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