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tensorflow/compiler/jit/xla_device_ops.h
\ REGISTER_KERNEL_BUILDER( \ Name("VarHandleOp").Device(DEVICE).HostMemory("resource"), VarHandleOp); \ REGISTER_KERNEL_BUILDER( \ Name("_VarHandlesOp").Device(DEVICE).HostMemory("resources"), \
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Nov 23 19:28:25 UTC 2021 - 17.1K bytes - Viewed (0) -
guava/src/com/google/common/base/Ascii.java
* * @since 8.0 */ public static final byte DLE = 16; /** * Device Control 1. Characters for the control of ancillary devices associated with data * processing or telecommunication systems, more especially switching devices "on" or "off." (If a * single "stop" control is required to interrupt or turn off ancillary devices, DC4 is the * preferred assignment.) * * @since 8.0 */
Registered: Wed Jun 12 16:38:11 UTC 2024 - Last Modified: Mon Jul 19 15:43:07 UTC 2021 - 21.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.h
// "replicate_on_last_tile_dim" and "last_tile_dims" should be deducted from the // real Tensor dimensions when tiled. // For example: // f32[8,512](sharding={devices=[1,1,2]0,1 last_tile_dims={REPLICATED}) // also means a replicated tensor over all devices. // // See xla_data.proto for detailed explanations on the fields. int GetDimsFromXLAShardingTiled(const xla::OpSharding& xla_sharding);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 28 22:18:34 UTC 2024 - 6K bytes - Viewed (0) -
tensorflow/c/eager/custom_device_testutil.cc
LoggingDevice* device = new LoggingDevice; device->arrived_flag = arrived_flag; device->executed_flag = executed_flag; device->device_name = name; device->underlying_device = "/job:localhost/replica:0/task:0/device:CPU:0"; device->strict_scope_placement = strict_scope_placement; TFE_RegisterCustomDevice(context, custom_device, name, device, status); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Mar 03 20:47:31 UTC 2021 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/cluster_formation.mlir
// CHECK-SAME: <{device = "tpu0"}> // CHECK: %[[B_OUTPUT:[0-9]*]] = "tf.B"(%[[A_OUTPUT]]) : (tensor<?xi32>) -> tensor<?xi32> %3 = "tf.B"(%2) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32> // CHECK: %[[C_OUTPUT:[0-9]*]] = "tf.C"(%[[A_OUTPUT]], %[[B_OUTPUT]]) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32> %4 = "tf.C"(%2, %3) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Oct 31 08:59:10 UTC 2023 - 14.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/tpu_space_to_depth_pass.mlir
func.func @main(%arg0: tensor<!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg1: tensor<!tf_type.variant> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg2: tensor<!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg3: tensor<!tf_type.variant> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg4: tensor<!tf_type.resource<tensor<7x7x3x64xf32>>> {tf.device = "/job:localhost/replica:0/task:0/device:TPU:0"},...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 37.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/fake_session.cc
} void FakeSession::BuildDeviceManager() { auto device = tensorflow::DeviceFactory::NewDevice("CPU", {}, kDeviceNamePrefix); device_mgr_ = std::make_unique<tensorflow::StaticDeviceMgr>(std::move(device)); } void FakeSession::InitVariables() { tensorflow::Device* device = nullptr; auto status = device_mgr_->LookupDevice(kDeviceName, &device); if (status != absl::OkStatus()) return;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Feb 26 03:47:51 UTC 2024 - 7.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/resource-device-inference.mlir
// RUN: tf-opt -split-input-file -verify-diagnostics -tf-resource-device-inference %s | FileCheck %s !tf_res = tensor<*x!tf_type.resource<tensor<32xf32>>> // Tests that the pass can correctly propagate device attributes inside the same // function. // CHECK-LABEL: func @propagate_in_function func.func @propagate_in_function( %arg0: !tf_res {tf.device = "/TPU:0"}, %arg1: !tf_res {tf.device = "/TPU:1"}) { tf_executor.graph {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 17 16:01:45 UTC 2022 - 18.2K 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/cc/framework/scope_internal.h
const string& op_name); Impl(const Scope& other, Tags::ControlDeps, std::vector<Operation> control_deps, bool clear_control_deps); Impl(const Scope& other, Tags::Device, const string& device); Impl(const Scope& other, Tags::SingleUseScope, const string& op_name); Impl(const Scope& other, Tags::ExitOnError); Impl(const Scope& other, Tags::KernelLabel, const string& kernel_label);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 05 15:46:43 UTC 2022 - 5.1K bytes - Viewed (0)