LocalAI/backend/python/tinygrad/vendor/__init__.py

"""Vendored upstream tinygrad reference code (MIT-licensed).

Source: https://github.com/tinygrad/tinygrad
These files are not part of the `tinygrad` pip package (the `extra/` tree is
excluded from `pyproject.toml` `packages`), so we carry a pinned copy here.
"""
feat(backend): add tinygrad multimodal backend (experimental) (#9364) * feat(backend): add tinygrad multimodal backend Wire tinygrad as a new Python backend covering LLM text generation with native tool-call extraction, embeddings, Stable Diffusion 1.x image generation, and Whisper speech-to-text from a single self-contained container. Backend (`backend/python/tinygrad/`): - `backend.py` gRPC servicer with LLM Predict/PredictStream (auto-detects Llama / Qwen2 / Mistral architecture from `config.json`, supports safetensors and GGUF), Embedding via mean-pooled last hidden state, GenerateImage via the vendored SD1.x pipeline, AudioTranscription + AudioTranscriptionStream via the vendored Whisper inference loop, plus Tokenize / ModelMetadata / Status / Free. - Vendored upstream model code under `vendor/` (MIT, headers preserved): llama.py with an added `qkv_bias` flag for Qwen2-family bias support and an `embed()` method that returns the last hidden state, plus clip.py, unet.py, stable_diffusion.py (trimmed to drop the MLPerf training branch that pulls `mlperf.initializers`), audio_helpers.py and whisper.py (trimmed to drop the pyaudio listener). - Pluggable tool-call parsers under `tool_parsers/`: hermes (Qwen2.5 / Hermes), llama3_json (Llama 3.1+), qwen3_xml (Qwen 3), mistral (Mistral / Mixtral). Auto-selected from model architecture or `Options`. - `install.sh` pins Python 3.11.14 (tinygrad >=0.12 needs >=3.11; the default portable python is 3.10). - `package.sh` bundles libLLVM.so.1 + libedit/libtinfo/libgomp/libsndfile into the scratch image. `run.sh` sets `CPU_LLVM=1` and `LLVM_PATH` so tinygrad's CPU device uses the in-process libLLVM JIT instead of shelling out to the missing `clang` binary. - Local unit tests for Health and the four parsers in `test.py`. Build wiring: - Root `Makefile`: `.NOTPARALLEL`, `prepare-test-extra`, `test-extra`, `BACKEND_TINYGRAD = tinygrad\|python\|.\|false\|true`, docker-build-target eval, and `docker-build-backends` aggregator. - `.github/workflows/backend.yml`: cpu / cuda12 / cuda13 build matrix entries (mirrors the transformers backend placement). - `backend/index.yaml`: `&tinygrad` meta + cpu/cuda12/cuda13 image entries (latest + development). E2E test wiring: - `tests/e2e-backends/backend_test.go` gains an `image` capability that exercises GenerateImage and asserts a non-empty PNG is written to `dst`. New `BACKEND_TEST_IMAGE_PROMPT` / `BACKEND_TEST_IMAGE_STEPS` knobs. - Five new make targets next to `test-extra-backend-vllm`: - `test-extra-backend-tinygrad` — Qwen2.5-0.5B-Instruct + hermes, mirrors the vllm target 1:1 (5/9 specs in ~57s). - `test-extra-backend-tinygrad-embeddings` — same model, embeddings via LLM hidden state (3/9 in ~10s). - `test-extra-backend-tinygrad-sd` — stable-diffusion-v1-5 mirror, health/load/image (3/9 in ~10min, 4 diffusion steps on CPU). - `test-extra-backend-tinygrad-whisper` — openai/whisper-tiny.en against jfk.wav from whisper.cpp samples (4/9 in ~49s). - `test-extra-backend-tinygrad-all` aggregate. All four targets land green on the first MVP pass: 15 specs total, 0 failures across LLM+tools, embeddings, image generation, and speech transcription. * refactor(tinygrad): collapse to a single backend image tinygrad generates its own GPU kernels (PTX renderer for CUDA, the autogen ctypes wrappers for HIP / Metal / WebGPU) and never links against cuDNN, cuBLAS, or any toolkit-version-tied library. The only runtime dependency that varies across hosts is the driver's libcuda.so.1 / libamdhip64.so, which are injected into the container at run time by the nvidia-container / rocm runtimes. So unlike torch- or vLLM-based backends, there is no reason to ship per-CUDA-version images. - Drop the cuda12-tinygrad and cuda13-tinygrad build-matrix entries from .github/workflows/backend.yml. The sole remaining entry is renamed to -tinygrad (from -cpu-tinygrad) since it is no longer CPU-only. - Collapse backend/index.yaml to a single meta + development pair. The meta anchor carries the latest uri directly; the development entry points at the master tag. - run.sh picks the tinygrad device at launch time by probing /usr/lib/... for libcuda.so.1 / libamdhip64.so. When libcuda is visible we set CUDA=1 + CUDA_PTX=1 so tinygrad uses its own PTX renderer (avoids any nvrtc/toolkit dependency); otherwise we fall back to HIP or CLANG. CPU_LLVM=1 + LLVM_PATH keep the in-process libLLVM JIT for the CLANG path. - backend.py's _select_tinygrad_device() is trimmed to a CLANG-only fallback since production device selection happens in run.sh. Re-ran test-extra-backend-tinygrad after the change: Ran 5 of 9 Specs in 56.541 seconds — 5 Passed, 0 Failed 2026-04-15 17:48:23 +00:00			`"""Vendored upstream tinygrad reference code (MIT-licensed).`

			`Source: https://github.com/tinygrad/tinygrad`
			These files are not part of the `tinygrad` pip package (the `extra/` tree is
			excluded from `pyproject.toml` `packages`), so we carry a pinned copy here.
			`"""`