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from typing import Dict, List, Any
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import PreTrainedTokenizerFast
from transformers import GenerationConfig
import transformers
import pandas as pd
import time
import numpy as np
from precious3_gpt_multi_modal import Custom_MPTForCausalLM
class EndpointHandler:
def __init__(self, path=""):
self.path = path
# load model and processor from path
self.model = Custom_MPTForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16).to('cuda')
self.tokenizer = PreTrainedTokenizerFast(tokenizer_file = os.path.join(path, "tokenizer.json"), unk_token="[UNK]",
pad_token="[PAD]",
eos_token="[EOS]",
bos_token="[BOS]")
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(os.path.join(path, 'p3_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()]
self.emb_gpt_genes = pd.read_pickle(os.path.join(self.path, 'multi-modal-data/emb_gpt_genes.pickle'))
self.emb_hgt_genes = pd.read_pickle(os.path.join(self.path, 'multi-modal-data/emb_hgt_genes.pickle'))
def create_prompt(self, prompt_config):
prompt = "[BOS]"
multi_modal_prefix = '<modality0><modality1><modality2><modality3>'*3
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':
prompt+=f'{multi_modal_prefix}<{k}>{v}</{k}>' if isinstance(v, str) else f'{multi_modal_prefix}<{k}>{" ".join(v)} </{k}>'
elif k=='down':
prompt+=f'{multi_modal_prefix}<{k}>{v}</{k}>' if isinstance(v, str) else f'{multi_modal_prefix}<{k}>{" ".join(v)} </{k}>'
else:
prompt+=f'<{k}>{v}</{k}>' if isinstance(v, str) else f'<{k}>{" ".join(v)} </{k}>'
return prompt
def custom_generate(self,
input_ids,
acc_embs_up_kg_mean,
acc_embs_down_kg_mean,
acc_embs_up_txt_mean,
acc_embs_down_txt_mean,
device,
max_new_tokens,
mode,
temperature=0.8,
top_p=0.2, top_k=3550,
n_next_tokens=50, num_return_sequences=1):
torch.manual_seed(137)
# 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
modality0_emb = torch.unsqueeze(torch.from_numpy(acc_embs_up_kg_mean), 0).to(device) if isinstance(acc_embs_up_kg_mean, np.ndarray) else None
modality1_emb = torch.unsqueeze(torch.from_numpy(acc_embs_down_kg_mean), 0).to(device) if isinstance(acc_embs_down_kg_mean, np.ndarray) else None
modality2_emb = torch.unsqueeze(torch.from_numpy(acc_embs_up_txt_mean), 0).to(device) if isinstance(acc_embs_up_txt_mean, np.ndarray) else None
modality3_emb = torch.unsqueeze(torch.from_numpy(acc_embs_down_txt_mean), 0).to(device) if isinstance(acc_embs_down_txt_mean, np.ndarray) else None
# 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,
modality0_emb=modality0_emb,
modality0_token_id=self.tokenizer.encode('<modality0>')[0], # 62191,
modality1_emb=modality1_emb,
modality1_token_id=self.tokenizer.encode('<modality1>')[0], # 62192,
modality2_emb=modality2_emb,
modality2_token_id=self.tokenizer.encode('<modality2>')[0], # 62193,
modality3_emb=modality3_emb,
modality3_token_id=self.tokenizer.encode('<modality3>')[0], # 62194
)[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]:
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 == 'meta2diff':
for output in outputs:
up_split_index = output.index(tokenizer.convert_tokens_to_ids('</up>'))
generated_up_raw = [i.strip() for i in 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(tokenizer.convert_tokens_to_ids('</down>'))
generated_down_raw = [i.strip() for i in 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
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
"""
Args:
data (:dict:):
The payload with the text prompt and generation parameters.
"""
torch.manual_seed(137)
device = "cuda"
config_data = data.pop("inputs", None)
parameters = data.pop("parameters", None)
mode = data.pop('mode', 'diff2compound')
prompt = self.create_prompt(config_data)
inputs = self.tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
max_new_tokens = 600 - len(input_ids[0])
try:
if set(["up", "down"]) & set(config_data.keys()):
acc_embs_up1 = []
acc_embs_up2 = []
for gs in config_data['up']:
try:
acc_embs_up1.append(self.emb_hgt_genes[self.emb_hgt_genes.gene_symbol==gs].embs.values[0])
acc_embs_up2.append(self.emb_gpt_genes[self.emb_gpt_genes.gene_symbol==gs].embs.values[0])
except Exception as e:
pass
acc_embs_up1_mean = np.array(acc_embs_up1).mean(0)
acc_embs_up2_mean = np.array(acc_embs_up2).mean(0)
acc_embs_down1 = []
acc_embs_down2 = []
for gs in config_data['down']:
try:
acc_embs_down1.append(self.emb_hgt_genes[self.emb_hgt_genes.gene_symbol==gs].embs.values[0])
acc_embs_down2.append(self.emb_gpt_genes[self.emb_gpt_genes.gene_symbol==gs].embs.values[0])
except Exception as e:
pass
acc_embs_down1_mean = np.array(acc_embs_down1).mean(0)
acc_embs_down2_mean = np.array(acc_embs_down2).mean(0)
else:
acc_embs_up1_mean, acc_embs_up2_mean, acc_embs_down1_mean, acc_embs_down2_mean = None, None, None, None
generated_sequence, raw_next_token_generation = self.custom_generate(input_ids = input_ids,
acc_embs_up_kg_mean=acc_embs_up1_mean,
acc_embs_down_kg_mean=acc_embs_down1_mean,
acc_embs_up_txt_mean=acc_embs_up2_mean,
acc_embs_down_txt_mean=acc_embs_down2_mean, 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'], "mode": mode, "message": "Done!", "input": prompt}
out = {"output": outputs, "mode": mode, "message": "Done!", "input": prompt}
elif mode == "meta2diff2compound":
outputs = {"up": generated_sequence['up'], "down": generated_sequence['down'], "mode": mode, "message": "Done!", "input": prompt}
out = {
"output": outputs, "compounds": next_token_generation, "raw_output": raw_next_token_generation, "mode": mode, "message": "Done!", "input": prompt}
else:
outputs = generated_sequence
out = {
"output": outputs, "compounds": next_token_generation, "raw_output": raw_next_token_generation, "mode": mode, "message": "Done!", "input": prompt}
except Exception as e:
print(e)
outputs, next_token_generation = [None], [None]
out = {"output": outputs, "mode": mode, 'message': f"{e}"}
return out |