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 numpy as np
  
  import topi
  import topi.testing
  import tvm
  from tvm import relay
  from tvm.relay.testing import check_grad, ctx_list, run_infer_type
  from tvm.relay.transform import gradient
  
  
  def verify_max_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode):
      x = relay.var("x", relay.TensorType(x_shape, "float32"))
      y = tvm.relay.nn.max_pool2d(x, pool_size=pool_size, strides=strides, padding=padding,
                                  ceil_mode=ceil_mode)
  
      fwd_func = relay.Function([x], y)
      fwd_func = run_infer_type(fwd_func)
      bwd_func = run_infer_type(gradient(fwd_func))
  
      data = np.random.rand(*x_shape).astype("float32")
      ph, pw = padding
      y_shape = topi.util.get_const_tuple(fwd_func.ret_type.shape)
      out_grad = np.ones(shape=y_shape)
      ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=pool_size, strides=strides,
                                             padding=[ph, pw, ph, pw],
                                             pool_type='max', ceil_mode=ceil_mode)
  
      for target, ctx in ctx_list():
          intrp = relay.create_executor(ctx=ctx, target=target)
          op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
          np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
  
  
  def test_max_pool2d_grad():
      verify_max_pool2d_grad((1, 4, 16, 16), pool_size=(2, 2), strides=(2, 2), padding=(0, 0), ceil_mode=False)
      verify_max_pool2d_grad((1, 4, 16, 16), pool_size=(1, 1), strides=(1, 1), padding=(1, 1), ceil_mode=False)
  
  
  def verify_avg_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode, count_include_pad):
      x = relay.var("x", relay.TensorType(x_shape, "float32"))
      y = tvm.relay.nn.avg_pool2d(x, pool_size=pool_size, strides=strides, padding=padding,
                                  ceil_mode=ceil_mode, count_include_pad=count_include_pad)
  
      fwd_func = relay.Function([x], y)
      fwd_func = run_infer_type(fwd_func)
      bwd_func = run_infer_type(gradient(fwd_func))
  
      data = np.random.rand(*x_shape).astype("float32")
      ph, pw = padding
      y_shape = topi.util.get_const_tuple(fwd_func.ret_type.shape)
      out_grad = np.ones(shape=y_shape)
      ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=pool_size, strides=strides,
                                             padding=[ph, pw, ph, pw],
                                             pool_type='avg', ceil_mode=ceil_mode)
  
      for target, ctx in ctx_list():
          intrp = relay.create_executor(ctx=ctx, target=target)
          op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
          np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
  
  def test_avg_pool2d_grad():
      verify_avg_pool2d_grad((1, 4, 16, 16), pool_size=(2, 2), strides=(2, 2), padding=(0, 0),
                             ceil_mode=False, count_include_pad=True)
      verify_avg_pool2d_grad((1, 4, 16, 16), pool_size=(1, 1), strides=(1, 1), padding=(1, 1),
                             ceil_mode=False, count_include_pad=False)
  
  
  def verify_global_avg_pool2d_grad(x_shape):
      x = relay.var("x", relay.TensorType(x_shape, "float32"))
      y = tvm.relay.nn.global_avg_pool2d(x)
  
      fwd_func = relay.Function([x], y)
      fwd_func = run_infer_type(fwd_func)
      bwd_func = run_infer_type(gradient(fwd_func))
  
      data = np.random.rand(*x_shape).astype("float32")
      y_shape = topi.util.get_const_tuple(fwd_func.ret_type.shape)
      out_grad = np.ones(shape=y_shape)
      ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=(x_shape[2], x_shape[3]), 
                                              strides=(1, 1), padding=[0, 0, 0, 0], pool_type='avg', 
                                              ceil_mode=False)
  
      for target, ctx in ctx_list():
          intrp = relay.create_executor(ctx=ctx, target=target)
          op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
          np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
  
  def test_global_avg_pool2d_grad():
      verify_global_avg_pool2d_grad((1, 4, 16, 16))
      verify_global_avg_pool2d_grad((1, 8, 8, 24))
  
  def verify_conv2d_grad(dshape, wshape, strides, padding, dilation, groups=1, mode='higher_order'):
      try:
          import torch
          import torch.nn.functional as F
      except ImportError:
          print('Skip because pytorch is not installed')
          return
  
      dtype = 'float32'
      data = relay.var('data', shape=dshape, dtype=dtype)
      weight = relay.var('weight', shape=wshape, dtype=dtype)
      conv = relay.nn.conv2d(data, weight, strides=strides, padding=padding, dilation=dilation,
                             groups=groups)
      fwd_func = relay.Function([data, weight], conv)
      fwd_func = run_infer_type(fwd_func)
      bwd_func = run_infer_type(gradient(fwd_func, mode=mode))
  
      data_pt = torch.randn(*dshape, dtype=torch.float32, requires_grad=True)
      weight_pt = torch.randn(*wshape, dtype=torch.float32, requires_grad=True)
      out_pt = F.conv2d(data_pt, weight_pt, stride=strides, padding=padding, dilation=dilation,
                        groups=groups)
      grad_output_pt = torch.ones(out_pt.shape)
      grad_input_pt = F.grad.conv2d_input(dshape, weight_pt, grad_output_pt, stride=strides,
                                          padding=padding, dilation=dilation, groups=groups) \
                            .detach().numpy()
      grad_weight_pt = F.grad.conv2d_weight(data_pt, wshape, grad_output_pt, stride=strides,
                                            padding=padding, dilation=dilation, groups=groups) \
                             .detach().numpy()
  
  
      for target, ctx in ctx_list():
          data = tvm.nd.array(data_pt.detach().numpy(), ctx)
          weight = tvm.nd.array(weight_pt.detach().numpy(), ctx)
          intrp = relay.create_executor(ctx=ctx, target=target)
          op_res, (grad_input, grad_weight) = intrp.evaluate(bwd_func)(data, weight)
          np.testing.assert_allclose(grad_input.asnumpy(), grad_input_pt, rtol=1e-4, atol=1e-4)
          np.testing.assert_allclose(grad_weight.asnumpy(), grad_weight_pt, rtol=1e-4, atol=1e-4)
  
  
  def test_conv2d_grad():
      verify_conv2d_grad((1, 4, 16, 16), (16, 4, 3, 3), [1, 1], [1, 1], [1, 1])
      verify_conv2d_grad((1, 4, 16, 16), (16, 4, 1, 1), [1, 1], [0, 0], [1, 1])
      verify_conv2d_grad((1, 4, 16, 16), (16, 4, 1, 1), [2, 2], [0, 0], [1, 1])
      verify_conv2d_grad((1, 4, 16, 16), (16, 4, 3, 3), [1, 1], [1, 1], [1, 1], mode='first_order')
  
  
  def verify_dense_grad(d_shape, w_shape):
      data = relay.var("data", relay.TensorType(d_shape, "float32"))
      weight = relay.var("weight", relay.TensorType(w_shape, "float32"))
      fwd_func = relay.Function([data, weight], relay.nn.dense(data, weight))
      check_grad(fwd_func)
  
  
  def test_dense_grad():
      verify_dense_grad((1, 8), (16, 8))
      verify_dense_grad((1, 4), (3, 4))
  
  
  def verify_batch_flatten_grad(d_shape):
      data = relay.var("data", relay.TensorType(d_shape, "float32"))
      fwd_func = relay.Function([data], relay.nn.batch_flatten(data))
      check_grad(fwd_func)
  
  
  def test_batch_flatten_grad():
      verify_batch_flatten_grad((1, 2, 3, 4))
      verify_batch_flatten_grad((1, 8))
  
  
  if __name__ == "__main__":
      test_max_pool2d_grad()
      test_avg_pool2d_grad()
      test_global_avg_pool2d_grad()
      test_conv2d_grad()
      test_dense_grad()
      test_batch_flatten_grad()
  