diff --git a/vllm_ascend/distributed/parallel_state.py b/vllm_ascend/distributed/parallel_state.py
index ce932fbb..1709d150 100644
--- a/vllm_ascend/distributed/parallel_state.py
+++ b/vllm_ascend/distributed/parallel_state.py
@@ -182,9 +182,8 @@ def init_ascend_model_parallel(parallel_config: ParallelConfig, ):
     global _SHARED_WEIGHT
     # TODO: Check if the model is Deepseek V3.2 with enabled SFA CP and activated shared weights. It will then be normalized within the PCP parameters. -- clrs97
     is_ds_v32 = hasattr(vllm_config.model_config.hf_config, "index_topk")
-    if enable_sp() and is_ds_v32:
+    if enable_sp() and is_ds_v32 and _SHARED_WEIGHT is None:
         _SHARED_WEIGHT = _create_shared_weight_group("CP_shared_weight")
-
     # TODO: Extract and unify the logic across different communication group.
     if flashcomm2_enable():
         flashcomm2_otp_size = get_ascend_config(
@@ -240,7 +239,9 @@ def init_ascend_model_parallel(parallel_config: ParallelConfig, ):
         # Create shared weight group for flashcomm2 oproj
         if flashcomm2_o_shared_enabled():
             assert flashcomm2_otp_size == 1, "flashcomm2_o_shared is only supported when flashcomm2_otp_size is 1"
-            _SHARED_WEIGHT = _create_shared_weight_group("flashcomm2_o_shared")
+            if _SHARED_WEIGHT is None:
+                _SHARED_WEIGHT = _create_shared_weight_group(
+                    "flashcomm2_o_shared")
 
     if get_ascend_config().multistream_overlap_gate:
         global _FC3_QUANT_X
diff --git a/vllm_ascend/spec_decode/mtp_proposer.py b/vllm_ascend/spec_decode/mtp_proposer.py
index 28882924..e6f856b5 100644
--- a/vllm_ascend/spec_decode/mtp_proposer.py
+++ b/vllm_ascend/spec_decode/mtp_proposer.py
@@ -8,6 +8,7 @@ import torch.nn.functional as F
 from vllm.config import (CUDAGraphMode, VllmConfig,
                          get_layers_from_vllm_config, set_current_vllm_config)
 from vllm.distributed import get_pcp_group
+from vllm.distributed.parallel_state import get_pp_group
 from vllm.forward_context import get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
@@ -205,9 +206,11 @@ class MtpProposer(Proposer):
         if self.vllm_config.model_config.is_deepseek_mla:
             # check if mtp model use main model's embedding and LMhead
             main_model = model
-            if torch.equal(self.model.model.embed_tokens.weight,
-                           main_model.model.embed_tokens.weight):
-                self.model.model.embed_tokens = main_model.model.embed_tokens
+            if get_pp_group().world_size == 1:
+                # If pp>1, the weights of mtp and the main model's embedding are not on the same device.
+                if torch.equal(self.model.model.embed_tokens.weight,
+                               main_model.model.embed_tokens.weight):
+                    self.model.model.embed_tokens = main_model.model.embed_tokens
             for _, layer_module in self.model.model.layers.items():
                 if torch.equal(layer_module.shared_head.head.weight,
                                main_model.lm_head.weight):