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/external/pytorch/docs/source/
Dtensors.rst5 torch.Tensor
8 A :class:`torch.Tensor` is a multi-dimensional matrix containing elements of
15 Torch defines tensor types with the following data types:
69 Data type CPU tensor GPU tensor
85 :class:`torch.Tensor` constructor is an alias for the default tensor type
91 A tensor can be constructed from a Python :class:`list` or sequence using the
92 :func:`torch.tensor` constructor:
96 >>> torch.tensor([[1., -1.], [1., -1.]])
97 tensor([[ 1.0000, -1.0000],
99 >>> torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
[all …]
/external/tensorflow/tensorflow/core/transforms/graph_compactor/tests/
Drename_lots.mlir12 %arg0: tensor<i1>,
13 %arg1: tensor<i1>,
14 %arg2: tensor<i1>,
15 %arg3: tensor<i1>,
16 %arg4: tensor<i1>,
17 %arg5: tensor<i1>,
18 %arg6: tensor<i1>,
19 %arg7: tensor<i1>,
20 %arg8: tensor<i1>,
21 %arg9: tensor<i1>,
[all …]
/external/pytorch/torch/csrc/autograd/
DFunctionsManual.h34 TORCH_API Tensor toNonOptFwGrad(const std::optional<Tensor>& t);
35 TORCH_API Tensor toNonOptPrimal(const std::optional<Tensor>& t);
36 TORCH_API Tensor toNonOptTensor(const std::optional<Tensor>& t);
38 TORCH_API inline std::optional<Tensor> wrap_opt_if( in wrap_opt_if()
39 const Tensor& t, in wrap_opt_if()
41 using OptTensor = std::optional<Tensor>; in wrap_opt_if()
45 TORCH_API Tensor
46 apply_loss_reduction(const Tensor& unreduced, int64_t reduction);
51 const at::Tensor& t);
55 at::ArrayRef<at::Tensor> t);
[all …]
/external/pytorch/torch/csrc/jit/runtime/
Dsymbolic_shape_registry_util.cpp13 {"aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
14 {"aten::_cast_Float(Tensor self, bool non_blocking) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
15 {"aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
16 {"aten::mul.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
17 {"aten::div.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
18 {"aten::eq.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
19 {"aten::ne.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
20 {"aten::ge.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
21 {"aten::gt.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
22 {"aten::le.Scalar(Tensor self, Scalar other) -> Tensor", "unary"}, in get_tensorexpr_elementwise_set()
[all …]
/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/stablehlo/tests/
Dverify_reduce_window.mlir4 func.func @reduce_window(%arg0: tensor<4x2xf32>, %arg1: tensor<4x2xi32>,
5 %init0: tensor<f32>, %init1: tensor<i32>) ->
6 (tensor<2x2xf32>, tensor<2x2xi32>) {
8 ^bb0(%a0: tensor<f32>, %a1: tensor<i32>,
9 %b0: tensor<f32>, %b1: tensor<i32>):
10 %2 = stablehlo.add %a0, %b0 : tensor<f32>
11 %3 = stablehlo.add %a1, %b1 : tensor<i32>
12 "stablehlo.return"(%2, %3) : (tensor<f32>, tensor<i32>) -> ()
14 { padding = dense<[[2, 2], [0, 0]]> : tensor<2x2xi64>,
15 window_dimensions = dense<[5, 1]> : tensor<2xi64>,
[all …]
Dverify_reduce.mlir8 func.func @reduce_valid(%arg0: tensor<4x4xf32>, %arg1 : tensor<4xf32>)
9 -> (tensor<4xf32>) {
12 ^bb0(%arg2: tensor<4xf32>, %arg3: tensor<4xf32> ):
13 %1 = "stablehlo.add"(%arg2, %arg3) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
14 "stablehlo.return"(%1) : (tensor<4xf32>) -> ()
16 }) {dimensions = dense<[0]> : tensor<1xi64>} : (tensor<4x4xf32>, tensor<4xf32>) -> tensor<4xf32>
18 func.return %0: tensor<4xf32>
24 func.func @reduce_complex_type(%arg0: tensor<1x2xcomplex<f32>>, %arg1 : tensor<complex<f32>>)
25 -> (tensor<1xcomplex<f32>>) {
28 ^bb0(%arg2: tensor<complex<f32>> loc("foo"), %arg3: tensor<complex<f32>> loc("foo")):
[all …]
Dprint_while_invalid.mlir5 func.func @while_with_different_types(%arg0: tensor<3xf32>) -> tensor<3xf32> {
6 %cst_0 = arith.constant dense<0> : tensor<1xi32>
7 %cst_1 = arith.constant dense<[100, 100]> : tensor<2xi32>
8 %cst_2 = arith.constant dense<1.00> : tensor<1xf32>
9 … between operand #2 and the matching condition block argument: 'tensor<1xf32>' vs 'tensor<3xf32>'}}
11 ^bb0(%arg1: tensor<1xi32>, %arg2: tensor<2xi32>, %arg3: tensor<3xf32>, %arg4: tensor<3xf32>):
12 %2 = arith.constant dense<0> : tensor<i32>
13 …= dense<[1]> : tensor<1xi64>, start_indices = dense<[0]> : tensor<1xi64>, strides = dense<1> : ten…
14 …hlo.compare"(%arg1, %3) {comparison_direction = "LT"} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1…
15 %5 = "stablehlo.reshape"(%4) : (tensor<1xi1>) -> tensor<i1>
[all …]
Dverify_select_and_scatter.mlir5 %arg0: tensor<10x24x24x64xf32>,
6 %arg1: tensor<10x12x12x64xf32>) -> tensor<10x24x24x64xf32> {
7 %0 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
10 ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>):
14 } : (tensor<f32>, tensor<f32>) -> tensor<i1>
15 "stablehlo.return"(%2) : (tensor<i1>) -> ()
17 ^bb0(%arg3: tensor<f32>, %arg4: tensor<f32>):
18 %2 = stablehlo.add %arg3, %arg4 : tensor<f32>
19 "stablehlo.return"(%2) : (tensor<f32>) -> ()
21 window_dimensions = dense<[1, 2, 2, 1]> : tensor<4xi64>,
[all …]
/external/pytorch/aten/src/ATen/native/
Dnative_functions.yaml9 - func: _cast_Byte(Tensor self, bool non_blocking=False) -> Tensor
13 - func: _cast_Char(Tensor self, bool non_blocking=False) -> Tensor
17 - func: _cast_Double(Tensor self, bool non_blocking=False) -> Tensor
21 - func: _cast_Float(Tensor self, bool non_blocking=False) -> Tensor
25 - func: _cast_Int(Tensor self, bool non_blocking=False) -> Tensor
29 - func: _cast_Long(Tensor self, bool non_blocking=False) -> Tensor
33 - func: _cast_Short(Tensor self, bool non_blocking=False) -> Tensor
37 - func: _cast_Half(Tensor self, bool non_blocking=False) -> Tensor
40 # Computes the gradient of current tensor w.r.t. graph leaves.
41 - func: _backward(Tensor self, Tensor[] inputs, Tensor? gradient=None, bool? retain_graph=None, boo…
[all …]
/external/tensorflow/tensorflow/compiler/mlir/hlo/tests/
Dmhlo_flatten_tuple.mlir4 // CHECK-SAME: %arg0: tensor<3xf32>) -> tensor<3xf32> {
5 // CHECK: %[[CST_0:.*]] = constant dense<0> : tensor<1xi32>
6 // CHECK: %[[CST_1:.*]] = constant dense<100> : tensor<2xi32>
7 // CHECK: %[[CST_2:.*]] = constant dense<1.000000e+00> : tensor<1xf32>
9 // CHECK: ^bb0(%arg1: tensor<1xi32>, %arg2: tensor<2xi32>, %arg3: tensor<1xf32>, %arg4: te…
10 …es = dense<1> : tensor<1xi64>, start_indices = dense<0> : tensor<1xi64>, strides = dense<1> : tens…
11 …e"(%arg1, %[[SLICE_0]]) {comparison_direction = "LT"} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1…
12 // CHECK: "mhlo.return"(%[[COMPARE_0]]) : (tensor<1xi1>) -> ()
14 // CHECK: ^bb0(%arg1: tensor<1xi32>, %arg2: tensor<2xi32>, %arg3: tensor<1xf32>, %arg4: te…
15 …cast_in_dim"(%arg3) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<…
[all …]
/external/tensorflow/tensorflow/compiler/mlir/tensorflow/tests/
Dshape_inference.mlir4 // CHECK-LABEL: func @main(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32>
5 func.func @main(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<*xi32> {
7 // CHECK-SAME: (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
8 // CHECK: return %[[RESULT]] : tensor<1xi32>
9 %0 = "tf.Cast"(%arg0) : (tensor<1xi32>) -> tensor<*xi32>
10 %1 = "tf.Cast"(%arg1) : (tensor<1xi32>) -> tensor<*xi32>
11 %2 = "tf.AddV2"(%0, %1) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
12 func.return %2 : tensor<*xi32>
16 func.func @simple_chain(%arg0: tensor<1xf32>) -> tensor<*xf32> {
17 // CHECK: %[[MUL:.*]] = "tf.Mul"{{.*}} (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
[all …]
Dcanonicalize.mlir4 func.func @tfAssertTrue(%arg0: tensor<1x1x6x2xf32>) {
5 %t = arith.constant dense<true> : tensor<i1>
7 "tf.Assert"(%t, %arg0) {summarize = 3} : (tensor<i1>, tensor<1x1x6x2xf32>) -> ()
12 func.func @tfAssertFalse(%arg0: tensor<1x1x6x2xf32>) {
13 %f = arith.constant dense<false> : tensor<i1>
15 "tf.Assert"(%f, %arg0) {summarize = 3} : (tensor<i1>, tensor<1x1x6x2xf32>) -> ()
21 func.func @testGatherToV2(%params: tensor<4x3xf32>, %indices: tensor<1x2xi32>) -> tensor<2x3xf32> {
22 // CHECK: %[[AXIS:.*]] = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
23 …g0, %arg1, %[[AXIS]]) {batch_dims = 0 : i64} : (tensor<4x3xf32>, tensor<1x2xi32>, tensor<i32>) -> …
24 %0 = "tf.Gather"(%params, %indices) : (tensor<4x3xf32>, tensor<1x2xi32>) -> tensor<2x3xf32>
[all …]
Dtf-ops.mlir17 // CHECK: "tf.TensorProtoIntTensor"() {bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4…
18 …"tf.TensorProtoIntTensor"(){bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4xi32>} : (…
19 // CHECK: "tf.TensorProtoFloatTensor"() {bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1…
20 …"tf.TensorProtoFloatTensor"(){bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4xf32>} :…
21 // CHECK: "tf.TensorProtoStringTensor"() {bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x…
22 …"tf.TensorProtoStringTensor"(){bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4x!tf_ty…
23 // CHECK: "tf.TensorProtoResourceTensor"() {bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<…
24 …"tf.TensorProtoResourceTensor"(){bar = #tf_type<tensor_proto : "0x68656C6C6F"> : tensor<2x1x4x!tf_…
45 func.func @testIdentity(%arg0: tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string> {
46 %0 = "tf.Identity"(%arg0) : (tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string>
[all …]
Dmerge_control_flow.mlir11 %0 = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>
12 %1 = "tf.Const"() {value = dense<false> : tensor<i1>} : () -> tensor<i1>
14 %2 = "tf.A"() : () -> (tensor<f32>)
18 }) {is_stateless = true} : (tensor<i1>) -> ()
20 %2 = "tf.B"() : () -> (tensor<f32>)
24 }) {is_stateless = true} : (tensor<i1>) -> ()
38 %0 = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>
39 %1 = "tf.Const"() {value = dense<false> : tensor<i1>} : () -> tensor<i1>
40 %3 = "tf.A"() : () -> (tensor<?xf32>)
41 %4 = "tf.B"() : () -> (tensor<i32>)
[all …]
/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/
Dverifier_reduce_window_op.mlir4 func.func @reduce_window(%arg0: tensor<4x2xf32>, %arg1: tensor<4x2xi32>,
5 %init0: tensor<f32>, %init1: tensor<i32>) ->
6 (tensor<2x2xf32>, tensor<2x2xi32>) {
8 ^bb0(%a0: tensor<f32>, %a1: tensor<i32>,
9 %b0: tensor<f32>, %b1: tensor<i32>):
10 %2 = mhlo.add %a0, %b0 : tensor<f32>
11 %3 = mhlo.add %a1, %b1 : tensor<i32>
12 "mhlo.return"(%2, %3) : (tensor<f32>, tensor<i32>) -> ()
14 { padding = dense<[[2, 2], [0, 0]]> : tensor<2x2xi64>,
15 window_dimensions = dense<[5, 1]> : tensor<2xi64>,
[all …]
Dverifier_reduce_op.mlir8 func.func @reduce_valid(%arg0: tensor<4x4xf32>, %arg1 : tensor<4xf32>)
9 -> (tensor<4xf32>) {
12 ^bb0(%arg2: tensor<4xf32>, %arg3: tensor<4xf32> ):
13 %1 = "mhlo.add"(%arg2, %arg3) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
14 "mhlo.return"(%1) : (tensor<4xf32>) -> ()
16 }) {dimensions = dense<[0]> : tensor<1xi64>} : (tensor<4x4xf32>, tensor<4xf32>) -> tensor<4xf32>
18 func.return %0: tensor<4xf32>
24 func.func @reduce_complex_type(%arg0: tensor<1x2xcomplex<f32>>, %arg1 : tensor<complex<f32>>)
25 -> (tensor<1xcomplex<f32>>) {
28 ^bb0(%arg2: tensor<complex<f32>> loc("foo"), %arg3: tensor<complex<f32>> loc("foo")):
[all …]
Dinvalid_while_op.mlir5 func.func @while_with_different_types(%arg0: tensor<3xf32>) -> tensor<3xf32> {
6 %cst_0 = arith.constant dense<0> : tensor<1xi32>
7 %cst_1 = arith.constant dense<[100, 100]> : tensor<2xi32>
8 %cst_2 = arith.constant dense<1.00> : tensor<1xf32>
9 … between operand #2 and the matching condition block argument: 'tensor<1xf32>' vs 'tensor<3xf32>'}}
11 ^bb0(%arg1: tensor<1xi32>, %arg2: tensor<2xi32>, %arg3: tensor<3xf32>, %arg4: tensor<3xf32>):
12 %2 = arith.constant dense<0> : tensor<i32>
13 …= dense<[1]> : tensor<1xi64>, start_indices = dense<[0]> : tensor<1xi64>, strides = dense<1> : ten…
14 …hlo.compare"(%arg1, %3) {comparison_direction = "LT"} : (tensor<1xi32>, tensor<1xi32>) -> tensor<1…
15 %5 = "mhlo.reshape"(%4) : (tensor<1xi1>) -> tensor<i1>
[all …]
/external/tensorflow/tensorflow/compiler/mlir/lite/tests/
Dlegalize-tf.mlir3 func.func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
4 %0 = "tf.Add"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
5 func.return %0: tensor<1xf32>
8 // CHECK: tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32>
12 func.func @sub(%arg0: tensor<1xi64>, %arg1: tensor<1xi64>) -> tensor<1xi64> {
13 %0 = "tf.Sub"(%arg0, %arg1) : (tensor<1xi64>, tensor<1xi64>) -> tensor<1xi64>
14 func.return %0: tensor<1xi64>
17 // CHECK: tfl.sub %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xi64>
22 …c @testAddHighDimsHaveSameShape(%arg0: tensor<1x2x3x4x5x6x7x8xi32>, %arg1: tensor<1x2x3x4x5x6x7x8x…
24 …%0 = "tf.Add"(%arg0, %arg1) : (tensor<1x2x3x4x5x6x7x8xi32>, tensor<1x2x3x4x5x6x7x8xi32>) -> tensor…
[all …]
Dfuse-tftext.mlir3 …ce_tokenizer_rank1(%arg0: tensor<1x!tf_type.string> {tf._user_specified_name = "input"}) -> (tenso…
4 %0 = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi64>} : () -> tensor<2xi64>
5 %1 = "tf.Const"() {value = dense<[]> : tensor<0xi64>} : () -> tensor<0xi64>
6 %2 = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>
7 %3 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
8 %4 = "tf.Const"() {value = dense<[[0], [1]]> : tensor<2x1xi64>} : () -> tensor<2x1xi64>
9 %5 = "tf.Const"() {value = dense<-1> : tensor<1xi32>} : () -> tensor<1xi32>
10 %6 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
11 %7 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
12 %8 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
[all …]
Dops.mlir7 func.func @testCos(tensor<? x f32>) -> tensor<? x f32> {
8 ^bb0(%arg0: tensor<? x f32>):
10 %0 = "tfl.cos"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
11 func.return %0 : tensor<? x f32>
17 func.func @testCosWithWrongInputType(tensor<?xi32>) -> tensor<?xi32> {
18 ^bb0(%arg0: tensor<?xi32>):
19 // expected-error @+1 {{tfl.cos' op operand #0 must be tensor of 32-bit float values}}
20 %0 = "tfl.cos"(%arg0): (tensor<?xi32>) -> tensor<?xi32>
21 func.return %0#0 : tensor<?xi32>
27 func.func @testExp(tensor<? x f32>) -> tensor<? x f32> {
[all …]
Dtfl_while_outline.mlir10 func.func @while() -> tensor<1xf32>
12 %cst = arith.constant dense<1> : tensor<i32> loc("dec")
13 %cst0 = arith.constant dense<5> : tensor<i32> loc("N")
14 %cst1 = arith.constant dense<3.0> : tensor<1xf32> loc("val")
16 ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
18 // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>)
19 %cst_0 = arith.constant dense<0> : tensor<i32>
20 %1 = "tfl.greater"(%arg2, %cst_0) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
21 "tfl.yield"(%1) : (tensor<i1>) -> ()
23 ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
[all …]
Dlower-static-tensor-list.mlir1 // RUN: tf-opt "-tfl-lower-static-tensor-list=allow-tensorlist-pass-through default-to-single-batch…
6 func.func @tensorlistConst(%arg0 : tensor<1xi32>) -> tensor<2x3xi32> {
7 …G: %[[ELEMENT0:.*]] = "tf.Const"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
8 …G: %[[ELEMENT1:.*]] = "tf.Const"() {value = dense<[3, 4, 5]> : tensor<3xi32>} : () -> tensor<3xi32>
9 ….Pack"(%[[ELEMENT0]], %[[ELEMENT1]]) {axis = 0 : i64} : (tensor<3xi32>, tensor<3xi32>) -> tensor<2…
10 …0333A5C3030335C3030335C3030345C30303522"> : tensor<!tf_type.variant>} : () -> tensor<!tf_type.vari…
13 …%1 = "tf.TensorListStack"(%0, %arg0) : (tensor<!tf_type.variant<tensor<3xi32>>>, tensor<1xi32>) ->…
14 func.return %1 : tensor<2x3xi32>
20 func.func @emptyTensorlistConst(%arg0 : tensor<1xi32>) -> tensor<0x3xi32> {
21 …030315C3032325C3030325C3031305C30303322"> : tensor<!tf_type.variant>} : () -> tensor<!tf_type.vari…
[all …]
/external/executorch/exir/dialects/edge/op/
Dsample_input.py16 "_log_softmax.default": { # (Tensor self, int dim, bool half_to_float) -> Tensor
18 InArg(ArgType.Tensor),
23 Return(ArgType.Tensor),
26 … { # (Tensor input, Tensor? weight, Tensor? bias, Tensor running_mean, Tensor running_var, float …
28 InArg(ArgType.Tensor, size=[2, 3, 4, 5]),
31 InArg(ArgType.Tensor, size=[3]),
32 InArg(ArgType.Tensor, size=[3]),
37 Return(ArgType.Tensor, argname="__ret0", size=[2, 3, 4, 5]),
38 Return(ArgType.Tensor, argname="__ret1", size=[0]),
39 Return(ArgType.Tensor, argname="__ret2", size=[0]),
[all …]
/external/pytorch/tools/autograd/
Ddeprecated.yaml4 - name: add(Tensor self, Scalar alpha, Tensor other) -> Tensor
7 - name: add_(Tensor(a!) self, Scalar alpha, Tensor other) -> Tensor(a!)
10 - name: add(Tensor self, Scalar alpha, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
13 - name: addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor
16 - name: addbmm_(Scalar beta, Tensor(a!) self, Scalar alpha, Tensor batch1, Tensor batch2) -> Tensor…
19 - name: addbmm(Scalar beta, Tensor self, Scalar alpha, Tensor batch1, Tensor batch2, *, Tensor(a!) …
22 - name: addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2) -> Tensor
25 - name: addbmm_(Scalar beta, Tensor(a!) self, Tensor batch1, Tensor batch2) -> Tensor(a!)
28 - name: addbmm(Scalar beta, Tensor self, Tensor batch1, Tensor batch2, *, Tensor(a!) out) -> Tensor…
31 - name: addcdiv(Tensor self, Scalar value, Tensor tensor1, Tensor tensor2) -> Tensor
[all …]
/external/tensorflow/tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/
Dlstm.mlir4 …tensor<1x4xf32>, %arg1: tensor<4x4xf32>, %arg2: tensor<4x4xf32>, %arg3: tensor<4x4xf32>, %arg4: te…
5 …%cst0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("…
6 …%cst1 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<1x4xf32>} : () -> tensor<1x4xf32> loc("…
7 …tensor<1x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf3…
8 func.return %24 : tensor<1x4xf32>
11 …%[[RES0:.*]] = "tfl.pseudo_const"() {value = dense<{{.*}}> : tensor<1x4xf32>} : () -> tensor<1x4xf…
12 …%[[RES1:.*]] = "tfl.pseudo_const"() {value = dense<{{.*}}> : tensor<1x4xf32>} : () -> tensor<1x4xf…
14 …tensor<1x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf3…
21 …tensor<1x528x!quant.uniform<i8:f32, 0.037248000502586365:-19>>, %arg1: tensor<2048x528x!quant.unif…
23 …tensor<0x!quant.uniform<i16:f32, 0.0049890000373125076>>, input_to_forget_intermediate = tensor<0x…
[all …]

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