Triton - a language and compiler for writing highly efficient custom Deep-Learning primitives.

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Triton

This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with higher flexibility than other existing DSLs.

The foundations of this project are described in the following MAPL2019 publication: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations. Please consider citing this work if you use Triton!

The official documentation contains installation instructions and tutorials.

Compatibility

Supported Platforms:

  • Linux

Supported Hardware:

  • NVIDIA GPUs (Compute Capability 7.0+)
  • Under development: AMD GPUs, CPUs

Contributing

Community contributions are more than welcome, whether it be to fix bugs or to add new features. Feel free to open Github issues about your contribution ideas, and we will review them. A contributor's guide containing general guidelines is coming soon!

Disclaimer

Triton is a fairly recent project, and it is under active development. We expect it to be pretty useful in a wide variety of cases, but don't be surprised if it's a bit rough around the edges :)

Comments
  • Changes to triton functions inside python script does not recompile

    Changes to triton functions inside python script does not recompile

    When I make changes to a function like this in the python tutorials and reran python ..., the new changes will not be recompiled.

    def leaky_relu(x):
        return tl.where(x >= 0, x, 0.01 * x)
    

    I had to manually clean the cache every time to compile the new code. Is there a better way to do this? Thanks.

    opened by pearlli98 26
  • What is the expected perf for int8 matmul?

    What is the expected perf for int8 matmul?

    Hi,

    I compared the matmul perf in fp16 and int8, using the tutorial code in https://triton-lang.org/master/getting-started/tutorials/03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py, and got the following result:

    ----------------------------------------------------------------
             M         N          K          Time(s)      Rate(TF/s)
    ----------------------------------------------------------------
         38400,       4096,       1024,       0.001457     221.026
         38400,       4096,       1024,       0.004148     77.664
    

    in A100 GPU. so for fp16 the TF/s is reasonable since the peak is 314 TF/s in tensorcore, for int8 it seems to be off by a lot, is this expected?

    question 
    opened by jerryzh168 22
  • [RFC] triton dequantize instruction

    [RFC] triton dequantize instruction

    implemente a dequantize instruction in triton

    def dequantize(input, scale, shift, nbit, dst_ty=float16, _builder=None):
    

    input is nbit (8, 4, or 2) integers packed into int16s or int32s. scale and shift are float16 scalars. For example, for nbit = 8, input is of type int32. The instruction will convert [{int8_0, int8_1, int8_2, int8_3}, {int8_4, int8_5, int8_6, int8_7}, ...] (every four int8s packed into one int32) to scale * [int8_0, int8_1, int8_2, int8_3, int8_4, int8_5, int8_6, int8_7, ..., ] + shift in float16s. If the size of input is N, the size of output is 4 * N. Similarly for int4 and int2, eight int4s are packed into one int32 and eight int2s are packed into one int16. See test file https://github.com/yuguo68/triton/blob/dequantize_inst/python/test/unit/language/test_dequantize.py for code examples.

    For our use case at Meta, the scale and shift are usually concatenated together with the quantized integers.

    input in memory: scale(16 bits), shift (16bits), int8_0, int8_1, int8_2, ..., 
    output = scale * ([int8_0, int8_1, int8_2, ...]) + shift
    

    similarly for int4 and int2.

    We find that using existing triton instruction (bit mask, bitwise cast etc) to unpack the quantized integers is slow. Hence we decide to implement the algorithm similar to https://github.com/pytorch/FBGEMM/blob/6a59bb6621ba9ec7d650ccb78b78ea24d62a3904/fbgemm_gpu/include/fbgemm_gpu/fbgemm_cuda_utils.cuh#L1566-L1619. We observe 2X speedup for Meta use case.

    During the implementation, we find that it is critical to make the nano tile size (nts_) https://github.com/openai/triton/blob/09cc2d454b442301e88d1df153214732bd8714d8/include/triton/codegen/analysis/layout.h#L232-L233 consistent between the input and output. For example, for 8-bit quantization with input size of 64 (output size 256), the output layout [0, 0, 0, 0, 1, 1, 1, 1, …, 31, 31, 31, 31, 0, 0, 0, 0, 1, 1, 1, 1, …, 31, 31, 31, 31] does not work with input layout [0, 0, 1, 1,…, 31, 31], but work with input layout [0,1,…,31; 0,1,…,31]. input layout [0, 0, 1, 1,…, 31, 31] works with output layout [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, …, 31, 31, 31, 31, 31, 31, 31, 31]. In general, supposing size(output)/size(input) = N, it requires nts_(output) = N * nts_(input).

    Currently we use tl.multiple_of hints https://github.com/yuguo68/triton/blob/2b3ba853a6f641584b0fb4c4ed8e15b772f7549c/python/test/unit/language/test_dequantize.py#L32-L38 to enforce the nano tile size consistency. Would love to hear better ways to enforce it, for example, in populate_starting_multiple_dequantize and populate_max_contiguous_dequantize.

    The PR author is new to Triton backend and would appreciate feedbacks/comments for improvement, especially for changes in lib/codegen/analysis/align.cc, lib/codegen/analysis/axes.cc. We are aware of the new MLIR backend, and would love to implement this instruction in the new backend as well. Comments on the feasibility in the new backend are appreciated. Thank you!

    @ngimel @jianyuh @ajtulloch

    opened by yuguo68 21
  • Fix Warnings and Enable Warnings as Errors

    Fix Warnings and Enable Warnings as Errors

    Enable Warnings-as-Errors

    I enabled HandleLLVMOptions in the top-level cmake project and used imported cmake option LLVM_ENABLE_WERROR to do the heavy lifting of setting compiler options correctly across compiler versions.

    Fixed Warnings

    I built triton-mlir with both GCC and Clang/LLVM and fixed all the result warnings. Most were harmless but i did find a couple of real issues during this work

    • signed/unsigned comparisons and unused code were the most common warnings
    • i fixed a couple of incorrect usages of std::move that were resulting in extra copies
    • replaced usage of tmpnam (unsafe) with built-in LLVM functionality
    • fix a place where code was checking if the success function existed rather than if a function returned success due to a typo.
    opened by manbearian 19
  • Support for Sparse-Dense Matrix Mulitplication

    Support for Sparse-Dense Matrix Mulitplication

    Hi, All

    Is there any support for using GPU tensor core in Sparse-Dense Matrix Multiplication (SpMM) or Sampled Dense-Dense Matrix Multiplication (SDDMM)?

    Thanks!

    opened by YukeWang96 19
  • TypeError: function takes exactly 16 arguments (13 given)

    TypeError: function takes exactly 16 arguments (13 given)

    (Issue was first posted in torchdynamo, but I'm reposting it here, since it seems like it is potentially an issue with triton instead.)

    The following seems to throws TypeError: function takes exactly 16 arguments (13 given) no matter what I do. I've reproduced it several times now.

    import torch
    from torch import tensor, device
    import torch.fx as fx
    from torchdynamo.testing import rand_strided
    from math import inf
    from torch.fx.experimental.proxy_tensor import make_fx
    
    # torch version: 1.14.0.dev20221009
    # torch cuda version: 11.7
    # torch git version: 0dbefb2414417e80371ef3d8224404d4a522f86e
    
    
    # CUDA Info:
    # nvcc: NVIDIA (R) Cuda compiler driver
    # Copyright (c) 2005-2022 NVIDIA Corporation
    # Built on Wed_Jun__8_16:49:14_PDT_2022
    # Cuda compilation tools, release 11.7, V11.7.99
    # Build cuda_11.7.r11.7/compiler.31442593_0
    
    # GPU Hardware Info:
    # NVIDIA A100-SXM4-40GB : 1
    
    
    from torch.nn import *
    class Repro(torch.nn.Module):
        def __init__(self):
            super().__init__()
    
    
    
        def forward(self, arg0_1, new_zeros_1):
            slice_scatter = torch.ops.aten.slice_scatter.default(new_zeros_1, arg0_1, 2, 0, 2048);  new_zeros_1 = arg0_1 = None
            return (slice_scatter,)
    
    args = [((16, 128, 2048), (262144, 2048, 1), torch.float32, 'cuda'), ((16, 128, 2112), (270336, 2112, 1), torch.float32, 'cuda')]
    args = [rand_strided(sh, st, dt, dev) for (sh, st, dt, dev) in args]
    mod = make_fx(Repro())(*args)
    
    from torchinductor.compile_fx import compile_fx_inner
    from torchdynamo.debug_utils import same_two_models
    
    compiled = compile_fx_inner(mod, args)
    compiled(*args)
    
    opened by typedfemale 18
  • [small] use torch.int for autotuning cache

    [small] use torch.int for autotuning cache

    For stupid reasons, ops on int8 are 3 times slower than on int, and for another set of stupid reasons we are not using cudaMemset for zero_, so using int8 buffer in do_bench makes it slow.

    opened by ngimel 16
  • triton==1.0.0.dev20210329 no longer installable via pip

    triton==1.0.0.dev20210329 no longer installable via pip

    Hi - would it be possible to reinstantiate triton==1.0.0.dev20210329 on pip, or make it clear how to update to the latest dev branch? The api seems to have changed significantly in the latest nightlies, and some function in https://github.com/microsoft/DeepSpeed rely on that particular interface.

    opened by sdtblck 15
  • Regression for caffe opencl branch.

    Regression for caffe opencl branch.

    The latest isaac code triggers many test failures with caffe's opencl branch. The good commit is: Templates/Reduce1D: now properly loading 2D scalars commit 6ac5e1f55b1cae5

    Since that commit, both "General: Internal code generator overhaul" and "JIT: No longer using fallbacks for stride[0] > 1" introduce some regressions.

    It's easy to build the Caffe's opencl branch as below:

    mkdir build

    cmake -DUSE_GREENTEA=ON -DUSE_ISAAC=ON ..

    cd build

    make -j8

    make runtest

    Then you will see many new failures with the above two commit.

    BTW It's better to use latest beignet driver as the OCL compiler. The good commit works great with beignet.

    @ptillet Could you look at this issue? Thanks.

    bug 
    opened by gongzg 15
  • Refresh cache when the source code of outlined functions are changed

    Refresh cache when the source code of outlined functions are changed

    Draft proposal to update the cache logic.

    Testing cases and document are incomplete.

    By revisiting the caching logic, my understanding is that we should update a binary/kernel whenever the following characteristics are changes:

    1. The signature of the kernel's source code.
    2. The signatures of inlined JIT functions being called.
    3. The signatures of outlined JIT functions being called.
    4. The length of any non-constexpr variables.
    5. The number of variables.
    6. The value of constexpr variables.
    7. The alignment, date type, and ptr length of tensors.
    8. The number of warps and stages.

    This patch tries to fix a corner case of characteristics 3 (C3).

    opened by Jokeren 14
  • Fix LLVM error for bfloat16

    Fix LLVM error for bfloat16

    I was encountering some LLVM "Cannot select" issues when using bf16 with certain ops, even with the latest bf16 patch. I've added a minimal reproducer as a test.

    I don't fully understand the source of the issue here, but switching to representing bfloat16 types as int16 seems to solve the issue and still give correct results. I also found that a couple other "Cannot select" errors were fixed by this as well, and removed those workarounds.

    Without this patch, the new test fails with the following error:

    triton/python/test/unit/operators/test_norm.py::test_normalized[dtype0] LLVM ERROR: Cannot select: 0x55b44dbdb690: bf16 = bitcast 0x55b44dbdea60
      0x55b44dbdea60: i16,ch,glue = CopyFromReg 0x55b44dbde9f8, Register:i16 %9, 0x55b44dbde9f8:1
        0x55b44dbde720: i16 = Register %9
        0x55b44dbde9f8: ch,glue = inlineasm 0x55b44dbde928, TargetExternalSymbol:i64'@$1 ld.global.b16 {$0}, [ $2 + 0];', MDNode:ch<null>, TargetConstant:i64<1>, TargetConstant:i32<196618>, Register:i16 %9, TargetConstant:i32<65545>, Register:i1 %10, TargetConstant:i32<851977>, Register:i64 %11, 0x55b44dbde928:1
          0x55b44dbde580: i64 = TargetExternalSymbol'@$1 ld.global.b16 {$0}, [ $2 + 0];'
          0x55b44dbde650: i64 = TargetConstant<1>
          0x55b44dbde6b8: i32 = TargetConstant<196618>
          0x55b44dbde720: i16 = Register %9
          0x55b44dbde858: i32 = TargetConstant<65545>
          0x55b44dbde788: i1 = Register %10
          0x55b44dbde990: i32 = TargetConstant<851977>
          0x55b44dbde8c0: i64 = Register %11
          0x55b44dbde928: ch,glue = CopyToReg 0x55b44dbde7f0, Register:i64 %11, 0x55b44dbde448, 0x55b44dbde7f0:1
            0x55b44dbde8c0: i64 = Register %11
            0x55b44dbde448: i64 = add 0x55b44dbdbbd8, 0x55b44dbe2b08
              0x55b44dbdbbd8: i64 = add 0x55b44dbdb830, 0x55b44dbde3e0
                0x55b44dbdb830: i64,ch = load<(dereferenceable invariant load 8 from `i64 addrspace(101)* null`, addrspace 101)> 0x55b44d28a958, TargetExternalSymbol:i64'_normalized_op_param_0', undef:i64
                  0x55b44dbdacd0: i64 = TargetExternalSymbol'_normalized_op_param_0'
                  0x55b44dbdada0: i64 = undef
                0x55b44dbde3e0: i64 = NVPTXISD::MUL_WIDE_SIGNED 0x55b44dbdb968, Constant:i32<2>
                  0x55b44dbdb968: i32 = mul 0x55b44dbdb900, 0x55b44dbdbb70
                    0x55b44dbdb900: i32 = llvm.nvvm.read.ptx.sreg.ctaid.x TargetConstant:i64<4999>
                      0x55b44dbdb898: i64 = TargetConstant<4999>
                    0x55b44dbdbb70: i32,ch = load<(dereferenceable invariant load 4 from `i32 addrspace(101)* null`, addrspace 101)> 0x55b44d28a958, TargetExternalSymbol:i64'_normalized_op_param_2', undef:i64
                      0x55b44dbdb010: i64 = TargetExternalSymbol'_normalized_op_param_2'
                      0x55b44dbdada0: i64 = undef
                  0x55b44dbe0f60: i32 = Constant<2>
              0x55b44dbe2b08: i64 = NVPTXISD::MUL_WIDE_SIGNED 0x55b44dbe29d0, Constant:i32<2>
                0x55b44dbe29d0: i32 = or 0x55b44dbe2e48, 0x55b44dbe2fe8
                  0x55b44dbe2e48: i32 = shl 0x55b44dbdb7c8, Constant:i32<5>
                    0x55b44dbdb7c8: i32 = srl 0x55b44dbdb558, Constant:i32<5>
                      0x55b44dbdb558: i32 = llvm.nvvm.read.ptx.sreg.tid.x TargetConstant:i64<5057>
    
                      0x55b44dbdee70: i32 = Constant<5>
                    0x55b44dbdee70: i32 = Constant<5>
                  0x55b44dbe2fe8: i32 = sub 0x55b44dbdb558, 0x55b44dbe2e48
                    0x55b44dbdb558: i32 = llvm.nvvm.read.ptx.sreg.tid.x TargetConstant:i64<5057>
                      0x55b44dbdb4f0: i64 = TargetConstant<5057>
                    0x55b44dbe2e48: i32 = shl 0x55b44dbdb7c8, Constant:i32<5>
                      0x55b44dbdb7c8: i32 = srl 0x55b44dbdb558, Constant:i32<5>
    
    
                      0x55b44dbdee70: i32 = Constant<5>
                0x55b44dbe0f60: i32 = Constant<2>
            0x55b44dbde7f0: ch,glue = CopyToReg 0x55b44d28a958, Register:i1 %10, 0x55b44dbde518
              0x55b44dbde788: i1 = Register %10
              0x55b44dbde518: i1 = setcc 0x55b44dbe29d0, 0x55b44dbdbb70, setlt:ch
                0x55b44dbe29d0: i32 = or 0x55b44dbe2e48, 0x55b44dbe2fe8
                  0x55b44dbe2e48: i32 = shl 0x55b44dbdb7c8, Constant:i32<5>
                    0x55b44dbdb7c8: i32 = srl 0x55b44dbdb558, Constant:i32<5>
                      0x55b44dbdb558: i32 = llvm.nvvm.read.ptx.sreg.tid.x TargetConstant:i64<5057>
    
                      0x55b44dbdee70: i32 = Constant<5>
                    0x55b44dbdee70: i32 = Constant<5>
                  0x55b44dbe2fe8: i32 = sub 0x55b44dbdb558, 0x55b44dbe2e48
                    0x55b44dbdb558: i32 = llvm.nvvm.read.ptx.sreg.tid.x TargetConstant:i64<5057>
                      0x55b44dbdb4f0: i64 = TargetConstant<5057>
                    0x55b44dbe2e48: i32 = shl 0x55b44dbdb7c8, Constant:i32<5>
                      0x55b44dbdb7c8: i32 = srl 0x55b44dbdb558, Constant:i32<5>
    
    
                      0x55b44dbdee70: i32 = Constant<5>
                0x55b44dbdbb70: i32,ch = load<(dereferenceable invariant load 4 from `i32 addrspace(101)* null`, addrspace 101)> 0x55b44d28a958, TargetExternalSymbol:i64'_normalized_op_param_2', undef:i64
                  0x55b44dbdb010: i64 = TargetExternalSymbol'_normalized_op_param_2'
                  0x55b44dbdada0: i64 = undef
    In function: _normalized_op
    Fatal Python error: Aborted
    
    opened by samsamoa 13
  • [Triton-MLIR][BACKEND] Make mmav1 works on basic cases

    [Triton-MLIR][BACKEND] Make mmav1 works on basic cases

    TODO:

    • Add more cases
    • Currently, we just set vec to 4 to make the basic cases pass

    Issue:

    • the vec in shared layout is different compared to master branch
      • when vec=1, it encounters CUDA misalignment error, it doesn't work in master branch as well
      • when setting vec to the value identical to master branch, the MMA works
    opened by Superjomn 0
  • is there a way to get cuda c code triton generated?

    is there a way to get cuda c code triton generated?

    I want to learn from the cuda c code triton generated in matmul, I believe this is a good way to get the knowledge about how triton works. Really curious about how triton implemented optimization in matmul in cuda c. But currently seems triton generate machine code(SASS) from triton-ir? image

    opened by Jack47 1
  • triton fp16 matmul introduces more noise than torch.matmul in fp16 when compared it to torch.matmul in fp32

    triton fp16 matmul introduces more noise than torch.matmul in fp16 when compared it to torch.matmul in fp32

    Hi, I found that triton fp16 gemm introduces more noise than torch.matmul when M is not large like 32 as shown in the figure. it can be larger than 6dB in SNR, which is saying the noise power can be >2x when taking the gemm output with the fp32 torch-matmul output as reference (and measure the SNR and compare it with the other SNR that measures the torch.matmul output in fp16 against torch.matmul output in fp32, more details can be found from the shared code). triton-matmul-K_vs_snr-M32-torch-vs-triton the test code I used is here https://gist.github.com/stephen-youn/9f39dfacfec8d777a385912110b44911 why the triton fp16 gemm introduces more noise than torch? is it expected? thanks

    opened by stephen-youn 0
  • AOT compilation

    AOT compilation

    Hi, I was just wondering if there had been any more thoughts on supporting AOT kernel compilation to allow execution outside of Python? Referencing https://github.com/openai/triton/issues/175

    opened by david-macleod 3
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