[LLM] First commit the llm deployment code

scripts/convert_ep_state_from_ep8.py

@@ -0,0 +1,59 @@
+"""
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+"""
+
+import paddle
+import paddle.distributed as dist
+from glob import glob
+import os
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_dir", type=str, required=True)
+args = parser.parse_args()
+
+rank = dist.get_rank()
+ep_num = dist.get_world_size()
+print("rank: ", rank)
+# merge tpn -> tp1
+model_dir = args.model_dir
+save_merged_pp_dir = os.path.join(model_dir, "merged_tp1_state_split")
+os.makedirs(save_merged_pp_dir, exist_ok=True)
+
+model_path_pp = glob(os.path.join(model_dir, "shangxianv1_ep_hadamard_quantmodel_to_eval_pp*"))
+for p in model_path_pp:
+    model_path_ep = os.path.join(p, f"model_state.ep0{rank}.pdparams")
+    print(p, model_path_ep)
+
+    state_dicts = paddle.load(model_path_ep, return_numpy=True)
+
+    print("merge ep")
+    print("p: ", p)
+    for k, v in state_dicts.items():
+        v = paddle.to_tensor(v)
+        if "mlp.experts" in k:
+            k_list = k.split(".")
+            export_id = rank * ep_num + int(k_list[5])
+            k_list[5] = str(export_id)
+            k = ".".join(k_list)
+            print(f"key: {k}")
+            save_split_path = os.path.join(save_merged_pp_dir, k)
+            paddle.save(v, save_split_path)
+        elif rank == 0:
+            save_split_path = os.path.join(save_merged_pp_dir, k)
+            paddle.save(paddle.to_tensor(v), save_split_path)
+    print(f"merge {p} end")
+print("merge end")