apr inspect

The apr inspect command reveals model structure, metadata, and tensor statistics. For microGPT, the equivalent is cargo run --example inspect_model.

Model metadata

$ cargo run --example inspect_model

microGPT Model Inspection
══════════════════════════════════════════════════

Architecture:
  Family:          microGPT (character-level, 1-layer GPT)
  Kernel Class:    Custom (MHA + RMSNorm + ReLU)
  Hidden Size:     16
  Attention Heads: 4 (head_dim=4)
  FFN Size:        64
  Vocab Size:      27
  Context Length:   16
  Parameters:      4192

Compare with a production model:

$ apr inspect qwen-1.5b-q4k.apr

  Architecture:
    Family: qwen2
    Hidden Size: 1536
    Intermediate Size: 8960
    Vocab Size: 151936
    Max Position: 32768
    RoPE Theta: 1000000

microGPT is ~360,000x smaller than Qwen-1.5B (1.5B / 4,192 ≈ 358K) but uses the same fundamental operations.

Tensor inventory

Equivalent to apr tensors <file> --stats:

Tensors (cf. `apr tensors`):
  Name                                  Shape   Numel  Stats
  ──────────────────────────────────────────────────────────────
  wte (token embeddings)         [27, 16]     432  μ=+0.010  σ=0.083
  wpe (position embeddings)      [16, 16]     256  μ=-0.010  σ=0.079
  wq[0] (query proj head 0)      [16,  4]      64  μ=-0.010  σ=0.081
  ...
  w_fc1 (MLP up projection)      [16, 64]    1024  μ=+0.000  σ=0.082
  w_fc2 (MLP down projection)    [64, 16]    1024  μ=+0.003  σ=0.079
  w_lm (LM head)                 [16, 27]     432  μ=+0.003  σ=0.078

All weights are initialized from N(0, 0.08²), matching Karpathy’s original Python implementation.

Kernel pipeline

Equivalent to apr explain --kernel:

┌─────────────────────────┬─────────────────────────────────┬─────────────────────────┐
│ Operation               │ Implementation                  │ Contract                │
├─────────────────────────┼─────────────────────────────────┼─────────────────────────┤
│ Embedding Lookup        │ one_hot @ wte (differentiable)  │ microgpt-v1 § one_hot   │
│ RMSNorm                 │ x * (1/rms) per row             │ microgpt-v1 § rms_norm  │
│ Attention (4-head MHA)  │ per-head Q/K/V/O matmul        │ attention-kernel-v1     │
│ Causal Mask             │ lower-tri 0, upper -1e9         │ microgpt-v1 § causal    │
│ Softmax                 │ aprender autograd softmax       │ softmax-kernel-v1       │
│ MLP (ReLU)              │ fc1 → ReLU → fc2               │ activation-kernel-v1    │
│ LM Head                 │ matmul to vocab logits          │ matmul-kernel-v1        │
└─────────────────────────┴─────────────────────────────────┴─────────────────────────┘

Every operation maps to a provable contract. The kernel pipeline for a production LLaMA model (via apr explain --kernel llama) shows the same structure at scale:

$ apr explain --kernel llama --proof-status

Kernel Pipeline (9 ops):
  MatVec (Q4K)    → matvec-kernel-v1         ◉ Tested
  Softmax         → softmax-kernel-v1        ◉ Tested
  Attention (GQA) → element-wise-ops-v1      ◉ Tested
  Normalization   → normalization-kernel-v1  ◉ Tested
  Activation      → element-wise-ops-v1      ◉ Tested
  MLP             → element-wise-ops-v1      ◉ Tested
  Position (RoPE) → rope-kernel-v1           ◉ Tested

Tensor roles

Equivalent to apr explain --tensor <name>: