GigaGPT presents itself as a Cerebras implementation of Andrei Karpathy's nanoGPT and impresses with its simplicity and compactness with only 565 lines of code. This release promises to push the boundaries of model size and exceed 100 billion parameters without resorting to third-party code additions or frameworks. This is made possible by leveraging the memory and processing power of the Cerebras hardware, which allows extensive training on the vanilla Torch.nn code without the need for modifications.
However, it is important to take a step back and look at this statement more closely. Although reducing the number of lines of code may seem attractive, the true measure of effectiveness lies in the usability, stability, and actual performance of the model. The fact that GigaGPT does not introduce code additions or use third-party frameworks can be seen as an advantage, but it also raises questions about robustness and adoption of established standards in the deep learning community. Furthermore, the exclusive dependence on memory.
Despite the team's claims, it is important to emphasize that GigaGPT's approach highlights the difficulties of training large transformers on a large number of GPUs. It highlights the argument that vanilla GPT models on newer GPUs are running out of memory beyond a few billion parameters, requiring complex distribution of models across multiple GPUs with workload coordination between them. Although GigaGPT claims to avoid this complexity by leveraging Cerebras' hardware capabilities, it is important to critically consider the ease of use and actual effectiveness of this approach compared to established LLM scaling frameworks such as Megatron, DeepSpeed, NeoX, Fairscale and other Mosaic Foundry.
Deploying a small model like nanoGPT reportedly requires only 639 lines of code, while implementing a 20 billion parameter model using Nvidia's Megatron model would be complex and require 20,507 lines of code. This represents a 32-fold increase in complexity. Although this comparison may seem sweeping, it is important to take a critical perspective. Simplifying code does not always mean ease of use, stability, or effective model performance.
The claim that gigaGPT offers the best of both worlds by combining a compact codebase and the ability to train GPT-3 sized models raises questions. The exclusive reliance on Cerebras hardware for large-scale training may limit the model's portability to other architectures, and the practical relevance of models with more than 100 billion parameters remains uncertain.
As for the specific implementation of gigaGPT, statements about its alignment with nanoGPT, use of learned position embeddings, default attention, and biases throughout the model raise questions about the diversity of architectures and approaches examined. GigaGPT validation appears to focus on functional correctness rather than more holistic criteria such as convergence, downstream performance or other meaningful metrics. Comparison with other GPT models that scale from millions to hundreds of billions of parameters without resorting to special parallelization techniques may seem impressive, but the question of the actual need for such scales remains open.
Although the team responsible presents gigaGPT as a remarkable advancement, it is important to view these claims with caution and maintain a critical perspective on the actual complexity, performance and relevance of the decisions made in developing this model.
Exploring Limits: GigaGPT and the Challenges of Scaling
After validating the Model 70B, an attempt was made to explore the limits of gigaGPT's scale by changing the dimensions of the model to match those in the original GPT-3 paper. Although the convergence results were inconclusive after a few training steps, the model maintained similar usage to the 70B configuration. However, it is important to note that the notion of scale limits remains subject to different interpretations and the relevance of more than 1,000 billion parameters gives rise to reservations.
When it comes to how gigaGPT works, the lack of sharding or pipelining techniques is highlighted as an option, as the model will be fully integrated into the system memory of the Cerebras hardware. The Cerebras Wafer Scale, MemoryX, and SwarmX cluster architecture discussions provide a technical overview, but it is important to remain critical when generalizing the effectiveness of this approach to other scenarios or architectures.
The depiction that gigaGPT consists primarily of model.py and train.py, with boring code and cosmetic differences from nanoGPT, raises questions about the actual originality and innovation that this implementation brings. Additionally, describing the code as fairly similar to succinct GPT implementations written for GPUs highlights the need to understand the trade-offs and performance implications associated with this simplicity.
The mention of the cerebras_pytorch package as a simplistic solution for training on a standard cluster deserves careful consideration. Although presented as key to the simplicity of the gigaGPT code, a critical assessment of the relevance of this solution in different contexts and understanding of its limitations is essential. In summary, while the overview of gigaGPT's internal workings is detailed, it is imperative to view these statements with caution and remain aware of the nuances and potential implications.
1 | for step, batch in enumerate(executor, start=global_step + 1): if step > config.num_steps: break loss = training_step(batch) log_loss(loss, step) save_checkpoint(step) |
First, let's take a closer look at the previously mentioned executor:
1 | dataloader = cstorch.utils.data.DataLoader( get_dataloader, data_path, config.sequence_length, config.batch_size, config.seed, ) executor = cstorch.utils.data.DataExecutor( dataloader, num_steps=config.num_steps – global_step, checkpoint_steps=config .checkpoint_steps, cs_config=cs_config,writer=writer, ) |
When running on the Cerebras system, dedicated CPU nodes are responsible for loading and passing data to the model. The purpose of using cstorch.utils.data.DataLoader is to define the instance of the data loader that runs on each of these worker nodes. This function takes as a parameter a function that returns a data loader instance, making it easier to implement independent and correctly sharded data loaders on each worker node. The resulting cstorch.utils.data.DataLoader object is then passed to a DataExecutor, which takes responsibility for the high-level coordination of all independent tasks required for execution.
Now let's take a closer look at the components that play a role in defining a single training step. Internally, the use of cerebras_pytorch relies on PyTorch LTC to draw the computational graph associated with the training task and convert it into operations executable on the Cerebras Wafer Scale Engine (WSE). Therefore, the first step in establishing the training logic is to create the model instance to enable its subsequent tracking. This operation is performed by the code at the beginning of train.py::main.
1 | backend = cstorch.backend(config.backend, use_cs_grad_accum=True) … with backend.device: model = GPTModel(model_config) compiled_model = cstorch.compile(model, backend) |
With this model definition, along with a learning rate optimizer and scheduler built with APIs that directly reflect PyTorch's APIs, it is possible to define the logic of a basic learning step.
1 | @cstorch.trace def training_step(batch): input_ids, labels = batch loss = compiled_model(input_ids, labels) loss.backward() Torch.nn.utils.clip_grad_norm_(list(all_params), config.max_gradient_norm)optimierer.step() lr_scheduler.step() optimized.zero_grad() returns loss |
The bulk of this function is pretty standard training code. The only interesting part is the decorator @cstorch.trace. It signals to the framework that the functional code should be tracked and executed on the CS system. Tensors cannot be eagerly executed in this area, which means that the code here cannot contain logging functions or Python conditionals. This requires another decorator:
1 | @cstorch.step_closure def log_loss(loss, step): rate = executor.profiler.rate() global_rate = executor.profiler.global_rate() logger.info( f”| Step={step}, ” f”Loss={loss .item():.5f}, ” f”Rate={rate:.2f} samples/sec., ” f”GlobalRate={global_rate:.2f} samples/sec.” ) write.add_scalar(“loss”, loss.item(), step)writer.add_scalar(“samples_per_second”, global_rate, step) |
This logging code requires eager execution of tensor values and does not need to run on the WSE, so it's wrapped in a @cstorch.step_closure decorator. The check code works the same way, except that it ensures that all checkpoint_steps are executed only on the checkpoint_steps value passed to the DataExecutor above. For this we have the decorator @cstorch.checkpoint_closure. The functions included in this decorator can be called at any time, but will only be executed if the current step is a checkpoint step.
1 | @cstorch.checkpoint_closure def save_checkpoint(step): checkpoint_path = out_dir.joinpath(f”checkpoint_{step}.mdl”) state_dict = { “model”: model.state_dict(), “optimizer”:optimierer.state_dict(), ” lr_scheduler”: lr_scheduler.state_dict(), “global_step”: step, “model_config”: asdict(model_config), } cstorch.save(state_dict, checkpoint_path) logger.info(f”Checkpoint saved in {checkpoint_path}”) |
The functions used in the main training loop have been specified. After adding a few lines of code to fix deficiencies related to checkpoint loading, configuration handling, etc., the result is a train.py that, in a total of 156 lines of code, is supposedly capable of seamlessly managing training coordination across large tasks , distributed clusters.
However, it is important to step back and carefully examine the validity of this statement. The requirement to smoothly coordinate training tasks across massive clusters distributed in so few lines of code raises questions about the true complexity of implementation and accounting for the various challenges and nuances that come with such large-scale operations. Accurately assessing these declarations requires a thorough assessment of the quality of the code, its robustness, and its long-term sustainability.
Source: Cerebras
And you ?
In your opinion, the simplicity claimed by GigaGPT in 565 lines of code does not affect the stability and actual performance of the model?
What are the advantages and disadvantages of using GigaGPT compared to other natural language models?
What are the potential risks associated with using GigaGPT for large-scale training of natural language models?
Is the exclusive reliance on Cerebras hardware memory a real advantage for GigaGPT or could this lead to portability issues on other architectures?
See also:
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Conservatives say ChatGPT is “woke” and worry about bias in OpenAI’s chatbot. They also accuse the chatbot of defending “left-wing values”.