colin
/
onnx-mlir
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
onnx-mlir/test/mlir/onnx/onnx_lowering.mlir

259 lines
14 KiB
MLIR
Raw Normal View History

Add test for checking lowering of Add op to KRNL IR (#385) * Add test for checking lowering of Add op to KRNL IR. * Add test file.
2019-11-27 08:29:18 +08:00
// RUN: onnf-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
[MLIR] Lower ONNX element-wise binary ops: Mul, Div, Sub, And, Or, Xor (#388) * Lower ONNX element-wise binary ops: Mul, Div, Sub, And, Or, Xor * Edit gen_doc.py to avoid changes about AnyTypeOf<[AnyMemRef, AnyTensor]> * Miss a space * Add tests * Shorten ONNXElementWiseBinaryOpLowering into ONNXEWBinaryOpLowering * Move lowering patterns into runOnModule() * Redundant space
2019-12-04 00:17:21 +08:00
func @test_add(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Add"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_add
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[ADDF:%.+]] = addf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[ADDF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_mul(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Mul"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_mul
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[MULF:%.+]] = mulf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[MULF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_div(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Div"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_div
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[DIVF:%.+]] = divf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[DIVF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_sub(%arg0 : tensor<?x10xf32>, %arg1 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Sub"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_sub
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xf32>
// CHECK: [[SUBF:%.+]] = subf [[LOAD1]], [[LOAD2]] : f32
// CHECK: store [[SUBF]], [[RES]][%arg2, %arg3] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
Add test for checking lowering of Add op to KRNL IR (#385) * Add test for checking lowering of Add op to KRNL IR. * Add test file.
2019-11-27 08:29:18 +08:00
}
[MLIR] Lower ONNX element-wise binary ops: Mul, Div, Sub, And, Or, Xor (#388) * Lower ONNX element-wise binary ops: Mul, Div, Sub, And, Or, Xor * Edit gen_doc.py to avoid changes about AnyTypeOf<[AnyMemRef, AnyTensor]> * Miss a space * Add tests * Shorten ONNXElementWiseBinaryOpLowering into ONNXEWBinaryOpLowering * Move lowering patterns into runOnModule() * Redundant space
2019-12-04 00:17:21 +08:00
func @test_and(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.And"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%0) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_and
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[AND:%.+]] = and [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[AND]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}
func @test_or(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.Or"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%0) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_or
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[OR:%.+]] = or [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[OR]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}
func @test_xor(%arg0 : tensor<?x10xi32>, %arg1 : tensor<?x10xi32>) -> tensor<*xi32> {
%0 = "onnx.Xor"(%arg0, %arg1) : (tensor<?x10xi32>, tensor<?x10xi32>) -> tensor<*xi32>
"std.return"(%0) : (tensor<*xi32>) -> ()
// CHECK-LABEL: test_xor
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xi32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xi32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg2 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg3 = 0 to 10) {
// CHECK: [[LOAD1:%.+]] = load %arg0[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[LOAD2:%.+]] = load %arg1[%arg2, %arg3] : memref<?x10xi32>
// CHECK: [[XOR:%.+]] = xor [[LOAD1]], [[LOAD2]] : i32
// CHECK: store [[XOR]], [[RES]][%arg2, %arg3] : memref<?x10xi32>
// CHECK: return [[RES]] : memref<?x10xi32>
}
[MLIR] Lower ONNX element-wise unary ops: Exp, Tanh, Sinh, Cosh, Sigmoid (#389) * Lower ExpOp * Lower ONNXTanhOp * Lower Exp Tanh, Sinh, and Cosh * Lower ONNX Sigmoid op * Merge * Specialize template lowerScalarOp * Unify ONNXEWUnaryOpLowering and ONNXEWBinaryOpLowering * Support multiple types * Reformat the code * Add test cases * Reformat the code * Change names * Apply clang-format * Update variable names
2019-12-06 09:08:09 +08:00
func @test_exp(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Exp"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_exp
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
// CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
// CHECK: [[EXP:%.+]] = exp [[LOAD]] : f32
// CHECK: store [[EXP]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_tanh(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Tanh"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_tanh
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
// CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
// CHECK: [[ZERO:%.+]] = constant {{0.+}} : f32
// CHECK: [[NLOAD:%.+]] = subf [[ZERO]], [[LOAD]] : f32
// CHECK: [[EXP:%.+]] = exp [[LOAD]] : f32
// CHECK: [[NEXP:%.+]] = exp [[NLOAD]] : f32
// CHECK: [[DIVIDEND:%.+]] = subf [[EXP]], [[NEXP]] : f32
// CHECK: [[DIVISOR:%.+]] = addf [[EXP]], [[NEXP]] : f32
// CHECK: [[TANH_RES:%.+]] = divf [[DIVIDEND]], [[DIVISOR]] : f32
// CHECK: store [[TANH_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_sinh(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Sinh"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_sinh
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
// CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
// CHECK: [[ZERO:%.+]] = constant {{0.+}} : f32
// CHECK: [[TWO:%.+]] = constant {{2.+}} : f32
// CHECK: [[NLOAD:%.+]] = subf [[ZERO]], [[LOAD]] : f32
// CHECK: [[EXP:%.+]] = exp [[LOAD]] : f32
// CHECK: [[NEXP:%.+]] = exp [[NLOAD]] : f32
// CHECK: [[DIVIDEND:%.+]] = subf [[EXP]], [[NEXP]] : f32
// CHECK: [[SINH_RES:%.+]] = divf [[DIVIDEND]], [[TWO]] : f32
// CHECK: store [[SINH_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_cosh(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Cosh"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_cosh
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
// CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
// CHECK: [[ZERO:%.+]] = constant {{0.+}} : f32
// CHECK: [[TWO:%.+]] = constant {{2.+}} : f32
// CHECK: [[NLOAD:%.+]] = subf [[ZERO]], [[LOAD]] : f32
// CHECK: [[EXP:%.+]] = exp [[LOAD]] : f32
// CHECK: [[NEXP:%.+]] = exp [[NLOAD]] : f32
// CHECK: [[DIVIDEND:%.+]] = addf [[EXP]], [[NEXP]] : f32
// CHECK: [[COSH_RES:%.+]] = divf [[DIVIDEND]], [[TWO]] : f32
// CHECK: store [[COSH_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}
func @test_sigmoid(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Sigmoid"(%arg0) : (tensor<?x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
// CHECK-LABEL: test_sigmoid
// CHECK: [[DIM_0:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: [[RES:%.+]] = alloc([[DIM_0]]) : memref<?x10xf32>
// CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
// CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops {
// CHECK: krnl.return_loops [[DEF_LOOPS]]#0, [[DEF_LOOPS]]#1
// CHECK: } : () -> (!krnl.loop, !krnl.loop)
// CHECK: [[DIM_2:%.+]] = dim %arg0, 0 : memref<?x10xf32>
// CHECK: krnl.iterate([[OPT_LOOPS]]#0, [[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to [[DIM_2]], [[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
// CHECK: [[LOAD:%.+]] = load %arg0[%arg1, %arg2] : memref<?x10xf32>
// CHECK: [[ZERO:%.+]] = constant {{0.+}} : f32
// CHECK: [[ONE:%.+]] = constant {{1.+}} : f32
// CHECK: [[NLOAD:%.+]] = subf [[ZERO]], [[LOAD]] : f32
// CHECK: [[NEXP:%.+]] = exp [[NLOAD]] : f32
// CHECK: [[DIVISOR:%.+]] = addf [[ONE]], [[NEXP]] : f32
// CHECK: [[SIGMOID_RES:%.+]] = divf [[ONE]], [[DIVISOR]] : f32
// CHECK: store [[SIGMOID_RES]], [[RES]][%arg1, %arg2] : memref<?x10xf32>
// CHECK: return [[RES]] : memref<?x10xf32>
}