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ad63753 a7727c4 b270a15 ad63753 c707ac1 3a9470d c707ac1 ad63753 c707ac1 3a9470d c707ac1 3a9470d c707ac1 3a9470d c707ac1 | 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 | """Frozen R51 generation path with unused adapter branches removed.
No model hooks or trained modules; see source_manifest.json and parity tests.
"""
import math
import time
import torch
from transformers import TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
def new_cache(model):
from transformers.models.granitemoehybrid.modeling_granitemoehybrid import (
HybridMambaAttentionDynamicCache,
)
parameter = next(model.parameters())
cache = HybridMambaAttentionDynamicCache(
model.config, batch_size=1, dtype=parameter.dtype, device=parameter.device
)
for layer in cache.transformer_layers:
shape = (
1,
model.config.num_key_value_heads,
0,
model.config.hidden_size // model.config.num_attention_heads,
)
cache.key_cache[layer] = torch.empty(shape, dtype=parameter.dtype, device=parameter.device)
cache.value_cache[layer] = torch.empty_like(cache.key_cache[layer])
return cache
def idle_frozen(model):
if model.training or any(p.requires_grad or p.grad is not None for p in model.parameters()):
raise ValueError("Runtime requires a frozen evaluation backbone")
attention = ALL_ATTENTION_FUNCTIONS["sdpa"]
if (
getattr(model, "_jev_feedback_active", False)
or getattr(attention, "_r17_controlled", False)
or getattr(attention, "_r18_controlled", False)
or any(m._forward_hooks or m._forward_pre_hooks for m in model.modules())
or torch.nn.modules.module._global_forward_hooks
or torch.nn.modules.module._global_forward_pre_hooks
):
raise RuntimeError("Model intervention already active")
def valid_ids(model, ids):
return (
isinstance(ids, list)
and bool(ids)
and all(type(i) is int and 0 <= i < model.config.vocab_size for i in ids)
)
def sample_setup(sampling, device):
if sampling is None:
return (None, [], None)
if (
not isinstance(sampling, dict)
or set(sampling) != {"seed", "temperature", "top_p"}
or type(sampling["seed"]) is not int
or (not 0 <= sampling["seed"] < 2**63)
or any(
type(sampling[k]) not in (int, float) or not math.isfinite(sampling[k])
for k in ("temperature", "top_p")
)
or (not 0 < sampling["temperature"] <= 2)
or (not 0 < sampling["top_p"] <= 1)
):
raise ValueError("Invalid sampling settings")
settings = dict(sampling, top_k=50)
generator = torch.Generator(device=device).manual_seed(settings["seed"])
warpers = [
TemperatureLogitsWarper(float(settings["temperature"])),
TopKLogitsWarper(50),
TopPLogitsWarper(float(settings["top_p"])),
]
return (generator, warpers, settings)
def generate(model, tok, ids, *, limit, eos, context_limit=32768, sampling=None, deadline=None):
idle_frozen(model)
if (
not valid_ids(model, ids)
or not valid_ids(model, eos)
or len(eos) != len(set(eos))
or (type(limit) is not int)
or (not 1 <= limit <= 2048)
or (type(context_limit) is not int)
or (not 1 <= context_limit <= 32768)
or (context_limit > model.config.max_position_embeddings)
or (len(ids) + limit > context_limit)
):
raise ValueError("Invalid tokens, generation limit or context")
device = next(model.parameters()).device
generator, warpers, settings = sample_setup(sampling, device)
pending, accepted, past = (list(ids), [], 0)
cache, traces = (new_cache(model), [])
tick = time.monotonic()
forward_pending, attempted_positions = False, 0
decoding_started = False
try:
with torch.no_grad():
for _ in range(limit):
if deadline:
deadline()
attempted_positions += len(pending)
forward_pending = True
out = model(
input_ids=torch.tensor([pending], device=device),
attention_mask=torch.ones(
(1, past + len(pending)), device=device, dtype=torch.long
),
past_key_values=cache,
use_cache=True,
cache_position=torch.arange(past, past + len(pending), device=device),
logits_to_keep=1,
)
past += len(pending)
forward_pending = False
cache = out.past_key_values
logits = out.logits[:, -1, :].float()
if not torch.isfinite(logits).all():
raise ValueError("Nonfinite generation logits")
if generator is None:
token = int(logits[0].argmax())
else:
for warper in warpers:
logits = warper(None, logits)
token = int(
torch.multinomial(torch.softmax(logits, dim=-1), 1, generator=generator)[
0, 0
]
)
accepted.append(token)
if token in eos:
break
pending = [token]
decoding_started = True
body = accepted[:-1] if accepted[-1] in eos else accepted
return dict(
prompt_token_ids=list(ids),
generated_token_ids=accepted,
text=tok.decode(body, skip_special_tokens=False, clean_up_tokenization_spaces=False),
finish_reason="eos" if accepted[-1] in eos else "length",
processed_tokens=len(ids) + len(accepted) - 1,
seconds=time.monotonic() - tick,
events=traces,
sampling=settings,
)
except BaseException as exc:
body = accepted[:-1] if accepted and accepted[-1] in eos else accepted
partial = dict(
prompt_token_ids=list(ids),
generated_token_ids=list(accepted),
text=None,
finish_reason=(
"time"
if isinstance(exc, TimeoutError)
else "error"
if isinstance(exc, Exception)
else "interrupted"
),
processed_tokens=past,
attempted_forward_positions=attempted_positions,
forward_work_unknown=forward_pending,
seconds=time.monotonic() - tick,
events=traces,
sampling=settings,
)
exc.partial_generation = partial
if not decoding_started:
try:
partial["text"] = tok.decode(
body, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
except BaseException as decoding_error:
partial["decoding_error_type"] = type(decoding_error).__name__
if not isinstance(decoding_error, Exception):
decoding_error.partial_generation = partial
raise
raise
|