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f8ab83c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | import torch
import torch.nn.functional as F
from birwkv7 import BiRWKV7Layer
def wkv7_forward_scan(r, w, k, v, a, sab_scale, init_state=None):
B, T, H, D = r.shape
r, w, k, v, a = [x.float() for x in (r, w, k, v, a)]
k = k * (D ** -0.5)
decay = torch.exp(-0.6065306597633104 * torch.sigmoid(w))
a = torch.sigmoid(a)
sab_s = float(sab_scale)
state = init_state.float().clone() if init_state is not None else \
torch.zeros(B, H, D, D, device=r.device, dtype=torch.float32)
outputs = []
for t in range(T):
kt, vt, rt, at, dt = k[:, t], v[:, t], r[:, t], a[:, t], decay[:, t]
sa = torch.einsum('bhij,bhj->bhi', state, -kt)
sab = torch.einsum('bhi,bhj->bhij', sa, kt * at)
state = state * dt.unsqueeze(-2) + sab_s * sab + \
torch.einsum('bhi,bhj->bhij', vt, kt)
state = state.clamp(-10.0, 10.0)
outputs.append(torch.einsum('bhij,bhj->bhi', state, rt))
return torch.stack(outputs, dim=1), state.detach()
class SpanEncoder:
def __init__(self, model, tokenizer, device, chunk_size=512):
self.model = model
self.tokenizer = tokenizer
self.device = device
self.chunk_size = chunk_size
self.birwkv_layers = []
self.birwkv_ids = {}
for m in model.modules():
if isinstance(m, BiRWKV7Layer):
self.birwkv_ids[id(m)] = len(self.birwkv_layers)
self.birwkv_layers.append(m)
self._originals = {}
self._hooked = False
self._active_states = [None] * len(self.birwkv_layers)
self.span_data = {}
def _hook(self):
if self._hooked:
return
for layer in self.birwkv_layers:
self._originals[id(layer)] = layer.forward
layer.forward = self._make_fwd(layer)
self._hooked = True
def _unhook(self):
if not self._hooked:
return
for layer in self.birwkv_layers:
layer.forward = self._originals[id(layer)]
self._originals.clear()
self._hooked = False
def _make_fwd(self, layer):
enc = self
idx = self.birwkv_ids[id(layer)]
def fwd(x, attention_mask=None, **kwargs):
B, T, C_ = x.shape
H, D = layer.num_heads, layer.head_size
prev = enc._active_states[idx]
if prev is not None:
x_prev = torch.cat([prev['last_x'], x[:, :-1]], dim=1)
else:
x_prev = F.pad(x[:, :-1], (0, 0, 1, 0))
def mix(mu):
return x + (x_prev - x) * torch.sigmoid(mu)
r = layer.W_r(mix(layer.mu_r)).view(B, T, H, D)
w = layer.W_w(mix(layer.mu_w)).view(B, T, H, D)
k = layer.W_k(mix(layer.mu_k)).view(B, T, H, D)
v = layer.W_v(mix(layer.mu_v)).view(B, T, H, D)
a = layer.W_a(mix(layer.mu_a)).view(B, T, H, D)
g = torch.sigmoid(layer.W_g(mix(layer.mu_g)))
sab_scale = torch.sigmoid(layer.sab_gate)
init_st = prev['wkv_state'] if prev else None
try:
from birwkv7_triton import wkv7_scan_triton
r_f, k_f, v_f = r.float(), k.float() * (D ** -0.5), v.float()
a_f = torch.sigmoid(a.float())
decay = torch.exp(-0.6065306597633104 * torch.sigmoid(w.float()))
out_fwd, wkv_state = wkv7_scan_triton(
r_f, decay, k_f, v_f, a_f, sab_scale,
return_state=True, init_state=init_st)
out_bwd = wkv7_scan_triton(
r_f.flip(1), decay.flip(1), k_f.flip(1),
v_f.flip(1), a_f.flip(1), sab_scale,
return_state=False).flip(1)
except (ImportError, Exception):
out_fwd, wkv_state = wkv7_forward_scan(
r, w, k, v, a, sab_scale, init_st)
out_bwd = wkv7_forward_scan(
r.flip(1), w.flip(1), k.flip(1),
v.flip(1), a.flip(1), sab_scale, None)[0].flip(1)
enc._active_states[idx] = {
'wkv_state': wkv_state,
'last_x': x[:, -1:].detach().clone(),
}
out = ((out_fwd + out_bwd) * 0.5).reshape(B, T, C_)
out = layer.group_norm(out.transpose(1, 2)).transpose(1, 2)
out = layer.W_o(out * g)
return out, None
return fwd
@torch.no_grad()
def _forward_encode_raw(self, text, init_states=None, max_length=8192):
self._hook()
if init_states is not None:
self._active_states = [
{k: v.clone() for k, v in s.items()} if s else None
for s in init_states
]
else:
self._active_states = [None] * len(self.birwkv_layers)
enc = self.tokenizer(text, return_tensors='pt', truncation=True,
max_length=max_length)
ids = enc['input_ids'].to(self.device)
mask = enc['attention_mask'].to(self.device)
h = self.model(input_ids=ids, attention_mask=mask).last_hidden_state
content = h[0, 1:-1, :].cpu()
n_content = content.shape[0]
final_states = [
{k: v.clone() for k, v in s.items()} if s else None
for s in self._active_states
]
self._unhook()
return content, n_content, final_states
def _chunk_hidden(self, content, return_residual=False):
T = content.shape[0]
chunks = []
last_end = 0
for start in range(0, T, self.chunk_size):
end = min(start + self.chunk_size, T)
if end - start < 32:
break
emb = F.normalize(content[start:end].mean(0, keepdim=True),
p=2, dim=-1)
chunks.append(emb)
last_end = end
if not chunks and T > 0:
chunks.append(F.normalize(content.mean(0, keepdim=True),
p=2, dim=-1))
last_end = T
if return_residual:
residual = content[last_end:] if last_end < T else None
return chunks, residual
return chunks
@torch.no_grad()
def encode_query(self, query):
assert not self._hooked
enc = self.tokenizer(query, return_tensors='pt', truncation=True,
max_length=512)
ids = enc['input_ids'].to(self.device)
mask = enc['attention_mask'].to(self.device)
h = self.model(input_ids=ids, attention_mask=mask).last_hidden_state
m = mask.unsqueeze(-1).float()
emb = (h * m).sum(1) / m.sum(1).clamp(min=1e-9)
return F.normalize(emb, p=2, dim=-1).cpu()
def encode_span(self, text, key):
content, n_tok, states = self._forward_encode_raw(text)
chunks, residual = self._chunk_hidden(content, return_residual=True)
self.span_data[key] = {
'layer_states': states,
'chunk_embs': chunks,
'n_tokens': n_tok,
'residual_hidden': residual,
}
return n_tok
def extend_right(self, piece_text, old_key, new_key):
old = self.span_data.pop(old_key)
content, n_new, states = self._forward_encode_raw(
piece_text, init_states=old['layer_states'])
if old.get('residual_hidden') is not None:
content = torch.cat([old['residual_hidden'], content], dim=0)
new_chunks, residual = self._chunk_hidden(
content, return_residual=True)
self.span_data[new_key] = {
'layer_states': states,
'chunk_embs': old['chunk_embs'] + new_chunks,
'n_tokens': old['n_tokens'] + n_new,
'residual_hidden': residual,
}
return n_new
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