[CI]Add norm and layout_plot

examples/norm/rms_norm.py

@@ -81,4 +81,4 @@ def ref_program(x):
     latency = profiler.do_bench(ref_program, warmup=500)
     print("Ref: {:.2f} ms".format(latency))
     latency = profiler.do_bench(warmup=500)
-    print("Tile-lang: {:.2f} ms".format(latency))
+    print("Tile-lang: {:.2f} ms".format(latency))

examples/norm/test_rms_norm.py

@@ -0,0 +1,78 @@
+# Copyright (c) Tile-AI Corporation.
+# Licensed under the MIT License.
+
+import torch
+import tilelang
+import tilelang.language as T
+
+
+def rms_norm_splitk(M, N, blk_m, blk_k):
+    dtype = "float"
+
+    @T.prim_func
+    def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)):
+        with T.Kernel(T.ceildiv(M, blk_m), threads=128) as bx:
+            A_shared = T.alloc_shared((blk_m, blk_k), dtype)
+            A_local = T.alloc_fragment((blk_m, blk_k), dtype)
+            A_powsum = T.alloc_fragment((blk_m,), dtype)
+
+            num_k_step = T.ceildiv(N, blk_k)
+            T.clear(A_local)
+            for k in range(num_k_step):
+                T.copy(A[bx * blk_m, k * blk_k], A_shared)
+                for i, j in T.Parallel(blk_m, blk_k):
+                    A_local[i, j] += A_shared[i, j] * A_shared[i, j]
+            T.reduce_sum(A_local, A_powsum, dim=1)
+            for i in T.Parallel(blk_m):
+                A_powsum[i] = T.rsqrt(A_powsum[i] / N) + 1e-12
+
+            for k in range(num_k_step):
+                # reverse, better cache hit rate
+                T.copy(A[bx * blk_m, (num_k_step - 1 - k) * blk_k], A_shared)
+                for i, j in T.Parallel(blk_m, blk_k):
+                    A_shared[i, j] *= A_powsum[i]
+                T.copy(A_shared, B[bx * blk_m, (num_k_step - 1 - k) * blk_k])
+
+    return main
+
+
+def rms_norm(M, N, blk_m):
+    dtype = "float"
+
+    @T.prim_func
+    def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)):
+        with T.Kernel(T.ceildiv(M, blk_m), threads=128) as bx:
+            A_shared = T.alloc_shared((blk_m, N), dtype)
+            A_pow_local = T.alloc_fragment((blk_m, N), dtype)
+            A_local = T.alloc_fragment((blk_m, N), dtype)
+            A_powsum = T.alloc_fragment((blk_m,), dtype)
+
+            T.copy(A[bx * blk_m:(bx + 1) * blk_m, :], A_shared)
+            T.copy(A_shared, A_local)
+            for i, j in T.Parallel(blk_m, N):
+                A_pow_local[i, j] = A_local[i, j] * A_local[i, j]
+            T.reduce_sum(A_pow_local, A_powsum, dim=1)
+            for i in T.Parallel(blk_m):
+                A_powsum[i] = T.rsqrt(A_powsum[i] / N) + 1e-12
+            for i, j in T.Parallel(blk_m, N):
+                A_local[i, j] *= A_powsum[i]
+            T.copy(A_local, B[bx * blk_m:(bx + 1) * blk_m, :])
+
+    return main
+
+
+def ref_program(x):
+    return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + 1e-12)
+
+
+def test_rms_norm():
+    M, N, blk_m = 8192, 8192, 1
+    program = rms_norm(M, N, blk_m)
+    kernel = tilelang.compile(
+        program,
+        out_idx=-1,
+        target="cuda",
+        execution_backend="cython",
+        pass_configs={"tl.disable_tma_lower": True})
+    profiler = kernel.get_profiler()
+    profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01)