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--- |
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license: apache-2.0 |
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datasets: |
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- c4 |
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language: |
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- en |
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inference: false |
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--- |
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# MosaicBERT-Base model |
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MosaicBERT-Base is a new BERT architecture and training recipe optimized for fast pretraining. |
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MosaicBERT trains faster and achieves higher pretraining and finetuning accuracy when benchmarked against |
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Hugging Face's [bert-base-uncased](https://huggingface.co/bert-base-uncased). |
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## Model Date |
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March 2023 |
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## Documentation |
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* [Blog post](https://www.mosaicml.com/blog/mosaicbert) |
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* [Github (mosaicml/examples/bert repo)](https://github.com/mosaicml/examples/tree/aab5ef7315715509cff9e08e862d41b3cbac83ad/examples/benchmarks/bert) |
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## How to use |
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```python |
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from transformers import AutoModelForMaskedLM |
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mlm = AutoModelForMaskedLM.from_pretrained('mosaicml/mosaic-bert-base', trust_remote_code=True) |
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``` |
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The tokenizer for this model is simply the Hugging Face `bert-base-uncased` tokenizer. |
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```python |
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from transformers import BertTokenizer |
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
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``` |
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To use this model directly for masked language modeling, use `pipeline`: |
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```python |
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from transformers import AutoModelForMaskedLM, BertTokenizer, pipeline |
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
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mlm = AutoModelForMaskedLM.from_pretrained('mosaicml/mosaic-bert-base', trust_remote_code=True) |
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classifier = pipeline('fill-mask', model=mlm, tokenizer=tokenizer) |
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classifier("I [MASK] to the store yesterday.") |
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``` |
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**To continue MLM pretraining**, follow the [MLM pre-training section of the mosaicml/examples/bert repo](https://github.com/mosaicml/examples/tree/main/examples/bert#mlm-pre-training). |
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**To fine-tune this model for classification**, follow the [Single-task fine-tuning section of the mosaicml/examples/bert repo](https://github.com/mosaicml/examples/tree/main/examples/bert#single-task-fine-tuning). |
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### Remote Code |
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This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we train using [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), which is not part of the `transformers` library and depends on [Triton](https://github.com/openai/triton) and some custom PyTorch code. Since this involves executing arbitrary code, you should consider passing a git `revision` argument that specifies the exact commit of the code, for example: |
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```python |
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mlm = AutoModelForMaskedLM.from_pretrained( |
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'mosaicml/mosaic-bert-base', |
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trust_remote_code=True, |
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revision='24512df', |
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) |
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``` |
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However, if there are updates to this model or code and you specify a revision, you will need to manually check for them and update the commit hash accordingly. |
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## Model description |
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In order to build MosaicBERT, we adopted architectural choices from the recent transformer literature. |
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These include [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi (Press et al. 2021)](https://arxiv.org/abs/2108.12409), |
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and [Gated Linear Units (Shazeer 2020)](https://arxiv.org/abs/2002.05202). In addition, we remove padding inside the transformer block, |
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and apply LayerNorm with low precision. |
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### Modifications to the Attention Mechanism |
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1. **FlashAttention**: Attention layers are core components of the transformer architecture. The recently proposed FlashAttention layer |
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reduces the number of read/write operations between the GPU HBM (high bandwidth memory, i.e. long-term memory) and the GPU SRAM |
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(i.e. short-term memory) [[Dao et al. 2022]](https://arxiv.org/pdf/2205.14135.pdf). We used the FlashAttention module built by |
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[hazy research](https://github.com/HazyResearch/flash-attention) with [OpenAI’s triton library](https://github.com/openai/triton). |
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2. **Attention with Linear Biases (ALiBi)**: In most BERT models, the positions of tokens in a sequence are encoded with a position embedding layer; |
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this embedding allows subsequent layers to keep track of the order of tokens in a sequence. ALiBi eliminates position embeddings and |
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instead conveys this information using a bias matrix in the attention operation. It modifies the attention mechanism such that nearby |
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tokens strongly attend to one another [[Press et al. 2021]](https://arxiv.org/abs/2108.12409). In addition to improving the performance of the final model, ALiBi helps the |
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model to handle sequences longer than it saw during training. Details on our ALiBi implementation can be found [in the mosaicml/examples repo here](https://github.com/mosaicml/examples/blob/d14a7c94a0f805f56a7c865802082bf6d8ac8903/examples/bert/src/bert_layers.py#L425). |
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3. **Unpadding**: Standard NLP practice is to combine text sequences of different lengths into a batch, and pad the sequences with empty |
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tokens so that all sequence lengths are the same. During training, however, this can lead to many superfluous operations on those |
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padding tokens. In MosaicBERT, we take a different approach: we concatenate all the examples in a minibatch into a single sequence |
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of batch size 1. Results from NVIDIA and others have shown that this approach leads to speed improvements during training, since |
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operations are not performed on padding tokens (see for example [Zeng et al. 2022](https://arxiv.org/pdf/2208.08124.pdf)). |
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Details on our “unpadding” implementation can be found [in the mosaicml/examples repo here](https://github.com/mosaicml/examples/blob/main/examples/bert/src/bert_padding.py). |
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4. **Low Precision LayerNorm**: this small tweak forces LayerNorm modules to run in float16 or bfloat16 precision instead of float32, improving utilization. |
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Our implementation can be found [in the mosaicml/examples repo here](https://docs.mosaicml.com/en/v0.12.1/method_cards/low_precision_layernorm.html). |
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### Modifications to the Feedforward Layers |
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5. **Gated Linear Units (GLU)**: We used Gated Linear Units for the feedforward sublayer of a transformer. GLUs were first proposed in 2016 [[Dauphin et al. 2016]](https://arxiv.org/abs/1612.08083), |
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and incorporate an extra learnable matrix that “gates” the outputs of the feedforward layer. More recent work has shown that |
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GLUs can improve performance quality in transformers [[Shazeer, 2020](https://arxiv.org/abs/2002.05202), [Narang et al. 2021](https://arxiv.org/pdf/2102.11972.pdf)]. We used the GeLU (Gaussian-error Linear Unit) |
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activation function with GLU, which is sometimes referred to as GeGLU. The GeLU activation function is a smooth, fully differentiable |
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approximation to ReLU; we found that this led to a nominal improvement over ReLU. More details on our implementation of GLU can be found here. |
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The extra gating matrix in a GLU model potentially adds additional parameters to a model; we chose to augment our BERT-Base model with |
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additional parameters due to GLU modules as it leads to a Pareto improvement across all timescales (which is not true of all larger |
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models such as BERT-Large). While BERT-Base has 110 million parameters, MosaicBERT-Base has 137 million parameters. Note that |
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MosaicBERT-Base trains faster than BERT-Base despite having more parameters. |
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## Training data |
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MosaicBERT is pretrained using a standard Masked Language Modeling (MLM) objective: the model is given a sequence of |
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text with some tokens hidden, and it has to predict these masked tokens. MosaicBERT is trained on |
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the English [“Colossal, Cleaned, Common Crawl” C4 dataset](https://github.com/allenai/allennlp/discussions/5056), which contains roughly 365 million curated text documents scraped |
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from the internet (equivalent to 156 billion tokens). We used this more modern dataset in place of traditional BERT pretraining |
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corpora like English Wikipedia and BooksCorpus. |
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## Pretraining Optimizations |
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Many of these pretraining optimizations below were informed by our [BERT results for the MLPerf v2.1 speed benchmark](https://www.mosaicml.com/blog/mlperf-nlp-nov2022). |
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1. **MosaicML Streaming Dataset**: As part of our efficiency pipeline, we converted the C4 dataset to [MosaicML’s StreamingDataset format](https://www.mosaicml.com/blog/mosaicml-streamingdataset) and used this |
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for both MosaicBERT-Base and the baseline BERT-Base. For all BERT-Base models, we chose the training duration to be 286,720,000 samples of sequence length 128; this covers 78.6% of C4. |
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2. **Higher Masking Ratio for the Masked Language Modeling Objective**: We used the standard Masked Language Modeling (MLM) pretraining objective. |
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While the original BERT paper also included a Next Sentence Prediction (NSP) task in the pretraining objective, |
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subsequent papers have shown this to be unnecessary [Liu et al. 2019](https://arxiv.org/abs/1907.11692). |
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However, we found that a 30% masking ratio led to slight accuracy improvements in both pretraining MLM and downstream GLUE performance. |
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We therefore included this simple change as part of our MosaicBERT training recipe. Recent studies have also found that this simple |
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change can lead to downstream improvements [Wettig et al. 2022](https://arxiv.org/abs/2202.08005). |
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3. **Bfloat16 Precision**: We use [bf16 (bfloat16) mixed precision training](https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus) for all the models, where a matrix multiplication layer uses bf16 |
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for the multiplication and 32-bit IEEE floating point for gradient accumulation. We found this to be more stable than using float16 mixed precision. |
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4. **Vocab Size as a Multiple of 64**: We increased the vocab size to be a multiple of 8 as well as 64 (i.e. from 30,522 to 30,528). |
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This small constraint is something of [a magic trick among ML practitioners](https://twitter.com/karpathy/status/1621578354024677377), and leads to a throughput speedup. |
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5. **Hyperparameters**: For all models, we use Decoupled AdamW with Beta_1=0.9 and Beta_2=0.98, and a weight decay value of 1.0e-5. |
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The learning rate schedule begins with a warmup to a maximum learning rate of 5.0e-4 followed by a linear decay to zero. |
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Warmup lasted for 6% of the full training duration. Global batch size was set to 4096, and microbatch size was 128; since global batch size was 4096, full pretraining consisted of 70,000 batches. |
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We set the maximum sequence length during pretraining to 128, and we used the standard embedding dimension of 768. |
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For MosaicBERT, we applied 0.1 dropout to the feedforward layers but no dropout to the FlashAttention module, as this was not possible with the OpenAI triton implementation. |
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Full configuration details for pretraining MosaicBERT-Base can be found in the configuration yamls [in the mosaicml/examples repo here](https://github.com/mosaicml/examples/tree/main/bert/yamls/main). |
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## Evaluation results |
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When fine-tuned on downstream tasks (following the [finetuning details here](https://github.com/mosaicml/examples/blob/main/examples/bert/yamls/finetuning/glue/mosaic-bert-base-uncased.yaml)), the MosaicBERT model achieves the following GLUE results: |
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| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average | |
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|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:| |
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| | 0.8495 | 0.9029 | 0.9074| 0.9246 | 0.5511 | 0.8927 | 0.9003 | 0.8136 | 0.8428 | |
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Note that this is averaged over n=5 pretraining seeds. |
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## Collection of MosaicBERT-Base models trained using ALiBi on different sequence lengths |
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ALiBi allows a model trained with a sequence length n to easily extrapolate to sequence lengths >2n during finetuning. For more details, see [Train Short, Test Long: Attention with Linear |
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Biases Enables Input Length Extrapolation (Press et al. 2022)](https://arxiv.org/abs/2108.12409) |
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This model is part of the **family of MosaicBERT-Base models** trained using ALiBi on different sequence lengths: |
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* mosaic-bert-base (trained on a sequence length of 128 tokens) |
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* [mosaic-bert-base-seqlen-256](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-256) |
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* [mosaic-bert-base-seqlen-512](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-512) |
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* [mosaic-bert-base-seqlen-1024](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-1024) |
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* [mosaic-bert-base-seqlen-2048](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) |
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The primary use case of these models is for research on efficient pretraining and finetuning for long context embeddings. |
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## Intended uses & limitations |
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This model is intended to be finetuned on downstream tasks. |
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## Citation |
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Please cite this model using the following format: |
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``` |
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@online{Portes2023MosaicBERT, |
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author = {Jacob Portes and Alex Trott and Daniel King and Sam Havens}, |
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title = {MosaicBERT: Pretraining BERT from Scratch for \$20}, |
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year = {2023}, |
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url = {https://www.mosaicml.com/blog/mosaicbert}, |
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note = {Accessed: 2023-03-28}, % change this date |
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urldate = {2023-03-28} % change this date |
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} |
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``` |
