diff --git a/models/segment-text/coreml/README.md b/models/segment-text/coreml/README.md new file mode 100644 index 0000000..a2a1ecc --- /dev/null +++ b/models/segment-text/coreml/README.md @@ -0,0 +1,66 @@ +# Segment Any Text CoreML +Segment Any Text is state-of-the-art sentence segmentation with 3 Transfomer layers. A pytorch version of the model is used in [wtsplit](https://github.com/segment-any-text/wtpsplit) and additional details can be found in this [paper](https://arxiv.org/abs/2406.16678). + +If you wish to skip the CoreML conversion, you can download a precompiled `SaT.mlmodelc` from [Hugging Face](https://huggingface.co/smdesai/SaT). + + +# CoreML Conversion + +## Environment Setup + +1. Install [uv](https://github.com/astral-sh/uv) if it is not already available. +2. Sync the project environment. + ```bash + uv sync + ``` +3. Activate the virtual environment: + ```bash + source .venv/bin/activate + ``` + +## Converting the Model + +Run the conversion script to create the SaT Core ML package: + +```bash +python convert-sat.py --model-id segment-any-text/sat-3l-sm --output-dir sat_coreml +``` + +This produces `SaT.mlpackage` in the `sat_coreml` directory. + +Here is the complete usage: +```bash +Usage: convert_sat.py [OPTIONS] + + Options + --model-id TEXT Model identifier to download + from HuggingFace model hub + [default: + segment-any-text/sat-3l-sm] + --output-dir PATH Directory to write mlpackage and + [default: sat_coreml] + --conversion-type -c TEXT Conversion methods to apply to + the model. Repeat the option to + chain conversions (allowed: + none, prune, quantize, + palettize; default: none). + [default: None] +``` + +## Compiling the Model + +Run the following to compile the model. +```bash +python compile_mlmodelc.py --coreml-dir sat_coreml +``` + +This produces `SaT.mlmodelc` in the `compiled` directory. + +Here is the complete usage: +```bash + Usage: compile_mlmodelc.py [OPTIONS] + + Options + --coreml-dir PATH Directory where mlpackages and metadata are written + [default: sat_coreml] +``` diff --git a/models/segment-text/coreml/compile_mlmodelc.py b/models/segment-text/coreml/compile_mlmodelc.py new file mode 100644 index 0000000..884c654 --- /dev/null +++ b/models/segment-text/coreml/compile_mlmodelc.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +import shutil +import subprocess +import sys +import typer +from pathlib import Path + +BASE_DIR = Path(__file__).resolve().parent +OUTPUT_ROOT = BASE_DIR / "compiled" + +app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) + + +def ensure_coremlcompiler() -> None: + """Ensure ``xcrun coremlcompiler`` is available for the active Xcode.""" + xcrun_path = shutil.which("xcrun") + if xcrun_path is None: + print("Error: 'xcrun' not found on PATH. Install Xcode command line tools.", file=sys.stderr) + sys.exit(1) + + try: + subprocess.run([ + xcrun_path, + "--find", + "coremlcompiler", + ], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + except subprocess.CalledProcessError: + print("Error: 'coremlcompiler' not found via xcrun. Check your Xcode installation.", file=sys.stderr) + sys.exit(1) + + +def gather_packages(dir: str) -> list[Path]: + """Return a list of all ``*.mlpackage`` bundles under the source dirs.""" + packages: list[Path] = [] + source = BASE_DIR / dir + if not source.exists(): + print(f"Warning: {source.relative_to(BASE_DIR)} does not exist; skipping", file=sys.stderr) + return packages + packages.extend(source.rglob("*.mlpackage")) + return packages + + +def compile_package(package: Path) -> None: + """Compile a single ``.mlpackage`` bundle using ``xcrun coremlcompiler``.""" + relative_pkg = package.relative_to(BASE_DIR) + #output_dir = OUTPUT_ROOT / relative_pkg.parent + output_dir = OUTPUT_ROOT + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / f"{package.stem}.mlmodelc" + + if output_path.exists(): + shutil.rmtree(output_path) + + cmd = [ + "xcrun", + "coremlcompiler", + "compile", + str(package), + str(output_dir), + ] + + print(f"Compiling {relative_pkg} -> {output_path.relative_to(BASE_DIR)}") + subprocess.run(cmd, check=True) + + +@app.command() +def compile( + coreml_dir: Path = typer.Option( + Path("sat_coreml"), + help="Directory where mlpackages and metadata are written", + ), +): + ensure_coremlcompiler() + packages = gather_packages(coreml_dir) + + if not packages: + print("No .mlpackage bundles found to compile.") + return + + for package in packages: + try: + compile_package(package) + except subprocess.CalledProcessError as exc: + print(f"Failed to compile {package}: {exc}", file=sys.stderr) + sys.exit(exc.returncode) + + print(f"Finished compiling {len(packages)} package(s) into {OUTPUT_ROOT.relative_to(BASE_DIR)}.") + + +if __name__ == "__main__": + app() diff --git a/models/segment-text/coreml/conversion_utils.py b/models/segment-text/coreml/conversion_utils.py new file mode 100644 index 0000000..48e06ce --- /dev/null +++ b/models/segment-text/coreml/conversion_utils.py @@ -0,0 +1,101 @@ +import json +import os +from enum import IntEnum + +import coremltools.optimize.coreml as cto_coreml + + +class Conversion(IntEnum): + NONE = 0 + PRUNE = 1 + QUANTIZE = 2 + PALETTIZE = 3 + + +def update_manifest_model_name(manifest_path: str, new_name: str) -> None: + with open(manifest_path, "r") as file: + manifest = json.load(file) + + for key, value in manifest["itemInfoEntries"].items(): + if value["name"] == "model.mlmodel": + value["name"] = f"{new_name}.mlmodel" + value["path"] = f"com.apple.CoreML/{new_name}.mlmodel" + + with open(manifest_path, "w") as file: + json.dump(manifest, file, indent=4) + + print(f"Manifest updated. Model name changed to {new_name}.mlmodel") + + old_model_path = os.path.join( + os.path.dirname(manifest_path), "Data/com.apple.CoreML/model.mlmodel" + ) + new_model_path = os.path.join( + os.path.dirname(manifest_path), + f"Data/com.apple.CoreML/{new_name}.mlmodel", + ) + if os.path.exists(old_model_path): + os.rename(old_model_path, new_model_path) + print(f"Model file renamed from model.mlmodel to {new_name}.mlmodel") + else: + print("Warning: model.mlmodel not found. Only manifest was updated.") + + +def palettize_model(mlpackage, *, bits: int = 8, weight_threshold: int = 512): + print(f"\nApplying {bits}-bit palettization...") + try: + op_config = cto_coreml.OpPalettizerConfig( + nbits=bits, + weight_threshold=weight_threshold, + ) + config = cto_coreml.OptimizationConfig(op_config) + return cto_coreml.palettize_weights(mlpackage, config) + except Exception as e: + print(f"Error palettization failed: {e}") + return None + + +def prune_model(mlpackage, *, threshold: float = 0.01): + print(f"\nApplying pruning quantization...") + try: + config = cto_coreml.OptimizationConfig( + global_config=cto_coreml.OpThresholdPrunerConfig(threshold=threshold) + ) + return cto_coreml.prune_weights(mlpackage, config) + except Exception as e: + print(f"Error pruning failed: {e}") + return None + + +def quantize_model(mlpackage, *, dtype: str = "int8", mode: str = "linear_symmetric"): + if str == "linear": + print(f"\nApplying {dtype} quantization...") + else: + print("\nApplying mixed precision quantization...") + + try: + op_config = cto_coreml.OpLinearQuantizerConfig( + mode=mode, + dtype=dtype, + granularity="per_block", + block_size=32, + ) + + config = cto_coreml.OptimizationConfig(global_config=op_config) + return cto_coreml.linear_quantize_weights(mlpackage, config) + except Exception as e: + print(f"INT8 quantization failed: {e}") + return None + + +def apply_conversion(mlpackage, conversion_type: Conversion): + match conversion_type: + case Conversion.NONE: + return mlpackage + case Conversion.PRUNE: + return prune_model(mlpackage) + case Conversion.QUANTIZE: + return quantize_model(mlpackage) + case Conversion.PALETTIZE: + return palettize_model(mlpackage) + case _: + raise ValueError(f"Unsupported conversion type: {conversion_type}") diff --git a/models/segment-text/coreml/convert_sat.py b/models/segment-text/coreml/convert_sat.py new file mode 100644 index 0000000..59ad399 --- /dev/null +++ b/models/segment-text/coreml/convert_sat.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +import os +from pathlib import Path + +import coremltools as ct +import numpy as np +import torch +import typer + +from transformers import AutoModelForTokenClassification, AutoTokenizer +import wtpsplit.models # registers SubwordXLM config/model types + +os.environ["TOKENIZERS_PARALLELISM"] = "false" + +DEFAULT_MODEL_ID = "segment-any-text/sat-3l-sm" + + +from conversion_utils import ( + Conversion, + apply_conversion, + update_manifest_model_name, +) + +app = typer.Typer(add_completion=False, pretty_exceptions_show_locals=False) + + +def parse_conversion_type(value: str | None) -> Conversion: + if value is None: + return Conversion.NONE + value_str = value.strip() + if not value_str: + return Conversion.NONE + + try: + return Conversion[value_str.upper()] + except KeyError as exc: + raise typer.BadParameter( + f"Invalid conversion type '{value}'. " + "Choose from 'none', 'prune', 'quantize', or 'palettize'." + ) from exc + + +def parse_conversion_types( + values: tuple[str, ...] | list[str] | None, +) -> list[Conversion]: + if not values: + return [Conversion.NONE] + + parsed: list[Conversion] = [] + for item in values: + parsed.append(parse_conversion_type(item)) + return parsed + + +@app.command() +def convert( + model_id: str = typer.Option( + DEFAULT_MODEL_ID, + "--model-id", + help="Model identifier to download from HuggingFace's model hub", + ), + output_dir: Path = typer.Option( + Path("sat_coreml"), + help="Directory where mlpackages and metadata will be written", + ), + conversion_types: list[str] = typer.Option( + None, + "--conversion-type", + "-c", + help=( + "Conversion methods to apply to the model. " + "Repeat the option to chain conversions " + "(allowed: none, prune, quantize, palettize; default: none)." + ), + ), +): + + conversions_to_apply = parse_conversion_types(conversion_types) + + model = AutoModelForTokenClassification.from_pretrained( + model_id, + return_dict=False, + torchscript=True, + trust_remote_code=True, + ).eval() + + tokenizer = AutoTokenizer.from_pretrained("facebookAI/xlm-roberta-base") + tokenized = tokenizer( + ["Sample input text to trace the model."], + return_tensors="pt", + max_length=512, # token sequence length + padding="max_length", + ) + + traced_model = torch.jit.trace( + model, + (tokenized["input_ids"], tokenized["attention_mask"]) + ) + + outputs = [ct.TensorType(name="output")] + + mlpackage = ct.convert( + traced_model, + convert_to="mlprogram", + inputs=[ + ct.TensorType( + f"{name}", + shape=tensor.shape, + dtype=np.int32, + ) + for name, tensor in tokenized.items() + ], + outputs=outputs, + compute_units=ct.ComputeUnit.ALL, + minimum_deployment_target=ct.target.iOS18, + ) + + try: + new_model = mlpackage + for conversion in conversions_to_apply: + new_model = apply_conversion(new_model, conversion) + except ValueError as e: + print(e) + return + + saved_name = "SaT" + saved_path = output_dir / f"{saved_name}.mlpackage" + new_model.save(saved_path) + + manifest_file = saved_path / "Manifest.json" + update_manifest_model_name(manifest_file, saved_name) + +if __name__ == "__main__": + app() diff --git a/models/segment-text/coreml/pyproject.toml b/models/segment-text/coreml/pyproject.toml new file mode 100644 index 0000000..998a6eb --- /dev/null +++ b/models/segment-text/coreml/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "coreml-conversion" +version = "0.1.0" +description = "Convert token classification models to Core ML packages" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "coremltools==8.3.0", + "numpy==2.3.4", + "torch==2.9.0", + "transformers==4.51.3", + "wtpsplit==2.1.6", + 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