[PYTHON] Added automated benchmark script (#63)

This adds a bench functionality to the setup.py that can be used to run the benchmark suite and generates a bunch of csv files (and optionally plots)

python setup.py bench
python setup.py bench --with-plots
python setup.py bench --filter=cross_entropy

python/test/test_cross_entropy.py

@@ -0,0 +1,33 @@
+import torch
+import triton
+import pytest
+
+@pytest.mark.parametrize("M, N, dtype, mode",
+    [
+    (M, N, dtype, mode) for M in [1024, 821]
+                        for N in [512, 857, 1871, 2089, 8573, 31000]
+                        for dtype in ['float16', 'float32']\
+                        for mode  in ['forward', 'backward']
+    ]
+                         )
+def test_op(M, N, dtype, mode):
+    dtype = {'float16': torch.float16, 'float32': torch.float32}[dtype]
+    # create inputs
+    x = torch.randn(M, N, dtype=dtype, device='cuda', requires_grad=True)
+    idx = 4 + torch.ones(M, dtype=torch.int64, device='cuda')
+    # forward pass
+    tt_y = triton.ops.cross_entropy(x, idx)
+    th_y = torch.nn.CrossEntropyLoss(reduction="none")(x, idx)
+    if mode == 'forward':
+        assert torch.allclose(th_y, tt_y, atol=1e-3, rtol=1e-2)
+    # backward pass
+    elif mode == 'backward':
+        dy = torch.randn_like(tt_y)
+        # triton backward
+        tt_y.backward(dy)
+        tt_dx = x.grad.clone()
+        # torch backward
+        x.grad.zero_()
+        th_y.backward(dy)
+        th_dx = x.grad.clone()
+        assert torch.allclose(th_dx, tt_dx, atol=1e-3, rtol=1e-2)