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* 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
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
"""
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Unit tests for the tinygrad gRPC backend.
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These tests cover the cheap paths that don't need a real model checkpoint:
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- Health responds OK
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- Tool-call parsers emit expected ToolCall structures
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The full LLM / embeddings / Stable Diffusion / Whisper paths are exercised by
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the root-level `make test-extra-backend-tinygrad-all` e2e targets, which boot
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the containerized backend against real HF checkpoints.
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"""
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import os
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import subprocess
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import sys
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import time
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import unittest
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import grpc
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import backend_pb2
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import backend_pb2_grpc
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sys.path.insert(0, os.path.dirname(__file__))
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from tool_parsers.hermes import HermesToolParser # noqa: E402
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class TestHealth(unittest.TestCase):
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def setUp(self):
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self.service = subprocess.Popen(
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["python3", "backend.py", "--addr", "localhost:50051"]
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)
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time.sleep(5)
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def tearDown(self):
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self.service.kill()
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self.service.wait()
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def test_health(self):
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with grpc.insecure_channel("localhost:50051") as channel:
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stub = backend_pb2_grpc.BackendStub(channel)
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response = stub.Health(backend_pb2.HealthMessage())
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self.assertEqual(response.message, b"OK")
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class TestHermesParser(unittest.TestCase):
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def test_single_tool_call(self):
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parser = HermesToolParser()
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text = (
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"Sure, let me check.\n"
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"<tool_call>\n"
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'{"name": "get_weather", "arguments": {"city": "Paris"}}\n'
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"</tool_call>\n"
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"Done."
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)
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content, calls = parser.parse(text)
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self.assertIn("Sure", content)
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self.assertIn("Done", content)
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self.assertEqual(len(calls), 1)
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self.assertEqual(calls[0].name, "get_weather")
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self.assertIn("Paris", calls[0].arguments)
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def test_multi_call_and_thinking(self):
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parser = HermesToolParser()
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text = (
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"<think>I need both.</think>"
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'<tool_call>{"name":"a","arguments":{"x":1}}</tool_call>'
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'<tool_call>{"name":"b","arguments":{}}</tool_call>'
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)
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result = parser.parse_full(text)
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self.assertEqual(result.reasoning, "I need both.")
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self.assertEqual([c.name for c in result.tool_calls], ["a", "b"])
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self.assertEqual(result.tool_calls[0].index, 0)
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self.assertEqual(result.tool_calls[1].index, 1)
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def test_no_tool_call_is_passthrough(self):
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parser = HermesToolParser()
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text = "plain assistant answer with no tool call"
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content, calls = parser.parse(text)
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self.assertEqual(content, text)
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self.assertEqual(calls, [])
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if __name__ == "__main__":
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unittest.main()
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