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"""
Train script for a single file
Need to set the TPU address first:
export XRT_TPU_CONFIG="localservice;0;localhost:51011"
"""
import torch.multiprocessing as mp
import threading
import time
import random
import sys
import argparse
import gzip
import json
import logging
import tqdm
import torch
from torch import nn
from torch.utils.data import DataLoader
import torch
import torch_xla
import torch_xla.core
import torch_xla.core.functions
import torch_xla.core.xla_model as xm
import torch_xla.distributed.xla_multiprocessing as xmp
import torch_xla.distributed.parallel_loader as pl
import os
from shutil import copyfile
from transformers import (
AdamW,
AutoModel,
AutoTokenizer,
get_linear_schedule_with_warmup,
set_seed,
)
class AutoModelForSentenceEmbedding(nn.Module):
def __init__(self, model_name, tokenizer, args):
super(AutoModelForSentenceEmbedding, self).__init__()
assert args.pooling in ['mean', 'cls']
self.model = AutoModel.from_pretrained(model_name)
self.normalize = not args.no_normalize
self.tokenizer = tokenizer
self.pooling = args.pooling
def forward(self, **kwargs):
model_output = self.model(**kwargs)
if self.pooling == 'mean':
embeddings = self.mean_pooling(model_output, kwargs['attention_mask'])
elif self.pooling == 'cls':
embeddings = self.cls_pooling(model_output, kwargs['attention_mask'])
if self.normalize:
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
return embeddings
def mean_pooling(self, model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def cls_pooling(self, model_output, attention_mask):
return model_output[0][:,0]
def save_pretrained(self, output_path):
if xm.is_master_ordinal():
self.tokenizer.save_pretrained(output_path)
self.model.config.save_pretrained(output_path)
xm.save(self.model.state_dict(), os.path.join(output_path, "pytorch_model.bin"))
def train_function(index, args, queue):
tokenizer = AutoTokenizer.from_pretrained(args.model)
model = AutoModelForSentenceEmbedding(args.model, tokenizer, args)
### Train Loop
device = xm.xla_device()
model = model.to(device)
# Instantiate optimizer
optimizer = AdamW(params=model.parameters(), lr=2e-5, correct_bias=True)
lr_scheduler = get_linear_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=args.steps,
)
# Now we train the model
cross_entropy_loss = nn.CrossEntropyLoss()
max_grad_norm = 1
model.train()
for global_step in tqdm.trange(args.steps, disable=not xm.is_master_ordinal()):
#### Get the batch data
batch = queue.get()
#print(index, "batch {}x{}".format(len(batch), ",".join([str(len(b)) for b in batch])))
if len(batch[0]) == 2: #(anchor, positive)
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length_a, truncation=True, padding="max_length")
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length_b, truncation=True, padding="max_length")
### Compute embeddings
embeddings_a = model(**text1.to(device))
embeddings_b = model(**text2.to(device))
### Gather all embedings
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
embeddings_b = torch_xla.core.functions.all_gather(embeddings_b)
### Compute similarity scores 512 x 512
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
### Compute cross-entropy loss
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
## Symmetric loss as in CLIP
loss = (cross_entropy_loss(scores, labels) + cross_entropy_loss(scores.transpose(0, 1), labels)) / 2
else: #(anchor, positive, negative)
text1 = tokenizer([b[0] for b in batch], return_tensors="pt", max_length=args.max_length_a, truncation=True, padding="max_length")
text2 = tokenizer([b[1] for b in batch], return_tensors="pt", max_length=args.max_length_b, truncation=True, padding="max_length")
text3 = tokenizer([b[2] for b in batch], return_tensors="pt", max_length=args.max_length_b, truncation=True, padding="max_length")
embeddings_a = model(**text1.to(device))
embeddings_b1 = model(**text2.to(device))
embeddings_b2 = model(**text3.to(device))
embeddings_a = torch_xla.core.functions.all_gather(embeddings_a)
embeddings_b1 = torch_xla.core.functions.all_gather(embeddings_b1)
embeddings_b2 = torch_xla.core.functions.all_gather(embeddings_b2)
embeddings_b = torch.cat([embeddings_b1, embeddings_b2])
### Compute similarity scores 512 x 1024
scores = torch.mm(embeddings_a, embeddings_b.transpose(0, 1)) * args.scale
### Compute cross-entropy loss
labels = torch.tensor(range(len(scores)), dtype=torch.long, device=embeddings_a.device) # Example a[i] should match with b[i]
## One-way loss
loss = cross_entropy_loss(scores, labels)
# Backward pass
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
xm.optimizer_step(optimizer, barrier=True)
lr_scheduler.step()
#Save model
if (global_step+1) % args.save_steps == 0:
output_path = os.path.join(args.output, str(global_step+1))
xm.master_print("save model: "+output_path)
model.save_pretrained(output_path)
output_path = os.path.join(args.output, "final")
xm.master_print("save model final: "+ output_path)
model.save_pretrained(output_path)
def produce_data(args, queue, filepaths, dataset_indices):
global_batch_size = args.batch_size*args.nprocs #Global batch size
num_same_dataset = int(args.nprocs / args.datasets_per_batch)
print("producer", "global_batch_size", global_batch_size)
print("producer", "num_same_dataset", num_same_dataset)
datasets = []
for filepath in filepaths:
if "reddit_" in filepath: #Special dataset class for Reddit files
data_obj = RedditDataset(filepath)
else:
data_obj = Dataset(filepath, args)
datasets.append(iter(data_obj))
# Store if dataset is in a 2 col or 3 col format
num_cols = {idx: len(next(dataset)) for idx, dataset in enumerate(datasets)}
while True:
texts_in_batch = set()
batch_format = None #2 vs 3 col format for this batch
#Add data from several sub datasets
for _ in range(args.datasets_per_batch):
valid_dataset = False #Check that datasets have the same 2/3 col format
while not valid_dataset:
data_idx = random.choice(dataset_indices)
if batch_format is None:
batch_format = num_cols[data_idx]
valid_dataset = True
else: #Check that this dataset has the same format
valid_dataset = (batch_format == num_cols[data_idx])
#Get data from this dataset
dataset = datasets[data_idx]
local_batch_size = args.batch_size
if batch_format == 3 and args.batch_size_triplets is not None:
local_batch_size = args.batch_size_triplets
for _ in range(num_same_dataset):
for _ in range(args.nprocs):
batch_device = [] #A batch for one device
while len(batch_device) < local_batch_size:
sample = next(dataset)
in_batch = False
for text in sample:
if text in texts_in_batch:
in_batch = True
break
if not in_batch:
for text in sample:
texts_in_batch.add(text)
batch_device.append(sample)
queue.put(batch_device)
class RedditDataset:
"""
A class that handles the reddit data files
"""
def __init__(self, filepath):
self.filepath = filepath
def __iter__(self):
while True:
with gzip.open(self.filepath, "rt") as fIn:
for line in fIn:
data = json.loads(line)
if "response" in data and "context" in data:
yield [data["response"], data["context"]]
class Dataset:
"""
A class that handles one dataset
"""
def __init__(self, filepath, args):
self.filepath = filepath
self.args = args
def __iter__(self):
max_dataset_size = 20*1000*1000 #Cache small datasets in memory
min_dataset_size = 50*1000 # Size for the small chunk of the dataset
dataset = []
min_dataset = []
data_format = None
print(self.filepath, "load")
while dataset is None or len(dataset) == 0:
with gzip.open(self.filepath, "rt") as fIn:
for line in fIn:
data = json.loads(line)
if isinstance(data, dict):
data = data['texts']
if data_format is None:
data_format = len(data)
#Ensure that all entries are of the same 2/3 col format
assert len(data) == data_format
if dataset is not None:
dataset.append(data)
if len(dataset) >= max_dataset_size and not self.args.no_data_streaming:
dataset = None
if self.args.no_data_streaming:
min_dataset.append(data)
if len(min_dataset) >= min_dataset_size:
random.shuffle(min_dataset)
for data in min_dataset:
yield data
min_dataset = []
else:
yield data
print(self.filepath, "fully loaded")
if len(min_dataset) > 0:
random.shuffle(min_dataset)
for data in min_dataset:
yield data
# Data loaded. Now stream to the queue
# Shuffle for each epoch
while True:
random.shuffle(dataset)
for data in dataset:
yield data
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='nreimers/MiniLM-L6-H384-uncased')
parser.add_argument('--steps', type=int, default=2000)
parser.add_argument('--save_steps', type=int, default=10000)
parser.add_argument('--warmup_steps', type=int, default=500)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--batch_size_triplets', type=int, default=None)
parser.add_argument('--max_length_a', type=int, default=128)
parser.add_argument('--max_length_b', type=int, default=128)
parser.add_argument('--nprocs', type=int, default=8)
parser.add_argument('--datasets_per_batch', type=int, default=2, help="Number of datasets per batch")
parser.add_argument('--scale', type=float, default=20, help="Use 20 for cossim, and 1 when you work with unnormalized embeddings with dot product")
parser.add_argument('--no_normalize', action="store_true", default=False, help="If set: Embeddings are not normalized")
parser.add_argument('--pooling', default='mean')
parser.add_argument('--data_folder', default="/data", help="Folder with your dataset files")
parser.add_argument('--no_data_streaming', action="store_true", default=False, help="If set: All data will first be loaded in memory")
parser.add_argument('data_config', help="A data_config.json file")
parser.add_argument('output')
args = parser.parse_args()
# Ensure num proc is devisible by datasets_per_batch
assert (args.nprocs % args.datasets_per_batch) == 0
logging.info("Output: "+args.output)
if os.path.exists(args.output):
print("Output folder already exists.")
input("Continue?")
# Write train script to output path
os.makedirs(args.output, exist_ok=True)
data_config_path = os.path.join(args.output, 'data_config.json')
copyfile(args.data_config, data_config_path)
train_script_path = os.path.join(args.output, 'train_script.py')
copyfile(__file__, train_script_path)
with open(train_script_path, 'a') as fOut:
fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
#Load data config
with open(args.data_config) as fIn:
data_config = json.load(fIn)
queue = mp.Queue(maxsize=100*args.nprocs)
filepaths = []
dataset_indices = []
for idx, data in enumerate(data_config):
filepaths.append(os.path.join(os.path.expanduser(args.data_folder), data['name']))
dataset_indices.extend([idx]*data['weight'])
# Start producer
p = mp.Process(target=produce_data, args=(args, queue, filepaths, dataset_indices))
p.start()
# Run training
print("Start processes:", args.nprocs)
xmp.spawn(train_function, args=(args, queue), nprocs=args.nprocs, start_method='fork')
print("Training done")
print("It might be that not all processes exit automatically. In that case you must manually kill this process.")
print("With 'pkill python' you can kill all remaining python processes")
p.kill()
exit()
# Script was called via:
#python train_many_data_files_v2.py --steps 200000 --batch_size 128 --model nreimers/MiniLM-L6-H384-uncased --max_length_a 64 --max_length_b 250 --scale 1 --pooling cls --no_normalize train_data_configs/multi-qa_v1.json output/multi-qa_v1-MiniLM-L6-cls_dot |