mlp.py

# Copyright (c) 2022, Tri Dao.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributed import ProcessGroup

try:
    from flash_attn.ops.fused_dense import ColumnParallelLinear, RowParallelLinear
except ImportError:
    ColumnParallelLinear, RowParallelLinear = None, None

try:
    from flash_attn.ops.fused_dense import FusedMLP, ParallelFusedMLP
except ImportError:
    FusedMLP, ParallelFusedMLP = None, None


class Mlp(nn.Module):

    def __init__(self, in_features, hidden_features=None, out_features=None, activation=F.gelu,
                 bias1=True, bias2=True, return_residual=False, device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features * 4
        self.return_residual = return_residual
        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias1, **factory_kwargs)
        self.activation = activation
        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias2, **factory_kwargs)

    def forward(self, x):
        y = self.fc1(x)
        y = self.activation(y)
        y = self.fc2(y)
        return y if not self.return_residual else (y, x)


class ParallelMLP(nn.Module):

    def __init__(self, in_features, hidden_features=None, out_features=None, activation=F.gelu,
                 process_group: ProcessGroup = None, sequence_parallel=True,
                 bias1=True, bias2=True, device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        assert ColumnParallelLinear is not None, "Need to install fused_dense"
        assert RowParallelLinear is not None, "Need to install fused_dense"
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features * 4
        self.fc1 = ColumnParallelLinear(in_features, hidden_features, process_group, bias=bias1,
                                        sequence_parallel=sequence_parallel, **factory_kwargs)
        self.activation = activation
        self.fc2 = RowParallelLinear(hidden_features, out_features, process_group, bias=bias2,
                                     sequence_parallel=sequence_parallel, **factory_kwargs)

    def forward(self, x):
        y = self.fc1(x)
        y = self.activation(y)
        y = self.fc2(y)
        return y


class GatedMlp(nn.Module):

    def __init__(self, in_features, hidden_features=None, out_features=None, activation=F.sigmoid,
                 bias1=True, bias2=True, multiple_of=256, return_residual=False,
                 device=None, dtype=None):
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or int(8 * in_features / 3)
        hidden_features = (hidden_features + multiple_of - 1) // multiple_of * multiple_of
        self.return_residual = return_residual
        self.fc1 = nn.Linear(in_features, 2 * hidden_features, bias=bias1, **factory_kwargs)
        self.activation = activation
        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias1, **factory_kwargs)

    def forward(self, x):
        y = self.fc1(x)
        if self.activation == F.sigmoid:  # Special case for GLU
            y = F.glu(y, dim=-1)
        else:
            y, gate = y.chunk(2, dim=-1)
            y = y * self.activation(gate)
        y = self.fc2(y)
        return y if not self.return_residual else (y, x)