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tritonbench/kernels/triton_fused_attention.py

@@ -458,10 +458,10 @@ def _attn_fwd_inner_ws(
             num_warps=w,
         )
     )
-    for BM in [128] #64, 128]
-    for BN in [128] #64, 128]
-    for s in [3] #3, 4, 7]
-    for w in [8] #4, 8]
+    for BM in [128]  # 64, 128]
+    for BN in [128]  # 64, 128]
+    for s in [3]  # 3, 4, 7]
+    for w in [8]  # 4, 8]
 ]
 # TMA, WS, and CompPipe
 configsTmaWS = [
@@ -1637,11 +1637,11 @@ def _attn_bwd_dkdv_ws(
         # Load m before computing qk to reduce pipeline stall.
         offs_m = curr_m + tl.arange(0, BLOCK_M1)
         m = tl.load(M + offs_m)
-        #with tl.async_task([0]):
+        # with tl.async_task([0]):
         #    do = tl.load(do_ptrs)
         with tl.async_task([1, 2]):
             qkT = tl.dot(k, qT)
-            #dpT = tl.dot(v, tl.trans(do)).to(tl.float32)
+            # dpT = tl.dot(v, tl.trans(do)).to(tl.float32)
             pT = tl.math.exp2(qkT - m[None, :])
             # Autoregressive masking.
             if MASK:
@@ -1746,10 +1746,10 @@ def keep2(conf):
                 "BLOCK_M2": BN,
                 "BLOCK_N2": BM,
             },
-            num_stages=s, #0 or s,
+            num_stages=s,  # 0 or s,
             num_warps=w,
-            num_buffers_warp_spec=0, #0 or 2,
-            num_consumer_groups=0, #0 or 1,
+            num_buffers_warp_spec=0,  # 0 or 2,
+            num_consumer_groups=0,  # 0 or 1,
         )
         if has_warp_spec
         else triton.Config(
@@ -1763,10 +1763,10 @@ def keep2(conf):
             num_warps=w,
         )
     )
-    for BM in [64] #32, 64] # BLOCK_N1 % BLOCK_M1 == 0
-    for BN in [128] #64, 128]
-    for s in [3]#, 4, 7]
-    for w in [4]#, 8]
+    for BM in [64]  # 32, 64] # BLOCK_N1 % BLOCK_M1 == 0
+    for BN in [128]  # 64, 128]
+    for s in [3]  # , 4, 7]
+    for w in [4]  # , 8]
 ]
 configsBwdWs = [
     (
@@ -1794,10 +1794,10 @@ def keep2(conf):
             num_warps=w,
         )
     )
-    for BM in [64] #32, 64] # BLOCK_N1 % BLOCK_M1 == 0
-    for BN in [128] #[64] #64, 128]
-    for s in [3]#, 4, 7]
-    for w in [4]#, 8]
+    for BM in [64]  # 32, 64] # BLOCK_N1 % BLOCK_M1 == 0
+    for BN in [128]  # [64] #64, 128]
+    for s in [3]  # , 4, 7]
+    for w in [4]  # , 8]
 ]
 
 
@@ -2698,8 +2698,8 @@ def backward(ctx, do):
         BATCH, N_HEAD, N_CTX = q.shape[:3]
         PRE_BLOCK = 128
 
-        #NUM_WARPS, NUM_STAGES = 4, 5
-        #BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 128, 128, 32
+        # NUM_WARPS, NUM_STAGES = 4, 5
+        # BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 128, 128, 32
 
         BLK_SLICE_FACTOR = 2
         RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2)
@@ -2720,8 +2720,7 @@ def backward(ctx, do):
             HEAD_DIM=ctx.HEAD_DIM,  #
         )
         grid = lambda args: (N_CTX // args["BLOCK_N1"], 1, BATCH * N_HEAD)
-        #grid = (N_CTX // BLOCK_N1, 1, BATCH * N_HEAD)
-        print(q.stride(0), q.stride(1), q.stride(2), q.stride(3))
+        # grid = (N_CTX // BLOCK_N1, 1, BATCH * N_HEAD)
         if ctx.bwdVariant == "base":
             _attn_bwd[grid](
                 q,
@@ -2740,14 +2739,14 @@ def backward(ctx, do):
                 q.stride(3),  #
                 N_HEAD,
                 N_CTX,  #
-                #BLOCK_M1=BLOCK_M1,
-                #BLOCK_N1=BLOCK_N1,  #
-                #BLOCK_M2=BLOCK_M2,
-                #BLOCK_N2=BLOCK_N2,  #
+                # BLOCK_M1=BLOCK_M1,
+                # BLOCK_N1=BLOCK_N1,  #
+                # BLOCK_M2=BLOCK_M2,
+                # BLOCK_N2=BLOCK_N2,  #
                 BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,  #
                 HEAD_DIM=ctx.HEAD_DIM,  #
-                #num_warps=NUM_WARPS,  #
-                #num_stages=NUM_STAGES,  #
+                # num_warps=NUM_WARPS,  #
+                # num_stages=NUM_STAGES,  #
             )
         elif ctx.bwdVariant == "ws":
             _attn_bwd_ws[grid](
@@ -2767,14 +2766,14 @@ def backward(ctx, do):
                 q.stride(3),  #
                 N_HEAD,
                 N_CTX,  #
-                #BLOCK_M1=BLOCK_M1,
-                #BLOCK_N1=BLOCK_N1,  #
-                #BLOCK_M2=BLOCK_M2,
-                #BLOCK_N2=BLOCK_N2,  #
+                # BLOCK_M1=BLOCK_M1,
+                # BLOCK_N1=BLOCK_N1,  #
+                # BLOCK_M2=BLOCK_M2,
+                # BLOCK_N2=BLOCK_N2,  #
                 BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,  #
                 HEAD_DIM=ctx.HEAD_DIM,  #
-                #num_warps=NUM_WARPS,  #
-                #num_stages=NUM_STAGES,  #
+                # num_warps=NUM_WARPS,  #
+                # num_stages=NUM_STAGES,  #
             )
 
         return dq, dk, dv, None, None, None, None

tritonbench/operators/flash_attention/operator.py

@@ -470,8 +470,6 @@ def tflops(
         BATCH, H, N_CTX, D_HEAD = q.shape
         flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD
         tflops = 2 * flops_per_matmul
-        print("causal, mode: ", self.causal, self.mode)
-        print("fn_name: ", fn_name, metrics.latency)
         if self.causal:
             tflops *= 0.5
         if self.mode == BenchmarkMode.BWD: