288 lines
9.7 KiB
ReStructuredText
288 lines
9.7 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 syntax of the Triton programming language
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- The best practices for creating PyTorch custom operators using the :code:`triton.kernel` Python API
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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 12-51
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Compute Kernel
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--------------------------
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Each compute kernel is declared using the :code:`__global__` attribute, and executed many times in parallel
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on different chunks of data (See the `Single Program, Multiple Data <(https://en.wikipedia.org/wiki/SPMD>`_)
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programming model for more details).
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.. code-block:: C
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__global__ void add(float* z, float* x, float* y, int N){
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// The `get_program_id(i)` returns the i-th coordinate
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// of the program in the overaching SPMD context
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// (a.k.a launch grid). This is what allows us to process
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// different chunks of data in parallel.
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// For those similar with CUDA, `get_program_id({0,1,2})`
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// is similar to blockIdx.{x,y,z}
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int pid = get_program_id(0);
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// In Triton, arrays are first-class citizen. In other words,
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// they are primitives data-types and are -- contrary to C and
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// CUDA -- not implemented as pointers to contiguous chunks of
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// memory.
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// In the few lines below, we create an array of `BLOCK` pointers
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// whose memory values are, e.g.:
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// [z + pid*BLOCK + 0, z + pid*BLOCK + 1, ..., z + pid*BLOCK + BLOCK - 1]
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// Note: here BLOCK is expected to be a pre-processor macro defined at compile-time
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int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;
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float* pz [BLOCK] = z + offset;
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float* px [BLOCK] = x + offset;
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float* py [BLOCK] = y + offset;
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// Simple element-wise control-flow for load/store operations can
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// be achieved using the the ternary operator `cond ? val_true : val_false`
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// or the conditional dereferencing operator `*?(cond)ptr
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// Here, we make sure that we do not access memory out-of-bounds when we
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// write-back `z`
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bool check[BLOCK] = offset < N;
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*?(check)pz = *?(check)px + *?(check)py;
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}
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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>`_.
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.. GENERATED FROM PYTHON SOURCE LINES 53-60
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Torch bindings
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--------------------------
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The only thing that matters when it comes to Triton and Torch is the :code:`triton.kernel` class. This allows you to transform the above C-like function into a callable python object that can be used to modify :code:`torch.tensor` objects. To create a :code:`triton.kernel`, you only need three things:
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- :code:`source: string`: the source-code of the kernel you want to create
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- :code:`device: torch.device`: the device you want to compile this code for
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- :code:`defines: dict`: the set of macros that you want the pre-processor to `#define` for you
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.. GENERATED FROM PYTHON SOURCE LINES 60-125
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.. code-block:: default
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import torch
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import triton
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# source-code for Triton compute kernel
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# here we just copy-paste the above code without the extensive comments.
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# you may prefer to store it in a .c file and load it from there instead.
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_src = """
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__global__ void add(float* z, float* x, float* y, int N){
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// program id
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int pid = get_program_id(0);
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// create arrays of pointers
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int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;
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float* pz[BLOCK] = z + offset;
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float* px[BLOCK] = x + offset;
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float* py[BLOCK] = y + offset;
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// bounds checking
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bool check[BLOCK] = offset < N;
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// write-back
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*?(check)pz = *?(check)px + *?(check)py;
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}
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"""
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# This function returns a callable `triton.kernel` object created from the above source code.
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# For portability, we maintain a cache of kernels for different `torch.device`
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# We compile the kernel with -DBLOCK=1024
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def make_add_kernel(device):
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cache = make_add_kernel.cache
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if device not in cache:
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defines = {'BLOCK': 1024}
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cache[device] = triton.kernel(_src, device=device, defines=defines)
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return cache[device]
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make_add_kernel.cache = dict()
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# This is a standard torch custom autograd Function;
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# The only difference is that we can now use the above kernel in the `forward` and `backward` functions.`
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class _add(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, y):
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# constraints of the op
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assert x.dtype == torch.float32
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# *allocate output*
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z = torch.empty_like(x)
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# *create launch grid*:
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# this is a function which takes compilation parameters `opt`
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# as input and returns a tuple of int (i.e., launch grid) for the kernel.
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# triton.cdiv is a shortcut for ceil division:
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# triton.cdiv(a, b) = (a + b - 1) // b
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N = z.shape[0]
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grid = lambda opt: (triton.cdiv(N, opt.BLOCK), )
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# *launch kernel*:
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# pointer to the data of torch tensors can be retrieved with
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# the `.data_ptr()` method
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kernel = make_add_kernel(z.device)
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kernel(z.data_ptr(), x.data_ptr(), y.data_ptr(), N, grid=grid)
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return z
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# Just like we standard PyTorch ops We use the :code:`.apply` method to create a callable object for our function
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add = _add.apply
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.. GENERATED FROM PYTHON SOURCE LINES 126-127
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We can now use the above function to compute the sum of two `torch.tensor` objects:
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.. GENERATED FROM PYTHON SOURCE LINES 129-133
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Unit Test
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--------------------------
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Of course, the first thing that we should check is that whether kernel is correct. This is pretty easy to test, as shown below:
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.. GENERATED FROM PYTHON SOURCE LINES 133-143
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.. code-block:: default
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torch.manual_seed(0)
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x = torch.rand(98432, device='cuda')
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y = torch.rand(98432, 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, ..., 0.6682, 1.1984, 1.2696], device='cuda:0')
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tensor([1.3713, 1.3076, 0.4940, ..., 0.6682, 1.1984, 1.2696], 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 144-145
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Seems like we're good to go!
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.. GENERATED FROM PYTHON SOURCE LINES 147-150
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Benchmarking
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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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.. GENERATED FROM PYTHON SOURCE LINES 150-178
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.. code-block:: default
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import matplotlib.pyplot as plt
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# There are three tensors of 4N bytes each. So the bandwidth of a given kernel
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# is 12N / time_ms * 1e-6 GB/s
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gbps = lambda N, ms: 12 * N / ms * 1e-6
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# We want to benchmark small and large vector alike
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sizes = [2**i for i in range(12, 25, 1)]
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triton_bw = []
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torch_bw = []
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for N in sizes:
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x = torch.rand(N, device='cuda', dtype=torch.float32)
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y = torch.rand(N, device='cuda', dtype=torch.float32)
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# Triton provide a do_bench utility function that can be used to benchmark
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# arbitrary workloads. It supports a `warmup` parameter that is used to stabilize
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# GPU clock speeds as well as a `rep` parameter that controls the number of times
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# the benchmark is repeated. Importantly, we set `clear_l2 = True` to make sure
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# that the L2 cache does not contain any element of x before each kernel call when
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# N is small.
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do_bench = lambda fn: gbps(N, triton.testing.do_bench(fn, warmup=10, rep=100, clear_l2=True))
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triton_bw += [do_bench(lambda: add(x, y))]
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torch_bw += [do_bench(lambda: x + y)]
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# We plot the results as a semi-log
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plt.semilogx(sizes, triton_bw, label='Triton')
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plt.semilogx(sizes, torch_bw, label='Torch')
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plt.legend()
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plt.show()
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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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.. GENERATED FROM PYTHON SOURCE LINES 179-179
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Seems like our simple element-wise operation operates at peak bandwidth. While this is a fairly low bar for a custom GPU programming language, this is a good start before we move to more advanced operations.
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 4.784 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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