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tensorflow/c/eager/c_api_experimental.h
// Creates a new eager Executor. Nodes in one executor are guaranteed to be // executed in sequence. Assigning nodes to different executors allows executing // nodes in parallel. // in_flight_nodes_limit: when is_async is true, this value controls the // maximum number of in flight async nodes. Enqueuing of additional async ops // after the limit is reached blocks until some inflight nodes finishes.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
CONTRIBUTING.md
- If a change is needed, the contributor is requested to make the suggested change. - You make the change and submit it for the review again. - This cycle repeats itself until the PR gets approved. - Note: As a friendly reminder, we may reach out to you if the PR is awaiting your response for more than 2 weeks. **4. Approved** - Once the PR is approved, it gets `kokoro:force-run` label applied and it
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tensorflow/c/eager/dlpack.cc
bool valid = true; int64_t expected_stride = 1; for (int i = ndim - 1; i >= 0; --i) { // Empty tensors are always compact regardless of strides. if (shape_arr[i] == 0) return true; // Note that dimensions with size=1 can have any stride. if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) { valid = false; } expected_stride *= shape_arr[i]; } return valid; }
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tensorflow/c/eager/c_api_experimental.cc
status->status = absl::OkStatus(); } TFE_Executor* TFE_NewExecutor(bool is_async, bool enable_streaming_enqueue, int in_flight_nodes_limit) { return new TFE_Executor(is_async, enable_streaming_enqueue, in_flight_nodes_limit); } void TFE_DeleteExecutor(TFE_Executor* executor) { delete executor; } bool TFE_ExecutorIsAsync(TFE_Executor* executor) {
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tensorflow/c/eager/c_api.cc
TFE_Context* ctx, TFE_ContextDevicePlacementPolicy policy) { tensorflow::unwrap(ctx)->SetThreadLocalDevicePlacementPolicy( static_cast<tensorflow::ContextDevicePlacementPolicy>(policy)); } // Note: this function looks up a thread local policy. So it should be called in // the appropriate client thread. In particular, in async mode, it may not be // safe to call this function from the async EagerExecutor threads.
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tensorflow/c/eager/tape.h
ids.push_back(i); } const int64_t op_id = next_op_id_++; std::vector<TapeTensor> tensors; tensors.reserve(output_tensors.size()); for (const TapeTensor& o : output_tensors) { // Note: the tensor can have already been watched and hence be in the tape, // so we cannot check that we're inserting it here. tensor_tape_[o.GetID()] = op_id; tensor_usage_[o.GetID()] = 1; tensors.push_back(o);
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tensorflow/c/c_api_experimental.cc
// A vector to keep track of newly created `tf::Tensor` objects. std::vector<Tensor> all_input_tensors; // Update the vector with information from `input_tensors` if provided. if (input_tensors != nullptr) { // Note that we take the address of the elements in `all_input_tensors` // below. Allocate enough space so that no reallocation happens, which will // make the pointers invalid. all_input_tensors.reserve(num_inputs);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 29.4K bytes - Viewed (0) -
tensorflow/c/c_api_function.cc
} } // Compute body nodes. std::vector<const Node*> body_nodes; status->status = tensorflow::ComputeBodyNodes( fn_body, fn_name, num_opers, opers, input_nodes, &body_nodes); if (TF_GetCode(status) != TF_OK) return nullptr; // Compute body nodes. std::vector<const Node*> control_output_nodes; control_output_nodes.reserve(ncontrol_outputs);
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
// parallel device. device_(device), executor_( TFE_NewExecutor(is_async, /*enable_streaming_enqueue=*/true, /*in_flight_nodes_limit=*/in_flight_nodes_limit)), op_(nullptr), thread_(tensorflow::Env::Default()->StartThread( tensorflow::ThreadOptions(), "parallel_device_execute", std::bind(&DeviceThread::Run, this))) {}
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tensorflow/c/eager/c_api_distributed_test.cc
// Inject failure to function instantiation if finding a node that contains // the given node name (error_node_) and requested device (error_device_). for (const auto node : graph->get()->nodes()) { if (node->name().find(error_node_) != string::npos && node->requested_device() == error_device_) { return tensorflow::errors::Internal("Injected graph pass error."); } }
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