Merge pull request #12599 from AUTOMATIC1111/ram_optim

RAM optimization round 2

extensions-builtin/Lora/networks.py

@@ -304,7 +304,10 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
 
     weights_backup = getattr(self, "network_weights_backup", None)
-    if weights_backup is None:
+    if weights_backup is None and wanted_names != ():
+        if current_names != ():
+            raise RuntimeError("no backup weights found and current weights are not unchanged")
+
         if isinstance(self, torch.nn.MultiheadAttention):
             weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
         else:

modules/sd_disable_initialization.py

@@ -155,10 +155,16 @@ class LoadStateDictOnMeta(ReplaceHelper):
     ```
     """
 
-    def __init__(self, state_dict, device):
+    def __init__(self, state_dict, device, weight_dtype_conversion=None):
         super().__init__()
         self.state_dict = state_dict
         self.device = device
+        self.weight_dtype_conversion = weight_dtype_conversion or {}
+        self.default_dtype = self.weight_dtype_conversion.get('')
+
+    def get_weight_dtype(self, key):
+        key_first_term, _ = key.split('.', 1)
+        return self.weight_dtype_conversion.get(key_first_term, self.default_dtype)
 
     def __enter__(self):
         if shared.cmd_opts.disable_model_loading_ram_optimization:
@@ -167,23 +173,60 @@ class LoadStateDictOnMeta(ReplaceHelper):
         sd = self.state_dict
         device = self.device
 
-        def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
+        def load_from_state_dict(original, module, 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 params:
+            for name, param in module._parameters.items():
-                if param.is_meta:
+                if param is None:
-                    self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
+                    continue
 
-            original(self, state_dict, prefix, *args, **kwargs)
-
-            for name, _ in params:
                 key = prefix + name
-                if key in sd:
+                sd_param = sd.pop(key, None)
-                    del sd[key]
+                if sd_param is not None:
+                    state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))
+                    used_param_keys.append(key)
 
+                if param.is_meta:
+                    dtype = sd_param.dtype if sd_param is not None else param.dtype
+                    module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+
+            for name in module._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(module, state_dict, prefix, *args, **kwargs)
+
+            for key in used_param_keys:
+                state_dict.pop(key, None)
+
+        def load_state_dict(original, module, 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(module, 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()

modules/sd_models.py

@@ -343,7 +343,10 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
         model.to(memory_format=torch.channels_last)
         timer.record("apply channels_last")
 
-    if not shared.cmd_opts.no_half:
+    if shared.cmd_opts.no_half:
+        model.float()
+        timer.record("apply float()")
+    else:
         vae = model.first_stage_model
         depth_model = getattr(model, 'depth_model', None)
 
@@ -518,6 +521,13 @@ def send_model_to_cpu(m):
     devices.torch_gc()
 
 
+def model_target_device():
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        return devices.cpu
+    else:
+        return devices.device
+
+
 def send_model_to_device(m):
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
@@ -579,7 +589,15 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     timer.record("create model")
 
-    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
+    if shared.cmd_opts.no_half:
+        weight_dtype_conversion = None
+    else:
+        weight_dtype_conversion = {
+            'first_stage_model': None,
+            '': torch.float16,
+        }
+
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(), weight_dtype_conversion=weight_dtype_conversion):
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
     timer.record("load weights from state dict")