tflite CLIP export

machine-learning/export/env.yaml

@@ -20,6 +20,7 @@ dependencies:
   - torchvision
   - transformers==4.*
   - pip:
-    - multilingual-clip
-    - onnx-simplifier
+      - multilingual-clip
+      - onnx-simplifier
+      - tensorflow==2.14.*
 category: main

machine-learning/export/models/tfclip.py

@@ -0,0 +1,72 @@
+import tempfile
+from pathlib import Path
+
+import tensorflow as tf
+from transformers import TFCLIPModel
+
+from .util import ModelType, get_model_path
+
+
+class _CLIPWrapper(tf.Module):
+    def __init__(self, model_name: str):
+        super(_CLIPWrapper)
+        self.model = TFCLIPModel.from_pretrained(model_name).half()
+
+    @tf.function()
+    def encode_image(self, input):
+        return self.model.get_image_features(input)
+
+    @tf.function()
+    def encode_text(self, input):
+        return self.model.get_text_features(input)
+
+
+# exported model signatures use batch size 2 because of the following reasons:
+# 1. ARM-NN cannot use dynamic batch sizes for complex models like CLIP ViT
+# 2. batch size 1 creates a larger TF-Lite model that uses a lot (50%) more RAM
+# 3. batch size 2 is ~50% faster on GPU than 1 while 4 (or larger) are not really faster
+# 4. batch size >2 wastes more computation if only a single image is processed
+BATCH_SIZE_IMAGE = 2
+# On most small-scale systems there will only be one query at a time, no sense in batching
+BATCH_SIZE_TEXT = 1
+
+SIGNATURE_TEXT = "encode_text"
+SIGNATURE_IMAGE = "encode_image"
+
+
+def to_tflite(
+    model_name,
+    output_path_image: Path | str | None,
+    output_path_text: Path | str | None,
+    context_length: int = 77,
+):
+    with tempfile.TemporaryDirectory() as tmpdir:
+        _export_temporary_tf_model(model_name, tmpdir, context_length)
+        if output_path_image is not None:
+            image_path = get_model_path(output_path_image, ModelType.TFLITE)
+            _export_tflite_model(tmpdir, SIGNATURE_IMAGE, image_path.as_posix())
+        if output_path_text is not None:
+            text_path = get_model_path(output_path_text, ModelType.TFLITE)
+            _export_tflite_model(tmpdir, SIGNATURE_TEXT, text_path.as_posix())
+
+
+def _export_temporary_tf_model(model_name, tmp_path: str, context_length: int):
+    wrapper = _CLIPWrapper(model_name)
+    conf = wrapper.model.config.vision_config
+    spec_visual = tf.TensorSpec(
+        shape=(BATCH_SIZE_IMAGE, conf.num_channels, conf.image_size, conf.image_size), dtype=tf.float32
+    )
+    encode_image = wrapper.encode_image.get_concrete_function(spec_visual)
+    spec_text = tf.TensorSpec(shape=(BATCH_SIZE_TEXT, context_length), dtype=tf.int32)
+    encode_text = wrapper.encode_text.get_concrete_function(spec_text)
+    signatures = { SIGNATURE_IMAGE: encode_image, SIGNATURE_TEXT: encode_text}
+    tf.saved_model.save(wrapper, tmp_path, signatures)
+
+
+def _export_tflite_model(tmp_path: str, signature: str, output_path: str):
+    converter = tf.lite.TFLiteConverter.from_saved_model(tmp_path, signature_keys=[signature])
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    converter.target_spec.supported_types = [tf.float32]
+    tflite_model = converter.convert()
+    with open(output_path, "wb") as f:
+        f.write(tflite_model)

machine-learning/export/models/util.py

@@ -1,12 +1,18 @@
 import json
+from enum import Enum
 from pathlib import Path
 from typing import Any
 
 
-def get_model_path(output_dir: Path | str) -> Path:
+class ModelType(Enum):
+    ONNX = "onnx"
+    TFLITE = "tflite"
+
+
+def get_model_path(output_dir: Path | str, model_type: ModelType = ModelType.ONNX) -> Path:
     output_dir = Path(output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
-    return output_dir / "model.onnx"
+    return output_dir / f"model.{model_type.value}"
 
 
 def save_config(config: Any, output_path: Path | str) -> None:

machine-learning/export/run.py

@@ -4,9 +4,10 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 
 from huggingface_hub import create_repo, login, upload_folder
-from models import mclip, openclip
 from rich.progress import Progress
 
+from models import mclip, openclip, tfclip
+
 models = [
     "RN50::openai",
     "RN50::yfcc15m",
@@ -37,9 +38,10 @@ models = [
     "M-CLIP/XLM-Roberta-Large-Vit-B-32",
     "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus",
     "M-CLIP/XLM-Roberta-Large-Vit-L-14",
+    "openai/clip-vit-base-patch32",
 ]
 
-login(token=os.environ["HF_AUTH_TOKEN"])
+# login(token=os.environ["HF_AUTH_TOKEN"])
 
 with Progress() as progress:
     task1 = progress.add_task("[green]Exporting models...", total=len(models))
@@ -65,6 +67,8 @@ with Progress() as progress:
                 textual_dir = tmpdir / model_name / "textual"
                 if model.startswith("M-CLIP"):
                     mclip.to_onnx(model, visual_dir, textual_dir)
+                elif "/" in model:
+                    tfclip.to_tflite(model, visual_dir.as_posix(), textual_dir.as_posix())
                 else:
                     name, _, pretrained = model_name.partition("__")
                     openclip.to_onnx(openclip.OpenCLIPModelConfig(name, pretrained), visual_dir, textual_dir)

machine-learning/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiocache"
@@ -3882,6 +3882,30 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tflite-runtime"
+version = "2.14.0"
+description = "TensorFlow Lite is for mobile and embedded devices."
+optional = false
+python-versions = "*"
+files = [
+    {file = "tflite_runtime-2.14.0-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:bb11df4283e281cd609c621ac9470ad0cb5674408593272d7593a2c6bde8a808"},
+    {file = "tflite_runtime-2.14.0-cp310-cp310-manylinux_2_34_aarch64.whl", hash = "sha256:d38c6885f5e9673c11a61ccec5cad7c032ab97340718d26b17794137f398b780"},
+    {file = "tflite_runtime-2.14.0-cp310-cp310-manylinux_2_34_armv7l.whl", hash = "sha256:7fe33f763263d1ff2733a09945a7547ab063d8bc311fd2a1be8144d850016ad3"},
+    {file = "tflite_runtime-2.14.0-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:195ab752e7e57329a68e54dd3dd5439fad888b9bff1be0f0dc042a3237a90e4d"},
+    {file = "tflite_runtime-2.14.0-cp311-cp311-manylinux_2_34_aarch64.whl", hash = "sha256:ce9fa5d770a9725c746dcbf6f59f3178233b3759f09982e8b2db8d2234c333b0"},
+    {file = "tflite_runtime-2.14.0-cp311-cp311-manylinux_2_34_armv7l.whl", hash = "sha256:c4e66a74165b18089c86788400af19fa551768ac782d231a9beae2f6434f7949"},
+    {file = "tflite_runtime-2.14.0-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:9f965054467f7890e678943858c6ac76a5197b17f61b48dcbaaba0af41d541a7"},
+    {file = "tflite_runtime-2.14.0-cp38-cp38-manylinux_2_34_aarch64.whl", hash = "sha256:437167fe3d8b12f50f5d694da8f45d268ab84a495e24c3dd810e02e1012125de"},
+    {file = "tflite_runtime-2.14.0-cp38-cp38-manylinux_2_34_armv7l.whl", hash = "sha256:79d8e17f68cc940df7e68a177b22dda60fcffba195fb9dd908d03724d65fd118"},
+    {file = "tflite_runtime-2.14.0-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:4aa740210a0fd9e4db4a46e9778914846b136e161525681b41575ca4896158fb"},
+    {file = "tflite_runtime-2.14.0-cp39-cp39-manylinux_2_34_aarch64.whl", hash = "sha256:be198b7dc4401204be54a15884d9e336389790eb707439524540f5a9329fdd02"},
+    {file = "tflite_runtime-2.14.0-cp39-cp39-manylinux_2_34_armv7l.whl", hash = "sha256:eca7672adca32727bbf5c0f1caf398fc17bbe222f2a684c7a2caea6fc6767203"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.2"
+
 [[package]]
 name = "threadpoolctl"
 version = "3.2.0"
@@ -4025,6 +4049,14 @@ dev = ["tokenizers[testing]"]
 docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
 
+[[package]]
+name = "torch"
+version = "2.0.1"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = false
+python-versions = "*"
+files = []
+
 [[package]]
 name = "torch"
 version = "2.1.0"
@@ -4772,4 +4804,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "bba5f87aa67bc1d2283a9f4b471ef78e572337f22413870d324e908014410d53"
+content-hash = "56614afdeeeec3b7f0b786771a8fcc126761c882b1033664056042833767e521"

machine-learning/pyproject.toml

@@ -29,6 +29,7 @@ python-multipart = "^0.0.6"
 orjson = "^3.9.5"
 safetensors = "0.3.2"
 gunicorn = "^21.1.0"
+tflite-runtime = "^2.14.0"
 
 [tool.poetry.group.dev.dependencies]
 mypy = "^1.3.0"