86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
import torch
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import struct
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import numpy as np
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def write_string(fp, v):
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v = v.encode("utf-8")
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fp.write( struct.pack("I", len(v)) )
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fp.write(v)
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def write_tuple(fp, v):
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fp.write( struct.pack("B", len(v)) )
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for i in v:
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fp.write( struct.pack("I", i) )
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def write_dtype(fp, v):
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sv = -1
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if v == np.int8:
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sv = 0
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elif v == np.float16:
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sv = 1
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if sv == -1:
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raise TypeError("Unknown dtype %s" % v)
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fp.write( struct.pack("B", sv) )
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def write_parameter(fp, name : str, value : torch.Tensor):
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write_string(fp, name)
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write_tuple(fp, value.size())
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value = np.ascontiguousarray(value.cpu().numpy())
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value_bytes = value.tobytes()
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fp.write( struct.pack("I", len(value_bytes)) )
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write_dtype(fp, value.dtype)
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fp.write(value_bytes)
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def split(x, s):
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sizes = []
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for it in x.size():
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sizes.append(it)
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assert sizes[0] % s == 0
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sizes = [s, sizes[0] // s ] + sizes[1:]
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return x.reshape(*sizes)
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def main(src_model_path, dst_model_path, layer_num):
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model = torch.load(src_model_path, map_location="cpu")
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params = {}
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params["input_embedding.weight"] = model["input_embedding.weight"].cpu()
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params["lm_head.weight"] = model["lm_head.weight"].cpu()
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params["output_layernorm.weight"] = (model["encoder.output_layernorm.weight"]).cpu()
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for i in range(layer_num):
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params[f"layers.{i}.ln_attn.weight"] = model[f"encoder.layers.{i}.self_att.layernorm_before_attention.weight"].cpu()
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params[f"layers.{i}.attn.project_q.weight"] = model[f"encoder.layers.{i}.self_att.self_attention.project_q.weight"]
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params[f"layers.{i}.attn.project_k.weight"] = model[f"encoder.layers.{i}.self_att.self_attention.project_k.weight"]
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params[f"layers.{i}.attn.project_v.weight"] = model[f"encoder.layers.{i}.self_att.self_attention.project_v.weight"]
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params[f"layers.{i}.attn.attn_out.weight"] = model[f"encoder.layers.{i}.self_att.self_attention.attention_out.weight"]
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params[f"layers.{i}.ln_ff.weight"] = model[f"encoder.layers.{i}.ffn.layernorm_before_ffn.weight"].cpu()
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params[f"layers.{i}.ff.w_in.weight"] = model[f"encoder.layers.{i}.ffn.ffn.w_in.w_0.weight"]
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params[f"layers.{i}.ff.w_gated.weight"] = model[f"encoder.layers.{i}.ffn.ffn.w_in.w_1.weight"]
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params[f"layers.{i}.ff.w_out.weight"] = model[f"encoder.layers.{i}.ffn.ffn.w_out.weight"]
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#转换后的模型
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fout = open(dst_model_path, "wb")
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fout.write( struct.pack("I", len(params)) )
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for name, value in params.items():
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write_parameter(fout, name, value)
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fout.close()
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if __name__ == '__main__':
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# 输入已有的源模型
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src_model_path = "./checkpoints-epoch-1/cpm9g-8b-sft-epoch-1.pt"
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# 格式转换后的模型地址
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dst_model_path = "model_8b.ckpt"
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# 百亿：32
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# 千亿：80
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layer_num = 32
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main(src_model_path, dst_model_path, layer_num) |