ros/LLaMA-Factory
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

video datasets

Browse Source 
Former-commit-id: 33f28ce82d9e44d2615909250dc56d6a4a03cd99
This commit is contained in:
hiyouga
2024-09-05 02:04:17 +08:00
parent 2c1eef34cb
commit 1874d579c5
25 changed files with 359 additions and 148 deletions
Download Patch File Download Diff File Expand all files Collapse all files

51
src/llamafactory/chat/hf_engine.py
View File

@@ -22,7 +22,7 @@ import torch
from transformers import GenerationConfig, TextIteratorStreamer
from ..data import get_template_and_fix_tokenizer
from ..extras.constants import IMAGE_PLACEHOLDER
from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_logits_processor
from ..model import load_model, load_tokenizer
@@ -30,11 +30,11 @@ from .base_engine import BaseEngine, Response
if TYPE_CHECKING:
from PIL.Image import Image
from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
from trl import PreTrainedModelWrapper
from ..data import Template
from ..data.mm_plugin import ImageInput, VideoInput
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
@@ -78,20 +78,30 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["Image"] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Tuple[Dict[str, Any], int]:
mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
if image is not None:
mm_input_dict.update({"images": [image], "imglens": [1]})
if IMAGE_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
messages = template.mm_plugin.process_messages(messages, [image], processor)
if video is not None:
mm_input_dict.update({"videos": [video], "vidlens": [1]})
if VIDEO_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = VIDEO_PLACEHOLDER + messages[0]["content"]
messages = template.mm_plugin.process_messages(
messages, mm_input_dict["images"], mm_input_dict["videos"], processor
)
paired_messages = messages + [{"role": "assistant", "content": ""}]
system = system or generating_args["default_system"]
prompt_ids, _ = template.encode_oneturn(tokenizer, paired_messages, system, tools)
if image is not None:
prompt_ids, _ = template.mm_plugin.process_token_ids(prompt_ids, None, [image], tokenizer, processor)
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)
@@ -154,13 +164,10 @@ class HuggingfaceEngine(BaseEngine):
logits_processor=get_logits_processor(),
)
if image is not None:
mm_inputs = template.mm_plugin.get_mm_inputs(
images=[image], imglens=[1], seqlens=[prompt_length], processor=processor
)
for key, value in mm_inputs.items():
value = value if isinstance(value, torch.Tensor) else torch.tensor(value)
gen_kwargs[key] = value.to(model.device)
mm_inputs = template.mm_plugin.get_mm_inputs(**mm_input_dict, seqlens=[prompt_length], processor=processor)
for key, value in mm_inputs.items():
value = value if isinstance(value, torch.Tensor) else torch.tensor(value)
gen_kwargs[key] = value.to(model.device)
return gen_kwargs, prompt_length
@@ -175,11 +182,12 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["Image"] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> List["Response"]:
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
)
generate_output = model.generate(**gen_kwargs)
response_ids = generate_output[:, prompt_length:]
@@ -210,11 +218,12 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["Image"] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Callable[[], str]:
gen_kwargs, _ = HuggingfaceEngine._process_args(
model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
gen_kwargs["streamer"] = streamer
@@ -267,7 +276,8 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["Image"] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
**input_kwargs,
) -> List["Response"]:
if not self.can_generate:
@@ -284,6 +294,7 @@ class HuggingfaceEngine(BaseEngine):
system,
tools,
image,
video,
input_kwargs,
)
async with self.semaphore:
@@ -295,7 +306,8 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["Image"] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:
@@ -312,6 +324,7 @@ class HuggingfaceEngine(BaseEngine):
system,
tools,
image,
video,
input_kwargs,
)
async with self.semaphore: