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Fix SBP settings for LayerNormGradOp to ensure correct gradient aggregation for gamma_diff and beta_diff Changes - Updated SBP strategy in LayerNormGradOp: Set gamma_diff and beta_diff to use PartialSum instead of Split to avoid dimension mismatches during distributed training. - Added consistency check for begin_norm_axis and begin_params_axis: Enforce equality to ensure proper alignment of normalization and parameter dimensions.
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因为 NPU GPT2 测试场景下，layer_norm 都是 affine 的，为减少 CANN 调用，对 layer_norm_grad 进行重构。在一个 kernel 内完成 gamma，beta，dx 的梯度计算。