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