wehub-resource-sync
335 lines
12 KiB
Python
335 lines
12 KiB
Python
# 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: E501
|
|
import re
|
|
|
|
import pytest
|
|
|
|
import tvm
|
|
import tvm.testing
|
|
from tvm.script import ir as I
|
|
from tvm.script import tirx as T
|
|
from tvm.testing import env
|
|
|
|
target = "opencl"
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_opencl(), reason="need opencl")
|
|
def test_opencl_ternary_expression():
|
|
def check_if_then_else(n, dtype):
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(A: T.Buffer((1,), dtype), C: T.Buffer((1,), dtype)):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i in T.thread_binding(1, thread="threadIdx.x"):
|
|
with T.sblock("C"):
|
|
v_i = T.axis.spatial(1, i)
|
|
T.reads(A[0])
|
|
T.writes(C[v_i])
|
|
C[v_i] = T.max(
|
|
T.Cast(dtype, 2),
|
|
T.if_then_else(
|
|
0 < T.Cast("int32", A[0]),
|
|
T.Cast(dtype, 1),
|
|
T.Cast(dtype, 3),
|
|
),
|
|
)
|
|
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
|
|
def run_and_check():
|
|
dev = tvm.device(target, 0)
|
|
a = tvm.runtime.empty((n,), dtype, dev)
|
|
c = tvm.runtime.empty((n,), dtype, dev)
|
|
fun(a, c)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
def check_select(n, dtype):
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(A: T.Buffer((1,), dtype), C: T.Buffer((1,), dtype)):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i in T.thread_binding(1, thread="threadIdx.x"):
|
|
with T.sblock("C"):
|
|
v_i = T.axis.spatial(1, i)
|
|
T.reads(A[0])
|
|
T.writes(C[v_i])
|
|
C[v_i] = T.max(
|
|
T.Cast(dtype, 2),
|
|
T.Select(
|
|
0 < T.Cast("int32", A[0]),
|
|
T.Cast(dtype, 1),
|
|
T.Cast(dtype, 3),
|
|
),
|
|
)
|
|
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
|
|
def run_and_check():
|
|
dev = tvm.device(target, 0)
|
|
a = tvm.runtime.empty((n,), dtype, dev)
|
|
c = tvm.runtime.empty((n,), dtype, dev)
|
|
fun(a, c)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
check_if_then_else(1, "int8")
|
|
check_if_then_else(1, "uint8")
|
|
check_if_then_else(1, "int16")
|
|
check_if_then_else(1, "uint16")
|
|
check_select(1, "int8")
|
|
check_select(1, "uint8")
|
|
check_select(1, "int16")
|
|
check_select(1, "uint16")
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_opencl(), reason="need opencl")
|
|
def test_opencl_inf_nan():
|
|
def check_inf_nan(n, value, dtype):
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(A: T.Buffer((1,), dtype), C: T.Buffer((1,), dtype)):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i in T.thread_binding(1, thread="threadIdx.x"):
|
|
with T.sblock("C"):
|
|
v_i = T.axis.spatial(1, i)
|
|
T.reads()
|
|
T.writes(C[v_i])
|
|
C[v_i] = T.Cast(dtype, value)
|
|
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
|
|
def run_and_check():
|
|
dev = tvm.device(target, 0)
|
|
a = tvm.runtime.empty((n,), dtype, dev)
|
|
c = tvm.runtime.empty((n,), dtype, dev)
|
|
fun(a, c)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
check_inf_nan(1, -float("inf"), "float32")
|
|
check_inf_nan(1, -float("inf"), "float64")
|
|
check_inf_nan(1, float("inf"), "float32")
|
|
check_inf_nan(1, float("inf"), "float64")
|
|
check_inf_nan(1, float("nan"), "float32")
|
|
check_inf_nan(1, float("nan"), "float64")
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_opencl(), reason="need opencl")
|
|
def test_opencl_max():
|
|
def check_max(n, dtype):
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(A: T.Buffer((1,), dtype), C: T.Buffer((1,), dtype)):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i in T.thread_binding(1, thread="threadIdx.x"):
|
|
with T.sblock("C"):
|
|
v_i = T.axis.spatial(1, i)
|
|
T.reads(A[0])
|
|
T.writes(C[v_i])
|
|
C[v_i] = T.max(A[0] + T.Cast(dtype, 1), T.Cast(dtype, 0))
|
|
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
|
|
def run_and_check():
|
|
dev = tvm.device(target, 0)
|
|
a = tvm.runtime.empty((n,), dtype, dev)
|
|
c = tvm.runtime.empty((n,), dtype, dev)
|
|
fun(a, c)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
check_max(1, "int8")
|
|
check_max(1, "uint8")
|
|
check_max(1, "int16")
|
|
check_max(1, "uint16")
|
|
check_max(1, "float32")
|
|
check_max(1, "float64")
|
|
|
|
|
|
def test_opencl_erf():
|
|
def check_erf(n, dtype):
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(A: T.Buffer((1,), dtype), C: T.Buffer((1,), dtype)):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i0 in T.thread_binding(1, thread="threadIdx.x"):
|
|
with T.sblock("C"):
|
|
v_i0 = T.axis.spatial(1, i0)
|
|
T.reads(A[v_i0])
|
|
T.writes(C[v_i0])
|
|
C[v_i0] = T.erf(A[v_i0])
|
|
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
|
|
source_str = fun.imports[0].inspect_source()
|
|
matches = re.findall("erf", source_str)
|
|
error_matches = re.findall("erff", source_str)
|
|
assert len(matches) == 1 and len(error_matches) == 0
|
|
|
|
check_erf(1, "float32")
|
|
check_erf(1, "float64")
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_opencl(), reason="need opencl")
|
|
def test_opencl_type_casting():
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(C: T.Buffer((32,), "float32")):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i_0 in T.thread_binding(8, thread="threadIdx.x"):
|
|
for i_1 in T.vectorized(4):
|
|
with T.sblock("C"):
|
|
v_i = T.axis.spatial(32, i_0 * 4 + i_1)
|
|
T.reads()
|
|
T.writes(C[v_i])
|
|
C[v_i] = T.Select(
|
|
v_i // 4 == 3 and v_i % 3 == 1, T.float32(1.0), T.float32(0.0)
|
|
)
|
|
|
|
def check_type_casting(n, dtype):
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
assembly = fun.imports[0].inspect_source()
|
|
lcond = "convert_int4(((convert_uint4(((uint4)(((convert_int(get_local_id(0))) == 3), ((convert_int(get_local_id(0))) == 3), ((convert_int(get_local_id(0))) == 3), ((convert_int(get_local_id(0))) == 3)))))"
|
|
rcond = "(convert_uint4(((((int4)(((convert_int(get_local_id(0))))+(1*0), ((convert_int(get_local_id(0))))+(1*1), ((convert_int(get_local_id(0))))+(1*2), ((convert_int(get_local_id(0))))+(1*3))) % ((int4)(3, 3, 3, 3))) == ((int4)(1, 1, 1, 1))))))))"
|
|
pattern_cond = f"({lcond} && {rcond})"
|
|
assert assembly.count(pattern_cond) != 0
|
|
|
|
def run_and_check():
|
|
dev = tvm.device(target, 0)
|
|
c = tvm.runtime.empty((n,), dtype, dev)
|
|
fun(c)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
check_type_casting(32, "float32")
|
|
# fp16 is not yet supported in ci
|
|
# check_type_casting(dev, 16, "float16")
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_opencl(), reason="need opencl")
|
|
@pytest.mark.parametrize(
|
|
"target",
|
|
[
|
|
pytest.param("opencl", marks=pytest.mark.gpu),
|
|
pytest.param({"kind": "opencl", "device": "adreno"}, marks=pytest.mark.gpu),
|
|
],
|
|
)
|
|
def test_opencl_ceil_log2(target):
|
|
if not tvm.testing.device_enabled(target):
|
|
pytest.skip(f"{target} not enabled")
|
|
|
|
def _check(target, n, dtype):
|
|
target_obj = tvm.target.Target(target)
|
|
is_adreno = "adreno" in target_obj.attrs.get("device", "")
|
|
inter_dtype = "float32" if is_adreno else "float64"
|
|
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(C: T.Buffer((n,), "int32")):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i in T.thread_binding(n, thread="threadIdx.x"):
|
|
with T.sblock("C"):
|
|
v_i = T.axis.spatial(n, i)
|
|
T.reads()
|
|
T.writes(C[v_i])
|
|
C[v_i] = T.Cast("int32", T.ceil(T.log2(T.Cast(inter_dtype, v_i))))
|
|
|
|
fun = tvm.tirx.build(Module, target=target)
|
|
assembly = fun.imports[0].inspect_source()
|
|
if is_adreno:
|
|
pattern = "convert_float"
|
|
else:
|
|
pattern = "convert_double"
|
|
assert assembly.count(pattern) != 0
|
|
|
|
_check(target, 32, "float32")
|
|
|
|
|
|
def _get_maximum_kernel_args(source):
|
|
def get_kernel_args(source):
|
|
import re
|
|
|
|
p = re.tirx.build(r"__kernel void .+\((.*)\)")
|
|
args = p.findall(source)
|
|
return args
|
|
|
|
args = get_kernel_args(source)
|
|
max_args = len(args[0].split(","))
|
|
for arg_line in args:
|
|
max_args = max(max_args, len(arg_line.split(",")))
|
|
return max_args
|
|
|
|
|
|
@pytest.mark.gpu
|
|
@pytest.mark.skipif(not env.has_opencl(), reason="need opencl")
|
|
def test_export_load_with_fallback(monkeypatch, tmp_path):
|
|
"""Force the codegen wrapper into the fallback branch, then export+load+run."""
|
|
import numpy as np
|
|
|
|
n = 1024
|
|
|
|
@I.ir_module(s_tir=True)
|
|
class Module:
|
|
@T.prim_func(s_tir=True)
|
|
def main(A: T.Buffer((n,), "float32"), B: T.Buffer((n,), "float32")):
|
|
T.func_attr({"tirx.noalias": True})
|
|
for i_0 in T.thread_binding(n // 32, thread="blockIdx.x"):
|
|
for i_1 in T.thread_binding(32, thread="threadIdx.x"):
|
|
with T.sblock("B"):
|
|
v_i = T.axis.spatial(n, i_0 * 32 + i_1)
|
|
T.reads(A[v_i])
|
|
T.writes(B[v_i])
|
|
B[v_i] = A[v_i] + 1.0
|
|
|
|
monkeypatch.setenv("TVM_COMPILE_FORCE_FALLBACK", "1")
|
|
host_lib = tvm.compile(Module, target=target)
|
|
monkeypatch.delenv("TVM_COMPILE_FORCE_FALLBACK")
|
|
|
|
lib_path = str(tmp_path / "lib.so")
|
|
host_lib.export_library(lib_path)
|
|
reloaded = tvm.runtime.load_module(lib_path)
|
|
|
|
a_np = np.random.uniform(size=(n,)).astype("float32")
|
|
b_np = np.zeros((n,), dtype="float32")
|
|
|
|
def run_and_check():
|
|
dev = tvm.device(target, 0)
|
|
a = tvm.runtime.tensor(a_np, dev)
|
|
b = tvm.runtime.tensor(b_np, dev)
|
|
reloaded["main"](a, b)
|
|
np.testing.assert_allclose(b.numpy(), a_np + 1.0, rtol=1e-5)
|
|
|
|
tvm.testing.run_with_gpu_lock(run_and_check)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tvm.testing.main()
|