File size: 7,384 Bytes
fb6b7f8 | 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 | import torch
from pathlib import Path
def build_input(user_input, tokenizer):
BOS = tokenizer.piece_to_id("[BOS]")
EOS = tokenizer.piece_to_id("[EOS]")
IM_START = tokenizer.piece_to_id("<|im_start|>")
IM_END = tokenizer.piece_to_id("<|im_end|>")
user_ids = (
[IM_START]
+ tokenizer.encode("user\n" + user_input.strip().lower(), out_type=int)
+ [IM_END]
)
assistant_prefix = [IM_START] + tokenizer.encode("model\n", out_type=int)
input_ids = [BOS] + user_ids + assistant_prefix
return input_ids, len(input_ids), EOS, IM_END
def decode_output(best, start, EOS, IM_END, tokenizer):
output_tokens = best["seq"][start:]
while output_tokens and output_tokens[-1] in (EOS, IM_END):
output_tokens.pop()
return tokenizer.decode(output_tokens)
def score(seq, log_prob, start):
out_len = max(len(seq) - start, 1)
return log_prob / (out_len ** 1.0)
def apply_penalty(logits, seq, penalty):
if penalty == 1.0:
return logits
logits = logits.clone()
for tid in set(seq):
if logits[tid] < 0:
logits[tid] *= penalty
else:
logits[tid] /= penalty
return logits
def get_banned_tokens(seq, n):
banned = set()
if n > 0 and len(seq) >= n:
prefix = tuple(seq[-(n - 1):])
for i in range(len(seq) - n + 1):
if tuple(seq[i:i + n - 1]) == prefix:
banned.add(seq[i + n - 1])
return banned
def forward_init(model, input_ids, max_beam_size, max_new_tokens, device):
prompt_len = len(input_ids)
max_total = prompt_len + max_new_tokens
kv_buffers = model.init_cache(max_beam_size, max_total, device)
prompt_tensor = torch.tensor([input_ids], dtype=torch.long, device=device)
with torch.inference_mode():
first_logits, present_cache = model.prefill(prompt_tensor, kv_cache=None)
for i, (k, v) in enumerate(present_cache):
kv_buffers[i][0][:, :, :prompt_len, :].copy_(k.expand(max_beam_size, -1, -1, -1))
kv_buffers[i][1][:, :, :prompt_len, :].copy_(v.expand(max_beam_size, -1, -1, -1))
return first_logits[0], kv_buffers, prompt_len
def forward_step(model, last_tokens, kv_buffers, cache_len):
with torch.inference_mode():
return model.decode_step(last_tokens, kv_buffers, cache_len)
def beam_core(model, input_ids, start, score_fn, penalty_fn, max_new_tokens, beam_size, no_repeat_ngram, penalty, device, EOS, IM_END, max_seq_len, early_stop=True, patience=10):
first_logits, kv_buffers, cache_len = forward_init(
model, input_ids, beam_size, max_new_tokens, device
)
first_logits = penalty_fn(first_logits, input_ids, penalty)
first_lp = torch.clamp(torch.log_softmax(first_logits, -1), -1e9, 0.0)
topk_lp, topk_tok = torch.topk(first_lp, beam_size)
seqs = [input_ids + [int(t)] for t in topk_tok.tolist()]
log_probs = topk_lp.tolist()
dones = [int(t) in (EOS, IM_END) for t in topk_tok.tolist()]
unique_sets = [set(input_ids) | {int(t)} for t in topk_tok.tolist()]
completed = []
K = beam_size * 3
patience_counter = 0
for _ in range(max_new_tokens - 1):
if all(dones):
break
last = torch.tensor([[seqs[i][-1]] for i in range(len(seqs))], dtype=torch.long, device=device)
logits_batch = forward_step(model, last, kv_buffers, cache_len)
cache_len += 1
n_beams = len(seqs)
if penalty != 1.0:
pen_mask = torch.ones(n_beams, logits_batch.shape[-1], device=device)
for i, uid in enumerate(unique_sets):
if dones[i]:
continue
idx = torch.tensor(list(uid), dtype=torch.long, device=device)
pen_mask[i, idx] = penalty
neg = logits_batch < 0
logits_batch = torch.where(neg, logits_batch * pen_mask, logits_batch / pen_mask)
lp_batch = torch.clamp(torch.log_softmax(logits_batch, -1), -1e9, 0.0)
topk_lp_b, topk_tok_b = torch.topk(lp_batch, K, dim=-1)
topk_lp_b = topk_lp_b.tolist()
topk_tok_b = topk_tok_b.tolist()
candidates = []
for beam_i in range(n_beams):
if dones[beam_i]:
continue
banned = get_banned_tokens(seqs[beam_i], no_repeat_ngram)
count = 0
for l, t in zip(topk_lp_b[beam_i], topk_tok_b[beam_i]):
if count >= beam_size:
break
if t in banned:
continue
new_seq = seqs[beam_i] + [t]
done = t in (EOS, IM_END) or len(new_seq) >= max_seq_len
candidates.append({
"beam_src": beam_i,
"seq": new_seq,
"log_prob": log_probs[beam_i] + l,
"done": done
})
count += 1
if not candidates:
break
candidates.sort(key=lambda x: score_fn(x["seq"], x["log_prob"], start), reverse=True)
kept, src = [], []
for c in candidates:
if c["done"]:
completed.append(c)
elif len(kept) < beam_size:
kept.append(c)
src.append(c["beam_src"])
if not kept:
break
if len(completed) >= beam_size:
best_done = max(completed, key=lambda x: score_fn(x["seq"], x["log_prob"], start))
best_alive = max(kept, key=lambda x: score_fn(x["seq"], x["log_prob"], start))
if score_fn(best_done["seq"], best_done["log_prob"], start) >= \
score_fn(best_alive["seq"], best_alive["log_prob"], start):
if early_stop:
break
else:
patience_counter += 1
if patience_counter >= patience:
break
else:
patience_counter = 0
if src != list(range(len(src))):
src_t = torch.tensor(src, dtype=torch.long, device=device)
for k_buf, v_buf in kv_buffers:
k_tmp = k_buf[src_t].clone()
v_tmp = v_buf[src_t].clone()
k_buf[:len(src)].copy_(k_tmp)
v_buf[:len(src)].copy_(v_tmp)
unique_sets = [unique_sets[src[i]] | {c["seq"][-1]} for i, c in enumerate(kept)]
seqs = [c["seq"] for c in kept]
log_probs = [c["log_prob"] for c in kept]
dones = [False] * len(kept)
pool = completed if completed else [{"seq": seqs[i], "log_prob": log_probs[i]} for i in range(len(seqs))]
return max(pool, key=lambda x: score_fn(x["seq"], x["log_prob"], start))
# ================= PUBLIC API =================
def generate(model, user_input, tokenizer,
max_new_tokens=200, beam_size=5,
no_repeat_ngram=3, penalty=1.2,
early_stop=False, patience=30):
device = model.lm_head.weight.device
ids, start, EOS, IM_END = build_input(user_input, tokenizer)
best = beam_core(
model, ids, start,
score, apply_penalty,
max_new_tokens, beam_size, no_repeat_ngram, penalty,
device, EOS, IM_END, model.max_seq_len,
early_stop=early_stop, patience=patience
)
return decode_output(best, start, EOS, IM_END, tokenizer)
|