[FRONTEND] Added tl.clock and tl.globaltimer (#485)

python/tutorials/01-vector-add.py

@@ -24,9 +24,11 @@ def add_kernel(
     y_ptr,  # *Pointer* to second input vector
     output_ptr,  # *Pointer* to output vector
     n_elements,  # Size of the vector
+    time_start_ptr, time_end_ptr,
     BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process
                  # NOTE: `constexpr` so it can be used as a shape value
 ):
+    tl.atomic_min(time_start_ptr, tl.clock())
     # There are multiple 'program's processing different data. We identify which program
     # we are here
     pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0
@@ -45,6 +47,7 @@ def add_kernel(
     output = x + y
     # Write x + y back to DRAM
     tl.store(output_ptr + offsets, output, mask=mask)
+    tl.atomic_max(time_end_ptr, tl.clock())
 
 
 # %%
@@ -53,6 +56,8 @@ def add_kernel(
 
 
 def add(x: torch.Tensor, y: torch.Tensor):
+    time_start = torch.zeros(1, dtype=torch.int64, device='cuda')
+    time_end = torch.zeros(1, dtype=torch.int64, device='cuda')
     # We need to preallocate the output
     output = torch.empty_like(x)
     assert x.is_cuda and y.is_cuda and output.is_cuda
@@ -65,9 +70,10 @@ def add(x: torch.Tensor, y: torch.Tensor):
     #  - 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
     #  - don't forget to pass meta-parameters as keywords arguments
-    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
+    add_kernel[grid](x, y, output, n_elements, time_start, time_end, BLOCK_SIZE=1024)
     # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
     # running asynchronously at this point.
+    print((time_end, time_start))
     return output