Not quite

modeling/inference_models/generic_hf_torch/class.py

@@ -238,7 +238,7 @@ class model_backend(HFTorchInferenceModel):
                     shutil.rmtree("cache/")
 
         self.patch_embedding()
-        lazy_loader.post_load_cleanup()
+        self.model.tie_weights()
 
         self.model.kai_model = self
         utils.koboldai_vars.modeldim = self.get_hidden_size()

modeling/inference_models/hf_torch.py

@@ -289,7 +289,8 @@ class HFTorchInferenceModel(HFInferenceModel):
             device_map = infer_auto_device_map(
                 model,
                 max_memory={0: "10GiB", 1: "7GiB", "cpu": "15GiB"},
-                no_split_module_classes=["GPTJBlock"],
+                no_split_module_classes=["GPTJBlock", "OPTDecoderLayer"],
+                dtype="float16",
             )
 
             return AutoModelForCausalLM.from_pretrained(

modeling/lazy_loader.py

@@ -46,6 +46,7 @@ POSSIBILITY OF SUCH DAMAGE.
 
 import contextlib
 from functools import reduce
+import time
 import zipfile
 import pickle
 import torch
@@ -55,6 +56,8 @@ import _codecs
 import os
 from typing import Any, Callable, Dict, Optional, Tuple, Type
 
+from torch.storage import UntypedStorage
+
 # Safetensors is a dependency for the local version, TPU/Colab doesn't
 # support it yet.
 try:
@@ -65,21 +68,9 @@ except ModuleNotFoundError:
     HAS_SAFETENSORS = False
 
 import utils
+from logger import logger
 
 
-STORAGE_TYPE_MAP = {
-    torch.float64: torch.DoubleStorage,
-    torch.float32: torch.FloatStorage,
-    torch.float16: torch.HalfStorage,
-    torch.int64: torch.LongStorage,
-    torch.int32: torch.IntStorage,
-    torch.int16: torch.ShortStorage,
-    torch.int8: torch.CharStorage,
-    torch.uint8: torch.ByteStorage,
-    torch.bool: torch.BoolStorage,
-    torch.bfloat16: torch.BFloat16Storage,
-}
-
 # Storage of zipfile handles for each shard
 torch_checkpoint_file_handles = {}
 
@@ -205,8 +196,8 @@ class TorchLazyTensor(LazyTensor):
         assert isinstance(checkpoint, zipfile.ZipFile)
 
         CheckpointChunkCache.handle.seek(self.seek_offset, os.SEEK_SET)
-        storage = STORAGE_TYPE_MAP[self.dtype].from_buffer(
-            CheckpointChunkCache.handle.read(nbytes), "little"
+        storage = UntypedStorage.from_buffer(
+            CheckpointChunkCache.handle.read(nbytes), "little", dtype=self.dtype
         )
 
         storage = torch.serialization._get_restore_location(map_location)(
@@ -421,6 +412,8 @@ def use_lazy_load(
             yield False
         return
 
+    begin_time = time.time()
+
     try:
         old_rebuild_tensor = torch._utils._rebuild_tensor
         torch._utils._rebuild_tensor = _rebuild_tensor
@@ -471,17 +464,23 @@ def use_lazy_load(
     finally:
         torch._utils._rebuild_tensor = old_rebuild_tensor
         torch.load = old_torch_load
+
+        post_load_cleanup()
+        logger.debug(
+            f"[lazy_load] Context closed in {round(time.time() - begin_time, 2)} seconds."
+        )
+
         if dematerialized_modules:
             init_empty_weights.__exit__(None, None, None)
 
 
 def post_load_cleanup() -> None:
+    """Close dangling file pointers and clear caches after the load is complete."""
     global torch_checkpoint_file_handles
 
-    print("CheckpointChunkCache Hit Data:", CheckpointChunkCache.hit_data)
-    CheckpointChunkCache.clear()
+    logger.debug(f"[lazy_load] CheckpointChunkCache Hit Data: {CheckpointChunkCache.hit_data}")
+    CheckpointChunkCache.clear(unload_model=True)
 
     for v in torch_checkpoint_file_handles.values():
         v.close()
-
     torch_checkpoint_file_handles = {}

modeling/patches.py

@@ -184,6 +184,10 @@ def patch_transformers_for_lazyload() -> None:
             state_dict[new_key] = state_dict.pop(old_key)
 
 # BEGIN PATCH
+        # TODO: Based on config
+        dtype = torch.float16
+        set_module_kwargs = {"dtype": dtype}
+
         for param_name, param in sorted(
             state_dict.items(),
             # State dict must be ordered in this manner to make the caching in
@@ -211,7 +215,6 @@ def patch_transformers_for_lazyload() -> None:
                 param_name = param_name[len(start_prefix) :]
 
             module_name = param_name
-            set_module_kwargs = {}
 
             # We convert floating dtypes to the `dtype` passed. We want to keep the buffers/params
             # in int/uint/bool and not cast them.
@@ -272,7 +275,7 @@ def patch_transformers_for_lazyload() -> None:
             elif not load_in_8bit:
                 # For backward compatibility with older versions of `accelerate`
                 set_module_tensor_to_device(
-                    model, param_name, param_device, **set_module_kwargs
+                    model, tensor_name=param_name, device=param_device, **set_module_kwargs
                 )
             else:
                 if (