text: # Licensed to the Apache Software Foundation (ASF) under one
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  # to you under the Apache License, Version 2.0 (the
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  #
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  # KIND, either express or implied.  See the License for the
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  # under the License.
  """Test code for relu activation"""
  import sys
  import os
  import numpy as np
  import tvm
  from tvm import te
  from tvm import topi
  import tvm.topi.testing
  from tvm.topi.utils import get_const_tuple
  from tvm.contrib.nvcc import have_fp16
  
  import pytest
  import tvm.testing
  
  
  m, n, dtype = tvm.testing.parameters(
      (10, 128, "float32"),
      (128, 64, "float16"),
      # Commented due to weird killed
      # (1024 * 100, 512, "float32"),
  )
  
  
  def test_relu(target, dev, m, n, dtype):
      A = te.placeholder((m, n), name="A", dtype=dtype)
      B = topi.nn.relu(A)
  
      a_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(A.shape)).astype(A.dtype)
      b_np = a_np * (a_np > 0)
  
      if dtype == "float16" and target == "cuda" and not have_fp16(tvm.cuda(0).compute_version):
          pytest.skip("Skip because %s does not have fp16 support" % target)
  
      print("Running on target: %s" % target)
      with tvm.target.Target(target):
          s = tvm.topi.testing.get_elemwise_schedule(target)(B)
  
      a = tvm.nd.array(a_np, dev)
      b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
      # Building with the CSE pass disabled
      with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
          foo = tvm.build(s, [A, B], target, name="relu")
      foo(a, b)
      tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
  
  
  size, alpha = tvm.testing.parameters((100, 0.1))
  
  
  def test_leaky_relu(size, alpha):
      A = te.placeholder((size,), name="A")
      B = topi.nn.leaky_relu(A, alpha)
      s = te.create_schedule([B.op])
  
      a_np = np.random.uniform(size=get_const_tuple(A.shape)).astype(A.dtype)
      b_np = a_np * (a_np > 0) + a_np * (a_np < 0) * alpha
      dev = tvm.cpu(0)
      a = tvm.nd.array(a_np, dev)
      b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
      # Building with the CSE pass disabled
      with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
          foo = tvm.build(s, [A, B], "llvm", name="leaky_relu")
      foo(a, b)
      tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-5)
  
  
  x, w, axis, weight_reshape = tvm.testing.parameters(
      ((1, 3, 2, 2), (3,), 1, (3, 1, 1)),
      ((1, 3, 2, 2), (2,), 2, (2, 1)),
      ((1, 3), (3,), 1, (3,)),
  )
  
  
  def test_prelu(x, w, axis, weight_reshape):
      X = te.placeholder((x), name="X")
      W = te.placeholder((w), name="W")
      x_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(X.shape)).astype(X.dtype)
      w_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(W.shape)).astype(W.dtype)
  
      def _prelu_numpy(x, W):
          return (x < 0) * (x * W.reshape(weight_reshape)) + (x >= 0) * x
  
      B = topi.nn.prelu(X, W, axis)
      s = te.create_schedule([B.op])
  
      dev = tvm.cpu(0)
      x_tvm = tvm.nd.array(x_np, dev)
      w_tvm = tvm.nd.array(w_np, dev)
  
      b = tvm.nd.array(np.zeros(get_const_tuple(X.shape), dtype=B.dtype), dev)
      # Building with the CSE pass disabled
      with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
          foo = tvm.build(s, [X, W, B], "llvm", name="prelu")
      foo(x_tvm, w_tvm, b)
      out_np = _prelu_numpy(x_np, w_np)
      tvm.testing.assert_allclose(b.numpy(), out_np, rtol=1e-5)
  
  
  if __name__ == "__main__":
      sys.exit(pytest.main(sys.argv))
  