[DOCS] Made documentation consistent with the new kernel API

docs/tutorials/custom-operation.rst

@@ -57,7 +57,8 @@ As you will see, a wrapper for the above Triton function can be created in just
     }
         """
         # create callable kernel for the source-code
-        kernel = triton.kernel(src)
+        # options: 4 warps and a -DTILE=1024
+        kernel = triton.kernel(src, defines = {'TILE': 1024}; num_warps = [4])
 
         # Forward pass
         @staticmethod
@@ -72,11 +73,7 @@ As you will see, a wrapper for the above Triton function can be created in just
             N = x.numel()
             grid = lambda opt: (triton.cdiv(N, opt.d('TILE')), )
             # launch kernel
-            # options: 4 warps and a -DTILE=1024
-            _add.kernel(z, x, y, N, 
-                        grid = grid, 
-                        num_warps = 4,
-                        defines = {'TILE': 1024})
+            _add.kernel(z, x, y, N, grid = grid)
             # return output
             return z
 

docs/tutorials/index.rst

@@ -8,4 +8,3 @@ Tutorials
    triton-vs-cuda
    matrix-transposition
    matrix-multiplication
-   putting-it-all-together

docs/tutorials/triton-vs-cuda.rst

@@ -98,11 +98,9 @@ Now assume that you want to tune the above code for different data types, tile s
 
 .. code-block:: python
  
-  _vector_add.kernel(y, x, N, grid=grid, 
-                     defines={'TILE': [256, 512, 1024]},
-                     num_warps = [2, 4, 8])
+  kernel = triton.kernel(src, defines = {'TILE': [256, 512, 1024]}, num_warps = [2, 4, 8])
 
-would benchmark our above triton-code for tile sizes of 256, 512 and 1024 executed with 2, 4 or 8 warps -- and cache the fastest kernel.
+would benchmark our above triton source-code for tile sizes of 256, 512 and 1024 executed with 2, 4 or 8 warps -- and cache the fastest kernel.
 
 =============================
 Going Further