[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_matmul.py

@@ -0,0 +1,96 @@
+import pytest
+import itertools
+import triton as tt
+import torch as th
+
+@pytest.mark.parametrize("TM, TN, TK, NWARP, M, N, K, AT, BT, DTYPE", itertools.chain(*[
+    [
+    # 1 warp
+    (16, 16, 16, 1, None, None, None, AT, BT, DTYPE),
+    (32, 16, 16, 1, None, None, None, AT, BT, DTYPE),
+    (16, 32, 16, 1, None, None, None, AT, BT, DTYPE),
+    (16, 16, 32, 1, None, None, None, AT, BT, DTYPE),
+    (32, 16, 32, 1, None, None, None, AT, BT, DTYPE),
+    (16, 32, 32, 1, None, None, None, AT, BT, DTYPE),
+    (16, 16, 64, 1, None, None, None, AT, BT, DTYPE),
+    (64, 16, 64, 1, None, None, None, AT, BT, DTYPE),
+    (16, 64, 64, 1, None, None, None, AT, BT, DTYPE),
+    # 2 warp
+    (64, 32, 64, 2, None, None, None, AT, BT, DTYPE),
+    (32, 64, 64, 2, None, None, None, AT, BT, DTYPE),
+    (64, 32, 16, 2, None, None, None, AT, BT, DTYPE),
+    (32, 64, 16, 2, None, None, None, AT, BT, DTYPE),
+    (128, 32, 32, 2, None, None, None, AT, BT, DTYPE),
+    (32, 128, 32, 2, None, None, None, AT, BT, DTYPE),
+    # 4 warp
+    (128, 64, 16, 4, None, None, None, AT, BT, DTYPE),
+    (64, 128, 16, 4, None, None, None, AT, BT, DTYPE),
+    (128, 32, 32, 4, None, None, None, AT, BT, DTYPE),
+    (32, 128, 32, 4, None, None, None, AT, BT, DTYPE),
+    (128, 32, 64, 4, None, None, None, AT, BT, DTYPE),
+    (32, 128, 64, 4, None, None, None, AT, BT, DTYPE),
+    # 8 warp
+    (128, 256, 16, 8, None, None, None, AT, BT, DTYPE),
+    (256, 128, 16, 8, None, None, None, AT, BT, DTYPE),
+    (256, 128, 32, 8, None, None, None, AT, BT, DTYPE),
+    # variable input
+    (128, 128, 32, 4, 256,  256,  256 , AT, BT, DTYPE),
+    (128, 128, 32, 4, 384,  128,  640 , AT, BT, DTYPE),
+    (128, 128, 32, 4, 107,  233,  256 , AT, BT, DTYPE),
+    (128, 128, 32, 4, 107,  233,  311 , AT, BT, DTYPE)
+    ]
+    for DTYPE in ['float16']
+    for AT in [False, True]
+    for BT in [False, True]
+]))
+def test_op(TM, TN, TK, NWARP, M, N, K, AT, BT, DTYPE):
+    DTYPE = {'float16': th.float16, 'float32': th.float32}[DTYPE]
+    th.manual_seed(0)
+    tt.ops._matmul.kernel = dict()
+    tt.ops._matmul.TM = [TM]
+    tt.ops._matmul.TN = [TN]
+    tt.ops._matmul.TK = [TK]
+    tt.ops._matmul.num_warps = [NWARP]
+    if M is None: M = TM
+    if N is None: N = TN
+    if K is None: K = TK
+    a = th.randn((K, M) if AT else (M, K), device='cuda', dtype=DTYPE) / K**.5
+    b = th.randn((N, K) if BT else (K, N), device='cuda', dtype=DTYPE) / K**.5
+    a = a.t() if AT else a
+    b = b.t() if BT else b
+    th_c = th.matmul(a, b)
+    tt_c = tt.ops.matmul(a, b)
+    rtol, atol = {th.float32: (1e-4, 1e-5),
+                  th.float16: (1e-2, 1e-3)}[DTYPE]
+    assert th.allclose(tt_c, th_c, atol=atol, rtol=rtol)
+
+
+def do_bench(fn, flops = 0, warmup = 10, rep = 50):
+    start_event = th.cuda.Event(enable_timing=True)
+    end_event   = th.cuda.Event(enable_timing=True)
+    ret = fn()
+    for i in range(warmup):
+        fn()
+    th.cuda.synchronize()
+    start_event.record()
+    for i in range(rep):
+        fn()
+    end_event.record()
+    th.cuda.synchronize()
+    time_ms = start_event.elapsed_time(end_event) / rep
+    return time_ms, flops/time_ms*1e-9, ret
+
+
+def perf_op(dtype=th.float16, warmup=10, rep=50):
+    AT, BT = False, False
+    configs = [(N, N, N) for N in [128, 8192]]
+    for M, N, K in configs:
+        a = th.randn((K, M) if AT else (M, K), device='cuda', dtype=dtype) / K**.5
+        b = th.randn((N, K) if BT else (K, N), device='cuda', dtype=dtype) / K**.5
+        if AT: a = a.t()
+        if BT: b = b.t()
+        a = a[::,::]
+        b = b[::,::]
+        TH_MS, TH_TFLOPS, _ = do_bench(lambda: th.matmul(a, b), flops = M*N*K*2, warmup = warmup, rep = rep)
+        TT_MS, TT_TFLOPS, _ = do_bench(lambda: tt.ops.matmul(a, b), flops = M*N*K*2, warmup = warmup, rep = rep)
+        print((M, N, K), TH_MS, TT_MS)