Yiyang Zhou
commited on
Commit
•
d6c79d5
1
Parent(s):
3d37fd1
up-hf
Browse files- README.md +7 -1
- adapter_config.json +21 -0
- adapter_model.bin +3 -0
- checkpoint-45400/README.md +9 -0
- checkpoint-45400/adapter_config.json +21 -0
- checkpoint-45400/adapter_model.bin +3 -0
- checkpoint-45400/global_step45400/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt +3 -0
- checkpoint-45400/global_step45400/zero_pp_rank_0_mp_rank_00_model_states.pt +3 -0
- checkpoint-45400/latest +1 -0
- checkpoint-45400/rng_state.pth +3 -0
- checkpoint-45400/special_tokens_map.json +24 -0
- checkpoint-45400/tokenizer.model +3 -0
- checkpoint-45400/tokenizer_config.json +35 -0
- checkpoint-45400/training_args.bin +3 -0
- checkpoint-45400/zero_to_fp32.py +578 -0
- config.json +45 -0
- non_lora_trainables.bin +3 -0
README.md
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---
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---
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library_name: peft
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---
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## Training procedure
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### Framework versions
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- PEFT 0.4.0
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adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": "/data/yiyang_zhou/workplace/LLaVA/checkpoint/llava-v1.5-7b/",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 256,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 128,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:56d4fd1630226ff241ee5484b8394dd1c54f36762ae691e8dc7d6e0b38ecfcd6
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size 159465805
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checkpoint-45400/README.md
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---
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library_name: peft
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---
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## Training procedure
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### Framework versions
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7 |
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- PEFT 0.4.0
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checkpoint-45400/adapter_config.json
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{
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"auto_mapping": null,
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"base_model_name_or_path": null,
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 256,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 128,
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"revision": null,
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"target_modules": [
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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checkpoint-45400/adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:acaf7d114713e6450ad1c3abbedc6837abb4325658f7c616d8564a492425fd61
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size 159469645
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checkpoint-45400/global_step45400/bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:6234bd1cc95bfc36d3415dd8e994bdc0e2826443556920ca9ba0d8f8f195566d
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size 956304503
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checkpoint-45400/global_step45400/zero_pp_rank_0_mp_rank_00_model_states.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:942728f3a39eb2e7c892c0eb4b7abda28148eebc0f1f8163e2db25a53306627d
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size 14126748223
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checkpoint-45400/latest
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global_step45400
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checkpoint-45400/rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c1031875f2ee7897569d1095d2376d589b45d477a84fc29d9f1462ee50cbbd2
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size 14575
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checkpoint-45400/special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"single_word": false
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"pad_token": "<unk>",
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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checkpoint-45400/tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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size 499723
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checkpoint-45400/tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"legacy": false,
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"model_max_length": 1048,
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"pad_token": null,
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"padding_side": "right",
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"sp_model_kwargs": {},
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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checkpoint-45400/training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c604bfee1fd2a162b480e073125a1a44d92e0df864e2c76bf9dd625ade59d55
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size 6075
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checkpoint-45400/zero_to_fp32.py
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#!/usr/bin/env python
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# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
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# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
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# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
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# application.
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#
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# example: python zero_to_fp32.py . pytorch_model.bin
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import argparse
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import torch
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import glob
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import math
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import os
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import re
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from collections import OrderedDict
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from dataclasses import dataclass
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# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
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# DeepSpeed data structures it has to be available in the current python environment.
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from deepspeed.utils import logger
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from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
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FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
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FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
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@dataclass
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class zero_model_state:
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buffers: dict()
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param_shapes: dict()
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shared_params: list
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ds_version: int
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frozen_param_shapes: dict()
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frozen_param_fragments: dict()
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debug = 0
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# load to cpu
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device = torch.device('cpu')
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def atoi(text):
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return int(text) if text.isdigit() else text
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def natural_keys(text):
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'''
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alist.sort(key=natural_keys) sorts in human order
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http://nedbatchelder.com/blog/200712/human_sorting.html
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(See Toothy's implementation in the comments)
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'''
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return [atoi(c) for c in re.split(r'(\d+)', text)]
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def get_model_state_file(checkpoint_dir, zero_stage):
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if not os.path.isdir(checkpoint_dir):
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raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
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# there should be only one file
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if zero_stage == 2:
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file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
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elif zero_stage == 3:
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file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
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if not os.path.exists(file):
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raise FileNotFoundError(f"can't find model states file at '{file}'")
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return file
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def get_checkpoint_files(checkpoint_dir, glob_pattern):
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# XXX: need to test that this simple glob rule works for multi-node setup too
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ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
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if len(ckpt_files) == 0:
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raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
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return ckpt_files
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def get_optim_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
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def get_model_state_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
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def parse_model_states(files):
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zero_model_states = []
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for file in files:
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state_dict = torch.load(file, map_location=device)
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if BUFFER_NAMES not in state_dict:
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raise ValueError(f"{file} is not a model state checkpoint")
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buffer_names = state_dict[BUFFER_NAMES]
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if debug:
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print("Found buffers:", buffer_names)
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# recover just the buffers while restoring them to fp32 if they were saved in fp16
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buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
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param_shapes = state_dict[PARAM_SHAPES]
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# collect parameters that are included in param_shapes
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param_names = []
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for s in param_shapes:
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for name in s.keys():
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param_names.append(name)
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# update with frozen parameters
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frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
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if frozen_param_shapes is not None:
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if debug:
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print(f"Found frozen_param_shapes: {frozen_param_shapes}")
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param_names += list(frozen_param_shapes.keys())
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# handle shared params
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shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
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ds_version = state_dict.get(DS_VERSION, None)
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frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
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z_model_state = zero_model_state(buffers=buffers,
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param_shapes=param_shapes,
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shared_params=shared_params,
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ds_version=ds_version,
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frozen_param_shapes=frozen_param_shapes,
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frozen_param_fragments=frozen_param_fragments)
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zero_model_states.append(z_model_state)
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return zero_model_states
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def parse_optim_states(files, ds_checkpoint_dir):
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total_files = len(files)
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state_dicts = []
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for f in files:
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state_dicts.append(torch.load(f, map_location=device))
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if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
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raise ValueError(f"{files[0]} is not a zero checkpoint")
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zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
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world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
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# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
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# parameters can be different from data parallelism for non-expert parameters. So we can just
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# use the max of the partition_count to get the dp world_size.
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if type(world_size) is list:
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world_size = max(world_size)
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if world_size != total_files:
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raise ValueError(
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f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
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"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
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)
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# the groups are named differently in each stage
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if zero_stage == 2:
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fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
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elif zero_stage == 3:
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fp32_groups_key = FP32_FLAT_GROUPS
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else:
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raise ValueError(f"unknown zero stage {zero_stage}")
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if zero_stage == 2:
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fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
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elif zero_stage == 3:
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# if there is more than one param group, there will be multiple flattened tensors - one
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# flattened tensor per group - for simplicity merge them into a single tensor
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#
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# XXX: could make the script more memory efficient for when there are multiple groups - it
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# will require matching the sub-lists of param_shapes for each param group flattened tensor
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fp32_flat_groups = [
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torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
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]
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return zero_stage, world_size, fp32_flat_groups
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def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
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"""
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Returns fp32 state_dict reconstructed from ds checkpoint
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Args:
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- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
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"""
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print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
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optim_files = get_optim_files(ds_checkpoint_dir)
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zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
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print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
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model_files = get_model_state_files(ds_checkpoint_dir)
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zero_model_states = parse_model_states(model_files)
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print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
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if zero_stage == 2:
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return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
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elif zero_stage == 3:
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return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
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def _zero2_merge_frozen_params(state_dict, zero_model_states):
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if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
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return
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+
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frozen_param_shapes = zero_model_states[0].frozen_param_shapes
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frozen_param_fragments = zero_model_states[0].frozen_param_fragments
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if debug:
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num_elem = sum(s.numel() for s in frozen_param_shapes.values())
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print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
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+
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wanted_params = len(frozen_param_shapes)
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wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
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avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
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print(f'Frozen params: Have {avail_numel} numels to process.')
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print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
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+
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total_params = 0
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total_numel = 0
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for name, shape in frozen_param_shapes.items():
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total_params += 1
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unpartitioned_numel = shape.numel()
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total_numel += unpartitioned_numel
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+
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state_dict[name] = frozen_param_fragments[name]
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+
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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+
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print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
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+
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+
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def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
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param_shapes = zero_model_states[0].param_shapes
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+
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# Reconstruction protocol:
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#
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# XXX: document this
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+
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if debug:
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for i in range(world_size):
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for j in range(len(fp32_flat_groups[0])):
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print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
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+
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+
# XXX: memory usage doubles here (zero2)
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+
num_param_groups = len(fp32_flat_groups[0])
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+
merged_single_partition_of_fp32_groups = []
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for i in range(num_param_groups):
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merged_partitions = [sd[i] for sd in fp32_flat_groups]
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+
full_single_fp32_vector = torch.cat(merged_partitions, 0)
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+
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
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avail_numel = sum(
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[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
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+
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if debug:
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wanted_params = sum([len(shapes) for shapes in param_shapes])
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+
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
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+
# not asserting if there is a mismatch due to possible padding
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print(f"Have {avail_numel} numels to process.")
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+
print(f"Need {wanted_numel} numels in {wanted_params} params.")
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+
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+
# params
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# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
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+
# out-of-core computing solution
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+
total_numel = 0
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+
total_params = 0
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+
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
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+
offset = 0
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+
avail_numel = full_single_fp32_vector.numel()
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284 |
+
for name, shape in shapes.items():
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285 |
+
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+
unpartitioned_numel = shape.numel()
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287 |
+
total_numel += unpartitioned_numel
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+
total_params += 1
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289 |
+
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+
if debug:
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+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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+
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
293 |
+
offset += unpartitioned_numel
|
294 |
+
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295 |
+
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
296 |
+
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
297 |
+
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
298 |
+
# live optimizer object, so we are checking that the numbers are within the right range
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299 |
+
align_to = 2 * world_size
|
300 |
+
|
301 |
+
def zero2_align(x):
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302 |
+
return align_to * math.ceil(x / align_to)
|
303 |
+
|
304 |
+
if debug:
|
305 |
+
print(f"original offset={offset}, avail_numel={avail_numel}")
|
306 |
+
|
307 |
+
offset = zero2_align(offset)
|
308 |
+
avail_numel = zero2_align(avail_numel)
|
309 |
+
|
310 |
+
if debug:
|
311 |
+
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
312 |
+
|
313 |
+
# Sanity check
|
314 |
+
if offset != avail_numel:
|
315 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
316 |
+
|
317 |
+
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
318 |
+
|
319 |
+
|
320 |
+
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states):
|
321 |
+
state_dict = OrderedDict()
|
322 |
+
|
323 |
+
# buffers
|
324 |
+
buffers = zero_model_states[0].buffers
|
325 |
+
state_dict.update(buffers)
|
326 |
+
if debug:
|
327 |
+
print(f"added {len(buffers)} buffers")
|
328 |
+
|
329 |
+
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
330 |
+
|
331 |
+
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
332 |
+
|
333 |
+
# recover shared parameters
|
334 |
+
for pair in zero_model_states[0].shared_params:
|
335 |
+
if pair[1] in state_dict:
|
336 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
337 |
+
|
338 |
+
return state_dict
|
339 |
+
|
340 |
+
|
341 |
+
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
342 |
+
remainder = unpartitioned_numel % world_size
|
343 |
+
padding_numel = (world_size - remainder) if remainder else 0
|
344 |
+
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
345 |
+
return partitioned_numel, padding_numel
|
346 |
+
|
347 |
+
|
348 |
+
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
349 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
350 |
+
return
|
351 |
+
|
352 |
+
if debug:
|
353 |
+
for i in range(world_size):
|
354 |
+
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
355 |
+
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
356 |
+
|
357 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
358 |
+
wanted_params = len(frozen_param_shapes)
|
359 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
360 |
+
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
361 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
362 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
363 |
+
|
364 |
+
total_params = 0
|
365 |
+
total_numel = 0
|
366 |
+
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
367 |
+
total_params += 1
|
368 |
+
unpartitioned_numel = shape.numel()
|
369 |
+
total_numel += unpartitioned_numel
|
370 |
+
|
371 |
+
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
372 |
+
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
373 |
+
|
374 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
375 |
+
|
376 |
+
if debug:
|
377 |
+
print(
|
378 |
+
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
379 |
+
)
|
380 |
+
|
381 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
382 |
+
|
383 |
+
|
384 |
+
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
385 |
+
param_shapes = zero_model_states[0].param_shapes
|
386 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
387 |
+
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
388 |
+
# param, re-consolidating each param, while dealing with padding if any
|
389 |
+
|
390 |
+
# merge list of dicts, preserving order
|
391 |
+
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
392 |
+
|
393 |
+
if debug:
|
394 |
+
for i in range(world_size):
|
395 |
+
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
396 |
+
|
397 |
+
wanted_params = len(param_shapes)
|
398 |
+
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
399 |
+
# not asserting if there is a mismatch due to possible padding
|
400 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
401 |
+
print(f"Trainable params: Have {avail_numel} numels to process.")
|
402 |
+
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
403 |
+
|
404 |
+
# params
|
405 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
406 |
+
# out-of-core computing solution
|
407 |
+
offset = 0
|
408 |
+
total_numel = 0
|
409 |
+
total_params = 0
|
410 |
+
for name, shape in param_shapes.items():
|
411 |
+
|
412 |
+
unpartitioned_numel = shape.numel()
|
413 |
+
total_numel += unpartitioned_numel
|
414 |
+
total_params += 1
|
415 |
+
|
416 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
417 |
+
|
418 |
+
if debug:
|
419 |
+
print(
|
420 |
+
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
421 |
+
)
|
422 |
+
|
423 |
+
# XXX: memory usage doubles here
|
424 |
+
state_dict[name] = torch.cat(
|
425 |
+
tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
|
426 |
+
0).narrow(0, 0, unpartitioned_numel).view(shape)
|
427 |
+
offset += partitioned_numel
|
428 |
+
|
429 |
+
offset *= world_size
|
430 |
+
|
431 |
+
# Sanity check
|
432 |
+
if offset != avail_numel:
|
433 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
434 |
+
|
435 |
+
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
436 |
+
|
437 |
+
|
438 |
+
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
|
439 |
+
state_dict = OrderedDict()
|
440 |
+
|
441 |
+
# buffers
|
442 |
+
buffers = zero_model_states[0].buffers
|
443 |
+
state_dict.update(buffers)
|
444 |
+
if debug:
|
445 |
+
print(f"added {len(buffers)} buffers")
|
446 |
+
|
447 |
+
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
448 |
+
|
449 |
+
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
450 |
+
|
451 |
+
# recover shared parameters
|
452 |
+
for pair in zero_model_states[0].shared_params:
|
453 |
+
if pair[1] in state_dict:
|
454 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
455 |
+
|
456 |
+
return state_dict
|
457 |
+
|
458 |
+
|
459 |
+
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
|
460 |
+
"""
|
461 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
462 |
+
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
463 |
+
via a model hub.
|
464 |
+
|
465 |
+
Args:
|
466 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder
|
467 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
468 |
+
|
469 |
+
Returns:
|
470 |
+
- pytorch ``state_dict``
|
471 |
+
|
472 |
+
Note: this approach may not work if your application doesn't have sufficient free CPU memory and
|
473 |
+
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
474 |
+
the checkpoint.
|
475 |
+
|
476 |
+
A typical usage might be ::
|
477 |
+
|
478 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
479 |
+
# do the training and checkpoint saving
|
480 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
481 |
+
model = model.cpu() # move to cpu
|
482 |
+
model.load_state_dict(state_dict)
|
483 |
+
# submit to model hub or save the model to share with others
|
484 |
+
|
485 |
+
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
486 |
+
application. i.e. you will need to re-initialize the deepspeed engine, since
|
487 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
488 |
+
|
489 |
+
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
490 |
+
|
491 |
+
"""
|
492 |
+
if tag is None:
|
493 |
+
latest_path = os.path.join(checkpoint_dir, 'latest')
|
494 |
+
if os.path.isfile(latest_path):
|
495 |
+
with open(latest_path, 'r') as fd:
|
496 |
+
tag = fd.read().strip()
|
497 |
+
else:
|
498 |
+
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
499 |
+
|
500 |
+
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
501 |
+
|
502 |
+
if not os.path.isdir(ds_checkpoint_dir):
|
503 |
+
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
504 |
+
|
505 |
+
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
|
506 |
+
|
507 |
+
|
508 |
+
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
|
509 |
+
"""
|
510 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
511 |
+
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
512 |
+
|
513 |
+
Args:
|
514 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
515 |
+
- ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
|
516 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
517 |
+
"""
|
518 |
+
|
519 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
520 |
+
print(f"Saving fp32 state dict to {output_file}")
|
521 |
+
torch.save(state_dict, output_file)
|
522 |
+
|
523 |
+
|
524 |
+
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
525 |
+
"""
|
526 |
+
1. Put the provided model to cpu
|
527 |
+
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
528 |
+
3. Load it into the provided model
|
529 |
+
|
530 |
+
Args:
|
531 |
+
- ``model``: the model object to update
|
532 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
533 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
534 |
+
|
535 |
+
Returns:
|
536 |
+
- ``model`: modified model
|
537 |
+
|
538 |
+
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
539 |
+
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
540 |
+
conveniently placed for you in the checkpoint folder.
|
541 |
+
|
542 |
+
A typical usage might be ::
|
543 |
+
|
544 |
+
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
545 |
+
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
546 |
+
# submit to model hub or save the model to share with others
|
547 |
+
|
548 |
+
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
549 |
+
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
550 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
551 |
+
|
552 |
+
"""
|
553 |
+
logger.info(f"Extracting fp32 weights")
|
554 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
555 |
+
|
556 |
+
logger.info(f"Overwriting model with fp32 weights")
|
557 |
+
model = model.cpu()
|
558 |
+
model.load_state_dict(state_dict, strict=False)
|
559 |
+
|
560 |
+
return model
|
561 |
+
|
562 |
+
|
563 |
+
if __name__ == "__main__":
|
564 |
+
|
565 |
+
parser = argparse.ArgumentParser()
|
566 |
+
parser.add_argument("checkpoint_dir",
|
567 |
+
type=str,
|
568 |
+
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
569 |
+
parser.add_argument(
|
570 |
+
"output_file",
|
571 |
+
type=str,
|
572 |
+
help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
|
573 |
+
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
574 |
+
args = parser.parse_args()
|
575 |
+
|
576 |
+
debug = args.debug
|
577 |
+
|
578 |
+
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file)
|
config.json
ADDED
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "/data/yiyang_zhou/workplace/LLaVA/checkpoint/llava-v1.5-7b/",
|
3 |
+
"architectures": [
|
4 |
+
"LlavaLlamaForCausalLM"
|
5 |
+
],
|
6 |
+
"bos_token_id": 1,
|
7 |
+
"eos_token_id": 2,
|
8 |
+
"freeze_mm_mlp_adapter": false,
|
9 |
+
"freeze_mm_vision_resampler": false,
|
10 |
+
"hidden_act": "silu",
|
11 |
+
"hidden_size": 4096,
|
12 |
+
"image_aspect_ratio": "pad",
|
13 |
+
"initializer_range": 0.02,
|
14 |
+
"intermediate_size": 11008,
|
15 |
+
"max_length": 4096,
|
16 |
+
"max_position_embeddings": 4096,
|
17 |
+
"mm_hidden_size": 1024,
|
18 |
+
"mm_projector_lr": 2e-05,
|
19 |
+
"mm_projector_type": "mlp2x_gelu",
|
20 |
+
"mm_resampler_type": null,
|
21 |
+
"mm_use_im_patch_token": false,
|
22 |
+
"mm_use_im_start_end": false,
|
23 |
+
"mm_vision_select_feature": "patch",
|
24 |
+
"mm_vision_select_layer": -2,
|
25 |
+
"mm_vision_tower": "openai/clip-vit-large-patch14-336",
|
26 |
+
"model_type": "llava",
|
27 |
+
"num_attention_heads": 32,
|
28 |
+
"num_hidden_layers": 32,
|
29 |
+
"num_key_value_heads": 32,
|
30 |
+
"pad_token_id": 0,
|
31 |
+
"pretraining_tp": 1,
|
32 |
+
"rms_norm_eps": 1e-05,
|
33 |
+
"rope_scaling": null,
|
34 |
+
"tie_word_embeddings": false,
|
35 |
+
"tokenizer_model_max_length": 1048,
|
36 |
+
"tokenizer_padding_side": "right",
|
37 |
+
"torch_dtype": "float16",
|
38 |
+
"transformers_version": "4.31.0",
|
39 |
+
"tune_mm_mlp_adapter": false,
|
40 |
+
"tune_mm_vision_resampler": false,
|
41 |
+
"unfreeze_mm_vision_tower": false,
|
42 |
+
"use_cache": true,
|
43 |
+
"use_mm_proj": true,
|
44 |
+
"vocab_size": 32000
|
45 |
+
}
|
non_lora_trainables.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cc6b9cc23771073378c3887f3524986a3b61cac85cbc085f322b318241bc6845
|
3 |
+
size 455
|