"""Implementation of the paper:
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://arxiv.org/abs/2304.15010
Port for Lit-GPT
"""
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Type
import torch
import torch.nn as nn
from typing_extensions import Self
import lit_gpt
from lit_gpt.adapter import GPT as BaseModel
from lit_gpt.adapter import Block as BaseBlock
from lit_gpt.adapter import CausalSelfAttention as BaseCausalSelfAttention
from lit_gpt.adapter import Config as BaseConfig
from lit_gpt.model import KVCache
from lit_gpt.utils import map_old_state_dict_weights
@dataclass
class Config(BaseConfig):
@property
def mlp_class(self) -> Type:
return getattr(lit_gpt.adapter_v2, self._mlp_class)
def adapter_filter(key: str, value: Any) -> bool:
adapter_substrings = (
# regular adapter v1 parameters
"adapter_wte",
"gating_factor",
# adapter v2: new bias and scale used in Linear
"adapter_scale",
"adapter_bias",
# adapter v2: Norm parameters are now trainable
"norm_1",
"norm_2",
"ln_f",
)
return any(s in key for s in adapter_substrings)
class AdapterV2Linear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, **kwargs) -> None:
super().__init__()
self.linear = torch.nn.Linear(in_features, out_features, **kwargs)
self.adapter_bias = torch.nn.Parameter(torch.zeros(out_features), requires_grad=False)
self.adapter_scale = torch.nn.Parameter(torch.ones(out_features), requires_grad=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.adapter_scale * (self.linear(x) + self.adapter_bias)
def reset_parameters(self) -> None:
nn.init.zeros_(self.adapter_bias)
nn.init.ones_(self.adapter_scale)
class GPT(BaseModel):
def __init__(self, config: Config) -> None:
# Skip the parent class __init__ altogether and replace it to avoid useless allocations
nn.Module.__init__(self)
assert config.padded_vocab_size is not None
self.config = config
self.lm_head = AdapterV2Linear(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
@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, AdapterV2Linear):
module.reset_parameters()
def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
"""For compatibility with base checkpoints."""
mapping = {"lm_head.weight": "lm_head.linear.weight", "lm_head.bias": "lm_head.linear.bias"}
state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
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.adapter.CausalSelfAttention` that uses the Adapter V2 Linear class"""
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)
shape = (config.n_head + 2 * config.n_query_groups) * config.head_size
# key, query, value projections for all heads, but in a batch
self.attn = AdapterV2Linear(in_features=config.n_embd, out_features=shape, bias=config.bias)
# output projection
self.proj = AdapterV2Linear(config.n_embd, config.n_embd, bias=config.bias)
# disabled by default
self.kv_cache: Optional[KVCache] = None
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
self.config = config
def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
"""For compatibility with base checkpoints."""
mapping = {
"attn.weight": "attn.linear.weight",
"attn.bias": "attn.linear.bias",
"proj.weight": "proj.linear.weight",
"proj.bias": "proj.linear.bias",
}
state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
# 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)
class GptNeoxMLP(lit_gpt.model.GptNeoxMLP):
def __init__(self, config: Config) -> None:
nn.Module.__init__(self)
self.fc = AdapterV2Linear(config.n_embd, config.intermediate_size, bias=config.bias)
self.proj = AdapterV2Linear(config.intermediate_size, config.n_embd, bias=config.bias)
self.config = config
def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
"""For compatibility with base checkpoints."""
mapping = {
"fc.weight": "fc.linear.weight",
"fc.bias": "fc.linear.bias",
"proj.weight": "proj.linear.weight",
"proj.bias": "proj.linear.bias",
}
state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
class LLaMAMLP(lit_gpt.model.LLaMAMLP):
def __init__(self, config: Config) -> None:
nn.Module.__init__(self)
self.fc_1 = AdapterV2Linear(config.n_embd, config.intermediate_size, bias=config.bias)
self.fc_2 = AdapterV2Linear(config.n_embd, config.intermediate_size, bias=config.bias)
self.proj = AdapterV2Linear(config.intermediate_size, config.n_embd, bias=config.bias)
def _load_from_state_dict(self, state_dict: Dict, prefix: str, *args: Any, **kwargs: Any) -> None:
"""For compatibility with base checkpoints."""
mapping = {
"fc_1.weight": "fc_1.linear.weight",
"fc_1.bias": "fc_1.linear.bias",
"fc_2.weight": "fc_2.linear.weight",
"fc_2.bias": "fc_2.linear.bias",
"proj.weight": "proj.linear.weight",
"proj.bias": "proj.linear.bias",
}
state_dict = map_old_state_dict_weights(state_dict, mapping, prefix)
super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
def mark_only_adapter_v2_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)
