support cp ,fix qwen3.5 gdn sp

cookbook/transformers/sp_fsdp_dense.py

@@ -19,9 +19,10 @@
     device_type=Platform.get_platform().device_prefix(),
 )]
 
-# FSDP + SP validation over 4 GPUs: dp=2, fsdp=2 (SP only affects input slicing)
+# FSDP + sequence-parallel validation over 4 GPUs: dp=2, fsdp=2.
+# In Transformers route, ulysses_size is the total sequence-parallel degree.
 device_mesh = DeviceMesh(
-    device_type='cuda',
+    device_type=Platform.get_platform().device_prefix(),
     mesh=np.arange(4).reshape(2, 2),
     mesh_dim_names=('dp', 'fsdp'),
     ulysses_size=2,

cookbook/transformers/sp_fsdp_dense.sh

@@ -1,5 +1,6 @@
 #!/bin/bash
-# To enabele sequence parallelism, please set ulysses_size > 1
+# To enable Transformers sequence parallelism, please set ulysses_size > 1.
+# ulysses_size is interpreted as the total sequence-parallel degree.
 # device_mesh = DeviceMesh(
 #     device_type="cuda",
 #     mesh=np.arange(4).reshape(2, 2),

src/twinkle/metric/loss.py

@@ -25,13 +25,19 @@ def accumulate(self, inputs: Union[InputFeature, List[InputFeature]], outputs: M
             return
         loss = outputs['loss']
         loss_reduction = kwargs.get('loss_reduction', 'mean')
+        ulysses_size = getattr(self.device_mesh, 'ulysses_size', None) or 1
         if loss_reduction == 'sum':
             if not isinstance(inputs, list):
                 inputs = [inputs]
             for input in inputs:
                 # `Transformers` models may use reduction=sum, to average grads before step
                 labels = input['labels']
                 self.num_tokens += (labels >= 0).sum().item()
+            # Sequence-parallel gathered loss is replicated on each ulysses rank, while
+            # local labels still count only the shard-local tokens. Normalize the loss
+            # contribution here so metric-side averaging matches the non-SP path.
+            if ulysses_size > 1:
+                loss = loss / float(ulysses_size)
         grad_norm = kwargs.get('grad_norm')
         if grad_norm is not None:
             self.grad_norm = grad_norm