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from typing import Dict, List, Any
# import transformers
# from transformers import AutoTokenizer
# import torch
from datetime import datetime
import torch
# torch.backends.cuda.matmul.allow_tf32 = True
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
class EndpointHandler():
def __init__(self, path=""):
# Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
self.pipe = self.pipe.to("cuda")
# self.pipe.enable_attention_slicing()
self.pipe.enable_xformers_memory_efficient_attention()
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.model.eval()
# self.model.to(device=device, dtype=self.torch_dtype)
# self.generate_kwargs = {
# 'max_new_tokens': 512,
# 'temperature': 0.0001,
# 'top_p': 1.0,
# 'top_k': 0,
# 'use_cache': True,
# 'do_sample': True,
# 'eos_token_id': self.tokenizer.eos_token_id,
# 'pad_token_id': self.tokenizer.pad_token_id,
# "repetition_penalty": 1.1
# }
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
"""
# streamer = TextIteratorStreamer(
# self.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
# )
## Model Parameters
# self.generate_kwargs['max_new_tokens'] = data['max_new_tokens'] if 'max_new_tokens' in data else self.generate_kwargs['max_new_tokens']
# self.generate_kwargs['temperature'] = data['temperature'] if 'temperature' in data else self.generate_kwargs['temperature']
# self.generate_kwargs['top_p'] = data['top_p'] if 'top_p' in data else self.generate_kwargs['top_p']
# self.generate_kwargs['top_k'] = data['top_k'] if 'top_k' in data else self.generate_kwargs['top_k']
# self.generate_kwargs['do_sample'] = data['do_sample'] if 'do_sample' in data else self.generate_kwargs['do_sample']
# self.generate_kwargs['repetition_penalty'] = data['repetition_penalty'] if 'repetition_penalty' in data else self.generate_kwargs['repetition_penalty']
## Prepare the inputs
# inputs = data.pop("inputs",data)
# input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
# input_ids = input_ids.to(self.model.device)
# pip install accelerate
batch_size = data.pop("batch_size",data)
now = datetime.now()
with torch.inference_mode():
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe([prompt]*batch_size, num_inference_steps=20)
# image.save("astronaut_rides_horse.png")
current = datetime.now()
# encoded_inp = self.tokenizer(inputs, return_tensors='pt', padding=True)
# for key, value in encoded_inp.items():
# encoded_inp[key] = value.to('cuda:0')
## Invoke the model
# with torch.no_grad():
# gen_tokens = self.model.generate(
# input_ids=encoded_inp['input_ids'],
# attention_mask=encoded_inp['attention_mask'],
# **generate_kwargs,
# )
# ## Decode using tokenizer
# decoded_gen = self.tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)
# with torch.no_grad():
# output_ids = self.model.generate(input_ids, **self.generate_kwargs)
# # Slice the output_ids tensor to get only new tokens
# new_tokens = output_ids[0, len(input_ids[0]) :]
# output_text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
return [{"batch_size":batch_size, "time_elapsed": str(current-now)}]
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