wehub-resource-sync
110 lines
3.6 KiB
Python
110 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 ir as I
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from tvm.script import tirx as T
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@pytest.mark.gpu
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def test_add_pipeline():
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"""Test extern-style add pipeline with vectorized operations."""
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nn = 64
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max_threads = 4
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# CPU version: serial loop with vectorized operations
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@I.ir_module
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class ModuleCPU:
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@T.prim_func(s_tir=True)
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def main(A: T.Buffer((64,), "float32"), C: T.Buffer((64,), "float32")):
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for i in T.serial((64 + 1) // 2):
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C[T.Ramp(i * 2, 1, 2)] = A[T.Ramp(i * 2, 1, 2)] + T.Broadcast(T.float32(1), 2)
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# GPU version: thread bindings with vectorized operations
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@I.ir_module
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class ModuleGPU:
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@T.prim_func(s_tir=True)
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def main(A: T.Buffer((64,), "float32"), C: T.Buffer((64,), "float32")):
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bx = T.launch_thread("blockIdx.x", (64 + 4 - 1) // 4)
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tx = T.launch_thread("threadIdx.x", 4)
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idx = bx * 4 + tx
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if T.likely(idx < 64):
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C[T.Ramp(idx * 2, 1, 2)] = A[T.Ramp(idx * 2, 1, 2)] + T.Broadcast(T.float32(1), 2)
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def check_target(target):
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if not tvm.testing.device_enabled(target):
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return
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mod = ModuleGPU if target in ["opencl", "cuda"] else ModuleCPU
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# build and invoke the kernel.
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f = tvm.compile(mod, target=target)
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n = nn
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def run_and_check():
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dev = tvm.device(target, 0)
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a = tvm.runtime.tensor(np.random.uniform(size=n).astype("float32"), dev)
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c = tvm.runtime.tensor(np.zeros(n, dtype="float32"), dev)
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f(a, c)
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tvm.testing.assert_allclose(c.numpy(), a.numpy() + 1)
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if target == "llvm":
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run_and_check()
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else:
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tvm.testing.run_with_gpu_lock(run_and_check)
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check_target("llvm")
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check_target("opencl")
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check_target("cuda")
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def test_pack_buffer_simple():
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"""Test call_packed with buffer arguments."""
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nn = 1024
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@I.ir_module
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class Module:
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@T.prim_func(s_tir=True)
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def main(A: T.Buffer((1024,), "float32"), C: T.Buffer((1024,), "float32")):
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T.evaluate(T.call_packed("my_extern_array_func1", A, C))
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@tvm.register_global_func
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def my_extern_array_func1(aa, bb):
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aa.copyto(bb)
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def check_target(target):
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if not tvm.testing.device_enabled(target):
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return
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# build and invoke the kernel.
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f = tvm.compile(Module, target=target)
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dev = tvm.cpu(0)
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# launch the kernel.
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n = nn
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a = tvm.runtime.tensor(np.random.uniform(size=n).astype("float32"), dev)
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c = tvm.runtime.tensor(np.zeros(n, dtype="float32"), dev)
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f(a, c)
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tvm.testing.assert_allclose(c.numpy(), a.numpy())
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check_target("llvm")
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if __name__ == "__main__":
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tvm.testing.main()
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