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# Copyright (c) 2023-2024 DeepSeek.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
from threading import Thread
from typing import List
import torch
torch.jit.script = lambda f: f
import transformers
from transformers import (
AutoModelForCausalLM,
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
from deepseek_vl.models import MultiModalityCausalLM, VLChatProcessor
from deepseek_vl.utils.conversation import Conversation
from transformers import BitsAndBytesConfig
nf8_config = BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_use_double_quant=True,
bnb_8bit_quant_type="nf8",
)
def load_model(model_path):
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
tokenizer = vl_chat_processor.tokenizer
vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
model_path, trust_remote_code=True, quantization_config=nf8_config
)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
return tokenizer, vl_gpt, vl_chat_processor
def convert_conversation_to_prompts(conversation: Conversation):
prompts = []
messages = conversation.messages
for i in range(0, len(messages), 2):
prompt = {
"role": messages[i][0],
"content": (
messages[i][1][0]
if isinstance(messages[i][1], tuple)
else messages[i][1]
),
"images": [messages[i][1][1]] if isinstance(messages[i][1], tuple) else [],
}
response = {"role": messages[i + 1][0], "content": messages[i + 1][1]}
prompts.extend([prompt, response])
return prompts
class StoppingCriteriaSub(StoppingCriteria):
def __init__(self, stops=[], encounters=1):
super().__init__()
self.stops = [stop.to("cuda") for stop in stops]
def __call__(
self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
):
for stop in self.stops:
if input_ids.shape[-1] < len(stop):
continue
if torch.all((stop == input_ids[0][-len(stop) :])).item():
return True
return False
@torch.inference_mode()
def deepseek_generate(
prompts: list,
vl_gpt: torch.nn.Module,
vl_chat_processor,
tokenizer: transformers.PreTrainedTokenizer,
stop_words: list,
max_length: int = 256,
temperature: float = 1.0,
top_p: float = 1.0,
repetition_penalty=1.1,
):
prompts = prompts
pil_images = list()
for message in prompts:
if "images" not in message:
continue
for pil_img in message["images"]:
pil_images.append(pil_img)
prepare_inputs = vl_chat_processor(
conversations=prompts, images=pil_images, force_batchify=True
).to(vl_gpt.device)
return generate(
vl_gpt,
tokenizer,
prepare_inputs,
max_length,
temperature,
repetition_penalty,
top_p,
stop_words,
)
@torch.inference_mode()
def generate(
vl_gpt,
tokenizer,
prepare_inputs,
max_gen_len: int = 256,
temperature: float = 0,
repetition_penalty=1.1,
top_p: float = 0.95,
stop_words: List[str] = [],
):
"""Stream the text output from the multimodality model with prompt and image inputs."""
inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
streamer = TextIteratorStreamer(tokenizer)
stop_words_ids = [
torch.tensor(tokenizer.encode(stop_word)) for stop_word in stop_words
]
stopping_criteria = StoppingCriteriaList(
[StoppingCriteriaSub(stops=stop_words_ids)]
)
generation_config = dict(
inputs_embeds=inputs_embeds,
attention_mask=prepare_inputs.attention_mask,
pad_token_id=tokenizer.eos_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=max_gen_len,
do_sample=True,
use_cache=True,
streamer=streamer,
stopping_criteria=stopping_criteria,
)
if temperature > 0:
generation_config.update(
{
"do_sample": True,
"top_p": top_p,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
}
)
else:
generation_config["do_sample"] = False
thread = Thread(target=vl_gpt.language_model.generate, kwargs=generation_config)
thread.start()
yield from streamer
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