mirror of
https://github.com/mudler/LocalAI.git
synced 2024-12-24 06:46:39 +00:00
feat(vllm): Initial vllm backend implementation
Related to: https://github.com/go-skynet/LocalAI/issues/1015 Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto
parent
cc74fc93b4
commit
c0bb5c4bf6
@ -11,7 +11,7 @@ ARG TARGETARCH
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ARG TARGETVARIANT
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ENV BUILD_TYPE=${BUILD_TYPE}
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ENV EXTERNAL_GRPC_BACKENDS="huggingface-embeddings:/build/extra/grpc/huggingface/huggingface.py,autogptq:/build/extra/grpc/autogptq/autogptq.py,bark:/build/extra/grpc/bark/ttsbark.py,diffusers:/build/extra/grpc/diffusers/backend_diffusers.py,exllama:/build/extra/grpc/exllama/exllama.py,vall-e-x:/build/extra/grpc/vall-e-x/ttsvalle.py"
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ENV EXTERNAL_GRPC_BACKENDS="huggingface-embeddings:/build/extra/grpc/huggingface/huggingface.py,autogptq:/build/extra/grpc/autogptq/autogptq.py,bark:/build/extra/grpc/bark/ttsbark.py,diffusers:/build/extra/grpc/diffusers/backend_diffusers.py,exllama:/build/extra/grpc/exllama/exllama.py,vall-e-x:/build/extra/grpc/vall-e-x/ttsvalle.py,vllm:/build/extra/grpc/vllm/backend_vllm.py"
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ENV GALLERIES='[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:go-skynet/model-gallery/huggingface.yaml","name":"huggingface"}]'
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ARG GO_TAGS="stablediffusion tts"
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@ -43,7 +43,7 @@ RUN if [ "${TARGETARCH}" = "amd64" ]; then \
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pip install git+https://github.com/suno-ai/bark.git diffusers invisible_watermark transformers accelerate safetensors;\
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fi
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RUN if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "amd64" ]; then \
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pip install torch && pip install auto-gptq https://github.com/jllllll/exllama/releases/download/0.0.10/exllama-0.0.10+cu${CUDA_MAJOR_VERSION}${CUDA_MINOR_VERSION}-cp39-cp39-linux_x86_64.whl;\
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pip install torch vllm && pip install auto-gptq https://github.com/jllllll/exllama/releases/download/0.0.10/exllama-0.0.10+cu${CUDA_MAJOR_VERSION}${CUDA_MINOR_VERSION}-cp39-cp39-linux_x86_64.whl;\
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fi
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RUN pip install -r /build/extra/requirements.txt && rm -rf /build/extra/requirements.txt
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1
Makefile
1
Makefile
@ -362,6 +362,7 @@ protogen-python:
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/bark/ --grpc_python_out=extra/grpc/bark/ pkg/grpc/proto/backend.proto
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/diffusers/ --grpc_python_out=extra/grpc/diffusers/ pkg/grpc/proto/backend.proto
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/vall-e-x/ --grpc_python_out=extra/grpc/vall-e-x/ pkg/grpc/proto/backend.proto
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/vllm/ --grpc_python_out=extra/grpc/vllm/ pkg/grpc/proto/backend.proto
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## GRPC
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61
extra/grpc/vllm/backend_pb2.py
Normal file
61
extra/grpc/vllm/backend_pb2.py
Normal file
File diff suppressed because one or more lines are too long
363
extra/grpc/vllm/backend_pb2_grpc.py
Normal file
363
extra/grpc/vllm/backend_pb2_grpc.py
Normal file
@ -0,0 +1,363 @@
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# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
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"""Client and server classes corresponding to protobuf-defined services."""
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import grpc
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import backend_pb2 as backend__pb2
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class BackendStub(object):
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"""Missing associated documentation comment in .proto file."""
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def __init__(self, channel):
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"""Constructor.
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Args:
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channel: A grpc.Channel.
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"""
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self.Health = channel.unary_unary(
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'/backend.Backend/Health',
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request_serializer=backend__pb2.HealthMessage.SerializeToString,
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response_deserializer=backend__pb2.Reply.FromString,
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)
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self.Predict = channel.unary_unary(
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'/backend.Backend/Predict',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.Reply.FromString,
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)
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self.LoadModel = channel.unary_unary(
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'/backend.Backend/LoadModel',
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request_serializer=backend__pb2.ModelOptions.SerializeToString,
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response_deserializer=backend__pb2.Result.FromString,
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)
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self.PredictStream = channel.unary_stream(
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'/backend.Backend/PredictStream',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.Reply.FromString,
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)
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self.Embedding = channel.unary_unary(
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'/backend.Backend/Embedding',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.EmbeddingResult.FromString,
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)
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self.GenerateImage = channel.unary_unary(
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'/backend.Backend/GenerateImage',
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request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
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response_deserializer=backend__pb2.Result.FromString,
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)
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self.AudioTranscription = channel.unary_unary(
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'/backend.Backend/AudioTranscription',
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request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
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response_deserializer=backend__pb2.TranscriptResult.FromString,
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)
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self.TTS = channel.unary_unary(
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'/backend.Backend/TTS',
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request_serializer=backend__pb2.TTSRequest.SerializeToString,
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response_deserializer=backend__pb2.Result.FromString,
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)
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self.TokenizeString = channel.unary_unary(
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'/backend.Backend/TokenizeString',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.TokenizationResponse.FromString,
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)
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self.Status = channel.unary_unary(
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'/backend.Backend/Status',
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request_serializer=backend__pb2.HealthMessage.SerializeToString,
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response_deserializer=backend__pb2.StatusResponse.FromString,
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)
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class BackendServicer(object):
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"""Missing associated documentation comment in .proto file."""
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def Health(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def Predict(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def LoadModel(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def PredictStream(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def Embedding(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def GenerateImage(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def AudioTranscription(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def TTS(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def TokenizeString(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def Status(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def add_BackendServicer_to_server(servicer, server):
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rpc_method_handlers = {
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'Health': grpc.unary_unary_rpc_method_handler(
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servicer.Health,
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request_deserializer=backend__pb2.HealthMessage.FromString,
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response_serializer=backend__pb2.Reply.SerializeToString,
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),
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'Predict': grpc.unary_unary_rpc_method_handler(
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servicer.Predict,
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request_deserializer=backend__pb2.PredictOptions.FromString,
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response_serializer=backend__pb2.Reply.SerializeToString,
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),
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'LoadModel': grpc.unary_unary_rpc_method_handler(
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servicer.LoadModel,
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request_deserializer=backend__pb2.ModelOptions.FromString,
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response_serializer=backend__pb2.Result.SerializeToString,
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),
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'PredictStream': grpc.unary_stream_rpc_method_handler(
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servicer.PredictStream,
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request_deserializer=backend__pb2.PredictOptions.FromString,
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response_serializer=backend__pb2.Reply.SerializeToString,
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),
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'Embedding': grpc.unary_unary_rpc_method_handler(
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servicer.Embedding,
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request_deserializer=backend__pb2.PredictOptions.FromString,
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response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
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),
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'GenerateImage': grpc.unary_unary_rpc_method_handler(
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servicer.GenerateImage,
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request_deserializer=backend__pb2.GenerateImageRequest.FromString,
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response_serializer=backend__pb2.Result.SerializeToString,
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),
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'AudioTranscription': grpc.unary_unary_rpc_method_handler(
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servicer.AudioTranscription,
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request_deserializer=backend__pb2.TranscriptRequest.FromString,
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response_serializer=backend__pb2.TranscriptResult.SerializeToString,
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),
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'TTS': grpc.unary_unary_rpc_method_handler(
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servicer.TTS,
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request_deserializer=backend__pb2.TTSRequest.FromString,
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response_serializer=backend__pb2.Result.SerializeToString,
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),
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'TokenizeString': grpc.unary_unary_rpc_method_handler(
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servicer.TokenizeString,
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request_deserializer=backend__pb2.PredictOptions.FromString,
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response_serializer=backend__pb2.TokenizationResponse.SerializeToString,
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),
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'Status': grpc.unary_unary_rpc_method_handler(
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servicer.Status,
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request_deserializer=backend__pb2.HealthMessage.FromString,
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response_serializer=backend__pb2.StatusResponse.SerializeToString,
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),
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}
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generic_handler = grpc.method_handlers_generic_handler(
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'backend.Backend', rpc_method_handlers)
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server.add_generic_rpc_handlers((generic_handler,))
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# This class is part of an EXPERIMENTAL API.
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class Backend(object):
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"""Missing associated documentation comment in .proto file."""
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@staticmethod
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def Health(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
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backend__pb2.HealthMessage.SerializeToString,
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backend__pb2.Reply.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def Predict(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
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backend__pb2.PredictOptions.SerializeToString,
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backend__pb2.Reply.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def LoadModel(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
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backend__pb2.ModelOptions.SerializeToString,
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backend__pb2.Result.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def PredictStream(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
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backend__pb2.PredictOptions.SerializeToString,
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backend__pb2.Reply.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def Embedding(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
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backend__pb2.PredictOptions.SerializeToString,
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backend__pb2.EmbeddingResult.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def GenerateImage(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
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backend__pb2.GenerateImageRequest.SerializeToString,
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backend__pb2.Result.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def AudioTranscription(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
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backend__pb2.TranscriptRequest.SerializeToString,
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backend__pb2.TranscriptResult.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def TTS(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
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backend__pb2.TTSRequest.SerializeToString,
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backend__pb2.Result.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def TokenizeString(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/TokenizeString',
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backend__pb2.PredictOptions.SerializeToString,
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backend__pb2.TokenizationResponse.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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@staticmethod
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def Status(request,
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target,
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options=(),
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channel_credentials=None,
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call_credentials=None,
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insecure=False,
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compression=None,
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wait_for_ready=None,
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timeout=None,
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metadata=None):
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return grpc.experimental.unary_unary(request, target, '/backend.Backend/Status',
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backend__pb2.HealthMessage.SerializeToString,
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backend__pb2.StatusResponse.FromString,
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options, channel_credentials,
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insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
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100
extra/grpc/vllm/backend_vllm.py
Normal file
100
extra/grpc/vllm/backend_vllm.py
Normal file
@ -0,0 +1,100 @@
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#!/usr/bin/env python3
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import grpc
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from concurrent import futures
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import time
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import backend_pb2
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import backend_pb2_grpc
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import argparse
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import signal
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import sys
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import os, glob
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from pathlib import Path
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from vllm import LLM, SamplingParams
|
||||
|
||||
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
|
||||
|
||||
# Implement the BackendServicer class with the service methods
|
||||
class BackendServicer(backend_pb2_grpc.BackendServicer):
|
||||
def generate(self,prompt, max_new_tokens):
|
||||
self.generator.end_beam_search()
|
||||
|
||||
# Tokenizing the input
|
||||
ids = self.generator.tokenizer.encode(prompt)
|
||||
|
||||
self.generator.gen_begin_reuse(ids)
|
||||
initial_len = self.generator.sequence[0].shape[0]
|
||||
has_leading_space = False
|
||||
decoded_text = ''
|
||||
for i in range(max_new_tokens):
|
||||
token = self.generator.gen_single_token()
|
||||
if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
|
||||
has_leading_space = True
|
||||
|
||||
decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
|
||||
if has_leading_space:
|
||||
decoded_text = ' ' + decoded_text
|
||||
|
||||
if token.item() == self.generator.tokenizer.eos_token_id:
|
||||
break
|
||||
return decoded_text
|
||||
def Health(self, request, context):
|
||||
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
|
||||
def LoadModel(self, request, context):
|
||||
try:
|
||||
# https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py
|
||||
self.llm = LLM(model=request.Model)
|
||||
except Exception as err:
|
||||
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
|
||||
return backend_pb2.Result(message="Model loaded successfully", success=True)
|
||||
|
||||
def Predict(self, request, context):
|
||||
sampling_params = SamplingParams(temperature=request.Temperature, top_p=request.TopP)
|
||||
outputs = self.llm.generate([request.Prompt], sampling_params)
|
||||
|
||||
generated_text = outputs[0].outputs[0].text
|
||||
|
||||
# Remove prompt from response if present
|
||||
if request.Prompt in generated_text:
|
||||
generated_text = generated_text.replace(request.Prompt, "")
|
||||
|
||||
return backend_pb2.Result(message=bytes(generated_text, encoding='utf-8'))
|
||||
|
||||
def PredictStream(self, request, context):
|
||||
# Implement PredictStream RPC
|
||||
#for reply in some_data_generator():
|
||||
# yield reply
|
||||
# Not implemented yet
|
||||
return self.Predict(request, context)
|
||||
|
||||
def serve(address):
|
||||
server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
|
||||
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
|
||||
server.add_insecure_port(address)
|
||||
server.start()
|
||||
print("Server started. Listening on: " + address, file=sys.stderr)
|
||||
|
||||
# Define the signal handler function
|
||||
def signal_handler(sig, frame):
|
||||
print("Received termination signal. Shutting down...")
|
||||
server.stop(0)
|
||||
sys.exit(0)
|
||||
|
||||
# Set the signal handlers for SIGINT and SIGTERM
|
||||
signal.signal(signal.SIGINT, signal_handler)
|
||||
signal.signal(signal.SIGTERM, signal_handler)
|
||||
|
||||
try:
|
||||
while True:
|
||||
time.sleep(_ONE_DAY_IN_SECONDS)
|
||||
except KeyboardInterrupt:
|
||||
server.stop(0)
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Run the gRPC server.")
|
||||
parser.add_argument(
|
||||
"--addr", default="localhost:50051", help="The address to bind the server to."
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
serve(args.addr)
|
||||
Loading…
Reference in New Issue
Block a user