mirror of https://github.com/mudler/LocalAI.git
feat: Openvino runtime for transformer backend and streaming support for Openvino and CUDA (#1892)
* fixes #1775 and #1774 Add BitsAndBytes Quantization and fixes embedding on CUDA devices * Manage 4bit and 8 bit quantization Manage different BitsAndBytes options with the quantization: parameter in yaml * fix compilation errors on non CUDA environment * OpenVINO draft First draft of OpenVINO integration in transformer backend * first working implementation * Streaming working * Small fix for regression on CUDA and XPU * use pip version of optimum[openvino] * Update backend/python/transformers/transformers_server.py Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> --------- Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
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@ -34,6 +34,7 @@ dependencies:
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- boto3==1.28.61
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- botocore==1.31.61
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- certifi==2023.7.22
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- coloredlogs==15.0.1
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- TTS==0.22.0
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- charset-normalizer==3.3.0
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- datasets==2.14.5
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@ -48,6 +49,7 @@ dependencies:
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- funcy==2.0
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- grpcio==1.59.0
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- huggingface-hub
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- humanfriendly==10.0
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- idna==3.4
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- jinja2==3.1.2
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- jmespath==1.0.1
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@ -57,7 +59,10 @@ dependencies:
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- multiprocess==0.70.15
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- networkx
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- numpy==1.26.0
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- optimum==1.17.1
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- onnx==1.15.0
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- openvino==2024.0.0
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- openvino-telemetry==2023.2.1
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- optimum[openvino]==1.17.1
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- packaging==23.2
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- pandas
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- peft==0.5.0
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@ -8,6 +8,7 @@ import argparse
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import signal
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import sys
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import os
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from threading import Thread
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import time
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import backend_pb2
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@ -17,13 +18,16 @@ import grpc
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import torch
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import torch.cuda
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XPU=os.environ.get("XPU", "0") == "1"
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if XPU:
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import intel_extension_for_pytorch as ipex
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from intel_extension_for_transformers.transformers.modeling import AutoModelForCausalLM
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from transformers import AutoTokenizer, AutoModel, set_seed
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from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer
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from optimum.intel.openvino import OVModelForCausalLM
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from openvino.runtime import Core
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else:
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from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed, BitsAndBytesConfig
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from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed, BitsAndBytesConfig, TextIteratorStreamer
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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@ -81,6 +85,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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compute=torch.bfloat16
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self.CUDA = request.CUDA
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self.OV=False
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device_map="cpu"
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@ -105,23 +110,55 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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bnb_4bit_compute_dtype = None,
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load_in_8bit=True,
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)
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try:
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if request.Type == "AutoModelForCausalLM":
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if XPU:
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if quantization == "xpu_4bit":
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device_map="xpu"
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compute=torch.float16
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if request.Quantization == "xpu_4bit":
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xpu_4bit = True
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self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode,
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device_map="xpu", load_in_4bit=xpu_4bit)
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xpu_8bit = False
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elif request.Quantization == "xpu_8bit":
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xpu_4bit = False
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xpu_8bit = True
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else:
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xpu_4bit = False
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xpu_8bit = False
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self.model = AutoModelForCausalLM.from_pretrained(model_name,
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trust_remote_code=request.TrustRemoteCode,
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use_safetensors=True,
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device_map=device_map,
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load_in_4bit=xpu_4bit,
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load_in_8bit=xpu_8bit,
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torch_dtype=compute)
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else:
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self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode, use_safetensors=True, quantization_config=quantization, device_map=device_map, torch_dtype=compute)
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self.model = AutoModelForCausalLM.from_pretrained(model_name,
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trust_remote_code=request.TrustRemoteCode,
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use_safetensors=True,
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quantization_config=quantization,
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device_map=device_map,
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torch_dtype=compute)
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elif request.Type == "OVModelForCausalLM":
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if "GPU" in Core().available_devices:
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device_map="GPU"
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else:
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device_map="CPU"
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self.model = OVModelForCausalLM.from_pretrained(model_name,
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compile=True,
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device=device_map)
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self.OV = True
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else:
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self.model = AutoModel.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode, use_safetensors=True, quantization_config=quantization, device_map=device_map, torch_dtype=compute)
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self.model = AutoModel.from_pretrained(model_name,
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trust_remote_code=request.TrustRemoteCode,
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use_safetensors=True,
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quantization_config=quantization,
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device_map=device_map,
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torch_dtype=compute)
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self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
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self.XPU = False
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if XPU:
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if XPU and self.OV == False:
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self.XPU = True
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try:
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print("Optimizing model", model_name, "to XPU.", file=sys.stderr)
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@ -130,6 +167,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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print("Not using XPU:", err, file=sys.stderr)
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except Exception as err:
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print("Error:", err, file=sys.stderr)
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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# Implement your logic here for the LoadModel service
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# Replace this with your desired response
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@ -167,7 +205,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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print("Embeddings:", sentence_embeddings, file=sys.stderr)
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return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
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def Predict(self, request, context):
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def Predict(self, request, context, streaming=False):
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"""
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Generates text based on the given prompt and sampling parameters.
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@ -186,15 +224,42 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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if request.Tokens > 0:
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max_tokens = request.Tokens
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inputs = self.tokenizer(request.Prompt, return_tensors="pt").input_ids
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inputs = self.tokenizer(request.Prompt, return_tensors="pt")
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if self.CUDA:
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inputs = inputs.to("cuda")
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if XPU:
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if XPU and self.OV == False:
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inputs = inputs.to("xpu")
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streaming = False
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outputs = self.model.generate(inputs,max_new_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP, do_sample=True, pad_token_id=self.tokenizer.eos_token_id)
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generated_text = self.tokenizer.batch_decode(outputs[:, inputs.shape[1]:], skip_special_tokens=True)[0]
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if streaming:
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streamer=TextIteratorStreamer(self.tokenizer,
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skip_prompt=True,
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skip_special_tokens=True)
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config=dict(inputs,
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max_new_tokens=max_tokens,
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temperature=request.Temperature,
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top_p=request.TopP,
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top_k=request.TopK,
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do_sample=True,
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attention_mask=inputs["attention_mask"],
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eos_token_id=self.tokenizer.eos_token_id,
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pad_token_id=self.tokenizer.eos_token_id,
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streamer=streamer)
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thread=Thread(target=self.model.generate, kwargs=config)
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thread.start()
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generated_text = ""
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for new_text in streamer:
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generated_text += new_text
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yield backend_pb2.Reply(message=bytes(new_text, encoding='utf-8'))
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else:
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outputs = self.model.generate(inputs["input_ids"],
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max_new_tokens=max_tokens,
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temperature=request.Temperature,
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top_p=request.TopP,
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top_k=request.TopK,
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do_sample=True,
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pad_token=self.tokenizer.eos_token_id)
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generated_text = self.tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]
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return backend_pb2.Reply(message=bytes(generated_text, encoding='utf-8'))
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def PredictStream(self, request, context):
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@ -208,7 +273,9 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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Returns:
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backend_pb2.Result: The predict stream result.
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"""
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yield self.Predict(request, context)
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iterations = self.Predict(request, context, streaming=True)
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for iteration in iterations:
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yield iteration
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def serve(address):
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