Fix offload bug in new dist infer

Fix offload bug in new dist infer

lightx2v/models/networks/wan/infer/audio/post_wan_audio_infer.py

@@ -18,7 +18,7 @@ class WanAudioPostInfer(WanPostInfer):
         self.scheduler = scheduler
 
     @torch.compile(disable=not CHECK_ENABLE_GRAPH_MODE())
-    def infer(self, weights, x, pre_infer_out):
+    def infer(self, x, pre_infer_out):
         x = x[:, : pre_infer_out.valid_patch_length]
         x = self.unpatchify(x, pre_infer_out.grid_sizes)
 

lightx2v/models/networks/wan/infer/post_infer.py

@@ -15,7 +15,7 @@ class WanPostInfer:
         self.scheduler = scheduler
 
     @torch.compile(disable=not CHECK_ENABLE_GRAPH_MODE())
-    def infer(self, weights, x, pre_infer_out):
+    def infer(self, x, pre_infer_out):
         x = self.unpatchify(x, pre_infer_out.grid_sizes)
 
         if self.clean_cuda_cache:

lightx2v/models/networks/wan/infer/transformer_infer.py

@@ -39,8 +39,8 @@ class WanTransformerInfer(BaseTransformerInfer):
             self.seq_p_group = None
 
         if self.config.get("cpu_offload", False):
-            if torch.cuda.get_device_capability(0) == (9, 0):
-                assert self.config["self_attn_1_type"] != "sage_attn2"
+            # if torch.cuda.get_device_capability(0) == (9, 0):
+            #     assert self.config["self_attn_1_type"] != "sage_attn2"
             if "offload_ratio" in self.config:
                 offload_ratio = self.config["offload_ratio"]
             else:

lightx2v/models/networks/wan/model.py

@@ -225,12 +225,10 @@ class WanModel:
 
         # Initialize weight containers
         self.pre_weight = self.pre_weight_class(self.config)
-        self.post_weight = self.post_weight_class(self.config)
         self.transformer_weights = self.transformer_weight_class(self.config)
 
         # Load weights into containers
         self.pre_weight.load(self.original_weight_dict)
-        self.post_weight.load(self.original_weight_dict)
         self.transformer_weights.load(self.original_weight_dict)
 
     def _load_weights_distribute(self, weight_dict, is_weight_loader):
@@ -303,12 +301,10 @@ class WanModel:
 
     def to_cpu(self):
         self.pre_weight.to_cpu()
-        self.post_weight.to_cpu()
         self.transformer_weights.to_cpu()
 
     def to_cuda(self):
         self.pre_weight.to_cuda()
-        self.post_weight.to_cuda()
         self.transformer_weights.to_cuda()
 
     @torch.no_grad()
@@ -318,7 +314,7 @@ class WanModel:
                 self.to_cuda()
             elif self.offload_granularity != "model":
                 self.pre_weight.to_cuda()
-                self.post_weight.to_cuda()
+                self.transformer_weights.post_weights_to_cuda()
 
         if self.transformer_infer.mask_map is None:
             _, c, h, w = self.scheduler.latents.shape
@@ -356,7 +352,7 @@ class WanModel:
                 self.to_cpu()
             elif self.offload_granularity != "model":
                 self.pre_weight.to_cpu()
-                self.post_weight.to_cpu()
+                self.transformer_weights.post_weights_to_cpu()
 
     @torch.no_grad()
     def _infer_cond_uncond(self, inputs, positive=True):
@@ -370,7 +366,7 @@ class WanModel:
         if self.config["seq_parallel"]:
             x = self._seq_parallel_post_process(x)
 
-        noise_pred = self.post_infer.infer(self.post_weight, x, pre_infer_out)[0]
+        noise_pred = self.post_infer.infer(x, pre_infer_out)[0]
 
         if self.clean_cuda_cache:
             del x, pre_infer_out

lightx2v/models/networks/wan/weights/transformer_weights.py

@@ -36,6 +36,16 @@ class WanTransformerWeights(WeightModule):
             for phase in block.compute_phases:
                 phase.clear()
 
+    def post_weights_to_cuda(self):
+        self.norm.to_cuda()
+        self.head.to_cuda()
+        self.head_modulation.to_cuda()
+
+    def post_weights_to_cpu(self):
+        self.norm.to_cpu()
+        self.head.to_cpu()
+        self.head_modulation.to_cpu()
+
 
 class WanTransformerAttentionBlock(WeightModule):
     def __init__(self, block_index, task, mm_type, config):