| |
| |
|
|
| from __future__ import annotations |
|
|
| from typing import TYPE_CHECKING, Optional, Tuple, Union |
|
|
| import torch |
| import torch.nn as nn |
| from einops import rearrange |
| from transformers.utils import logging |
|
|
| from fla.modules import RotaryEmbedding |
| from fla.ops.nsa.parallel import parallel_nsa |
|
|
| if TYPE_CHECKING: |
| from fla.models.utils import Cache |
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class NativeSparseAttention(nn.Module): |
|
|
| def __init__( |
| self, |
| hidden_size: int = 2048, |
| num_heads: int = 64, |
| num_kv_heads: Optional[int] = 4, |
| head_dim: int = 64, |
| qkv_bias: bool = False, |
| block_size: Optional[int] = 64, |
| block_counts: Optional[Union[torch.LongTensor, int]] = 16, |
| window_size: Optional[int] = 512, |
| rope_theta: Optional[float] = 10000., |
| max_position_embeddings: Optional[int] = None, |
| layer_idx: int = None |
| ): |
| super().__init__() |
|
|
| self.hidden_size = hidden_size |
| self.num_heads = num_heads |
| if num_kv_heads is None: |
| self.num_kv_heads = self.num_heads |
| else: |
| self.num_kv_heads = num_kv_heads |
| self.num_kv_groups = num_heads // self.num_kv_heads |
| self.head_dim = head_dim |
| self.kv_dim = self.num_kv_heads * self.head_dim |
| self.qkv_bias = qkv_bias |
|
|
| self.block_size = block_size |
| self.block_counts = block_counts |
| self.window_size = window_size |
| self.rope_theta = rope_theta |
| self.max_position_embeddings = max_position_embeddings |
| self.layer_idx = layer_idx |
|
|
| self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=self.qkv_bias) |
| self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) |
| self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=self.qkv_bias) |
| self.g_proj = nn.Linear(self.hidden_size, self.num_heads * 3, bias=False) |
| self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) |
|
|
| self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta) |
|
|
| def forward( |
| self, |
| hidden_states: torch.Tensor, |
| attention_mask: Optional[torch.LongTensor] = None, |
| past_key_values: Optional[Cache] = None, |
| output_attentions: bool = False, |
| use_cache: bool = False, |
| **kwargs, |
| ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
| if attention_mask is not None: |
| assert len(attention_mask.shape) == 2, ( |
| "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " |
| "for padding purposes (0 indicating padding). " |
| "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." |
| ) |
|
|
| batch_size, seq_len, _ = hidden_states.size() |
|
|
| q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) |
| k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) |
| v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim) |
| g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=3) |
| g_cmp, g_slc, g_swa = g.sigmoid().unbind(-1) |
|
|
| cu_seqlens = kwargs.get('cu_seqlens', None) |
|
|
| seqlen_offset, max_seqlen = 0, seq_len |
| if past_key_values is not None: |
| seqlen_offset = past_key_values.get_seq_length(self.layer_idx) |
| max_seqlen = q.shape[1] + seqlen_offset |
|
|
| if attention_mask is not None: |
| |
| seqlen_offset = seqlen_offset + attention_mask.sum(-1) - attention_mask.shape[-1] |
| max_seqlen = q.shape[1] + max(seqlen_offset) |
|
|
| if self.max_position_embeddings is not None: |
| max_seqlen = max(max_seqlen, self.max_position_embeddings) |
| q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens) |
|
|
| if past_key_values is not None: |
| cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0 |
| k_cached, v_cached = past_key_values.update( |
| attn_state=(k.flatten(-2, -1), v.flatten(-2, -1)), |
| layer_idx=self.layer_idx, |
| offset=seq_len, |
| cache_kwargs=dict(window_size=self.window_size) |
| )['attn_state'] |
| if cache_has_content: |
| k, v = k_cached, v_cached |
| k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim) |
| v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim) |
|
|
| o = parallel_nsa( |
| q=q, |
| k=k, |
| v=v, |
| g_cmp=g_cmp, |
| g_slc=g_slc, |
| g_swa=g_swa, |
| block_size=self.block_size, |
| block_counts=self.block_counts, |
| window_size=self.window_size, |
| cu_seqlens=cu_seqlens, |
| head_first=False |
| ) |
| o = o.reshape(batch_size, seq_len, -1) |
| o = self.o_proj(o) |
|
|
| if not output_attentions: |
| attentions = None |
|
|
| return o, attentions, past_key_values |
|
|
