.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "getting-started/tutorials/01-vector-add.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_getting-started_tutorials_01-vector-add.py: Vector Addition ================= In this tutorial, you will write a simple vector addition using Triton and learn about: - The basic programming model used by Triton - The `triton.jit` decorator, which constitutes the main entry point for writing Triton kernels. - The best practices for validating and benchmarking custom ops against native reference implementations .. GENERATED FROM PYTHON SOURCE LINES 12-14 Compute Kernel -------------------------- .. GENERATED FROM PYTHON SOURCE LINES 14-42 .. code-block:: default import torch import triton @triton.jit def _add( X, # *Pointer* to first input vector Y, # *Pointer* to second input vector Z, # *Pointer* to output vector N, # Size of the vector **meta # Optional meta-parameters for the kernel ): pid = triton.program_id(0) # Create an offset for the blocks of pointers to be # processed by this program instance offsets = pid * meta['BLOCK'] + triton.arange(0, meta['BLOCK']) # Create a mask to guard memory operations against # out-of-bounds accesses mask = offsets < N # Load x x = triton.load(X + offsets, mask=mask) y = triton.load(Y + offsets, mask=mask) # Write back x + y z = x + y triton.store(Z + offsets, z) .. GENERATED FROM PYTHON SOURCE LINES 43-45 We can also declara a helper function that handles allocating the output vector and enqueueing the kernel. .. GENERATED FROM PYTHON SOURCE LINES 45-63 .. code-block:: default def add(x, y): z = torch.empty_like(x) N = z.shape[0] # The SPMD launch grid denotes the number of kernel instances that should execute in parallel. # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int] grid = lambda meta: (triton.cdiv(N, meta['BLOCK']), ) # NOTE: # - torch.tensor objects are implicitly converted to pointers to their first element. # - `triton.jit`'ed functions can be subscripted with a launch grid to obtain a callable GPU kernel # - don't forget to pass meta-parameters as keywords arguments _add[grid](x, y, z, N, BLOCK=1024) # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still # running asynchronously. return z .. GENERATED FROM PYTHON SOURCE LINES 64-65 We can now use the above function to compute the sum of two `torch.tensor` objects and test our results: .. GENERATED FROM PYTHON SOURCE LINES 65-76 .. code-block:: default torch.manual_seed(0) size = 98432 x = torch.rand(size, device='cuda') y = torch.rand(size, 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))}') .. rst-class:: sphx-glr-script-out Out: .. code-block:: none 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 .. GENERATED FROM PYTHON SOURCE LINES 77-78 Seems like we're good to go! .. GENERATED FROM PYTHON SOURCE LINES 80-85 Benchmark ----------- 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. .. GENERATED FROM PYTHON SOURCE LINES 85-111 .. code-block:: default @triton.testing.perf_report( triton.testing.Benchmark( x_names=['size'], # argument names to use as an x-axis for the plot x_vals=[2**i for i in range(12, 28, 1)], # different possible values for `x_name` x_log=True, # x axis is logarithmic y_name='provider', # argument name whose value corresponds to a different line in the plot y_vals=['torch', 'triton'], # possible keys for `y_name` y_lines=["Torch", "Triton"], # label name for the lines ylabel="GB/s", # label name for the y-axis plot_name="vector-add-performance", # name for the plot. Used also as a file name for saving the plot. args={} # values for function arguments not in `x_names` and `y_name` ) ) def benchmark(size, provider): x = torch.rand(size, device='cuda', dtype=torch.float32) y = torch.rand(size, device='cuda', dtype=torch.float32) if provider == 'torch': ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y) if provider == 'triton': ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y)) gbps = lambda ms: 12 * size / ms * 1e-6 return gbps(ms), gbps(max_ms), gbps(min_ms) .. GENERATED FROM PYTHON SOURCE LINES 112-114 We can now run the decorated function above. Pass `show_plots=True` to see the plots and/or `save_path='/path/to/results/' to save them to disk along with raw CSV data .. GENERATED FROM PYTHON SOURCE LINES 114-114 .. code-block:: default benchmark.run(show_plots=True) .. image:: /getting-started/tutorials/images/sphx_glr_01-vector-add_001.png :alt: 01 vector add :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 5.812 seconds) .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 01-vector-add.py <01-vector-add.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 01-vector-add.ipynb <01-vector-add.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_