triton/python/tutorials/01-vector-add.py

"""
Vector Addition
=================
In this tutorial, we will see how to construct a simple, high-performance vector addition using Triton. You will learn:
* The basic syntax of the Triton programming language
* The best practices for creating PyTorch custom operators using the `triton.kernel` Python API
* The best practices for validating and benchmarking custom ops against native reference implementations
"""

# %%
# Writing the Compute Kernel
# --------------------------
#
# Each compute kernel is declared using the :code:`__global__` attribute, and executed many times in parallel
# on different chunks of data (See the `Single Program, Multiple Data <(https://en.wikipedia.org/wiki/SPMD>`_)
# programming model for more details).
#
#  .. code-block:: C
#
#    __global__ void add(float* z, float* x, float* y, int N){
#        // The `get_program_id(i)` returns the i-th coordinate
#        // of the program in the overaching SPMD context
#        // (a.k.a launch grid). This is what allows us to process
#        // different chunks of data in parallel.
#        // For those similar with CUDA, `get_program_id({0,1,2})`
#        // is similar to blockIdx.{x,y,z}
#        int pid = get_program_id(0);
#        // In Triton, arrays are first-class citizen. In other words,
#        // they are primitives data-types and are -- contrary to C and
#        // CUDA -- not implemented as pointers to contiguous chunks of
#        // memory.
#        // In the few lines below, we create an array of `BLOCK` pointers
#        // whose memory values are, e.g.:
#        // [z + pid*BLOCK + 0, z + pid*BLOCK + 1, ..., z + pid*BLOCK + BLOCK - 1]
#        // Note: here BLOCK is expected to be a pre-processor macro defined at compile-time
#        int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;
#        float* pz [BLOCK] = z + offset;
#        float* px [BLOCK] = x + offset;
#        float* py [BLOCK] = y + offset;
#        // Simple element-wise control-flow for load/store operations can
#        // be achieved using the the ternary operator `cond ? val_true : val_false`
#        // or the conditional dereferencing operator `*?(cond)ptr
#        // Here, we make sure that we do not access memory out-of-bounds when we
#        // write-back `z`
#        bool check[BLOCK] = offset < N;
#        *?(check)pz = *?(check)px + *?(check)py;
#    }
#
# The existence of arrays as a primitive data-type for Triton comes with a number of advantages that are highlighted in the `MAPL'2019 Triton paper <http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tillet-kung-cox.pdf>`_.

# %%
# Writing the Torch bindings
# --------------------------
# The only thing that matters when it comes to Triton and Torch is the `triton.kernel` class. This allows you to transform the above C-like function into a callable python object that can be used to modify `torch.tensor` objects.
#
# To create a `triton.kernel`, you only need three things:
# - `source: string`: the source-code of the kernel you want to create
# - `device: torch.device`: the device you want to compile this code for
# - `defines: dict`: the set of macros that you want the pre-processor to `#define` for you

import torch
import triton

# %%
# source-code for Triton compute kernel
# here we just copy-paste the above code without the extensive comments.
# you may prefer to store it in a .c file and load it from there instead.

_src = """
__global__ void add(float* z, float* x, float* y, int N){
    // program id
    int pid = get_program_id(0);
    // create arrays of pointers
    int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;
    float* pz[BLOCK] = z + offset;
    float* px[BLOCK] = x + offset;
    float* py[BLOCK] = y + offset;
    // bounds checking
    bool check[BLOCK] = offset < N;
    // write-back
    *?(check)pz = *?(check)px + *?(check)py;
}
    """

# %%
# This function returns a callable `triton.kernel` object
# created from the above source code.
# For portability, we maintain a cache of kernels for different `torch.device`
# We compile the kernel with -DBLOCK=1024


def make_add_kernel(device):
    cache = make_add_kernel.cache
    if device not in cache:
        defines = {'BLOCK': 1024}
        cache[device] = triton.kernel(_src, device=device, defines=defines)
    return cache[device]


make_add_kernel.cache = dict()

# %%
# This is a standard torch custom autograd Function
# The only difference is that we can now use the above kernel
# in the `forward` and `backward` functions.`


class _add(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, y):
        # constraints of the op
        assert x.dtype == torch.float32
        # *allocate output*
        z = torch.empty_like(x)
        # *create launch grid*:
        # this is a function which takes compilation parameters `opt`
        # as input and returns a tuple of int (i.e., launch grid) for the kernel.
        # triton.cdiv is a shortcut for ceil division:
        # triton.cdiv(a, b) = (a + b - 1) // b
        N = z.shape[0]
        grid = lambda opt: (triton.cdiv(N, opt.BLOCK), )
        # *launch kernel*:
        # pointer to the data of torch tensors can be retrieved with
        # the `.data_ptr()` method
        kernel = make_add_kernel(z.device)
        kernel(z.data_ptr(), x.data_ptr(), y.data_ptr(), N, grid=grid)
        return z


# Just like we standard PyTorch ops We use the `.apply` method to create a callable object for our function
add = _add.apply

# %%
# Writing a Unit Test
# --------------------------
torch.manual_seed(0)
x = torch.rand(98432, device='cuda')
y = torch.rand(98432, device='cuda')
za = x + y
zb = add(x, y)
print(za)
print(zb)
print(f'The maximum difference between torch and triton is ' f'{torch.max(torch.abs(za - zb))}')

# %%
# Writing a Benchmark
# --------------------------
# We can now benchmark our custom op for vectors of increasing sizes to get a sense of how it does

warmup = 10
rep = 200
for N in [2**i for i in range(17, 26, 1)]:
    x = torch.rand(N, device='cuda')
    y = torch.rand(N, device='cuda')
    triton_ms = triton.testing.do_bench(lambda: add(x, y), warmup=warmup, rep=rep)
    torch_ms = triton.testing.do_bench(lambda: x + y, warmup=warmup, rep=rep)
    # print the performance of triton and torch as well as the achieved bandwidth
    print(f'{N} {triton_ms:.3f} {torch_ms:.3f}')