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ff6ec5b | 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 | """Shared batched generation and measured, log-friendly progress (no prompt text)."""
import gc
import inspect
import sys
import threading
import time
from itertools import groupby
from token_cache import padded_batch
class Progress:
def __init__(self, label, total=0, quiet=False, interval=5):
self.label, self.total, self.quiet = label, total, quiet
self.interval = interval
self.done = self.tokens = 0
self.stage = 'starting'
self.started = time.monotonic()
self.last = 0.0
self.lock = threading.RLock()
self.stop = threading.Event()
def update(self, stage=None, done=None, tokens=None, force=False):
with self.lock:
if stage is not None:
self.stage = stage
if done is not None:
self.done = done
if tokens is not None:
self.tokens = tokens
self.render(force)
def render(self, force=False):
now = time.monotonic()
if self.quiet or (not force and now-self.last < self.interval):
return
self.last = now
elapsed = max(now-self.started, 1e-6)
rate = self.done/elapsed
eta = f'{(self.total-self.done)/rate:.0f}s' if rate and self.total else 'pending'
print(f'{self.label}: {self.stage} | {self.done}/{self.total} complete | '
f'{self.tokens} generated tokens | {rate:.2f}/s | elapsed {elapsed:.0f}s | ETA {eta}',
file=sys.stderr, flush=True)
def __enter__(self):
self.update(force=True)
def heartbeat():
while not self.stop.wait(self.interval):
with self.lock:
self.render()
self.thread = threading.Thread(target=heartbeat, daemon=True)
if not self.quiet:
self.thread.start()
return self
def __exit__(self, kind, value, tb):
self.stop.set()
if self.thread.is_alive():
self.thread.join(timeout=1)
self.update(stage='failed' if kind else 'finished', force=True)
def eos_ids(model, tokenizer):
value = getattr(model.generation_config, 'eos_token_id', None)
if value is None:
value = tokenizer.eos_token_id
return set(value if isinstance(value, (list, tuple)) else [] if value is None else [value])
class TokenProgress:
"""Transformers streamer: count actual decode steps without decoding text."""
def __init__(self, progress, batch_size, eos, base_tokens=0):
self.progress = progress
self.active = [True]*batch_size
self.eos = eos
self.first = True
self.count = 0
self.base = base_tokens
def put(self, value):
if self.first: # Initial prompt (or decoder-start IDs), not generated tokens.
self.first = False
return
values = value.tolist()
for i, token in enumerate(values):
if self.active[i]:
self.count += 1
if token in self.eos:
self.active[i] = False
self.progress.update(stage='decoding', tokens=self.base+self.count)
def end(self):
pass
def trim_after_eos(sequences, eos):
result = []
for tokens in sequences:
end = next((i+1 for i,t in enumerate(tokens) if t in eos), len(tokens))
result.append(tokens[:end])
return result
def generate_batches(model, tokenizer, compiled, rows, budget_for, batch_size, emit,
progress, is_seq2seq=False, keep_special_tokens=False):
"""Length/budget buckets, KV cache, greedy decoding, and CUDA OOM backoff."""
import torch
ordered = sorted(rows, key=lambda q: (budget_for(q), len(compiled[q['id']]['input_ids'])))
eos = eos_ids(model, tokenizer)
done = tokens_done = 0
effective_batch = batch_size
stats = dict(batches=0, oom_retries=0, generated_tokens=0, input_tokens=0, padded_input_tokens=0)
# Models exposing this documented optimization need only the next-token logits
# during generation, not a vocabulary projection for every prefill position.
generation_options = {}
forward = getattr(model, 'forward', None)
if forward is not None and not is_seq2seq:
parameters = inspect.signature(forward).parameters
for name in ('logits_to_keep', 'num_logits_to_keep'):
if name in parameters:
generation_options[name] = 1
break
stats['last_token_logits'] = bool(generation_options)
def generate(group, budget):
data = padded_batch([compiled[q['id']] for q in group], tokenizer.pad_token_id, tokenizer.padding_side)
width = data['input_ids'].shape[1]
device = model.get_input_embeddings().weight.device
data = {k:v.to(device) for k,v in data.items()}
progress.update(stage=f'prefill batch={len(group)} input_width={width}', force=True)
stream = TokenProgress(progress, len(group), eos, tokens_done)
# Explicit defaults prevent a checkpoint's sampling/beam config changing the task.
output = model.generate(**data, max_new_tokens=budget, do_sample=False, num_beams=1,
num_return_sequences=1, pad_token_id=tokenizer.pad_token_id,
use_cache=True, streamer=stream, return_dict_in_generate=False,
output_scores=False, **generation_options)
prefix = 1 if is_seq2seq else width
sequences = trim_after_eos(output[:, prefix:].tolist(), eos)
answers = tokenizer.batch_decode(sequences, skip_special_tokens=not keep_special_tokens,
clean_up_tokenization_spaces=False)
if len(answers) != len(group):
raise RuntimeError('Generation returned an unexpected number of sequences')
return answers, [len(s) for s in sequences], width
with torch.inference_mode():
for budget, family in groupby(ordered, key=budget_for):
family = list(family)
offset = 0
while offset < len(family):
group = family[offset:offset+effective_batch]
try:
answers, lengths, width = generate(group, budget)
except torch.cuda.OutOfMemoryError:
if len(group) == 1:
raise
effective_batch = max(1, len(group)//2)
stats['oom_retries'] += 1
progress.update(stage=f'CUDA OOM; retry batch={effective_batch}', tokens=tokens_done, force=True)
else:
emit([(q['id'], text, length) for q,text,length in zip(group,answers,lengths)])
done += len(group)
tokens_done += sum(lengths)
stats['batches'] += 1
stats['input_tokens'] += sum(len(compiled[q['id']]['input_ids']) for q in group)
stats['padded_input_tokens'] += width*len(group)
progress.update(stage='batch saved', done=done, tokens=tokens_done, force=True)
offset += len(group)
continue
# Release the exception traceback (and its tensors) before GC.
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
stats.update(generated_tokens=tokens_done, final_batch_size=effective_batch)
return stats
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