[GH-PAGES] Updated website

_sources/getting-started/tutorials/01-vector-add.rst.txt

@@ -200,63 +200,75 @@ Of course, the first thing that we should check is that whether kernel is correc
 
 Seems like we're good to go!
 
-.. GENERATED FROM PYTHON SOURCE LINES 147-150
+.. GENERATED FROM PYTHON SOURCE LINES 147-152
 
 Benchmarking
 --------------------------
 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 150-178
+.. GENERATED FROM PYTHON SOURCE LINES 152-178
 
 .. code-block:: default
 
 
-    import matplotlib.pyplot as plt
 
-    # There are three tensors of 4N bytes each. So the bandwidth of a given kernel
-    # is 12N / time_ms * 1e-6 GB/s
-    gbps = lambda N, ms: 12 * N / ms * 1e-6
-    # We want to benchmark small and large vector alike
-    sizes = [2**i for i in range(12, 25, 1)]
-    triton_bw = []
-    torch_bw = []
-    for N in sizes:
-        x = torch.rand(N, device='cuda', dtype=torch.float32)
-        y = torch.rand(N, device='cuda', dtype=torch.float32)
-        # Triton provide a do_bench utility function that can be used to benchmark
-        # arbitrary workloads. It supports a `warmup` parameter that is used to stabilize
-        # GPU clock speeds as well as a `rep` parameter that controls the number of times
-        # the benchmark is repeated. Importantly, we set `clear_l2 = True` to make sure
-        # that the L2 cache does not contain any element of x before each kernel call when
-        # N is small.
-        do_bench = lambda fn: gbps(N, triton.testing.do_bench(fn, warmup=10, rep=100, clear_l2=True))
-        triton_bw += [do_bench(lambda: add(x, y))]
-        torch_bw += [do_bench(lambda: x + y)]
-    # We plot the results as a semi-log
-    plt.semilogx(sizes, triton_bw, label='Triton')
-    plt.semilogx(sizes, torch_bw, label='Torch')
-    plt.legend()
-    plt.show()
+    @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 179-181
+
+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 181-181
+
+.. code-block:: default
+
+    benchmark.run(show_plots=True)
+
+
 .. image:: /getting-started/tutorials/images/sphx_glr_01-vector-add_001.png
-    :alt: 01 vector add
+    :alt: vector-add-performance
     :class: sphx-glr-single-img
 
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 179-179
-
-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.
-
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  4.784 seconds)
+   **Total running time of the script:** ( 0 minutes  5.768 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_01-vector-add.py: