Update convert_hf_fm9g.py

quick_start_clean/convert_hf_fm9g.py

@@ -0,0 +1,98 @@
+# coding=utf-8
+# Copyright 2022 The OpenBMB team.
+#
+# 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.
+
+from collections import OrderedDict
+
+import torch
+import argparse
+import os
+
+parser = argparse.ArgumentParser(description='Load and save model weights with specified paths.')
+parser.add_argument('--model_path', type=str, required=True, help='Path to the model directory.')
+parser.add_argument('--output_path', type=str, required=True, help='Path to save the new weights.')
+parser.add_argument('--model_type',type=str,default='fm9g',help='The model type need to be one of "fm9g" or "9g-8b"')
+parser.add_argument('--task',type=str,default='pt2bin',help='The task need to be one of "pt2bin" or "bin2pt"')
+# parser.add_argument('--layer_num', type=int, required=True, help='The layers of model')
+
+args = parser.parse_args()
+
+src_path = args.model_path
+dst_path = args.output_path if args.output_path.endswith('/') else args.output_path + ('/')
+model_type = args.model_type
+task = args.task
+
+assert model_type in ['fm9g'], 'The "model_type" must be one of "fm9g"!'
+assert task in ['pt2bin','bin2pt'], 'The task need to be one of "pt2bin" or "bin2pt"!'
+
+if model_type == 'fm9g':
+    layer_num = 40
+
+if not os.path.exists(dst_path):
+    os.makedirs(dst_path)
+
+
+def convert_hf_to_fm9g():
+    # 2B模型转换bin2pt
+    ckpt = torch.load(src_path)
+    new_ckpt = OrderedDict()
+
+    new_ckpt['input_embedding.weight'] = ckpt['model.embed_tokens.weight']
+    new_ckpt["encoder.output_layernorm.weight"] = ckpt['model.norm.weight']
+    for i in range(layer_num):
+        new_ckpt[f"encoder.layers.{i}.self_att.self_attention.project_q.weight"] = ckpt[f"model.layers.{i}.self_attn.q_proj.weight"]
+        new_ckpt[f"encoder.layers.{i}.self_att.self_attention.project_k.weight"] = ckpt[f"model.layers.{i}.self_attn.k_proj.weight"]
+        new_ckpt[f"encoder.layers.{i}.self_att.self_attention.project_v.weight"] = ckpt[f"model.layers.{i}.self_attn.v_proj.weight"]
+        new_ckpt[f"encoder.layers.{i}.self_att.self_attention.attention_out.weight"] = ckpt[f"model.layers.{i}.self_attn.o_proj.weight"]
+        new_ckpt[f"encoder.layers.{i}.self_att.layernorm_before_attention.weight"] = ckpt[f"model.layers.{i}.input_layernorm.weight"]
+        new_ckpt[f"encoder.layers.{i}.ffn.layernorm_before_ffn.weight"] = ckpt[f"model.layers.{i}.post_attention_layernorm.weight"]
+        
+        new_ckpt[f"encoder.layers.{i}.ffn.ffn.w_in.w_0.weight"] = ckpt[f'model.layers.{i}.mlp.gate_proj.weight']
+        new_ckpt[f"encoder.layers.{i}.ffn.ffn.w_in.w_1.weight"] = ckpt[f'model.layers.{i}.mlp.up_proj.weight']
+        new_ckpt[f"encoder.layers.{i}.ffn.ffn.w_out.weight"] = ckpt[f'model.layers.{i}.mlp.down_proj.weight']
+
+    torch.save(new_ckpt, f"{dst_path}fm9g.pt")
+
+def convert_fm9g_to_hf():
+    # 2B模型转换pt2bin
+    state = torch.load(src_path)
+
+    new_state = {}
+    new_state["model.embed_tokens.weight"] = state["input_embedding.weight"]
+    new_state["model.norm.weight"] = state["encoder.output_layernorm.weight"]
+    for lid in range(layer_num):
+        print(lid)
+        new_state[f"model.layers.{lid}.self_attn.q_proj.weight"] = state[f"encoder.layers.{lid}.self_att.self_attention.project_q.weight"]
+        new_state[f"model.layers.{lid}.self_attn.k_proj.weight"] = state[f"encoder.layers.{lid}.self_att.self_attention.project_k.weight"]
+        new_state[f"model.layers.{lid}.self_attn.v_proj.weight"] = state[f"encoder.layers.{lid}.self_att.self_attention.project_v.weight"]
+
+        new_state[f"model.layers.{lid}.self_attn.o_proj.weight"] = state[f"encoder.layers.{lid}.self_att.self_attention.attention_out.weight"]
+        new_state[f"model.layers.{lid}.mlp.gate_proj.weight"] = state[f"encoder.layers.{lid}.ffn.ffn.w_in.w_0.weight"]
+        new_state[f"model.layers.{lid}.mlp.up_proj.weight"] = state[f"encoder.layers.{lid}.ffn.ffn.w_in.w_1.weight"]
+        new_state[f"model.layers.{lid}.mlp.down_proj.weight"] = state[f"encoder.layers.{lid}.ffn.ffn.w_out.weight"]
+
+        new_state[f"model.layers.{lid}.input_layernorm.weight"] = state[f"encoder.layers.{lid}.self_att.layernorm_before_attention.weight"]
+        new_state[f"model.layers.{lid}.post_attention_layernorm.weight"] = state[f"encoder.layers.{lid}.ffn.layernorm_before_ffn.weight"]
+    del state
+    state = None
+    torch.save(new_state, f"{dst_path}fm9g.bin")  
+
+
+if __name__ == "__main__":
+    if model_type == 'fm9g' and task == 'bin2pt':
+        convert_hf_to_fm9g()
+    elif model_type == 'fm9g' and task == 'pt2bin':
+        convert_fm9g_to_hf()
+    else:
+        raise ValueError('Please check the model type and task!')