wehub-resource-sync
125 lines
4.2 KiB
Python
125 lines
4.2 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: F401, F841
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import pytest
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import tvm
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import tvm.script
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from tvm import relax, tirx
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from tvm.ir import assert_structural_equal
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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_basic():
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@tvm.script.ir_module
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class Before:
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@T.prim_func(s_tir=True)
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def tir_matmul(x: T.handle, y: T.handle, z: T.handle) -> None:
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m = T.int64()
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n = T.int64()
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k = T.int64()
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A = T.match_buffer(x, (m, n))
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B = T.match_buffer(y, (n, k))
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C = T.match_buffer(z, (m, k))
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for i, j, k in T.grid(m, k, n):
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with T.sblock("matmul"):
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vi, vj, vk = T.axis.remap("SSR", [i, j, k])
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with T.init():
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C[vi, vj] = T.float32(0)
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C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
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@R.function(private=True)
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def main(
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x: R.Tensor(("m", "n"), "float32"), w: R.Tensor(("n", "k"), "float32")
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) -> R.Tensor:
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m, n, k = T.int64(), T.int64(), T.int64()
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gv0 = R.call_tir(Before.tir_matmul, (x, w), R.Tensor((m, k), dtype="float32"))
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return gv0
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@tvm.script.ir_module
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class Expected:
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@T.prim_func(s_tir=True)
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def tir_matmul(x: T.handle, y: T.handle, z: T.handle) -> None:
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T.func_attr({"global_symbol": "tir_matmul"})
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m = T.int64()
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n = T.int64()
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k = T.int64()
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A = T.match_buffer(x, (m, n))
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B = T.match_buffer(y, (n, k))
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C = T.match_buffer(z, (m, k))
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for i, j, k in T.grid(m, k, n):
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with T.sblock("matmul"):
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vi, vj, vk = T.axis.remap("SSR", [i, j, k])
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with T.init():
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C[vi, vj] = T.float32(0)
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C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj]
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@R.function
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def main(
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x: R.Tensor(("m", "n"), "float32"), w: R.Tensor(("n", "k"), "float32")
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) -> R.Tensor:
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m, n, k = T.int64(), T.int64(), T.int64()
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gv0 = R.call_tir(Expected.tir_matmul, (x, w), R.Tensor((m, k), dtype="float32"))
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return gv0
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before = Before
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expected = Expected
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after = relax.transform.AttachGlobalSymbol()(before)
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assert_structural_equal(after, expected)
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def test_system_lib_prefix():
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@tvm.script.ir_module
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class Before:
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I.module_attrs({"system_lib_prefix": "hello_"})
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@T.prim_func(private=True, s_tir=True)
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def tir_zeros(x: T.Buffer((2), "float32")) -> None:
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x[0] = T.float32(0)
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@R.function(private=True)
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def main() -> R.Tensor:
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gv0 = R.call_tir(Before.tir_zeros, (), R.Tensor((2,), dtype="float32"))
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return gv0
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@tvm.script.ir_module
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class Expected:
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I.module_attrs({"system_lib_prefix": "hello_"})
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@T.prim_func(s_tir=True)
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def hello_tir_zeros(x: T.Buffer((2), "float32")) -> None:
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T.func_attr({"global_symbol": "hello_tir_zeros"})
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x[0] = T.float32(0)
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@R.function
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def main() -> R.Tensor:
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gv0 = R.call_tir(Expected.hello_tir_zeros, (), R.Tensor((2,), dtype="float32"))
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return gv0
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before = Before
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after = relax.transform.AttachGlobalSymbol()(before)
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assert_structural_equal(after, Expected)
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if __name__ == "__main__":
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pytest.main([__file__])
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