File size: 4,821 Bytes
a33ae41 fbee3b9 18097d1 a33ae41 fc124cf fbee3b9 fc124cf a33ae41 18097d1 a33ae41 2e8c95f a33ae41 a894d5f a33ae41 2e8c95f a33ae41 2e8c95f 18097d1 2e8c95f 9df03e0 2e8c95f a33ae41 9cd72d1 a33ae41 63c82ae a33ae41 f866daa a33ae41 f866daa a33ae41 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
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
# hf_logging.set_verbosity_debug()
logging.basicConfig(level=logging.INFO)
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["inputs"]
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)
logging.info(f"OUTPUTS 1: {outputs} length: {outputs.shape}")
outputs = outputs[:, inputs["input_ids"].shape[1] :]
logging.info(f"OUTPUTS 2: {outputs.shape}")
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]))
|