ValueError: operands could not be broadcast together with shapes (38,384) (37,384)

Got this error while using this library to train an embedding model:

File "/usr/local/lib/python3.8/dist-packages/angle_emb/angle.py", line 986, in on_epoch_end
corrcoef, accuracy = self.evaluate_fn(self.valid_ds)
File "/usr/local/lib/python3.8/dist-packages/angle_emb/angle.py", line 1470, in evaluate
pred = (x_vecs[::2] * x_vecs[1::2]).sum(1)
ValueError: operands could not be broadcast together with shapes (38,384) (37,384)

I confirmed that valid_ds and train_ds were of even length, so ultimately I just modified one line of the evaluate method of the AnglE class. After this line: x_vecs = l2_normalize(x_vecs) I added:

if len(x_vecs) % 2 != 0:
    x_vecs = x_vecs[:-1]

Hopefully that doesn't break anything/everything else? Any thougths on what else might be the source of the issue?

Also, I attempted to restart training by running the same angle.fit() as I did when I started it but adjusting the from_pretrained to point to the most recent checkpoint: angle = AnglE.from_pretrained('/checkpoint-1100', max_length=512, pooling_strategy='cls').cuda()

I don't see a resume_from_checkpoint=True argument option anywhere... so it's not clear that it's aware of how many epochs have already been run etc.

hi @williambarberjr, to resume training, you can try to specify the checkpoint path to pretrained_lora_path if it is a LoRA model or to pretrained_model_path if it is a standard model, as follows:

from angle_emb import AnglE

angle = AnglE.from_pretrained(
    backbone='your_choice_backbone',
    pretrained_model_path='/checkpoint-1100',
    max_length=512,
    pooling_strategy='cls').cuda()

Thanks so much for your help. I was able to get it started again but I do have two other questions.

I launched the training like this:

from datasets import load_dataset, DatasetDict
from angle_emb import AnglE, AngleDataTokenizer, DatasetFormats

# Load the dataset
ds = load_dataset("mydataset-here")

# Instantiate your AnglE model
angle = AnglE.from_pretrained('mixedbread-ai/mxbai-embed-large-v1', max_length=512, pooling_strategy='cls').cuda()

angle.set_prompt(prompt="Represent this sentence for searching relevant passages: ")

# Tokenize the data
train_ds = ds['train'].shuffle().map(AngleDataTokenizer(angle.tokenizer, angle.max_length), num_proc=8)
valid_ds = ds['validation'].map(AngleDataTokenizer(angle.tokenizer, angle.max_length), num_proc=8)
test_ds = ds['test'].map(AngleDataTokenizer(angle.tokenizer, angle.max_length), num_proc=8)

angle.fit(train_ds=train_ds,
    valid_ds=test_ds,
    output_dir='/',
    batch_size=13,
    epochs=5,
    learning_rate=2e-5,
    save_steps=30,
    eval_steps=1000,
    warmup_steps=0,
    gradient_accumulation_steps=315,
    loss_kwargs={
        'w1':0, 
        'w2':20,
        'w3':1,
        'cosine_tau':20,
        'ibn_tau':20,
        'angle_tau':20,
    },
    fp16=True,
    logging_steps=100)

So I'm just going to assume it's not a LoRA model, it's just a pretrained_model_path.

On a different note, when trying to test the model checkpoint like this:

from typing import Dict
import torch
import numpy as np
from transformers import AutoModel, AutoTokenizer
from sentence_transformers.util import cos_sim

# The model works really well with cls pooling (default) but also with mean poolin.
def pooling(outputs: torch.Tensor, inputs: Dict,  strategy: str = 'cls') -> np.ndarray:
    if strategy == 'cls':
        outputs = outputs[:, 0]
    elif strategy == 'mean':
        outputs = torch.sum(
            outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"])
    else:
        raise NotImplementedError
    return outputs.detach().cpu().numpy()

# 1. load model
model_id = '/path/to/folder/checkpoint-1100'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id).cuda()

query = 'Represent this sentence for searching relevant passages: CCTV camera systems for warehouses'

docs = [
    query,
    "Vertical Lines Technologies FZE is a company that provides complete solutions in the sector of data collection and automatic identification. They offer a range of products, including barcode scanners, printers, PDAs, POS systems, and CCTV cameras, as well as services such as asset tracking, library management, retail inventory, school management, shipping and cargo, time and attendance, and warehouse management. Their target industries include automobile, facilities management, glass and aluminum, healthcare, jewelry, logistics and distribution, retail and chain stores, and warehouses. The company's values include quality, prompt delivery, integrity, customer focus, and respect and teamwork. They strive to provide the best quality products and services to their clients, with competitive prices and on-time delivery."
]

# 2. encode
inputs = tokenizer(docs, padding=True, return_tensors='pt')
for k, v in inputs.items():
    inputs[k] = v.cuda()

outputs = model(**inputs).last_hidden_state
embeddings = pooling(outputs, inputs, 'cls')

similarities = cos_sim(embeddings[0], embeddings[1:])
print('similarities:', similarities)

outputs is printing out as:

tensor([[[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]],

        [[nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         ...,
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan],
         [nan, nan, nan,  ..., nan, nan, nan]]], device='cuda:0',
       grad_fn=<NativeLayerNormBackward0>)

Which naturally means that the similarities get printed as: similarities: tensor([[nan]])

I tried a few different ways of replicating the code here: https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1

But couldn't seem to make the checkpoint work for me. I'm just attempting to validate that the model is getting better or at least staying the same "in domain" as a sanity check that I haven't messed anything else up.

hi @williambarberjr , I reproduced your problem. Here is the solution:

mixedbread-ai/mxbai-embed-large-v1 uses torch.float16 by default, we need to load it in torch.float32, as follows:

angle = AnglE.from_pretrained(
    'mixedbread-ai/mxbai-embed-large-v1', 
    torch_dtype=torch.float32,
    max_length=512,
    pooling_strategy='cls').cuda()

B.T.W, here are some tips to improve the training:

1) don't use set_prompt() in the training stage, it doesn't work in the training stage. You have to specify the prompt for your data manually. For example, you can do it as follows:

ds = ds.map(lambda x: {'text': 'Represent this sentence for searching relevant passages: {x["text"]}'})

train_ds = ds['train'].shuffle().map(AngleDataTokenizer(angle.tokenizer, angle.max_length), num_proc=8)
...

2) It is better to set a small learning rate (< 1e-5)

Here, I provide a training example for NLI, hopefully it can help :) colab: https://colab.research.google.com/drive/1B2pTa2N4BDoKaFjghNkAGXRSuwKbnF8P?usp=sharing

Very helpful. I got it running on a single GPU setup and then on a multi-GPU setup to speed things up. However, I spot tested the most recent checkpoint with two query positive pairs and noticed that the similarity scores had both gotten worse than the original scores from the "mixedbread-ai/mxbai-embed-large-v1" model. But I'll either do a more comprehensive test or let it run out further before I assume I've done something wrong.

I was having trouble getting it running distributed across multiple GPUs. But one small tweak got it working. I'll leave the revised version below incase it's helpful to someone else or in the event you have some constructive criticism or feedback for me on how to improve it. In the colab you linked, you set w2 to 2, should I have done that as well - for context, I only intend to use the embeddings for retrieval.

from datasets import load_dataset, DatasetDict, Dataset
from angle_emb import AnglE, AngleDataTokenizer, DatasetFormats
import os
import torch
import random
import json
from transformers import AutoTokenizer
from angle_emb import AnglE, AngleDataTokenizer

os.environ['WANDB_MODE'] = 'disabled'

data = []

with open('/root/train_small.jsonl', 'r') as file:
    for line in file:
        obj = json.loads(line)
        obj['text'] = f'Represent this sentence for searching relevant passages: {obj["text"]}'
        data.append(obj)

random.shuffle(data)
train_ds = Dataset.from_list(data)

test_data = []

with open('/root/test.jsonl', 'r') as file:
    for line in file:
        obj = json.loads(line)
        obj['text'] = f'Represent this sentence for searching relevant passages: {obj["text"]}'
        test_data.append(obj)

random.shuffle(test_data)
test_ds = Dataset.from_list(test_data)

validation_data = []

with open('/root/validation.jsonl', 'r') as file:
    for line in file:
        obj = json.loads(line)
        obj['text'] = f'Represent this sentence for searching relevant passages: {obj["text"]}'
        validation_data.append(obj)

random.shuffle(validation_data)
valid_ds = Dataset.from_list(validation_data)

max_len = 512
model_id = 'mixedbread-ai/mxbai-embed-large-v1'

tokenizer = AutoTokenizer.from_pretrained(model_id)
train_ds = train_ds.shuffle().map(AngleDataTokenizer(tokenizer, max_len), num_proc=8)
valid_ds = valid_ds.map(AngleDataTokenizer(tokenizer, max_len), num_proc=8)
test_ds = test_ds.map(AngleDataTokenizer(tokenizer, max_len), num_proc=8)

angle = AnglE.from_pretrained(model_id, max_length=max_len, torch_dtype=torch.float32, pooling_strategy='cls').cuda()

angle.fit(train_ds=train_ds,
    valid_ds=valid_ds,
    output_dir='/root',
    batch_size=3,
    epochs=5,
    learning_rate=1e-5,
    save_steps=100,
    eval_steps=1000,
    warmup_steps=0,
    gradient_accumulation_steps= 2050,
    loss_kwargs={
        'w1':0, 
        'w2':20,
        'w3':1,
        'cosine_tau':20,
        'ibn_tau':20,
        'angle_tau':20,
    },
    fp16=True,
    logging_steps=10)

I'm calling the code with this command on a 8 x RTX 4090 setup: NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 torchrun --nproc_per_node=8 /root/runMultiGPUTraining.py 2>&1 | tee /root/output.txt

hi @williambarberjr , i checked it in multi-GPU setting and did not reproduce your problem. It works fine in my training.

I fixed the NaN issue several minutes ago. Could you update the angle_emb to v0.3.9 and try to train the model using the train_cli.py

Here is an example to train with train_cli.py:

1) prepare the data into a jsonl file, as follows:

$ head -3 snli_5k.jsonl

{"text": "A person on a horse jumps over a broken down airplane.", "positive": "A person is outdoors, on a horse.", "negative": "A person is at a diner, ordering an omelette."}
{"text": "Children smiling and waving at camera", "positive": "There are children present", "negative": "The kids are frowning"}
{"text": "A boy is jumping on skateboard in the middle of a red bridge.", "positive": "The boy does a skateboarding trick.", "negative": "The boy skates down the sidewalk."}

2) train the model using following script:

NCCL_P2P_DISABLE=1 NCCL_IB_DISABLE=1 CUDA_VISIBLE_DEVICES=0,1 WANDB_MODE=disabled CUDA_VISIBLE_DEVICES=2,3 torchrun --nproc_per_node=2 --master_port=2345 train_cli.py \
--model_name_or_path mixedbread-ai/mxbai-embed-large-v1 \
--train_name_or_path ./snli_5k.jsonl --save_dir mxbai-snli-ckpts \
--w1 0. --w2 20.0 --w3 1.0 --angle_tau 20.0 --learning_rate 3e-6 --maxlen 64 \
--pooling_strategy cls \
--epochs 1 \
--batch_size 32 \
--logging_steps 100 \
--warmup_steps 200 \
--save_steps 1000 --seed 42 --gradient_accumulation_steps 2 --fp16 1 --torch_dtype 'float32'

When the training is done, the model will be saved to mxbai-snli-ckpts, you can load it as follows:

from angle_emb import AnglE

angle = AnglE.from_pretrained('mixedbread-ai/mxbai-embed-large-v1', pretrained_model_path='mxbai-snli-ckpts').cuda()

...

Thanks for your help. The training is running smoothly now distributed across several GPUs. I've got one running with an effective batch size of 30k+ and another with an effective batch size of about 4k.

Vs for the 30k+ batch size:

The grad_norm values for the 4k batch size seem to be a bit all over the place - does that suggest an issue to you?

SeanLee97 / AnglE

ValueError: operands could not be broadcast together with shapes (38,384) (37,384) #61