mirror of
https://github.com/headroomlabs-ai/headroom.git
synced 2026-08-27 14:17:10 -04:00
c71592d421
## Description
On Apple-Silicon Macs — especially fanless models like the MacBook Air
(M5) — running the proxy with memory context injection can pin the CPU
while embedding. The embedding work runs an uncapped session on the CPU,
saturating multiple cores, which starves the proxy's asyncio loop and
leads to request timeouts.
This PR adds an **opt-in** runtime that offloads the memory embedder to
the Apple GPU (MPS). Setting `HEADROOM_EMBEDDER_RUNTIME=pytorch_mps`
routes embedding through the torch `sentence-transformers` backend on
MPS instead of the default ONNX CPU embedder, moving the work off the
CPU and keeping the proxy responsive.
The default behavior is unchanged — the feature is strictly opt-in,
env-var only, and falls through to the existing default embedder
selection (with a warning) whenever MPS or the torch dependencies are
unavailable.
Fixes: N/A — no tracking issue (surfaced while running codex auto-review
through
the proxy on a fanless MacBook Air (M5)).
## Type of Change
- [ ] Bug fix (non-breaking change that fixes an issue)
- [x] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [x] Performance improvement
- [ ] Code refactoring (no functional changes)
## Changes Made
- **Runtime selection** (`headroom/proxy/memory_handler.py`): read
`HEADROOM_EMBEDDER_RUNTIME`; when set to `pytorch_mps` **and** MPS is
actually available, route the memory embedder to the torch
`sentence-transformers` backend (Apple GPU).
- If MPS is unavailable or torch/sentence-transformers is not installed,
log a warning and fall through to the existing default embedder
selection (ONNX when available, else the pre-existing local
sentence-transformers fallback) — no crash. The default (env var unset)
is unchanged. Env-var only
- **MPS serialization** (`headroom/memory/adapters/embedders.py`):
`LocalEmbedder` now funnels every `encode()` through a dedicated
single-worker `ThreadPoolExecutor` when the resolved device is MPS.
torch-MPS is not thread-safe, and the existing `run_in_executor(None,
...)` dispatch would otherwise let concurrent proxy requests call MPS
from multiple threads. CPU/CUDA keep the shared default executor
(behavior unchanged). `close()` also drops the cached model so re-use
after close re-initializes cleanly.
- **Packaging** (`pyproject.toml`): new `pytorch-mps` extra (`torch` +
`sentence-transformers`), **platform-gated to macOS** (`; sys_platform
== 'darwin'`) since MPS is Apple-Silicon-only. Deliberately left out of
`[all]` (its deps already arrive via `[ml]`/`[memory]`).
- **Tests** (`tests/test_memory/test_embedder_mps_serialization.py`):
regression coverage for the serialized executor, concurrency safety (no
SIGABRT), CPU-path default behavior, and close/re-use re-initialization.
- **Docs**: `wiki/{configuration,memory,macos-deployment}.md`,
`docs/content/docs/{configuration,installation,memory}.mdx`,
`README.md`, `CHANGELOG.md`.
## Testing
- [x] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [x] Type checking passes (`mypy headroom`)
- [x] New tests added for new functionality
- [x] Manual testing performed (CPU-offload + concurrency profiling on
Apple Silicon)
## Test Output
```
$ pytest -v tests/test_memory/test_embedder_mps_serialization.py
collected 4 items
tests/test_memory/test_embedder_mps_serialization.py::test_cpu_uses_shared_executor PASSED [ 25%]
tests/test_memory/test_embedder_mps_serialization.py::test_mps_creates_single_worker_executor PASSED [ 50%]
tests/test_memory/test_embedder_mps_serialization.py::test_mps_concurrent_embeds_do_not_crash PASSED [ 75%]
tests/test_memory/test_embedder_mps_serialization.py::test_mps_reembed_after_close_recreates_executor PASSED [100%]
============================== 4 passed in 6.92s ===============================
$ ruff check headroom/memory/adapters/embedders.py headroom/proxy/memory_handler.py tests/test_memory/test_embedder_mps_serialization.py
All checks passed!
$ mypy headroom/memory/adapters/embedders.py headroom/proxy/memory_handler.py
Success: no issues found in 2 source files
$ pytest -q tests/test_memory/ tests/test_memory_handler_concurrent_init.py tests/test_memory_handler_native_ops.py
553 passed, 1 skipped in 13.51s
```
## Checklist
- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [x] New and existing unit tests pass locally with my changes
- [x] I have updated the CHANGELOG.md if applicable
## Additional Notes
**Why MPS (and not CoreML or a thread cap):** measured on an
Apple-Silicon Mac, the default uncapped CPU embedding session saturates
the cores; the same model on MPS runs at a fraction of the CPU (≈8x
lower sustained CPU utilization in profiling) while producing
**byte-identical embeddings** (cosine distance ≈ 0 across runtimes), so
relevance/ranking is unchanged. A CoreML execution-provider path was
evaluated and rejected: the default optimized ONNX model uses fused ops
that fall back to CPU under CoreML (no offload), and a full-precision
re-export was impractical (very low throughput + multi-GB memory). MPS
via `sentence-transformers` was the only practical GPU offload.
**Why serialization is mandatory:** torch-MPS is not thread-safe —
concurrent encode calls from a multi-worker executor abort with
`-[IOGPUMetalCommandBuffer validate]: failed assertion 'commit an
already committed command buffer'` (reproduced deterministically; a
single-worker executor resolves it).
Under concurrent load the serialized single-GPU-stream throughput meets
or exceeds the parallel CPU path while using a fraction of the cores.
**Scope / boundary:** this targets the Python **memory** embedder, which
is live on the proxy request path (memory context injection). The
Rust-backed SmartCrusher compression path is unaffected and remains
non-configurable from Python by design.
**Safety:** default behavior is unchanged (ONNX, no torch). The feature
is opt-in, env-var only, macOS-gated at the packaging layer, and
degrades gracefully (warn + the existing default embedder selection)
when MPS or the dependencies are unavailable.
|
||
|---|---|---|
| .. | ||
| app | ||
| components | ||
| content/docs | ||
| overrides | ||
| screenshots | ||
| spec | ||
| .gitignore | ||
| auth-modes.md | ||
| bedrock.md | ||
| bun.lock | ||
| next.config.mjs | ||
| observability.md | ||
| package-lock.json | ||
| package.json | ||
| postcss.config.mjs | ||
| proxy.ts | ||
| README.md | ||
| rtk-architecture.md | ||
| source.config.ts | ||
| tsconfig.json | ||
docs
This is a Next.js application generated with Create Fumadocs.
Run development server:
npm run dev
# or
pnpm dev
# or
yarn dev
Open http://localhost:3000 with your browser to see the result.
Explore
In the project, you can see:
lib/source.ts: Code for content source adapter,loader()provides the interface to access your content.lib/layout.shared.tsx: Shared options for layouts, optional but preferred to keep.
| Route | Description |
|---|---|
app/(home) |
The route group for your landing page and other pages. |
app/docs |
The documentation layout and pages. |
app/api/search/route.ts |
The Route Handler for search. |
Fumadocs MDX
A source.config.ts config file has been included, you can customise different options like frontmatter schema.
Read the Introduction for further details.
Learn More
To learn more about Next.js and Fumadocs, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
- Fumadocs - learn about Fumadocs