[Perf] Wan2.2 I2V optimization: convert datatype from FP32 to BF16 in vae (#2391)

Signed-off-by: KexiongYu <yukexiong1@huawei.com>
Signed-off-by: Canlin Guo <961750412@qq.com>
Co-authored-by: Canlin Guo <961750412@qq.com>

vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from contextlib import nullcontext
 from typing import Any
 
 import torch
@@ -15,11 +16,38 @@ from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor impor
     GridSpec,
     TileTask,
 )
+from vllm_omni.platforms import current_omni_platform
 
 logger = init_logger(__name__)
 
 
-class DistributedAutoencoderKLWan(AutoencoderKLWan, DistributedVaeMixin):
+class OmniAutoencoderKLWan(AutoencoderKLWan):
+    def _execution_context(self):
+        try:
+            first_param = next(self.parameters())
+        except StopIteration:
+            return nullcontext()
+
+        dtype = first_param.dtype
+        if dtype not in (torch.float16, torch.bfloat16):
+            return nullcontext()
+
+        return current_omni_platform.create_autocast_context(
+            device_type=first_param.device.type,
+            dtype=dtype,
+            enabled=True,
+        )
+
+    def encode(self, x: torch.Tensor, return_dict: bool = True):
+        with self._execution_context():
+            return super().encode(x, return_dict=return_dict)
+
+    def decode(self, z: torch.Tensor, return_dict: bool = True):
+        with self._execution_context():
+            return super().decode(z, return_dict=return_dict)
+
+
+class DistributedAutoencoderKLWan(OmniAutoencoderKLWan, DistributedVaeMixin):
     @classmethod
     def from_pretrained(cls, *args: Any, **kwargs: Any):
         model = super().from_pretrained(*args, **kwargs)
@@ -84,11 +112,12 @@ class DistributedAutoencoderKLWan(AutoencoderKLWan, DistributedVaeMixin):
         """Decode a single latent tile into RGB space."""
         self.clear_cache()
         time = []
-        for k in range(len(task.tensor)):
-            self._conv_idx = [0]
-            tile = self.post_quant_conv(task.tensor[k])
-            decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=(k == 0))
-            time.append(decoded)
+        with self._execution_context():
+            for k in range(len(task.tensor)):
+                self._conv_idx = [0]
+                tile = self.post_quant_conv(task.tensor[k])
+                decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=(k == 0))
+                time.append(decoded)
         result = torch.cat(time, dim=2)
         return result