| Here’s a menu of additional, “pure-PyTorch” extensions that can close the gap even further to a production-grade LLM: | |
| ⸻ | |
| 1. Native Low-Rank & MoE Layers (DO LAST) | |
| Why: Expert mixtures and low-rank adapters let you balloon effective parameter count without proportional compute. | |
| • Mixture-of-Experts: Implement a tiny gating network (one or two linear layers) that routes each token’s representation to one of E experts (each a small FFN). Only that expert runs on that position, so compute per token stays constant while total capacity grows by E×. | |
| • PyTorch sketch: | |
| class MoE(nn.Module): | |
| def __init__(self, d_model, d_ff, n_experts=4): | |
| super.__init__ | |
| self.gate = nn.Linear(d_model, n_experts) | |
| self.experts = nn.ModuleList( | |
| [nn.Sequential(nn.Linear(d_model, d_ff), nn.GELU, nn.Linear(d_ff, d_model)) | |
| for _ in range(n_experts)] | |
| ) | |
| def forward(self, x): | |
| # x: [T,B,D] | |
| logits = self.gate(x) # [T,B,E] | |
| w = F.softmax(logits, dim=-1) # [T,B,E] | |
| y = torch.stack([expert(x) for expert in self.experts], -1) | |
| # y: [T,B,D,E] → weighted sum: | |
| out = (y * w.unsqueeze(2)).sum(-1) | |
| return out | |
| • Trade-off: You’ll need a load-balancing loss term (e.g. encourage the gate to spread load) and telemetry on expert usage, but the code stays pure PyTorch. | |
| ⸻ | |
| 2. [x] Adaptive Computation Time (ACT) | |
| Why: Let the model learn to spend more depth on “hard” bits and skip layers on easier ones. | |
| • Implementation: Add a tiny halting unit after each layer—e.g. a single linear+sigmoid per token that predicts stop/pause. Accumulate “halt probability” across layers and stop processing tokens once they cross a threshold. | |
| • Benefit: On average you’ll do fewer layer passes per token, reducing compute without touching PyTorch internals. | |
| ⸻ | |
| 3. [x] Advanced PyTorch-Native Quantization | |
| Why: Move beyond static 4-bit packaging to full QAT / dynamic quant. | |
| • FX-graph QAT: Use torch.quantization.prepare_qat_fx on your SparseQuantTransformerLayer with a custom 4-bit observer (we sketched one earlier). Then convert_fx to int8 or 4-bit for weights—no external libs needed. | |
| • Dynamic quant for inference: Wrap your model in torch.quantization.quantize_dynamic(...), quantizing only Linear modules to int8 on-the-fly. Gives a big speed/memory win at inference time on CPU. | |
| ⸻ | |
| 4. [x] Chunked & Overlapping Attention | |
| Why: Emulate sparse attention with pure PyTorch and no for-loops. | |
| • How: Break your sequence into fixed-size chunks (e.g. 512 bits), attend within each chunk plus a small overlap window to neighbors. | |
| • Pure PyTorch: Use unfold + batched torch.matmul to compute all chunked attention in parallel: | |
| x: [B, L, D], chunk_size=C, overlap=O | |
| pads = (O, O) | |
| x_padded = F.pad(x, (0,0) + pads) # pad on seq dim | |
| chunks = x_padded.unfold(1, C+2*O, C) # [B, n_chunks, C+2O, D] | |
| Then project Q,K,V per-chunk and do fused matmuls batchwise | |
| • Benefit: You get an O(L·(C+2O)) algorithm without Python loops, all in tensor ops. | |
| ⸻ | |
| 5. Functorch-Based Vectorization & vmap | |
| Why: Fuse your per-head or per-expert loops automatically. | |
| • Use functorch.vmap to turn your per-head attention code (the one inside the for t in range(T)) into a single batched kernel. | |
| • Benefit: Cleaner code, fewer Python loops, and TorchInductor can fuse it just as well as hand-written loops. | |
| ⸻ | |
| 6. [x] Fully-Sharded DataParallel & Pipeline Parallel (PyTorch-Native) | |
| Why: Scale out to multiple GPUs without external frameworks. | |
| • FSDP: Wrap your model in torch.distributed.fsdp.FullyShardedDataParallel to shard both parameters and optimizer state across GPUs. | |
| • Pipe: Use torch.distributed.pipeline.sync.Pipe to split your 40+ layer model across GPUs as pipeline stages. | |
| • Benefit: Zero external deps—pure PyTorch DDP/FS/PIPE—so you can train 100M+ parameter models. | |
| ⸻ | |
| 7. [x] Mixed Precision & Autocast on CPU (bfloat16) | |
| Why: PyTorch now supports `torch.amp.autocast('cpu')` for bfloat16 on some architectures. | |
| • Surround your forward in with `torch.amp.autocast('cpu')`: to cut memory and speed up linear/attention kernels, even on CPU. | |
| ⸻ | |
| 8. [x] Optimized Learning-Rate Schedules & Optimizers | |
| Why: Achieve GPT-level convergence behavior… | |
| • Implement OneCycleLR or CosineAnnealingWarmRestarts directly via torch.optim.lr_scheduler. | |
| • Swap to AdamW with decoupled weight decay (torch.optim.AdamW) and dynamic gradient clipping (torch.nn.utils.clip_grad_norm_). | |
| • All of these live in core PyTorch. | |
| ⸻ | |
| Putting It All Together | |
| 1. MoE + ACT will let you scale capacity (E× experts) while controlling average compute. | |
| 2. FX/QAT + dynamic quant gives you 4-bit int inference with no external libs. | |
| 3. Chunked attention + vmap replaces loops with giant fused tensor ops. | |
| 4. FSDP + Pipe moves you onto multi-GPU purely in torch.distributed. | |
| 5. Autocast (bfloat16) on CPU/GPU for mixed precision speed. | |
| By layering these techniques, you can: | |
| • Reach hundreds of millions (even billions) of effective parameters | |
| • Maintain single-library purity (just PyTorch) | |
| • Hit LLM-class throughputs (100’s of tokens/sec GPU, 10’s CPU) | |
| • Keep full NRB telemetry available for safety checks | |