diff --git a/allennlp/common/mmap.py b/allennlp/common/mmap.py
new file mode 100644
index 00000000000..ef81c756c24
--- /dev/null
+++ b/allennlp/common/mmap.py
@@ -0,0 +1,312 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import lru_cache
+import os
+import shutil
+import struct
+import numpy as np
+import torch
+from allennlp.data.fields import DataArray
+
+dtypes = {
+    1: np.uint8,
+    2: np.int8,
+    3: np.int16,
+    4: np.int32,
+    5: np.int64,
+    6: np.float,
+    7: np.double,
+    8: np.uint16,
+}
+
+
+def code(dtype):
+    for k in dtypes.keys():
+        if dtypes[k] == dtype:
+            return k
+    raise ValueError(dtype)
+
+
+def index_file_path(prefix_path):
+    return f"{prefix_path}.idx"
+
+
+def data_file_path(prefix_path):
+    return f"{prefix_path}.bin"
+
+
+def _warmup_mmap_file(path):
+    with open(path, "rb") as stream:
+        while stream.read(100 * 1024 * 1024):
+            pass
+
+
+class MMapCacheReader:
+    class Index(object):
+        _HDR_MAGIC = b"MMIDIDX\x00\x00"
+
+        @classmethod
+        def writer(cls, path, dtype):
+            class _Writer(object):
+                def __enter__(self):
+                    self._file = open(path, "wb")
+
+                    self._file.write(cls._HDR_MAGIC)
+                    self._file.write(struct.pack("<Q", 1))
+                    self._file.write(struct.pack("<B", code(dtype)))
+
+                    return self
+
+                @staticmethod
+                def _get_pointers(sizes):
+                    dtype_size = dtype().itemsize
+                    address = 0
+                    pointers = []
+
+                    for size in sizes:
+                        pointers.append(address)
+                        address += size * dtype_size
+
+                    return pointers
+
+                def write(self, sizes):
+                    pointers = self._get_pointers(sizes)
+
+                    self._file.write(struct.pack("<Q", len(sizes)))
+
+                    sizes = np.array(sizes, dtype=np.int32)
+                    self._file.write(sizes.tobytes(order="C"))
+                    del sizes
+
+                    pointers = np.array(pointers, dtype=np.int64)
+                    self._file.write(pointers.tobytes(order="C"))
+                    del pointers
+
+                def __exit__(self, exc_type, exc_val, exc_tb):
+                    self._file.close()
+
+            return _Writer()
+
+        def __init__(self, path):
+            with open(path, "rb") as stream:
+                magic_test = stream.read(9)
+                assert self._HDR_MAGIC == magic_test, (
+                    "Index file doesn't match expected format. "
+                    "Make sure that --dataset-impl is configured properly."
+                )
+                version = struct.unpack("<Q", stream.read(8))
+                assert (1,) == version
+
+                (dtype_code,) = struct.unpack("<B", stream.read(1))
+                self._dtype = dtypes[dtype_code]
+                self._dtype_size = self._dtype().itemsize
+
+                self._len = struct.unpack("<Q", stream.read(8))[0]
+                offset = stream.tell()
+
+            _warmup_mmap_file(path)
+
+            self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
+            self._bin_buffer = memoryview(self._bin_buffer_mmap)
+            self._sizes = np.frombuffer(
+                self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
+            )
+            self._pointers = np.frombuffer(
+                self._bin_buffer,
+                dtype=np.int64,
+                count=self._len,
+                offset=offset + self._sizes.nbytes,
+            )
+
+        def __del__(self):
+            self._bin_buffer_mmap._mmap.close()
+            del self._bin_buffer_mmap
+
+        @property
+        def dtype(self):
+            return self._dtype
+
+        @property
+        def sizes(self):
+            return self._sizes
+
+        @lru_cache(maxsize=8)
+        def __getitem__(self, i):
+            return self._pointers[i], self._sizes[i]
+
+        def __len__(self):
+            return self._len
+
+    def __init__(self, path):
+        super().__init__()
+
+        self._path = None
+        self._index = None
+        self._bin_buffer = None
+
+        self._do_init(path)
+
+    def __getstate__(self):
+        return self._path
+
+    def __setstate__(self, state):
+        self._do_init(state)
+
+    def _do_init(self, path):
+        self._path = path
+        self._index = self.Index(index_file_path(self._path))
+
+        _warmup_mmap_file(data_file_path(self._path))
+        self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode="r", order="C")
+        self._bin_buffer = memoryview(self._bin_buffer_mmap)
+
+    def __del__(self):
+        self._bin_buffer_mmap._mmap.close()
+        del self._bin_buffer_mmap
+        del self._index
+
+    def __len__(self):
+        return len(self._index)
+
+    @lru_cache(maxsize=8)
+    def __getitem__(self, i):
+        ptr, size = self._index[i]
+        # self._index.dtype will be different everytime
+        np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr)
+        if self._index.dtype != np.int64:
+            np_array = np_array.astype(np.int64)
+        #To tensor_dict() here.
+        return torch.from_numpy(np_array)
+
+    @property
+    def sizes(self):
+        return self._index.sizes
+
+    @staticmethod
+    def exists(path):
+        return os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))
+
+
+class MMapCacheBuilder(object):
+    def __init__(self, out_file):
+        self._data_file = open(out_file, "wb")
+        self._sizes = []
+        self._field_names = None
+        self._dtype = np.int32
+
+    def add_instance(self, instance: Instance):
+        tensor_dict = instance.as_tensor_dict()
+        flattened_dict = self.flatten_dict(tensor_dict)
+        if not self._field_names:
+            self._field_names = list(sorted(flattened_dict.keys()))
+            assert self._field_names
+            # TODO: what if some instances have a different set of field names, i.e missing some, for test instances, we don't have supervision.....
+            # for now we will just write the name of every field next_field_names
+            self.add_tensor(key, value)
+
+    @classmethod
+    def flatten_dict(cls, tensor_dict: Dict, prefix=None):
+        flat_dict = {}
+        for field_name, value in tensor_dict.items():
+            if isinstance(value, torch.Tensor):
+                name = f"{prefix}___{field_name}" if prefix else field_name
+                flat_dict[name] = value
+            elif isinstance(value, dict):
+                flat_dict.update(cls.flatten_dict(value, prefix=field_name))
+            else:
+                raise ValueError("You gave me a MetadataField")
+        return flat_dict
+
+    def add_tensor(self, name, tensor):
+        np_array = tensor.contiguous().detach().numpy()
+        np_array_b = np_array.tobytes(order="C")
+        name_b = name.encode()
+        self._sizes += [len(name_b), len(np_array_b), np_array.size]
+        self._data_file.write(name_b)
+        self._data_file.write(np_array_b)
+
+    def merge_file_(self, another_file):
+        # Concatenate index
+        index = MMapCacheReader.Index(index_file_path(another_file))
+        assert index.dtype == self._dtype
+
+        for size in index.sizes:
+            self._sizes.append(size)
+
+        # Concatenate data
+        with open(data_file_path(another_file), "rb") as f:
+            shutil.copyfileobj(f, self._data_file)
+
+    def finalize(self, index_file):
+        self._data_file.close()
+
+        with MMapCacheReader.Index.writer(index_file, self._dtype) as index:
+            index.write(self._sizes)
+
+
+
+class MMapCache:
+    def __init__(
+        self,
+        cache_path: str,
+    ) -> None:
+        self.cache_path = cache_path
+        self._builder = None
+        self._cache = None
+        
+        if os.path.exists(self.cache_path)):
+            if self.is_finalized(self.cache_path):
+                #scenario 2, we can read.
+                self._cache = MMapCacheReader(self.cache_path)
+            else:
+                #scenario 3, another training process is currently writing to it or was interrupted while it was writing.
+                pass
+        else:
+            self._builder = MMapCacheBuilder(self.cache_path)
+            #scenario 1, we need to write to it.
+            
+    def get_instances(
+        self,
+        data_path: str,
+    ) -> Optional[Iterable[Dict[str, DataArray]]]:
+        #dont need data_path here
+        assert self._cache
+        for i in range(len(self._cache)):
+            yield self._cache[i]
+        
+
+    def set_instances(
+        self,
+        instances: Iterable[Dict[str, DataArray]],
+    ) -> Iterable[Dict[str, DataArray]]:
+        assert self._builder:
+        for instance in instances:
+            self._builder.add_instance(instance)
+        return instances
+
+            
+
+    def get_vocabulary(self) -> Optional[Vocabulary]:
+        pass
+
+    def set_vocabulary(self, vocab: Vocabulary) -> None:
+        pass
+
+    def finalize(self) -> None:
+        pass
+
+    @classmethod
+    def hash_config(cls, config: Params) -> str:
+        pass
+    
+    @classmethod
+    def is_finalized(cls,path):
+        return True
+
+    
+
+    # Similar to the DatasetReader class, the Cache class will also have
+    # getters and setters for WorkerInfo and DistributedInfo.
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