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/external/llvm-project/mlir/test/IR/
Drepro_b120295301.mlir5 %1 = "std.constant"() {value = dense<0.1> : tensor<1xf32>} : () -> (tensor<1xf32>)
6 %2 = "std.constant"() {value = dense<0.1> : tensor<2xf32>} : () -> (tensor<2xf32>)
7 %3 = "std.constant"() {value = dense<0.1> : tensor<3xf32>} : () -> (tensor<3xf32>)
8 %4 = "std.constant"() {value = dense<0.1> : tensor<4xf32>} : () -> (tensor<4xf32>)
9 %5 = "std.constant"() {value = dense<0.1> : tensor<5xf32>} : () -> (tensor<5xf32>)
10 %6 = "std.constant"() {value = dense<0.1> : tensor<6xf32>} : () -> (tensor<6xf32>)
11 %7 = "std.constant"() {value = dense<0.1> : tensor<7xf32>} : () -> (tensor<7xf32>)
12 %8 = "std.constant"() {value = dense<0.1> : tensor<8xf32>} : () -> (tensor<8xf32>)
13 %9 = "std.constant"() {value = dense<0.1> : tensor<9xf32>} : () -> (tensor<9xf32>)
14 %10 = "std.constant"() {value = dense<0.1> : tensor<10xf32>} : () -> (tensor<10xf32>)
[all …]
Ddense-elements-hex.mlir4 // HEX: dense<"0x000020410000A040"> : tensor<2xf32>
5 "foo.op"() {dense.attr = dense<[10.0, 5.0]> : tensor<2xf32>} : () -> ()
7 // HEX: dense<"0x00000000000024400000000000001440"> : tensor<2xf64>
8 "foo.op"() {dense.attr = dense<[10.0, 5.0]> : tensor<2xf64>} : () -> ()
10 // CHECK: dense<[1.000000e+01, 5.000000e+00]> : tensor<2xf32>
11 "foo.op"() {dense.attr = dense<"0x000020410000A040"> : tensor<2xf32>} : () -> ()
13 // CHECK: dense<[1.000000e+01, 5.000000e+00]> : tensor<2xf64>
14 "foo.op"() {dense.attr = dense<"0x00000000000024400000000000001440"> : tensor<2xf64>} : () -> ()
16 // CHECK: dense<(1.000000e+01,5.000000e+00)> : tensor<2xcomplex<f32>>
17 "foo.op"() {dense.attr = dense<"0x000020410000A040000020410000A040"> : tensor<2xcomplex<f32>>} : ()…
[all …]
Dattribute.mlir361 // CHECK: scalar_f32_attr = dense<5.000000e+00> : tensor<2xf32>
362 // CHECK: tensor_f64_attr = dense<6.000000e+00> : tensor<4x8xf64>
363 scalar_f32_attr = dense<5.0> : tensor<2xf32>,
364 tensor_f64_attr = dense<6.0> : tensor<4x8xf64>
374 scalar_f32_attr = dense<5.0> : tensor<2xf64>,
375 tensor_f64_attr = dense<6.0> : tensor<4x8xf64>
385 scalar_f32_attr = dense<5.0> : tensor<2xf32>,
386 tensor_f64_attr = dense<6.0> : tensor<4xf64>
399 // CHECK: dense<"example">
400 scalar_string_attr = dense<"example"> : tensor<2x!unknown<"">>
[all …]
/external/tensorflow/tensorflow/compiler/mlir/lite/tests/
Dconst-fold.mlir5 %0 = constant dense<4.5> : tensor<f32>
6 %1 = constant dense<1.5> : tensor<f32>
8 %2 = constant dense< 3.5> : tensor<4xf32>
9 %3 = constant dense<-0.5> : tensor<4xf32>
11 // CHECK: %[[CST:.*]] = constant dense<3.500000e+00> : tensor<4xf32>
12 // CHECK: %[[CST_0:.*]] = constant dense<-5.000000e-01> : tensor<4xf32>
13 // CHECK: %[[CST_1:.*]] = constant dense<6.000000e+00> : tensor<f32>
14 // CHECK: %[[CST_2:.*]] = constant dense<4.000000e+00> : tensor<4xf32>
15 // CHECK: %[[CST_3:.*]] = constant dense<5.000000e+00> : tensor<4xf32>
16 // CHECK: %[[CST_4:.*]] = constant dense<3.000000e+00> : tensor<4xf32>
[all …]
Dprepare-quantize-post-training.mlir7 %cst_3 = constant dense<1.0> : tensor<20x20xf32>
8 %cst_7 = constant dense<1.0> : tensor<20xf32>
9 %cst_11 = constant dense<1.0> : tensor<20x28xf32>
10 %recurrent_input = constant dense<1.0> : tensor<1x20xf32>
11 …%recurrent_stats = "quant.stats"(%recurrent_input) {layerStats = dense<[-2.0, 1.0]> : tensor<2xf32…
12 %cell_input = constant dense<1.0> : tensor<1x20xf32>
13 …%cell_stats = "quant.stats"(%cell_input) {layerStats = dense<[-2.73090601, 7.94872093]> : tensor<2…
30 …%1 = "quant.stats"(%0) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<1x28x20xf32>) -…
33 // CHECK: %[[cell_input:.*]] = constant dense<1.000000e+00> : tensor<1x20xf32>
43 …%input = "quant.stats"(%arg0) {layerStats = dense<[-1.2, 1.5]> : tensor<2xf32>} : (tensor<1x5xf32>…
[all …]
Doptimize.mlir51 %cst = constant dense<1.5> : tensor<16xf32>
52 …%cst_0 = constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0…
57 …// CHECK: %cst = constant dense<[2.500000e+00, 3.500000e+00, 4.500000e+00, 5.500000e+00, 6.500000e…
63 %cst = constant dense<0.5> : tensor<16xf32>
64 …%cst_0 = constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0…
69 …// CHECK: %cst = constant dense<[5.000000e-01, 1.500000e+00, 2.500000e+00, 3.500000e+00, 4.500000e…
75 %cst = constant dense<1.5> : tensor<32xf32>
76 …%cst_0 = constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0…
77 %cst_1 = constant dense<[1, 64, 84, 32]> : tensor<4xi32>
78 %cst_2 = constant dense<1.0> : tensor<32x4x4x128xf32>
[all …]
/external/tensorflow/tensorflow/compiler/mlir/hlo/tests/
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]>
14 %0 = mhlo.constant dense<1> : tensor<4xi64>
15 %1 = mhlo.constant dense<5> : tensor<4xi64>
16 // CHECK: mhlo.constant dense<6>
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>
25 // CHECK: mhlo.constant dense<[6.000000e+00, 8.000000e+00, 1.000000e+01, 1.200000e+01]>
32 %0 = mhlo.constant dense<5> : tensor<4xi64>
[all …]
Doptimize-hlo.mlir5 // CHECK: [[CST:%.+]] = mhlo.constant dense<0> : tensor<i64>
6 …+]] = "mhlo.dynamic-slice"(%arg0, %arg1, [[CST]], [[CST]]) {slice_sizes = dense<[1, 1, 2]> : tenso…
10 collapsed_slice_dims = dense<0> : tensor<1xi64>,
12 offset_dims = dense<[0, 1]> : tensor<2xi64>,
13 start_index_map = dense<0> : tensor<1xi64>
15 slice_sizes = dense<[1, 1, 2]> : tensor<3xi64>
24 // CHECK: [[CST:%.+]] = mhlo.constant dense<0> : tensor<i64>
26 …"mhlo.dynamic-slice"(%arg0, [[RESHAPE]], [[CST]], [[CST]]) {slice_sizes = dense<[1, 1, 2]> : tenso…
31 collapsed_slice_dims = dense<0> : tensor<1xi64>,
33 offset_dims = dense<[0, 1]> : tensor<2xi64>,
[all …]
Dlhlo_gpu_ops.mlir39 input_spatial_dimensions = dense<[2,3]> : tensor<2xi64>,
42 kernel_spatial_dimensions = dense<[2,3]> : tensor<2xi64>,
45 output_spatial_dimensions = dense<[2,3]> : tensor<2xi64>},
46 window_strides = dense<[1, 1]> : tensor<2xi64>,
47 padding = dense<[0,0]> : tensor<2xi64>,
48 lhs_dilation = dense<[1,1]> : tensor<2xi64>,
49 rhs_dilation = dense<[1,1]> : tensor<2xi64>,
74 input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>,
77 kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
80 output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>},
[all …]
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>
35 // CHECK: %[[TMP_2:.*]] = mhlo.constant dense<1.797{{.*}}E+308>
40 // CHECK: %[[TMP_7:.*]] = mhlo.constant dense<2.000{{.*}}e+00>
44 // CHECK: %[[TMP_11:.*]] = mhlo.constant dense<1.000{{.*}}e+00>
52 // CHECK: %[[TMP_19:.*]] = mhlo.constant dense<1.000{{.*}}e+00>
55 // CHECK: %[[TMP_22:.*]] = mhlo.constant dense<1.000{{.*}}e+00>
65 // CHECK: %[[TMP_32:.*]] = mhlo.constant dense<1.000{{.*}}e+00>
81 // CHECK: %[[RESULT:.*]] = mhlo.constant dense<3.200000e+00> : tensor<1x2xf32>
[all …]
Dhlo-legalize-gather-to-torch-index-select.mlir10 …dense<0> : tensor<1xi64>, index_vector_dim = 2 : i64, offset_dims = dense<2> : tensor<1xi64>, star…
23 …dense<0> : tensor<1xi64>, index_vector_dim = 0 : i64, offset_dims = dense<[0, 1]> : tensor<2xi64>,…
32 …dense<0> : tensor<1xi64>, index_vector_dim = 2 : i64, offset_dims = dense<2> : tensor<1xi64>, star…
39 …dense<0> : tensor<1xi64>, index_vector_dim = 2 : i64, offset_dims = dense<2> : tensor<1xi64>, star…
Dconvert.mlir73 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<42> : tensor<i32>
74 %cst = mhlo.constant dense<42> : tensor<i32>
84 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<42> : tensor<i32>
85 %cst = mhlo.constant dense<42.0> : tensor<f32>
95 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<4.{{0*}}e+00> : tensor<f32>
96 %cst = mhlo.constant dense<4> : tensor<i32>
106 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<-4.{{0*}}e+00> : tensor<f32>
107 %cst = mhlo.constant dense<-4> : tensor<i32>
117 // CHECK-NEXT: [[CST:%.+]] = mhlo.constant dense<4.{{0*}}e+00> : tensor<bf16>
118 %cst = mhlo.constant dense<4> : tensor<i32>
[all …]
Dlower-general-dot.mlir8 …dense<[]> : tensor<0xi64>, lhs_contracting_dimensions = dense<2> : tensor<1xi64>, rhs_batching_dim…
17 …// CHECK-DAG: [[R0:%.+]] = "mhlo.transpose"(%arg0) {permutation = dense<[1, 0]> : tensor<2xi64>} :…
18 …// CHECK-DAG: [[R1:%.+]] = "mhlo.transpose"(%arg1) {permutation = dense<[2, 0, 1]> : tensor<3xi64>…
23 …dense<[]> : tensor<0xi64>, lhs_contracting_dimensions = dense<0> : tensor<1xi64>, rhs_batching_dim…
32 …dense<[0]> : tensor<1xi64>, lhs_contracting_dimensions = dense<1> : tensor<1xi64>, rhs_batching_di…
/external/tensorflow/tensorflow/python/keras/legacy_tf_layers/
Dcore_test.py48 dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')
49 self.assertEqual(dense.units, 2)
50 self.assertEqual(dense.activation, nn_ops.relu)
51 self.assertEqual(dense.kernel_regularizer, None)
52 self.assertEqual(dense.bias_regularizer, None)
53 self.assertEqual(dense.activity_regularizer, None)
54 self.assertEqual(dense.use_bias, True)
57 dense = core_layers.Dense(2, activation=nn_ops.relu)
58 dense.apply(random_ops.random_uniform((5, 2)))
59 self.assertEqual(dense.name, 'dense_1')
[all …]
/external/tensorflow/tensorflow/compiler/mlir/tensorflow/tests/
Dtf_saved_model_lift_variables.mlir7 …0x50xf32>>> {tf.resource_name = "dense/kernel"}, %arg1: tensor<!tf.resource<tensor<50xf32>>> {tf.r…
9 ….VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = ""…
11 …f.VarHandleOp"() {_class = ["loc:@dense/bias"], allowed_devices = [], container = "", device = "",…
17 // CHECK: sym_name = "dense/kernel"
19 // CHECK: sym_name = "dense/bias"
21 … %arg0: tensor<!tf.resource<tensor<100x50xf32>>> {tf_saved_model.bound_input = @"dense/kernel"},
22 …// CHECK: %arg1: tensor<!tf.resource<tensor<50xf32>>> {tf_saved_model.bound_input = @"dense/bia…
31 …0x50xf32>>> {tf.resource_name = "dense/kernel"}, %arg1: tensor<!tf.resource<tensor<50xf32>>> {tf.r…
33 ….VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = ""…
35 …f.VarHandleOp"() {_class = ["loc:@dense/bias"], allowed_devices = [], container = "", device = "",…
[all …]
Dconstant-fold.mlir7 // CHECK: tf.Const{{.*}} dense<> : tensor<0xi32>
12 // CHECK: "tf.Const"() {value = dense<[1, 32, 32, 16]> : tensor<4xi32>} : () -> tensor<?xi32>
25 %cst_zero = constant dense<0.0> : tensor<f32>
26 %cst_one = constant dense<1.0> : tensor<f32>
31 …// CHECK-DAG: %[[POW_ZERO:.*]] = "tf.Const"() {value = dense<1.000000e+00> : tensor<4xf32>} : () -…
43 %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>
45 // CHECK: [[VAL:%.+]] = "tf.Const"() {value = dense<0> : tensor<5xi32>}
53 %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>
55 // CHECK: [[VAL:%.+]] = "tf.Const"() {value = dense<0> : tensor<5xi64>}
63 %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>
[all …]
Dtf_optimize.mlir5 …%cst0 = constant dense<[[[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], [[7.0, 8.0], [9.0, 10.0], [11.0, 12…
6 %cst2 = constant dense<[1.0, 2.0]> : tensor<2xf32>
11 // CHECK: %[[CST:.*]] = "tf.Const{{.*}} dense<
22 …%cst0 = constant dense<[[[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], [[7.0, 8.0], [9.0, 10.0], [11.0, 12…
23 %cst2 = constant dense<3.0> : tensor<23x2xf32>
28 // CHECK: %cst_0 = constant dense<3.000000e+00> : tensor<23x2xf32>
37 %cst_1 = constant dense<[1, 8, 6, 1, 6, 1, 1, 18]> : tensor<8xi64>
39 %cst_2 = constant dense<[8, 6, 6, 18]> : tensor<4xi64>
43 …// CHECK: %[[CST:.*]] = "tf.Const"() {value = dense<[8, 1, 1, 18]> : tensor<4xi64>} : () -> tensor…
44 …// CHECK: %[[CST1:.*]] = "tf.Const"() {value = dense<[8, 6, 6, 18]> : tensor<4xi64>} : () -> tens…
[all …]
/external/tensorflow/tensorflow/core/util/
Dstrided_slice_op.cc81 const StridedSliceSparseSpec& sparse, StridedSliceDenseSpec* dense) { in BuildDenseSpec() argument
84 dense->begin.resize(dense->dims); in BuildDenseSpec()
85 dense->end.resize(dense->dims); in BuildDenseSpec()
86 dense->strides.resize(dense->dims); in BuildDenseSpec()
87 dense->input_shape_gather_indices_sparse.resize(dense->dims); in BuildDenseSpec()
89 dense->begin_mask = 0; in BuildDenseSpec()
90 dense->end_mask = 0; in BuildDenseSpec()
91 dense->shrink_axis_mask = 0; in BuildDenseSpec()
96 dense->begin_valid = sparse.begin_tensor != nullptr; in BuildDenseSpec()
97 dense->end_valid = sparse.end_tensor != nullptr; in BuildDenseSpec()
[all …]
/external/llvm-project/mlir/integration_test/Dialect/Vector/CPU/
Dtest-print-int.mlir10 %0 = std.constant dense<[true, false, -1, 0, 1]> : vector<5xi1>
14 %1 = std.constant dense<[true, false, -1, 0]> : vector<4xsi1>
18 %2 = std.constant dense<[true, false, 0, 1]> : vector<4xui1>
22 %3 = std.constant dense<[-128, -127, -1, 0, 1, 127, 128, 254, 255]> : vector<9xi8>
26 %4 = std.constant dense<[-128, -127, -1, 0, 1, 127]> : vector<6xsi8>
30 %5 = std.constant dense<[0, 1, 127, 128, 254, 255]> : vector<6xui8>
34 %6 = std.constant dense<[-32768, -32767, -1, 0, 1, 32767, 32768, 65534, 65535]> : vector<9xi16>
38 %7 = std.constant dense<[-32768, -32767, -1, 0, 1, 32767]> : vector<6xsi16>
42 %8 = std.constant dense<[0, 1, 32767, 32768, 65534, 65535]> : vector<6xui16>
46 %9 = std.constant dense<[-2147483648, -2147483647, -1, 0, 1,
[all …]
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/
Dlegalize-tf-BatchMatMulV2.mlir18 …c_broadcast_in_dim"([[LHS]], [[LHSSHAPEEXTENTS]]) {broadcast_dimensions = dense<[0, 1, 2]> : tenso…
20 …dense<0> : tensor<1xi64>, lhs_contracting_dimensions = dense<2> : tensor<1xi64>, rhs_batching_dime…
30 // CHECK: "mhlo.dynamic_broadcast_in_dim"({{.*}}, {{.*}}) {broadcast_dimensions = dense<[…
32 // CHECK-SAME: lhs_batching_dimensions = dense<0> : tensor<1xi64>,
33 // CHECK-SAME: lhs_contracting_dimensions = dense<2> : tensor<1xi64>,
34 // CHECK-SAME: rhs_batching_dimensions = dense<0> : tensor<1xi64>,
35 // CHECK-SAME: rhs_contracting_dimensions = dense<1> : tensor<1xi64>}}
42 // CHECK: "mhlo.dynamic_broadcast_in_dim"({{.*}}, {{.*}}) {broadcast_dimensions = dense<[…
44 // CHECK-SAME: lhs_batching_dimensions = dense<0> : tensor<1xi64>,
45 // CHECK-SAME: lhs_contracting_dimensions = dense<2> : tensor<1xi64>,
[all …]
Dlegalize-tf.mlir65 // CHECK: [[DUMMY:%.*]] = mhlo.constant dense<0.000000e+00> : tensor<0xf32>
99 // CHECK: %[[FACTOR:.*]] = mhlo.constant dense<1.00195694>
102 // CHECK-DAG: %[[ALPHA:.*]] = mhlo.constant dense<0.199999988>
103 // CHECK-DAG: %[[BETA:.*]] = mhlo.constant dense<8.000000e-01>
157 // CHECK-NEXT: %[[eps:.*]] = mhlo.constant dense<1.000000e-03> : tensor<f32>
159 …// CHECK-NEXT: %[[add:.*]] = chlo.broadcast_add %arg4, %[[eps]] {broadcast_dimensions = dense<> : …
162 … = "mhlo.dynamic_broadcast_in_dim"(%arg3, {{.*}}) {broadcast_dimensions = dense<3> : tensor<1xi64>…
165 // CHECK-NEXT: mhlo.constant dense<[0, 1, 2]> : tensor<3xi64>
167 // CHECK-NEXT: %[[init:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
172 …// CHECK-NEXT: }) {dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<8x8x8x8xf32>, tensor<f…
[all …]
/external/tensorflow/tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/
Dconstants.mlir6 // CHECK: value = dense<[false, true, true, false]> : tensor<4xi1>
7 …%0 = "tfl.pseudo_const"() { value = dense<[false, true, true, false]> : tensor<4xi1> } : () -> ten…
27 // %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf16> } : () -> tens…
33 // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
34 …%0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4…
40 // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf64>
41 …%0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf64> } : () -> tensor<4…
47 // CHECK: value = dense<[1, 2, 3, 4]> : tensor<4xi8>
48 %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 4]> : tensor<4xi8> } : () -> tensor<4xi8>
54 // CHECK: value = dense<[1, 2, 3, 258]> : tensor<4xi16>
[all …]
/external/rust/crates/regex/src/
Dsparse.rs19 dense: Vec<usize>, field
30 dense: Vec::with_capacity(size), in new()
36 self.dense.len() in len()
40 self.dense.is_empty() in is_empty()
44 self.dense.capacity() in capacity()
50 self.dense.push(value); in insert()
56 self.dense.get(i) == Some(&value) in contains()
60 self.dense.clear(); in clear()
66 write!(f, "SparseSet({:?})", self.dense) in fmt()
74 &self.dense in deref()
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/translate/
Dlayouts_and_names.mlir13 input_spatial_dimensions = dense<[ 1, 2 ]> : tensor<2xi64>,
16 kernel_spatial_dimensions = dense<[ 1, 2 ]> : tensor<2xi64>,
19 output_spatial_dimensions = dense<[ 2, 3 ]> : tensor<2xi64>
22 lhs_dilations = dense<1> : tensor<2xi64>,
23 minor_to_major = dense<[ 1, 3, 2, 0 ]> : tensor<4xindex>,
24 padding = dense<3> : tensor<2x2xi64>,
26 rhs_dilations = dense<1> : tensor<2xi64>,
27 window_strides = dense<2> : tensor<2xi64>
31 …%cst_1 = "std.constant"() {value = dense<[[42]]> : tensor<1x1xi32>, minor_to_major = dense<[0, 1]>…
/external/llvm-project/mlir/test/Dialect/Quant/
Dconvert-const.mlir14 // CHECK: %cst = constant dense<-64> : tensor<4xi8>
16 %cst = constant dense<0.5> : tensor<4xf32>
26 // CHECK: %cst = constant dense<63> : tensor<4xi8>
28 %cst = constant dense<0.5> : tensor<4xf32>
38 // CHECK: %cst = constant dense<64> : tensor<4xi8>
40 %cst = constant dense<0.5> : tensor<4xf32>
50 // CHECK: %cst = constant dense<-64> : tensor<4xi8>
51 %cst = constant dense<-0.5> : tensor<4xf32>
58 // Verifies i8 fixedpoint quantization on a dense tensor, sweeping values.
61 // CHECK: %cst = constant dense<[-128, -128, -64, 0, 64, 127, 127]> : tensor<7xi8>
[all …]

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