| # Substrate candidate spec (for automated kernel authors) |
|
|
| A substrate is a CORRECT CUDA implementation of one KernelBench level1 problem, |
| used as the mutation target. It is NOT the correctness oracle (the official |
| PyTorch reference is). Every candidate must pass an automated gate comparing it |
| against the official reference on multiple input suites before admission. |
|
|
| ## Deliverable |
|
|
| One Python file: `substrates/candidate_<name>.py` (snake_case short name). |
| It must be import-safe (no GPU work at import time) and define exactly: |
| |
| ```python |
| def register(K, torch, ctypes): |
| K.PROBLEMS["<name>"] = dict( |
| kb_file="<original KernelBench filename>", |
| cuda=CUDA_SOURCE, # device-only source, see rules below |
| kernel_name="<kernel symbol>", |
| ref=<callable>, # torch reference, MUST match the KB Model's |
| # forward() semantics exactly (params from |
| # get_init_inputs() defaults) |
| launch=<callable>, # (inputs, torch) -> (out_tensor, grid3, |
| # block3, [(ctypes_type, value), ...]) |
| suites=<callable>, # () -> [(name, n_trials, builder)] |
| probe=<callable>, # (torch) -> [(name, [small cpu tensors])] |
| ) |
| ``` |
| |
| ## CUDA source rules |
|
|
| - Device code ONLY: `__global__` kernel(s) + `#include <cuda_fp16.h>` (and |
| `<math_constants.h>` if needed). NO torch headers, NO host wrapper. |
| It is compiled with NVRTC; the launch config lives in the Python `launch`. |
| - fp32 in/out. Use `long long` for any index that can exceed 2^31. |
| - One thread-block pattern from the proven set when applicable: |
| elementwise 1D grid; block-per-row with 256-thread shared-memory reduction; |
| 32x32 shared-memory tiles for matmul; column-thread + row-loop for |
| dim-1 reductions. Look at ../problems_batch2.py for working examples of all |
| four patterns, including suites/probe conventions. |
| - Structure the source so mutation sites are visible: keep bounds guards as |
| single-line `if (...) { ... }` or `if (...) return;`, use ceil-division |
| `(x + K - 1) / K`, label repeated `__syncthreads();` with distinct trailing |
| comments (e.g. `// sync-after-load`). |
| |
| ## Suites rules |
| |
| - `T0_original`: EXACTLY the shapes/distribution of the problem's |
| `get_inputs()` (5 trials, seed `1000 + t`). |
| - Plus 2-3 targeted suites: signed values, misaligned shapes (dims not |
| divisible by 32/256), and a structure-specific stressor (spike/large values) |
| where meaningful. Seed every builder deterministically. |
| - CRITICAL: no suite may false-kill a correct implementation. Avoid |
| large-magnitude SIGNED values feeding long accumulations (catastrophic |
| cancellation exceeds the 1e-2 tolerance between two legitimate fp32 |
| implementations); use positive-only values for large-magnitude suites. |
| |
| ## Probe rules |
| |
| Tiny CPU tensors (<1 MB), 2 entries: one aligned T0-like, one misaligned. |
| |
| ## Semantics rules |
| |
| - Read the KernelBench problem file CAREFULLY: match forward() exactly |
| (e.g. L1Norm divides by mean(|x|), NOT sum; reductions may or may not |
| keepdim; losses reduce to a scalar with 'mean'). |
| - Scalar outputs: return a 0-dim or shape-(1,) tensor from launch's out and |
| make ref produce the matching shape. |
| - Integer-output ops (argmax/argmin): out dtype int64 in launch, ref returns |
| the indices tensor; comparison is exact. |
| |
