Merge pull request #12599 from AUTOMATIC1111/ram_optim

RAM optimization round 2

2 files changed, 73 insertions, 12 deletions

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 695c5736..8863107a 100644
--- a/modules/sd_disable_initialization.py
+++ b/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):

-            params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]

+        def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs):

+            used_param_keys = []

 

-            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)

+            for name, param in module._parameters.items():

+                if param is None:

+                    continue

 

-            original(self, state_dict, prefix, *args, **kwargs)

+                key = prefix + name

+                sd_param = sd.pop(key, None)

+                if sd_param is not None:

+                    state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))

+                    used_param_keys.append(key)

 

-            for name, _ in params:

+                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

-                if key in sd:

-                    del sd[key]

 

+                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()

diff --git a/modules/sd_models.py b/modules/sd_models.py
index f6fbdcd6..685585b1 100644
--- a/modules/sd_models.py
+++ b/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")