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Types & classes28 in github.com/RightNow-AI/autokernel

↓ 7 callersClass_Timeout
Context-manager wall-clock timeout. Works on both Unix (SIGALRM) and Windows (thread-based fallback).
bench.py:43
↓ 4 callersClassRMSNorm
models/llama_7b.py:22
↓ 3 callersClassGPUSpec
profile.py:77
↓ 3 callersClassKernelBenchProblem
A single KernelBench problem.
kernelbench/bridge.py:48
↓ 2 callersClassGPUSpec
bench.py:90
↓ 2 callersClassKernelReplacement
Describes a single kernel replacement: what to replace and with what.
verify.py:63
↓ 2 callersClassOptimizedModelContext
Context manager that patches a model's submodules to use optimized Triton kernels. Usage: with OptimizedModelContext(model, replacem
verify.py:510
↓ 2 callersClassTransformerBlock
models/llama_7b.py:101
↓ 1 callersClassAttention
models/llama_7b.py:51
↓ 1 callersClassBenchTimeoutError
bench.py:39
↓ 1 callersClassBertLayer
models/bert_base.py:55
↓ 1 callersClassBertMLP
models/bert_base.py:43
↓ 1 callersClassBertSelfAttention
models/bert_base.py:16
↓ 1 callersClassBlock
models/gpt2.py:73
↓ 1 callersClassCausalSelfAttention
models/gpt2.py:18
↓ 1 callersClassFeedForward
SwiGLU MLP (LLaMA-style).
models/llama_7b.py:88
↓ 1 callersClassKernelRecord
Aggregated stats for one kernel (name + shape combo).
profile.py:505
↓ 1 callersClassMLP
models/gpt2.py:57
↓ 1 callersClassVerificationResult
Full verification result.
verify.py:73
↓ 1 callersClass_LayerNormWrapper
Wraps nn.LayerNorm to use an optimized kernel_fn.
verify.py:441
↓ 1 callersClass_LinearWrapper
Wraps nn.Linear to use an optimized matmul kernel_fn.
verify.py:408
↓ 1 callersClass_RMSNormWrapper
Wraps RMSNorm-like modules to use an optimized kernel_fn.
verify.py:478
↓ 1 callersClass_Timeout
Context manager that raises TimeoutError after `seconds`.
kernelbench/bench_kb.py:68
ClassBertModel
BERT-base: hidden_size=768, num_layers=12, num_heads=12, intermediate=3072 (110M params).
models/bert_base.py:69
ClassGPT2
GPT-2 124M (small) by default. Configurable via constructor args. Sizes: - GPT-2 Small: n_layer=12, n_head=12, n_embd=768 (124M par
models/gpt2.py:87
ClassLlamaModel
Compact LLaMA (160M params) -- fits on any GPU, good for testing AutoKernel. Config: dim=768, n_layers=12, n_heads=12, n_kv_heads=4, hidden_
models/llama_7b.py:115
ClassLlamaModel7B
LLaMA-7B scale -- requires ~14GB VRAM in fp16. Config: dim=4096, n_layers=32, n_heads=32, n_kv_heads=8, hidden_dim=11008
models/llama_7b.py:164
ClassMyModel
Example custom model -- a simple CNN + MLP. Replace this with your own architecture.
models/custom.py:13