From 86221269f98ef9b21a6e6c9d04b86e2fb5cb33d3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 16 Aug 2023 09:55:35 +0300
Subject: RAM optimization round 2

---
 modules/sd_disable_initialization.py | 51 +++++++++++++++++++++++++++++++-----
 1 file changed, 44 insertions(+), 7 deletions(-)

(limited to 'modules/sd_disable_initialization.py')

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 695c5736..719eeb93 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -168,22 +168,59 @@ class LoadStateDictOnMeta(ReplaceHelper):
         device = self.device
 
         def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
-            params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
+            used_param_keys = []
+
+            for name, param in self._parameters.items():
+                if param is None:
+                    continue
+
+                key = prefix + name
+                sd_param = sd.pop(key, None)
+                if sd_param is not None:
+                    state_dict[key] = sd_param
+                    used_param_keys.append(key)
 
-            for name, param in params:
                 if param.is_meta:
-                    self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+                    dtype = sd_param.dtype if sd_param is not None else param.dtype
+                    self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+
+            for name in self._buffers:
+                key = prefix + name
+
+                sd_param = sd.pop(key, None)
+                if sd_param is not None:
+                    state_dict[key] = sd_param
+                    used_param_keys.append(key)
 
             original(self, state_dict, prefix, *args, **kwargs)
 
-            for name, _ in params:
-                key = prefix + name
-                if key in sd:
-                    del sd[key]
+            for key in used_param_keys:
+                state_dict.pop(key, None)
+
+        def load_state_dict(original, self, state_dict, strict=True):
+            """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
+            because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
+            all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
+
+            In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
+
+            The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
+            the function and does not call the original) the state dict will just fail to load because weights
+            would be on the meta device.
+            """
+
+            if state_dict == sd:
+                state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
+
+            original(self, state_dict, strict=strict)
 
+        module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
+        module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
         linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
         conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
         mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
+        layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
+        group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.restore()
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