// 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, %c = transform.structured.fuse %matmul [10, 20]
: (!transform.any_op) -> (!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.for %[[IV0:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG0:.+]] = %[[INIT]])
// CHECK: scf.for %[[IV1:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG1:.+]] = %[[ITERARG0]])
// 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 %[[ITERARG1]][%[[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: %[[INSERT:.+]] = tensor.insert_slice %[[GEMM_TILE]] into %[[ITERARG1]][%[[IV0]], %[[IV1]]]
// CHECK: scf.yield %[[INSERT]]
// -----
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, %c = transform.structured.fuse %generic [10, 20]
: (!transform.any_op) -> (!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.for %[[IV0:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG0:.+]] = %[[INIT]])
// CHECK: scf.for %[[IV1:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG1:.+]] = %[[ITERARG0]])
// 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 %[[ITERARG1]][%[[IV0]], %[[IV1]]]
// CHECK: %[[GENERIC_TILE:.+]] = linalg.generic
// CHECK-SAME: ins(%[[GEMM_TILE]], %[[BIAS_TILE]] :
// CHECK-SAME: outs(%[[OUTS_TILE]] :
// CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[GENERIC_TILE]] into %[[ITERARG1]][%[[IV0]], %[[IV1]]]
// CHECK: scf.yield %[[INSERT]]
// -----
func.func @gemm_gemm_fusion(%lhs0 : tensor<?x?xf32>, %rhs0 : tensor<?x?xf32>, %rhs1 : 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 %lhs0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %rhs0, %c1 : tensor<?x?xf32>
%init0 = tensor.empty(%d0, %d1) : tensor<?x?xf32>
%fill0 = linalg.fill ins(%cst : f32) outs(%init0 : tensor<?x?xf32>) -> tensor<?x?xf32>
%gemm0 = linalg.matmul
ins(%lhs0, %rhs0 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill0 : tensor<?x?xf32>) -> tensor<?x?xf32>
%d2 = tensor.dim %rhs1, %c1 : tensor<?x?xf32>
%init1 = tensor.empty(%d0, %d2) : tensor<?x?xf32>
%fill1 = linalg.fill ins(%cst : f32) outs(%init1 : tensor<?x?xf32>) -> tensor<?x?xf32>
%gemm1 = linalg.matmul
ins(%gemm0, %rhs1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill1 : tensor<?x?xf32>) -> tensor<?x?xf32>
return %gemm1 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%matmuls = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%mm1, %mm2 = transform.split_handle %matmuls
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
%a, %b = transform.structured.fuse %mm2 [10]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func.func @gemm_gemm_fusion(
// CHECK-SAME: %[[LHS0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[RHS0:[a-zA-Z0-9]+]]: tensor<?x?xf32>,
// CHECK-SAME: %[[RHS1:[a-zA-Z0-9]+]]: tensor<?x?xf32>)
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[D0:.+]] = tensor.dim %[[LHS0]], %[[C0]]
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[RHS0]], %[[C1]]
// CHECK-DAG: %[[INIT0:.+]] = tensor.empty(%[[D0]], %[[D1]])
// CHECK-DAG: %[[D2:.+]] = tensor.dim %[[RHS1]], %[[C1]]
// CHECK: %[[INIT1:.+]] = tensor.empty(%[[D0]], %[[D2]])
// CHECK: scf.for %[[IV:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG:.+]] = %[[INIT1]])
// CHECK-DAG: %[[LHS0_TILE:.+]] = tensor.extract_slice %[[LHS0]][%[[IV]], 0]
// CHECK-DAG: %[[RHS0_TILE:.+]] = tensor.extract_slice %[[RHS0]][0, 0]
// CHECK-DAG: %[[INIT0_TILE:.+]] = tensor.extract_slice %[[INIT0]][%[[IV]], 0]
// CHECK: %[[FILL0_TILE:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT0_TILE]] :
// CHECK: %[[GEMM0_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS0_TILE]], %[[RHS0_TILE]] :
// CHECK-SAME: outs(%[[FILL0_TILE]] :
// CHECK-DAG: %[[RHS1_TILE:.+]] = tensor.extract_slice %[[RHS1]][0, 0]
// CHECK-DAG: %[[INIT1_TILE:.+]] = tensor.extract_slice %[[ITERARG]][%[[IV]], 0]
// CHECK: %[[FILL1_TILE:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT1_TILE]] :
// CHECK: %[[GEMM1_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[GEMM0_TILE]], %[[RHS1_TILE]] :
// CHECK-SAME: outs(%[[FILL1_TILE]] :
// CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[GEMM1_TILE]] into %[[ITERARG]][%[[IV]], 0]
// CHECK: scf.yield %[[INSERT]]
// -----
func.func @gemm_transpose_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>
%init0 = tensor.empty(%d0, %d1) : tensor<?x?xf32>
%fill = linalg.fill ins(%cst : f32) outs(%init0 : 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>
%init1 = tensor.empty(%d1, %d0) : tensor<?x?xf32>
%transpose = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1, d0)>],
iterator_types = ["parallel", "parallel"]}
ins(%gemm : tensor<?x?xf32>) outs(%init1 : tensor<?x?xf32>) {
^bb0(%b0 : f32, %b1 : f32):
linalg.yield %b0 : f32
} -> tensor<?x?xf32>
return %transpose : 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, %c = transform.structured.fuse %generic [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func.func @gemm_transpose_fusion(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>)
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG1]], %[[C1]]
// CHECK-DAG: %[[INIT0:.+]] = tensor.empty(%[[D0]], %[[D1]])
// CHECK-DAG: %[[INIT1:.+]] = tensor.empty(%[[D1]], %[[D0]])
// CHECK: scf.for %[[IV0:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG0:.+]] = %[[INIT1]])
// CHECK: scf.for %[[IV1:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG1:.+]] = %[[ITERARG0]])
// CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0]
// CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]]
// CHECK-DAG: %[[INIT0_TILE:.+]] = tensor.extract_slice %[[INIT0]][%[[IV0]], %[[IV1]]]
// CHECK: %[[FILL_TILE:.+]] = linalg.fill
// CHECK-SAME: outs(%[[INIT0_TILE]] :
// CHECK: %[[GEMM_TILE:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] :
// CHECK-SAME: outs(%[[FILL_TILE]] :
// CHECK-DAG: %[[OUTS_TILE:.+]] = tensor.extract_slice %[[ITERARG1]][%[[IV1]], %[[IV0]]]
// CHECK: %[[GENERIC_TILE:.+]] = linalg.generic
// CHECK-SAME: ins(%[[GEMM_TILE]] :
// CHECK-SAME: outs(%[[OUTS_TILE]] :
// CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[GENERIC_TILE]] into %[[ITERARG1]][%[[IV1]], %[[IV0]]]
// CHECK: scf.yield %[[INSERT]]
// -----
func.func @interchange_matmul_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
%cst = arith.constant 0.0 : f32
%0 = tensor.empty(%d0, %d1) : tensor<?x?xf32>
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
%2 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%1 : tensor<?x?xf32>) -> tensor<?x?xf32>
%3 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%2 : tensor<?x?xf32>) outs(%0 : tensor<?x?xf32>) {
^bb0(%b0 : f32, %b1 : f32):
%4 = arith.addf %b0, %b0 : f32
linalg.yield %4 : f32
} -> tensor<?x?xf32>
return %3 : 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, %c = transform.structured.fuse %generic [10, 20] interchange[1, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func.func @interchange_matmul_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.for %[[IV0:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG0:.+]] = %[[INIT]])
// CHECK: scf.for %[[IV1:[a-zA-Z0-9]+]] =
// CHECK-SAME: iter_args(%[[ITERARG1:.+]] = %[[ITERARG0]])
// CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV1]], 0]
// CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV0]]]
// CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT]][%[[IV1]], %[[IV0]]]
// 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: %[[INIT_TILE_2:.+]] = tensor.extract_slice %[[ITERARG1]][%[[IV1]], %[[IV0]]]
// CHECK: %[[GENERIC_TILE:.+]] = linalg.generic
// CHECK-SAME: ins(%[[GEMM_TILE]] :
// CHECK-SAME: outs(%[[INIT_TILE_2]] :
// CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[GENERIC_TILE]] into %[[ITERARG1]][%[[IV1]], %[[IV0]]]
// CHECK: scf.yield %[[INSERT]]
// -----
func.func @matmul_plus_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
%arg2: tensor<?x?xf32>) -> tensor<?x?xf32>{
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = tensor.dim %arg2, %c0 : tensor<?x?xf32>
%1 = tensor.dim %arg2, %c1 : tensor<?x?xf32>
%2 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
%3 = tensor.dim %2, %c0 : tensor<?x?xf32>
%4 = tensor.dim %2, %c1 : tensor<?x?xf32>
%5 = tensor.empty(%3, %4) : tensor<?x?xf32>
%6 = linalg.generic
{indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%2, %2 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%5 : tensor<?x?xf32>) {
^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32) :
%7 = arith.addf %arg3, %arg4 : f32
linalg.yield %7 : f32
} -> tensor<?x?xf32>
return %6 : 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, %c = transform.structured.fuse %generic [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func @matmul_plus_matmul
// 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<?x?xf32>
// CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9_]+]]
// CHECK-SAME: iter_args(%[[ARG4:.+]] = %{{[a-zA-Z0-9_]+}})
// CHECK: %[[YIELD:.+]] = scf.for %[[IV1:[a-zA-Z0-9_]+]]
// CHECK-SAME: iter_args(%[[ARG6:.+]] = %[[ARG4]])
// CHECK-DAG: %[[ST_ARG0:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0]
// CHECK-DAG: %[[ST_ARG1:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]]
// CHECK-DAG: %[[ST_ARG2:.+]] = tensor.extract_slice %[[ARG2]][%[[IV0]], %[[IV1]]]
// CHECK: %[[MATMUL:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[ST_ARG0]], %[[ST_ARG1]] :
// CHECK-SAME: outs(%[[ST_ARG2]] :
// CHECK: %[[ST_ARG6:.+]] = tensor.extract_slice %[[ARG6]][%[[IV0]], %[[IV1]]]
// CHECK: %[[ST_RESULT:.+]] = linalg.generic
// CHECK-SAME: ins(%[[MATMUL]], %[[MATMUL]] :
// CHECK-SAME: outs(%[[ST_ARG6]] :
// CHECK: %[[UPDATE:.+]] = tensor.insert_slice %[[ST_RESULT]]
// CHECK-SAME: into %[[ARG6]][%[[IV0]], %[[IV1]]]
// CHECK: scf.yield %[[UPDATE]]
// CHECK: scf.yield %[[YIELD]]
// CHECK: return %[[RESULT]]
// -----
func.func @matmul_plus_transpose_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
%arg2: tensor<?x?xf32>) -> tensor<?x?xf32>{
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = tensor.dim %arg2, %c0 : tensor<?x?xf32>
%1 = tensor.dim %arg2, %c1 : tensor<?x?xf32>
%2 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
%3 = tensor.dim %2, %c0 : tensor<?x?xf32>
%4 = tensor.dim %2, %c1 : tensor<?x?xf32>
%5 = tensor.empty(%3, %4) : tensor<?x?xf32>
%6 = linalg.generic
{indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d1, d0)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%2, %2 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%5 : tensor<?x?xf32>) {
^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32) :
%7 = arith.addf %arg3, %arg4 : f32
linalg.yield %7 : f32
} -> tensor<?x?xf32>
return %6 : 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, %c = transform.structured.fuse %generic [10, 20]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func @matmul_plus_transpose_matmul
// 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<?x?xf32>
// CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9_]+]]
// CHECK-SAME: iter_args(%[[ARG4:.+]] = %{{[a-zA-Z0-9_]+}})
// CHECK: %[[YIELD:.+]] = scf.for %[[IV1:[a-zA-Z0-9_]+]]
// CHECK-SAME: iter_args(%[[ARG6:.+]] = %[[ARG4]])
// CHECK-DAG: %[[ST_ARG0:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], 0]
// CHECK-DAG: %[[ST_ARG1:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV1]]]
// CHECK-DAG: %[[ST_ARG2:.+]] = tensor.extract_slice %[[ARG2]][%[[IV0]], %[[IV1]]]
// CHECK: %[[LHS:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[ST_ARG0]], %[[ST_ARG1]]
// CHECK-SAME: : tensor<?x?xf32>, tensor<?x?xf32>)
// CHECK-SAME: outs(%[[ST_ARG2]] : tensor<?x?xf32>)
// CHECK-DAG: %[[STR_ARG0:.+]] = tensor.extract_slice %[[ARG0]][%[[IV1]], 0]
// CHECK-DAG: %[[STR_ARG1:.+]] = tensor.extract_slice %[[ARG1]][0, %[[IV0]]]
// CHECK-DAG: %[[STR_ARG2:.+]] = tensor.extract_slice %[[ARG2]][%[[IV1]], %[[IV0]]]
// CHECK: %[[RHS:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[STR_ARG0]], %[[STR_ARG1]] :
// CHECK-SAME: outs(%[[STR_ARG2]] :
// CHECK: %[[ST_ARG6:.+]] = tensor.extract_slice %[[ARG6]][%[[IV0]], %[[IV1]]]
// CHECK: %[[ST_RESULT:.+]] = linalg.generic
// CHECK-SAME: ins(%[[LHS]], %[[RHS]] :
// CHECK-SAME: outs(%[[ST_ARG6]] :
// CHECK: %[[UPDATE:.+]] = tensor.insert_slice %[[ST_RESULT]]
// CHECK-SAME: into %[[ARG6]][%[[IV0]], %[[IV1]]]
// CHECK: scf.yield %[[UPDATE]]
// CHECK: scf.yield %[[YIELD]]
// CHECK: return %[[RESULT]]
// -----
func.func @matmul_sequence_fusion(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
%arg2: tensor<?x?xf32>, %arg3: tensor<?x?xf32>, %arg4: tensor<?x?xf32>,
%arg5: tensor<?x?xf32>, %arg6: tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32> // [M, N0] * [N0, N1]
%1 = linalg.matmul ins(%0, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg4 : tensor<?x?xf32>) -> tensor<?x?xf32> // [M, N1] * [N1, N2]
%2 = linalg.matmul ins(%1, %arg5 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg6 : tensor<?x?xf32>) -> tensor<?x?xf32> // [M, N2] * [N2, N3]
return %2 : tensor<?x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%matmuls = transform.structured.match ops{["linalg.matmul"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%mm1, %mm2, %mm3 = transform.split_handle %matmuls
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
%a, %b = transform.structured.fuse %mm3 [10]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: #[[MAP:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 10)>
// CHECK: func @matmul_sequence_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<?x?xf32>
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG4:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG5:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG6:[a-zA-Z0-9_]+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-DAG: %[[ORIG_GEMM1:.+]] = linalg.matmul ins(%[[ARG0]], %[[ARG1]] :
// CHECK-DAG: %[[ORIG_GEMM2:.+]] = linalg.matmul ins(%[[ORIG_GEMM1]], %[[ARG3]] :
// CHECK-DAG: %[[M:.+]] = tensor.dim %[[ORIG_GEMM2]], %[[C0]]
// CHECK-DAG: %[[N2:.+]] = tensor.dim %[[ORIG_GEMM2]], %[[C1]]
// CHECK-DAG: %[[N3:.+]] = tensor.dim %[[ARG5]], %[[C1]]
// CHECK: %[[R0:.+]] = scf.for %[[IV:[a-zA-Z0-9_]+]] =
// CHECK-SAME: iter_args(%[[ARG8:.+]] = %[[ARG6]]) -> (tensor<?x?xf32>) {
// CHECK-DAG: %[[N1:.+]] = tensor.dim %[[ORIG_GEMM1]], %[[C1]]
// CHECK-DAG: %[[N0:.+]] = tensor.dim %[[ARG0]], %[[C1]]
// CHECK-DAG: %[[TILE_M:.+]] = affine.min #[[MAP]](%[[IV]])[%[[M]]]
// CHECK-DAG: %[[SLICE_ARG0:.+]] = tensor.extract_slice %[[ARG0]][%[[IV]], 0] [%[[TILE_M]], %[[N0]]]
// CHECK-DAG: %[[SLICE_ARG1:.+]] = tensor.extract_slice %[[ARG1]][0, 0] [%[[N0]], %[[N1]]]
// CHECK-DAG: %[[SLICE_ARG2:.+]] = tensor.extract_slice %[[ARG2]][%[[IV]], 0] [%[[TILE_M]], %[[N1]]]
// CHECK-DAG: %[[TILE_GEMM1:.+]] = linalg.matmul ins(%[[SLICE_ARG0]], %[[SLICE_ARG1]] :
// CHECK-SAME: outs(%[[SLICE_ARG2]] :
// CHECK-DAG: %[[SLICE_ARG3:.+]] = tensor.extract_slice %[[ARG3]][0, 0] [%[[N1]], %[[N2]]]
// CHECK-DAG: %[[SLICE_ARG4:.+]] = tensor.extract_slice %[[ARG4]][%[[IV]], 0] [%[[TILE_M]], %[[N2]]]
// CHECK-DAG: %[[TILE_GEMM2:.+]] = linalg.matmul ins(%[[TILE_GEMM1]], %[[SLICE_ARG3]] :
// CHECK-SAME: outs(%[[SLICE_ARG4]] :
// CHECK-DAG: %[[SLICE_ARG5:.+]] = tensor.extract_slice %[[ARG5]][0, 0] [%[[N2]], %[[N3]]]
// CHECK-DAG: %[[SLICE_ARG6:.+]] = tensor.extract_slice %[[ARG8]][%[[IV]], 0] [%[[TILE_M]], %[[N3]]]
// CHECK-DAG: %[[TILE_GEMM3:.+]] = linalg.matmul
// CHECK-SAME: ins(%[[TILE_GEMM2]], %[[SLICE_ARG5]] :
// CHECK-SAME: outs(%[[SLICE_ARG6]] :
// CHECK: %[[UPDATE:.+]] = tensor.insert_slice %[[TILE_GEMM3]] into %[[ARG8]][%[[IV]], 0] [%[[TILE_M]], %[[N3]]]
// CHECK: scf.yield %[[UPDATE]]
// -----
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.structured.fuse %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.for %[[IV:[a-zA-Z0-9]+]]
// CHECK-SAME: iter_args(%[[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-DAG: %[[INSERTSLICE:.+]] = tensor.insert_slice %[[GENERIC2]] into %[[ITERARG0]][%[[IV]], 0]
// CHECK: scf.yield %[[INSERTSLICE]]
// CHECK: return %[[RESULT]]
// -----
func.func @pad_producer_fusion(%arg0 : tensor<10xf32>) -> tensor<16xf32> {
%0 = tensor.empty() : tensor<10xf32>
%1 = linalg.generic {
indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>],
iterator_types = ["parallel"]}
ins(%arg0 : tensor<10xf32>) outs(%0 : tensor<10xf32>) {
^bb0(%b0 : f32, %b1 : f32):
%2 = arith.addf %b0, %b0: f32
linalg.yield %2 : f32
} -> tensor<10xf32>
%cst = arith.constant 0.0 : f32
%2 = tensor.pad %1 low[4] high[2] {
^bb0(%arg1 : index):
tensor.yield %cst : f32
} : tensor<10xf32> to tensor<16xf32>
return %2 : tensor<16xf32>
}
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
%pad = transform.structured.match ops{["tensor.pad"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %b = transform.structured.fuse %pad [8]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-LABEL: func @pad_producer_fusion
// CHECK-SAME: %[[ARG0:.+]]: tensor<10xf32>
// CHECK: %[[FOR_RESULT:.+]] = scf.for
// CHECK: %[[IF_RESULT:.+]] = scf.if
// CHECK: else
// CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: ins(%[[SLICE]] :
// CHECK: %[[PAD:.+]] = tensor.pad %[[GENERIC]]
// CHECK: %[[CAST:.+]] = tensor.cast %[[PAD]]
// CHECK: scf.yield %[[CAST]]
// CHECK: %[[INSERT_SLICE:.+]] = tensor.insert_slice %[[IF_RESULT]]
// CHECK: scf.yield %[[INSERT_SLICE]]
// CHECK: return %[[FOR_RESULT]]
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