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from typing import Optional
from transformers.models.llama.modeling_llama import (
LlamaConfig,
LlamaModel,
LlamaForCausalLM,
LlamaAttention,
LlamaFlashAttention2,
LlamaSdpaAttention,
LlamaMLP,
LlamaDecoderLayer,
)
from mybitnet.bitnet import BitLinear
from torch import nn
class BitLlamaConfig(LlamaConfig):
model_type = "bit_llama"
def __init__(self, bits=8, **kwargs):
super().__init__(**kwargs)
self.bits = bits
class BitLlamaMLP(LlamaMLP):
def __init__(self, config):
super().__init__(config)
self.gate_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, bits=config.bits, flg_before_linear=True)
self.up_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, bits=config.bits, flg_before_linear=True)
self.down_proj = BitLinear(self.intermediate_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=False)
class BitLlamaAttention(LlamaAttention):
def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
super().__init__(config)
self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=True)
class BitLlamaFlashAttention2(LlamaFlashAttention2):
def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=True)
class BitLlamaSdpaAttention(LlamaSdpaAttention):
def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, bits=config.bits, flg_before_linear=True)
self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, bits=config.bits, flg_before_linear=True)
BITLLAMA_ATTENTION_CLASSES = {
"eager": BitLlamaAttention,
"flash_attention_2": BitLlamaFlashAttention2,
"sdpa": BitLlamaSdpaAttention,
}
class BitLlamaDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: BitLlamaConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = BITLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = BitLlamaMLP(config)
class BitLlamaModel(LlamaModel):
config_class = BitLlamaConfig
def __init__(self, config: BitLlamaConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[BitLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
class BitLlamaForCausalLM(LlamaForCausalLM):
config_class = BitLlamaConfig
def __init__(self, config: BitLlamaConfig):
super().__init__(config)
self.model = BitLlamaModel(config)
self.lm_head = BitLinear(config.hidden_size, config.vocab_size, bias=False, bits=config.bits, flg_before_linear=True)
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