why close ag overlap when is_grad_enabled is False

When I test model with full recomputation, the forward all-gather communication is not overlapped. Because is_grad_enabled is false when forward with full recomputation. I see the following code in _LayerNormLinear class:

    if ub_overlap_ag:
        tp_world_size = get_distributed_world_size(tp_group)
        if tp_world_size == 1 or (not is_grad_enabled):
            ub_overlap_ag = False
    if ub_overlap_ag:
        dim_size = list(inputmat.size())
        dim_size[0] = dim_size[0] * tp_world_size
        ub_obj_lnout = get_ub(ub_name + "_fprop")
        if return_layernorm_output:
            # First prepare LN output in higher precision,
            # which will be later copied to a FP8 UB
            ln_out = torch.empty_like(inputmat, memory_format=torch.contiguous_format)
        else:
            ln_out = ub_obj_lnout.get_ubuf_output(0)

Why ub_overlap_ag is set to False in '(not is_grad_enabled)' condition?

Start by locating the _LayerNormLinear implementation and tracing the full-recomputation forward path around the is_grad_enabled guard. Review the related forward all-gather overlap logic and determine whether the condition is intentional; done means its rationale and effect are clearly explained or a concrete change is identified.