diff --git a/python/triton/compiler.py b/python/triton/compiler.py
index b40fe65fb..bf102b612 100644
--- a/python/triton/compiler.py
+++ b/python/triton/compiler.py
@@ -905,7 +905,7 @@ def ttir_to_ttgir(mod, num_warps, num_stages, compute_capability):
     pm.add_licm_pass()
     pm.add_tritongpu_combine_pass(compute_capability)
     pm.add_cse_pass()
-    # pm.add_tritongpu_optimize_load_convert_pass()
+    pm.add_tritongpu_optimize_load_convert_pass()
     pm.add_tritongpu_decompose_conversions_to_dot_operand_pass()
     pm.add_cse_pass()
     pm.add_symbol_dce_pass()
diff --git a/python/tutorials/06-fused-attention.py b/python/tutorials/06-fused-attention.py
index 9da192a8b..e7455903e 100644
--- a/python/tutorials/06-fused-attention.py
+++ b/python/tutorials/06-fused-attention.py
@@ -44,13 +44,13 @@ def _fwd_kernel(
     acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
     # load q: it will stay in SRAM throughout
     q = tl.load(q_ptrs)
-    q *= (q.to(tl.float32) * sm_scale).to(tl.float16)
     # loop over k, v and update accumulator
     for start_n in range(0, (start_m + 1) * BLOCK_M, BLOCK_N):
         # -- compute qk ----
         k = tl.load(k_ptrs)
         qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
         qk += tl.dot(q, k)
+        qk *= sm_scale
         qk = tl.where(offs_m[:, None] >= (start_n + offs_n[None, :]), qk, float("-inf"))
         # compute new m
         m = tl.maximum(tl.max(qk, 1), m_prev)
@@ -337,7 +337,7 @@ BATCH, N_HEADS, N_CTX, D_HEAD = 4, 48, 4096, 64
 # vary seq length for fixed head and batch=4
 configs = [triton.testing.Benchmark(
     x_names=['N_CTX'],
-    x_vals=[2**i for i in range(10, 13)],
+    x_vals=[2**i for i in range(10, 17)],
     line_arg='provider',
     line_vals=['triton'],
     line_names=['Triton'],
@@ -345,7 +345,7 @@ configs = [triton.testing.Benchmark(
     ylabel='ms',
     plot_name=f'fused-attention-batch{BATCH}-head{N_HEADS}-d{D_HEAD}-{mode}',
     args={'H': N_HEADS, 'BATCH': BATCH, 'D_HEAD': D_HEAD, 'dtype': torch.float16, 'mode': mode}
-) for mode in ['bwd']]
+) for mode in ['fwd', 'bwd']]
 
 
 @triton.testing.perf_report(configs)
@@ -367,7 +367,7 @@ def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.f
         flops_per_matmul = 2. * BATCH * H * N_CTX * N_CTX * D_HEAD * 0.5
         total_flops = 2 * flops_per_matmul
         # print(total_flops/ms*1e-9)
-        print(ms)
+        # print(ms)
         return ms
     if provider == "flash":
         lengths = torch.full((BATCH,), fill_value=N_CTX, device=device)
@@ -383,4 +383,4 @@ def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, mode, provider, dtype=torch.f
         return ms
 
 
-bench_flash_attention.run(save_path='.', print_data=True)
+# bench_flash_attention.run(save_path='.', print_data=True)