update model with additional 1.8ish epochs training
Browse files- .gitattributes +0 -1
- config.json +1 -1
- latest +1 -1
- pytorch_model.bin +1 -1
- tokenizer.json +6 -3
- trainer_state.json +2164 -0
- training_args.bin +2 -2
- zero_to_fp32.py +17 -1
.gitattributes
CHANGED
|
@@ -1,7 +1,6 @@
|
|
| 1 |
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
-
*.bin.* filter=lfs diff=lfs merge=lfs -text
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| 5 |
*.bz2 filter=lfs diff=lfs merge=lfs -text
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| 6 |
*.ftz filter=lfs diff=lfs merge=lfs -text
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| 7 |
*.gz filter=lfs diff=lfs merge=lfs -text
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| 1 |
*.7z filter=lfs diff=lfs merge=lfs -text
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| 2 |
*.arrow filter=lfs diff=lfs merge=lfs -text
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| 3 |
*.bin filter=lfs diff=lfs merge=lfs -text
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| 4 |
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
*.ftz filter=lfs diff=lfs merge=lfs -text
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| 6 |
*.gz filter=lfs diff=lfs merge=lfs -text
|
config.json
CHANGED
|
@@ -75,7 +75,7 @@
|
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| 75 |
},
|
| 76 |
"tokenizer_class": "GPT2Tokenizer",
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| 77 |
"torch_dtype": "bfloat16",
|
| 78 |
-
"transformers_version": "4.
|
| 79 |
"use_cache": false,
|
| 80 |
"vocab_size": 50257,
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| 81 |
"window_size": 256
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|
|
|
| 75 |
},
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| 76 |
"tokenizer_class": "GPT2Tokenizer",
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| 77 |
"torch_dtype": "bfloat16",
|
| 78 |
+
"transformers_version": "4.18.0",
|
| 79 |
"use_cache": false,
|
| 80 |
"vocab_size": 50257,
|
| 81 |
"window_size": 256
|
latest
CHANGED
|
@@ -1 +1 @@
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| 1 |
-
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| 1 |
+
global_step1794
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 11142172078
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|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d3ed96d4f16a77132786cd79f6f7a262d707e6b1be5c57f6facc06fd852fcb0f
|
| 3 |
size 11142172078
|
tokenizer.json
CHANGED
|
@@ -17,17 +17,20 @@
|
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| 17 |
"pre_tokenizer": {
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| 18 |
"type": "ByteLevel",
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| 19 |
"add_prefix_space": false,
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| 20 |
-
"trim_offsets": true
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| 21 |
},
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| 22 |
"post_processor": {
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| 23 |
"type": "ByteLevel",
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| 24 |
"add_prefix_space": true,
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| 25 |
-
"trim_offsets": false
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| 26 |
},
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| 27 |
"decoder": {
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| 28 |
"type": "ByteLevel",
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| 29 |
"add_prefix_space": true,
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| 30 |
-
"trim_offsets": true
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| 31 |
},
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| 32 |
"model": {
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| 33 |
"type": "BPE",
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| 17 |
"pre_tokenizer": {
|
| 18 |
"type": "ByteLevel",
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| 19 |
"add_prefix_space": false,
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| 20 |
+
"trim_offsets": true,
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| 21 |
+
"use_regex": true
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| 22 |
},
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| 23 |
"post_processor": {
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| 24 |
"type": "ByteLevel",
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| 25 |
"add_prefix_space": true,
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| 26 |
+
"trim_offsets": false,
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| 27 |
+
"use_regex": true
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| 28 |
},
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| 29 |
"decoder": {
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| 30 |
"type": "ByteLevel",
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| 31 |
"add_prefix_space": true,
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| 32 |
+
"trim_offsets": true,
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| 33 |
+
"use_regex": true
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| 34 |
},
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| 35 |
"model": {
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| 36 |
"type": "BPE",
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trainer_state.json
ADDED
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@@ -0,0 +1,2164 @@
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"trial_name": null,
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| 2164 |
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}
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ccc24300f7e033dad3fb336ef3c16599d6de8427088ebd5afd5853b0fd9fa708
|
| 3 |
+
size 4207
|
zero_to_fp32.py
CHANGED
|
@@ -12,6 +12,7 @@ import torch
|
|
| 12 |
import glob
|
| 13 |
import math
|
| 14 |
import os
|
|
|
|
| 15 |
from collections import OrderedDict
|
| 16 |
|
| 17 |
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
|
@@ -34,6 +35,19 @@ debug = 0
|
|
| 34 |
device = torch.device('cpu')
|
| 35 |
|
| 36 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 37 |
def get_model_state_file(checkpoint_dir, zero_stage):
|
| 38 |
if not os.path.isdir(checkpoint_dir):
|
| 39 |
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
|
@@ -52,7 +66,9 @@ def get_model_state_file(checkpoint_dir, zero_stage):
|
|
| 52 |
|
| 53 |
def get_optim_files(checkpoint_dir):
|
| 54 |
# XXX: need to test that this simple glob rule works for multi-node setup too
|
| 55 |
-
optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
|
|
|
|
|
|
|
| 56 |
|
| 57 |
if len(optim_files) == 0:
|
| 58 |
raise FileNotFoundError(
|
|
|
|
| 12 |
import glob
|
| 13 |
import math
|
| 14 |
import os
|
| 15 |
+
import re
|
| 16 |
from collections import OrderedDict
|
| 17 |
|
| 18 |
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
|
|
|
| 35 |
device = torch.device('cpu')
|
| 36 |
|
| 37 |
|
| 38 |
+
def atoi(text):
|
| 39 |
+
return int(text) if text.isdigit() else text
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
def natural_keys(text):
|
| 43 |
+
'''
|
| 44 |
+
alist.sort(key=natural_keys) sorts in human order
|
| 45 |
+
http://nedbatchelder.com/blog/200712/human_sorting.html
|
| 46 |
+
(See Toothy's implementation in the comments)
|
| 47 |
+
'''
|
| 48 |
+
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
| 49 |
+
|
| 50 |
+
|
| 51 |
def get_model_state_file(checkpoint_dir, zero_stage):
|
| 52 |
if not os.path.isdir(checkpoint_dir):
|
| 53 |
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
|
|
|
| 66 |
|
| 67 |
def get_optim_files(checkpoint_dir):
|
| 68 |
# XXX: need to test that this simple glob rule works for multi-node setup too
|
| 69 |
+
optim_files = sorted(glob.glob(os.path.join(checkpoint_dir,
|
| 70 |
+
"*_optim_states.pt")),
|
| 71 |
+
key=natural_keys)
|
| 72 |
|
| 73 |
if len(optim_files) == 0:
|
| 74 |
raise FileNotFoundError(
|