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ba3bbc7 | 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 | """Stage-1 vision projector training for CORTEX AI.
Trains *only* ``VisionProjector`` on top of a frozen real vision encoder and a frozen small
causal LM::
IMAGE -> real vision encoder -> VisionProjector -> PrefixFusion -> frozen causal LM -> logits
Nothing here touches the DeepSeek-V4-Pro shards: the ``model-*-of-*.safetensors`` files are only
read to hash their LFS pointers, and the checkpoint written by this script is a separate file.
Examples::
python examples/train_vision_projector.py --config configs/vision_projector.json
python examples/train_vision_projector.py --max-steps 20 --batch-size 2
"""
from __future__ import annotations
import argparse
import json
import sys
from dataclasses import asdict, fields
from pathlib import Path
import torch
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from cortex_ai.vision.train import ( # noqa: E402
ProjectorTrainingConfig,
build_fusion_pipeline,
count_parameters,
iterate_caption_batches,
run_training,
save_projector,
)
def load_dataset(config: ProjectorTrainingConfig) -> tuple[list, list[str]]:
"""Load real images and captions from the configured Hub dataset."""
from datasets import load_dataset as hf_load_dataset
ds = hf_load_dataset(config.dataset, split=config.dataset_split, streaming=True)
images, captions = [], []
limit = config.limit or 0
for example in ds:
image = example.get("jpg") or example.get("image")
text = example.get("txt") or example.get("caption") or example.get("text")
if image is None or not text:
continue
if isinstance(text, list):
text = text[0]
images.append(image.convert("RGB"))
captions.append(str(text))
if limit and len(images) >= limit:
break
if not images:
raise SystemExit(f"no usable image/caption pairs found in {config.dataset}")
return images, captions
def parse_args(argv: list[str] | None = None) -> ProjectorTrainingConfig:
p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--config", help="JSON file of ProjectorTrainingConfig fields")
defaults = ProjectorTrainingConfig()
for f in fields(defaults):
value = getattr(defaults, f.name)
if isinstance(value, bool):
p.add_argument(f"--{f.name.replace('_', '-')}", type=lambda s: s.lower() == "true", default=None)
else:
p.add_argument(
f"--{f.name.replace('_', '-')}",
type=type(value) if value is not None else str,
default=None,
)
args = p.parse_args(argv)
values = {}
if args.config:
values.update(json.loads(Path(args.config).read_text()))
for f in fields(defaults):
v = getattr(args, f.name.replace("-", "_"))
if v is not None:
values[f.name] = v
return ProjectorTrainingConfig(**values)
def main(argv: list[str] | None = None) -> int:
config = parse_args(argv)
torch.manual_seed(config.seed)
print("=" * 78)
print("CORTEX AI - stage-1 vision projector training (experimental)")
print("=" * 78)
print(f"config : {json.dumps(asdict(config), indent=2)}")
from transformers import AutoImageProcessor, AutoModelForCausalLM, AutoTokenizer, SiglipVisionModel
dtype = config.resolved_dtype()
device = config.resolved_device()
print(f"\n[1] loading real vision encoder : {config.vision_model}")
image_processor = AutoImageProcessor.from_pretrained(config.vision_model)
vision_encoder = SiglipVisionModel.from_pretrained(config.vision_model).to(dtype=dtype, device=device)
print(f"[2] loading real frozen LM : {config.language_model}")
core = AutoModelForCausalLM.from_pretrained(config.language_model, dtype=dtype).to(device)
tokenizer = AutoTokenizer.from_pretrained(config.language_model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("[3] assembling fusion pipeline (encoder + LM frozen, projector trainable)")
fusion, vision_hidden, lm_hidden = build_fusion_pipeline(
vision_encoder, core, config, image_processor=image_processor
)
projector_params = count_parameters(fusion.projector)
print(f" vision_hidden_size = {vision_hidden}")
print(f" lm_hidden_size = {lm_hidden}")
print(f" projector_type = {config.projector_type}")
print(f" projector_parameters = {projector_params:,}")
print(f" dtype / device = {config.dtype} / {config.device}")
print(f"\n[4] loading real dataset : {config.dataset} (split={config.dataset_split})")
images, captions = load_dataset(config)
print(f" {len(images)} image/caption pairs")
print(f" sample caption: {captions[0][:70]!r}")
batches = iterate_caption_batches(images, captions, tokenizer, image_processor, config)
def on_step(step: int, loss: float) -> None:
if step == 1 or step % config.log_every == 0:
print(f" step {step:4d} loss={loss:.4f}")
print(f"\n[5] training projector for up to {config.max_steps} steps (projector only)")
report = run_training(
fusion, batches, config, projector_params, vision_hidden, lm_hidden, on_step=on_step
)
path = save_projector(fusion.projector, config, report)
report.checkpoint_path = str(path)
print("\n" + "=" * 78)
print("RESULT")
print("=" * 78)
print(report.to_json())
print(f"\ncheckpoint written: {path}")
print("\nPROVEN by this run:")
print(" - real image -> real encoder -> projector -> PrefixFusion -> frozen LM executed")
print(" - projector received gradients and its parameters changed")
print(" - vision encoder and causal LM parameters are unchanged")
print("\nNOT PROVEN:")
print(" - that this projector works with the real DeepSeek-V4-Pro core")
print(" - native multimodal understanding of the 1.65T checkpoint")
return 0
if __name__ == "__main__":
raise SystemExit(main()) |