File size: 11,624 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 |
# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import tqdm
import argparse
from dataclasses import dataclass
import torch
from deepspeed.checkpoint import DeepSpeedCheckpoint
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument(
"--folder", default=None, type=str, help="DeepSpeed Checkpoint folder"
)
parser.add_argument(
"--model_type",
default="GPT",
type=str,
help="Type of the model",
choices=["GPT", "BLOOM", "LLAMA"],
)
args = parser.parse_args()
print(f"args = {args}")
return args
def show_3d(ds_checkpoint):
src_3d = ds_checkpoint.zero_checkpoint.src_3d
dp, tp, pp = src_3d.dp_degree, src_3d.tp_degree, src_3d.pp_degree
print(f"3D configuration: DP={dp} TP={tp} PP={pp}")
def get_layer_patterns_for_non_sharded(model_type):
if model_type == "GPT":
return [
"position_embeddings.weight",
"input_layernorm.weight",
"input_layernorm.bias",
"self_attention.query_key_value.bias",
"self_attention.dense.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"mlp.dense_h_to_4h.bias",
"mlp.dense_4h_to_h.bias",
"weight",
"bias",
]
elif model_type == "BLOOM":
return [
"input_layernorm.weight",
"input_layernorm.bias",
"self_attention.query_key_value.bias",
"self_attention.dense.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"mlp.dense_h_to_4h.bias",
"mlp.dense_4h_to_h.bias",
"weight",
"bias",
]
elif model_type == "LLAMA":
return [
"input_layernorm.weight",
"input_layernorm.bias",
"self_attention.query_key_value.bias",
"self_attention.dense.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"mlp.dense_h_to_4h.bias",
"mlp.dense_4h_to_h.bias",
"weight",
"bias",
]
def get_zero_patterns_for_non_sharded(model_type):
if model_type == "GPT":
patterns = [
r"tied_modules.embed.word_embeddings.norm.weight",
r"tied_modules.embed.word_embeddings.norm.bias",
r"tied_modules.embed.position_embeddings.weight",
r"\d+.self_attention.query_key_value.bias",
r"\d+.self_attention.dense.bias",
r"\d+.mlp.dense_h_to_4h.bias",
r"\d+.mlp.dense_4h_to_h.bias",
r"\d+.input_layernorm.weight",
r"\d+.input_layernorm.bias",
r"\d+.post_attention_layernorm.weight",
r"\d+.post_attention_layernorm.bias",
r"\d+.weight",
r"\d+.bias",
]
return patterns
if model_type == "BLOOM":
patterns = [
r"tied_modules.embed.word_embeddings.norm.weight",
r"tied_modules.embed.word_embeddings.norm.bias",
r"\d+.self_attention.query_key_value.bias",
r"\d+.self_attention.dense.bias",
r"\d+.mlp.dense_h_to_4h.bias",
r"\d+.mlp.dense_4h_to_h.bias",
r"\d+.input_layernorm.weight",
r"\d+.input_layernorm.bias",
r"\d+.post_attention_layernorm.weight",
r"\d+.post_attention_layernorm.bias",
r"\d+.weight",
r"\d+.bias",
]
return patterns
if model_type == "LLAMA":
patterns = [
r"\d+.word_embeddings.bias",
r"\d+.self_attention.query_key_value.bias",
r"\d+.self_attention.dense.bias",
r"\d+.mlp.dense_h_to_4h.bias",
r"\d+.mlp.dense_4h_to_h.bias",
r"\d+.input_layernorm.weight",
r"\d+.input_layernorm.bias",
r"\d+.post_attention_layernorm.weight",
r"\d+.post_attention_layernorm.bias",
r"\d+.weight",
r"\d+.bias",
]
return patterns
@dataclass
class ParamInfo:
pp: int
tp: int
dp: int
data: torch.Tensor
numel: int
def get_zero_pp_stage_non_sharded_params(
ds_checkpoint, model_type, pp_stage, dp_stage
):
patterns = get_zero_patterns_for_non_sharded(model_type)
params = {}
for tp_stage in tqdm.tqdm(range(ds_checkpoint.tp_degree), desc="bf16 zero files"):
sd = ds_checkpoint.get_zero_checkpoint_state(
pp_index=pp_stage, tp_index=tp_stage, dp_index=dp_stage
)
optim_sd = sd["optimizer_state_dict"]
param_slice_mappings = optim_sd["param_slice_mappings"]
state_groups = optim_sd["base_optimizer_state"]["state"]
fp32_groups = optim_sd["single_partition_of_fp32_groups"]
for param_group_id in range(len(state_groups)):
flat_state = dict(
exp_avg=state_groups[param_group_id]["exp_avg"],
exp_avg_sq=state_groups[param_group_id]["exp_avg_sq"],
fp32=fp32_groups[param_group_id],
)
for name, fragment_mapping in param_slice_mappings[param_group_id].items():
if not any(re.match(pattern, name) for pattern in patterns):
continue
for state_key in flat_state.keys():
tensor = (
flat_state[state_key]
.narrow(
dim=0,
start=fragment_mapping.start,
length=fragment_mapping.numel,
)
.clone()
)
info = ParamInfo(
pp=pp_stage,
tp=tp_stage,
dp=dp_stage,
data=tensor,
numel=fragment_mapping.numel,
)
full_name = name + ".__" + state_key
if full_name not in params:
params[full_name] = []
params[full_name].append(info)
return params
def verify_equal_params(params, tp):
failed = 0
report = {}
for name, info in params.items():
n = len(info)
if n != tp:
ok = False
print(f"{name}: FAILED expected n={n} == tp={tp}")
elif n == 1:
ok = True
else:
ok = all([(x.numel == info[0].numel) for x in info[1:]])
if not ok:
print(f"{name}: FAILED numel comparison [n={n}]")
else:
ok = all([x.data.eq(info[0].data).all().item() for x in info[1:]])
if not ok:
print(f"{name}: FAILED data comparison [n={n}]")
failed += ok == False
report[name] = (ok, n)
if ok:
print(f"{name}: OK [n={n}]")
return failed, report
def update_layer_non_sharded_params(params, model_type, filename, pp_index, tp_index):
layer_id, file_tp_index = re.search("layer_(\d+)-model_(\d+)", filename).groups()
layer_id = int(layer_id)
file_tp_index = int(file_tp_index)
# assert tp_index == file_tp_index, f'Inconsistent tp index tp_index={tp_index} file_tp_index={file_tp_index}'
if tp_index != file_tp_index:
print("bad")
sd = torch.load(filename, map_location=torch.device("cpu"))
sequential_layers = get_layer_patterns_for_non_sharded(model_type)
for key in sd.keys():
if key in sequential_layers:
param_key = str(layer_id) + "." + key
if param_key not in params:
params[param_key] = []
info = ParamInfo(
pp=pp_index, tp=tp_index, dp=-1, data=sd[key], numel=sd[key].numel()
)
params[param_key].append(info)
return params
def verify_layer_files(ds_checkpoint, model_type):
src_3d = ds_checkpoint.zero_checkpoint.src_3d
dp, tp, pp = src_3d.dp_degree, src_3d.tp_degree, src_3d.pp_degree
total_failed = 0
for pp_index in range(pp):
print(f"\nChecking pp_stage={pp_index}")
params = {}
if pp_index == 0:
for tp_index in range(tp):
for filename in ds_checkpoint.tp_to_embedding_map[tp_index]:
update_layer_non_sharded_params(
params, model_type, filename, pp_index, tp_index
)
for tp_index in range(tp):
for filename_list in ds_checkpoint.transformer_file_map[
(tp_index, pp_index)
]:
for filename in filename_list:
update_layer_non_sharded_params(
params, model_type, filename, pp_index, tp_index
)
if pp_index == (pp - 1):
for tp_index in range(tp):
for filename in ds_checkpoint.tp_to_final_norm_map[tp_index]:
update_layer_non_sharded_params(
params, model_type, filename, pp_index, tp_index
)
failed, report = verify_equal_params(params, tp)
total_failed += failed
return total_failed
def verify_zero_files(ds_checkpoint, model_type):
src_3d = ds_checkpoint.zero_checkpoint.src_3d
dp, tp, pp = src_3d.dp_degree, src_3d.tp_degree, src_3d.pp_degree
total_failed = 0
for i in range(pp):
for j in range(dp):
print(f"\nChecking pp_stage={i} dp_stage={j}")
params = get_zero_pp_stage_non_sharded_params(
ds_checkpoint, model_type, pp_stage=i, dp_stage=j
)
failed, report = verify_equal_params(params, tp)
total_failed += failed
return total_failed
def verify_checkpoint(folder, model_type):
final_layer_norm_idx = -2 if model_type == "LLAMA" else -1
ds_checkpoint = DeepSpeedCheckpoint(
folder, final_layer_norm_idx=final_layer_norm_idx
)
ds_checkpoint.validate_files()
show_3d(ds_checkpoint)
print("\nVerify ** layer_ ** files")
total_failed_layer = verify_layer_files(ds_checkpoint, model_type)
if total_failed_layer == 0:
print("\nCheckpoint layer files OK")
else:
print(f"\nCheckpoint layer files BAD with total_failed={total_failed_layer}")
print("\nVerify ** bf16_zero_ ** files")
total_failed_zero = verify_zero_files(ds_checkpoint, model_type)
if total_failed_zero == 0:
print("\nCheckpoint zero files OK")
else:
print(f"\nCheckpoint zero files BAD with total_failed={total_failed_zero}")
return (total_failed_layer + total_failed_zero) == 0
def main():
print(f"Verify DeepSpeed Checkpoint consistency for non-TP-sharded parameters")
args = parse_arguments()
assert (
verify_checkpoint(args.folder, args.model_type) is True
), "Checkpoint verification failed"
if __name__ == "__main__":
main()
|