ffn.py

"""MPT Blocks used for the MPT Model."""

import logging
from copy import deepcopy
from functools import partial
from typing import Any, Callable, Optional, Union
import torch
import torch.nn as nn
from .fc import FC_CLASS_REGISTRY

try:
    import transformer_engine.pytorch as te
except:
    te = None
log = logging.getLogger(__name__)
_FFN_ACT_FN_DEFAULT = {"name": "gelu", "approximate": "none"}


def resolve_ffn_act_fn(
    config: Optional[dict] = None,
) -> Callable[[torch.Tensor], torch.Tensor]:
    """Resolve the activation function for the feed-forward network.
    Args:
        config (Optional[dict]): The configuration dictionary for the activation function.
            The dict config must specify the 'name' of a torch.nn.functional activation
            function. All of other key values pairs are bound to the function as a partial.
    Returns:
        Callable[[torch.Tensor], torch.Tensor]: The activation function.
    """
    if config is None:
        config = _FFN_ACT_FN_DEFAULT
    config = deepcopy(config)
    name = config.pop("name")
    if not hasattr(torch.nn.functional, name):
        raise ValueError(f"Unrecognised activation function name ({name}).")
    act = getattr(torch.nn.functional, name)
    return partial(act, **config)


_DEFAULT_ACT_FN = resolve_ffn_act_fn(_FFN_ACT_FN_DEFAULT)


def resolve_ffn_hidden_size(
    d_model: int,
    expansion_ratio: Union[int, float],
    ffn_hidden_size: Optional[int] = None,
) -> int:
    """Resolve the hidden size of the feed-forward network.
    Args:
        d_model (int): The dimension of the input and output of the feed-forward network.
        expansion_ratio (Union[int, float]): The expansion ratio of the feed-forward network.
        ffn_hidden_size (Optional[int]): The hidden size of the feed-forward network.
    Returns:
        int: The hidden size of the feed-forward network.
    """
    if ffn_hidden_size is not None:
        log.info(
            f"`expansion_ratio` (={expansion_ratio}) ignored when `ffn_hidden_size` (={ffn_hidden_size}) is specified."
        )
    else:
        ffn_hidden_size = int(d_model * expansion_ratio)
        if ffn_hidden_size != d_model * expansion_ratio:
            raise ValueError(
                f"`d_model * expansion_ratio` must be an integer (d_model={d_model!r}; expansion_ratio={expansion_ratio!r}; d_model * expansion_ratio={d_model * expansion_ratio!r})."
            )
    return ffn_hidden_size


class MPTMLP(nn.Module):

    def __init__(
        self,
        d_model: int,
        expansion_ratio: Union[int, float],
        fc_type: str = "torch",
        ffn_hidden_size: Optional[int] = None,
        act_fn: Callable[[torch.Tensor], torch.Tensor] = _DEFAULT_ACT_FN,
        device: Optional[str] = None,
        bias: bool = True,
    ):
        super().__init__()
        ffn_hidden_size = resolve_ffn_hidden_size(
            d_model, expansion_ratio, ffn_hidden_size
        )
        self.fc_kwargs: dict[str, Any] = {"bias": bias}
        if fc_type != "te":
            self.fc_kwargs["device"] = device
        self.up_proj = FC_CLASS_REGISTRY[fc_type](
            d_model, ffn_hidden_size, **self.fc_kwargs
        )
        self.act = act_fn
        self.down_proj = FC_CLASS_REGISTRY[fc_type](
            ffn_hidden_size, d_model, **self.fc_kwargs
        )
        self.down_proj._is_residual = True

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(self.act(self.up_proj(x)))


class MPTGLU(MPTMLP):

    def __init__(
        self,
        d_model: int,
        expansion_ratio: Union[int, float],
        fc_type: str = "torch",
        ffn_hidden_size: Optional[int] = None,
        act_fn: Callable[[torch.Tensor], torch.Tensor] = _DEFAULT_ACT_FN,
        device: Optional[str] = None,
        bias: bool = True,
    ):
        super().__init__(
            d_model=d_model,
            expansion_ratio=expansion_ratio,
            fc_type=fc_type,
            ffn_hidden_size=ffn_hidden_size,
            act_fn=act_fn,
            device=device,
            bias=bias,
        )
        self.gate_proj = FC_CLASS_REGISTRY[fc_type](
            d_model, self.up_proj.out_features, **self.fc_kwargs
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(self.act(self.gate_proj(x)) * self.up_proj(x))


FFN_CLASS_REGISTRY = {"mptmlp": MPTMLP, "mptglu": MPTGLU}
if te is not None:
    te.LayerNormMLP._has_norm = True
    FFN_CLASS_REGISTRY["te_ln_mlp"] = te.LayerNormMLP


def build_ffn(
    d_model: int,
    expansion_ratio: Union[int, float],
    fc_type: str = "torch",
    ffn_hidden_size: Optional[int] = None,
    ffn_act_fn: Optional[dict] = None,
    device: Optional[str] = None,
    bias: bool = True,
    **kwargs: Any,
) -> nn.Module:
    ffn_type = kwargs.pop("ffn_type")
    if ffn_type in ["mptmlp", "mptglu"]:
        if len(kwargs) > 0:
            raise ValueError(
                f"MPTMLP (or MPTGLU) got an unexpected keyword argument: {kwargs}"
            )
        return FFN_CLASS_REGISTRY[ffn_type](
            d_model=d_model,
            expansion_ratio=expansion_ratio,
            fc_type=fc_type,
            act_fn=resolve_ffn_act_fn(ffn_act_fn),
            ffn_hidden_size=ffn_hidden_size,
            device=device,
            bias=bias,
        )
    elif ffn_type == "te_ln_mlp":
        assert te is not None
        ffn_hidden_size = resolve_ffn_hidden_size(
            d_model, expansion_ratio, ffn_hidden_size
        )
        if ffn_act_fn is not None:
            raise ValueError(
                f"Transformer Engine block does not support custom activation functions."
            )
        return te.LayerNormMLP(
            hidden_size=d_model, ffn_hidden_size=ffn_hidden_size, bias=bias, **kwargs
        )
    raise ValueError(f"ffn_type={ffn_type!r} not recognized.")