Commit
•
ed562a3
1
Parent(s):
dc920e1
ckpt added
Browse files- README.md +22 -1
- config.json +44 -0
- generation_config.json +8 -0
- pytorch_model-00001-of-00003.bin +3 -0
- pytorch_model-00002-of-00003.bin +3 -0
- pytorch_model-00003-of-00003.bin +3 -0
- pytorch_model.bin.index.json +805 -0
- special_tokens_map.json +24 -0
- tokenizer.model +3 -0
- tokenizer_config.json +35 -0
- trainer_state.json +0 -0
- training_args.bin +3 -0
- zero_to_fp32.py +578 -0
README.md
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---
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-
license:
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---
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---
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license: apache-2.0
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---
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<br>
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<br>
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# PosterLLaVA Model Card
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## Model details
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**Model type:**
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PoserLLaVA is an layout generation model with LLaVa achitecture. The current model is fine-tuned with the LLaVA-v1.5-13B checkpoint.
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**Model date:**
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posterllava_v0 was trained in March 2024.
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## Training dataset
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- 7k banner layouts from Ad Banner dataset
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- 60k commerical poster layouts from CGL dataset and PosterLayout with text constraints
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- 4k social media poster layouts from QB-Poster dataset
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## Evaluation dataset
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the combination of the corresponding test sets of the above training datasets
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config.json
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{
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"_name_or_path": "llava-v1.5-13b-posterllava_v0",
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"architectures": [
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"LlavaLlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"freeze_mm_mlp_adapter": false,
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"freeze_mm_vision_resampler": false,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"image_aspect_ratio": "pad",
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"image_grid_pinpoints": null,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_length": 4096,
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"max_position_embeddings": 4096,
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"mm_hidden_size": 1024,
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"mm_projector_lr": null,
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"mm_projector_type": "mlp2x_gelu",
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"mm_resampler_type": null,
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"mm_use_im_patch_token": false,
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"mm_use_im_start_end": false,
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"mm_vision_select_feature": "patch",
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"mm_vision_select_layer": -2,
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"mm_vision_tower": "openai/clip-vit-large-patch14-336",
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"model_type": "llava",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.31.0",
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"tune_mm_mlp_adapter": false,
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"tune_mm_vision_resampler": false,
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"unfreeze_mm_vision_tower": false,
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"use_cache": false,
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"use_mm_proj": true,
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"vocab_size": 32000
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_length": 4096,
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"pad_token_id": 0,
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"transformers_version": "4.31.0"
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}
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pytorch_model-00001-of-00003.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c1823336b8b48881295a6e7e162413745c2f18fbccc63c42bc6ba79aa9561481
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size 9948726510
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pytorch_model-00002-of-00003.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:de6e1402c891d2d7d9722482ae9e490b3503f019a351b525036f4d716c971dae
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size 9904162976
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pytorch_model-00003-of-00003.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7048ef91349e14be4e9114dc6e4e9f4398b1d5953fc823f829192c58f1ae688f
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size 6849082169
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pytorch_model.bin.index.json
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tokenizer_config.json
ADDED
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trainer_state.json
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|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
|
| 3 |
+
# Copyright (c) Microsoft Corporation.
|
| 4 |
+
# SPDX-License-Identifier: Apache-2.0
|
| 5 |
+
|
| 6 |
+
# DeepSpeed Team
|
| 7 |
+
|
| 8 |
+
# This script extracts fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints. It gets
|
| 9 |
+
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
| 10 |
+
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
+
# application.
|
| 12 |
+
#
|
| 13 |
+
# example: python zero_to_fp32.py . pytorch_model.bin
|
| 14 |
+
|
| 15 |
+
import argparse
|
| 16 |
+
import torch
|
| 17 |
+
import glob
|
| 18 |
+
import math
|
| 19 |
+
import os
|
| 20 |
+
import re
|
| 21 |
+
from collections import OrderedDict
|
| 22 |
+
from dataclasses import dataclass
|
| 23 |
+
|
| 24 |
+
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
| 25 |
+
# DeepSpeed data structures it has to be available in the current python environment.
|
| 26 |
+
from deepspeed.utils import logger
|
| 27 |
+
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
| 28 |
+
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
| 29 |
+
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
@dataclass
|
| 33 |
+
class zero_model_state:
|
| 34 |
+
buffers: dict()
|
| 35 |
+
param_shapes: dict()
|
| 36 |
+
shared_params: list
|
| 37 |
+
ds_version: int
|
| 38 |
+
frozen_param_shapes: dict()
|
| 39 |
+
frozen_param_fragments: dict()
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
debug = 0
|
| 43 |
+
|
| 44 |
+
# load to cpu
|
| 45 |
+
device = torch.device('cpu')
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
def atoi(text):
|
| 49 |
+
return int(text) if text.isdigit() else text
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
def natural_keys(text):
|
| 53 |
+
'''
|
| 54 |
+
alist.sort(key=natural_keys) sorts in human order
|
| 55 |
+
http://nedbatchelder.com/blog/200712/human_sorting.html
|
| 56 |
+
(See Toothy's implementation in the comments)
|
| 57 |
+
'''
|
| 58 |
+
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
def get_model_state_file(checkpoint_dir, zero_stage):
|
| 62 |
+
if not os.path.isdir(checkpoint_dir):
|
| 63 |
+
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
| 64 |
+
|
| 65 |
+
# there should be only one file
|
| 66 |
+
if zero_stage == 2:
|
| 67 |
+
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
| 68 |
+
elif zero_stage == 3:
|
| 69 |
+
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
| 70 |
+
|
| 71 |
+
if not os.path.exists(file):
|
| 72 |
+
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
| 73 |
+
|
| 74 |
+
return file
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
| 78 |
+
# XXX: need to test that this simple glob rule works for multi-node setup too
|
| 79 |
+
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
| 80 |
+
|
| 81 |
+
if len(ckpt_files) == 0:
|
| 82 |
+
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
| 83 |
+
|
| 84 |
+
return ckpt_files
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
def get_optim_files(checkpoint_dir):
|
| 88 |
+
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
def get_model_state_files(checkpoint_dir):
|
| 92 |
+
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def parse_model_states(files):
|
| 96 |
+
zero_model_states = []
|
| 97 |
+
for file in files:
|
| 98 |
+
state_dict = torch.load(file, map_location=device)
|
| 99 |
+
|
| 100 |
+
if BUFFER_NAMES not in state_dict:
|
| 101 |
+
raise ValueError(f"{file} is not a model state checkpoint")
|
| 102 |
+
buffer_names = state_dict[BUFFER_NAMES]
|
| 103 |
+
if debug:
|
| 104 |
+
print("Found buffers:", buffer_names)
|
| 105 |
+
|
| 106 |
+
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
| 107 |
+
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
| 108 |
+
param_shapes = state_dict[PARAM_SHAPES]
|
| 109 |
+
|
| 110 |
+
# collect parameters that are included in param_shapes
|
| 111 |
+
param_names = []
|
| 112 |
+
for s in param_shapes:
|
| 113 |
+
for name in s.keys():
|
| 114 |
+
param_names.append(name)
|
| 115 |
+
|
| 116 |
+
# update with frozen parameters
|
| 117 |
+
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
| 118 |
+
if frozen_param_shapes is not None:
|
| 119 |
+
if debug:
|
| 120 |
+
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
| 121 |
+
param_names += list(frozen_param_shapes.keys())
|
| 122 |
+
|
| 123 |
+
# handle shared params
|
| 124 |
+
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
| 125 |
+
|
| 126 |
+
ds_version = state_dict.get(DS_VERSION, None)
|
| 127 |
+
|
| 128 |
+
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
| 129 |
+
|
| 130 |
+
z_model_state = zero_model_state(buffers=buffers,
|
| 131 |
+
param_shapes=param_shapes,
|
| 132 |
+
shared_params=shared_params,
|
| 133 |
+
ds_version=ds_version,
|
| 134 |
+
frozen_param_shapes=frozen_param_shapes,
|
| 135 |
+
frozen_param_fragments=frozen_param_fragments)
|
| 136 |
+
zero_model_states.append(z_model_state)
|
| 137 |
+
|
| 138 |
+
return zero_model_states
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
def parse_optim_states(files, ds_checkpoint_dir):
|
| 142 |
+
|
| 143 |
+
total_files = len(files)
|
| 144 |
+
state_dicts = []
|
| 145 |
+
for f in files:
|
| 146 |
+
state_dicts.append(torch.load(f, map_location=device))
|
| 147 |
+
|
| 148 |
+
if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
| 149 |
+
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
| 150 |
+
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
| 151 |
+
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
| 152 |
+
|
| 153 |
+
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
| 154 |
+
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
| 155 |
+
# use the max of the partition_count to get the dp world_size.
|
| 156 |
+
|
| 157 |
+
if type(world_size) is list:
|
| 158 |
+
world_size = max(world_size)
|
| 159 |
+
|
| 160 |
+
if world_size != total_files:
|
| 161 |
+
raise ValueError(
|
| 162 |
+
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
| 163 |
+
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
| 164 |
+
)
|
| 165 |
+
|
| 166 |
+
# the groups are named differently in each stage
|
| 167 |
+
if zero_stage == 2:
|
| 168 |
+
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
| 169 |
+
elif zero_stage == 3:
|
| 170 |
+
fp32_groups_key = FP32_FLAT_GROUPS
|
| 171 |
+
else:
|
| 172 |
+
raise ValueError(f"unknown zero stage {zero_stage}")
|
| 173 |
+
|
| 174 |
+
if zero_stage == 2:
|
| 175 |
+
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
| 176 |
+
elif zero_stage == 3:
|
| 177 |
+
# if there is more than one param group, there will be multiple flattened tensors - one
|
| 178 |
+
# flattened tensor per group - for simplicity merge them into a single tensor
|
| 179 |
+
#
|
| 180 |
+
# XXX: could make the script more memory efficient for when there are multiple groups - it
|
| 181 |
+
# will require matching the sub-lists of param_shapes for each param group flattened tensor
|
| 182 |
+
|
| 183 |
+
fp32_flat_groups = [
|
| 184 |
+
torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
|
| 185 |
+
]
|
| 186 |
+
|
| 187 |
+
return zero_stage, world_size, fp32_flat_groups
|
| 188 |
+
|
| 189 |
+
|
| 190 |
+
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
|
| 191 |
+
"""
|
| 192 |
+
Returns fp32 state_dict reconstructed from ds checkpoint
|
| 193 |
+
|
| 194 |
+
Args:
|
| 195 |
+
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
| 196 |
+
|
| 197 |
+
"""
|
| 198 |
+
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
| 199 |
+
|
| 200 |
+
optim_files = get_optim_files(ds_checkpoint_dir)
|
| 201 |
+
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
| 202 |
+
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
| 203 |
+
|
| 204 |
+
model_files = get_model_state_files(ds_checkpoint_dir)
|
| 205 |
+
|
| 206 |
+
zero_model_states = parse_model_states(model_files)
|
| 207 |
+
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
| 208 |
+
|
| 209 |
+
if zero_stage == 2:
|
| 210 |
+
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
|
| 211 |
+
elif zero_stage == 3:
|
| 212 |
+
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
|
| 213 |
+
|
| 214 |
+
|
| 215 |
+
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
| 216 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 217 |
+
return
|
| 218 |
+
|
| 219 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 220 |
+
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
| 221 |
+
|
| 222 |
+
if debug:
|
| 223 |
+
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
| 224 |
+
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 225 |
+
|
| 226 |
+
wanted_params = len(frozen_param_shapes)
|
| 227 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 228 |
+
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
| 229 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 230 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 231 |
+
|
| 232 |
+
total_params = 0
|
| 233 |
+
total_numel = 0
|
| 234 |
+
for name, shape in frozen_param_shapes.items():
|
| 235 |
+
total_params += 1
|
| 236 |
+
unpartitioned_numel = shape.numel()
|
| 237 |
+
total_numel += unpartitioned_numel
|
| 238 |
+
|
| 239 |
+
state_dict[name] = frozen_param_fragments[name]
|
| 240 |
+
|
| 241 |
+
if debug:
|
| 242 |
+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 243 |
+
|
| 244 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 245 |
+
|
| 246 |
+
|
| 247 |
+
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 248 |
+
param_shapes = zero_model_states[0].param_shapes
|
| 249 |
+
|
| 250 |
+
# Reconstruction protocol:
|
| 251 |
+
#
|
| 252 |
+
# XXX: document this
|
| 253 |
+
|
| 254 |
+
if debug:
|
| 255 |
+
for i in range(world_size):
|
| 256 |
+
for j in range(len(fp32_flat_groups[0])):
|
| 257 |
+
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
| 258 |
+
|
| 259 |
+
# XXX: memory usage doubles here (zero2)
|
| 260 |
+
num_param_groups = len(fp32_flat_groups[0])
|
| 261 |
+
merged_single_partition_of_fp32_groups = []
|
| 262 |
+
for i in range(num_param_groups):
|
| 263 |
+
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
| 264 |
+
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
| 265 |
+
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
| 266 |
+
avail_numel = sum(
|
| 267 |
+
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
| 268 |
+
|
| 269 |
+
if debug:
|
| 270 |
+
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
| 271 |
+
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
| 272 |
+
# not asserting if there is a mismatch due to possible padding
|
| 273 |
+
print(f"Have {avail_numel} numels to process.")
|
| 274 |
+
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
| 275 |
+
|
| 276 |
+
# params
|
| 277 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 278 |
+
# out-of-core computing solution
|
| 279 |
+
total_numel = 0
|
| 280 |
+
total_params = 0
|
| 281 |
+
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
| 282 |
+
offset = 0
|
| 283 |
+
avail_numel = full_single_fp32_vector.numel()
|
| 284 |
+
for name, shape in shapes.items():
|
| 285 |
+
|
| 286 |
+
unpartitioned_numel = shape.numel()
|
| 287 |
+
total_numel += unpartitioned_numel
|
| 288 |
+
total_params += 1
|
| 289 |
+
|
| 290 |
+
if debug:
|
| 291 |
+
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 292 |
+
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
| 293 |
+
offset += unpartitioned_numel
|
| 294 |
+
|
| 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
|
| 299 |
+
align_to = 2 * world_size
|
| 300 |
+
|
| 301 |
+
def zero2_align(x):
|
| 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)
|