JavaScript & Swift SDK

.github/workflows/release.yml

@@ -113,10 +113,14 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: "main"
+      - name: Install dependencies
+        run: |
+          sudo apt update && 
+          sudo apt install -y doxygen graphviz dia git && 
+          pip install sphinx==5.3.0 sphinx-js==3.2.1 breathe==4.35.0 furo==2023.3.27 m2r2==0.3.3.post2 sphinxcontrib-googleanalytics==0.2.dev20220708 sphinxcontrib-jquery==4.1 && 
+          npm install -g jsdoc
       - name: Setup GitHub Pages
         uses: actions/configure-pages@v2
-      - name: Install dependencies
-        run: sudo apt update && sudo apt install -y doxygen graphviz dia git && pip install sphinx==7.1.2 breathe furo m2r2 sphinxcontrib-googleanalytics==0.2.dev20220708 sphinxcontrib-jquery toml
       - name: Install UForm from PyPi
         run: pip install uform
       - name: Build documentation

.gitignore

@@ -4,7 +4,21 @@ test
 build/
 package-lock.json
 *.egg-info
-*.onnx
 __pycache__
 .build
-.swiftpm
+.swiftpm
+.hf_token
+
+dictionary*
+vocab*
+
+# Tensors & ML Model
+*.onnx
+*.pt
+*.safetensors
+*.mlpackage
+
+# NodeJS
+node_modules
+node_build
+yarn-error.log

.vscode/launch.json

@@ -5,11 +5,29 @@
     "version": "0.2.0",
     "configurations": [
         {
-            "name": "Python Debugger: Current File with Arguments",
+            "name": "Python Debugger",
             "type": "debugpy",
             "request": "launch",
             "program": "${file}",
             "console": "integratedTerminal",
+        },
+        {
+            "name": "PyTest Debugger",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "pytest",
+            "console": "integratedTerminal",
+            "args": [
+                "${file}",
+                "-s",
+                "-x",
+            ],
+        },
+        {
+            "name": "NodeJS Debugger",
+            "type": "node-terminal",
+            "request": "launch",
+            "command": "npm run test",
         }
     ]
 }

.vscode/settings.json

@@ -1,8 +1,10 @@
 {
     "cSpell.words": [
         "arange",
+        "astype",
         "CFURL",
         "coreml",
+        "crossattn",
         "cumsum",
         "dtype",
         "embs",
@@ -19,26 +21,37 @@
         "ndarray",
         "numpy",
         "ONNX",
+        "onnxconverter",
         "onnxruntime",
+        "opset",
         "packbits",
         "preprocess",
         "pretrained",
         "probs",
         "pypi",
+        "pytest",
+        "randn",
         "rerank",
         "reranker",
         "reranking",
+        "sandbeach",
         "sess",
         "SIMD",
         "softmax",
+        "Tensorrt",
+        "torchvision",
         "transfromers",
         "uform",
         "unimodal",
         "unsqueeze",
-        "Vardanian"
+        "Vardanian",
+        "whitespaces"
     ],
     "[python]": {
         "editor.defaultFormatter": "ms-python.black-formatter"
     },
-    "python.formatting.provider": "none"
+    "python.formatting.provider": "none",
+    "window.autoDetectColorScheme": true,
+    "workbench.colorTheme": "Default Dark+",
+    "workbench.preferredDarkColorTheme": "Default Dark+"
 }

BENCHMARKS.md

@@ -0,0 +1,182 @@
+# UForm Model Benchmarks
+
+## Accuracy
+
+### Embedding Models
+
+Few retrieval benchmarks exist for multimodal embeddings.
+The most famous ones for English are "MS-COCO" and "Flickr30k".
+Evaluating `uform-vl-english` model, one can expect the following numbers for search quality.
+
+| Dataset   | Recall @ 1 | Recall @ 5 | Recall @ 10 |
+| :-------- | ---------: | ---------: | ----------: |
+| Flickr    |      0.727 |      0.915 |       0.949 |
+| MS-COCO ¹ |      0.510 |      0.761 |       0.838 |
+
+For multilingual benchmarks, we've created the [`unum-cloud/coco-sm`](https://github.com/unum-cloud/coco-sm) repository².
+Evaluating the `unum-cloud/uform-vl-multilingual-v2` model, one can expect the following metrics for text-to-image search, compared against `xlm-roberta-base-ViT-B-32` [OpenCLIP](https://github.com/mlfoundations/open_clip) model.
+
+| Language  | OpenCLIP @ 1 | UForm @ 1 | OpenCLIP @ 5 | UForm @ 5 | OpenCLIP @ 10 | UForm @ 10 | Speakers |
+| :-------- | -----------: | --------: | -----------: | --------: | ------------: | ---------: | -------: |
+| English 🇺🇸 |     __37.8__ |      37.7 |         63.5 |  __65.0__ |          73.5 |   __75.9__ |  1'452 M |
+| Chinese 🇨🇳 |         27.3 |  __32.2__ |         51.3 |  __59.0__ |          62.1 |   __70.5__ |  1'118 M |
+| Hindi 🇮🇳   |         20.7 |  __31.3__ |         42.5 |  __57.9__ |          53.7 |   __69.6__ |    602 M |
+| Spanish 🇪🇸 |         32.6 |  __35.6__ |         58.0 |  __62.8__ |          68.8 |   __73.7__ |    548 M |
+| Arabic 🇸🇦  |         22.7 |  __31.7__ |         44.9 |  __57.8__ |          55.8 |   __69.2__ |    274 M |
+| French 🇫🇷  |         31.3 |  __35.4__ |         56.5 |  __62.6__ |          67.4 |   __73.3__ |    274 M |
+
+
+All languages:
+
+| Language             | OpenCLIP @ 1 |    UForm @ 1 | OpenCLIP @ 5 |    UForm @ 5 | OpenCLIP @ 10 |   UForm @ 10 | Speakers |
+| :------------------- | -----------: | -----------: | -----------: | -----------: | ------------: | -----------: | -------: |
+| Arabic 🇸🇦             |         22.7 |     __31.7__ |         44.9 |     __57.8__ |          55.8 |     __69.2__ |    274 M |
+| Armenian 🇦🇲           |          5.6 |     __22.0__ |         14.3 |     __44.7__ |          20.2 |     __56.0__ |      4 M |
+| Chinese 🇨🇳            |         27.3 |     __32.2__ |         51.3 |     __59.0__ |          62.1 |     __70.5__ |  1'118 M |
+| English 🇺🇸            |     __37.8__ |         37.7 |         63.5 |     __65.0__ |          73.5 |     __75.9__ |  1'452 M |
+| French 🇫🇷             |         31.3 |     __35.4__ |         56.5 |     __62.6__ |          67.4 |     __73.3__ |    274 M |
+| German 🇩🇪             |         31.7 |     __35.1__ |         56.9 |     __62.2__ |          67.4 |     __73.3__ |    134 M |
+| Hebrew 🇮🇱             |         23.7 |     __26.7__ |         46.3 |     __51.8__ |          57.0 |     __63.5__ |      9 M |
+| Hindi 🇮🇳              |         20.7 |     __31.3__ |         42.5 |     __57.9__ |          53.7 |     __69.6__ |    602 M |
+| Indonesian 🇮🇩         |         26.9 |     __30.7__ |         51.4 |     __57.0__ |          62.7 |     __68.6__ |    199 M |
+| Italian 🇮🇹            |         31.3 |     __34.9__ |         56.7 |     __62.1__ |          67.1 |     __73.1__ |     67 M |
+| Japanese 🇯🇵           |         27.4 |     __32.6__ |         51.5 |     __59.2__ |          62.6 |     __70.6__ |    125 M |
+| Korean 🇰🇷             |         24.4 |     __31.5__ |         48.1 |     __57.8__ |          59.2 |     __69.2__ |     81 M |
+| Persian 🇮🇷            |         24.0 |     __28.8__ |         47.0 |     __54.6__ |          57.8 |     __66.2__ |     77 M |
+| Polish 🇵🇱             |         29.2 |     __33.6__ |         53.9 |     __60.1__ |          64.7 |     __71.3__ |     41 M |
+| Portuguese 🇵🇹         |         31.6 |     __32.7__ |         57.1 |     __59.6__ |          67.9 |     __71.0__ |    257 M |
+| Russian 🇷🇺            |         29.9 |     __33.9__ |         54.8 |     __60.9__ |          65.8 |     __72.0__ |    258 M |
+| Spanish 🇪🇸            |         32.6 |     __35.6__ |         58.0 |     __62.8__ |          68.8 |     __73.7__ |    548 M |
+| Thai 🇹🇭               |         21.5 |     __28.7__ |         43.0 |     __54.6__ |          53.7 |     __66.0__ |     61 M |
+| Turkish 🇹🇷            |         25.5 |     __33.0__ |         49.1 |     __59.6__ |          60.3 |     __70.8__ |     88 M |
+| Ukranian 🇺🇦           |         26.0 |     __30.6__ |         49.9 |     __56.7__ |          60.9 |     __68.1__ |     41 M |
+| Vietnamese 🇻🇳         |         25.4 |     __28.3__ |         49.2 |     __53.9__ |          60.3 |     __65.5__ |     85 M |
+|                      |              |              |              |              |               |              |          |
+| Mean                 |     26.5±6.4 | __31.8±3.5__ |     49.8±9.8 | __58.1±4.5__ |     60.4±10.6 | __69.4±4.3__ |        - |
+| Google Translate     |     27.4±6.3 | __31.5±3.5__ |     51.1±9.5 | __57.8±4.4__ |     61.7±10.3 | __69.1±4.3__ |        - |
+| Microsoft Translator |     27.2±6.4 | __31.4±3.6__ |     50.8±9.8 | __57.7±4.7__ |     61.4±10.6 | __68.9±4.6__ |        - |
+| Meta NLLB            |     24.9±6.7 | __32.4±3.5__ |    47.5±10.3 | __58.9±4.5__ |     58.2±11.2 | __70.2±4.3__ |        - |
+
+### Generative Models
+
+| Model                | LLM Size |  SQA |    MME | MMBench | Average¹ |
+| :------------------- | -------: | ---: | -----: | ------: | -------: |
+| UForm-Gen2-Qwen-500m |     0.5B | 45.5 |  880.1 |    42.0 |    29.31 |
+| MobileVLM v2         |     1.4B | 52.1 | 1302.8 |    57.7 |    36.81 |
+| LLaVA-Phi            |     2.7B | 68.4 | 1335.1 |    59.8 |    42.95 |
+
+For captioning evaluation we measure CLIPScore and RefCLIPScore³.
+
+| Model                               | Size | Caption Length | CLIPScore | RefCLIPScore |
+| :---------------------------------- | ---: | -------------: | --------: | -----------: |
+| `llava-hf/llava-1.5-7b-hf`          |   7B |           Long |     0.878 |        0.529 |
+| `llava-hf/llava-1.5-7b-hf`          |   7B |          Short |     0.886 |        0.531 |
+|                                     |      |                |           |              |
+| `Salesforce/instructblip-vicuna-7b` |   7B |           Long |     0.902 |        0.534 |
+| `Salesforce/instructblip-vicuna-7b` |   7B |          Short |     0.848 |        0.523 |
+|                                     |      |                |           |              |
+| `unum-cloud/uform-gen`              | 1.5B |           Long |     0.847 |        0.523 |
+| `unum-cloud/uform-gen`              | 1.5B |          Short |     0.842 |        0.522 |
+|                                     |      |                |           |              |
+| `unum-cloud/uform-gen-chat`         | 1.5B |           Long |     0.860 |        0.525 |
+| `unum-cloud/uform-gen-chat`         | 1.5B |          Short |     0.858 |        0.525 |
+
+Results for VQAv2 evaluation.
+
+| Model                      | Size | Accuracy |
+| :------------------------- | ---: | -------: |
+| `llava-hf/llava-1.5-7b-hf` |   7B |     78.5 |
+| `unum-cloud/uform-gen`     | 1.5B |     66.5 |
+
+<br/>
+
+> ¹ Train split was in training data. <br/>
+> ² Lacking a broad enough evaluation dataset, we translated the [COCO Karpathy test split](https://www.kaggle.com/datasets/shtvkumar/karpathy-splits) with multiple public and proprietary translation services, averaging the scores across all sets, and breaking them down in the bottom section. <br/>
+> ³ We used `apple/DFN5B-CLIP-ViT-H-14-378` CLIP model.
+
+## Speed
+
+### Embedding Models
+
+UForm comes pre-packaged with speed benchmarks for the models.
+
+```bash
+$ python python/scripts/bench_encoders.py --help
+usage: bench_encoders.py [-h] [--filter-out FILTER_OUT] [--batch-size BATCH_SIZE]
+
+options:
+  -h, --help            show this help message and exit
+  --filter-out FILTER_OUT
+                        Filter out models, backends, or devices with a Regular Expression.
+  --batch-size BATCH_SIZE
+                        Batch size for the benchmark. Batch size 1 measures latency. Large batch sizes may not fit on every GPU.
+```
+
+Running that script for a fairly small batch size of 50 on an Nvidia H100 GPU and 
+
+| Model Name                                     | Device | Backend | Images Preprocessed/s | Images Encoded/s | Texts Preprocessed/s | Texts Encoded/s |
+| :--------------------------------------------- | :----- | :------ | --------------------: | :--------------- | :------------------- | :-------------- |
+| unum-cloud/uform3-image-text-english-base      | cpu    | torch   |                 23.03 | 76.57            | 15,978.03            | 562.28          |
+| unum-cloud/uform3-image-text-english-base      | cpu    | onnx    |                 23.11 | 77.75            | 13,880.27            | 1,067.40        |
+| unum-cloud/uform3-image-text-english-base      | cuda   | torch   |                 22.87 | 1,060.40         | 12,348.94            | 13,242.83       |
+| unum-cloud/uform3-image-text-english-large     | cpu    | torch   |                 22.41 | 10.84            | 13,350.45            | 145.12          |
+| unum-cloud/uform3-image-text-english-large     | cpu    | onnx    |                 23.13 | 19.60            | 18,031.85            | 960.09          |
+| unum-cloud/uform3-image-text-english-large     | cuda   | torch   |                 22.78 | 244.86           | 13,226.40            | 10,204.04       |
+| unum-cloud/uform3-image-text-english-small     | cpu    | torch   |                 20.08 | 71.68            | 12,147.05            | 249.63          |
+| unum-cloud/uform3-image-text-english-small     | cpu    | onnx    |                 22.84 | 195.27           | 13,636.99            | 1,385.25        |
+| unum-cloud/uform3-image-text-english-small     | cuda   | torch   |                 22.63 | 2,662.16         | 14,731.18            | 14,694.87       |
+| unum-cloud/uform3-image-text-multilingual-base | cpu    | torch   |                 22.98 | 64.28            | 10,129.27            | 209.76          |
+| unum-cloud/uform3-image-text-multilingual-base | cpu    | onnx    |                 23.06 | 66.81            | 8,963.13             | 1,104.32        |
+| unum-cloud/uform3-image-text-multilingual-base | cuda   | torch   |                 22.88 | 1,051.95         | 15,639.72            | 12,416.12       |
+
+If you are interested in performance numbers on consumer grade hardware, compared to third-party models, here are some rough estimates.
+On Nvidia RTX 3090:
+
+| Model                                            | Multilingual |                  Speed |    Speedup |
+| :----------------------------------------------- | -----------: | ---------------------: | ---------: |
+| `bert-base-uncased`                              |           No | 1'612 sequences/second |            |
+| `distilbert-base-uncased`                        |           No | 3'174 sequences/second |     x 1.96 |
+| `sentence-transformers/all-MiniLM-L12-v2`        |      __Yes__ | 3'604 sequences/second |     x 2.24 |
+| `unum-cloud/uform3-image-text-multilingual-base` |      __Yes__ | 6'809 sequences/second | __x 4.22__ |
+
+Given the small size of the model it also work well on mobile devices.
+On Apple M2 Arm chips the energy efficiency of inference can exceed that of the RTX 3090 GPU and other Ampere-generation cards.
+
+| Device                 |               Speed | Device TDP |        Efficiency |
+| :--------------------- | ------------------: | ---------: | ----------------: |
+| Nvidia RTX 3090        | ~ 140 tokens/second |     < 350W | 0.40 tokens/joule |
+| Apple M2 Pro unplugged |  ~ 19 tokens/second |      < 20W | 0.95 tokens/joule |
+| Apple M2 Max unplugged |  ~ 38 tokens/second |      < 36W | 1.06 tokens/joule |
+| Apple M2 Max plugged   |  ~ 56 tokens/second |      < 89W | 0.63 tokens/joule |
+
+### Generative Models
+
+```bash
+$ python python/scripts/bench_decoders.py --help
+usage: bench_decoders.py [-h] [--filter-out FILTER_OUT] [--batch-size BATCH_SIZE]
+
+options:
+  -h, --help            show this help message and exit
+  --batch-size BATCH_SIZE
+                        Batch size for the benchmark. Batch size 1 measures latency. Large batch sizes may not fit on every GPU.
+  --max-length MAX_LENGTH
+                        Maximum length of the generated text in tokens.
+```
+
+On Nvidia H100 GPU, the following performance is expected on text token generation using `float16`, equivalent PyTorch settings, and greedy decoding.
+
+| Model                               |  Size | Decoding Speed |    Decoding Parallel Streams |
+| :---------------------------------- | ----: | -------------: | ---------------------------: |
+| `llava-hf/llava-1.5-7b-hf`          |   7 B | ~ 141 tokens/s |  ~ 4 K tokens/s (32 streams) |
+| `Salesforce/instructblip-vicuna-7b` |   7 B | ~ 211 tokens/s |  ~ 2 K tokens/s (32 streams) |
+| `unum-cloud/uform-gen`              | 1.5 B | ~ 252 tokens/s | ~ 3 K tokens/s (128 streams) |
+| `unum-cloud/uform-gen2-dpo`         | 1.2 B | ~ 372 tokens/s | ~ 10 K tokens/s (64 streams) |
+
+On Nvidia RTX 3090, the following performance is expected on text token generation using `float16`, equivalent PyTorch settings, and greedy decoding.
+
+| Model                               |  Size | Decoding Speed |   Speedup |
+| :---------------------------------- | ----: | -------------: | --------: |
+| `llava-hf/llava-1.5-7b-hf`          |   7 B |  ~ 40 tokens/s |           |
+| `Salesforce/instructblip-vicuna-7b` |   7 B |  ~ 40 tokens/s |           |
+| `unum-cloud/uform-gen`              | 1.5 B | ~ 140 tokens/s | __x 3.5__ |
+

CONTRIBUTING.md

@@ -7,19 +7,25 @@ We welcome contributions to UForm!
 Before submitting any changes, please make sure that the tests pass.
 
 ```sh
-pip install -e .                # For core dependencies
-
+pip install -e ".[dev]"         # For development dependencies
 pip install -e ".[torch]"       # For PyTorch
 pip install -e ".[onnx]"        # For ONNX on CPU
 pip install -e ".[onnx-gpu]"    # For ONNX on GPU, available for some platforms
-pip install -e ".[torch,onnx]"  # For PyTorch and ONNX Python tests
+pip install -e ".[torch,onnx,onnx-gpu,dev]"  # For all
 
 pytest python/scripts/ -s -x -Wd -v
 pytest python/scripts/ -s -x -Wd -v -k onnx # To run only ONNX tests without loading Torch
 ```
 
 ## Swift
 
+To build and test the Swift package, use the following command:
+
+```bash
+swift build
+swift test
+```
+
 Swift formatting is enforced with `swift-format` default utility from Apple.
 To install and run it on all the files in the project, use the following command:
 
@@ -30,3 +36,31 @@ swift-format . -i -r
 
 The style is controlled by the `.swift-format` JSON file in the root of the repository.
 As there is no standard for Swift formatting, even Apple's own `swift-format` tool and Xcode differ in their formatting rules, and available settings.
+
+## JavaScript
+
+For rapid development you can avoid the TypeScript precompilation step:
+
+```sh
+npm install -g ts-node
+ts-node javascript/embeddings.mts
+```
+
+Before submitting any changes, please make sure that the tests pass.
+
+```sh
+npm install
+npm run test
+```
+
+## Benchmarking
+
+If you want to double check, how fast the model may work on your hardware, you can clone the library and repeat the benchmarks locally.
+The following benchmark will exclude PyTorch backend, CUDA-capable devices, and all the `-base` and `-large` models, running only the ONNX benchmarks on the CPU.
+
+```sh
+git clone https://github.com/unum-cloud/uform --depth 1 # Clone the repository
+cd uform && pip install -e ".[torch,onnx,onnx-gpu,dev]" # Install all dependencies
+python python/scripts/bench_encoders.py --filter-out "torch|cuda|base|large"
+```
+

Package.resolved

@@ -14,7 +14,7 @@
       "kind" : "remoteSourceControl",
       "location" : "https://github.com/ashvardanian/swift-transformers",
       "state" : {
-        "revision" : "9ef46a51eca46978b62773f8887926dfe72b0ab4"
+        "revision" : "89fb5d97e1df347f9f588f62fc538dcad6fdb16c"
       }
     }
   ],