text: # 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 tvm
  from tvm import te
  import tvm.testing
  import re
  
  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")
          s = te.create_schedule(C.op)
          s[C].bind(s[C].op.axis[0], te.thread_axis("threadIdx.x"))
          fun = tvm.build(s, [A, C], target)
  
          a = tvm.nd.empty((n,), A.dtype, dev)
          c = tvm.nd.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")
          s = te.create_schedule(C.op)
          s[C].bind(s[C].op.axis[0], te.thread_axis("threadIdx.x"))
          fun = tvm.build(s, [A, C], target)
  
          a = tvm.nd.empty((n,), A.dtype, dev)
          c = tvm.nd.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")
          s = te.create_schedule(C.op)
          s[C].bind(s[C].op.axis[0], te.thread_axis("threadIdx.x"))
          fun = tvm.build(s, [A, C], target)
          a = tvm.nd.empty((n,), A.dtype, dev)
          c = tvm.nd.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")
          s = te.create_schedule(C.op)
          s[C].bind(s[C].op.axis[0], te.thread_axis("threadIdx.x"))
          fun = tvm.build(s, [A, C], target)
  
          a = tvm.nd.empty((n,), A.dtype, dev)
          c = tvm.nd.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")
          s = te.create_schedule(C.op)
          s[C].bind(s[C].op.axis[0], te.thread_axis("threadIdx.x"))
          fun = tvm.build(s, [A, C], target)
          source_str = fun.imported_modules[0].get_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")
  
  
  if __name__ == "__main__":
      test_opencl_ternary_expression()
      test_opencl_inf_nan()
      test_opencl_erf()
  