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alexbadenalexbaden
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use attr variable in lit test
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‎test/TritonIntelGPU/optimize-block-io-encoding.mlir

Lines changed: 12 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -3,9 +3,9 @@
33
#blocked = #ttg.blocked<{sizePerThread = [4, 4], threadsPerWarp = [1, 16], warpsPerCTA = [8, 4], order = [1, 0]}>
44
#blocked1 = #ttg.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 4], warpsPerCTA = [32, 1], order = [1, 0]}>
55
#blocked2 = #ttg.blocked<{sizePerThread = [1, 8], threadsPerWarp = [1, 16], warpsPerCTA = [16, 2], order = [1, 0]}>
6-
// CHECK: #mma = #ttig.subgroup_2d_block<{warpsPerCTA = [8, 4], instrShape = [8, 16], numBlocks=2, order=[1, 0], kWidth=1, threadsPerWarp=16}>
7-
// CHECK: #mma1 = #ttig.subgroup_2d_block<{warpsPerCTA = [8, 4], instrShape = [16, 16], numBlocks=2, order=[0, 1], kWidth=2, threadsPerWarp=16}>
8-
// CHECK: #mma2 = #ttig.dpas<{repeatCount = 8, systolicDepth = 8, executionSize = 16, opsPerChan = 2, threadsPerWarp = 16, warpsPerCTA = [8, 4], repCluster = [4, 2], A = [32, 16], B = [16, 32], C = [32, 32]}>
6+
// CHECK: #[[$SUBGROUP_BLOCK_A:.+]] = #ttig.subgroup_2d_block<{warpsPerCTA = [8, 4], instrShape = [8, 16], numBlocks=2, order=[1, 0], kWidth=1, threadsPerWarp=16}>
7+
// CHECK: #[[$SUBGROUP_BLOCK_B:.+]] = #ttig.subgroup_2d_block<{warpsPerCTA = [8, 4], instrShape = [16, 16], numBlocks=2, order=[0, 1], kWidth=2, threadsPerWarp=16}>
8+
// CHECK: #[[$DPAS:.+]] = #ttig.dpas<{repeatCount = 8, systolicDepth = 8, executionSize = 16, opsPerChan = 2, threadsPerWarp = 16, warpsPerCTA = [8, 4], repCluster = [4, 2], A = [32, 16], B = [16, 32], C = [32, 32]}>
99
#mma = #ttig.dpas<{repeatCount = 8, systolicDepth = 8, executionSize = 16, opsPerChan = 2, threadsPerWarp = 16, warpsPerCTA = [8, 4], repCluster = [4, 2], A = [32, 16], B = [16, 32], C = [32, 32]}>
1010
module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 32 : i32, "ttg.threads-per-warp" = 16 : i32, ttig.min_sg_size = 16 : i32, ttig.support_dpas, ttig.support_sg_2d_block} {
1111
tt.func public @matmul_kernel_with_block_pointers(%arg0: !tt.ptr<f16>, %arg1: !tt.ptr<f16>, %arg2: !tt.ptr<f16>) {
@@ -30,30 +30,30 @@ module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 32 : i32, "ttg.th
3030
%7 = arith.remsi %0, %c64_i32 : i32
3131
%8 = arith.divsi %7, %4 : i32
3232
%9 = arith.muli %6, %c256_i32 : i32
33-
// CHECK: %[[MAKE_TENSOR_PTR_A:.*]] = tt.make_tensor_ptr {{.*}} : <tensor<256x32xf16, #mma>>
33+
// CHECK: %[[MAKE_TENSOR_PTR_A:.*]] = tt.make_tensor_ptr {{.*}} : <tensor<256x32xf16, #[[$SUBGROUP_BLOCK_A]]>>
3434
%10 = tt.make_tensor_ptr %arg0, [%c1024_i64, %c5120_i64], [%c5120_i64, %c1_i64], [%9, %c0_i32] {order = array<i32: 1, 0>} : <tensor<256x32xf16, #blocked1>>
3535
%11 = arith.muli %8, %c256_i32 : i32
36-
// CHECK: %[[MAKE_TENSOR_PTR_B:.*]] = tt.make_tensor_ptr {{.*}} : <tensor<32x256xf16, #mma1>>
36+
// CHECK: %[[MAKE_TENSOR_PTR_B:.*]] = tt.make_tensor_ptr {{.*}} : <tensor<32x256xf16, #[[$SUBGROUP_BLOCK_B]]>>
3737
%12 = tt.make_tensor_ptr %arg1, [%c5120_i64, %c4096_i64], [%c4096_i64, %c1_i64], [%c0_i32, %11] {order = array<i32: 1, 0>} : <tensor<32x256xf16, #blocked2>>
3838
// CHECK: scf.for {{.*}} iter_args({{.*}} = {{.*}}, %[[ARG5:.*]] = %[[MAKE_TENSOR_PTR_A]], %[[ARG6:.*]] = %[[MAKE_TENSOR_PTR_B]])
3939
%13:3 = scf.for %arg3 = %c0_i32 to %c5120_i32 step %c32_i32 iter_args(%arg4 = %cst, %arg5 = %10, %arg6 = %12) -> (tensor<256x256xf32, #blocked>, !tt.ptr<tensor<256x32xf16, #blocked1>>, !tt.ptr<tensor<32x256xf16, #blocked2>>) : i32 {
4040
%17 = tt.load %arg5 {boundaryCheck = array<i32: 0, 1>, ttig.block_io = "row_major"} : !tt.ptr<tensor<256x32xf16, #blocked1>>
41-
// CHECK: %[[A_LOAD:.*]] = tt.load %[[ARG5]] {boundaryCheck = array<i32: 0, 1>, ttig.block_io = "row_major"} : !tt.ptr<tensor<256x32xf16, #mma>>
42-
// CHECK: {{.*}} = ttg.convert_layout %[[A_LOAD]] : tensor<256x32xf16, #mma> -> tensor<256x32xf16, #blocked1>
41+
// CHECK: %[[A_LOAD:.*]] = tt.load %[[ARG5]] {boundaryCheck = array<i32: 0, 1>, ttig.block_io = "row_major"} : !tt.ptr<tensor<256x32xf16, #[[$SUBGROUP_BLOCK_A]]>>
42+
// CHECK: {{.*}} = ttg.convert_layout %[[A_LOAD]] : tensor<256x32xf16, #[[$SUBGROUP_BLOCK_A]]> -> tensor<256x32xf16, #blocked1>
4343
%18 = tt.load %arg6 {boundaryCheck = array<i32: 0, 1>, ttig.block_io = "row_major"} : !tt.ptr<tensor<32x256xf16, #blocked2>>
44-
// CHECK: %[[B_LOAD:.*]] = tt.load %[[ARG6]] {boundaryCheck = array<i32: 0, 1>, ttig.block_io = "row_major"} : !tt.ptr<tensor<32x256xf16, #mma1>>
45-
// CHECK: {{.*}} = ttg.convert_layout %[[B_LOAD]] : tensor<32x256xf16, #mma1> -> tensor<32x256xf16, #blocked2>
44+
// CHECK: %[[B_LOAD:.*]] = tt.load %[[ARG6]] {boundaryCheck = array<i32: 0, 1>, ttig.block_io = "row_major"} : !tt.ptr<tensor<32x256xf16, #[[$SUBGROUP_BLOCK_B]]>>
45+
// CHECK: {{.*}} = ttg.convert_layout %[[B_LOAD]] : tensor<32x256xf16, #[[$SUBGROUP_BLOCK_B]]> -> tensor<32x256xf16, #blocked2>
4646
%19 = ttg.convert_layout %17 : tensor<256x32xf16, #blocked1> -> tensor<256x32xf16, #ttg.dot_op<{opIdx = 0, parent = #blocked}>>
4747
%20 = ttg.convert_layout %18 : tensor<32x256xf16, #blocked2> -> tensor<32x256xf16, #ttg.dot_op<{opIdx = 1, parent = #blocked}>>
4848
%21 = ttg.convert_layout %arg4 : tensor<256x256xf32, #blocked> -> tensor<256x256xf32, #mma>
4949
%22 = ttg.convert_layout %19 : tensor<256x32xf16, #ttg.dot_op<{opIdx = 0, parent = #blocked}>> -> tensor<256x32xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 1}>>
5050
%23 = ttg.convert_layout %20 : tensor<32x256xf16, #ttg.dot_op<{opIdx = 1, parent = #blocked}>> -> tensor<32x256xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 2}>>
51-
// CHECK: tt.dot {{.*}} : tensor<256x32xf16, #ttg.dot_op<{opIdx = 0, parent = #mma2, kWidth = 1}>> * tensor<32x256xf16, #ttg.dot_op<{opIdx = 1, parent = #mma2, kWidth = 2}>> -> tensor<256x256xf32, #mma2>
51+
// CHECK: tt.dot {{.*}} : tensor<256x32xf16, #ttg.dot_op<{opIdx = 0, parent = #[[$DPAS]], kWidth = 1}>> * tensor<32x256xf16, #ttg.dot_op<{opIdx = 1, parent = #[[$DPAS]], kWidth = 2}>> -> tensor<256x256xf32, #[[$DPAS]]>
5252
%24 = tt.dot %22, %23, %21, inputPrecision = tf32 : tensor<256x32xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 1}>> * tensor<32x256xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 2}>> -> tensor<256x256xf32, #mma>
5353
%25 = ttg.convert_layout %24 : tensor<256x256xf32, #mma> -> tensor<256x256xf32, #blocked>
54-
// CHECK: %[[ADVANCE_A:.*]] = tt.advance {{.*}} : <tensor<256x32xf16, #mma>>
54+
// CHECK: %[[ADVANCE_A:.*]] = tt.advance {{.*}} : <tensor<256x32xf16, #[[$SUBGROUP_BLOCK_A]]>>
5555
%26 = tt.advance %arg5, [%c0_i32, %c32_i32] : <tensor<256x32xf16, #blocked1>>
56-
// CHECK: %[[ADVANCE_B:.*]] = tt.advance {{.*}} : <tensor<32x256xf16, #mma1>>
56+
// CHECK: %[[ADVANCE_B:.*]] = tt.advance {{.*}} : <tensor<32x256xf16, #[[$SUBGROUP_BLOCK_B]]>>
5757
%27 = tt.advance %arg6, [%c32_i32, %c0_i32] : <tensor<32x256xf16, #blocked2>>
5858
// CHECK: scf.yield {{.*}}, %[[ADVANCE_A]], %[[ADVANCE_B]]
5959
scf.yield %25, %26, %27 : tensor<256x256xf32, #blocked>, !tt.ptr<tensor<256x32xf16, #blocked1>>, !tt.ptr<tensor<32x256xf16, #blocked2>>

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