File size: 8,065 Bytes
5f0e4a2 | 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 | """Atomic writer for fresh Predictor-v2 step targets and four-layer context KV."""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Any, Mapping
import torch
from safetensors.torch import save_file
from .v2_schema import (
CONTEXT_BLOCK_IDS,
DEFAULT_CONTEXT_KV_FRAMES,
DEFAULT_TEXT_KV_TOKENS,
SCHEMA_VERSION_V2,
TOKENS_PER_FRAME,
validate_case_tensors_v2,
validate_step_tensors_v2,
validate_vision_context_v2,
)
def _convert(tensor: torch.Tensor, *, timestep: bool = False) -> torch.Tensor:
tensor = tensor.detach()
if timestep:
tensor = tensor.to(dtype=torch.float32)
elif tensor.is_floating_point():
tensor = tensor.to(dtype=torch.bfloat16)
return tensor.to(device="cpu").contiguous()
class PredictorV2DatasetWriter:
def __init__(
self,
root: str | os.PathLike[str],
*,
text_kv_tokens: int = DEFAULT_TEXT_KV_TOKENS,
context_kv_frames: int = DEFAULT_CONTEXT_KV_FRAMES,
) -> None:
self.root = Path(root).resolve()
self.text_kv_tokens = int(text_kv_tokens)
self.context_kv_frames = int(context_kv_frames)
if self.text_kv_tokens <= 0 or self.context_kv_frames < 0:
raise ValueError("Invalid padded KV sizes")
self.case_dir = self.root / "cases"
self.step_dir = self.root / "steps"
self.context_dir = self.root / "context"
self.manifest_path = self.root / "manifest.jsonl"
self.train_eval_manifest_path = self.root / "train_eval_manifest.jsonl"
for path in (self.case_dir, self.step_dir, self.context_dir):
path.mkdir(parents=True, exist_ok=True)
self._manifest_keys = self._load_existing_keys()
def _load_existing_keys(self) -> set[tuple[int, int, int]]:
keys: set[tuple[int, int, int]] = set()
if not self.manifest_path.exists():
return keys
with self.manifest_path.open("r", encoding="utf-8") as handle:
for line in handle:
if line.strip():
item = json.loads(line)
keys.add((int(item["case_id"]), int(item["seed"]), int(item["chunk_id"])))
return keys
def case_path(self, case_id: int) -> Path:
return self.case_dir / f"case_{case_id:02d}.safetensors"
def step_path(self, case_id: int, seed: int, chunk_id: int) -> Path:
return self.step_dir / f"case_{case_id:02d}_seed_{seed}" / f"chunk_{chunk_id:02d}.safetensors"
def context_path(self, block_id: int, case_id: int, seed: int, chunk_id: int) -> Path:
return (
self.context_dir
/ f"block_{block_id:02d}"
/ f"case_{case_id:02d}_seed_{seed}"
/ f"chunk_{chunk_id:02d}.safetensors"
)
def is_chunk_complete(self, case_id: int, seed: int, chunk_id: int) -> bool:
key = (case_id, seed, chunk_id)
return (
key in self._manifest_keys
and self.step_path(*key).is_file()
and all(self.context_path(block_id, *key).is_file() for block_id in CONTEXT_BLOCK_IDS)
)
@staticmethod
def _atomic_save(path: Path, tensors: Mapping[str, torch.Tensor]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
tmp = path.with_suffix(path.suffix + f".tmp.{os.getpid()}")
save_file(dict(tensors), str(tmp))
os.replace(tmp, path)
@staticmethod
def _pad_sequence(
tensor: torch.Tensor,
target_tokens: int,
*,
name: str,
) -> torch.Tensor:
valid_tokens = int(tensor.shape[2])
if valid_tokens > target_tokens:
raise ValueError(
f"{name} has {valid_tokens} tokens, exceeding padded size {target_tokens}"
)
if valid_tokens == target_tokens:
return tensor
padded = tensor.new_zeros((*tensor.shape[:2], target_tokens, tensor.shape[3]))
padded[:, :, :valid_tokens].copy_(tensor)
return padded
def save_case(self, case_id: int, tensors: Mapping[str, torch.Tensor]) -> Path:
converted = {name: _convert(tensor) for name, tensor in tensors.items()}
first_key = f"block_{CONTEXT_BLOCK_IDS[0]:02d}_k_txt"
valid_tokens = int(converted[first_key].shape[2])
converted["text_valid_mask"] = (
torch.arange(self.text_kv_tokens)[None] < valid_tokens
)
for block_id in CONTEXT_BLOCK_IDS:
for field in ("k_txt", "v_txt"):
name = f"block_{block_id:02d}_{field}"
converted[name] = self._pad_sequence(
converted[name], self.text_kv_tokens, name=name
)
validate_case_tensors_v2(converted)
path = self.case_path(case_id)
self._atomic_save(path, converted)
return path
def save_chunk(
self,
*,
case_id: int,
seed: int,
chunk_id: int,
step_tensors: Mapping[str, torch.Tensor],
context_by_block: Mapping[int, Mapping[str, torch.Tensor]],
metadata: Mapping[str, Any],
) -> Path:
key = (case_id, seed, chunk_id)
if self.is_chunk_complete(*key):
return self.step_path(*key)
converted_steps = {
name: _convert(tensor, timestep="timestep" in name)
for name, tensor in step_tensors.items()
}
validate_step_tensors_v2(converted_steps)
context_frames = None
converted_context: dict[int, dict[str, torch.Tensor]] = {}
for block_id in CONTEXT_BLOCK_IDS:
if block_id not in context_by_block:
raise ValueError(f"Missing context block {block_id}")
cache = {name: _convert(tensor) for name, tensor in context_by_block[block_id].items()}
valid_tokens = int(cache["k_vision"].shape[2])
target_tokens = self.context_kv_frames * TOKENS_PER_FRAME
cache["context_valid_mask"] = (
torch.arange(target_tokens)[None] < valid_tokens
)
for field in ("k_vision", "v_vision"):
cache[field] = self._pad_sequence(
cache[field], target_tokens, name=f"block_{block_id}_{field}"
)
frames = validate_vision_context_v2(block_id, cache)
if context_frames is None:
context_frames = frames
elif frames != context_frames:
raise ValueError("Context frame count differs between blocks")
converted_context[block_id] = cache
step_path = self.step_path(*key)
self._atomic_save(step_path, converted_steps)
context_files: dict[str, str] = {}
for block_id, cache in converted_context.items():
path = self.context_path(block_id, *key)
self._atomic_save(path, cache)
context_files[str(block_id)] = str(path.relative_to(self.root))
record = {
"schema_version": SCHEMA_VERSION_V2,
"case_id": case_id,
"seed": seed,
"chunk_id": chunk_id,
"step_tensor_file": str(step_path.relative_to(self.root)),
"case_tensor_file": str(self.case_path(case_id).relative_to(self.root)),
"context_tensor_files": context_files,
"context_block_ids": list(CONTEXT_BLOCK_IDS),
"context_frames": int(context_frames or 0),
"text_kv_tokens": self.text_kv_tokens,
"context_kv_tokens": self.context_kv_frames * TOKENS_PER_FRAME,
"padding_masks": True,
**dict(metadata),
}
line = json.dumps(record, ensure_ascii=False, sort_keys=True) + "\n"
for manifest in (self.manifest_path, self.train_eval_manifest_path):
with manifest.open("a", encoding="utf-8") as handle:
handle.write(line)
handle.flush()
os.fsync(handle.fileno())
self._manifest_keys.add(key)
return step_path
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