Skip to content

Latest commit

 

History

History
199 lines (144 loc) · 9.96 KB

README.md

File metadata and controls

199 lines (144 loc) · 9.96 KB
Raw

triton-shared

A shared middle-layer for the Triton Compiler.

Currently the middle layer is not complete but has enough functionality to demonstrate how it can work. The general idea is that Triton IR is lowered into an MLIR core dialect to allow it to be both shared across Triton targets as well as allow back-ends to be shared with other languages.

The basic intended architecture looks like this:

[Triton IR] -> [Middle Layer] -> [HW specific IR]

The middle-layer uses MLIR's Linalg and Tensor Dialects for operations on Triton block values. Operations on Triton pointers use the Memref Dialect.

Motivation

This talk at the 2023 Triton Developer Conferene gives some background on the project and its goals.

Usage

This repo now includes triton as a submodule and builds as an out-of-tree backend.

To build this repo clone triton-shared to a folder called triton_shared (notice the underscore). Triton will use this folder name to create a module under triton.runtime for the reference CPU backend.

You need to set the TRITON_PLUGIN_DIRS environment variable to the location of your triton-shared directory for triton to find it.

export TRITON_PLUGIN_DIRS=$(pwd)/triton_shared

git clone --recurse-submodules https://github.com/microsoft/triton-shared.git triton_shared
cd triton_shared/triton/python

To build with Clang:

python3 -m pip install --upgrade pip
python3 -m pip install cmake==3.24 ninja pytest-xdist
sudo apt-get update -y
sudo apt-get install -y ccache clang lld
TRITON_BUILD_WITH_CLANG_LLD=true TRITON_BUILD_WITH_CCACHE=true python3 -m pip install --no-build-isolation -vvv '.[tests]'

To build with a virtualenv:

python3 -m venv .venv --prompt triton
source .venv/bin/activate

pip3 install ninja cmake wheel pytest
pip3 install -e python --no-build-isolation

The resulting triton-shared binaries will be placed under triton/python/build/{current_cmake_version}/third_party/triton_shared

1. Stand-Alone

The middle layer can be used as a stand-alone component to convert Triton dialect to the middle layer dialects. This is intended for testing and validation purposes, but could potentially be used before sending the IR to another MLIR complier.

Stand-alone example:

triton-shared-opt --triton-to-linalg %file

2. Backend Component

The intended use of the Triton middle layer is to be used as a component in a Triton back-end. This can be accomplished by adding the cmake targets it produces and its headers files to that back-end. An example back-end will be published at a later date.

3. Reference CPU backend

We also include an experimental reference CPU backend that leverages all existing mlir passes. After building, the CPU backend can be used by setting triton's active driver:

import triton
from triton.backends.triton_shared.driver import CPUDriver

triton.runtime.driver.set_active(CPUDriver())

For more examples, please refer to python/examples.

Implementation details

Even though a valid triton program can perform load and store in arbitrary memory locations, the prototype only supports lowering programs that have structured memory access patterns.

Analyses

As part of the conversion process, there are three important analyses:

  1. Pointer analysis:

    • This analysis is responsible for extracting structured memory access patterns from a triton program during load and store; it walks the IR and visits relevant instructions to build strided memory accesses in the memref dialect. The analysis is still in its early stage and does not support all scenarios.

  2. Use analysis:

    • After "Pointer analysis", instructions that are part of memory address calculation will no longer be necessary in a triton program because their semantics have now been captured by memref operations representing strided memory accesses. To aid with removing these instructions safely, we perform Use analysis to mark which instructions are used only in address calculation (called MetaUse) or used in both address calculation and data manipulation (called MixedUse) operations. Those that are MixedUse are cloned and have their users adjusted accordingly with the goal of separating out the MetaUse ops so that they can be safely deleted.

  3. Mask analysis:

    • This analysis is responsible for handling masked loads and stores.

Conversion strategy

We introduce the TritonToLinalg pass that converts the triton dialect to the linalg dialect on tensors. This means the resulting IR is fully compatible with linalg tiling and fusion transformation passes. As mentioned in the Pointer analysis's description, we do however have to deal with memref instructions at the load and store boundaries and have to convert them to tensors using bufferization.to_tensor. Here's a simple example of what the IR looks like:

tt.func @kernel(%afloat : !tt.ptr<bf16>, %res : !tt.ptr<bf16>) {
  %0 = tt.make_range {end = 128 : i32, start = 0 : i32} : tensor<128xi32>
  %1 = tt.splat %afloat : (!tt.ptr<bf16>) -> tensor<128x!tt.ptr<bf16>>
  %2 = tt.addptr %1, %0 : tensor<128x!tt.ptr<bf16>>, tensor<128xi32>
  %afm = tt.load %2 : tensor<128x!tt.ptr<bf16>>
  %3 = "tt.reduce"(%afm) ({
  ^bb0(%arg5: bf16, %arg6: bf16):
    %21 = arith.addf %arg5, %arg6 : bf16
    tt.reduce.return %21 : bf16
  }) {axis = 0 : i32} : (tensor<128xbf16>) -> bf16
  tt.store %res, %3 : !tt.ptr<bf16>
  tt.return
}

after conversion:

func.func @kernel(%arg0: memref<*xbf16>, %arg1: memref<*xbf16>, %arg2: i32, %arg3: i32, %arg4: i32) {
    %cst = arith.constant 0.000000e+00 : f32
    %reinterpret_cast = memref.reinterpret_cast %arg0 to offset: [0], sizes: [128], strides: [1] :
        memref<*xbf16> to memref<128xbf16, strided<[1]>>
    %alloc = memref.alloc() : memref<128xbf16>
    memref.copy %reinterpret_cast, %alloc : memref<128xbf16, strided<[1]>> to memref<128xbf16>
    %0 = bufferization.to_tensor %alloc restrict writable : memref<128xbf16>
    %1 = bufferization.alloc_tensor() : tensor<f32>
    %inserted = tensor.insert %cst into %1[] : tensor<f32>
    %reduced = linalg.reduce ins(%0 : tensor<128xbf16>) outs(%inserted : tensor<f32>) dimensions = [0]
      (%in: bf16, %init: f32) {
        %3 = arith.extf %in : bf16 to f32
        %4 = arith.addf %3, %init : f32
        linalg.yield %4 : f32
      }
    %extracted = tensor.extract %reduced[] : tensor<f32>
    %2 = arith.truncf %extracted : f32 to bf16
    %reinterpret_cast_0 = memref.reinterpret_cast %arg1 to offset: [0], sizes: [1], strides: [1] :
        memref<*xbf16> to memref<1xbf16, strided<[1]>>
    affine.store %2, %reinterpret_cast_0[0] : memref<1xbf16, strided<[1]>>
    return

}

Important details to note:

  • tt.load (together with all of its related address calculation instructions such as tt.addptr and tt.splat) are lowered to a combination of memref.reinterpret_cast, memref.alloc, and memref.copy. After the initialization of the local buffer, we convert the memref back to a tensor using bufferization.to_tensor; this op is automatically removed during bufferization.

  • tt.store lowers to a combination of memref.reinterpret_cast and either affine.store or memref.tensor_store:

%reinterpret_cast = memref.reinterpret_cast %arg2 to offset: [...] memref<*xf32> to memref<1024xf32>
%extracted_slice = tensor.extract_slice %15[0] [%21] [1] : tensor<1024xf32> to tensor<?xf32>
%subview = memref.subview %reinterpret_cast[0] [%21] [1] : memref<1024xf32> to memref<?xf32>
bufferization.materialize_in_destination %extracted_slice in writable %subview
  • element-wise arith and math operators are converted to their corresponding linalg.generic version.
  • tt.dot becomes linalg.matmul.
  • tt.reduce becomes linalg.reduce; known limitation: only support addf and maxf reduction in the reduction body for now.

Testing

The prototype was tested on the following triton kernel examples:

  1. vector addition
  2. fused softmax
  3. matrix multiplication
  4. layer normalization
  5. fused attention

The Python tests are setup to run with Pytest and you will need to set the following environment variables to run them:

export LLVM_BINARY_DIR=<path-to-your-llvm-binaries>
export TRITON_SHARED_OPT_PATH=$TRITON_PLUGIN_DIRS/triton/python/build/<your-cmake-directory>/third_party/triton_shared_opt/triton_shared-opt

pytest <path-to-triton-shared>/python/examples

In addition to testing on the tutorial kernels, there are many lit tests covering various scenarios.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.