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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import os
from typing import Dict
import torch
from .reshape_3d_utils import model_3d_desc
from .reshape_utils import (basic_folder_validation, merge_state, partition_data, get_files, get_files_with_prefix)
from .constants import (MODEL_FILE_PREFIX, LAYER_FILE_PREFIX)
from .reshape_meg_2d import reshape_meg_2d_parallel, meg_2d_parallel_map
from .zero_checkpoint import ZeROCheckpoint
from .constants import *
EMBEDDING_LAYER_INDEX = 0
FINAL_LAYER_NORM_INDEX = -1
ARGS_KEY = 'args'
CHECKPOINT_INFO_KEY = 'checkpoint_info'
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, dp_degree=None):
self.dir = dir
pipeline_parallel = len(get_files_with_prefix(get_files(dir), LAYER_FILE_PREFIX)) > 0
self._validate_folder(dir, pipeline_parallel)
self.zero_checkpoint = ZeROCheckpoint(dir)
self.file_list = get_files(dir)
self.layer_files = get_files_with_prefix(self.file_list, LAYER_FILE_PREFIX)
self.mp_rank_files = get_files_with_prefix(self.file_list, MODEL_FILE_PREFIX)
self.layer_keys = self._get_layer_keys()
self.layer_count = len(self.layer_keys)
self.tp_degree = self.zero_checkpoint.get_src_tp_degree() if tp_degree is None else tp_degree
self.pp_degree = self.zero_checkpoint.get_src_pp_degree() if pp_degree is None else pp_degree
self.dp_degree = self.zero_checkpoint.get_src_dp_degree() if dp_degree is None else dp_degree
self.original_world_size = self.zero_checkpoint.get_src_tp_degree() * self.zero_checkpoint.get_src_pp_degree(
) * self.zero_checkpoint.get_src_dp_degree()
self.world_size = self.tp_degree * self.pp_degree * self.dp_degree
self.old_2d_map = meg_2d_parallel_map(self.zero_checkpoint.get_src_pp_degree(),
self.zero_checkpoint.get_src_tp_degree())
self.old_2d_map.simple_init()
self.new_2d_map = reshape_meg_2d_parallel(old_pp_degree=self.zero_checkpoint.get_src_pp_degree(),
old_tp_degree=self.zero_checkpoint.get_src_tp_degree(),
new_pp_degree=self.pp_degree,
new_tp_degree=self.tp_degree)
if self.is_change_pp_degree() or self.is_change_tp_degree() or self.is_change_dp_degree():
self.zero_checkpoint.reshape(model_3d_desc(self.pp_degree, self.tp_degree, self.dp_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()
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 is_change_tp_degree(self):
return self.tp_degree != self.zero_checkpoint.get_src_tp_degree()
def is_change_pp_degree(self):
return self.pp_degree != self.zero_checkpoint.get_src_pp_degree()
def is_change_dp_degree(self):
return self.dp_degree != self.zero_checkpoint.get_src_dp_degree()
def show_2d_mapping(self):
print(f'reshaped 2d map ---- begin')
for i in range(self.pp_degree):
for j in range(self.tp_degree):
file_list = self.get_2d_parallel_files(pp_index=i, tp_index=j)
print(f'[{i}, {j}] = {file_list}')
print(f'reshaped 2d map ---- end')
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_transformer_map(self):
self._dump_mapping(self.pp_to_transformer_map, 'pp_to_transformer_layers')
def show_transformer_file_map(self):
self._dump_mapping(self.transformer_file_map, 'rank_to_transformer_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_zero_checkpoint_state(self, pp_index, tp_index, dp_index) -> dict:
return self.zero_checkpoint.get_state_for_rank(pp_index=pp_index,
tp_index=tp_index,
dp_index=dp_index,
keys_to_ignore=[PARAM_SHAPES])
def get_zero_files(self, pp_index, tp_index, dp_index) -> list:
return self.zero_checkpoint.get_files_for_rank(pp_index=pp_index, tp_index=tp_index, dp_index=dp_index)
def get_embedding_layer_id(self):
return self.layer_keys[EMBEDDING_LAYER_INDEX]
def get_final_norm_layer_id(self):
return self.layer_keys[FINAL_LAYER_NORM_INDEX]
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_embedding_files(self, tp_index: int) -> list:
assert tp_index in self.tp_to_embedding_map.keys()
return self.tp_to_embedding_map[tp_index]
def _get_checkpoint_value(self, key):
if not key in self.global_state:
sd = torch.load(self.mp_rank_files[0], map_location=torch.device('cpu'))
self.global_state[key] = sd.get(key, None)
return self.global_state[key]
def get_args(self):
return self._get_checkpoint_value(ARGS_KEY)
def get_checkpoint_info(self, info_key=CHECKPOINT_INFO_KEY):
return self._get_checkpoint_value(info_key)
def get_2d_parallel_state(self, tp_index: int, pp_index: int) -> dict:
assert tp_index < self.tp_degree
assert pp_index < self.pp_degree
fname_list = self.get_2d_parallel_files(tp_index=tp_index, pp_index=pp_index)
sd_list = [torch.load(fname, map_location=torch.device('cpu')) for fname in fname_list]
merged_sd = None
for sd in sd_list:
if merged_sd is None:
merged_sd = sd
else:
merged_sd = merge_state(merged_sd, sd)
return merged_sd
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_pp_transformer_map(self, pp_index: int) -> list:
assert pp_index < self.pp_degree
return self.pp_to_transformer_map[pp_index]
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 get_final_norm_files(self, tp_index: int) -> list:
assert tp_index in self.tp_to_final_norm_map.keys()
return self.tp_to_final_norm_map[tp_index]
def _build_tp_other_layer_map(self, layer_index: int):
data_map = {}
if len(self.layer_files) < 1:
return data_map
assert layer_index <= len(self.layer_files)
layer_files = get_files_with_prefix(self.layer_files, self.layer_keys[layer_index])
layer_file_partitions = partition_data(layer_files, self.tp_degree)
data_map = {i: flist for i, flist in enumerate(layer_file_partitions)}
return data_map
def get_2d_parallel_files(self, tp_index: int, pp_index: int) -> list:
assert tp_index < self.tp_degree
assert pp_index < self.pp_degree
file_indices = self.new_2d_map.get_data(pp_index=pp_index, tp_index=tp_index)
return [self.mp_rank_files[i] for i in file_indices]
def _build_pp_transformer_map(self):
data_map = {}
if self.pp_degree > 0:
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 = {}
# XXX: this is not guaranteed
layers_per_pp = 1
if self.pp_degree > 0:
layers_per_pp = len(transformer_layer_keys) // self.pp_degree
#print(f"{transformer_layer_keys} {layers_per_pp}")
for key_index, layer_key in enumerate(transformer_layer_keys):
pp_index = key_index // layers_per_pp
layer_files = get_files_with_prefix(self.layer_files, layer_key)
layer_file_partitions = 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 self.zero_checkpoint.num_files % (self.pp_degree * self.tp_degree) == 0
assert self.zero_checkpoint.num_files % (self.tp_degree) == 0
# XXX: fix me - isn't always the case
# only true with --pp-partition-method 'type:transformer|embedding' \
# assert (len(self.layer_keys) - 2) % self.pp_degree == 0
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_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 _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
def _validate_folder(self, dir, pipeline_parallel):
basic_folder_validation(dir)
file_list = get_files(dir)
file_prefix_list = [MODEL_FILE_PREFIX]
if pipeline_parallel:
file_prefix_list.extend([LAYER_FILE_PREFIX, f'{LAYER_FILE_PREFIX}01'])
for file_prefix in file_prefix_list:
ckpt_files = get_files_with_prefix(file_list, file_prefix)
assert len(
ckpt_files
) > 0, f'{dir} seems a bogus DeepSpeed checkpoint folder: Cannot find {file_prefix}* files in there.'
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