Datasets:
PallasBench Robust GPU Benchmark: 45 Pallas kernels, original+fixed source, GPU compilation fixes, robust eval framework, IR artifacts
99477fc verified | """Level 3: Triangle Multiplicative Update via Pallas. | |
| Implements the core triangular multiplicative update from AlphaFold2/3's | |
| Evoformer / Pairformer: for each pair (i,j), aggregate information from | |
| all intermediate positions k via element-wise product of edges (i,k) | |
| and (k,j), enabling triplet reasoning for 3D structure consistency. | |
| This is the computational heart of protein structure prediction and | |
| is the most expensive operation in the Pairformer stack. | |
| Provenance: google-deepmind/alphafold3 Pairformer triangle multiplication | |
| "Triangle Multiplication Is All You Need" (arXiv:2510.18870) | |
| """ | |
| from pallasbench.provenance import describe_task as _describe_task | |
| __doc__ = _describe_task("L3/triangle_update", __doc__) | |
| import jax | |
| import jax.numpy as jnp | |
| from jax.experimental import pallas as pl | |
| def _triangle_update_kernel(pair_ref, mask_ref, o_ref): | |
| pair = pair_ref[...] | |
| mask = mask_ref[...] | |
| n, _, c = pair.shape | |
| left_proj = pair * mask[:, :, None] | |
| right_proj = pair * mask[:, :, None] | |
| # Triangle update: out[i,j] = sum_k left[i,k] * right[k,j] | |
| # This is effectively a batched matmul over the channel dimension | |
| left_t = left_proj.transpose(2, 0, 1) | |
| right_t = right_proj.transpose(2, 0, 1) | |
| update = jnp.sum(left_t[:, :, :, None] * right_t[:, None, :, :], axis=2) | |
| update = update.transpose(1, 2, 0) | |
| o_ref[...] = pair + update | |
| def pallas_triangle_update(pair: jax.Array, mask: jax.Array) -> jax.Array: | |
| n, _, c = pair.shape | |
| return pl.pallas_call( | |
| _triangle_update_kernel, | |
| out_shape=jax.ShapeDtypeStruct(pair.shape, pair.dtype), | |
| grid=(1,), | |
| in_specs=[ | |
| pl.BlockSpec(pair.shape, lambda i: (0, 0, 0)), | |
| pl.BlockSpec(mask.shape, lambda i: (0, 0)), | |
| ], | |
| out_specs=pl.BlockSpec(pair.shape, lambda i: (0, 0, 0)), | |
| )(pair, mask) | |
| pallas_kernel = pallas_triangle_update | |
| task_name = "triangle_update" | |
| input_shapes = [(64, 64, 32), (64, 64)] | |
| category = "genomics" | |
| level = 3 | |