pipeline parallelism

MAPL treats inter-stage activation compression in pipeline parallelism as a learnable orthogonal projection under Stiefel manifold constraints, letting each stage adapt its own task-optimal subspace. Combined with factorized anchor embeddings and residual vector quantization, it achieves high compression with negligible performance loss across LLaMA models from 150M to 1B parameters.