wehub-resource-sync
135 lines
3.6 KiB
Python
135 lines
3.6 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: F841
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import numpy as np
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import pytest
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import tvm
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import tvm.testing
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from tvm.script import relax as R
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exec_mode = tvm.testing.parameter("bytecode", "compiled")
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pytestmark = pytest.mark.skipif(not tvm.testing.device_enabled("llvm"), reason="llvm not enabled")
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def test_pass_tensor_to_function(exec_mode):
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target = "llvm"
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dev = tvm.device(target)
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@R.function
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def relax_func(
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A: R.Tensor([16], "int32"),
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callback: R.Callable([R.Tensor([16], "int32")], R.Tuple([])),
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):
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B = R.multiply(A, R.const(2))
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_ = callback(B)
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return R.tuple()
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ex = tvm.relax.build(
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tvm.IRModule.from_expr(relax_func),
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target=target,
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exec_mode=exec_mode,
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)
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vm = tvm.relax.VirtualMachine(ex, dev)
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from_callback = None
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def custom_callback(arr):
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nonlocal from_callback
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from_callback = arr
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np_A = np.arange(16, dtype="int32")
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tvm_A = tvm.runtime.tensor(np_A)
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vm["relax_func"](tvm_A, custom_callback)
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assert from_callback is not None
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np.testing.assert_array_equal(np_A * 2, from_callback.numpy())
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def test_generate_tensor_in_function(exec_mode):
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target = "llvm"
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dev = tvm.device(target)
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@R.function
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def relax_func(
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callback: R.Callable([], R.Tensor([16], "int32")),
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):
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A = callback()
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B = R.multiply(A, R.const(2))
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return B
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ex = tvm.relax.build(
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tvm.IRModule.from_expr(relax_func),
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target=target,
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exec_mode=exec_mode,
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)
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vm = tvm.relax.VirtualMachine(ex, dev)
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np_A = np.arange(16, dtype="int32")
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def custom_callback():
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return tvm.runtime.tensor(np_A)
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output = vm["relax_func"](custom_callback)
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np.testing.assert_array_equal(np_A * 2, output.numpy())
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def test_catch_exception_with_full_stack_trace(exec_mode):
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target = "llvm"
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dev = tvm.device(target)
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@R.function
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def relax_func(
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callback: R.Callable([], R.Tensor([16], "int32")),
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):
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A = callback()
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return A
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ex = tvm.relax.build(
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tvm.IRModule.from_expr(relax_func),
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target=target,
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exec_mode=exec_mode,
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)
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vm = tvm.relax.VirtualMachine(ex, dev)
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# custom callback that raises an error in python
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def custom_callback():
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local_var = 42
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raise RuntimeError("Error thrown from callback")
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try:
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vm["relax_func"](custom_callback)
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except RuntimeError as err:
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stack = err.__traceback__
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while stack.tb_next is not None:
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stack = stack.tb_next
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frame = stack.tb_frame
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assert frame.f_code.co_filename.find("test_vm_callback_function.py") != -1, (
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"Inner-most stack frame should be from Python callback"
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)
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else:
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raise RuntimeError("Exception thrown in callback was not propagated to calling scope")
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if __name__ == "__main__":
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tvm.testing.main()
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