nreimers commited on
Commit
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1 Parent(s): 4e50e9a
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 1024,
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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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+ }
README.md ADDED
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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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+
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+ # all_datasets_v3_roberta-large
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+
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+ This is a roberta-large model trained on all the dataset of the 1B+ train corpus. It was trained with the v3 setup. See data_config.json and train_script.py in this respository how the model was trained and which datasets have been used.
config.json ADDED
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+ {
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+ "_name_or_path": "roberta-large",
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+ "architectures": [
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+ "RobertaForMaskedLM"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "model_type": "roberta",
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.8.2",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 50265
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+ }
config_sentence_transformers.json ADDED
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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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+ }
data_config.json ADDED
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+ },
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+ "name": "2",
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+ "path": "2_Normalize",
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+ "type": "sentence_transformers.models.Normalize"
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+ }
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+ ]
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train_script.py ADDED
@@ -0,0 +1,344 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 32 --model roberta-large train_data_configs/all_datasets_v3.json output/all_datasets_v3_roberta-large
vocab.json ADDED
The diff for this file is too large to render. See raw diff