Repository: vllm-project/vllm
File: vllm/config/vllm.py (Python)

Code before:
    from vllm.utils.flashinfer import has_flashinfer

    return (
        cfg.parallel_config.tensor_parallel_size > 1
        and current_platform.is_cuda()
        and has_flashinfer()
        and (
            current_platform.is_device_capability(100)
            or current_platform.is_device_capability(90)
        )
        # tp-dp combination broken:
        # https://github.com/vllm-project/vllm/issues/34458
        and cfg.parallel_config.data_parallel_size == 1
    )


def enable_rope_kvcache_fusion(cfg: "VllmConfig") -> bool:
    """Enable if rotary embedding custom op is active and
    use_inductor_graph_partition is enabled.
    """
    from vllm._aiter_ops import rocm_aiter_ops

    return (
        rocm_aiter_ops.is_enabled()
        and cfg.compilation_config.is_custom_op_enabled("rotary_embedding")
        and cfg.compilation_config.use_inductor_graph_partition
    )


def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
    """Enable if using AITER RMSNorm and AITER Triton GEMMs
    and hidden size is 2880 i.e. gpt-oss; otherwise Inductor handles fusion."""
    from _aiter_ops import rocm_aiter_ops

    return (
        rocm_aiter_ops.is_rmsnorm_enabled()
        and not rocm_aiter_ops.is_triton_gemm_enabled()
        and cfg.model_config is not None
        and cfg.model_config.get_hidden_size() == 2880
    )


OPTIMIZATION_LEVEL_00 = {
    "compilation_config": {
        "pass_config": {
            "fuse_norm_quant": False,
            "fuse_act_quant": False,
            "fuse_allreduce_rms": False,
            "fuse_attn_quant": False,
            "enable_sp": False,
            "fuse_gemm_comms": False,

Review comment (performance):
So are you guys using inductor graph partition on rocm by default? Otherwise we should also return true here I'd dynamo partition and kv cache op not in splitting ops

Code after:
    from vllm.utils.flashinfer import has_flashinfer

    return (
        cfg.parallel_config.tensor_parallel_size > 1
        and current_platform.is_cuda()
        and has_flashinfer()
        and (
            current_platform.is_device_capability(100)
            or current_platform.is_device_capability(90)
        )
        # tp-dp combination broken:
        # https://github.com/vllm-project/vllm/issues/34458
        and cfg.parallel_config.data_parallel_size == 1
    )


def enable_rope_kvcache_fusion(cfg: "VllmConfig") -> bool:
    """Enable if rotary embedding custom op is active and
    use_inductor_graph_partition is enabled.
    """
    from vllm._aiter_ops import rocm_aiter_ops

    return (
        rocm_aiter_ops.is_enabled()
        and cfg.compilation_config.is_custom_op_enabled("rotary_embedding")
        and cfg.compilation_config.use_inductor_graph_partition
    )


def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
    """Enable if using AITER RMSNorm and AITER Triton GEMMs
    and hidden size is 2880 i.e. gpt-oss; otherwise Inductor handles fusion."""
    from vllm._aiter_ops import rocm_aiter_ops

    return (
        rocm_aiter_ops.is_rmsnorm_enabled()
        and not rocm_aiter_ops.is_triton_gemm_enabled()
        and cfg.model_config is not None
        and cfg.model_config.get_hidden_size() == 2880
    )


OPTIMIZATION_LEVEL_00 = {
    "compilation_config": {
        "pass_config": {
            "fuse_norm_quant": False,
            "fuse_act_quant": False,
            "fuse_allreduce_rms": False,
            "fuse_attn_quant": False,
            "enable_sp": False,
            "fuse_gemm_comms": False,