Refactor and add more REALM arguments

hashed_index.py

@@ -5,7 +5,7 @@ from megatron import get_args
 from megatron import mpu
 from megatron.checkpointing import get_checkpoint_tracker_filename, get_checkpoint_name
 from megatron.data.bert_dataset import get_indexed_dataset_
-from megatron.data.realm_dataset import InverseClozeDataset
+from megatron.data.realm_dataset import ICTDataset
 from megatron.data.realm_index import detach, BlockData, RandProjectionLSHIndex
 from megatron.data.samplers import DistributedBatchSampler
 from megatron.initialize import initialize_megatron
@@ -150,7 +150,7 @@ def get_ict_dataset():
         short_seq_prob=0.0001,  # doesn't matter
         seed=1
     )
-    dataset = InverseClozeDataset(**kwargs)
+    dataset = ICTDataset(**kwargs)
     return dataset
 
 

megatron/arguments.py

@@ -245,6 +245,8 @@ def _add_checkpointing_args(parser):
                        help='Directory containing a model checkpoint.')
     group.add_argument('--ict-load', type=str, default=None,
                        help='Directory containing an ICTBertModel checkpoint')
+    group.add_argument('--bert-load', type=str, default=None,
+                       help='Directory containing an BertModel checkpoint (needed to start REALM)')
     group.add_argument('--no-load-optim', action='store_true',
                        help='Do not load optimizer when loading checkpoint.')
     group.add_argument('--no-load-rng', action='store_true',
@@ -326,6 +328,8 @@ def _add_data_args(parser):
                        help='Path to pickled BlockData data structure')
     group.add_argument('--block-index-path', type=str, default=None,
                        help='Path to pickled data structure for efficient block indexing')
+    group.add_argument('--block-top-k', type=int, default=5,
+                       help='Number of blocks to use as top-k during retrieval')
     group.add_argument('--split', type=str, default='969, 30, 1',
                        help='Comma-separated list of proportions for training,'
                        ' validation, and test split. For example the split '

megatron/checkpointing.py

@@ -131,11 +131,15 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler):
 def load_checkpoint(model, optimizer, lr_scheduler):
     """Load a model checkpoint and return the iteration."""
     args = get_args()
+    load_dir = args.load
+    from megatron.model.bert_model import BertModel
+    if isinstance(model, BertModel) and args.bert_load is not None:
+        load_dir = args.bert_load
 
     if isinstance(model, torchDDP):
         model = model.module
     # Read the tracker file and set the iteration.
-    tracker_filename = get_checkpoint_tracker_filename(args.load)
+    tracker_filename = get_checkpoint_tracker_filename(load_dir)
 
     # If no tracker file, return iretation zero.
     if not os.path.isfile(tracker_filename):
@@ -164,7 +168,7 @@ def load_checkpoint(model, optimizer, lr_scheduler):
         tracker_filename)
 
     # Checkpoint.
-    checkpoint_name = get_checkpoint_name(args.load, iteration, release)
+    checkpoint_name = get_checkpoint_name(load_dir, iteration, release)
     if mpu.get_data_parallel_rank() == 0:
         print('global rank {} is loading checkpoint {}'.format(
             torch.distributed.get_rank(), checkpoint_name))

megatron/data/dataset_utils.py

@@ -454,8 +454,8 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
     print_split_stats('test', 2)
 
     def build_dataset(index, name):
-        from megatron.data.realm_dataset import InverseClozeDataset
-        from megatron.data.realm_dataset import RealmDataset
+        from megatron.data.realm_dataset import ICTDataset
+        from megatron.data.realm_dataset import REALMDataset
         dataset = None
         if splits[index + 1] > splits[index]:
             # Get the pointer to the original doc-idx so we can set it later.
@@ -478,13 +478,13 @@ def build_train_valid_test_datasets(data_prefix, data_impl, splits_string,
             )
 
             if dataset_type == 'ict':
-                dataset = InverseClozeDataset(
+                dataset = ICTDataset(
                     block_dataset=indexed_dataset,
                     title_dataset=title_dataset,
                     **kwargs
                 )
             else:
-                dataset_cls = BertDataset if dataset_type == 'standard_bert' else RealmDataset
+                dataset_cls = BertDataset if dataset_type == 'standard_bert' else REALMDataset
                 dataset = dataset_cls(
                     indexed_dataset=indexed_dataset,
                     masked_lm_prob=masked_lm_prob,

megatron/data/realm_dataset.py

@@ -15,7 +15,7 @@ from megatron.data.dataset_utils import create_masked_lm_predictions, pad_and_co
 #qa_nlp = spacy.load('en_core_web_lg')
 
 
-class RealmDataset(BertDataset):
+class REALMDataset(BertDataset):
     """Dataset containing simple masked sentences for masked language modeling.
 
     The dataset should yield sentences just like the regular BertDataset
@@ -28,7 +28,7 @@ class RealmDataset(BertDataset):
     def __init__(self, name, indexed_dataset, data_prefix,
                  num_epochs, max_num_samples, masked_lm_prob,
                  max_seq_length, short_seq_prob, seed):
-        super(RealmDataset, self).__init__(name, indexed_dataset, data_prefix,
+        super(REALMDataset, self).__init__(name, indexed_dataset, data_prefix,
                                            num_epochs, max_num_samples, masked_lm_prob,
                                            max_seq_length, short_seq_prob, seed)
         self.build_sample_fn = build_simple_training_sample
@@ -81,7 +81,7 @@ def spacy_ner(block_text):
     candidates['answers'] = answers
 
 
-class InverseClozeDataset(Dataset):
+class ICTDataset(Dataset):
     """Dataset containing sentences and their blocks for an inverse cloze task."""
     def __init__(self, name, block_dataset, title_dataset, data_prefix,
                  num_epochs, max_num_samples, max_seq_length,

megatron/model/__init__.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 
 from .distributed import *
-from .bert_model import BertModel, ICTBertModel, REALMBertModel, REALMRetriever
+from .bert_model import BertModel
+from megatron.model.realm_model import ICTBertModel, REALMRetriever, REALMBertModel
 from .gpt2_model import GPT2Model
 from .utils import get_params_for_weight_decay_optimization

megatron/model/bert_model.py

@@ -15,14 +15,9 @@
 
 """BERT model."""
 
-import pickle
-
-import numpy as np
 import torch
-import torch.nn.functional as F
 
 from megatron import get_args
-from megatron.data.realm_index import detach
 from megatron.model.language_model import parallel_lm_logits
 from megatron.model.language_model import get_language_model
 from megatron.model.transformer import LayerNorm
@@ -224,198 +219,3 @@ class BertModel(MegatronModule):
                 state_dict[self._ict_head_key], strict=strict)
 
 
-class REALMBertModel(MegatronModule):
-    def __init__(self, retriever):
-        super(REALMBertModel, self).__init__()
-        bert_args = dict(
-            num_tokentypes=1,
-            add_binary_head=False,
-            parallel_output=True
-        )
-        self.lm_model = BertModel(**bert_args)
-        self._lm_key = 'realm_lm'
-
-        self.retriever = retriever
-        self._retriever_key = 'retriever'
-
-    def forward(self, tokens, attention_mask):
-        # [batch_size x 5 x seq_length]
-        top5_block_tokens, top5_block_attention_mask = self.retriever.retrieve_evidence_blocks(tokens, attention_mask)
-        batch_size = tokens.shape[0]
-
-        seq_length = top5_block_tokens.shape[2]
-        top5_block_tokens = torch.cuda.LongTensor(top5_block_tokens).reshape(-1, seq_length)
-        top5_block_attention_mask = torch.cuda.LongTensor(top5_block_attention_mask).reshape(-1, seq_length)
-
-        # [batch_size x 5 x embed_size]
-        true_model = self.retriever.ict_model.module.module
-        fresh_block_logits = true_model.embed_block(top5_block_tokens, top5_block_attention_mask).reshape(batch_size, 5, -1)
-
-        # [batch_size x embed_size x 1]
-        query_logits = true_model.embed_query(tokens, attention_mask).unsqueeze(2)
-
-
-        # [batch_size x 5]
-        fresh_block_scores = torch.matmul(fresh_block_logits, query_logits).squeeze()
-        block_probs = F.softmax(fresh_block_scores, dim=1)
-
-        # [batch_size * 5 x seq_length]
-        tokens = torch.stack([tokens.unsqueeze(1)] * 5, dim=1).reshape(-1, seq_length)
-        attention_mask = torch.stack([attention_mask.unsqueeze(1)] * 5, dim=1).reshape(-1, seq_length)
-
-        # [batch_size * 5 x 2 * seq_length]
-        all_tokens = torch.cat((tokens, top5_block_tokens), axis=1)
-        all_attention_mask = torch.cat((attention_mask, top5_block_attention_mask), axis=1)
-        all_token_types = torch.zeros(all_tokens.shape).type(torch.int64).cuda()
-
-        # [batch_size x 5 x 2 * seq_length x vocab_size]
-        lm_logits, _ = self.lm_model.forward(all_tokens, all_attention_mask, all_token_types)
-        lm_logits = lm_logits.reshape(batch_size, 5, 2 * seq_length, -1)
-        return lm_logits, block_probs
-
-    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
-                                       keep_vars=False):
-        """For easy load when model is combined with other heads,
-        add an extra key."""
-
-        state_dict_ = {}
-        state_dict_[self._lm_key] = self.lm_model.state_dict_for_save_checkpoint(destination, prefix, keep_vars)
-        return state_dict_
-
-
-class REALMRetriever(MegatronModule):
-    """Retriever which uses a pretrained ICTBertModel and a HashedIndex"""
-    def __init__(self, ict_model, ict_dataset, block_data, hashed_index, top_k=5):
-        super(REALMRetriever, self).__init__()
-        self.ict_model = ict_model
-        self.ict_dataset = ict_dataset
-        self.block_data = block_data
-        self.hashed_index = hashed_index
-        self.top_k = top_k
-
-    def retrieve_evidence_blocks_text(self, query_text):
-        """Get the top k evidence blocks for query_text in text form"""
-        print("-" * 100)
-        print("Query: ", query_text)
-        padless_max_len = self.ict_dataset.max_seq_length - 2
-        query_tokens = self.ict_dataset.encode_text(query_text)[:padless_max_len]
-
-        query_tokens, query_pad_mask = self.ict_dataset.concat_and_pad_tokens(query_tokens)
-        query_tokens = torch.cuda.LongTensor(np.array(query_tokens).reshape(1, -1))
-        query_pad_mask = torch.cuda.LongTensor(np.array(query_pad_mask).reshape(1, -1))
-
-        top5_block_tokens, _ = self.retrieve_evidence_blocks(query_tokens, query_pad_mask)
-        for i, block in enumerate(top5_block_tokens[0]):
-            block_text = self.ict_dataset.decode_tokens(block)
-            print('\n    > Block {}: {}'.format(i, block_text))
-
-    def retrieve_evidence_blocks(self, query_tokens, query_pad_mask):
-        """Embed blocks to be used in a forward pass"""
-        with torch.no_grad():
-            true_model = self.ict_model.module.module
-            query_embeds = detach(true_model.embed_query(query_tokens, query_pad_mask))
-        _, block_indices = self.hashed_index.search_mips_index(query_embeds, top_k=self.top_k, reconstruct=False)
-        all_top5_tokens, all_top5_pad_masks = [], []
-        for indices in block_indices:
-            # [k x meta_dim]
-            top5_metas = np.array([self.block_data.meta_data[idx] for idx in indices])
-            top5_block_data = [self.ict_dataset.get_block(*block_meta) for block_meta in top5_metas]
-            top5_tokens, top5_pad_masks = zip(*top5_block_data)
-
-            all_top5_tokens.append(np.array(top5_tokens))
-            all_top5_pad_masks.append(np.array(top5_pad_masks))
-
-        # [batch_size x k x seq_length]
-        return np.array(all_top5_tokens), np.array(all_top5_pad_masks)
-
-
-class ICTBertModel(MegatronModule):
-    """Bert-based module for Inverse Cloze task."""
-    def __init__(self,
-                 ict_head_size,
-                 num_tokentypes=1,
-                 parallel_output=True,
-                 only_query_model=False,
-                 only_block_model=False):
-        super(ICTBertModel, self).__init__()
-        bert_args = dict(
-            num_tokentypes=num_tokentypes,
-            add_binary_head=False,
-            ict_head_size=ict_head_size,
-            parallel_output=parallel_output
-        )
-        assert not (only_block_model and only_query_model)
-        self.use_block_model = not only_query_model
-        self.use_query_model = not only_block_model
-
-        if self.use_query_model:
-            # this model embeds (pseudo-)queries - Embed_input in the paper
-            self.query_model = BertModel(**bert_args)
-            self._query_key = 'question_model'
-
-        if self.use_block_model:
-            # this model embeds evidence blocks - Embed_doc in the paper
-            self.block_model = BertModel(**bert_args)
-            self._block_key = 'context_model'
-
-    def forward(self, query_tokens, query_attention_mask, block_tokens, block_attention_mask, only_query=False, only_block=False):
-        """Run a forward pass for each of the models and compute the similarity scores."""
-
-        if only_query:
-            return self.embed_query(query_tokens, query_attention_mask)
-
-        if only_block:
-            return self.embed_block(block_tokens, block_attention_mask)
-
-
-        query_logits = self.embed_query(query_tokens, query_attention_mask)
-        block_logits = self.embed_block(block_tokens, block_attention_mask)
-
-        # [batch x embed] * [embed x batch]
-        retrieval_scores = query_logits.matmul(torch.transpose(block_logits, 0, 1))
-        return retrieval_scores
-
-    def embed_query(self, query_tokens, query_attention_mask):
-        """Embed a batch of tokens using the query model"""
-        if self.use_query_model:
-            query_types = torch.zeros(query_tokens.shape).type(torch.int64).cuda()
-            query_ict_logits, _ = self.query_model.forward(query_tokens, query_attention_mask, query_types)
-            return query_ict_logits
-        else:
-            raise ValueError("Cannot embed query without query model.")
-
-    def embed_block(self, block_tokens, block_attention_mask):
-        """Embed a batch of tokens using the block model"""
-        if self.use_block_model:
-            block_types = torch.zeros(block_tokens.shape).type(torch.int64).cuda()
-            block_ict_logits, _ = self.block_model.forward(block_tokens, block_attention_mask, block_types)
-            return block_ict_logits
-        else:
-            raise ValueError("Cannot embed block without block model.")
-
-    def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
-        """Save dict with state dicts of each of the models."""
-        state_dict_ = {}
-        if self.use_query_model:
-            state_dict_[self._query_key] \
-                = self.query_model.state_dict_for_save_checkpoint(
-                destination, prefix, keep_vars)
-
-        if self.use_block_model:
-            state_dict_[self._block_key] \
-                = self.block_model.state_dict_for_save_checkpoint(
-                destination, prefix, keep_vars)
-
-        return state_dict_
-
-    def load_state_dict(self, state_dict, strict=True):
-        """Load the state dicts of each of the models"""
-        if self.use_query_model:
-            print("Loading ICT query model", flush=True)
-            self.query_model.load_state_dict(
-                state_dict[self._query_key], strict=strict)
-
-        if self.use_block_model:
-            print("Loading ICT block model", flush=True)
-            self.block_model.load_state_dict(
-                state_dict[self._block_key], strict=strict)

megatron/model/realm_model.py

+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from megatron.checkpointing import load_checkpoint
+from megatron.data.realm_index import detach
+from megatron.model import BertModel
+from megatron.module import MegatronModule
+
+
+class REALMBertModel(MegatronModule):
+    def __init__(self, retriever):
+        super(REALMBertModel, self).__init__()
+        bert_args = dict(
+            num_tokentypes=1,
+            add_binary_head=False,
+            parallel_output=True
+        )
+        self.lm_model = BertModel(**bert_args)
+        load_checkpoint(self.lm_model, optimizer=None, lr_scheduler=None)
+        self._lm_key = 'realm_lm'
+
+        self.retriever = retriever
+        self._retriever_key = 'retriever'
+
+    def forward(self, tokens, attention_mask):
+        # [batch_size x 5 x seq_length]
+        top5_block_tokens, top5_block_attention_mask = self.retriever.retrieve_evidence_blocks(tokens, attention_mask)
+        batch_size = tokens.shape[0]
+
+        seq_length = top5_block_tokens.shape[2]
+        top5_block_tokens = torch.cuda.LongTensor(top5_block_tokens).reshape(-1, seq_length)
+        top5_block_attention_mask = torch.cuda.LongTensor(top5_block_attention_mask).reshape(-1, seq_length)
+
+        # [batch_size x 5 x embed_size]
+        true_model = self.retriever.ict_model.module.module
+        fresh_block_logits = true_model.embed_block(top5_block_tokens, top5_block_attention_mask).reshape(batch_size, 5, -1)
+
+        # [batch_size x embed_size x 1]
+        query_logits = true_model.embed_query(tokens, attention_mask).unsqueeze(2)
+
+        # [batch_size x 5]
+        fresh_block_scores = torch.matmul(fresh_block_logits, query_logits).squeeze()
+        block_probs = F.softmax(fresh_block_scores, dim=1)
+
+        # [batch_size * 5 x seq_length]
+        tokens = torch.stack([tokens.unsqueeze(1)] * 5, dim=1).reshape(-1, seq_length)
+        attention_mask = torch.stack([attention_mask.unsqueeze(1)] * 5, dim=1).reshape(-1, seq_length)
+
+        # [batch_size * 5 x 2 * seq_length]
+        all_tokens = torch.cat((tokens, top5_block_tokens), axis=1)
+        all_attention_mask = torch.cat((attention_mask, top5_block_attention_mask), axis=1)
+        all_token_types = torch.zeros(all_tokens.shape).type(torch.int64).cuda()
+
+        # [batch_size x 5 x 2 * seq_length x vocab_size]
+        lm_logits, _ = self.lm_model.forward(all_tokens, all_attention_mask, all_token_types)
+        lm_logits = lm_logits.reshape(batch_size, 5, 2 * seq_length, -1)
+        return lm_logits, block_probs
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        """For easy load when model is combined with other heads,
+        add an extra key."""
+
+        state_dict_ = {}
+        state_dict_[self._lm_key] = self.lm_model.state_dict_for_save_checkpoint(destination, prefix, keep_vars)
+        state_dict_[self._retriever_key] = self.retriever.state_dict_for_save_checkpoint(destination, prefix, keep_vars)
+
+        return state_dict_
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Load the state dicts of each of the models"""
+        self.lm_model.load_state_dict(state_dict[self._lm_key], strict)
+        self.retriever.load_state_dict(state_dict[self._retriever_key], strict)
+
+
+class REALMRetriever(MegatronModule):
+    """Retriever which uses a pretrained ICTBertModel and a HashedIndex"""
+    def __init__(self, ict_model, ict_dataset, block_data, hashed_index, top_k=5):
+        super(REALMRetriever, self).__init__()
+        self.ict_model = ict_model
+        self.ict_dataset = ict_dataset
+        self.block_data = block_data
+        self.hashed_index = hashed_index
+        self.top_k = top_k
+        self._ict_key = 'ict_model'
+
+    def retrieve_evidence_blocks_text(self, query_text):
+        """Get the top k evidence blocks for query_text in text form"""
+        print("-" * 100)
+        print("Query: ", query_text)
+        padless_max_len = self.ict_dataset.max_seq_length - 2
+        query_tokens = self.ict_dataset.encode_text(query_text)[:padless_max_len]
+
+        query_tokens, query_pad_mask = self.ict_dataset.concat_and_pad_tokens(query_tokens)
+        query_tokens = torch.cuda.LongTensor(np.array(query_tokens).reshape(1, -1))
+        query_pad_mask = torch.cuda.LongTensor(np.array(query_pad_mask).reshape(1, -1))
+
+        top5_block_tokens, _ = self.retrieve_evidence_blocks(query_tokens, query_pad_mask)
+        for i, block in enumerate(top5_block_tokens[0]):
+            block_text = self.ict_dataset.decode_tokens(block)
+            print('\n    > Block {}: {}'.format(i, block_text))
+
+    def retrieve_evidence_blocks(self, query_tokens, query_pad_mask):
+        """Embed blocks to be used in a forward pass"""
+        with torch.no_grad():
+            true_model = self.ict_model.module.module
+            query_embeds = detach(true_model.embed_query(query_tokens, query_pad_mask))
+        _, block_indices = self.hashed_index.search_mips_index(query_embeds, top_k=self.top_k, reconstruct=False)
+        all_top5_tokens, all_top5_pad_masks = [], []
+        for indices in block_indices:
+            # [k x meta_dim]
+            top5_metas = np.array([self.block_data.meta_data[idx] for idx in indices])
+            top5_block_data = [self.ict_dataset.get_block(*block_meta) for block_meta in top5_metas]
+            top5_tokens, top5_pad_masks = zip(*top5_block_data)
+
+            all_top5_tokens.append(np.array(top5_tokens))
+            all_top5_pad_masks.append(np.array(top5_pad_masks))
+
+        # [batch_size x k x seq_length]
+        return np.array(all_top5_tokens), np.array(all_top5_pad_masks)
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='',
+                                       keep_vars=False):
+        """For easy load when model is combined with other heads,
+        add an extra key."""
+
+        state_dict_ = {}
+        state_dict_[self._ict_key] = self.ict_model.state_dict_for_save_checkpoint(destination, prefix, keep_vars)
+        return state_dict_
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Load the state dicts of each of the models"""
+        self.ict_model.load_state_dict(state_dict[self._ict_key], strict)
+
+
+class ICTBertModel(MegatronModule):
+    """Bert-based module for Inverse Cloze task."""
+    def __init__(self,
+                 ict_head_size,
+                 num_tokentypes=1,
+                 parallel_output=True,
+                 only_query_model=False,
+                 only_block_model=False):
+        super(ICTBertModel, self).__init__()
+        bert_args = dict(
+            num_tokentypes=num_tokentypes,
+            add_binary_head=False,
+            ict_head_size=ict_head_size,
+            parallel_output=parallel_output
+        )
+        assert not (only_block_model and only_query_model)
+        self.use_block_model = not only_query_model
+        self.use_query_model = not only_block_model
+
+        if self.use_query_model:
+            # this model embeds (pseudo-)queries - Embed_input in the paper
+            self.query_model = BertModel(**bert_args)
+            self._query_key = 'question_model'
+
+        if self.use_block_model:
+            # this model embeds evidence blocks - Embed_doc in the paper
+            self.block_model = BertModel(**bert_args)
+            self._block_key = 'context_model'
+
+    def forward(self, query_tokens, query_attention_mask, block_tokens, block_attention_mask, only_query=False, only_block=False):
+        """Run a forward pass for each of the models and compute the similarity scores."""
+
+        if only_query:
+            return self.embed_query(query_tokens, query_attention_mask)
+
+        if only_block:
+            return self.embed_block(block_tokens, block_attention_mask)
+
+
+        query_logits = self.embed_query(query_tokens, query_attention_mask)
+        block_logits = self.embed_block(block_tokens, block_attention_mask)
+
+        # [batch x embed] * [embed x batch]
+        retrieval_scores = query_logits.matmul(torch.transpose(block_logits, 0, 1))
+        return retrieval_scores
+
+    def embed_query(self, query_tokens, query_attention_mask):
+        """Embed a batch of tokens using the query model"""
+        if self.use_query_model:
+            query_types = torch.zeros(query_tokens.shape).type(torch.int64).cuda()
+            query_ict_logits, _ = self.query_model.forward(query_tokens, query_attention_mask, query_types)
+            return query_ict_logits
+        else:
+            raise ValueError("Cannot embed query without query model.")
+
+    def embed_block(self, block_tokens, block_attention_mask):
+        """Embed a batch of tokens using the block model"""
+        if self.use_block_model:
+            block_types = torch.zeros(block_tokens.shape).type(torch.int64).cuda()
+            block_ict_logits, _ = self.block_model.forward(block_tokens, block_attention_mask, block_types)
+            return block_ict_logits
+        else:
+            raise ValueError("Cannot embed block without block model.")
+
+    def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
+        """Save dict with state dicts of each of the models."""
+        state_dict_ = {}
+        if self.use_query_model:
+            state_dict_[self._query_key] \
+                = self.query_model.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+
+        if self.use_block_model:
+            state_dict_[self._block_key] \
+                = self.block_model.state_dict_for_save_checkpoint(
+                destination, prefix, keep_vars)
+
+        return state_dict_
+
+    def load_state_dict(self, state_dict, strict=True):
+        """Load the state dicts of each of the models"""
+        if self.use_query_model:
+            print("Loading ICT query model", flush=True)
+            self.query_model.load_state_dict(
+                state_dict[self._query_key], strict=strict)
+
+        if self.use_block_model:
+            print("Loading ICT block model", flush=True)
+            self.block_model.load_state_dict(
+                state_dict[self._block_key], strict=strict)

pretrain_realm.py

@@ -44,7 +44,7 @@ def model_provider():
     hashed_index = FaissMIPSIndex(index_type='flat_l2', embed_size=128)
     hashed_index.add_block_embed_data(all_block_data)
 
-    retriever = REALMRetriever(ict_model, ict_dataset, all_block_data, hashed_index)
+    retriever = REALMRetriever(ict_model, ict_dataset, all_block_data, hashed_index, args.block_top_k)
     # TODO: REALMBertModel should accept a path to a pretrained bert-base
     model = REALMBertModel(retriever)