// RUN: mlir-opt --transform-interpreter --cse --split-input-file %s | FileCheck %s
func.func @gemm_fill_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cst = arith.constant 0.0 : f32
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
%init = tensor.empty(%d0, %d1) : tensor<?x?xf32>
%fill = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32>
%gemm = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
return %gemm : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.fuse_using_forall %matmul [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func.func @gemm_fill_fusion(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>)
// CHECK: %[[INIT:.+]] = tensor.empty
// CHECK: scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) =
// CHECK-SAME: shared_outs(%[[ITERARG0:.+]] = %[[INIT]])
// CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0]
// CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]]
// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV0]], %[[IV1]]]
// CHECK: %[[FILL_TILE:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT_TILE]] :
// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :
// CHECK-SAME: outs(%[[FILL_TILE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[GEMM_TILE]] into %[[ITERARG0]][%[[IV0]], %[[IV1]]]
// CHECK: }
// -----
func.func @gemm_generic_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
%arg2 : tensor<?xf32>) -> tensor<?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cst = arith.constant 0.0 : f32
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
%init = tensor.empty(%d0, %d1) : tensor<?x?xf32>
%fill = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32>
%gemm = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
%generic = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%gemm, %arg2 : tensor<?x?xf32>, tensor<?xf32>) outs(%init : tensor<?x?xf32>) {
^bb0(%b0 : f32, %b1 : f32, %b2 : f32):
%add = arith.addf %b0, %b1 : f32
linalg.yield %add : f32
} -> tensor<?x?xf32>
return %generic : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generic = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.test.fuse_using_forall %generic [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func.func @gemm_generic_fusion(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>,
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?xf32>)
// CHECK: %[[INIT:.+]] = tensor.empty
// CHECK: scf.forall (%[[IV0:[a-zA-Z0-9]+]], %[[IV1:[a-zA-Z0-9]+]]) =
// CHECK-SAME: shared_outs(%[[ITERARG0:.+]] = %[[INIT]])
// CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0]
// CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]]
// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT]][%[[IV0]], %[[IV1]]]
// CHECK: %[[FILL_TILE:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT_TILE]] :
// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :
// CHECK-SAME: outs(%[[FILL_TILE]] :
// CHECK-DAG: %[[BIAS_TILE:.+]] = tensor.extract_slice %[[ARG2]][%[[IV1]]]
// CHECK-DAG: %[[OUTS_TILE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV0]], %[[IV1]]]
// CHECK: %[[GENERIC_TILE:.+]] = linalg.generic
// CHECK-SAME: ins(%[[GEMM_TILE]], %[[BIAS_TILE]] :
// CHECK-SAME: outs(%[[OUTS_TILE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[GENERIC_TILE]] into %[[ITERARG0]][%[[IV0]], %[[IV1]]]
// CHECK: }
// -----
func.func @reduction_sequence(%arg0: tensor<30x3xf32>) -> tensor<30x3xf32> {
%cst = arith.constant 0.000000e+00 : f32
%cst_0 = arith.constant 0xFF800000 : f32
%0 = tensor.empty() : tensor<30xf32>
%1 = linalg.fill ins(%cst_0 : f32) outs(%0 : tensor<30xf32>) -> tensor<30xf32>
%2 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<30x3xf32>) outs(%1 : tensor<30xf32>) {
^bb0(%arg1: f32, %arg2: f32):
%8 = arith.maximumf %arg2, %arg1 : f32
linalg.yield %8 : f32
} -> tensor<30xf32>
%3 = tensor.empty() : tensor<30x3xf32>
%4 = linalg.fill ins(%cst : f32) outs(%0 : tensor<30xf32>) -> tensor<30xf32>
%5:2 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0)>, affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0, %2 : tensor<30x3xf32>, tensor<30xf32>) outs(%4, %3 : tensor<30xf32>, tensor<30x3xf32>) {
^bb0(%arg1: f32, %arg2: f32, %arg3: f32, %arg4: f32):
%8 = arith.subf %arg1, %arg2 : f32
%9 = math.exp %8 : f32
%10 = arith.addf %arg3, %9 : f32
linalg.yield %10, %9 : f32, f32
} -> (tensor<30xf32>, tensor<30x3xf32>)
%6 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%5#1, %5#0 : tensor<30x3xf32>, tensor<30xf32>) outs(%3 : tensor<30x3xf32>) {
^bb0(%arg1: f32, %arg2: f32, %arg3: f32):
%8 = arith.divf %arg1, %arg2 : f32
linalg.yield %8 : f32
} -> tensor<30x3xf32>
return %6 : tensor<30x3xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generics = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%generic1, %generic2, %generic3 = transform.split_handle %generics
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
%a, %b = transform.test.fuse_using_forall %generic3 [10]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func @reduction_sequence(%[[ARG0:.+]]: tensor<30x3xf32>)
// CHECK-DAG: %[[INIT0:.+]] = tensor.empty() : tensor<30xf32>
// CHECK-DAG: %[[INIT1:.+]] = tensor.empty() : tensor<30x3xf32>
// CHECK: %[[RESULT:[a-zA-Z0-9]+]] = scf.forall (%[[IV:[a-zA-Z0-9]+]])
// CHECK-SAME: shared_outs(%[[ITERARG0:[a-zA-Z0-9]+]] = %[[INIT1]])
// CHECK-DAG: %[[ARG0_SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV]], 0]
// CHECK-DAG: %[[INIT0_SLICE:.+]] = tensor.extract_slice %[[INIT0]][%[[IV]]]
// CHECK: %[[FILL0:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT0_SLICE]] :
// CHECK: %[[GENERIC0:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0_SLICE]] :
// CHECK-SAME: outs(%[[FILL0]] :
// CHECK: %[[FILL1:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT0_SLICE]] :
// CHECK: %[[INIT1_SLICE:.+]] = tensor.extract_slice %[[INIT1]][%[[IV]], 0]
// CHECK: %[[GENERIC1:.+]]:2 = linalg.generic
// CHECK-SAME: ins(%[[ARG0_SLICE]], %[[GENERIC0]] :
// CHECK-SAME: outs(%[[FILL1]], %[[INIT1_SLICE]] :
// CHECK: %[[ITERARG0_SLICE:.+]] = tensor.extract_slice %[[ITERARG0]][%[[IV]], 0]
// CHECK: %[[GENERIC2:.+]] = linalg.generic
// CHECK-SAME: ins(%[[GENERIC1]]#1, %[[GENERIC1]]#0 :
// CHECK-SAME: outs(%[[ITERARG0_SLICE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[GENERIC2]] into %[[ITERARG0]][%[[IV]], 0]
// CHECK: }
// CHECK: return %[[RESULT]]
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