Use less RAM when creating models

1 files changed, 101 insertions, 5 deletions

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
index 9fc89dc6..695c5736 100644
--- a/modules/sd_disable_initialization.py
+++ b/modules/sd_disable_initialization.py
@@ -3,8 +3,31 @@ import open_clip
 import torch

 import transformers.utils.hub

 

+from modules import shared

 

-class DisableInitialization:

+

+class ReplaceHelper:

+    def __init__(self):

+        self.replaced = []

+

+    def replace(self, obj, field, func):

+        original = getattr(obj, field, None)

+        if original is None:

+            return None

+

+        self.replaced.append((obj, field, original))

+        setattr(obj, field, func)

+

+        return original

+

+    def restore(self):

+        for obj, field, original in self.replaced:

+            setattr(obj, field, original)

+

+        self.replaced.clear()

+

+

+class DisableInitialization(ReplaceHelper):

     """

     When an object of this class enters a `with` block, it starts:

     - preventing torch's layer initialization functions from working

@@ -21,7 +44,7 @@ class DisableInitialization:
     """

 

     def __init__(self, disable_clip=True):

-        self.replaced = []

+        super().__init__()

         self.disable_clip = disable_clip

 

     def replace(self, obj, field, func):

@@ -86,8 +109,81 @@ class DisableInitialization:
             self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)

 

     def __exit__(self, exc_type, exc_val, exc_tb):

-        for obj, field, original in self.replaced:

-            setattr(obj, field, original)

+        self.restore()

 

-        self.replaced.clear()

 

+class InitializeOnMeta(ReplaceHelper):

+    """

+    Context manager that causes all parameters for linear/conv2d/mha layers to be allocated on meta device,

+    which results in those parameters having no values and taking no memory. model.to() will be broken and

+    will need to be repaired by using LoadStateDictOnMeta below when loading params from state dict.

+

+    Usage:

+    ```

+    with sd_disable_initialization.InitializeOnMeta():

+        sd_model = instantiate_from_config(sd_config.model)

+    ```

+    """

+

+    def __enter__(self):

+        if shared.cmd_opts.disable_model_loading_ram_optimization:

+            return

+

+        def set_device(x):

+            x["device"] = "meta"

+            return x

+

+        linear_init = self.replace(torch.nn.Linear, '__init__', lambda *args, **kwargs: linear_init(*args, **set_device(kwargs)))

+        conv2d_init = self.replace(torch.nn.Conv2d, '__init__', lambda *args, **kwargs: conv2d_init(*args, **set_device(kwargs)))

+        mha_init = self.replace(torch.nn.MultiheadAttention, '__init__', lambda *args, **kwargs: mha_init(*args, **set_device(kwargs)))

+        self.replace(torch.nn.Module, 'to', lambda *args, **kwargs: None)

+

+    def __exit__(self, exc_type, exc_val, exc_tb):

+        self.restore()

+

+

+class LoadStateDictOnMeta(ReplaceHelper):

+    """

+    Context manager that allows to read parameters from state_dict into a model that has some of its parameters in the meta device.

+    As those parameters are read from state_dict, they will be deleted from it, so by the end state_dict will be mostly empty, to save memory.

+    Meant to be used together with InitializeOnMeta above.

+

+    Usage:

+    ```

+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict):

+        model.load_state_dict(state_dict, strict=False)

+    ```

+    """

+

+    def __init__(self, state_dict, device):

+        super().__init__()

+        self.state_dict = state_dict

+        self.device = device

+

+    def __enter__(self):

+        if shared.cmd_opts.disable_model_loading_ram_optimization:

+            return

+

+        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]

+

+            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)

+

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

+

+            for name, _ in params:

+                key = prefix + name

+                if key in sd:

+                    del sd[key]

+

+        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))

+

+    def __exit__(self, exc_type, exc_val, exc_tb):

+        self.restore()