| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| import tvm |
| import tvm.testing |
| |
| from tvm.script import relax as R |
| |
| import numpy as np |
| |
| exec_mode = tvm.testing.parameter("bytecode", "compiled") |
| |
| pytestmark = tvm.testing.parametrize_targets("llvm") |
| |
| |
| def test_pass_tensor_to_function(exec_mode, target, dev): |
| @R.function |
| def relax_func( |
| A: R.Tensor([16], "int32"), |
| callback: R.Callable([R.Tensor([16], "int32")], R.Tuple([])), |
| ): |
| B = R.multiply(A, R.const(2)) |
| _ = callback(B) |
| return R.tuple() |
| |
| ex = tvm.relax.build( |
| tvm.IRModule.from_expr(relax_func), |
| target=target, |
| exec_mode=exec_mode, |
| ) |
| vm = tvm.relax.VirtualMachine(ex, dev) |
| |
| from_callback = None |
| |
| def custom_callback(arr): |
| nonlocal from_callback |
| from_callback = arr |
| |
| np_A = np.arange(16, dtype="int32") |
| tvm_A = tvm.runtime.tensor(np_A) |
| |
| vm["relax_func"](tvm_A, custom_callback) |
| |
| assert from_callback is not None |
| np.testing.assert_array_equal(np_A * 2, from_callback.numpy()) |
| |
| |
| def test_generate_tensor_in_function(exec_mode, target, dev): |
| @R.function |
| def relax_func( |
| callback: R.Callable([], R.Tensor([16], "int32")), |
| ): |
| A = callback() |
| B = R.multiply(A, R.const(2)) |
| return B |
| |
| ex = tvm.relax.build( |
| tvm.IRModule.from_expr(relax_func), |
| target=target, |
| exec_mode=exec_mode, |
| ) |
| vm = tvm.relax.VirtualMachine(ex, dev) |
| |
| np_A = np.arange(16, dtype="int32") |
| |
| def custom_callback(): |
| return tvm.runtime.tensor(np_A) |
| |
| output = vm["relax_func"](custom_callback) |
| |
| np.testing.assert_array_equal(np_A * 2, output.numpy()) |
| |
| |
| def test_catch_exception_with_full_stack_trace(exec_mode, target, dev): |
| @R.function |
| def relax_func( |
| callback: R.Callable([], R.Tensor([16], "int32")), |
| ): |
| A = callback() |
| return A |
| |
| ex = tvm.relax.build( |
| tvm.IRModule.from_expr(relax_func), |
| target=target, |
| exec_mode=exec_mode, |
| ) |
| vm = tvm.relax.VirtualMachine(ex, dev) |
| |
| # custom callback that raises an error in python |
| def custom_callback(): |
| local_var = 42 |
| raise RuntimeError("Error thrown from callback") |
| |
| try: |
| vm["relax_func"](custom_callback) |
| except RuntimeError as err: |
| stack = err.__traceback__ |
| while stack.tb_next is not None: |
| stack = stack.tb_next |
| frame = stack.tb_frame |
| assert ( |
| frame.f_code.co_filename.find("test_vm_callback_function.py") != -1 |
| ), "Inner-most stack frame should be from Python callback" |
| |
| else: |
| raise RuntimeError("Exception thrown in callback was not propagated to calling scope") |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |