diff --git a/tests/models/test_transformers_ulysses.py b/tests/models/test_transformers_ulysses.py
index c3e6eef9021..a3c7f579b0f 100644
--- a/tests/models/test_transformers_ulysses.py
+++ b/tests/models/test_transformers_ulysses.py
@@ -29,12 +29,12 @@
 from verl.utils.distributed import initialize_global_process_group
 from verl.utils.model import compute_position_id_with_mask, create_random_mask
 from verl.utils.ulysses import (
-    FSDPUlyssesShardingManager,
     gather_outputs_and_unpad,
     get_ulysses_sequence_parallel_world_size,
     set_ulysses_sequence_parallel_group,
     ulysses_pad_and_slice_inputs,
 )
+from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager
 
 if get_device_name() == "cuda":
     from flash_attn.bert_padding import index_first_axis, rearrange, unpad_input
@@ -50,6 +50,7 @@ class SequenceParallelConfig:
     config: PretrainedConfig
     sp_size: int
     is_valid: bool
+    attn_implementation: str = "flash_attention_2"
 
 
 def test_configs():
@@ -86,6 +87,20 @@ def test_configs():
             )
         )
 
+    try:
+        from transformers import GlmMoeDsaConfig
+
+        configs.append(
+            SequenceParallelConfig(
+                GlmMoeDsaConfig(num_hidden_layers=2, n_routed_experts=4, vocab_size=1024, index_topk=128),
+                sp_size=4,
+                is_valid=True,
+                attn_implementation="sdpa",
+            )
+        )
+    except ImportError:
+        pass
+
     return configs
 
 
@@ -103,13 +118,18 @@ def test_hf_casual_fwd_bwd(test_config):
     context = contextlib.nullcontext() if test_config.is_valid else pytest.raises(AssertionError)
     with context:
         world_size = torch.distributed.get_world_size()
-        _hf_casual_fwd_bwd(test_config.config, test_config.sp_size, world_size // test_config.sp_size)
+        _hf_casual_fwd_bwd(
+            test_config.config,
+            test_config.sp_size,
+            world_size // test_config.sp_size,
+            attn_implementation=test_config.attn_implementation,
+        )
 
     # TODO: seems not work, will cause `socketStartConnect: Connect to xxx failed : Software caused connection abort`
     # torch.distributed.destroy_process_group()
 
 
-def _hf_casual_fwd(config, sp_size, dp_size):
+def _hf_casual_fwd(config, sp_size, dp_size, attn_implementation="flash_attention_2"):
     assert get_torch_device().device_count() >= 2, "need at least 2 gpus for test"
 
     ulysses_device_mesh = init_device_mesh(
@@ -124,7 +144,7 @@ def _hf_casual_fwd(config, sp_size, dp_size):
     # patch before load
     with torch.device(get_device_name()):
         model = AutoModelForCausalLM.from_config(
-            config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
+            config=config, torch_dtype=torch.bfloat16, attn_implementation=attn_implementation
         )
         apply_monkey_patch(model, sp_size)
         model = model.to(device=get_device_name())
@@ -188,7 +208,7 @@ def _hf_casual_fwd(config, sp_size, dp_size):
     torch.testing.assert_close(mean_local, mean_full, rtol=1e-2, atol=1e-5)
 
 
-def _hf_casual_fwd_bwd(config, sp_size, dp_size):
+def _hf_casual_fwd_bwd(config, sp_size, dp_size, attn_implementation="flash_attention_2"):
     assert get_torch_device().device_count() >= 2, "need at least 2 gpus for test"
 
     ulysses_device_mesh = init_device_mesh(
@@ -203,7 +223,7 @@ def _hf_casual_fwd_bwd(config, sp_size, dp_size):
     # patch before load
     with torch.device(get_device_name()):
         model = AutoModelForCausalLM.from_config(
-            config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
+            config=config, torch_dtype=torch.bfloat16, attn_implementation=attn_implementation
         )
         apply_monkey_patch(model, sp_size)
         model = model.to(device=get_device_name())
@@ -271,13 +291,17 @@ def _hf_casual_fwd_bwd(config, sp_size, dp_size):
     mean_full.backward()
     mean_local.backward()
 
-    # 3. check the gradients
-    grad = model.model.layers[0].self_attn.q_proj.weight.grad
-    grad_full = model_no_sp.model.layers[0].self_attn.q_proj.weight.grad
+    # 3. check the gradients (MLA models use q_a_proj instead of q_proj)
+    sp_attn = model.model.layers[0].self_attn
+    no_sp_attn = model_no_sp.model.layers[0].self_attn
+    q_proj_name = "q_proj" if hasattr(sp_attn, "q_proj") else "q_a_proj"
+    grad = getattr(sp_attn, q_proj_name).weight.grad
+    grad_full = getattr(no_sp_attn, q_proj_name).weight.grad
+
     torch.testing.assert_close(mean_local, mean_full, rtol=1e-2, atol=3e-5)
     # The check should be less strict because the gradient is not an averaged value.
     torch.testing.assert_close(grad, grad_full, rtol=1e-2, atol=1e-3)
 
 
 if __name__ == "__main__":
-    pytest.main([__file__, "-svv"])
+    pytest.main([__file__, "-svv", "-c", "/dev/null"])
diff --git a/verl/models/transformers/glm_moe_dsa.py b/verl/models/transformers/glm_moe_dsa.py
new file mode 100644
index 00000000000..3ddeb468b09
--- /dev/null
+++ b/verl/models/transformers/glm_moe_dsa.py
@@ -0,0 +1,273 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from transformers.cache_utils import Cache
+
+from verl.utils.ulysses import (
+    gather_heads_scatter_seq,
+    gather_seq_scatter_heads,
+    get_ulysses_sequence_parallel_world_size,
+)
+
+
+def _build_causal_mask_from_position_ids(position_ids: torch.Tensor, seq_len: int, dtype: torch.dtype):
+    """Build a 4D causal mask [B, 1, S, S] from position_ids that respects sequence boundaries.
+
+    In packed sequences, position_ids resets at sequence boundaries:
+    e.g., [0, 1, 2, 0, 1, 2, 3, 0, 1] for 3 packed sequences.
+
+    The mask ensures:
+    - Causal: position i can only attend to positions j where j <= i
+    - Cross-sequence isolation: position i can only attend to positions in the same sequence
+    """
+    batch_size = position_ids.shape[0]
+    device = position_ids.device
+
+    boundaries = torch.zeros(batch_size, seq_len, device=device, dtype=torch.long)
+    if seq_len > 1:
+        boundaries[:, 1:] = (position_ids[:, 1:] <= position_ids[:, :-1]).long()
+    segment_ids = boundaries.cumsum(dim=1)  # [B, S]
+
+    same_segment = segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)  # [B, S, S]
+
+    indices = torch.arange(seq_len, device=device)
+    causal = indices.unsqueeze(0) <= indices.unsqueeze(1)  # [S, S], causal[i, j] = (j <= i)
+
+    mask = same_segment & causal.unsqueeze(0)  # [B, S, S]
+
+    float_mask = torch.where(mask, torch.zeros(1, device=device, dtype=dtype), torch.finfo(dtype).min)
+    return float_mask.unsqueeze(1)  # [B, 1, S, S]
+
+
+def glm_moe_dsa_attn_forward_with_dsa(
+    self,
+    hidden_states: torch.Tensor,
+    position_embeddings: tuple[torch.Tensor, torch.Tensor],
+    attention_mask: Optional[torch.Tensor] = None,
+    past_key_values: Optional[Cache] = None,
+    prev_topk_indices: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    **kwargs,
+):
+    """Drop-in replacement for GlmMoeDsaAttention.forward with use_remove_padding + Ulysses SP.
+
+    When attention_mask is None (use_remove_padding mode), generates a causal mask
+    from position_ids that respects packed sequence boundaries.
+    DSA indexer is fully preserved.
+
+    When Ulysses SP > 1, uses all-to-all to gather full sequence / scatter heads
+    for attention, and all-gathers hidden_states for the indexer.
+    """
+    from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+
+    batch_size, seq_length = hidden_states.shape[:-1]
+    cos, sin = position_embeddings
+    ulysses_sp_size = get_ulysses_sequence_parallel_world_size()
+
+    # Always rebuild a float additive mask from position_ids.
+    # The model's create_causal_mask may return a bool mask (incompatible with the DSA indexer
+    # which expects float 0/-inf) or a local-sized mask (wrong when ulysses_sp_size > 1).
+    if position_ids is not None and position_ids.dim() == 1:
+        position_ids = position_ids.unsqueeze(0)
+
+    if ulysses_sp_size > 1 and position_ids is not None:
+        from verl.utils.ulysses import get_ulysses_sequence_parallel_group
+
+        sp_group = get_ulysses_sequence_parallel_group()
+        position_ids_full = _all_gather_seq(position_ids, sp_group, ulysses_sp_size)
+        full_seq_length = seq_length * ulysses_sp_size
+        attention_mask = _build_causal_mask_from_position_ids(position_ids_full, full_seq_length, hidden_states.dtype)
+    elif position_ids is not None:
+        attention_mask = _build_causal_mask_from_position_ids(position_ids, seq_length, hidden_states.dtype)
+
+    # ===== Query path (MLA) =====
+    if self.q_lora_rank is None:
+        query_states = self.q_proj(hidden_states)
+        q_resid = None
+    else:
+        q_resid = self.q_a_layernorm(self.q_a_proj(hidden_states))
+        query_states = self.q_b_proj(q_resid)
+    query_states = query_states.view(batch_size, seq_length, -1, self.qk_head_dim).transpose(1, 2)
+    q_nope, q_pe = torch.split(query_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+    q_pe = _apply_rotary_pos_emb(q_pe, cos, sin, unsqueeze_dim=1)
+
+    # ===== KV path (MLA compressed) =====
+    compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+    k_compressed, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+    k_compressed = self.kv_a_layernorm(k_compressed)
+
+    kv_expanded = self.kv_b_proj(k_compressed)
+    kv_expanded = kv_expanded.view(batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)
+    k_nope, value_states = torch.split(kv_expanded, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+    k_nope = k_nope.transpose(1, 2)
+    value_states = value_states.transpose(1, 2)
+
+    k_pe = k_pe.view(batch_size, 1, seq_length, self.qk_rope_head_dim)
+    k_pe = _apply_rotary_pos_emb(k_pe, cos, sin, unsqueeze_dim=1)
+    k_pe = k_pe.expand(-1, k_nope.shape[1], -1, -1)
+
+    # Assemble full Q and K
+    query_states = torch.cat([q_nope, q_pe], dim=-1)
+    key_states = torch.cat([k_nope, k_pe], dim=-1)
+
+    # Cache update
+    if past_key_values is not None:
+        key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+    # ===== Ulysses SP: all-to-all to gather full seq, scatter heads =====
+    if ulysses_sp_size > 1:
+        # [B, H, S_local, D] -> [B, H/sp, S_full, D]
+        query_states = gather_seq_scatter_heads(query_states, seq_dim=2, head_dim=1)
+        key_states = gather_seq_scatter_heads(key_states, seq_dim=2, head_dim=1)
+        value_states = gather_seq_scatter_heads(value_states, seq_dim=2, head_dim=1)
+        full_seq_length = query_states.shape[2]
+    else:
+        full_seq_length = seq_length
+
+    # ===== Indexer (DSA sparse mask) =====
+    if not self.skip_topk or prev_topk_indices is None:
+        if ulysses_sp_size > 1:
+            # Indexer needs full-sequence hidden_states and q_resid
+            from verl.utils.ulysses import get_ulysses_sequence_parallel_group
+
+            sp_group = get_ulysses_sequence_parallel_group()
+            # All-gather hidden_states along seq dim for indexer
+            hidden_states_full = _all_gather_seq(hidden_states, sp_group, ulysses_sp_size)
+            q_resid_full = _all_gather_seq(q_resid, sp_group, ulysses_sp_size) if q_resid is not None else None
+            # Position embeddings also need full seq
+            cos_full = _all_gather_seq(cos, sp_group, ulysses_sp_size)
+            sin_full = _all_gather_seq(sin, sp_group, ulysses_sp_size)
+            position_embeddings_full = (cos_full, sin_full)
+        else:
+            hidden_states_full = hidden_states
+            q_resid_full = q_resid
+            position_embeddings_full = position_embeddings
+
+        indexer_mask = (
+            attention_mask[:, 0, :, :]
+            if attention_mask is not None and attention_mask.dim() == 4
+            else attention_mask.unsqueeze(1)
+            if attention_mask is not None
+            else None
+        )
+        topk_indices = self.indexer(
+            hidden_states_full,
+            q_resid_full,
+            position_embeddings_full,
+            indexer_mask,
+            use_cache=past_key_values is not None,
+        )
+    else:
+        topk_indices = prev_topk_indices
+
+    # Build combined DSA + causal mask
+    total_len = key_states.shape[2]
+    # topk_indices is [B, S_full, topk]
+    index_mask = torch.full(
+        (batch_size, full_seq_length, total_len),
+        float("-inf"),
+        device=hidden_states.device,
+        dtype=query_states.dtype,
+    )
+    index_mask.scatter_(-1, topk_indices, 0.0)
+    index_mask = index_mask.unsqueeze(1)  # [B, 1, S_full, T]
+    if attention_mask is not None and attention_mask.dim() == 4:
+        causal_mask = attention_mask[..., :total_len]
+        combined_mask = index_mask + causal_mask
+    else:
+        if attention_mask is not None:
+            if attention_mask.dim() == 2:
+                attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            elif attention_mask.dim() == 3:
+                attention_mask = attention_mask.unsqueeze(1)
+            combined_mask = index_mask + attention_mask
+        else:
+            combined_mask = index_mask
+
+    # DSA produces a dense 4D float mask incompatible with flash attention.
+    attn_impl = "sdpa"
+
+    # SDPA requires Q/K/V to have same head_dim; pad V if needed
+    if self.qk_head_dim != self.v_head_dim:
+        value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])
+
+    attention_interface = ALL_ATTENTION_FUNCTIONS.get_interface(attn_impl, _eager_attention_forward)
+
+    attn_output, attn_weights = attention_interface(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        combined_mask,
+        dropout=0.0 if not self.training else self.attention_dropout,
+        scaling=self.scaling,
+        **kwargs,
+    )
+
+    if self.qk_head_dim != self.v_head_dim:
+        attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+    # ===== Ulysses SP: all-to-all back to scatter seq, gather heads =====
+    # attention_interface returns [B, S_full, H/sp, v_head_dim] (already transposed internally)
+    if ulysses_sp_size > 1:
+        attn_output = gather_heads_scatter_seq(attn_output, seq_dim=1, head_dim=2)
+        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
+    else:
+        attn_output = attn_output.reshape(batch_size, full_seq_length, -1).contiguous()
+
+    attn_output = self.o_proj(attn_output)
+
+    topk_for_next = topk_indices if self.next_skip_topk else None
+    return attn_output, attn_weights, topk_for_next
+
+
+def _all_gather_seq(tensor: torch.Tensor, group, sp_size: int) -> torch.Tensor:
+    """All-gather tensor along sequence dimension (dim=1)."""
+    if sp_size <= 1:
+        return tensor
+    gathered = [torch.empty_like(tensor) for _ in range(sp_size)]
+    torch.distributed.all_gather(gathered, tensor.contiguous(), group=group)
+    return torch.cat(gathered, dim=1)
+
+
+def _rotate_half(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def _apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    return (x * cos) + (_rotate_half(x) * sin)
+
+
+def _is_flash_attention_requested(config):
+    try:
+        from transformers.utils.generic import is_flash_attention_requested
+
+        return is_flash_attention_requested(config)
+    except ImportError:
+        return getattr(config, "_attn_implementation", None) in ("flash_attention_2", "flash_attention_3")
+
+
+def _eager_attention_forward(module, query, key, value, attention_mask, **kwargs):
+    """Fallback eager attention (imported at runtime to avoid circular deps)."""
+    from transformers.models.glm_moe_dsa.modeling_glm_moe_dsa import eager_attention_forward
+
+    return eager_attention_forward(module, query, key, value, attention_mask, **kwargs)
diff --git a/verl/models/transformers/monkey_patch.py b/verl/models/transformers/monkey_patch.py
index 1d9e8ce0587..58eddd37c83 100644
--- a/verl/models/transformers/monkey_patch.py
+++ b/verl/models/transformers/monkey_patch.py
@@ -474,6 +474,18 @@ def state_dict(self, *args, **kwargs):
         if ulysses_sp_size > 1:
             patch_vlm_for_ulysses_input_slicing(Glm4vTextModel)
 
+    elif model.config.model_type == "glm_moe_dsa":
+        if use_fused_kernels:
+            print("Fused kernels are not supported for glm_moe_dsa. Disabling use_fused_kernels.")
+            use_fused_kernels = False
+        if use_remove_padding or ulysses_sp_size > 1:
+            from transformers.models.glm_moe_dsa.modeling_glm_moe_dsa import GlmMoeDsaAttention
+
+            from verl.models.transformers.glm_moe_dsa import glm_moe_dsa_attn_forward_with_dsa
+
+            GlmMoeDsaAttention.forward = glm_moe_dsa_attn_forward_with_dsa
+            print(f"Monkey patch {model.__class__.__name__} attention layer with DSA")
+
     elif model.config.model_type == "kimi_vl":
         if use_remove_padding or ulysses_sp_size > 1:
             # TODO: Changes need to be made when transformers are adapted.
diff --git a/verl/utils/flops_counter.py b/verl/utils/flops_counter.py
index 57fa62030e8..f980c795958 100644
--- a/verl/utils/flops_counter.py
+++ b/verl/utils/flops_counter.py
@@ -535,6 +535,82 @@ def _estimate_gpt_oss_flops(config, tokens_sum, batch_seqlens, delta_time):
     return flops_achieved
 
 
+def _estimate_glm_moe_dsa_flops(config, tokens_sum, batch_seqlens, delta_time):
+    hidden_size = config.hidden_size
+    vocab_size = config.vocab_size
+    moe_intermediate_size = config.moe_intermediate_size
+    num_hidden_layers = config.num_hidden_layers
+    num_experts = config.n_routed_experts
+
+    moe_topk = config.num_experts_per_tok
+    share_expert_num = config.n_shared_experts
+
+    num_attention_heads = config.num_attention_heads
+    q_lora_rank = config.q_lora_rank
+    kv_lora_rank = config.kv_lora_rank
+    qk_head_dim = config.qk_head_dim
+    qk_rope_head_dim = config.qk_rope_head_dim
+    qk_nope_head_dim = config.qk_nope_head_dim
+    v_head_dim = config.v_head_dim
+
+    # DSA indexer config
+    index_head_dim = config.index_head_dim
+    index_n_heads = config.index_n_heads
+    index_topk = config.index_topk
+
+    # MLA attention linear params per layer:
+    # q_a_proj + q_b_proj + kv_a_proj + kv_b_proj + o_proj
+    # When q_lora_rank is None, q_proj directly maps hidden_size -> num_attention_heads * qk_head_dim
+    if q_lora_rank is None:
+        q_proj_N = hidden_size * (num_attention_heads * qk_head_dim)
+        indexer_q_N = hidden_size * (index_n_heads * index_head_dim)
+    else:
+        q_proj_N = hidden_size * q_lora_rank + q_lora_rank * (num_attention_heads * qk_head_dim)
+        indexer_q_N = q_lora_rank * (index_n_heads * index_head_dim)
+
+    attn_linear_N = (
+        q_proj_N
+        + hidden_size * (kv_lora_rank + qk_rope_head_dim)
+        + kv_lora_rank * (num_attention_heads * (qk_nope_head_dim + v_head_dim))
+        + (num_attention_heads * v_head_dim) * hidden_size
+    )
+
+    # DSA indexer linear params per layer:
+    # wq_b: q_lora_rank -> index_n_heads * index_head_dim
+    # wk: kv_lora_rank -> index_n_heads * index_head_dim
+    # weights_proj: index_n_heads -> num_attention_heads
+    indexer_linear_N = (
+        indexer_q_N + kv_lora_rank * (index_n_heads * index_head_dim) + index_n_heads * num_attention_heads
+    )
+
+    moe_gata_N = hidden_size * num_experts
+    moe_expertmlp_N = hidden_size * moe_intermediate_size * (moe_topk + share_expert_num) * 3
+    moe_mlp_N = moe_gata_N + moe_expertmlp_N
+
+    emd_and_lm_head_N = vocab_size * hidden_size * 2
+
+    dense_N = (moe_mlp_N + attn_linear_N + indexer_linear_N) * num_hidden_layers + emd_and_lm_head_N
+
+    dense_N_flops = 6 * dense_N * tokens_sum
+
+    # DSA attention FLOPs per layer:
+    # Stage 1 (indexer): Q_idx × K_idx^T over full seq -> seq × seq × index_head_dim × index_n_heads
+    # Stage 2 (sparse MLA): Q × K^T and attn × V only over topk tokens
+    #   -> seq × topk × (qk_head_dim + v_head_dim) × num_attention_heads
+    indexer_qk_flops = 0
+    sparse_attn_flops = 0
+    for seqlen in batch_seqlens:
+        effective_topk = min(index_topk, seqlen)
+        indexer_qk_flops += seqlen * seqlen * index_head_dim * index_n_heads
+        sparse_attn_flops += seqlen * effective_topk * (qk_head_dim + v_head_dim) * num_attention_heads
+
+    attn_qkv_flops = 6 * (indexer_qk_flops + sparse_attn_flops) * num_hidden_layers
+
+    flops_all_token = dense_N_flops + attn_qkv_flops
+    flops_achieved = flops_all_token * (1.0 / delta_time) / 1e12
+    return flops_achieved
+
+
 def _estimate_unknown_flops(config, tokens_sum, batch_seqlens, delta_time):
     return 0
 
@@ -559,6 +635,7 @@ def _estimate_unknown_flops(config, tokens_sum, batch_seqlens, delta_time):
     "glm4v": _estimate_qwen2_flops,
     "gpt_oss": _estimate_gpt_oss_flops,
     "mimo": _estimate_qwen2_flops,
+    "glm_moe_dsa": _estimate_glm_moe_dsa_flops,
 }