[PYTHON][TESTS][DOC] Various improvement of the API and code quality:

* Simplified `triton.kernel` API to achieve lower latency:
  > .data_ptr() must now be passed as kernel argument. No more implicit
conversion from torch.tensor
  > compilation options are now constant attributes, i.e., opt.d('VAR')
becomes opt.VAR
  > torch.device must now be passed explicitly to triton.kernel (no
longer inferred from torch.tensor arguments)
* C++ tests moved to `python/tests/`
* C++ tutorial created in `tutorials/`
* Python tutorial created in python/tutorials/
* Version changed to 1.0alpha
* No longer copying C++ headers into the Python package
* added python/triton/ops/ package for pre-written Triton ops

python/tests/test_blocksparse.py

@@ -0,0 +1,50 @@
+import itertools
+import torch
+import triton as tt
+import pytest
+
+def sparsify_tensor(x, mask, block):
+  ret = torch.empty((x.size(0), mask.sum(), block, block), dtype=x.dtype, device=x.device)
+  for idx, (h, i, j) in enumerate(zip(*mask.nonzero(as_tuple=True))):
+    ret[:, idx, :, :] = x[:, h, i*block: (i+1)*block, j*block: (j+1)*block]
+  return ret
+
+def mask_tensor(x, mask, block, value = 0):
+  ret = x.clone()
+  for h, i, j in zip(*(mask == 0).nonzero(as_tuple=True)):
+    ret[:, h, i*block: (i+1)*block, j*block: (j+1)*block] = value
+  return ret
+
+@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_op(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 = tt.ops.blocksparse.matmul(layout, BLOCK, MODE, trans_a=TRANS_A, trans_b=TRANS_B)
+  ra = sparsify_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
+  rb = sparsify_tensor(b, layout, BLOCK) if MODE == 'dds' else b
+  rc  = op(ra, rb)
+  # torch result
+  ta = mask_tensor(a, layout, BLOCK) if MODE == 'dsd' else a
+  tb = 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 = mask_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
+  tc = sparsify_tensor(tc, layout, BLOCK) if MODE == 'sdd' else tc
+  # compare
+  rtol, atol = {torch.float32: (1e-4, 1e-5),
+                torch.float16: (1e-2, 1e-3)}[DTYPE]
+  assert torch.allclose(rc, tc, rtol=rtol, atol=atol)