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import warnings |
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from typing import Optional, Tuple |
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from transformers.models.llama.modeling_llama import ( |
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LlamaConfig, |
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LlamaModel, |
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LlamaForCausalLM, |
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LlamaAttention, |
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LlamaFlashAttention2, |
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LlamaSdpaAttention, |
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LlamaMLP, |
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LlamaDecoderLayer, |
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) |
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from mybitnet.bitnet import BitLinear, BitLinear158b |
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import torch |
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from torch import nn |
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class BitLlamaConfig(LlamaConfig): |
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model_type = "bit_llama" |
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def __init__(self, bitnet_type="1.58b", bits=8, **kwargs): |
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super().__init__(**kwargs) |
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self.bitnet_type = bitnet_type |
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if self.bitnet_type not in ["1.58b", "1b"]: |
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raise ValueError("bitnet_type must be either '1.58b' or '1b'.") |
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self.bits = bits |
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class BitLlamaMLP(LlamaMLP): |
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def __init__(self, config): |
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super().__init__(config) |
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if config.bitnet_type=="1b": |
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self.gate_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=False) |
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self.up_proj = BitLinear(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.down_proj = BitLinear(self.intermediate_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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elif config.bitnet_type=="1.58b": |
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self.gate_proj = BitLinear158b(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.up_proj = BitLinear158b(self.hidden_size, self.intermediate_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.down_proj = BitLinear158b(self.intermediate_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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else: |
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raise ValueError("bitnet_type must be either '1.58b' or '1b'.") |
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class BitLlamaAttention(LlamaAttention): |
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def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None): |
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super().__init__(config) |
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if config.bitnet_type=="1b": |
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self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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elif config.bitnet_type=="1.58b": |
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self.q_proj = BitLinear158b(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.k_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.v_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.o_proj = BitLinear158b(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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else: |
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raise ValueError("bitnet_type must be either '1.58b' or '1b'.") |
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class BitLlamaFlashAttention2(LlamaFlashAttention2): |
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def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None): |
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super().__init__(config, layer_idx) |
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if config.bitnet_type=="1b": |
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self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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elif config.bitnet_type=="1.58b": |
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self.q_proj = BitLinear158b(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.k_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.v_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.o_proj = BitLinear158b(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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else: |
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raise ValueError("bitnet_type must be either '1.58b' or '1b'.") |
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class BitLlamaSdpaAttention(LlamaSdpaAttention): |
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def __init__(self, config: BitLlamaConfig, layer_idx: Optional[int] = None): |
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super().__init__(config, layer_idx) |
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if config.bitnet_type=="1b": |
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self.q_proj = BitLinear(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.k_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.v_proj = BitLinear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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self.o_proj = BitLinear(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits, flg_before_linear=True) |
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elif config.bitnet_type=="1.58b": |
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self.q_proj = BitLinear158b(self.hidden_size, self.num_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.k_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.v_proj = BitLinear158b(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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self.o_proj = BitLinear158b(self.hidden_size, self.hidden_size, bias=False, rms_norm_eps=config.rms_norm_eps, bits=config.bits) |
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else: |
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raise ValueError("bitnet_type must be either '1.58b' or '1b'.") |
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BITLLAMA_ATTENTION_CLASSES = { |
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"eager": BitLlamaAttention, |
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"flash_attention_2": BitLlamaFlashAttention2, |
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"sdpa": BitLlamaSdpaAttention, |
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} |
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class BitLlamaDecoderLayer(LlamaDecoderLayer): |
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def __init__(self, config: BitLlamaConfig, layer_idx: int): |
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super().__init__(config, layer_idx) |
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self.self_attn = BITLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) |
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self.mlp = BitLlamaMLP(config) |
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del self.input_layernorm |
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del self.post_attention_layernorm |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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attention_mask: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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past_key_value: Optional[Tuple[torch.Tensor]] = None, |
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output_attentions: Optional[bool] = False, |
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use_cache: Optional[bool] = False, |
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cache_position: Optional[torch.LongTensor] = None, |
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**kwargs, |
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: |
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""" |
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refers: https://github.com/huggingface/transformers/blob/c5f0288bc7d76f65996586f79f69fba8867a0e67/src/transformers/models/llama/modeling_llama.py#L693 |
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""" |
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if "padding_mask" in kwargs: |
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warnings.warn( |
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"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`" |
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) |
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residual = hidden_states |
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hidden_states, self_attn_weights, present_key_value = self.self_attn( |
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hidden_states=hidden_states, |
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attention_mask=attention_mask, |
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position_ids=position_ids, |
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past_key_value=past_key_value, |
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output_attentions=output_attentions, |
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use_cache=use_cache, |
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cache_position=cache_position, |
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**kwargs, |
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) |
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hidden_states = residual + hidden_states |
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residual = hidden_states |
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hidden_states = self.mlp(hidden_states) |
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hidden_states = residual + hidden_states |
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outputs = (hidden_states,) |
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if output_attentions: |
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outputs += (self_attn_weights,) |
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if use_cache: |
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outputs += (present_key_value,) |
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return outputs |
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class BitLlamaModel(LlamaModel): |
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config_class = BitLlamaConfig |
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def __init__(self, config: BitLlamaConfig): |
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super().__init__(config) |
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self.layers = nn.ModuleList( |
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[BitLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] |
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) |
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class BitLlamaForCausalLM(LlamaForCausalLM): |
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config_class = BitLlamaConfig |
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def __init__(self, config: BitLlamaConfig): |
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super().__init__(config) |
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self.model = BitLlamaModel(config) |
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self.lm_head = BitLinear(config.hidden_size, config.vocab_size, bias=False, bits=config.bits, flg_before_linear=True) |
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