From f252d5e18091d9fe99380dc455fcc9a71775569b Mon Sep 17 00:00:00 2001 From: "J.R. Murray" Date: Tue, 7 Jul 2026 15:49:29 -0400 Subject: [PATCH 1/2] feat: add Apple Silicon MPS acceleration for embedding (#1133) Rebuild of PR #1133 (yangqingggui-a11y) onto current develop. CONFLICTING against develop on GitHub since it predates the embeddinggemma multilingual-model feature -- this needed real reconciliation, not a mechanical merge, so it's not a straight cherry-pick of their commit the way a same-shape single-conflict port would be. Adds a new "mps" embedding_device that routes through PyTorch + sentence-transformers instead of ONNX Runtime. On Apple Silicon, ChromaDB's bundled ONNX embedding function enables CoreMLExecutionProvider by default, which falls back op-by-op to CPU for all-MiniLM-L6-v2 because some ops aren't implemented in CoreML's MLProgram lowering -- the resulting ANE<->CPU copies cost more than they save (yangqingggui-a11y's benchmark: 60-256x slowdown vs. PyTorch MPS on the same hardware). Routing through PyTorch MPS bypasses CoreML entirely. auto now prefers mps over coreml on Apple Silicon when the extra is installed. mps ships as an optional extra (mempalace[mps] = sentence-transformers + torch), same pattern as the existing gpu/dml/coreml extras -- not a required base dependency. Reconciliation beyond the original PR's diff: 1. Docstring/model-selection conflict: develop's embedding.py now supports two embedding MODELS (minilm default, embeddinggemma multilingual) selected independently of hardware backend/device. The PR's diff assumed a single fixed model. Merged the docstrings to describe both axes (model choice vs. backend/device) rather than picking one side. 2. A real correctness gap in the original PR's design (not present in the original problem statement, since embeddinggemma didn't exist yet): _build_mps_ef hardcodes model_name="all-MiniLM-L6-v2" with no equivalent for embeddinggemma. Silently taking the mps path for model="embeddinggemma" would build MiniLM vectors under an EF a palace still expects to be embeddinggemma-shaped -- a vector-space correctness bug, not just a missed speedup. Added an explicit guard: when device resolves to mps and model=="embeddinggemma", warn once and fall back to embeddinggemma's own (CPU) ONNX path instead of building the wrong model. Verified interactively: get_embedding_function(device="mps", model="embeddinggemma") correctly returns EmbeddinggemmaONNX, not the MiniLM MPS EF. 3. Unrelated adjacent addition on develop (_intra_op_session_options / _resolve_intra_op_threads, an ORT thread-cap fix for the same underlying issue #1068 this PR also targets, via a different mechanism) -- kept both; they're complementary, not conflicting (thread-capping helps the ONNX/CoreML path, MPS-routing avoids CoreML entirely). 4. pyproject.toml: develop has both [project.optional-dependencies].dev and a separately-listed [dependency-groups].dev (also grown with pytest-rerunfailures/hypothesis/pre-commit/mypy since the PR's base). Added sentence-transformers/torch to both so `pip install -e ".[dev]"` picks them up, not just the PEP 735 dependency-groups table the original diff touched. Verified for real on Apple Silicon hardware (MPS actually available): auto resolves to mps, get_embedding_function('mps') builds a real sentence-transformers EF and produces genuine 384-dim vectors via the actual Metal backend -- not just mocked unit tests. tests/test_embedding.py: 24 passed (real torch/sentence-transformers installed, not mocked-only). Full suite passed, ruff check/format clean. Co-Authored-By: yangqingggui-a11y --- README.md | 35 ++++ benchmarks/apple_silicon_bench.py | 270 ++++++++++++++++++++++++++++++ mempalace/config.py | 11 +- mempalace/embedding.py | 203 ++++++++++++++++++++-- mempalace/mcp_server.py | 15 +- pyproject.toml | 30 +++- tests/test_embedding.py | 188 +++++++++++++++++++++ 7 files changed, 722 insertions(+), 30 deletions(-) create mode 100644 benchmarks/apple_silicon_bench.py diff --git a/README.md b/README.md index 4983008d81..c6ab13ee2c 100644 --- a/README.md +++ b/README.md @@ -286,6 +286,41 @@ For per-message recall on top of the file-level chunks the hooks produce, run `mempalace sweep ` periodically — it stores one verbatim drawer per user/assistant message, idempotent and resume-safe. +## Embedding device + +For the default `minilm` model (`all-MiniLM-L6-v2`), switching device does +not invalidate an existing palace. Set the device with `config.json` or the +`MEMPALACE_EMBEDDING_DEVICE` env var: + +| Device | Extra to install | Notes | +|---|---|---| +| `auto` (default) | — | resolve at runtime: mps ▸ cuda ▸ coreml ▸ dml ▸ cpu | +| `cpu` | — | bundled ONNX Runtime, works everywhere | +| `cuda` | `mempalace[gpu]` | NVIDIA via ONNX Runtime CUDAExecutionProvider | +| `coreml` | `mempalace[coreml]` | Apple ANE via ONNX Runtime CoreML provider | +| `dml` | `mempalace[dml]` | Windows AMD/Intel/NVIDIA via DirectML | +| `mps` | `mempalace[mps]` | Apple Metal via PyTorch + sentence-transformers (`minilm` only) | + +The MPS path currently supports `minilm` only. With `embeddinggemma`, an +`mps` selection falls back to the model's CPU ONNX path; `openai-compat` +uses its configured endpoint and ignores the local device setting. + +On Apple Silicon, the `mps` device is materially faster than `coreml` +because ChromaDB's bundled ONNX path enables `CoreMLExecutionProvider`, +which silently falls back op-by-op to CPU for `all-MiniLM-L6-v2` — the +ANE↔CPU copies cost more than they save. Measured on M5, 200 real +chunks: `coreml` ≈ 2 chunks/s, `cpu` ≈ 45, `mps` ≈ 523. See +[`benchmarks/apple_silicon_bench.py`](benchmarks/apple_silicon_bench.py) +to reproduce. + +A note on reproducibility: same-model embeddings agree to ~1e-6 across +runtimes (ONNX vs PyTorch FP arithmetic ordering), well below cosine +retrieval's noise floor. HNSW *index construction* however is order- and +value-sensitive, so an index built end-to-end on one device is not +bit-identical to one built on another — query results stay correct, but +exact `recall@k` can shift by a hit or two between devices. Pin +`MEMPALACE_EMBEDDING_DEVICE` if you need strict reproducibility. + --- ## Requirements diff --git a/benchmarks/apple_silicon_bench.py b/benchmarks/apple_silicon_bench.py new file mode 100644 index 0000000000..4e012c9b7b --- /dev/null +++ b/benchmarks/apple_silicon_bench.py @@ -0,0 +1,270 @@ +#!/usr/bin/env python3 +""" +Apple Silicon embedding backend benchmark. + +Compares embedding throughput across: + - onnxruntime with default providers (current mempalace behavior) + - onnxruntime CPU-only + - onnxruntime CoreML-only (if available) + - sentence-transformers CPU + - sentence-transformers MPS (Metal Performance Shaders, Apple GPU) + +Usage: + python apple_silicon_bench.py # default 500 chunks + python apple_silicon_bench.py --n-chunks 2000 # more samples + python apple_silicon_bench.py --batch-size 64 # try batch sizes + python apple_silicon_bench.py --source ~/.claude/projects # use real data + +Output: markdown table ready to paste into PR description. +""" + +import argparse +import json +import platform +import random +import statistics +import time +from pathlib import Path +from typing import Callable, List + +# ---- sample data -------------------------------------------------------- +# Default: generate synthetic text chunks that look like conversation exchanges. +# With --source: sample real JSONL chunks from a user's ~/.claude/projects. + + +def synthetic_chunks(n: int, seed: int = 42) -> List[str]: + """Generate realistic-length text chunks (100-400 chars, like real convo pairs).""" + random.seed(seed) + pool = [ + "The quick brown fox jumps over the lazy dog near the riverbank at sunset.", + "When we debugged the auth token refresh flow last sprint, the race condition turned out to be in the middleware chain.", + "Postgres versus SQLite for this workload — concurrent writes, dataset growing past 10GB, want to avoid Mongo.", + "Here's the patch I applied to fix the memory leak: the reference cycle was between the event emitter and the state manager.", + "Let's switch the embedding backend from default ONNX to sentence-transformers with MPS device for Apple Silicon machines.", + ] + out = [] + for i in range(n): + # Simulate 2-5 sentences per chunk + k = random.randint(2, 5) + out.append(" ".join(random.choices(pool, k=k)) + f" [chunk_{i}]") + return out + + +def real_chunks(source_dir: Path, n: int, seed: int = 42) -> List[str]: + """Sample n chunks of conversation text from JSONL files.""" + random.seed(seed) + jsonls = list(source_dir.rglob("*.jsonl")) + random.shuffle(jsonls) + chunks = [] + for path in jsonls: + if len(chunks) >= n: + break + try: + with path.open() as f: + for line in f: + try: + entry = json.loads(line) + msg = entry.get("message", {}) + if not isinstance(msg, dict): + continue + content = msg.get("content", "") + if isinstance(content, list): + text = " ".join( + c.get("text", "") for c in content if isinstance(c, dict) + ) + else: + text = str(content) + text = text.strip() + if 50 < len(text) < 2000: + chunks.append(text[:1500]) + if len(chunks) >= n: + break + except Exception: + continue + except Exception: + continue + return chunks[:n] + + +# ---- benchmark runners -------------------------------------------------- + + +def time_it(fn: Callable[[], None], warmup: int = 1, runs: int = 3) -> dict: + """Run fn() `runs` times after `warmup` warmups. Return timing stats.""" + for _ in range(warmup): + fn() + samples = [] + for _ in range(runs): + t0 = time.perf_counter() + fn() + samples.append(time.perf_counter() - t0) + return { + "mean": statistics.mean(samples), + "min": min(samples), + "max": max(samples), + "std": statistics.stdev(samples) if len(samples) > 1 else 0, + } + + +def bench_onnx_default(chunks: List[str], batch_size: int) -> dict: + """ChromaDB's ONNXMiniLM_L6_V2 with default providers (current mempalace).""" + from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2 + + ef = ONNXMiniLM_L6_V2() + # warmup downloads model + loads session + _ = ef(chunks[:2]) + + def fn(): + for i in range(0, len(chunks), batch_size): + _ = ef(chunks[i : i + batch_size]) + + return time_it(fn) + + +def bench_onnx_cpu_only(chunks: List[str], batch_size: int) -> dict: + from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2 + + ef = ONNXMiniLM_L6_V2(preferred_providers=["CPUExecutionProvider"]) + _ = ef(chunks[:2]) + + def fn(): + for i in range(0, len(chunks), batch_size): + _ = ef(chunks[i : i + batch_size]) + + return time_it(fn) + + +def bench_onnx_coreml(chunks: List[str], batch_size: int) -> dict: + """Explicitly force CoreML — expected to raise or silently fall back.""" + try: + import onnxruntime + + if "CoreMLExecutionProvider" not in onnxruntime.get_available_providers(): + return {"skipped": "CoreML provider not available"} + from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ( + ONNXMiniLM_L6_V2, + ) + + ef = ONNXMiniLM_L6_V2( + preferred_providers=["CoreMLExecutionProvider", "CPUExecutionProvider"] + ) + _ = ef(chunks[:2]) + + def fn(): + for i in range(0, len(chunks), batch_size): + _ = ef(chunks[i : i + batch_size]) + + return time_it(fn) + except Exception as e: + return {"error": str(e)[:120]} + + +def bench_sentence_transformers(chunks: List[str], batch_size: int, device: str) -> dict: + try: + from sentence_transformers import SentenceTransformer + except ImportError: + return {"skipped": "sentence-transformers not installed"} + + try: + model = SentenceTransformer("all-MiniLM-L6-v2", device=device) + except Exception as e: + return {"error": f"load failed on {device}: {str(e)[:100]}"} + + # warmup + _ = model.encode(chunks[:2], batch_size=batch_size, show_progress_bar=False) + + def fn(): + _ = model.encode(chunks, batch_size=batch_size, show_progress_bar=False) + + return time_it(fn) + + +# ---- main --------------------------------------------------------------- + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--n-chunks", type=int, default=500) + parser.add_argument("--batch-size", type=int, default=32) + parser.add_argument("--source", type=str, default=None) + parser.add_argument("--runs", type=int, default=3) + parser.add_argument( + "--skip", + type=str, + default="", + help="Comma-separated backend names to skip (e.g. 'onnx_coreml,st_cuda')", + ) + args = parser.parse_args() + skip_set = {s.strip() for s in args.skip.split(",") if s.strip()} + + print(f"\n{'=' * 70}") + print(" Apple Silicon Embedding Benchmark") + print(f"{'=' * 70}") + print(f" Machine: {platform.machine()} / {platform.processor()}") + print(f" System: {platform.system()} {platform.release()}") + print(f" N chunks: {args.n_chunks}") + print(f" Batch size: {args.batch_size}") + print(f" Runs/warm: {args.runs}/1") + print(f"{'-' * 70}\n") + + if args.source: + print(f"Loading real chunks from {args.source}...") + chunks = real_chunks(Path(args.source).expanduser(), args.n_chunks) + print(f" loaded {len(chunks)} chunks") + else: + print("Generating synthetic chunks...") + chunks = synthetic_chunks(args.n_chunks) + if not chunks: + print("ERROR: no chunks to benchmark") + return + + backends = [ + ("onnx_default", lambda: bench_onnx_default(chunks, args.batch_size)), + ("onnx_cpu_only", lambda: bench_onnx_cpu_only(chunks, args.batch_size)), + ("onnx_coreml", lambda: bench_onnx_coreml(chunks, args.batch_size)), + ("st_cpu", lambda: bench_sentence_transformers(chunks, args.batch_size, "cpu")), + ("st_mps", lambda: bench_sentence_transformers(chunks, args.batch_size, "mps")), + ] + + results = {} + for name, runner in backends: + if name in skip_set: + print(f"Running: {name:20}... SKIP (--skip)") + results[name] = {"skipped": "user --skip"} + continue + print(f"Running: {name:20}...", end=" ", flush=True) + try: + results[name] = runner() + r = results[name] + if "skipped" in r: + print(f"SKIP ({r['skipped']})") + elif "error" in r: + print(f"ERROR ({r['error']})") + else: + rate = len(chunks) / r["mean"] + print(f"mean {r['mean']:.2f}s ({rate:.0f} chunks/s) std {r['std']:.2f}") + except Exception as e: + results[name] = {"error": str(e)[:120]} + print(f"CRASH ({str(e)[:80]})") + + # --- markdown table --- + print(f"\n{'=' * 70}") + print(" Results (markdown, ready for PR)") + print(f"{'=' * 70}\n") + print("| Backend | Mean (s) | Rate (chunks/s) | vs baseline |") + print("|---|---|---|---|") + baseline = results.get("onnx_default", {}).get("mean") + for name, r in results.items(): + if "skipped" in r or "error" in r: + print(f"| {name} | — | — | {r.get('skipped') or r.get('error')} |") + continue + rate = len(chunks) / r["mean"] + if baseline: + speedup = baseline / r["mean"] + print(f"| {name} | {r['mean']:.2f} | {rate:.0f} | {speedup:.2f}x |") + else: + print(f"| {name} | {r['mean']:.2f} | {rate:.0f} | — |") + + +if __name__ == "__main__": + main() diff --git a/mempalace/config.py b/mempalace/config.py index 25d0af3929..be15ba8729 100644 --- a/mempalace/config.py +++ b/mempalace/config.py @@ -727,11 +727,16 @@ def set_entity_languages(self, languages): @property def embedding_device(self): - """Hardware device for the ONNX embedding model. + """Hardware device for the embedding model. Values: ``"auto"`` (default), ``"cpu"``, ``"cuda"``, ``"coreml"``, - ``"dml"``. Read from env ``MEMPALACE_EMBEDDING_DEVICE`` first, then - ``embedding_device`` in ``config.json``, then ``"auto"``. + ``"dml"``, ``"mps"``. Read from env ``MEMPALACE_EMBEDDING_DEVICE`` + first, then ``embedding_device`` in ``config.json``, then ``"auto"``. + + ``cpu`` / ``cuda`` / ``coreml`` / ``dml`` are ONNX Runtime execution + providers; ``mps`` routes through PyTorch + sentence-transformers + instead, bypassing CoreML's op-fallback thrash on Apple Silicon + (see :mod:`mempalace.embedding` docstring for the full diagnosis). ``auto`` resolves to the first available accelerator at runtime via :mod:`mempalace.embedding`; requesting an unavailable accelerator diff --git a/mempalace/embedding.py b/mempalace/embedding.py index 7d4eb5cd7d..dcf798eccc 100644 --- a/mempalace/embedding.py +++ b/mempalace/embedding.py @@ -1,8 +1,9 @@ """Embedding function factory with hardware acceleration. -Returns a ChromaDB-compatible embedding function — either a local ONNX model -bound to a user-selected ONNX Runtime execution provider, or an -OpenAI-compatible HTTP ``/v1/embeddings`` endpoint. +Returns a ChromaDB-compatible embedding function using one of three execution +paths: a local ONNX model bound to a user-selected ONNX Runtime provider, +PyTorch + sentence-transformers on Apple Metal, or an OpenAI-compatible HTTP +``/v1/embeddings`` endpoint. Three embedding-model options are available, selected via ``MEMPALACE_EMBEDDING_MODEL`` or ``embedding_model`` in @@ -28,17 +29,48 @@ switching to/from this backend also requires ``mempalace repair rebuild-index``. Stays local when the endpoint is on your machine/LAN. +For ``minilm``, the backend can be either ONNX Runtime (default) or, for +the ``mps`` device specifically, PyTorch + sentence-transformers. Switching +the device for that model does not invalidate an existing palace — the same +model weights and vector space are used either way. The MPS path currently +supports ``minilm`` only: ``embeddinggemma`` falls back to its CPU ONNX path, +and ``openai-compat`` ignores the local device setting. + Supported devices (env ``MEMPALACE_EMBEDDING_DEVICE`` or ``embedding_device`` in ``~/.mempalace/config.json``): -* ``auto`` — prefer CUDA ▸ CoreML ▸ DirectML, fall back to CPU -* ``cpu`` — force CPU (the historical default) +* ``auto`` — prefer ``mps`` ▸ ``cuda`` ▸ ``coreml`` ▸ ``dml``, fall back to CPU +* ``cpu`` — force ONNX Runtime on CPU (the historical default) * ``cuda`` — NVIDIA GPU via ``onnxruntime-gpu`` (``pip install mempalace[gpu]``) -* ``coreml`` — Apple Neural Engine (macOS) +* ``coreml`` — Apple Neural Engine via ONNX Runtime CoreML provider (macOS) * ``dml`` — DirectML (Windows / AMD / Intel GPUs) - -Requesting an unavailable accelerator emits a warning and falls back to CPU -rather than hard-failing — mining must still work on a laptop without CUDA. +* ``mps`` — Apple Metal GPU via PyTorch + sentence-transformers + (``pip install mempalace[mps]``) + +Why ``mps`` is its own path: on Apple Silicon, ChromaDB's bundled ONNX +embedding function enables ``CoreMLExecutionProvider`` by default, which +silently falls back op-by-op to CPU for ``all-MiniLM-L6-v2`` because some +ops are not yet implemented in CoreML's MLProgram lowering. The resulting +ANE↔CPU copies cost more than they save (measured 60–256× slowdown vs. +PyTorch MPS on the same hardware, M5, 200 real chunks). Routing +``mps`` through sentence-transformers + PyTorch bypasses CoreML +entirely. The ``coreml`` device is retained for users who want ONNX +Runtime's CoreML provider explicitly. + +The ``mps`` and ``cuda``/``coreml``/``dml`` paths produce embeddings that +differ by ~1e-6 (numerical FP drift between ONNX Runtime and PyTorch +implementations of the same MiniLM weights). This drift is well below the +noise floor of cosine retrieval, so backends can be switched on an +existing palace without re-mining. The HNSW *index* itself is +order- and value-sensitive, so an index built end-to-end on one backend +will not be bit-identical to one built on another (graph neighbor lists +differ); query results stay correct, but exact ``recall@k`` can shift by +a hit or two between runtimes. Pin ``MEMPALACE_EMBEDDING_DEVICE`` if you +need strict reproducibility. + +Requesting an unavailable accelerator emits a warning and falls back to +CPU rather than hard-failing — mining must still work on a laptop without +CUDA, MPS, or CoreML. """ from __future__ import annotations @@ -53,20 +85,33 @@ logger = logging.getLogger(__name__) +# Sentinel returned in the provider list to mark the PyTorch-MPS path. +# Anything that consumes a (providers, device) pair must treat this as a +# signal to take the sentence-transformers branch rather than passing the +# providers through to ONNX Runtime. +_MPS_SENTINEL = "__mempalace_torch_mps__" + _PROVIDER_MAP = { "cpu": ["CPUExecutionProvider"], "cuda": ["CUDAExecutionProvider", "CPUExecutionProvider"], "coreml": ["CoreMLExecutionProvider", "CPUExecutionProvider"], "dml": ["DmlExecutionProvider", "CPUExecutionProvider"], + # "mps" is resolved without consulting onnxruntime — the sentinel exists so + # cache_key / EF-construction code can stay uniform across backends. + "mps": [_MPS_SENTINEL, "CPUExecutionProvider"], } _DEVICE_EXTRA = { "cuda": "mempalace[gpu]", "coreml": "mempalace[coreml]", "dml": "mempalace[dml]", + "mps": "mempalace[mps]", } -_AUTO_ORDER = [ +# auto-resolution order. MPS is preferred over CoreML on Apple Silicon +# because sentence-transformers + torch.mps avoids the CoreML op-fallback +# thrash that pins ``onnx_default`` to ~2 chunks/s on this model. +_AUTO_ORDER_ONNX = [ ("CUDAExecutionProvider", "cuda"), ("CoreMLExecutionProvider", "coreml"), ("DmlExecutionProvider", "dml"), @@ -80,14 +125,54 @@ _WARNED: set = set() +def _torch_mps_available() -> bool: + """Return True iff PyTorch is installed AND its MPS backend is usable. + + Wrapped in a broad ``except`` because torch can raise platform-specific + errors on non-Apple Silicon hosts (NotImplementedError, RuntimeError), + and we want a clean False fallback rather than a stack trace. + """ + try: + import torch + + return ( + hasattr(torch.backends, "mps") + and torch.backends.mps.is_available() + and torch.backends.mps.is_built() + ) + except Exception: + return False + + +def _sentence_transformers_available() -> bool: + """True iff both ``torch`` and ``sentence_transformers`` import cleanly.""" + try: + import sentence_transformers # noqa: F401 + import torch # noqa: F401 + + return True + except ImportError: + return False + + def _resolve_providers(device: str) -> tuple[list, str]: """Return ``(provider_list, effective_device)`` for ``device``. + For ONNX devices the first element of ``provider_list`` is the + onnxruntime provider name. For the ``mps`` device the first element is + :data:`_MPS_SENTINEL` and the rest of the system reads ``effective`` + to take the sentence-transformers branch. + Falls back to CPU (with a one-shot warning) when the requested - accelerator is not compiled into the installed ``onnxruntime``. + accelerator is not compiled into the installed ``onnxruntime`` or when + the optional ``mempalace[mps]`` extra is missing. """ device = (device or "auto").strip().lower() + # MPS is special: it does not consult onnxruntime providers at all. + if device == "mps": + return _resolve_mps_or_cpu() + try: import onnxruntime as ort @@ -96,7 +181,12 @@ def _resolve_providers(device: str) -> tuple[list, str]: return (["CPUExecutionProvider"], "cpu") if device == "auto": - for provider, name in _AUTO_ORDER: + # Prefer MPS on Apple Silicon when the optional extra is installed — + # CoreML thrashes on this model, so MPS is materially faster even + # though both target the same physical GPU. + if _torch_mps_available() and _sentence_transformers_available(): + return ([_MPS_SENTINEL, "CPUExecutionProvider"], "mps") + for provider, name in _AUTO_ORDER_ONNX: if provider in available: return ([provider, "CPUExecutionProvider"], name) return (["CPUExecutionProvider"], "cpu") @@ -157,6 +247,50 @@ def _resolve_intra_op_threads() -> int: return 0 +def _resolve_mps_or_cpu() -> tuple[list, str]: + """Return MPS providers if torch+MPS+ST available, else warn and fall to CPU.""" + if not _sentence_transformers_available(): + if "mps" not in _WARNED: + logger.warning( + "embedding_device='mps' requested but the optional 'mps' extra " + "is not installed — falling back to CPU. Install %s.", + _DEVICE_EXTRA["mps"], + ) + _WARNED.add("mps") + return (["CPUExecutionProvider"], "cpu") + if not _torch_mps_available(): + if "mps" not in _WARNED: + logger.warning( + "embedding_device='mps' requested but torch.backends.mps is " + "not available on this host — falling back to CPU." + ) + _WARNED.add("mps") + return (["CPUExecutionProvider"], "cpu") + return ([_MPS_SENTINEL, "CPUExecutionProvider"], "mps") + + +def _resolve_providers_for_model(device: str, model: str) -> tuple[list, str]: + """Resolve a local device while enforcing model/backend compatibility.""" + normalized_model = (model or "minilm").strip().lower() + if normalized_model == "openai-compat": + return ([], "openai-compat") + + providers, effective = _resolve_providers(device) + if effective == "mps" and normalized_model == "embeddinggemma": + # _build_mps_ef only has a verified build for all-MiniLM-L6-v2. + # Silently taking the MPS path here would build MiniLM vectors under + # an EF the palace expects to be embeddinggemma-shaped — a vector-space + # correctness bug, not merely a missed speedup. + if "mps-embeddinggemma" not in _WARNED: + logger.warning( + "embedding_device='mps' has no embeddinggemma implementation; " + "using CPU for model='embeddinggemma' instead." + ) + _WARNED.add("mps-embeddinggemma") + return (["CPUExecutionProvider"], "cpu") + return (providers, effective) + + def _build_ef_class(): """Subclass ``ONNXMiniLM_L6_V2`` with name ``"default"``. @@ -621,6 +755,33 @@ def _vectors_from_response(self, data, n: int) -> list: return (arr / norms).tolist() +def _build_mps_ef(model_name: str = "all-MiniLM-L6-v2"): + """Build a sentence-transformers EF on torch MPS, named ``"default"``. + + The ``name()`` override mirrors :func:`_build_ef_class` — it lets a + palace persisted by ChromaDB's bundled default EF reopen with this EF + without tripping the 1.x ``Embedding function conflict`` guard. The + embeddings produced agree with ONNX Runtime's to ~1e-6 (same MiniLM + weights, FP32 vs ANE/MPS arithmetic ordering), well below cosine + retrieval's noise floor. + + Only builds ``all-MiniLM-L6-v2`` — there is no sentence-transformers + equivalent wired up for ``embeddinggemma`` here (only its ONNX artifact + via :class:`EmbeddinggemmaONNX`). Callers must not route + ``model="embeddinggemma"`` through this function. + """ + from chromadb.utils.embedding_functions.sentence_transformer_embedding_function import ( + SentenceTransformerEmbeddingFunction, + ) + + class _MempalaceMPS(SentenceTransformerEmbeddingFunction): + @staticmethod + def name() -> str: + return "default" + + return _MempalaceMPS(model_name=model_name, device="mps") + + def get_embedding_function(device: Optional[str] = None, model: Optional[str] = None): """Return a cached embedding function for the requested device + model. @@ -673,7 +834,8 @@ def get_embedding_function(device: Optional[str] = None, model: Optional[str] = ) return ef - providers, effective = _resolve_providers(device) + providers, effective = _resolve_providers_for_model(device, model) + cache_key = (model, tuple(providers)) cached = _EF_CACHE.get(cache_key) # lock-free fast path; dict.get is GIL-atomic if cached is not None: @@ -686,6 +848,8 @@ def get_embedding_function(device: Optional[str] = None, model: Optional[str] = threads = _resolve_intra_op_threads() if model == "embeddinggemma": ef = EmbeddinggemmaONNX(preferred_providers=providers, intra_op_num_threads=threads) + elif providers and providers[0] == _MPS_SENTINEL: + ef = _build_mps_ef() else: # Default: minilm (or anything we don't recognize — back-compat win). ef_cls = _build_ef_class() @@ -701,7 +865,7 @@ def get_embedding_function(device: Optional[str] = None, model: Optional[str] = return ef -def describe_device(device: Optional[str] = None) -> str: +def describe_device(device: Optional[str] = None, model: Optional[str] = None) -> str: """Return a short human-readable label for the resolved embedding backend. Used by the miner CLI header / MCP status so users can see at a glance @@ -709,15 +873,18 @@ def describe_device(device: Optional[str] = None) -> str: that embeddings are served by a remote endpoint rather than local hardware (in which case the ``embedding_device`` accelerator label is irrelevant). """ - if device is None: + if device is None or model is None: from .config import MempalaceConfig cfg = MempalaceConfig() - if cfg.embedding_model == "openai-compat": + if model is None: + model = cfg.embedding_model + if model == "openai-compat": url = cfg.embedding_api_url return f"openai-compat ({url})" if url else "openai-compat" - device = cfg.embedding_device - _, effective = _resolve_providers(device) + if device is None: + device = cfg.embedding_device + _, effective = _resolve_providers_for_model(device, model) return effective diff --git a/mempalace/mcp_server.py b/mempalace/mcp_server.py index ff4f28fd7a..b99f0444ab 100644 --- a/mempalace/mcp_server.py +++ b/mempalace/mcp_server.py @@ -7163,7 +7163,13 @@ def _http_serve_sync(handler, path: str) -> bool: _NODE_PROFILE_TTL_S = 60.0 _node_profile_cache: dict = {} -_ACCELERATOR_NAMES = {"cuda": "CUDA", "dml": "DirectML", "coreml": "CoreML", "cpu": "CPU"} +_ACCELERATOR_NAMES = { + "cuda": "CUDA", + "dml": "DirectML", + "coreml": "CoreML", + "mps": "MPS", + "cpu": "CPU", +} def _node_profile() -> dict: @@ -7193,13 +7199,14 @@ def _node_profile() -> dict: logger.debug("node profile: logstream authorship unavailable", exc_info=True) accelerator = None try: - from .embedding import _resolve_providers, current_model_name + from .embedding import _resolve_providers_for_model, current_model_name device = getattr(_config, "embedding_device", None) or "auto" - _providers, effective = _resolve_providers(device) + model = current_model_name() + _providers, effective = _resolve_providers_for_model(device, model) accelerator = { "provider": _ACCELERATOR_NAMES.get(effective, effective), - "embedder": current_model_name(), + "embedder": model, } roles.append("compute") except Exception: diff --git a/pyproject.toml b/pyproject.toml index ebfc95bdfe..b2ce74f0b8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -98,6 +98,12 @@ dev = [ # Run ad-hoc with ``mypy mempalace/``; CI doesn't gate on it yet but # contributors should fix mypy errors they see before pushing. "mypy>=1.0", + # dev installs include the [mps] extra so the embedding test suite can + # exercise the sentence-transformers branch without a separate install + # step. CI runners on Linux pull torch CPU wheels (no ROCm / CUDA + # needed for tests). + "sentence-transformers>=2.2.0", + "torch>=2.0.0", ] spellcheck = ["autocorrect>=2.0"] # Opt-in Milvus backend. Defaults to per-palace Milvus Lite @@ -112,14 +118,23 @@ milvus = [ # MEMPALACE_BACKEND=pgvector / --backend pgvector; never required for the # default (Chroma) install. pgvector = ["psycopg[binary]>=3.1"] -# Hardware acceleration for the ONNX embedding model. Install exactly one: -# pip install mempalace[gpu] — NVIDIA CUDA -# pip install mempalace[dml] — DirectML (Windows AMD/Intel/NVIDIA) -# pip install mempalace[coreml] — macOS Neural Engine -# After install, set MEMPALACE_EMBEDDING_DEVICE=cuda|dml|coreml (or "auto"). +# Hardware acceleration for the embedding model. Install exactly one: +# pip install mempalace[gpu] — NVIDIA CUDA (ONNX Runtime CUDAExecutionProvider) +# pip install mempalace[dml] — DirectML (Windows AMD/Intel/NVIDIA via ONNX Runtime) +# pip install mempalace[coreml] — Apple ANE (ONNX Runtime CoreMLExecutionProvider) +# pip install mempalace[mps] — Apple Metal (PyTorch + sentence-transformers, bypasses ONNX) +# After install, set MEMPALACE_EMBEDDING_DEVICE=cuda|dml|coreml|mps (or "auto"). +# +# Why both [coreml] and [mps] for macOS: the CoreML execution provider falls +# back op-by-op to CPU for all-MiniLM-L6-v2 because some MLProgram lowerings +# are unimplemented; the resulting ANE↔CPU copies thrash the bus and pin +# throughput at ~2 chunks/s on the measured M5. Routing through PyTorch MPS +# instead (sentence-transformers, same weights) measured ~523 chunks/s on that +# system. Both remain explicit options because performance can vary by host. gpu = ["onnxruntime-gpu>=1.16"] dml = ["onnxruntime-directml>=1.16"] coreml = ["onnxruntime>=1.16"] +mps = ["sentence-transformers>=2.2.0", "torch>=2.0.0"] # Multilingual extra kept as a no-op alias for back-compat with # `pip install mempalace[multilingual]` — these deps now ship in core. multilingual = [] @@ -140,6 +155,9 @@ extract = [ ] [dependency-groups] +# dev installs include the [mps] extra so the embedding test suite can +# exercise the sentence-transformers branch without a separate install step. +# CI runners on Linux pull torch CPU wheels (no ROCm / CUDA needed for tests). dev = [ "pytest>=7.0", "pytest-cov>=4.0", @@ -152,6 +170,8 @@ dev = [ "hypothesis>=6.0", "pre-commit>=3.0", "mypy>=1.0", + "sentence-transformers>=2.2.0", + "torch>=2.0.0", ] [build-system] diff --git a/tests/test_embedding.py b/tests/test_embedding.py index 83ccd7cde0..75d481d35a 100644 --- a/tests/test_embedding.py +++ b/tests/test_embedding.py @@ -7,6 +7,11 @@ def isolate_embedding_state(monkeypatch): monkeypatch.setattr(embedding, "_EF_CACHE", {}) monkeypatch.setattr(embedding, "_WARNED", set()) + # Default-off MPS for the onnx-focused tests below so they keep their + # original meaning even on an Apple Silicon dev machine where the [mps] + # extra is installed. MPS-specific tests re-enable these explicitly. + monkeypatch.setattr(embedding, "_torch_mps_available", lambda: False) + monkeypatch.setattr(embedding, "_sentence_transformers_available", lambda: False) def test_auto_picks_cuda(monkeypatch): @@ -318,3 +323,186 @@ def _explode(*_, **__): monkeypatch.setattr(embedding, "get_embedding_function", _explode) assert ew._embed_texts([]) == [] + + +# --------------------------------------------------------------------------- +# MPS device — sentence-transformers / torch.mps branch +# --------------------------------------------------------------------------- +# +# Why MPS is its own path: ChromaDB's bundled ONNXMiniLM_L6_V2 enables +# CoreMLExecutionProvider by default, which silently falls back op-by-op +# to CPU for all-MiniLM-L6-v2 on Apple Silicon. The ANE↔CPU copies cost +# more than they save (measured 60-256x slowdown). Routing through +# sentence-transformers + torch.mps bypasses CoreML entirely and runs +# the model directly on the Metal GPU. + + +def test_mps_explicit_resolves_to_sentinel_when_available(monkeypatch): + """Explicit device='mps' returns the MPS sentinel when torch+ST+MPS line up.""" + monkeypatch.setattr(embedding, "_torch_mps_available", lambda: True) + monkeypatch.setattr(embedding, "_sentence_transformers_available", lambda: True) + + providers, effective = embedding._resolve_providers("mps") + assert providers[0] == embedding._MPS_SENTINEL + assert effective == "mps" + + +def test_mps_missing_extra_warns_and_falls_to_cpu(monkeypatch, caplog): + """device='mps' without the [mps] extra installed -> CPU + actionable warning.""" + monkeypatch.setattr(embedding, "_torch_mps_available", lambda: False) + monkeypatch.setattr(embedding, "_sentence_transformers_available", lambda: False) + + providers, effective = embedding._resolve_providers("mps") + assert providers == ["CPUExecutionProvider"] + assert effective == "cpu" + assert "mempalace[mps]" in caplog.text + + +def test_mps_st_installed_but_no_metal_warns(monkeypatch, caplog): + """ST installed but torch.backends.mps unavailable (e.g. Linux/Intel Mac).""" + monkeypatch.setattr(embedding, "_sentence_transformers_available", lambda: True) + monkeypatch.setattr(embedding, "_torch_mps_available", lambda: False) + + providers, effective = embedding._resolve_providers("mps") + assert providers == ["CPUExecutionProvider"] + assert effective == "cpu" + assert "torch.backends.mps" in caplog.text + + +def test_auto_prefers_mps_over_coreml_when_torch_mps_available(monkeypatch): + """The whole point of this PR: MPS wins on Apple Silicon, even if CoreML works.""" + monkeypatch.setattr(embedding, "_torch_mps_available", lambda: True) + monkeypatch.setattr(embedding, "_sentence_transformers_available", lambda: True) + monkeypatch.setattr( + "onnxruntime.get_available_providers", + lambda: ["CoreMLExecutionProvider", "CPUExecutionProvider"], + ) + + providers, effective = embedding._resolve_providers("auto") + assert effective == "mps" + assert providers[0] == embedding._MPS_SENTINEL + + +def test_auto_falls_to_coreml_when_torch_unavailable(monkeypatch): + """Without the [mps] extra, auto on Apple Silicon should still use CoreML.""" + monkeypatch.setattr(embedding, "_torch_mps_available", lambda: False) + monkeypatch.setattr(embedding, "_sentence_transformers_available", lambda: False) + monkeypatch.setattr( + "onnxruntime.get_available_providers", + lambda: ["CoreMLExecutionProvider", "CPUExecutionProvider"], + ) + + providers, effective = embedding._resolve_providers("auto") + assert effective == "coreml" + assert providers == ["CoreMLExecutionProvider", "CPUExecutionProvider"] + + +def test_get_embedding_function_routes_mps_to_st_branch(monkeypatch): + """When the resolver returns the MPS sentinel, get_embedding_function must + take the sentence-transformers path (``_build_mps_ef``), not the ONNX path.""" + + class DummySTEF: + pass + + onnx_called = False + + def fake_build_ef_class(): + nonlocal onnx_called + onnx_called = True + + class _Dummy: + def __init__(self, **_): + pass + + return _Dummy + + monkeypatch.setattr(embedding, "_build_ef_class", fake_build_ef_class) + monkeypatch.setattr(embedding, "_build_mps_ef", lambda: DummySTEF()) + monkeypatch.setattr( + embedding, + "_resolve_providers", + lambda device: ([embedding._MPS_SENTINEL, "CPUExecutionProvider"], "mps"), + ) + + ef = embedding.get_embedding_function("mps") + assert isinstance(ef, DummySTEF) + assert onnx_called is False # ONNX class was never built — proves routing + + +def test_get_embedding_function_caches_mps_branch(monkeypatch): + """The MPS branch must hit the same ``_EF_CACHE`` as the ONNX branch.""" + + class DummySTEF: + pass + + monkeypatch.setattr(embedding, "_build_mps_ef", lambda: DummySTEF()) + monkeypatch.setattr( + embedding, + "_resolve_providers", + lambda device: ([embedding._MPS_SENTINEL, "CPUExecutionProvider"], "mps"), + ) + + a = embedding.get_embedding_function("mps") + b = embedding.get_embedding_function("mps") + assert a is b + + +def test_embeddinggemma_mps_falls_back_to_cpu_onnx(monkeypatch, caplog): + """MPS must never build MiniLM vectors for an EmbeddingGemma palace.""" + captured = {} + + class DummyGemma: + def __init__(self, preferred_providers=None, intra_op_num_threads=0): + captured["providers"] = preferred_providers + + monkeypatch.setattr(embedding, "EmbeddinggemmaONNX", DummyGemma) + monkeypatch.setattr( + embedding, + "_resolve_providers", + lambda device: ([embedding._MPS_SENTINEL, "CPUExecutionProvider"], "mps"), + ) + monkeypatch.setattr( + embedding, + "_build_mps_ef", + lambda: (_ for _ in ()).throw(AssertionError("must not build MiniLM MPS EF")), + ) + + ef = embedding.get_embedding_function("mps", "embeddinggemma") + + assert isinstance(ef, DummyGemma) + assert captured["providers"] == ["CPUExecutionProvider"] + assert "using CPU" in caplog.text + + +def test_describe_device_reports_model_compatible_mps_fallback(monkeypatch): + monkeypatch.setattr( + embedding, + "_resolve_providers", + lambda device: ([embedding._MPS_SENTINEL, "CPUExecutionProvider"], "mps"), + ) + + assert embedding.describe_device("mps", "minilm") == "mps" + assert embedding.describe_device("mps", "embeddinggemma") == "cpu" + + +def test_unknown_device_does_not_match_mps_sentinel(monkeypatch, caplog): + """Regression guard: arbitrary unknown strings must not accidentally route to MPS.""" + monkeypatch.setattr("onnxruntime.get_available_providers", lambda: ["CPUExecutionProvider"]) + providers, effective = embedding._resolve_providers("__mempalace_torch_mps__") + assert effective == "cpu" + assert providers == ["CPUExecutionProvider"] + + +def test_torch_mps_available_returns_bool_without_torch(monkeypatch): + """Helper must not raise when torch is missing — important for non-Apple CI.""" + import builtins + + real_import = builtins.__import__ + + def fake_import(name, *args, **kwargs): + if name == "torch": + raise ImportError("no torch in this venv") + return real_import(name, *args, **kwargs) + + monkeypatch.setattr(builtins, "__import__", fake_import) + assert embedding._torch_mps_available() is False From f6bf2607fd787ffa759787b91661f83cabb2fed7 Mon Sep 17 00:00:00 2001 From: yangqingggui-a11y Date: Tue, 18 Aug 2026 15:11:49 +0800 Subject: [PATCH 2/2] fix: refresh MPS benchmark and lockfile Honor the benchmark --runs option across every backend and regenerate uv.lock for the optional MPS dependencies. --- benchmarks/apple_silicon_bench.py | 41 +- uv.lock | 2432 ++++++++++++++++++++++++----- 2 files changed, 2083 insertions(+), 390 deletions(-) diff --git a/benchmarks/apple_silicon_bench.py b/benchmarks/apple_silicon_bench.py index 4e012c9b7b..df175cc448 100644 --- a/benchmarks/apple_silicon_bench.py +++ b/benchmarks/apple_silicon_bench.py @@ -106,7 +106,7 @@ def time_it(fn: Callable[[], None], warmup: int = 1, runs: int = 3) -> dict: } -def bench_onnx_default(chunks: List[str], batch_size: int) -> dict: +def bench_onnx_default(chunks: List[str], batch_size: int, runs: int) -> dict: """ChromaDB's ONNXMiniLM_L6_V2 with default providers (current mempalace).""" from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2 @@ -118,10 +118,10 @@ def fn(): for i in range(0, len(chunks), batch_size): _ = ef(chunks[i : i + batch_size]) - return time_it(fn) + return time_it(fn, runs=runs) -def bench_onnx_cpu_only(chunks: List[str], batch_size: int) -> dict: +def bench_onnx_cpu_only(chunks: List[str], batch_size: int, runs: int) -> dict: from chromadb.utils.embedding_functions.onnx_mini_lm_l6_v2 import ONNXMiniLM_L6_V2 ef = ONNXMiniLM_L6_V2(preferred_providers=["CPUExecutionProvider"]) @@ -131,10 +131,10 @@ def fn(): for i in range(0, len(chunks), batch_size): _ = ef(chunks[i : i + batch_size]) - return time_it(fn) + return time_it(fn, runs=runs) -def bench_onnx_coreml(chunks: List[str], batch_size: int) -> dict: +def bench_onnx_coreml(chunks: List[str], batch_size: int, runs: int) -> dict: """Explicitly force CoreML — expected to raise or silently fall back.""" try: import onnxruntime @@ -154,12 +154,12 @@ def fn(): for i in range(0, len(chunks), batch_size): _ = ef(chunks[i : i + batch_size]) - return time_it(fn) + return time_it(fn, runs=runs) except Exception as e: return {"error": str(e)[:120]} -def bench_sentence_transformers(chunks: List[str], batch_size: int, device: str) -> dict: +def bench_sentence_transformers(chunks: List[str], batch_size: int, device: str, runs: int) -> dict: try: from sentence_transformers import SentenceTransformer except ImportError: @@ -176,7 +176,7 @@ def bench_sentence_transformers(chunks: List[str], batch_size: int, device: str) def fn(): _ = model.encode(chunks, batch_size=batch_size, show_progress_bar=False) - return time_it(fn) + return time_it(fn, runs=runs) # ---- main --------------------------------------------------------------- @@ -219,11 +219,26 @@ def main(): return backends = [ - ("onnx_default", lambda: bench_onnx_default(chunks, args.batch_size)), - ("onnx_cpu_only", lambda: bench_onnx_cpu_only(chunks, args.batch_size)), - ("onnx_coreml", lambda: bench_onnx_coreml(chunks, args.batch_size)), - ("st_cpu", lambda: bench_sentence_transformers(chunks, args.batch_size, "cpu")), - ("st_mps", lambda: bench_sentence_transformers(chunks, args.batch_size, "mps")), + ( + "onnx_default", + lambda: bench_onnx_default(chunks, args.batch_size, args.runs), + ), + ( + "onnx_cpu_only", + lambda: bench_onnx_cpu_only(chunks, args.batch_size, args.runs), + ), + ( + "onnx_coreml", + lambda: bench_onnx_coreml(chunks, args.batch_size, args.runs), + ), + ( + "st_cpu", + lambda: bench_sentence_transformers(chunks, args.batch_size, "cpu", args.runs), + ), + ( + "st_mps", + lambda: bench_sentence_transformers(chunks, args.batch_size, "mps", args.runs), + ), ] results = {} diff --git a/uv.lock b/uv.lock index f01121357d..808ad80752 100644 --- a/uv.lock +++ b/uv.lock @@ -43,9 +43,9 @@ resolution-markers = [ "python_full_version < '3.10'", ] dependencies = [ - { name = "exceptiongroup" }, - { name = "idna" }, - { name = "typing-extensions" }, + { name = "exceptiongroup", marker = "python_full_version < '3.10'" }, + { name = "idna", marker = "python_full_version < '3.10'" }, + { name = "typing-extensions", marker = "python_full_version < '3.10'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" } wheels = [ @@ -71,9 +71,9 @@ resolution-markers = [ "python_full_version == '3.10.*' and sys_platform != 'win32'", ] dependencies = [ - { name = "exceptiongroup", marker = "python_full_version < '3.11'" }, - { name = "idna" }, - { name = "typing-extensions", marker = "python_full_version < '3.13'" }, + { name = "exceptiongroup", marker = "python_full_version == '3.10.*'" }, + { name = "idna", marker = "python_full_version >= '3.10'" }, + { name = "typing-extensions", marker = "python_full_version >= '3.10' and python_full_version < '3.13'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" } wheels = [ @@ -210,8 +210,8 @@ name = "beautifulsoup4" version = "4.14.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "soupsieve" }, - { name = "typing-extensions" }, + { name = "soupsieve", marker = "python_full_version >= '3.10'" }, + { name = "typing-extensions", marker = "python_full_version >= '3.10'" }, ] sdist = { url = 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