wehub-resource-sync
123 lines
4.0 KiB
Python
123 lines
4.0 KiB
Python
#!/usr/bin/env python3
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# 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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import numpy as np
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import tvm
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import tvm.testing
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from tvm import relax, te
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from tvm.runtime import Executable
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from tvm.script import ir as I
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from tvm.script import relax as R
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from tvm.script import tirx as T
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def test_compile_tir():
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"""Test tvm.compile with TIR input."""
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n = te.var("n")
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A = te.placeholder((n,), name="A")
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B = te.placeholder((n,), name="B")
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C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
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func = te.create_prim_func([A, B, C])
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# Test compile with PrimFunc
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exec_prim = tvm.compile(func)
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assert isinstance(exec_prim, Executable)
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# Test compile with IRModule containing PrimFunc
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mod = tvm.IRModule.from_expr(func)
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exec_mod = tvm.compile(mod)
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assert isinstance(exec_mod, Executable)
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# Verify the compiled module works
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dev = tvm.cpu(0)
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a_np = np.random.uniform(size=10).astype(np.float32)
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b_np = np.random.uniform(size=10).astype(np.float32)
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a = tvm.runtime.tensor(a_np, dev)
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b = tvm.runtime.tensor(b_np, dev)
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c = tvm.runtime.tensor(np.zeros(10, dtype=np.float32), dev)
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exec_prim(a, b, c)
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tvm.testing.assert_allclose(c.numpy(), a_np + b_np)
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exec_mod(a, b, c)
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tvm.testing.assert_allclose(c.numpy(), a_np + b_np)
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def test_compile_relax():
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"""Test tvm.compile with Relax input."""
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# Define a simple Relax program
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@I.ir_module
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class MyModule:
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@R.function
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def main(x: R.Tensor((3, 4), "float32"), y: R.Tensor((3, 4), "float32")) -> R.Tensor:
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z = R.add(x, y)
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return z
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# Test compile with Relax module
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target = tvm.target.Target("llvm")
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exec_relax = tvm.compile(MyModule, target)
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assert isinstance(exec_relax, Executable)
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# Verify the compiled module works
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dev = tvm.cpu(0)
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x_np = np.random.uniform(size=(3, 4)).astype(np.float32)
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y_np = np.random.uniform(size=(3, 4)).astype(np.float32)
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x = tvm.runtime.tensor(x_np, dev)
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y = tvm.runtime.tensor(y_np, dev)
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vm = relax.VirtualMachine(exec_relax, dev)
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z = vm["main"](x, y)
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tvm.testing.assert_allclose(z.numpy(), x_np + y_np)
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@tvm.testing.skip_if_32bit(reason="skipping test for i386.")
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def test_compile_mixed_module():
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@tvm.script.ir_module
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class MyModule:
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@T.prim_func(s_tir=True)
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def add_one(X: T.Buffer((4,), "float32"), Y: T.Buffer((4,), "float32")):
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for i in range(4):
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Y[i] = X[i] + 1
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@R.function
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def main(x: R.Tensor((4,), "float32")):
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cls = MyModule
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with R.dataflow():
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y = R.call_tir(cls.add_one, [x], R.Tensor((4,), "float32"))
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return y
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# Test with custom pipeline
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target = tvm.target.Target("c")
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ex = tvm.compile(MyModule, target)
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assert isinstance(ex, Executable)
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dev = tvm.cpu(0)
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x = tvm.runtime.tensor(np.array([1, 2, 3, 4], dtype=np.float32), dev)
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y = tvm.runtime.tensor(np.zeros(4, dtype=np.float32), dev)
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# For tirx function, we can directly call the function
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ex["add_one"](x, y)
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tvm.testing.assert_allclose(y.numpy(), x.numpy() + 1)
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# For relax function, we need to use the vm to call the function
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vm = relax.VirtualMachine(ex, dev)
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z = vm["main"](x)
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tvm.testing.assert_allclose(z.numpy(), x.numpy() + 1)
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if __name__ == "__main__":
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tvm.testing.main()
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