mlir-hlo/tests/move_up_dynamic_broadcasts_...

288 lines
13 KiB
MLIR

// RUN: mlir-hlo-opt --split-input-file --allow-unregistered-dialect --mhlo-move-up-dynamic-broadcasts-for-fusion --canonicalize --cse %s | FileCheck %s
// Shape computations shall be reified.
// CHECK-LABEL: @shape_of_unary
// CHECK-SAME: (%[[ARG:.*]]: tensor<?x32xi16>)
func @shape_of_unary(%arg : tensor<?x32xi16>) {
// CHECK: %[[SHAPE:.*]] = shape.shape_of %[[ARG]] : tensor<?x32xi16> -> tensor<?xindex>
// CHECK: "use"(%[[SHAPE]])
%0 = "mhlo.convert"(%arg) : (tensor<?x32xi16>) -> tensor<?x32xf16>
%1 = shape.shape_of %0 : tensor<?x32xf16> -> tensor<?xindex>
"use"(%1) : (tensor<?xindex>) -> ()
return
}
// -----
// Shape computations shall be reified.
// CHECK-LABEL: @shape_of_nary
// CHECK-SAME: (%[[ARG0:.*]]: tensor<?x32xf16>, %[[ARG1:.*]]: tensor<?x32xf16>)
func @shape_of_nary(%arg0 : tensor<?x32xf16>, %arg1 : tensor<?x32xf16>) {
// CHECK: %[[SHAPE:.*]] = shape.shape_of %[[ARG0]] : tensor<?x32xf16> -> tensor<?xindex>
// CHECK: "use"(%[[SHAPE]])
%0 = mhlo.subtract %arg0, %arg1 : tensor<?x32xf16>
%1 = mhlo.subtract %0, %arg1 : tensor<?x32xf16>
%2 = shape.shape_of %1 : tensor<?x32xf16> -> tensor<?xindex>
"use"(%2) : (tensor<?xindex>) -> ()
return
}
// -----
// Broadcasts can be moved up over unary shape-preserving operations.
// CHECK-LABEL: @bcast_unary
// CHECK-SAME: (%[[ARG:.*]]: tensor<?x32xi16>, %[[OUT_DIMS:.*]]: tensor<3xindex>)
func @bcast_unary(%arg : tensor<?x32xi16>, %out_dims : tensor<3xindex>)
-> tensor<?x?x32xf16> {
// CHECK: %[[BCASTED_OPERAND:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG]], %[[OUT_DIMS]])
// CHECK-SAME: broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>} : (tensor<?x32xi16>, tensor<3xindex>) -> tensor<?x?x32xi16>
// CHECK: "mhlo.convert"(%[[BCASTED_OPERAND]]) : (tensor<?x?x32xi16>) -> tensor<?x?x32xf16>
%0 = "mhlo.convert"(%arg) : (tensor<?x32xi16>) -> tensor<?x32xf16>
%1 = "mhlo.dynamic_broadcast_in_dim"(%0, %out_dims) {
broadcast_dimensions = dense<[0, 1]> : tensor<2xi64> } :
(tensor<?x32xf16>, tensor<3xindex>) -> tensor<?x?x32xf16>
return %1 : tensor<?x?x32xf16>
}
// -----
// Broadcasts can be moved up over n-ary shape-preserving operations.
// CHECK-LABEL: @bcast_nary
// CHECK-SAME: (%[[ARG0:.*]]: tensor<?x32xf32>, %[[ARG1:.*]]: tensor<?x32xf32>, %[[OUT_DIMS:.*]]: tensor<3xindex>)
func @bcast_nary(%arg0 : tensor<?x32xf32>, %arg1 : tensor<?x32xf32>,
%out_dims : tensor<3xindex>) -> tensor<?x?x32xf32> {
// CHECK-NOT: subtract
// CHECK: %[[BCASTED_ARG0:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %[[OUT_DIMS]])
// CHECK: %[[BCASTED_ARG1:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %[[OUT_DIMS]])
// CHECK: %{{.*}} = mhlo.subtract %[[BCASTED_ARG0]], %[[BCASTED_ARG1]] : tensor<?x?x32xf32>
%0 = mhlo.subtract %arg0, %arg1 : tensor<?x32xf32>
%1 = "mhlo.dynamic_broadcast_in_dim"(%0, %out_dims) {
broadcast_dimensions = dense<[0, 1]> : tensor<2xi64> } :
(tensor<?x32xf32>, tensor<3xindex>) -> tensor<?x?x32xf32>
return %1 : tensor<?x?x32xf32>
}
// -----
// Exemplary IR as it appears in the lowering with `tf.Sub` and `tf.Cast`.
// CHECK-LABEL: @cast_sub
// CHECK-SAME: (%[[ARG0:.*]]: tensor<?x32xi16>, %[[ARG1:.*]]: tensor<?x?x32xf16>) -> tensor<?x?x32xf16>
func @cast_sub(%arg0: tensor<?x32xi16>, %arg1: tensor<?x?x32xf16>)
-> tensor<?x?x32xf16> {
// CHECK-NOT: convert
// CHECK: %[[BCASTED_ARG1:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG1]], %{{.*}})
// CHECK: %[[BCASTED_ARG0:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[ARG0]], %{{.*}})
// CHECK: %[[CONVERTED_BCASTED_ARG0:.*]] = "mhlo.convert"(%[[BCASTED_ARG0]]) : (tensor<?x?x32xi16>) -> tensor<?x?x32xf16>
// CHECK: %{{.*}} = mhlo.subtract %[[BCASTED_ARG1]], %[[CONVERTED_BCASTED_ARG0]] : tensor<?x?x32xf16>
%0 = "mhlo.convert"(%arg0) : (tensor<?x32xi16>) -> tensor<?x32xf16>
%1 = shape.shape_of %arg1 : tensor<?x?x32xf16> -> tensor<?xindex>
%2 = shape.shape_of %0 : tensor<?x32xf16> -> tensor<?xindex>
%3 = shape.cstr_broadcastable %1, %2 : tensor<?xindex>, tensor<?xindex>
%4 = shape.assuming %3 -> (tensor<?x?x32xf16>) {
%5 = shape.shape_of %arg1 : tensor<?x?x32xf16> -> tensor<?xindex>
%6 = shape.shape_of %0 : tensor<?x32xf16> -> tensor<?xindex>
%7 = shape.broadcast %5, %6 : tensor<?xindex>, tensor<?xindex>
-> tensor<?xindex>
%8 = tensor.cast %7 : tensor<?xindex> to tensor<3xindex>
%9 = "mhlo.dynamic_broadcast_in_dim"(%arg1, %8) {
broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} :
(tensor<?x?x32xf16>, tensor<3xindex>) -> tensor<?x?x32xf16>
%10 = "mhlo.dynamic_broadcast_in_dim"(%0, %8) {
broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} :
(tensor<?x32xf16>, tensor<3xindex>) -> tensor<?x?x32xf16>
%11 = mhlo.subtract %9, %10 : tensor<?x?x32xf16>
shape.assuming_yield %11 : tensor<?x?x32xf16>
}
return %4 : tensor<?x?x32xf16>
}
// -----
// CHECK-LABEL: @inline_bcasted_shape_operands
// CHECK-SAME: (%[[A:.*]]: tensor<?xindex>, %[[B:.*]]: tensor<?xindex>, %[[C:.*]]: tensor<?xindex>)
func @inline_bcasted_shape_operands(%a : tensor<?xindex>, %b : tensor<?xindex>,
%c : tensor<?xindex>) -> !shape.witness {
// CHECK-NOT: shape.broadcast
// CHECK: %[[WITNESS:.*]] = shape.cstr_broadcastable %[[A]], %[[B]], %[[C]]
// CHECK: return %[[WITNESS]] : !shape.witness
%0 = shape.broadcast %a, %b : tensor<?xindex>, tensor<?xindex>
-> tensor<?xindex>
%1 = shape.cstr_broadcastable %0, %c : tensor<?xindex>, tensor<?xindex>
return %1 : !shape.witness
}
// -----
// CHECK-LABEL: @move_shape_of_into_assuming
// CHECK-SAME: (%[[ARG0:.*]]: !shape.witness, %[[ARG1:.*]]: tensor<?x32xf32>)
func @move_shape_of_into_assuming(%arg0 : !shape.witness,
%arg1 : tensor<?x32xf32>) -> tensor<3xindex> {
// CHECK: %[[ASSUMING_RESULTS:.*]]:3 = shape.assuming %[[ARG0]] -> (tensor<?x32xf32>, tensor<?x32xf32>, tensor<3xindex>) {
// CHECK: %[[DUMMY_TENSOR:.*]] = "dummy.tensor"() : () -> tensor<?x32xf32>
// CHECK: %[[SHAPE:.*]] = shape.shape_of %[[DUMMY_TENSOR]]
// CHECK: shape.assuming_yield %[[ARG1]], %[[DUMMY_TENSOR]], %[[SHAPE]]
// CHECK: }
// CHECK-NOT: shape_of
// CHECK: return %[[ASSUMING_RESULTS]]#2
%0:2 = shape.assuming %arg0 -> (tensor<?x32xf32>, tensor<?x32xf32>) {
%1 = "dummy.tensor"() : () -> tensor<?x32xf32>
shape.assuming_yield %arg1, %1 : tensor<?x32xf32>, tensor<?x32xf32>
}
%2 = shape.shape_of %0#1 : tensor<?x32xf32> -> tensor<3xindex>
return %2 : tensor<3xindex>
}
// -----
// CHECK-LABEL: @move_cstr_broadcastable_into_assuming
// CHECK-SAME: (%[[ARG0:.*]]: !shape.witness, %[[ARG1:.*]]: tensor<2xindex>)
func @move_cstr_broadcastable_into_assuming(%arg0 : !shape.witness,
%arg1 : tensor<2xindex>) -> !shape.witness {
// CHECK: %[[ASSUMING_RESULTS:.*]]:3 = shape.assuming %[[ARG0]] -> (tensor<2xindex>, tensor<3xindex>, !shape.witness) {
// CHECK: %[[DUMMY_TENSOR:.*]] = "dummy.tensor"() : () -> tensor<3xindex>
// CHECK: %[[WITNESS:.*]] = shape.cstr_broadcastable %[[ARG1]], %[[DUMMY_TENSOR]]
// CHECK: shape.assuming_yield %[[ARG1]], %[[DUMMY_TENSOR]], %[[WITNESS]]
// CHECK: }
// CHECK-NOT: cstr_broadcastable
// CHECK: return %[[ASSUMING_RESULTS]]#2
%0:2 = shape.assuming %arg0 -> (tensor<2xindex>, tensor<3xindex>) {
%1 = "dummy.tensor"() : () -> tensor<3xindex>
shape.assuming_yield %arg1, %1 : tensor<2xindex>, tensor<3xindex>
}
%1 = shape.cstr_broadcastable %arg1, %0#1 : tensor<2xindex>, tensor<3xindex>
return %1 : !shape.witness
}
// -----
// CHECK-LABEL: @not_move_shape_of_into_assuming
func @not_move_shape_of_into_assuming(%arg0 : !shape.witness,
%arg1 : tensor<?x32xf32>, %arg2 : tensor<?x32xf32>) -> tensor<3xindex> {
// CHECK: shape.assuming
// CHECK-SAME: {
// CHECK-NOT: shape_of
// CHECK: }
// CHECK: "some.other.op"
// CHECK: shape_of
%0:2 = shape.assuming %arg0 -> (tensor<?x32xf32>, tensor<?x32xf32>) {
shape.assuming_yield %arg1, %arg2 : tensor<?x32xf32>, tensor<?x32xf32>
}
"some.other.op"() : () -> ()
%2 = shape.shape_of %0#1 : tensor<?x32xf32> -> tensor<3xindex>
return %2 : tensor<3xindex>
}
// -----
// CHECK-LABEL: @move_cstr_broadcastable_out_of_assuming
// CHECK-SAME: (%[[ARG0:.*]]: !shape.witness, %[[ARG1:.*]]: tensor<2xindex>, %[[ARG2:.*]]: tensor<3xindex>)
func @move_cstr_broadcastable_out_of_assuming(%arg0 : !shape.witness,
%arg1 : tensor<2xindex>, %arg2 : tensor<3xindex>) -> !shape.witness {
// CHECK: %[[WITNESS:.*]] = shape.cstr_broadcastable %[[ARG1]], %[[ARG2]]
// CHECK-NOT: assuming
// CHECK-NOT: cstr_broadcastable
// CHECK: return %[[WITNESS]]
%0 = shape.assuming %arg0 -> (!shape.witness) {
%1 = shape.cstr_broadcastable %arg1, %arg2 : tensor<2xindex>, tensor<3xindex>
shape.assuming_yield %1 : !shape.witness
}
return %0 : !shape.witness
}
// -----
// CHECK-LABEL: @move_shape_of_out_of_assuming
// CHECK-SAME: (%[[ARG0:.*]]: !shape.witness, %[[ARG1:.*]]: tensor<2x?xf32>)
func @move_shape_of_out_of_assuming(%arg0 : !shape.witness,
%arg1 : tensor<2x?xf32>) -> tensor<2xindex> {
// CHECK: %[[SHAPE:.*]] = shape.shape_of %[[ARG1]]
// CHECK-NOT: assuming
// CHECK-NOT: cstr_broadcastable
// CHECK: return %[[SHAPE]]
%0 = shape.assuming %arg0 -> (tensor<2xindex>) {
%1 = shape.shape_of %arg1 : tensor<2x?xf32> -> tensor<2xindex>
shape.assuming_yield %1 : tensor<2xindex>
}
return %0 : tensor<2xindex>
}
// -----
// CHECK-LABEL: @move_shape_of_out_of_assuming
// CHECK-SAME: (%[[ARG0:.*]]: !shape.witness, %[[ARG1:.*]]: tensor<2x?xf32>)
func @move_shape_of_out_of_assuming(%arg0 : !shape.witness,
%arg1 : tensor<2x?xf32>) -> tensor<2xindex> {
// CHECK: %[[SHAPE:.*]] = shape.shape_of %[[ARG1]]
// CHECK: %{{.*}} = shape.assuming %[[ARG0]] -> (tensor<2x?xf32>) {
// CHECK: %[[SOME_VAL:.*]] = "some.op"() : () -> tensor<2x?xf32>
// CHECK: shape.assuming_yield %[[SOME_VAL]] : tensor<2x?xf32>
// CHECK: }
// CHECK: return %[[SHAPE]]
%0:2 = shape.assuming %arg0 -> (tensor<2x?xf32>, tensor<2xindex>) {
%1 = "some.op"() : () -> (tensor<2x?xf32>)
%2 = shape.shape_of %arg1 : tensor<2x?xf32> -> tensor<2xindex>
shape.assuming_yield %1, %2 : tensor<2x?xf32>, tensor<2xindex>
}
return %0#1 : tensor<2xindex>
}
// -----
// CHECK-LABEL: @not_move_shape_of_out_of_assuming
// CHECK-SAME: (%[[ARG0:.*]]: !shape.witness, %[[ARG1:.*]]: tensor<2x?xf32>)
func @not_move_shape_of_out_of_assuming(%arg0 : !shape.witness,
%arg1 : tensor<2x?xf32>) -> tensor<2xindex> {
// CHECK-NOT: shape_of
// CHECK: shape.assuming
// CHECK-SAME: {
// CHECK: "some.tensor"
// CHECK: shape_of
// CHECK: }
%0 = shape.assuming %arg0 -> (tensor<2xindex>) {
%1 = "some.tensor"() : () -> tensor<2x?xf32>
%2 = shape.shape_of %1 : tensor<2x?xf32> -> tensor<2xindex>
shape.assuming_yield %2 : tensor<2xindex>
}
return %0 : tensor<2xindex>
}
// -----
// CHECK: @merge_assuming_ops
// CHECK: (%[[ARG0:.*]]: tensor<?x32xf16>, %[[ARG1:.*]]: tensor<?x32xf16>, %[[ARG2:.*]]: tensor<?x?x32xf16>)
func @merge_assuming_ops(%arg0: tensor<?x32xf16>, %arg1 : tensor<?x32xf16>,
%arg2: tensor<?x?x32xf16>) -> tensor<?x?x32xf16> {
// CHECK: %[[SHAPE0:.*]] = shape.shape_of %[[ARG0]]
// CHECK: %[[SHAPE1:.*]] = shape.shape_of %[[ARG1]]
// CHECK: %[[SHAPE2:.*]] = shape.shape_of %[[ARG2]]
// CHECK: %[[WITNESS0:.*]] = shape.cstr_broadcastable %[[SHAPE0]], %[[SHAPE1]]
// CHECK: %[[WITNESS1:.*]] = shape.cstr_broadcastable %[[SHAPE0]], %[[SHAPE1]], %[[SHAPE2]]
// CHECK: %[[WITNESS_MERGED:.*]] = shape.cstr_broadcastable %[[SHAPE0]], %[[SHAPE1]], %[[SHAPE2]]
// CHECK: %[[MERGED:.*]]:2 = shape.assuming %[[WITNESS_MERGED]]
// CHECK-SAME: {
// CHECK: "some.op"
// CHECK: %[[RESULT0:.*]] = "some.producer"
// CHECK: "another.op"
// CHECK: %[[RESULT1:.*]] = "another.producer"
// CHECK: shape.assuming_yield %[[RESULT0]], %[[RESULT1]]
// CHECK: }
// CHECK: return %[[MERGED]]#1
%0 = shape.shape_of %arg0 : tensor<?x32xf16> -> tensor<2xindex>
%1 = shape.shape_of %arg1 : tensor<?x32xf16> -> tensor<2xindex>
%2 = shape.shape_of %arg2 : tensor<?x?x32xf16> -> tensor<3xindex>
%3 = shape.cstr_broadcastable %0, %1 : tensor<2xindex>, tensor<2xindex>
%4 = shape.cstr_broadcastable %0, %1, %2 : tensor<2xindex>, tensor<2xindex>,
tensor<3xindex>
%5 = shape.assuming %3 -> (tensor<?x32xf16>) {
"some.op"() : () -> ()
%6 = "some.producer"() : () -> tensor<?x32xf16>
shape.assuming_yield %6 : tensor<?x32xf16>
}
%7 = shape.assuming %4 -> (tensor<?x?x32xf16>) {
"another.op"() : () -> ()
%8 = "another.producer"() : () -> tensor<?x?x32xf16>
shape.assuming_yield %8 : tensor<?x?x32xf16>
}
return %7 : tensor<?x?x32xf16>
}