mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-06 10:37:22 +00:00
471b1b248b
Migrates package structure to src/ layout following Python packaging best practices. All code moved from `llama_stack/` to `src/llama_stack/`. Public API unchanged - imports remain `import llama_stack.*`. Updated build configs, pre-commit hooks, scripts, and GitHub workflows accordingly. All hooks pass, package builds cleanly. **Developer note**: Reinstall after pulling: `pip install -e .`
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
# All rights reserved.
|
|
#
|
|
# This source code is licensed under the terms described in the LICENSE file in
|
|
# the root directory of this source tree.
|
|
|
|
import asyncio
|
|
import base64
|
|
import platform
|
|
import struct
|
|
from typing import TYPE_CHECKING
|
|
|
|
import torch
|
|
|
|
from llama_stack.log import get_logger
|
|
|
|
if TYPE_CHECKING:
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
from llama_stack.apis.inference import (
|
|
ModelStore,
|
|
OpenAIEmbeddingData,
|
|
OpenAIEmbeddingsRequestWithExtraBody,
|
|
OpenAIEmbeddingsResponse,
|
|
OpenAIEmbeddingUsage,
|
|
)
|
|
|
|
EMBEDDING_MODELS = {}
|
|
|
|
DARWIN = "Darwin"
|
|
|
|
|
|
log = get_logger(name=__name__, category="providers::utils")
|
|
|
|
|
|
class SentenceTransformerEmbeddingMixin:
|
|
model_store: ModelStore
|
|
|
|
async def openai_embeddings(
|
|
self,
|
|
params: OpenAIEmbeddingsRequestWithExtraBody,
|
|
) -> OpenAIEmbeddingsResponse:
|
|
# Convert input to list format if it's a single string
|
|
input_list = [params.input] if isinstance(params.input, str) else params.input
|
|
if not input_list:
|
|
raise ValueError("Empty list not supported")
|
|
|
|
# Get the model and generate embeddings
|
|
model_obj = await self.model_store.get_model(params.model)
|
|
embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id)
|
|
embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False)
|
|
|
|
# Convert embeddings to the requested format
|
|
data = []
|
|
for i, embedding in enumerate(embeddings):
|
|
if params.encoding_format == "base64":
|
|
# Convert float array to base64 string
|
|
float_bytes = struct.pack(f"{len(embedding)}f", *embedding)
|
|
embedding_value = base64.b64encode(float_bytes).decode("ascii")
|
|
else:
|
|
# Default to float format
|
|
embedding_value = embedding.tolist()
|
|
|
|
data.append(
|
|
OpenAIEmbeddingData(
|
|
embedding=embedding_value,
|
|
index=i,
|
|
)
|
|
)
|
|
|
|
# Not returning actual token usage
|
|
usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
|
|
return OpenAIEmbeddingsResponse(
|
|
data=data,
|
|
model=params.model,
|
|
usage=usage,
|
|
)
|
|
|
|
async def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer":
|
|
global EMBEDDING_MODELS
|
|
|
|
loaded_model = EMBEDDING_MODELS.get(model)
|
|
if loaded_model is not None:
|
|
return loaded_model
|
|
|
|
log.info(f"Loading sentence transformer for {model}...")
|
|
|
|
def _load_model():
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
platform_name = platform.system()
|
|
if platform_name == DARWIN:
|
|
# PyTorch's OpenMP kernels can segfault on macOS when spawned from background
|
|
# threads with the default parallel settings, so force a single-threaded CPU run.
|
|
log.debug(f"Constraining torch threads on {platform_name} to a single worker")
|
|
torch.set_num_threads(1)
|
|
|
|
return SentenceTransformer(model, trust_remote_code=True)
|
|
|
|
loaded_model = await asyncio.to_thread(_load_model)
|
|
EMBEDDING_MODELS[model] = loaded_model
|
|
return loaded_model
|