TVM.TL

Not the official and Release, but you can use tl via BitBLAS

TVM.TL is an extention of TVMScript to write simple and high performance GPU kernels with tensorcores. TVM.TL is currently supported on CUDA deivces with Ampere (sm_80+), Turing (sm_75) and Volta(sm_70).

Let's get started with a simple GEMM example.

import tvm.tl.language as T
def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype = "float"):
    @T.prim_func
    def main(
        A: T.Buffer((M, K), dtype),
        B: T.Buffer((K, N), dtype),
        C: T.Buffer((M, N), dtype),
        bias: T.Buffer([N], dtype),
    ):
        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
            A_shared = T.alloc_shared((block_M, block_K), dtype)
            B_shared = T.alloc_shared((block_K, block_N), dtype)
            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
            bias_local = T.alloc_fragment((block_N,), dtype)
            T.clear(C_local)
            for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
                T.copy(A[by * block_M, k * block_K], A_shared)
                T.copy(B[k * block_K, bx * block_N], B_shared)
                T.gemm(A_shared, B_shared, C_local)
            T.copy(bias[bx * block_N], bias_local)
            for i, j in T.Parallel(block_M, block_N):
                C_local[i, j] += bias_local[j]
            T.copy(C_local, C[by * block_M, bx * block_N])

    return main

Despite this simple examples, tvm.tl can be used to write more complicated examples including convolutions, flash-attention-v2 (fwd & bwd), normalizations, these examples can be found under folder tl_scripts.

The performance of our flash-attention is comparable to the manually implementation. (see Link).

Install

Install is similar to tvm. First, fill in USE_CUDA and USE_LLVM in cmake/config.cmake, like this:

set(USE_LLVM "/path/to/llvm-config --link-static")
set(HIDE_PRIVATE_SYMBOLS ON)
set(USE_CUDA /usr/local/cuda)

Then build tvm

mkdir -p build && cd build && cp ../cmake/config.cmake . && cmake .. && make -j && cd -
export PYTHONPATH="$PYTHONPATH:$PWD/python"
# some python package required by tvm
pip install torch attrs cloudpickle decorator psutil synr tornado xgboost

We also need to prepare the cutlass headers, the default version of cutlass in TVM does not work correctly

git clone https://github.com/NVIDIA/cutlass.git -b v3.2.2
export TL_CUTLASS_PATH=/path/to/cutlass/include

Note 1: It is recommeneded to use the latest cuda toolkit, because we requires nvcc to jit compile the generated CUDA code.

Note 2: Don't forget to clone the submodules.

Language reference

Still in progress.

See tl_doc/language_ref.md