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CONTRIBUTING.md
***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository. ### Contributing code If you have improvements to TensorFlow, send us your pull requests! For those just getting started, GitHub has a [how-to](https://help.github.com/articles/using-pull-requests/).
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tensorflow/c/eager/c_api_experimental.h
// Causes the calling thread to block till all ops dispatched in this executor // have been executed. Note that "execution" here refers to kernel execution / // scheduling of copies, etc. Similar to sync execution, it doesn't guarantee // that lower level device queues (like GPU streams) have been flushed. // // This call may not block for execution of ops enqueued concurrently with this // call.
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tensorflow/c/eager/tape.h
// have all gradients available. This set usually is a subset of the set of // targets (not all since targets which have outputs in the tape will not have // gradients available initially). // // Then we repeatedly pop an entry from the stack, run its backprop, and update // the gradients of its inputs. Once we have computed all gradients for a single
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tensorflow/c/eager/dlpack.cc
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; } } // namespace
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ci/official/utilities/code_check_full.bats
} @test "API compatibility test passes, ensuring no unexpected changes to the TF API" { bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test echo "You have to re-generate the TF API goldens and have the API changes reviewed." echo "Look at the instructions for ':api_compatibility_test -- --update_goldens=True'" } # See b/279852433 (internal).
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> await_status( TF_NewStatus(), TF_DeleteStatus); // Wait until all pending nodes have completed since they may have a // reference to default_cancellation_manager_. We ignore the status return // since we already have a bad status to propagate. TFE_ContextAsyncWait(context, await_status.get()); // Reset the cancellation manager on a bad status. Otherwise we'll cancel
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tensorflow/c/c_api_experimental.cc
} const tensorflow::OpDef_ArgDef& input_arg = op_def->input_arg()[input_index]; if (input_arg.number_attr().empty()) { status->status = tensorflow::errors::NotFound( op_name, " does not have number_attr() defined."); return nullptr; } // The returned string is owned by OpRegistry, so liveness is not a concern. return input_arg.number_attr().c_str(); }
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tensorflow/c/c_api_function.cc
// artificial restriction and require that when num_opers=-1, such // nodes must have a single output. if (node->num_outputs() != 1) { return InvalidArgument( "When `num_opers` is set to -1, nodes referenced in `inputs` " "must have a single output. Node ", node->name(), " has ", node->num_outputs(),
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tensorflow/c/eager/c_api_distributed_test.cc
TFE_TensorHandle* h1 = TestVariable(ctx, 2.0, task2_name); TFE_TensorHandle* h2 = TestVariable(ctx, 3.0, task0_name); // 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.
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