"""Implementation of the paper:
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://arxiv.org/abs/2303.16199
Port for Lit-GPT
"""
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from typing_extensions import Self
from lit_gpt.config import Config as BaseConfig
from lit_gpt.model import GPT as BaseModel
from lit_gpt.model import Block as BaseBlock
from lit_gpt.model import CausalSelfAttention as BaseCausalSelfAttention
@dataclass
class Config(BaseConfig):
adapter_prompt_length: int = 10
adapter_start_layer: int = 2
class GPT(BaseModel):
"""The implementation is identical to `lit_gpt.model.GPT` with the exception that
the `Block` saves the layer index and passes it down to the attention layer."""
def __init__(self, config: Config) -> None:
nn.Module.__init__(self)
assert config.padded_vocab_size is not None
self.config = config
self.lm_head = nn.Linear(config.n_embd, config.padded_vocab_size, bias=config.lm_head_bias)
self.transformer = nn.ModuleDict(
dict(
wte=nn.Embedding(config.padded_vocab_size, config.n_embd),
h=nn.ModuleList(Block(config, i) for i in range(config.n_layer)),
ln_f=config.norm_class(config.n_embd, eps=config.norm_eps),
)
)
self.max_seq_length = self.config.block_size
self.mask_cache: Optional[torch.Tensor] = None
def forward(
self, idx: torch.Tensor, input_pos: Optional[torch.Tensor] = None, lm_head_chunk_size: int = 0
) -> Union[torch.Tensor, List[torch.Tensor]]:
T = idx.size(1)
if self.max_seq_length < T:
raise ValueError(f"Cannot forward sequence of length {T}, max seq length is only {self.max_seq_length}.")
if input_pos is not None: # use the kv cache
cos = self.cos.index_select(0, input_pos)
sin = self.sin.index_select(0, input_pos)
if self.mask_cache is None:
raise TypeError("You need to call `gpt.set_kv_cache()`")
mask = self.mask_cache.index_select(2, input_pos)
else:
cos = self.cos[:T]
sin = self.sin[:T]
mask = None
x = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
for block in self.transformer.h:
x = block(x, cos, sin, mask, input_pos)
x = self.transformer.ln_f(x)
if lm_head_chunk_size > 0:
# chunk the lm head logits to reduce the peak memory used by autograd
return [self.lm_head(x_i) for x_i in x.split(lm_head_chunk_size, dim=1)]
return self.lm_head(x) # (b, t, vocab_size)
@classmethod
def from_name(cls, name: str, **kwargs: Any) -> Self:
return cls(Config.from_name(name, **kwargs))
def _init_weights(self, module: nn.Module) -> None:
"""Meant to be used with `gpt.apply(gpt._init_weights)`. Unused method left for completeness."""
super()._init_weights(module)
if isinstance(module, CausalSelfAttention):
module.reset_parameters()
class Block(BaseBlock):
"""The implementation is identical to `lit_gpt.model.Block` with the exception that
we replace the attention layer where adaption is implemented."""
def __init__(self, config: Config, block_idx: int) -> None:
# Skip the parent class __init__ altogether and replace it to avoid useless allocations
nn.Module.__init__(self)
self.norm_1 = config.norm_class(config.n_embd, eps=config.norm_eps)
self.attn = CausalSelfAttention(config, block_idx)
if not config.shared_attention_norm:
self.norm_2 = config.norm_class(config.n_embd, eps=config.norm_eps)
self.mlp = config.mlp_class(config)
self.config = config
class CausalSelfAttention(BaseCausalSelfAttention):
"""A modification of `lit_gpt.model.CausalSelfAttention` that adds the attention
over the adaption prompt."""
def __init__(self, config: Config, block_idx: int) -> None:
super().__init__(config)
if block_idx >= config.adapter_start_layer:
# adapter embedding layer
self.adapter_wte = nn.Embedding(config.adapter_prompt_length, config.n_embd)
# gate for adaption
self.gating_factor = torch.nn.Parameter(torch.zeros(1, 1, config.n_head, 1))
# kv cache for inference
self.adapter_kv_cache: Optional[Tuple[torch.Tensor, torch.Tensor]] = None
self.block_idx = block_idx
def scaled_dot_product_attention(
self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
y = super().scaled_dot_product_attention(q, k, v, mask)
if self.block_idx < self.config.adapter_start_layer:
return y
aT = self.config.adapter_prompt_length
if self.adapter_kv_cache is not None:
# since this uses the wte weights as the prefix and the kv cache is only used during inference, ak and av
# are the same every call
ak, av = self.adapter_kv_cache
else:
prefix = self.adapter_wte.weight.reshape(1, aT, self.config.n_embd)
aqkv = self.attn(prefix)
q_per_kv = self.config.n_head // self.config.n_query_groups
aqkv = aqkv.view(1, aT, self.config.n_query_groups, q_per_kv + 2, self.config.head_size)
aqkv = aqkv.permute(0, 2, 3, 1, 4)
_, ak, av = aqkv.split((q_per_kv, 1, 1), dim=2)
if self.config.n_query_groups != 1:
# for MHA this is a no-op
ak = ak.repeat_interleave(q_per_kv, dim=2)
av = av.repeat_interleave(q_per_kv, dim=2)
ak = ak.view(1, -1, aT, self.config.head_size) # (1, nh_ak, aT, hs)
av = av.view(1, -1, aT, self.config.head_size) # (1, nh_av, aT, hs)
self.adapter_kv_cache = (ak, av)
T = q.size(2)
amask = torch.ones(T, aT, dtype=torch.bool, device=q.device)
ay = super().scaled_dot_product_attention(q, ak, av, amask)
return y + self.gating_factor * ay
def reset_parameters(self) -> None:
torch.nn.init.zeros_(self.gating_factor)
def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
"""For compatibility with older checkpoints."""
if (key := prefix + "gating_factor") in state_dict and state_dict[key].size(1) == self.config.n_head:
state_dict[key] = state_dict[key].permute(0, 2, 1, 3)
super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
def mark_only_adapter_as_trainable(model: GPT) -> None:
"""Sets `requires_grad=False` for all non-adapter weights."""
for name, param in model.named_parameters():
param.requires_grad = adapter_filter(name, param)
def adapter_filter(key: str, value: Any) -> bool:
return "adapter_wte" in key or "gating_factor" in key
