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Results 161 - 170 of 245 for tpu0 (0.03 sec)
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tensorflow/compiler/mlir/tensorflow/transforms/prepare_tpu_computation_for_tf_export.cc
func.getLoc(), op.getOperandTypes(), /*dynamic_key=*/dynamic_key, op.getSendKeyAttr(), /*device_ordinal=*/rewriter.getI64IntegerAttr(0), rewriter.getStringAttr("TPU")); for (auto result : llvm::zip(cloned_func.getArguments(), recv_at_host->getResults())) { std::get<0>(result).replaceAllUsesWith(std::get<1>(result)); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tpu_identity_pruning.cc
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h" #include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h" namespace mlir { namespace TFTPU { namespace { // This pass removes Identity/IdentityN ops from the TPU computation and // reachable functions. // TODO(lyandy): Remove this pass once resource op lifting is migrated to use // resource alias analysis and support region based control flow. Removing
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 05 23:50:19 UTC 2022 - 4.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto
// TODO(b/263528090): Check the condition when this feature is beneficial. bool enable_two_input_tensors = 11; // Supports TPU model quantization. If the target model for the quantization // is already converted for TPU, this flag may be helpful. Note that this // feature may be unstable as it is under the experimental stage. bool experimental_enable_tpu_model_support = 12;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 19 06:31:19 UTC 2024 - 9.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/tests/mlrt/tpu_conversions.mlir
// RUN: tf-tfrt-opt --split-input-file -pass-pipeline='builtin.module(pre-parallel-tf-to-mlrt{use-tpu-host-allocator-for-inputs=true},tf-mlrt-parallelization{tfrt-cost-threshold=4},tf-to-mlrt)' %s | FileCheck %s --dump-input=fail --dump-input-filter=all func.func @callee(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>) { func.return %arg0: tensor<i32> } // CHECK-LABEL: func @batch_function func.func @batch_function(%arg0: tensor<i32>) -> (tensor<i32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 04 21:25:31 UTC 2023 - 11K bytes - Viewed (0) -
tensorflow/compiler/jit/xla_compile_util.cc
// We store information about the JIT-compiled XLA computation in the // ResourceMgr. The DeviceCompiler (which contains the DeviceCompilationCache) // is stored in the tfrt_global ResourceMgr for TPU and the Device ResourceMgr // for CPU/GPU. This is to make sure the DeviceCompiler's lifecycle is // maintained appropriately. ResourceMgr* rm = nullptr; if (device_type == DEVICE_TPU) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 09:53:30 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_side_effects.h
}; struct DatasetIterator : ::mlir::SideEffects::Resource::Base<DatasetIterator> { StringRef getName() final { return "DatasetIterator"; } }; // Special resource type to track TPU Embedding specific ops, which must execute // but do not have side effects with one another or with resource variable ops. struct TPUEmbedding : ::mlir::SideEffects::Resource::Base<TPUEmbedding> {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Dec 26 18:45:40 UTC 2023 - 4.7K bytes - Viewed (0) -
.github/bot_config.yml
* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task. * All you need is a good internet connection and you are all set. * Try to build TF from sources by changing CPU optimization flags. *Please let us know if this helps.*
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jun 03 04:55:57 UTC 2024 - 4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/BUILD
"//tensorflow/compiler/mlir/quantization/...", "//tensorflow/compiler/mlir/tf2xla/transforms/...", "//tensorflow/lite/...", "//third_party/cloud_tpu/inference_converter/...", # TPU Inference Converter V1 ] + internal_visibility_allowlist(), ) package( # copybara:uncomment default_applicable_licenses = ["@stablehlo//:license"], default_visibility = [
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 20:18:36 UTC 2024 - 7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/xla_rewrite_util_test.cc
#include "tensorflow/compiler/mlir/tensorflow/utils/serialize_mlir_module_utils.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/protobuf/tpu/topology.pb.h" #include "tensorflow/core/util/device_name_utils.h" // #include <gmock/gmock.h> // #include <gtest/gtest.h> namespace tensorflow { namespace {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Apr 26 09:37:10 UTC 2024 - 4.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/rewrite_tpu_embedding_ops.cc
RecvTPUEmbeddingActivationsOp recv_op; if (failed(GetOp(region, &recv_op))) return failure(); SendTPUEmbeddingGradientsOp send_op; if (failed(GetOp(region, &send_op))) return failure(); // No TPU embedding ops. if (!recv_op && !send_op) return success(); Location loc = recv_op ? recv_op.getLoc() : send_op.getLoc(); StringRef config = recv_op ? recv_op.getConfig() : send_op.getConfig();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 15 22:55:42 UTC 2024 - 4.5K bytes - Viewed (0)