File size: 13,637 Bytes
e1acc15 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 | """D2-DATA d2.manifest.v1 reader and small synthetic internal block fixtures."""
from __future__ import annotations
import hashlib
import json
import bisect
from pathlib import Path
from typing import Protocol, TypedDict
import torch
from torch import Tensor
from safetensors import safe_open
from .model import Config
from .synthetic import inputs
INPUT_NAMES = ("taps", "engine_mx", "has_taps", "positions", "mask", "anchor", "P")
TARGET_NAMES = ("previous", "top_ids", "top_logp", "tail_mass", "labels", "present", "position_mask")
class Block(TypedDict):
inputs: dict[str, Tensor]
targets: dict[str, Tensor]
request: str
class Blocks(Protocol):
fingerprint: str
def __len__(self) -> int: ...
def __getitem__(self, index: int) -> Block: ...
def collate(blocks: list[Block]) -> Block:
if not blocks:
raise ValueError("empty block batch")
width = max(b["inputs"]["positions"].shape[1] for b in blocks)
batches: dict[str, list[Tensor]] = {n: [] for n in INPUT_NAMES}
for block in blocks:
inp = block["inputs"]
pad = width-inp["positions"].shape[1]
for name in INPUT_NAMES:
t = inp[name]
if pad and name not in ("anchor", "P"):
if name == "positions":
before = torch.arange(int(t[0, 0])-pad, int(t[0, 0]), dtype=t.dtype)[None]
else:
before = torch.zeros(1, pad, *t.shape[2:], dtype=t.dtype)
t = torch.cat((before, t), 1)
batches[name].append(t)
return {"inputs": {n: torch.cat(v, 0) for n, v in batches.items()},
"targets": {n: torch.cat([b["targets"][n] for b in blocks], 0) for n in TARGET_NAMES},
"request": "batch"}
def sha256(path: Path) -> str:
h = hashlib.sha256()
with path.open("rb") as f:
for b in iter(lambda: f.read(4 << 20), b""):
h.update(b)
return h.hexdigest()
def source_families(split: str, units: dict[str, list[tuple[str, str]]]) -> dict[str, str]:
"""Union requests sharing source documents (or a source-less prompt identity).
Run after reading ALL shard sidecars of a split: a later request can bridge
two earlier components. Hash sorted component units so traversal order and
task labels cannot change the resampling key. Never expose source IDs.
"""
parent = {request: request for request in units}
def find(request: str) -> str:
while parent[request] != request:
parent[request] = parent[parent[request]]
request = parent[request]
return request
owners: dict[tuple[str, str], str] = {}
for request, sources in units.items():
for unit in sources:
other = owners.setdefault(unit, request)
a, b = find(request), find(other)
parent[max(a, b)] = min(a, b)
components: dict[str, set[tuple[str, str]]] = {}
for request, sources in units.items():
components.setdefault(find(request), set()).update(sources)
identities = {root: hashlib.sha256(json.dumps([split, sorted(sources)], separators=(",", ":")).encode()).hexdigest()
for root, sources in components.items()}
return {request: identities[find(request)] for request in units}
class SyntheticBlocks:
def __init__(self, c: Config, count: int = 8):
self.c, self.count = c, count
self.fingerprint = f"synthetic-v1-{c}-{count}"
def __len__(self) -> int:
return self.count
def __getitem__(self, index: int) -> Block:
inp = inputs(self.c, batch=1, P=12+index)
g = torch.Generator().manual_seed(111+index)
full = torch.randn(1, 5, self.c.vocab, generator=g).softmax(-1)
p, ids = full.topk(20, -1)
labels = torch.multinomial(full.flatten(0, 1), 1, generator=g).reshape(1, 5)
previous = torch.cat((inp["anchor"][:, None], labels[:, :-1]), -1)
target = {"previous": previous, "top_ids": ids, "top_logp": p.log(), "tail_mass": 1-p.sum(-1),
"labels": labels, "present": torch.ones(1, 5, dtype=torch.bool),
"position_mask": torch.ones(1, 5, dtype=torch.bool)}
return {"inputs": inp, "targets": target, "request": f"synthetic-{index}"}
class D2Blocks:
"""Memory-map one bounded D2 shard at a time; hash all split metadata first.
Blocks follow DATASET.md §4 exactly: query anchor P+1, targets P+2..P+6,
and teacher-forced Markov input P+1..P+5. Tail blocks are position masked.
"""
def __init__(self, manifest: Path, split: str):
self.root = manifest.parent.resolve()
self.manifest = json.loads(manifest.read_text())
if self.manifest.get("schema") != "d2.manifest.v1":
raise ValueError("expected D2-DATA d2.manifest.v1")
checks = self.manifest.get("checks", {})
if "plumbing_errors" in checks and checks["plumbing_errors"] != 0:
raise ValueError("dataset records plumbing errors")
self.sequences: list[tuple[Path, list[int], str]] = []
self.request_families: dict[str, str] = {}
self.request_tasks: dict[str, str] = {}
self.sequence_b: list[int] = [] # V1 counts even sequences with zero eligible blocks
self.ends: list[int] = []
seen: dict[str, str] = {}
docs: dict[str, str] = {}
total = 0
for split_name, spec in self.manifest["splits"].items():
split_b = 0
units: dict[str, list[tuple[str, str]]] = {}
for entry in spec["files"]:
path = self.verified(entry["file"], entry["sha256"])
side = self.verified(entry["sidecar"], entry["sidecar_sha256"])
meta = json.loads(side.read_text())
constants = {"schema": "d2.shard.v1", "split": split_name, "W": 128, "K": 5, "topk": 20,
"tap_layers": [37, 38, 39], "hidden": 5120, "noise_token": 128799}
if any(meta.get(k) != v for k, v in constants.items()) or meta["sha256"] != entry["sha256"]:
raise ValueError(f"D2 shard sidecar mismatch {side}")
with safe_open(path, framework="pt", device="cpu") as f:
if f.metadata() != {"schema": "d2.shard.v1", "split": split_name}:
raise ValueError(f"D2 safetensors metadata mismatch {path}")
index = f.get_tensor("seq_index")
if index.dtype != torch.int64 or index.shape != (len(meta["sequences"]), 8):
raise ValueError(f"D2 sequence index mismatch {path}")
expected = (0, 0, 0)
for j, seq in enumerate(meta["sequences"]):
req = seq["seq_id"]
if req in seen:
raise ValueError("duplicate request across shards/splits")
seen[req] = split_name
if not isinstance(seq.get("family"), str) or not seq["family"]:
raise ValueError("D2 request missing task label")
self.request_tasks[req] = seq["family"]
source_docs = seq.get("source_docs")
if not isinstance(source_docs, list) or any(not isinstance(d, str) or not d for d in source_docs):
raise ValueError("D2 request invalid source documents")
if source_docs:
units[req] = [("document", doc) for doc in source_docs]
else:
prompt = seq.get("prompt_sha256")
if not isinstance(prompt, str) or not prompt:
raise ValueError("D2 source-less request missing prompt identity")
units[req] = [("prompt", prompt)]
for doc in source_docs:
if doc in docs and docs[doc] != split_name:
raise ValueError("source-document split leakage")
docs[doc] = split_name
if split_name in ("train", "dev") and seq["family"] in ("chronology", "motion_outline"):
raise ValueError("held-out family in train/dev")
row = index[j].tolist()
tok_off, L, gen, r0, row_off, nr, span_off, ns = row
if not 1 <= gen < L or (L, gen) != (seq["L"], seq["gen_start"]) or r0 != max(0, gen-128) or nr != L-r0 or ns != L-gen:
raise ValueError("D2 sequence offset/length mismatch")
if (tok_off, row_off, span_off) != expected:
raise ValueError("D2 sequence offsets are not cumulative")
tokens = f.get_slice("tokens")[tok_off:tok_off+L]
if tokens.dtype != torch.int32 or tokens.shape != (L,) or bool(((tokens < 0) | (tokens >= 129280)).any()):
raise ValueError("D2 token shape/dtype/vocabulary mismatch")
digest = hashlib.sha256(tokens.contiguous().numpy().astype("<i4", copy=False).tobytes()).hexdigest()
if digest != seq.get("tokens_sha256"):
raise ValueError("D2 per-sequence token hash differs")
rp = f.get_slice("row_pos")[row_off:row_off+nr]
if rp.dtype != torch.int32 or not torch.equal(rp, torch.arange(r0, L, dtype=torch.int32)):
raise ValueError("D2 row positions differ from sequence positions")
expected = tok_off+L, row_off+nr, span_off+ns
if split_name == split and L-gen > 1:
total += L-gen-1
self.ends.append(total)
self.sequences.append((path, row, req))
self.sequence_b.append(split_b)
split_b += 1
shapes = [f.get_slice(n).get_shape()[0] for n in ("tokens", "row_pos", "top_lp")]
if tuple(shapes) != expected or f.get_slice("taps").get_shape() != [expected[1], 15360]:
raise ValueError("D2 aggregate tensor lengths differ from sequence offsets")
self.request_families.update(source_families(split_name, units))
if not total:
raise ValueError(f"empty D2 block split {split}")
self.fingerprint = hashlib.sha256((sha256(manifest)+split).encode()).hexdigest()
def verified(self, relative: str, expected: str) -> Path:
path = (self.root/relative).resolve()
if not path.is_relative_to(self.root) or sha256(path) != expected:
raise ValueError(f"D2 file hash/path mismatch: {relative}")
return path
def __len__(self) -> int:
return self.ends[-1]
def supervised_tokens(self, index: int) -> int:
if index < 0 or index >= len(self):
raise IndexError(index)
seq_i = bisect.bisect_right(self.ends, index)
_, row, _ = self.sequences[seq_i]
_, length, gen = row[:3]
offset = index-(self.ends[seq_i-1] if seq_i else 0)
return min(5, length-(gen-1+offset)-2)
def __getitem__(self, index: int) -> Block:
if index < 0 or index >= len(self):
raise IndexError(index)
seq_i = bisect.bisect_right(self.ends, index)
path, row, req = self.sequences[seq_i]
tok_off, L, gen, r0, row_off, nr, span_off, ns = row
offset = index-(self.ends[seq_i-1] if seq_i else 0)
P = gen-1+offset
first = max(0, P-127)
positions = torch.arange(first, P+1)
with safe_open(path, framework="pt", device="cpu") as f:
tokens = f.get_slice("tokens")[tok_off:tok_off+L].long()
lo, hi = row_off+first-r0, row_off+P-r0+1
taps = f.get_slice("taps")[lo:hi]
actual_pos = f.get_slice("row_pos")[lo:hi].long()
if taps.dtype != torch.bfloat16 or taps.shape != (positions.numel(), 15360) or not torch.equal(positions, actual_pos):
raise ValueError("tap shape/dtype/absolute positions differ")
target_pos = torch.arange(P+2, P+7)
mask = target_pos < L
n = int(mask.sum())
start = span_off+P+2-gen
ids = torch.full((5, 20), -1, dtype=torch.long)
lp = torch.full((5, 20), -torch.inf)
resid = torch.ones(5)
ids[:n] = f.get_slice("top_ids")[start:start+n].long()
lp[:n] = f.get_slice("top_lp")[start:start+n]
resid[:n] = f.get_slice("resid")[start:start+n]
labels = torch.full((5,), -1, dtype=torch.long)
previous = torch.zeros(5, dtype=torch.long)
labels[:n] = tokens[P+2:P+2+n]
previous[:n] = tokens[P+1:P+1+n]
return {"inputs": {"taps": taps[None], "engine_mx": torch.zeros(1, positions.numel(), 5120),
"has_taps": torch.ones(1, positions.numel(), dtype=torch.bool), "positions": positions[None],
"mask": torch.ones(1, positions.numel(), dtype=torch.bool), "anchor": tokens[P+1:P+2],
"P": torch.tensor([P])},
"targets": {"previous": previous[None], "top_ids": ids[None], "top_logp": lp[None],
"tail_mass": resid[None], "labels": labels[None], "present": mask[None],
"position_mask": mask[None]}, "request": req}
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