feat(backend): add tinygrad multimodal backend (experimental)#9364
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feat(backend): add tinygrad multimodal backend (experimental)#9364
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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.
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
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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.pygRPC servicer with LLM Predict/PredictStream (auto-detects Llama / Qwen2 / Mistral architecture fromconfig.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.vendor/(MIT, headers preserved): llama.py with an addedqkv_biasflag for Qwen2-family bias support and anembed()method that returns the last hidden state, plus clip.py, unet.py, stable_diffusion.py (trimmed to drop the MLPerf training branch that pullsmlperf.initializers), audio_helpers.py and whisper.py (trimmed to drop the pyaudio listener).tool_parsers/: hermes (Qwen2.5 / Hermes), llama3_json (Llama 3.1+), qwen3_xml (Qwen 3), mistral (Mistral / Mixtral). Auto-selected from model architecture orOptions.install.shpins Python 3.11.14 (tinygrad >=0.12 needs >=3.11; the default portable python is 3.10).package.shbundles libLLVM.so.1 + libedit/libtinfo/libgomp/libsndfile into the scratch image.run.shsetsCPU_LLVM=1andLLVM_PATHso tinygrad's CPU device uses the in-process libLLVM JIT instead of shelling out to the missingclangbinary.test.py.Build wiring:
Makefile:.NOTPARALLEL,prepare-test-extra,test-extra,BACKEND_TINYGRAD = tinygrad|python|.|false|true, docker-build-target eval, anddocker-build-backendsaggregator..github/workflows/backend.yml: cpu / cuda12 / cuda13 build matrix entries (mirrors the transformers backend placement).backend/index.yaml:&tinygradmeta + cpu/cuda12/cuda13 image entries (latest + development).E2E test wiring:
tests/e2e-backends/backend_test.gogains animagecapability that exercises GenerateImage and asserts a non-empty PNG is written todst. NewBACKEND_TEST_IMAGE_PROMPT/BACKEND_TEST_IMAGE_STEPSknobs.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-allaggregate.All four targets land green on the first MVP pass: 15 specs total, 0 failures across LLM+tools, embeddings, image generation, and speech transcription.
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This PR fixes #
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