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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 asyncio
import concurrent.futures
import os
from threading import Thread
from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Dict, List, Optional, Sequence, Tuple, Union
import torch
from transformers import GenerationConfig, TextIteratorStreamer
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
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
from .base_engine import BaseEngine, Response
if TYPE_CHECKING:
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
logger = get_logger(__name__)
class HuggingfaceEngine(BaseEngine):
def __init__(
self,
model_args: "ModelArguments",
data_args: "DataArguments",
finetuning_args: "FinetuningArguments",
generating_args: "GeneratingArguments",
) -> None:
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" if self.can_generate else "right"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args)
self.model = load_model(
self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
) # must after fixing tokenizer to resize vocab
self.generating_args = generating_args.to_dict()
try:
asyncio.get_event_loop()
except RuntimeError:
logger.warning("There is no current event loop, creating a new one.")
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1")))
@staticmethod
def _process_args(
model: "PreTrainedModel",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: Dict[str, Any],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = 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"]
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)
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)
do_sample: Optional[bool] = input_kwargs.pop("do_sample", None)
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 stop is not None:
logger.warning("Stop parameter is not supported by the huggingface engine yet.")
generating_args = generating_args.copy()
generating_args.update(
dict(
do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
temperature=temperature if temperature is not None else generating_args["temperature"],
top_p=top_p if top_p is not None else generating_args["top_p"],
top_k=top_k if top_k is not None else generating_args["top_k"],
num_return_sequences=num_return_sequences,
repetition_penalty=repetition_penalty
if repetition_penalty is not None
else generating_args["repetition_penalty"],
length_penalty=length_penalty if length_penalty is not None else generating_args["length_penalty"],
eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
pad_token_id=tokenizer.pad_token_id,
)
)
if isinstance(num_return_sequences, int) and num_return_sequences > 1: # do_sample needs temperature > 0
generating_args["do_sample"] = True
generating_args["temperature"] = generating_args["temperature"] or 1.0
if not generating_args["temperature"]:
generating_args["do_sample"] = False
if not generating_args["do_sample"]:
generating_args.pop("temperature", None)
generating_args.pop("top_p", None)
if max_length:
generating_args.pop("max_new_tokens", None)
generating_args["max_length"] = max_length
if max_new_tokens:
generating_args.pop("max_length", None)
generating_args["max_new_tokens"] = max_new_tokens
gen_kwargs = dict(
inputs=inputs,
attention_mask=attention_mask,
generation_config=GenerationConfig(**generating_args),
logits_processor=get_logits_processor(),
)
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
@staticmethod
@torch.inference_mode()
def _chat(
model: "PreTrainedModel",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: Dict[str, Any],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = 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, video, input_kwargs
)
generate_output = model.generate(**gen_kwargs)
response_ids = generate_output[:, prompt_length:]
response = tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
results = []
for i in range(len(response)):
eos_index = (response_ids[i] == tokenizer.eos_token_id).nonzero()
response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i])
results.append(
Response(
response_text=response[i],
response_length=response_length,
prompt_length=prompt_length,
finish_reason="stop" if len(eos_index) else "length",
)
)
return results
@staticmethod
@torch.inference_mode()
def _stream_chat(
model: "PreTrainedModel",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
template: "Template",
generating_args: Dict[str, Any],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = 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, video, input_kwargs
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
gen_kwargs["streamer"] = streamer
thread = Thread(target=model.generate, kwargs=gen_kwargs, daemon=True)
thread.start()
def stream():
try:
return streamer.__next__()
except StopIteration:
raise StopAsyncIteration()
return stream
@staticmethod
@torch.inference_mode()
def _get_scores(
model: "PreTrainedModelWrapper",
tokenizer: "PreTrainedTokenizer",
batch_input: List[str],
input_kwargs: Optional[Dict[str, Any]] = {},
) -> List[float]:
max_length: Optional[int] = input_kwargs.pop("max_length", None)
device = getattr(model.pretrained_model, "device", "cuda")
inputs: Dict[str, "torch.Tensor"] = tokenizer(
batch_input,
padding=True,
truncation=True,
max_length=max_length or getattr(model.config, "max_position_embeddings", 1024),
return_tensors="pt",
add_special_tokens=False,
).to(device)
values: "torch.Tensor" = model(**inputs, return_dict=True, use_cache=False)[-1]
scores = values.gather(dim=-1, index=(inputs["attention_mask"].sum(dim=-1, keepdim=True) - 1))
return scores
@override
async def chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
**input_kwargs,
) -> List["Response"]:
if not self.can_generate:
raise ValueError("The current model does not support `chat`.")
loop = asyncio.get_running_loop()
input_args = (
self.model,
self.tokenizer,
self.processor,
self.template,
self.generating_args,
messages,
system,
tools,
image,
video,
input_kwargs,
)
async with self.semaphore:
with concurrent.futures.ThreadPoolExecutor() as pool:
return await loop.run_in_executor(pool, self._chat, *input_args)
@override
async def stream_chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:
raise ValueError("The current model does not support `stream_chat`.")
loop = asyncio.get_running_loop()
input_args = (
self.model,
self.tokenizer,
self.processor,
self.template,
self.generating_args,
messages,
system,
tools,
image,
video,
input_kwargs,
)
async with self.semaphore:
with concurrent.futures.ThreadPoolExecutor() as pool:
stream = self._stream_chat(*input_args)
while True:
try:
yield await loop.run_in_executor(pool, stream)
except StopAsyncIteration:
break
@override
async def get_scores(
self,
batch_input: List[str],
**input_kwargs,
) -> List[float]:
if self.can_generate:
raise ValueError("Cannot get scores using an auto-regressive model.")
loop = asyncio.get_running_loop()
input_args = (self.model, self.tokenizer, batch_input, input_kwargs)
async with self.semaphore:
with concurrent.futures.ThreadPoolExecutor() as pool:
return await loop.run_in_executor(pool, self._get_scores, *input_args)
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