nreimers
commited on
Commit
•
6a0d20a
1
Parent(s):
de7b898
upload
Browse files- 1_Pooling/config.json +7 -0
- README.md +11 -0
- config.json +24 -0
- config_sentence_transformers.json +7 -0
- data_config.json +1452 -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
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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README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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---
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# all_datasets_v4_MiniLM-L6
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This is a MiniLM-L6 model trained on all the dataset of the 1B+ train corpus. It was trained with the v4 setup. See data_config.json and train_script.py in this respository how the model was trained and which datasets have been used.
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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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|
| 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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|
|