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tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h
// By default this is the sum of the cost of individual cost for each op. virtual double GetFuncCost(func::FuncOp* func) const; // Returns true if 'op' can run on this Hardware. virtual bool IsOpSupported(mlir::Operation* op) const; // Switching cost between from hardware and this hardware. // If both the hardwares are the same, the transfer cost is basically 0.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 09 21:39:59 UTC 2023 - 7.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h
return GetInferenceTypeEnum(device_name_str); } // InferenceDeviceType is a combination of the hardware with inference type. struct InferenceDeviceType { std::string hardware; InferenceType inference_type; bool operator==(const InferenceDeviceType& other) const { return (hardware == other.hardware) && (inference_type == other.inference_type); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 06 03:08:33 UTC 2023 - 4.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/target_annotation.cc
auto* hardware = this->GetTargetHardware(device); if (hardware == nullptr) continue; if (hardware->IsOpSupported(op)) { SetAnnotation(op, kDevice, device, builder); device_is_set = true; break; } } } else { for (const auto* hardware : module_->GetAvailableHardwares()) { if (hardware == nullptr) continue;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 5.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/cost_model.cc
auto hardware = GetTargetAnnotation(op); if (!hardware) return; float cost = GetCostForOp(op, *hardware); UpdateCost(op, cost, &builder); } }); } } // namespace float GetCostForOp(Operation* op, const std::string& hardware) { auto* device_hardware = GetTargetHardware(hardware); if (device_hardware == nullptr) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/execution_metadata_exporter.cc
} }); } // Build the flatbuffer. std::vector<flatbuffers::Offset<flatbuffers::String>> hardwares; for (const auto& kv : *hardware_names) { hardwares.push_back(builder->CreateString(kv.first)); } return CreateHardwareMetadata(*builder, builder->CreateVector(hardwares)); } } // namespace std::optional<std::string> ExportRuntimeMetadata(mlir::ModuleOp module) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 06:11:34 UTC 2024 - 7.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tac_filter.proto
// A list of filters for TAC users to run ops/functions on ML hardwares. The // intuition is that, for ops/functions that can be run on ML hardware (e.g. // EdgeTPU) and TFLite CPU, TAC users give a hint that they're more performant // to run on TFLite CPU. These filters give the TAC users freedom to specify the // parts that they want to use other hardware to accelerate. message TacFilters {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 19 19:32:06 UTC 2023 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tac_module.h
absl::Status RunTacPasses(mlir::ModuleOp* module, bool debug_mode = false); // Create instances of all registered hardwares. std::vector<std::unique_ptr<tac::TargetHardware>> InstantiateBackends(); std::unique_ptr<TacImporter> importer_; std::unique_ptr<TacExporter> exporter_; // Owned list of all target hardware backends. std::vector<std::unique_ptr<tac::TargetHardware>> backends_;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 01:19:25 UTC 2023 - 4.3K bytes - Viewed (0) -
src/runtime/os_linux_arm.go
// reflect the CPU capabilities. Assume that every Android arm device // has the necessary floating point hardware available. if GOOS == "android" { return } if cpu.HWCap&_HWCAP_VFP == 0 && goarmsoftfp == 0 { print("runtime: this CPU has no floating point hardware, so it cannot run\n") print("a binary compiled for hard floating point. Recompile adding ,softfloat\n") print("to GOARM.\n") exit(1)
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Dec 05 20:34:30 UTC 2023 - 1.5K bytes - Viewed (0) -
src/runtime/os_linux_s390x.go
func checkS390xCPU() { // Check if the present z-system has the hardware capability to carryout // floating point operations. Check if hwcap reflects CPU capability for the // necessary floating point hardware (HasVX) availability. // Starting with Go1.19, z13 is the minimum machine level for running Go on LoZ if cpu.HWCap&_HWCAP_VX == 0 { print("runtime: This CPU has no floating point hardware, so this program cannot be run. \n") exit(1) }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Aug 01 17:36:28 UTC 2023 - 825 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tac_module.cc
// large functions (and maybe other metadata as well). } const tac::TargetHardware* TacModule::GetTargetHardware( const std::string& hardware_name) const { for (auto& hardware : backends_) { if (GetHardwareName(hardware.get()) == hardware_name) return hardware.get(); } return nullptr; } absl::Status TacModule::RunTacPasses(mlir::ModuleOp* module, bool debug_mode) { mlir::PassManager pm((*module)->getName(),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 08 01:19:25 UTC 2023 - 5.6K bytes - Viewed (0)