Improve linear.py to load sharded weights & remove the dependency of Parameters from vllm (#2784)

Co-authored-by: SangBin Cho rkooo567@gmail.com

3rdparty/amd/tuning/benchmark_moe_rocm.py

@@ -10,7 +10,10 @@ import triton.language as tl
 from tqdm import tqdm
 from transformers import AutoConfig
 
-from sglang.srt.layers.fused_moe_triton.fused_moe import fused_moe, get_config_file_name
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
+    fused_moe,
+    get_config_file_name,
+)
 
 padding_size = 128 if bool(int(os.getenv("MOE_PADDING", "0"))) else 0
 

python/sglang/srt/layers/attention/__init__.py

@@ -66,7 +66,14 @@ class AttentionBackend(ABC):
         if forward_batch.forward_mode.is_decode():
             return self.forward_decode(q, k, v, layer, forward_batch, save_kv_cache)
         else:
-            return self.forward_extend(q, k, v, layer, forward_batch, save_kv_cache)
+            return self.forward_extend(
+                q,
+                k,
+                v,
+                layer,
+                forward_batch,
+                save_kv_cache,
+            )
 
     def forward_decode(
         self,

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -347,6 +347,8 @@ class FlashInferAttnBackend(AttentionBackend):
             else forward_batch.encoder_out_cache_loc
         )
 
+        logits_soft_cap = layer.logit_cap
+
         if not self.forward_metadata.use_ragged:
             if k is not None:
                 assert v is not None
@@ -359,7 +361,7 @@ class FlashInferAttnBackend(AttentionBackend):
                 causal=not layer.is_cross_attention,
                 sm_scale=layer.scaling,
                 window_left=layer.sliding_window_size,
-                logits_soft_cap=layer.logit_cap,
+                logits_soft_cap=logits_soft_cap,
             )
         else:
             o1, s1 = self.prefill_wrapper_ragged.forward_return_lse(
@@ -368,7 +370,7 @@ class FlashInferAttnBackend(AttentionBackend):
                 v.contiguous().view(-1, layer.tp_v_head_num, layer.head_dim),
                 causal=True,
                 sm_scale=layer.scaling,
-                logits_soft_cap=layer.logit_cap,
+                logits_soft_cap=logits_soft_cap,
             )
 
             if self.forward_metadata.extend_no_prefix:

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -204,6 +204,7 @@ class FusedMoE(torch.nn.Module):
         prefix: str = "",
         custom_routing_function: Optional[Callable] = None,
         correction_bias: Optional[torch.Tensor] = None,
+        use_presharded_weights: bool = False,
     ):
         super().__init__()
 
@@ -243,6 +244,7 @@ class FusedMoE(torch.nn.Module):
             params_dtype=params_dtype,
             weight_loader=self.weight_loader,
         )
+        self.use_presharded_weights = use_presharded_weights
 
     def _load_per_tensor_weight_scale(
         self,
@@ -395,10 +397,7 @@ class FusedMoE(torch.nn.Module):
         weight_name: str,
         shard_id: str,
         expert_id: int,
-        use_presharded_weights: bool = False,
     ) -> None:
-        self.use_presharded_weights = use_presharded_weights
-
         # compressed-tensors checkpoints with packed weights are stored flipped
         # TODO (mgoin): check self.quant_method.quant_config.quant_format
         # against known CompressionFormat enum values that have this quality

python/sglang/srt/layers/parameter.py

+"""
+Adapted from vLLM (0.6.4.post1).
+https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/parameter.py
+"""
+
+import logging
+from fractions import Fraction
+from typing import Callable, Optional, Union
+
+import torch
+from torch.nn import Parameter
+from vllm.distributed import get_tensor_model_parallel_rank
+
+__all__ = [
+    "BasevLLMParameter",
+    "PackedvLLMParameter",
+    "PerTensorScaleParameter",
+    "ModelWeightParameter",
+    "ChannelQuantScaleParameter",
+    "GroupQuantScaleParameter",
+    "PackedColumnParameter",
+    "RowvLLMParameter",
+]
+
+logger = logging.getLogger(__name__)
+
+
+class BasevLLMParameter(Parameter):
+    """
+    Base parameter for vLLM linear layers. Extends the torch.nn.parameter
+    by taking in a linear weight loader. Will copy the loaded weight
+    into the parameter when the provided weight loader is called.
+    """
+
+    def __new__(cls, data: torch.Tensor, **kwargs):
+
+        return super().__new__(cls, data=data, requires_grad=False)
+
+    def __init__(self, data: torch.Tensor, weight_loader: Callable):
+        """
+        Initialize the BasevLLMParameter
+
+        :param data: torch tensor with the parameter data
+        :param weight_loader: weight loader callable
+
+        :returns: a torch.nn.parameter
+        """
+
+        self._weight_loader = weight_loader
+
+    @property
+    def weight_loader(self):
+        return self._weight_loader
+
+    def _assert_and_load(self, loaded_weight: torch.Tensor):
+        assert self.data.shape == loaded_weight.shape
+        self.data.copy_(loaded_weight)
+
+    def load_column_parallel_weight(self, loaded_weight: torch.Tensor):
+        self._assert_and_load(loaded_weight)
+
+    def load_row_parallel_weight(self, loaded_weight: torch.Tensor):
+        self._assert_and_load(loaded_weight)
+
+    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
+        self._assert_and_load(loaded_weight)
+
+    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
+        self._assert_and_load(loaded_weight)
+
+
+class _ColumnvLLMParameter(BasevLLMParameter):
+    """
+    Private class defining weight loading functionality
+    (load_merged_column_weight, load_qkv_weight)
+    for parameters being loaded into linear layers with column
+    parallelism. This includes QKV and MLP layers which are
+    not already fused on disk. Requires an output dimension
+    to be defined. Called within the weight loader of
+    each of the column parallel linear layers.
+    """
+
+    def __init__(self, output_dim: int, **kwargs):
+        self._output_dim = output_dim
+        super().__init__(**kwargs)
+
+    @property
+    def output_dim(self):
+        return self._output_dim
+
+    def load_column_parallel_weight(self, loaded_weight: torch.Tensor):
+        tp_rank = get_tensor_model_parallel_rank()
+        shard_size = self.data.shape[self.output_dim]
+        loaded_weight = loaded_weight.narrow(
+            self.output_dim, tp_rank * shard_size, shard_size
+        )
+        assert self.data.shape == loaded_weight.shape
+        self.data.copy_(loaded_weight)
+
+    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
+
+        shard_offset = kwargs.get("shard_offset")
+        shard_size = kwargs.get("shard_size")
+        use_presharded_weights = kwargs.get("use_presharded_weights")
+        if (
+            isinstance(self, (PackedColumnParameter, PackedvLLMParameter))
+            and self.packed_dim == self.output_dim
+        ):
+            shard_size, shard_offset = self.adjust_shard_indexes_for_packing(
+                shard_offset=shard_offset, shard_size=shard_size
+            )
+
+        param_data = self.data
+
+        tp_rank = get_tensor_model_parallel_rank()
+        param_data = param_data.narrow(self.output_dim, shard_offset, shard_size)
+        if not use_presharded_weights:
+            loaded_weight = loaded_weight.narrow(
+                self.output_dim, tp_rank * shard_size, shard_size
+            )
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
+    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
+
+        shard_offset = kwargs.get("shard_offset")
+        shard_size = kwargs.get("shard_size")
+        shard_id = kwargs.get("shard_id")
+        num_heads = kwargs.get("num_heads")
+
+        if (
+            isinstance(self, (PackedColumnParameter, PackedvLLMParameter))
+            and self.output_dim == self.packed_dim
+        ):
+            shard_size, shard_offset = self.adjust_shard_indexes_for_packing(
+                shard_offset=shard_offset, shard_size=shard_size
+            )
+
+        param_data = self.data
+        tp_rank = get_tensor_model_parallel_rank()
+        shard_id = tp_rank if shard_id == "q" else tp_rank // num_heads
+        param_data = param_data.narrow(self.output_dim, shard_offset, shard_size)
+        loaded_weight = loaded_weight.narrow(
+            self.output_dim, shard_id * shard_size, shard_size
+        )
+
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
+
+class RowvLLMParameter(BasevLLMParameter):
+    """
+    Parameter class defining weight_loading functionality
+    (load_row_parallel_weight) for parameters being loaded
+    into linear layers with row parallel functionality.
+    Requires an input_dim to be defined.
+    """
+
+    def __init__(self, input_dim: int, **kwargs):
+        self._input_dim = input_dim
+        super().__init__(**kwargs)
+
+    @property
+    def input_dim(self):
+        return self._input_dim
+
+    def load_row_parallel_weight(self, loaded_weight: torch.Tensor, **kwargs):
+        use_presharded_weights = kwargs.get("use_presharded_weights")
+        tp_rank = get_tensor_model_parallel_rank()
+        shard_size = self.data.shape[self.input_dim]
+        if not use_presharded_weights:
+            loaded_weight = loaded_weight.narrow(
+                self.input_dim, tp_rank * shard_size, shard_size
+            )
+
+        if len(loaded_weight.shape) == 0:
+            loaded_weight = loaded_weight.reshape(1)
+
+        assert self.data.shape == loaded_weight.shape
+        self.data.copy_(loaded_weight)
+
+
+class ModelWeightParameter(_ColumnvLLMParameter, RowvLLMParameter):
+    """
+    Parameter class for linear layer weights. Uses both column and
+    row parallelism.
+    """
+
+    pass
+
+
+class GroupQuantScaleParameter(_ColumnvLLMParameter, RowvLLMParameter):
+    """
+    Parameter class for weight scales loaded for weights with
+    grouped quantization. Uses both column and row parallelism.
+    """
+
+    pass
+
+
+class ChannelQuantScaleParameter(_ColumnvLLMParameter):
+    """
+    Parameter class for weight scales loaded for weights with
+    channel-wise quantization. Equivalent to _ColumnvLLMParameter.
+    """
+
+    pass
+
+
+class PerTensorScaleParameter(BasevLLMParameter):
+    """
+    Parameter class for scales where the number of scales is
+    equivalent to the number of logical matrices in fused linear
+    layers (e.g. for QKV, there are 3 scales loaded from disk).
+    This is relevant to weights with per-tensor quantization.
+    Adds functionality to map the scalers to a shard during
+    weight loading.
+
+    Note: additional parameter manipulation may be handled
+    for each quantization config specifically, within
+    process_weights_after_loading
+    """
+
+    def __init__(self, **kwargs):
+        self.qkv_idxs = {"q": 0, "k": 1, "v": 2}
+        super().__init__(**kwargs)
+
+    def _shard_id_as_int(self, shard_id: Union[str, int]) -> int:
+        if isinstance(shard_id, int):
+            return shard_id
+
+        # if not int, assume shard_id for qkv
+        # map to int and return
+        assert isinstance(shard_id, str)
+        assert shard_id in self.qkv_idxs
+        return self.qkv_idxs[shard_id]
+
+    # For row parallel layers, no sharding needed
+    # load weight into parameter as is
+    def load_row_parallel_weight(self, *args, **kwargs):
+        super().load_row_parallel_weight(*args, **kwargs)
+
+    def load_merged_column_weight(self, *args, **kwargs):
+        self._load_into_shard_id(*args, **kwargs)
+
+    def load_qkv_weight(self, *args, **kwargs):
+        self._load_into_shard_id(*args, **kwargs)
+
+    def load_column_parallel_weight(self, *args, **kwargs):
+        super().load_row_parallel_weight(*args, **kwargs)
+
+    def _load_into_shard_id(
+        self, loaded_weight: torch.Tensor, shard_id: Union[str, int], **kwargs
+    ):
+        """
+        Slice the parameter data based on the shard id for
+        loading.
+        """
+
+        param_data = self.data
+        shard_id = self._shard_id_as_int(shard_id)
+
+        # AutoFP8 scales do not have a shape
+        # compressed-tensors scales do have a shape
+        if len(loaded_weight.shape) != 0:
+            assert loaded_weight.shape[0] == 1
+            loaded_weight = loaded_weight[0]
+
+        param_data = param_data[shard_id]
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
+
+class PackedColumnParameter(_ColumnvLLMParameter):
+    """
+    Parameter for model parameters which are packed on disk
+    and support column parallelism only. See PackedvLLMParameter
+    for more details on the packed properties.
+    """
+
+    def __init__(
+        self,
+        packed_factor: Union[int, Fraction],
+        packed_dim: int,
+        marlin_tile_size: Optional[int] = None,
+        **kwargs
+    ):
+        self._packed_factor = packed_factor
+        self._packed_dim = packed_dim
+        self._marlin_tile_size = marlin_tile_size
+        super().__init__(**kwargs)
+
+    @property
+    def packed_dim(self):
+        return self._packed_dim
+
+    @property
+    def packed_factor(self):
+        return self._packed_factor
+
+    @property
+    def marlin_tile_size(self):
+        return self._marlin_tile_size
+
+    def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
+        return _adjust_shard_indexes_for_packing(
+            shard_size=shard_size,
+            shard_offset=shard_offset,
+            packed_factor=self.packed_factor,
+            marlin_tile_size=self.marlin_tile_size,
+        )
+
+
+class PackedvLLMParameter(ModelWeightParameter):
+    """
+    Parameter for model weights which are packed on disk.
+    Example: GPTQ Marlin weights are int4 or int8, packed into int32.
+    Extends the ModelWeightParameter to take in the
+    packed factor, the packed dimension, and optionally, marlin
+    tile size for marlin kernels. Adjusts the shard_size and
+    shard_offset for fused linear layers model weight loading
+    by accounting for packing and optionally, marlin tile size.
+    """
+
+    def __init__(
+        self,
+        packed_factor: Union[int, Fraction],
+        packed_dim: int,
+        marlin_tile_size: Optional[int] = None,
+        **kwargs
+    ):
+        self._packed_factor = packed_factor
+        self._packed_dim = packed_dim
+        self._marlin_tile_size = marlin_tile_size
+        super().__init__(**kwargs)
+
+    @property
+    def packed_dim(self):
+        return self._packed_dim
+
+    @property
+    def packed_factor(self):
+        return self._packed_factor
+
+    @property
+    def marlin_tile_size(self):
+        return self._marlin_tile_size
+
+    def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
+        return _adjust_shard_indexes_for_packing(
+            shard_size=shard_size,
+            shard_offset=shard_offset,
+            packed_factor=self.packed_factor,
+            marlin_tile_size=self.marlin_tile_size,
+        )
+
+
+def permute_param_layout_(
+    param: BasevLLMParameter, input_dim: int, output_dim: int, **kwargs
+) -> BasevLLMParameter:
+    """
+    Permute a parameter's layout to the specified input and output dimensions,
+    useful for forcing the parameter into a known layout, for example, if I need
+    a packed (quantized) weight matrix to be in the layout
+        {input_dim = 0, output_dim = 1, packed_dim = 0}
+    then I can call:
+        permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
+    to ensure x is in the correct layout (permuting it to the correct layout if
+    required, asserting if it cannot get it to the correct layout)
+    """
+
+    curr_input_dim = getattr(param, "input_dim", None)
+    curr_output_dim = getattr(param, "output_dim", None)
+
+    if curr_input_dim is None or curr_output_dim is None:
+        assert param.data.dim() == 2, (
+            "permute_param_layout_ only supports 2D parameters when either "
+            "input_dim or output_dim is not set"
+        )
+
+    # if one of the dimensions is not set, set it to the opposite of the other
+    #  we can only do this since we asserted the parameter is 2D above
+    if curr_input_dim is None:
+        assert curr_output_dim is not None, "either input or output dim must be set"
+        curr_input_dim = (curr_output_dim + 1) % 2
+    if curr_output_dim is None:
+        assert curr_input_dim is not None, "either input or output dim must be set"
+        curr_output_dim = (curr_input_dim + 1) % 2
+
+    # create permutation from the current layout to the layout with
+    # self.input_dim at input_dim and self.output_dim at output_dim preserving
+    # other dimensions
+    perm = [
+        i for i in range(param.data.dim()) if i not in [curr_input_dim, curr_output_dim]
+    ]
+    perm.insert(input_dim, curr_input_dim)
+    perm.insert(output_dim, curr_output_dim)
+
+    if "packed_dim" in kwargs:
+        assert (
+            hasattr(param, "packed_dim")
+            and param.packed_dim == perm[kwargs["packed_dim"]]
+        ), "permute_param_layout_ currently doesn't support repacking"
+
+    param.data = param.data.permute(*perm)
+    if hasattr(param, "_input_dim"):
+        param._input_dim = input_dim
+    if hasattr(param, "_output_dim"):
+        param._output_dim = output_dim
+    if "packed_dim" in kwargs and hasattr(param, "_packed_dim"):
+        param._packed_dim = kwargs["packed_dim"]
+
+    return param
+
+
+def _adjust_shard_indexes_for_marlin(shard_size, shard_offset, marlin_tile_size):
+    return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
+
+
+def _adjust_shard_indexes_for_packing(
+    shard_size, shard_offset, packed_factor, marlin_tile_size
+):
+    shard_size = shard_size // packed_factor
+    shard_offset = shard_offset // packed_factor
+    if marlin_tile_size is not None:
+        return _adjust_shard_indexes_for_marlin(
+            shard_size=shard_size,
+            shard_offset=shard_offset,
+            marlin_tile_size=marlin_tile_size,
+        )
+    return shard_size, shard_offset

python/sglang/srt/layers/quantization/fp8.py

@@ -25,9 +25,9 @@ from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     per_tensor_dequantize,
     requantize_with_max_scale,
 )
-from vllm.model_executor.parameter import ModelWeightParameter, PerTensorScaleParameter
 
 from sglang.srt.layers.linear import LinearMethodBase, UnquantizedLinearMethod
+from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
 from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,

python/sglang/srt/layers/vocab_parallel_embedding.py

@@ -12,8 +12,8 @@ from vllm.distributed import (
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
 )
-from vllm.model_executor.parameter import BasevLLMParameter
 
+from sglang.srt.layers.parameter import BasevLLMParameter
 from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,

python/sglang/srt/managers/session_controller.py

@@ -99,7 +99,7 @@ class Session:
 
         if last_req is not None:
             # trim bos token if it is an append
-            if req.input_ids[0] == tokenizer.bos_token_id:
+            if tokenizer is not None and req.input_ids[0] == tokenizer.bos_token_id:
                 req.input_ids = req.input_ids[1:]
 
             input_ids = (

python/sglang/srt/model_executor/forward_batch_info.py

@@ -106,6 +106,9 @@ class ForwardMode(IntEnum):
     def is_dummy_first(self):
         return self == ForwardMode.DUMMY_FIRST
 
+    def is_decode_or_idle(self):
+        return self == ForwardMode.DECODE or self == ForwardMode.IDLE
+
 
 class CaptureHiddenMode(IntEnum):
     NULL = auto()

python/sglang/srt/model_executor/model_runner.py

@@ -205,7 +205,7 @@ class ModelRunner:
         if self.device == "cuda":
             backend = "nccl"
         elif self.device == "xpu":
-            # TODO(liangan1):Just use gloo to bypass the initilization fail
+            # TODO(liangan1): Just use gloo to bypass the initilization fail
             # Need to use xccl for xpu backend in the future
             backend = "gloo"
         elif self.device == "hpu":
@@ -634,7 +634,6 @@ class ModelRunner:
             )
 
     def init_double_sparsity_channel_config(self, selected_channel):
-
         selected_channel = "." + selected_channel + "_proj"
         self.sorted_channels = []
         # load channel config

python/sglang/srt/models/grok.py

@@ -57,6 +57,7 @@ class Grok1MLP(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
         reduce_results=True,
+        use_presharded_weights: bool = False,
     ) -> None:
         super().__init__()
         self.gate_up_proj = MergedColumnParallelLinear(
@@ -65,6 +66,7 @@ class Grok1MLP(nn.Module):
             bias=False,
             quant_config=quant_config,
             prefix=f"{prefix}.gate_up_proj",
+            use_presharded_weights=use_presharded_weights,
         )
         self.down_proj = RowParallelLinear(
             intermediate_size,
@@ -73,6 +75,7 @@ class Grok1MLP(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.down_proj",
             reduce_results=reduce_results,
+            use_presharded_weights=use_presharded_weights,
         )
         self.act_fn = GeluAndMul(approximate="tanh")
 
@@ -103,6 +106,7 @@ class Grok1MoE(nn.Module):
         quant_config: Optional[QuantizationConfig] = None,
         tp_size: Optional[int] = None,
         reduce_results=True,
+        use_presharded_weights: bool = False,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -129,6 +133,7 @@ class Grok1MoE(nn.Module):
             renormalize=False,
             quant_config=quant_config,
             tp_size=tp_size,
+            use_presharded_weights=use_presharded_weights,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -156,6 +161,7 @@ class Grok1Attention(nn.Module):
         max_position: int = 4096 * 32,
         rope_theta: float = 10000,
         quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
     ) -> None:
         super().__init__()
         self.config = config
@@ -194,6 +200,7 @@ class Grok1Attention(nn.Module):
             hidden_size,
             bias=False,
             quant_config=quant_config,
+            reduce_results=reduce_results,
         )
         self.rotary_emb = get_rope(
             self.head_dim,
@@ -234,10 +241,12 @@ class Grok1DecoderLayer(nn.Module):
         config: PretrainedConfig,
         layer_id: int = 0,
         quant_config: Optional[QuantizationConfig] = None,
+        use_presharded_weights: bool = False,
     ) -> None:
         super().__init__()
         self.num_experts = config.num_local_experts
         self.hidden_size = config.hidden_size
+        self.layer_id = layer_id
 
         rope_theta = getattr(config, "rope_theta", 10000)
         self.self_attn = Grok1Attention(
@@ -262,6 +271,7 @@ class Grok1DecoderLayer(nn.Module):
             ),
             quant_config=quant_config,
             reduce_results=True,
+            use_presharded_weights=use_presharded_weights,
         )
         self.pre_attn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -299,6 +309,7 @@ class Grok1Model(nn.Module):
         self,
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
+        use_presharded_weights: bool = False,
     ) -> None:
         super().__init__()
         self.config = config
@@ -311,7 +322,12 @@ class Grok1Model(nn.Module):
         )
         self.layers = nn.ModuleList(
             [
-                Grok1DecoderLayer(config, i, quant_config=quant_config)
+                Grok1DecoderLayer(
+                    config,
+                    i,
+                    quant_config=quant_config,
+                    use_presharded_weights=use_presharded_weights,
+                )
                 for i in range(config.num_hidden_layers)
             ]
         )
@@ -347,11 +363,7 @@ class Grok1ForCausalLM(nn.Module):
         super().__init__()
         self.config = config
         self.quant_config = quant_config
-        self.model = Grok1Model(config, quant_config=quant_config)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-        self.logits_processor = LogitsProcessor(config)
 
-        # Monkey patch _prepare_weights to load pre-sharded weights
         if (
             self.config.num_local_experts > 0
             and get_tensor_model_parallel_world_size() > 1
@@ -361,6 +373,14 @@ class Grok1ForCausalLM(nn.Module):
         else:
             self.use_presharded_weights = False
 
+        self.model = Grok1Model(
+            config,
+            quant_config=quant_config,
+            use_presharded_weights=self.use_presharded_weights,
+        )
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.logits_processor = LogitsProcessor(config)
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -376,10 +396,7 @@ class Grok1ForCausalLM(nn.Module):
     def load_weights(
         self,
         weights: Iterable[Tuple[str, torch.Tensor]],
-        use_presharded_weights: Optional[bool] = None,
     ):
-        if use_presharded_weights is None:
-            use_presharded_weights = self.use_presharded_weights
         num_experts = self.config.num_local_experts
 
         stacked_params_mapping = [
@@ -435,20 +452,12 @@ class Grok1ForCausalLM(nn.Module):
                         continue
                     name = name.replace(weight_name, param_name)
 
-                    if use_presharded_weights:
-                        extra_kwargs = {
-                            "use_presharded_weights": use_presharded_weights
-                        }
-                    else:
-                        extra_kwargs = {}
-
                     load_weight_wrapper(
                         name,
                         loaded_weight,
                         name,
                         shard_id=shard_id,
                         expert_id=expert_id,
-                        **extra_kwargs,
                     )
                     break
                 else:

python/sglang/srt/server.py

@@ -544,7 +544,12 @@ def launch_server(
 
     # Send a warmup request
     t = threading.Thread(
-        target=_wait_and_warmup, args=(server_args, pipe_finish_writer)
+        target=_wait_and_warmup,
+        args=(
+            server_args,
+            pipe_finish_writer,
+            tokenizer_manager.image_token_id,
+        ),
     )
     t.start()
 
@@ -614,7 +619,7 @@ def _set_envs_and_config(server_args: ServerArgs):
     mp.set_start_method("spawn", force=True)
 
 
-def _wait_and_warmup(server_args, pipe_finish_writer):
+def _wait_and_warmup(server_args, pipe_finish_writer, image_token_text):
     headers = {}
     url = server_args.url()
     if server_args.api_key:

python/sglang/srt/speculative/eagle_utils.py

@@ -14,7 +14,7 @@ from sglang.srt.speculative.build_eagle_tree import build_tree_kernel
 from sglang.srt.speculative.spec_info import SpecInfo
 
 if TYPE_CHECKING:
-    from python.sglang.srt.managers.schedule_batch import ScheduleBatch
+    from sglang.srt.managers.schedule_batch import ScheduleBatch
     from sglang.srt.server_args import ServerArgs
 
 

scripts/killall_sglang.sh

@@ -7,6 +7,7 @@ nvidia-smi
 kill -9 $(ps aux | grep 'sglang::' | grep -v 'grep' | awk '{print $2}') 2>/dev/null
 kill -9 $(ps aux | grep 'sglang.launch_server' | grep -v 'grep' | awk '{print $2}') 2>/dev/null
 kill -9 $(ps aux | grep 'sglang.bench' | grep -v 'grep' | awk '{print $2}') 2>/dev/null
+kill -9 $(ps aux | grep 'sglang.data_parallel' | grep -v 'grep' | awk '{print $2}') 2>/dev/null
 
 # Clean all GPU processes if any argument is provided
 if [ $# -gt 0 ]; then