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from typing import Dict, List, Any
import os
import torch
from transformers import AutoTokenizer, AutoModel
import pandas as pd
import time
import numpy as np
class EndpointHandler:
def __init__(self, path="insilicomedicine/precious3-gpt"):
self.model = AutoModel.from_pretrained(path, trust_remote_code=True).to('cuda')
self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
self.model.config.pad_token_id = self.tokenizer.pad_token_id
self.model.config.bos_token_id = self.tokenizer.bos_token_id
self.model.config.eos_token_id = self.tokenizer.eos_token_id
unique_entities_p3 = pd.read_csv('https://huggingface.co/insilicomedicine/precious3-gpt/raw/main/all_entities_with_type.csv')
self.unique_compounds_p3 = [i.strip() for i in unique_entities_p3[unique_entities_p3.type=='compound'].entity.to_list()]
self.unique_genes_p3 = [i.strip() for i in unique_entities_p3[unique_entities_p3.type=='gene'].entity.to_list()]
def create_prompt(self, prompt_config):
prompt = "[BOS]"
multi_modal_prefix = ''
for k, v in prompt_config.items():
if k=='instruction':
prompt+=f'<{v}>' if isinstance(v, str) else "".join([f'<{v_i}>' for v_i in v])
elif k=='up':
if v:
prompt+=f'{multi_modal_prefix}<{k}>{v} </{k}>' if isinstance(v, str) else f'{multi_modal_prefix}<{k}>{" ".join(v)} </{k}>'
elif k=='down':
if v:
prompt+=f'{multi_modal_prefix}<{k}>{v} </{k}>' if isinstance(v, str) else f'{multi_modal_prefix}<{k}>{" ".join(v)} </{k}>'
elif k=='age':
if isinstance(v, int):
if prompt_config['species'].strip() == 'human':
prompt+=f'<{k}_individ>{v} </{k}_individ>'
elif prompt_config['species'].strip() == 'macaque':
prompt+=f'<{k}_individ>Macaca-{int(v/20)} </{k}_individ>'
else:
if v:
prompt+=f'<{k}>{v.strip()} </{k}>' if isinstance(v, str) else f'<{k}>{" ".join(v)} </{k}>'
else:
prompt+=f'<{k}></{k}>'
return prompt
def custom_generate(self,
input_ids,
device,
max_new_tokens,
mode,
temperature=0.8,
top_p=0.2, top_k=3550,
n_next_tokens=50, num_return_sequences=1, random_seed=137):
torch.manual_seed(random_seed)
# Set parameters
# temperature - Higher value for more randomness, lower for more control
# top_p - Probability threshold for nucleus sampling (aka top-p sampling)
# top_k - Ignore logits below the top-k value to reduce randomness (if non-zero)
# n_next_tokens - Number of top next tokens when predicting compounds
# Generate sequences
outputs = []
next_token_compounds = []
for _ in range(num_return_sequences):
start_time = time.time()
generated_sequence = []
current_token = input_ids.clone()
for _ in range(max_new_tokens): # Maximum length of generated sequence
# Forward pass through the model
logits = self.model.forward(
input_ids=current_token
)[0]
# Apply temperature to logits
if temperature != 1.0:
logits = logits / temperature
# Apply top-p sampling (nucleus sampling)
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
if top_k > 0:
sorted_indices_to_remove[..., top_k:] = 1
# Set the logit values of the removed indices to a very small negative value
inf_tensor = torch.tensor(float("-inf")).type(torch.bfloat16).to(logits.device)
logits = logits.where(sorted_indices_to_remove, inf_tensor)
# Sample the next token
if current_token[0][-1] == self.tokenizer.encode('<drug>')[0] and len(next_token_compounds)==0:
next_token_compounds.append(torch.topk(torch.softmax(logits, dim=-1)[0][len(current_token[0])-1, :].flatten(), n_next_tokens).indices)
next_token = torch.multinomial(torch.softmax(logits, dim=-1)[0], num_samples=1)[len(current_token[0])-1, :].unsqueeze(0)
# Append the sampled token to the generated sequence
generated_sequence.append(next_token.item())
# Stop generation if an end token is generated
if next_token == self.tokenizer.eos_token_id:
break
# Prepare input for the next iteration
current_token = torch.cat((current_token, next_token), dim=-1)
print(time.time()-start_time)
outputs.append(generated_sequence)
# Process generated up/down lists
processed_outputs = {"up": [], "down": []}
if mode in ['meta2diff', 'meta2diff2compound']:
for output in outputs:
up_split_index = output.index(self.tokenizer.convert_tokens_to_ids('</up>'))
generated_up_raw = [i.strip() for i in self.tokenizer.convert_ids_to_tokens(output[:up_split_index])]
generated_up = sorted(set(generated_up_raw) & set(self.unique_genes_p3), key = generated_up_raw.index)
processed_outputs['up'].append(generated_up)
down_split_index = output.index(self.tokenizer.convert_tokens_to_ids('</down>'))
generated_down_raw = [i.strip() for i in self.tokenizer.convert_ids_to_tokens(output[up_split_index:down_split_index+1])]
generated_down = sorted(set(generated_down_raw) & set(self.unique_genes_p3), key = generated_down_raw.index)
processed_outputs['down'].append(generated_down)
else:
processed_outputs = outputs
predicted_compounds_ids = [self.tokenizer.convert_ids_to_tokens(j) for j in next_token_compounds]
predicted_compounds = []
for j in predicted_compounds_ids:
predicted_compounds.append([i.strip() for i in j])
return processed_outputs, predicted_compounds, random_seed
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
"""
Args:
data (:dict:):
The payload with the text prompt and generation parameters.
"""
device = "cuda"
parameters = data.pop("parameters", None)
config_data = data.pop("inputs", None)
mode = data.pop('mode', 'Not specified')
prompt = self.create_prompt(config_data)
inputs = self.tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
max_new_tokens = self.model.config.max_seq_len - len(input_ids[0])
try:
generated_sequence, raw_next_token_generation, out_seed = self.custom_generate(input_ids = input_ids,
max_new_tokens=max_new_tokens, mode=mode,
device=device, **parameters)
next_token_generation = [sorted(set(i) & set(self.unique_compounds_p3), key = i.index) for i in raw_next_token_generation]
if mode == "meta2diff":
outputs = {"up": generated_sequence['up'], "down": generated_sequence['down']}
out = {"output": outputs, "mode": mode, "message": "Done!", "input": prompt, 'random_seed': out_seed}
elif mode == "meta2diff2compound":
outputs = {"up": generated_sequence['up'], "down": generated_sequence['down']}
out = {
"output": outputs, "compounds": next_token_generation, "raw_output": raw_next_token_generation, "mode": mode,
"message": "Done!", "input": prompt, 'random_seed': out_seed}
elif mode == "diff2compound":
outputs = generated_sequence
out = {
"output": outputs, "compounds": next_token_generation, "raw_output": raw_next_token_generation, "mode": mode,
"message": "Done!", "input": prompt, 'random_seed': out_seed}
else:
out = {"message": f"Specify one of the following modes: meta2diff, meta2diff2compound, diff2compound. Your mode is: {mode}"}
except Exception as e:
print(e)
outputs, next_token_generation = [None], [None]
out = {"output": outputs, "mode": mode, 'message': f"{e}", "input": prompt, 'random_seed': 137}
return out |