File size: 6,168 Bytes
20962c9 | 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 | """Lazy prompt-sharded access for large Predictor training datasets."""
from __future__ import annotations
from pathlib import Path
from typing import Any
import torch
from safetensors import safe_open
from torch.utils.data import Dataset
class LazyLayer17Dataset(Dataset):
"""Read only the tensors needed by one Layer-17 sample on demand."""
def __init__(
self,
root: str | Path,
layer_id: int = 17,
include_previous_hidden: bool = True,
) -> None:
self.root = Path(root).resolve()
self.layer_id = int(layer_id)
self.include_previous_hidden = bool(include_previous_hidden)
def __len__(self) -> int:
return 1000
def __getitem__(self, index: tuple[int, int, int]) -> dict[str, Any]:
prompt_id, chunk, target_step = (int(value) for value in index)
if not 0 <= prompt_id < 1000:
raise ValueError(index)
if chunk < 1 or chunk > 6 or target_step < 1 or target_step > 3:
raise ValueError(index)
anchor_step = target_step - 1
prompt_dir = self.root / f"prompt_{prompt_id:04d}"
trajectory_path = prompt_dir / "trajectory.safetensors"
context_path = prompt_dir / "chunk0_context" / "trajectory.safetensors"
with safe_open(trajectory_path, framework="pt", device="cpu") as handle:
prefix = f"chunk_{chunk:02d}_step_{target_step:02d}"
anchor_prefix = f"chunk_{chunk:02d}_step_{anchor_step:02d}"
noisy = handle.get_tensor(f"{prefix}_noisy_latent").squeeze(0)
timestep = handle.get_tensor(f"{prefix}_timestep").squeeze(0)
anchor_timestep = handle.get_tensor(
f"{anchor_prefix}_timestep"
).squeeze(0)
anchor_hidden = handle.get_tensor(
f"{anchor_prefix}_final_hidden"
).squeeze(0)
target_hidden = handle.get_tensor(
f"{prefix}_final_hidden"
).squeeze(0)
target_flow = handle.get_tensor(f"{prefix}_flow").squeeze(0)
if self.include_previous_hidden and chunk > 1:
previous_hidden = handle.get_tensor(
f"chunk_{chunk - 1:02d}_step_{target_step:02d}_final_hidden"
).squeeze(0)
else:
previous_hidden = None
if self.include_previous_hidden and previous_hidden is None:
with safe_open(context_path, framework="pt", device="cpu") as handle:
previous_hidden = handle.get_tensor(
f"chunk_00_step_{target_step:02d}_final_hidden"
).squeeze(0)
elif not self.include_previous_hidden:
# Keep the common batch interface while the DisCa model physically
# omits this channel from its fusion layer.
previous_hidden = torch.zeros_like(anchor_hidden)
prefeature_path = (
prompt_dir
/ "clean_prefeatures"
/ f"block_{self.layer_id:02d}.safetensors"
)
context_prefeature_path = (
prompt_dir
/ "chunk0_context"
/ "clean_prefeatures"
/ f"block_{self.layer_id:02d}.safetensors"
)
history_parts = []
with safe_open(
context_prefeature_path, framework="pt", device="cpu"
) as context_handle, safe_open(
prefeature_path, framework="pt", device="cpu"
) as handle:
for history_chunk in range(chunk):
source = context_handle if history_chunk == 0 else handle
history_parts.append(
source.get_tensor(f"chunk_{history_chunk:02d}").squeeze(0)
)
history_prefeature = torch.cat(history_parts, dim=0).contiguous()
cross_path = prompt_dir / "cross_attention.safetensors"
with safe_open(cross_path, framework="pt", device="cpu") as handle:
cross_k = handle.get_tensor(
f"block_{self.layer_id:02d}_k"
).squeeze(0)
cross_v = handle.get_tensor(
f"block_{self.layer_id:02d}_v"
).squeeze(0)
return {
"prompt_id": prompt_id,
"chunk": chunk,
"target_step": target_step,
"noisy_latent": noisy,
"timestep": timestep,
"anchor_timestep": anchor_timestep,
"anchor_distance": (
timestep.float() - anchor_timestep.float()
).abs().mean(),
"anchor_hidden": anchor_hidden,
"previous_hidden": previous_hidden,
"target_hidden": target_hidden,
"target_flow": target_flow,
"history_prefeature": history_prefeature,
"cross_k": cross_k,
"cross_v": cross_v,
}
def collate_lazy_samples(samples: list[dict[str, Any]]) -> dict[str, Any]:
if not samples:
raise ValueError("Empty batch")
chunks = {sample["chunk"] for sample in samples}
target_steps = {sample["target_step"] for sample in samples}
if len(chunks) != 1 or len(target_steps) != 1:
raise ValueError("A batch must share chunk and target_step")
return {
"prompt_ids": [sample["prompt_id"] for sample in samples],
"chunk": samples[0]["chunk"],
"anchor_step": samples[0]["target_step"] - 1,
"target_step": samples[0]["target_step"],
**{
key: torch.stack([sample[key] for sample in samples])
for key in (
"noisy_latent",
"timestep",
"anchor_timestep",
"anchor_distance",
"anchor_hidden",
"previous_hidden",
"target_hidden",
"target_flow",
"history_prefeature",
"cross_k",
"cross_v",
)
},
}
class ScheduledBatchSampler:
def __init__(self, batches: list[list[tuple[int, int, int]]]) -> None:
self.batches = batches
def __iter__(self):
return iter(self.batches)
def __len__(self) -> int:
return len(self.batches)
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