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bolt/deps/llvm-18.1.8/mlir/test/Dialect/SCF/one-shot-bufferize.mlir

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Embed LLVM 18.1.8
2025-02-14 19:21:04 +01:00
// RUN: mlir-opt %s -allow-unregistered-dialect -one-shot-bufferize="allow-return-allocs-from-loops bufferize-function-boundaries" -cse -canonicalize -drop-equivalent-buffer-results -split-input-file | FileCheck %s
// Run fuzzer with different seeds.
// RUN: mlir-opt %s -allow-unregistered-dialect -one-shot-bufferize="allow-return-allocs-from-loops test-analysis-only analysis-fuzzer-seed=23 bufferize-function-boundaries" -split-input-file -o /dev/null
// RUN: mlir-opt %s -allow-unregistered-dialect -one-shot-bufferize="allow-return-allocs-from-loops test-analysis-only analysis-fuzzer-seed=59 bufferize-function-boundaries" -split-input-file -o /dev/null
// RUN: mlir-opt %s -allow-unregistered-dialect -one-shot-bufferize="allow-return-allocs-from-loops test-analysis-only analysis-fuzzer-seed=91 bufferize-function-boundaries" -split-input-file -o /dev/null
// Test bufferization using memref types that have no layout map.
// RUN: mlir-opt %s -allow-unregistered-dialect -one-shot-bufferize="allow-return-allocs-from-loops unknown-type-conversion=identity-layout-map function-boundary-type-conversion=identity-layout-map bufferize-function-boundaries" -split-input-file -o /dev/null
// CHECK-LABEL: func @scf_for_yield_only(
// CHECK-SAME: %[[A:[a-zA-Z0-9]*]]: memref<?xf32, strided<[?], offset: ?>>,
// CHECK-SAME: %[[t:[a-zA-Z0-9]*]]: memref<?xf32, strided<[?], offset: ?>>
// CHECK-SAME: ) -> memref<?xf32> {
func.func @scf_for_yield_only(
%A : tensor<?xf32> {bufferization.writable = false},
%B : tensor<?xf32> {bufferization.writable = true},
%lb : index, %ub : index, %step : index)
-> (tensor<?xf32>, tensor<?xf32>)
{
// CHECK: %[[ALLOC_FOR_A:.*]] = memref.alloc
// CHECK: memref.copy %[[A]], %[[ALLOC_FOR_A]]
// The first scf.for remains but just turns into dead code.
%r0 = scf.for %i = %lb to %ub step %step iter_args(%t = %A) -> (tensor<?xf32>) {
scf.yield %t : tensor<?xf32>
}
// The second scf.for remains but just turns into dead code.
%r1 = scf.for %i = %lb to %ub step %step iter_args(%t = %B) -> (tensor<?xf32>) {
scf.yield %t : tensor<?xf32>
}
// CHECK: return %[[ALLOC_FOR_A]] : memref<?xf32>
// CHECK-NOT: dealloc
return %r0, %r1: tensor<?xf32>, tensor<?xf32>
}
// -----
// CHECK-LABEL: func @scf_for_is_reading(
// CHECK-SAME: %[[A:.*]]: memref<?xf32, strided<[?], offset: ?>>, %[[B:.*]]: memref<?xf32, strided<[?], offset: ?>>
func.func @scf_for_is_reading(%A : tensor<?xf32>, %B : tensor<?xf32>,
%lb : index, %ub : index)
-> (f32, f32)
{
%c1 = arith.constant 1 : index
%cst = arith.constant 0.0 : f32
// This is a regression test to make sure that an alloc + copy is emitted.
// CHECK: %[[alloc:.*]] = memref.alloc
// CHECK: memref.copy %[[A]], %[[alloc]]
// CHECK: scf.for {{.*}} iter_args(%{{.*}} = %[[alloc]])
%0 = scf.for %iv = %lb to %ub step %c1 iter_args(%1 = %A) -> tensor<?xf32> {
%r = linalg.fill ins(%cst : f32) outs(%1 : tensor<?xf32>) -> tensor<?xf32>
scf.yield %B : tensor<?xf32>
}
%1 = tensor.extract %0[%c1] : tensor<?xf32>
%2 = tensor.extract %A[%c1] : tensor<?xf32>
return %1, %2 : f32, f32
}
// -----
// Ensure that the function bufferizes without error. This tests pre-order
// traversal of scf.for loops during bufferization. No need to check the IR,
// just want to make sure that it does not crash.
// CHECK-LABEL: func @nested_scf_for
func.func @nested_scf_for(%A : tensor<?xf32> {bufferization.writable = true},
%v : vector<5xf32>) -> tensor<?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c10 = arith.constant 10 : index
%r1 = scf.for %i = %c0 to %c10 step %c1 iter_args(%B = %A) -> tensor<?xf32> {
%r2 = scf.for %j = %c0 to %c10 step %c1 iter_args(%C = %B) -> tensor<?xf32> {
%w = vector.transfer_write %v, %C[%c0] : vector<5xf32>, tensor<?xf32>
scf.yield %w : tensor<?xf32>
}
scf.yield %r2 : tensor<?xf32>
}
return %r1 : tensor<?xf32>
}
// -----
// CHECK-LABEL: func @scf_for_with_tensor.insert_slice
// CHECK-SAME: %[[A:[a-zA-Z0-9]*]]: memref<?xf32, strided<[?], offset: ?>>
// CHECK-SAME: %[[B:[a-zA-Z0-9]*]]: memref<?xf32, strided<[?], offset: ?>>
// CHECK-SAME: %[[C:[a-zA-Z0-9]*]]: memref<4xf32, strided<[?], offset: ?>>
func.func @scf_for_with_tensor.insert_slice(
%A : tensor<?xf32> {bufferization.writable = false},
%B : tensor<?xf32> {bufferization.writable = true},
%C : tensor<4xf32> {bufferization.writable = false},
%lb : index, %ub : index, %step : index)
-> (tensor<?xf32>, tensor<?xf32>)
{
// CHECK: %[[ALLOC_FOR_A:.*]] = memref.alloc
// CHECK: memref.copy %[[A]], %[[ALLOC_FOR_A]]
// CHECK: scf.for {{.*}}
// CHECK-NOT: iter_args
%r0:2 = scf.for %i = %lb to %ub step %step iter_args(%tA = %A, %tB = %B)
-> (tensor<?xf32>, tensor<?xf32>)
{
// %ttA bufferizes to direct copy of %BUFFER_CAST_C into %svA
// CHECK: %[[svA:.*]] = memref.subview %[[ALLOC_FOR_A]][0] [4] [1]
// CHECK: memref.copy %[[C]], %[[svA]]
%ttA = tensor.insert_slice %C into %tA[0][4][1] : tensor<4xf32> into tensor<?xf32>
// %ttB bufferizes to direct copy of %BUFFER_CAST_C into %BUFFER_CAST_B
// CHECK: %[[svB:.*]] = memref.subview %[[B]][0] [4] [1]
// CHECK: memref.copy %[[C]], %[[svB]]
%ttB = tensor.insert_slice %C into %tB[0][4][1] : tensor<4xf32> into tensor<?xf32>
// CHECK-NOT: scf.yield
scf.yield %ttA, %ttB : tensor<?xf32>, tensor<?xf32>
}
// CHECK: return %[[ALLOC_FOR_A]] : memref<?xf32>
return %r0#0, %r0#1: tensor<?xf32>, tensor<?xf32>
}
// -----
// CHECK-LABEL: func @execute_region_with_conflict(
// CHECK-SAME: %[[m1:.*]]: memref<?xf32
func.func @execute_region_with_conflict(
%t1 : tensor<?xf32> {bufferization.writable = true})
-> (f32, tensor<?xf32>, f32)
{
%f1 = arith.constant 0.0 : f32
%idx = arith.constant 7 : index
// scf.execute_region is canonicalized away after bufferization. So just the
// memref.store is left over.
// CHECK: %[[alloc:.*]] = memref.alloc
// CHECK: memref.copy %[[m1]], %[[alloc]]
// CHECK: memref.store %{{.*}}, %[[alloc]][%{{.*}}]
%0, %1, %2 = scf.execute_region -> (f32, tensor<?xf32>, f32) {
%t2 = tensor.insert %f1 into %t1[%idx] : tensor<?xf32>
scf.yield %f1, %t2, %f1 : f32, tensor<?xf32>, f32
}
// CHECK: %[[load:.*]] = memref.load %[[m1]]
%3 = tensor.extract %t1[%idx] : tensor<?xf32>
// CHECK: return %{{.*}}, %[[alloc]], %[[load]] : f32, memref<?xf32>, f32
return %0, %1, %3 : f32, tensor<?xf32>, f32
}
// -----
// CHECK-LABEL: func @scf_if_inplace(
// CHECK-SAME: %[[cond:.*]]: i1, %[[t1:.*]]: memref<?xf32{{.*}}>, %[[v:.*]]: vector
func.func @scf_if_inplace(%cond: i1,
%t1: tensor<?xf32> {bufferization.writable = true},
%v: vector<5xf32>, %idx: index) -> tensor<?xf32> {
// CHECK: scf.if %[[cond]] {
// CHECK-NEXT: } else {
// CHECK-NEXT: vector.transfer_write %[[v]], %[[t1]]
// CHECK-NEXT: }
// CHECK-NEXT: return
%r = scf.if %cond -> (tensor<?xf32>) {
scf.yield %t1 : tensor<?xf32>
} else {
%t2 = vector.transfer_write %v, %t1[%idx] : vector<5xf32>, tensor<?xf32>
scf.yield %t2 : tensor<?xf32>
}
return %r : tensor<?xf32>
}
// -----
// CHECK-LABEL: func @scf_if_inside_scf_for
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[c10:.*]] = arith.constant 10 : index
// CHECK: scf.for %{{.*}} = %[[c0]] to %[[c10]] step %[[c1]] {
// CHECK: scf.if %{{.*}} {
// CHECK: } else {
// CHECK: vector.transfer_write
// CHECK: }
// CHECK: }
func.func @scf_if_inside_scf_for(
%t1: tensor<?xf32> {bufferization.writable = true},
%v: vector<5xf32>, %idx: index,
%cond: i1)
-> tensor<?xf32>
{
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c10 = arith.constant 10 : index
%r = scf.for %iv = %c0 to %c10 step %c1 iter_args(%bb = %t1) -> (tensor<?xf32>) {
%r2 = scf.if %cond -> (tensor<?xf32>) {
scf.yield %bb : tensor<?xf32>
} else {
%t2 = vector.transfer_write %v, %bb[%idx] : vector<5xf32>, tensor<?xf32>
scf.yield %t2 : tensor<?xf32>
}
scf.yield %r2 : tensor<?xf32>
}
return %r : tensor<?xf32>
}
// -----
// CHECK-LABEL: func @scf_if_non_equiv_yields(
// CHECK-SAME: %[[cond:.*]]: i1, %[[A:.*]]: memref<{{.*}}>, %[[B:.*]]: memref<{{.*}}>) -> memref<{{.*}}>
func.func @scf_if_non_equiv_yields(
%b : i1,
%A : tensor<4xf32> {bufferization.writable = false},
%B : tensor<4xf32> {bufferization.writable = false})
-> tensor<4xf32>
{
// CHECK: %[[r:.*]] = arith.select %[[cond]], %[[A]], %[[B]]
%r = scf.if %b -> (tensor<4xf32>) {
scf.yield %A : tensor<4xf32>
} else {
scf.yield %B : tensor<4xf32>
}
// CHECK: return %[[r]]
return %r: tensor<4xf32>
}
// -----
// Note: This bufferization is inefficient, but it bufferizes correctly.
// CHECK-LABEL: func @scf_execute_region_yield_non_equivalent(
// CHECK: %[[alloc:.*]] = memref.alloc(%{{.*}})
// CHECK: %[[r:.*]] = memref.load %[[alloc]][%{{.*}}]
// CHECK: return %[[r]]
func.func @scf_execute_region_yield_non_equivalent(%i: index, %j: index) -> f32 {
%r = scf.execute_region -> (tensor<?xf32>) {
%t2 = bufferization.alloc_tensor(%i) : tensor<?xf32>
scf.yield %t2 : tensor<?xf32>
}
%f = tensor.extract %r[%j] : tensor<?xf32>
return %f : f32
}
// -----
// Note: This bufferizes to inefficient code, but bufferization should not see
// such IR in the first place. The iter_arg would canonicalize away. This test
// case is just to ensure that the bufferization generates correct code.
// CHECK-LABEL: func @scf_for_yield_non_equivalent(
// CHECK-SAME: %[[t:.*]]: memref<?xf32
// CHECK: %[[alloc:.*]] = memref.alloc(%{{.*}})
// CHECK: memref.copy %[[t]], %[[alloc]]
// CHECK: %[[for:.*]] = scf.for {{.*}} iter_args(%[[iter:.*]] = %[[alloc]])
// CHECK-DAG: %[[alloc2:.*]] = memref.alloc(%{{.*}})
// CHECK: memref.copy %[[t]], %[[alloc2]]
// CHECK: scf.yield %[[alloc2]]
// CHECK: return %[[for]]
func.func @scf_for_yield_non_equivalent(
%t: tensor<?xf32>, %lb : index, %ub : index, %step : index) -> tensor<?xf32> {
%r = scf.for %i = %lb to %ub step %step iter_args(%a = %t) -> tensor<?xf32> {
scf.yield %t : tensor<?xf32>
}
return %r : tensor<?xf32>
}
// -----
// CHECK-LABEL: func @scf_for_yield_allocation(
// CHECK-SAME: %[[t:.*]]: memref<?xf32
// CHECK: %[[for:.*]] = scf.for {{.*}} iter_args(%[[iter:.*]] = %[[t]])
// CHECK-DAG: %[[alloc:.*]] = memref.alloc(%{{.*}})
// CHECK: %[[casted:.*]] = memref.cast %[[alloc]]
// CHECK: scf.yield %[[casted]]
// CHECK: return %[[for]]
func.func @scf_for_yield_allocation(%t: tensor<?xf32>, %lb : index, %ub : index,
%step : index) -> tensor<?xf32> {
%r = scf.for %i = %lb to %ub step %step iter_args(%a = %t) -> tensor<?xf32> {
%t2 = bufferization.alloc_tensor(%i) : tensor<?xf32>
scf.yield %t2 : tensor<?xf32>
}
return %r : tensor<?xf32>
}
// -----
// TODO: The scf.yield could bufferize to 1 alloc and 2 copies (instead of
// 2 allocs and 2 copies).
// CHECK-LABEL: func @scf_for_swapping_yields(
// CHECK-SAME: %[[A:.*]]: memref<?xf32, strided{{.*}}>, %[[B:.*]]: memref<?xf32, strided{{.*}}>
func.func @scf_for_swapping_yields(
%A : tensor<?xf32>, %B : tensor<?xf32> {bufferization.writable = true},
%C : tensor<4xf32>, %lb : index, %ub : index, %step : index)
-> (f32, f32)
{
// CHECK: %[[for:.*]]:2 = scf.for {{.*}} iter_args(%[[iter1:.*]] = %[[A]], %[[iter2:.*]] = %[[B]])
%r0:2 = scf.for %i = %lb to %ub step %step iter_args(%tA = %A, %tB = %B)
-> (tensor<?xf32>, tensor<?xf32>)
{
// CHECK: %[[sv1:.*]] = memref.subview %[[iter1]]
// CHECK: memref.copy %{{.*}}, %[[sv1]]
%ttA = tensor.insert_slice %C into %tA[0][4][1] : tensor<4xf32> into tensor<?xf32>
// CHECK: %[[sv2:.*]] = memref.subview %[[iter2]]
// CHECK: memref.copy %{{.*}}, %[[sv2]]
%ttB = tensor.insert_slice %C into %tB[0][4][1] : tensor<4xf32> into tensor<?xf32>
// CHECK: %[[alloc2:.*]] = memref.alloc(%{{.*}})
// CHECK: memref.copy %[[iter2]], %[[alloc2]]
// CHECK: %[[alloc1:.*]] = memref.alloc(%{{.*}})
// CHECK: memref.copy %[[iter1]], %[[alloc1]]
// CHECK: %[[casted2:.*]] = memref.cast %[[alloc2]]
// CHECK: %[[casted1:.*]] = memref.cast %[[alloc1]]
// CHECK: scf.yield %[[casted2]], %[[casted1]]
// Yield tensors in different order.
scf.yield %ttB, %ttA : tensor<?xf32>, tensor<?xf32>
}
// CHECK: %[[r0:.*]] = memref.load %[[for]]#0
// CHECK: %[[r1:.*]] = memref.load %[[for]]#1
%f0 = tensor.extract %r0#0[%step] : tensor<?xf32>
%f1 = tensor.extract %r0#1[%step] : tensor<?xf32>
// CHECK: return %[[r0]], %[[r1]]
return %f0, %f1: f32, f32
}
// -----
// CHECK-LABEL: func @scf_while(
// CHECK-SAME: %[[arg0:.*]]: memref<?xi1, strided{{.*}}>
func.func @scf_while(%arg0: tensor<?xi1>, %idx: index) -> tensor<?xi1> {
// CHECK: scf.while : () -> () {
%res:2 = scf.while (%arg1 = %arg0, %i = %idx) :
(tensor<?xi1>, index) -> (tensor<?xi1>, index) {
// CHECK: %[[condition:.*]] = memref.load %[[arg0]]
// CHECK: scf.condition(%[[condition]])
%condition = tensor.extract %arg1[%idx] : tensor<?xi1>
scf.condition(%condition) %arg1, %idx : tensor<?xi1>, index
} do {
^bb0(%arg2: tensor<?xi1>, %i: index):
// CHECK: } do {
// CHECK: memref.store %{{.*}}, %[[arg0]]
// CHECK: scf.yield
// CHECK: }
%pos = "dummy.some_op"() : () -> (index)
%val = "dummy.another_op"() : () -> (i1)
%1 = tensor.insert %val into %arg2[%pos] : tensor<?xi1>
scf.yield %1, %i : tensor<?xi1>, index
}
// CHECK: return
return %res#0 : tensor<?xi1>
}
// -----
// The loop condition yields non-equivalent buffers.
// CHECK-LABEL: func @scf_while_non_equiv_condition(
// CHECK-SAME: %[[arg0:.*]]: memref<5xi1, strided{{.*}}>, %[[arg1:.*]]: memref<5xi1, strided{{.*}}>
func.func @scf_while_non_equiv_condition(%arg0: tensor<5xi1>,
%arg1: tensor<5xi1>,
%idx: index)
-> (tensor<5xi1>, tensor<5xi1>)
{
// CHECK: %[[loop:.*]]:2 = scf.while (%[[w0:.*]] = %[[arg0]], %[[w1:.*]] = %[[arg1]]) {{.*}} {
%r0, %r1 = scf.while (%w0 = %arg0, %w1 = %arg1)
: (tensor<5xi1>, tensor<5xi1>) -> (tensor<5xi1>, tensor<5xi1>) {
// CHECK: %[[condition:.*]] = memref.load %[[w0]]
// CHECK: %[[a1:.*]] = memref.alloc() {{.*}} : memref<5xi1>
// CHECK: memref.copy %[[w1]], %[[a1]]
// CHECK: %[[a0:.*]] = memref.alloc() {{.*}} : memref<5xi1>
// CHECK: memref.copy %[[w0]], %[[a0]]
// CHECK: scf.condition(%[[condition]]) %[[a1]], %[[a0]]
%condition = tensor.extract %w0[%idx] : tensor<5xi1>
scf.condition(%condition) %w1, %w0 : tensor<5xi1>, tensor<5xi1>
} do {
^bb0(%b0: tensor<5xi1>, %b1: tensor<5xi1>):
// CHECK: } do {
// CHECK: ^bb0(%[[b0:.*]]: memref<5xi1>, %[[b1:.*]]: memref<5xi1>):
// CHECK: memref.store %{{.*}}, %[[b0]]
// CHECK: %[[casted0:.*]] = memref.cast %[[b0]] : memref<5xi1> to memref<5xi1, strided{{.*}}>
// CHECK: %[[casted1:.*]] = memref.cast %[[b1]] : memref<5xi1> to memref<5xi1, strided{{.*}}>
// CHECK: scf.yield %[[casted0]], %[[casted1]]
// CHECK: }
%pos = "dummy.some_op"() : () -> (index)
%val = "dummy.another_op"() : () -> (i1)
%1 = tensor.insert %val into %b0[%pos] : tensor<5xi1>
scf.yield %1, %b1 : tensor<5xi1>, tensor<5xi1>
}
// CHECK: return %[[loop]]#0, %[[loop]]#1
return %r0, %r1 : tensor<5xi1>, tensor<5xi1>
}
// -----
// Both the loop condition and the loop buffer yield non-equivalent buffers.
// CHECK-LABEL: func @scf_while_non_equiv_condition_and_body(
// CHECK-SAME: %[[arg0:.*]]: memref<5xi1, strided{{.*}}>, %[[arg1:.*]]: memref<5xi1, strided{{.*}}>
func.func @scf_while_non_equiv_condition_and_body(%arg0: tensor<5xi1>,
%arg1: tensor<5xi1>,
%idx: index)
-> (tensor<5xi1>, tensor<5xi1>)
{
// CHECK: %[[loop:.*]]:2 = scf.while (%[[w0:.*]] = %[[arg0]], %[[w1:.*]] = %[[arg1]]) {{.*}} {
%r0, %r1 = scf.while (%w0 = %arg0, %w1 = %arg1)
: (tensor<5xi1>, tensor<5xi1>) -> (tensor<5xi1>, tensor<5xi1>) {
// CHECK: %[[condition:.*]] = memref.load %[[w0]]
// CHECK: %[[a1:.*]] = memref.alloc() {{.*}} : memref<5xi1>
// CHECK: memref.copy %[[w1]], %[[a1]]
// CHECK: %[[a0:.*]] = memref.alloc() {{.*}} : memref<5xi1>
// CHECK: memref.copy %[[w0]], %[[a0]]
// CHECK: scf.condition(%[[condition]]) %[[a1]], %[[a0]]
%condition = tensor.extract %w0[%idx] : tensor<5xi1>
scf.condition(%condition) %w1, %w0 : tensor<5xi1>, tensor<5xi1>
} do {
^bb0(%b0: tensor<5xi1>, %b1: tensor<5xi1>):
// CHECK: } do {
// CHECK: ^bb0(%[[b0:.*]]: memref<5xi1>, %[[b1:.*]]: memref<5xi1>):
// CHECK: memref.store %{{.*}}, %[[b0]]
// CHECK: %[[casted1:.*]] = memref.cast %[[b1]]
// CHECK: %[[casted0:.*]] = memref.cast %[[b0]]
// CHECK: scf.yield %[[casted1]], %[[casted0]]
// CHECK: }
%pos = "dummy.some_op"() : () -> (index)
%val = "dummy.another_op"() : () -> (i1)
%1 = tensor.insert %val into %b0[%pos] : tensor<5xi1>
scf.yield %b1, %1 : tensor<5xi1>, tensor<5xi1>
}
// CHECK: return %[[loop]]#0, %[[loop]]#1
return %r0, %r1 : tensor<5xi1>, tensor<5xi1>
}
// -----
// CHECK-LABEL: func @scf_while_iter_arg_result_mismatch(
// CHECK-SAME: %[[arg0:.*]]: memref<5xi1, strided{{.*}}>, %[[arg1:.*]]: memref<5xi1, strided{{.*}}>
// CHECK: scf.while (%[[arg3:.*]] = %[[arg1]]) : (memref<5xi1, strided{{.*}}) -> () {
// CHECK-DAG: %[[load:.*]] = memref.load %[[arg0]]
// CHECK: scf.condition(%[[load]])
// CHECK: } do {
// CHECK: %[[alloc2:.*]] = memref.alloc() {{.*}} : memref<5xi1>
// CHECK: memref.copy %[[arg0]], %[[alloc2]]
// CHECK: memref.store %{{.*}}, %[[alloc2]]
// CHECK: %[[casted:.*]] = memref.cast %[[alloc2]] : memref<5xi1> to memref<5xi1, strided{{.*}}>
// CHECK: scf.yield %[[casted]]
// CHECK: }
func.func @scf_while_iter_arg_result_mismatch(%arg0: tensor<5xi1>,
%arg1: tensor<5xi1>,
%arg2: index) {
scf.while (%arg3 = %arg1) : (tensor<5xi1>) -> () {
%0 = tensor.extract %arg0[%arg2] : tensor<5xi1>
%1 = tensor.extract %arg3[%arg2] : tensor<5xi1>
"dummy.use"(%1) : (i1) -> ()
scf.condition(%0)
} do {
%0 = "dummy.some_op"() : () -> index
%1 = "dummy.another_op"() : () -> i1
%2 = tensor.insert %1 into %arg0[%0] : tensor<5xi1>
scf.yield %2 : tensor<5xi1>
}
return
}
// -----
// CHECK-LABEL: func.func @parallel_insert_slice_no_conflict(
// CHECK-SAME: %[[idx:.*]]: index, %[[idx2:.*]]: index,
// CHECK-SAME: %[[arg1:.*]]: memref<?xf32, strided{{.*}}>,
// CHECK-SAME: %[[arg2:.*]]: memref<?xf32, strided{{.*}}>
func.func @parallel_insert_slice_no_conflict(
%idx: index,
%idx2: index,
%arg1: tensor<?xf32> {bufferization.writable = true},
%arg2: tensor<?xf32> {bufferization.writable = true}) -> (tensor<?xf32>, f32) {
%cst = arith.constant 4.200000e+01 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
// CHECK: scf.forall (%[[tidx:.*]]) in (%[[idx2]])
%2 = scf.forall (%arg3) in (%idx2) shared_outs(%o = %arg2) -> (tensor<?xf32>) {
// CHECK: %[[subview:.*]] = memref.subview %[[arg2]][5] [%[[idx]]] [1]
%6 = tensor.extract_slice %o[5] [%idx] [%c1] : tensor<?xf32> to tensor<?xf32>
// CHECK: linalg.fill ins(%{{.*}}) outs(%[[subview]] : memref<?xf32
%8 = linalg.fill ins(%cst : f32) outs(%6 : tensor<?xf32>) -> tensor<?xf32>
// CHECK-NOT: memref.copy
// Empty terminator is elided from pretty-printing.
// CHECK-NOT: scf.forall.in_parallel
// CHECK-NOT: parallel_insert_slice
scf.forall.in_parallel {
tensor.parallel_insert_slice %8 into %o[5] [%idx] [%c1] :
tensor<?xf32> into tensor<?xf32>
}
}
// CHECK: %[[load:.*]] = memref.load %[[arg2]]
%f = tensor.extract %2[%c0] : tensor<?xf32>
// CHECK: return %[[load]] : f32
return %2, %f : tensor<?xf32>, f32
}
// -----
// CHECK-LABEL: func.func @parallel_insert_slice_with_conflict(
// CHECK-SAME: %[[idx:.*]]: index, %[[idx2:.*]]: index,
// CHECK-SAME: %[[arg1:.*]]: memref<?xf32, strided{{.*}}>,
// CHECK-SAME: %[[arg2:.*]]: memref<?xf32, strided{{.*}}>
func.func @parallel_insert_slice_with_conflict(
%idx: index,
%idx2: index,
%arg1: tensor<?xf32> {bufferization.writable = true},
%arg2: tensor<?xf32> {bufferization.writable = true}) -> (f32, f32)
{
%cst = arith.constant 4.200000e+01 : f32
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
// The parallel_insert_slice_op bufferizes out-of-place due to a RAW conflict
// on %arg2, so we need an allocation.
// CHECK: %[[alloc1:.*]] = memref.alloc
// CHECK: memref.copy %[[arg2]], %[[alloc1]]
// CHECK: scf.forall (%[[tidx:.*]]) in (%[[idx2]])
%2 = scf.forall (%arg3) in (%idx2) shared_outs(%o = %arg2) -> (tensor<?xf32>) {
// CHECK: %[[subview1:.*]] = memref.subview %[[alloc1]][5] [%[[idx]]] [1]
%6 = tensor.extract_slice %o[5] [%idx] [%c1] : tensor<?xf32> to tensor<?xf32>
// CHECK: linalg.fill ins(%{{.*}}) outs(%[[subview1]] : memref<?xf32
%8 = linalg.fill ins(%cst : f32) outs(%6 : tensor<?xf32>) -> tensor<?xf32>
// CHECK-NOT: memref.copy
// Empty terminator is elided from pretty-printing.
// CHECK-NOT: scf.forall.in_parallel
// CHECK-NOT: parallel_insert_slice
scf.forall.in_parallel {
tensor.parallel_insert_slice %8 into %o[5] [%idx] [%c1] :
tensor<?xf32> into tensor<?xf32>
}
}
// CHECK: %[[load:.*]] = memref.load %[[arg2]]
// CHECK: %[[load2:.*]] = memref.load %[[alloc1]]
%f = tensor.extract %arg2[%c0] : tensor<?xf32>
%f2 = tensor.extract %2[%c0] : tensor<?xf32>
// CHECK: return %[[load2]], %[[load]] : f32, f32
return %f2, %f : f32, f32
}
// -----
#map0 = affine_map<(d0) -> (d0 * 4)>
#map1 = affine_map<(d0) -> (d0 * 2)>
// CHECK-LABEL: func.func @matmul
func.func @matmul(%arg0: tensor<8x8xf32>, %arg1: tensor<8x8xf32>, %arg2: tensor<8x8xf32> {bufferization.writable = true}) -> tensor<8x8xf32> {
%c2 = arith.constant 2 : index
%c4 = arith.constant 4 : index
// CHECK: scf.forall {{.*}}
%0 = scf.forall (%arg3, %arg4) in (%c2, %c4) shared_outs(%o = %arg2) -> (tensor<8x8xf32>) {
%1 = affine.apply #map0(%arg3)
%3 = tensor.extract_slice %arg0[%1, 0] [4, 8] [1, 1] : tensor<8x8xf32> to tensor<4x8xf32>
%4 = affine.apply #map1(%arg4)
%6 = tensor.extract_slice %arg1[0, %4] [8, 4] [1, 1] : tensor<8x8xf32> to tensor<8x4xf32>
%7 = tensor.extract_slice %o[%1, %4] [4, 4] [1, 1] : tensor<8x8xf32> to tensor<4x4xf32>
// CHECK: linalg.matmul ins({{.*}}memref<4x8xf32, strided<[?, ?], offset: ?>>, memref<8x4xf32, strided<[?, ?], offset: ?>>) outs({{.*}} : memref<4x4xf32, strided<[?, ?], offset: ?>>)
%8 = linalg.matmul ins(%3, %6 : tensor<4x8xf32>, tensor<8x4xf32>) outs(%7 : tensor<4x4xf32>) -> tensor<4x4xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %8 into %o[%1, %4] [4, 4] [1, 1] : tensor<4x4xf32> into tensor<8x8xf32>
}
}
return %0 : tensor<8x8xf32>
}
// -----
// CHECK-LABEL: func @scf_foreach_private_var(
// CHECK-SAME: %[[t:.*]]: memref<10xf32
func.func @scf_foreach_private_var(%t: tensor<10xf32>) -> f32 {
%c2 = arith.constant 2 : index
%c5 = arith.constant 5 : index
// A copy is inserted for the uses of %t in the loop.
// CHECK: %[[t_copy:.*]] = memref.alloc() {{.*}} : memref<10xf32>
// CHECK: memref.copy %[[t]], %[[t_copy]]
// CHECK: scf.forall (%{{.*}}) in (2) {
// Load from the original and store into the copy.
// CHECK: %[[subview:.*]] = memref.subview %[[t_copy]]
// CHECK: memref.load %[[t]]
// CHECK: memref.store %{{.*}}, %[[subview]]
%0 = scf.forall (%tid) in (%c2) shared_outs(%o = %t) -> tensor<10xf32> {
%offset = arith.muli %c5, %tid : index
%slice = tensor.extract_slice %o[%offset] [5] [1]
: tensor<10xf32> to tensor<5xf32>
%r2 = tensor.extract %t[%tid] : tensor<10xf32>
%i = tensor.insert %r2 into %slice[%c2] : tensor<5xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %i into %o[%offset] [5] [1]
: tensor<5xf32> into tensor<10xf32>
}
}
%r = tensor.extract %0[%c2] : tensor<10xf32>
return %r : f32
}
// -----
// CHECK-LABEL: func.func @scf_foreach_privatized_but_not_copied(
// CHECK-SAME: %[[t0:.*]]: memref<10xf32, {{.*}}>, %[[t1:.*]]: memref<10xf32
func.func @scf_foreach_privatized_but_not_copied(
%t0: tensor<10xf32>, %t1: tensor<10xf32>) -> f32 {
%c2 = arith.constant 2 : index
%c5 = arith.constant 5 : index
// CHECK-NOT: memref.alloc
// CHECK-NOT: memref.copy
// CHECK: scf.forall {{.*}} {
%0 = scf.forall (%tid) in (%c2) shared_outs(%o = %t0) -> tensor<10xf32> {
%offset = arith.muli %c5, %tid : index
%slice = tensor.extract_slice %o[%offset] [5] [1]
: tensor<10xf32> to tensor<5xf32>
// %t1 is never written in here, so no copy is needed
// CHECK: memref.load %[[t1]]
%r2 = tensor.extract %t1[%tid] : tensor<10xf32>
%i = tensor.insert %r2 into %slice[%c2] : tensor<5xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %i into %o[%offset] [5] [1]
: tensor<5xf32> into tensor<10xf32>
}
}
%r = tensor.extract %0[%c2] : tensor<10xf32>
return %r : f32
}
// -----
// CHECK-LABEL: func @scf_if_memory_space
func.func @scf_if_memory_space(%c: i1, %f: f32, %cst: f32) -> (f32, f32)
{
%c0 = arith.constant 0 : index
// CHECK: %[[alloc:.*]] = memref.alloc() {{.*}} : memref<5xf32, 1>
%alloc = bufferization.alloc_tensor() {memory_space = 1 : i64} : tensor<5xf32>
// CHECK: linalg.fill {{.*}} outs(%[[alloc]] : memref<5xf32, 1>)
%filled = linalg.fill ins(%cst : f32) outs(%alloc : tensor<5xf32>) -> tensor<5xf32>
// CHECK: scf.if %{{.*}} -> (memref<5xf32, 1>) {
%1 = scf.if %c -> tensor<5xf32> {
// CHECK: scf.yield %[[alloc]]
scf.yield %filled : tensor<5xf32>
} else {
// CHECK: %[[alloc2:.*]] = memref.alloc() {{.*}} : memref<5xf32, 1>
// CHECK: memref.store %{{.*}}, %[[alloc2]]
// CHECK: scf.yield %[[alloc2]]
%2 = tensor.insert %f into %filled[%c0] : tensor<5xf32>
scf.yield %2 : tensor<5xf32>
}
%r0 = tensor.extract %filled[%c0] : tensor<5xf32>
%r1 = tensor.extract %1[%c0] : tensor<5xf32>
return %r0, %r1 : f32, f32
}
// -----
// CHECK-LABEL: func @scf_execute_region_memory_space
// CHECK: memref.alloc() {{.*}} : memref<5xf32, 1>
// CHECK: memref.store
// CHECK: memref.load
func.func @scf_execute_region_memory_space(%f: f32) -> f32 {
%c0 = arith.constant 0 : index
%0 = scf.execute_region -> tensor<5xf32> {
%1 = bufferization.alloc_tensor() {memory_space = 1 : i64} : tensor<5xf32>
%2 = tensor.insert %f into %1[%c0] : tensor<5xf32>
scf.yield %2 : tensor<5xf32>
}
%r = tensor.extract %0[%c0] : tensor<5xf32>
return %r : f32
}
// -----
// Additional allocs are inserted in the loop body. We just check that all
// allocs have the correct memory space.
// CHECK-LABEL: func @scf_for_swapping_yields_memory_space
func.func @scf_for_swapping_yields_memory_space(
%sz: index, %C : tensor<4xf32>, %lb : index, %ub : index, %step : index)
-> (f32, f32)
{
// CHECK: memref.alloc(%{{.*}}) {{.*}} : memref<?xf32, 1>
// CHECK: memref.alloc(%{{.*}}) {{.*}} : memref<?xf32, 1>
%A = bufferization.alloc_tensor(%sz) {memory_space = 1 : i64} : tensor<?xf32>
%B = bufferization.alloc_tensor(%sz) {memory_space = 1 : i64} : tensor<?xf32>
// CHECK: scf.for {{.*}} {
%r0:2 = scf.for %i = %lb to %ub step %step iter_args(%tA = %A, %tB = %B)
-> (tensor<?xf32>, tensor<?xf32>)
{
// CHECK: memref.alloc(%{{.*}}) {{.*}} : memref<?xf32, 1>
// CHECK: memref.alloc(%{{.*}}) {{.*}} : memref<?xf32, 1>
%ttA = tensor.insert_slice %C into %tA[0][4][1] : tensor<4xf32> into tensor<?xf32>
%ttB = tensor.insert_slice %C into %tB[0][4][1] : tensor<4xf32> into tensor<?xf32>
// Yield tensors in different order.
scf.yield %ttB, %ttA : tensor<?xf32>, tensor<?xf32>
}
// CHECK: }
%f0 = tensor.extract %r0#0[%step] : tensor<?xf32>
%f1 = tensor.extract %r0#1[%step] : tensor<?xf32>
return %f0, %f1: f32, f32
}
// -----
// CHECK-LABEL: func @scf_for_yield_alias_of_non_equivalent(
func.func @scf_for_yield_alias_of_non_equivalent(%sz: index) -> tensor<?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cst = arith.constant 5.0 : f32
// CHECK: %[[generate:.*]] = memref.alloc
%0 = tensor.generate %sz {
^bb0(%i: index):
tensor.yield %cst : f32
} : tensor<?xf32>
// A copy is inserted because %t is used inside the loop.
// CHECK: %[[generate_copy:.*]] = memref.alloc
// CHECK: memref.copy %[[generate]], %[[generate_copy]]
// CHECK: scf.for
%r = scf.for %iv = %c0 to %sz step %c1 iter_args(%t = %0) -> tensor<?xf32> {
%iv_sub = arith.subi %iv, %c1 : index
// CHECK: memref.subview %[[generate]]
%ll = tensor.extract_slice %0[%iv_sub][%sz][1] : tensor<?xf32> to tensor<?xf32>
%l = tensor.extract %ll[%c0] : tensor<?xf32>
%double = arith.mulf %cst, %l : f32
// CHECK: memref.store %{{.*}}, %[[generate_copy]]
%s = tensor.insert %double into %t[%iv] : tensor<?xf32>
scf.yield %s : tensor<?xf32>
}
// CHECK: return %[[generate_copy]]
return %r : tensor<?xf32>
}
// -----
// We just check that this example bufferizes to valid IR.
// CHECK-LABEL: func @scf_for_buffer_type_mismatch
func.func @scf_for_buffer_type_mismatch(%sz: index, %sz2: index) -> f32 {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c10 = arith.constant 10 : index
%0 = bufferization.alloc_tensor(%sz) : tensor<?xf32>
%e2 = tensor.extract_slice %0[1][%sz2][1] : tensor<?xf32> to tensor<?xf32>
// init_arg and iter_arg have different buffer types. This must be resolved
// with casts.
%r = scf.for %iv = %c0 to %c10 step %c1 iter_args(%t = %e2) -> tensor<?xf32> {
%s = "test.dummy"() : () -> (index)
%e = tensor.extract_slice %t[1][%s][1] : tensor<?xf32> to tensor<?xf32>
scf.yield %e : tensor<?xf32>
}
%x = tensor.extract %r[%c1] : tensor<?xf32>
return %x : f32
}
// -----
// We just check that this example bufferizes to valid IR.
// CHECK-LABEL: func @scf_while_buffer_type_mismatch
func.func @scf_while_buffer_type_mismatch(%sz: index, %sz2: index) -> f32 {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c10 = arith.constant 10 : index
%cst = arith.constant 5.5 : f32
%0 = bufferization.alloc_tensor(%sz) : tensor<?xf32>
%e2 = tensor.extract_slice %0[1][%sz2][1] : tensor<?xf32> to tensor<?xf32>
// init_arg and iter_arg have different buffer types. This must be resolved
// with casts.
%r = scf.while (%t = %e2) : (tensor<?xf32>) -> (tensor<?xf32>) {
%c = "test.condition"() : () -> (i1)
%s = "test.dummy"() : () -> (index)
%e = tensor.extract_slice %t[1][%s][1] : tensor<?xf32> to tensor<?xf32>
scf.condition(%c) %e : tensor<?xf32>
} do {
^bb0(%b0: tensor<?xf32>):
%s2 = "test.dummy"() : () -> (index)
%n = tensor.insert %cst into %b0[%s2] : tensor<?xf32>
scf.yield %n : tensor<?xf32>
}
%x = tensor.extract %r[%c1] : tensor<?xf32>
return %x : f32
}
// -----
// CHECK-LABEL: func @non_tensor_for_arg
func.func @non_tensor_for_arg(%A : tensor<?xf32> {bufferization.writable = true})
-> tensor<?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2.0 : f32
%c10 = arith.constant 10 : index
%r1:2 = scf.for %i = %c0 to %c10 step %c1 iter_args(%idx = %c1, %t = %A) -> (index, tensor<?xf32>) {
%t2 = tensor.insert %c2 into %t[%idx] : tensor<?xf32>
scf.yield %idx, %t2 : index, tensor<?xf32>
}
return %r1#1 : tensor<?xf32>
}
// -----
// This is a regression test. Just check that the IR bufferizes.
// CHECK-LABEL: func @buffer_type_of_collapse_shape
func.func @buffer_type_of_collapse_shape(%arg0: tensor<f64>) {
%true = arith.constant true
%0 = scf.while (%arg1 = %arg0) : (tensor<f64>) -> (tensor<f64>) {
scf.condition(%true) %arg1 : tensor<f64>
} do {
^bb0(%_: tensor<f64>):
%3 = bufferization.alloc_tensor() : tensor<1xf64>
%16 = tensor.collapse_shape %3 [] : tensor<1xf64> into tensor<f64>
scf.yield %16 : tensor<f64>
}
return
}
// -----
// This is a regression test. Just check that the IR bufferizes.
// CHECK-LABEL: func @non_block_argument_yield
func.func @non_block_argument_yield() {
%true = arith.constant true
%0 = bufferization.alloc_tensor() : tensor<i32>
%1 = scf.while (%arg0 = %0) : (tensor<i32>) -> (tensor<i32>) {
scf.condition(%true) %arg0 : tensor<i32>
} do {
^bb0(%arg0: tensor<i32>):
%ret = scf.while (%arg1 = %0) : (tensor<i32>) -> (tensor<i32>) {
scf.condition(%true) %arg1 : tensor<i32>
} do {
^bb0(%arg7: tensor<i32>):
scf.yield %0 : tensor<i32>
}
scf.yield %ret : tensor<i32>
}
return
}
// -----
// This is a regression test. Make sure that bufferization succeeds.
// CHECK-LABEL: func @regression_cast_in_loop(
func.func @regression_cast_in_loop() -> tensor<2xindex> {
%false = arith.constant false
%c0 = arith.constant 0 : index
%0 = bufferization.alloc_tensor() : tensor<2xindex>
// CHECK: scf.while (%{{.*}} = %{{.*}}) : (memref<2xindex>) -> memref<2xindex>
%1 = scf.while (%arg0 = %0) : (tensor<2xindex>) -> tensor<2xindex> {
scf.condition(%false) %arg0 : tensor<2xindex>
} do {
// CHECK: ^bb0(%{{.*}}: memref<2xindex>):
^bb0(%arg0: tensor<2xindex>):
%cast = tensor.cast %0 : tensor<2xindex> to tensor<?xindex>
%inserted = tensor.insert %c0 into %cast[%c0] : tensor<?xindex>
%cast_0 = tensor.cast %inserted : tensor<?xindex> to tensor<2xindex>
scf.yield %cast_0 : tensor<2xindex>
}
return %1 : tensor<2xindex>
}
// -----
// This test does not compute anything meaningful but it tests that
// bufferizesToMemoryWrite is correctly propagated through regions.
// CHECK-LABEL: func @elide_copy_of_non_writing_scf_if(
func.func @elide_copy_of_non_writing_scf_if(%c: i1, %p1: index, %p2: index, %f: f32)
-> (tensor<10xf32>, f32)
{
%r = scf.if %c -> tensor<10xf32> {
// CHECK: memref.alloc
%t1 = bufferization.alloc_tensor() : tensor<10xf32>
scf.yield %t1 : tensor<10xf32>
} else {
// CHECK: memref.alloc
%t2 = bufferization.alloc_tensor() : tensor<10xf32>
scf.yield %t2 : tensor<10xf32>
}
// No copy should be inserted because %r does not bufferize to a memory write.
// I.e., %r does not have defined contents and the copy can be elided.
// CHECK-NOT: memref.alloc
// CHECK-NOT: memref.copy
%r2 = tensor.insert %f into %r[%p1] : tensor<10xf32>
%r3 = tensor.extract %r[%p2] : tensor<10xf32>
return %r2, %r3 : tensor<10xf32>, f32
}
// -----
// CHECK-LABEL: func @index_switch(
// CHECK-SAME: %[[pred:.*]]: index, %[[b:.*]]: memref<{{.*}}>, %[[c:.*]]: memref<{{.*}}>) -> memref<{{.*}}>
func.func @index_switch(%pred: index, %b: tensor<5xf32>, %c: tensor<5xf32>) -> tensor<5xf32> {
// Throw in a tensor that bufferizes to a different layout map.
// CHECK: %[[a:.*]] = memref.alloc() {{.*}} : memref<5xf32>
%a = bufferization.alloc_tensor() : tensor<5xf32>
// CHECK: %[[r:.*]] = scf.index_switch %[[pred]] -> memref<5xf32, strided<[?], offset: ?>>
%0 = scf.index_switch %pred -> tensor<5xf32>
// CHECK: case 2 {
// CHECK: %[[cast:.*]] = memref.cast %[[a]] : memref<5xf32> to memref<5xf32, strided<[?], offset: ?>>
// CHECK: scf.yield %[[cast]]
case 2 {
scf.yield %a: tensor<5xf32>
}
// CHECK: case 5 {
// CHECK: scf.yield %[[b]] : memref<5xf32, strided<[?], offset: ?>>
case 5 {
scf.yield %b: tensor<5xf32>
}
// CHECK: default {
// CHECK: scf.yield %[[c]] : memref<5xf32, strided<[?], offset: ?>>
default {
scf.yield %c: tensor<5xf32>
}
// CHECK: return %[[r]]
return %0 : tensor<5xf32>
}
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