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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 >
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< p > In this tutorial, you will write a simple vector addition using Triton and learn about:< / p >
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< ul class = "simple" >
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< li > < p > The basic programming model used by Triton< / p > < / li >
< li > < p > The < cite > triton.jit< / cite > decorator, which constitutes the main entry point for writing Triton kernels.< / p > < / li >
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< li > < p > The best practices for validating and benchmarking 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 >
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< span class = "nd" > @triton< / span > < span class = "o" > .< / span > < span class = "n" > jit< / 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 = "c1" > # *Pointer* to first input vector< / span >
< span class = "n" > Y< / span > < span class = "p" > ,< / span > < span class = "c1" > # *Pointer* to second input vector< / span >
< span class = "n" > Z< / span > < span class = "p" > ,< / span > < span class = "c1" > # *Pointer* to output vector< / span >
< span class = "n" > N< / span > < span class = "p" > ,< / span > < span class = "c1" > # Size of the vector< / span >
< span class = "o" > **< / span > < span class = "n" > meta< / span > < span class = "c1" > # Optional meta-parameters for the kernel< / span >
< span class = "p" > ):< / span >
< span class = "n" > pid< / span > < span class = "o" > =< / span > < span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > program_id< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > )< / span >
< span class = "c1" > # Create an offset for the blocks of pointers to be< / span >
< span class = "c1" > # processed by this program instance< / span >
< span class = "n" > offsets< / span > < span class = "o" > =< / span > < span class = "n" > pid< / span > < span class = "o" > *< / span > < span class = "n" > meta< / span > < span class = "p" > [< / span > < span class = "s1" > ' BLOCK' < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "n" > triton< / 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" > meta< / span > < span class = "p" > [< / span > < span class = "s1" > ' BLOCK' < / span > < span class = "p" > ])< / span >
< span class = "c1" > # Create a mask to guard memory operations against< / span >
< span class = "c1" > # 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< / span >
< span class = "c1" > # Load x< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > X< / 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" > triton< / span > < span class = "o" > .< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > Y< / 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 = "c1" > # Write back x + y< / span >
< span class = "n" > z< / span > < span class = "o" > =< / span > < span class = "n" > x< / span > < span class = "o" > +< / span > < span class = "n" > y< / span >
< span class = "n" > triton< / span > < span class = "o" > .< / span > < span class = "n" > store< / span > < span class = "p" > (< / span > < span class = "n" > Z< / span > < span class = "o" > +< / span > < span class = "n" > offsets< / span > < span class = "p" > ,< / span > < span class = "n" > z< / span > < span class = "p" > )< / span >
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< / pre > < / div >
< / div >
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< p > We can also declara a helper function that handles allocating the output vector
and enqueueing the kernel.< / 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" > y< / span > < span class = "p" > ):< / span >
< span class = "n" > z< / 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 = "n" > N< / span > < span class = "o" > =< / span > < span class = "n" > z< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "c1" > # The SPMD launch grid denotes the number of kernel instances that should execute 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 = "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< / span > < span class = "p" > ,< / span > < span class = "n" > meta< / span > < span class = "p" > [< / span > < span class = "s1" > ' BLOCK' < / span > < span class = "p" > ]),< / span > < span class = "p" > )< / span >
< span class = "c1" > # NOTE:< / span >
< span class = "c1" > # - torch.tensor objects are implicitly converted to pointers to their first element.< / span >
< span class = "c1" > # - `triton.jit`' ed functions can be subscripted 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< / 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" > z< / span > < span class = "p" > ,< / span > < span class = "n" > N< / span > < span class = "p" > ,< / span > < span class = "n" > BLOCK< / 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.< / span >
< span class = "k" > return< / span > < span class = "n" > z< / span >
< / pre > < / div >
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< / div >
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< p > We can now use the above function to compute the sum of two < cite > torch.tensor< / cite > objects and test our results:< / p >
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< 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 >
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< 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 >
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< span class = "n" > za< / span > < span class = "o" > =< / span > < span class = "n" > x< / span > < span class = "o" > +< / span > < span class = "n" > y< / span >
< span class = "n" > zb< / 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" > za< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > zb< / 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" > za< / span > < span class = "o" > -< / span > < span class = "n" > zb< / 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 >
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< 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' )
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The maximum difference between torch and triton is 0.0
< / pre > < / div >
< / div >
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< p > Seems like we’ < / p >
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< / div >
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< div class = "section" id = "benchmark" >
< h2 > Benchmark< a class = "headerlink" href = "#benchmark" title = "Permalink to this headline" > ¶< / a > < / h2 >
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< p > We can now benchmark our custom op for 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 our custom op.
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 = "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" > y_name< / 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" > y_vals< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s1" > ' torch' < / span > < span class = "p" > ,< / span > < span class = "s1" > ' triton' < / span > < span class = "p" > ],< / span > < span class = "c1" > # possible keys for `y_name`< / span >
< span class = "n" > y_lines< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s2" > " Torch" < / span > < span class = "p" > ,< / span > < span class = "s2" > " Triton" < / span > < span class = "p" > ],< / span > < span class = "c1" > # label name for the lines< / span >
< span class = "n" > ylabel< / span > < span class = "o" > =< / span > < span class = "s2" > " 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 = "s2" > " 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 >
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< / pre > < / div >
< / div >
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< p > We can now run the decorated function above. Pass < cite > show_plots=True< / cite > to see the plots 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" > show_plots< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
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