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feat(transformers): merge sentencetransformers backend (#4624)

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* merge sentencetransformers

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Add alias to silently redirect sentencetransformers to transformers

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Add alias also for transformers-musicgen

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Drop from makefile

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Move tests from sentencetransformers

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Remove sentencetransformers

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Remove tests from CI (part of transformers)

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Do not always try to load the tokenizer

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Adapt tests

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Fix typo

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* Tiny adjustments

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto 2025-01-18 18:30:30 +01:00 • committed by GitHub
parent 4bd8434ae0
commit 1e9bf19c8d
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27 changed files with 104 additions and 354 deletions
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38
backend/python/transformers/backend.py
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@ -25,6 +25,8 @@ from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreame
from transformers import AutoProcessor, MusicgenForConditionalGeneration
from scipy.io import wavfile
import outetts
from sentence_transformers import SentenceTransformer
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
@ -88,10 +90,12 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
self.CUDA = torch.cuda.is_available()
self.OV=False
self.OuteTTS=False
self.SentenceTransformer = False
device_map="cpu"
quantization = None
autoTokenizer = True
if self.CUDA:
from transformers import BitsAndBytesConfig, AutoModelForCausalLM
@ -195,9 +199,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
device=device_map)
self.OV = True
elif request.Type == "MusicgenForConditionalGeneration":
autoTokenizer = False
self.processor = AutoProcessor.from_pretrained(model_name)
self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
elif request.Type == "OuteTTS":
autoTokenizer = False
options = request.Options
MODELNAME = "OuteAI/OuteTTS-0.3-1B"
TOKENIZER = "OuteAI/OuteTTS-0.3-1B"
@ -235,6 +241,10 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
self.speaker = self.interface.create_speaker(audio_path=self.AudioPath)
else:
self.speaker = self.interface.load_default_speaker(name=SPEAKER)
elif request.Type == "SentenceTransformer":
autoTokenizer = False
self.model = SentenceTransformer(model_name, trust_remote_code=request.TrustRemoteCode)
self.SentenceTransformer = True
else:
print("Automodel", file=sys.stderr)
self.model = AutoModel.from_pretrained(model_name,
@ -250,7 +260,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
else:
self.max_tokens = 512
if request.Type != "MusicgenForConditionalGeneration":
if autoTokenizer:
self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
self.XPU = False
@ -286,18 +296,26 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
max_length = 512
if request.Tokens != 0:
max_length = request.Tokens
encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
# Create word embeddings
if self.CUDA:
encoded_input = encoded_input.to("cuda")
embeds = None
with torch.no_grad():
model_output = self.model(**encoded_input)
if self.SentenceTransformer:
print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
embeds = self.model.encode(request.Embeddings)
else:
encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt")
# Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
# Create word embeddings
if self.CUDA:
encoded_input = encoded_input.to("cuda")
with torch.no_grad():
model_output = self.model(**encoded_input)
# Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeds = sentence_embeddings[0]
return backend_pb2.EmbeddingResult(embeddings=embeds)
async def _predict(self, request, context, streaming=False):
set_seed(request.Seed)