File size: 8,195 Bytes
d0afae8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import os
from typing import Dict
import torch
ZERO_FILE_PREFIX = 'zero_pp_rank_'
LAYER_FILE_PREFIX = 'layer_'
MP_RANK_FILE_PREFIX = 'mp_rank_'
EMBEDDING_LAYER_INDEX = 0
FINAL_LAYER_NORM_INDEX = -1
ARGS_KEY = 'args'
ITERATION_KEY = 'iteration'
SEQUENTIAL_LAYERS = [
'input_layernorm.weight', 'input_layernorm.bias',
'self_attention.dense.bias',
'post_attention_layernorm.weight', 'post_attention_layernorm.bias',
'mlp.dense_4h_to_h.bias',
'position_embeddings.weight'
]
LAYER_CONCAT_DIM = {
'self_attention.dense.weight': 1,
'mlp.dense_4h_to_h.weight': 1
}
class DeepSpeedCheckpoint(object):
def __init__(self, dir, tp_degree=None, pp_degree=None, no_pp=False):
self.dir = dir
self.no_pp = no_pp
self.file_list = self._get_files(dir)
self.zero_files = self._get_files_with_prefix(self.file_list, ZERO_FILE_PREFIX)
self.layer_files = self._get_files_with_prefix(self.file_list, LAYER_FILE_PREFIX)
self.mp_rank_files = self._get_files_with_prefix(self.file_list, MP_RANK_FILE_PREFIX)
self.layer_keys = self._get_layer_keys()
self.layer_count = len(self.layer_keys)
if not self.no_pp:
self.original_tp_degree = len(self._get_files_with_prefix(self.layer_files, f'{LAYER_FILE_PREFIX}01'))
self.original_pp_degree = len(self.mp_rank_files) // self.original_tp_degree
else:
self.original_tp_degree = len(self.mp_rank_files)
self.original_pp_degree = 1
self.dp_degree = len(self.zero_files) // (self.original_pp_degree * self.original_tp_degree)
self.tp_degree = self.original_tp_degree if tp_degree is None else tp_degree
self.pp_degree = self.original_pp_degree if pp_degree is None else pp_degree
self.global_state = {}
self._sanity_check()
self.pp_to_transformer_map = self._build_pp_transformer_map()
self.transformer_file_map = self._build_transformer_file_map()
if not self.no_pp:
self.tp_to_embedding_map = self._build_tp_other_layer_map(EMBEDDING_LAYER_INDEX)
self.tp_to_final_norm_map = self._build_tp_other_layer_map(FINAL_LAYER_NORM_INDEX)
self._build_global_state()
def show_tp_embedding_map(self):
self._dump_mapping(self.tp_to_embedding_map, 'tp_to_embedding_layers')
def show_tp_final_norm_map(self):
self._dump_mapping(self.tp_to_final_norm_map, 'tp_to_final_norm_layers')
def show_pp_tranformer_map(self):
self._dump_mapping(self.pp_to_transformer_map, 'pp_to_tranformer_layers')
def show_transformer_file_map(self):
self._dump_mapping(self.transformer_file_map, 'rank_to_tranformer_files')
def _build_global_state(self):
sd = torch.load(self.mp_rank_files[0], map_location=torch.device('cpu'))
self.global_state[ITERATION_KEY] = sd.get(ITERATION_KEY, 0)
self.global_state[ARGS_KEY] = sd.get(ARGS_KEY, None)
def get_iteration(self):
if not ITERATION_KEY in self.global_state:
sd = torch.load(self.mp_rank_files[0], map_location=torch.device('cpu'))
self.global_state[ITERATION_KEY] = sd.get(ITERATION_KEY, 0)
return self.global_state[ITERATION_KEY]
def get_embedding_state(self, tp_index: int) -> Dict:
assert tp_index in self.tp_to_embedding_map.keys()
sd_list = [torch.load(fname, map_location=torch.device('cpu')) for fname in self.tp_to_embedding_map[tp_index]]
sd = self._merge_state_dicts(sd_list)
return sd
def get_args(self):
if not ARGS_KEY in self.global_state:
sd = torch.load(self.mp_rank_files[0], map_location=torch.device('cpu'))
self.global_state[ARGS_KEY] = sd.get(ARGS_KEY, None)
return self.global_state[ARGS_KEY]
def get_transformer_state(self, tp_index: int, pp_index: int) -> list:
assert tp_index < self.tp_degree
assert pp_index < self.pp_degree
t_list = []
for fname_list in self.transformer_file_map[(tp_index, pp_index)]:
sd_list = [torch.load(fname, map_location=torch.device('cpu')) for fname in fname_list]
sd = self._merge_state_dicts(sd_list)
t_list.append(sd)
return t_list
def get_final_norm_state(self, tp_index:int) -> Dict:
assert tp_index in self.tp_to_final_norm_map.keys()
sd = torch.load(self.tp_to_final_norm_map[tp_index][0], map_location=torch.device('cpu'))
return sd
def _build_tp_other_layer_map(self, layer_index:int):
assert layer_index < len(self.layer_files)
layer_files = self._get_files_with_prefix(self.layer_files, self.layer_keys[layer_index])
layer_file_partitions = self._partition_data(layer_files, self.tp_degree)
data_map = {i:flist for i, flist in enumerate(layer_file_partitions)}
return data_map
def _build_pp_transformer_map(self):
data_map = {}
transformer_layers = self.layer_keys[1:-1]
layers_per_pp = len(transformer_layers) // self.pp_degree
data_map = {i:transformer_layers[i*layers_per_pp:(i+1)*layers_per_pp] for i in range(0, self.pp_degree)}
return data_map
def _dump_mapping(self, data_map, map_tag = None):
if map_tag is not None:
print(f'Dump mapping: {map_tag}')
for k, v in data_map.items():
print(f'{k} = {v}')
def _build_transformer_file_map(self):
transformer_layer_keys = self.layer_keys[1:-1]
file_map = {}
layers_per_pp = len(transformer_layer_keys) // self.pp_degree
for key_index, layer_key in enumerate(transformer_layer_keys):
pp_index = key_index // layers_per_pp
layer_files = self._get_files_with_prefix(self.layer_files, layer_key)
layer_file_partitions = self._partition_data(layer_files, self.tp_degree)
for tp_index in range(self.tp_degree):
map_key = (tp_index, pp_index)
if not map_key in file_map.keys():
file_map[map_key] = []
file_map[map_key].append(layer_file_partitions[tp_index])
return file_map
def _sanity_check(self):
assert len(self.mp_rank_files) % self.tp_degree == 0
assert len(self.zero_files) % (self.pp_degree * self.tp_degree) == 0
if not self.no_pp:
assert len(self.layer_keys) > 2
assert (len(self.layer_keys) - 2) % self.pp_degree == 0
def _get_files_with_prefix(self, all_files, prefix):
file_list = []
for file_path in all_files:
_, fname = os.path.split(file_path)
if fname.startswith(prefix):
file_list.append(file_path)
return sorted(file_list)
def validate_files(self):
for file in self.file_list:
if not os.path.isfile(file):
print(f'Error: {file} is not existent')
def _get_files(self, dir):
file_list = []
for root, dirs, files in os.walk(dir):
for file in files:
file_list.append(os.path.join(root, file))
return file_list
def _get_layer_keys(self):
key_set = set()
key_len = len(LAYER_FILE_PREFIX) + 2
for file_path in self.layer_files:
_, fname = os.path.split(file_path)
key_set.add(fname[:key_len])
return sorted(list(key_set))
def _partition_data(self, data_list, num_partitions):
num_elems = len(data_list)
assert num_elems % num_partitions == 0
partition_size = num_elems // num_partitions
partitions_list = [data_list[i:i+partition_size] for i in range(0, num_elems, partition_size)]
return partitions_list
def _merge_state_dicts(self, sd_list):
merged_sd = {}
for key in sd_list[0].keys():
if not key in SEQUENTIAL_LAYERS:
cat_dim = LAYER_CONCAT_DIM.get(key, 0)
merged_sd[key] = torch.cat([sd[key] for sd in sd_list], dim=cat_dim)
else:
merged_sd[key] = sd_list[0][key]
return merged_sd
|