Commit
•
79f8440
1
Parent(s):
756239c
Add new SentenceTransformer model.
Browse files- .gitattributes +3 -2
- 1_Pooling/config.json +7 -0
- config.json +24 -0
- config_sentence_transformers.json +7 -0
- data_config.json +1452 -0
- modules.json +20 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- train_script.py +344 -0
- vocab.txt +0 -0
.gitattributes
CHANGED
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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{
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"word_embedding_dimension": 384,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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config.json
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{
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"_name_or_path": "nreimers/MiniLM-L6-H384-uncased",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.8.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.6.1",
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"pytorch": "1.8.1"
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}
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}
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data_config.json
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+
]
|
modules.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c3a85f238711653950f6a79ece63eb0ea93d76f6a6284be04019c53733baf256
|
3 |
+
size 90888945
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 256,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "name_or_path": "nreimers/MiniLM-L6-H384-uncased", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer", "model_max_length": 512}
|
train_script.py
ADDED
@@ -0,0 +1,344 @@
|
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|
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|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""
|
2 |
+
Train script for a single file
|
3 |
+
|
4 |
+
Need to set the TPU address first:
|
5 |
+
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
|
6 |
+
"""
|
7 |
+
|
8 |
+
import torch.multiprocessing as mp
|
9 |
+
import threading
|
10 |
+
import time
|
11 |
+
import random
|
12 |
+
import sys
|
13 |
+
import argparse
|
14 |
+
import gzip
|
15 |
+
import json
|
16 |
+
import logging
|
17 |
+
import tqdm
|
18 |
+
import torch
|
19 |
+
from torch import nn
|
20 |
+
from torch.utils.data import DataLoader
|
21 |
+
import torch
|
22 |
+
import torch_xla
|
23 |
+
import torch_xla.core
|
24 |
+
import torch_xla.core.functions
|
25 |
+
import torch_xla.core.xla_model as xm
|
26 |
+
import torch_xla.distributed.xla_multiprocessing as xmp
|
27 |
+
import torch_xla.distributed.parallel_loader as pl
|
28 |
+
import os
|
29 |
+
from shutil import copyfile
|
30 |
+
|
31 |
+
|
32 |
+
from transformers import (
|
33 |
+
AdamW,
|
34 |
+
AutoModel,
|
35 |
+
AutoTokenizer,
|
36 |
+
get_linear_schedule_with_warmup,
|
37 |
+
set_seed,
|
38 |
+
)
|
39 |
+
|
40 |
+
class AutoModelForSentenceEmbedding(nn.Module):
|
41 |
+
def __init__(self, model_name, tokenizer, normalize=True):
|
42 |
+
super(AutoModelForSentenceEmbedding, self).__init__()
|
43 |
+
|
44 |
+
self.model = AutoModel.from_pretrained(model_name)
|
45 |
+
self.normalize = normalize
|
46 |
+
self.tokenizer = tokenizer
|
47 |
+
|
48 |
+
def forward(self, **kwargs):
|
49 |
+
model_output = self.model(**kwargs)
|
50 |
+
embeddings = self.mean_pooling(model_output, kwargs['attention_mask'])
|
51 |
+
if self.normalize:
|
52 |
+
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
|
53 |
+
|
54 |
+
return embeddings
|
55 |
+
|
56 |
+
def mean_pooling(self, model_output, attention_mask):
|
57 |
+
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
|
58 |
+
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
59 |
+
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
60 |
+
|
61 |
+
def save_pretrained(self, output_path):
|
62 |
+
if xm.is_master_ordinal():
|
63 |
+
self.tokenizer.save_pretrained(output_path)
|
64 |
+
self.model.config.save_pretrained(output_path)
|
65 |
+
|
66 |
+
xm.save(self.model.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
|
67 |
+
|
68 |
+
|
69 |
+
|
70 |
+
|
71 |
+
def train_function(index, args, queue):
|
72 |
+
tokenizer = AutoTokenizer.from_pretrained(args.model)
|
73 |
+
model = AutoModelForSentenceEmbedding(args.model, tokenizer)
|
74 |
+
|
75 |
+
|
76 |
+
### Train Loop
|
77 |
+
device = xm.xla_device()
|
78 |
+
model = model.to(device)
|
79 |
+
|
80 |
+
# Instantiate optimizer
|
81 |
+
optimizer = AdamW(params=model.parameters(), lr=2e-5, correct_bias=True)
|
82 |
+
|
83 |
+
lr_scheduler = get_linear_schedule_with_warmup(
|
84 |
+
optimizer=optimizer,
|
85 |
+
num_warmup_steps=500,
|
86 |
+
num_training_steps=args.steps,
|
87 |
+
)
|
88 |
+
|
89 |
+
# Now we train the model
|
90 |
+
cross_entropy_loss = nn.CrossEntropyLoss()
|
91 |
+
max_grad_norm = 1
|
92 |
+
|
93 |
+
model.train()
|
94 |
+
|
95 |
+
for global_step in tqdm.trange(args.steps, disable=not xm.is_master_ordinal()):
|
96 |
+
#### Get the batch data
|
97 |
+
batch = queue.get()
|
98 |
+
#print(index, "batch {}x{}".format(len(batch), ",".join([str(len(b)) for b in batch])))
|
99 |
+
|
100 |
+
|
101 |
+
if len(batch[0]) == 2: #(anchor, positive)
|
102 |
+
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
103 |
+
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
104 |
+
|
105 |
+
### Compute embeddings
|
106 |
+
embeddings_a = model(**text1.to(device))
|
107 |
+
embeddings_b = model(**text2.to(device))
|
108 |
+
|
109 |
+
### Gather all embedings
|
110 |
+
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
|
111 |
+
embeddings_b = torch_xla.core.functions.all_gather(embeddings_b)
|
112 |
+
|
113 |
+
### Compute similarity scores 512 x 512
|
114 |
+
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
|
115 |
+
|
116 |
+
### Compute cross-entropy loss
|
117 |
+
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
|
118 |
+
|
119 |
+
## Symmetric loss as in CLIP
|
120 |
+
loss = (cross_entropy_loss(scores, labels) + cross_entropy_loss(scores.transpose(0, 1), labels)) / 2
|
121 |
+
|
122 |
+
else: #(anchor, positive, negative)
|
123 |
+
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
124 |
+
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
125 |
+
text3 = tokenizer([b[2] for b in batch], return_tensors="pt", max_length=args.max_length, truncation=True, padding="max_length")
|
126 |
+
|
127 |
+
embeddings_a = model(**text1.to(device))
|
128 |
+
embeddings_b1 = model(**text2.to(device))
|
129 |
+
embeddings_b2 = model(**text3.to(device))
|
130 |
+
|
131 |
+
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
|
132 |
+
embeddings_b1 = torch_xla.core.functions.all_gather(embeddings_b1)
|
133 |
+
embeddings_b2 = torch_xla.core.functions.all_gather(embeddings_b2)
|
134 |
+
|
135 |
+
embeddings_b = torch.cat([embeddings_b1, embeddings_b2])
|
136 |
+
|
137 |
+
### Compute similarity scores 512 x 1024
|
138 |
+
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
|
139 |
+
|
140 |
+
### Compute cross-entropy loss
|
141 |
+
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
|
142 |
+
|
143 |
+
## One-way loss
|
144 |
+
loss = cross_entropy_loss(scores, labels)
|
145 |
+
|
146 |
+
|
147 |
+
# Backward pass
|
148 |
+
optimizer.zero_grad()
|
149 |
+
loss.backward()
|
150 |
+
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
|
151 |
+
|
152 |
+
xm.optimizer_step(optimizer, barrier=True)
|
153 |
+
lr_scheduler.step()
|
154 |
+
|
155 |
+
|
156 |
+
#Save model
|
157 |
+
if (global_step+1) % args.save_steps == 0:
|
158 |
+
output_path = os.path.join(args.output, str(global_step+1))
|
159 |
+
xm.master_print("save model: "+output_path)
|
160 |
+
model.save_pretrained(output_path)
|
161 |
+
|
162 |
+
|
163 |
+
output_path = os.path.join(args.output, "final")
|
164 |
+
xm.master_print("save model final: "+ output_path)
|
165 |
+
model.save_pretrained(output_path)
|
166 |
+
|
167 |
+
|
168 |
+
def produce_data(args, queue, filepaths, dataset_indices):
|
169 |
+
global_batch_size = args.batch_size*args.nprocs #Global batch size
|
170 |
+
size_per_dataset = int(global_batch_size / args.datasets_per_batch) #How many datasets per batch
|
171 |
+
num_same_dataset = int(size_per_dataset / args.batch_size)
|
172 |
+
print("producer", "global_batch_size", global_batch_size)
|
173 |
+
print("producer", "size_per_dataset", size_per_dataset)
|
174 |
+
print("producer", "num_same_dataset", num_same_dataset)
|
175 |
+
|
176 |
+
datasets = []
|
177 |
+
for filepath in filepaths:
|
178 |
+
if "reddit_" in filepath: #Special dataset class for Reddit files
|
179 |
+
data_obj = RedditDataset(filepath)
|
180 |
+
else:
|
181 |
+
data_obj = Dataset(filepath)
|
182 |
+
datasets.append(iter(data_obj))
|
183 |
+
|
184 |
+
# Store if dataset is in a 2 col or 3 col format
|
185 |
+
num_cols = {idx: len(next(dataset)) for idx, dataset in enumerate(datasets)}
|
186 |
+
|
187 |
+
while True:
|
188 |
+
texts_in_batch = set()
|
189 |
+
batch_format = None #2 vs 3 col format for this batch
|
190 |
+
|
191 |
+
#Add data from several sub datasets
|
192 |
+
for _ in range(args.datasets_per_batch):
|
193 |
+
valid_dataset = False #Check that datasets have the same 2/3 col format
|
194 |
+
while not valid_dataset:
|
195 |
+
data_idx = random.choice(dataset_indices)
|
196 |
+
if batch_format is None:
|
197 |
+
batch_format = num_cols[data_idx]
|
198 |
+
valid_dataset = True
|
199 |
+
else: #Check that this dataset has the same format
|
200 |
+
valid_dataset = (batch_format == num_cols[data_idx])
|
201 |
+
|
202 |
+
#Get data from this dataset
|
203 |
+
dataset = datasets[data_idx]
|
204 |
+
for _ in range(num_same_dataset):
|
205 |
+
for _ in range(args.nprocs):
|
206 |
+
batch_device = [] #A batch for one device
|
207 |
+
while len(batch_device) < args.batch_size:
|
208 |
+
sample = next(dataset)
|
209 |
+
in_batch = False
|
210 |
+
for text in sample:
|
211 |
+
if text in texts_in_batch:
|
212 |
+
in_batch = True
|
213 |
+
break
|
214 |
+
|
215 |
+
if not in_batch:
|
216 |
+
for text in sample:
|
217 |
+
texts_in_batch.add(text)
|
218 |
+
batch_device.append(sample)
|
219 |
+
|
220 |
+
queue.put(batch_device)
|
221 |
+
|
222 |
+
|
223 |
+
class RedditDataset:
|
224 |
+
"""
|
225 |
+
A class that handles the reddit data files
|
226 |
+
"""
|
227 |
+
def __init__(self, filepath):
|
228 |
+
self.filepath = filepath
|
229 |
+
|
230 |
+
def __iter__(self):
|
231 |
+
while True:
|
232 |
+
with gzip.open(self.filepath, "rt") as fIn:
|
233 |
+
for line in fIn:
|
234 |
+
data = json.loads(line)
|
235 |
+
|
236 |
+
if "response" in data and "context" in data:
|
237 |
+
yield [data["response"], data["context"]]
|
238 |
+
|
239 |
+
class Dataset:
|
240 |
+
"""
|
241 |
+
A class that handles one dataset
|
242 |
+
"""
|
243 |
+
def __init__(self, filepath):
|
244 |
+
self.filepath = filepath
|
245 |
+
|
246 |
+
def __iter__(self):
|
247 |
+
max_dataset_size = 10*1000*1000 #Cache small datasets in memory
|
248 |
+
dataset = []
|
249 |
+
data_format = None
|
250 |
+
|
251 |
+
while dataset is None or len(dataset) == 0:
|
252 |
+
with gzip.open(self.filepath, "rt") as fIn:
|
253 |
+
for line in fIn:
|
254 |
+
data = json.loads(line)
|
255 |
+
if isinstance(data, dict):
|
256 |
+
data = data['texts']
|
257 |
+
|
258 |
+
if data_format is None:
|
259 |
+
data_format = len(data)
|
260 |
+
|
261 |
+
#Ensure that all entries are of the same 2/3 col format
|
262 |
+
assert len(data) == data_format
|
263 |
+
|
264 |
+
if dataset is not None:
|
265 |
+
dataset.append(data)
|
266 |
+
if len(dataset) >= max_dataset_size:
|
267 |
+
dataset = None
|
268 |
+
|
269 |
+
yield data
|
270 |
+
|
271 |
+
# Data loaded. Now stream to the queue
|
272 |
+
# Shuffle for each epoch
|
273 |
+
while True:
|
274 |
+
random.shuffle(dataset)
|
275 |
+
for data in dataset:
|
276 |
+
yield data
|
277 |
+
|
278 |
+
|
279 |
+
|
280 |
+
if __name__ == "__main__":
|
281 |
+
parser = argparse.ArgumentParser()
|
282 |
+
parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased')
|
283 |
+
parser.add_argument('--steps', type=int, default=2000)
|
284 |
+
parser.add_argument('--save_steps', type=int, default=10000)
|
285 |
+
parser.add_argument('--batch_size', type=int, default=64)
|
286 |
+
parser.add_argument('--max_length', type=int, default=128)
|
287 |
+
parser.add_argument('--nprocs', type=int, default=8)
|
288 |
+
parser.add_argument('--datasets_per_batch', type=int, default=2, help="Number of datasets per batch")
|
289 |
+
parser.add_argument('--scale', type=float, default=20, help="Use 20 for cossim, and 1 when you work with unnormalized embeddings with dot product")
|
290 |
+
parser.add_argument('--data_folder', default="/data", help="Folder with your dataset files")
|
291 |
+
parser.add_argument('data_config', help="A data_config.json file")
|
292 |
+
parser.add_argument('output')
|
293 |
+
args = parser.parse_args()
|
294 |
+
|
295 |
+
# Ensure global batch size is divisble by data_sample_size
|
296 |
+
assert (args.batch_size*args.nprocs) % args.datasets_per_batch == 0
|
297 |
+
|
298 |
+
logging.info("Output: "+args.output)
|
299 |
+
if os.path.exists(args.output):
|
300 |
+
print("Output folder already exists.")
|
301 |
+
input("Continue?")
|
302 |
+
|
303 |
+
# Write train script to output path
|
304 |
+
os.makedirs(args.output, exist_ok=True)
|
305 |
+
|
306 |
+
data_config_path = os.path.join(args.output, 'data_config.json')
|
307 |
+
copyfile(args.data_config, data_config_path)
|
308 |
+
|
309 |
+
train_script_path = os.path.join(args.output, 'train_script.py')
|
310 |
+
copyfile(__file__, train_script_path)
|
311 |
+
with open(train_script_path, 'a') as fOut:
|
312 |
+
fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
|
313 |
+
|
314 |
+
|
315 |
+
|
316 |
+
#Load data config
|
317 |
+
with open(args.data_config) as fIn:
|
318 |
+
data_config = json.load(fIn)
|
319 |
+
|
320 |
+
queue = mp.Queue(maxsize=100*args.nprocs)
|
321 |
+
|
322 |
+
filepaths = []
|
323 |
+
dataset_indices = []
|
324 |
+
for idx, data in enumerate(data_config):
|
325 |
+
filepaths.append(os.path.join(os.path.expanduser(args.data_folder), data['name']))
|
326 |
+
dataset_indices.extend([idx]*data['weight'])
|
327 |
+
|
328 |
+
# Start producer
|
329 |
+
p = mp.Process(target=produce_data, args=(args, queue, filepaths, dataset_indices))
|
330 |
+
p.start()
|
331 |
+
|
332 |
+
# Run training
|
333 |
+
print("Start processes:", args.nprocs)
|
334 |
+
xmp.spawn(train_function, args=(args, queue), nprocs=args.nprocs, start_method='fork')
|
335 |
+
print("Training done")
|
336 |
+
print("It might be that not all processes exit automatically. In that case you must manually kill this process.")
|
337 |
+
print("With 'pkill python' you can kill all remaining python processes")
|
338 |
+
p.kill()
|
339 |
+
exit()
|
340 |
+
|
341 |
+
|
342 |
+
|
343 |
+
# Script was called via:
|
344 |
+
#python train_many_data_files_v2.py --steps 1000000 --batch_size 128 --model nreimers/MiniLM-L6-H384-uncased train_data_configs/all_datasets_v4.json output/all_datasets_v4_MiniLM-L6-H384-uncased-batch128
|
vocab.txt
ADDED
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See raw diff
|
|