mirror of
https://github.com/qdrant/fastembed.git
synced 2026-07-23 11:20:51 -05:00
implement data-parallel inference and up version
This commit is contained in:
generall
@@ -8,6 +8,7 @@ The default embedding supports "query" and "passage" prefixes for the input text
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- Quantized model weights
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- ONNX Runtime, no PyTorch dependency
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- CPU-first design
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- Data-parallelism for encoding of large datasets
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2. Accuracy/Recall
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- Better than OpenAI Ada-002
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@@ -3,8 +3,10 @@ import os
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import shutil
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import tarfile
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from abc import ABC, abstractmethod
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from itertools import islice
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from multiprocessing import get_all_start_methods
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from pathlib import Path
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from typing import Dict, Iterable, List, Union
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from typing import Dict, Iterable, List, Union, Generator, Any, Tuple
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import onnxruntime as ort
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import numpy as np
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@@ -12,6 +14,21 @@ import requests
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from tokenizers import Tokenizer, AddedToken
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from tqdm import tqdm
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from fastembed.parallel_processor import Worker, ParallelWorkerPool
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def iter_batch(iterable: Union[Iterable, Generator], size: int) -> Iterable:
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"""
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>>> list(iter_batch([1,2,3,4,5], 3))
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[[1, 2, 3], [4, 5]]
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"""
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source_iter = iter(iterable)
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while source_iter:
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b = list(islice(source_iter, size))
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if len(b) == 0:
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break
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yield b
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def normalize(input_array, p=2, dim=1, eps=1e-12):
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# Calculate the Lp norm along the specified dimension
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@@ -21,6 +38,122 @@ def normalize(input_array, p=2, dim=1, eps=1e-12):
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return normalized_array
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class EmbeddingModel(ABC):
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@classmethod
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def load_tokenizer(cls, model_dir: Path, max_length: int = 512) -> Tokenizer:
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config_path = model_dir / "config.json"
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if not config_path.exists():
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raise ValueError(f"Could not find config.json in {model_dir}")
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tokenizer_path = model_dir / "tokenizer.json"
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if not tokenizer_path.exists():
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raise ValueError(f"Could not find tokenizer.json in {model_dir}")
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tokenizer_config_path = model_dir / "tokenizer_config.json"
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if not tokenizer_config_path.exists():
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raise ValueError(f"Could not find tokenizer_config.json in {model_dir}")
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tokens_map_path = model_dir / "special_tokens_map.json"
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if not tokens_map_path.exists():
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raise ValueError(f"Could not find special_tokens_map.json in {model_dir}")
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config = json.load(open(str(config_path)))
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tokenizer_config = json.load(open(str(tokenizer_config_path)))
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tokens_map = json.load(open(str(tokens_map_path)))
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tokenizer = Tokenizer.from_file(str(tokenizer_path))
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tokenizer.enable_truncation(max_length=min(tokenizer_config["model_max_length"], max_length))
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tokenizer.enable_padding(pad_id=config["pad_token_id"], pad_token=tokenizer_config["pad_token"])
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for token in tokens_map.values():
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if isinstance(token, str):
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tokenizer.add_special_tokens([token])
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elif isinstance(token, dict):
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tokenizer.add_special_tokens([AddedToken(**token)])
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return tokenizer
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def __init__(
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self,
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path: Path,
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model_name: str,
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max_length: int = 512,
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max_threads: int = None,
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):
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self.path = path
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self.model_name = model_name
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model_path = self.path / "model.onnx"
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optimized_model_path = self.path / "model_optimized.onnx"
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# List of Execution Providers: https://onnxruntime.ai/docs/execution-providers
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onnx_providers = ["CPUExecutionProvider"]
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if not model_path.exists():
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# Rename file model_optimized.onnx to model.onnx if it exists
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if optimized_model_path.exists():
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optimized_model_path.rename(model_path)
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else:
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raise ValueError(f"Could not find model.onnx in {self.path}")
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# Hacky support for multilingual model
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self.exclude_token_type_ids = False
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if model_name == "intfloat/multilingual-e5-large":
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self.exclude_token_type_ids = True
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so = ort.SessionOptions()
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so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
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if max_threads is not None:
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so.intra_op_num_threads = max_threads
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so.inter_op_num_threads = max_threads
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self.tokenizer = self.load_tokenizer(self.path, max_length=max_length)
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self.model = ort.InferenceSession(str(model_path), providers=onnx_providers, sess_options=so)
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def onnx_embed(self, documents: List[str]) -> np.ndarray:
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encoded = self.tokenizer.encode_batch(documents)
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input_ids = np.array([e.ids for e in encoded])
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attention_mask = np.array([e.attention_mask for e in encoded])
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onnx_input = {
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"input_ids": np.array(input_ids, dtype=np.int64),
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"attention_mask": np.array(attention_mask, dtype=np.int64),
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}
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if not self.exclude_token_type_ids:
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onnx_input["token_type_ids"] = np.array(
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[np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64
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)
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model_output = self.model.run(None, onnx_input)
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last_hidden_state = model_output[0][:, 0]
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embeddings = normalize(last_hidden_state).astype(np.float32)
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return embeddings
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class EmbeddingWorker(Worker):
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def __init__(
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self,
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path: Path,
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model_name: str,
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max_length: int = 512,
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):
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self.model = EmbeddingModel(path=path, model_name=model_name, max_length=max_length, max_threads=1)
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@classmethod
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def start(cls, path: Path, model_name: str, max_length: int = 512, **kwargs: Any) -> "EmbeddingWorker":
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return cls(
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path=path,
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model_name=model_name,
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max_length=max_length,
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)
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def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
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for idx, batch in items:
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embeddings = self.model.onnx_embed(batch)
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yield idx, embeddings
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class Embedding(ABC):
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"""
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Abstract class for embeddings.
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@@ -220,7 +353,7 @@ class Embedding(ABC):
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for i in range(0, len(texts), batch_size):
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# Prepend "passage: " to each text
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yield from self.embed([f"passage: {t}" for t in texts[i : i + batch_size]])
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yield from self.embed([f"passage: {t}" for t in texts[i: i + batch_size]])
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def query_embed(self, query: str) -> Iterable[np.ndarray]:
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"""
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@@ -249,51 +382,19 @@ class FlagEmbedding(Embedding):
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Embedding (_type_): _description_
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"""
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@classmethod
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def load_tokenizer(cls, model_dir: Path, max_length: int = 512) -> Tokenizer:
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config_path = model_dir / "config.json"
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if not config_path.exists():
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raise ValueError(f"Could not find config.json in {model_dir}")
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tokenizer_path = model_dir / "tokenizer.json"
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if not tokenizer_path.exists():
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raise ValueError(f"Could not find tokenizer.json in {model_dir}")
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tokenizer_config_path = model_dir / "tokenizer_config.json"
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if not tokenizer_config_path.exists():
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raise ValueError(f"Could not find tokenizer_config.json in {model_dir}")
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tokens_map_path = model_dir / "special_tokens_map.json"
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if not tokens_map_path.exists():
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raise ValueError(f"Could not find special_tokens_map.json in {model_dir}")
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config = json.load(open(str(config_path)))
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tokenizer_config = json.load(open(str(tokenizer_config_path)))
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tokens_map = json.load(open(str(tokens_map_path)))
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tokenizer = Tokenizer.from_file(str(tokenizer_path))
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tokenizer.enable_truncation(max_length=min(tokenizer_config["model_max_length"], max_length))
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tokenizer.enable_padding(pad_id=config["pad_token_id"], pad_token=tokenizer_config["pad_token"])
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for token in tokens_map.values():
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if isinstance(token, str):
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tokenizer.add_special_tokens([token])
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elif isinstance(token, dict):
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tokenizer.add_special_tokens([AddedToken(**token)])
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return tokenizer
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def __init__(
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self,
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model_name: str = "BAAI/bge-small-en",
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max_length: int = 512,
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cache_dir: str = None,
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self,
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model_name: str = "BAAI/bge-small-en",
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max_length: int = 512,
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cache_dir: str = None,
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threads: int = None,
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):
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"""
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Args:
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model_name (str): The name of the model to use.
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max_length (int, optional): The maximum number of tokens. Defaults to 512. Unknown behavior for values > 512.
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cache_dir (str, optional): The path to the cache directory. Defaults to `local_cache` in the current directory.
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threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
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Raises:
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ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
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@@ -304,75 +405,48 @@ class FlagEmbedding(Embedding):
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cache_dir = Path(".").resolve() / "local_cache"
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cache_dir.mkdir(parents=True, exist_ok=True)
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model_dir = self.retrieve_model(model_name, cache_dir)
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self._cache_dir = cache_dir
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self._model_dir = self.retrieve_model(model_name, cache_dir)
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self._max_length = max_length
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model_path = model_dir / "model.onnx"
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optimized_model_path = model_dir / "model_optimized.onnx"
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self.model = EmbeddingModel(self._model_dir, self.model_name, max_length=max_length,
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max_threads=threads)
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# List of Execution Providers: https://onnxruntime.ai/docs/execution-providers
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onnx_providers = ["CPUExecutionProvider"]
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if not model_path.exists():
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# Rename file model_optimized.onnx to model.onnx if it exists
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if optimized_model_path.exists():
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optimized_model_path.rename(model_path)
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else:
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raise ValueError(f"Could not find model.onnx in {model_dir}")
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# Hacky support for multilingual model
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self.exclude_token_type_ids = False
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if model_name == "intfloat/multilingual-e5-large":
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self.exclude_token_type_ids = True
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so = ort.SessionOptions()
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so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
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self.tokenizer = self.load_tokenizer(model_dir, max_length=max_length)
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self.model = ort.InferenceSession(str(model_path), providers=onnx_providers, sess_options=so)
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def onnx_embed(self, documents: List[str]) -> np.ndarray:
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encoded = self.tokenizer.encode_batch(documents)
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input_ids = np.array([e.ids for e in encoded])
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attention_mask = np.array([e.attention_mask for e in encoded])
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onnx_input = {
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"input_ids": np.array(input_ids, dtype=np.int64),
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"attention_mask": np.array(attention_mask, dtype=np.int64),
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}
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if not self.exclude_token_type_ids:
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onnx_input["token_type_ids"] = np.array(
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[np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64
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)
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model_output = self.model.run(None, onnx_input)
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last_hidden_state = model_output[0][:, 0]
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embeddings = normalize(last_hidden_state).astype(np.float32)
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return embeddings
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def embed(self, documents: List[str], batch_size: int = 256) -> Iterable[np.ndarray]:
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def embed(
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self, documents: Union[str, Iterable[str]], batch_size: int = 256, parallel: int = 0
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) -> Iterable[np.ndarray]:
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"""
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Encode a list of documents into list of embeddings.
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We use mean pooling with attention so that the model can handle variable-length inputs.
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Args:
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documents: List of documents to embed
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documents: Iterator of documents or single document to embed
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batch_size: Batch size for encoding -- higher values will use more memory, but be faster
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parallel:
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If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
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If 0, will use default threading configuration.
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Returns:
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List of embeddings, one per document
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"""
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if type(documents) == str:
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if isinstance(documents, str):
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documents = [documents]
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# TODO: Replace loop with parallelized batching
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if len(documents) >= batch_size:
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for i in range(0, len(documents), batch_size):
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batch = documents[i : i + batch_size]
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yield from self.onnx_embed(batch)
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if parallel == 0:
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parallel = os.cpu_count()
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if parallel == 0:
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for batch in iter_batch(documents, batch_size):
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yield from self.model.onnx_embed(batch)
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else:
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vectors = self.onnx_embed(documents)
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yield from vectors
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start_method = "forkserver" if "forkserver" in get_all_start_methods() else "spawn"
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params = {
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"path": self._model_dir,
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"model_name": self.model_name,
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"max_length": self._max_length,
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}
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pool = ParallelWorkerPool(parallel, EmbeddingWorker, start_method=start_method)
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for batch in pool.ordered_map(iter_batch(documents, batch_size), **params):
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yield from batch
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class DefaultEmbedding(FlagEmbedding):
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@@ -384,12 +458,13 @@ class DefaultEmbedding(FlagEmbedding):
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"""
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def __init__(
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self,
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model_name: str = "BAAI/bge-small-en",
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max_length: int = 512,
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cache_dir: str = None,
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self,
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model_name: str = "BAAI/bge-small-en",
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max_length: int = 512,
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cache_dir: str = None,
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threads: int = None,
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):
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super().__init__(model_name, max_length=max_length, cache_dir=cache_dir)
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super().__init__(model_name, max_length=max_length, cache_dir=cache_dir, threads=threads)
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class OpenAIEmbedding(Embedding):
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207
fastembed/parallel_processor.py
Normal file
207
fastembed/parallel_processor.py
Normal file
@@ -0,0 +1,207 @@
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import logging
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import os
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from collections import defaultdict
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from enum import Enum
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from multiprocessing import Queue, get_context
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from multiprocessing.context import BaseContext
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from multiprocessing.process import BaseProcess
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from multiprocessing.sharedctypes import Synchronized as BaseValue
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from queue import Empty
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from typing import Any, Dict, Iterable, List, Optional, Type, Tuple
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# Single item should be processed in less than:
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processing_timeout = 10 * 60 # seconds
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max_internal_batch_size = 200
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class QueueSignals(str, Enum):
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stop = "stop"
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confirm = "confirm"
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error = "error"
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class Worker:
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@classmethod
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def start(cls, **kwargs: Any) -> "Worker":
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raise NotImplementedError()
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def process(self, items: Iterable[Tuple[int, Any]]) -> Iterable[Tuple[int, Any]]:
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raise NotImplementedError()
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def _worker(
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worker_class: Type[Worker],
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input_queue: Queue,
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output_queue: Queue,
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num_active_workers: BaseValue,
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worker_id: int,
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kwargs: Optional[Dict[str, Any]] = None,
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) -> None:
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"""
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A worker that pulls data pints off the input queue, and places the execution result on the output queue.
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When there are no data pints left on the input queue, it decrements
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num_active_workers to signal completion.
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"""
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if kwargs is None:
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kwargs = {}
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logging.info(f"Reader worker: {worker_id} PID: {os.getpid()}")
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try:
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worker = worker_class.start(**kwargs)
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# Keep going until you get an item that's None.
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def input_queue_iterable() -> Iterable[Any]:
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while True:
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item = input_queue.get()
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if item == QueueSignals.stop:
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break
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yield item
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for processed_item in worker.process(input_queue_iterable()):
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output_queue.put(processed_item)
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except Exception as e: # pylint: disable=broad-except
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logging.exception(e)
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output_queue.put(QueueSignals.error)
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finally:
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# It's important that we close and join the queue here before
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# decrementing num_active_workers. Otherwise our parent may join us
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# before the queue's feeder thread has passed all buffered items to
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# the underlying pipe resulting in a deadlock.
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#
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# See:
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# https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#pipes-and-queues
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# https://docs.python.org/3.6/library/multiprocessing.html?highlight=process#programming-guidelines
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output_queue.close()
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output_queue.join_thread()
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||||
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||||
with num_active_workers.get_lock():
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num_active_workers.value -= 1
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||||
|
||||
logging.info(f"Reader worker {worker_id} finished")
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||||
|
||||
|
||||
class ParallelWorkerPool:
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def __init__(self, num_workers: int, worker: Type[Worker], start_method: Optional[str] = None):
|
||||
self.worker_class = worker
|
||||
self.num_workers = num_workers
|
||||
self.input_queue: Optional[Queue] = None
|
||||
self.output_queue: Optional[Queue] = None
|
||||
self.ctx: BaseContext = get_context(start_method)
|
||||
self.processes: List[BaseProcess] = []
|
||||
self.queue_size = self.num_workers * max_internal_batch_size
|
||||
|
||||
self.num_active_workers: Optional[BaseValue] = None
|
||||
|
||||
def start(self, **kwargs: Any) -> None:
|
||||
self.input_queue = self.ctx.Queue(self.queue_size)
|
||||
self.output_queue = self.ctx.Queue(self.queue_size)
|
||||
|
||||
ctx_value = self.ctx.Value("i", self.num_workers)
|
||||
assert isinstance(ctx_value, BaseValue)
|
||||
self.num_active_workers = ctx_value
|
||||
|
||||
for worker_id in range(0, self.num_workers):
|
||||
assert hasattr(self.ctx, "Process")
|
||||
process = self.ctx.Process(
|
||||
target=_worker,
|
||||
args=(
|
||||
self.worker_class,
|
||||
self.input_queue,
|
||||
self.output_queue,
|
||||
self.num_active_workers,
|
||||
worker_id,
|
||||
kwargs.copy(),
|
||||
),
|
||||
)
|
||||
process.start()
|
||||
self.processes.append(process)
|
||||
|
||||
def ordered_map(self, stream: Iterable[Any], *args: Any, **kwargs: Any) -> Iterable[Any]:
|
||||
buffer = defaultdict(Any)
|
||||
next_expected = 0
|
||||
|
||||
for idx, item in self.semi_ordered_map(stream, *args, **kwargs):
|
||||
buffer[idx] = item
|
||||
while next_expected in buffer:
|
||||
yield buffer.pop(next_expected)
|
||||
next_expected += 1
|
||||
|
||||
def semi_ordered_map(self, stream: Iterable[Any], *args: Any, **kwargs: Any) -> Iterable[Tuple[int, Any]]:
|
||||
try:
|
||||
self.start(**kwargs)
|
||||
|
||||
assert self.input_queue is not None, "Input queue was not initialized"
|
||||
assert self.output_queue is not None, "Output queue was not initialized"
|
||||
|
||||
pushed = 0
|
||||
read = 0
|
||||
for idx, item in enumerate(stream):
|
||||
if pushed - read < self.queue_size:
|
||||
try:
|
||||
out_item = self.output_queue.get_nowait()
|
||||
except Empty:
|
||||
out_item = None
|
||||
else:
|
||||
try:
|
||||
out_item = self.output_queue.get(timeout=processing_timeout)
|
||||
except Empty as e:
|
||||
self.join_or_terminate()
|
||||
raise e
|
||||
|
||||
if out_item is not None:
|
||||
if out_item == QueueSignals.error:
|
||||
self.join_or_terminate()
|
||||
raise RuntimeError("Thread unexpectedly terminated")
|
||||
yield out_item
|
||||
read += 1
|
||||
|
||||
self.input_queue.put((idx, item))
|
||||
pushed += 1
|
||||
|
||||
for _ in range(self.num_workers):
|
||||
self.input_queue.put(QueueSignals.stop)
|
||||
|
||||
while read < pushed:
|
||||
out_item = self.output_queue.get(timeout=processing_timeout)
|
||||
if out_item == QueueSignals.error:
|
||||
self.join_or_terminate()
|
||||
raise RuntimeError("Thread unexpectedly terminated")
|
||||
yield out_item
|
||||
read += 1
|
||||
finally:
|
||||
assert self.input_queue is not None, "Input queue is None"
|
||||
assert self.output_queue is not None, "Output queue is None"
|
||||
self.input_queue.close()
|
||||
self.output_queue.close()
|
||||
|
||||
def join_or_terminate(self, timeout: Optional[int] = 1) -> None:
|
||||
"""
|
||||
Emergency shutdown
|
||||
@param timeout:
|
||||
@return:
|
||||
"""
|
||||
for process in self.processes:
|
||||
process.join(timeout=timeout)
|
||||
if process.is_alive():
|
||||
process.terminate()
|
||||
self.processes.clear()
|
||||
|
||||
def join(self) -> None:
|
||||
for process in self.processes:
|
||||
process.join()
|
||||
self.processes.clear()
|
||||
|
||||
def __del__(self) -> None:
|
||||
"""
|
||||
Terminate processes if the user hasn't joined. This is necessary as
|
||||
leaving stray processes running can corrupt shared state. In brief,
|
||||
we've observed shared memory counters being reused (when the memory was
|
||||
free from the perspective of the parent process) while the stray
|
||||
workers still held a reference to them.
|
||||
For a discussion of using destructors in Python in this manner, see
|
||||
https://eli.thegreenplace.net/2009/06/12/safely-using-destructors-in-python/.
|
||||
"""
|
||||
for process in self.processes:
|
||||
process.terminate()
|
||||
511
poetry.lock
generated
511
poetry.lock
generated
@@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
@@ -251,13 +251,13 @@ uvloop = ["uvloop (>=0.15.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "bleach"
|
||||
version = "6.0.0"
|
||||
version = "6.1.0"
|
||||
description = "An easy safelist-based HTML-sanitizing tool."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "bleach-6.0.0-py3-none-any.whl", hash = "sha256:33c16e3353dbd13028ab4799a0f89a83f113405c766e9c122df8a06f5b85b3f4"},
|
||||
{file = "bleach-6.0.0.tar.gz", hash = "sha256:1a1a85c1595e07d8db14c5f09f09e6433502c51c595970edc090551f0db99414"},
|
||||
{file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
|
||||
{file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -265,7 +265,7 @@ six = ">=1.9.0"
|
||||
webencodings = "*"
|
||||
|
||||
[package.extras]
|
||||
css = ["tinycss2 (>=1.1.0,<1.2)"]
|
||||
css = ["tinycss2 (>=1.1.0,<1.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "cairocffi"
|
||||
@@ -1008,13 +1008,13 @@ referencing = ">=0.28.0"
|
||||
|
||||
[[package]]
|
||||
name = "jupyter-client"
|
||||
version = "8.3.1"
|
||||
version = "8.4.0"
|
||||
description = "Jupyter protocol implementation and client libraries"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupyter_client-8.3.1-py3-none-any.whl", hash = "sha256:5eb9f55eb0650e81de6b7e34308d8b92d04fe4ec41cd8193a913979e33d8e1a5"},
|
||||
{file = "jupyter_client-8.3.1.tar.gz", hash = "sha256:60294b2d5b869356c893f57b1a877ea6510d60d45cf4b38057f1672d85699ac9"},
|
||||
{file = "jupyter_client-8.4.0-py3-none-any.whl", hash = "sha256:6a2a950ec23a8f62f9e4c66acec7f0ea6c7d1f80ba0992e747b10c56ce2e6dbe"},
|
||||
{file = "jupyter_client-8.4.0.tar.gz", hash = "sha256:dc1b857d5d7d76ac101766c6e9b646bf18742721126e72e5d484c75a993cada2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1031,13 +1031,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
|
||||
|
||||
[[package]]
|
||||
name = "jupyter-core"
|
||||
version = "5.3.2"
|
||||
version = "5.4.0"
|
||||
description = "Jupyter core package. A base package on which Jupyter projects rely."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupyter_core-5.3.2-py3-none-any.whl", hash = "sha256:a4af53c3fa3f6330cebb0d9f658e148725d15652811d1c32dc0f63bb96f2e6d6"},
|
||||
{file = "jupyter_core-5.3.2.tar.gz", hash = "sha256:0c28db6cbe2c37b5b398e1a1a5b22f84fd64cd10afc1f6c05b02fb09481ba45f"},
|
||||
{file = "jupyter_core-5.4.0-py3-none-any.whl", hash = "sha256:66e252f675ac04dcf2feb6ed4afb3cd7f68cf92f483607522dc251f32d471571"},
|
||||
{file = "jupyter_core-5.4.0.tar.gz", hash = "sha256:e4b98344bb94ee2e3e6c4519a97d001656009f9cb2b7f2baf15b3c205770011d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1051,13 +1051,13 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "jupyter-events"
|
||||
version = "0.7.0"
|
||||
version = "0.8.0"
|
||||
description = "Jupyter Event System library"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupyter_events-0.7.0-py3-none-any.whl", hash = "sha256:4753da434c13a37c3f3c89b500afa0c0a6241633441421f6adafe2fb2e2b924e"},
|
||||
{file = "jupyter_events-0.7.0.tar.gz", hash = "sha256:7be27f54b8388c03eefea123a4f79247c5b9381c49fb1cd48615ee191eb12615"},
|
||||
{file = "jupyter_events-0.8.0-py3-none-any.whl", hash = "sha256:81f07375c7673ff298bfb9302b4a981864ec64edaed75ca0fe6f850b9b045525"},
|
||||
{file = "jupyter_events-0.8.0.tar.gz", hash = "sha256:fda08f0defce5e16930542ce60634ba48e010830d50073c3dfd235759cee77bf"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1091,13 +1091,13 @@ jupyter-server = ">=1.1.2"
|
||||
|
||||
[[package]]
|
||||
name = "jupyter-server"
|
||||
version = "2.7.3"
|
||||
version = "2.8.0"
|
||||
description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupyter_server-2.7.3-py3-none-any.whl", hash = "sha256:8e4b90380b59d7a1e31086c4692231f2a2ea4cb269f5516e60aba72ce8317fc9"},
|
||||
{file = "jupyter_server-2.7.3.tar.gz", hash = "sha256:d4916c8581c4ebbc534cebdaa8eca2478d9f3bfdd88eae29fcab0120eac57649"},
|
||||
{file = "jupyter_server-2.8.0-py3-none-any.whl", hash = "sha256:c57270faa6530393ae69783a2d2f1874c718b9f109080581ea076b05713249fa"},
|
||||
{file = "jupyter_server-2.8.0.tar.gz", hash = "sha256:b11e2ba80667c75f55630faf8ac3d5809f8734f9006d65cce117c46a0a516ab8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1146,13 +1146,13 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov",
|
||||
|
||||
[[package]]
|
||||
name = "jupyterlab"
|
||||
version = "4.0.6"
|
||||
version = "4.0.7"
|
||||
description = "JupyterLab computational environment"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "jupyterlab-4.0.6-py3-none-any.whl", hash = "sha256:7d9dacad1e3f30fe4d6d4efc97fda25fbb5012012b8f27cc03a2283abcdee708"},
|
||||
{file = "jupyterlab-4.0.6.tar.gz", hash = "sha256:6c43ae5a6a1fd2fdfafcb3454004958bde6da76331abb44cffc6f9e436b19ba1"},
|
||||
{file = "jupyterlab-4.0.7-py3-none-any.whl", hash = "sha256:08683045117cc495531fdb39c22ababb9aaac6977a45e67cfad20046564c9c7c"},
|
||||
{file = "jupyterlab-4.0.7.tar.gz", hash = "sha256:48792efd9f962b2bcda1f87d72168ff122c288b1d97d32109e4a11b33dc862be"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1216,20 +1216,20 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
|
||||
|
||||
[[package]]
|
||||
name = "markdown"
|
||||
version = "3.4.4"
|
||||
version = "3.5"
|
||||
description = "Python implementation of John Gruber's Markdown."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "Markdown-3.4.4-py3-none-any.whl", hash = "sha256:a4c1b65c0957b4bd9e7d86ddc7b3c9868fb9670660f6f99f6d1bca8954d5a941"},
|
||||
{file = "Markdown-3.4.4.tar.gz", hash = "sha256:225c6123522495d4119a90b3a3ba31a1e87a70369e03f14799ea9c0d7183a3d6"},
|
||||
{file = "Markdown-3.5-py3-none-any.whl", hash = "sha256:4afb124395ce5fc34e6d9886dab977fd9ae987fc6e85689f08278cf0c69d4bf3"},
|
||||
{file = "Markdown-3.5.tar.gz", hash = "sha256:a807eb2e4778d9156c8f07876c6e4d50b5494c5665c4834f67b06459dfd877b3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
|
||||
|
||||
[package.extras]
|
||||
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.0)", "mkdocs-nature (>=0.4)"]
|
||||
docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
|
||||
testing = ["coverage", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
@@ -1375,13 +1375,13 @@ mkdocs = ">=1.1"
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-material"
|
||||
version = "9.4.3"
|
||||
version = "9.4.6"
|
||||
description = "Documentation that simply works"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs_material-9.4.3-py3-none-any.whl", hash = "sha256:3274a47a4e55a541b25bd8fa4937cf3f3c82a51763453511661e0052062758b9"},
|
||||
{file = "mkdocs_material-9.4.3.tar.gz", hash = "sha256:5c9abc3f6ba8f88be1f9f13df23d695ca4dddbdd8a3538e4e6279c055c3936bc"},
|
||||
{file = "mkdocs_material-9.4.6-py3-none-any.whl", hash = "sha256:78802035d5768a78139c84ad7dce0c6493e8f7dc4861727d36ed91d1520a54da"},
|
||||
{file = "mkdocs_material-9.4.6.tar.gz", hash = "sha256:09665e60df7ee9e5ff3a54af173f6d45be718b1ee7dd962bcff3102b81fb0c14"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1508,13 +1508,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
|
||||
|
||||
[[package]]
|
||||
name = "nbconvert"
|
||||
version = "7.9.0"
|
||||
version = "7.9.2"
|
||||
description = "Converting Jupyter Notebooks"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "nbconvert-7.9.0-py3-none-any.whl", hash = "sha256:50248c86cbbfda7be6acc734427ef44b928663c95cd051e84c45f8838d78d46e"},
|
||||
{file = "nbconvert-7.9.0.tar.gz", hash = "sha256:66918deab9717af2bdb8ae1fac1b0fb5b408574b8f11b0004fa7f115c158fc7e"},
|
||||
{file = "nbconvert-7.9.2-py3-none-any.whl", hash = "sha256:39fe4b8bdd1b0104fdd86fc8a43a9077ba64c720bda4c6132690d917a0a154ee"},
|
||||
{file = "nbconvert-7.9.2.tar.gz", hash = "sha256:e56cc7588acc4f93e2bb5a34ec69028e4941797b2bfaf6462f18a41d1cc258c9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1578,13 +1578,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "notebook"
|
||||
version = "7.0.4"
|
||||
version = "7.0.5"
|
||||
description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "notebook-7.0.4-py3-none-any.whl", hash = "sha256:ee738414ac01773c1ad6834cf76cc6f1ce140ac8197fd13b3e2d44d89e257f72"},
|
||||
{file = "notebook-7.0.4.tar.gz", hash = "sha256:0c1b458f72ce8774445c8ef9ed2492bd0b9ce9605ac996e2b066114f69795e71"},
|
||||
{file = "notebook-7.0.5-py3-none-any.whl", hash = "sha256:f26bd66accd54fcd96cc6696fb6c2911f15843b1c524318fd7cbdb32a763e6a6"},
|
||||
{file = "notebook-7.0.5.tar.gz", hash = "sha256:9e7c7a91de138bc8b5ee50486a20e70fa4d82d407b5622ec8beac9e13e773181"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1703,35 +1703,35 @@ lint = ["lintrunner (>=0.10.0)", "lintrunner-adapters (>=0.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "onnxruntime"
|
||||
version = "1.16.0"
|
||||
version = "1.16.1"
|
||||
description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "onnxruntime-1.16.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:69c86ba3d90c166944c4a3c8a5b2a24a7bc45e68ae5997d83279af21ffd0f5f3"},
|
||||
{file = "onnxruntime-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:604a46aa2ad6a51f2fc4df1a984ea571a43aa02424aea93464c32ce02d23b3bb"},
|
||||
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[package.dependencies]
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@@ -1838,65 +1838,65 @@ files = [
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[[package]]
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name = "pillow"
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description = "Python Imaging Library (Fork)"
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optional = false
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||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -3034,13 +3039,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "websocket-client"
|
||||
version = "1.6.3"
|
||||
version = "1.6.4"
|
||||
description = "WebSocket client for Python with low level API options"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
{file = "websocket-client-1.6.3.tar.gz", hash = "sha256:3aad25d31284266bcfcfd1fd8a743f63282305a364b8d0948a43bd606acc652f"},
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{file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
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]
|
||||
|
||||
[package.extras]
|
||||
@@ -3066,4 +3071,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.0,<3.12"
|
||||
content-hash = "5d9a19b39f38cba3e1f6fe98955b561fd947f07a117475b77be3c55844aa461b"
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content-hash = "ea9bf064edc1bdd86349fc438be503f89ec32ab0eb3effc46f1980b61b7ea2b7"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "fastembed"
|
||||
version = "0.0.5"
|
||||
version = "0.1.0"
|
||||
description = "Fast, light, accurate library built for retrieval embedding generation"
|
||||
authors = ["NirantK <nirant.bits@gmail.com>"]
|
||||
license = "Apache License"
|
||||
@@ -18,7 +18,8 @@ tqdm = "^4.65"
|
||||
requests = "^2.31"
|
||||
tokenizers = "^0.13"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^7.4.2"
|
||||
ruff = "^0.0.277"
|
||||
isort = "^5.12.0"
|
||||
black = "^23.7.0"
|
||||
@@ -28,7 +29,7 @@ mkdocstrings = "^0.22.0"
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||||
pillow = "^10.0.0"
|
||||
cairosvg = "^2.7.1"
|
||||
mknotebooks = "^0.8.0"
|
||||
pytest = "^7.4.0"
|
||||
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
from fastembed.embedding import DefaultEmbedding, Embedding
|
||||
|
||||
@@ -33,3 +34,17 @@ def test_batch_embedding():
|
||||
embeddings = np.stack(embeddings, axis=0)
|
||||
|
||||
assert embeddings.shape == (200, 384)
|
||||
|
||||
|
||||
def test_parallel_processing():
|
||||
model = DefaultEmbedding()
|
||||
|
||||
docs = ["hello world", "flag embedding"] * 100
|
||||
embeddings = list(model.embed(docs, batch_size=10, parallel=2))
|
||||
embeddings = np.stack(embeddings, axis=0)
|
||||
|
||||
embeddings_2 = list(model.embed(docs, batch_size=10, parallel=0))
|
||||
embeddings_2 = np.stack(embeddings_2, axis=0)
|
||||
|
||||
assert embeddings.shape == (200, 384)
|
||||
assert np.allclose(embeddings, embeddings_2, atol=1e-3)
|
||||
|
||||
Reference in New Issue
Block a user