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from typing import Dict, List, Any
from transformers import pipeline
from PIL import Image
import requests
from transformers import AutoModelForCausalLM, LlamaTokenizer
import torch
from accelerate import (
init_empty_weights,
infer_auto_device_map,
load_checkpoint_and_dispatch,
)
class EndpointHandler:
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# self.pipeline = pipeline(
# "text-generation", model="THUDM/cogvlm-chat-hf", trust_remote_code=True
# )
# self.model = AutoModelForCausalLM.from_pretrained(
# "THUDM/cogvlm-chat-hf", trust_remote_code=True
# )
self.tokenizer = LlamaTokenizer.from_pretrained("lmsys/vicuna-7b-v1.5")
# self.model = (
# AutoModelForCausalLM.from_pretrained(
# "THUDM/cogvlm-chat-hf",
# torch_dtype=torch.bfloat16,
# low_cpu_mem_usage=True,
# trust_remote_code=True,
# )
# .to("cuda")
# .eval()
# )
# DISTRIBUTED GPUS
with init_empty_weights():
self.model = AutoModelForCausalLM.from_pretrained(
"THUDM/cogvlm-chat-hf",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
)
device_map = infer_auto_device_map(
self.model,
max_memory={
0: "16GiB",
1: "16GiB",
2: "16GiB",
3: "16GiB",
"cpu": "180GiB",
},
no_split_module_classes=["CogVLMDecoderLayer"],
)
self.model = load_checkpoint_and_dispatch(
self.model,
"~/.cache/huggingface/modules/transformers_modules/THUDM/cogvlm-chat-hf/8abca878c4257412c4c38eeafaed3fe27a036730", # typical, '~/.cache/huggingface/hub/models--THUDM--cogvlm-chat-hf/snapshots/balabala'
# "/home/ec2-user/.cache/huggingface/hub/models--THUDM--cogvlm-chat-hf/snapshots/8abca878c4257412c4c38eeafaed3fe27a036730", # typical, '~/.cache/huggingface/hub/models--THUDM--cogvlm-chat-hf/snapshots/balabala'
device_map=device_map,
no_split_module_classes=["CogVLMDecoderLayer"],
)
self.model = self.model.eval()
## DISTRIBUTED GPUS
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
query = data["query"]
img_uri = data["img_uri"]
image = Image.open(
requests.get(
img_uri,
stream=True,
).raw
).convert("RGB")
inputs = self.model.build_conversation_input_ids(
self.tokenizer,
query=query,
history=[],
images=[image],
template_version="vqa",
) # vqa mode
inputs = {
"input_ids": inputs["input_ids"].unsqueeze(0).to("cuda"),
"token_type_ids": inputs["token_type_ids"].unsqueeze(0).to("cuda"),
"attention_mask": inputs["attention_mask"].unsqueeze(0).to("cuda"),
"images": [[inputs["images"][0].to("cuda").to(torch.bfloat16)]],
}
gen_kwargs = {"max_length": 2048, "do_sample": False}
with torch.no_grad():
outputs = self.model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs["input_ids"].shape[1] :]
response = self.tokenizer.decode(outputs[0])
return response
# query = "How many houses are there in this cartoon?"
# image = Image.open(
# requests.get(
# "https://github.com/THUDM/CogVLM/blob/main/examples/3.jpg?raw=true", stream=True
# ).raw
# ).convert("RGB")
# inputs = model.build_conversation_input_ids(
# tokenizer, query=query, history=[], images=[image], template_version="vqa"
# ) # vqa mode
# inputs = {
# "input_ids": inputs["input_ids"].unsqueeze(0).to("cuda"),
# "token_type_ids": inputs["token_type_ids"].unsqueeze(0).to("cuda"),
# "attention_mask": inputs["attention_mask"].unsqueeze(0).to("cuda"),
# "images": [[inputs["images"][0].to("cuda").to(torch.bfloat16)]],
# }
# gen_kwargs = {"max_length": 2048, "do_sample": False}
# with torch.no_grad():
# outputs = model.generate(**inputs, **gen_kwargs)
# outputs = outputs[:, inputs["input_ids"].shape[1] :]
# print(tokenizer.decode(outputs[0]))
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