[diffusion] parallel: pad tokens for video models under sp

python/sglang/multimodal_gen/configs/pipeline_configs/base.py

@@ -102,6 +102,14 @@ def shard_rotary_emb_for_sp(emb):
         return emb
 
 
+def maybe_unpad_latents(latents, batch):
+    # If SP padding was applied, remove extra tokens before reshaping
+    target_tokens = batch.raw_latent_shape[-1] * batch.raw_latent_shape[-2]
+    if latents.shape[1] > target_tokens:
+        latents = latents[:, :target_tokens, :]
+    return latents
+
+
 # config for a single pipeline
 @dataclass
 class PipelineConfig:
@@ -310,6 +318,7 @@ def get_neg_prompt_embeds(self, batch):
         return batch.negative_prompt_embeds
 
     def post_denoising_loop(self, latents, batch):
+        latents = maybe_unpad_latents(latents, batch)
         return latents
 
     def prepare_pos_cond_kwargs(self, batch, device, rotary_emb, dtype):
@@ -655,10 +664,7 @@ def _unpad_and_unpack_latents(self, latents, batch):
         height = 2 * (int(batch.height) // (vae_scale_factor * 2))
         width = 2 * (int(batch.width) // (vae_scale_factor * 2))
 
-        # If SP padding was applied, remove extra tokens before reshaping
-        target_tokens = (height // 2) * (width // 2)
-        if latents.shape[1] > target_tokens:
-            latents = latents[:, :target_tokens, :]
+        latents = maybe_unpad_latents(latents, batch)
 
         latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
         latents = latents.permute(0, 3, 1, 4, 2, 5)

python/sglang/multimodal_gen/configs/sample/sampling_params.py

@@ -228,7 +228,7 @@ def _adjust(
             logger.debug(f"Setting num_frames to 1 because this is an image-gen model")
             self.num_frames = 1
             self.data_type = DataType.IMAGE
-        else:
+        elif self.adjust_frames:
             # NOTE: We must apply adjust_num_frames BEFORE the SP alignment logic below.
             # If we apply it after, adjust_num_frames might modify the frame count
             # and break the divisibility constraint (alignment) required by num_gpus.
@@ -536,8 +536,8 @@ def add_cli_args(parser: Any) -> Any:
             default=SamplingParams.adjust_frames,
             help=(
                 "Enable/disable adjusting num_frames to evenly split latent frames across GPUs "
-                "and satisfy model temporal constraints. Default: true. "
-                "Examples: --adjust-frames, --adjust-frames true, --adjust-frames false."
+                "and satisfy model temporal constraints. If disabled, tokens might be padded for SP."
+                "Default: true. Examples: --adjust-frames, --adjust-frames true, --adjust-frames false."
             ),
         )
         return parser

python/sglang/multimodal_gen/runtime/layers/usp.py

@@ -51,10 +51,10 @@ def _usp_input_all_to_all(x: torch.Tensor, head_dim: int = 1) -> torch.Tensor:
     Perform Ulysses-style input all-to-all over the head dimension.
 
     Default layout expects heads at dim=1 and sequence at dim=2:
-        [b, h, s_local, d] -> [b, h // world_size, s_global, d]
+        [b, h, s_local, d] -> [b, h_local, s_global, d]
 
     If heads are at dim=2 (input is [b, s_local, h, d]), set head_dim=2, and the
-    function returns [b, s_global, h // world_size, d], preserving the original
+    function returns [b, s_global, h+local, d], preserving the original
     head/sequence dim ordering.
 
     Args:
@@ -83,11 +83,11 @@ def _usp_input_all_to_all(x: torch.Tensor, head_dim: int = 1) -> torch.Tensor:
         h % world_size == 0
     ), f"h ({h}) must be divisible by world_size ({world_size})"
 
-    # [b, h, s, d] -> [h, b, s, d]
+    # [b, h, s_local, d] -> [h, b, s_local, d]
     x_c = x_c.permute(1, 0, 2, 3).contiguous()
     # all-to-all along h
     x_c = _usp_all_to_all_single(x_c)
-    # -> [b, h // world, s * world, d]
+    # -> [b, h_local, s, d]
     x_c = (
         x_c.reshape(world_size, h // world_size, b, -1, d)
         .permute(2, 1, 0, 3, 4)
@@ -109,7 +109,7 @@ def _usp_output_all_to_all(x: torch.Tensor, head_dim: int = 1) -> torch.Tensor:
     Perform Ulysses-style output all-to-all over the head dimension (inverse of input).
 
     Default layout expects heads at dim=1 and sequence at dim=2:
-        [b, h // world_size, s_global, d] -> [b, h, s_local, d]
+        [b, h_local, s, d] -> [b, h, s_local, d]
 
     If heads are at dim=2 (input is [b, s_global, h // world_size, d]), set head_dim=2,
     and the function returns [b, s_local, h, d], preserving the original head/sequence
@@ -141,10 +141,10 @@ def _usp_output_all_to_all(x: torch.Tensor, head_dim: int = 1) -> torch.Tensor:
         s % world_size == 0
     ), f"s ({s}) must be divisible by world_size ({world_size})"
 
-    # [b, h, s, d] -> [s, b, h, d]
+    # [b, h_local, s, d] -> [s, b, h_local, d]
     x_c = x_c.permute(2, 0, 1, 3).contiguous()
     x_c = _usp_all_to_all_single(x_c)
-    # -> [b, h * world, s // world, d]
+    # -> [b, h, s_local, d]
     x_c = (
         x_c.reshape(world_size, s // world_size, b, -1, d)
         .permute(2, 0, 3, 1, 4)