[BACKEND] Keren/shared memory barrier (#59)
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Keren Zhou
GitHub
178
test/Analysis/test-membar.mlir
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178
test/Analysis/test-membar.mlir
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// RUN: triton-opt %s -split-input-file --mlir-disable-threading -test-print-membar 2>&1 | FileCheck %s
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#AL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
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#BL = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [1, 32], warpsPerCTA = [4, 1], order = [1, 0]}>
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#A = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
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#B = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
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#C = #triton_gpu.mma<{version = 2, warpsPerCTA = [4, 1]}>
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// CHECK-LABEL: matmul_loop
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func @matmul_loop(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>) {
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%a_ptr_init = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
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%b_ptr_init = tt.broadcast %B : (!tt.ptr<f16>) -> tensor<32x128x!tt.ptr<f16>, #BL>
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%a_mask = arith.constant dense<true> : tensor<128x32xi1, #AL>
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%a_other = arith.constant dense<0.00e+00> : tensor<128x32xf16, #AL>
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%b_mask = arith.constant dense<true> : tensor<32x128xi1, #BL>
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%b_other = arith.constant dense<0.00e+00> : tensor<32x128xf16, #BL>
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%c_init = arith.constant dense<0.00e+00> : tensor<128x128xf32, #C>
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%a_off = arith.constant dense<4> : tensor<128x32xi32, #AL>
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%b_off = arith.constant dense<4> : tensor<32x128xi32, #BL>
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scf.for %iv = %lb to %ub step %step iter_args(%a_ptr = %a_ptr_init, %b_ptr = %b_ptr_init, %prev_c = %c_init) -> (tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>) {
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%a_ = tt.load %a_ptr, %a_mask, %a_other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
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%a = triton_gpu.convert_layout %a_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
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%b_ = tt.load %b_ptr, %b_mask, %b_other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<32x128xf16, #BL>
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%b = triton_gpu.convert_layout %b_ : (tensor<32x128xf16, #BL>) -> tensor<32x128xf16, #B>
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// CHECK: Membar 13
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%c = tt.dot %a, %b, %prev_c {allowTF32 = true} : tensor<128x32xf16, #A> * tensor<32x128xf16, #B> -> tensor<128x128xf32, #C>
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%next_a_ptr = tt.getelementptr %a_ptr, %a_off : tensor<128x32x!tt.ptr<f16>, #AL>
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%next_b_ptr = tt.getelementptr %b_ptr, %b_off : tensor<32x128x!tt.ptr<f16>, #BL>
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scf.yield %next_a_ptr, %next_b_ptr, %c : tensor<128x32x!tt.ptr<f16>, #AL>, tensor<32x128x!tt.ptr<f16>, #BL>, tensor<128x128xf32, #C>
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}
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return
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}
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// CHECK-LABEL: raw_single_block
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func @raw_single_block(%A : !tt.ptr<f16>) {
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%cst1 = arith.constant dense<true> : tensor<128x32xi1, #AL>
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%cst2 = arith.constant dense<0.000000e+00> : tensor<128x32xf16, #AL>
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%a_ptr = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
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%a1_ = tt.load %a_ptr, %cst1, %cst2 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
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%a1 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
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// CHECK: Membar 5
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%a2 = triton_gpu.convert_layout %a1 : (tensor<128x32xf16, #A>) -> tensor<128x32xf16, #A>
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return
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}
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// CHECK-LABEL: war_single_block
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func @war_single_block(%A : !tt.ptr<f16>) {
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%cst1 = arith.constant dense<true> : tensor<128x32xi1, #AL>
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%cst2 = arith.constant dense<0.000000e+00> : tensor<128x32xf16, #AL>
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%a_ptr = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<128x32x!tt.ptr<f16>, #AL>
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%a1_ = tt.load %a_ptr, %cst1, %cst2 {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<128x32xf16, #AL>
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%a1 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
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// CHECK: Membar 5
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%a2 = triton_gpu.convert_layout %a1 : (tensor<128x32xf16, #A>) -> tensor<128x32xf16, #AL>
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// CHECK-NEXT: Membar 7
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%a3 = triton_gpu.convert_layout %a1_ : (tensor<128x32xf16, #AL>) -> tensor<128x32xf16, #A>
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return
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}
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// CHECK-LABEL: scratch
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func @scratch() {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK: Membar 1
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%a = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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// CHECK-NEXT: Membar 3
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%b = tt.reduce %a {redOp = 1 : i32, axis = 0 : i32} : tensor<32x16xf16, #A> -> tensor<16xf16, #A>
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return
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}
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// CHECK-LABEL: async_wait
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func @async_wait() {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK: Membar 1
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%a = tt.cat %cst0, %cst0 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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triton_gpu.async_wait {num = 4 : i32}
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// CHECK-NEXT: Membar 4
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%a_ = triton_gpu.convert_layout %a : (tensor<32x16xf16, #A>) -> tensor<32x16xf16, #AL>
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return
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}
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// If branch inserted a barrier for %cst0 and %cst1, but else didn't, then the barrier should be inserted in the parent region
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// CHECK-LABEL: multi_blocks
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func @multi_blocks(%i1 : i1) {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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scf.if %i1 {
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// CHECK: Membar 2
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%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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} else {
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%cst2 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%cst3 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK-NEXT: Membar 7
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%b = tt.cat %cst2, %cst3 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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}
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// CHECK-NEXT: Membar 10
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%c = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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return
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}
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// Both branches inserted a barrier for %cst0 and %cst1, then the barrier doesn't need to be inserted in the parent region
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// CHECK-LABEL: multi_blocks_join_barrier
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func @multi_blocks_join_barrier(%i1 : i1) {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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scf.if %i1 {
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// CHECK: Membar 2
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%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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} else {
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// CHECK-NEXT: Membar 5
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%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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}
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%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
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return
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}
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// Read yielded tensor requires a barrier
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// CHECK-LABEL: multi_blocks_yield
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func @multi_blocks_yield(%i1 : i1) {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%a = scf.if %i1 -> (tensor<32x16xf16, #A>) {
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// CHECK: Membar 2
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%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield %a : tensor<32x16xf16, #A>
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} else {
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// CHECK-NEXT: Membar 5
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%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield %b : tensor<32x16xf16, #A>
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}
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%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
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// CHECK-NEXT: Membar 9
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%b = tt.cat %a, %a {axis = 0} : (tensor<32x16xf16, #A>, tensor<32x16xf16, #A>) -> tensor<64x16xf16, #A>
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return
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}
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// Conservatively add a barrier as if the branch (%i1) is never taken
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// CHECK-LABEL: multi_blocks_noelse
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func @multi_blocks_noelse(%i1 : i1) {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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scf.if %i1 {
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// CHECK: Membar 2
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%a = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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}
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%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
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return
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}
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// Conservatively add a barrier as if the branch (%i2) is never taken
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// CHECK-LABEL: multi_blocks_nested_scf
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func @multi_blocks_nested_scf(%i1 : i1, %i2 : i1) {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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scf.if %i1 {
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scf.if %i2 {
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// CHECK: Membar 2
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%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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}
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scf.yield
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} else {
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// CHECK-NEXT: Membar 6
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%b = tt.cat %cst0, %cst1 {axis = 0} : (tensor<16x16xf16, #A>, tensor<16x16xf16, #A>) -> tensor<32x16xf16, #A>
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scf.yield
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}
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// CHECK-NEXT: Membar 9
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%a_ = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #A>) -> tensor<16x16xf16, #AL>
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return
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}
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