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feat(ml): add more search models (#11468)

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* update export code

* add uuid glob, sort model names

* add new models to ml, sort names

* add new models to server, sort by dims and name

* typo in name

* update export dependencies

* onnx save function

* format
This commit is contained in:
Mert 2024-07-31 00:34:45 -04:00 committed by GitHub
parent 2423bb36c4
commit 41580696c7
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9 changed files with 3804 additions and 2923 deletions
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4
machine-learning/ann/ann.py
View file

@ -65,7 +65,7 @@ class Ann(metaclass=_Singleton):
self.input_shapes: dict[int, tuple[tuple[int], ...]] = {} self.input_shapes: dict[int, tuple[tuple[int], ...]] = {}
self.ann: int | None = None self.ann: int | None = None
self.new() self.new()
if self.tuning_file is not None: if self.tuning_file is not None:
# make sure tuning file exists (without clearing contents) # make sure tuning file exists (without clearing contents)
# once filled, the tuning file reduces the cost/time of the first # once filled, the tuning file reduces the cost/time of the first
@ -105,7 +105,7 @@ class Ann(metaclass=_Singleton):
raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx") raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx")
if not exists(model_path): if not exists(model_path):
raise ValueError("model_path must point to an existing file!") raise ValueError("model_path must point to an existing file!")
save_cached_network = False save_cached_network = False
if cached_network_path is not None and not exists(cached_network_path): if cached_network_path is not None and not exists(cached_network_path):
save_cached_network = True save_cached_network = True

53
machine-learning/app/models/constants.py
View file

@ -2,53 +2,64 @@ from app.config import clean_name
from app.schemas import ModelSource from app.schemas import ModelSource
_OPENCLIP_MODELS = { _OPENCLIP_MODELS = {
"RN50__openai",
"RN50__yfcc15m",
"RN50__cc12m",
"RN101__openai", "RN101__openai",
"RN101__yfcc15m", "RN101__yfcc15m",
"RN50x4__openai", "RN50__cc12m",
"RN50__openai",
"RN50__yfcc15m",
"RN50x16__openai", "RN50x16__openai",
"RN50x4__openai",
"RN50x64__openai", "RN50x64__openai",
"ViT-B-32__openai", "ViT-B-16-SigLIP-256__webli",
"ViT-B-16-SigLIP-384__webli",
"ViT-B-16-SigLIP-512__webli",
"ViT-B-16-SigLIP-i18n-256__webli",
"ViT-B-16-SigLIP__webli",
"ViT-B-16-plus-240__laion400m_e31",
"ViT-B-16-plus-240__laion400m_e32",
"ViT-B-16__laion400m_e31",
"ViT-B-16__laion400m_e32",
"ViT-B-16__openai",
"ViT-B-32__laion2b-s34b-b79k",
"ViT-B-32__laion2b_e16", "ViT-B-32__laion2b_e16",
"ViT-B-32__laion400m_e31", "ViT-B-32__laion400m_e31",
"ViT-B-32__laion400m_e32", "ViT-B-32__laion400m_e32",
"ViT-B-32__laion2b-s34b-b79k", "ViT-B-32__openai",
"ViT-B-16__openai", "ViT-H-14-378-quickgelu__dfn5b",
"ViT-B-16__laion400m_e31", "ViT-H-14-quickgelu__dfn5b",
"ViT-B-16__laion400m_e32", "ViT-H-14__laion2b-s32b-b79k",
"ViT-B-16-plus-240__laion400m_e31", "ViT-L-14-336__openai",
"ViT-B-16-plus-240__laion400m_e32", "ViT-L-14-quickgelu__dfn2b",
"ViT-L-14__openai", "ViT-L-14__laion2b-s32b-b82k",
"ViT-L-14__laion400m_e31", "ViT-L-14__laion400m_e31",
"ViT-L-14__laion400m_e32", "ViT-L-14__laion400m_e32",
"ViT-L-14__laion2b-s32b-b82k", "ViT-L-14__openai",
"ViT-L-14-336__openai", "ViT-L-16-SigLIP-256__webli",
"ViT-H-14__laion2b-s32b-b79k", "ViT-L-16-SigLIP-384__webli",
"ViT-SO400M-14-SigLIP-384__webli",
"ViT-g-14__laion2b-s12b-b42k", "ViT-g-14__laion2b-s12b-b42k",
"ViT-L-14-quickgelu__dfn2b", "XLM-Roberta-Base-ViT-B-32__laion5b_s13b_b90k",
"ViT-H-14-quickgelu__dfn5b",
"ViT-H-14-378-quickgelu__dfn5b",
"XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k", "XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k",
"nllb-clip-base-siglip__mrl",
"nllb-clip-base-siglip__v1", "nllb-clip-base-siglip__v1",
"nllb-clip-large-siglip__mrl",
"nllb-clip-large-siglip__v1", "nllb-clip-large-siglip__v1",
} }
_MCLIP_MODELS = { _MCLIP_MODELS = {
"LABSE-Vit-L-14", "LABSE-Vit-L-14",
"XLM-Roberta-Large-Vit-B-32",
"XLM-Roberta-Large-Vit-B-16Plus", "XLM-Roberta-Large-Vit-B-16Plus",
"XLM-Roberta-Large-Vit-B-32",
"XLM-Roberta-Large-Vit-L-14", "XLM-Roberta-Large-Vit-L-14",
} }
_INSIGHTFACE_MODELS = { _INSIGHTFACE_MODELS = {
"antelopev2", "antelopev2",
"buffalo_l",
"buffalo_m",
"buffalo_s", "buffalo_s",
"buffalo_m",
"buffalo_l",
} }

6426
machine-learning/export/conda-lock.yml
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File diff suppressed because it is too large Load diff

6
machine-learning/export/env.yaml
View file

@ -2,7 +2,7 @@ name: base
channels: channels:
- conda-forge - conda-forge
- nvidia - nvidia
- pytorch-nightly - pytorch
platforms: platforms:
- linux-64 - linux-64
dependencies: dependencies:
@ -13,7 +13,7 @@ dependencies:
- orjson==3.* - orjson==3.*
- pip - pip
- python==3.11.* - python==3.11.*
- pytorch - pytorch>=2.3
- rich==13.* - rich==13.*
- safetensors==0.* - safetensors==0.*
- setuptools==68.* - setuptools==68.*
@ -21,5 +21,5 @@ dependencies:
- transformers==4.* - transformers==4.*
- pip: - pip:
- multilingual-clip - multilingual-clip
- onnx-simplifier - onnxsim
category: main category: main

22
machine-learning/export/models/mclip.py
View file

@ -1,3 +1,4 @@
import os
import tempfile import tempfile
import warnings import warnings
from pathlib import Path from pathlib import Path
@ -8,7 +9,6 @@ from transformers import AutoTokenizer
from .openclip import OpenCLIPModelConfig from .openclip import OpenCLIPModelConfig
from .openclip import to_onnx as openclip_to_onnx from .openclip import to_onnx as openclip_to_onnx
from .optimize import optimize
from .util import get_model_path from .util import get_model_path
_MCLIP_TO_OPENCLIP = { _MCLIP_TO_OPENCLIP = {
@ -23,18 +23,20 @@ def to_onnx(
model_name: str, model_name: str,
output_dir_visual: Path | str, output_dir_visual: Path | str,
output_dir_textual: Path | str, output_dir_textual: Path | str,
) -> None: ) -> tuple[Path, Path]:
textual_path = get_model_path(output_dir_textual) textual_path = get_model_path(output_dir_textual)
with tempfile.TemporaryDirectory() as tmpdir: with tempfile.TemporaryDirectory() as tmpdir:
model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir) model = MultilingualCLIP.from_pretrained(model_name, cache_dir=os.environ.get("CACHE_DIR", tmpdir))
AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual) AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
model.eval()
for param in model.parameters(): for param in model.parameters():
param.requires_grad_(False) param.requires_grad_(False)
export_text_encoder(model, textual_path) export_text_encoder(model, textual_path)
openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual) visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual)
optimize(textual_path) assert visual_path is not None, "Visual model export failed"
return visual_path, textual_path
def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None: def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None:
@ -58,10 +60,10 @@ def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> Non
args, args,
output_path.as_posix(), output_path.as_posix(),
input_names=["input_ids", "attention_mask"], input_names=["input_ids", "attention_mask"],
output_names=["text_embedding"], output_names=["embedding"],
opset_version=17, opset_version=17,
dynamic_axes={ # dynamic_axes={
"input_ids": {0: "batch_size", 1: "sequence_length"}, # "input_ids": {0: "batch_size", 1: "sequence_length"},
"attention_mask": {0: "batch_size", 1: "sequence_length"}, # "attention_mask": {0: "batch_size", 1: "sequence_length"},
}, # },
) )

23
machine-learning/export/models/openclip.py
View file

@ -1,3 +1,4 @@
import os
import tempfile import tempfile
import warnings import warnings
from dataclasses import dataclass, field from dataclasses import dataclass, field
@ -7,7 +8,6 @@ import open_clip
import torch import torch
from transformers import AutoTokenizer from transformers import AutoTokenizer
from .optimize import optimize
from .util import get_model_path, save_config from .util import get_model_path, save_config
@ -23,25 +23,28 @@ class OpenCLIPModelConfig:
if open_clip_cfg is None: if open_clip_cfg is None:
raise ValueError(f"Unknown model {self.name}") raise ValueError(f"Unknown model {self.name}")
self.image_size = open_clip_cfg["vision_cfg"]["image_size"] self.image_size = open_clip_cfg["vision_cfg"]["image_size"]
self.sequence_length = open_clip_cfg["text_cfg"]["context_length"] self.sequence_length = open_clip_cfg["text_cfg"].get("context_length", 77)
def to_onnx( def to_onnx(
model_cfg: OpenCLIPModelConfig, model_cfg: OpenCLIPModelConfig,
output_dir_visual: Path | str | None = None, output_dir_visual: Path | str | None = None,
output_dir_textual: Path | str | None = None, output_dir_textual: Path | str | None = None,
) -> None: ) -> tuple[Path | None, Path | None]:
visual_path = None
textual_path = None
with tempfile.TemporaryDirectory() as tmpdir: with tempfile.TemporaryDirectory() as tmpdir:
model = open_clip.create_model( model = open_clip.create_model(
model_cfg.name, model_cfg.name,
pretrained=model_cfg.pretrained, pretrained=model_cfg.pretrained,
jit=False, jit=False,
cache_dir=tmpdir, cache_dir=os.environ.get("CACHE_DIR", tmpdir),
require_pretrained=True, require_pretrained=True,
) )
text_vision_cfg = open_clip.get_model_config(model_cfg.name) text_vision_cfg = open_clip.get_model_config(model_cfg.name)
model.eval()
for param in model.parameters(): for param in model.parameters():
param.requires_grad_(False) param.requires_grad_(False)
@ -53,8 +56,6 @@ def to_onnx(
save_config(text_vision_cfg, output_dir_visual.parent / "config.json") save_config(text_vision_cfg, output_dir_visual.parent / "config.json")
export_image_encoder(model, model_cfg, visual_path) export_image_encoder(model, model_cfg, visual_path)
optimize(visual_path)
if output_dir_textual is not None: if output_dir_textual is not None:
output_dir_textual = Path(output_dir_textual) output_dir_textual = Path(output_dir_textual)
textual_path = get_model_path(output_dir_textual) textual_path = get_model_path(output_dir_textual)
@ -62,7 +63,7 @@ def to_onnx(
tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32") tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32")
AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual) AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual)
export_text_encoder(model, model_cfg, textual_path) export_text_encoder(model, model_cfg, textual_path)
optimize(textual_path) return visual_path, textual_path
def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None: def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None:
@ -83,9 +84,9 @@ def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig,
args, args,
output_path.as_posix(), output_path.as_posix(),
input_names=["image"], input_names=["image"],
output_names=["image_embedding"], output_names=["embedding"],
opset_version=17, opset_version=17,
dynamic_axes={"image": {0: "batch_size"}}, # dynamic_axes={"image": {0: "batch_size"}},
) )
@ -107,7 +108,7 @@ def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, o
args, args,
output_path.as_posix(), output_path.as_posix(),
input_names=["text"], input_names=["text"],
output_names=["text_embedding"], output_names=["embedding"],
opset_version=17, opset_version=17,
dynamic_axes={"text": {0: "batch_size"}}, # dynamic_axes={"text": {0: "batch_size"}},
) )

21
machine-learning/export/models/optimize.py
View file

@ -5,13 +5,26 @@ import onnxruntime as ort
import onnxsim import onnxsim
def save_onnx(model: onnx.ModelProto, output_path: Path | str) -> None:
try:
onnx.save(model, output_path)
except ValueError as e:
if "The proto size is larger than the 2 GB limit." in str(e):
onnx.save(model, output_path, save_as_external_data=True, size_threshold=1_000_000)
else:
raise e
def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None: def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None:
model_path = Path(model_path) model_path = Path(model_path)
output_path = Path(output_path) output_path = Path(output_path)
model = onnx.load(model_path.as_posix()) model = onnx.load(model_path.as_posix())
model, check = onnxsim.simplify(model, skip_shape_inference=True) model, check = onnxsim.simplify(model)
assert check, "Simplified ONNX model could not be validated" assert check, "Simplified ONNX model could not be validated"
onnx.save(model, output_path.as_posix()) for file in model_path.parent.iterdir():
if file.name.startswith("Constant") or "onnx" in file.name or file.suffix == ".weight":
file.unlink()
save_onnx(model, output_path)
def optimize_ort( def optimize_ort(
@ -33,6 +46,4 @@ def optimize(model_path: Path | str) -> None:
model_path = Path(model_path) model_path = Path(model_path)
optimize_ort(model_path, model_path) optimize_ort(model_path, model_path)
# onnxsim serializes large models as a blob, which uses much more memory when loading the model at runtime optimize_onnxsim(model_path, model_path)
if not any(file.name.startswith("Constant") for file in model_path.parent.iterdir()):
optimize_onnxsim(model_path, model_path)

115
machine-learning/export/run.py
View file

@ -3,74 +3,111 @@ import os
from pathlib import Path from pathlib import Path
from tempfile import TemporaryDirectory from tempfile import TemporaryDirectory
from huggingface_hub import create_repo, login, upload_folder import torch
from huggingface_hub import create_repo, upload_folder
from models import mclip, openclip from models import mclip, openclip
from models.optimize import optimize
from rich.progress import Progress from rich.progress import Progress
models = [ models = [
"RN50::openai", "M-CLIP/LABSE-Vit-L-14",
"RN50::yfcc15m", "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus",
"RN50::cc12m", "M-CLIP/XLM-Roberta-Large-Vit-B-32",
"M-CLIP/XLM-Roberta-Large-Vit-L-14",
"RN101::openai", "RN101::openai",
"RN101::yfcc15m", "RN101::yfcc15m",
"RN50x4::openai", "RN50::cc12m",
"RN50::openai",
"RN50::yfcc15m",
"RN50x16::openai", "RN50x16::openai",
"RN50x4::openai",
"RN50x64::openai", "RN50x64::openai",
"ViT-B-32::openai", "ViT-B-16-SigLIP-256::webli",
"ViT-B-16-SigLIP-384::webli",
"ViT-B-16-SigLIP-512::webli",
"ViT-B-16-SigLIP-i18n-256::webli",
"ViT-B-16-SigLIP::webli",
"ViT-B-16-plus-240::laion400m_e31",
"ViT-B-16-plus-240::laion400m_e32",
"ViT-B-16::laion400m_e31",
"ViT-B-16::laion400m_e32",
"ViT-B-16::openai",
"ViT-B-32::laion2b-s34b-b79k",
"ViT-B-32::laion2b_e16", "ViT-B-32::laion2b_e16",
"ViT-B-32::laion400m_e31", "ViT-B-32::laion400m_e31",
"ViT-B-32::laion400m_e32", "ViT-B-32::laion400m_e32",
"ViT-B-32::laion2b-s34b-b79k", "ViT-B-32::openai",
"ViT-B-16::openai", "ViT-H-14-378-quickgelu::dfn5b",
"ViT-B-16::laion400m_e31", "ViT-H-14-quickgelu::dfn5b",
"ViT-B-16::laion400m_e32", "ViT-H-14::laion2b-s32b-b79k",
"ViT-B-16-plus-240::laion400m_e31", "ViT-L-14-336::openai",
"ViT-B-16-plus-240::laion400m_e32", "ViT-L-14-quickgelu::dfn2b",
"ViT-L-14::openai", "ViT-L-14::laion2b-s32b-b82k",
"ViT-L-14::laion400m_e31", "ViT-L-14::laion400m_e31",
"ViT-L-14::laion400m_e32", "ViT-L-14::laion400m_e32",
"ViT-L-14::laion2b-s32b-b82k", "ViT-L-14::openai",
"ViT-L-14-336::openai", "ViT-L-16-SigLIP-256::webli",
"ViT-H-14::laion2b-s32b-b79k", "ViT-L-16-SigLIP-384::webli",
"ViT-SO400M-14-SigLIP-384::webli",
"ViT-g-14::laion2b-s12b-b42k", "ViT-g-14::laion2b-s12b-b42k",
"M-CLIP/LABSE-Vit-L-14", "nllb-clip-base-siglip::mrl",
"M-CLIP/XLM-Roberta-Large-Vit-B-32", "nllb-clip-base-siglip::v1",
"M-CLIP/XLM-Roberta-Large-Vit-B-16Plus", "nllb-clip-large-siglip::mrl",
"M-CLIP/XLM-Roberta-Large-Vit-L-14", "nllb-clip-large-siglip::v1",
"xlm-roberta-base-ViT-B-32::laion5b_s13b_b90k",
"xlm-roberta-large-ViT-H-14::frozen_laion5b_s13b_b90k",
] ]
login(token=os.environ["HF_AUTH_TOKEN"]) # glob to delete old UUID blobs when reuploading models
uuid_char = "[a-fA-F0-9]"
uuid_glob = uuid_char * 8 + "-" + uuid_char * 4 + "-" + uuid_char * 4 + "-" + uuid_char * 4 + "-" + uuid_char * 12
# remote repo files to be deleted before uploading
# deletion is in the same commit as the upload, so it's atomic
delete_patterns = ["**/*onnx*", "**/Constant*", "**/*.weight", "**/*.bias", f"**/{uuid_glob}"]
with Progress() as progress: with Progress() as progress:
task1 = progress.add_task("[green]Exporting models...", total=len(models)) task = progress.add_task("[green]Exporting models...", total=len(models))
task2 = progress.add_task("[yellow]Uploading models...", total=len(models)) token = os.environ.get("HF_AUTH_TOKEN")
torch.backends.mha.set_fastpath_enabled(False)
with TemporaryDirectory() as tmp: with TemporaryDirectory() as tmp:
tmpdir = Path(tmp) tmpdir = Path(tmp)
for model in models: for model in models:
model_name = model.split("/")[-1].replace("::", "__") model_name = model.split("/")[-1].replace("::", "__")
hf_model_name = model_name.replace("xlm-roberta-large", "XLM-Roberta-Large")
hf_model_name = model_name.replace("xlm-roberta-base", "XLM-Roberta-Base")
config_path = tmpdir / model_name / "config.json" config_path = tmpdir / model_name / "config.json"
def upload() -> None:
progress.update(task2, description=f"[yellow]Uploading {model_name}")
repo_id = f"immich-app/{model_name}"
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=tmpdir / model_name)
progress.update(task2, advance=1)
def export() -> None: def export() -> None:
progress.update(task1, description=f"[green]Exporting {model_name}") progress.update(task, description=f"[green]Exporting {hf_model_name}")
visual_dir = tmpdir / model_name / "visual" visual_dir = tmpdir / hf_model_name / "visual"
textual_dir = tmpdir / model_name / "textual" textual_dir = tmpdir / hf_model_name / "textual"
if model.startswith("M-CLIP"): if model.startswith("M-CLIP"):
mclip.to_onnx(model, visual_dir, textual_dir) visual_path, textual_path = mclip.to_onnx(model, visual_dir, textual_dir)
else: else:
name, _, pretrained = model_name.partition("__") name, _, pretrained = model_name.partition("__")
openclip.to_onnx(openclip.OpenCLIPModelConfig(name, pretrained), visual_dir, textual_dir) config = openclip.OpenCLIPModelConfig(name, pretrained)
visual_path, textual_path = openclip.to_onnx(config, visual_dir, textual_dir)
progress.update(task, description=f"[green]Optimizing {hf_model_name} (visual)")
optimize(visual_path)
progress.update(task, description=f"[green]Optimizing {hf_model_name} (textual)")
optimize(textual_path)
progress.update(task1, advance=1)
gc.collect() gc.collect()
def upload() -> None:
progress.update(task, description=f"[yellow]Uploading {hf_model_name}")
repo_id = f"immich-app/{hf_model_name}"
create_repo(repo_id, exist_ok=True)
upload_folder(
repo_id=repo_id,
folder_path=tmpdir / hf_model_name,
delete_patterns=delete_patterns,
token=token,
)
export() export()
upload() if token is not None:
upload()
progress.update(task, advance=1)

57
server/src/constants.ts
View file

@ -93,39 +93,50 @@ export const supportedPresetTokens = [
type ModelInfo = { dimSize: number }; type ModelInfo = { dimSize: number };
export const CLIP_MODEL_INFO: Record<string, ModelInfo> = { export const CLIP_MODEL_INFO: Record<string, ModelInfo> = {
RN50__openai: { dimSize: 1024 },
RN50__yfcc15m: { dimSize: 1024 },
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RN50__openai: { dimSize: 1024 },
RN50__yfcc15m: { dimSize: 1024 },
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}; };