[GH-PAGES] Updated website

_sources/getting-started/tutorials/02-fused-softmax.rst.txt

@@ -250,45 +250,50 @@ Benchmarking
 Here we will benchmark our operation as a function of the number of columns in the input matrix -- assuming 4096 rows.
 We will then compare its performance against (1) :code:`torch.softmax` and (2) the :code:`naive_softmax` defined above.
 
-.. GENERATED FROM PYTHON SOURCE LINES 181-204
+.. GENERATED FROM PYTHON SOURCE LINES 181-209
 
 .. code-block:: default
 
 
-    import matplotlib.pyplot as plt
 
-    M = 4096
-    Ns = [256 * i for i in range(2, 50)]
-    tri_bw = []
-    ref_bw = []
-    def_bw = []
-    for N in Ns:
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=['N'],  # argument names to use as an x-axis for the plot
+            x_vals=[256 * i for i in range(2, 50)],  # different possible values for `x_name`
+            y_name='provider',  # argument name whose value corresponds to a different line in the plot
+            y_vals=['torch', 'triton', 'naive'],  # possible keys for `y_name`
+            y_lines=["Torch", "Triton", 'Naive'],  # label name for the lines
+            ylabel="GB/s",  # label name for the y-axis
+            plot_name="softmax-performance",  # name for the plot. Used also as a file name for saving the plot.
+            args={'M': 4096}  # values for function arguments not in `x_names` and `y_name`
+        )
+    )
+    def benchmark(M, N, provider):
         x = torch.randn(M, N, device='cuda', dtype=torch.float32)
-        gbps = lambda ms: x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)
-        do_bench = lambda fn: gbps(triton.testing.do_bench(fn, warmup=10, rep=100, clear_l2=True))
-        tri_bw += [do_bench(lambda: softmax(x))]
-        ref_bw += [do_bench(lambda: torch.softmax(x, axis=1))]
-        def_bw += [do_bench(lambda: naive_softmax(x))]
-    plt.xlabel('N')
-    plt.ylabel('Bandwidth (GB/s)')
-    plt.plot(Ns, tri_bw, label='Triton')
-    plt.plot(Ns, ref_bw, label='Torch')
-    plt.plot(Ns, def_bw, label='Naive')
-    plt.legend()
-    plt.show()
+        if provider == 'torch':
+            ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1))
+        if provider == 'triton':
+            ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x))
+        if provider == 'naive':
+            ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x))
+        gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3)
+        return gbps(ms), gbps(max_ms), gbps(min_ms)
+
+
+    benchmark.run(show_plots=True)
 
 
 
 
 .. image:: /getting-started/tutorials/images/sphx_glr_02-fused-softmax_001.png
-    :alt: 02 fused softmax
+    :alt: softmax-performance
     :class: sphx-glr-single-img
 
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 205-210
+.. GENERATED FROM PYTHON SOURCE LINES 210-215
 
 In the above plot, we can see that:
 
@@ -300,7 +305,7 @@ In the above plot, we can see that:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  33.773 seconds)
+   **Total running time of the script:** ( 0 minutes  21.653 seconds)
 
 
 .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py: