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< p class = "caption" role = "heading" > < span class = "caption-text" > Getting Started< / span > < / p >
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< li class = "toctree-l2 current" > < a class = "current reference internal" href = "#" > Vector Addition< / a > < ul >
< li class = "toctree-l3" > < a class = "reference internal" href = "#compute-kernel" > Compute Kernel< / a > < / li >
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< p class = "admonition-title" > Note< / p >
< p > Click < a class = "reference internal" href = "#sphx-glr-download-getting-started-tutorials-01-vector-add-py" > < span class = "std std-ref" > here< / span > < / a >
to download the full example code< / p >
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< div class = "sphx-glr-example-title section" id = "vector-addition" >
< span id = "sphx-glr-getting-started-tutorials-01-vector-add-py" > < / span > < h1 > Vector Addition< a class = "headerlink" href = "#vector-addition" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > In this tutorial, you will write a simple vector addition using Triton and learn about:< / p >
< ul class = "simple" >
< li > < p > The basic programming model of Triton< / p > < / li >
< li > < p > The < cite > triton.jit< / cite > decorator, which is used to define Triton kernels.< / p > < / li >
< li > < p > The best practices for validating and benchmarking your custom ops against native reference implementations< / p > < / li >
< / ul >
< div class = "section" id = "compute-kernel" >
< h2 > Compute Kernel< a class = "headerlink" href = "#compute-kernel" title = "Permalink to this headline" > ¶< / a > < / h2 >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > torch< / span >
< span class = "kn" > import< / span > < span class = "nn" > triton< / span >
< span class = "kn" > import< / span > < span class = "nn" > triton.language< / span > < span class = "k" > as< / span > < span class = "nn" > tl< / span >
< span class = "nd" > @triton< / span > < span class = "o" > .< / span > < span class = "n" > jit< / span >
< span class = "k" > def< / span > < span class = "nf" > add_kernel< / span > < span class = "p" > (< / span >
< span class = "n" > x_ptr< / span > < span class = "p" > ,< / span > < span class = "c1" > # *Pointer* to first input vector< / span >
< span class = "n" > y_ptr< / span > < span class = "p" > ,< / span > < span class = "c1" > # *Pointer* to second input vector< / span >
< span class = "n" > output_ptr< / span > < span class = "p" > ,< / span > < span class = "c1" > # *Pointer* to output vector< / span >
< span class = "n" > n_elements< / span > < span class = "p" > ,< / span > < span class = "c1" > # Size of the vector< / span >
< span class = "n" > BLOCK_SIZE< / span > < span class = "p" > :< / span > < span class = "n" > tl< / span > < span class = "o" > .< / span > < span class = "n" > constexpr< / span > < span class = "p" > ,< / span > < span class = "c1" > # Number of elements each program should process< / span >
< span class = "c1" > # NOTE: `constexpr` so it can be used as a shape value< / span >
< span class = "p" > ):< / span >
< span class = "c1" > # There are multiple ' program' s processing different data. We identify which program< / span >
< span class = "c1" > # we are here< / span >
< span class = "n" > pid< / span > < span class = "o" > =< / span > < span class = "n" > tl< / span > < span class = "o" > .< / span > < span class = "n" > program_id< / span > < span class = "p" > (< / span > < span class = "n" > axis< / span > < span class = "o" > =< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span > < span class = "c1" > # We use a 1D launch grid so axis is 0< / span >
< span class = "c1" > # This program will process inputs that are offset from the initial data.< / span >
< span class = "c1" > # for instance, if you had a vector of length 256 and block_size of 64, the programs< / span >
< span class = "c1" > # would each access the elements [0:64, 64:128, 128:192, 192:256].< / span >
< span class = "c1" > # Note that offsets is a list of pointers< / span >
< span class = "n" > block_start< / span > < span class = "o" > =< / span > < span class = "n" > pid< / span > < span class = "o" > *< / span > < span class = "n" > BLOCK_SIZE< / span >
< span class = "n" > offsets< / span > < span class = "o" > =< / span > < span class = "n" > block_start< / span > < span class = "o" > +< / span > < span class = "n" > tl< / span > < span class = "o" > .< / span > < span class = "n" > arange< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > BLOCK_SIZE< / span > < span class = "p" > )< / span >
< span class = "c1" > # Create a mask to guard memory operations against out-of-bounds accesses< / span >
< span class = "n" > mask< / span > < span class = "o" > =< / span > < span class = "n" > offsets< / span > < span class = "o" > < < / span > < span class = "n" > n_elements< / span >
< span class = "c1" > # Load x and y from DRAM, masking out any extra elements in case the input is not a< / span >
< span class = "c1" > # multiple of the block size< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > tl< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > x_ptr< / span > < span class = "o" > +< / span > < span class = "n" > offsets< / span > < span class = "p" > ,< / span > < span class = "n" > mask< / span > < span class = "o" > =< / span > < span class = "n" > mask< / span > < span class = "p" > )< / span >
< span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > tl< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > y_ptr< / span > < span class = "o" > +< / span > < span class = "n" > offsets< / span > < span class = "p" > ,< / span > < span class = "n" > mask< / span > < span class = "o" > =< / span > < span class = "n" > mask< / span > < span class = "p" > )< / span >
< span class = "n" > output< / span > < span class = "o" > =< / span > < span class = "n" > x< / span > < span class = "o" > +< / span > < span class = "n" > y< / span >
< span class = "c1" > # Write x + y back to DRAM< / span >
< span class = "n" > tl< / span > < span class = "o" > .< / span > < span class = "n" > store< / span > < span class = "p" > (< / span > < span class = "n" > output_ptr< / span > < span class = "o" > +< / span > < span class = "n" > offsets< / span > < span class = "p" > ,< / span > < span class = "n" > output< / span > < span class = "p" > ,< / span > < span class = "n" > mask< / span > < span class = "o" > =< / span > < span class = "n" > mask< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Let’ < cite > z< / cite > tensor
and (2) enqueue the above kernel with appropriate grid/block sizes.< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > def< / span > < span class = "nf" > add< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > :< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > Tensor< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > :< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > Tensor< / span > < span class = "p" > ):< / span >
< span class = "c1" > # We need to preallocate the output< / span >
< span class = "n" > output< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > empty_like< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > assert< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > is_cuda< / span > < span class = "ow" > and< / span > < span class = "n" > y< / span > < span class = "o" > .< / span > < span class = "n" > is_cuda< / span > < span class = "ow" > and< / span > < span class = "n" > output< / span > < span class = "o" > .< / span > < span class = "n" > is_cuda< / span >
< span class = "n" > n_elements< / span > < span class = "o" > =< / span > < span class = "n" > output< / span > < span class = "o" > .< / span > < span class = "n" > numel< / span > < span class = "p" > ()< / span >
< span class = "c1" > # The SPMD launch grid denotes the number of kernel instances that run in parallel.< / span >
< span class = "c1" > # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]< / span >
< span class = "c1" > # In this case, we use a 1D grid where the size is the number of blocks< / span >
< span class = "n" > grid< / span > < span class = "o" > =< / span > < span class = "k" > lambda< / span > < span class = "n" > meta< / span > < span class = "p" > :< / span > < span class = "p" > (< / span > < span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > cdiv< / span > < span class = "p" > (< / span > < span class = "n" > n_elements< / span > < span class = "p" > ,< / span > < span class = "n" > meta< / span > < span class = "p" > [< / span > < span class = "s1" > ' BLOCK_SIZE' < / span > < span class = "p" > ]),)< / span >
< span class = "c1" > # NOTE:< / span >
< span class = "c1" > # - each torch.tensor object is implicitly converted into a pointer to its first element.< / span >
< span class = "c1" > # - `triton.jit`' ed functions can be index with a launch grid to obtain a callable GPU kernel< / span >
< span class = "c1" > # - don' t forget to pass meta-parameters as keywords arguments< / span >
< span class = "n" > add_kernel< / span > < span class = "p" > [< / span > < span class = "n" > grid< / span > < span class = "p" > ](< / span > < span class = "n" > x< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > ,< / span > < span class = "n" > output< / span > < span class = "p" > ,< / span > < span class = "n" > n_elements< / span > < span class = "p" > ,< / span > < span class = "n" > BLOCK_SIZE< / span > < span class = "o" > =< / span > < span class = "mi" > 1024< / span > < span class = "p" > )< / span >
< span class = "c1" > # We return a handle to z but, since `torch.cuda.synchronize()` hasn' t been called, the kernel is still< / span >
< span class = "c1" > # running asynchronously at this point.< / span >
< span class = "k" > return< / span > < span class = "n" > output< / span >
< / pre > < / div >
< / div >
< p > We can now use the above function to compute the element-wise sum of two < cite > torch.tensor< / cite > objects and test its correctness:< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > manual_seed< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span >
< span class = "n" > size< / span > < span class = "o" > =< / span > < span class = "mi" > 98432< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > rand< / span > < span class = "p" > (< / span > < span class = "n" > size< / span > < span class = "p" > ,< / span > < span class = "n" > device< / span > < span class = "o" > =< / span > < span class = "s1" > ' cuda' < / span > < span class = "p" > )< / span >
< span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > rand< / span > < span class = "p" > (< / span > < span class = "n" > size< / span > < span class = "p" > ,< / span > < span class = "n" > device< / span > < span class = "o" > =< / span > < span class = "s1" > ' cuda' < / span > < span class = "p" > )< / span >
< span class = "n" > output_torch< / span > < span class = "o" > =< / span > < span class = "n" > x< / span > < span class = "o" > +< / span > < span class = "n" > y< / span >
< span class = "n" > output_triton< / span > < span class = "o" > =< / span > < span class = "n" > add< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > output_torch< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > output_triton< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span >
< span class = "sa" > f< / span > < span class = "s1" > ' The maximum difference between torch and triton is ' < / span >
< span class = "sa" > f< / span > < span class = "s1" > ' < / span > < span class = "si" > {< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > max< / span > < span class = "p" > (< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > abs< / span > < span class = "p" > (< / span > < span class = "n" > output_torch< / span > < span class = "o" > -< / span > < span class = "n" > output_triton< / span > < span class = "p" > ))< / span > < span class = "si" > }< / span > < span class = "s1" > ' < / span >
< span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device=' cuda:0' )
tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device=' cuda:0' )
The maximum difference between torch and triton is 0.0
< / pre > < / div >
< / div >
< p > Seems like we’ < / p >
< / div >
< div class = "section" id = "benchmark" >
< h2 > Benchmark< a class = "headerlink" href = "#benchmark" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of your custom ops
for different problem sizes.< / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "nd" > @triton< / span > < span class = "o" > .< / span > < span class = "n" > testing< / span > < span class = "o" > .< / span > < span class = "n" > perf_report< / span > < span class = "p" > (< / span >
< span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > testing< / span > < span class = "o" > .< / span > < span class = "n" > Benchmark< / span > < span class = "p" > (< / span >
< span class = "n" > x_names< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' size' < / span > < span class = "p" > ],< / span > < span class = "c1" > # argument names to use as an x-axis for the plot< / span >
< span class = "n" > x_vals< / span > < span class = "o" > =< / span > < span class = "p" > [< / span >
< span class = "mi" > 2< / span > < span class = "o" > **< / span > < span class = "n" > i< / span > < span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 12< / span > < span class = "p" > ,< / span > < span class = "mi" > 28< / span > < span class = "p" > ,< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "p" > ],< / span > < span class = "c1" > # different possible values for `x_name`< / span >
< span class = "n" > x_log< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "c1" > # x axis is logarithmic< / span >
< span class = "n" > line_arg< / span > < span class = "o" > =< / span > < span class = "s1" > ' provider' < / span > < span class = "p" > ,< / span > < span class = "c1" > # argument name whose value corresponds to a different line in the plot< / span >
< span class = "n" > line_vals< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' triton' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' torch' < / span > < span class = "p" > ],< / span > < span class = "c1" > # possible values for `line_arg`< / span >
< span class = "n" > line_names< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' Triton' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' Torch' < / span > < span class = "p" > ],< / span > < span class = "c1" > # label name for the lines< / span >
< span class = "n" > styles< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' blue' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' -' < / span > < span class = "p" > ),< / span > < span class = "p" > (< / span > < span class = "s1" > ' green' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' -' < / span > < span class = "p" > )],< / span > < span class = "c1" > # line styles< / span >
< span class = "n" > ylabel< / span > < span class = "o" > =< / span > < span class = "s1" > ' GB/s' < / span > < span class = "p" > ,< / span > < span class = "c1" > # label name for the y-axis< / span >
< span class = "n" > plot_name< / span > < span class = "o" > =< / span > < span class = "s1" > ' vector-add-performance' < / span > < span class = "p" > ,< / span > < span class = "c1" > # name for the plot. Used also as a file name for saving the plot.< / span >
< span class = "n" > args< / span > < span class = "o" > =< / span > < span class = "p" > {},< / span > < span class = "c1" > # values for function arguments not in `x_names` and `y_name`< / span >
< span class = "p" > )< / span >
< span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > benchmark< / span > < span class = "p" > (< / span > < span class = "n" > size< / span > < span class = "p" > ,< / span > < span class = "n" > provider< / span > < span class = "p" > ):< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > rand< / span > < span class = "p" > (< / span > < span class = "n" > size< / span > < span class = "p" > ,< / span > < span class = "n" > device< / span > < span class = "o" > =< / span > < span class = "s1" > ' cuda' < / span > < span class = "p" > ,< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > rand< / span > < span class = "p" > (< / span > < span class = "n" > size< / span > < span class = "p" > ,< / span > < span class = "n" > device< / span > < span class = "o" > =< / span > < span class = "s1" > ' cuda' < / span > < span class = "p" > ,< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > torch< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > provider< / span > < span class = "o" > ==< / span > < span class = "s1" > ' torch' < / span > < span class = "p" > :< / span >
< span class = "n" > ms< / span > < span class = "p" > ,< / span > < span class = "n" > min_ms< / span > < span class = "p" > ,< / span > < span class = "n" > max_ms< / span > < span class = "o" > =< / span > < span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > testing< / span > < span class = "o" > .< / span > < span class = "n" > do_bench< / span > < span class = "p" > (< / span > < span class = "k" > lambda< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > +< / span > < span class = "n" > y< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "n" > provider< / span > < span class = "o" > ==< / span > < span class = "s1" > ' triton' < / span > < span class = "p" > :< / span >
< span class = "n" > ms< / span > < span class = "p" > ,< / span > < span class = "n" > min_ms< / span > < span class = "p" > ,< / span > < span class = "n" > max_ms< / span > < span class = "o" > =< / span > < span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > testing< / span > < span class = "o" > .< / span > < span class = "n" > do_bench< / span > < span class = "p" > (< / span > < span class = "k" > lambda< / span > < span class = "p" > :< / span > < span class = "n" > add< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > ))< / span >
< span class = "n" > gbps< / span > < span class = "o" > =< / span > < span class = "k" > lambda< / span > < span class = "n" > ms< / span > < span class = "p" > :< / span > < span class = "mi" > 12< / span > < span class = "o" > *< / span > < span class = "n" > size< / span > < span class = "o" > /< / span > < span class = "n" > ms< / span > < span class = "o" > *< / span > < span class = "mf" > 1e-6< / span >
< span class = "k" > return< / span > < span class = "n" > gbps< / span > < span class = "p" > (< / span > < span class = "n" > ms< / span > < span class = "p" > ),< / span > < span class = "n" > gbps< / span > < span class = "p" > (< / span > < span class = "n" > max_ms< / span > < span class = "p" > ),< / span > < span class = "n" > gbps< / span > < span class = "p" > (< / span > < span class = "n" > min_ms< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > We can now run the decorated function above. Pass < cite > print_data=True< / cite > to see the performance number, < cite > show_plots=True< / cite > to plot them, and/or
< a href = "#id1" > < span class = "problematic" id = "id2" > `< / span > < / a > save_path=’ ’ < / p >
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > benchmark< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > (< / span > < span class = "n" > print_data< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > show_plots< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< img alt = "01 vector add" class = "sphx-glr-single-img" src = "../../_images/sphx_glr_01-vector-add_001.png" / >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > vector-add-performance:
size Triton Torch
0 4096.0 9.600000 9.600000
1 8192.0 19.200000 19.200000
2022-06-11 00:48:41 +00:00
2 16384.0 38.400001 38.400001
2022-06-27 00:48:22 +00:00
3 32768.0 76.800002 76.800002
2022-06-05 21:05:02 +00:00
4 65536.0 127.999995 127.999995
5 131072.0 219.428568 219.428568
2022-06-27 00:48:22 +00:00
6 262144.0 341.333321 384.000001
2022-06-05 21:05:02 +00:00
7 524288.0 472.615390 472.615390
8 1048576.0 614.400016 614.400016
2022-06-26 00:50:12 +00:00
9 2097152.0 722.823517 722.823517
2022-06-05 21:05:02 +00:00
10 4194304.0 780.190482 780.190482
11 8388608.0 812.429770 812.429770
12 16777216.0 833.084721 833.084721
2022-06-26 00:50:12 +00:00
13 33554432.0 842.004273 842.004273
2022-06-05 21:05:02 +00:00
14 67108864.0 847.448255 848.362445
15 134217728.0 849.737435 850.656574
< / pre > < / div >
< / div >
2022-06-27 00:48:22 +00:00
< p class = "sphx-glr-timing" > < strong > Total running time of the script:< / strong > ( 1 minutes 34.829 seconds)< / p >
2022-06-05 21:05:02 +00:00
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< p > < a class = "reference download internal" download = "" href = "../../_downloads/62d97d49a32414049819dd8bb8378080/01-vector-add.py" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Python< / span > < span class = "pre" > source< / span > < span class = "pre" > code:< / span > < span class = "pre" > 01-vector-add.py< / span > < / code > < / a > < / p >
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