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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,
)
import os
import logging
from transformers import logging as hf_logging
logging.basicConfig(level=logging.INFO)
hf_logging.set_verbosity_debug()
def list_files(directory, depth, max_depth=5):
# Lists all files and directories in the given directory
for filename in os.listdir(directory):
print(os.path.join(directory, filename))
if not os.path.isfile(filename) and depth < max_depth:
list_files(os.path.join(directory, filename), depth + 1, max_depth)
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,
# )
# # print("LISTING FILES IN ", "/root/.cache/huggingface")
# # list_files("/root/.cache/huggingface", 0, 5)
# device_map = infer_auto_device_map(
# self.model,
# max_memory={
# 0: "12GiB",
# 1: "12GiB",
# 2: "12GiB",
# 3: "12GiB",
# "cpu": "180GiB",
# },
# no_split_module_classes=["CogVLMDecoderLayer"],
# )
# self.model = load_checkpoint_and_dispatch(
# self.model,
# "/root/.cache/huggingface/hub/models--THUDM--cogvlm-chat-hf/snapshots/8abca878c4257412c4c38eeafaed3fe27a036730",
# 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)
print("outputs 1: ", outputs)
outputs = outputs[:, inputs["input_ids"].shape[1] :]
print("outputs 2: ", outputs)
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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