added train script

train_script.py

@@ -0,0 +1,361 @@
+"""
+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=500,
+        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)
+        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):
+        self.filepath = filepath
+
+    def __iter__(self):
+        max_dataset_size = 20*1000*1000    #Cache small datasets in memory
+        dataset = []
+        data_format = None
+
+        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:
+                            dataset = None
+
+                    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('--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('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 train_data_configs/multi-qa_v1.json output/multi-qa_v1-MiniLM-L6-mean_cos