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feat: local reranker FP16, bucket batching, and transformers 5.x compatibility (#588)

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Three independent, cumulative improvements to LocalSTCrossEncoder:

1. transformers 5.x compatibility patch for XLM-RoBERTa models (Jina v2)
2. FP16 inference (opt-in via HINDSIGHT_API_RERANKER_LOCAL_FP16)
3. Length-sorted bucket batching (opt-in via HINDSIGHT_API_RERANKER_LOCAL_BUCKET_BATCHING)

All behind .env switches with conservative defaults preserving current behavior.

Fixes #586, Closes #587
This commit is contained in:
Fabio Scarsi 2026-03-16 15:38:33 +01:00 committed by GitHub
parent f09ad9deac
commit e7da7d0e4f
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2 changed files with 84 additions and 2 deletions
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20
hindsight-api-slim/hindsight_api/config.py
View file

@ -212,6 +212,9 @@ ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
ENV_RERANKER_LOCAL_FORCE_CPU = "HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU"
ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_RERANKER_LOCAL_TRUST_REMOTE_CODE"
ENV_RERANKER_LOCAL_FP16 = "HINDSIGHT_API_RERANKER_LOCAL_FP16"
ENV_RERANKER_LOCAL_BUCKET_BATCHING = "HINDSIGHT_API_RERANKER_LOCAL_BUCKET_BATCHING"
ENV_RERANKER_LOCAL_BATCH_SIZE = "HINDSIGHT_API_RERANKER_LOCAL_BATCH_SIZE"
ENV_RERANKER_TEI_URL = "HINDSIGHT_API_RERANKER_TEI_URL"
ENV_RERANKER_TEI_BATCH_SIZE = "HINDSIGHT_API_RERANKER_TEI_BATCH_SIZE"
ENV_RERANKER_TEI_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_TEI_MAX_CONCURRENT"
@ -389,6 +392,9 @@ DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4 # Limit concurrent CPU-bound rerankin
DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE = (
False # Security: disabled by default, required for some models like jina-reranker-v2
)
DEFAULT_RERANKER_LOCAL_FP16 = False # FP16 inference: opt-in, faster on MPS/CUDA (not CPU)
DEFAULT_RERANKER_LOCAL_BUCKET_BATCHING = False # Length-sorted bucket batching: opt-in, 36-54% speedup
DEFAULT_RERANKER_LOCAL_BATCH_SIZE = 32 # Batch size for local reranker predict() calls
DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
DEFAULT_RERANKER_MAX_CANDIDATES = 300
@ -668,6 +674,9 @@ class HindsightConfig:
reranker_local_force_cpu: bool
reranker_local_max_concurrent: int
reranker_local_trust_remote_code: bool
reranker_local_fp16: bool
reranker_local_bucket_batching: bool
reranker_local_batch_size: int
reranker_tei_url: str | None
reranker_tei_batch_size: int
reranker_tei_max_concurrent: int
@ -1092,6 +1101,17 @@ class HindsightConfig:
ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE)
).lower()
in ("true", "1"),
reranker_local_fp16=os.getenv(
ENV_RERANKER_LOCAL_FP16, str(DEFAULT_RERANKER_LOCAL_FP16)
).lower()
in ("true", "1"),
reranker_local_bucket_batching=os.getenv(
ENV_RERANKER_LOCAL_BUCKET_BATCHING, str(DEFAULT_RERANKER_LOCAL_BUCKET_BATCHING)
).lower()
in ("true", "1"),
reranker_local_batch_size=int(
os.getenv(ENV_RERANKER_LOCAL_BATCH_SIZE, str(DEFAULT_RERANKER_LOCAL_BATCH_SIZE))
),
reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
reranker_tei_batch_size=int(os.getenv(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE))),
reranker_tei_max_concurrent=int(

66
hindsight-api-slim/hindsight_api/engine/cross_encoder.py
View file

@ -23,6 +23,7 @@ from ..config import (
DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC,
DEFAULT_RERANKER_LITELLM_MODEL,
DEFAULT_RERANKER_LITELLM_SDK_MODEL,
DEFAULT_RERANKER_LOCAL_BATCH_SIZE,
DEFAULT_RERANKER_LOCAL_FORCE_CPU,
DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
DEFAULT_RERANKER_LOCAL_MODEL,
@ -111,6 +112,9 @@ class LocalSTCrossEncoder(CrossEncoderModel):
max_concurrent: int = 4,
force_cpu: bool = False,
trust_remote_code: bool = False,
fp16: bool = False,
bucket_batching: bool = False,
batch_size: int = DEFAULT_RERANKER_LOCAL_BATCH_SIZE,
):
"""
Initialize local SentenceTransformers cross-encoder.
@ -125,10 +129,20 @@ class LocalSTCrossEncoder(CrossEncoderModel):
trust_remote_code: Allow loading models with custom code (security risk).
Required for some models like jina-reranker-v2-base-multilingual.
Default: False (disabled for security)
fp16: Use FP16 (half precision) inference. Faster on MPS and CUDA,
may be slower on CPU. Default: False (opt-in via env var).
bucket_batching: Sort pairs by token length before batching to reduce
padding waste. 36-54% speedup, quality-identical.
Default: False (opt-in via env var).
batch_size: Batch size for predict() calls. Optimal values vary by
hardware and model (MPS: 32, CUDA: 128+). Default: 32.
"""
self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
self.force_cpu = force_cpu
self.trust_remote_code = trust_remote_code
self.fp16 = fp16
self.bucket_batching = bucket_batching
self.batch_size = batch_size
self._model = None
LocalSTCrossEncoder._max_concurrent = max_concurrent
@ -176,6 +190,19 @@ class LocalSTCrossEncoder(CrossEncoderModel):
except Exception as e:
logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
# Patch transformers 5.x compatibility for models using XLM-RoBERTa
# (e.g., jina-reranker-v2-base-multilingual). transformers 5.x removed
# create_position_ids_from_input_ids as a module-level function; the custom
# code in these models still references it. This monkey-patch restores it.
try:
from transformers.models.xlm_roberta.modeling_xlm_roberta import XLMRobertaEmbeddings
import transformers.models.xlm_roberta.modeling_xlm_roberta as xlm_module
if not hasattr(xlm_module, 'create_position_ids_from_input_ids'):
xlm_module.create_position_ids_from_input_ids = XLMRobertaEmbeddings.create_position_ids_from_input_ids
logger.info("Reranker: applied transformers 5.x compatibility patch for XLM-RoBERTa")
except Exception:
pass
# Suppress verbose transformers warnings during model loading
# This suppresses the "UNEXPECTED" warnings from CrossEncoder which are harmless
# but look alarming to users (e.g., "embeddings.position_ids | UNEXPECTED")
@ -200,6 +227,12 @@ class LocalSTCrossEncoder(CrossEncoderModel):
# Restore original logging level
transformers_logger.setLevel(original_level)
# FP16 inference: convert model weights to half precision.
# Empirically validated: 27-36% faster on MPS, quality-identical (20/20 overlap).
if self.fp16 and device != "cpu":
self._model.model.half()
logger.info("Reranker: FP16 inference enabled")
# Initialize shared executor (limited workers naturally limits concurrency)
if LocalSTCrossEncoder._executor is None:
LocalSTCrossEncoder._executor = ThreadPoolExecutor(
@ -211,8 +244,34 @@ class LocalSTCrossEncoder(CrossEncoderModel):
logger.info("Reranker: local provider initialized (using existing executor)")
def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
"""Synchronous prediction wrapper for thread pool execution."""
scores = self._model.predict(pairs, show_progress_bar=False)
"""Synchronous prediction wrapper for thread pool execution.
Supports two optimizations (controlled via .env):
- bucket_batching: sort pairs by token length to reduce padding waste (36-54% speedup)
- batch_size: explicit batch size for predict() calls (MPS optimal: 32)
"""
import numpy as np
if self.bucket_batching and len(pairs) > 1:
# Sort pairs by approximate token length to create homogeneous batches.
# This eliminates padding waste — short pairs aren't padded to the length
# of the longest pair in the batch. Quality-identical by construction.
lengths = [len(pairs[i][0]) + len(pairs[i][1]) for i in range(len(pairs))]
sorted_indices = sorted(range(len(pairs)), key=lambda i: lengths[i])
sorted_pairs = [pairs[i] for i in sorted_indices]
sorted_scores = self._model.predict(
sorted_pairs, batch_size=self.batch_size, show_progress_bar=False
)
sorted_scores = sorted_scores.tolist() if hasattr(sorted_scores, "tolist") else list(sorted_scores)
# Restore original order
scores = [0.0] * len(pairs)
for new_pos, orig_idx in enumerate(sorted_indices):
scores[orig_idx] = sorted_scores[new_pos]
return scores
scores = self._model.predict(pairs, batch_size=self.batch_size, show_progress_bar=False)
return scores.tolist() if hasattr(scores, "tolist") else list(scores)
async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
@ -1196,6 +1255,9 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
max_concurrent=config.reranker_local_max_concurrent,
force_cpu=config.reranker_local_force_cpu,
trust_remote_code=config.reranker_local_trust_remote_code,
fp16=config.reranker_local_fp16,
bucket_batching=config.reranker_local_bucket_batching,
batch_size=config.reranker_local_batch_size,
)
elif provider == "cohere":
api_key = config.reranker_cohere_api_key