[AMD] Disable pingpong with non-local load input. (#5718)

Pingpong pass only expects to handle local load ops as A/B.
Avoid using the transform when different op is detected.
Enabling it is the next step.

Author: jungpark-mlir

test/TritonGPU/amd/amd-block-pingpong.mlir

@@ -128,7 +128,6 @@ module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 4 : i32, ttg.targ
 
 #blocked = #ttg.blocked<{sizePerThread = [1, 8], threadsPerWarp = [8, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
 #blocked1 = #ttg.blocked<{sizePerThread = [8, 1], threadsPerWarp = [8, 8], warpsPerCTA = [1, 8], order = [0, 1]}>
-#loc = loc("/home/jung/rocm/triton/python/perf-kernels/tools/tune_gemm/matmul_kernel.py":6:0)
 #mma = #ttg.amd_mfma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [2, 4], instrShape = [16, 16], isTransposed = true}>#shared = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [1, 0], hasLeadingOffset = false}>
 #shared1 = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [0, 1], hasLeadingOffset = false}>
 module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.target = "hip:gfx942", "ttg.threads-per-warp" = 64 : i32} {
@@ -227,7 +226,6 @@ module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.targ
 
 #blocked = #ttg.blocked<{sizePerThread = [1, 8], threadsPerWarp = [8, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
 #blocked1 = #ttg.blocked<{sizePerThread = [8, 1], threadsPerWarp = [8, 8], warpsPerCTA = [1, 8], order = [0, 1]}>
-#loc = loc("/home/jung/rocm/triton/python/perf-kernels/tools/tune_gemm/matmul_kernel.py":6:0)
 #mma = #ttg.amd_mfma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [2, 4], instrShape = [16, 16], isTransposed = true}>#shared = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [1, 0], hasLeadingOffset = false}>
 #shared1 = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [0, 1], hasLeadingOffset = false}>
 module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.target = "hip:gfx942", "ttg.threads-per-warp" = 64 : i32} {
@@ -288,6 +286,77 @@ module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.targ
 
 // -----
 
+// CHECK-LABEL: pingpong_medium_cast
+// CHECK-COUNT-2: local_load
+// CHECK-NOT: setprio
+// CHECK-NOT: barrier
+
+#blocked = #ttg.blocked<{sizePerThread = [1, 8], threadsPerWarp = [8, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
+#blocked1 = #ttg.blocked<{sizePerThread = [8, 1], threadsPerWarp = [8, 8], warpsPerCTA = [1, 8], order = [0, 1]}>
+#mma = #ttg.amd_mfma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [2, 4], instrShape = [16, 16], isTransposed = true}>#shared = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [1, 0], hasLeadingOffset = false}>
+#shared1 = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [0, 1], hasLeadingOffset = false}>
+module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.target = "hip:gfx942", "ttg.threads-per-warp" = 64 : i32} {
+  tt.func public @pingpong_medium_cast(%arg0: !tt.ptr<f16> {tt.divisibility = 16 : i32, tt.pointer_range = 32 : i32}, %arg1: !tt.ptr<f16> {tt.divisibility = 16 : i32, tt.pointer_range = 32 : i32}, %arg2: !tt.ptr<f16> {tt.divisibility = 16 : i32, tt.pointer_range = 32 : i32}, %arg3: i32 {tt.divisibility = 16 : i32}, %arg4: i32 {tt.divisibility = 16 : i32}, %arg5: i32 {tt.divisibility = 16 : i32}, %arg6: i32 {tt.divisibility = 16 : i32}, %arg7: i32 {tt.divisibility = 16 : i32}, %arg8: i32 {tt.divisibility = 16 : i32}, %arg9: i32 {tt.divisibility = 16 : i32}) attributes {noinline = false} {
+    %cst = arith.constant dense<0.000000e+00> : tensor<256x128xf32, #mma>
+    %c1_i32 = arith.constant 1 : i32
+    %cst_0 = arith.constant dense<64> : tensor<64x128xi32, #blocked>
+    %cst_1 = arith.constant dense<64> : tensor<256x64xi32, #blocked1>
+    %c0_i32 = arith.constant 0 : i32
+    %c64_i32 = arith.constant 64 : i32
+    %0 = tt.splat %arg0 : !tt.ptr<f16> -> tensor<256x1x!tt.ptr<f16>, #blocked1>
+    %1 = tt.get_program_id x : i32
+    %2 = tt.splat %1 : i32 -> tensor<256xi32, #ttg.slice<{dim = 1, parent = #blocked1}>>
+    %3 = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32, #ttg.slice<{dim = 1, parent = #blocked1}>>
+    %4 = arith.addi %2, %3 : tensor<256xi32, #ttg.slice<{dim = 1, parent = #blocked1}>>
+    %5 = tt.expand_dims %4 {axis = 1 : i32} : tensor<256xi32, #ttg.slice<{dim = 1, parent = #blocked1}>> -> tensor<256x1xi32, #blocked1>
+    %6 = tt.splat %arg6 : i32 -> tensor<256x1xi32, #blocked1>
+    %7 = arith.muli %5, %6 : tensor<256x1xi32, #blocked1>
+    %8 = tt.addptr %0, %7 : tensor<256x1x!tt.ptr<f16>, #blocked1>, tensor<256x1xi32, #blocked1>
+    %9 = tt.broadcast %8 : tensor<256x1x!tt.ptr<f16>, #blocked1> -> tensor<256x64x!tt.ptr<f16>, #blocked1>
+    %10 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #ttg.slice<{dim = 0, parent = #blocked1}>>
+    %11 = tt.expand_dims %10 {axis = 0 : i32} : tensor<64xi32, #ttg.slice<{dim = 0, parent = #blocked1}>> -> tensor<1x64xi32, #blocked1>
+    %12 = tt.broadcast %11 : tensor<1x64xi32, #blocked1> -> tensor<256x64xi32, #blocked1>
+    %13 = tt.addptr %9, %12 : tensor<256x64x!tt.ptr<f16>, #blocked1>, tensor<256x64xi32, #blocked1>
+    %14 = tt.splat %arg1 : !tt.ptr<f16> -> tensor<64x1x!tt.ptr<f16>, #blocked>
+    %15 = tt.make_range {end = 64 : i32, start = 0 : i32} : tensor<64xi32, #ttg.slice<{dim = 1, parent = #blocked}>>
+    %16 = tt.expand_dims %15 {axis = 1 : i32} : tensor<64xi32, #ttg.slice<{dim = 1, parent = #blocked}>> -> tensor<64x1xi32, #blocked>
+    %17 = tt.addptr %14, %16 : tensor<64x1x!tt.ptr<f16>, #blocked>, tensor<64x1xi32, #blocked>
+    %18 = tt.broadcast %17 : tensor<64x1x!tt.ptr<f16>, #blocked> -> tensor<64x128x!tt.ptr<f16>, #blocked>
+    %19 = tt.splat %arg7 : i32 -> tensor<64x128xi32, #blocked>
+    %20 = tt.addptr %18, %19 : tensor<64x128x!tt.ptr<f16>, #blocked>, tensor<64x128xi32, #blocked>
+    %21 = ttg.local_alloc  : () -> !ttg.memdesc<1x256x64xf16, #shared, #ttg.shared_memory, mutable>
+    %22 = ttg.local_alloc  : () -> !ttg.memdesc<1x64x128xi16, #shared1, #ttg.shared_memory, mutable>
+    %23 = ttg.memdesc_subview %21[%c0_i32, %c0_i32, %c0_i32] : !ttg.memdesc<1x256x64xf16, #shared, #ttg.shared_memory, mutable> -> !ttg.memdesc<256x64xf16, #shared, #ttg.shared_memory, mutable>
+    %24 = ttg.memdesc_subview %22[%c0_i32, %c0_i32, %c0_i32] : !ttg.memdesc<1x64x128xi16, #shared1, #ttg.shared_memory, mutable> -> !ttg.memdesc<64x128xi16, #shared1, #ttg.shared_memory, mutable>
+    %25:6 = scf.for %arg10 = %c0_i32 to %c64_i32 step %c1_i32 iter_args(%arg11 = %cst, %arg12 = %13, %arg13 = %20, %arg14 = %c0_i32, %arg15 = %23, %arg16 = %24) -> (tensor<256x128xf32, #mma>, tensor<256x64x!tt.ptr<f16>, #blocked1>, tensor<64x128x!tt.ptr<f16>, #blocked>, i32, !ttg.memdesc<256x64xf16, #shared, #ttg.shared_memory, mutable>, !ttg.memdesc<64x128xi16, #shared1, #ttg.shared_memory, mutable>)  : i32 {
+      %26 = tt.addptr %arg12, %cst_1 : tensor<256x64x!tt.ptr<f16>, #blocked1>, tensor<256x64xi32, #blocked1>
+      %27 = tt.load %26 : tensor<256x64x!tt.ptr<f16>, #blocked1>
+      %28 = tt.addptr %arg13, %cst_0 : tensor<64x128x!tt.ptr<f16>, #blocked>, tensor<64x128xi32, #blocked>
+      %29 = tt.load %28 : tensor<64x128x!tt.ptr<f16>, #blocked>
+      %cast2 = tt.bitcast %29 : tensor<64x128xf16, #blocked> -> tensor<64x128xi16, #blocked>
+      %30 = ttg.local_load %arg15 : !ttg.memdesc<256x64xf16, #shared, #ttg.shared_memory, mutable> -> tensor<256x64xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>>
+      %31 = ttg.local_load %arg16 : !ttg.memdesc<64x128xi16, #shared1, #ttg.shared_memory, mutable> -> tensor<64x128xi16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>>
+      %cast = tt.bitcast %31 : tensor<64x128xi16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>> ->  tensor<64x128xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>>
+      %32 = tt.dot %30, %cast, %arg11 : tensor<256x64xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>> * tensor<64x128xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>> -> tensor<256x128xf32, #mma>
+      %33 = arith.addi %arg14, %c1_i32 : i32
+      %34 = arith.cmpi slt, %33, %c1_i32 : i32
+      %35 = arith.select %34, %33, %c0_i32 : i32
+      %36 = ttg.memdesc_subview %21[%35, %c0_i32, %c0_i32] : !ttg.memdesc<1x256x64xf16, #shared, #ttg.shared_memory, mutable> -> !ttg.memdesc<256x64xf16, #shared, #ttg.shared_memory, mutable>
+      ttg.local_store %27, %36 : tensor<256x64xf16, #blocked1> -> !ttg.memdesc<256x64xf16, #shared, #ttg.shared_memory, mutable>
+      %37 = ttg.memdesc_subview %22[%35, %c0_i32, %c0_i32] : !ttg.memdesc<1x64x128xi16, #shared1, #ttg.shared_memory, mutable> -> !ttg.memdesc<64x128xi16, #shared1, #ttg.shared_memory, mutable>
+      ttg.local_store %cast2, %37 : tensor<64x128xi16, #blocked> -> !ttg.memdesc<64x128xi16, #shared1, #ttg.shared_memory, mutable>
+      scf.yield %32, %26, %28, %35, %36, %37 : tensor<256x128xf32, #mma>, tensor<256x64x!tt.ptr<f16>, #blocked1>, tensor<64x128x!tt.ptr<f16>, #blocked>, i32, !ttg.memdesc<256x64xf16, #shared, #ttg.shared_memory, mutable>, !ttg.memdesc<64x128xi16, #shared1, #ttg.shared_memory, mutable>
+    }
+    ttg.local_dealloc %21 : !ttg.memdesc<1x256x64xf16, #shared, #ttg.shared_memory, mutable>
+    ttg.local_dealloc %22 : !ttg.memdesc<1x64x128xi16, #shared1, #ttg.shared_memory, mutable>
+    tt.return
+  }
+}
+
+
+// -----
+
+
 // CHECK-LABEL: pingpong_reject
 // CHECK-COUNT-2: local_load
 // CHECK-NOT: local_load
@@ -296,7 +365,6 @@ module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.targ
 
 #blocked = #ttg.blocked<{sizePerThread = [1, 8], threadsPerWarp = [8, 8], warpsPerCTA = [8, 1], order = [1, 0]}>
 #blocked1 = #ttg.blocked<{sizePerThread = [8, 1], threadsPerWarp = [8, 8], warpsPerCTA = [1, 8], order = [0, 1]}>
-#loc = loc("/home/jung/rocm/triton/python/perf-kernels/tools/tune_gemm/matmul_kernel.py":6:0)
 #mma = #ttg.amd_mfma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [2, 4], instrShape = [16, 16], isTransposed = true}>#shared = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [1, 0], hasLeadingOffset = false}>
 #shared1 = #ttg.shared<{vec = 4, perPhase = 1, maxPhase = 16, order = [0, 1], hasLeadingOffset = false}>
 module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 8 : i32, ttg.target = "hip:gfx942", "ttg.threads-per-warp" = 64 : i32} {

third_party/amd/lib/TritonAMDGPUTransforms/BlockPingpong.cpp

@@ -151,7 +151,11 @@ LogicalResult Pingponger::genLocalSlice(OpBuilder &builder, Value v,
                                         int64_t sliceWidth) {
   SmallVector<Operation *> slices;
   SmallVector<Operation *> subviews;
-  auto memDesc = v.getDefiningOp()->getOperand(0);
+  // TODO: support transformed input to dot
+  auto localLoad = v.getDefiningOp<ttg::LocalLoadOp>();
+  if (!localLoad)
+    return failure();
+  auto memDesc = localLoad.getSrc();
   auto type = cast<ttg::MemDescType>(memDesc.getType());
   SmallVector<int64_t> shape = llvm::to_vector(type.getShape());
   Type elementType = type.getElementType();