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from __future__ import annotations |
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from typing import TYPE_CHECKING, Dict, Optional, Tuple |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from einops import rearrange |
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from fla.modules import FusedRMSNormGated, ShortConvolution |
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from fla.modules.fused_norm_gate import rms_norm_swish_gate_linear |
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from fla.ops.gla import chunk_gla, fused_recurrent_gla |
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if TYPE_CHECKING: |
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from transformers.processing_utils import Unpack |
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from fla.models.utils import Cache |
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class LightNetAttention(nn.Module): |
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def __init__( |
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self, |
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mode: str = 'chunk', |
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hidden_size: int = 1024, |
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num_heads: Optional[int] = None, |
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expand_ratio: Optional[int] = 128, |
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use_short_conv: bool = False, |
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conv_size: int = 4, |
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conv_bias: bool = False, |
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gate_low_rank_dim: int = 128, |
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elementwise_affine: Optional[bool] = True, |
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norm_eps: float = 1e-5, |
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layer_idx: int = None |
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) -> LightNetAttention: |
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super().__init__() |
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self.mode = mode |
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self.hidden_size = hidden_size |
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if expand_ratio is None and num_heads is not None: |
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expand_ratio = hidden_size // num_heads |
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elif expand_ratio is not None and num_heads is None: |
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num_heads = hidden_size // expand_ratio |
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elif expand_ratio is None and num_heads is None: |
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raise RuntimeError("One of `expand_ratio` or `num_heads` should be provided.") |
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self.num_heads = num_heads |
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self.expand_ratio = expand_ratio |
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self.use_short_conv = use_short_conv |
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self.conv_size = conv_size |
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self.conv_bias = conv_bias |
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self.key_dim = int(self.num_heads * self.expand_ratio) |
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self.value_dim = hidden_size |
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self.gate_low_rank_dim = gate_low_rank_dim |
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self.layer_idx = layer_idx |
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assert mode in ['chunk', 'fused_chunk'], f"Not suppoerted mode `{mode}`." |
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assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}" |
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assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}" |
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self.head_f_dim = self.expand_ratio |
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self.head_i_dim = self.hidden_size // num_heads |
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self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) |
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self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) |
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self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) |
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if use_short_conv: |
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self.conv_size = conv_size |
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self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation=None) |
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self.k_conv1d = ShortConvolution(self.key_dim, conv_size, activation=None) |
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self.v_conv1d = ShortConvolution(self.value_dim, conv_size, activation=None) |
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self.g_proj = nn.Sequential( |
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nn.Linear(hidden_size, gate_low_rank_dim, bias=False), |
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nn.Linear(gate_low_rank_dim, hidden_size, bias=False) |
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) |
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self.g_norm = FusedRMSNormGated( |
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hidden_size=hidden_size, |
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elementwise_affine=elementwise_affine, |
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eps=norm_eps |
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) |
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self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) |
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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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past_key_values: Optional[Cache] = None, |
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use_cache: Optional[bool] = False, |
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output_attentions: Optional[bool] = False, |
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**kwargs: Unpack[Dict] |
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]: |
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if attention_mask is not None: |
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assert len(attention_mask.shape) == 2, ( |
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"Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " |
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"for padding purposes (0 indicating padding). " |
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"Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." |
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) |
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mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode |
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last_state = None |
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if past_key_values is not None and len(past_key_values) > self.layer_idx: |
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last_state = past_key_values[self.layer_idx] |
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cu_seqlens = kwargs.get('cu_seqlens', None) |
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if self.use_short_conv: |
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conv_state_q, conv_state_k, conv_state_v = None, None, None |
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if last_state is not None: |
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conv_state_q, conv_state_k, conv_state_v = last_state['conv_state'] |
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conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None |
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q, conv_state_q = self.q_conv1d( |
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x=self.q_proj(hidden_states), |
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mask=conv_mask, |
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cache=conv_state_q, |
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output_final_state=use_cache, |
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cu_seqlens=cu_seqlens |
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) |
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k, conv_state_k = self.k_conv1d( |
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x=self.k_proj(hidden_states), |
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mask=conv_mask, |
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cache=conv_state_k, |
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output_final_state=use_cache, |
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cu_seqlens=cu_seqlens |
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) |
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v, conv_state_v = self.v_conv1d( |
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x=self.v_proj(hidden_states), |
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mask=conv_mask, |
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cache=conv_state_v, |
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output_final_state=use_cache, |
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cu_seqlens=cu_seqlens |
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) |
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else: |
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q = self.q_proj(hidden_states) |
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k = self.k_proj(hidden_states) |
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v = self.v_proj(hidden_states) |
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if attention_mask is not None: |
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v = v.mul_(attention_mask[:, -v.shape[-2]:, None]) |
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q = F.silu(q) |
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q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_f_dim), (q, k)) |
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v = rearrange(v, '... (h d) -> ... h d', d=self.head_i_dim) |
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z = k.float().logcumsumexp(1) |
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if cu_seqlens is not None: |
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raise NotImplementedError("LightNet does not support variable-length sequences for now.") |
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k, g = torch.exp(k - z).to(k.dtype), (torch.cat((z[:, :1], z[:, :-1]), 1) - z).to(k.dtype) |
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recurrent_state = last_state['recurrent_state'] if last_state is not None else None |
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if mode == 'fused_recurrent': |
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o, recurrent_state = fused_recurrent_gla( |
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q=q, |
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k=k, |
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v=v, |
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gk=g, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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cu_seqlens=cu_seqlens, |
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head_first=False |
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) |
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elif mode == 'chunk': |
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o, recurrent_state = chunk_gla( |
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q=q, |
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k=k, |
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v=v, |
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g=g, |
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initial_state=recurrent_state, |
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output_final_state=use_cache, |
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cu_seqlens=cu_seqlens, |
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head_first=False |
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) |
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else: |
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raise NotImplementedError(f"Not supported mode `{mode}`.") |
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if past_key_values is not None: |
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past_key_values.update( |
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recurrent_state=recurrent_state, |
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conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None, |
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layer_idx=self.layer_idx, |
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offset=q.shape[1] |
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) |
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o = rms_norm_swish_gate_linear( |
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rearrange(o, 'b t h d -> b t (h d)'), |
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self.g_proj(hidden_states), |
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self.g_norm.weight, |
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self.g_norm.bias, |
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self.o_proj.weight, |
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self.o_proj.bias |
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) |
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return o, None, past_key_values |
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def state_size(self, **kwargs) -> int: |
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state_size = self.key_dim * self.head_i_dim |
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for module in self.children(): |
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if isinstance(module, ShortConvolution): |
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state_size += module.state_size |
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return state_size |
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