fastforks 0.1.0

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,100 @@
+---
+library_name: "fastforks"
+pipeline_tag: "image-segmentation"
+license: "cc-by-nc-4.0"
+tags:
+- "segmentation"
+- "semantic-segmentation"
+- "cuda"
+- "pytorch"
+- "fastforks"
+---
+# Mask2Former (fastforks)
+
+Mask2Former (Cheng et al. 2022): Swin backbone, multi-scale deformable attention pixel decoder and a masked-attention transformer decoder; semantic logits from mask classification.
+
+This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/huggingface/transformers> at `c4c4605f9618`.
+
+Author weight repos: <https://huggingface.co/facebook/mask2former-swin-large-ade-semantic>.
+
+Task: segmentation. Tier: stable. Outputs: `logits`. Backends: `portable`, `kernels`.
+
+Input: one RGB image. Output: per-pixel class labels (logits with `logits=True`).
+
+## Variants
+
+Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/segmentation/models/mask2former/card.toml).
+
+| variant | weights (author link) | SHA-256 | weights licence | commercial use |
+|---|---|---|---|---|
+| `mask2former-swinl-ade20k@1` | [mask2former-swinl-ade20k.config.json](https://huggingface.co/facebook/mask2former-swin-large-ade-semantic/resolve/aa25c92404a40599614215e76514c79b427c7527/config.json)<br>[mask2former-swinl-ade20k.safetensors](https://huggingface.co/facebook/mask2former-swin-large-ade-semantic/resolve/aa25c92404a40599614215e76514c79b427c7527/model.safetensors) | `573b9330846e`<br>`b143c144341c` | CC-BY-NC-4.0 | **no** |
+
+## Licence
+
+Original-code strings, recorded as they are (research-use package; check them before any other use). Code: Apache-2.0. Files: .
+
+Training data: ImageNet-21k / ImageNet-22k (full ImageNet, fall 2011 release), ADE20K (SceneParsing, 150 classes).
+
+Licence evidence: <https://github.com/facebookresearch/Mask2Former/blob/9b0651c6c1d5b3af2e6da0589b719c514ec0d69a/MODEL_ZOO.md#license>
+
+## Input size
+
+any H x W >= 1, resized or padded inside the model
+Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `mask2former-swinl-ade20k` 3000x4000 (14.1 GiB peak allocated, 15.1 reserved)
+
+## Speed and memory
+
+### Segmentation, synthetic, 1024x2048
+
+| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `mask2former-swinl-ade20k` | 244 | 145 | 103 | 60.4 | 2.4x | 1.7x | 3572 / 3878 | 1164 / 1400 | 1164 / 1400 |
+
+### Startup and first call
+
+| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- |
+| `mask2former-swinl-ade20k` | 4.86 (512x512) | 3.03 | 3.17† | 0.76 (512x512) | 0.13 | 0.07† |
+
+### torch.compile and TensorRT
+
+| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `mask2former-swinl-ade20k` | 1024x2048 | 103 | 103.2 | 0.4 | 55.6 | 129 | 60.4 | 1164 / 1400 | 1155 / 1338 | 1816 |
+
+### Fixed-size speed and peak memory, all models
+
+| model | mode | 480x640 | 720x1280 | 1080x1920 | 12 MP 3000x4000 |
+| --- | --- | --- | --- | --- | --- |
+| `mask2former-swinl-ade20k` | portable | 71.9 ms / 1194 / 1912 | 96.1 ms / 1698 / 3182 | 104.1 ms / 2985 / 4500 | 152.8 ms / 14457 / 15480 |
+| `mask2former-swinl-ade20k` | kernels | 45.3 ms / 1194 / 1816 | 60.5 ms / 1699 / 3056 | 68.5 ms / 2986 / 4374 | 126.0 ms / 14457 / 15344 |
+
+Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
+
+Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or `(fp32)` where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
+
+## Accuracy
+
+### Parity with the original code
+
+Every variant is checked against reference outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.001 and p99 <= 0.01 (absolute error of the output `logits`).
+
+## Usage
+
+```python
+from fastforks import segmentation
+
+# non-commercial weights: run with FASTFORKS_NONCOMMERCIAL=1 in the environment
+model = segmentation.load("mask2former-swinl-ade20k")  # mode="auto": the preferred backend from recorded measurements
+result = model("photo.jpg")
+result.labels  # [H, W] per-pixel class index
+result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
+```
+
+Install with `pip install fastforks` (see <https://github.com/kleinicke/fastforks>). Other variants: `mask2former-swinl-ade20k`.
+
+## Kernels backend
+
+`segmentation.load("mask2former-swinl-ade20k", mode="kernels")` runs our CUDA kernels (`fvm_seg`, `seg_up`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.
+
+<!-- staged from fastforks feccac41 (2026-11-06) -->