diff --git a/Dockerfile b/Dockerfile index 4e27ae60..a21accf3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -160,9 +160,8 @@ COPY server/Makefile server/Makefile # Install server COPY proto proto COPY server server -RUN cd server && \ - make gen-server && \ - pip install ".[accelerate]" --no-cache-dir +# RUN --mount=type=cache,target=/root/.cache/pip cd server && make gen-server && pip install ".[accelerate, openvino]" +RUN cd server && make gen-server && pip install ".[accelerate, openvino]" --no-cache-dir # Patch codegen model changes into transformers 4.35 RUN cp server/transformers_patch/modeling_codegen.py ${SITE_PACKAGES}/transformers/models/codegen/modeling_codegen.py @@ -311,7 +310,8 @@ RUN --mount=type=bind,from=auto-gptq-cache,src=/usr/src/auto-gptq-wheel,target=/ # Install server COPY proto proto COPY server server -RUN cd server && make gen-server && pip install ".[accelerate, onnx-gpu, quantize]" --no-cache-dir +# RUN --mount=type=cache,target=/root/.cache/pip cd server && make gen-server && pip install ".[accelerate, onnx-gpu, openvino, quantize]" +RUN cd server && make gen-server && pip install ".[accelerate, onnx-gpu, openvino, quantize]" --no-cache-dir # Patch codegen model changes into transformers 4.35 RUN cp server/transformers_patch/modeling_codegen.py ${SITE_PACKAGES}/transformers/models/codegen/modeling_codegen.py diff --git a/server/poetry.lock b/server/poetry.lock index 4a11d7a2..6a6d1619 100644 --- a/server/poetry.lock +++ b/server/poetry.lock @@ -1,10 +1,15 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + [[package]] name = "accelerate" version = "0.26.1" description = "Accelerate" -category = "main" optional = true python-versions = ">=3.8.0" +files = [ + {file = "accelerate-0.26.1-py3-none-any.whl", hash = "sha256:04df826b84ac7bad8a0a8ab90e6aeacdecb1ea5a2d744d7e94f6735c29183227"}, + {file = "accelerate-0.26.1.tar.gz", hash = "sha256:bf63716b6bd9460d87da970cf4d833abb824ca0aa633be36b741e63a1b504f89"}, +] [package.dependencies] huggingface-hub = "*" @@ -12,1095 +17,26 @@ numpy = ">=1.17" packaging = ">=20.0" psutil = "*" pyyaml = "*" -safetensors = ">=0.3.1" -torch = ">=1.10.0" - 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"sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"}, ] + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[extras] +accelerate = ["accelerate"] +bnb = ["bitsandbytes"] +onnx = ["onnx", "onnxruntime", "optimum"] +onnx-gpu = ["onnx", "onnxruntime-gpu", "optimum"] +openvino = ["optimum-intel"] +quantize = ["datasets", "texttable"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.11.0,<3.13" +content-hash = "538b2c46193948faef6cc812c6de5600ab23ea7693b1dcddf49e197aaebc9ea1" diff --git a/server/pyproject.toml b/server/pyproject.toml index 32d07539..29b2eddc 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -23,6 +23,7 @@ datasets = { version = "^2.15.0", optional = true } texttable = { version = "^1.7.0", optional = true } transformers = "4.37.1" optimum = { version = "^1.16.2", extras = ["onnxruntime-gpu"], optional = true } +optimum-intel = { version = ">=1.14.0", extras = ["openvino,nncf"], optional = true } onnxruntime = { version = "^1.16.3", optional = true } onnxruntime-gpu = { version = "^1.16.3", optional = true } onnx = { version = "^1.15.0", optional = true } @@ -41,6 +42,7 @@ accelerate = ["accelerate"] bnb = ["bitsandbytes"] onnx = ["optimum", "onnxruntime", "onnx"] onnx-gpu = ["optimum", "onnxruntime-gpu", "onnx"] +openvino = ["optimum-intel"] # These are only required if using the quantize cli command quantize = ["datasets", "texttable"] diff --git a/server/text_generation_server/inference_engine/hf_optimum_ov.py b/server/text_generation_server/inference_engine/hf_optimum_ov.py new file mode 100644 index 00000000..0fc80ea6 --- /dev/null +++ b/server/text_generation_server/inference_engine/hf_optimum_ov.py @@ -0,0 +1,99 @@ +""" +OpenVINO integration for text-generation-inference. + +Usage: set DEPLOYMENT_FRAMEWORK environment variable to hf_optimum_ov or use the +`--deployment-framework=hf_optimum_ov` flag with text-generation-launcher. + +Example, including model conversion: + +``` +pip install optimum[openvino,nncf] +optimum-cli export openvino -m facebook/opt-2.7b data/opt-2.7b-ov +volume=$PWD/data +mkdir $volume +MODEL=/data/opt-2.7b-ov +IMAGE_ID= [text-generation-server IMAGE_ID] +docker run -p 8033:8033 -p 3000:3000 -e OPENVINO_CONFIG=/data/openvino_config.json \ +-v $volume:/data $IMAGE_ID text-generation-launcher --model-name $MODEL --deployment-framework hf_optimum_ov + +``` + +OPENVINO_CONFIG is optional. If used, it must point to a json file with an openvino configuration. +If no specific config is specified, it is set to: +{"CACHE_DIR": "", "PERFORMANCE_HINT": "LATENCY", "PERFORMANCE_HINT_NUM_REQUESTS": 1}. + +Known limitations: + +- Seq2Seq models are not supported yet in this integration. +- Only CPU device is supported at the moment. + +""" + +import json +import os +from pathlib import Path +from typing import Any, Optional, Union + +import torch +from openvino.runtime import get_version +from optimum.intel import OVModelForCausalLM +from optimum.intel.version import __version__ +from text_generation_server.inference_engine.engine import BaseInferenceEngine +from text_generation_server.utils.hub import TRUST_REMOTE_CODE +from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM + + +class InferenceEngine(BaseInferenceEngine): + def __init__( + self, + model_path: str, + model_class: Union[AutoModelForCausalLM, AutoModelForSeq2SeqLM], + dtype: torch.dtype, + quantize: Optional[str], # not used by OpenVINO + model_config: Optional[Any], + max_sequence_length: Optional[int], + ) -> None: + super().__init__(model_path, model_config) + print(f"Optimum Intel version: {__version__}") + print(f"OpenVINO version: {get_version()}") + print("model_path:", model_path) + os.environ["OPENVINO_LOG_LEVEL"] = "4" + + if model_class == AutoModelForCausalLM: + model_class = OVModelForCausalLM + elif model_class == AutoModelForSeq2SeqLM: + raise ValueError( + "Seq2Seq models are not yet supported by the hf_optimum_ov deployment framework" + ) + + ov_config_file = os.getenv("OPENVINO_CONFIG") + if ov_config_file is not None: + ov_config = json.loads(Path(ov_config_file).read_text()) + else: + ov_config = {} + + # Set good default options for latency-optimized workflow + if "PERFORMANCE_HINT" not in ov_config: + ov_config["PERFORMANCE_HINT"] = "LATENCY" + if "NUM_STREAMS" not in ov_config: + ov_config["NUM_STREAMS"] = 1 + + print(f"ov_config: {ov_config}") + + kwargs = { + "model_id": model_path, + "trust_remote_code": TRUST_REMOTE_CODE, + "export": False, + "ov_config": ov_config, + "use_cache": True, + "load_in_8bit": None, + } + + model_is_ov = any(f.endswith("_model.xml") for f in os.listdir(model_path)) + + if model_is_ov: + self.model = model_class.from_pretrained(**kwargs) + + else: + kwargs["export"] = True + self.model = model_class.from_pretrained(**kwargs) diff --git a/server/text_generation_server/models/causal_lm.py b/server/text_generation_server/models/causal_lm.py index ba43ea0a..d8c67853 100644 --- a/server/text_generation_server/models/causal_lm.py +++ b/server/text_generation_server/models/causal_lm.py @@ -571,16 +571,19 @@ def __init__( else: self.tokenizer.add_special_tokens({"pad_token": "[PAD]"}) - # Perform a forward pass to determine the structure of the past_key_values - one_token = torch.tensor([[1]], device=inference_engine.get_device()) - _, past_key_values, _ = self.forward(input_ids=one_token, attention_mask=one_token) - if torch.is_tensor(past_key_values[0]): - self.batch_type = CombinedKVCausalLMBatch + if deployment_framework == "hf_optimum_ov" and self.model.stateful: + self.batch_type = CausalLMBatch else: - # check the ordering of the key tensor dimensions - key_past, value_past = past_key_values[0] - keys_head_dim_last = key_past.shape[-1] == value_past.shape[-1] - self.batch_type = CausalLMBatch if keys_head_dim_last else KeysDimTransposedCausalLMBatch + # Perform a forward pass to determine the structure of the past_key_values + one_token = torch.tensor([[1]], device=inference_engine.get_device()) + _, past_key_values, _ = self.forward(input_ids=one_token, attention_mask=one_token) + if torch.is_tensor(past_key_values[0]): + self.batch_type = CombinedKVCausalLMBatch + else: + # check the ordering of the key tensor dimensions + key_past, value_past = past_key_values[0] + keys_head_dim_last = key_past.shape[-1] == value_past.shape[-1] + self.batch_type = CausalLMBatch if keys_head_dim_last else KeysDimTransposedCausalLMBatch @property def batch_type(self) -> Type[CausalLMBatch]: