Lazy loader Python 3.6 compatibility

The current lazy loader relies on a feature of the Python zipfile module
that was added in Python 3.7.0:

https://bugs.python.org/issue22908

This commit adds compatibility for Python 3.6.

torch_lazy_loader.py

@@ -95,7 +95,7 @@ class LazyTensor:
         nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
         if isinstance(checkpoint, zipfile.ZipFile):
             f = checkpoint.open(f"archive/data/{self.key}", "r")
-            f.seek(self.seek_offset)
+            f.read(self.seek_offset)
         else:
             f = checkpoint
         try:

tpu_mtj_backend.py

@@ -887,6 +887,7 @@ def read_neox_checkpoint(state, path, config, checkpoint_shards=2):
     output_shards = config["cores_per_replica"] // checkpoint_shards
 
     import torch
+    import torch.utils.dlpack
     from tqdm.auto import tqdm
 
     move_xmap = jax.experimental.maps.xmap(
@@ -1154,12 +1155,14 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
     import torch_lazy_loader
     import torch
     from tqdm.auto import tqdm
+    import functools
 
     def callback(model_dict, f, **_):
         with zipfile.ZipFile(f, "r") as z:
             try:
                 last_storage_key = None
                 f = None
+                current_offset = 0
                 print("\n\n\nThis model has  ", f"{hk.data_structures.tree_size(network.state['params']):,d}".replace(",", " "), "  parameters.\n")
                 for key in tqdm(sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)), desc="Loading model tensors"):
 
@@ -1178,17 +1181,22 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
                         if isinstance(f, zipfile.ZipExtFile):
                             f.close()
                         f = z.open(f"archive/data/{storage_key}")
-                    current_offset = f.tell()
+                        current_offset = 0
                     if current_offset != model_dict[key].seek_offset:
-                        f.seek(model_dict[key].seek_offset - current_offset, 1)
+                        f.read(model_dict[key].seek_offset - current_offset)
+                        current_offset = model_dict[key].seek_offset
                     spec = model_spec[key]
                     transforms = set(spec.get("transforms", ()))
                     if not isinstance(model_dict[key], torch_lazy_loader.LazyTensor):
                         error = f"Duplicate key {repr(key)}"
                         print("\n\nERROR:  " + error, file=sys.stderr)
                         raise RuntimeError(error)
+                    size = functools.reduce(lambda x, y: x * y, model_dict[key].shape, 1)
+                    dtype = model_dict[key].dtype
+                    nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3)
                     tensor = model_dict[key].materialize(f, map_location="cpu")
                     model_dict[key] = tensor.to("meta")
+                    current_offset += nbytes
 
                     # MTJ requires certain mathematical operations to be performed
                     # on tensors in order for them to be in the correct format