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03223d7 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | from __future__ import annotations
import json
import torch
def encode_piece(tok, text: str) -> list[int]:
return tok(text, add_special_tokens=False).input_ids
def state_to_text(state) -> str:
if isinstance(state, str):
return state
return json.dumps(state, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
def _clip_head_tail(ids: list[int], limit: int) -> list[int]:
"""Deterministically keep evidence from both ends of a long field."""
if limit <= 0:
return []
if len(ids) <= limit:
return ids
head = (limit + 1) // 2
tail = limit - head
return ids[:head] + (ids[-tail:] if tail else [])
def _assemble(tok, state_ids, q_ids, option_ids, max_length: int):
ids = [tok.bos_token_id] if tok.bos_token_id is not None else []
ids += encode_piece(tok, "State:\n")
ids += state_ids
ids += encode_piece(tok, "\n\nQuestion:\n")
ids += q_ids
ids += encode_piece(tok, "\n\nOptions:\n")
option_positions = []
option_spans = []
for idx, opt_ids in enumerate(option_ids):
ids += encode_piece(tok, f"- [{idx}] ")
span_start = len(ids)
ids += opt_ids
span_end = len(ids)
option_spans.append((span_start, span_end))
# Represent each option by its final semantic token, after it has seen
# the full option text but before the newline delimiter.
option_positions.append(len(ids) - 1)
ids += encode_piece(tok, "\n")
ids += encode_piece(tok, "\nDecision:")
decide_position = len(ids) - 1
if len(ids) > max_length:
return None
return ids, option_positions, decide_position, option_spans
def pack_question(tok, state, question: dict, max_length: int):
"""Pack a decision example without dropping long states.
Priority order is structural markers/options/question first, then state.
Long state is head+tail truncated. If question/options themselves are huge,
they are bounded as a second-stage fallback. Returning None is reserved for
structurally impossible cases (e.g. too many options for max_length).
"""
if max_length < 32:
return None
state_ids = encode_piece(tok, state_to_text(state))
q_ids = encode_piece(tok, str(question["instruction"]))
option_ids = [encode_piece(tok, str(opt["text"])) for opt in question["options"]]
if not option_ids or any(not x for x in option_ids):
return None
was_truncated = False
# First discover how much room remains for the state while preserving the
# complete question and option text.
probe = _assemble(tok, [], q_ids, option_ids, max_length=10**9)
if probe is None:
return None
structural_len = len(probe[0])
if structural_len <= max_length:
state_budget = max_length - structural_len
was_truncated = len(state_ids) > state_budget
packed = _assemble(tok, _clip_head_tail(state_ids, state_budget), q_ids, option_ids, max_length)
else:
was_truncated = True
# Extremely verbose question/tool schemas: cap semantic fields rather
# than dropping the sample. Typical training examples never hit this.
nopt = len(option_ids)
q_cap = min(len(q_ids), max(16, max_length // 8))
# Start modestly; then shrink until the structural representation fits.
opt_cap = max(8, min(96, max_length // max(8, nopt * 2)))
q_fit = _clip_head_tail(q_ids, q_cap)
opts_fit = [_clip_head_tail(x, opt_cap) for x in option_ids]
packed = _assemble(tok, [], q_fit, opts_fit, max_length)
while packed is None and (q_cap > 8 or opt_cap > 4):
q_cap = max(8, q_cap // 2)
opt_cap = max(4, opt_cap // 2)
q_fit = _clip_head_tail(q_ids, q_cap)
opts_fit = [_clip_head_tail(x, opt_cap) for x in option_ids]
packed = _assemble(tok, [], q_fit, opts_fit, max_length)
if packed is not None:
# If the shrunken question/options leave room, fill it with state.
base_len = len(packed[0])
state_budget = max(0, max_length - base_len)
was_truncated = was_truncated or len(state_ids) > state_budget
packed = _assemble(tok, _clip_head_tail(state_ids, state_budget), q_fit, opts_fit, max_length)
if packed is None:
return None
ids, option_positions, decide_position, option_spans = packed
return {
"input_ids": ids,
"option_positions": option_positions,
"option_spans": option_spans,
"decide_position": decide_position,
"target": question.get("target_distribution"),
"was_truncated": was_truncated,
}
def collate_packed(tok, packed: list[dict]) -> dict[str, torch.Tensor]:
if not packed:
raise RuntimeError("No packable examples")
bsz = len(packed)
max_seq = max(len(x["input_ids"]) for x in packed)
max_opts = max(len(x["option_positions"]) for x in packed)
pad_id = tok.pad_token_id if tok.pad_token_id is not None else 0
input_ids = torch.full((bsz, max_seq), pad_id, dtype=torch.long)
attention_mask = torch.zeros((bsz, max_seq), dtype=torch.long)
option_positions = torch.zeros((bsz, max_opts), dtype=torch.long)
option_mask = torch.zeros((bsz, max_opts), dtype=torch.bool)
decide_positions = torch.zeros((bsz,), dtype=torch.long)
targets = torch.zeros((bsz, max_opts), dtype=torch.float32)
option_starts = torch.zeros((bsz, max_opts), dtype=torch.long)
option_ends = torch.zeros((bsz, max_opts), dtype=torch.long)
has_targets = all(x.get("target") is not None for x in packed)
for i, x in enumerate(packed):
n = len(x["input_ids"])
m = len(x["option_positions"])
input_ids[i, :n] = torch.tensor(x["input_ids"], dtype=torch.long)
attention_mask[i, :n] = 1
option_positions[i, :m] = torch.tensor(x["option_positions"], dtype=torch.long)
spans = x.get("option_spans") or [(int(v), int(v)+1) for v in x["option_positions"]]
option_starts[i, :m] = torch.tensor([a for a, _ in spans], dtype=torch.long)
option_ends[i, :m] = torch.tensor([b for _, b in spans], dtype=torch.long)
option_mask[i, :m] = True
decide_positions[i] = x["decide_position"]
if has_targets:
targets[i, :m] = torch.tensor(x["target"], dtype=torch.float32)
out = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"option_positions": option_positions,
"option_starts": option_starts,
"option_ends": option_ends,
"option_mask": option_mask,
"decide_positions": decide_positions,
}
if has_targets:
out["targets"] = targets
return out
def pack_shared_request(tok, state, questions: list[dict], max_length: int):
"""Pack one common state prefix plus causal question suffixes.
Every question sees exactly the same serialized/clipped state. This is the
representation needed for safe KV-prefix reuse at inference time.
"""
if max_length < 32 or not questions:
return None
bos = [tok.bos_token_id] if tok.bos_token_id is not None else []
state_marker = encode_piece(tok, "State:\n")
state_ids = encode_piece(tok, state_to_text(state))
suffixes = []
max_suffix_len = 0
for question in questions:
q_ids = encode_piece(tok, str(question["instruction"]))
option_ids = [encode_piece(tok, str(opt["text"])) for opt in question["options"]]
if not option_ids or any(not x for x in option_ids):
return None
ids = encode_piece(tok, "\n\nQuestion:\n") + q_ids + encode_piece(tok, "\n\nOptions:\n")
option_positions = []
for idx, opt_ids in enumerate(option_ids):
ids += encode_piece(tok, f"- [{idx}] ")
ids += opt_ids
option_positions.append(len(ids) - 1)
ids += encode_piece(tok, "\n")
ids += encode_piece(tok, "\nDecision:")
decide_position = len(ids) - 1
suffixes.append({
"input_ids": ids,
"option_positions": option_positions,
"decide_position": decide_position,
})
max_suffix_len = max(max_suffix_len, len(ids))
fixed_prefix_len = len(bos) + len(state_marker)
state_budget = max_length - fixed_prefix_len - max_suffix_len
if state_budget < 0:
return None
clipped = _clip_head_tail(state_ids, state_budget)
was_truncated = len(clipped) < len(state_ids)
prefix_ids = bos + state_marker + clipped
for s in suffixes:
s["was_truncated"] = was_truncated
if len(prefix_ids) + len(s["input_ids"]) > max_length:
return None
return {"prefix_ids": prefix_ids, "suffixes": suffixes, "was_truncated": was_truncated}
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