244 lines
6.6 KiB
ReStructuredText
244 lines
6.6 KiB
ReStructuredText
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.. DO NOT EDIT.
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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
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.. "getting-started/tutorials/01-vector-add.py"
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.. LINE NUMBERS ARE GIVEN BELOW.
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.. only:: html
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.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_getting-started_tutorials_01-vector-add.py>`
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to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_getting-started_tutorials_01-vector-add.py:
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Vector Addition
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=================
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In this tutorial, you will write a simple vector addition using Triton and learn about:
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- The basic programming model used by Triton
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- The `triton.jit` decorator, which constitutes the main entry point for writing Triton kernels.
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- The best practices for validating and benchmarking custom ops against native reference implementations
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.. GENERATED FROM PYTHON SOURCE LINES 12-14
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Compute Kernel
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--------------------------
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.. GENERATED FROM PYTHON SOURCE LINES 14-42
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.. code-block:: default
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import torch
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import triton
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@triton.jit
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def _add(
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X, # *Pointer* to first input vector
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Y, # *Pointer* to second input vector
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Z, # *Pointer* to output vector
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N, # Size of the vector
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**meta # Optional meta-parameters for the kernel
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):
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pid = triton.program_id(0)
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# Create an offset for the blocks of pointers to be
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# processed by this program instance
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offsets = pid * meta['BLOCK'] + triton.arange(0, meta['BLOCK'])
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# Create a mask to guard memory operations against
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# out-of-bounds accesses
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mask = offsets < N
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# Load x
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x = triton.load(X + offsets, mask=mask)
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y = triton.load(Y + offsets, mask=mask)
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# Write back x + y
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z = x + y
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triton.store(Z + offsets, z)
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.. GENERATED FROM PYTHON SOURCE LINES 43-45
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We can also declara a helper function that handles allocating the output vector
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and enqueueing the kernel.
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.. GENERATED FROM PYTHON SOURCE LINES 45-63
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.. code-block:: default
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def add(x, y):
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z = torch.empty_like(x)
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N = z.shape[0]
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# The SPMD launch grid denotes the number of kernel instances that should execute in parallel.
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# It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
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grid = lambda meta: (triton.cdiv(N, meta['BLOCK']), )
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# NOTE:
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# - torch.tensor objects are implicitly converted to pointers to their first element.
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# - `triton.jit`'ed functions can be subscripted with a launch grid to obtain a callable GPU kernel
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# - don't forget to pass meta-parameters as keywords arguments
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_add[grid](x, y, z, N, BLOCK=1024)
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# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
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# running asynchronously.
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return z
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.. GENERATED FROM PYTHON SOURCE LINES 64-65
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We can now use the above function to compute the sum of two `torch.tensor` objects and test our results:
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.. GENERATED FROM PYTHON SOURCE LINES 65-76
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.. code-block:: default
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torch.manual_seed(0)
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size = 98432
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x = torch.rand(size, device='cuda')
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y = torch.rand(size, device='cuda')
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za = x + y
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zb = add(x, y)
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print(za)
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print(zb)
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print(f'The maximum difference between torch and triton is ' f'{torch.max(torch.abs(za - zb))}')
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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tensor([1.3713, 1.3076, 0.4940, ..., 0.6724, 1.2141, 0.9733], device='cuda:0')
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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
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.. GENERATED FROM PYTHON SOURCE LINES 77-78
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Seems like we're good to go!
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.. GENERATED FROM PYTHON SOURCE LINES 80-85
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Benchmark
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-----------
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We can now benchmark our custom op for vectors of increasing sizes to get a sense of how it does relative to PyTorch.
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To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of our custom op.
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for different problem sizes.
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.. GENERATED FROM PYTHON SOURCE LINES 85-111
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.. code-block:: default
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=['size'], # argument names to use as an x-axis for the plot
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x_vals=[2**i for i in range(12, 28, 1)], # different possible values for `x_name`
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x_log=True, # x axis is logarithmic
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y_name='provider', # argument name whose value corresponds to a different line in the plot
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y_vals=['torch', 'triton'], # possible keys for `y_name`
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y_lines=["Torch", "Triton"], # label name for the lines
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ylabel="GB/s", # label name for the y-axis
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plot_name="vector-add-performance", # name for the plot. Used also as a file name for saving the plot.
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args={} # values for function arguments not in `x_names` and `y_name`
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)
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)
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def benchmark(size, provider):
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x = torch.rand(size, device='cuda', dtype=torch.float32)
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y = torch.rand(size, device='cuda', dtype=torch.float32)
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if provider == 'torch':
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y)
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if provider == 'triton':
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y))
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gbps = lambda ms: 12 * size / ms * 1e-6
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return gbps(ms), gbps(max_ms), gbps(min_ms)
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.. GENERATED FROM PYTHON SOURCE LINES 112-114
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We can now run the decorated function above. Pass `show_plots=True` to see the plots and/or
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`save_path='/path/to/results/' to save them to disk along with raw CSV data
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.. GENERATED FROM PYTHON SOURCE LINES 114-114
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.. code-block:: default
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benchmark.run(show_plots=True)
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.. image:: /getting-started/tutorials/images/sphx_glr_01-vector-add_001.png
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:alt: 01 vector add
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:class: sphx-glr-single-img
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 5.812 seconds)
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.. _sphx_glr_download_getting-started_tutorials_01-vector-add.py:
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.. only :: html
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.. container:: sphx-glr-footer
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:class: sphx-glr-footer-example
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.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: 01-vector-add.py <01-vector-add.py>`
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: 01-vector-add.ipynb <01-vector-add.ipynb>`
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.. only:: html
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.. rst-class:: sphx-glr-signature
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
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