export cli

machine-learning/export/immich_model_exporter/exporters/constants.py

@@ -0,0 +1,23 @@
+from enum import StrEnum
+from typing import NamedTuple
+
+
+class ModelSource(StrEnum):
+    INSIGHTFACE = "insightface"
+    MCLIP = "mclip"
+    OPENCLIP = "openclip"
+
+
+class SourceMetadata(NamedTuple):
+    name: str
+    link: str
+    type: str
+
+
+SOURCE_TO_METADATA = {
+    ModelSource.MCLIP: SourceMetadata("M-CLIP", "https://huggingface.co/M-CLIP", "CLIP"),
+    ModelSource.OPENCLIP: SourceMetadata("OpenCLIP", "https://github.com/mlfoundations/open_clip", "CLIP"),
+    ModelSource.INSIGHTFACE: SourceMetadata(
+        "InsightFace", "https://github.com/deepinsight/insightface/tree/master", "facial recognition"
+    ),
+}

machine-learning/export/immich_model_exporter/exporters/onnx/__init__.py

@@ -0,0 +1,20 @@
+from pathlib import Path
+
+from ..constants import ModelSource
+from .models import mclip, openclip
+
+
+def export(
+    model_name: str, model_source: ModelSource, output_dir: Path, opset_version: int = 19, no_cache: bool = False
+) -> None:
+    visual_dir = output_dir / "visual"
+    textual_dir = output_dir / "textual"
+    match model_source:
+        case ModelSource.MCLIP:
+            mclip.to_onnx(model_name, opset_version, visual_dir, textual_dir, no_cache=no_cache)
+        case ModelSource.OPENCLIP:
+            name, _, pretrained = model_name.partition("__")
+            config = openclip.OpenCLIPModelConfig(name, pretrained)
+            openclip.to_onnx(config, opset_version, visual_dir, textual_dir, no_cache=no_cache)
+        case _:
+            raise ValueError(f"Unsupported model source {model_source}")

machine-learning/export/immich_model_exporter/exporters/onnx/models/mclip.py

@@ -0,0 +1,76 @@
+import warnings
+from pathlib import Path
+
+from .openclip import OpenCLIPModelConfig
+from .openclip import to_onnx as openclip_to_onnx
+from .util import get_model_path
+
+_MCLIP_TO_OPENCLIP = {
+    "M-CLIP/XLM-Roberta-Large-Vit-B-32": OpenCLIPModelConfig("ViT-B-32", "openai"),
+    "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus": OpenCLIPModelConfig("ViT-B-16-plus-240", "laion400m_e32"),
+    "M-CLIP/LABSE-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"),
+    "M-CLIP/XLM-Roberta-Large-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"),
+}
+
+
+def to_onnx(
+    model_name: str,
+    opset_version: int,
+    output_dir_visual: Path | str,
+    output_dir_textual: Path | str,
+    no_cache: bool = False,
+) -> tuple[Path, Path]:
+    textual_path = get_model_path(output_dir_textual)
+    if no_cache or not textual_path.exists():
+        import torch
+        from multilingual_clip.pt_multilingual_clip import MultilingualCLIP
+        from transformers import AutoTokenizer
+
+        torch.backends.mha.set_fastpath_enabled(False)
+
+        model = MultilingualCLIP.from_pretrained(model_name)
+        AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual)
+
+        model.eval()
+        for param in model.parameters():
+            param.requires_grad_(False)
+
+        _export_text_encoder(model, textual_path, opset_version)
+    else:
+        print(f"Model {textual_path} already exists, skipping")
+    visual_path, _ = openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual, no_cache=no_cache)
+    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, opset_version: int) -> None:
+    import torch
+    from multilingual_clip.pt_multilingual_clip import MultilingualCLIP
+
+    output_path = Path(output_path)
+
+    def forward(self: MultilingualCLIP, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
+        embs = self.transformer(input_ids, attention_mask)[0]
+        embs = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
+        embs = self.LinearTransformation(embs)
+        return torch.nn.functional.normalize(embs, dim=-1)
+
+    # unfortunately need to monkeypatch for tracing to work here
+    # otherwise it hits the 2GiB protobuf serialization limit
+    MultilingualCLIP.forward = forward
+
+    args = (torch.ones(1, 77, dtype=torch.int32), torch.ones(1, 77, dtype=torch.int32))
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        torch.onnx.export(
+            model,
+            args,
+            output_path.as_posix(),
+            input_names=["input_ids", "attention_mask"],
+            output_names=["embedding"],
+            opset_version=opset_version,
+            # dynamic_axes={
+            #     "input_ids": {0: "batch_size", 1: "sequence_length"},
+            #     "attention_mask": {0: "batch_size", 1: "sequence_length"},
+            # },
+        )

machine-learning/export/immich_model_exporter/exporters/onnx/models/openclip.py

@@ -0,0 +1,149 @@
+import warnings
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import Any
+
+from .util import get_model_path, save_config
+
+
+@dataclass
+class OpenCLIPModelConfig:
+    name: str
+    pretrained: str
+
+    @cached_property
+    def model_config(self) -> dict[str, Any]:
+        import open_clip
+
+        config = open_clip.get_model_config(self.name)
+        if config is None:
+            raise ValueError(f"Unknown model {self.name}")
+        return config
+
+    @property
+    def image_size(self) -> int:
+        return self.model_config["vision_cfg"]["image_size"]
+
+    @property
+    def sequence_length(self) -> int:
+        return self.model_config["text_cfg"].get("context_length", 77)
+
+
+def to_onnx(
+    model_cfg: OpenCLIPModelConfig,
+    opset_version: int,
+    output_dir_visual: Path | str | None = None,
+    output_dir_textual: Path | str | None = None,
+    no_cache: bool = False,
+) -> tuple[Path | None, Path | None]:
+    visual_path = None
+    textual_path = None
+    if output_dir_visual is not None:
+        output_dir_visual = Path(output_dir_visual)
+        visual_path = get_model_path(output_dir_visual)
+
+    if output_dir_textual is not None:
+        output_dir_textual = Path(output_dir_textual)
+        textual_path = get_model_path(output_dir_textual)
+
+    if not no_cache and (
+        (textual_path is None or textual_path.exists()) and (visual_path is None or visual_path.exists())
+    ):
+        print(f"Models {textual_path} and {visual_path} already exist, skipping")
+        return visual_path, textual_path
+
+    import open_clip
+    import torch
+    from transformers import AutoTokenizer
+
+    torch.backends.mha.set_fastpath_enabled(False)
+
+    model = open_clip.create_model(
+        model_cfg.name,
+        pretrained=model_cfg.pretrained,
+        jit=False,
+        require_pretrained=True,
+    )
+
+    text_vision_cfg = open_clip.get_model_config(model_cfg.name)
+
+    model.eval()
+    for param in model.parameters():
+        param.requires_grad_(False)
+
+    if visual_path is not None:
+        if no_cache or not visual_path.exists():
+            save_config(
+                open_clip.get_model_preprocess_cfg(model),
+                output_dir_visual / "preprocess_cfg.json",
+            )
+            save_config(text_vision_cfg, output_dir_visual.parent / "config.json")
+            _export_image_encoder(model, model_cfg, visual_path, opset_version)
+        else:
+            print(f"Model {visual_path} already exists, skipping")
+
+    if textual_path is not None:
+        if no_cache or not textual_path.exists():
+            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)
+            _export_text_encoder(model, model_cfg, textual_path, opset_version)
+        else:
+            print(f"Model {textual_path} already exists, skipping")
+    return visual_path, textual_path
+
+
+def _export_image_encoder(
+    model: "open_clip.CLIP", model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int
+) -> None:
+    import torch
+
+    output_path = Path(output_path)
+
+    def encode_image(image: torch.Tensor) -> torch.Tensor:
+        output = model.encode_image(image, normalize=True)
+        assert isinstance(output, torch.Tensor)
+        return output
+
+    args = (torch.randn(1, 3, model_cfg.image_size, model_cfg.image_size),)
+    traced = torch.jit.trace(encode_image, args)  # type: ignore[no-untyped-call]
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        torch.onnx.export(
+            traced,
+            args,
+            output_path.as_posix(),
+            input_names=["image"],
+            output_names=["embedding"],
+            opset_version=opset_version,
+            # dynamic_axes={"image": {0: "batch_size"}},
+        )
+
+
+def _export_text_encoder(
+    model: "open_clip.CLIP", model_cfg: OpenCLIPModelConfig, output_path: Path | str, opset_version: int
+) -> None:
+    import torch
+
+    output_path = Path(output_path)
+
+    def encode_text(text: torch.Tensor) -> torch.Tensor:
+        output = model.encode_text(text, normalize=True)
+        assert isinstance(output, torch.Tensor)
+        return output
+
+    args = (torch.ones(1, model_cfg.sequence_length, dtype=torch.int32),)
+    traced = torch.jit.trace(encode_text, args)  # type: ignore[no-untyped-call]
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", UserWarning)
+        torch.onnx.export(
+            traced,
+            args,
+            output_path.as_posix(),
+            input_names=["text"],
+            output_names=["embedding"],
+            opset_version=opset_version,
+            # dynamic_axes={"text": {0: "batch_size"}},
+        )

machine-learning/export/immich_model_exporter/exporters/onnx/models/util.py

@@ -0,0 +1,15 @@
+import json
+from pathlib import Path
+from typing import Any
+
+
+def get_model_path(output_dir: Path | str) -> Path:
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    return output_dir / "model.onnx"
+
+
+def save_config(config: Any, output_path: Path | str) -> None:
+    output_path = Path(output_path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    json.dump(config, output_path.open("w"))

machine-learning/export/immich_model_exporter/exporters/rknn.py

@@ -0,0 +1,77 @@
+from pathlib import Path
+
+RKNN_SOCS = ["rk3566", "rk3576", "rk3588"]
+
+
+def _export_platform(
+    model_dir: Path,
+    target_platform: str,
+    dynamic_input=None,
+    fuse_matmul_softmax_matmul_to_sdpa: bool = True,
+    no_cache: bool = False,
+):
+    from rknn.api import RKNN
+
+    input_path = model_dir / "model.onnx"
+    output_path = model_dir / "rknpu" / target_platform / "model.rknn"
+    if not no_cache and output_path.exists():
+        print(f"Model {input_path} already exists at {output_path}, skipping")
+        return
+
+    print(f"Exporting model {input_path} to {output_path}")
+
+    rknn = RKNN(verbose=False)
+
+    rknn.config(
+        target_platform=target_platform,
+        dynamic_input=dynamic_input,
+        disable_rules=["fuse_matmul_softmax_matmul_to_sdpa"] if not fuse_matmul_softmax_matmul_to_sdpa else [],
+        enable_flash_attention=True,
+        model_pruning=True,
+    )
+    ret = rknn.load_onnx(model=input_path.as_posix())
+
+    if ret != 0:
+        raise RuntimeError("Load failed!")
+
+    ret = rknn.build(do_quantization=False)
+
+    if ret != 0:
+        raise RuntimeError("Build failed!")
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    ret = rknn.export_rknn(output_path.as_posix())
+    if ret != 0:
+        raise RuntimeError("Export rknn model failed!")
+
+
+def _export_platforms(model_dir: Path, dynamic_input=None, no_cache: bool = False):
+    fuse_matmul_softmax_matmul_to_sdpa = True
+    for soc in RKNN_SOCS:
+        try:
+            _export_platform(model_dir, soc, dynamic_input, fuse_matmul_softmax_matmul_to_sdpa)
+        except Exception as e:
+            print(f"Failed to export model for {soc}: {e}")
+            if "inputs or 'outputs' must be set" in str(e):
+                print("Retrying without fuse_matmul_softmax_matmul_to_sdpa")
+                fuse_matmul_softmax_matmul_to_sdpa = False
+                _export_platform(model_dir, soc, dynamic_input, fuse_matmul_softmax_matmul_to_sdpa)
+
+
+def export(model_dir: Path, no_cache: bool = False):
+    textual = model_dir / "textual"
+    visual = model_dir / "visual"
+    detection = model_dir / "detection"
+    recognition = model_dir / "recognition"
+
+    if textual.is_dir():
+        _export_platforms(textual, no_cache=no_cache)
+
+    if visual.is_dir():
+        _export_platforms(visual, no_cache=no_cache)
+
+    if detection.is_dir():
+        _export_platforms(detection, dynamic_input=[[[1, 3, 640, 640]]], no_cache=no_cache)
+
+    if recognition.is_dir():
+        _export_platforms(recognition, dynamic_input=[[[1, 3, 112, 112]]], no_cache=no_cache)