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https://github.com/meta-llama/llama-stack.git
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cb6a5e2687
# What does this PR do? Fix segfault with load model The cc-vec integration failed with segfault when used with default embedding model on macOS `model_id: nomic-ai/nomic-embed-text-v1.5` and `provider_id: sentence-transformers` Checked crash report and see this is due to torch OPENMP settings. Constrainting to 1 thread works without crashes. ## Test Plan Tested with cc-vec integration 1. start server llama stack run starter 2. Do the setup in https://github.com/raghotham/cc-vec to set env variables and try `uv run cc-vec index --url-patterns "%.github.io" --vector-store-name "ml-research" --limit 50 --chunk-size 800 --overlap 400`
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import asyncio
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import base64
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import platform
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import struct
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from typing import TYPE_CHECKING
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import torch
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from llama_stack.log import get_logger
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if TYPE_CHECKING:
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from sentence_transformers import SentenceTransformer
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from llama_stack.apis.inference import (
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ModelStore,
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OpenAIEmbeddingData,
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OpenAIEmbeddingsRequestWithExtraBody,
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OpenAIEmbeddingsResponse,
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OpenAIEmbeddingUsage,
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)
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EMBEDDING_MODELS = {}
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DARWIN = "Darwin"
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log = get_logger(name=__name__, category="providers::utils")
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class SentenceTransformerEmbeddingMixin:
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model_store: ModelStore
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async def openai_embeddings(
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self,
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params: OpenAIEmbeddingsRequestWithExtraBody,
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) -> OpenAIEmbeddingsResponse:
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# Convert input to list format if it's a single string
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input_list = [params.input] if isinstance(params.input, str) else params.input
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if not input_list:
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raise ValueError("Empty list not supported")
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# Get the model and generate embeddings
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model_obj = await self.model_store.get_model(params.model)
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embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id)
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embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False)
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# Convert embeddings to the requested format
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data = []
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for i, embedding in enumerate(embeddings):
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if params.encoding_format == "base64":
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# Convert float array to base64 string
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float_bytes = struct.pack(f"{len(embedding)}f", *embedding)
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embedding_value = base64.b64encode(float_bytes).decode("ascii")
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else:
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# Default to float format
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embedding_value = embedding.tolist()
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data.append(
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OpenAIEmbeddingData(
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embedding=embedding_value,
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index=i,
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)
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)
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# Not returning actual token usage
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usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
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return OpenAIEmbeddingsResponse(
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data=data,
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model=params.model,
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usage=usage,
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)
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async def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer":
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global EMBEDDING_MODELS
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loaded_model = EMBEDDING_MODELS.get(model)
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if loaded_model is not None:
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return loaded_model
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log.info(f"Loading sentence transformer for {model}...")
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def _load_model():
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from sentence_transformers import SentenceTransformer
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platform_name = platform.system()
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if platform_name == DARWIN:
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# PyTorch's OpenMP kernels can segfault on macOS when spawned from background
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# threads with the default parallel settings, so force a single-threaded CPU run.
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log.debug(f"Constraining torch threads on {platform_name} to a single worker")
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torch.set_num_threads(1)
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return SentenceTransformer(model, trust_remote_code=True)
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loaded_model = await asyncio.to_thread(_load_model)
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EMBEDDING_MODELS[model] = loaded_model
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return loaded_model
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