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b7751e5 | 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 | """Autoregressive decoding using forward calls, including batched cached beams."""
from typing import Callable
import torch
from torch import Tensor
ModelForward = Callable[[Tensor], Tensor]
def _check(input_ids, max_new_tokens):
"""Validate the prompt tensor shape and nonnegative generation budget."""
if input_ids.ndim != 2 or input_ids.shape[1] == 0 or max_new_tokens < 0:
raise ValueError(
"Expected nonempty (batch, sequence) prompts and nonnegative length"
)
def _forward(model, ids, mask, cache):
# Hugging Face models expose config; simple callable test models need only ids.
"""Return next-token logits and an optional cache using only model forward calls."""
if hasattr(model, "config") and hasattr(model.config, "model_type"):
new_ids = ids if cache is None else ids[:, -1:]
positions = mask.long().cumsum(-1).sub(1).clamp_min(0)[:, -new_ids.shape[1] :]
result = model.forward(
input_ids=new_ids,
attention_mask=mask,
position_ids=positions,
past_key_values=cache,
use_cache=True,
)
return result.logits[:, -1].float(), getattr(result, "past_key_values", None)
result = (
model.forward(ids, attention_mask=mask)
if hasattr(model, "forward")
else model(ids)
)
logits = result.logits if hasattr(result, "logits") else result
return logits[:, -1].float(), None
def _reorder(cache, indices):
"""Reorder cached beam states according to their selected parent indices."""
if cache is None:
return None
if hasattr(cache, "reorder_cache"):
cache.reorder_cache(indices)
return cache
return tuple(tuple(t.index_select(0, indices) for t in layer) for layer in cache)
@torch.no_grad()
def _sample(
model,
input_ids,
max_new_tokens,
mode,
k=None,
p=None,
temperature=1.0,
eos_token_id=None,
attention_mask=None,
):
"""Append tokens using greedy, top-k or nucleus selection.
Maintain attention masks and optional cached states. Finished sequences
append only EOS while other sequences continue within the token budget.
"""
_check(input_ids, max_new_tokens)
if temperature <= 0:
raise ValueError("temperature must be positive")
ids = input_ids.clone()
mask = torch.ones_like(ids) if attention_mask is None else attention_mask.clone()
finished = torch.zeros(ids.shape[0], device=ids.device, dtype=torch.bool)
cache = None
for _ in range(max_new_tokens):
logits, cache = _forward(model, ids, mask, cache)
if mode == "greedy":
token = logits.argmax(-1)
else:
logits = logits / temperature
if mode == "top_k":
values, indices = logits.topk(min(k, logits.shape[-1]), dim=-1)
choice = torch.multinomial(values.softmax(-1), 1)
token = indices.gather(1, choice).squeeze(1)
else:
values, indices = logits.sort(descending=True, dim=-1, stable=True)
probs = values.softmax(-1)
remove = probs.cumsum(-1) - probs >= p
values = values.masked_fill(remove, -torch.inf)
choice = torch.multinomial(values.softmax(-1), 1)
token = indices.gather(1, choice).squeeze(1)
if eos_token_id is not None:
token = torch.where(finished, eos_token_id, token)
finished |= token.eq(eos_token_id)
ids = torch.cat((ids, token[:, None]), -1)
mask = torch.cat((mask, torch.ones_like(token[:, None])), -1)
if bool(finished.all()):
break
return ids
def greedy_decode(model_forward, input_ids, max_new_tokens, **kwargs):
"""Append the highest-logit token at each step until EOS or the token limit."""
return _sample(model_forward, input_ids, max_new_tokens, "greedy", **kwargs)
def top_k_decode(model_forward, input_ids, k, max_new_tokens, **kwargs):
"""Sample among the highest k logits; k=1 uses deterministic greedy decoding."""
if k < 1:
raise ValueError("k must be positive")
if k == 1:
return greedy_decode(model_forward, input_ids, max_new_tokens, **kwargs)
return _sample(model_forward, input_ids, max_new_tokens, "top_k", k=k, **kwargs)
def top_p_decode(model_forward, input_ids, p, max_new_tokens, **kwargs):
"""Sample from the smallest prefix reaching the requested mass."""
if not 0 < p <= 1:
raise ValueError("p must be in (0, 1]")
return _sample(model_forward, input_ids, max_new_tokens, "top_p", p=p, **kwargs)
@torch.no_grad()
def beam_search(
model_forward,
input_ids,
width,
max_new_tokens,
eos_token_id=None,
length_penalty=0.0,
attention_mask=None,
):
"""Return the best cumulative-log-probability beam for each prompt.
Keep the requested number of hypotheses and reorder caches after pruning.
Completed beams keep their scores; optional length normalization is used
only to choose the final hypothesis. Width one matches greedy decoding.
"""
_check(input_ids, max_new_tokens)
if width < 1 or length_penalty < 0:
raise ValueError("width must be positive and length_penalty nonnegative")
if width == 1:
return greedy_decode(
model_forward,
input_ids,
max_new_tokens,
eos_token_id=eos_token_id,
attention_mask=attention_mask,
)
if max_new_tokens == 0:
return input_ids.clone()
b, prompt_length = input_ids.shape
ids = input_ids.repeat_interleave(width, 0)
mask = (
torch.ones_like(input_ids) if attention_mask is None else attention_mask
).repeat_interleave(width, 0)
scores = torch.full((b, width), -torch.inf, device=ids.device)
scores[:, 0] = 0.0
finished = torch.zeros(b, width, device=ids.device, dtype=torch.bool)
lengths = torch.zeros(b, width, device=ids.device, dtype=torch.long)
cache = None
for _ in range(max_new_tokens):
logits, cache = _forward(model_forward, ids, mask, cache)
vocab = logits.shape[-1]
if width > vocab:
raise ValueError("beam width exceeds vocabulary size")
log_probs = logits.log_softmax(-1).reshape(b, width, vocab)
if eos_token_id is not None:
log_probs = log_probs.masked_fill(finished[..., None], -torch.inf)
log_probs[:, :, eos_token_id] = torch.where(
finished, 0.0, log_probs[:, :, eos_token_id]
)
candidates = scores[..., None] + log_probs
scores, flat_indices = candidates.reshape(b, -1).topk(width, -1)
parent, token = flat_indices // vocab, flat_indices % vocab
global_parent = (
parent + torch.arange(b, device=ids.device)[:, None] * width
).reshape(-1)
old_finished = finished.gather(1, parent)
lengths = lengths.gather(1, parent) + (~old_finished).long()
finished = (
old_finished
if eos_token_id is None
else old_finished | token.eq(eos_token_id)
)
ids = torch.cat((ids.index_select(0, global_parent), token.reshape(-1, 1)), -1)
mask = torch.cat(
(
mask.index_select(0, global_parent),
torch.ones(b * width, 1, dtype=mask.dtype, device=ids.device),
),
-1,
)
cache = _reorder(cache, global_parent)
if bool(finished.all()):
break
normalized = scores / lengths.clamp_min(1).float().pow(length_penalty)
best = normalized.argmax(-1) + torch.arange(b, device=ids.device) * width
return ids.index_select(0, best)
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