Embedding Runtime on NeuralSpeed (opea-project#448)

* neural-speed release 0.1

* added docker compose & modified neuralspeed

* updated neural-speed dynamic batching

* updated neural-speed dynamic batching

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* bge done

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* update doc & cleancode

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update client_multibatch.py

* release update

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: chen, suyue <[email protected]>

comps/embeddings/neural-speed/README.md

@@ -0,0 +1,35 @@
+# build Mosec endpoint docker image
+
+```
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t langchain-mosec:neuralspeed -f comps/embeddings/neural-speed/neuralspeed-docker/Dockerfile .
+```
+
+# build embedding microservice docker image
+
+```
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t opea/embedding-langchain-mosec:neuralspeed -f comps/embeddings/neural-speed/docker/Dockerfile .
+```
+
+Note: Please contact us to request model files before building images.
+
+# launch Mosec endpoint docker container
+
+```
+docker run -d --name="embedding-langchain-mosec-endpoint" -p 6001:8000  langchain-mosec:neuralspeed
+```
+
+# launch embedding microservice docker container
+
+```
+export MOSEC_EMBEDDING_ENDPOINT=http://{mosec_embedding_host_ip}:6001
+docker run -d --name="embedding-langchain-mosec-server" -e http_proxy=$http_proxy -e https_proxy=$https_proxy -p 6000:6000 --ipc=host -e MOSEC_EMBEDDING_ENDPOINT=$MOSEC_EMBEDDING_ENDPOINT opea/embedding-langchain-mosec:neuralspeed
+```
+
+# run client test
+
+```
+curl localhost:6000/v1/embeddings \
+     -X POST \
+     -d '{"text":"Hello, world!"}' \
+     -H 'Content-Type: application/json'
+```

comps/embeddings/neural-speed/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

comps/embeddings/neural-speed/docker/Dockerfile

@@ -0,0 +1,30 @@
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM langchain/langchain:latest
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+USER user
+
+COPY comps /home/user/comps
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r /home/user/comps/embeddings/neural-speed/requirements.txt
+
+RUN pip3 install llmspec mosec msgspec httpx requests
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+WORKDIR /home/user/comps/embeddings/neural-speed
+
+ENTRYPOINT ["python", "embedding_neuralspeed_svc.py"]
+

comps/embeddings/neural-speed/docker/docker_compose_embedding.yaml

@@ -0,0 +1,22 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3.8"
+
+services:
+  embedding:
+    image: opea/embedding-langchain-mosec:neuralspeed
+    container_name: embedding-langchain-mosec-server
+    ports:
+      - "6000:6000"
+    ipc: host
+    environment:
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      MOSEC_EMBEDDING_ENDPOINT: ${MOSEC_EMBEDDING_ENDPOINT}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge

comps/embeddings/neural-speed/embedding_neuralspeed_svc.py

@@ -0,0 +1,83 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import time
+from typing import List, Optional
+
+import httpx
+import msgspec
+import requests
+from langchain_community.embeddings import OpenAIEmbeddings
+from langsmith import traceable
+
+from comps import (
+    EmbedDoc,
+    ServiceType,
+    TextDoc,
+    opea_microservices,
+    register_microservice,
+    register_statistics,
+    statistics_dict,
+)
+
+
+class MosecEmbeddings(OpenAIEmbeddings):
+
+    def _get_len_safe_embeddings(
+        self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
+    ) -> List[List[float]]:
+        _chunk_size = chunk_size or self.chunk_size
+        batched_embeddings: List[List[float]] = []
+        response = self.client.create(input=texts, **self._invocation_params)
+        if not isinstance(response, dict):
+            response = response.model_dump()
+        batched_embeddings.extend(r["embedding"] for r in response["data"])
+
+        _cached_empty_embedding: Optional[List[float]] = None
+
+        def empty_embedding() -> List[float]:
+            nonlocal _cached_empty_embedding
+            if _cached_empty_embedding is None:
+                average_embedded = self.client.create(input="", **self._invocation_params)
+                if not isinstance(average_embedded, dict):
+                    average_embedded = average_embedded.model_dump()
+                _cached_empty_embedding = average_embedded["data"][0]["embedding"]
+            return _cached_empty_embedding
+
+        return [e if e is not None else empty_embedding() for e in batched_embeddings]
+
+
+@register_microservice(
+    name="opea_service@embedding_mosec",
+    service_type=ServiceType.EMBEDDING,
+    endpoint="/v1/embeddings",
+    host="0.0.0.0",
+    port=6000,
+    input_datatype=TextDoc,
+    output_datatype=EmbedDoc,
+)
+@traceable(run_type="embedding")
+@register_statistics(names=["opea_service@embedding_mosec"])
+def embedding(input: TextDoc) -> EmbedDoc:
+    start = time.time()
+    req = {
+        "query": input.text,
+    }
+    request_url = MOSEC_EMBEDDING_ENDPOINT + "/inference"
+    resp = requests.post(request_url, data=msgspec.msgpack.encode(req))
+
+    embed_vector = msgspec.msgpack.decode(resp.content)["embeddings"]
+    res = EmbedDoc(text=req["query"][0], embedding=embed_vector)
+    statistics_dict["opea_service@embedding_mosec"].append_latency(time.time() - start, None)
+    return res
+
+
+if __name__ == "__main__":
+    MOSEC_EMBEDDING_ENDPOINT = os.environ.get("MOSEC_EMBEDDING_ENDPOINT", "http://127.0.0.1:6001")
+    os.environ["OPENAI_API_BASE"] = MOSEC_EMBEDDING_ENDPOINT
+    os.environ["OPENAI_API_KEY"] = "Dummy key"
+    MODEL_ID = os.environ.get("MODEL_ID", "BAAI/bge-base-en-v1.5")
+    embeddings = MosecEmbeddings(model=MODEL_ID)
+    print("NeuralSpeed Embedding Microservice Initialized.")
+    opea_microservices["opea_service@embedding_mosec"].start()

comps/embeddings/neural-speed/neuralspeed-docker/Dockerfile

@@ -0,0 +1,26 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+From ubuntu:22.04
+ARG DEBIAN_FRONTEND=noninteractive
+
+ENV GLIBC_TUNABLES glibc.cpu.x86_shstk=permissive
+
+COPY comps /root/comps
+COPY neural_speed-0.1.dev117+gafc0030.d20240815-cp310-cp310-linux_x86_64.whl /root/
+COPY bge-base-q8.bin /root/
+
+RUN apt update && apt install -y python3 python3-pip 
+RUN pip3 install -r /root/comps/embeddings/neural-speed/neuralspeed-docker/requirements.txt
+RUN pip3 install llmspec mosec msgspec httpx requests
+RUN pip3 install /root/neural_speed-0.1.dev117+gafc0030.d20240815-cp310-cp310-linux_x86_64.whl
+
+RUN cd /root/ && export HF_ENDPOINT=https://hf-mirror.com && huggingface-cli download --resume-download BAAI/bge-base-en-v1.5 --local-dir /root/bge-base-en-v1.5
+
+
+ENV LD_PRELOAD=/root/libstdc++.so.6
+
+
+WORKDIR /root/comps/embeddings/neural-speed/neuralspeed-docker
+
+CMD ["python3", "server.py"]

comps/embeddings/neural-speed/neuralspeed-docker/client.py

@@ -0,0 +1,31 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+from http import HTTPStatus
+
+import httpx
+import msgspec
+import requests
+
+input_text = "what a nice day"
+req = {
+    "query": input_text,
+}
+
+httpx_response = httpx.post("http://127.0.0.1:6001/inference", content=msgspec.msgpack.encode(req))
+
+requests_response = requests.post("http://127.0.0.1:6001/inference", data=msgspec.msgpack.encode(req))
+
+MOSEC_EMBEDDING_ENDPOINT = os.environ.get("MOSEC_EMBEDDING_ENDPOINT", "http://127.0.0.1:6001")
+
+request_url = MOSEC_EMBEDDING_ENDPOINT + "/inference"
+print(f"request_url = {request_url}")
+resp_3 = requests.post(request_url, data=msgspec.msgpack.encode(req))
+
+if httpx_response.status_code == HTTPStatus.OK and requests_response.status_code == HTTPStatus.OK:
+    print(f"OK: \n {msgspec.msgpack.decode(httpx_response.content)}")
+    print(f"OK: \n {msgspec.msgpack.decode(requests_response.content)}")
+    print(f"OK: \n {msgspec.msgpack.decode(resp_3.content)}")
+else:
+    print(f"err[{httpx_response.status_code}] {httpx_response.text}")

comps/embeddings/neural-speed/neuralspeed-docker/client_multibatch.py

@@ -0,0 +1,40 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from http import HTTPStatus
+from threading import Thread
+
+import httpx
+import msgspec
+
+req = {
+    "query": "Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data. Thread identifiers may be recycled when a thread exits and another thread is created.",
+}
+reqs = []
+BATCH = 32
+for i in range(BATCH):
+    reqs.append(msgspec.msgpack.encode(req))
+
+
+def post_func(threadIdx):
+    resp = httpx.post("http://127.0.0.1:6001/inference", content=reqs[threadIdx])
+    ret = f"thread {threadIdx} \n"
+    if resp.status_code == HTTPStatus.OK:
+        ret += f"OK: {msgspec.msgpack.decode(resp.content)['embeddings'][:16]}"
+    else:
+        ret += f"err[{resp.status_code}] {resp.text}"
+    print(ret)
+
+
+threads = []
+for i in range(BATCH):
+    t = Thread(
+        target=post_func,
+        args=[
+            i,
+        ],
+    )
+    threads.append(t)
+
+for i in range(BATCH):
+    threads[i].start()

comps/embeddings/neural-speed/neuralspeed-docker/requirements.txt

@@ -0,0 +1,16 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+accelerate
+cmake
+datasets
+huggingface_hub
+matplotlib
+numpy
+peft
+protobuf<3.20
+py-cpuinfo
+sentencepiece
+tiktoken
+torch
+transformers
+transformers_stream_generator
+zipfile38

comps/embeddings/neural-speed/neuralspeed-docker/server.py

@@ -0,0 +1,81 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import time
+from typing import Any, List
+
+import numpy
+from mosec import Server, Worker, get_logger
+from mosec.mixin import TypedMsgPackMixin
+from msgspec import Struct
+from neural_speed import Model
+from transformers import AutoTokenizer
+
+logger = get_logger()
+
+INFERENCE_BATCH_SIZE = 32
+INFERENCE_MAX_WAIT_TIME = 30
+INFERENCE_WORKER_NUM = 1
+INFERENCE_CONTEXT = 512
+
+TorchModel = "/root/bge-base-en-v1.5"
+NS_Bin = "/root/bge-base-q8.bin"
+
+NS_Model = "bert"
+
+
+class Request(Struct, kw_only=True):
+    query: str
+
+
+class Response(Struct, kw_only=True):
+    embeddings: List[float]
+
+
+class Inference(TypedMsgPackMixin, Worker):
+
+    def __init__(self):
+        super().__init__()
+        self.tokenizer = AutoTokenizer.from_pretrained(TorchModel)
+        self.model = Model()
+        self.model.init_from_bin(
+            NS_Model,
+            NS_Bin,
+            batch_size=INFERENCE_BATCH_SIZE,
+            n_ctx=INFERENCE_CONTEXT + 2,
+        )
+
+    def forward(self, data: List[Request]) -> List[Response]:
+        batch = len(data)
+        sequences = [d.query for d in data]
+        inputs = self.tokenizer(
+            sequences,
+            padding=True,
+            truncation=True,
+            max_length=INFERENCE_CONTEXT,
+            return_tensors="pt",
+        )
+        st = time.time()
+        ns_outputs = self.model(
+            **inputs,
+            reinit=True,
+            logits_all=True,
+            continuous_batching=False,
+            ignore_padding=True,
+        )
+        logger.info(f"batch {batch} input shape {inputs.input_ids.shape} time {time.time()-st}")
+        ns_outputs = ns_outputs[:, 0]
+        ns_outputs = ns_outputs / numpy.linalg.norm(ns_outputs, axis=1, keepdims=True)
+        resps = []
+        for i in range(batch):
+            resp = Response(embeddings=ns_outputs[i].tolist())
+            resps.append(resp)
+        return resps
+
+
+if __name__ == "__main__":
+    server = Server()
+    server.append_worker(
+        Inference, max_batch_size=INFERENCE_BATCH_SIZE, max_wait_time=INFERENCE_MAX_WAIT_TIME, num=INFERENCE_WORKER_NUM
+    )
+    server.run()

comps/embeddings/neural-speed/requirements.txt

@@ -0,0 +1,11 @@
+docarray[full]
+fastapi
+langchain
+langchain_community
+openai
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+prometheus-fastapi-instrumentator
+shortuuid
+uvicorn