Use torch custom_ops to avoid graph breaks with torch.compile

Hopefully finally fixes the torch.compile VRAM issues...

custom_linear.py

@@ -1,14 +1,52 @@
 import torch
 import torch.nn as nn
 from accelerate import init_empty_weights
+from .gguf.gguf_utils import GGUFParameter, dequantize_gguf_tensor
+
+@torch.library.custom_op("wanvideo::apply_lora", mutates_args=())
+def apply_lora(weight: torch.Tensor, lora_diff_0: torch.Tensor, lora_diff_1: torch.Tensor, lora_diff_2: float, lora_strength: float) -> torch.Tensor:
+    patch_diff = torch.mm(
+        lora_diff_0.flatten(start_dim=1),
+        lora_diff_1.flatten(start_dim=1)
+    ).reshape(weight.shape)
+
+    alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 != 0.0 else 1.0
+    scale = lora_strength * alpha
+
+    return weight.add(patch_diff, alpha=scale)
+
+@apply_lora.register_fake
+def _(weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
+    # Return weight with same metadata
+    return weight.clone()
+
+@torch.library.custom_op("wanvideo::apply_single_lora", mutates_args=())
+def apply_single_lora(weight: torch.Tensor, lora_diff: torch.Tensor, lora_strength: float) -> torch.Tensor:
+    return weight.add(lora_diff, alpha=lora_strength)
+
+@apply_single_lora.register_fake
+def _(weight, lora_diff, lora_strength):
+    # Return weight with same metadata
+    return weight.clone()
+
+@torch.library.custom_op("wanvideo::linear_forward", mutates_args=())
+def linear_forward(input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor | None) -> torch.Tensor:
+    return torch.nn.functional.linear(input, weight, bias)
+
+@linear_forward.register_fake
+def _(input, weight, bias):
+    # Calculate output shape: (..., out_features)
+    out_features = weight.shape[0]
+    output_shape = list(input.shape[:-1]) + [out_features]
+    return input.new_empty(output_shape)
 
 #based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/quantizers/gguf/utils.py
-def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, scale_weights=None, compile_args=None):
+def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, scale_weights=None, compile_args=None, modules_to_not_convert=[]):
-   
+
     has_children = list(model.children())
     if not has_children:
         return
-    
+
     allow_compile = False
 
     for name, module in model.named_children():
@@ -16,13 +54,22 @@ def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, s
             allow_compile = compile_args.get("allow_unmerged_lora_compile", False)
         module_prefix = prefix + name + "."
         module_prefix = module_prefix.replace("_orig_mod.", "")
-        _replace_linear(module, compute_dtype, state_dict, module_prefix, patches, scale_weights, compile_args)
+        _replace_linear(module, compute_dtype, state_dict, module_prefix, patches, scale_weights, compile_args, modules_to_not_convert)
 
-        if isinstance(module, nn.Linear) and "loras" not in module_prefix:
+        if isinstance(module, nn.Linear) and "loras" not in module_prefix and name not in modules_to_not_convert:
-            in_features = state_dict[module_prefix + "weight"].shape[1]
+            weight_key = module_prefix + "weight"
-            out_features = state_dict[module_prefix + "weight"].shape[0]
+            if weight_key not in state_dict:
-            if scale_weights is not None:
+                continue
+
+            in_features = state_dict[weight_key].shape[1]
+            out_features = state_dict[weight_key].shape[0]
+
+            is_gguf = isinstance(state_dict[weight_key], GGUFParameter)
+
+            scale_weight = None
+            if not is_gguf and scale_weights is not None:
                 scale_key = f"{module_prefix}scale_weight"
+                scale_weight = scale_weights.get(scale_key)
 
             with init_empty_weights():
                 model._modules[name] = CustomLinear(
@@ -30,8 +77,9 @@ def _replace_linear(model, compute_dtype, state_dict, prefix="", patches=None, s
                     out_features,
                     module.bias is not None,
                     compute_dtype=compute_dtype,
-                    scale_weight=scale_weights.get(scale_key) if scale_weights else None,
+                    scale_weight=scale_weight,
-                    allow_compile=allow_compile
+                    allow_compile=allow_compile,
+                    is_gguf=is_gguf
                 )
             model._modules[name].source_cls = type(module)
             model._modules[name].requires_grad_(False)
@@ -84,7 +132,8 @@ class CustomLinear(nn.Linear):
         compute_dtype=None,
         device=None,
         scale_weight=None,
-        allow_compile=False
+        allow_compile=False,
+        is_gguf=False
     ) -> None:
         super().__init__(in_features, out_features, bias, device)
         self.compute_dtype = compute_dtype
@@ -93,11 +142,51 @@ class CustomLinear(nn.Linear):
         self.scale_weight = scale_weight
         self.lora_strengths = []
         self.allow_compile = allow_compile
+        self.is_gguf = is_gguf
 
         if not allow_compile:
-            self._get_weight_with_lora = torch.compiler.disable()(self._get_weight_with_lora)
+            # Disable compilation for both methods
-            self.forward = torch.compiler.disable()(self.forward)
+            #self._get_weight_with_lora = torch.compiler.disable()(self._get_weight_with_lora)
-    
+            #self.forward = torch.compiler.disable()(self.forward)
+            # Use regular implementations instead of custom ops
+            self._apply_lora_impl = self._apply_lora_custom_op
+            self._apply_single_lora_impl = self._apply_single_lora_custom_op
+            self._linear_forward_impl = self._linear_forward_custom_op
+        else:
+            self._apply_lora_impl = self._apply_lora_direct
+            self._apply_single_lora_impl = self._apply_single_lora_direct
+            self._linear_forward_impl = self._linear_forward_direct
+
+
+    # Direct implementations (no custom ops)
+    def _apply_lora_direct(self, weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
+        patch_diff = torch.mm(
+            lora_diff_0.flatten(start_dim=1),
+            lora_diff_1.flatten(start_dim=1)
+        ).reshape(weight.shape)
+        alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 != 0.0 else 1.0
+        scale = lora_strength * alpha
+        return weight.add(patch_diff, alpha=scale)
+
+    def _apply_single_lora_direct(self, weight, lora_diff, lora_strength):
+        return weight.add(lora_diff, alpha=lora_strength)
+
+    def _linear_forward_direct(self, input, weight, bias):
+        return torch.nn.functional.linear(input, weight, bias)
+
+    # Custom op implementations
+    def _apply_lora_custom_op(self, weight, lora_diff_0, lora_diff_1, lora_diff_2, lora_strength):
+        return torch.ops.wanvideo.apply_lora(weight, lora_diff_0, lora_diff_1,
+            float(lora_diff_2) if lora_diff_2 is not None else 0.0,
+            float(lora_strength)
+        )
+
+    def _apply_single_lora_custom_op(self, weight, lora_diff, lora_strength):
+        return torch.ops.wanvideo.apply_single_lora(weight, lora_diff, float(lora_strength))
+
+    def _linear_forward_custom_op(self, input, weight, bias):
+        return torch.ops.wanvideo.linear_forward(input, weight, bias)
+
     def set_lora_diffs(self, lora_diffs, device=torch.device("cpu")):
         self.lora_diffs = []
         for i, diff in enumerate(lora_diffs):
@@ -111,10 +200,10 @@ class CustomLinear(nn.Linear):
                 self.lora_diffs.append(f"lora_diff_{i}_0")
 
     def _get_weight_with_lora(self, weight):
-        """Apply LoRA outside compiled region"""
+        """Apply LoRA using custom ops to avoid graph breaks"""
         if not hasattr(self, "lora_diff_0_0"):
             return weight
-        
+
         for lora_diff_names, lora_strength in zip(self.lora_diffs, self.lora_strengths):
             if isinstance(lora_strength, list):
                 lora_strength = lora_strength[self.step]
@@ -122,39 +211,50 @@ class CustomLinear(nn.Linear):
                     continue
             elif lora_strength == 0.0:
                 continue
+
             if isinstance(lora_diff_names, tuple):
                 lora_diff_0 = getattr(self, lora_diff_names[0])
                 lora_diff_1 = getattr(self, lora_diff_names[1])
                 lora_diff_2 = getattr(self, lora_diff_names[2])
-                patch_diff = torch.mm(
+
-                    lora_diff_0.flatten(start_dim=1),
+                weight = self._apply_lora_impl(
-                    lora_diff_1.flatten(start_dim=1)
+                    weight, lora_diff_0, lora_diff_1,
-                ).reshape(weight.shape) + 0
+                    float(lora_diff_2) if lora_diff_2 is not None else 0.0,
-                alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 is not None else 1.0
+                    float(lora_strength)
-                scale = lora_strength * alpha
+                )
-                weight = weight.add(patch_diff, alpha=scale)
             else:
                 lora_diff = getattr(self, lora_diff_names)
-                weight = weight.add(lora_diff, alpha=lora_strength)
+                weight = self._apply_single_lora_impl(weight, lora_diff,float(lora_strength))
+        return weight
+
+    def _prepare_weight(self, input):
+        """Prepare weight tensor - handles both regular and GGUF weights"""
+        if self.is_gguf:
+            weight = dequantize_gguf_tensor(self.weight).to(self.compute_dtype)
+        else:
+            weight = self.weight.to(input)
         return weight
 
     def forward(self, input):
+        weight = self._prepare_weight(input)
+
         if self.bias is not None:
-            bias = self.bias.to(input)
+            bias = self.bias.to(input if not self.is_gguf else self.compute_dtype)
         else:
             bias = None
-        weight = self.weight.to(input)
 
-        if self.scale_weight is not None:
+        # Only apply scale_weight for non-GGUF models
+        if not self.is_gguf and self.scale_weight is not None:
             if weight.numel() < input.numel():
                 weight = weight * self.scale_weight
             else:
                 input = input * self.scale_weight
 
         weight = self._get_weight_with_lora(weight)
+        out = self._linear_forward_impl(input, weight, bias)
+        del weight, input, bias
+        return out
 
-        return torch.nn.functional.linear(input, weight, bias)
-    
 def remove_lora_from_module(module):
     for name, submodule in module.named_modules():
         if hasattr(submodule, "lora_diffs"):

gguf/gguf.py

@@ -1,15 +1,11 @@
 import torch
-import torch.nn as nn
 import numpy as np
 import gguf
-from accelerate import init_empty_weights
 
-from .gguf_utils import GGUFParameter, dequantize_gguf_tensor
+from .gguf_utils import GGUFParameter
-from ..utils import log
 
 def load_gguf(model_path):
-    from gguf import GGUFReader
+    reader = gguf.GGUFReader(model_path)
-    reader = GGUFReader(model_path)
     parsed_parameters = {}
     for tensor in reader.tensors:
         # if the tensor is a torch supported dtype do not use GGUFParameter
@@ -18,144 +14,12 @@ def load_gguf(model_path):
         parsed_parameters[tensor.name] = GGUFParameter(meta_tensor, quant_type=tensor.tensor_type) if is_gguf_quant else meta_tensor
     return parsed_parameters, reader
 
-#based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/quantizers/gguf/utils.py
+from ..custom_linear import _replace_linear, set_lora_params, CustomLinear
+
 def _replace_with_gguf_linear(model, compute_dtype, state_dict, prefix="", modules_to_not_convert=[], patches=None, compile_args=None):
-    def _should_convert_to_gguf(state_dict, prefix):
+    return _replace_linear(model, compute_dtype, state_dict, prefix, patches, None, compile_args, modules_to_not_convert)
-        weight_key = prefix + "weight"
-        return weight_key in state_dict and isinstance(state_dict[weight_key], GGUFParameter)
-
-    has_children = list(model.children())
-    if not has_children:
-        return
-    
-    allow_compile = False
-
-    for name, module in model.named_children():
-        if compile_args is not None:
-            allow_compile = compile_args.get("allow_unmerged_lora_compile", False)
-        module_prefix = prefix + name + "."
-        _replace_with_gguf_linear(module, compute_dtype, state_dict, module_prefix, modules_to_not_convert, patches, compile_args)
-
-        if (
-            isinstance(module, nn.Linear)
-            and not isinstance(module, GGUFLinear) 
-            and _should_convert_to_gguf(state_dict, module_prefix)
-            and name not in modules_to_not_convert
-        ):
-            in_features = state_dict[module_prefix + "weight"].shape[1]
-            out_features = state_dict[module_prefix + "weight"].shape[0]
-
-            with init_empty_weights():
-                model._modules[name] = GGUFLinear(
-                    in_features,
-                    out_features,
-                    module.bias is not None,
-                    compute_dtype=compute_dtype,
-                    allow_compile=allow_compile
-                )
-            
-            model._modules[name].source_cls = type(module)
-            model._modules[name].requires_grad_(False)
-    return model
 
 def set_lora_params_gguf(module, patches, module_prefix="", device=torch.device("cpu")):
-    # Recursively set lora_diffs and lora_strengths for all GGUFLinear layers
+    return set_lora_params(module, patches, module_prefix, device)
-    for name, child in module.named_children():
-        params = list(child.parameters())
-        if params:
-            device = params[0].device
-        else:
-            device = torch.device("cpu")
-        child_prefix = (f"{module_prefix}{name}.")
-        set_lora_params_gguf(child, patches, child_prefix, device)
-    if isinstance(module, GGUFLinear):
-        key = f"diffusion_model.{module_prefix}weight"
-        patch = patches.get(key, [])
-        #print(f"Processing LoRA patches for {key}: {len(patch)} patches found")
-        if len(patch) == 0:
-            key = key.replace("_orig_mod.", "")
-            patch = patches.get(key, [])
-        if len(patch) != 0:
-            lora_diffs = []
-            for p in patch:
-                lora_obj = p[1]
-                if "head" in key:
-                    continue  # For now skip LoRA for head layers
-                elif hasattr(lora_obj, "weights"):
-                    lora_diffs.append(lora_obj.weights)
-                elif isinstance(lora_obj, tuple) and lora_obj[0] == "diff":
-                    lora_diffs.append(lora_obj[1])
-                else:
-                    continue
-            module.lora_strengths = [p[0] for p in patch]
-            module.set_lora_diffs(lora_diffs, device=device)
-            module.step = 0  # Initialize step for LoRA scheduling
 
-
+GGUFLinear = CustomLinear
-class GGUFLinear(nn.Linear):
-    def __init__(
-        self,
-        in_features,
-        out_features,
-        bias=False,
-        compute_dtype=None,
-        device=None,
-        allow_compile=False
-    ) -> None:
-        super().__init__(in_features, out_features, bias, device)
-        self.compute_dtype = compute_dtype
-        self.lora_diffs = []
-        self.lora_strengths = []
-        self.step = 0
-        self.allow_compile = allow_compile
-
-        if not allow_compile:
-            self._get_weight_with_lora = torch.compiler.disable()(self._get_weight_with_lora)
-
-    def forward(self, inputs):
-        weight = dequantize_gguf_tensor(self.weight).to(self.compute_dtype)
-        bias = self.bias.to(self.compute_dtype) if self.bias is not None else None
-
-        weight = self._get_weight_with_lora(weight)#.to(self.compute_dtype)
-
-        return torch.nn.functional.linear(inputs, weight, bias)
-    
-    def set_lora_diffs(self, lora_diffs, device=torch.device("cpu")):
-        self.lora_diffs = []
-        for i, diff in enumerate(lora_diffs):
-            if len(diff) > 1:
-                self.register_buffer(f"lora_diff_{i}_0", diff[0].to(device, self.compute_dtype))
-                self.register_buffer(f"lora_diff_{i}_1", diff[1].to(device, self.compute_dtype))
-                setattr(self, f"lora_diff_{i}_2", diff[2])
-                self.lora_diffs.append((f"lora_diff_{i}_0", f"lora_diff_{i}_1", f"lora_diff_{i}_2"))
-            else:
-                self.register_buffer(f"lora_diff_{i}_0", diff[0].to(device, self.compute_dtype))
-                self.lora_diffs.append(f"lora_diff_{i}_0")
-
-    def _get_weight_with_lora(self, weight):
-        """Apply LoRA outside compiled region"""
-        if not hasattr(self, "lora_diff_0_0"):
-            return weight
-        
-        for lora_diff_names, lora_strength in zip(self.lora_diffs, self.lora_strengths):
-            if isinstance(lora_strength, list):
-                lora_strength = lora_strength[self.step]
-                if lora_strength == 0.0:
-                    continue
-            elif lora_strength == 0.0:
-                continue
-            if isinstance(lora_diff_names, tuple):
-                lora_diff_0 = getattr(self, lora_diff_names[0])
-                lora_diff_1 = getattr(self, lora_diff_names[1])
-                lora_diff_2 = getattr(self, lora_diff_names[2])
-                patch_diff = torch.mm(
-                    lora_diff_0.flatten(start_dim=1),
-                    lora_diff_1.flatten(start_dim=1)
-                ).reshape(weight.shape) + 0
-                alpha = lora_diff_2 / lora_diff_1.shape[0] if lora_diff_2 is not None else 1.0
-                scale = lora_strength * alpha
-                weight = weight.add(patch_diff, alpha=scale)
-            else:
-                lora_diff = getattr(self, lora_diff_names)
-                weight = weight.add(lora_diff, alpha=lora_strength)
-        return weight

wanvideo/modules/attention.py

@@ -18,14 +18,21 @@ except Exception as e:
 # Sage Attention imports
 try:
     from sageattention import sageattn
-    @torch.compiler.disable()
+    
-    def sageattn_func(q, k, v, attn_mask=None, dropout_p=0, is_causal=False, tensor_layout="HND"):
+    @torch.library.custom_op("wanvideo::sageattn", mutates_args=())
+    def sageattn_func(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, attn_mask: torch.Tensor | None = None, dropout_p: float = 0.0, is_causal: bool = False, tensor_layout: str = "HND"
+    ) -> torch.Tensor:
         if not (q.dtype == k.dtype == v.dtype):
             return sageattn(q, k.to(q.dtype), v.to(q.dtype), attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, tensor_layout=tensor_layout)
         elif q.dtype == torch.float32:
             return sageattn(q.to(torch.float16), k.to(torch.float16), v.to(torch.float16), attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, tensor_layout=tensor_layout).to(torch.float32)
         else:
             return sageattn(q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal, tensor_layout=tensor_layout)
+    
+    @sageattn_func.register_fake
+    def _(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False, tensor_layout="HND"):
+        # Return tensor with same shape as q
+        return q.clone()
 
     def sageattn_func_compiled(q, k, v, attn_mask=None, dropout_p=0, is_causal=False, tensor_layout="HND"):
         if not (q.dtype == k.dtype == v.dtype):
@@ -42,18 +49,12 @@ except Exception as e:
         log.warning("sageattention DLL loading error, sageattention will not be available")
     sageattn_func = None
 
-try:
-    from sageattn3 import sageattn3_blackwell as sageattn_blackwell
-except:
-    try:
-        from sageattn import sageattn_blackwell
-    except:
-        SAGE3_AVAILABLE = False
-
 try: 
     from sageattention import sageattn_varlen
-    @torch.compiler.disable()
+    
-    def sageattn_varlen_func(q, k, v, q_lens, k_lens, max_seqlen_q, max_seqlen_k, dropout_p=0, is_causal=False):
+    @torch.library.custom_op("wanvideo::sageattn_varlen", mutates_args=())
+    def sageattn_varlen_func(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_lens: list, k_lens: list, max_seqlen_q: int, max_seqlen_k: int, dropout_p: float = 0.0, is_causal: bool = False
+    ) -> torch.Tensor:
         cu_seqlens_q = torch.tensor([0] + list(torch.cumsum(torch.tensor(q_lens), dim=0)), device=q.device, dtype=torch.int32)
         cu_seqlens_k = torch.tensor([0] + list(torch.cumsum(torch.tensor(k_lens), dim=0)), device=q.device, dtype=torch.int32)
         if not (q.dtype == k.dtype == v.dtype):
@@ -62,9 +63,23 @@ try:
             return sageattn_varlen(q.to(torch.float16), k.to(torch.float16), v.to(torch.float16), cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p=dropout_p, is_causal=is_causal).to(torch.float32)
         else:
             return sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p=dropout_p, is_causal=is_causal)
+    
+    @sageattn_varlen_func.register_fake
+    def _(q, k, v, q_lens, k_lens, max_seqlen_q, max_seqlen_k, dropout_p=0.0, is_causal=False):
+        # Return tensor with same shape as q
+        return q.clone()
 except: 
     sageattn_varlen_func = None
 
+try:
+    from sageattn3 import sageattn3_blackwell as sageattn_blackwell
+except:
+    try:
+        from sageattn import sageattn_blackwell
+    except:
+        SAGE3_AVAILABLE = False
+
+
 __all__ = [
     'flash_attention',
     'attention',
@@ -228,7 +243,7 @@ def attention(
             per_block_mean=False #seems necessary for reasonable VRAM usage, not sure of other implications
             ).transpose(1,2).contiguous()
     elif attention_mode == 'sageattn_varlen':
-        return sageattn_varlen_func(
+        return torch.ops.wanvideo.sageattn_varlen(
                 q,k,v,
                 q_lens=q_lens,
                 k_lens=k_lens,
@@ -238,4 +253,4 @@ def attention(
     elif attention_mode == 'sageattn_compiled':
         return sageattn_func_compiled(q, k, v, tensor_layout="NHD").contiguous()
     else:
-        return sageattn_func(q, k, v, tensor_layout="NHD").contiguous()
+        return torch.ops.wanvideo.sageattn(q, k, v, tensor_layout="NHD").contiguous()