ColossalAI/colossalai/shardformer/layer/_operation.py

import torch
import torch.distributed as dist
import torch.nn.functional as F

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


class MatmulWithAsyncCommunication(torch.autograd.Function):
    """
    Linear layer execution with asynchronous communication in backprop.
    """

    @staticmethod
    def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce):
        ctx.save_for_backward(input_, weight)
        ctx.use_bias = bias is not None
        ctx.process_group = process_group
        ctx.async_grad_allreduce = async_grad_allreduce

        output = torch.matmul(input_, weight)

        if bias is not None:
            output = output + bias
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, weight = ctx.saved_tensors
        use_bias = ctx.use_bias

        total_input = input
        grad_input = grad_output.matmul(weight.T)
        grad_output = grad_output.contiguous()
        # Convert the tensor shapes to 2D for execution compatibility
        if len(grad_output.shape) > 2:
            grad_output = grad_output.view(-1, grad_output.shape[-1])
            total_input = total_input.view(-1, total_input.shape[-1])

        if ctx.async_grad_allreduce:
            # Asynchronous all-reduce
            handle = dist.all_reduce(grad_input, group=ctx.process_group, async_op=True)
            # Delay the start of weight gradient computation shortly (3us) to have
            # all-reduce scheduled first and have GPU resources allocated
            _ = torch.empty(1, device=grad_output.device) + 1

        grad_weight = total_input.t().matmul(grad_output)
        grad_bias = grad_output.sum(dim=0) if use_bias else None

        if ctx.async_grad_allreduce:
            handle.wait()

        return grad_input, grad_weight, grad_bias, None, None, None


class LinearWithAsyncCommunication(torch.autograd.Function):
    """
    Linear layer execution with asynchronous communication in backprop.
    """

    @staticmethod
    def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce):
        ctx.save_for_backward(input_, weight)
        ctx.use_bias = bias is not None
        ctx.process_group = process_group
        ctx.async_grad_allreduce = async_grad_allreduce

        if bias is not None:
            output = F.linear(input_, weight, bias)
        else:
            output = F.linear(input_, weight)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, weight = ctx.saved_tensors
        use_bias = ctx.use_bias

        total_input = input
        grad_input = grad_output.matmul(weight)
        grad_output = grad_output.contiguous()
        # Convert the tensor shapes to 2D for execution compatibility
        if len(grad_output.shape) > 2:
            grad_output = grad_output.view(-1, grad_output.shape[-1])
            total_input = total_input.view(-1, total_input.shape[-1])

        if ctx.async_grad_allreduce:
            # Asynchronous all-reduce
            handle = dist.all_reduce(grad_input, group=ctx.process_group, async_op=True)
            # Delay the start of weight gradient computation shortly (3us) to have
            # all-reduce scheduled first and have GPU resources allocated
            _ = torch.empty(1, device=grad_output.device) + 1

        grad_weight = grad_output.t().matmul(total_input)
        grad_bias = grad_output.sum(dim=0) if use_bias else None

        if ctx.async_grad_allreduce:
            handle.wait()

        return grad_input, grad_weight, grad_bias, None, None, None


class _SplitForwardGatherBackward(torch.autograd.Function):
    """
    Split the input and keep only the corresponding chuck to the rank.

    Args:
        input_ (`torch.Tensor`): input matrix.
        dim (int): the dimension to perform split and gather
        process_group (`torch.distributed.ProcessGroup`): the process group used for collective communication

    """

    @staticmethod
    def forward(ctx, input_, dim, process_group):
        ctx.process_group = process_group
        ctx.dim = dim
        return _split(input_, dim, process_group)

    @staticmethod
    def backward(ctx, grad_output):
        return _gather(grad_output, ctx.dim, ctx.process_group), None, None


class _ReduceForward(torch.autograd.Function):
    """
    All-reduce the input from the model parallel region.

    Args:
        input_: input matrix.
        parallel_mode: parallel mode.
    """

    @staticmethod
    def forward(ctx, input_, process_group):
        return _reduce(input_, process_group)

    @staticmethod
    def backward(ctx, grad_output):
        return grad_output, None


class _ReduceBackward(torch.autograd.Function):
    """
    All-reduce the input from the model parallel region.

    Args:
        input_: input matrix.
        parallel_mode: parallel mode.
    """

    @staticmethod
    def forward(ctx, input_, process_group):
        ctx.process_group = process_group
        return input_

    @staticmethod
    def backward(ctx, grad_output):
        return _reduce(grad_output, ctx.process_group), None


def _reduce(input_, process_group):
    # skip if only one rank involved
    if dist.get_world_size(process_group) == 1:
        return input_
    else:
        dist.all_reduce(input_, group=process_group)
        return input_


def _split(input_, dim=-1, process_group=None):
    # skip if only one rank involved
    world_size = dist.get_world_size(process_group)
    if world_size == 1:
        return input_

    # Split along last dimension.
    dim_size = input_.size(dim)
    assert dim_size % world_size == 0, \
        f'The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), ' \
        f'cannot split tensor evenly'

    tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
    rank = dist.get_rank(process_group)
    output = tensor_list[rank].contiguous()

    return output


def _gather(input_, dim=-1, process_group=None):
    # skip if only one rank involved
    world_size = dist.get_world_size(process_group)
    if world_size == 1:
        return input_

    # all gather
    rank = dist.get_rank(process_group)
    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
    tensor_list[rank] = input_
    torch.distributed.all_gather(tensor_list, input_, group=process_group)

    # concat
    output = torch.cat(tensor_list, dim=dim).contiguous()

    return output


class _GatherForwardSplitBackward(torch.autograd.Function):
    """Gather the input from model parallel region and concatenate.

    Args:
        input_: input matrix.
        parallel_mode: parallel mode.
        dim: dimension
    """

    @staticmethod
    def forward(ctx, input_, dim, process_group):
        ctx.process_group = process_group
        ctx.dim = dim
        return _gather(input_, dim, process_group)

    @staticmethod
    def backward(ctx, grad_output):
        return _split(grad_output, ctx.dim, ctx.process_group), None, None


def matmul_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce):
    return MatmulWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce)


def linear_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce):
    return LinearWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce)


def gather_forward_split_backward(input_, dim, process_group):
    return _GatherForwardSplitBackward.apply(input_, dim, process_group)


def split_forward_gather_backward(input_, dim, process_group):
    return _SplitForwardGatherBackward.apply(input_, dim, process_group)


def reduce_forward(input_, process_group):
    return _ReduceForward.apply(input_, process_group)


def reduce_backward(input_, process_group):
    return _ReduceBackward.apply(input_, process_group)