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ae8ade0 | 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 | #!/usr/bin/env python3
"""Add the minimal chunk-0 context required by Stage-1 Predictor training."""
from __future__ import annotations
import argparse
import json
import os
import shutil
import time
from pathlib import Path
import build_predictor_offline_data as base
torch = base.torch
OmegaConf = base.OmegaConf
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--gpu", default=base.PHYSICAL_GPU)
parser.add_argument("--dataset_root", type=Path, required=True)
parser.add_argument(
"--config_path",
type=Path,
default=Path("configs/causal_forcing_dmd_chunkwise.yaml"),
)
parser.add_argument(
"--checkpoint_path",
type=Path,
default=Path("checkpoints/chunkwise/causal_forcing.pt"),
)
parser.add_argument("--prompt_ids", type=int, nargs="*", default=None)
parser.add_argument("--generation_seed", type=int, default=0)
parser.add_argument("--max_new_prompts", type=int, default=None)
parser.add_argument("--min_free_gib", type=float, default=500.0)
return parser.parse_args()
@torch.inference_mode()
def generate_chunk0(
pipeline,
recorder: base.TrajectoryRecorder,
prompt: str,
generation_seed: int,
device: torch.device,
):
base.set_seed(generation_seed)
base.reset_caches(pipeline, 1, torch.bfloat16, device)
recorder.clean_prefeatures = {layer: [] for layer in recorder.layers}
conditional = pipeline.text_encoder(text_prompts=[prompt])
# Draw the full 21-latent noise tensor so the RNG state and chunk-0 slice
# exactly match the original offline rollout.
full_noise = torch.randn(
1, 21, base.LATENT_CHANNELS, base.LATENT_HEIGHT, base.LATENT_WIDTH,
dtype=torch.bfloat16, device=device,
)
noisy_input = full_noise[:, : pipeline.num_frame_per_block]
timesteps = pipeline.denoising_step_list.to(device=device)
trajectory = {}
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
started = time.perf_counter()
timestep = None
denoised_pred = None
for step, current_timestep in enumerate(timesteps):
timestep = torch.ones(
[1, pipeline.num_frame_per_block], device=device, dtype=torch.int64
) * current_timestep
recorder.start_denoising_step()
_, denoised_pred = pipeline.generator(
noisy_image_or_video=noisy_input,
conditional_dict=conditional,
timestep=timestep,
kv_cache=pipeline.kv_cache1,
crossattn_cache=pipeline.crossattn_cache,
current_start=0,
)
trajectory[f"chunk_00_step_{step:02d}_final_hidden"] = (
recorder.finish_denoising_step()
)
if step < len(timesteps) - 1:
next_timestep = timesteps[step + 1]
flat = denoised_pred.flatten(0, 1)
noisy_input = pipeline.scheduler.add_noise(
flat,
torch.randn_like(flat),
next_timestep * torch.ones(
[pipeline.num_frame_per_block],
device=device,
dtype=torch.long,
),
).unflatten(0, denoised_pred.shape[:2])
if denoised_pred is None or timestep is None:
raise RuntimeError("Chunk-0 denoising produced no output")
recorder.start_clean_pass()
pipeline.generator(
noisy_image_or_video=denoised_pred,
conditional_dict=conditional,
timestep=torch.ones_like(timestep) * pipeline.args.context_noise,
kv_cache=pipeline.kv_cache1,
crossattn_cache=pipeline.crossattn_cache,
current_start=0,
)
recorder.finish_clean_pass()
torch.cuda.synchronize()
return (
trajectory,
recorder.clean_prefeatures,
time.perf_counter() - started,
torch.cuda.max_memory_allocated() / 1024**3,
)
def save_context(prompt_dir, trajectory, prefeatures, elapsed_s, peak_gib):
destination = prompt_dir / "chunk0_context"
partial = prompt_dir / "chunk0_context.partial"
if partial.exists():
shutil.rmtree(partial)
partial.mkdir(parents=True)
metadata = {"dataset_version": "2", "kind": "chunk0_context"}
base.atomic_save_safetensors(
trajectory, partial / "trajectory.safetensors", metadata
)
for layer in range(30):
values = prefeatures[layer]
if len(values) != 1:
raise RuntimeError(f"Layer {layer} captured {len(values)} values")
base.atomic_save_safetensors(
{"chunk_00": values[0]},
partial / "clean_prefeatures" / f"block_{layer:02d}.safetensors",
{**metadata, "block_id": str(layer)},
)
base.atomic_write_json(
partial / "metadata.json",
{
"kind": "context_only",
"chunk": 0,
"is_training_target": False,
"hidden_steps": [0, 1, 2, 3],
"layers": list(range(30)),
"elapsed_s": elapsed_s,
"peak_gpu_gib": peak_gib,
},
)
(partial / "_SUCCESS").write_text("ok\n", encoding="utf-8")
if destination.exists():
shutil.rmtree(destination)
os.replace(partial, destination)
return sum(p.stat().st_size for p in destination.rglob("*") if p.is_file())
def main() -> None:
args = parse_args()
root = args.dataset_root.resolve()
config_path = base.resolve_path(args.config_path)
checkpoint_path = base.resolve_path(args.checkpoint_path)
selected = json.loads((root / "prompt_selection.json").read_text())["prompts"]
requested = (
set(range(len(selected)))
if args.prompt_ids is None
else set(args.prompt_ids)
)
pending = [
(index, item)
for index, item in enumerate(selected)
if index in requested
and not (root / f"prompt_{index:04d}" / "chunk0_context" / "_SUCCESS").exists()
]
if not pending:
print("[context] all requested prompt contexts exist", flush=True)
return
config = OmegaConf.merge(
OmegaConf.load(base.REPO_ROOT / "configs/default_config.yaml"),
OmegaConf.load(config_path),
)
device = torch.device("cuda")
pipeline = base.build_pipeline(config, checkpoint_path, device)
recorder = base.TrajectoryRecorder(pipeline.generator.model, list(range(30)))
generated = 0
try:
for index, selection in pending:
if args.max_new_prompts is not None and generated >= args.max_new_prompts:
break
free_gib = shutil.disk_usage(root).free / 1024**3
if free_gib < args.min_free_gib:
raise RuntimeError(f"Only {free_gib:.1f} GiB free")
trajectory, prefeatures, elapsed_s, peak_gib = generate_chunk0(
pipeline, recorder, selection["prompt"], args.generation_seed, device
)
size = save_context(
root / f"prompt_{index:04d}", trajectory, prefeatures,
elapsed_s, peak_gib,
)
generated += 1
print(
f"[context] saved prompt_{index:04d}: {size / 1024**3:.3f} GiB, "
f"{elapsed_s:.1f}s, peak={peak_gib:.1f} GiB",
flush=True,
)
del trajectory, prefeatures
torch.cuda.empty_cache()
finally:
recorder.close()
print(f"[context] generated {generated} prompt contexts", flush=True)
if __name__ == "__main__":
main()
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