| # 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. |
| import re |
| |
| import tvm |
| import tvm.testing |
| from tvm import te |
| |
| target = "opencl" |
| |
| |
| @tvm.testing.requires_gpu |
| @tvm.testing.requires_opencl |
| def test_opencl_ternary_expression(): |
| def check_if_then_else(dev, n, dtype): |
| A = te.placeholder((n,), name="A", dtype=dtype) |
| true_value = tvm.tir.const(1, dtype=dtype) |
| false_value = tvm.tir.const(3, dtype=dtype) |
| max_lhs = tvm.tir.const(2, dtype=dtype) |
| max_rhs = tvm.tir.if_then_else(A[0] > 0, true_value, false_value) |
| C = te.compute((n,), lambda i: tvm.te.max(max_lhs, max_rhs), name="C") |
| |
| func = te.create_prim_func([A, C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| sch.bind(x, "threadIdx.x") |
| fun = tvm.tir.build(sch.mod, target=target) |
| a = tvm.runtime.empty((n,), A.dtype, dev) |
| c = tvm.runtime.empty((n,), A.dtype, dev) |
| # Only need to test compiling here |
| fun(a, c) |
| |
| def check_select(dev, n, dtype): |
| A = te.placeholder((n,), name="A", dtype=dtype) |
| true_value = tvm.tir.const(1, dtype=dtype) |
| false_value = tvm.tir.const(3, dtype=dtype) |
| max_lhs = tvm.tir.const(2, dtype=dtype) |
| max_rhs = tvm.tir.Select(A[0] > 0, true_value, false_value) |
| C = te.compute((n,), lambda i: tvm.te.max(max_lhs, max_rhs), name="C") |
| func = te.create_prim_func([A, C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| sch.bind(x, "threadIdx.x") |
| fun = tvm.tir.build(sch.mod, target=target) |
| |
| a = tvm.runtime.empty((n,), A.dtype, dev) |
| c = tvm.runtime.empty((n,), A.dtype, dev) |
| # Only need to test compiling here |
| fun(a, c) |
| |
| dev = tvm.device(target, 0) |
| |
| check_if_then_else(dev, 1, "int8") |
| check_if_then_else(dev, 1, "uint8") |
| check_if_then_else(dev, 1, "int16") |
| check_if_then_else(dev, 1, "uint16") |
| check_select(dev, 1, "int8") |
| check_select(dev, 1, "uint8") |
| check_select(dev, 1, "int16") |
| check_select(dev, 1, "uint16") |
| |
| |
| @tvm.testing.requires_gpu |
| @tvm.testing.requires_opencl |
| def test_opencl_inf_nan(): |
| def check_inf_nan(dev, n, value, dtype): |
| A = te.placeholder((n,), name="A", dtype=dtype) |
| inf_value = tvm.tir.const(value, dtype=dtype) |
| C = te.compute((n,), lambda i: inf_value, name="C") |
| func = te.create_prim_func([A, C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| sch.bind(x, "threadIdx.x") |
| fun = tvm.tir.build(sch.mod, target=target) |
| a = tvm.runtime.empty((n,), A.dtype, dev) |
| c = tvm.runtime.empty((n,), A.dtype, dev) |
| # Only need to test compiling here |
| fun(a, c) |
| |
| dev = tvm.device(target, 0) |
| |
| check_inf_nan(dev, 1, -float("inf"), "float32") |
| check_inf_nan(dev, 1, -float("inf"), "float64") |
| check_inf_nan(dev, 1, float("inf"), "float32") |
| check_inf_nan(dev, 1, float("inf"), "float64") |
| check_inf_nan(dev, 1, float("nan"), "float32") |
| check_inf_nan(dev, 1, float("nan"), "float64") |
| |
| |
| @tvm.testing.requires_gpu |
| @tvm.testing.requires_opencl |
| def test_opencl_max(): |
| def check_max(dev, n, dtype): |
| A = te.placeholder((n,), name="A", dtype=dtype) |
| max_lhs = A[0] + tvm.tir.const(1, dtype=dtype) |
| max_rhs = tvm.tir.const(0, dtype=dtype) |
| C = te.compute((n,), lambda i: tvm.te.max(max_lhs, max_rhs), name="C") |
| func = te.create_prim_func([A, C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| sch.bind(x, "threadIdx.x") |
| fun = tvm.tir.build(sch.mod, target=target) |
| |
| a = tvm.runtime.empty((n,), A.dtype, dev) |
| c = tvm.runtime.empty((n,), A.dtype, dev) |
| # Only need to test compiling here |
| fun(a, c) |
| |
| dev = tvm.device(target, 0) |
| |
| check_max(dev, 1, "int8") |
| check_max(dev, 1, "uint8") |
| check_max(dev, 1, "int16") |
| check_max(dev, 1, "uint16") |
| check_max(dev, 1, "float32") |
| check_max(dev, 1, "float64") |
| |
| |
| def test_opencl_erf(): |
| def check_erf(dev, n, dtype): |
| A = te.placeholder((n,), name="A", dtype=dtype) |
| C = te.compute(A.shape, lambda *i: te.erf(A(*i)), name="C") |
| func = te.create_prim_func([A, C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| sch.bind(x, "threadIdx.x") |
| fun = tvm.tir.build(sch.mod, 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 |
| |
| dev = tvm.device(target, 0) |
| |
| check_erf(dev, 1, "float32") |
| check_erf(dev, 1, "float64") |
| |
| |
| @tvm.testing.requires_gpu |
| @tvm.testing.requires_opencl |
| def test_opencl_type_casting(): |
| def check_type_casting(ctx, n, dtype): |
| block_size = 4 |
| C = te.compute( |
| (n,), |
| lambda i: tvm.tir.Select( |
| tvm.tir.all( |
| *[ |
| i // block_size == tvm.tir.const(3, "int32"), |
| i % 3 == tvm.tir.const(1, "int32"), |
| ] |
| ), |
| tvm.tir.const(1, dtype), |
| tvm.tir.const(0, dtype), |
| ), |
| name="C", |
| ) |
| # NOTE: test simple convert pattern |
| func = te.create_prim_func([C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| tx, vx = sch.split(x, factors=[None, block_size]) |
| sch.bind(tx, "threadIdx.x") |
| sch.vectorize(vx) |
| |
| fun = tvm.tir.build(sch.mod, target=target) |
| c = tvm.runtime.empty((n,), dtype, ctx) |
| 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 = "({} && {})".format(lcond, rcond) |
| assert assembly.count(pattern_cond) != 0 |
| fun(c) |
| |
| dev = tvm.device(target, 0) |
| |
| check_type_casting(dev, 32, "float32") |
| # fp16 is not yet supported in ci |
| # check_type_casting(dev, 16, "float16") |
| |
| |
| @tvm.testing.requires_gpu |
| @tvm.testing.requires_opencl |
| @tvm.testing.parametrize_targets("opencl", "opencl -device=adreno") |
| def test_opencl_ceil_log2(target): |
| def _check(target, n, dtype): |
| with tvm.target.Target(target): |
| C = te.compute( |
| (n,), |
| lambda i: tvm.topi.ceil_log2(i), |
| name="C", |
| ) |
| func = te.create_prim_func([C]) |
| sch = tvm.tir.Schedule(func) |
| (x,) = sch.get_loops(sch.get_block("C")) |
| sch.bind(x, "threadIdx.x") |
| |
| fun = tvm.tir.build(sch.mod, target=target) |
| assembly = fun.imports[0].inspect_source() |
| if "adreno" in target: |
| 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.tir.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 |
| |
| |
| if __name__ == "__main__": |
| tvm.testing.main() |
