Support cpu offload for hunyuan

lightx2v/text2v/models/networks/hunyuan/infer/transformer_infer.py

@@ -28,17 +28,13 @@ class HunyuanTransformerInfer:
     def infer(self, weights, img, txt, vec, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis):
         return self.infer_func(weights, img, txt, vec, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis)
 
-    def _infer_with_offload(
-        self, weights, img, txt, vec, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis
-    ):
+    def _infer_with_offload(self, weights, img, txt, vec, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis):
         txt_seq_len = txt.shape[0]
         img_seq_len = img.shape[0]
 
         for double_block_idx in range(self.double_blocks_num):
             if double_block_idx == 0:
-                self.double_weights_stream_mgr.active_weights[
-                    0
-                ] = weights.double_blocks_weights[0]
+                self.double_weights_stream_mgr.active_weights[0] = weights.double_blocks_weights[0]
                 self.double_weights_stream_mgr.active_weights[0].to_cuda()
 
             with torch.cuda.stream(self.double_weights_stream_mgr.compute_stream):
@@ -53,18 +49,14 @@ class HunyuanTransformerInfer:
                 )
 
             if double_block_idx < self.double_blocks_num - 1:
-                self.double_weights_stream_mgr.prefetch_weights(
-                    double_block_idx + 1, weights.double_blocks_weights
-                )
+                self.double_weights_stream_mgr.prefetch_weights(double_block_idx + 1, weights.double_blocks_weights)
             self.double_weights_stream_mgr.swap_weights()
 
         x = torch.cat((img, txt), 0)
 
         for single_block_idx in range(self.single_blocks_num):
             if single_block_idx == 0:
-                self.single_weights_stream_mgr.active_weights[
-                    0
-                ] = weights.single_blocks_weights[0]
+                self.single_weights_stream_mgr.active_weights[0] = weights.single_blocks_weights[0]
                 self.single_weights_stream_mgr.active_weights[0].to_cuda()
             with torch.cuda.stream(self.single_weights_stream_mgr.compute_stream):
                 x = self.infer_single_block(
@@ -77,9 +69,7 @@ class HunyuanTransformerInfer:
                     freqs_cis,
                 )
             if single_block_idx < self.single_blocks_num - 1:
-                self.single_weights_stream_mgr.prefetch_weights(
-                    single_block_idx + 1, weights.single_blocks_weights
-                )
+                self.single_weights_stream_mgr.prefetch_weights(single_block_idx + 1, weights.single_blocks_weights)
             self.single_weights_stream_mgr.swap_weights()
 
         img = x[:img_seq_len, ...]
@@ -116,9 +106,7 @@ class HunyuanTransformerInfer:
         img = x[:img_seq_len, ...]
         return img, vec
 
-    def infer_double_block(
-        self, weights, img, txt, vec, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis
-    ):
+    def infer_double_block(self, weights, img, txt, vec, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis):
         vec_silu = torch.nn.functional.silu(vec)
 
         img_mod_out = weights.img_mod.apply(vec_silu)
@@ -141,12 +129,8 @@ class HunyuanTransformerInfer:
             txt_mod2_gate,
         ) = txt_mod_out.chunk(6, dim=-1)
 
-        img_q, img_k, img_v = self.infer_double_block_img_pre_atten(
-            weights, img, img_mod1_scale, img_mod1_shift, freqs_cis
-        )
-        txt_q, txt_k, txt_v = self.infer_double_block_txt_pre_atten(
-            weights, txt, txt_mod1_scale, txt_mod1_shift
-        )
+        img_q, img_k, img_v = self.infer_double_block_img_pre_atten(weights, img, img_mod1_scale, img_mod1_shift, freqs_cis)
+        txt_q, txt_k, txt_v = self.infer_double_block_txt_pre_atten(weights, txt, txt_mod1_scale, txt_mod1_shift)
 
         q = torch.cat((img_q, txt_q), dim=0)
         k = torch.cat((img_k, txt_k), dim=0)
@@ -197,18 +181,12 @@ class HunyuanTransformerInfer:
         )
         return img, txt
 
-    def infer_double_block_img_pre_atten(
-        self, weights, img, img_mod1_scale, img_mod1_shift, freqs_cis
-    ):
-        img_modulated = torch.nn.functional.layer_norm(
-            img, (img.shape[1],), None, None, 1e-6
-        )
+    def infer_double_block_img_pre_atten(self, weights, img, img_mod1_scale, img_mod1_shift, freqs_cis):
+        img_modulated = torch.nn.functional.layer_norm(img, (img.shape[1],), None, None, 1e-6)
         img_modulated = img_modulated * (1 + img_mod1_scale) + img_mod1_shift
         img_qkv = weights.img_attn_qkv.apply(img_modulated)
 
-        img_q, img_k, img_v = rearrange(
-            img_qkv, "L (K H D) -> K L H D", K=3, H=self.heads_num
-        )
+        img_q, img_k, img_v = rearrange(img_qkv, "L (K H D) -> K L H D", K=3, H=self.heads_num)
 
         img_q = weights.img_attn_q_norm.apply(img_q)
         img_k = weights.img_attn_k_norm.apply(img_k)
@@ -216,18 +194,12 @@ class HunyuanTransformerInfer:
         img_q, img_k = apply_rotary_emb(img_q, img_k, freqs_cis)
         return img_q, img_k, img_v
 
-    def infer_double_block_txt_pre_atten(
-        self, weights, txt, txt_mod1_scale, txt_mod1_shift
-    ):
-        txt_modulated = torch.nn.functional.layer_norm(
-            txt, (txt.shape[1],), None, None, 1e-6
-        )
+    def infer_double_block_txt_pre_atten(self, weights, txt, txt_mod1_scale, txt_mod1_shift):
+        txt_modulated = torch.nn.functional.layer_norm(txt, (txt.shape[1],), None, None, 1e-6)
         txt_modulated = txt_modulated * (1 + txt_mod1_scale) + txt_mod1_shift
         txt_qkv = weights.txt_attn_qkv.apply(txt_modulated)
 
-        txt_q, txt_k, txt_v = rearrange(
-            txt_qkv, "L (K H D) -> K L H D", K=3, H=self.heads_num
-        )
+        txt_q, txt_k, txt_v = rearrange(txt_qkv, "L (K H D) -> K L H D", K=3, H=self.heads_num)
 
         txt_q = weights.txt_attn_q_norm.apply(txt_q)
         txt_k = weights.txt_attn_k_norm.apply(txt_k)
@@ -279,9 +251,7 @@ class HunyuanTransformerInfer:
         txt = txt + out
         return txt
 
-    def infer_single_block(
-        self, weights, x, vec, txt_seq_len, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis
-    ):
+    def infer_single_block(self, weights, x, vec, txt_seq_len, cu_seqlens_qkv, max_seqlen_qkv, freqs_cis):
         out = torch.nn.functional.silu(vec)
         out = weights.modulation.apply(out)
         mod_shift, mod_scale, mod_gate = out.chunk(3, dim=-1)
@@ -291,9 +261,7 @@ class HunyuanTransformerInfer:
 
         x_mod = weights.linear1.apply(x_mod)
 
-        qkv, mlp = torch.split(
-            x_mod, [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
-        )
+        qkv, mlp = torch.split(x_mod, [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = rearrange(qkv, "L (K H D) -> K L H D", K=3, H=self.heads_num)
 

lightx2v/text2v/models/networks/hunyuan/model.py

@@ -101,4 +101,4 @@ class HunyuanModel:
         self.scheduler.noise_pred = self.post_infer.infer(self.post_weight, img, vec, self.scheduler.latents.shape)
         if self.config["cpu_offload"]:
             self.pre_weight.to_cpu()
-            self.post_weight.to_cpu()
+            self.post_weight.to_cpu()
\ No newline at end of file

lightx2v/text2v/models/networks/hunyuan/weights/transformer_weights.py

@@ -136,7 +136,6 @@ class HunyuanTransformerSingleBlock:
                 mm_weight.set_config(self.config["mm_config"])
                 mm_weight.load(weight_dict)
 
-
     def to_cpu(self):
         for mm_weight in self.weight_list:
             if isinstance(mm_weight, (MMWeightTemplate, RMSWeightTemplate)):