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tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/input_output_names_attr.mlir
// Tests input and output names from FlatBuffer are added to `tf.entry_function` attribute. // CHECK-LABEL: @main func.func @main(%arg0: tensor<4xi8>, %arg1: tensor<4xi32>) -> (tensor<4xi32>, tensor<4xi8>) // CHECK: attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}} attributes {tf.entry_function = {inputs = "input0,input1", outputs = "output0,output1"}} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 07:35:24 UTC 2022 - 726 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/multi_output_op.json
], "name": "output0", "quantization": { } }, { "shape": [ 128, 32, 32, 3 ], "name": "output1", "quantization": { } } ], "inputs": [ 0, 1 ], "outputs": [ 2, 3 ],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Dec 03 00:08:31 UTC 2022 - 1.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/mlir2graphdef/output-shapes-attr.mlir
// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s func.func @main(%arg0: tensor<10xi32>) -> tensor<10xi32> attributes {tf.entry_function = {inputs = "input0", outputs = "output0"}} { %graph = tf_executor.graph { tf_executor.fetch %arg0 : tensor<10xi32> } func.return %graph : tensor<10xi32> } // CHECK: node { // CHECK-NEXT: name: "input0" // CHECK-NEXT: op: "_Arg" // CHECK: key: "T"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 25 12:28:56 UTC 2022 - 3.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tf-executor-to-functional.mlir
%graph_result = tf_executor.graph { %output0, %control0 = tf_executor.island { %a = "tf.opA"(%arg0) : (tensor<i32>) -> tensor<i32> tf_executor.yield %a : tensor<i32> } %output1, %control1 = tf_executor.island { %b = "tf.opB"(%output0) : (tensor<i32>) -> tensor<i32> tf_executor.yield %b : tensor<i32> } tf_executor.fetch %output1 : tensor<i32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 28 12:06:33 UTC 2022 - 3.6K bytes - Viewed (0) -
tensorflow/cc/tools/freeze_saved_model_test.cc
std::unordered_set<string> outputs; TF_ASSERT_OK(FreezeSavedModel(saved_model_bundle, &frozen_graph_def, &inputs, &outputs)); std::unordered_set<string> expected_inputs = {"input0:0", "input1:0"}; std::unordered_set<string> expected_outputs = {"output0:0", "output1:0"}; EXPECT_EQ(expected_inputs, inputs); EXPECT_EQ(expected_outputs, outputs); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 07 13:30:31 UTC 2022 - 21.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/add_dump_tensor_op.mlir
// WholeModel-DAG: "tf.DumpTensor"(%[[output1]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_1", node_name = "Conv2D_1"}> : (tensor<*xf32>) -> () // WholeModel-DAG: return %[[output0]], %[[output1]] // IntPerLayer-LABEL: func @conv // IntPerLayer-DAG: %[[w:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}1.600000e-01, 1.000000e-01
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 37.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_bridge_v1/end_to_end.mlir
func.func @main() { // CHECK: arith.constant // CHECK: TPUCompile // CHECK: TPUExecute // CHECK-NOT: func @_func tf_executor.graph { %outputs, %control = tf_executor.island wraps "arith.constant"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32> %outputs_0, %control_1 = tf_executor.island wraps "arith.constant"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 13 21:23:47 UTC 2024 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/canonicalize_compile_and_replicate_attributes.mlir
%outputs_0, %control_0 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT", name = "y", shape = "tfshape$dim { }"} : () -> tensor<0xf32> %outputs_1, %control_1 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_0) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input1"} : (tensor<0xf32>) -> tensor<0xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 3.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/modify_io_nodes.mlir
} func.func @not_modified(%arg0: tensor<f32>, %arg1: tensor<1x224x224x3xf32>) -> (tensor<1x401408xf32>, tensor<1x224x224x3xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "input0,input1", outputs = "output0,output1"}} { %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 19.9K bytes - Viewed (0) -
src/cmd/compile/internal/ssa/_gen/ARMOps.go
gp21carry = regInfo{inputs: []regMask{gpg, gpg}, outputs: []regMask{gp, 0}} gp2flags = regInfo{inputs: []regMask{gpg, gpg}} gp2flags1 = regInfo{inputs: []regMask{gp, gp}, outputs: []regMask{gp}} gp22 = regInfo{inputs: []regMask{gpg, gpg}, outputs: []regMask{gp, gp}} gp31 = regInfo{inputs: []regMask{gp, gp, gp}, outputs: []regMask{gp}} gp31carry = regInfo{inputs: []regMask{gp, gp, gp}, outputs: []regMask{gp, 0}}
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Feb 24 00:21:13 UTC 2023 - 41K bytes - Viewed (0)