| """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] |
|
|
| |
| |
| 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 |
|
|
