<filename>colossalai/nn/layer/parallel_1d/_operation.py
import torch

try:
    import fused_mix_prec_layer_norm_cuda
except:
    fused_mix_prec_layer_norm_cuda = None


class FusedLayerNormAffineFunction1D(torch.autograd.Function):
    r"""Layernorm

    Args:
        input: input matrix.
        weight: weight matrix.
        bias: bias matrix.
        normalized_shape: input shape from an expected input of size.
            :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
            If a single integer is used, it is treated as a singleton list, and this module will
            normalize over the last dimension which is expected to be of that specific size.
        eps: a value added to the denominator for numerical stability
  """

    @staticmethod
    def forward(ctx, input, weight, bias, normalized_shape, eps):
        ctx.normalized_shape = normalized_shape
        ctx.eps = eps
        input_ = input.contiguous()
        weight_ = weight.contiguous()
        bias_ = bias.contiguous()
        output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(input_, ctx.normalized_shape, weight_,
                                                                             bias_, ctx.eps)
        ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input_, weight_, bias_, mean, invvar = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None
        grad_input, grad_weight, grad_bias \
          = fused_mix_prec_layer_norm_cuda.backward_affine(
            grad_output.contiguous(), mean, invvar,
            input_, ctx.normalized_shape,
            weight_, bias_, ctx.eps)

        return grad_input, grad_weight, grad_bias, None, None

<filename>colossalai/nn/layer/parallel_1d/_operation.py
import torch

try:
    import fused_mix_prec_layer_norm_cuda
except:
    fused_mix_prec_layer_norm_cuda = None


class FusedLayerNormAffineFunction1D(torch.autograd.Function):
    r"""Layernorm

    Args:
        input: input matrix.
        weight: weight matrix.
        bias: bias matrix.
        normalized_shape: input shape from an expected input of size.
            :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
            If a single integer is used, it is treated as a singleton list, and this module will
            normalize over the last dimension which is expected to be of that specific size.
        eps: a value added to the denominator for numerical stability
  """

    @staticmethod
    def forward(ctx, input, weight, bias, normalized_shape, eps):
        ctx.normalized_shape = normalized_shape
        ctx.eps = eps
        input_ = input.contiguous()
        weight_ = weight.contiguous()
        bias_ = bias.contiguous()
        output, mean, invvar = fused_mix_prec_layer_norm_cuda.forward_affine(input_, ctx.normalized_shape, weight_,
                                                                             bias_, ctx.eps)
        ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input_, weight_, bias_, mean, invvar = ctx.saved_tensors
        grad_input = grad_weight = grad_bias = None
        grad_input, grad_weight, grad_bias \
          = fused_mix_prec_layer_norm_cuda.backward_affine(
            grad_output.contiguous(), mean, invvar,
            input_, ctx.normalized_shape,
            weight_, bias_, ctx.eps)

        return grad_input, grad_weight, grad_bias, None, None

Layernorm      Args:         input: input matrix.         weight: weight matrix.         bias: bias matrix.         normalized_shape: input shape from an expected input of size.             :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`             If a single integer is used, it is treated as a singleton list, and this module will             normalize over the last dimension which is expected to be of that specific size.         eps: a value added to the denominator for numerical stability