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# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import uuid
from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence, Union
from ..data import get_template_and_fix_tokenizer
from ..extras.logging import get_logger
from ..extras.misc import get_device_count
from ..extras.packages import is_vllm_available, is_vllm_version_greater_than_0_5, is_vllm_version_greater_than_0_5_1
from ..model import load_config, load_tokenizer
from ..model.model_utils.quantization import QuantizationMethod
from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
from .base_engine import BaseEngine, Response
if is_vllm_available():
from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
from vllm.lora.request import LoRARequest
if is_vllm_version_greater_than_0_5_1():
pass
elif is_vllm_version_greater_than_0_5():
from vllm.multimodal.image import ImagePixelData
else:
from vllm.sequence import MultiModalData
if TYPE_CHECKING:
from numpy.typing import NDArray
from transformers.image_processing_utils import BaseImageProcessor
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
class VllmEngine(BaseEngine):
def __init__(
self,
model_args: "ModelArguments",
data_args: "DataArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
) -> None:
config = load_config(model_args) # may download model from ms hub
if getattr(config, "quantization_config", None): # gptq models should use float16
quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
quant_method = quantization_config.get("quant_method", "")
if quant_method == QuantizationMethod.GPTQ and model_args.infer_dtype == "auto":
model_args.infer_dtype = "float16"
self.can_generate = finetuning_args.stage == "sft"
tokenizer_module = load_tokenizer(model_args)
self.tokenizer = tokenizer_module["tokenizer"]
self.processor = tokenizer_module["processor"]
self.tokenizer.padding_side = "left"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
self.generating_args = generating_args.to_dict()
engine_args = {
"model": model_args.model_name_or_path,
"trust_remote_code": True,
"download_dir": model_args.cache_dir,
"dtype": model_args.infer_dtype,
"max_model_len": model_args.vllm_maxlen,
"tensor_parallel_size": get_device_count() or 1,
"gpu_memory_utilization": model_args.vllm_gpu_util,
"disable_log_stats": True,
"disable_log_requests": True,
"enforce_eager": model_args.vllm_enforce_eager,
"enable_lora": model_args.adapter_name_or_path is not None,
"max_lora_rank": model_args.vllm_max_lora_rank,
}
if model_args.visual_inputs:
image_size = config.vision_config.image_size
patch_size = config.vision_config.patch_size
self.image_feature_size = (image_size // patch_size) ** 2
engine_args["image_input_type"] = "pixel_values"
engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids(self.template.image_token)
engine_args["image_input_shape"] = "1,3,{},{}".format(image_size, image_size)
engine_args["image_feature_size"] = self.image_feature_size
if getattr(config, "is_yi_vl_derived_model", None):
import vllm.model_executor.models.llava
logger.info("Detected Yi-VL model, applying projector patch.")
vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
if model_args.adapter_name_or_path is not None:
self.lora_request = LoRARequest("default", 1, model_args.adapter_name_or_path[0])
else:
self.lora_request = None
async def _generate(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncIterator["RequestOutput"]:
request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
if (
self.processor is not None
and image is not None
and not hasattr(self.processor, "image_seq_length")
and self.template.image_token not in messages[0]["content"]
): # llava-like models (TODO: paligemma models)
messages[0]["content"] = self.template.image_token * self.image_feature_size + messages[0]["content"]
paired_messages = messages + [{"role": "assistant", "content": ""}]
system = system or self.generating_args["default_system"]
prompt_ids, _ = self.template.encode_oneturn(
tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
)
if self.processor is not None and image is not None: # add image features
image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
pixel_values = image_processor(image, return_tensors="pt")["pixel_values"]
if is_vllm_version_greater_than_0_5_1():
multi_modal_data = {"image": pixel_values}
elif is_vllm_version_greater_than_0_5():
multi_modal_data = ImagePixelData(image=pixel_values)
else: # TODO: remove vllm 0.4.3 support
multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
else:
multi_modal_data = None
prompt_length = len(prompt_ids)
use_beam_search: bool = self.generating_args["num_beams"] > 1
temperature: Optional[float] = input_kwargs.pop("temperature", None)
top_p: Optional[float] = input_kwargs.pop("top_p", None)
top_k: Optional[float] = input_kwargs.pop("top_k", None)
num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
max_length: Optional[int] = input_kwargs.pop("max_length", None)
max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
if "max_new_tokens" in self.generating_args:
max_tokens = self.generating_args["max_new_tokens"]
elif "max_length" in self.generating_args:
if self.generating_args["max_length"] > prompt_length:
max_tokens = self.generating_args["max_length"] - prompt_length
else:
max_tokens = 1
if max_length:
max_tokens = max_length - prompt_length if max_length > prompt_length else 1
if max_new_tokens:
max_tokens = max_new_tokens
sampling_params = SamplingParams(
n=num_return_sequences,
repetition_penalty=(
repetition_penalty if repetition_penalty is not None else self.generating_args["repetition_penalty"]
)
or 1.0, # repetition_penalty must > 0
temperature=temperature if temperature is not None else self.generating_args["temperature"],
top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0, # top_p must > 0
top_k=top_k if top_k is not None else self.generating_args["top_k"],
use_beam_search=use_beam_search,
length_penalty=length_penalty if length_penalty is not None else self.generating_args["length_penalty"],
stop=stop,
stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
max_tokens=max_tokens,
skip_special_tokens=True,
)
result_generator = self.model.generate(
inputs={"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data},
sampling_params=sampling_params,
request_id=request_id,
lora_request=self.lora_request,
)
return result_generator
async def chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> List["Response"]:
final_output = None
generator = await self._generate(messages, system, tools, image, **input_kwargs)
async for request_output in generator:
final_output = request_output
results = []
for output in final_output.outputs:
results.append(
Response(
response_text=output.text,
response_length=len(output.token_ids),
prompt_length=len(final_output.prompt_token_ids),
finish_reason=output.finish_reason,
)
)
return results
async def stream_chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["NDArray"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
generated_text = ""
generator = await self._generate(messages, system, tools, image, **input_kwargs)
async for result in generator:
delta_text = result.outputs[0].text[len(generated_text) :]
generated_text = result.outputs[0].text
yield delta_text
async def get_scores(
self,
batch_input: List[str],
**input_kwargs,
) -> List[float]:
raise NotImplementedError("vLLM engine does not support get_scores.")
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