mirror of
https://github.com/infiniflow/ragflow.git
synced 2026-05-21 08:37:05 +08:00
811e9826d0
### What problem does this PR solve? Both tokenizer (`rag/flow/tokenizer/tokenizer.py`) and `BuiltinEmbed.encode` (`rag/llm/embedding_model.py`) currently accumulate embedding batches via `np.concatenate` inside the per-batch loop. `np.concatenate` allocates a new array and copies all existing data on every call, so accumulating N batches is O(N²) in both time and peak memory. Replacing the incremental concatenate with a list-of-batches + a single `np.vstack` at the end gives O(N) total work. For tokenizer the title-vector broadcast `np.concatenate([vts[0]] * N)` is also replaced by `np.tile`, which does the same job with a single contiguous allocation instead of building a Python list of references. This is purely a CPU/memory optimisation — output shape and dtype are unchanged. Measured impact grows with document size: - 1k chunks (batch 512, 2 iters): ~negligible - 10k chunks (20 iters): ~10× speedup on this stage - 100k chunks (195 iters): ~100× speedup, and peak RAM drops from O(N) extra to near-zero ### Type of change - [x] Performance Improvement Co-authored-by: yoan sapienza <Yoan Sapienza yoan.sapienza@orange.fr Yoan Sapienza zappy@macbookpro.home>
204 lines
9.1 KiB
Python
204 lines
9.1 KiB
Python
#
|
|
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import logging
|
|
import random
|
|
import re
|
|
|
|
import numpy as np
|
|
|
|
from common.constants import LLMType
|
|
from api.db.services.knowledgebase_service import KnowledgebaseService
|
|
from api.db.services.llm_service import LLMBundle
|
|
from api.db.joint_services.tenant_model_service import get_tenant_default_model_by_type, get_model_config_by_id, get_model_config_by_type_and_name
|
|
from common.connection_utils import timeout
|
|
from rag.flow.base import ProcessBase, ProcessParamBase
|
|
from rag.flow.parser.pdf_chunk_metadata import finalize_pdf_chunk
|
|
from rag.flow.tokenizer.schema import TokenizerFromUpstream
|
|
from rag.nlp import rag_tokenizer
|
|
from common import settings
|
|
from rag.svr.task_executor import embed_limiter
|
|
from common.token_utils import truncate
|
|
|
|
from common.misc_utils import thread_pool_exec
|
|
|
|
class TokenizerParam(ProcessParamBase):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.search_method = ["full_text", "embedding"]
|
|
self.filename_embd_weight = 0.1
|
|
self.fields = ["text"]
|
|
|
|
def check(self):
|
|
for v in self.search_method:
|
|
self.check_valid_value(v.lower(), "Chunk method abnormal.", ["full_text", "embedding"])
|
|
|
|
def get_input_form(self) -> dict[str, dict]:
|
|
return {}
|
|
|
|
|
|
class Tokenizer(ProcessBase):
|
|
component_name = "Tokenizer"
|
|
|
|
async def _embedding(self, name, chunks):
|
|
# Tokenization may legitimately produce zero chunks; embedding should be a no-op.
|
|
if not chunks:
|
|
return [], 0
|
|
|
|
parts = sum(["full_text" in self._param.search_method, "embedding" in self._param.search_method])
|
|
token_count = 0
|
|
if self._canvas._kb_id:
|
|
e, kb = KnowledgebaseService.get_by_id(self._canvas._kb_id)
|
|
if kb.tenant_embd_id:
|
|
embd_model_config = get_model_config_by_id(kb.tenant_embd_id)
|
|
else:
|
|
embd_model_config = get_model_config_by_type_and_name(self._canvas._tenant_id, LLMType.EMBEDDING, kb.embd_id)
|
|
else:
|
|
embd_model_config = get_tenant_default_model_by_type(self._canvas._tenant_id, LLMType.EMBEDDING)
|
|
embedding_model = LLMBundle(self._canvas._tenant_id, embd_model_config)
|
|
texts = []
|
|
valid_pairs = []
|
|
for i, c in enumerate(chunks):
|
|
txt = ""
|
|
if isinstance(self._param.fields, str):
|
|
self._param.fields=[self._param.fields]
|
|
for f in self._param.fields:
|
|
f = c.get(f)
|
|
if isinstance(f, str):
|
|
txt += f
|
|
elif isinstance(f, list):
|
|
txt += "\n".join(f)
|
|
cleaned_txt = re.sub(r"</?(table|td|caption|tr|th)( [^<>]{0,12})?>", " ", txt).strip()
|
|
if not cleaned_txt:
|
|
continue
|
|
texts.append(cleaned_txt)
|
|
valid_pairs.append((i, c))
|
|
|
|
if not texts:
|
|
return chunks, token_count
|
|
|
|
vts, c = embedding_model.encode([name])
|
|
token_count += c
|
|
tts = np.tile(vts[0], (len(texts), 1))
|
|
|
|
@timeout(60)
|
|
def batch_encode(txts):
|
|
nonlocal embedding_model
|
|
return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts])
|
|
|
|
cnts_batches = []
|
|
for i in range(0, len(texts), settings.EMBEDDING_BATCH_SIZE):
|
|
async with embed_limiter:
|
|
vts, c = await thread_pool_exec(batch_encode,texts[i : i + settings.EMBEDDING_BATCH_SIZE],)
|
|
cnts_batches.append(vts)
|
|
token_count += c
|
|
if i % 33 == 32:
|
|
self.callback(i * 1.0 / len(texts) / parts / settings.EMBEDDING_BATCH_SIZE + 0.5 * (parts - 1))
|
|
cnts_ = np.vstack(cnts_batches) if cnts_batches else np.array([])
|
|
|
|
cnts = cnts_
|
|
title_w = float(self._param.filename_embd_weight)
|
|
vects = (title_w * tts + (1 - title_w) * cnts) if len(tts) == len(cnts) else cnts
|
|
|
|
assert len(vects) == len(valid_pairs)
|
|
for i, (_, ck) in enumerate(valid_pairs):
|
|
v = vects[i].tolist()
|
|
ck["q_%d_vec" % len(v)] = v
|
|
return chunks, token_count
|
|
|
|
async def _invoke(self, **kwargs):
|
|
try:
|
|
chunks = kwargs.get("chunks")
|
|
if chunks is not None:
|
|
kwargs["chunks"] = [c for c in chunks if c is not None]
|
|
|
|
from_upstream = TokenizerFromUpstream.model_validate(kwargs)
|
|
except Exception as e:
|
|
self.set_output("_ERROR", f"Input error: {str(e)}")
|
|
return
|
|
|
|
self.set_output("output_format", "chunks")
|
|
parts = sum(["full_text" in self._param.search_method, "embedding" in self._param.search_method])
|
|
if "full_text" in self._param.search_method:
|
|
self.callback(random.randint(1, 5) / 100.0, "Start to tokenize.")
|
|
# Branch on the declared upstream format so an empty chunk list stays on the chunk path.
|
|
if from_upstream.output_format == "chunks":
|
|
chunks = from_upstream.chunks or []
|
|
for i, ck in enumerate(chunks):
|
|
ck["chunk_order_int"] = i
|
|
ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name))
|
|
ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"])
|
|
if ck.get("questions"):
|
|
ck["question_kwd"] = ck["questions"].split("\n")
|
|
ck["question_tks"] = rag_tokenizer.tokenize(str(ck["questions"]))
|
|
if ck.get("keywords"):
|
|
ck["important_kwd"] = ck["keywords"].split(",")
|
|
ck["important_tks"] = rag_tokenizer.tokenize(str(ck["keywords"]))
|
|
if ck.get("summary"):
|
|
ck["content_ltks"] = rag_tokenizer.tokenize(str(ck["summary"]))
|
|
ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
|
|
elif ck.get("text"):
|
|
ck["content_ltks"] = rag_tokenizer.tokenize(ck["text"])
|
|
ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
|
|
if i % 100 == 99:
|
|
self.callback(i * 1.0 / len(chunks) / parts)
|
|
|
|
elif from_upstream.output_format in ["markdown", "text", "html"]:
|
|
if from_upstream.output_format == "markdown":
|
|
payload = from_upstream.markdown_result
|
|
elif from_upstream.output_format == "text":
|
|
payload = from_upstream.text_result
|
|
else:
|
|
payload = from_upstream.html_result
|
|
|
|
if not payload:
|
|
return ""
|
|
|
|
ck = {"text": payload}
|
|
if "full_text" in self._param.search_method:
|
|
ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name))
|
|
ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"])
|
|
ck["content_ltks"] = rag_tokenizer.tokenize(payload)
|
|
ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
|
|
chunks = [ck]
|
|
else:
|
|
# Empty JSON payloads are valid and should remain empty downstream.
|
|
chunks = from_upstream.json_result or []
|
|
for i, ck in enumerate(chunks):
|
|
ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name))
|
|
ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"])
|
|
if not ck.get("text"):
|
|
continue
|
|
ck["content_ltks"] = rag_tokenizer.tokenize(ck["text"])
|
|
ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
|
|
if i % 100 == 99:
|
|
self.callback(i * 1.0 / len(chunks) / parts)
|
|
|
|
self.callback(1.0 / parts, "Finish tokenizing.")
|
|
|
|
if "embedding" in self._param.search_method:
|
|
self.callback(random.randint(1, 5) / 100.0 + 0.5 * (parts - 1), "Start embedding inference.")
|
|
|
|
if from_upstream.name.strip() == "":
|
|
logging.warning("Tokenizer: empty name provided from upstream, embedding may be not accurate.")
|
|
|
|
chunks, token_count = await self._embedding(from_upstream.name, chunks)
|
|
self.set_output("embedding_token_consumption", token_count)
|
|
|
|
self.callback(1.0, "Finish embedding.")
|
|
|
|
chunks = [finalize_pdf_chunk(ck) for ck in chunks]
|
|
|
|
self.set_output("chunks", chunks)
|