[Triton-MLIR] Fix some typos (#874)

Fix some typos

python/tutorials/01-vector-add.py

@@ -63,7 +63,7 @@ def add(x: torch.Tensor, y: torch.Tensor):
     grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
     # NOTE:
     #  - each torch.tensor object is implicitly converted into a pointer to its first element.
-    #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
+    #  - `triton.jit`'ed functions can be indexed with a launch grid to obtain a callable GPU kernel
     #  - don't forget to pass meta-parameters as keywords arguments
     add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
     # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still

python/tutorials/02-fused-softmax.py

@@ -80,7 +80,7 @@ def softmax_kernel(
     row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf'))
     # Subtract maximum for numerical stability
     row_minus_max = row - tl.max(row, axis=0)
-    # Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA)
+    # Note that exponentiation in Triton is fast but approximate (i.e., think __expf in CUDA)
     numerator = tl.exp(row_minus_max)
     denominator = tl.sum(numerator, axis=0)
     softmax_output = numerator / denominator
@@ -188,4 +188,4 @@ benchmark.run(show_plots=True, print_data=True)
 #
 #  - Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here.
 #  - Triton is noticeably faster than :code:`torch.softmax` -- in addition to being **easier to read, understand and maintain**.
-#    Note however that the PyTorch `softmax` operation is more general and will works on tensors of any shape.
+#    Note however that the PyTorch `softmax` operation is more general and will work on tensors of any shape.

python/tutorials/06-fused-attention.py

@@ -15,7 +15,7 @@ import triton.language as tl
 @triton.jit
 def _fwd_kernel(
     Q, K, V, sm_scale,
-    TMP, L, M,  # NOTE: TMP is a scratchpad buffer to workaround a compiler bug
+    TMP, L, M,  # NOTE: TMP is a scratchpad buffer to work around a compiler bug
     Out,
     stride_qz, stride_qh, stride_qm, stride_qk,
     stride_kz, stride_kh, stride_kn, stride_kk,