def __enter__()

in optimum/exporters/openvino/model_patcher.py [0:0]

• 26 lines of code
• 13 McCabe index (conditional complexity)

    def __enter__(self):
        super().__enter__()

        # currently, long RoPE can not be traced for long context support, disable it for avoid potential accuracy issues
        if self._model.config.max_position_embeddings != getattr(
            self._model.config, "original_max_position_embeddings", self._model.config.max_position_embeddings
        ):
            self._model.config.max_position_embeddings = self._model.config.original_max_position_embeddings

        if is_transformers_version(">=", "4.42.0") and is_transformers_version("<", "4.48.0"):
            self._model.model._orig_forward = self._model.model.forward
            self._model.model.forward = types.MethodType(phi3_442_forward, self._model.model)

        # https://github.com/huggingface/transformers/blob/30ee508c6c92a1c0aa0281d193c7c0fb815b8d2f/src/transformers/models/phi3/modeling_phi3.py#L113
        # init inv_freq for torchscript tracing
        # 4.48 transformers version phi3 fixed, but issue still visible with trust_remote_true=True (trust_remote_code has _support_sdpa = False)
        for layer in self._model.model.layers:
            if (
                is_torch_version(">=", "2.1.0")
                and is_transformers_version("<", "4.48.0")
                or not getattr(self._model, "_supports_sdpa", False)
            ):
                orig_self_attn_fwd = layer.self_attn.forward
                layer.self_attn.forward = types.MethodType(_phi3_self_attn_sdpa_forward, layer.self_attn)
                layer.self_attn._orig_forward = orig_self_attn_fwd

            if (
                hasattr(layer.self_attn, "rotary_emb")
                and getattr(layer.self_attn.rotary_emb, "inv_freq", None) is None
            ):
                rotary_emb = layer.self_attn.rotary_emb
                layer.self_attn.rotary_emb.inv_freq = 1.0 / (
                    rotary_emb.base ** (torch.arange(0, rotary_emb.dim, 2, dtype=torch.int64).float() / rotary_emb.dim)
                )