Remove useless
Meng Zhang
parent
0cd5482eaf
commit
65743ba1b9
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@ -1,253 +0,0 @@
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# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
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# Modified by Brendan Dolan-Gavitt, 2022
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# Modified by Meng Zhang, 2023
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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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import argparse
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import configparser
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import multiprocessing
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import os
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import sys
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from pathlib import Path
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import numpy as np
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import torch
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from transformers import GPTJForCausalLM
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dir_path = os.path.dirname(os.path.realpath(__file__))
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sys.path.append(dir_path + "/../../../..")
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sys.path.append(dir_path)
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def get_weight_data_type(data_type):
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if data_type == "fp32":
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return np.float32
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elif data_type == "fp16":
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return np.float16
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else:
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assert False, f"Invalid weight data type {data_type}"
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def split_and_convert_process(i, saved_dir, factor, key, val):
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if (
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key.find("input_layernorm.weight") != -1
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or key.find("input_layernorm.bias") != -1
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or key.find("attention.dense.bias") != -1
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or key.find("post_attention_layernorm.weight") != -1
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or key.find("post_attention_layernorm.bias") != -1
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or key.find("mlp.dense_4h_to_h.bias") != -1
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or key.find("final_layernorm.weight") != -1
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or key.find("final_layernorm.bias") != -1
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):
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# shared weights, only need to convert the weights of rank 0
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if i == 0:
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saved_path = saved_dir + "/model." + key + ".bin"
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val.tofile(saved_path)
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elif (
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key.find("attention.dense.weight") != -1
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or key.find("mlp.dense_4h_to_h.weight") != -1
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):
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split_vals = np.split(val, factor, axis=0)
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for j in range(factor):
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saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j)
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split_vals[j].tofile(saved_path)
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elif (
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key.find("mlp.dense_h_to_4h.weight") != -1
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or key.find("mlp.dense_h_to_4h.bias") != -1
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):
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split_vals = np.split(val, factor, axis=-1)
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for j in range(factor):
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saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j)
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split_vals[j].tofile(saved_path)
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elif key.find("attention.query_key_value.weight") != -1:
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split_vals = np.split(val, factor, axis=-1)
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for j in range(factor):
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saved_path = saved_dir + "/model." + key + ".%d.bin" % (i * factor + j)
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split_vals[j].tofile(saved_path)
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else:
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print("[ERROR] cannot find key '{}'".format(key))
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def split_and_convert(args):
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saved_dir = args.saved_dir + "/%d-gpu/" % args.infer_gpu_num
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if os.path.exists(saved_dir) is False:
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os.makedirs(saved_dir)
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t_gpu_num = args.trained_gpu_num
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i_gpu_num = args.infer_gpu_num
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assert i_gpu_num % t_gpu_num == 0
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factor = (int)(i_gpu_num / t_gpu_num)
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model = GPTJForCausalLM.from_pretrained(args.in_file)
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try:
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config = configparser.ConfigParser()
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config["gpt"] = {}
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for key in vars(args):
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config["gpt"][key] = f"{vars(args)[key]}"
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for k, v in vars(model.config).items():
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config["gpt"][k] = f"{v}"
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config["gpt"]["weight_data_type"] = args.weight_data_type
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with open((Path(saved_dir) / "config.ini").as_posix(), "w") as configfile:
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config.write(configfile)
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except Exception:
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print("Fail to save the config in config.ini.")
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np_weight_data_type = get_weight_data_type(args.weight_data_type)
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huggingface_model_name_pattern = [
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"ln_1.bias",
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"ln_1.weight",
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"attn.q_proj.weight",
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"attn.out_proj.weight",
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"mlp.fc_in.bias",
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"mlp.fc_in.weight",
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"mlp.fc_out.bias",
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"mlp.fc_out.weight",
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]
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ft_model_name_pattern = [
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"input_layernorm.bias",
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"input_layernorm.weight",
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"attention.query_key_value.weight",
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"attention.dense.weight",
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"mlp.dense_h_to_4h.bias",
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"mlp.dense_h_to_4h.weight",
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"mlp.dense_4h_to_h.bias",
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"mlp.dense_4h_to_h.weight",
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]
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torch.multiprocessing.set_start_method("spawn")
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pool = multiprocessing.Pool(args.processes)
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for name, param in model.named_parameters():
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if name.find("weight") == -1 and name.find("bias") == -1:
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continue
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print(name)
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if name == "transformer.wte.weight":
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param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
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saved_dir + "model.wte.bin"
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)
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elif name == "transformer.ln_f.bias":
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param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
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saved_dir + "model.final_layernorm.bias.bin"
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)
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elif name == "transformer.ln_f.weight":
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param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
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saved_dir + "model.final_layernorm.weight.bin"
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)
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elif name == "lm_head.weight":
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param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
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saved_dir + "model.lm_head.weight.bin"
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)
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elif name == "lm_head.bias":
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param.detach().cpu().numpy().astype(np_weight_data_type).tofile(
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saved_dir + "model.lm_head.bias.bin"
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)
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else:
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for i in range(len(huggingface_model_name_pattern)):
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if name.find(huggingface_model_name_pattern[i]) != -1:
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# Special case for QKV weights
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if name.find("attn.q_proj.weight") != -1:
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layer = name.split(".")[2]
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base_k = f"transformer.h.{layer}."
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w = model.state_dict()
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QKV_w = torch.stack(
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[
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w[base_k + "attn.q_proj.weight"],
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w[base_k + "attn.k_proj.weight"],
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w[base_k + "attn.v_proj.weight"],
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]
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) # [qkv, n_heads * dim_head, latent_space]
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QKV_w = QKV_w.permute(2, 0, 1)
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weights = (
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QKV_w.detach().cpu().numpy().astype(np_weight_data_type)
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)
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else:
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weights = (
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param.detach().cpu().numpy().astype(np_weight_data_type)
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)
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# Some weights need to be transposed
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if (
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name.find("mlp.fc_in.weight") != -1
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or name.find("mlp.fc_out.weight") != -1
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or name.find("attn.out_proj.weight") != -1
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):
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weights = weights.T
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new_name = name.replace("transformer.h.", "layers.").replace(
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huggingface_model_name_pattern[i], ft_model_name_pattern[i]
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)
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pool.starmap(
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split_and_convert_process,
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[(0, saved_dir, factor, new_name, weights)],
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)
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pool.close()
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pool.join()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)
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parser.add_argument(
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"-saved_dir", "-o", type=str, help="file name of output file", required=True
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)
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parser.add_argument(
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"-in_file", "-i", type=str, help="HF model name or directory", required=True
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)
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parser.add_argument(
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"-trained_gpu_num",
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"-t_g",
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type=int,
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help="How many gpus for training",
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default=1,
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)
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parser.add_argument(
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"-infer_gpu_num",
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"-i_g",
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type=int,
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help="How many gpus for inference",
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required=True,
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)
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parser.add_argument(
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"-processes",
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"-p",
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type=int,
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help="How many processes to spawn for conversion (default: 4)",
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default=4,
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)
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parser.add_argument(
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"-weight_data_type",
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type=str,
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default="fp32",
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choices=["fp32", "fp16"],
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help="output weight data type",
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)
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args = parser.parse_args()
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print("\n=============== Argument ===============")
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for key in vars(args):
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print("{}: {}".format(key, vars(args)[key]))
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print("========================================")
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split_and_convert(args)
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[gpt]
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saved_dir = out
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in_file = hf-internal-testing/tiny-random-gptj
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trained_gpu_num = 1
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infer_gpu_num = 1
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processes = 4
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weight_data_type = fp32
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vocab_size = 1000
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n_positions = 512
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n_embd = 32
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n_layer = 5
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n_head = 4
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n_inner = None
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rotary_dim = 4
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activation_function = gelu_new
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resid_pdrop = 0.0
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embd_pdrop = 0.0
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attn_pdrop = 0.0
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layer_norm_epsilon = 1e-05
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initializer_range = 0.02
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use_cache = True
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bos_token_id = 98
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eos_token_id = 98
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return_dict = True
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output_hidden_states = False
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output_attentions = False
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torchscript = False
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torch_dtype = None
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use_bfloat16 = False
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tf_legacy_loss = False
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pruned_heads = {}
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tie_word_embeddings = False
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is_encoder_decoder = False
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is_decoder = False
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cross_attention_hidden_size = None
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add_cross_attention = False
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tie_encoder_decoder = False
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max_length = 20
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min_length = 0
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do_sample = False
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early_stopping = False
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num_beams = 1
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num_beam_groups = 1
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diversity_penalty = 0.0
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temperature = 1.0
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top_k = 50
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top_p = 1.0
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typical_p = 1.0
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repetition_penalty = 1.0
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length_penalty = 1.0
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no_repeat_ngram_size = 0
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encoder_no_repeat_ngram_size = 0
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bad_words_ids = None
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num_return_sequences = 1
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chunk_size_feed_forward = 0
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output_scores = False
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return_dict_in_generate = False
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forced_bos_token_id = None
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forced_eos_token_id = None
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remove_invalid_values = False
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exponential_decay_length_penalty = None
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suppress_tokens = None
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begin_suppress_tokens = None
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architectures = None
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finetuning_task = None
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id2label = {0: 'LABEL_0', 1: 'LABEL_1'}
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label2id = {'LABEL_0': 0, 'LABEL_1': 1}
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tokenizer_class = None
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prefix = None
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pad_token_id = 98
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sep_token_id = None
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decoder_start_token_id = None
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task_specific_params = None
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problem_type = None
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_name_or_path = hf-internal-testing/tiny-random-gptj
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_commit_hash = b96595a4bcdeb272096214589efa0314259853a0
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transformers_version = 4.11.0.dev0
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attention_probs_dropout_prob = 0.0
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gradient_checkpointing = False
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hidden_act = gelu
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hidden_dropout_prob = 0.0
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intermediate_size = 37
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model_type = gptj
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n_ctx = 512
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scale_attn_weights = True
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type_vocab_size = 16
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:38723a2e5e8a17aa7950dc008209944e898f69a7bd10a23c839d341e935fd5ca
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size 128
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:b638277a8690e175a9137feff1e43c067f9faf4e2f600caf468fb05b0403b717
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size 128
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:125bd07dc5e7a5c6444f7689ba78df0f4c7959b6dcfbaf7c89edd0634a147ea0
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size 4096
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:11fd52f2b94fad5fe54e2e03c5848050703ec4d798a43a4c1813f109a6703883
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size 12288
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
|
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oid sha256:38723a2e5e8a17aa7950dc008209944e898f69a7bd10a23c839d341e935fd5ca
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size 128
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@ -1,3 +0,0 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:b638277a8690e175a9137feff1e43c067f9faf4e2f600caf468fb05b0403b717
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size 128
|
|
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||||||
Loading…
Reference in New Issue