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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "9db57e75-ba95-4e96-836a-ce2eb9689c7b",
"metadata": {},
"outputs": [],
"source": [
"!pip install torch\n",
"\n",
"\n",
"from torch import Tensor\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.nn import Transformer\n",
"import math\n",
"DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"import os\n",
"from argparse import Namespace\n",
"from collections import Counter\n",
"import json\n",
"import re\n",
"import string\n",
"import datetime\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.nn import functional as F\n",
"from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n",
"import torch.optim as optima\n",
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"class Vocabulary(object):\n",
" \"\"\"Class to process text and extract vocabulary for mapping\"\"\"\n",
"\n",
" def __init__(self, token_to_idx=None):\n",
" \"\"\"\n",
" Args:\n",
" token_to_idx (dict): a pre-existing map of tokens to indices\n",
" \"\"\"\n",
"\n",
" if token_to_idx is None:\n",
" token_to_idx = {}\n",
" self._token_to_idx = token_to_idx\n",
"\n",
" self._idx_to_token = {idx: token \n",
" for token, idx in self._token_to_idx.items()}\n",
" \n",
" def to_serializable(self):\n",
" \"\"\" returns a dictionary that can be serialized \"\"\"\n",
" return {'token_to_idx': self._token_to_idx}\n",
"\n",
" @classmethod\n",
" def from_serializable(cls, contents):\n",
" \"\"\" instantiates the Vocabulary from a serialized dictionary \"\"\"\n",
" return cls(**contents)\n",
"\n",
" def add_token(self, token):\n",
" \"\"\"Update mapping dicts based on the token.\n",
"\n",
" Args:\n",
" token (str): the item to add into the Vocabulary\n",
" Returns:\n",
" index (int): the integer corresponding to the token\n",
" \"\"\"\n",
" if token in self._token_to_idx:\n",
" index = self._token_to_idx[token]\n",
" else:\n",
" index = len(self._token_to_idx)\n",
" self._token_to_idx[token] = index\n",
" self._idx_to_token[index] = token\n",
" return index\n",
" \n",
" def add_many(self, tokens):\n",
" \"\"\"Add a list of tokens into the Vocabulary\n",
" \n",
" Args:\n",
" tokens (list): a list of string tokens\n",
" Returns:\n",
" indices (list): a list of indices corresponding to the tokens\n",
" \"\"\"\n",
" return [self.add_token(token) for token in tokens]\n",
"\n",
" def lookup_token(self, token):\n",
" \"\"\"Retrieve the index associated with the token \n",
" \n",
" Args:\n",
" token (str): the token to look up \n",
" Returns:\n",
" index (int): the index corresponding to the token\n",
" \"\"\"\n",
" return self._token_to_idx[token]\n",
"\n",
" def lookup_index(self, index):\n",
" \"\"\"Return the token associated with the index\n",
" \n",
" Args: \n",
" index (int): the index to look up\n",
" Returns:\n",
" token (str): the token corresponding to the index\n",
" Raises:\n",
" KeyError: if the index is not in the Vocabulary\n",
" \"\"\"\n",
" if index not in self._idx_to_token:\n",
" raise KeyError(\"the index (%d) is not in the Vocabulary\" % index)\n",
" return self._idx_to_token[index]\n",
"\n",
" def __str__(self):\n",
" return \"<Vocabulary(size=%d)>\" % len(self)\n",
"\n",
" def __len__(self):\n",
" return len(self._token_to_idx)\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"class SequenceVocabulary(Vocabulary):\n",
" def __init__(self, token_to_idx=None, unk_token=\"<UNK>\",\n",
" mask_token=\"<MASK>\", begin_seq_token=\"<BEGIN>\",\n",
" end_seq_token=\"<END>\"):\n",
"\n",
" super(SequenceVocabulary, self).__init__(token_to_idx)\n",
"\n",
" self._mask_token = mask_token\n",
" self._unk_token = unk_token\n",
" self._begin_seq_token = begin_seq_token\n",
" self._end_seq_token = end_seq_token\n",
"\n",
" self.mask_index = self.add_token(self._mask_token)\n",
" self.unk_index = self.add_token(self._unk_token)\n",
" self.begin_seq_index = self.add_token(self._begin_seq_token)\n",
" self.end_seq_index = self.add_token(self._end_seq_token)\n",
"\n",
" def to_serializable(self):\n",
" contents = super(SequenceVocabulary, self).to_serializable()\n",
" contents.update({'unk_token': self._unk_token,\n",
" 'mask_token': self._mask_token,\n",
" 'begin_seq_token': self._begin_seq_token,\n",
" 'end_seq_token': self._end_seq_token})\n",
" return contents\n",
"\n",
" def lookup_token(self, token):\n",
" \"\"\"Retrieve the index associated with the token \n",
" or the UNK index if token isn't present.\n",
" \n",
" Args:\n",
" token (str): the token to look up \n",
" Returns:\n",
" index (int): the index corresponding to the token\n",
" Notes:\n",
" `unk_index` needs to be >=0 (having been added into the Vocabulary) \n",
" for the UNK functionality \n",
" \"\"\"\n",
" if self.unk_index >= 0:\n",
" return self._token_to_idx.get(token, self.unk_index)\n",
" else:\n",
" return self._token_to_idx[token]\n",
" \n",
"\n",
"\n",
"\n",
"class NMTVectorizer(object):\n",
" \"\"\" The Vectorizer which coordinates the Vocabularies and puts them to use\"\"\" \n",
" def __init__(self, source_vocab, target_vocab, max_source_length, max_target_length):\n",
" \"\"\"\n",
" Args:\n",
" source_vocab (SequenceVocabulary): maps source words to integers\n",
" target_vocab (SequenceVocabulary): maps target words to integers\n",
" max_source_length (int): the longest sequence in the source dataset\n",
" max_target_length (int): the longest sequence in the target dataset\n",
" \"\"\"\n",
" self.source_vocab = source_vocab\n",
" self.target_vocab = target_vocab\n",
" \n",
" self.max_source_length = max_source_length\n",
" self.max_target_length = max_target_length\n",
" \n",
"\n",
" def _vectorize(self, indices, vector_length=-1, mask_index=0):\n",
" \"\"\"Vectorize the provided indices\n",
" \n",
" Args:\n",
" indices (list): a list of integers that represent a sequence\n",
" vector_length (int): an argument for forcing the length of index vector\n",
" mask_index (int): the mask_index to use; almost always 0\n",
" \"\"\"\n",
" if vector_length < 0:\n",
" vector_length = len(indices)\n",
" \n",
" vector = np.zeros(vector_length, dtype=np.int64)\n",
" vector[:len(indices)] = indices\n",
" vector[len(indices):] = mask_index\n",
"\n",
" return vector\n",
" \n",
" def _get_source_indices(self, text):\n",
" \"\"\"Return the vectorized source text\n",
" \n",
" Args:\n",
" text (str): the source text; tokens should be separated by spaces\n",
" Returns:\n",
" indices (list): list of integers representing the text\n",
" \"\"\"\n",
" indices = [self.source_vocab.begin_seq_index]\n",
" indices.extend(self.source_vocab.lookup_token(token) for token in text.split(\" \"))\n",
" indices.append(self.source_vocab.end_seq_index)\n",
" return indices\n",
" \n",
" def _get_target_indices(self, text):\n",
" \"\"\"Return the vectorized source text\n",
" \n",
" Args:\n",
" text (str): the source text; tokens should be separated by spaces\n",
" Returns:\n",
" a tuple: (x_indices, y_indices)\n",
" x_indices (list): list of integers representing the observations in target decoder \n",
" y_indices (list): list of integers representing predictions in target decoder\n",
" \"\"\"\n",
" indices = [self.target_vocab.lookup_token(token) for token in text.split(\" \")]\n",
" x_indices = [self.target_vocab.begin_seq_index] + indices\n",
" y_indices = indices + [self.target_vocab.end_seq_index]\n",
" return x_indices, y_indices\n",
" \n",
" def vectorize(self, source_text, target_text, use_dataset_max_lengths=True):\n",
" \"\"\"Return the vectorized source and target text\n",
" \n",
" The vetorized source text is just the a single vector.\n",
" The vectorized target text is split into two vectors in a similar style to \n",
" the surname modeling in Chapter 7.\n",
" At each timestep, the first vector is the observation and the second vector is the target. \n",
" \n",
" \n",
" Args:\n",
" source_text (str): text from the source language\n",
" target_text (str): text from the target language\n",
" use_dataset_max_lengths (bool): whether to use the global max vector lengths\n",
" Returns:\n",
" The vectorized data point as a dictionary with the keys: \n",
" source_vector, target_x_vector, target_y_vector, source_length\n",
" \"\"\"\n",
" source_vector_length = -1\n",
" target_vector_length = -1\n",
" \n",
" if use_dataset_max_lengths:\n",
" source_vector_length = self.max_source_length + 2\n",
" target_vector_length = self.max_target_length + 1\n",
" \n",
" source_indices = self._get_source_indices(source_text)\n",
" source_vector = self._vectorize(source_indices, \n",
" vector_length=source_vector_length, \n",
" mask_index=self.source_vocab.mask_index)\n",
" \n",
" target_x_indices, target_y_indices = self._get_target_indices(target_text)\n",
" target_x_vector = self._vectorize(target_x_indices,\n",
" vector_length=target_vector_length,\n",
" mask_index=self.target_vocab.mask_index)\n",
" target_y_vector = self._vectorize(target_y_indices,\n",
" vector_length=target_vector_length,\n",
" mask_index=self.target_vocab.mask_index)\n",
" return {\"source_vector\": source_vector, \n",
" \"target_x_vector\": target_x_vector, \n",
" \"target_y_vector\": target_y_vector, \n",
" \"source_length\": len(source_indices)}\n",
" \n",
" @classmethod\n",
" def from_dataframe(cls, bitext_df):\n",
" \"\"\"Instantiate the vectorizer from the dataset dataframe\n",
" \n",
" Args:\n",
" bitext_df (pandas.DataFrame): the parallel text dataset\n",
" Returns:\n",
" an instance of the NMTVectorizer\n",
" \"\"\"\n",
" source_vocab = SequenceVocabulary()\n",
" target_vocab = SequenceVocabulary()\n",
" \n",
" max_source_length = 50\n",
" max_target_length = 25\n",
"\n",
" for _, row in bitext_df.iterrows():\n",
" source_tokens = row[\"source_language\"].split(\" \")\n",
" if len(source_tokens) > max_source_length:\n",
" max_source_length = len(source_tokens)\n",
" for token in source_tokens:\n",
" source_vocab.add_token(token)\n",
" \n",
" target_tokens = row[\"target_language\"].split(\" \")\n",
" if len(target_tokens) > max_target_length:\n",
" max_target_length = len(target_tokens)\n",
" for token in target_tokens:\n",
" target_vocab.add_token(token)\n",
" \n",
" return cls(source_vocab, target_vocab, max_source_length, max_target_length)\n",
"\n",
" @classmethod\n",
" def from_serializable(cls, contents):\n",
" source_vocab = SequenceVocabulary.from_serializable(contents[\"source_vocab\"])\n",
" target_vocab = SequenceVocabulary.from_serializable(contents[\"target_vocab\"])\n",
" \n",
" return cls(source_vocab=source_vocab, \n",
" target_vocab=target_vocab, \n",
" max_source_length=contents[\"max_source_length\"], \n",
" max_target_length=contents[\"max_target_length\"])\n",
"\n",
" def to_serializable(self):\n",
" return {\"source_vocab\": self.source_vocab.to_serializable(), \n",
" \"target_vocab\": self.target_vocab.to_serializable(), \n",
" \"max_source_length\": self.max_source_length,\n",
" \"max_target_length\": self.max_target_length}\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"class NMTDataset(Dataset):\n",
" def __init__(self, text_df, vectorizer):\n",
" \"\"\"\n",
" Args:\n",
" surname_df (pandas.DataFrame): the dataset\n",
" vectorizer (SurnameVectorizer): vectorizer instatiated from dataset\n",
" \"\"\"\n",
" self.text_df = text_df\n",
" self._vectorizer = vectorizer\n",
"\n",
" self.train_df = self.text_df[self.text_df.split=='train']\n",
" self.train_size = len(self.train_df)\n",
"\n",
" self.val_df = self.text_df[self.text_df.split=='val']\n",
" self.validation_size = len(self.val_df)\n",
"\n",
" self.test_df = self.text_df[self.text_df.split=='test']\n",
" self.test_size = len(self.test_df)\n",
"\n",
" self._lookup_dict = {'train': (self.train_df, self.train_size),\n",
" 'val': (self.val_df, self.validation_size),\n",
" 'test': (self.test_df, self.test_size)}\n",
"\n",
" self.set_split('train')\n",
"\n",
" @classmethod\n",
" def load_dataset_and_make_vectorizer(cls, dataset_csv):\n",
" \"\"\"Load dataset and make a new vectorizer from scratch\n",
" \n",
" Args:\n",
" surname_csv (str): location of the dataset\n",
" Returns:\n",
" an instance of SurnameDataset\n",
" \"\"\"\n",
" text_df = pd.read_csv(dataset_csv).fillna(' ')\n",
" train_subset = text_df[text_df.split=='train']\n",
" return cls(text_df, NMTVectorizer.from_dataframe(train_subset))\n",
"\n",
" @classmethod\n",
" def load_dataset_and_load_vectorizer(cls, dataset_csv, vectorizer_filepath):\n",
" \"\"\"Load dataset and the corresponding vectorizer. \n",
" Used in the case in the vectorizer has been cached for re-use\n",
" \n",
" Args:\n",
" surname_csv (str): location of the dataset\n",
" vectorizer_filepath (str): location of the saved vectorizer\n",
" Returns:\n",
" an instance of SurnameDataset\n",
" \"\"\"\n",
" text_df = pd.read_csv(dataset_csv).fillna(' ')\n",
" vectorizer = cls.load_vectorizer_only(vectorizer_filepath)\n",
" return cls(text_df, vectorizer)\n",
"\n",
" @staticmethod\n",
" def load_vectorizer_only(vectorizer_filepath):\n",
" \"\"\"a static method for loading the vectorizer from file\n",
" \n",
" Args:\n",
" vectorizer_filepath (str): the location of the serialized vectorizer\n",
" Returns:\n",
" an instance of SurnameVectorizer\n",
" \"\"\"\n",
" with open(vectorizer_filepath) as fp:\n",
" return NMTVectorizer.from_serializable(json.load(fp))\n",
"\n",
" def save_vectorizer(self, vectorizer_filepath):\n",
" \"\"\"saves the vectorizer to disk using json\n",
" \n",
" Args:\n",
" vectorizer_filepath (str): the location to save the vectorizer\n",
" \"\"\"\n",
" with open(vectorizer_filepath, \"w\") as fp:\n",
" json.dump(self._vectorizer.to_serializable(), fp)\n",
"\n",
" def get_vectorizer(self):\n",
" \"\"\" returns the vectorizer \"\"\"\n",
" return self._vectorizer\n",
"\n",
" def set_split(self, split=\"train\"):\n",
" self._target_split = split\n",
" self._target_df, self._target_size = self._lookup_dict[split]\n",
"\n",
" def __len__(self):\n",
" return self._target_size\n",
"\n",
" def __getitem__(self, index):\n",
" \"\"\"the primary entry point method for PyTorch datasets\n",
" \n",
" Args:\n",
" index (int): the index to the data point \n",
" Returns:\n",
" a dictionary holding the data point: (x_data, y_target, class_index)\n",
" \"\"\"\n",
" row = self._target_df.iloc[index]\n",
"\n",
" vector_dict = self._vectorizer.vectorize(row.source_language, row.target_language)\n",
"\n",
" return {\"x_source\": vector_dict[\"source_vector\"], \n",
" \"x_target\": vector_dict[\"target_x_vector\"],\n",
" \"y_target\": vector_dict[\"target_y_vector\"], \n",
" \"x_source_length\": vector_dict[\"source_length\"]}\n",
" \n",
" def get_num_batches(self, batch_size):\n",
" \"\"\"Given a batch size, return the number of batches in the dataset\n",
" \n",
" Args:\n",
" batch_size (int)\n",
" Returns:\n",
" number of batches in the dataset\n",
" \"\"\"\n",
" return len(self) // batch_size\n",
" \n",
"\n",
"\n",
"\n",
"def generate_nmt_batches(dataset, batch_size, shuffle=True, \n",
" drop_last=True, device=\"cpu\"):\n",
" \"\"\"A generator function which wraps the PyTorch DataLoader. The NMT Version \"\"\"\n",
" dataloader = DataLoader(dataset=dataset, batch_size=batch_size,\n",
" shuffle=shuffle, drop_last=drop_last)\n",
"\n",
" for data_dict in dataloader:\n",
" lengths = data_dict['x_source_length'].numpy()\n",
" # Get the indices according to sorted length\n",
" sorted_length_indices = lengths.argsort()[::-1].tolist()\n",
" \n",
" # Sort the minibatch\n",
" out_data_dict = {}\n",
" for name, tensor in data_dict.items():\n",
" out_data_dict[name] = data_dict[name][sorted_length_indices].to(device)\n",
" yield out_data_dict\n",
"\n",
"\n",
"\n",
"\n",
"class PositionalEncoding(nn.Module):\n",
" def __init__(self, emb_size, drop_out, max_len:int = 200):\n",
" super(PositionalEncoding, self).__init__()\n",
" den = torch.exp(-torch.arange(0, emb_size,2)*math.log(10000)/emb_size)\n",
" pos = torch.arange(0,max_len).reshape(max_len,1)\n",
" pos_embedding = torch.zeros((max_len, emb_size))\n",
" pos_embedding[:,0::2]= torch.sin(pos*den)\n",
" pos_embedding[:,1::2] = torch.cos(pos*den)\n",
" pos_embedding = pos_embedding.unsqueeze(-2)\n",
" self.dropout = nn.Dropout(drop_out)\n",
" self.register_buffer('pos_embedding', pos_embedding)\n",
"\n",
" def forward(self, token_embedding:Tensor):\n",
" return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0),:])\n",
"\n",
"class TokenEmbedding(nn.Module):\n",
" def __init__(self, vocab_size:int, emb_size):\n",
" super(TokenEmbedding, self).__init__()\n",
" self.embedding = nn.Embedding(vocab_size, emb_size)\n",
" self.emb_size = emb_size\n",
"\n",
" def forward(self, tokens:Tensor):\n",
" return self.embedding(tokens.long())*math.sqrt(self.emb_size)\n",
"\n",
"\n",
"class Seq2SeqTransformer(nn.Module):\n",
" def __init__(self, num_encoder_layers,num_decoder_layers, emb_size, nhead,src_vocab_size,tgt_vocab_size, dim_feedforward = 512, dropout = 0.1):\n",
" super(Seq2SeqTransformer,self).__init__()\n",
" self.transformer = Transformer(d_model = emb_size, nhead = nhead, num_encoder_layers = num_encoder_layers, num_decoder_layers = num_decoder_layers, dim_feedforward = dim_feedforward, dropout = dropout, norm_first = True)\n",
" self.generator = nn.Linear(emb_size, tgt_vocab_size)\n",
" self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)\n",
" self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)\n",
" self.positional_encoding = PositionalEncoding(emb_size, drop_out = dropout)\n",
"\n",
" def forward(self, src:Tensor, trg:Tensor, src_mask:Tensor, tgt_mask: Tensor, src_padding_mask: Tensor, tgt_padding_mask: Tensor, memory_key_padding_mask: Tensor):\n",
" src_emb = self.positional_encoding(self.src_tok_emb(src))\n",
" tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))\n",
" outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None, src_padding_mask, tgt_padding_mask, memory_key_padding_mask)\n",
" return self.generator(outs)\n",
"\n",
" def encode(self, src, src_mask):\n",
" return self.transformer.encoder(self.positional_encoding(self.src_tok_emb(src)),src_mask)\n",
"\n",
" def decode(self, tgt:Tensor, memory:Tensor, tgt_mask:Tensor):\n",
" return self.transformer.decoder(self.positional_encoding(self.tgt_tok_emb(tgt)), memory, tgt_mask)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def set_seed_everywhere(seed, cuda):\n",
" #seed = self.seed\n",
" #cuda = self.cuda\n",
" np.random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" print(seed)\n",
" if cuda:\n",
" torch.cuda.manual_seed_all(seed)\n",
"\n",
"\n",
"def generate_square_subsequent_mask(sz):\n",
" mask = (torch.triu(torch.ones((sz, sz), device=DEVICE)) == 1).transpose(0, 1)\n",
" mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\n",
" return mask\n",
"\n",
"\n",
"\n",
"def handle_dirs(save_dirs):\n",
" dirpath = save_dir\n",
" if not os.path.exists(dirpath):\n",
" os.makedirs(dirpath)\n",
"\n",
"\n",
"\n",
"def create_mask(src, tgt,PAD_IDX):\n",
" src_seq_len = src.shape[0]\n",
" tgt_seq_len = tgt.shape[0]\n",
" \n",
" tgt_mask = generate_square_subsequent_mask(tgt_seq_len)\n",
" src_mask = torch.zeros((src_seq_len, src_seq_len),device=DEVICE).type(torch.bool)\n",
" \n",
" src_padding_mask = (src == PAD_IDX).transpose(0, 1)\n",
" tgt_padding_mask = (tgt == PAD_IDX).transpose(0, 1)\n",
" return src_mask, tgt_mask, src_padding_mask, tgt_padding_mask\n",
"\n",
"\n",
"\n",
"def train_epoch(batch_size, device, model, dataset, split_value, optimizer, PAD_IDX, loss_fn):\n",
" BATCH_SIZE = batch_size\n",
" model.train()\n",
" losses = 0\n",
" print(dataset.__len__())\n",
" train_dataloader = DataLoader(dataset, batch_size=BATCH_SIZE)\n",
" #print(BATCH_SIZE,len(list(train_dataloader)))\n",
" dataset.set_split(split_value)\n",
" batch_generator = generate_nmt_batches(dataset, batch_size=BATCH_SIZE, device = device)\n",
" print(\"printing batch generator\",batch_generator)\n",
" ctr = 0\n",
" for batch_index, batch_dict in enumerate(batch_generator):\n",
" ctr = ctr+1\n",
" #optimizer.zero_grad()\n",
" #print(torch.cat((torch.transpose(batch_dict['x_source'],0,1),torch.transpose(batch_dict['x_target'],0,1),torch.transpose(batch_dict['y_target'],0,1)),1).numpy().shape)\n",
" #print(torch.transpose(batch_dict['x_target'],0,1))\n",
" #print(torch.transpose(batch_dict['y_target'],0,1))\n",
" src=torch.transpose(batch_dict['x_source'],0,1)\n",
" tgt=torch.transpose(batch_dict['y_target'],0,1)\n",
" tgt_input = tgt[:-1,:]\n",
" src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src,tgt_input, PAD_IDX)\n",
" logits = model(src,tgt_input, src_mask, tgt_mask, src_padding_mask, tgt_padding_mask, src_padding_mask)\n",
" optimizer.zero_grad()\n",
" tgt_out = tgt[1:,:]\n",
" loss = loss_fn(logits.reshape(-1, logits.shape[-1]),tgt_out.reshape(-1))\n",
" loss.backward()\n",
" optimizer.step()\n",
" losses += loss.item()\n",
" if ctr%50==0:\n",
" #print('source_shape',src.shape, 'target_shape',tgt.shape)\n",
" print(\"ctr: \",ctr,\" losses: \",losses/ctr,'time',datetime.datetime.now())#,\" len_train_dataloader: \",len(list(train_dataloader)))\n",
" return losses/len(list(train_dataloader))\n",
"\n",
"\n",
"def evaluate(batch_size,device,model, dataset,split_value,PAD_IDX,loss_fn):\n",
" model.eval()\n",
" losses = 0\n",
" dataset.set_split(split_value)\n",
" val_dataloader=DataLoader(dataset, batch_size=batch_size)\n",
" batch_generator=generate_nmt_batches(dataset, batch_size=batch_size, device=device)\n",
" ctr = 0\n",
" for batch_index, batch_dict in enumerate(batch_generator):\n",
" src = torch.transpose(batch_dict['x_source'],0,1)\n",
" tgt = torch.transpose(batch_dict['y_target'],0,1)\n",
" tgt_input = tgt[:-1,:]\n",
" src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src,tgt_input, PAD_IDX)\n",
" logits = model(src,tgt_input,src_mask,tgt_mask, src_padding_mask, tgt_padding_mask, src_padding_mask)\n",
" tgt_out=tgt[1:,:]\n",
" loss = loss_fn(logits.reshape(-1, logits.shape[-1]),tgt_out.reshape(-1))#loss_fn(logits.reshape[-1],tgt_out.reshape[-1])\n",
" losses += loss.item()\n",
" ctr = ctr+1\n",
" print(ctr,\"validation\",losses/ctr)\n",
"\n",
" \"\"\"for src, tgt in val_dataloader:\n",
" src = src.to(DEVICE)\n",
" tgt = tgt.to(DEVICE)\n",
"\n",
" tgt_input = tgt[:-1, :]\n",
"\n",
" src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)\n",
"\n",
" logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)\n",
"\n",
" tgt_out = tgt[1:, :]\n",
" loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))\n",
" losses += loss.item()\"\"\"\n",
" return losses/len(list(val_dataloader))\n",
"\n",
"\n",
"\n",
"def greedy_decode(DEVICE, model, src, src_mask, max_len, start_symbol, EOS_IDX):\n",
" src = src.to(DEVICE)\n",
" src_mask=src_mask.to(DEVICE)\n",
" memory = model.encode(src, src_mask)\n",
" ys = torch.ones(1,1).fill_(start_symbol).type(torch.long).to(DEVICE)\n",
" for i in range(max_len):\n",
" #print(i,'ys',ys)\n",
" memory = memory.to(DEVICE)\n",
" tgt_mask = (generate_square_subsequent_mask(ys.size(0)).type(torch.bool)).to(DEVICE)\n",
" #print('tgt_mask',tgt_mask)\n",
" out = model.decode(ys,memory, tgt_mask)#.squeeze()\n",
" #print(\"out\",out,'out_shape',out.shape)\n",
" out = out.transpose(0,1)\n",
" #print(\"out transpose\",out,'out_transpose_shape',out.shape)\n",
" prob = model.generator(out)[:,-1]\n",
" _, next_word = torch.max(prob, dim=1)\n",
" next_word = next_word.item()\n",
" #print('next_word = ',next_word)\n",
" ys = torch.cat([ys, torch.ones(1,1).type_as(src.data).fill_(next_word)], dim = 0)\n",
" #print('ys',ys)\n",
" if next_word == EOS_IDX:\n",
" break\n",
" return ys\n",
"\n",
"\n",
"\n",
"def translate( device,model:torch.nn.Module, src_sentence:str, BOS_IDX, EOS_IDX):\n",
" model.eval()\n",
" src= src_sentence\n",
" #print('src',src)\n",
" num_tokens = src.shape[0]\n",
" #print(num_tokens)\n",
" src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)\n",
" #print('src_mask',src_mask)\n",
" tgt_tokens = greedy_decode(device,model, src, src_mask, max_len = num_tokens, start_symbol=BOS_IDX, EOS_IDX=EOS_IDX).flatten()\n",
" return tgt_tokens\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"input_df = 'dataset_for_APE_hinglish_to_english2.csv'\n",
"fpath = \"nmt_IITB_APE2\"\n",
"\n",
"\n",
"#dataset = NMTDataset.load_dataset_and_make_vectorizer('IITB_dataset_1.csv')\n",
"#dataset.save_vectorizer(\"vectorizer_transformer_3layer_IITB1mill.json\")\n",
"\n",
"\n",
"\n",
"#dataloader = DataLoader(dataset=dataset, batch_size=1024,shuffle=False, drop_last=True)\n",
"\n",
"dataset_csv = 'dataset_for_APE_hinglish_to_english2.csv'\n",
"vectorizer_file = 'vectorizer_APE_2.json'\n",
"print(vectorizer_file)\n",
"model_state_file = 'APE_2.pth'\n",
"save_dir = \"nmt_DG2_FFNN8192\"#'GenV1_Transforemer_1',\n",
"print(save_dir)\n",
"reload_from_files = True\n",
"cuda = False\n",
"seed = 13\n",
"learning_rate = 8e-3\n",
"batch_size = 1024\n",
"batch_size_val = 1\n",
"num_epochs = 40\n",
"source_embedding_size = 256\n",
"target_embedding_size = 256\n",
"encoding_size = 256\n",
"use_glove = False\n",
"expand_filepaths_to_save_dir = True\n",
"early_stopping_criteria = 10\n",
"dataset_to_evaluate = 'dataset_for_APE_hinglish_to_english2.csv'\n",
"path_to_save = 'APE_1_new.csv'\n",
"saved_model_path = 'APE_1_new.pt'\n",
"file_exist = 0\n",
"existing_file_name = 'dataset_for_APE_hinglish_to_english2.csv'\n",
"\n",
"\n",
"dataset_path = fpath\n",
"existing_file_name = input_df\n",
"fname = existing_file_name\n",
"dataset_csv = fname\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"model_state_file = model_state_file\n",
"save_dir = save_dir\n",
"print(save_dir)\n",
"reload_from_files = reload_from_files\n",
"expand_filepaths_to_save_dir = expand_filepaths_to_save_dir\n",
"cuda = cuda\n",
"seed = seed\n",
"learning_rate = learning_rate\n",
"batch_size = batch_size\n",
"batch_size_val = batch_size_val\n",
"num_epochs = num_epochs\n",
"early_stopping_criteria = True#self.early_stopping_criteria\n",
"source_embedding_size = source_embedding_size\n",
"target_embedding_size = target_embedding_size\n",
"encoding_size = encoding_size\n",
"use_glove = False\n",
"catch_keyboard_interrupt = True\n",
"if expand_filepaths_to_save_dir:\n",
" vectorizer_file = os.path.join(save_dir, vectorizer_file)\n",
"model_state_file = os.path.join(save_dir, model_state_file)\n",
"if not torch.cuda.is_available():\n",
" cuda = False\n",
"device = torch.device(\"cuda\" if cuda else \"cpu\")\n",
"set_seed_everywhere(seed,cuda)\n",
"handle_dirs(save_dir)\n",
"if reload_from_files and os.path.exists(vectorizer_file):\n",
" dataset = NMTDataset.load_dataset_and_load_vectorizer(dataset_csv, vectorizer_file)\n",
" print('load_dataset_and_load_vectorizer______')\n",
"else:\n",
" dataset = NMTDataset.load_dataset_and_make_vectorizer(dataset_csv)\n",
" dataset.save_vectorizer(vectorizer_file)\n",
" print('_________load_dataset_and_make_vectorizer______')\n",
"vectorizer = dataset.get_vectorizer()\n",
"PAD_IDX = vectorizer.to_serializable()['target_vocab']['token_to_idx']['<MASK>']\n",
"BOS_IDX = vectorizer.to_serializable()['target_vocab']['token_to_idx']['<BEGIN>']\n",
"EOS_IDX = vectorizer.to_serializable()['target_vocab']['token_to_idx']['<END>']\n",
"SRC_VOCAB_SIZE = len(vectorizer.to_serializable()['source_vocab']['token_to_idx'])\n",
"TGT_VOCAB_SiZE = len(vectorizer.to_serializable()['target_vocab']['token_to_idx'])\n",
"print('target vocab size',TGT_VOCAB_SiZE)\n",
"print('dataset_size 1: ', dataset.__len__(), dataset_path, dataset_csv)\n",
"print(' dataset csv length',len(pd.read_csv(dataset_csv)))\n",
"EMB_SIZE = 256\n",
"NHEAD = 16\n",
"FFN_HID_DIM =8192\n",
"BATCH_SIZE = 128\n",
"NUM_ENCODER_LAYERS = 3\n",
"NUM_DECODER_LAYERS = 3\n",
"batch_size = BATCH_SIZE\n",
"transformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE, NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SiZE, FFN_HID_DIM)\n",
"transformer = transformer.to(DEVICE)\n",
"loss_fn = torch.nn.CrossEntropyLoss(ignore_index=PAD_IDX)\n",
"optimizer = torch.optim.Adam(transformer.parameters(), lr=0.004, betas = (0.99, 0.99), eps = 1e-9)\n",
"from timeit import default_timer as timer\n",
"NUM_EPOCHS = num_epochs\n",
"for epoch in range(1, NUM_EPOCHS+1):\n",
" print(\"==================Training started==================\",epoch)\n",
" start_time = timer()\n",
" split_value_train = 'train'\n",
" split_value_validate = 'val'\n",
" train_loss = train_epoch(batch_size,device,transformer, dataset, split_value_train, optimizer, PAD_IDX, loss_fn)\n",
" end_time = timer()\n",
" torch.save(transformer,'epoch'+str(epoch)+'_APE_2_new.pt')\n",
"#torch.save(transformer, save_dir+\"/\"+saved_model_path+\"_epoch\")\n",
" #val_loss = evaluate(batch_size,device,transformer, dataset, split_value_validate, PAD_IDX, loss_fn)\n",
"torch.save(transformer, save_dir+\"/\"+saved_model_path)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37a50cf7-d754-4c19-aaa5-4e094cfd87e6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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|