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tensorflow/compiler/mlir/lite/experimental/tac/tac.py
# limitations under the License. # ============================================================================== """Target aware conversion for TFLite model.""" from tensorflow.compiler.mlir.lite.experimental.tac.py_wrapper import _pywrap_tac_wrapper def run_tac(model_path, targets, output_path): """Run target aware conversion for the given tflite model file. Args: model_path: Path to the tflite model file.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jul 21 01:22:53 UTC 2021 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/tac-filter.mlir
// RUN: tac-opt-all-backends -tfl-tac-filter='use-test-setting=true' %s -split-input-file -verify-diagnostics | FileCheck %s // expected-remark@below {{Tac filter (0): filter type: function filter SKIP_TARGET_ANNOTATION, filter_pattern: "^testFunction"}} // expected-remark@below {{Tac filter (1): filter type: function filter INCLUDE_TARGET_ANNOTATION, filter_pattern: "testFunctionInclude"}} // expected-remark@below {{Tac filter (1) specified but not applied to any op}}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 24 01:08:29 UTC 2023 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-op-cost.mlir
// CHECK: tac.cost = 1.000000e+03 %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<10x10x10xf32>, tensor<10xf32>) -> tensor<10x10x10xf32> // CHECK: tac.cost = 1.000000e+03 %1 = "tfl.mul"(%0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<10x10x10xf32>, tensor<10xf32>) -> tensor<10x10x10xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 24 05:29:10 UTC 2022 - 5.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/README.md
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 29 18:32:13 UTC 2022 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/target-annotation.mlir
// CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT" %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32> // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 6.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/pick-subgraphs.mlir
%0 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6", tac.device = "GPU"} : tensor<100xf32> func.return %0 : tensor<100xf32> } func.func @func_2_GPU_FLOAT(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.cost = 8.040000e+01 : f32, tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 24.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
// CHECK: %[[VAL_3:.*]] = tfl.add %[[VAL_0]], %[[VAL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32> // CHECK: %[[VAL_4:.*]] = tfl.mul %[[VAL_3]], %[[VAL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/raise-target-subgraphs.mlir
// CHECK: %[[VAL_5:.*]] = tfl.add %[[VAL_0]], %[[VAL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32> // CHECK: %[[VAL_6:.*]] = tfl.mul %[[VAL_5]], %[[VAL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tac_module.h
#include "mlir/Pass/PassManager.h" // from @llvm-project #include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h" #include "tensorflow/compiler/mlir/lite/experimental/tac/tac_importer_exporter.h" namespace mlir { namespace TFL { namespace tac { // Main class for using Target Aware Conversion (TAC). // To run TAC: // 1) users should create object form this class, with desired options // (TacModule::Options).
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 01:19:25 UTC 2023 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tac_module.cc
pass_manager->addPass(mlir::TFL::tac::CreateTargetAnnotationPass(this)); pass_manager->addPass(mlir::TFL::tac::CreateRaiseTargetSubgraphsPass()); pass_manager->addPass(mlir::TFL::tac::CreateFoldConstantsToSubgraphPass( /*fold_all_constants=*/false)); pass_manager->addPass( mlir::TFL::tac::CreateAlternativeSubgraphPass(device_specs)); if (options_.legalize_to_tflite_ops) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 01:19:25 UTC 2023 - 5.6K bytes - Viewed (0)