02ebf24d35
The purpose of this PR is analyzing shared memory aliases so that we can fix memory allocation bugs and save memory allocations in triton code involving complex control flows. Changes to memory bar and allocation are on the way. Co-authored-by: Philippe Tillet <phil@openai.com>
217 lines
11 KiB
MLIR
217 lines
11 KiB
MLIR
// RUN: triton-opt %s --mlir-disable-threading -test-print-alias -split-input-file 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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// CHECK: %4 -> %4
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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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// CHECK-NEXT: %6 -> %6
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%b = triton_gpu.convert_layout %b_ : (tensor<32x128xf16, #BL>) -> tensor<32x128xf16, #B>
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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: alloc
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func @alloc(%A : !tt.ptr<f16>) {
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// CHECK: %cst -> %cst
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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, #AL>
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return
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}
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// CHECK-LABEL: convert
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func @convert(%A : !tt.ptr<f16>) {
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #AL>
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// CHECK: %0 -> %0
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%cst1 = triton_gpu.convert_layout %cst0 : (tensor<16x16xf16, #AL>) -> tensor<16x16xf16, #A>
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return
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}
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// CHECK-LABEL: copy_async
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func @copy_async(%A : !tt.ptr<f16>, %i1 : i1) {
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%a_ptr = tt.broadcast %A : (!tt.ptr<f16>) -> tensor<16x16x!tt.ptr<f16>, #AL>
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%mask = tt.splat %i1 : (i1) -> tensor<16x16xi1, #AL>
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%other = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #AL>
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// CHECK: %2 -> %2
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%a = triton_gpu.copy_async %a_ptr, %mask, %other {cache = 1 : i32, evict = 1 : i32, isOtherUnspecified = false, isVolatile = false} : tensor<16x16x!tt.ptr<f16>, #AL> -> tensor<16x16xf16, #A>
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return
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}
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// COM: Enable the following test once we support view on shared memory tensors
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// COM: // CHECK-LABEL: view
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// COM: func @view(%A : !tt.ptr<f16>) {
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// COM: // CHECK: res0:0 -> 0
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// COM: %cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// COM: // CHECK-NEXT: res1:0 -> 0
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// COM: %cst1 = tt.view %cst0 : (tensor<16x16xf16, #A>) -> tensor<32x8xf16, #A>
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// COM: return
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// COM: }
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// CHECK-LABEL: if_cat
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func @if_cat(%i1 : i1) {
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// CHECK: %cst -> %cst
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK: %cst_0 -> %cst_0
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK: %0 -> %1,%1
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%cst2 = scf.if %i1 -> tensor<32x16xf16, #A> {
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// CHECK: %1 -> %1
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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: %1 -> %1
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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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return
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}
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// CHECK-LABEL: if_alias
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func @if_alias(%i1 : i1) {
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// CHECK: %cst -> %cst
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%cst0 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK-NEXT: %cst_0 -> %cst_0
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%cst1 = arith.constant dense<0.000000e+00> : tensor<16x16xf16, #A>
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// CHECK-NEXT: %0 -> %cst,%cst_0
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%cst2 = scf.if %i1 -> tensor<16x16xf16, #A> {
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scf.yield %cst0 : tensor<16x16xf16, #A>
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} else {
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scf.yield %cst1 : tensor<16x16xf16, #A>
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}
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return
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}
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// CHECK-LABEL: for
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func @for(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>) {
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// CHECK: %cst -> %cst
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%a_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// CHECK-NEXT: %cst_0 -> %cst_0
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%b_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// CHECK-NEXT: %cst_1 -> %cst_1
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%c_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// CHECK-NEXT: %arg6 -> %cst
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// CHECK-NEXT: %arg7 -> %cst_0
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// CHECK-NEXT: %arg8 -> %cst_1
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// CHECK-NEXT: %0#0 -> %cst,%cst_0
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// CHECK-NEXT: %0#1 -> %cst,%cst_0
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// CHECK-NEXT: %0#2 -> %cst,%cst_0
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%a_shared, %b_shared, %c_shared = scf.for %iv = %lb to %ub step %step iter_args(%a_shared = %a_shared_init, %b_shared = %b_shared_init, %c_shared = %c_shared_init) -> (tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>) {
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scf.yield %b_shared, %a_shared, %a_shared : tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>
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}
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return
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}
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// COM: // Enable the following test once we support view on shared memory tensors
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// COM: // CHECK-LABEL: for_if
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// COM: func @for_if(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>, %i1 : i1) {
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// COM: // CHECK: res0:0 -> 0
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// COM: %a_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: // CHECK-NEXT: res1:0 -> 1
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// COM: %b_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: // CHECK-NEXT: res2:0 -> 2
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// COM: %c_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: // CHECK-NEXT: arg3:0 -> 0
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// COM: // CHECK-NEXT: arg3:1 -> 1
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// COM: // CHECK-NEXT: arg3:2 -> 2
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// COM: // CHECK-NEXT: res3:0 -> 0,1
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// COM: // CHECK-NEXT: res3:1 -> 0,1
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// COM: // CHECK-NEXT: res3:2 -> 0,1
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// COM: %a_shared, %b_shared, %c_shared = scf.for %iv = %lb to %ub step %step iter_args(%a_shared = %a_shared_init, %b_shared = %b_shared_init, %c_shared = %c_shared_init) -> (tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>) {
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// COM: scf.if %i1 {
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// COM: // CHECK-NEXT: res5:0 -> 0,1
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// COM: %cst0 = tt.view %a_shared : (tensor<128x32xf16, #A>) -> tensor<32x128xf16, #A>
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// COM: scf.yield
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// COM: }
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// COM: scf.yield %b_shared, %a_shared, %a_shared : tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>
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// COM: }
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// COM: return
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// COM: }
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// COM: // Enable the following test once we support view on shared memory tensors
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// COM: // CHECK-LABEL: for_if_else
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// COM: func @for_if_else(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>, %i1 : i1) {
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// COM: // CHECK: res0:0 -> 0
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// COM: %a_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: // CHECK-NEXT: res1:0 -> 1
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// COM: %b_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: // CHECK-NEXT: res2:0 -> 2
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// COM: %c_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: // CHECK-NEXT: arg3:0 -> 0
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// COM: // CHECK-NEXT: arg3:1 -> 1
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// COM: // CHECK-NEXT: arg3:2 -> 2
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// COM: // CHECK-NEXT: res3:0 -> 0
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// COM: // CHECK-NEXT: res3:1 -> 1
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// COM: // CHECK-NEXT: res3:2 -> 0,7
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// COM: %a_shared, %b_shared, %c_shared = scf.for %iv = %lb to %ub step %step iter_args(%a_shared = %a_shared_init, %b_shared = %b_shared_init, %c_shared = %c_shared_init) -> (tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>) {
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// COM: // CHECK-NEXT: res4:0 -> 0,7
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// COM: %c_shared_next = scf.if %i1 -> tensor<128x32xf16, #A> {
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// COM: // CHECK-NEXT: res5:0 -> 0
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// COM: %cst0 = tt.view %a_shared : (tensor<128x32xf16, #A>) -> tensor<128x32xf16, #A>
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// COM: scf.yield %cst0 : tensor<128x32xf16, #A>
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// COM: } else {
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// COM: // CHECK-NEXT: res7:0 -> 7
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// COM: %cst0 = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// COM: scf.yield %cst0 : tensor<128x32xf16, #A>
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// COM: }
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// COM: scf.yield %a_shared, %b_shared, %c_shared_next : tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>
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// COM: }
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// COM: return
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// COM: }
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// CHECK-LABEL: for_if_for
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func @for_if_for(%lb : index, %ub : index, %step : index, %A : !tt.ptr<f16>, %B : !tt.ptr<f16>, %i1 : i1) {
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// CHECK: %cst -> %cst
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%a_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// CHECK-NEXT: %cst_0 -> %cst_0
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%b_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// CHECK-NEXT: %cst_1 -> %cst_1
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%c_shared_init = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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// CHECK-NEXT: %arg7 -> %cst
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// CHECK-NEXT: %arg8 -> %cst_0
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// CHECK-NEXT: %arg9 -> %cst_1
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// CHECK-NEXT: %0#0 -> %cst
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// CHECK-NEXT: %0#1 -> %cst_0
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// CHECK-NEXT: %0#2 -> %cst_2,%cst_2
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%a_shared, %b_shared, %c_shared = scf.for %iv = %lb to %ub step %step iter_args(%a_shared = %a_shared_init, %b_shared = %b_shared_init, %c_shared = %c_shared_init) -> (tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>) {
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// CHECK-NEXT: %arg11 -> %cst_1,%cst_2,%cst_2
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// CHECK-NEXT: %1 -> %cst_2,%cst_2
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%c_shared_next = scf.for %jv = %lb to %ub step %step iter_args(%c_shared_next = %c_shared) -> (tensor<128x32xf16, #A>) {
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// CHECK-NEXT: %2 -> %cst_2,%cst_2
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%c_shared_next_next = scf.if %i1 -> tensor<128x32xf16, #A> {
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// CHECK-NEXT: %cst_2 -> %cst_2
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%cst0 = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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scf.yield %cst0 : tensor<128x32xf16, #A>
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} else {
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// CHECK-NEXT: %cst_2 -> %cst_2
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%cst0 = arith.constant dense<0.00e+00> : tensor<128x32xf16, #A>
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scf.yield %cst0 : tensor<128x32xf16, #A>
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}
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scf.yield %c_shared_next_next : tensor<128x32xf16, #A>
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}
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scf.yield %a_shared, %b_shared, %c_shared_next : tensor<128x32xf16, #A>, tensor<128x32xf16, #A>, tensor<128x32xf16, #A>
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}
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return
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}
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