refactor rl get_name_mappings_to_training (#2847)

* refactor rl get_name_mappings_to_training

* fix tp>1

* change variable name(ffn1->up_gate_proj/ffn2->down_proj)

* change variable name(linear_weight->weight/linear_bias->bias)

* add rl names mapping for vl

* fix ernie 0.3B error

* fix develop code

* fix

fastdeploy/model_executor/layers/hydra_head.py

@@ -14,16 +14,13 @@
 # limitations under the License.
 """
 
-from paddleformers.utils.log import logger
-
 import paddle
 import paddle.nn.functional as F
 from paddle import nn
 from paddle.distributed import fleet
-from paddle.distributed.fleet.meta_parallel import (
-    ColumnParallelLinear,
-    VocabParallelEmbedding,
-)
+from paddle.distributed.fleet.meta_parallel import (ColumnParallelLinear,
+                                                    VocabParallelEmbedding)
+from paddleformers.utils.log import logger
 
 from .utils import get_tensor
 
@@ -130,7 +127,7 @@ class HydraHead(nn.Layer):
                 ]
             )
 
-        self.word_embeddings = VocabParallelEmbedding(
+        self.embeddings = VocabParallelEmbedding(
             vocab_size,
             hidden_size,
             mp_group=fleet.get_hybrid_communicate_group().get_model_parallel_group(),
@@ -170,8 +167,8 @@ class HydraHead(nn.Layer):
                 get_tensor(state_dict.pop(f"1.{hydra_head_idx}.weight"))
             )
 
-        self.word_embeddings.weight.set_value(
-            get_tensor(state_dict.pop("word_embeddings.weight"))
+        self.embeddings.weight.set_value(
+            get_tensor(state_dict.pop("embeddings.weight"))
         )
 
     def set_state_dict(self, state_dict):
@@ -183,7 +180,7 @@ class HydraHead(nn.Layer):
         """
         is_custom = True
         for key in state_dict.keys():
-            if key != "word_embeddings.weight" and (
+            if key != "embeddings.weight" and (
                 "hydra_mlp" in key or "hydra_head" in key
             ):
                 is_custom = False
@@ -207,7 +204,7 @@ class HydraHead(nn.Layer):
             hidden_states: [batch_size, hidden_size] The hidden_states of the last accept_tokens
         """
         hydra_inputs = [hidden_states]
-        input_embeds = self.word_embeddings(input_ids)
+        input_embeds = self.embeddings(input_ids)
         for hydra_head_idx in range(self.hydra_num_heads):
             hydra_inputs.append(input_embeds)
             head_input = paddle.concat(hydra_inputs, axis=-1)
@@ -217,4 +214,4 @@ class HydraHead(nn.Layer):
             _, topk_tokens = paddle.topk(probs, k=1, axis=-1)
             next_tokens[:, 1 + hydra_head_idx : 2 + hydra_head_idx] = topk_tokens[:]
 
-            input_embeds = self.word_embeddings(next_tokens[:, 1 + hydra_head_idx])
+            input_embeds = self.embeddings(next_tokens[:, 1 + hydra_head_idx])