Add support for FluxControlLoRALayer in CustomLinear layers and add a unit test for it.

invokeai/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/custom_linear.py

@@ -4,17 +4,80 @@ from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.cast_
 from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.custom_modules.custom_module_mixin import (
     CustomModuleMixin,
 )
-from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.custom_modules.utils import (
-    add_nullable_tensors,
-)
+from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
+from invokeai.backend.patches.layers.concatenated_lora_layer import ConcatenatedLoRALayer
+from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
+from invokeai.backend.patches.layers.lora_layer import LoRALayer
+
+
+def linear_lora_forward(input: torch.Tensor, lora_layer: LoRALayer, lora_weight: float) -> torch.Tensor:
+    """An optimized implementation of the residual calculation for a sidecar linear LoRALayer."""
+    x = torch.nn.functional.linear(input, lora_layer.down)
+    if lora_layer.mid is not None:
+        x = torch.nn.functional.linear(x, lora_layer.mid)
+    x = torch.nn.functional.linear(x, lora_layer.up, bias=lora_layer.bias)
+    x *= lora_weight * lora_layer.scale()
+    return x
+
+
+def concatenated_lora_forward(
+    input: torch.Tensor, concatenated_lora_layer: ConcatenatedLoRALayer, lora_weight: float
+) -> torch.Tensor:
+    """An optimized implementation of the residual calculation for a sidecar ConcatenatedLoRALayer."""
+    x_chunks: list[torch.Tensor] = []
+    for lora_layer in concatenated_lora_layer.lora_layers:
+        x_chunk = torch.nn.functional.linear(input, lora_layer.down)
+        if lora_layer.mid is not None:
+            x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.mid)
+        x_chunk = torch.nn.functional.linear(x_chunk, lora_layer.up, bias=lora_layer.bias)
+        x_chunk *= lora_weight * lora_layer.scale()
+        x_chunks.append(x_chunk)
+
+    # TODO(ryand): Generalize to support concat_axis != 0.
+    assert concatenated_lora_layer.concat_axis == 0
+    x = torch.cat(x_chunks, dim=-1)
+    return x
+
+
+def autocast_linear_forward_sidecar_patches(
+    orig_module: torch.nn.Linear, input: torch.Tensor, patches_and_weights: list[tuple[BaseLayerPatch, float]]
+) -> torch.Tensor:
+    """A function that runs a linear layer (quantized or non-quantized) with sidecar patches for a linear layer.
+    Compatible with both quantized and non-quantized Linear layers.
+    """
+    # First, apply the original linear layer.
+    # NOTE: We slice the input to match the original weight shape in order to work with FluxControlLoRAs, which
+    # change the linear layer's in_features.
+    orig_input = input
+    input = orig_input[..., : orig_module.in_features]
+    output = orig_module._autocast_forward(input)
+
+    # Then, apply layers for which we have optimized implementations.
+    unprocessed_patches_and_weights: list[tuple[BaseLayerPatch, float]] = []
+    for patch, patch_weight in patches_and_weights:
+        if isinstance(patch, FluxControlLoRALayer):
+            # Note that we use the original input here, not the sliced input.
+            output += linear_lora_forward(orig_input, patch, patch_weight)
+        elif isinstance(patch, LoRALayer):
+            output += linear_lora_forward(input, patch, patch_weight)
+        elif isinstance(patch, ConcatenatedLoRALayer):
+            output += concatenated_lora_forward(input, patch, patch_weight)
+        else:
+            unprocessed_patches_and_weights.append((patch, patch_weight))
+
+    # Finally, apply any remaining patches.
+    if len(unprocessed_patches_and_weights) > 0:
+        aggregated_param_residuals = orig_module._aggregate_patch_parameters(unprocessed_patches_and_weights)
+        output += torch.nn.functional.linear(
+            input, aggregated_param_residuals["weight"], aggregated_param_residuals.get("bias", None)
+        )
+
+    return output
 
 
 class CustomLinear(torch.nn.Linear, CustomModuleMixin):
     def _autocast_forward_with_patches(self, input: torch.Tensor) -> torch.Tensor:
-        aggregated_param_residuals = self._aggregate_patch_parameters(self._patches_and_weights)
-        weight = add_nullable_tensors(self.weight, aggregated_param_residuals["weight"])
-        bias = add_nullable_tensors(self.bias, aggregated_param_residuals.get("bias", None))
-        return torch.nn.functional.linear(input, weight, bias)
+        return autocast_linear_forward_sidecar_patches(self, input, self._patches_and_weights)
 
     def _autocast_forward(self, input: torch.Tensor) -> torch.Tensor:
         weight = cast_to_device(self.weight, input.device)