wehub-resource-sync
81 lines
2.8 KiB
Python
81 lines
2.8 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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from functools import partial
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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 ir as I
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from tvm.script import tirx as T
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from tvm.testing import env
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@pytest.mark.gpu
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@pytest.mark.skipif(not env.has_gpu(), reason="need gpu")
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@pytest.mark.parametrize(
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"target",
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[
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pytest.param("cuda", marks=pytest.mark.gpu),
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pytest.param("metal", marks=pytest.mark.gpu),
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pytest.param({"kind": "vulkan", "supports_int64": True}, marks=pytest.mark.gpu),
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pytest.param("opencl", marks=pytest.mark.gpu),
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],
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)
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@pytest.mark.parametrize("dtype", ["int32", "uint32", "int64", "uint64"])
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def test_int_intrin(target, dtype):
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if not tvm.testing.device_enabled(target):
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pytest.skip(f"{target} not enabled")
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test_funcs = [
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(T.clz, lambda x, dtype: int(dtype[-2:]) - (len(bin(x)) - 2)),
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]
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for tvm_intrin, np_func in test_funcs:
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n = 128
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@I.ir_module(s_tir=True)
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class Module:
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@T.prim_func(s_tir=True)
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def main(
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A: T.Buffer((n,), dtype),
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B: T.Buffer((n,), dtype),
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):
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T.func_attr({"tirx.noalias": True})
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for i0 in T.thread_binding(n, thread="threadIdx.x"):
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with T.sblock("B"):
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v_i0 = T.axis.spatial(n, i0)
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T.reads(A[v_i0])
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T.writes(B[v_i0])
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B[v_i0] = tvm_intrin(A[v_i0])
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f = tvm.compile(Module, target=target)
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def run_and_check():
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dev = tvm.device(target["kind"] if isinstance(target, dict) else target)
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a = tvm.runtime.tensor(np.random.randint(0, 100000, size=n).astype(dtype), dev)
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b = tvm.runtime.tensor(np.zeros(shape=(n,)).astype(dtype), dev)
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f(a, b)
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ref = np.vectorize(partial(np_func, dtype=dtype))(a.numpy())
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tvm.testing.assert_allclose(b.numpy(), ref)
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tvm.testing.run_with_gpu_lock(run_and_check)
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if __name__ == "__main__":
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tvm.testing.main()
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