[DEV] fix(megatron-fsdp): preserve non-meta tensors during meta device materialization#4155
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xuwchen wants to merge 1 commit intoNVIDIA:devfrom
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[DEV] fix(megatron-fsdp): preserve non-meta tensors during meta device materialization#4155xuwchen wants to merge 1 commit intoNVIDIA:devfrom
xuwchen wants to merge 1 commit intoNVIDIA:devfrom
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shjwudp
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Apr 7, 2026
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Good catch! Some bias parameters are stored as nn.Module buffers (though that’s not a standard PyTorch practice). These buffers weren’t initialized on the meta device and shouldn’t be converted to empty tensors. This PR fixes that issue and also resolves the unexpected NaNs observed in the functional tests with the MoE layer.
| for name, param in module.named_parameters(recurse=False): | ||
| if param.is_meta: | ||
| new = torch.empty_like(param, device=device) | ||
| setattr(module, name, torch.nn.Parameter(new, requires_grad=param.requires_grad)) |
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Please do not reconfigures module parameters, which might cause loss of existing attributes on those parameters or invalidate maps that used these parameters as keys.
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main PR: #4154
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