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import torch
import torch.nn as nn
import torch.nn.functional as F
import json
import os
# ======================================================================
# RWKV-Mamba Hybrid Recurrence
# ======================================================================
class RWKVMambaHybrid(nn.Module):
def __init__(self, d_model, d_state=64):
super().__init__()
self.d_model = d_model
self.d_state = d_state
self.w_mix = nn.Parameter(torch.ones(d_model) * 0.5)
self.A = nn.Parameter(torch.randn(d_state, d_state) * 0.01)
self.B = nn.Parameter(torch.randn(d_state, d_model) * 0.01)
self.C = nn.Parameter(torch.randn(d_model, d_state) * 0.01)
self.D = nn.Parameter(torch.ones(d_model) * 0.1)
def forward(self, x):
B, T, C = x.shape
h = torch.zeros(B, C, device=x.device)
s = torch.zeros(B, self.d_state, device=x.device)
outputs = []
for t in range(T):
x_t = x[:, t, :]
h = self.w_mix * h + (1 - self.w_mix) * x_t
s = s @ self.A.T + x_t @ self.B.T
y_t = s @ self.C.T + h * self.D
outputs.append(y_t)
return torch.stack(outputs, dim=1)
# ======================================================================
# Full Multi-Head Attention
# ======================================================================
class FullAttention(nn.Module):
def __init__(self, d_model, n_heads=16):
super().__init__()
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
assert d_model % n_heads == 0, "d_model must be divisible by n_heads"
self.qkv = nn.Linear(d_model, d_model * 3)
self.out_proj = nn.Linear(d_model, d_model)
def forward(self, x, mask=None):
B, T, C = x.shape
qkv = self.qkv(x)
q, k, v = qkv.chunk(3, dim=-1)
q = q.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
attn = (q @ k.transpose(-2, -1)) / (self.head_dim ** 0.5)
if mask is not None:
mask = mask.expand(B, self.n_heads, T, T).bool()
attn = attn.masked_fill(mask == 0, float('-inf'))
attn = F.softmax(attn, dim=-1)
out = attn @ v
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.out_proj(out)
# ======================================================================
# i3 Hybrid Block
# ======================================================================
class i3HybridBlock(nn.Module):
def __init__(self, d_model, d_state=64, ffn_mult=4):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.hybrid = RWKVMambaHybrid(d_model, d_state)
self.ln2 = nn.LayerNorm(d_model)
d_ff = d_model * ffn_mult
self.ffn = nn.Sequential(
nn.Linear(d_model, d_ff),
nn.GELU(),
nn.Linear(d_ff, d_model)
)
def forward(self, x, mask=None):
x = x + self.hybrid(self.ln1(x))
x = x + self.ffn(self.ln2(x))
return x
# ======================================================================
# i3 Attention Block
# ======================================================================
class i3AttentionBlock(nn.Module):
def __init__(self, d_model, n_heads=16, ffn_mult=4):
super().__init__()
self.ln1 = nn.LayerNorm(d_model)
self.attn = FullAttention(d_model, n_heads)
self.ln2 = nn.LayerNorm(d_model)
d_ff = d_model * ffn_mult
self.ffn = nn.Sequential(
nn.Linear(d_model, d_ff),
nn.GELU(),
nn.Linear(d_ff, d_model)
)
def forward(self, x, mask=None):
x = x + self.attn(self.ln1(x), mask)
x = x + self.ffn(self.ln2(x))
return x
# ======================================================================
# Full i3 Model
# ======================================================================
class i3Model(nn.Module):
def __init__(self, vocab_size, d_model=512, n_heads=16, max_seq_len=256, d_state=32):
super().__init__()
self.vocab_size = vocab_size
self.d_model = d_model
self.max_seq_len = max_seq_len
self.embed = nn.Embedding(vocab_size, d_model)
self.pos_embed = nn.Embedding(max_seq_len, d_model)
hybrid_layers = [i3HybridBlock(d_model, d_state=d_state) for _ in range(10)]
attention_layers = [i3AttentionBlock(d_model, n_heads=n_heads) for _ in range(6)]
self.layers = nn.ModuleList(hybrid_layers + attention_layers)
self.ln_f = nn.LayerNorm(d_model)
self.head = nn.Linear(d_model, vocab_size)
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
module.weight.data.normal_(0.0, 0.02)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def forward(self, idx, targets=None):
B, T = idx.shape
pos = torch.arange(T, device=idx.device).unsqueeze(0)
x = self.embed(idx) + self.pos_embed(pos)
mask = torch.tril(torch.ones(T, T, device=idx.device)).view(1, 1, T, T)
for layer in self.layers:
x = layer(x, mask)
x = self.ln_f(x)
logits = self.head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.max_seq_len else idx[:, -self.max_seq_len:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, 1)
idx = torch.cat((idx, idx_next), dim=1)
return idx
# ======================================================================
# ChunkTokenizer
# ======================================================================
class ChunkTokenizer:
def __init__(self, vocab_only=False):
self.chunk_to_idx = {}
self.idx_to_chunk = {}
self.vocab_size = 0
self.unk_token = '<UNK>'
self.unk_idx = 0
self.common_trigrams = frozenset({
'the', 'and', 'ing', 'ion', 'tio', 'for', 'tha',
'ter', 'hat', 'his', 'ere', 'ent', 'her', 'was',
'you', 'are', 'not', 'but', 'can', 'all', 'whi',
'one', 'our', 'out', 'whe', 'hav', 'thi', 'wit'
})
def _should_use_3char(self, pos, text):
if pos + 3 > len(text):
return False
chunk_3 = text[pos:pos+3]
if chunk_3 in self.common_trigrams:
return True
if pos > 0 and text[pos-1] == ' ':
return True
if pos + 3 < len(text) and text[pos+3] == ' ':
return True
return False
def encode(self, text):
text = text.lower()
pos, indices = 0, []
while pos < len(text):
chunk_len = 3 if self._should_use_3char(pos, text) else 2
chunk_len = min(chunk_len, len(text) - pos)
chunk = text[pos:pos+chunk_len]
if chunk in self.chunk_to_idx:
indices.append(self.chunk_to_idx[chunk])
else:
indices.append(self.unk_idx)
pos += chunk_len
return indices
def decode(self, indices):
return ''.join([self.idx_to_chunk.get(int(i), self.unk_token) for i in indices])
def save(self, path):
data = {
'chunk_to_idx': self.chunk_to_idx,
'idx_to_chunk': {int(k): v for k, v in self.idx_to_chunk.items()},
'vocab_size': self.vocab_size,
'unk_token': self.unk_token,
'unk_idx': self.unk_idx
}
with open(path, 'w') as f:
json.dump(data, f)
def load(self, path):
with open(path, 'r') as f:
data = json.load(f)
self.chunk_to_idx = data['chunk_to_idx']
self.idx_to_chunk = {int(k): v for k, v in data['idx_to_chunk'].items()}
self.vocab_size = data['vocab_size']
self.unk_token = data.get('unk_token', '<UNK>')
self.unk_idx = data.get('unk_idx', 0)
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