wehub-resource-sync
148 lines
5.0 KiB
Python
148 lines
5.0 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
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import re
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import subprocess
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import tempfile
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import numpy as np
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import pytest
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import tvm
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from tvm.script import tirx as T
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from tvm.target import codegen
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from tvm.testing import env
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@pytest.fixture(scope="session")
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def sve_device_vector_length():
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c_code = r"""
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#include <stdio.h>
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#include <arm_sve.h>
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int main() {
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printf("%ld\n", svcntb() * 8);
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}
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"""
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with tempfile.TemporaryDirectory() as tmp_dir:
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c_path = f"{tmp_dir}/vl.c"
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o_path = f"{tmp_dir}/out.o"
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with open(c_path, "w") as f:
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f.write(c_code)
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tvm.support.cc.create_executable(o_path, c_path, ["-march=native"])
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out = subprocess.check_output(o_path, shell=True).strip().decode()
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return int(out)
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@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
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def test_scalable_div(sve_device_vector_length):
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np.random.seed(0)
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target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
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dev = tvm.cpu(0)
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@T.prim_func(s_tir=True)
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def my_func(a: T.handle):
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A = T.match_buffer(a, (1,), "int32")
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T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
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A[0] = T.Div(10000, 4 * T.vscale())
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mod = tvm.compile(my_func, target=target)
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A_nd = tvm.runtime.tensor(np.empty((1,), dtype="int32"), device=dev)
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mod(A_nd)
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ref = 10000 // (sve_device_vector_length // 32)
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tvm.testing.assert_allclose(A_nd.numpy()[0], ref)
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@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
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def test_scalable_buffer_load_store(sve_device_vector_length):
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np.random.seed(0)
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target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
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num_elements = sve_device_vector_length // 32
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dev = tvm.cpu(0)
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@T.prim_func(s_tir=True)
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def my_func(a: T.handle, b: T.handle):
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A = T.match_buffer(a, (num_elements,), "float32")
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B = T.match_buffer(b, (num_elements,), "float32")
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T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
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B[T.ramp(0, 1, 4 * T.vscale())] = A[T.ramp(0, 1, 4 * T.vscale())]
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mod = tvm.compile(my_func, target=target)
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A_np = np.random.uniform(size=(num_elements,)).astype("float32")
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B_np = np.zeros((num_elements,)).astype("float32")
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A_nd = tvm.runtime.tensor(A_np, device=dev)
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B_nd = tvm.runtime.tensor(B_np, device=dev)
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mod(A_nd, B_nd)
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tvm.testing.assert_allclose(B_nd.numpy(), A_np)
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@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
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def test_scalable_loop_bound(sve_device_vector_length):
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np.random.seed(0)
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dtype = "float32"
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num_elements = sve_device_vector_length // 32
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target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
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dev = tvm.cpu(0)
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@T.prim_func(s_tir=True)
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def my_func(a: T.handle, b: T.handle):
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A = T.match_buffer(a, (num_elements,), "float32")
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B = T.match_buffer(b, (num_elements,), "float32")
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T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
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for i in T.serial(0, 4 * T.vscale()):
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B[i] = A[i]
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mod = tvm.compile(my_func, target=target)
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A_np = np.random.uniform(size=(num_elements,)).astype(dtype)
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B_np = np.zeros((num_elements,)).astype(dtype)
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A_nd = tvm.runtime.tensor(A_np, device=dev)
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B_nd = tvm.runtime.tensor(B_np, device=dev)
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mod(A_nd, B_nd)
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tvm.testing.assert_allclose(B_nd.numpy(), A_np)
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@pytest.mark.skipif(not env.has_cpu_feature("sve"), reason="need aarch64 sve")
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def test_scalable_broadcast(sve_device_vector_length):
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target = {"kind": "llvm", "mtriple": "aarch64-linux-gnu", "mattr": ["+sve"]}
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num_elements = sve_device_vector_length // 32
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dev = tvm.cpu(0)
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@T.prim_func(s_tir=True)
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def my_func(a: T.handle):
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A = T.match_buffer(a, (num_elements,), "float32")
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T.func_attr({"global_symbol": "my_module", "tirx.noalias": True})
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A[T.ramp(0, 1, 4 * T.vscale())] = T.broadcast(1, 4 * T.vscale())
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mod = tvm.compile(my_func, target=target)
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A_np = np.zeros((num_elements,)).astype("float32")
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A_nd = tvm.runtime.tensor(A_np, device=dev)
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mod(A_nd)
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ref = np.ones((num_elements,))
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tvm.testing.assert_allclose(A_nd.numpy(), ref)
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