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Results 1 - 10 of 476 for kDevice (0.16 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/transforms/raise_target_subgraphs.cc
// `{ tac.device = "GPU", tac.inference_type = "FLOAT"}` to a function // with the matching attributes. Assumed is that device type "CPU" // is the only device that is allowed to call other devices. I.e. ancestors of a // "CPU" `Operation` may only `Operations` without a device or other "CPU" // `Operations`. Implied is that "CPU" ops may contain subgraphs of different
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/sparsecore/embedding_sequencing.cc
// TODO(bfontain): Check for other attributes. replicated_output->setAttr(kDevice, builder.getStringAttr("")); TF::TPUReplicatedInputOp input = builder.create<TF::TPUReplicatedInputOp>( op->getLoc(), result.getType(), replicated_output.getResults()); input->setAttr(kDevice, builder.getStringAttr("")); mlir::Value new_value = input.getOutput();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 39.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/execution_metadata_exporter.cc
return std::nullopt; if (!HasValidHardwareTarget(op)) return std::nullopt; auto device = op->getAttrOfType<mlir::StringAttr>(mlir::TFL::tac::kDevice); if (device == nullptr) return std::nullopt; llvm::StringRef device_name_str = device.getValue(); return device_name_str.str(); } std::optional<std::vector<float>> GetPerDeviceCosts(
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/tensorflow/transforms/sparsecore/embedding_pipelining.cc
// TODO(bfontain): Check for other attributes. replicated_output->setAttr(kDevice, builder.getStringAttr("")); TF::TPUReplicatedInputOp input = builder.create<TF::TPUReplicatedInputOp>( op->getLoc(), result.getType(), replicated_output.getResults()); input->setAttr(kDevice, builder.getStringAttr("")); mlir::Value new_value = input.getOutput();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 92.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/e2e/device-transform-nnapi.mlir
// RUN: tac-translate -input-mlir -output-mlir -device-specs=NNAPI %s -o - 2>&1 | FileCheck %s module { // CHECK-LABEL: main func.func @main(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> { %0 = "tfl.squared_difference"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> func.return %0 : tensor<4xf32> // CHECK: [[VAL_0:%.*]] = tfl.sub %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/device-transform-gpu.mlir
// RUN: tac-opt-all-backends -tfl-device-transform-gpu %s -split-input-file -verify-diagnostics | FileCheck %s func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> { %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32> func.return %0 : tensor<2x1xf32> } // CHECK: func @pack(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 15.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/device-transform-nnapi.mlir
// RUN: tac-opt-all-backends -tfl-device-transform-nnapi %s -split-input-file -verify-diagnostics | FileCheck %s func.func @mean_4d_keepdim(%arg0: tensor<1x48x48x512xf32>) -> tensor<1x1x1x512xf32> { %cst = arith.constant dense<[1, 2]> : tensor<2xi32> %0 = "tfl.mean"(%arg0, %cst) {keep_dims = true} : (tensor<1x48x48x512xf32>, tensor<2xi32>) -> tensor<1x1x1x512xf32> func.return %0 : tensor<1x1x1x512xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/internal/passes/clustering_passes.h
// Creates a pass that extracts outside compilation (Host ops inside device // cluster) at head/tail of Device cluster to run before/after XLA computation. std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>> CreateExtractHeadTailOutsideCompilationPass(); // Creates a pass that extract outside compilation (Host ops inside cevice // cluster) ops to a separate parallel_execute region to run on CPU.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 02:01:13 UTC 2024 - 3.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/device_util.cc
mlir::Builder* builder) { // Parse GPU device compute capability from physical device description. static auto* r = new llvm::Regex("compute capability: ([0-9]+)\\.([0-9]+)"); llvm::SmallVector<llvm::StringRef, 3> cc; if (r->match(device.attributes().physical_device_desc(), &cc)) { return mlir::TF::GpuDeviceMetadata::get(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/convert_to_legacy_compile_and_replicate_attributes.mlir
%outputs, %control = tf_executor.island wraps "tf.GuaranteeConst"(%arg1) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32> %outputs_0, %control_1 = tf_executor.island wraps "tf.GuaranteeConst"(%arg2) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32> %control_2 = tf_executor.island wraps "tf.NoOp"() {_pivot_for_cluster = "cluster", device = ""} : () -> ()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 22:03:30 UTC 2024 - 6.1K bytes - Viewed (0)