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support vllm 0.6.0

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Former-commit-id: b6681d7198acf4acbebfe271dd22095e236bc430
This commit is contained in:
hiyouga 2024-09-08 02:26:20 +08:00
parent f6f58ebef0
commit 78cf256067
5 changed files with 12 additions and 51 deletions
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2
setup.py
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@ -45,7 +45,7 @@ extra_require = {
"gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
"awq": ["autoawq"],
"aqlm": ["aqlm[gpu]>=1.1.0"],
"vllm": ["vllm>=0.4.3"],
"vllm": ["vllm>=0.4.3,<=0.6.0"],
"galore": ["galore-torch"],
"badam": ["badam>=1.2.1"],
"adam-mini": ["adam-mini"],

1
src/llamafactory/chat/hf_engine.py
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@ -103,7 +103,6 @@ class HuggingfaceEngine(BaseEngine):
prompt_ids, _ = template.mm_plugin.process_token_ids(
prompt_ids, None, mm_input_dict["images"], mm_input_dict["videos"], tokenizer, processor
)
prompt_length = len(prompt_ids)
inputs = torch.tensor([prompt_ids], device=model.device)
attention_mask = torch.ones_like(inputs, dtype=torch.bool)

48
src/llamafactory/chat/vllm_engine.py
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@ -21,7 +21,7 @@ from ..data import get_template_and_fix_tokenizer
from ..extras.constants import IMAGE_PLACEHOLDER
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 ..extras.packages import is_vllm_available
from ..model import load_config, load_tokenizer
from ..model.model_utils.quantization import QuantizationMethod
from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
@ -32,17 +32,8 @@ 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 transformers.image_processing_utils import BaseImageProcessor
from ..data.mm_plugin import ImageInput, VideoInput
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
@ -88,19 +79,11 @@ class VllmEngine(BaseEngine):
"max_lora_rank": model_args.vllm_max_lora_rank,
}
if getattr(config, "model_type", None) == "llava":
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
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
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:
@ -118,29 +101,13 @@ class VllmEngine(BaseEngine):
**input_kwargs,
) -> AsyncIterator["RequestOutput"]:
request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
if image is not None:
if IMAGE_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
messages = self.template.mm_plugin.process_messages(messages, [image], self.processor)
paired_messages = messages + [{"role": "assistant", "content": ""}]
system = system or self.generating_args["default_system"]
prompt_ids, _ = self.template.encode_oneturn(self.tokenizer, paired_messages, system, 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
@ -185,6 +152,11 @@ class VllmEngine(BaseEngine):
skip_special_tokens=True,
)
if image is not None: # add image features
multi_modal_data = {"image": image}
else:
multi_modal_data = None
result_generator = self.model.generate(
inputs={"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data},
sampling_params=sampling_params,

10
src/llamafactory/extras/packages.py
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@ -85,13 +85,3 @@ def is_uvicorn_available():
def is_vllm_available():
return _is_package_available("vllm")
@lru_cache
def is_vllm_version_greater_than_0_5():
return _get_package_version("vllm") >= version.parse("0.5.0")
@lru_cache
def is_vllm_version_greater_than_0_5_1():
return _get_package_version("vllm") >= version.parse("0.5.1")

2
src/llamafactory/hparams/parser.py
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@ -123,7 +123,7 @@ def _check_extra_dependencies(
require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")
if model_args.infer_backend == "vllm":
require_version("vllm>=0.4.3", "To fix: pip install vllm>=0.4.3")
require_version("vllm>=0.4.3,<=0.6.0", "To fix: pip install vllm>=0.4.3,<=0.6.0")
if finetuning_args.use_galore:
require_version("galore_torch", "To fix: pip install galore_torch")