mirror of
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86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import paddle
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import paddle.distributed as dist
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import pdb
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from glob import glob
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import os
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import numpy as np
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_dir", type=str, required=True)
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args = parser.parse_args()
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rank = dist.get_rank()
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print("rank: ", rank)
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# merge tpn -> tp1
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model_dir = args.model_dir
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model_path_pp = glob(os.path.join(model_dir, f"pp{rank}"))
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model_path_pp_tp = []
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for p in model_path_pp:
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model_path_tp = glob(os.path.join(p, "model_state*"))
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model_path_tp = sorted(model_path_tp)
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save_merged_pp_path = os.path.join(p, "merged_tp1_state.pdparams")
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save_merged_pp_dir = os.path.join(model_dir, "merged_tp1_state_split")
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os.makedirs(save_merged_pp_dir, exist_ok=True)
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print(p, model_path_tp)
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state_dicts = [paddle.load(path, return_numpy=True) for path in model_path_tp]
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state = state_dicts[0]
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print("merge tp")
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print("p: ", p)
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for k, v in state.items():
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save_split_path = os.path.join(save_merged_pp_dir, k)
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state_now = []
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for i in range(len(state_dicts)):
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state_now.append(state_dicts[i][k])
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print("k: ", k, ", v.shape: ", v.shape)
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if "qkv_proj" in k:
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"""not need prmt"""
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# qkv not prmt
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ori_q = [s[:, :1024] for s in state_now]
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ori_k = [s[:, 1024:1152] for s in state_now]
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ori_v = [s[:, 1152:] for s in state_now]
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new_q = np.concatenate(ori_q, axis=1)
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new_k = np.concatenate(ori_k, axis=1)
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new_v = np.concatenate(ori_v, axis=1)
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print(new_q.shape)
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print(new_k.shape)
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print(new_v.shape)
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new_w = np.concatenate([new_q, new_k, new_v], axis=1)
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# new_w = np.concatenate(state_now, axis=1)
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elif "o_proj" in k or "down_proj" in k:
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new_w = np.concatenate(state_now, axis=0)
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elif "embed_tokens" in k:
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new_w = np.concatenate(state_now, axis=0)
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elif "up_gate_proj" in k:
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dim = state_now[0].shape[1]
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half_ffn1_1 = [s[:, :(dim // 2)] for s in state_now]
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half_ffn1_2 = [s[:, (dim // 2):] for s in state_now]
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new_ffn1_1 = np.concatenate(half_ffn1_1, axis=1)
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new_ffn1_2 = np.concatenate(half_ffn1_2, axis=1)
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new_w = np.concatenate([new_ffn1_1, new_ffn1_2], axis=1)
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elif "lm_head" in k or "mtp_linear_proj" in k:
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new_w = np.concatenate(state_now, axis=1)
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else:
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new_w = v
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print("merged_shape: ", new_w.shape)
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paddle.save(paddle.to_tensor(new_w), save_split_path)
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print("merge end") |