T z7$shKPs5ukk)*lDi08%k7f*7(`LSPE4)IgFWR*d7A(%RN|NT+nnPs;6ju^o7EHLmr^(&Pv3P!NAqp1!;};`}M4gY?Yp1Tvfb?p}+ 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+ + + + + + + + + + + + + + + + + + + ML Model + Onnx + , TF + + TVM + Frontend + + + Relay/Relax + Graph + + + TVM Graph Partitioning + + + + + + Supported + Operators + + + Relay/ + Relax.build + () + + + TVM + Default + + + UMA + Pipeline + + + + + + + + + .c + files + + + .c / . + ll + files + + HW or + HW + - + Simulator + + + Meta Scheduler + + Unsupporte + d + Operators + Relay/Relax Graph + Optimization + + + + UMA + Partitioner + + + Accelerator + A + Partitioner + + + Accelerator A + Partitioner + + + + + + Register pattern + e.g. + hard rules + and relay passes + + From b593e11389ee4aeef44af0b19c7209ef8c639818 Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Mon, 28 Feb 2022 09:55:54 +0000 Subject: [PATCH 02/17] uma rfc update --- ...A_Unified_Modular_Accelerator_Interface.md | 33 +-- rfcs/uma_pipeline.png | Bin 116020 -> 91570 bytes rfcs/uma_pipeline.svg | 45 ++- rfcs/uma_toplevel.png | Bin 96001 -> 105827 bytes rfcs/uma_toplevel.svg | 264 ++++++------------ 5 files changed, 141 insertions(+), 201 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 9e717807..2211c973 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -61,12 +61,13 @@ In the following the tasks and the functionality of each block in the figure bel ![](uma_toplevel.png) -UMA Partitioning: +UMA Partitioner: * Register relay passes * Register patterns - supported sub-graph operations * Order: pre-partitioning passes, Graph partitioning, post-partitioning passes -* UMAPartitioner baseclass (Python only) has to be inherited by accelerator-specific Partitioners (e.g. Accelerator A Partitioner, etc) - +* API level: + * UMA Partitioner creates a wrapper API to TVM core-compiler APIs + * *UMAPartitioner* baseclass (Python only) has to be inherited by accelerator-specific Partitioners (e.g. Accelerator A Partitioner, etc) The figure below described the *UMA Pipeline*. The blocks are described below: @@ -101,8 +102,8 @@ NS-TIR: Non-Schedulable TIR UMA provides a mostly python-based API. On the C++ side, new targets are registered using target hooks (RFC #0010). A generic `codegen.cc` handles the calls to the python side. ``` . -�"?�"?�"? codegen.cc -�""�"?�"? targets.cc +├── codegen.cc +└── targets.cc ``` ```cpp TVM_REGISTER_TARGET_KIND("accelerator_A", kDLCPU) @@ -116,17 +117,17 @@ TVM_REGISTER_TARGET_KIND("accelerator_B", kDLCPU) The python API is structured as shown below. Two base classes for relay graph partitioning and modification `UMAPartitioner`, and lowering from relay to TIR `UMALower` are building the core API. New custom accelerators are added in subdirectories by inheriting these two base classes. ``` . -�"?�"?�"? partitioner.py -�"?�"?�"? lower.py -�"?�"?�"? utils.py -�"?�"?�"? accelerator_A -�"?   �"?�"?�"? partitioner.py -�"?   �"?�"?�"? lower.py -�"?   �"?�"?�"? passes.py -�"?   �"?�"?�"? patterns.py -�"?   �""�"?�"? schedules.py -�""�"?�"? accelerator_B - �""�"?�"? ... +├── partitioner.py +├── lower.py +├── utils.py +├── accelerator_A +│ ├── partitioner.py +│ ├── lower.py +│ ├── passes.py +│ ├── patterns.py +│ └── schedules.py +└── accelerator_B + └── ... ``` The `UMAPartitioner` base class performs a target specific relay graph partitioning. 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style="font-style:normal;font-variant:normal;font-weight:400;font-size:3.88607001px;font-family:'Bosch Office Sans';fill:#000000;stroke-width:0.26458299" + id="tspan62499">Relay/ Relax.build() .c / . - ll + style="font-style:normal;font-variant:normal;font-weight:400;font-size:6.3499999px;font-family:'Bosch Office Sans';fill:#000000;stroke-width:0.26458299" + id="tspan62581">.c / .ll Unsupporte - d - UnsupportedOperators + x="173.61946" + y="191.81377" + style="text-align:center;text-anchor:middle;stroke-width:0.26458299" + id="tspan199">Operators Relay/Relax Graph UMA Partitioner Register pattern - Register pattern e.g. hard rulese.g. - hard rules - and relay passes + x="127.95663" + y="281.28998" + style="text-align:center;text-anchor:middle;stroke-width:0.26458299" + id="tspan201">and relay passes
From 716a40ad7cc21a4840991b5704d88b8ed1952432 Mon Sep 17 00:00:00 2001 From: PaulPalomeroBernardo Date: Fri, 4 Mar 2022 11:37:41 +0100 Subject: [PATCH 03/17] Update 00xx_UMA_Unified_Modular_Accelerator_Interface.md Update code snippets --- ...A_Unified_Modular_Accelerator_Interface.md | 74 +++++++++++++------ 1 file changed, 50 insertions(+), 24 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 2211c973..a9473564 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -114,45 +114,71 @@ TVM_REGISTER_TARGET_KIND("accelerator_B", kDLCPU) .set_attr("RelayToTIR", relay::contrib::generic::RelayToTIR("accelerator_B")); .set_attr("TIRToRuntime", relay::contrib::generic::accelerator_B::TIRToRuntime); ``` -The python API is structured as shown below. Two base classes for relay graph partitioning and modification `UMAPartitioner`, and lowering from relay to TIR `UMALower` are building the core API. New custom accelerators are added in subdirectories by inheriting these two base classes. +The python API is structured as shown below. The base class `UMABackend` functions as the core API. It uses the API points described in the [flow description](#flow-description) to register elements for the different stages. ``` . -├── partitioner.py -├── lower.py -├── utils.py -├── accelerator_A -│ ├── partitioner.py +├── backend.py +├── api +│ ├── codegen.py │ ├── lower.py +│ ├── partitioner.py +│ └── utils.py +├── accelerator_A +│ ├── backend.py +│ ├── codegen.py │ ├── passes.py │ ├── patterns.py -│ └── schedules.py +│ ├── schedules.py +│ └── strategies.py └── accelerator_B └── ... ``` -The `UMAPartitioner` base class performs a target specific relay graph partitioning. New custom accelerators can control this process by registering supported patterns and relay passes using the provided API. +A custom accelerator is added by inheriting the `UMABackend`. New elements (e.g., passes, schedules) are added using a registration machanism. ```python -class MyCustomAcceleratorPartitioner(UMAPartitioner): - @property - def target_name(self): - return "my_custom_accelerator" +"""UMA backend for the UltraTrail accelerator""" + +class UltraTrailBackend(UMABackend): + def __init__(self): + super(UltraTrailBackend, self).__init__() - def _register_patterns(self): + # Relay to Relay function registration self._register_pattern("conv1d_relu", conv1d_relu_pattern()) - - def _register_relay_passes(self): + self._register_relay_pass(1, ConfigGenerator()) self._register_relay_pass(2, BufferScopeAnnotator()) -``` -The `UMALower` base class performs a lowering from relay to TIR. New custom accelerators can control this process by registering custom schedules and TIR passes using the provided API. -```python -class MyCustomAcceleratorLower(UMALower): - def __init__(self): - super(MyCustomAcceleratorLower, self).__init__() - def _register_tir_schedules(self): + # Relay to TIR function registration + self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy, plevel=9) + self._register_tir_schedule(insert_extern_calls) - def _register_tir_passes(self): - self._register_tir_pass(0, GenerateConstants()) + self._register_tir_pass(0, CodegenGenerateConfig()) + self._register_tir_pass(0, CodegenGenerateConstants()) + + # TIR to runtime function registration + self._register_codegen(format="c", includes=None, constants=None, replace_call_extern=None) + + @property + def target_name(self): + return "ultra_trail" +``` +Once a `UMABackend` is registered, it hooks into the usual `relay.build` process to create the code for the target accelerator. +``` +# Load model +mod, params = relay.frontend.from_pytorch(scripted_model, [("input_data", input_shape)]) + +# Register a UMA backend +UltraTrailBackend().register() +mod = UltraTrailBackend().partition(mod) + +# Relay build (AOT C target) +TARGET = tvm.target.Target("c") +RUNTIME = tvm.relay.backend.Runtime("crt") +EXECUTOR = tvm.relay.backend.Executor("aot", {"unpacked-api": True}) + +with tvm.transform.PassContext( + opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["AlterOpLayout"] +): + module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) ``` From 8ffd21cfa18ac6557068135984c203736039c790 Mon Sep 17 00:00:00 2001 From: Christoph Gerum Date: Mon, 14 Mar 2022 17:28:02 +0100 Subject: [PATCH 04/17] Update 00xx_UMA_Unified_Modular_Accelerator_Interface.md --- rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index a9473564..02d92abd 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -105,15 +105,7 @@ UMA provides a mostly python-based API. On the C++ side, new targets are registe ├── codegen.cc └── targets.cc ``` -```cpp -TVM_REGISTER_TARGET_KIND("accelerator_A", kDLCPU) - .set_attr("RelayToTIR", relay::contrib::generic::RelayToTIR("accelerator_A")) - .set_attr("TIRToRuntime", relay::contrib::generic::accelerator_A::TIRToRuntime); - -TVM_REGISTER_TARGET_KIND("accelerator_B", kDLCPU) - .set_attr("RelayToTIR", relay::contrib::generic::RelayToTIR("accelerator_B")); - .set_attr("TIRToRuntime", relay::contrib::generic::accelerator_B::TIRToRuntime); -``` + The python API is structured as shown below. The base class `UMABackend` functions as the core API. It uses the API points described in the [flow description](#flow-description) to register elements for the different stages. ``` . From fee37a52ee71d0e1c481f2580c278e2e37dccdba Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Mon, 14 Mar 2022 18:34:59 +0000 Subject: [PATCH 05/17] UMA: Update reference level and guide level --- ...A_Unified_Modular_Accelerator_Interface.md | 89 ++++++++++++------- 1 file changed, 58 insertions(+), 31 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index a9473564..2aaf8ea0 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -28,12 +28,26 @@ The features and proposals of *Target registered compiler flow customization* [T -## Motivation +## Goal and Motivation A number of accelerators have already been integrated into TVM, e.g. VTA, ARM EthosU. These are similar in both the structure of their build flow and the operations that they can offload. Nonetheless, due to incremental independent development, the TVM interfaces and processing steps used are quite different with little commonality. A consistent, unified, infrastructure would simplify accelerator integration making it accessible to smaller, hardware-focused, development teams. +The **goal** of UMA is to establish two API layers with a different target groups of users: + +**Porcelain Layer**: UMA + - Straight-forward, *Python-only* and stable API wrapper of plumbing layer + - Easy and clearly-defined template for integration of accelerators + - Short learning period for hardware/software engineers new to TVM + +**Plumbing Layer**: + - Collage-like API + other TVM APIs + - Powerful API to core-compiler + other TVM features + - Target audience is experienced TVM users/developers + - C++ and Python APIs + + ## Focus UMA's primary objective is to enable straight-forward TVM integration of loosely-coupled processor/microcontroller controlled accelerators. That is, accelerators capable of executing complete tensor operations or operation-graphs without host processor intervention. @@ -49,8 +63,8 @@ Accelerator support or optimization functions **outside** the scope of UMA are: * Real-time execution (to be handled by executor/run-time) * High-level support for parameter conversion like quantization or sparsity exploitation (to be realized via model pre-processing or in accelerator backends) +## Guide-level explanation -## Reference-level explanation ### Flow description @@ -90,13 +104,51 @@ UMA Pipelining: * UMACodegen baseclass has to be inherited by accelerator-specific Codegen classes (e.g. Accelerator A Codegen, etc) * Output: Target .c files -The intention is to use TensorIR and Relax with MetaScheduler for optimization. +The intention is to use TensorIR with MetaScheduler for optimization and Relax in later versions. Abbreviations: S-TIR: Schedulable TIR NS-TIR: Non-Schedulable TIR +### Adding a New Custom Accelerator + +A custom accelerator is added by inheriting the `UMABackend`. New elements (e.g., passes, schedules) are added using a registration machanism. +```python +"""UMA backend for the UltraTrail accelerator""" + +class UltraTrailBackend(UMABackend): + def __init__(self): + super(UltraTrailBackend, self).__init__() + + # Configuration parameters + self._register_config({"partitioning.enable_MergeCompilerRegions": False}) # Example config parameter + + # Relay to Relay function registration + self._register_pattern("conv1d_relu", conv1d_relu_pattern()) + + self._register_relay_pass(1, ConfigGenerator()) + self._register_relay_pass(2, BufferScopeAnnotator()) + + # Relay to TIR function registration + self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy, plevel=9) + + self._register_tir_schedule(insert_extern_calls) + + self._register_tir_pass(0, CodegenGenerateConfig()) + self._register_tir_pass(0, CodegenGenerateConstants()) + + # TIR to runtime function registration + self._register_codegen(format="c", includes=None, constants=None, replace_call_extern=None) + + @property + def target_name(self): + return "ultra_trail" +``` + +## Reference-level explanation + + ### File and class structure and Snippets as example for integration UMA provides a mostly python-based API. On the C++ side, new targets are registered using target hooks (RFC #0010). A generic `codegen.cc` handles the calls to the python side. @@ -133,36 +185,10 @@ The python API is structured as shown below. The base class `UMABackend` functio └── accelerator_B └── ... ``` -A custom accelerator is added by inheriting the `UMABackend`. New elements (e.g., passes, schedules) are added using a registration machanism. -```python -"""UMA backend for the UltraTrail accelerator""" -class UltraTrailBackend(UMABackend): - def __init__(self): - super(UltraTrailBackend, self).__init__() - - # Relay to Relay function registration - self._register_pattern("conv1d_relu", conv1d_relu_pattern()) +### UMABackend class - self._register_relay_pass(1, ConfigGenerator()) - self._register_relay_pass(2, BufferScopeAnnotator()) - - # Relay to TIR function registration - self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy, plevel=9) - - self._register_tir_schedule(insert_extern_calls) - - self._register_tir_pass(0, CodegenGenerateConfig()) - self._register_tir_pass(0, CodegenGenerateConstants()) - - # TIR to runtime function registration - self._register_codegen(format="c", includes=None, constants=None, replace_call_extern=None) - - @property - def target_name(self): - return "ultra_trail" -``` -Once a `UMABackend` is registered, it hooks into the usual `relay.build` process to create the code for the target accelerator. +Once a `UMABackend` is registered (as shown in Guide-level explanation), it hooks into the usual `relay.build` process to create the code for the target accelerator. ``` # Load model mod, params = relay.frontend.from_pytorch(scripted_model, [("input_data", input_shape)]) @@ -182,3 +208,4 @@ with tvm.transform.PassContext( module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) ``` +### From 0cdbbeff49dc91e51af46e1d25ce709443148911 Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Wed, 16 Mar 2022 10:43:39 +0000 Subject: [PATCH 06/17] uma:added _register_config reference and example --- ...A_Unified_Modular_Accelerator_Interface.md | 27 ++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 2aaf8ea0..0e66fb61 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -208,4 +208,29 @@ with tvm.transform.PassContext( module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) ``` -### +####UMABackend References + +#### _register_config +```python +UMABackend._register_config(parameters: dict) +``` +The following ```parameters``` are allow to be passed via ```_register_config```. + +Table of supported parameters: + +|Parameter name|Type|Default|Description| +|--------------|-----------|-----------|-----------| +|partitioning.enable_MergeCompilerRegions|bool |True |MergeCompilerRegions pass is used for partitioning + +This list is not complete and it is the intend of **THIS RFC** to find a small set of necessary parameters. + +Example usage: +```python +UMABackend._register_config({"partitioning.enable_MergeCompilerRegions": False}) +``` + +#### _register_pattern +```python +UMABackend.self._register_pattern() +``` +TODO \ No newline at end of file From d943a5e6ffec6262b3afbf946f7dd93c37e50d02 Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Wed, 16 Mar 2022 10:45:27 +0000 Subject: [PATCH 07/17] uma: minor update --- rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 0e66fb61..02a4195c 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -208,7 +208,7 @@ with tvm.transform.PassContext( module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) ``` -####UMABackend References +#### UMABackend References #### _register_config ```python From 06b5458d53762c72501c0ab70369b387670a534c Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Fri, 18 Mar 2022 18:24:12 +0000 Subject: [PATCH 08/17] uma-rfc: update to questions/comments added --- rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 02a4195c..80f5be2d 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -42,7 +42,7 @@ The **goal** of UMA is to establish two API layers with a different target group - Short learning period for hardware/software engineers new to TVM **Plumbing Layer**: - - Collage-like API + other TVM APIs + - Collage-like API [COLLAGE-RFC](https://github.com/mbs-octoml/mbs-tvm-rfcs/blob/mbs-rfcs-collage/rfcs/xxxx-collage.md) + other TVM APIs - Powerful API to core-compiler + other TVM features - Target audience is experienced TVM users/developers - C++ and Python APIs @@ -88,7 +88,7 @@ The figure below described the *UMA Pipeline*. The blocks are described below: ![](uma_pipeline.png) UMA Pipelining: -* Consists of UMALower and UMACogen, which implement the target hook Relay-to-TIR and TIR-to-Runtime (proposed in [TVM-RFC0010]) +* Consists of UMALower and UMACodgen, which implement the target hook Relay-to-TIR and TIR-to-Runtime (proposed in [TVM-RFC0010]) * UMALower * Input: Partitioned composite functions * Custom primitives can be registered @@ -104,7 +104,7 @@ UMA Pipelining: * UMACodegen baseclass has to be inherited by accelerator-specific Codegen classes (e.g. Accelerator A Codegen, etc) * Output: Target .c files -The intention is to use TensorIR with MetaScheduler for optimization and Relax in later versions. +The intention is to use TensorIR with MetaScheduler for optimization and Relax (a possible succesor of Relay [video link](https://www.youtube.com/watch?v=xVbkjJDMexo)) in later versions. Abbreviations: From 0dda395eda42e7c286e06c503f6215e30bd1a2f8 Mon Sep 17 00:00:00 2001 From: PaulPalomeroBernardo Date: Fri, 1 Apr 2022 11:30:54 +0200 Subject: [PATCH 09/17] Update 00xx_UMA_Unified_Modular_Accelerator_Interface.md * Add descriptions for all API functions * Clarify backend registration and add target hook explanation * Remove schedules from API and corresponding descriptions --- ...A_Unified_Modular_Accelerator_Interface.md | 196 ++++++++++++++---- 1 file changed, 156 insertions(+), 40 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 6e4e0e5b..bc531a89 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -81,7 +81,6 @@ UMA Partitioner: * Order: pre-partitioning passes, Graph partitioning, post-partitioning passes * API level: * UMA Partitioner creates a wrapper API to TVM core-compiler APIs - * *UMAPartitioner* baseclass (Python only) has to be inherited by accelerator-specific Partitioners (e.g. Accelerator A Partitioner, etc) The figure below described the *UMA Pipeline*. The blocks are described below: @@ -93,15 +92,13 @@ UMA Pipelining: * Input: Partitioned composite functions * Custom primitives can be registered * Lowering from Relay to S-TIR, using TOPI or custom primitives - * Interface for registering accelerator-specific schedules and passes - * Execution of UMA schedules and passes on S-TIR + * Interface for registering accelerator-specific passes + * Execution of UMA passes on S-TIR and NS-TIR * Output: NS-TIR(including tir.extern calls) - * UMALower baseclass (Python only) has to be inherited by accelerator-specific Lower classes (e.g. Accelerator A Lower, etc) * UMACodegen * Input: NS-TIR(including tir.extern calls) * Defaults to standard TVM codegen * Intend is to provide a Python interface to insert/emit target code - * UMACodegen baseclass has to be inherited by accelerator-specific Codegen classes (e.g. Accelerator A Codegen, etc) * Output: Target .c files The intention is to use TensorIR with MetaScheduler for optimization and Relax (a possible succesor of Relay [video link](https://www.youtube.com/watch?v=xVbkjJDMexo)) in later versions. @@ -113,16 +110,13 @@ NS-TIR: Non-Schedulable TIR ### Adding a New Custom Accelerator -A custom accelerator is added by inheriting the `UMABackend`. New elements (e.g., passes, schedules) are added using a registration machanism. +A custom accelerator is added by inheriting the `UMABackend`. New elements (e.g., passes) are added using a registration machanism. Below example shows a backend that makes use of all available registration functions. ```python """UMA backend for the UltraTrail accelerator""" class UltraTrailBackend(UMABackend): def __init__(self): super(UltraTrailBackend, self).__init__() - - # Configuration parameters - self._register_config({"partitioning.enable_MergeCompilerRegions": False}) # Example config parameter # Relay to Relay function registration self._register_pattern("conv1d_relu", conv1d_relu_pattern()) @@ -131,15 +125,13 @@ class UltraTrailBackend(UMABackend): self._register_relay_pass(2, BufferScopeAnnotator()) # Relay to TIR function registration - self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy, plevel=9) - - self._register_tir_schedule(insert_extern_calls) + self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy) self._register_tir_pass(0, CodegenGenerateConfig()) - self._register_tir_pass(0, CodegenGenerateConstants()) + self._register_tir_pass(0, CodegenGenerateExternCalls()) # TIR to runtime function registration - self._register_codegen(format="c", includes=None, constants=None, replace_call_extern=None) + self._register_codegen(format="c", includes=gen_includes, replace_call_extern=None) @property def target_name(self): @@ -148,17 +140,9 @@ class UltraTrailBackend(UMABackend): ## Reference-level explanation - - ### File and class structure and Snippets as example for integration -UMA provides a mostly python-based API. On the C++ side, new targets are registered using target hooks (RFC #0010). A generic `codegen.cc` handles the calls to the python side. -``` -. -├── codegen.cc -└── targets.cc -``` -The python API is structured as shown below. The base class `UMABackend` functions as the core API. It uses the API points described in the [flow description](#flow-description) to register elements for the different stages. +UMA provides a fully python-based API. The API is structured as shown below. The base class `UMABackend` functions as the core API. It uses the API points described in the [flow description](#flow-description) to register elements for the different stages. ``` . ├── backend.py @@ -172,7 +156,6 @@ The python API is structured as shown below. The base class `UMABackend` functio │ ├── codegen.py │ ├── passes.py │ ├── patterns.py -│ ├── schedules.py │ └── strategies.py └── accelerator_B └── ... @@ -180,14 +163,15 @@ The python API is structured as shown below. The base class `UMABackend` functio ### UMABackend class -Once a `UMABackend` is registered (as shown in Guide-level explanation), it hooks into the usual `relay.build` process to create the code for the target accelerator. +A new custom backend is created by implementing the `UMABackend` as shown in the [Guide-level explanation](#adding-a-new-custom-accelerator). To use the backend it simply needs to be registered using `backend.register()`. Once a `UMABackend` is registered, it hooks into the usual `relay.build` process to create the code for the target accelerator. ``` # Load model mod, params = relay.frontend.from_pytorch(scripted_model, [("input_data", input_shape)]) # Register a UMA backend -UltraTrailBackend().register() -mod = UltraTrailBackend().partition(mod) +ut_backend = UltraTrailBackend() +ut_backend.register() +mod = ut_backend.partition(mod) # Relay build (AOT C target) TARGET = tvm.target.Target("c") @@ -200,29 +184,161 @@ with tvm.transform.PassContext( module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) ``` -#### UMABackend References +### UMA Target Hooks + +UMA uses target hooks (RFC #0010) to perform the lowering of the partitioned, accelerator specific functions from relay to TIR and TIR to runtime. To register the hooks from the python side, a global function `RegisterTarget` is registered on the C++ side and used during the backend registration. + +```cpp +TVM_REGISTER_GLOBAL("relay.backend.contrib.uma.RegisterTarget") + .set_body_typed([](String target_name){ + ::tvm::TargetKindRegEntry::RegisterOrGet(target_name) + .set_name() + .set_device_type(kDLCPU) + .add_attr_option>("keys") + .add_attr_option("tag") + .add_attr_option("device") + .add_attr_option("model") + .add_attr_option>("libs") + .add_attr_option("host") + .add_attr_option("from_device") + .set_attr("RelayToTIR", relay::contrib::uma::RelayToTIR(target_name)) + .set_attr("TIRToRuntime", relay::contrib::uma::TIRToRuntime); + }); +``` + +### UMABackend References + +UMA should function as an easy to use API, that also helps new users gain orientation in the codebase. To this end, all API functions are typed and documented with examples of their use and required input. + +#### `_register_relay_pass` +```python +_register_relay_pass(self, phase: int, relay_pass: tvm.transform.Pass) -> None +``` + +|Parameter|Description| +|---------|-----------| +|phase|The phase at which the pass is registered.| +|relay_pass|The relay pass to be registered.| + +Example usage: +```python +self._register_relay_pass(0, MyPassA) + +# Where a relay pass can look like this: +@tvm.ir.transform.module_pass(opt_level=0) +class MyPassA: + def transform_module(self, mod, ctx): + # My pass functionality... + return mod +``` -#### _register_config +#### `_register_pattern` ```python -UMABackend._register_config(parameters: dict) +_register_pattern(self, name: str, pattern: tvm.relay.dataflow_pattern.DFPattern,) -> None ``` -The following ```parameters``` are allow to be passed via ```_register_config```. -Table of supported parameters: +|Parameter|Description| +|---------|-----------| +|name|The name of the pattern.| +|pattern|The dataflow pattern.| + +Example usage: +```python +self._register_pattern("conv1d", conv1d_pattern) + +# Where a dataflow pattern can look like this: +conv1d_pattern = is_op("nn.conv1d")(wildcard(), wildcard()) +optional_bias = lambda x: is_op("nn.bias_add")(x, wildcard()) +optional_relu = lambda x: is_op("nn.relu")(x) +conv1d_pattern = conv1d_pattern.optional(optional_bias).optional(optional_relu) +``` -|Parameter name|Type|Default|Description| -|--------------|-----------|-----------|-----------| -|partitioning.enable_MergeCompilerRegions|bool |True |MergeCompilerRegions pass is used for partitioning +#### `_register_operator_strategy` +```python +_register_operator_strategy(self, op: str, strategy: Callable[[tvm.ir.Attrs, tvm.ir.Array, tvm.ir.TensorType, tvm.target.Target], tvm.relay.op.op.OpStrategy], plevel: Optional[int] = 11) -> None +``` -This list is not complete and it is the intend of **THIS RFC** to find a small set of necessary parameters. +|Parameter|Description| +|---------|-----------| +|op|The name of the operator for which this strategy will be registered.| +|strategy|The strategy function.| +|plevel|The priority level of the strategy. Higher plevel equals higher priorization. The TVM default for topi strategies is 10 so by default new UMA strategies are always used.| Example usage: ```python -UMABackend._register_config({"partitioning.enable_MergeCompilerRegions": False}) +self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy) + +# Where a strategy function can look like this: +@relay.op.strategy.override_native_generic_func("custom_conv1d_strategy") +def custom_conv1d_strategy(attrs, inputs, out_type, target): + strategy = _op.OpStrategy() + strategy.add_implementation( + wrap_compute_conv1d(custom_conv1d_compute), + wrap_topi_schedule(custom_conv1d_schedule), + name="custom_conv1d.generic", + return strategy ``` -#### _register_pattern +#### `_register_tir_pass` ```python -UMABackend.self._register_pattern() +_register_tir_pass(self, phase: int, tir_pass: tvm.tir.transform.PrimFuncPass) -> None ``` -TODO \ No newline at end of file + +|Parameter|Description| +|---------|-----------| +|phase|The phase at which the pass is registered.| +|tir_pass|The relay pass to be registered.| + +Example usage: +```python +self._register_tir_pass(0, MyPassA) + +# Where a TIR pass can look like this: +@tvm.tir.transform.prim_func_pass(opt_level=0) +class MyPassA: + def transform_function(self, func, mod, ctx): + # My pass functionality... + return func +``` + +#### `_register_codegen` +```python +_register_codegen(self, fmt: str = "c", **kwargs) -> None +``` + +|Parameter|Description| +|---------|-----------| +|fmt|The codegen format. For now, only C-codegen is supported by UMA.| +|**kwargs|Keyword arguments for the chosen codegen.| + +Example usage: +```python +self._register_codegen(fmt="c", includes=gen_includes, replace_call_extern=gen_replace_call_extern) + +# The C-codegen provides two hooks which allows the user to insert code through the python API. +# - `includes` hooks into the include stream and allows insertion of custom includes. +# - `replace_call_extern` hooks into the expression visitor and allows the user to insert custom code for a given extern call. +# +# The code generation functions can look like this: + +def gen_includes() -> str: + includes = "#include \n" + return includes + +def gen_replace_call_extern(args: tvm.ir.container.Array) -> str: + return "my_custom_api_function({}, {}, {})".format(*args) +``` + +#### Configuration + +The `UMABackend` can be further configured through parameters during initialization. However, the amount of configurability should be kept to a minimum, since UMA tries to streamline and simplify the compilation pipeline. +```python +class UltraTrailBackend(UMABackend): + def __init__(self): + super(UltraTrailBackend, self).__init__(merge_compiler_regions=False) +``` + +Below is a list of currently planned configuration parameters for the initial version of UMA proposed in this RFC. +|Parameter|Type|Description| +|---------|----|-----------| +|merge_compiler_regions|bool (default: True)|Enables/disables the `MergeCompilerRegions` pass during partitioning.| From 9d7e6b5c085aeb33e49f7d57139443462ef0d649 Mon Sep 17 00:00:00 2001 From: "Klaiber Michael (CR/AEX2)" Date: Wed, 6 Apr 2022 17:17:16 +0200 Subject: [PATCH 10/17] uma_pipeline image update --- rfcs/uma_pipeline.svg | 267 +++++++++++++++++++++--------------------- 1 file changed, 135 insertions(+), 132 deletions(-) diff --git a/rfcs/uma_pipeline.svg b/rfcs/uma_pipeline.svg index 3967d261..60da36d3 100644 --- a/rfcs/uma_pipeline.svg +++ b/rfcs/uma_pipeline.svg @@ -2,23 +2,23 @@ + inkscape:export-ydpi="120" + xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape" + xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd" + xmlns="http://www.w3.org/2000/svg" + xmlns:svg="http://www.w3.org/2000/svg" + xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" + xmlns:cc="http://creativecommons.org/ns#" + xmlns:dc="http://purl.org/dc/elements/1.1/"> @@ -45,16 +45,58 @@ fit-margin-right="0" fit-margin-bottom="0" inkscape:zoom="0.74118967" - inkscape:cx="155.83056" - inkscape:cy="282.6537" - inkscape:window-width="1916" - inkscape:window-height="946" - inkscape:window-x="0" - inkscape:window-y="31" + inkscape:cx="491.77696" + inkscape:cy="116.70427" + inkscape:window-width="1920" + inkscape:window-height="1017" + inkscape:window-x="-8" + inkscape:window-y="-8" inkscape:window-maximized="1" inkscape:current-layer="layer1" /> + + + + + + + + + @@ -97,6 +139,20 @@ height="6" x="0" y="0" /> + + + TIR - - - - + + + Execute - Execute + UMA - UMA + Passes + id="text63559">Passes + files - Codegen - - - Execute - UMA - Schedules - Use + +
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phases as enums --- ...A_Unified_Modular_Accelerator_Interface.md | 56 +++++++++++++++++-- 1 file changed, 50 insertions(+), 6 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index bc531a89..0a93a1f7 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -184,14 +184,22 @@ with tvm.transform.PassContext( module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) ``` +### Pass Phases + +UMA allows users to register passes in different stages of the compilation pipeline called pass phases. They are motivated by the existing [TIR pass phases](https://tvm.apache.org/docs/how_to/extend_tvm/low_level_custom_pass.html?highlight=phase#glue-to-lowering) and provide new users with some orientation as to where they should put their custom passes in the entire compilation process. To be more expressive, pass phases are named enums of type `PassPhase`, e.g., `PassPhase.PRE_PARTITIONING`, `PassPhase.POST_PARTITIONING`. This makes them easier to extend and more intuitive to use than an int-based representation. + +Passes are divided into relay and TIR passes and can be registered as such (`_register_relay_pass`, `_register_tir_pass`). Each pass gets registered for a specific pass phase and the order of registration defines the order of exection within the same phase. + +Future versions of UMA could also provide a finer interface for where to register a pass by providing the passes dependencies, e.g., `_register_relay_pass(MyPass, before=PassA, after=(PassB, PassC))`. However, this would first require pass dependencies to be implemented in the TVM core since UMA only tries to wrap around existing TVM APIs. + ### UMA Target Hooks -UMA uses target hooks (RFC #0010) to perform the lowering of the partitioned, accelerator specific functions from relay to TIR and TIR to runtime. To register the hooks from the python side, a global function `RegisterTarget` is registered on the C++ side and used during the backend registration. +UMA uses target hooks (RFC [#0010](https://github.com/apache/tvm-rfcs/blob/main/rfcs/0010-target-registered-compiler-flow-customisation.md)) to perform the lowering of the partitioned, accelerator specific functions from relay to TIR and TIR to runtime. To register the hooks from the python side, a global function `RegisterTarget` is registered on the C++ side and used during backend registration. To allow customization of the target, it is possible to pass target-specific attribute options which are registered in the UMABackend. -```cpp +```cpp TVM_REGISTER_GLOBAL("relay.backend.contrib.uma.RegisterTarget") - .set_body_typed([](String target_name){ - ::tvm::TargetKindRegEntry::RegisterOrGet(target_name) + .set_body_typed([](String target_name, Map attr_options){ + auto target_kind = ::tvm::TargetKindRegEntry::RegisterOrGet(target_name) .set_name() .set_device_type(kDLCPU) .add_attr_option>("keys") @@ -203,16 +211,52 @@ TVM_REGISTER_GLOBAL("relay.backend.contrib.uma.RegisterTarget") .add_attr_option("from_device") .set_attr("RelayToTIR", relay::contrib::uma::RelayToTIR(target_name)) .set_attr("TIRToRuntime", relay::contrib::uma::TIRToRuntime); + + for (auto &attr_option : attr_options) { + try { + target_kind.add_attr_option(attr_option.first, Downcast(attr_option.second)); + continue; + } catch (...) {} + try { + target_kind.add_attr_option(attr_option.first, Downcast(attr_option.second)); + continue; + } catch (...) {} + try { + target_kind.add_attr_option(attr_option.first, Downcast(attr_option.second)); + continue; + } catch (...) { + LOG(FATAL) << "Attribute option of type " << attr_option.second->GetTypeKey() + << " can not be added. Only String, Integer, or Bool are supported."; + } + } }); ``` +Future versions of UMA could support further customization of the target. One option that has been discussed is the possibility to also allow registration of target preprocessors (e.g., `.add_attrs_preprocessor(Preprocessor)`). A motivation for it in described in this [RFC](https://github.com/apache/tvm-rfcs/blob/1901b7361891fc7235b3bb8e80c89130cfe2c91d/rfcs/0070-target-preprocessing.md). + ### UMABackend References UMA should function as an easy to use API, that also helps new users gain orientation in the codebase. To this end, all API functions are typed and documented with examples of their use and required input. +#### `_register_target_attr` +```python +_register_target_attr(self, name: str, default: Optional[Union[str, int, bool]] = "") -> None +``` + +|Parameter|Description| +|---------|-----------| +|name|The name of the target attribute.| +|default|A default value for the attribute. If none is provided, the attribute will be treated as a string.| + +Example usage: +```python +self._register_target_attr("attrA", default=0) +self._register_target_attr("attrB", default=False) +``` + #### `_register_relay_pass` ```python -_register_relay_pass(self, phase: int, relay_pass: tvm.transform.Pass) -> None +_register_relay_pass(self, phase: PassPhase, relay_pass: tvm.transform.Pass) -> None ``` |Parameter|Description| @@ -281,7 +325,7 @@ def custom_conv1d_strategy(attrs, inputs, out_type, target): #### `_register_tir_pass` ```python -_register_tir_pass(self, phase: int, tir_pass: tvm.tir.transform.PrimFuncPass) -> None +_register_tir_pass(self, phase: PassPhase, tir_pass: tvm.tir.transform.PrimFuncPass) -> None ``` |Parameter|Description| From 8a2731a80d60b059dc05517ce9164a9da328a2ab Mon Sep 17 00:00:00 2001 From: PaulPalomeroBernardo Date: Mon, 23 May 2022 14:07:21 +0200 Subject: [PATCH 13/17] Fix code snippets --- ...xx_UMA_Unified_Modular_Accelerator_Interface.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md index 0a93a1f7..7f7155d4 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md @@ -117,18 +117,20 @@ A custom accelerator is added by inheriting the `UMABackend`. New elements (e.g. class UltraTrailBackend(UMABackend): def __init__(self): super(UltraTrailBackend, self).__init__() + + # Target configuration + self._register_target_attr("dimension", default=8) # Relay to Relay function registration self._register_pattern("conv1d_relu", conv1d_relu_pattern()) - self._register_relay_pass(1, ConfigGenerator()) - self._register_relay_pass(2, BufferScopeAnnotator()) + self._register_relay_pass(PassPhase.POST_PARTITIONING, ConfigGenerator()) + self._register_relay_pass(PassPhase.POST_PARTITIONING, BufferScopeAnnotator()) # Relay to TIR function registration self._register_operator_strategy("nn.conv1d", custom_conv1d_strategy) - self._register_tir_pass(0, CodegenGenerateConfig()) - self._register_tir_pass(0, CodegenGenerateExternCalls()) + self._register_tir_pass(PassPhase.TIR_PHASE_0, CodegenGenerateExternCalls()) # TIR to runtime function registration self._register_codegen(format="c", includes=gen_includes, replace_call_extern=None) @@ -266,7 +268,7 @@ _register_relay_pass(self, phase: PassPhase, relay_pass: tvm.transform.Pass) -> Example usage: ```python -self._register_relay_pass(0, MyPassA) +self._register_relay_pass(PassPhase.RELAY_PASS_PHASE, MyPassA) # Where a relay pass can look like this: @tvm.ir.transform.module_pass(opt_level=0) @@ -335,7 +337,7 @@ _register_tir_pass(self, phase: PassPhase, tir_pass: tvm.tir.transform.PrimFuncP Example usage: ```python -self._register_tir_pass(0, MyPassA) +self._register_tir_pass(PassPhase.TIR_PASS_PHASE, MyPassA) # Where a TIR pass can look like this: @tvm.tir.transform.prim_func_pass(opt_level=0) From e591bbf91e6fe0b56f0b3f522d1fbf3f0028a41c Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Wed, 25 May 2022 07:16:41 +0000 Subject: [PATCH 14/17] [UMA] update --- ..._Unified_Modular_Accelerator_Interface.md} | 6 +++--- rfcs/{ => assets/0060}/uma_pipeline.png | Bin rfcs/{ => assets/0060}/uma_pipeline.svg | 0 rfcs/assets/0060/uma_toplevel.png | Bin 0 -> 105226 bytes rfcs/{ => assets/0060}/uma_toplevel.svg | 18 ++++++++++-------- rfcs/uma_toplevel.png | Bin 105827 -> 0 bytes 6 files changed, 13 insertions(+), 11 deletions(-) rename rfcs/{00xx_UMA_Unified_Modular_Accelerator_Interface.md => 0060_UMA_Unified_Modular_Accelerator_Interface.md} (99%) rename rfcs/{ => assets/0060}/uma_pipeline.png (100%) rename rfcs/{ => assets/0060}/uma_pipeline.svg (100%) create mode 100644 rfcs/assets/0060/uma_toplevel.png rename rfcs/{ => assets/0060}/uma_toplevel.svg (98%) delete mode 100644 rfcs/uma_toplevel.png diff --git a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md similarity index 99% rename from rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md rename to rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md index 7f7155d4..233b2506 100644 --- a/rfcs/00xx_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md @@ -9,7 +9,7 @@ Johannes Partzsch - TU Dresden Andrew Stevens - Infineon Technologies RFC PR: TBD - GitHub Issue: TBD + GitHub Tracking Issue: https://github.com/apache/tvm/issues/11260 ## Summary @@ -73,7 +73,7 @@ Accelerator support or optimization functions **outside** the scope of UMA are: The figure below describes the UMA interface from a top level. An *Accelerator Partitioner* which is a specialization of the *UMA Partitioner* takes the Relay graph and matches for supported and unsupported operators. Unsupported operators are processed with the default TVM flow. Supported operator are processed with **UMA Pipeline**. In the following the tasks and the functionality of each block in the figure below is described: -![](uma_toplevel.png) +![](assests/0060/uma_toplevel.png) UMA Partitioner: * Register relay passes @@ -84,7 +84,7 @@ UMA Partitioner: The figure below described the *UMA Pipeline*. 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ztHQ0)bV4+~1E|2LXswI=YBe*h4^PwbkYg2HHi(fph>nEaBe1lJH;>(L4*AQmV`oX9 zcs=m19gNeIlTx3(t(BZXYAP_*hQSp*9`(S3BNwI&#}aRL#iLcD4+o#+5`LvHka!@a zBztI2A|*FGV+6;p0XpsAo;|%JAc|(!tY!G$q=!*of8>c$A{Ug9N;_AqjnFFLVAIPo zU&TBVg(OTQVUGNS2@}eFR(|>H@{pc){qg~FN%L#vG{62^PVU6-r+SIE`}#`wLB_e~ q-i6oD+q`_h%P(&sZm|46-9u6j>b+FL From fa9c27333541917b530b02c9143498af14e64aa2 Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Wed, 25 May 2022 07:18:57 +0000 Subject: [PATCH 15/17] [UMA] update --- rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md index 233b2506..8802bb9f 100644 --- a/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md @@ -73,7 +73,7 @@ Accelerator support or optimization functions **outside** the scope of UMA are: The figure below describes the UMA interface from a top level. An *Accelerator Partitioner* which is a specialization of the *UMA Partitioner* takes the Relay graph and matches for supported and unsupported operators. Unsupported operators are processed with the default TVM flow. Supported operator are processed with **UMA Pipeline**. In the following the tasks and the functionality of each block in the figure below is described: -![](assests/0060/uma_toplevel.png) +![](assets/0060/uma_toplevel.png) UMA Partitioner: * Register relay passes @@ -84,7 +84,7 @@ UMA Partitioner: The figure below described the *UMA Pipeline*. The blocks are described below: -![](assests/0060/uma_pipeline.png) +![](assets/0060/uma_pipeline.png) UMA Pipelining: * Consists of UMALower and UMACodgen, which implement the target hook Relay-to-TIR and TIR-to-Runtime (proposed in [TVM-RFC0010]) From 87229f14ef3a681074c23a99d5388d6b4e452e03 Mon Sep 17 00:00:00 2001 From: "Michael J. Klaiber" Date: Wed, 25 May 2022 07:47:09 +0000 Subject: [PATCH 16/17] [UMA] RFC PR added --- rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md index 8802bb9f..a66d1d46 100644 --- a/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md @@ -8,7 +8,7 @@ Philipp van Kempen @philippvk, Rafael Stahl @r.stahl, Daniel Müller-Gritschneder - Technical University of Munich Johannes Partzsch - TU Dresden Andrew Stevens - Infineon Technologies - RFC PR: TBD + RFC PR: https://github.com/apache/tvm-rfcs/pull/60 GitHub Tracking Issue: https://github.com/apache/tvm/issues/11260 ## Summary From a00d87510dd6619072eaf49f36982194a9d3b1d3 Mon Sep 17 00:00:00 2001 From: PaulPalomeroBernardo Date: Tue, 31 May 2022 22:12:44 +0200 Subject: [PATCH 17/17] Move backend usage to guide-level explanation --- ...A_Unified_Modular_Accelerator_Interface.md | 46 +++++++++---------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md index a66d1d46..6b402a52 100644 --- a/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md +++ b/rfcs/0060_UMA_Unified_Modular_Accelerator_Interface.md @@ -140,6 +140,29 @@ class UltraTrailBackend(UMABackend): return "ultra_trail" ``` +### Using a Custom Backend + +A new custom backend is created by implementing the `UMABackend` as shown in the [previous section](#adding-a-new-custom-accelerator). To use the backend it simply needs to be registered using `backend.register()`. Once a `UMABackend` is registered, it hooks into the usual `relay.build` process to create the code for the target accelerator. +``` +# Load model +mod, params = relay.frontend.from_pytorch(scripted_model, [("input_data", input_shape)]) + +# Register a UMA backend +ut_backend = UltraTrailBackend() +ut_backend.register() +mod = ut_backend.partition(mod) + +# Relay build (AOT C target) +TARGET = tvm.target.Target("c") +RUNTIME = tvm.relay.backend.Runtime("crt") +EXECUTOR = tvm.relay.backend.Executor("aot", {"unpacked-api": True}) + +with tvm.transform.PassContext( + opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["AlterOpLayout"] +): + module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) +``` + ## Reference-level explanation ### File and class structure and Snippets as example for integration @@ -163,29 +186,6 @@ UMA provides a fully python-based API. The API is structured as shown below. The └── ... ``` -### UMABackend class - -A new custom backend is created by implementing the `UMABackend` as shown in the [Guide-level explanation](#adding-a-new-custom-accelerator). To use the backend it simply needs to be registered using `backend.register()`. Once a `UMABackend` is registered, it hooks into the usual `relay.build` process to create the code for the target accelerator. -``` -# Load model -mod, params = relay.frontend.from_pytorch(scripted_model, [("input_data", input_shape)]) - -# Register a UMA backend -ut_backend = UltraTrailBackend() -ut_backend.register() -mod = ut_backend.partition(mod) - -# Relay build (AOT C target) -TARGET = tvm.target.Target("c") -RUNTIME = tvm.relay.backend.Runtime("crt") -EXECUTOR = tvm.relay.backend.Executor("aot", {"unpacked-api": True}) - -with tvm.transform.PassContext( - opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["AlterOpLayout"] -): - module = relay.build(mod, target=TARGET, runtime=RUNTIME, executor=EXECUTOR, params=params) -``` - ### Pass Phases UMA allows users to register passes in different stages of the compilation pipeline called pass phases. They are motivated by the existing [TIR pass phases](https://tvm.apache.org/docs/how_to/extend_tvm/low_level_custom_pass.html?highlight=phase#glue-to-lowering) and provide new users with some orientation as to where they should put their custom passes in the entire compilation process. To be more expressive, pass phases are named enums of type `PassPhase`, e.g., `PassPhase.PRE_PARTITIONING`, `PassPhase.POST_PARTITIONING`. This makes them easier to extend and more intuitive to use than an int-based representation.