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/external/tensorflow/tensorflow/compiler/mlir/hlo/tests/
Dchlo_legalize_to_mhlo.mlir7 // CHECK: mhlo.constant dense<3.389{{.*}}e+38>
16 // CHECK: mhlo.constant dense<6.550{{.*}}e+04>
25 // CHECK: mhlo.constant dense<3.402{{.*}}E+38>
33 // CHECK: %[[TMP_0:.*]] = "mhlo.sign"(%[[ARG]])
34 // CHECK: %[[TMP_1:.*]] = "mhlo.abs"(%[[ARG]])
35 // CHECK: %[[TMP_2:.*]] = mhlo.constant dense<1.797{{.*}}E+308>
36 // CHECK: %[[TMP_3:.*]] = "mhlo.sqrt"(%[[TMP_2]])
37 // CHECK: %[[TMP_4:.*]] = "mhlo.compare"(%[[TMP_1]], %[[TMP_3]]) {comparison_direction = "GE"}
38 // CHECK: %[[TMP_5:.*]] = "mhlo.abs"(%[[ARG]])
39 // CHECK: %[[TMP_6:.*]] = "mhlo.log"(%[[TMP_5]])
[all …]
Dlower-complex.mlir1 // RUN: mlir-hlo-opt %s -chlo-legalize-to-hlo -mhlo-test-lower-complex | FileCheck %s
5 %2 = "mhlo.complex"(%arg0, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> (tensor<2xcomplex<f32>>)
6 %3 = "mhlo.complex"(%arg2, %arg3) : (tensor<2xf32>, tensor<2xf32>) -> (tensor<2xcomplex<f32>>)
8 // CHECK-DAG: [[VAL0:%.+]] = mhlo.add %arg0, %arg2
9 // CHECK-DAG: [[VAL1:%.+]] = mhlo.add %arg1, %arg3
10 …%4 = "mhlo.add"(%2, %3) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> (tensor<2xcomplex<f3…
11 %5 = "mhlo.real"(%4) : (tensor<2xcomplex<f32>>) -> (tensor<2xf32>)
12 %6 = "mhlo.imag"(%4) : (tensor<2xcomplex<f32>>) -> (tensor<2xf32>)
20 %2 = "mhlo.complex"(%arg0, %arg1) : (tensor<*xf32>, tensor<*xf32>) -> (tensor<*xcomplex<f32>>)
21 %3 = "mhlo.complex"(%arg2, %arg3) : (tensor<*xf32>, tensor<*xf32>) -> (tensor<*xcomplex<f32>>)
[all …]
Dcanonicalize.mlir5 %0 = mhlo.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
6 %1 = mhlo.constant dense<[5, 6, 7, 8]> : tensor<4xi64>
7 // CHECK: mhlo.constant dense<[6, 8, 10, 12]>
8 %2 = "mhlo.add"(%0, %1) : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>)
14 %0 = mhlo.constant dense<1> : tensor<4xi64>
15 %1 = mhlo.constant dense<5> : tensor<4xi64>
16 // CHECK: mhlo.constant dense<6>
17 %2 = "mhlo.add"(%0, %1) : (tensor<4xi64>, tensor<4xi64>) -> (tensor<4xi64>)
23 %0 = mhlo.constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64>
24 %1 = mhlo.constant dense<[5.0, 6.0, 7.0, 8.0]> : tensor<4xf64>
[all …]
Dsink-constants-to-control-flow.mlir1 // RUN: mlir-hlo-opt %s -mhlo-sink-constants-to-control-flow | FileCheck %s
7 // CHECK-NEXT: mhlo.while
8 %c0 = mhlo.constant dense<1> : tensor<i64>
9 %c1 = mhlo.constant dense<2> : tensor<i64>
10 %0 = "mhlo.while"(%arg0) ( {
13 // CHECK: %[[C0:.+]] = mhlo.constant dense<1> : tensor<i64>
14 // CHECK: "mhlo.compare"(%[[C0]], %[[ARG1A]])
15 …%1 = "mhlo.compare"(%c0, %arg1) {comparison_direction = "LT"} : (tensor<i64>, tensor<i64>) -> tens…
16 "mhlo.return"(%1) : (tensor<i1>) -> ()
20 // CHECK-DAG: %[[C1:.+]] = mhlo.constant dense<2> : tensor<i64>
[all …]
Dmhlo-fusion.mlir1 // RUN: mlir-hlo-opt %s -mhlo-fusion -split-input-file | FileCheck %s
5 %0 = "mhlo.add"(%arg0, %arg1) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
6 %1 = "mhlo.subtract"(%arg0, %0) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
7 %2 = "mhlo.add"(%1, %1) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
8 // CHECK: %[[RET:.*]]:2 = "mhlo.fusion"
9 // CHECK-NEXT: mhlo.add
10 // CHECK-NEXT: mhlo.subtract
11 // CHECK-NEXT: mhlo.add
12 // CHECK-NEXT: mhlo.return
20 %0 = "mhlo.abs"(%arg0) : (tensor<?x?xf32>) -> tensor<?x?xf32>
[all …]
Dconvert.mlir8 %0 = "mhlo.convert"(%arg) : (tensor<f32>) -> tensor<f32>
18 // CHECK-NEXT: [[RES:%.+]] = "mhlo.convert"([[ARG]]) : (tensor<i32>) -> tensor<i64>
19 %0 = "mhlo.convert"(%arg) : (tensor<i32>) -> tensor<i64>
29 // CHECK-NEXT: [[RES:%.+]] = "mhlo.convert"([[ARG]]) : (tensor<i32>) -> tensor<i16>
30 %0 = "mhlo.convert"(%arg) : (tensor<i32>) -> tensor<i16>
40 // CHECK-NEXT: [[RES:%.+]] = "mhlo.convert"([[ARG]]) : (tensor<f32>) -> tensor<i32>
41 %0 = "mhlo.convert"(%arg) : (tensor<f32>) -> tensor<i32>
51 // CHECK-NEXT: [[RES:%.+]] = "mhlo.convert"([[ARG]]) : (tensor<i32>) -> tensor<f32>
52 %0 = "mhlo.convert"(%arg) : (tensor<i32>) -> tensor<f32>
62 // CHECK-NEXT: [[RES:%.+]] = "mhlo.convert"([[ARG]]) : (tensor<2x3xi32>) -> tensor<2x3xf32>
[all …]
Dreshape.mlir5 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<42> : tensor<i32>
6 %cst = mhlo.constant dense<42> : tensor<1x1xi32>
7 %0 = "mhlo.reshape"(%cst) : (tensor<1x1xi32>) -> tensor<i32>
16 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<42> : tensor<2xi32>
17 %cst = mhlo.constant dense<42> : tensor<1x2xi32>
18 %0 = "mhlo.reshape"(%cst) : (tensor<1x2xi32>) -> tensor<2xi32>
27 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<42> : tensor<1xi32>
28 %cst = mhlo.constant dense<42> : tensor<i32>
29 %0 = "mhlo.reshape"(%cst) : (tensor<i32>) -> tensor<1xi32>
38 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<42> : tensor<16xi64>
[all …]
Dops.mlir6 // CHECK-LABEL: func private @token_type() -> !mhlo.token
7 func private @token_type() -> !mhlo.token
11 // expected-error@+1 {{unknown mhlo type: foobar}}
12 func private @invalid_type() -> !mhlo.foobar
18 %0 = "mhlo.all_to_all"(%data) {
31 %0 = "mhlo.all_to_all"(%data) {
44 %0 = "mhlo.all_to_all"(%data) {
57 …%0 = "mhlo.broadcast"(%arg0) {broadcast_sizes = dense<[1, 2]> : tensor<2xi64>} : (tensor<3xi32>) -…
65 …%0 = "mhlo.broadcast"(%arg0) {broadcast_sizes = dense<[[1, 2]]> : tensor<1x2xi64>} : (tensor<3xi32…
73 …%0 = "mhlo.broadcast"(%arg0) {broadcast_sizes = dense<[2]> : tensor<1xi64>} : (tensor<3xi32>) -> t…
[all …]
Dlegalize-control-flow.mlir1 // RUN: mlir-hlo-opt -mhlo-legalize-control-flow %s -o - | FileCheck %s
7 //CHECK: [[VAL1:%.+]] = "mhlo.compare"([[VAL0]], [[VAL0]])
11 //CHECK: [[VAL4:%.+]] = mhlo.add [[VAL3]], [[VAL3]]
14 %0 = "mhlo.while"(%arg0) ( {
16 …%1 = "mhlo.compare"(%arg1, %arg1) {comparison_direction = "LT", name = "compare.2"} : (tensor<i64>…
17 "mhlo.return"(%1) : (tensor<i1>) -> ()
20 %1 = mhlo.add %arg1, %arg1 {name = "compare.0"} : tensor<i64>
21 "mhlo.return"(%1) : (tensor<i64>) -> ()
33 …// CHECK: [[VAL0:%.+]] = "mhlo.compare"(%arg0, [[C0]]) {comparison_direction = "LT"} : (tensor<f…
34 …%0 = "mhlo.compare"(%arg0, %cst) {comparison_direction = "LT"} : (tensor<f32>, tensor<f32>) -> ten…
[all …]
Doptimize-hlo.mlir1 // RUN: mlir-hlo-opt %s -pass-pipeline='func(mhlo-test-optimize)' | FileCheck %s
5 // CHECK: [[CST:%.+]] = mhlo.constant dense<0> : tensor<i64>
6 …// CHECK: [[SLICE:%.+]] = "mhlo.dynamic-slice"(%arg0, %arg1, [[CST]], [[CST]]) {slice_sizes = dens…
7 // CHECK: [[RESHAPE:%.+]] = "mhlo.reshape"([[SLICE]])
8 %res = "mhlo.gather"(%arg0, %arg1) {
24 // CHECK: [[CST:%.+]] = mhlo.constant dense<0> : tensor<i64>
25 // CHECK: [[RESHAPE:%.+]] = "mhlo.reshape"(%arg1)
26 …// CHECK: [[SLICE:%.+]] = "mhlo.dynamic-slice"(%arg0, [[RESHAPE]], [[CST]], [[CST]]) {slice_sizes …
27 // CHECK: [[RES:%.+]] = "mhlo.reshape"([[SLICE]])
29 %res = "mhlo.gather"(%arg0, %arg1) {
[all …]
Dunfuse_batch_norm.mlir1 // RUN: mlir-hlo-opt -split-input-file -mhlo-test-unfuse-batch-norm -verify-diagnostics %s | FILECH…
13 // CHECK-DAG: %[[EPS_BCAST:.+]] = mhlo.constant dense<1.001000e-05> : tensor<256xf32>
14 // CHECK-DAG: %[[VARIANCE_EPS:.+]] = mhlo.add %[[VARIANCE]], %[[EPS_BCAST]] : tensor<256xf32>
15 …// CHECK-DAG: %[[STDDEV:.+]] = "mhlo.sqrt"(%[[VARIANCE_EPS]]) : (tensor<256xf32>) -> tensor<256xf3…
16 …// CHECK-DAG: %[[STDDEV_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[STDDEV]]) {broadcast_dimensions = …
17 …// CHECK-DAG: %[[SCALE_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[SCALE]]) {broadcast_dimensions = de…
18 …// CHECK-DAG: %[[OFFSET_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[OFFSET]]) {broadcast_dimensions = …
19 …// CHECK-DAG: %[[MEAN_BCAST:.+]] = "mhlo.broadcast_in_dim"(%[[MEAN]]) {broadcast_dimensions = dens…
20 // CHECK-DAG: %[[X_CENTER:.+]] = mhlo.subtract %[[X]], %[[MEAN_BCAST]] : tensor<4x256xf32>
21 // CHECK-DAG: %[[X_SCALED:.+]] = mhlo.multiply %[[X_CENTER]], %[[SCALE_BCAST]] : tensor<4x256xf32>
[all …]
Dlegalize-to-std.mlir1 // RUN: mlir-hlo-opt -mhlo-legalize-to-std %s -o - | FileCheck %s
6 %0 = "mhlo.add"(%arg0, %arg1) {name = "add.3"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
9 %1 = "mhlo.multiply"(%0, %arg1) {name = "mul.4"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
12 %2 = "mhlo.subtract"(%1, %arg1) {name = "sub.5"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
15 %3 = "mhlo.divide"(%2, %arg1) {name = "div.6"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
18 %4 = "mhlo.remainder"(%3, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
27 %0 = "mhlo.add"(%arg0, %arg1) {name = "add.3"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
30 %1 = "mhlo.multiply"(%0, %arg1) {name = "mul.4"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
33 %2 = "mhlo.subtract"(%1, %arg1) {name = "sub.5"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
36 %3 = "mhlo.divide"(%2, %arg1) {name = "div.6"} : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>
[all …]
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/
Dlegalize-tf-communication.mlir4 // `mhlo.send` per operand and `mhlo.recv` per result. Channel Id's are uniquely
5 // assigned per mhlo communcation op, and frontend attributes (modified keys)
20 // CHECK: [[INIT_TOKEN:%.*]] = "mhlo.create_token"
22 // CHECK: [[SEND_ARG0_TOKEN:%.*]] = "mhlo.send"([[ARG0]], [[INIT_TOKEN]])
25 …// CHECK-SAME: mhlo.frontend_attributes = {_xla_host_transfer_original_type = "s32", _xla_host_tra…
26 // CHECK-SAME: mhlo.sharding = "\08\01\1A\01\01\22\01\00"
27 // CHECK-SAME: (tensor<i32>, !mhlo.token) -> !mhlo.token
29 // CHECK: [[SEND_ARG1_TOKEN:%.*]] = "mhlo.send"([[ARG1]], [[INIT_TOKEN]])
32 …// CHECK-SAME: mhlo.frontend_attributes = {_xla_host_transfer_original_type = "s64", _xla_host_tra…
33 // CHECK-SAME: mhlo.sharding = "\08\01\1A\01\01\22\01\00"
[all …]
Dlegalize-tf-control-flow.mlir6 …// CHECK: [[VAL0:%.+]] = "mhlo.compare"([[ARG0]], [[ARG1]]) {comparison_direction = "GT"} : (tenso…
7 …%0 = "mhlo.compare"(%arg0, %arg1) {comparison_direction = "GT"} : (tensor<f32>, tensor<f32>) -> te…
8 // CHECK: [[VAL1:%.+]] = "mhlo.tuple"([[ARG0]], [[ARG1]])
9 // CHECK: [[VAL2:%.+]] = "mhlo.if"([[VAL0]], [[VAL1]], [[VAL1]]) ( {
11 // CHECK: [[VAL4:%.+]] = "mhlo.get_tuple_element"([[THEN_ARG]]) {index = 0 : i32}
12 // CHECK: [[VAL5:%.+]] = "mhlo.get_tuple_element"([[THEN_ARG]]) {index = 1 : i32}
14 // CHECK: [[VAL7:%.+]] = "mhlo.tuple"([[VAL6]])
15 // CHECK: "mhlo.return"([[VAL7]]) : (tuple<tensor<f32>>) -> ()
18 // CHECK: [[VAL4:%.+]] = "mhlo.get_tuple_element"([[ELSE_ARG]]) {index = 0 : i32}
19 // CHECK: [[VAL5:%.+]] = "mhlo.get_tuple_element"([[ELSE_ARG]]) {index = 1 : i32}
[all …]
Dlegalize-tf.mlir16 …// CHECK: "mhlo.batch_norm_inference"(%arg0, %arg1, %arg2, %arg3, %arg4) {epsilon = 1.000000e-03 :…
34 …// CHECK: "mhlo.batch_norm_inference"({{.*}}, %arg1, %arg2, %arg3, %arg4) {epsilon = 1.000000e-03 …
41 …// CHECK: %[[RESULT0:.*]] = "mhlo.batch_norm_training"({{.*}}, %arg1, %arg2) {epsilon = 1.000000e-…
43 …// CHECK: "mhlo.get_tuple_element"(%[[RESULT0]]) {index = 0 : i32} : (tuple<tensor<8x8x8x8xf32>, t…
44 …// CHECK: "mhlo.get_tuple_element"(%[[RESULT0]]) {index = 1 : i32} : (tuple<tensor<8x8x8x8xf32>, t…
45 …// CHECK: %[[VAR:.*]] = "mhlo.get_tuple_element"(%[[RESULT0]]) {index = 2 : i32} : (tuple<tensor<8…
46 // CHECK: mhlo.constant
53 …// CHECK: "mhlo.batch_norm_inference"({{.*}}, %arg1, %arg2, %arg3, %arg4) {epsilon = 1.000000e-03 …
61 …// CHECK: [[CONVERT_X:%.*]] = "mhlo.convert"([[X]]) : (tensor<8x8x8x8xbf16>) -> tensor<8x8x8x8xf32>
62 …// CHECK: [[Y:%.*]] = "mhlo.batch_norm_inference"([[CONVERT_X]], [[SCALE]], [[OFFSET]], [[MEAN]], …
[all …]
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/translate/
Dlocation_to_op_metadata.mlir4 func @main(%arg0: !mhlo.token) -> !mhlo.token {
5 %0 = "mhlo.after_all"(%arg0) : (!mhlo.token) -> !mhlo.token loc(unknown)
6 return %0 : !mhlo.token
15 func @main(%arg0: !mhlo.token) -> !mhlo.token {
16 %0 = "mhlo.after_all"(%arg0) : (!mhlo.token) -> !mhlo.token loc("AfterAll")
17 return %0 : !mhlo.token
26 func @main(%arg0: !mhlo.token) -> !mhlo.token {
27 %0 = "mhlo.after_all"(%arg0) : (!mhlo.token) -> !mhlo.token loc("name@function")
28 return %0 : !mhlo.token
37 func @main(%arg0: !mhlo.token) -> !mhlo.token {
[all …]
Ddynamic_parameter_binding_invalid.mlir3 // Test bad `mhlo.padding_map` attribute type.
5 func @main(%arg0: tensor<i32>, %arg1: tensor<10xf32> {mhlo.padding_map = ""}) {
9 // CHECK: requires 'mhlo.padding_map' dict attribute at arg 1
13 // Test missing `shape_indices` attribute in `mhlo.padding_map`.
15 func @main(%arg0: tensor<i32>, %arg1: tensor<10xf32> {mhlo.padding_map = {}}) {
19 // CHECK: requires 'shape_indices' array attribute in 'mhlo.padding_map' dict at arg 1
23 // Test bad `shape_indices` attribute type in `mhlo.padding_map`.
25 func @main(%arg0: tensor<i32>, %arg1: tensor<10xf32> {mhlo.padding_map = {shape_indices = ""}}) {
29 // CHECK: requires 'shape_indices' array attribute in 'mhlo.padding_map' dict at arg 1
33 // Test missing `padding_arg_indices` attribute in `mhlo.padding_map`.
[all …]
Dfully_connected_reference_model.hlotxt14 …// CHECK-NEXT: %[[VAL_2:.*]] = "mhlo.reshape"(%[[VAL_0]]) : (tensor<1x300xf32>) -> tensor<1x300xf3…
17 …// CHECK-NEXT: %[[VAL_3:.*]] = "mhlo.transpose"(%[[VAL_2]]) {permutation = dense<[1, 0]> : tensor<…
20 …// CHECK-NEXT: %[[VAL_4:.*]] = "mhlo.reshape"(%[[VAL_3]]) : (tensor<300x1xf32>) -> tensor<300x1x1x…
23 …// CHECK-NEXT: %[[VAL_5:.*]] = "mhlo.reshape"(%[[VAL_4]]) : (tensor<300x1x1xf32>) -> tensor<300x1x…
26 …// CHECK-NEXT: %[[VAL_6:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_5]]) {broadcast_dimensions = dense<[…
29 // CHECK-NEXT: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
32 …// CHECK-NEXT: %[[VAL_8:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_7]]) {broadcast_dimensions = dense<>…
35 // CHECK-NEXT: %[[VAL_9:.*]] = mhlo.multiply %[[VAL_6]], %[[VAL_8]] : tensor<300x1x5xf32>
38 // CHECK-NEXT: %[[VAL_10:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
41 …// CHECK-NEXT: %[[VAL_11:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_10]]) {broadcast_dimensions = dense…
[all …]
Dif.mlir7 %0 = "mhlo.get_tuple_element"(%arg0) {index = 0 : i32} : (tuple<tensor<f32>>) -> tensor<f32>
10 %1 = "mhlo.log"(%0) : (tensor<f32>) -> tensor<f32>
13 %2 = "mhlo.tuple"(%1) : (tensor<f32>) -> tuple<tensor<f32>>
21 %0 = "mhlo.get_tuple_element"(%arg0) {index = 0 : i32} : (tuple<tensor<f32>>) -> tensor<f32>
24 %1 = "mhlo.exponential"(%0) : (tensor<f32>) -> tensor<f32>
27 %2 = "mhlo.tuple"(%1) : (tensor<f32>) -> tuple<tensor<f32>>
38 …%0 = "mhlo.compare"(%arg0, %cst) {comparison_direction = "LT"} : (tensor<f32>, tensor<f32>) -> ten…
41 %1 = "mhlo.tuple"(%arg0) : (tensor<f32>) -> tuple<tensor<f32>>
44 %2 = "mhlo.if"(%0, %1, %1) ( {
46 %6 = "mhlo.get_tuple_element"(%arg1) {index = 0 : i32} : (tuple<tensor<f32>>) -> tensor<f32>
[all …]
Dexport.mlir5 func @main(%arg0: !mhlo.token, %arg1: !mhlo.token) -> !mhlo.token {
6 %0 = "mhlo.after_all"(%arg0, %arg1) : (!mhlo.token, !mhlo.token) -> !mhlo.token
7 return %0 : !mhlo.token
19 %0 = "mhlo.all_reduce"(%arg0) ({
22 %max = mhlo.maximum %lhs, %rhs : tensor<f32>
23 "mhlo.return"(%max) : (tensor<f32>) -> ()
48 %0 = "mhlo.all_reduce"(%arg0) ({
51 %max = mhlo.maximum %lhs, %rhs : tensor<f32>
52 "mhlo.return"(%max) : (tensor<f32>) -> ()
76 …%0 = "mhlo.batch_norm_grad" (%input, %scale, %mean, %variance, %grad_output) {epsilon = 0.001 : f3…
[all …]
/external/tensorflow/tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/
Dchlo_legalize_to_hlo.cc59 rewriter.replaceOpWithNewOp<mhlo::ConstOp>( in matchAndRewrite()
68 Value constant = rewriter.create<mhlo::ConstOp>(loc, op.value()); in matchAndRewrite()
75 rewriter.replaceOpWithNewOp<mhlo::DynamicBroadcastInDimOp>( in matchAndRewrite()
87 poly = rewriter.create<mhlo::MulOp>(loc, x.getType(), poly, x); in MaterializePolynomialApproximation()
88 poly = rewriter.create<mhlo::AddOp>( in MaterializePolynomialApproximation()
128 Value x_sq = rewriter.create<mhlo::MulOp>(loc, x, x); in MaterializeErfcApproximationF64ForMagnituteGEOne()
129 Value z = rewriter.create<mhlo::NegOp>(loc, x_sq); in MaterializeErfcApproximationF64ForMagnituteGEOne()
133 Value exp_z = rewriter.create<mhlo::ExpOp>(loc, z); in MaterializeErfcApproximationF64ForMagnituteGEOne()
134 Value abs_x = rewriter.create<mhlo::AbsOp>(loc, x); in MaterializeErfcApproximationF64ForMagnituteGEOne()
137 Value exp_z_mul_poly_p = rewriter.create<mhlo::MulOp>(loc, exp_z, poly_p); in MaterializeErfcApproximationF64ForMagnituteGEOne()
[all …]
Dhlo_legalize_to_lhlo.cc43 namespace mhlo { namespace
128 rewriter.create<mhlo::HloToLhloOp<HloOpTy>>(op->getLoc(), llvm::None, in matchAndRewrite()
142 class HloToLhloOpConverter<mhlo::DotOp> : public BaseOpConversion<mhlo::DotOp> {
144 using BaseOpConversion<mhlo::DotOp>::BaseOpConversion;
146 mhlo::DotOp hloOp, ArrayRef<Value> operands, in matchAndRewrite()
161 auto dimension_numbers = mhlo::DotDimensionNumbers::get( in matchAndRewrite()
171 : public BaseOpConversion<mhlo::CustomCallOp> {
173 using BaseOpConversion<mhlo::CustomCallOp>::BaseOpConversion;
176 mhlo::CustomCallOp hloOp, ArrayRef<Value> operands, in matchAndRewrite()
197 : public BaseOpConversion<mhlo::ReshapeOp> {
[all …]
/external/tensorflow/tensorflow/compiler/mlir/xla/
Dhlo_utils.cc261 mlir::mhlo::GatherDimensionNumbers CreateGatherDimensionNumbers( in CreateGatherDimensionNumbers()
279 return mlir::mhlo::GatherDimensionNumbers::get( in CreateGatherDimensionNumbers()
287 if (isa<mlir::mhlo::ConstOp, mlir::lmhlo::ConstOp>(op)) { in MhloToHloOpcode()
289 } else if (isa<mlir::mhlo::IotaOp, mlir::lmhlo::IotaOp>(op)) { in MhloToHloOpcode()
291 } else if (isa<mlir::mhlo::ConvertOp, mlir::lmhlo::ConvertOp>(op)) { in MhloToHloOpcode()
293 } else if (isa<mlir::mhlo::AddOp, mlir::lmhlo::AddOp>(op)) { in MhloToHloOpcode()
295 } else if (isa<mlir::mhlo::Atan2Op, mlir::lmhlo::Atan2Op>(op)) { in MhloToHloOpcode()
297 } else if (isa<mlir::mhlo::DivOp, mlir::lmhlo::DivOp>(op)) { in MhloToHloOpcode()
299 } else if (isa<mlir::mhlo::MaxOp, mlir::lmhlo::MaxOp>(op)) { in MhloToHloOpcode()
301 } else if (isa<mlir::mhlo::MinOp, mlir::lmhlo::MinOp>(op)) { in MhloToHloOpcode()
[all …]
/external/tensorflow/tensorflow/compiler/mlir/hlo/include/mlir-hlo/Dialect/mhlo/transforms/
Dmap_chlo_to_hlo_op.h29 static mhlo::ComplexOp CreateOp(BroadcastComplexOp from_op, Type result_type, in CreateOp()
32 return builder.create<mhlo::ComplexOp>(from_op.getLoc(), result_type, in CreateOp()
46 static mhlo::CompareOp CreateOp(BroadcastCompareOp from_op, Type result_type, in CreateOp()
49 return builder.create<mhlo::CompareOp>( in CreateOp()
67 POPULATE_BCAST(BroadcastAddOp, mhlo::AddOp); in PopulateForBroadcastingBinaryOp()
68 POPULATE_BCAST(BroadcastAndOp, mhlo::AndOp); in PopulateForBroadcastingBinaryOp()
69 POPULATE_BCAST(BroadcastAtan2Op, mhlo::Atan2Op); in PopulateForBroadcastingBinaryOp()
70 POPULATE_BCAST(BroadcastDivOp, mhlo::DivOp); in PopulateForBroadcastingBinaryOp()
71 POPULATE_BCAST(BroadcastMaxOp, mhlo::MaxOp); in PopulateForBroadcastingBinaryOp()
72 POPULATE_BCAST(BroadcastMinOp, mhlo::MinOp); in PopulateForBroadcastingBinaryOp()
[all …]
/external/tensorflow/tensorflow/compiler/mlir/hlo/
DBUILD38 exports_files(["include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td"])
40 exports_files(["include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.td"])
45 "include/mlir-hlo/Dialect/mhlo/IR/chlo_ops.td",
46 "include/mlir-hlo/Dialect/mhlo/IR/hlo_ops.td",
47 "include/mlir-hlo/Dialect/mhlo/IR/hlo_ops_base.td",
48 "include/mlir-hlo/Dialect/mhlo/IR/hlo_ops_base_enums.td",
49 "include/mlir-hlo/Dialect/mhlo/IR/hlo_ops_base_structs.td",
50 "include/mlir-hlo/Dialect/mhlo/IR/hlo_utils.td",
51 "include/mlir-hlo/Dialect/mhlo/IR/infer_fusibility_op_interface.td",
52 "include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.td",
[all …]

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