model_runner.py

# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ModelRunner runs the forward passes of the models."""

import gc
import json
import logging
import time
from typing import Optional

import torch
import torch.distributed as dist
from vllm.distributed import (
    get_tp_group,
    init_distributed_environment,
    initialize_model_parallel,
    set_custom_all_reduce,
)

from sglang.srt.configs.device_config import DeviceConfig
from sglang.srt.configs.load_config import LoadConfig
from sglang.srt.configs.model_config import AttentionArch, ModelConfig
from sglang.srt.layers.attention.double_sparsity_backend import DoubleSparseAttnBackend
from sglang.srt.layers.attention.flashinfer_backend import FlashInferAttnBackend
from sglang.srt.layers.attention.torch_native_backend import TorchNativeAttnBackend
from sglang.srt.layers.attention.triton_backend import TritonAttnBackend
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.layers.sampler import Sampler
from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
from sglang.srt.lora.lora_manager import LoRAManager
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.mem_cache.memory_pool import (
    DoubleSparseTokenToKVPool,
    MHATokenToKVPool,
    MLATokenToKVPool,
    ReqToTokenPool,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader import get_model
from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import (
    enable_show_time_cost,
    get_available_gpu_memory,
    init_custom_process_group,
    is_hip,
    monkey_patch_vllm_gguf_config,
    monkey_patch_vllm_p2p_access_check,
    set_cpu_offload_max_bytes,
)

logger = logging.getLogger(__name__)


class ModelRunner:
    """ModelRunner runs the forward passes of the models."""

    def __init__(
        self,
        model_config: ModelConfig,
        mem_fraction_static: float,
        gpu_id: int,
        tp_rank: int,
        tp_size: int,
        nccl_port: int,
        server_args: ServerArgs,
    ):
        # Parse args
        self.model_config = model_config
        self.mem_fraction_static = mem_fraction_static
        self.device = server_args.device
        self.gpu_id = gpu_id
        self.tp_rank = tp_rank
        self.tp_size = tp_size
        self.dist_port = nccl_port
        self.server_args = server_args
        self.is_generation = model_config.is_generation
        self.is_multimodal = model_config.is_multimodal

        # Model-specific adjustment
        if (
            self.model_config.attention_arch == AttentionArch.MLA
            and not self.server_args.disable_mla
        ):
            logger.info("MLA optimization is turned on. Use triton backend.")
            self.server_args.attention_backend = "triton"

        if self.server_args.enable_double_sparsity:
            logger.info(
                "Double sparsity optimization is turned on. Use triton backend without CUDA graph."
            )
            self.server_args.attention_backend = "triton"
            self.server_args.disable_cuda_graph = True
            if self.server_args.ds_heavy_channel_type is None:
                raise ValueError(
                    "Please specify the heavy channel type for double sparsity optimization."
                )
            self.init_double_sparsity_channel_config(
                self.server_args.ds_heavy_channel_type
            )

        if self.is_multimodal:
            server_args.chunked_prefill_size = -1
            self.mem_fraction_static *= 0.95
            logger.info(
                f"Automatically reduce --mem-fraction-static to {self.mem_fraction_static:.3f} "
                f"and turn off chunked prefill "
                f"because this is a multimodal model."
            )
            # TODO: qwen2-vl does not support radix cache now, set disable_radix_cache=True automatically
            if self.model_config.hf_config.architectures == [
                "Qwen2VLForConditionalGeneration"
            ]:
                server_args.disable_radix_cache = True

        # Global vars
        if server_args.show_time_cost:
            enable_show_time_cost()
        if server_args.disable_outlines_disk_cache:
            from outlines.caching import disable_cache

            disable_cache()

        global_server_args_dict.update(
            {
                "attention_backend": server_args.attention_backend,
                "sampling_backend": server_args.sampling_backend,
                "triton_attention_reduce_in_fp32": server_args.triton_attention_reduce_in_fp32,
                "disable_mla": server_args.disable_mla,
                "torchao_config": server_args.torchao_config,
                "enable_nan_detection": server_args.enable_nan_detection,
                "enable_dp_attention": server_args.enable_dp_attention,
                "enable_ep_moe": server_args.enable_ep_moe,
            }
        )

        set_cpu_offload_max_bytes(int(server_args.cpu_offload_gb * 1024**3))

        # Get memory before model loading
        min_per_gpu_memory = self.init_torch_distributed()

        # Load the model
        self.sampler = Sampler()
        self.load_model()

        # Apply torch TP if the model supports it
        supports_torch_tp = getattr(self.model, "supports_torch_tp", False)
        if self.tp_size > 1 and supports_torch_tp:
            self.apply_torch_tp()
            self.torch_tp_applied = True
        else:
            self.torch_tp_applied = False

        apply_torchao_config_to_model(
            self.model, global_server_args_dict["torchao_config"]
        )

        # Init memory pool and attention backends
        if server_args.lora_paths is not None:
            self.init_lora_manager()
        self.init_memory_pool(
            min_per_gpu_memory,
            server_args.max_running_requests,
            server_args.max_total_tokens,
        )
        if self.device == "cuda":
            self.init_cublas()
            self.init_attention_backend()
            self.init_cuda_graphs()
        else:
            self.cuda_graph_runner = None
            self.init_attention_backend()

    def init_torch_distributed(self):
        logger.info("Init torch distributed begin.")
        # Init torch distributed
        torch.get_device_module(self.device).set_device(self.gpu_id)
        if self.device == "cuda":
            backend = "nccl"
        # ToDO(liangan1):Just use gloo to bypass the initilization fail
        # Need to use xccl for xpu backend in the future
        elif self.device == "xpu":
            backend = "gloo"
        elif self.device == "hpu":
            backend = "hccl"

        if not self.server_args.enable_p2p_check:
            monkey_patch_vllm_p2p_access_check(self.gpu_id)
        if self.server_args.dist_init_addr:
            dist_init_method = f"tcp://{self.server_args.dist_init_addr}"
        else:
            dist_init_method = f"tcp://127.0.0.1:{self.dist_port}"
        set_custom_all_reduce(not self.server_args.disable_custom_all_reduce)
        init_distributed_environment(
            backend=backend,
            world_size=self.tp_size,
            rank=self.tp_rank,
            local_rank=self.gpu_id,
            distributed_init_method=dist_init_method,
        )
        initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
        min_per_gpu_memory = get_available_gpu_memory(
            self.device, self.gpu_id, distributed=self.tp_size > 1
        )
        self.tp_group = get_tp_group()

        # Check memory for tensor parallelism
        if self.tp_size > 1:
            local_gpu_memory = get_available_gpu_memory(self.device, self.gpu_id)
            if min_per_gpu_memory < local_gpu_memory * 0.9:
                raise ValueError(
                    "The memory capacity is unbalanced. Some GPUs may be occupied by other processes."
                )

        return min_per_gpu_memory

    def load_model(self):
        logger.info(
            f"Load weight begin. avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
        )

        # This can reduce thread conflicts and speed up weight loading.
        torch.set_num_threads(1)
        if self.device == "cuda":
            if torch.cuda.get_device_capability()[0] < 8:
                logger.info(
                    "Compute capability below sm80. Use float16 due to lack of bfloat16 support."
                )
                self.server_args.dtype = "float16"
                self.model_config.dtype = torch.float16
                if torch.cuda.get_device_capability()[1] < 5:
                    raise RuntimeError("SGLang only supports sm75 and above.")

        # Prepare the vllm model config
        self.load_config = LoadConfig(
            load_format=self.server_args.load_format,
            download_dir=self.server_args.download_dir,
        )

        if self.server_args.load_format == "gguf":
            monkey_patch_vllm_gguf_config()
        self.model = get_model(
            model_config=self.model_config,
            load_config=self.load_config,
            device_config=DeviceConfig(self.device),
        )

        self.sliding_window_size = (
            self.model.get_attention_sliding_window_size()
            if hasattr(self.model, "get_attention_sliding_window_size")
            else None
        )
        self.dtype = self.model_config.dtype

        logger.info(
            f"Load weight end. "
            f"type={type(self.model).__name__}, "
            f"dtype={self.dtype}, "
            f"avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
        )

    def update_weights_from_disk(self, model_path: str, load_format: str):
        """Update engine weights online from disk."""
        from sglang.srt.model_loader.loader import (
            DefaultModelLoader,
            device_loading_context,
            get_model_loader,
        )
        from sglang.srt.model_loader.utils import set_default_torch_dtype

        logger.info(
            f"Update engine weights online from disk begin. "
            f"avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
        )

        target_device = torch.device(self.device)
        self.model_config.model_path = model_path
        load_config = LoadConfig(load_format=load_format)

        # Only support vllm DefaultModelLoader for now
        loader = get_model_loader(load_config)
        if not isinstance(loader, DefaultModelLoader):
            message = f"Failed to get model loader: {loader}."
            return False, message

        def get_weight_iter(config):
            iter = loader._get_weights_iterator(
                DefaultModelLoader.Source(
                    config.model_path,
                    revision=config.revision,
                    fall_back_to_pt=getattr(
                        self.model, "fall_back_to_pt_during_load", True
                    ),
                )
            )
            return iter

        def model_load_weights(model, iter):
            model.load_weights(iter)
            for _, module in self.model.named_modules():
                quant_method = getattr(module, "quant_method", None)
                if quant_method is not None:
                    with device_loading_context(module, target_device):
                        quant_method.process_weights_after_loading(module)
            return model

        with set_default_torch_dtype(self.model_config.dtype):
            try:
                iter = get_weight_iter(self.model_config)
            except Exception as e:
                message = f"Failed to get weights iterator: {e}."
                return False, message
            try:
                model = model_load_weights(self.model, iter)
            except Exception as e:
                message = (
                    f"Failed to update weights: {e}.\nRolling back to original weights."
                )
                del iter
                gc.collect()
                iter = get_weight_iter(self.model_config)
                self.model = model_load_weights(self.model, iter)
                return False, message

        self.model = model
        self.server_args.model_path = model_path
        self.server_args.load_format = load_format
        self.load_config = load_config

        logger.info("Update weights end.")
        return True, "Succeeded to update model weights."

    def init_weights_update_group(
        self,
        master_address,
        master_port,
        rank_offset,
        world_size,
        group_name,
        backend="nccl",
    ):
        """Initialize the Torch process group for model parameter updates.

        `_model_update_group` is used in the RLHF workflow, where rank
        0 is the actor model in the training engine, and the other ranks are
        the inference engine, which is used for rollout.

        In the RLHF workflow, the training engine updates the model
        weights/parameters online, and broadcasts them to the inference
        engine through the `_model_update_group` process group.
        """
        assert (
            torch.distributed.is_initialized()
        ), "Default torch process group must be initialized"
        assert group_name != "", "Group name cannot be empty"

        rank = rank_offset + self.tp_rank

        logger.info(
            f"init custom process group: master_address={master_address}, master_port={master_port}, "
            f"rank_offset={rank_offset}, world_size={world_size}, group_name={group_name}, backend={backend}"
        )

        try:
            self._model_update_group = init_custom_process_group(
                backend=backend,
                init_method=f"tcp://{master_address}:{master_port}",
                world_size=world_size,
                rank=rank,
                group_name=group_name,
            )
            dist.barrier(group=self._model_update_group, device_ids=[rank])
            return True, "Succeeded to initialize custom process group."
        except Exception as e:
            message = f"Failed to initialize custom process group: {e}."
            logger.error(message)
            return False, message

    def update_weights_from_distributed(self, name, dtype, shape):
        """
        Update specific parameter in the model weights online
        through `_model_update_group` process group.

        Args:
            name: the name of the parameter to be updated.
            dtype: the data type of the parameter to be updated.
            shape: the shape of the parameter to be updated.
        """
        target_dtype = (
            dtype if isinstance(dtype, torch.dtype) else getattr(torch, dtype)
        )
        current_dtype = self.dtype if isinstance(self.dtype, str) else self.dtype

        assert (
            self._model_update_group is not None
        ), "model update group must be initialized"

        try:
            weights = torch.empty(shape, dtype=target_dtype, device=self.device)
            torch.distributed.broadcast(weights, src=0, group=self._model_update_group)
            self.model.load_weights([(name, weights)])
            return True, f"Succeeded to update parameter {name} online."

        except Exception as e:
            error_msg = (
                f"Failed to update parameter online: {e}. "
                f"The full weights of the ModelRunner are partially updated. "
                f"Please discard the whole weights."
            )
            logger.error(error_msg)
            return False, error_msg

    def get_weights_by_name(
        self, name: str, truncate_size: int = 100
    ) -> Optional[torch.Tensor]:
        """Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face.

        Only used for unit test with an unoptimized performance.
        For optimized performance, please use torch.save and torch.load.
        """
        # TODO: (chenyang) Add support for Qwen models.
        try:
            return self.model.get_weights_by_name(
                name, truncate_size, tp_size=self.tp_size
            )
        except Exception as e:
            logger.error(f"Error when getting parameter {name}: {e}")
            return None

    def init_lora_manager(self):
        self.lora_manager = LoRAManager(
            base_model=self.model,
            lora_paths=self.server_args.lora_paths,
            base_hf_config=self.model_config.hf_config,
            max_loras_per_batch=self.server_args.max_loras_per_batch,
            load_config=self.load_config,
            dtype=self.dtype,
        )
        logger.info("LoRA manager ready.")

    def profile_max_num_token(self, total_gpu_memory: int):
        available_gpu_memory = get_available_gpu_memory(
            self.device, self.gpu_id, distributed=self.tp_size > 1
        )
        if (
            self.model_config.attention_arch == AttentionArch.MLA
            and not self.server_args.disable_mla
        ):
            cell_size = (
                (self.model_config.kv_lora_rank + self.model_config.qk_rope_head_dim)
                * self.model_config.num_hidden_layers
                * torch._utils._element_size(self.kv_cache_dtype)
            )
        else:
            cell_size = (
                self.model_config.get_num_kv_heads(self.tp_size)
                * self.model_config.head_dim
                * self.model_config.num_hidden_layers
                * 2
                * torch._utils._element_size(self.kv_cache_dtype)
            )
        rest_memory = available_gpu_memory - total_gpu_memory * (
            1 - self.mem_fraction_static
        )
        max_num_token = int(rest_memory * (1 << 30) // cell_size)
        return max_num_token

    def init_memory_pool(
        self,
        total_gpu_memory: int,
        max_num_reqs: Optional[int] = None,
        max_total_tokens: Optional[int] = None,
    ):
        if self.server_args.kv_cache_dtype == "auto":
            self.kv_cache_dtype = self.dtype
        elif self.server_args.kv_cache_dtype == "fp8_e5m2":
            if is_hip():  # Using natively supported format
                self.kv_cache_dtype = torch.float8_e5m2fnuz
            else:
                self.kv_cache_dtype = torch.float8_e5m2
        else:
            raise ValueError(
                f"Unsupported kv_cache_dtype: {self.server_args.kv_cache_dtype}."
            )

        self.max_total_num_tokens = self.profile_max_num_token(total_gpu_memory)
        if max_total_tokens is not None:
            if max_total_tokens > self.max_total_num_tokens:
                logging.warning(
                    f"max_total_tokens={max_total_tokens} is larger than the profiled value "
                    f"{self.max_total_num_tokens}. "
                    f"Use the profiled value instead."
                )
            self.max_total_num_tokens = min(self.max_total_num_tokens, max_total_tokens)

        if self.max_total_num_tokens <= 0:
            raise RuntimeError(
                "Not enough memory. Please try to increase --mem-fraction-static."
            )

        if max_num_reqs is None:
            max_num_reqs = min(
                max(
                    int(
                        self.max_total_num_tokens / self.model_config.context_len * 512
                    ),
                    2048,
                ),
                4096,
            )

        self.req_to_token_pool = ReqToTokenPool(
            size=max_num_reqs + 1,
            max_context_len=self.model_config.context_len + 4,
            device=self.device,
            use_records=False,
        )
        if (
            self.model_config.attention_arch == AttentionArch.MLA
            and not self.server_args.disable_mla
        ):
            self.token_to_kv_pool = MLATokenToKVPool(
                self.max_total_num_tokens,
                dtype=self.kv_cache_dtype,
                kv_lora_rank=self.model_config.kv_lora_rank,
                qk_rope_head_dim=self.model_config.qk_rope_head_dim,
                layer_num=self.model_config.num_hidden_layers,
                device=self.device,
            )
        elif self.server_args.enable_double_sparsity:
            self.token_to_kv_pool = DoubleSparseTokenToKVPool(
                self.max_total_num_tokens,
                dtype=self.kv_cache_dtype,
                head_num=self.model_config.get_num_kv_heads(self.tp_size),
                head_dim=self.model_config.head_dim,
                layer_num=self.model_config.num_hidden_layers,
                device=self.device,
                heavy_channel_num=self.server_args.ds_heavy_channel_num,
            )
        else:
            self.token_to_kv_pool = MHATokenToKVPool(
                self.max_total_num_tokens,
                dtype=self.kv_cache_dtype,
                head_num=self.model_config.get_num_kv_heads(self.tp_size),
                head_dim=self.model_config.head_dim,
                layer_num=self.model_config.num_hidden_layers,
                device=self.device,
            )
        logger.info(
            f"Memory pool end. "
            f"avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
        )

    def init_cublas(self):
        """We need to run a small matmul to init cublas. Otherwise, it will raise some errors later."""
        dtype = torch.float16
        device = "cuda"
        a = torch.ones((16, 16), dtype=dtype, device=device)
        b = torch.ones((16, 16), dtype=dtype, device=device)
        c = a @ b
        return c

    def init_attention_backend(self):
        """Init attention kernel backend."""
        if self.server_args.attention_backend == "flashinfer":
            self.attn_backend = FlashInferAttnBackend(self)
        elif self.server_args.attention_backend == "triton":
            assert self.sliding_window_size is None, (
                "Window attention is not supported in the triton attention backend. "
                "Please use `--attention-backend flashinfer`."
            )
            assert not self.model_config.is_encoder_decoder, (
                "Cross attention is not supported in the triton attention backend. "
                "Please use `--attention-backend flashinfer`."
            )
            if self.server_args.enable_double_sparsity:
                self.attn_backend = DoubleSparseAttnBackend(self)
            else:
                self.attn_backend = TritonAttnBackend(self)
        elif self.server_args.attention_backend == "torch_native":
            self.attn_backend = TorchNativeAttnBackend(self)
        else:
            raise ValueError(
                f"Invalid attention backend: {self.server_args.attention_backend}"
            )

    def init_double_sparsity_channel_config(self, selected_channel):

        selected_channel = "." + selected_channel + "_proj"
        self.sorted_channels = []
        # load channel config
        with open(self.server_args.ds_channel_config_path, "r") as f:
            channel_config = json.load(f)

        for i in range(self.model_config.num_hidden_layers):
            key = "model.layers." + str(i) + ".self_attn" + selected_channel
            self.sorted_channels.append(
                torch.tensor(channel_config[key])[
                    :, : self.server_args.ds_heavy_channel_num
                ]
                .contiguous()
                .cuda()
            )

    def init_cuda_graphs(self):
        """Capture cuda graphs."""
        from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner

        self.cuda_graph_runner = None

        if not self.is_generation:
            # TODO: Currently, cuda graph only captures decode steps, which only exists for generation models
            return

        if self.server_args.disable_cuda_graph:
            return

        tic = time.time()
        logger.info("Capture cuda graph begin. This can take up to several minutes.")
        self.cuda_graph_runner = CudaGraphRunner(self)
        logger.info(f"Capture cuda graph end. Time elapsed: {time.time() - tic:.2f} s")

    def apply_torch_tp(self):
        logger.info(f"Enabling torch tensor parallelism on {self.tp_size} devices.")
        from sglang.srt.model_parallel import tensor_parallel

        device_mesh = torch.distributed.init_device_mesh(self.device, (self.tp_size,))
        tensor_parallel(self.model, device_mesh)

    def forward_decode(self, forward_batch: ForwardBatch):
        if self.cuda_graph_runner and self.cuda_graph_runner.can_run(forward_batch):
            return self.cuda_graph_runner.replay(forward_batch)

        forward_batch.positions = (forward_batch.seq_lens - 1).to(torch.int64)
        self.attn_backend.init_forward_metadata(forward_batch)
        return self.model.forward(
            forward_batch.input_ids, forward_batch.positions, forward_batch
        )

    def forward_extend(self, forward_batch: ForwardBatch):
        self.attn_backend.init_forward_metadata(forward_batch)
        if self.is_generation:
            if forward_batch.input_embeds is None:
                return self.model.forward(
                    forward_batch.input_ids, forward_batch.positions, forward_batch
                )
            else:
                return self.model.forward(
                    forward_batch.input_ids,
                    forward_batch.positions,
                    forward_batch,
                    input_embeds=forward_batch.input_embeds.bfloat16(),
                )
        else:
            # Only embedding models have get_embedding parameter
            return self.model.forward(
                forward_batch.input_ids,
                forward_batch.positions,
                forward_batch,
                get_embedding=True,
            )

    def forward_idle(self, forward_batch: ForwardBatch):
        if self.cuda_graph_runner and self.cuda_graph_runner.can_run(forward_batch):
            return self.cuda_graph_runner.replay(forward_batch)

        return self.model.forward(
            forward_batch.input_ids, forward_batch.positions, forward_batch
        )

    def forward(self, forward_batch: ForwardBatch) -> LogitsProcessorOutput:
        if forward_batch.forward_mode.is_decode():
            return self.forward_decode(forward_batch)
        elif forward_batch.forward_mode.is_extend():
            return self.forward_extend(forward_batch)
        elif forward_batch.forward_mode.is_idle():
            return self.forward_idle(forward_batch)
        else:
            raise ValueError(f"Invaid forward mode: {forward_batch.forward_mode}")

    def sample(
        self, logits_output: LogitsProcessorOutput, forward_batch: ForwardBatch
    ) -> torch.Tensor:
        sampling_info = forward_batch.sampling_info
        if sampling_info.sampling_info_done:
            # Overlap mode: the function update_regex_vocab_mask was executed
            # in process_batch_result of the last batch.
            if sampling_info.grammars:
                sampling_info.sampling_info_done.wait()
        else:
            # Normal mode: Put CPU-heavy tasks here. They will be overlapped with the forward pass.
            sampling_info.update_regex_vocab_mask()
            sampling_info.update_penalties()
        logits = self.apply_logits_bias(logits_output.next_token_logits, sampling_info)

        # Sample the next tokens.
        next_token_ids = self.sampler(logits, sampling_info)
        return next_token_ids

    def apply_logits_bias(self, logits: torch.Tensor, sampling_info: SamplingBatchInfo):
        # Apply logit_bias
        if sampling_info.logit_bias is not None:
            logits.add_(sampling_info.logit_bias)

        # min-token, presence, frequency
        if sampling_info.linear_penalties is not None:
            logits.add_(sampling_info.linear_penalties)

        # repetition
        if sampling_info.scaling_penalties is not None:
            logits = torch.where(
                logits > 0,
                logits / sampling_info.scaling_penalties,
                logits * sampling_info.scaling_penalties,
            )

        # Apply regex vocab_mask
        if sampling_info.vocab_mask is not None:
            sampling_info.apply_mask(logits=logits, vocab_mask=sampling_info.vocab_mask)

        return logits

    @property
    def model_is_mrope(self) -> bool:
        """Detect if the model has "mrope" rope_scaling type.
        mrope requires keep "rope_deltas" between prompt and decoding phases."""
        rope_scaling = getattr(self.model_config.hf_config, "rope_scaling", {})
        if rope_scaling is None:
            return False
        return rope_scaling.get("type", None) == "mrope"