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.github/workflows/release-branch-cherrypick.yml
# input the branch name and paste the cherry-pick commit and click Run. A PR # will be created. name: Release Branch Cherrypick on: workflow_dispatch: inputs: # We use this instead of the "run on branch" argument because GitHub looks # on that branch for a workflow.yml file, and we'd have to cherry-pick # this file into those branches. release_branch:
Others - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Sep 12 14:49:29 GMT 2023 - 3.1K bytes - Viewed (0) -
README.md
apply fixes to bugs or security vulnerabilities: * Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch `r2.8` for version 2.8. * Apply (that is, cherry-pick) the desired changes and resolve any code conflicts. * Run TensorFlow tests and ensure they pass. * [Build](https://www.tensorflow.org/install/source) the TensorFlow pip package from source.
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tensorflow/c/eager/parallel_device/parallel_device_testlib.h
TF_Status* status); // Helper to un-pack `num_replicas` TFE_TensorHandles from one parallel handle. template <std::size_t num_replicas> void ExtractPerDeviceValues( TFE_Context* context, TFE_TensorHandle* input, std::array<TensorHandlePtr, num_replicas>* components, TF_Status* status); // Helper to pack `num_replicas` TFE_TensorHandles into one parallel handle. template <std::size_t num_replicas>
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tensorflow/c/eager/parallel_device/parallel_device.cc
// A ParallelDevice on its own is not registered with a TFE_Context, and so has // no device name (e.g. for `tf.device`). `NamedParallelDevice` associates a // name with it, which lets us pack its `ParallelTensor`s into TFE_TensorHandles // placed on the parallel device. class NamedParallelDevice { public: NamedParallelDevice(const std::string& name,
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tensorflow/c/eager/parallel_device/parallel_device_test.cc
Multiply(context.get(), second_combined_value.get(), second_negative_one.get(), status.get())); ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get()); // Un-pack the parallel tensor to verify that the operation was // successful. The resulting structure should be: // second_device{first_device{1. * 3., 2. * 3.}, 3. * 3.}. std::array<TensorHandlePtr, 2> second_components;
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tensorflow/c/eager/parallel_device/parallel_device_testlib.cc
&FloatDeallocator, nullptr), TF_DeleteTensor); return TensorHandlePtr(TFE_NewTensorHandle(tensor.get(), status)); } // Helper to un-pack `num_replicas` TFE_TensorHandles from one parallel handle. template <std::size_t num_replicas> void ExtractPerDeviceValues( TFE_Context* context, TFE_TensorHandle* input,
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
if (first_bad_status != nullptr) { TF_SetStatus(status, TF_GetCode(first_bad_status.get()), TF_Message(first_bad_status.get())); return result; } // For each output of the original operation, pack the per-device // TensorHandles we've computed into a single parallel TensorHandle. std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs; per_device_outputs.reserve(first_op_output_count);
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tensorflow/c/eager/c_api_distributed_test.cc
// Add a sync point to make sure that variables have been initialized // before the function execution starts. TFE_ContextAsyncWait(ctx, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); // Pack 3 variable handles into one TFE_TensorHandle. // When remote is false, function device is placed on task0. Handle types are // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
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tensorflow/c/eager/c_api_experimental.h
// custom device. // // Many devices will want to simply return an "unimplemented" status // here. This is the default behavior if `pack` is null when passed to // TFE_RegisterCustomDevice. TFE_TensorHandle* (*pack)(TFE_Context* context, TFE_TensorHandle** handles, int num_handles, TF_Status* s, void* device_info) = nullptr;
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tensorflow/c/eager/c_api.cc
TFE_DeleteTensorHandle(outputs[i]); } } return status.status; } tensorflow::Status Pack(absl::Span<ImmediateExecutionTensorHandle*> handles, ImmediateExecutionTensorHandle** result) override { TF_Status status; *result = tensorflow::unwrap(device_.pack(context_, tensorflow::wrap(handles.data()),
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