| # 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 numpy as np |
| import pytest |
| |
| import tvm |
| import tvm.script |
| import tvm.testing |
| from tvm import relax |
| from tvm.script import relax as R, ir as I |
| |
| |
| def test_multinomial_from_uniform(): |
| @I.ir_module |
| class CallSample: |
| @R.function |
| def foo(x: R.Tensor((3, 5), "float32"), y: R.Tensor((3, 1), "float32")): |
| z = R.call_pure_packed( |
| "vm.builtin.multinomial_from_uniform", |
| x, |
| y, |
| sinfo_args=(R.Tensor((3, 1), dtype="int64")), |
| ) |
| return z |
| |
| mod = CallSample |
| target = tvm.target.Target("llvm", host="llvm") |
| ex = tvm.compile(mod, target) |
| np_rand = np.random.rand(3, 5).astype(np.float32) |
| # normalize it to get the random prob |
| np_prob = np_rand / np_rand.sum(axis=1, keepdims=True) |
| nd_prob = tvm.runtime.tensor(np_prob) |
| # special sample to get deterministic results |
| nd_sample = tvm.runtime.tensor(np.array([[1.0], [0], [1]]).astype(np.float32)) |
| |
| vm = relax.VirtualMachine(ex, tvm.cpu()) |
| res = vm["foo"](nd_prob, nd_sample) |
| tvm.testing.assert_allclose(res.numpy(), np.array([[4], [0], [4]]).astype(np.int64)) |
| |
| |
| @tvm.testing.parametrize_targets("cuda") |
| def test_alloc_tensor_raises_out_of_memory(target, dev): |
| """Out-of-memory exceptions may be raised from VM |
| |
| This is a regression test. In previous implementations, the Relax |
| VM would segfault if the built-in function |
| "vm.builtin.alloc_storage" was unable to allocate the requested |
| buffer. |
| """ |
| |
| @I.ir_module |
| class Module: |
| @R.function |
| def main(): |
| # Allocate a 1-petabyte tensor to trigger OOM. If the CI |
| # ever runs on a device with more than 1 petabyte of GPU |
| # memory, this test will need to be updated. |
| output = R.builtin.alloc_tensor(R.shape([1024, 1024, 1024, 1024, 1024]), "uint8", 0) |
| return output |
| |
| built = tvm.compile(Module, target=target) |
| vm = relax.VirtualMachine(built, dev) |
| |
| with pytest.raises(Exception, match="CUDA: out of memory"): |
| vm["main"]() |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |