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import torch.nn as nn
import torch.nn.functional as F
import math
from functools import partial
from einops import rearrange, repeat
from typing import Optional, Tuple, Union
Linear = partial(nn.Linear, bias=False)
LayerNorm = partial(nn.LayerNorm, bias=False)
def rotate_half(x, interleaved=False):
if not interleaved:
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
else:
x1, x2 = x[..., ::2], x[..., 1::2]
return rearrange(
torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
)
def apply_rotary_emb_torch(x, cos, sin, interleaved=False, _inplace=False):
"""
x: (batch_size, seqlen, nheads, headdim)
cos, sin: (seqlen, rotary_dim / 2)
"""
ro_dim = cos.shape[-1] * 2
assert ro_dim <= x.shape[-1]
seqlen = x.size(1)
cos = cos[:seqlen]
sin = sin[:seqlen]
cos = repeat(cos, "s d -> s 1 (2 d)")
sin = repeat(sin, "s d -> s 1 (2 d)")
return torch.cat(
[
x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
x[..., ro_dim:],
],
dim=-1,
)
class RotaryEmbedding(torch.nn.Module):
def __init__(
self,
dim: int,
base=10000.0,
interleaved=False,
scale_base=None,
scaling_factor=1.0,
pos_idx_in_fp32=True,
device=None,
):
super().__init__()
self.dim = dim
self.base = float(base)
self.pos_idx_in_fp32 = pos_idx_in_fp32
# Generate and save the inverse frequency buffer (non trainable)
self.interleaved = interleaved
self.scale_base = scale_base
self.scaling_factor = scaling_factor
self.device = device
self._seq_len_cached = 0
self._cos_cached = None
self._sin_cached = None
self._cos_k_cached = None
self._sin_k_cached = None
self.reset_parameters()
def reset_parameters(self):
inv_freq = self._compute_inv_freq(self.device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
arange = torch.arange(0, self.dim, 2, device=self.device, dtype=torch.float32)
scale = (
(arange + 0.4 * self.dim) / (1.4 * self.dim)
if self.scale_base is not None
else None
)
self.register_buffer("scale", scale)
def _compute_inv_freq(self, device=None):
return 1 / (
self.base
** (
torch.arange(0, self.dim, 2, device=device, dtype=torch.float32)
/ self.dim
)
)
def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
if (
seqlen > self._seq_len_cached
or self._cos_cached is None
or self._cos_cached.device != device
or self._cos_cached.dtype != dtype
or (self.training and self._cos_cached.is_inference())
):
self._seq_len_cached = seqlen
if self.pos_idx_in_fp32:
t = torch.arange(seqlen, device=device, dtype=torch.float32)
t /= self.scaling_factor
if self.inv_freq.dtype != torch.float32:
inv_freq = self.inv_freq.to(torch.float32)
else:
inv_freq = self.inv_freq
else:
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
t /= self.scaling_factor
inv_freq = self.inv_freq
freqs = torch.outer(t, inv_freq)
if self.scale is None:
self._cos_cached = torch.cos(freqs).to(dtype)
self._sin_cached = torch.sin(freqs).to(dtype)
else:
power = (
torch.arange(
seqlen, dtype=self.scale.dtype, device=self.scale.device
)
- seqlen // 2
) / self.scale_base
scale = self.scale.to(device=power.device) ** power.unsqueeze(-1)
self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
q: (batch, seqlen, nheads, headdim)
k: (batch, seqlen, nheads, headdim)
"""
self._update_cos_sin_cache(q.shape[1], device=q.device, dtype=q.dtype)
assert self._cos_cached is not None
assert self._sin_cached is not None
if self.scale is None:
return (
apply_rotary_emb_torch(
q,
self._cos_cached,
self._sin_cached,
self.interleaved,
True, # inplace=True
),
apply_rotary_emb_torch(
k,
self._cos_cached,
self._sin_cached,
self.interleaved,
True, # inplace=True
),
) # type: ignore
else:
assert False
class MultiHeadAttention(nn.Module):
def __init__(self, hidden_size: int, n_heads: int, rotary: bool = True):
super().__init__()
self.hidden_size = hidden_size
self.n_heads = n_heads
self.d_head = self.hidden_size // self.n_heads
self.layernorm_qkv = nn.Sequential(
LayerNorm(hidden_size), Linear(hidden_size, hidden_size * 3)
)
self.out_proj = Linear(hidden_size, hidden_size)
self.q_ln = LayerNorm(hidden_size, bias=False)
self.k_ln = LayerNorm(hidden_size, bias=False)
self.reshaper = partial(rearrange, pattern="b s (h d) -> b h s d", h=n_heads)
self.rotary = RotaryEmbedding(hidden_size // n_heads) if rotary else None
def _apply_rotary(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
q = q.unflatten(-1, (self.n_heads, self.d_head))
k = k.unflatten(-1, (self.n_heads, self.d_head))
q, k = self.rotary(q, k)
q = q.flatten(-2, -1)
k = k.flatten(-2, -1)
return q, k
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
# attention mask already prepped for sdpa shape (bs, 1, seq_len, seq_len)
qkv = self.layernorm_qkv(x) # (bs, seq_len, d_model * 3)
q, k, v = torch.chunk(qkv, 3, dim=-1) # (bs, seq_len, hidden_size)
q, k = self.q_ln(q).to(q.dtype), self.k_ln(k).to(q.dtype)
if self.rotary:
q, k = self._apply_rotary(q, k)
q, k, v = map(self.reshaper, (q, k, v)) # (bs, n_heads, seq_len, d_head)
a = F.scaled_dot_product_attention(q, k, v, attention_mask) # (bs, n_heads, seq_len, d_head)
a = rearrange(a, "b h s d -> b s (h d)") # (bs, seq_len, n_heads * d_head)
return self.out_proj(a) # (bs, seq_len, hidden_size)
class PAttention(nn.Module):
"""
Cross-attention mechanism for token-parameter-attention (b, L, d) -> (b, L, n_tokens) -> (b, L, d)
"""
def __init__(
self,
hidden_size: int,
n_tokens: int,
dropout: float = 0.2,
):
super(PAttention, self).__init__()
self.n_tokens = n_tokens
self.Wq = Linear(hidden_size, hidden_size)
self.Pk = nn.Parameter(torch.randn(1, n_tokens, hidden_size))
self.Pv = nn.Parameter(torch.randn(1, n_tokens, hidden_size))
self.dropout = nn.Dropout(dropout)
def forward(
self,
x: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
b, L, _ = x.size()
if attention_mask is not None:
attention_mask = attention_mask[:, None, :].expand(b, self.n_token, self.L).bool()
q = self.Wq(x) # (b, L, d)
out = F.scaled_dot_product_attention(q, self.Pk, self.Pv, attn_mask=attention_mask, is_causal=False) # (b, L, d)
return self.dropout(out)
class AttentionLogitsSequence(nn.Module):
"""
Cross-attention mechanism for token-parameter-attention (b, L, d) -> (b, L, num_labels) -> (b, num_labels)
"""
def __init__(self, hidden_size: int, num_labels: int = 1, sim_type: str = 'dot'):
super(AttentionLogitsSequence, self).__init__()
self.num_labels = num_labels
self.Wp = nn.Parameter(torch.randn(1, hidden_size, num_labels))
self.Wx = Linear(hidden_size, hidden_size)
self.sim_type = sim_type
def mean_pooling(self, emb: torch.Tensor, attention_mask: Optional[torch.Tensor] = None): # (b, L, d) -> (b, d)
if attention_mask is None:
return emb.mean(dim=1)
else:
return (emb * attention_mask).sum(dim=1) / attention_mask.sum(dim=1) # (b, d)
def dot_product(self, x: torch.Tensor, p: torch.Tensor): # (b, L, d) * (b, d, num_labels) -> (b, L, num_labels)
return torch.matmul(x, p)
def euclidean_distance(self, x: torch.Tensor, p: torch.Tensor): # (b, L, d) * (b, d, num_labels) -> (b, L, num_labels)
# x: (b, L, d), p: (b, d, num_labels)
x_exp = x.unsqueeze(-1) # (b, L, d, 1)
p_exp = p.unsqueeze(1) # (b, 1, d, num_labels)
dist = torch.abs(torch.norm(x_exp - p_exp, p=2, dim=2)) # (b, L, num_labels)
return -dist
def cosine_similarity(
self,
x: torch.Tensor,
p: torch.Tensor,
attention_mask: torch.Tensor = None,
) -> torch.Tensor: # (b, L, d) * (b, d, num_labels) -> (b, L, num_labels)
x = x * attention_mask
x = F.normalize(x, p=2, dim=-1)
p = F.normalize(p, p=2, dim=1)
cos_sims = torch.matmul(x, p)
assert cos_sims.max().item() <= 1.0 and cos_sims.min().item() >= -1.0, "Cosine similarity values should be between -1 and 1"
return cos_sims
def forward(
self,
x: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
b, l, _ = x.size()
p = self.Wp.expand(b, -1, -1) # (b, d, num_labels)
x = self.Wx(x) # (b, L, d)
if attention_mask is None:
attention_mask = torch.ones(b, l, device=x.device, dtype=x.dtype)
attention_mask = attention_mask.unsqueeze(-1)
if self.sim_type == 'dot':
y = self.dot_product(x, p)
elif self.sim_type == 'euclidean':
y = self.euclidean_distance(x, p)
elif self.sim_type == 'cosine':
y = self.cosine_similarity(x, p, attention_mask)
else:
raise ValueError(f"Invalid similarity type: {self.sim_type}")
# y (b, L, num_labels)
logits = self.mean_pooling(y, attention_mask) # (b, num_labels)
return logits, y, x
class AttentionLogitsToken(nn.Module):
"""
Cross-attention mechanism for token-parameter-attention (b, L, d) -> (b, L, num_labels)
"""
def __init__(self, hidden_size: int, num_labels: int = 1, sim_type: str = 'dot'):
super(AttentionLogitsToken, self).__init__()
self.num_labels = num_labels
self.Wp = nn.Parameter(torch.randn(1, hidden_size, num_labels))
self.Wx = Linear(hidden_size, hidden_size)
self.sim_type = sim_type
def dot_product(self, x: torch.Tensor, p: torch.Tensor):
return torch.matmul(x, p)
def euclidean_distance(self, x: torch.Tensor, p: torch.Tensor):
return torch.norm(x - p, p=2, dim=-1)
def cosine_similarity(
self,
x: torch.Tensor,
p: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if attention_mask is not None:
x = x * attention_mask.unsqueeze(-1)
x = F.normalize(x, p=2, dim=-1)
p = F.normalize(p, p=2, dim=1)
cos_sims = torch.matmul(x, p)
assert cos_sims.max().item() <= 1.0 and cos_sims.min().item() >= -1.0, "Cosine similarity values should be between -1 and 1"
return cos_sims
def forward(
self,
x: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
b, L, d = x.size()
p = self.Wp.expand(b, -1, -1) # (b, d, num_labels)
x = self.Wx(x) # (b, L, d)
if self.sim_type == 'dot':
logits = self.dot_product(x, p)
elif self.sim_type == 'euclidean':
logits = self.euclidean_distance(x, p)
elif self.sim_type == 'cosine':
logits = self.cosine_similarity(x, p, attention_mask)
else:
raise ValueError(f"Invalid similarity type: {self.sim_type}")
return logits # (b, L, num_labels)
class MultiHeadPAttention(nn.Module):
def __init__(
self,
hidden_size: int,
n_heads: int,
n_tokens: int,
dropout: float = 0.2,
rotary: bool = True,
causal: bool = False,
):
super().__init__()
self.hidden_size = hidden_size
self.n_heads = n_heads
self.d_head = self.hidden_size // self.n_heads
self.Wq = PAttention(hidden_size, n_tokens=n_tokens, dropout=dropout)
self.Wk = PAttention(hidden_size, n_tokens=n_tokens, dropout=dropout)
self.Wv = PAttention(hidden_size, n_tokens=n_tokens, dropout=dropout)
self.out_proj = Linear((hidden_size // n_heads) * n_heads, hidden_size)
self.q_ln = LayerNorm(hidden_size)
self.k_ln = LayerNorm(hidden_size)
self.reshaper = partial(rearrange, pattern="b s (h d) -> b h s d", h=n_heads)
self.rotary = RotaryEmbedding(hidden_size // n_heads) if rotary else None
self.causal = causal
def _apply_rotary(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
q = q.unflatten(-1, (self.n_heads, self.d_head))
k = k.unflatten(-1, (self.n_heads, self.d_head))
q, k = self.rotary(q, k)
q = q.flatten(-2, -1)
k = k.flatten(-2, -1)
return q, k
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
# attention mask already prepped for sdpa shape (bs, 1, seq_len, seq_len)
b, L, _ = x.shape
if attention_mask is not None and attention_mask.dim() == 2:
attention_mask = attention_mask[:, None, None, :].expand(b, 1, L, L).bool()
q = self.Wq(x)
k = self.Wk(x)
v = self.Wv(x)
q, k = self.q_ln(q).to(q.dtype), self.k_ln(k).to(q.dtype)
if self.rotary:
q, k = self._apply_rotary(q, k)
q, k, v = map(self.reshaper, (q, k, v)) # (bs, n_heads, seq_len, d_head)
a = F.scaled_dot_product_attention(q, k, v, attention_mask if not self.causal else None, is_causal=self.causal) # (bs, n_heads, seq_len, d_head)
a = rearrange(a, "b h s d -> b s (h d)") # (bs, seq_len, n_heads * d_head)
return self.out_proj(a) # (bs, seq_len, hidden_size)
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