wip

masahi · masahi · commit 54c686443e37 · 2022-05-17T10:29:17.000+09:00
diff --git a/tests/python/unittest/test_mma_16x8x16_fp16_4k_tune_trans.py b/tests/python/unittest/test_mma_16x8x16_fp16_4k_tune_trans.py
@@ -135,16 +135,18 @@ def mma_sync_desc(a: T.handle, b: T.handle, c: T.handle) -> None:
         for i, j, k in T.grid(16, 16, 16):
             with T.block("C"):
                 i, j, k = T.axis.remap("SSR", [i, j, k])
+                thread_id_C, local_id_C = shared_16x16_to_ldmatrix_32x8_layout(i, j)
+                thread_id_A, local_id_A = shared_16x16_to_ldmatrix_32x8_layout(i, k)
+                thread_id_B, local_id_B = shared_16x16_to_ldmatrix_32x8_layout(j, k)
+
                 T.reads(
-                    C[i % 8 * 4 + j % 8 // 2, j % 16 // 8 * 4 + i % 16 // 8 * 2 + j % 2],
-                    A[i % 8 * 4 + k % 8 // 2, k % 16 // 8 * 4 + i % 16 // 8 * 2 + k % 2],
-                    B[j % 8 * 4 + k % 8 // 2, k % 16 // 8 * 4 + j % 16 // 8 * 2 + k % 2],
+                    C[thread_id_C, local_id_C],
+                    A[thread_id_A, local_id_A],
+                    B[thread_id_B, local_id_B],
                 )
-                T.writes(C[i % 8 * 4 + j % 8 // 2, j % 16 // 8 * 4 + i % 16 // 8 * 2 + j % 2])
-                C[i % 8 * 4 + j % 8 // 2, j % 16 // 8 * 4 + i % 16 // 8 * 2 + j % 8 % 2] = (
-                    C[i % 8 * 4 + j % 8 // 2, j % 16 // 8 * 4 + i % 16 // 8 * 2 + j % 8 % 2]
-                    + A[i % 8 * 4 + k % 8 // 2, k % 16 // 8 * 4 + i % 16 // 8 * 2 + k % 8 % 2]
-                    * B[j % 8 * 4 + k % 8 // 2, k % 16 // 8 * 4 + j % 16 // 8 * 2 + k % 8 % 2]
+                T.writes(C[thread_id_C, local_id_C])
+                C[thread_id_C, local_id_C] += (
+                    A[thread_id_A, local_id_A] * B[thread_id_B, local_id_B]
                 )
 
 
@@ -207,14 +209,13 @@ def mma_store_desc(a: T.handle, c: T.handle) -> None:
     with T.block("root"):
         T.reads(C_warp[0:32, 0:8])
         T.writes(C[0:16, 0:16])
-        for ax1_0, i0, i1 in T.grid(2, 32, 4):
+        for i0, i1 in T.grid(16, 16):
             with T.block("C_warp"):
-                v0 = T.axis.spatial(16, i1 // 2 * 8 + i0 // 4)
-                v1 = T.axis.spatial(16, ax1_0 * 8 + i0 % 4 * 2 + i1 % 2)
-
-                T.reads(C_warp[v0 % 8 * 4 + v1 % 8 // 2, v1 // 8 * 4 + v0 // 8 * 2 + v1 % 2])
+                v0, v1 = T.axis.remap("SS", [i0, i1])
+                thread_id, local_id = shared_16x16_to_ldmatrix_32x8_layout(v0, v1)
+                T.reads(C_warp[thread_id, local_id])
                 T.writes(C[v0, v1])
-                C[v0, v1] = C_warp[v0 % 8 * 4 + v1 % 8 // 2, v1 // 8 * 4 + v0 // 8 * 2 + v1 % 2]
+                C[v0, v1] = C_warp[thread_id, local_id]
 
 
 @T.prim_func
@@ -247,21 +248,13 @@ def mma_fill_desc(a: T.handle) -> None:
     with T.block("root"):
         T.reads()
         T.writes(C_warp[0:32, 0:8])
-        for i0, i1 in T.grid(32, 8):
+        for i0, i1 in T.grid(16, 16):
             with T.block("C_warp"):
-                i_init = T.axis.spatial(16, i1 // 4 * 8 + i0 // 4)
-                j_init = T.axis.spatial(16, (i0 % 4) * 4 + i1 % 4)
+                i_init, j_init = T.axis.remap("SS", [i0, i1])
+                thread_id, local_id = shared_16x16_to_ldmatrix_32x8_layout(i_init, j_init)
                 T.reads()
-                T.writes(
-                    C_warp[
-                        i_init % 8 * 4 + j_init % 8 // 2,
-                        j_init % 16 // 8 * 4 + i_init % 16 // 8 * 2 + j_init % 2,
-                    ]
-                )
-                C_warp[
-                    i_init % 8 * 4 + j_init % 8 // 2,
-                    j_init % 16 // 8 * 4 + i_init % 16 // 8 * 2 + j_init % 8 % 2,
-                ] = T.float16(0)
+                T.writes(C_warp[thread_id, local_id])
+                C_warp[thread_id, local_id] = T.float16(0)
 
 
 @T.prim_func
@@ -387,8 +380,6 @@ def fetch_to_shared(block, idx, ndim):
     A_sh = fetch_to_shared(block_outer, 0, 2)
     B_sh = fetch_to_shared(block_outer, 1, 2)
 
-    loop = sch.get_loops(block_outer)[-1]
-
     A_warp = sch.cache_read(block_outer, 0, "warp")
     B_warp = sch.cache_read(block_outer, 1, "warp")
 
@@ -403,7 +394,8 @@ def fetch_to_shared(block, idx, ndim):
     jo, ji = sch.split(jj, factors=[None, 16])
     sch.reorder(io, jo, ii, ji)
 
-    block_init_c = sch.decompose_reduction(block_outer, sch.get_loops(block_outer)[3])
+    sch.decompose_reduction(block_outer, sch.get_loops(block_outer)[3])
+    block_init_c = sch.get_block("C_init")
 
     def tile_wmma_fragment(block_read, height):
         i, j = sch.get_loops(block_read)[-2:]
@@ -412,47 +404,25 @@ def tile_wmma_fragment(block_read, height):
         sch.reorder(i0, j0, i1, j1)
         return i1
 
-    def shared_16x16_to_ldmatrix_32x8_layout(i, j):
-        i_0 = i // 16
-        j_0 = j // 16
-
-        i = i % 16
-        j = j % 16
-
-        thread_id = 4 * (i % 8) + (j % 8) // 2
-        return i_0, j_0, thread_id, 4 * (j // 8) + (i // 8) * 2 + (j % 8) % 2
-
     loop_a = tile_wmma_fragment(A_warp, 16)
     loop_b = tile_wmma_fragment(B_warp, 16)
 
-    sch.transform_layout(A_warp, 0, "write", index_map=shared_16x16_to_ldmatrix_32x8_layout)
-    sch.transform_layout(B_warp, 0, "write", index_map=shared_16x16_to_ldmatrix_32x8_layout)
-    sch.transform_layout(C_warp, 0, "read", index_map=shared_16x16_to_ldmatrix_32x8_layout)
+    def index_map(i, j):
+        return (
+            i // 16,
+            j // 16,
+            *shared_16x16_to_ldmatrix_32x8_layout(i % 16, j % 16),
+        )
+
+    sch.transform_layout(A_warp, 0, "write", index_map)
+    sch.transform_layout(B_warp, 0, "write", index_map)
+    sch.transform_layout(C_warp, 0, "read", index_map)
 
     sch.tensorize(loop_a, "mma.ldmatrix_a")
     sch.tensorize(loop_b, "mma.ldmatrix_b")
-
-    mma_loop = sch.get_loops(block_inner)[-3]
-    sch.tensorize(mma_loop, "mma_sync")
-
-    block_init_c = sch.get_block("C_init")
-    init_loop1, init_loop2 = sch.get_loops(block_init_c)[-2:]
-    f_0, f_1 = sch.split(init_loop1, factors=[None, 8])
-    f_2, f_3 = sch.split(init_loop2, factors=[None, 4])
-    sch.reorder(f_1, f_2, f_0, f_3)
-    fused_1 = sch.fuse(f_1, f_2)
-    fused_2 = sch.fuse(f_0, f_3)
-    sch.tensorize(fused_1, "mma_fill")
-
-    warp_loop1, warp_loop2 = sch.get_loops(C_warp)[-2:]
-    f_0, f_1 = sch.split(warp_loop1, factors=[None, 8])
-    outer, f_2, f_3 = sch.split(warp_loop2, factors=[2, 4, 2])
-    sch.reorder(outer, f_1, f_2, f_0, f_3)
-    fused_1 = sch.fuse(f_1, f_2)
-    fused_2 = sch.fuse(f_0, f_3)
-    sch.tensorize(outer, "mma_store")
-    # print(sch.mod.script())
-    # return
+    sch.tensorize(sch.get_loops(block_inner)[-3], "mma_sync")
+    sch.tensorize(sch.get_loops(block_init_c)[-2], "mma_fill")
+    sch.tensorize(sch.get_loops(C_warp)[-2], "mma_store")
 
 
 ir_module = tvm.IRModule({"main": workload})
