"""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"} 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)