Release/v26.5#90
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The dispatch/MLP MoE activations are already expert-sharded via activation_exp. Since AI-Hypercomputer#4007, their batch dim also maps to activation_batch_moe, which includes 'expert'. Under single-node expert parallelism (ici_expert_parallelism=-1) this double-maps two tensor dims onto the 'expert' mesh axis, so GSPMD falls back from expert-parallel AllToAll to FSDP-style AllGather+ReduceScatter, regressing throughput for few-large-expert models (e.g. Mixtral-8x7b: ~7.4k -> ~10.9k tok/s/device at bs=11 on 8x MI355X). Add a config flag moe_dispatch_no_expert_sharding (default false) that selects a new activation_batch_no_exp rule ([data, fsdp, fsdp_transpose], no 'expert') for the training dispatch/MLP batch axis. Enable it for mixtral-8x7b. Default-false keeps every other model and all TPU/non-EP paths byte-identical; the flag only changes sharding when the 'expert' mesh axis size > 1.
…e-expert geometry as 8x7b, so it benefits from the same expert-parallel MoE dispatch/MLP sharding.
…oe_dispatch_no_expert_sharding the expert dim is sharded by 'expert' and the batch dim is not, guarding the expert-parallel dispatch/MLP sharding.
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