[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_blocksparse.py

@@ -0,0 +1,78 @@
+import torch
+import triton
+import pytest
+
+
+@pytest.mark.parametrize("MODE, TRANS_A, TRANS_B, BLOCK",
+    [
+    (mode, at, bt, block) for mode in ['sdd', 'dsd', 'dds']\
+                          for at   in [False, True]\
+                          for bt   in [False, True]\
+                          for block in [16, 32, 64]
+    ]
+                         )
+def test_matmul(MODE, TRANS_A, TRANS_B, BLOCK, DTYPE=torch.float16, Z=3, H=2, M=128, N=256, K=384):
+    # set seed
+    torch.random.manual_seed(0)
+    # create inputs
+    a = torch.randn((Z, H, K, M) if TRANS_A else (Z, H, M, K), dtype=DTYPE, device='cuda')
+    b = torch.randn((Z, H, N, K) if TRANS_B else (Z, H, K, N), dtype=DTYPE, device='cuda')
+    shape = {'sdd': (M, N), 'dsd': (a.shape[2], a.shape[3]), 'dds': (b.shape[2], b.shape[3])}[MODE]
+    layout = torch.randint(2, (H, shape[0] // BLOCK, shape[1] // BLOCK))
+    # triton result
+    op = triton.ops.blocksparse.matmul(layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B)
+    ra = triton.testing.sparsify_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
+    rb = triton.testing.sparsify_tensor(b, layout, BLOCK) if MODE == 'dds' else b
+    rc = op(ra, rb)
+    # torch result
+    ta = triton.testing.mask_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
+    tb = triton.testing.mask_tensor(b, layout, BLOCK) if MODE == 'dds' else b
+    ta = ta.transpose(2, 3) if TRANS_A else ta
+    tb = tb.transpose(2, 3) if TRANS_B else tb
+    tc = torch.matmul(ta, tb)
+    tc = triton.testing.mask_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
+    tc = triton.testing.sparsify_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
+    # compare
+    assert triton.testing.allclose(rc, tc)
+
+@pytest.mark.parametrize("BLOCK, WIDTH",
+                         [
+    (block, width) for block in [32]\
+                   for width in [256, 576, 1024, 1792]
+                         ]
+                         )
+def test_softmax(BLOCK, WIDTH, DTYPE=torch.float16):
+    # set seed
+    torch.random.manual_seed(0)
+    Z, H, M, N = 2, 4, WIDTH, WIDTH
+    scale = 0.4
+    # create inputs
+    layout = torch.randint(2, (H, M // BLOCK, N // BLOCK))
+    x = torch.randn((Z, H, M, N), dtype=DTYPE, requires_grad=True, device='cuda')
+    at_mask = torch.randint(low=0, high=2, size=(N, N), \
+                            dtype=torch.bool, requires_grad=False, device='cuda')
+    kp_mask = torch.randint(low=0, high=2, size=(Z, N), \
+                            dtype=DTYPE, requires_grad=False, device='cuda')
+    kp_mask[kp_mask == 1.] = float('-inf')
+    # triton result
+    op = triton.ops.blocksparse.softmax(layout, BLOCK)
+    tx = triton.testing.sparsify_tensor(x, layout, BLOCK)
+    ty = op(tx,
+            scale=scale,
+            key_padding_mask=kp_mask,
+            key_padding_mask_mode='add',
+            attn_mask=at_mask.to(DTYPE),
+            attn_mask_mode='mul')
+    # torch result
+    rx = triton.testing.mask_tensor(x, layout, BLOCK, value=float('-inf'))
+    if at_mask is not None:
+        # broadcast at_mask to the same shape as rx
+        M = at_mask[None, None, :, :] + torch.zeros_like(rx)
+        rx[M == 0] = float('-inf')
+    if kp_mask is not None:
+        rx += kp_mask[:, None, None, :]
+    ry = torch.softmax(rx * scale, -1)
+    ry = torch.softmax(rx * scale, -1)
+    ry = triton.testing.sparsify_tensor(ry, layout, BLOCK)
+    # compare
+    assert triton.testing.allclose(ry, ty)