Speed

modeling/lazy_loader.py

@@ -46,7 +46,6 @@ POSSIBILITY OF SUCH DAMAGE.
 
 import contextlib
 from functools import reduce
-import itertools
 import zipfile
 import pickle
 import torch
@@ -54,8 +53,7 @@ import numpy as np
 import collections
 import _codecs
 import os
-from torch.nn import Module
+from typing import Any, Callable, Dict, Optional, Tuple, Type
-from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
 # Safetensors is a dependency for the local version, TPU/Colab doesn't
 # support it yet.
@@ -85,6 +83,22 @@ STORAGE_TYPE_MAP = {
 # Storage of zipfile handles for each shard
 torch_checkpoint_file_handles = {}
 
+
+class CheckpointChunkCache:
+    """Storage for common checkpoint weight files to speed up loading. In order
+    for this to be effective at all, weights must be loaded in ascending order
+    of (key, seek_offset)."""
+    file_name = None
+    key = None
+    handle = None
+
+    @classmethod
+    def clear(cls) -> None:
+        cls.file_name = None
+        cls.key = None
+        cls.handle = None
+
+
 class LazyTensor:
     pass
 
@@ -121,36 +135,37 @@ class TorchLazyTensor(LazyTensor):
 
     def materialize(
         self,
-        checkpoint: Union[zipfile.ZipFile, zipfile.ZipExtFile] = None,
         map_location=None,
         no_grad=True,
-        filename="pytorch_model.bin",
     ) -> torch.Tensor:
 
+        checkpoint = torch_checkpoint_file_handles[self.file_name]
 
+        filename = os.path.basename(os.path.normpath(self.file_name)).split(".")[0]
 
-        # if f not in torch_tensor_container_file_map:
+        # Most of the operations are just seeks, let's see if we can optimize that.
-        #     torch_tensor_container_file_map[f] = []
+        if (
+            CheckpointChunkCache.file_name != filename
+            or CheckpointChunkCache.key != self.key
+            or not CheckpointChunkCache.handle
+        ):
+            # Flush cache if invalid
+            print("!", end="", flush=True)
 
-        #     with zipfile.ZipFile(f, "r") as z:
+            if CheckpointChunkCache.handle:
-        #         paths = z.namelist()
+                CheckpointChunkCache.handle.close()
 
-        #     for name in paths:
+            CheckpointChunkCache.file_name = filename
-        #         val = name.split("/data/")[-1]
+            CheckpointChunkCache.key = self.key
-        #         if not val.isdecimal():
+            try:
-        #             continue
+                CheckpointChunkCache.handle = checkpoint.open(
-        #         torch_tensor_container_file_map[f].append(int(val))
+                    f"archive/data/{self.key}", "r"
-        #     torch_tensor_container_file_map[f].sort()
+                )
-        #     print(torch_tensor_container_file_map)
+            except KeyError:
+                CheckpointChunkCache.handle = checkpoint.open(
+                    f"{filename}/data/{self.key}", "r"
+                )
 
-
-
-
-        if not checkpoint:
-            checkpoint = torch_checkpoint_file_handles[self.file_name]
-        filename = self.file_name
-
-        filename = os.path.basename(os.path.normpath(filename)).split(".")[0]
         size = reduce(lambda x, y: x * y, self.shape, 1)
         dtype = self.dtype
         nbytes = (
@@ -163,21 +178,12 @@ class TorchLazyTensor(LazyTensor):
             )
         )
 
-        if isinstance(checkpoint, zipfile.ZipFile):
+        assert isinstance(checkpoint, zipfile.ZipFile)
-            try:
-                f = checkpoint.open(f"archive/data/{self.key}", "r")
-            except:
-                f = checkpoint.open(f"{filename}/data/{self.key}", "r")
-            f.seek(self.seek_offset, os.SEEK_CUR)
-            # f.read(self.seek_offset)
-        else:
-            f = checkpoint
 
-        try:
+        CheckpointChunkCache.handle.seek(self.seek_offset, os.SEEK_SET)
-            storage = STORAGE_TYPE_MAP[dtype].from_buffer(f.read(nbytes), "little")
+        storage = STORAGE_TYPE_MAP[dtype].from_buffer(
-        finally:
+            CheckpointChunkCache.handle.read(nbytes), "little"
-            if isinstance(checkpoint, zipfile.ZipFile):
+        )
-                f.close()
 
         storage = torch.serialization._get_restore_location(map_location)(
             storage, self.location
@@ -277,9 +283,7 @@ class _LazyUnpickler(RestrictedUnpickler):
             typename == "storage"
         ), f"Unknown typename for persistent_load, expected 'storage' but got '{typename}'"
         storage_type, key, location, _ = saved_id[1:]
-        return TorchLazyTensor(
+        return TorchLazyTensor(storage_type, key, location)
-            storage_type, key, location
-        )
 
     def load(self, *args, **kwargs):
         retval = super().load(*args, **kwargs)
@@ -361,13 +365,6 @@ def patch_safetensors(callback):
 
     transformers.modeling_utils.safe_load_file = safetensors_load
 
-def get_torch_tensor_file(file: str, lazy_tensor: TorchLazyTensor):
-    with zipfile.ZipFile(file, "r") as z:
-        storage_key = lazy_tensor.key
-        ziproot = z.namelist()[0].split("/")[0]
-        f = z.open(f"{ziproot}/data/{storage_key}")
-        # TODO: Maybe some file seeking
-        return f
 
 @contextlib.contextmanager
 def use_custom_unpickler(unpickler: Type[pickle.Unpickler] = RestrictedUnpickler):
@@ -418,7 +415,7 @@ def use_lazy_load(
             if f not in torch_checkpoint_file_handles:
                 torch_checkpoint_file_handles[f] = zipfile.ZipFile(f, "r")
 
-            for k,v in model_dict.items():
+            for k, v in model_dict.items():
                 v.file_name = f
 
             if callback is not None:

modeling/patches.py

@@ -126,7 +126,6 @@ def patch_transformers_generation() -> None:
     transformers.generation.logits_process.NoBadWordsLogitsProcessor.__init__ = new_init
 
 
-CURRENT_CHECKPOINT = None
 def patch_transformers_for_lazyload() -> None:
     import torch
     import inspect
@@ -158,8 +157,6 @@ def patch_transformers_for_lazyload() -> None:
 
         """
 
-        print("DEVMAP", device_map)
-
         # XXX: remaining features to implement to be fully compatible with _load_state_dict_into_model
         # - deepspeed zero 3 support
         # - need to copy metadata if any - see _load_state_dict_into_model
@@ -186,13 +183,22 @@ def patch_transformers_for_lazyload() -> None:
         for old_key, new_key in zip(old_keys, new_keys):
             state_dict[new_key] = state_dict.pop(old_key)
 
-        for param_name, param in state_dict.items():
+# BEGIN PATCH
+        for param_name, param in sorted(
+            state_dict.items(),
+            # State dict must be ordered in this manner to make the caching in
+            # lazy_loader.py effective
+            key=lambda x: (
+                # NOTE: Assuming key is just decimal
+                int(x[1].key),
+                x[1].seek_offset,
+            ),
+        ):
 
-            # BEGIN PATCH
             if isinstance(param, LazyTensor):
                 print(".", end="", flush=True)
                 param = param.materialize()
-            # END PATCH
+# END PATCH
 
             # First part of the test is always true as load_state_dict_keys always contains state_dict keys.
             if (