[TUTORIALS] Removed #noformat in layer norm tutorial

python/tutorials/05-layer-norm.py

@@ -16,7 +16,6 @@ try:
 except ModuleNotFoundError:
     HAS_APEX = False
 
-# fmt: off
 
 @triton.jit
 def _layer_norm_fwd_fused(
@@ -37,17 +36,17 @@ def _layer_norm_fwd_fused(
     _mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
     for off in range(0, N, BLOCK_SIZE):
         cols = off + tl.arange(0, BLOCK_SIZE)
-        a = tl.load(A + cols, mask=cols<N, other=0., eviction_policy="evict_last").to(tl.float32)
+        a = tl.load(A + cols, mask=cols < N, other=0., eviction_policy="evict_last").to(tl.float32)
         _mean += a
-    mean = tl.sum(_mean, axis = 0) / N
+    mean = tl.sum(_mean, axis=0) / N
     # compute variance
     _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
     for off in range(0, N, BLOCK_SIZE):
         cols = off + tl.arange(0, BLOCK_SIZE)
-        a = tl.load(A + cols, mask=cols<N, other=0., eviction_policy="evict_last").to(tl.float32)
-        a = tl.where(cols<N, a - mean, 0.)
+        a = tl.load(A + cols, mask=cols < N, other=0., eviction_policy="evict_last").to(tl.float32)
+        a = tl.where(cols < N, a - mean, 0.)
         _var += a * a
-    var = tl.sum(_var, axis = 0) / N
+    var = tl.sum(_var, axis=0) / N
     rstd = 1 / tl.sqrt(var + eps)
     # write-back mean/rstd
     tl.store(Mean + row, mean)
@@ -65,6 +64,8 @@ def _layer_norm_fwd_fused(
         tl.store(Out + cols, out, mask=mask)
 
 # Backward pass (DA + partial DW + partial DB)
+
+
 @triton.jit
 def _layer_norm_bwd_dx_fused(
     _DA,
@@ -78,9 +79,9 @@ def _layer_norm_bwd_dx_fused(
     # position of elements processed by this program
     pid = tl.program_id(0)
     row = pid
-    A = _A + row*stride
-    DOut = _DOut + row*stride
-    DA = _DA + row*stride
+    A = _A + row * stride
+    DOut = _DOut + row * stride
+    DA = _DA + row * stride
     mean = tl.load(Mean + row)
     rstd = tl.load(Rstd + row)
     # load data to SRAM
@@ -129,12 +130,12 @@ def _layer_norm_bwd_dwdb(
     db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
     for i in range(0, M, BLOCK_SIZE_M):
         rows = i + tl.arange(0, BLOCK_SIZE_M)
-        mask = (rows[:, None] < M) & (cols[None,:] < N)
-        offs = rows[:, None] * N + cols[None,:]
+        mask = (rows[:, None] < M) & (cols[None, :] < N)
+        offs = rows[:, None] * N + cols[None, :]
         a = tl.load(A + offs, mask=mask, other=0.).to(tl.float32)
         dout = tl.load(DOut + offs, mask=mask, other=0.).to(tl.float32)
-        mean = tl.load(Mean + rows, mask=rows<M, other=0.)
-        rstd = tl.load(Var + rows, mask=rows<M, other=0.)
+        mean = tl.load(Mean + rows, mask=rows < M, other=0.)
+        rstd = tl.load(Var + rows, mask=rows < M, other=0.)
         a_hat = (a - mean[:, None]) * rstd[:, None]
         dw += dout * a_hat
         db += dout
@@ -235,6 +236,7 @@ class LayerNorm(torch.autograd.Function):
 def layer_norm(a, normalized_shape, weight, bias, eps):
     return LayerNorm.apply(a, normalized_shape, weight, bias, eps)
 
+
 def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
     torch.manual_seed(0)
     # create data