[LLM] First commit the llm deployment code

scripts/convert_ep_state_from_tp8.py

@@ -0,0 +1,86 @@
+"""
+# Copyright (c) 2025 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
+import pdb
+from glob import glob
+import os
+import numpy as np
+
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_dir", type=str, required=True)
+args = parser.parse_args()
+
+rank = dist.get_rank()
+print("rank: ", rank)
+# merge tpn -> tp1
+model_dir = args.model_dir
+model_path_pp = glob(os.path.join(model_dir, f"pp{rank}"))
+model_path_pp_tp = []
+for p in model_path_pp:
+    model_path_tp = glob(os.path.join(p, "model_state*"))
+    model_path_tp = sorted(model_path_tp)
+    save_merged_pp_path = os.path.join(p, "merged_tp1_state.pdparams")
+    save_merged_pp_dir = os.path.join(model_dir, "merged_tp1_state_split")
+    os.makedirs(save_merged_pp_dir, exist_ok=True)
+    print(p, model_path_tp)
+
+    state_dicts = [paddle.load(path, return_numpy=True) for path in model_path_tp]
+    state = state_dicts[0]
+
+    print("merge tp")
+    print("p: ", p)
+    for k, v in state.items():
+        save_split_path = os.path.join(save_merged_pp_dir, k)
+        state_now = []
+        for i in range(len(state_dicts)):
+            state_now.append(state_dicts[i][k])
+        print("k: ", k, ", v.shape: ", v.shape)
+        if "qkv_proj" in k:
+            """not need prmt"""
+            # qkv not prmt
+            ori_q = [s[:, :1024] for s in state_now]
+            ori_k = [s[:, 1024:1152] for s in state_now]
+            ori_v = [s[:, 1152:] for s in state_now]
+            new_q = np.concatenate(ori_q, axis=1)
+            new_k = np.concatenate(ori_k, axis=1)
+            new_v = np.concatenate(ori_v, axis=1)
+            print(new_q.shape)
+            print(new_k.shape)
+            print(new_v.shape)
+            new_w = np.concatenate([new_q, new_k, new_v], axis=1)
+            # new_w = np.concatenate(state_now, axis=1)
+        elif "o_proj" in k or "down_proj" in k:
+            new_w = np.concatenate(state_now, axis=0)
+        elif "embed_tokens" in k:
+            new_w = np.concatenate(state_now, axis=0)
+        elif "up_gate_proj" in k:
+            dim = state_now[0].shape[1]
+            half_ffn1_1 = [s[:, :(dim // 2)] for s in state_now]
+            half_ffn1_2 = [s[:, (dim // 2):] for s in state_now]
+            new_ffn1_1 = np.concatenate(half_ffn1_1, axis=1)
+            new_ffn1_2 = np.concatenate(half_ffn1_2, axis=1)
+            new_w = np.concatenate([new_ffn1_1, new_ffn1_2], axis=1)
+        elif "lm_head" in k or "mtp_linear_proj" in k:
+            new_w = np.concatenate(state_now, axis=1)
+        else:
+            new_w = v
+        print("merged_shape: ", new_w.shape)
+        paddle.save(paddle.to_tensor(new_w), save_split_path)
+    print("merge end")