[GENERAL] Merged v1.0alpha into master. Added features are:

- A100 support via mma.16816
- Thread swizzling for conflict-free shared memory accesses without
padding
- Complete overhaul of the LLVM code generation in
codegen/selection/generator.cc to remove overengineering
- Added debugging capabilities in the Python binding
- Compilation error for kernels that spill

python/examples/tutorials/trans.py

@@ -0,0 +1,76 @@
+import torch
+import triton
+
+class _transpose(torch.autograd.Function):
+    src = """
+    __global__ void transpose(TYPE * X, TYPE * Y,
+                              int M __retune,
+                              int N __retune,
+                              int ldx __multipleof(8), int ldy __multipleof(8)) {
+          // extract program ID
+          int pidm = get_program_id(0); //(1)
+          int pidn = get_program_id(1); //(2)
+
+          // create 1D range along the two matrix's axes
+          int rm[TM] = pidm * TM + 0 ... TM; //(3)
+          int rn[TN] = pidn * TN + 0 ... TN; //(4)
+
+          // create 2D array of pointers
+          TYPE* px[TM, TN] = X + rm[:, newaxis] * ldx + rn[newaxis, :]; //(5)
+          TYPE* py[TN, TM] = Y + rm[newaxis, :] + rn[:, newaxis] * ldy; //(6)
+
+          // create bounds-checking mask
+          bool checkx[TM, TN] = (rm[:, newaxis] < M) && (rn[newaxis, :] < N); //(7a)
+          bool checky[TN, TM] = (rn[:, newaxis] < N) && (rm[newaxis, :] < M); //(7b)
+
+          // conditional write-back using the conditional dereferencing operatior '*?()'
+          *?(checky)py = ^(*?(checkx)px); //(7)
+        }
+    """
+
+    kernel = None ### initialize later when we know the sizes
+
+    @staticmethod
+    def forward(ctx, x):
+
+       M, N = x.shape
+
+       ldx = N
+       ldy = M
+
+       dtype = x.dtype
+
+       y = torch.empty((N,M)).cuda()
+
+       defines= {
+           'TYPE' : dtype,
+           'TM'   : [32,64,128],
+           'TN'   : [32,64,128],
+       }
+
+       grid = lambda opt: [triton.cdiv(M, opt.d('TM')), triton.cdiv(N, opt.d('TN'))]
+
+       if _transpose.kernel is None:
+           _transpose.kernel = triton.kernel(_transpose.src, defines=defines, num_warps=[4])
+
+       _transpose.kernel(x, y, M, N, ldx, ldy, grid=grid)
+
+       return y
+
+transpose = _transpose.apply
+
+# test
+torch.manual_seed(0)
+x = torch.randn(1024,128).cuda()
+
+print(x)
+
+ya = torch.t(x)
+yb = transpose(x)
+print()
+print(ya)
+print()
+print(yb)
+print(torch.allclose(ya, yb))
+
+print(ya == yb)