Here’s a menu of additional, “pure-PyTorch” extensions that can close the gap even further to a production-grade LLM:
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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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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