content: # SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
  # SPDX-License-Identifier: LicenseRef-NvidiaProprietary
  #
  # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
  # property and proprietary rights in and to this material, related
  # documentation and any modifications thereto. Any use, reproduction,
  # disclosure or distribution of this material and related documentation
  # without an express license agreement from NVIDIA CORPORATION or
  # its affiliates is strictly prohibited.
  
  """Custom PyTorch ops for efficient bias and activation."""
  
  import os
  import numpy as np
  import torch
  from ldm.modules.neus.third_party import dnnlib
  
  from .. import custom_ops
  from .. import misc
  
  #----------------------------------------------------------------------------
  
  activation_funcs = {
      'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
      'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
      'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
      'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
      'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
      'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
      'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
      'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
      'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
  }
  
  #----------------------------------------------------------------------------
  
  _plugin = None
  _null_tensor = torch.empty([0])
  
  def _init():
      global _plugin
      if _plugin is None:
          _plugin = custom_ops.get_plugin(
              module_name='bias_act_plugin',
              sources=['bias_act.cpp', 'bias_act.cu'],
              headers=['bias_act.h'],
              source_dir=os.path.dirname(__file__),
              extra_cuda_cflags=['--use_fast_math'],
          )
      return True
  
  #----------------------------------------------------------------------------
  
  def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
      r"""Fused bias and activation function.
  
      Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
      and scales the result by `gain`. Each of the steps is optional. In most cases,
      the fused op is considerably more efficient than performing the same calculation
      using standard PyTorch ops. It supports first and second order gradients,
      but not third order gradients.
  
      Args:
          x:      Input activation tensor. Can be of any shape.
          b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
                  as `x`. The shape must be known, and it must match the dimension of `x`
                  corresponding to `dim`.
          dim:    The dimension in `x` corresponding to the elements of `b`.
                  The value of `dim` is ignored if `b` is not specified.
          act:    Name of the activation function to evaluate, or `"linear"` to disable.
                  Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
                  See `activation_funcs` for a full list. `None` is not allowed.
          alpha:  Shape parameter for the activation function, or `None` to use the default.
          gain:   Scaling factor for the output tensor, or `None` to use default.
                  See `activation_funcs` for the default scaling of each activation function.
                  If unsure, consider specifying 1.
          clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
                  the clamping (default).
          impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
  
      Returns:
          Tensor of the same shape and datatype as `x`.
      """
      assert isinstance(x, torch.Tensor)
      assert impl in ['ref', 'cuda']
      if impl == 'cuda' and x.device.type == 'cuda' and _init():
          return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
      return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
  
  #----------------------------------------------------------------------------
  
  @misc.profiled_function
  def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
      """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
      """
      assert isinstance(x, torch.Tensor)
      assert clamp is None or clamp >= 0
      spec = activation_funcs[act]
      alpha = float(alpha if alpha is not None else spec.def_alpha)
      gain = float(gain if gain is not None else spec.def_gain)
      clamp = float(clamp if clamp is not None else -1)
  
      # Add bias.
      if b is not None:
          assert isinstance(b, torch.Tensor) and b.ndim == 1
          assert 0 <= dim < x.ndim
          assert b.shape[0] == x.shape[dim]
          x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
  
      # Evaluate activation function.
      alpha = float(alpha)
      x = spec.func(x, alpha=alpha)
  
      # Scale by gain.
      gain = float(gain)
      if gain != 1:
          x = x * gain
  
      # Clamp.
      if clamp >= 0:
          x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
      return x
  
  #----------------------------------------------------------------------------
  
  _bias_act_cuda_cache = dict()
  
  def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
      """Fast CUDA implementation of `bias_act()` using custom ops.
      """
      # Parse arguments.
      assert clamp is None or clamp >= 0
      spec = activation_funcs[act]
      alpha = float(alpha if alpha is not None else spec.def_alpha)
      gain = float(gain if gain is not None else spec.def_gain)
      clamp = float(clamp if clamp is not None else -1)
  
      # Lookup from cache.
      key = (dim, act, alpha, gain, clamp)
      if key in _bias_act_cuda_cache:
          return _bias_act_cuda_cache[key]
  
      # Forward op.
      class BiasActCuda(torch.autograd.Function):
          @staticmethod
          def forward(ctx, x, b): # pylint: disable=arguments-differ
              ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format
              x = x.contiguous(memory_format=ctx.memory_format)
              b = b.contiguous() if b is not None else _null_tensor
              y = x
              if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
                  y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
              ctx.save_for_backward(
                  x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
                  b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
                  y if 'y' in spec.ref else _null_tensor)
              return y
  
          @staticmethod
          def backward(ctx, dy): # pylint: disable=arguments-differ
              dy = dy.contiguous(memory_format=ctx.memory_format)
              x, b, y = ctx.saved_tensors
              dx = None
              db = None
  
              if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
                  dx = dy
                  if act != 'linear' or gain != 1 or clamp >= 0:
                      dx = BiasActCudaGrad.apply(dy, x, b, y)
  
              if ctx.needs_input_grad[1]:
                  db = dx.sum([i for i in range(dx.ndim) if i != dim])
  
              return dx, db
  
      # Backward op.
      class BiasActCudaGrad(torch.autograd.Function):
          @staticmethod
          def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
              ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format
              dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
              ctx.save_for_backward(
                  dy if spec.has_2nd_grad else _null_tensor,
                  x, b, y)
              return dx
  
          @staticmethod
          def backward(ctx, d_dx): # pylint: disable=arguments-differ
              d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
              dy, x, b, y = ctx.saved_tensors
              d_dy = None
              d_x = None
              d_b = None
              d_y = None
  
              if ctx.needs_input_grad[0]:
                  d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
  
              if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
                  d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
  
              if spec.has_2nd_grad and ctx.needs_input_grad[2]:
                  d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
  
              return d_dy, d_x, d_b, d_y
  
      # Add to cache.
      _bias_act_cuda_cache[key] = BiasActCuda
      return BiasActCuda
  
  #----------------------------------------------------------------------------
  