[BUG] RuntimeError: weight should have at least three dimensions

Describe the bug
I'm now using deepspeed to initialize a new torch module which is not in the original huggingface model.
at first it pop out the error similar to https://github.com/deepspeedai/DeepSpeed/issues/5326, then i used the method in it and resolve the weight-import-error
but now i face this error, i believe it's caused by deepspeed

my code is presented below:

class RemoteClipLoader:
    def __init__(self, model_name="RN50", ckpt_path=None, dim=0, device="cuda"):
        self.model, _, _ = open_clip.create_model_and_transforms(model_name)
        self.device = device
        self.model.to(self.device).eval()

        self.mlp = nn.Linear(
            self.model.visual.attnpool.c_proj.out_features if hasattr(self.model.visual, "attnpool") else 512,
            dim
        ).to(self.device)

        if ckpt_path is not None:
            state_dict = torch.load(ckpt_path, map_location="cpu")
            self._load_state_dict(self.model, state_dict)

    def _load_state_dict(self, module_to_load, state_dict, start_prefix=""):
        metadata = getattr(state_dict, "_metadata", None)
        state_dict = state_dict.copy()
        if metadata is not None:
            state_dict._metadata = metadata
        error_msgs = []
        def load(module, state_dict, prefix=""):
            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
            args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
            if any(key.startswith(prefix) for key in state_dict):
                named_parameters = dict(module.named_parameters(prefix=prefix[:-1], recurse=False))
                params_to_gather = [named_parameters[k] for k in state_dict.keys() if k in named_parameters]
                if params_to_gather:
                    with deepspeed.zero.GatheredParameters(params_to_gather, modifier_rank=0):
                        rank = dist.get_rank() if dist.is_initialized() else 0
                        if rank == 0:
                            module._load_from_state_dict(*args)
                else:
                    module._load_from_state_dict(*args)
            else:
                module._load_from_state_dict(*args)
            for name, child in module._modules.items():
                if child is not None:
                    load(child, state_dict, prefix + name + ".")
        load(module_to_load, state_dict, start_prefix)
        if error_msgs:
            raise RuntimeError(
                f"Error(s) in loading state_dict for {module_to_load.__class__.__name__}:\n\t" +
                "\n\t".join(error_msgs)
            )
        print("RemoteCLIP checkpoint loaded successfully!")

    @torch.no_grad()
    def encode_image(self, x):
        if x.ndim == 3:
            B, N, feat = x.shape  # (B, 256, 1176)
            p = int(math.sqrt(feat // (2 * 3)))  # 14
            x = x.view(B * N, 2, 3, p, p)        # (B*256, 2, 3, 14, 14)
            x = x[:, 0]                   
            x = torch.nn.functional.interpolate(x, size=(224,224), mode="bilinear")
        elif x.ndim == 4 and x.shape[1] == 3:
            B = x.size(0)
            patches = x.unfold(2,14,14).unfold(3,14,14)  # (B,3,16,16,14,14)
            patches = patches.permute(0,2,3,1,4,5).reshape(B,256,3,14,14)
            x = patches.reshape(B*256,3,14,14)
            x = torch.nn.functional.interpolate(x, size=(224,224), mode="bilinear")
        else:
            raise ValueError(f"Unsupported input shape {tuple(x.shape)}")

        x = x.to(self.device)
        feat = self.model.encode_image(x)   # (B*256, d_model)
        out = self.mlp(feat)                # (B*256, dim)

        return out.view(-1, out.size(-1))   # [256, dim]

    def __call__(self, x):
        return self.encode_image(x)

Start with the provided RemoteClipLoader example, especially _load_state_dict and the deepspeed.zero.GatheredParameters block, and capture the full traceback, dependency versions, and a minimal reproducible case. Compare the failing parameter shape with the module state_dict during loading; done means identifying whether DeepSpeed or the custom loading path causes the dimensionality error and documenting or fixing that path.