SOC Dataset Memory Mystery: 80GB VRAM With Batch Size 1?

Hey everyone! Let's dive into a head-scratcher we've been seeing with the SOC (Spin-Orbit Coupling) dataset when training models. You know how sometimes you set up your training, expecting things to run smoothly, and then BAM! Your GPU memory just explodes? Yeah, that's kind of what's happening here, and it's a pretty wild situation. We're talking about massive memory consumption, even when we're just feeding the model one sample at a time. Stick around as we break down what's going on, why it might be happening, and what we can do about it.

The Unexplained VRAM Hoard: What's Happening?

So, the core of the issue is this: we're seeing extremely high GPU memory usage, hitting around 80GB per GPU, even when the batch_size is set to a tiny 1. To put that into perspective, we're using a setup with 8 NVIDIA H100 GPUs, each boasting a hefty 80GB of VRAM. That means a single data point is nearly maxing out one of these powerful GPUs! This is definitely not what we expect, and it's a major roadblock for training effectively, especially if we want to use larger batch sizes down the line. The setup is pretty beefy: NVIDIA H100 80GB HBM3 GPUs, and we're running CUDA 12.4. The dataset in question is the SOC data, which we know can be quite complex. The question on everyone's mind is: Is this memory usage expected for single samples from the SOC dataset? Given the nature of spin-orbit coupling and the complexity it introduces, could it be that processing just one sample inherently requires such a colossal amount of VRAM? Or is there something else at play here? We've attached the config file, and we're really hoping to get some insights into whether specific parameters, maybe related to layer sizes or the precision settings, might be contributing to this memory footprint. The ultimate goal, of course, is to figure out how to optimize this setup to allow for a larger batch size, which would significantly speed up our training process and make better use of our hardware. It's a real puzzle, and we're keen to unravel it together.

Diving Deep: Configuration Analysis for Memory Leaks

Alright guys, let's get our hands dirty and dissect this configuration file, because that's often where the secrets lie when it comes to unexpected memory usage. We're looking for any parameters that might be silently gobbling up our precious VRAM, especially when dealing with the intricacies of the SOC dataset. First off, we see batch_size: 1 under dataset_params. Yep, confirmed, we're already at the minimum. No surprises there. Then we move to losses_metrics. Here, we've got loss_weight: 27.211 for the Mean Absolute Error (MAE) on the Hamiltonian. This weight itself probably isn't the culprit, but the complexity of calculating this loss for a single sample could be significant, especially if intermediate computations are memory-intensive. Moving onto optim_params, things like lr, lr_decay, gradient_clip_val, and epoch settings are unlikely to directly cause this VRAM spike. They're more about the training dynamics. The real meat seems to be in output_nets and representation_nets. Under output_nets, we have HamGNN_out with several key settings: ham_only: true (which is good, we're not fitting both H and S, saving some computation), ham_type: openmx (this specifies the type of Hamiltonian), nao_max: 26. Now, nao_max (maximum number of atomic orbitals) is a potential flag. A higher number of atomic orbitals can definitely lead to larger matrices and tensors, thus increasing memory needs. If the SOC dataset has systems with many atomic orbitals, this parameter could be a major factor. add_H0: True and symmetrize: True are also operations that add computational overhead and might require storing intermediate results. The soc_switch: True is particularly interesting – fitting the SOC Hamiltonian is precisely what we're doing, and this inherently adds complexity and potentially more parameters or calculations compared to non-SOC cases. Now, let's peek at representation_nets and the HamGNN_pre parameters. This is where the graph neural network architecture is defined. We see cutoff: 26.0 and cutoff_func: cos, standard stuff. However, the irreps_edge_sh and irreps_node_features are quite extensive. For instance, irreps_node_features: 64x0e+64x0o+32x1o+16x1e+12x2o+25x2e+18x3o+9x3e+4x4o+9x4e+4x5o+4x5e+2x6e defines a very rich set of irreducible representations for node features. Similarly, irreps_edge_sh: 0e + 1o + 2e + 3o + 4e + 5o defines the edge features. The num_layers: 3, num_radial: 64, and radial_MLP: [64, 64] also indicate a fairly deep and wide network. Each of these can contribute significantly to the model's parameter count and the size of intermediate activations during the forward pass, which are stored in VRAM. The combination of a high nao_max, a complex network architecture with many irreducible representations, and the inherent complexity of SOC calculations seems to be the prime suspect for this 80GB VRAM usage. We'll need to explore if these can be reduced without sacrificing too much accuracy.

Tackling the Memory Beast: Optimization Strategies

Okay, so we've identified some likely culprits for our memory woes on the SOC dataset – mainly the complexity arising from nao_max, the rich irreducible representations, and the inherent demands of SOC calculations. Now, let's talk about strategies to tame this VRAM-hungry beast and, hopefully, pave the way for a larger batch size. The first and perhaps most direct approach is to experiment with reducing nao_max. If the majority of your systems in the SOC dataset don't require the full 26 atomic orbitals, consider if a lower value, say 19 or even 14 (if your data allows), could suffice. This directly shrinks the size of the tensors involved in calculations. We need to carefully evaluate the trade-off between memory savings and prediction accuracy here. Another avenue lies within the representation_nets, specifically the irreps_node_features and irreps_edge_sh. While these are designed to capture rich physical information, extremely high numbers of irreducible representations can inflate the model's capacity and memory footprint. Could we simplify the set of irreps? Perhaps start with a smaller set and gradually increase if accuracy suffers. This requires some trial and error, but it's a powerful way to fine-tune the model's complexity. Look closely at num_layers and num_radial as well. While a deeper network (more layers) or more radial basis functions (num_radial) can improve performance, they also increase the number of parameters and intermediate activations. Reducing the number of layers or num_radial might offer substantial memory savings. Again, this is a delicate balancing act with accuracy. When we talk about precision, the config shows precision: 32. This means we're using full 32-bit floating-point numbers. Switching to precision: 16 (half-precision), particularly with libraries that support mixed-precision training (like PyTorch with torch.cuda.amp), can drastically cut down VRAM usage, often by nearly half, and can even speed up training on modern GPUs like the H100s. This is usually the first optimization step for memory issues. We also need to consider the gradient checkpointing technique, although it's not explicitly in this config. Gradient checkpointing allows you to trade computation for memory by recomputing certain activations during the backward pass instead of storing them all. This can be a lifesaver for very deep or memory-intensive models. Finally, let's consider data loading. While num_workers: 16 is generally good for throughput, ensure that the data preprocessing itself isn't creating excessively large intermediate objects before they even reach the model. Sometimes, the issue isn't just the model, but how data is prepared. Each of these optimization strategies requires careful experimentation. You'll want to make one change at a time, measure the VRAM usage and training performance, and iterate. It’s a process of tuning, but by systematically adjusting these parameters, we should be able to find a sweet spot that allows for more reasonable memory consumption and, hopefully, larger batch sizes on your SOC dataset.