[8/n] decouple quantization impl from vllm dependency - gguf srt (#11964)

Co-authored-by: Peng Zhang <zhuangsen.zp@antgroup.com>

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

@@ -12,7 +12,6 @@ try:
     from vllm.model_executor.layers.quantization.bitsandbytes import BitsAndBytesConfig
     from vllm.model_executor.layers.quantization.deepspeedfp import DeepSpeedFPConfig
     from vllm.model_executor.layers.quantization.experts_int8 import ExpertsInt8Config
-    from vllm.model_executor.layers.quantization.gguf import GGUFConfig
     from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
         GPTQMarlin24Config,
     )
@@ -32,9 +31,7 @@ except ImportError as e:
 
     AQLMConfig = BitsAndBytesConfig = CompressedTensorsConfig = DeepSpeedFPConfig = (
         ExpertsInt8Config
-    ) = GGUFConfig = GPTQMarlin24Config = MarlinConfig = QQQConfig = Int8TpuConfig = (
-        DummyConfig
-    )
+    ) = GPTQMarlin24Config = MarlinConfig = QQQConfig = Int8TpuConfig = DummyConfig
 
 
 from sglang.srt.layers.quantization.awq import AWQConfig, AWQMarlinConfig
@@ -45,6 +42,7 @@ from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import
 )
 from sglang.srt.layers.quantization.fp8 import Fp8Config
 from sglang.srt.layers.quantization.fpgemm_fp8 import FBGEMMFp8Config
+from sglang.srt.layers.quantization.gguf import GGUFConfig
 from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
 from sglang.srt.layers.quantization.modelopt_quant import (
     ModelOptFp4Config,
@@ -75,6 +73,7 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "w8a8_fp8": W8A8Fp8Config,
     "awq": AWQConfig,
     "awq_marlin": AWQMarlinConfig,
+    "gguf": GGUFConfig,
     "gptq": GPTQConfig,
     "gptq_marlin": GPTQMarlinConfig,
     "moe_wna16": MoeWNA16Config,
@@ -108,7 +107,6 @@ VLLM_QUANTIZATION_METHODS = {
     "deepspeedfp": DeepSpeedFPConfig,
     "tpu_int8": Int8TpuConfig,
     "marlin": MarlinConfig,
-    "gguf": GGUFConfig,
     "gptq_marlin_24": GPTQMarlin24Config,
     "bitsandbytes": BitsAndBytesConfig,
     "qqq": QQQConfig,

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

+# SPDX-License-Identifier: Apache-2.0
+# Adapted from: https://github.com/vllm-project/vllm/blob/ab3e80042eac24dd362408e6d63ad98768046359/vllm/model_executor/layers/quantization/gguf.py
+from __future__ import annotations
+
+import logging
+import warnings
+from typing import TYPE_CHECKING, Any, List, Optional
+
+import gguf
+import torch
+from gguf import GGMLQuantizationType as WeightType
+from torch.nn.parameter import Parameter, UninitializedParameter
+
+from sglang.srt.layers.linear import LinearBase
+from sglang.srt.layers.moe import MoeRunnerConfig
+from sglang.srt.layers.quantization.base_config import (
+    FusedMoEMethodBase,
+    LinearMethodBase,
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
+from sglang.srt.utils import is_cuda, is_hip, is_xpu, set_weight_attrs
+
+if TYPE_CHECKING:
+    from sglang.srt.layers.moe.token_dispatcher import (
+        CombineInput,
+        StandardDispatchOutput,
+    )
+
+_is_cuda = is_cuda()
+_is_hip = is_hip()
+_is_xpu = is_xpu()
+
+if _is_cuda:
+    from sgl_kernel import gelu_and_mul, moe_align_block_size, moe_sum, silu_and_mul
+    from sgl_kernel.quantization import (
+        ggml_dequantize,
+        ggml_moe_a8,
+        ggml_moe_a8_vec,
+        ggml_moe_get_block_size,
+        ggml_mul_mat_a8,
+        ggml_mul_mat_vec_a8,
+    )
+else:
+    warnings.warn(f"Only CUDA support GGUF q uantization currently.")
+
+logger = logging.getLogger(__name__)
+
+
+class GGUFConfig(QuantizationConfig):
+    """Config class for GGUF."""
+
+    def __init__(self, modules_to_not_convert: list[str] | None = None) -> None:
+        super().__init__()
+        self.modules_to_not_convert = modules_to_not_convert or []
+
+    def __repr__(self) -> str:
+        return "GGUFConfig()"
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+    def get_name(self) -> "str":
+        return "gguf"
+
+    def get_supported_act_dtypes(self) -> list[torch.dtype]:
+        return [torch.half, torch.bfloat16, torch.float32]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return []  # no extra configs.
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "GGUFConfig":
+        modules_to_not_convert = cls.get_from_keys_or(
+            config, ["modules_to_not_convert"], None
+        )
+        return cls(modules_to_not_convert)
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+        from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped_gguf(prefix, self.modules_to_not_convert):
+                return UnquantizedLinearMethod()
+            return GGUFLinearMethod(self)
+        elif isinstance(layer, VocabParallelEmbedding):
+            return GGUFEmbeddingMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return GGUFMoEMethod(self)
+        return None
+
+
+def is_layer_skipped_gguf(prefix: str, modules_to_not_convert: list[str]):
+    return any(module_name in prefix for module_name in modules_to_not_convert)
+
+
+UNQUANTIZED_TYPES = {WeightType.F32, WeightType.F16, WeightType.BF16}
+STANDARD_QUANT_TYPES = {
+    WeightType.Q4_0,
+    WeightType.Q4_1,
+    WeightType.Q5_0,
+    WeightType.Q5_1,
+    WeightType.Q8_0,
+    WeightType.Q8_1,
+}
+KQUANT_TYPES = {
+    WeightType.Q2_K,
+    WeightType.Q3_K,
+    WeightType.Q4_K,
+    WeightType.Q5_K,
+    WeightType.Q6_K,
+}
+IMATRIX_QUANT_TYPES = {
+    WeightType.IQ1_M,
+    WeightType.IQ1_S,
+    WeightType.IQ2_XXS,
+    WeightType.IQ2_XS,
+    WeightType.IQ2_S,
+    WeightType.IQ3_XXS,
+    WeightType.IQ3_S,
+    WeightType.IQ4_XS,
+    WeightType.IQ4_NL,
+}
+# TODO(Isotr0py): Currently, we don't have MMQ kernel for I-Matrix quantization.
+# Consolidate DEQUANT_TYPES, MMVQ_QUANT_TYPES and MMQ_QUANT_TYPES after we add
+# MMQ kernel for I-Matrix quantization.
+DEQUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
+MMVQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
+MMQ_QUANT_TYPES = STANDARD_QUANT_TYPES | KQUANT_TYPES
+
+
+def fused_mul_mat_gguf(
+    x: torch.Tensor, qweight: torch.Tensor, qweight_type: int
+) -> torch.Tensor:
+    if qweight_type in IMATRIX_QUANT_TYPES:
+        mmvq_safe = 8 if qweight.shape[0] > 5120 else 16
+    else:
+        mmvq_safe = 2 if qweight.shape[0] > 5120 else 6
+    # HACK: when doing chunked prefill we don't generate output tokens
+    # so input to logits generator is empty which causes invalid parameter
+    if x.shape[0] == 0:
+        return torch.empty(x.shape[0], qweight.shape[0], dtype=x.dtype, device=x.device)
+    # there is no need to call any kernel for fp16/bf16
+    if qweight_type in UNQUANTIZED_TYPES:
+        return x @ qweight.T
+    # enable MMVQ in contiguous batching with batch_size=1
+    if x.shape[0] <= mmvq_safe and qweight_type in MMVQ_QUANT_TYPES:
+        y = ggml_mul_mat_vec_a8(qweight, x, qweight_type, qweight.shape[0])
+    # Use MMQ Kernel if it's available (standard + k-quants)
+    elif qweight_type in MMQ_QUANT_TYPES:
+        y = ggml_mul_mat_a8(qweight, x, qweight_type, qweight.shape[0])
+    # If there is no available MMQ kernel, fallback to dequantize
+    elif qweight_type in DEQUANT_TYPES:
+        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
+        shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
+        weight = ggml_dequantize(qweight, qweight_type, *shape, x.dtype)
+        y = x @ weight.T
+    else:
+        # Raise an error if the quantization type is not supported.
+        # Might be useful if llama.cpp adds a new quantization type.
+        # Wrap to GGMLQuantizationType IntEnum to make sure it's a valid type.
+        qweight_type = WeightType(qweight_type)
+        raise NotImplementedError(f"Unsupported GGUF quantization type: {qweight_type}")
+    return y
+
+
+def fused_moe_gguf(
+    x: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    qweight_type: int,
+    qweight_type2: int,
+    activation: str,
+) -> torch.Tensor:
+    def act(x: torch.Tensor):
+        d = x.shape[-1] // 2
+        output_shape = x.shape[:-1] + (d,)
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        if activation == "silu":
+            silu_and_mul(out, x)
+        elif activation == "gelu":
+            gelu_and_mul(out, x)
+        else:
+            raise ValueError(f"Unsupported activation: {activation}")
+        return out
+
+    out_hidden_states = torch.empty_like(x)
+    # unless we decent expert reuse we are better off running moe_vec kernel
+    if (
+        qweight_type2 in MMQ_QUANT_TYPES
+        and qweight_type in MMQ_QUANT_TYPES
+        and x.shape[0] > 64
+    ):
+        num_tokens, _ = x.shape
+        E, N, _ = w1.shape
+        top_k = topk_ids.shape[1]
+        BLOCK_SIZE = ggml_moe_get_block_size(qweight_type)
+
+        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
+            topk_ids, BLOCK_SIZE, E
+        )
+        out = ggml_moe_a8(
+            x,
+            w1,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            qweight_type,
+            N,
+            top_k,
+            num_tokens,
+        )
+        out = act(out)
+        out = ggml_moe_a8(
+            out,
+            w2,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            qweight_type2,
+            w2.shape[1],
+            1,
+            num_tokens * top_k,
+        )
+        out = out.reshape(num_tokens, top_k, w2.shape[1]).mul_(
+            topk_weights.view(num_tokens, top_k, 1)
+        )
+        # TODO(FlamingoPg): maybe we can use moe_sum_reduce here?
+        moe_sum(out, out_hidden_states)
+    elif qweight_type2 in MMVQ_QUANT_TYPES and qweight_type in MMVQ_QUANT_TYPES:
+        num_tokens, _ = x.shape
+        E, N, _ = w1.shape
+        top_k = topk_ids.shape[1]
+
+        out = ggml_moe_a8_vec(x, w1, topk_ids, top_k, qweight_type, N, num_tokens)
+        out = act(out)
+
+        out = ggml_moe_a8_vec(
+            out, w2, topk_ids, 1, qweight_type2, w2.shape[1], num_tokens * top_k
+        )
+        out = out.reshape(num_tokens, top_k, w2.shape[1]).mul_(
+            topk_weights.view(num_tokens, top_k, 1)
+        )
+        moe_sum(out, out_hidden_states)
+    else:
+        logger.warning_once(
+            "There is no support for fast MoE kernel "
+            "for current quantization method. "
+            "Falling back to slow implementation. "
+        )
+        for tok, (w, idx) in enumerate(zip(topk_weights, topk_ids)):
+            inp = x[tok].reshape((1,) + x.shape[1:])
+            current_hidden_state = None
+            for ww, ii in zip(w, idx):
+                expert_up = w1[ii]
+
+                out = fused_mul_mat_gguf(inp, expert_up, qweight_type)
+                out = act(out)
+
+                expert_down = w2[ii]
+                current_state = fused_mul_mat_gguf(
+                    out, expert_down, qweight_type2
+                ).mul_(ww)
+                if current_hidden_state is None:
+                    current_hidden_state = current_state
+                else:
+                    current_hidden_state.add_(current_state)
+            out_hidden_states[tok] = current_hidden_state
+    return out_hidden_states
+
+
+def apply_gguf_embedding(
+    x: torch.Tensor,
+    qweight: torch.Tensor,
+    qweight_type: int,
+    hidden_size: int,
+    dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    if qweight_type in UNQUANTIZED_TYPES:
+        return torch.embedding(qweight, x)
+    elif qweight_type in DEQUANT_TYPES:
+        block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
+        x_flat = x.flatten()
+        assert hidden_size == qweight.shape[1] // type_size * block_size
+        quant = torch.index_select(qweight, dim=0, index=x_flat)
+        dequant = ggml_dequantize(
+            quant, qweight_type, hidden_size, x_flat.shape[0], dtype
+        )
+        return dequant.view(*x.shape, hidden_size)
+    else:
+        qweight_type = WeightType(qweight_type)
+        raise NotImplementedError(f"Unsupported GGUF quantization type: {qweight_type}")
+
+
+class GGUFLinearMethod(LinearMethodBase):
+    """Linear method for GGUF.
+
+    Args:
+        quant_config: The GGUF quantization config.
+    """
+
+    def __init__(self, quant_config: GGUFConfig):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        self.params_dtype = params_dtype
+        output_size_per_partition = sum(output_partition_sizes)
+
+        tensor_shape = (output_size_per_partition, input_size_per_partition)
+        qweight = GGUFUninitializedParameter(requires_grad=False)
+        set_weight_attrs(
+            qweight,
+            {
+                "input_dim": 1,
+                "output_dim": 0,
+                "tensor_shape": tensor_shape,
+                "is_gguf_weight": True,
+                "data_container": [],
+                "shard_id": [],
+                "shard_id_map": {},
+            },
+        )
+        set_weight_attrs(qweight, extra_weight_attrs)
+        layer.register_parameter("qweight", qweight)
+
+        qweight_type = Parameter(
+            torch.empty(len(output_partition_sizes), dtype=torch.uint8),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            qweight_type,
+            {
+                "is_gguf_weight_type": True,
+                "weight_type": 0,
+                "shard_weight_type": {},
+                "ignore_warning": True,
+            },
+        )
+        set_weight_attrs(qweight_type, extra_weight_attrs)
+        layer.register_parameter("qweight_type", qweight_type)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        qweight_type = layer.qweight_type.weight_type
+        if not (qweight_type in UNQUANTIZED_TYPES or qweight_type in DEQUANT_TYPES):
+            qweight_type = WeightType(qweight_type)
+            raise ValueError(
+                f"Unsupported GGUF quantization type {qweight_type} in layer {layer}."
+            )
+        # For MergedColumnParallelLinear and QKVParallelLinear, we need to
+        # materialize the padded weight parameter for CUDA Graph compatibility.
+        self._create_padded_weight_param(layer)
+
+    def _create_padded_weight_param(self, layer: torch.nn.Module):
+        """Create padded weight parameter for GGUF MergedLinear layer."""
+        qweight = layer.qweight
+        shard_id_map = qweight.shard_id_map
+        shard_id = qweight.shard_id
+        if len(data_container := qweight.data_container) > 1:
+            dtype = {data.dtype for data in data_container}
+            assert len(dtype) == 1, ValueError(
+                f"Data container has mixed dtypes: {dtype}"
+            )
+            dtype = next(iter(dtype))
+            # concat dim0 and pad dim1
+            padded_side = max(x.size(1) for x in data_container)
+            concat_side = sum(x.size(0) for x in data_container)
+            # Pad the quantized weights to dense tensor, and create a map
+            # with the location of each shard in the padded tensor.
+            padded_data = torch.zeros(
+                (concat_side, padded_side), dtype=dtype, device=qweight.device
+            )
+            # (dim0_start, dim0_end, dim1_size)
+            shard_offset_map = dict[str, tuple[int, int, int]]()
+            for idx in shard_id:
+                id_in_container = shard_id_map[idx]
+                start = sum(x.size(0) for x in data_container[:id_in_container])
+                end = start + data_container[id_in_container].size(0)
+                size = data_container[id_in_container].size(1)
+                padded_data[start:end, :size] = data_container[id_in_container]
+                shard_offset_map[idx] = (start, end, size)
+            qweight.data_container.clear()
+            padded_param = Parameter(padded_data, requires_grad=False)
+            set_weight_attrs(padded_param, vars(qweight))
+            set_weight_attrs(padded_param, {"shard_offset_map": shard_offset_map})
+            layer.register_parameter("qweight", padded_param)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        shard_id = layer.qweight.shard_id
+
+        if shard_id:
+            # dequantize shard weights respectively
+            shard_id = ["q", "k", "v"] if "q" in shard_id else shard_id
+            qweight = layer.qweight
+            result = []
+            for idx in shard_id:
+                start, end, offset = layer.qweight.shard_offset_map[idx]
+                qweight_type = layer.qweight_type.shard_weight_type[idx]
+                result.append(
+                    fused_mul_mat_gguf(
+                        x, qweight[start:end, :offset].contiguous(), qweight_type
+                    )
+                )
+            out = torch.cat(result, axis=1)
+        else:
+            qweight = layer.qweight
+            qweight_type = layer.qweight_type.weight_type
+            out = fused_mul_mat_gguf(x, qweight, qweight_type)
+        if bias is not None:
+            out.add_(bias)
+        return out
+
+
+class GGUFMoEMethod(FusedMoEMethodBase):
+    """MoE method for GGUF.
+
+    Args:
+        quant_config: The GGUF quantization config.
+    """
+
+    def __init__(self, quant_config: GGUFConfig):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size_per_partition: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        tensor_shape = (num_experts, 2 * intermediate_size_per_partition, hidden_size)
+        # gate up proj
+        w13_qweight = GGUFUninitializedParameter(requires_grad=False)
+        set_weight_attrs(
+            w13_qweight,
+            {
+                "input_dim": 1,
+                "output_dim": 0,
+                "tensor_shape": tensor_shape,
+                "is_gguf_weight": True,
+                "data_container": [],
+            },
+        )
+        set_weight_attrs(w13_qweight, extra_weight_attrs)
+        layer.register_parameter("w13_qweight", w13_qweight)
+
+        w13_qweight_type = Parameter(
+            torch.empty(1, dtype=torch.uint8), requires_grad=False
+        )
+        set_weight_attrs(
+            w13_qweight_type,
+            {"is_gguf_weight_type": True, "weight_type": 0, "ignore_warning": True},
+        )
+        set_weight_attrs(w13_qweight_type, extra_weight_attrs)
+        layer.register_parameter("w13_qweight_type", w13_qweight_type)
+
+        tensor_shape = (num_experts, intermediate_size_per_partition, hidden_size)
+        # gate down proj
+        w2_qweight = GGUFUninitializedParameter(requires_grad=False)
+        set_weight_attrs(
+            w2_qweight,
+            {
+                "input_dim": 1,
+                "output_dim": 0,
+                "tensor_shape": tensor_shape,
+                "is_gguf_weight": True,
+                "data_container": [],
+            },
+        )
+        set_weight_attrs(w2_qweight, extra_weight_attrs)
+        layer.register_parameter("w2_qweight", w2_qweight)
+
+        w2_qweight_type = Parameter(
+            torch.empty(1, dtype=torch.uint8), requires_grad=False
+        )
+        set_weight_attrs(
+            w2_qweight_type,
+            {"is_gguf_weight_type": True, "weight_type": 0, "ignore_warning": True},
+        )
+
+        set_weight_attrs(w2_qweight_type, extra_weight_attrs)
+        layer.register_parameter("w2_qweight_type", w2_qweight_type)
+
+    def create_moe_runner(
+        self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
+    ):
+        self.moe_runner_config = moe_runner_config
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        dispatch_output: StandardDispatchOutput,
+    ) -> CombineInput:
+        assert self.fused_experts is None
+
+        from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput
+
+        assert (
+            self.moe_runner_config.activation == "silu"
+        ), "Only SiLU activation is supported."
+
+        x = dispatch_output.hidden_states
+        topk_output = dispatch_output.topk_output
+
+        moe_runner_config = self.moe_runner_config
+
+        topk_weights, topk_ids, _ = topk_output
+        output = fused_moe_gguf(
+            x=x,
+            w1=layer.w13_qweight,
+            w2=layer.w2_qweight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            qweight_type=layer.w13_qweight_type.weight_type,
+            qweight_type2=layer.w2_qweight_type.weight_type,
+            activation=moe_runner_config.activation,
+        )
+        return StandardCombineInput(hidden_states=output)
+
+
+class GGUFEmbeddingMethod(GGUFLinearMethod):
+    """Embedding method for GGUF.
+
+    Args:
+        quant_config: The GGUF quantization config.
+    """
+
+    def embedding(self, layer: torch.nn.Module, x: torch.Tensor) -> torch.Tensor:
+        qweight = layer.qweight
+        qweight_type = layer.qweight_type.weight_type
+        hidden_size = qweight.tensor_shape[1]
+
+        return apply_gguf_embedding(
+            x, qweight, qweight_type, hidden_size, dtype=self.params_dtype
+        )
+
+
+class GGUFUninitializedParameter(UninitializedParameter):
+    cls_to_become = Parameter
+    data_container: list[torch.Tensor]

python/sglang/srt/model_executor/model_runner.py

@@ -140,7 +140,6 @@ from sglang.srt.utils import (
     is_sm100_supported,
     log_info_on_rank0,
     monkey_patch_p2p_access_check,
-    monkey_patch_vllm_gguf_config,
     set_cuda_arch,
     slow_rank_detector,
     xpu_has_xmx_support,
@@ -858,8 +857,6 @@ class ModelRunner:
             self.model_config = adjust_config_with_unaligned_cpu_tp(
                 self.model_config, self.load_config, self.tp_size
             )
-        if self.server_args.load_format == "gguf":
-            monkey_patch_vllm_gguf_config()
 
         if self.server_args.load_format == LoadFormat.REMOTE_INSTANCE:
             if self.tp_rank == 0:

python/sglang/srt/utils/common.py

@@ -95,7 +95,7 @@ from sglang.srt.environ import envs
 from sglang.srt.metrics.func_timer import enable_func_timer
 
 if TYPE_CHECKING:
-    from sglang.srt.layers.quantization.base_config import QuantizeMethodBase
+    pass
 
 logger = logging.getLogger(__name__)
 
@@ -1069,32 +1069,6 @@ def monkey_patch_p2p_access_check():
     setattr(CustomAllreduce, "__del__", lambda *args, **kwargs: None)
 
 
-def monkey_patch_vllm_gguf_config():
-    try:
-        from vllm.model_executor.layers.quantization.gguf import (
-            GGUFConfig,
-            GGUFEmbeddingMethod,
-            GGUFLinearMethod,
-        )
-    except ImportError:
-        return
-
-    from sglang.srt.layers.linear import LinearBase
-    from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
-
-    def get_quant_method_with_embedding_replaced(
-        self, layer: torch.nn.Module, prefix: str
-    ) -> Optional[QuantizeMethodBase]:
-        if isinstance(layer, LinearBase):
-            return GGUFLinearMethod(self)
-        elif isinstance(layer, VocabParallelEmbedding):
-            # patch to own VocabParallelEmbedding
-            return GGUFEmbeddingMethod(self)
-        return None
-
-    setattr(GGUFConfig, "get_quant_method", get_quant_method_with_embedding_replaced)
-
-
 def set_ulimit(target_soft_limit=65535):
     # number of open files
     resource_type = resource.RLIMIT_NOFILE

test/srt/run_suite.py

@@ -197,7 +197,7 @@ suites = {
         TestFile("test_bnb.py", 5),
         TestFile("test_gptqmodel_dynamic.py", 102),
         TestFile("test_vllm_dependency.py", 185),
-        # TestFile("test_gguf.py", 96),
+        TestFile("test_gguf.py", 96),
     ],
     # If the test cases take too long, considering adding them to nightly tests instead of per-commit tests
     "nightly-1-gpu": [],