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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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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