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new: add clip exporter #220

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5f9a29f
new: add clip exporter
joein Apr 30, 2024
7b51486
fix: clean output
joein Apr 30, 2024
27e7079
Merge branch 'main' into clip-export
NirantK May 5, 2024
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323 changes: 323 additions & 0 deletions experiments/03_CLIP_TO_ONNX.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "f7f8e989-cdc9-475e-918d-af20530fcfe6",
"metadata": {
"is_executing": true
},
"outputs": [],
"source": [
"!pip3 install -q torch transformers optimum pillow"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8c9e276-58f2-45a4-af40-6d7bacc30eec",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from pathlib import Path\n",
"from typing import Optional, Dict, Union, Tuple\n",
"\n",
"import torch\n",
"import numpy as np\n",
"from PIL import Image\n",
"from transformers import (\n",
" CLIPVisionModelWithProjection,\n",
" CLIPTextModelWithProjection,\n",
" CLIPImageProcessor,\n",
" CLIPTokenizerFast,\n",
")\n",
"from transformers.models.clip.modeling_clip import (\n",
" CLIPTextModelOutput,\n",
" CLIPVisionModelOutput,\n",
" CLIPModel,\n",
")\n",
"from optimum.onnxruntime import ORTModelForCustomTasks\n",
"from optimum.exporters.onnx.model_configs import CLIPTextWithProjectionOnnxConfig, ViTOnnxConfig\n",
"from optimum.exporters.onnx import export_models"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbceb40f-22cd-4d92-be6e-fe14f16f7bc2",
"metadata": {},
"outputs": [],
"source": [
"model_id = \"openai/clip-vit-base-patch32\"\n",
"output_dir = \"split-clip-onnx\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f96ff7fb-518e-405e-a7e0-46f836ffdec8",
"metadata": {},
"outputs": [],
"source": [
"class CLIPVisionModelWithProjectionOnnxConfig(ViTOnnxConfig):\n",
" @property\n",
" def outputs(self) -> Dict[str, Dict[int, str]]:\n",
" return {\n",
" \"image_embeds\": {0: \"batch_size\"},\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18863f0b-6bd5-463f-bebc-38bf40d51b9c",
"metadata": {},
"outputs": [],
"source": [
"class CLIPTextModelWithProjectionAndAttentionOnnxConfig(CLIPTextWithProjectionOnnxConfig):\n",
" @property\n",
" def inputs(self) -> Dict[str, Dict[int, str]]:\n",
" return {\n",
" \"input_ids\": {0: \"batch_size\", 1: \"sequence_length\"},\n",
" \"attention_mask\": {0: \"batch_size\", 1: \"sequence_length\"},\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5d37b16a-ec30-40e1-8404-0f0d51abfa76",
"metadata": {},
"outputs": [],
"source": [
"class CLIPTextModelWithProjectionNormalized(CLIPTextModelWithProjection):\n",
" def forward(\n",
" self,\n",
" input_ids: Optional[torch.Tensor] = None,\n",
" attention_mask: Optional[torch.Tensor] = None,\n",
" position_ids: Optional[torch.Tensor] = None,\n",
" output_attentions: Optional[bool] = None,\n",
" output_hidden_states: Optional[bool] = None,\n",
" return_dict: Optional[bool] = None,\n",
" ) -> Union[Tuple, CLIPTextModelOutput]:\n",
" text_outputs = super().forward(\n",
" input_ids,\n",
" attention_mask,\n",
" position_ids,\n",
" output_attentions,\n",
" output_hidden_states,\n",
" return_dict,\n",
" )\n",
" normalized_text_embeds = text_outputs.text_embeds / text_outputs.text_embeds.norm(\n",
" p=2, dim=-1, keepdim=True\n",
" )\n",
" return CLIPTextModelOutput(\n",
" text_embeds=normalized_text_embeds,\n",
" last_hidden_state=text_outputs.last_hidden_state,\n",
" hidden_states=text_outputs.hidden_states,\n",
" attentions=text_outputs.attentions,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cd05d33-5f4f-4b36-aa2c-43fd97d5061d",
"metadata": {},
"outputs": [],
"source": [
"class CLIPVisionModelWithProjectionNormalized(CLIPVisionModelWithProjection):\n",
" def forward(\n",
" self,\n",
" pixel_values: Optional[torch.FloatTensor] = None,\n",
" output_attentions: Optional[bool] = None,\n",
" output_hidden_states: Optional[bool] = None,\n",
" return_dict: Optional[bool] = None,\n",
" ) -> Union[Tuple, CLIPVisionModelOutput]:\n",
" vision_outputs = super().forward(pixel_values, return_dict)\n",
" normalized_image_embeds = vision_outputs.image_embeds / vision_outputs.image_embeds.norm(\n",
" p=2, dim=-1, keepdim=True\n",
" )\n",
" return CLIPVisionModelOutput(\n",
" image_embeds=normalized_image_embeds,\n",
" last_hidden_state=vision_outputs.last_hidden_state,\n",
" hidden_states=vision_outputs.hidden_states,\n",
" attentions=vision_outputs.attentions,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb5e5617-f8ce-4dcf-9148-68ddd91854c9",
"metadata": {},
"outputs": [],
"source": [
"text_model = CLIPTextModelWithProjectionNormalized.from_pretrained(model_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f578131-c6bb-460e-8200-d0b7f0aa4135",
"metadata": {},
"outputs": [],
"source": [
"vision_model = CLIPVisionModelWithProjectionNormalized.from_pretrained(model_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c57db080-642f-4b62-96ad-75af9c0ab277",
"metadata": {},
"outputs": [],
"source": [
"text_config = CLIPTextModelWithProjectionAndAttentionOnnxConfig(text_model.config)\n",
"vision_config = CLIPVisionModelWithProjectionOnnxConfig(vision_model.config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cdb01aab-0dff-4fd5-9297-d16599274fdb",
"metadata": {},
"outputs": [],
"source": [
"text_model.config.save_pretrained(f\"./{output_dir}/text\")\n",
"vision_model.config.save_pretrained(f\"./{output_dir}/image\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "133bcd2c-57ae-4132-a691-3d129057f275",
"metadata": {},
"outputs": [],
"source": [
"export_models(\n",
" models_and_onnx_configs={\n",
" \"text_model\": (text_model, text_config),\n",
" \"vision_model\": (vision_model, vision_config),\n",
" },\n",
" output_dir=Path(f\"./{output_dir}\"),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd9167b9-0d00-4a24-8f5e-41392d923b95",
"metadata": {},
"outputs": [],
"source": [
"os.rename(f\"./{output_dir}/text_model.onnx\", f\"./{output_dir}/text/model.onnx\")\n",
"os.rename(f\"./{output_dir}/vision_model.onnx\", f\"./{output_dir}/image/model.onnx\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2281cc75-1027-4dbe-a7a5-86ee1dad4c3e",
"metadata": {},
"outputs": [],
"source": [
"ort_vision_model = ORTModelForCustomTasks.from_pretrained(\n",
" f\"./{output_dir}/image\", config=vision_config\n",
")\n",
"image_processor = CLIPImageProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"image_input = image_processor(images=Image.open(\"assets/image.jpeg\"), return_tensors=\"pt\")\n",
"\n",
"with torch.inference_mode():\n",
" image_outputs = ort_vision_model(**image_input)\n",
"image_processor.save_pretrained(f\"./{output_dir}/image\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ce61045-b1e7-4b62-a47b-4bcb27ac7288",
"metadata": {},
"outputs": [],
"source": [
"ort_text_model = ORTModelForCustomTasks.from_pretrained(f\"./{output_dir}/text\", config=text_config)\n",
"text_processor = CLIPTokenizerFast.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"text_input = text_processor(\"What am I using?\", return_tensors=\"pt\")\n",
"\n",
"with torch.inference_mode():\n",
" text_outputs = ort_text_model(**text_input)\n",
"text_processor.save_pretrained(f\"./{output_dir}/text\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee1e3d13-6884-4aa3-a6ab-aaa41fc17134",
"metadata": {},
"outputs": [],
"source": [
"clip_model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
"inputs = {**text_input, **image_input}\n",
"clip_model.eval()\n",
"with torch.inference_mode():\n",
" gt_output = clip_model(**inputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec07ce55-a0e7-42d2-b1f4-ba1370ab45b7",
"metadata": {},
"outputs": [],
"source": [
"print(np.allclose(gt_output.text_embeds.numpy(), text_outputs.text_embeds, atol=1e-6))\n",
"print(np.allclose(gt_output.image_embeds.numpy(), image_outputs.image_embeds, atol=1e-6))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9b15597-3054-40f1-a080-fea19d378d88",
"metadata": {},
"outputs": [],
"source": [
"token = \"<token>\"\n",
"# create_repo(repo_id='Qdrant/clip-ViT-B-32-vision', exist_ok=True, token=token)\n",
"# create_repo(repo_id='Qdrant/clip-ViT-B-32-text', exist_ok=True, token=token)\n",
"\n",
"ort_text_model.push_to_hub(\n",
" save_directory=f\"./{output_dir}/text/\",\n",
" repository_id=\"Qdrant/clip-ViT-B-32-text\",\n",
" use_auth_token=token,\n",
")\n",
"ort_vision_model.push_to_hub(\n",
" save_directory=f\"./{output_dir}/image\",\n",
" repository_id=\"Qdrant/clip-ViT-B-32-vision\",\n",
" use_auth_token=token,\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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