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