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import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import AutoModel, AutoConfig, AutoTokenizer
import pandas as pd
from optimum.intel import OVModelForQuestionAnswering
import openvino.inference_engine as ie
import os
import gradio as gr
from multiprocessing import cpu_count
AUTH_TOKEN = "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"
tokenizer = AutoTokenizer.from_pretrained('nguyenvulebinh/vi-mrc-base',
use_auth_token=AUTH_TOKEN)
pad_token_id = tokenizer.pad_token_id
# Load the model
model_xml = "openvino_stage1/stage1.xml"
model_bin = "openvino_stage1/stage1.bin"
# Create an Inference Engine object
ie_core = ie.IECore()
# Read the IR files"
net = ie_core.read_network(model=model_xml, weights=model_bin)
class PairwiseModel_modify(nn.Module):
def __init__(self, model_name, max_length=384, batch_size=16, device="cpu"):
super(PairwiseModel_modify, self).__init__()
self.max_length = max_length
self.batch_size = batch_size
self.device = device
# self.model = AutoModel.from_pretrained(model_name , use_auth_token=AUTH_TOKEN)
self.config = AutoConfig.from_pretrained(model_name, use_auth_token=AUTH_TOKEN)
self.fc = nn.Linear(768, 1).to(self.device)
def forward(self, ids, masks):
# Export the model to ONNX format
ids_np = ids.cpu().numpy().astype(np.int64)
masks_np = masks.cpu().numpy().astype(np.int64)
ids_device = torch.from_numpy(ids_np).to(self.device)
masks_device = torch.from_numpy(masks_np).to(self.device)
input_feed = {"input_ids": ids_device, "attention_mask": masks_device}
# Specify the input shapes (batch_size, max_sequence_length)
input_shapes = {"input_ids": ids.shape, "attention_mask": masks.shape}
# Set the input shapes in the network
net.reshape(input_shapes)
# Load the network with the specified input shapes
exec_net = ie_core.load_network(network=net, device_name="CPU")
outputs = exec_net.infer(input_feed)
# Get the output tensor and apply the linear layer
out = torch.from_numpy(outputs["output"]).to(self.device)
out = out[:, 0]
return out
def stage1_ranking(self, question, texts):
tmp = pd.DataFrame()
tmp["text"] = [" ".join(x.split()) for x in texts]
tmp["question"] = question
valid_dataset = SiameseDatasetStage1(tmp, tokenizer, self.max_length, is_test=True)
valid_loader = DataLoader(valid_dataset, batch_size=self.batch_size, collate_fn=collate_fn,
num_workers=cpu_count(), shuffle=False, pin_memory=True)
preds = []
with torch.no_grad():
bar = enumerate(valid_loader)
for step, data in bar:
ids = data["ids"].to(self.device)
masks = data["masks"].to(self.device)
preds.append(torch.sigmoid(self(ids, masks)).view(-1))
preds = torch.concat(preds)
return preds.cpu().numpy()
class SiameseDatasetStage1(Dataset):
def __init__(self, df, tokenizer, max_length, is_test=False):
self.df = df
self.max_length = max_length
self.tokenizer = tokenizer
self.is_test = is_test
self.content1 = tokenizer.batch_encode_plus(list(df.question.values), max_length=max_length, truncation=True)[
"input_ids"]
self.content2 = tokenizer.batch_encode_plus(list(df.text.values), max_length=max_length, truncation=True)[
"input_ids"]
if not self.is_test:
self.targets = self.df.label
def __len__(self):
return len(self.df)
def __getitem__(self, index):
return {
'ids1': torch.tensor(self.content1[index], dtype=torch.long),
'ids2': torch.tensor(self.content2[index][1:], dtype=torch.long),
'target': torch.tensor(0) if self.is_test else torch.tensor(self.targets[index], dtype=torch.float)
}
class SiameseDatasetStage2(Dataset):
def __init__(self, df, tokenizer, max_length, is_test=False):
self.df = df
self.max_length = max_length
self.tokenizer = tokenizer
self.is_test = is_test
self.df["content1"] = self.df.apply(lambda row: row.question + f" {tokenizer.sep_token} " + row.answer, axis=1)
self.df["content2"] = self.df.apply(lambda row: row.title + f" {tokenizer.sep_token} " + row.candidate, axis=1)
self.content1 = tokenizer.batch_encode_plus(list(df.content1.values), max_length=max_length, truncation=True)[
"input_ids"]
self.content2 = tokenizer.batch_encode_plus(list(df.content2.values), max_length=max_length, truncation=True)[
"input_ids"]
if not self.is_test:
self.targets = self.df.label
def __len__(self):
return len(self.df)
def __getitem__(self, index):
return {
'ids1': torch.tensor(self.content1[index], dtype=torch.long),
'ids2': torch.tensor(self.content2[index][1:], dtype=torch.long),
'target': torch.tensor(0) if self.is_test else torch.tensor(self.targets[index], dtype=torch.float)
}
def collate_fn(batch):
ids = [torch.cat([x["ids1"], x["ids2"]]) for x in batch]
targets = [x["target"] for x in batch]
max_len = np.max([len(x) for x in ids])
masks = []
for i in range(len(ids)):
if len(ids[i]) < max_len:
ids[i] = torch.cat((ids[i], torch.tensor([pad_token_id, ] * (max_len - len(ids[i])), dtype=torch.long)))
masks.append(ids[i] != pad_token_id)
# print(tokenizer.decode(ids[0]))
outputs = {
"ids": torch.vstack(ids),
"masks": torch.vstack(masks),
"target": torch.vstack(targets).view(-1)
}
return outputs
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