Feature DeepSeek V3/R1 INT8 Quantization (block-wise) (#3730)

Co-authored-by: HandH1998 <1335248067@qq.com>

python/sglang/srt/layers/linear.py

@@ -38,6 +38,7 @@ WEIGHT_LOADER_V2_SUPPORTED = [
     "AWQLinearMethod",
     "GPTQMarlinLinearMethod",
     "Fp8LinearMethod",
+    "BlockInt8LinearMethod",
     "MarlinLinearMethod",
     "QQQLinearMethod",
     "GPTQMarlin24LinearMethod",

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

@@ -15,7 +15,13 @@ from vllm import _custom_ops as ops
 
 from sglang.srt.layers.moe.topk import select_experts
 from sglang.srt.layers.quantization.fp8_kernel import per_token_group_quant_fp8
-from sglang.srt.utils import direct_register_custom_op, get_device_name, is_hip
+from sglang.srt.layers.quantization.int8_kernel import per_token_group_quant_int8
+from sglang.srt.utils import (
+    direct_register_custom_op,
+    get_device_name,
+    is_cuda_available,
+    is_hip,
+)
 
 is_hip_flag = is_hip()
 
@@ -86,6 +92,7 @@ def fused_moe_kernel(
     top_k: tl.constexpr,
     compute_type: tl.constexpr,
     use_fp8_w8a8: tl.constexpr,
+    use_int8_w8a8: tl.constexpr,
     use_int8_w8a16: tl.constexpr,
     even_Ks: tl.constexpr,
 ):
@@ -159,7 +166,7 @@ def fused_moe_kernel(
         )
         b_scale = tl.load(b_scale_ptrs)
 
-    if use_fp8_w8a8:
+    if use_fp8_w8a8 or use_int8_w8a8:
         if group_k > 0 and group_n > 0:
             a_scale_ptrs = a_scale_ptr + (offs_token // top_k) * stride_asm
             offs_bsn = offs_bn // group_n
@@ -198,7 +205,7 @@ def fused_moe_kernel(
         # We accumulate along the K dimension.
         if use_int8_w8a16:
             accumulator = tl.dot(a, b.to(compute_type), acc=accumulator)
-        elif use_fp8_w8a8:
+        elif use_fp8_w8a8 or use_int8_w8a8:
             if group_k > 0 and group_n > 0:
                 k_start = k * BLOCK_SIZE_K
                 offs_ks = k_start // group_k
@@ -221,7 +228,7 @@ def fused_moe_kernel(
         accumulator = accumulator * moe_weight[:, None]
     if use_int8_w8a16:
         accumulator = (accumulator * b_scale).to(compute_type)
-    elif use_fp8_w8a8:
+    elif use_fp8_w8a8 or use_int8_w8a8:
         if group_k > 0 and group_n > 0:
             accumulator = accumulator.to(compute_type)
         else:
@@ -477,6 +484,7 @@ def invoke_fused_moe_kernel(
     config: Dict[str, Any],
     compute_type: tl.dtype,
     use_fp8_w8a8: bool,
+    use_int8_w8a8: bool,
     use_int8_w8a16: bool,
     block_shape: Optional[List[int]] = None,
 ) -> None:
@@ -499,6 +507,18 @@ def invoke_fused_moe_kernel(
             assert triton.cdiv(A.shape[-1], block_k) == A_scale.shape[-1]
             assert triton.cdiv(B.shape[-2], block_n) == B_scale.shape[-2]
             assert triton.cdiv(B.shape[-1], block_k) == B_scale.shape[-1]
+    elif use_int8_w8a8:
+        assert B_scale is not None
+        if block_shape is None:
+            padded_size = padding_size
+            A, A_scale = ops.scaled_int8_quant(A, A_scale)
+        else:
+            assert len(block_shape) == 2
+            block_n, block_k = block_shape[0], block_shape[1]
+            A, A_scale = per_token_group_quant_int8(A, block_k)
+            assert triton.cdiv(A.shape[-1], block_k) == A_scale.shape[-1]
+            assert triton.cdiv(B.shape[-2], block_n) == B_scale.shape[-2]
+            assert triton.cdiv(B.shape[-1], block_k) == B_scale.shape[-1]
     elif use_int8_w8a16:
         assert B_scale is not None
     else:
@@ -548,6 +568,7 @@ def invoke_fused_moe_kernel(
         top_k=top_k,
         compute_type=compute_type,
         use_fp8_w8a8=use_fp8_w8a8,
+        use_int8_w8a8=use_int8_w8a8,
         use_int8_w8a16=use_int8_w8a16,
         even_Ks=even_Ks,
         **config,
@@ -701,9 +722,12 @@ def get_config_dtype_str(
     dtype: torch.dtype,
     use_int8_w8a16: Optional[bool] = False,
     use_fp8_w8a8: Optional[bool] = False,
+    use_int8_w8a8: Optional[bool] = False,
 ):
     if use_fp8_w8a8:
         return "fp8_w8a8"
+    elif use_int8_w8a8:
+        return "int8_w8a8"
     elif use_int8_w8a16:
         return "int8_w8a16"
     elif dtype == torch.float:
@@ -721,6 +745,7 @@ def inplace_fused_experts(
     topk_ids: torch.Tensor,
     activation: str = "silu",
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -737,6 +762,7 @@ def inplace_fused_experts(
         True,
         activation,
         use_fp8_w8a8,
+        use_int8_w8a8,
         use_int8_w8a16,
         w1_scale,
         w2_scale,
@@ -754,6 +780,7 @@ def inplace_fused_experts_fake(
     topk_ids: torch.Tensor,
     activation: str = "silu",
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -780,6 +807,7 @@ def outplace_fused_experts(
     topk_ids: torch.Tensor,
     activation: str = "silu",
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -796,6 +824,7 @@ def outplace_fused_experts(
         False,
         activation,
         use_fp8_w8a8,
+        use_int8_w8a8,
         use_int8_w8a16,
         w1_scale,
         w2_scale,
@@ -813,6 +842,7 @@ def outplace_fused_experts_fake(
     topk_ids: torch.Tensor,
     activation: str = "silu",
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -840,6 +870,7 @@ def fused_experts(
     inplace: bool = False,
     activation: str = "silu",
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -856,6 +887,7 @@ def fused_experts(
             topk_ids,
             activation,
             use_fp8_w8a8,
+            use_int8_w8a8,
             use_int8_w8a16,
             w1_scale,
             w2_scale,
@@ -873,6 +905,7 @@ def fused_experts(
             topk_ids,
             activation,
             use_fp8_w8a8,
+            use_int8_w8a8,
             use_int8_w8a16,
             w1_scale,
             w2_scale,
@@ -891,6 +924,7 @@ def fused_experts_impl(
     inplace: bool = False,
     activation: str = "silu",
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -899,7 +933,7 @@ def fused_experts_impl(
     block_shape: Optional[List[int]] = None,
 ):
     padded_size = padding_size
-    if not use_fp8_w8a8 or block_shape is not None:
+    if not use_fp8_w8a8 or not use_int8_w8a8 or block_shape is not None:
         padded_size = 0
 
     # Check constraints.
@@ -918,6 +952,7 @@ def fused_experts_impl(
     M = min(num_tokens, CHUNK_SIZE)
     config_dtype = get_config_dtype_str(
         use_fp8_w8a8=use_fp8_w8a8,
+        use_int8_w8a8=use_int8_w8a8,
         use_int8_w8a16=use_int8_w8a16,
         dtype=hidden_states.dtype,
     )
@@ -1001,6 +1036,7 @@ def fused_experts_impl(
             config,
             compute_type=compute_type,
             use_fp8_w8a8=use_fp8_w8a8,
+            use_int8_w8a8=use_int8_w8a8,
             use_int8_w8a16=use_int8_w8a16,
             block_shape=block_shape,
         )
@@ -1034,6 +1070,7 @@ def fused_experts_impl(
             config,
             compute_type=compute_type,
             use_fp8_w8a8=use_fp8_w8a8,
+            use_int8_w8a8=use_int8_w8a8,
             use_int8_w8a16=use_int8_w8a16,
             block_shape=block_shape,
         )
@@ -1078,6 +1115,7 @@ def fused_moe(
     topk_group: Optional[int] = None,
     custom_routing_function: Optional[Callable] = None,
     use_fp8_w8a8: bool = False,
+    use_int8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -1105,6 +1143,8 @@ def fused_moe(
         note: Deepseek V2/V3/R1 series models use grouped_topk
     - use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner
         products for w1 and w2. Defaults to False.
+    - use_int8_w8a8 (bool): If True, use int8 arithmetic to compute the inner
+        products for w1 and w2. Defaults to False.
     - use_int8_w8a16 (bool): If True, use fp8 arithmetic to compute the inner
         products for w1 and w2. Defaults to False.
     - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
@@ -1144,6 +1184,7 @@ def fused_moe(
         inplace=inplace,
         activation=activation,
         use_fp8_w8a8=use_fp8_w8a8,
+        use_int8_w8a8=use_int8_w8a8,
         use_int8_w8a16=use_int8_w8a16,
         w1_scale=w1_scale,
         w2_scale=w2_scale,

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

@@ -24,6 +24,7 @@ from vllm.model_executor.layers.quantization.qqq import QQQConfig
 from vllm.model_executor.layers.quantization.tpu_int8 import Int8TpuConfig
 
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
 from sglang.srt.layers.quantization.fp8 import Fp8Config
 from sglang.srt.layers.quantization.modelopt_quant import ModelOptFp8Config
 from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
@@ -34,6 +35,7 @@ QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "deepspeedfp": DeepSpeedFPConfig,
     "tpu_int8": Int8TpuConfig,
     "fp8": Fp8Config,
+    "blockwise_int8": BlockInt8Config,
     "fbgemm_fp8": FBGEMMFp8Config,
     "marlin": MarlinConfig,
     "modelopt": ModelOptFp8Config,

python/sglang/srt/layers/quantization/blockwise_int8.py

@@ -0,0 +1,406 @@
+# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
+
+import logging
+from typing import Any, Callable, Dict, List, Optional
+
+import torch
+from torch.nn import Module
+from vllm.model_executor.layers.quantization.utils.quant_utils import is_layer_skipped
+
+from sglang.srt.distributed import get_tensor_model_parallel_world_size
+from sglang.srt.layers.linear import (
+    LinearBase,
+    LinearMethodBase,
+    UnquantizedLinearMethod,
+)
+from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
+from sglang.srt.layers.quantization.base_config import (
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.srt.layers.quantization.fp8_utils import BlockQuantScaleParameter
+from sglang.srt.layers.quantization.int8_utils import apply_w8a8_block_int8_linear
+from sglang.srt.utils import set_weight_attrs
+
+ACTIVATION_SCHEMES = ["static", "dynamic"]
+
+logger = logging.getLogger(__name__)
+
+
+class BlockInt8Config(QuantizationConfig):
+    """Config class for INT8."""
+
+    def __init__(
+        self,
+        is_checkpoint_int8_serialized: bool = False,
+        activation_scheme: str = "dynamic",
+        ignored_layers: Optional[List[str]] = None,
+        weight_block_size: List[int] = None,
+    ) -> None:
+        self.is_checkpoint_int8_serialized = is_checkpoint_int8_serialized
+        if is_checkpoint_int8_serialized:
+            logger.warning(
+                "Detected int8 checkpoint. Please note that the "
+                "format is experimental and subject to change."
+            )
+        if activation_scheme not in ACTIVATION_SCHEMES:
+            raise ValueError(f"Unsupported activation scheme {activation_scheme}")
+        self.activation_scheme = activation_scheme
+        self.ignored_layers = ignored_layers or []
+        if weight_block_size is not None:
+            if not is_checkpoint_int8_serialized:
+                raise ValueError(
+                    f"The block-wise quantization only supports int8-serialized checkpoint for now."
+                )
+            if len(weight_block_size) != 2:
+                raise ValueError(
+                    f"The quantization block size of weight must have 2 dimensions, but got {len(weight_block_size)} dimensions."
+                )
+            if activation_scheme != "dynamic":
+                raise ValueError(
+                    f"The block-wise quantization only supports dynamic activation scheme for now, but got {activation_scheme} activation scheme."
+                )
+        self.weight_block_size = weight_block_size
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "blockwise_int8"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return []
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "BlockInt8Config":
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        is_checkpoint_int8_serialized = "int8" in quant_method
+        activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
+        ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
+        weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None)
+        return cls(
+            is_checkpoint_int8_serialized=is_checkpoint_int8_serialized,
+            activation_scheme=activation_scheme,
+            ignored_layers=ignored_layers,
+            weight_block_size=weight_block_size,
+        )
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.ignored_layers):
+                return UnquantizedLinearMethod()
+            return BlockInt8LinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return BlockInt8MoEMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class BlockInt8LinearMethod(LinearMethodBase):
+    """Linear method for INT8.
+    Supports loading INT8 checkpoints with static weight scale and
+    dynamic activation scale.
+
+    Limitations:
+    Only support block-wise int8 quantization and int8 checkpoint
+
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __init__(self, quant_config: BlockInt8Config):
+        self.quant_config = quant_config
+        assert self.quant_config.weight_block_size is not None
+        assert self.quant_config.is_checkpoint_int8_serialized
+
+    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,
+    ):
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        tp_size = get_tensor_model_parallel_world_size()
+
+        block_n, block_k = (
+            self.quant_config.weight_block_size[0],
+            self.quant_config.weight_block_size[1],
+        )
+        # Required by row parallel
+        if tp_size > 1 and input_size // input_size_per_partition == tp_size:
+            if input_size_per_partition % block_k != 0:
+                raise ValueError(
+                    f"Weight input_size_per_partition = "
+                    f"{input_size_per_partition} is not divisible by "
+                    f"weight quantization block_k = {block_k}."
+                )
+        # Required by collum parallel or enabling merged weights
+        if (tp_size > 1 and output_size // output_size_per_partition == tp_size) or len(
+            output_partition_sizes
+        ) > 1:
+            for output_partition_size in output_partition_sizes:
+                if output_partition_size % block_n != 0:
+                    raise ValueError(
+                        f"Weight output_partition_size = "
+                        f"{output_partition_size} is not divisible by "
+                        f"weight quantization block_n = {block_n}."
+                    )
+
+        layer.logical_widths = output_partition_sizes
+
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.orig_dtype = params_dtype
+
+        # WEIGHT
+        weight_dtype = (
+            torch.int8
+            if self.quant_config.is_checkpoint_int8_serialized
+            else params_dtype
+        )
+
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition, input_size_per_partition, dtype=weight_dtype
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+
+        scale = BlockQuantScaleParameter(
+            data=torch.empty(
+                (output_size_per_partition + block_n - 1) // block_n,
+                (input_size_per_partition + block_k - 1) // block_k,
+                dtype=torch.float32,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale_inv", scale)
+
+        # INPUT ACTIVATION SCALE
+        assert self.quant_config.activation_scheme == "dynamic"
+        layer.register_parameter("input_scale", None)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        # Block quant doesn't need to process weights after loading
+        # Use torch Parameter to avoid cuda graph capturing issue
+        layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
+        layer.weight_scale_inv = torch.nn.Parameter(
+            layer.weight_scale_inv.data, requires_grad=False
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return apply_w8a8_block_int8_linear(
+            input=x,
+            weight=layer.weight,
+            block_size=self.quant_config.weight_block_size,
+            weight_scale=layer.weight_scale_inv,
+            input_scale=None,
+            bias=bias,
+        )
+
+
+class BlockInt8MoEMethod:
+    """MoE method for INT8.
+    Supports loading INT8 checkpoints with static weight scale and
+    dynamic activation scale.
+
+    Limitations:
+    Only support block-wise int8 quantization and int8 checkpoint
+
+    Args:
+        quant_config: The quantization config.
+    """
+
+    def __new__(cls, *args, **kwargs):
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoEMethodBase
+
+        if not hasattr(cls, "_initialized"):
+            original_init = cls.__init__
+            new_cls = type(
+                cls.__name__,
+                (FusedMoEMethodBase,),
+                {
+                    "__init__": original_init,
+                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
+                },
+            )
+            obj = super(new_cls, new_cls).__new__(new_cls)
+            obj.__init__(*args, **kwargs)
+            return obj
+        return super().__new__(cls)
+
+    def __init__(self, quant_config):
+        self.quant_config = quant_config
+        assert self.quant_config.weight_block_size is not None
+        assert self.quant_config.is_checkpoint_int8_serialized
+
+    def create_weights(
+        self,
+        layer: Module,
+        num_experts: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
+        if self.quant_config.is_checkpoint_int8_serialized:
+            params_dtype = torch.int8
+        tp_size = get_tensor_model_parallel_world_size()
+
+        block_n, block_k = (
+            self.quant_config.weight_block_size[0],
+            self.quant_config.weight_block_size[1],
+        )
+        # NOTE(HandH1998): To ensure proper alignment of the block-wise quantization scales, the output_size of the weights for both the gate and up layers must be divisible by block_n.
+        # Required by collum parallel or enabling merged weights
+        if intermediate_size % block_n != 0:
+            raise ValueError(
+                f"The output_size of gate's and up's weight = "
+                f"{intermediate_size} is not divisible by "
+                f"weight quantization block_n = {block_n}."
+            )
+        if tp_size > 1:
+            # Required by row parallel
+            if intermediate_size % block_k != 0:
+                raise ValueError(
+                    f"The input_size of down's weight = "
+                    f"{intermediate_size} is not divisible by "
+                    f"weight quantization block_k = {block_k}."
+                )
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts, hidden_size, intermediate_size, dtype=params_dtype
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                2 * ((intermediate_size + block_n - 1) // block_n),
+                (hidden_size + block_k - 1) // block_k,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                (hidden_size + block_n - 1) // block_n,
+                (intermediate_size + block_k - 1) // block_k,
+                dtype=torch.float32,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
+        layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
+        )
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        assert self.quant_config.activation_scheme == "dynamic"
+        layer.w13_input_scale = None
+        layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        # Block quant doesn't need to process weights after loading
+        return
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
+    ) -> torch.Tensor:
+        from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
+        from sglang.srt.layers.moe.topk import select_experts
+
+        # Expert selection
+        topk_weights, topk_ids = select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            correction_bias=correction_bias,
+        )
+
+        # Expert fusion with INT8 quantization
+        return fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            use_int8_w8a8=True,
+            w1_scale=(layer.w13_weight_scale_inv),
+            w2_scale=(layer.w2_weight_scale_inv),
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            block_shape=self.quant_config.weight_block_size,
+        )

python/sglang/srt/layers/quantization/int8_kernel.py

@@ -1,7 +1,17 @@
+import functools
+import json
+import logging
+import os
+from typing import Any, Dict, List, Optional, Tuple
+
 import torch
 import triton
 import triton.language as tl
 
+from sglang.srt.utils import get_device_name
+
+logger = logging.getLogger(__name__)
+
 
 @triton.jit
 def _per_token_quant_int8(
@@ -52,3 +62,320 @@ def per_token_quant_int8(x):
     )
 
     return x_q, scales
+
+
+@triton.jit
+def _per_token_group_quant_int8(
+    # Pointers to inputs and output
+    y_ptr,
+    y_q_ptr,
+    y_s_ptr,
+    # Stride of input
+    y_stride,
+    # Collums of input
+    N,
+    # Avoid to divide zero
+    eps,
+    # Information for int8
+    int8_min,
+    int8_max,
+    # Meta-parameters
+    BLOCK: tl.constexpr,
+):
+    """A Triton-accelerated function to perform per-token-group quantization on a
+    tensor.
+
+    This function converts the tensor values into int8 values.
+    """
+    # Map the program id to the row of X and Y it should compute.
+    g_id = tl.program_id(0)
+    y_ptr += g_id * y_stride
+    y_q_ptr += g_id * y_stride
+    y_s_ptr += g_id
+
+    cols = tl.arange(0, BLOCK)  # N <= BLOCK
+    mask = cols < N
+
+    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
+    # Quant
+    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
+    y_s = _absmax / int8_max
+    y_q = tl.clamp(y / y_s, int8_min, int8_max).to(y_q_ptr.dtype.element_ty)
+
+    tl.store(y_q_ptr + cols, y_q, mask=mask)
+    tl.store(y_s_ptr, y_s)
+
+
+def per_token_group_quant_int8(
+    x: torch.Tensor,
+    group_size: int,
+    eps: float = 1e-10,
+    dtype: torch.dtype = torch.int8,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Function to perform per-token-group quantization on an input tensor `x`.
+
+    It converts the tensor values into signed int8 values and returns the
+    quantized tensor along with the scaling factor used for quantization.
+
+    Args:
+        x: The input tenosr with ndim >= 2.
+        group_size: The group size used for quantization.
+        eps: The minimum to avoid dividing zero.
+        dtype: The dype of output tensor. Note that only `torch.int8` is supported for now.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
+    """
+    assert (
+        x.shape[-1] % group_size == 0
+    ), "the last dimension of `x` cannot be divisible by `group_size`"
+    assert x.is_contiguous(), "`x` is not contiguous"
+
+    iinfo = torch.iinfo(dtype)
+    int8_max = iinfo.max
+    int8_min = iinfo.min
+
+    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
+    M = x.numel() // group_size
+    N = group_size
+    x_s = torch.empty(
+        x.shape[:-1] + (x.shape[-1] // group_size,),
+        device=x.device,
+        dtype=torch.float32,
+    )
+
+    BLOCK = triton.next_power_of_2(N)
+    # heuristics for number of warps
+    num_warps = min(max(BLOCK // 256, 1), 8)
+    num_stages = 1
+    _per_token_group_quant_int8[(M,)](
+        x,
+        x_q,
+        x_s,
+        group_size,
+        N,
+        eps,
+        int8_min=int8_min,
+        int8_max=int8_max,
+        BLOCK=BLOCK,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+
+    return x_q, x_s
+
+
+@triton.jit
+def _w8a8_block_int8_matmul(
+    # Pointers to inputs and output
+    A,
+    B,
+    C,
+    As,
+    Bs,
+    # Shape for matmul
+    M,
+    N,
+    K,
+    # Block size for block-wise quantization
+    group_n,
+    group_k,
+    # Stride for inputs and output
+    stride_am,
+    stride_ak,
+    stride_bk,
+    stride_bn,
+    stride_cm,
+    stride_cn,
+    stride_As_m,
+    stride_As_k,
+    stride_Bs_k,
+    stride_Bs_n,
+    # Meta-parameters
+    BLOCK_SIZE_M: tl.constexpr,
+    BLOCK_SIZE_N: tl.constexpr,
+    BLOCK_SIZE_K: tl.constexpr,
+    GROUP_SIZE_M: tl.constexpr,
+):
+    """Triton-accelerated function used to perform linear operations (dot
+    product) on input tensors `A` and `B` with block-wise quantization, and store the result in output
+    tensor `C`.
+    """
+
+    pid = tl.program_id(axis=0)
+    num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
+    num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
+    num_pid_in_group = GROUP_SIZE_M * num_pid_n
+    group_id = pid // num_pid_in_group
+    first_pid_m = group_id * GROUP_SIZE_M
+    group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
+    pid_m = first_pid_m + (pid % group_size_m)
+    pid_n = (pid % num_pid_in_group) // group_size_m
+
+    offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
+    offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+    a_ptrs = A + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
+    b_ptrs = B + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
+
+    As_ptrs = As + offs_am * stride_As_m
+    offs_bsn = offs_bn // group_n
+    Bs_ptrs = Bs + offs_bsn * stride_Bs_n
+
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
+        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
+        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
+
+        k_start = k * BLOCK_SIZE_K
+        offs_ks = k_start // group_k
+        a_s = tl.load(As_ptrs + offs_ks * stride_As_k)
+        b_s = tl.load(Bs_ptrs + offs_ks * stride_Bs_k)
+
+        accumulator += tl.dot(a, b).to(tl.float32) * a_s[:, None] * b_s[None, :]
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    if C.dtype.element_ty == tl.bfloat16:
+        c = accumulator.to(tl.bfloat16)
+    elif C.dtype.element_ty == tl.float16:
+        c = accumulator.to(tl.float16)
+    else:
+        c = accumulator.to(tl.float32)
+
+    offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+    c_ptrs = C + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
+    c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
+    tl.store(c_ptrs, c, mask=c_mask)
+
+
+@functools.lru_cache
+def get_w8a8_block_int8_configs(
+    N: int, K: int, block_n: int, block_k: int
+) -> Optional[Dict[int, Any]]:
+    """
+    Return optimized configurations for the w8a8 block fp8 kernel.
+
+    The return value will be a dictionary that maps an irregular grid of
+    batch sizes to configurations of the w8a8 block fp8 kernel. To evaluate the
+    kernel on a given batch size bs, the closest batch size in the grid should
+    be picked and the associated configuration chosen to invoke the kernel.
+    """
+
+    # First look up if an optimized configuration is available in the configs
+    # directory
+    device_name = get_device_name().replace(" ", "_")
+    json_file_name = f"N={N},K={K},device_name={device_name},dtype=int8_w8a8,block_shape=[{block_n}, {block_k}].json"
+
+    config_file_path = os.path.join(
+        os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name
+    )
+    if os.path.exists(config_file_path):
+        with open(config_file_path) as f:
+            logger.info(
+                "Using configuration from %s for W8A8 Block INT8 kernel.",
+                config_file_path,
+            )
+            # If a configuration has been found, return it
+            return {int(key): val for key, val in json.load(f).items()}
+
+    # If no optimized configuration is available, we will use the default
+    # configuration
+    logger.warning(
+        (
+            "Using default W8A8 Block INT8 kernel config. Performance might be sub-optimal! "
+            "Config file not found at %s"
+        ),
+        config_file_path,
+    )
+    return None
+
+
+def w8a8_block_int8_matmul(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    block_size: List[int],
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    """This function performs matrix multiplication with block-wise quantization.
+
+    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
+    The output is returned in the specified `output_dtype`.
+
+    Args:
+        A: The input tensor, e.g., activation.
+        B: The input tensor, e.g., weight.
+        As: The per-token-group quantization scale for `A`.
+        Bs: The per-block quantization scale for `B`.
+        block_size: The block size for per-block quantization. It should be 2-dim, e.g., [128, 128].
+        output_dytpe: The dtype of the returned tensor.
+
+    Returns:
+        torch.Tensor: The result of matmul.
+    """
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+
+    assert A.shape[-1] == B.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
+    assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
+    M = A.numel() // A.shape[-1]
+
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    N, K = B.shape
+    assert triton.cdiv(N, block_n) == Bs.shape[0]
+    assert triton.cdiv(K, block_k) == Bs.shape[1]
+
+    C_shape = A.shape[:-1] + (N,)
+    C = A.new_empty(C_shape, dtype=output_dtype)
+
+    configs = get_w8a8_block_int8_configs(N, K, block_size[0], block_size[1])
+    if configs:
+        # If an optimal configuration map has been found, look up the
+        # optimal config
+        config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
+    else:
+        # Default config
+        # Block-wise quant: BLOCK_SIZE_K must be divisable by block_size[1]
+        config = {
+            "BLOCK_SIZE_M": 64,
+            "BLOCK_SIZE_N": block_size[0],
+            "BLOCK_SIZE_K": block_size[1],
+            "GROUP_SIZE_M": 32,
+            "num_warps": 4,
+            "num_stages": 3,
+        }
+
+    def grid(META):
+        return (
+            triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        )
+
+    _w8a8_block_int8_matmul[grid](
+        A,
+        B,
+        C,
+        As,
+        Bs,
+        M,
+        N,
+        K,
+        block_n,
+        block_k,
+        A.stride(-2),
+        A.stride(-1),
+        B.stride(1),
+        B.stride(0),
+        C.stride(-2),
+        C.stride(-1),
+        As.stride(-2),
+        As.stride(-1),
+        Bs.stride(1),
+        Bs.stride(0),
+        **config,
+    )
+
+    return C

python/sglang/srt/layers/quantization/int8_utils.py

@@ -0,0 +1,73 @@
+from typing import List, Optional, Tuple
+
+import torch
+
+from sglang.srt.layers.quantization.int8_kernel import (
+    per_token_group_quant_int8,
+    w8a8_block_int8_matmul,
+)
+
+
+def apply_w8a8_block_int8_linear(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    block_size: List[int],
+    weight_scale: torch.Tensor,
+    input_scale: Optional[torch.Tensor] = None,
+    bias: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    assert input_scale is None
+    # View input as 2D matrix for fp8 methods
+    input_2d = input.view(-1, input.shape[-1])
+    output_shape = [*input.shape[:-1], weight.shape[0]]
+
+    q_input, x_scale = per_token_group_quant_int8(input_2d, block_size[1])
+    output = w8a8_block_int8_matmul(
+        q_input, weight, x_scale, weight_scale, block_size, output_dtype=input.dtype
+    )
+
+    if bias is not None:
+        output = output + bias
+    return output.to(dtype=input.dtype).view(*output_shape)
+
+
+def input_to_int8(
+    x: torch.Tensor, dtype: torch.dtype = torch.int8
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """This function quantizes input values to int8 values with tensor-wise quantization."""
+    iinfo = torch.iinfo(dtype)
+    min_val, max_val = x.aminmax()
+    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
+    int8_min, int8_max = iinfo.min, iinfo.max
+    scale = int8_max / amax
+    x_scl_sat = (x * scale).clamp(min=int8_min, max=int8_max)
+    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
+
+
+def block_dequant(
+    x_q_block: torch.Tensor,
+    x_s: torch.Tensor,
+    block_size: List[int],
+) -> torch.Tensor:
+    """This function conducts block-wise dequantization.
+    The inputs are block-wise quantization tensor `x_q_block`, block-wise quantization scale
+    and the block size.
+    The outputs are dequantized tensor.
+    """
+    block_n, block_k = block_size[0], block_size[1]
+    n, k = x_q_block.shape
+    n_tiles = (n + block_n - 1) // block_n
+    k_tiles = (k + block_k - 1) // block_k
+    assert n_tiles == x_s.shape[0]
+    assert k_tiles == x_s.shape[1]
+
+    x_dq_block = x_q_block.to(torch.float32)
+
+    for i in range(k_tiles):
+        for j in range(n_tiles):
+            x_dq_block[
+                j * block_n : min((j + 1) * block_n, n),
+                i * block_k : min((i + 1) * block_k, k),
+            ] *= x_s[j][i]
+
+    return x_dq_block

python/sglang/srt/models/deepseek_v2.py

@@ -47,6 +47,9 @@ from sglang.srt.layers.quantization.fp8_utils import (
     input_to_float8,
     normalize_e4m3fn_to_e4m3fnuz,
 )
+from sglang.srt.layers.quantization.int8_utils import (
+    block_dequant as int8_block_dequant,
+)
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.rotary_embedding import get_rope, get_rope_wrapper
 from sglang.srt.layers.vocab_parallel_embedding import (
@@ -994,6 +997,18 @@ class DeepseekV2ForCausalLM(nn.Module):
                             weight, weight_scale, weight_block_size
                         )
                         self_attn.w_scale = scale
+                if (
+                    hasattr(self.quant_config, "weight_block_size")
+                    and w.dtype == torch.int8
+                ):
+                    weight_block_size = self.quant_config.weight_block_size
+                    if weight_block_size is not None:
+                        assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
+                        weight = w
+                        weight_scale = self_attn.kv_b_proj.weight_scale_inv
+                        w = int8_block_dequant(
+                            weight, weight_scale, weight_block_size
+                        ).to(torch.bfloat16)
                 w_kc, w_vc = w.unflatten(
                     0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
                 ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)

test/srt/run_suite.py

@@ -55,6 +55,7 @@ suites = {
         "test_vision_openai_server.py",
         "test_w8a8_quantization.py",
         "test_fp8_kernel.py",
+        "test_block_int8.py",
     ],
     "nightly": [
         "test_nightly_gsm8k_eval.py",

test/srt/test_block_int8.py

@@ -0,0 +1,221 @@
+import itertools
+import unittest
+
+import torch
+
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_moe
+
+
+# For test
+def native_per_token_group_quant_int8(x, group_size, eps=1e-10, dtype=torch.int8):
+    """Function to perform per-token-group quantization on an input tensor `x` using native torch.
+
+    It converts the tensor values into float8 values and returns the
+    quantized tensor along with the scaling factor used for quantization.
+    Note that only `torch.float8_e4m3fn` is supported for now.
+    """
+    assert (
+        x.shape[-1] % group_size == 0
+    ), "the last dimension of `x` cannot be divisible by `group_size`"
+    assert x.is_contiguous(), "`x` is not contiguous"
+
+    iinfo = torch.iinfo(dtype)
+    int8_min = iinfo.min
+    int8_max = iinfo.max
+
+    x_ = x.reshape(x.numel() // group_size, group_size)
+    amax = x_.abs().max(dim=-1, keepdim=True)[0].clamp(min=eps).to(torch.float32)
+    x_s = amax / int8_max
+    x_q = (x_ / x_s).clamp(min=int8_min, max=int8_max).to(dtype)
+    x_q = x_q.reshape(x.shape)
+    x_s = x_s.reshape(x.shape[:-1] + (x.shape[-1] // group_size,))
+
+    return x_q, x_s
+
+
+# For test
+def native_w8a8_block_int8_matmul(A, B, As, Bs, block_size, output_dtype=torch.float16):
+    """This function performs matrix multiplication with block-wise quantization using native torch.
+
+    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
+    The output is returned in the specified `output_dtype`.
+    """
+
+    A = A.to(torch.float32)
+    B = B.to(torch.float32)
+    assert A.shape[-1] == B.shape[-1]
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+    assert (A.shape[-1] + block_k - 1) // block_k == As.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1]
+
+    M = A.numel() // A.shape[-1]
+    N, K = B.shape
+    origin_C_shape = A.shape[:-1] + (N,)
+    A = A.reshape(M, A.shape[-1])
+    As = As.reshape(M, As.shape[-1])
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+    assert n_tiles == Bs.shape[0]
+    assert k_tiles == Bs.shape[1]
+
+    C_shape = (M, N)
+    C = torch.zeros(C_shape, dtype=torch.float32, device=A.device)
+
+    A_tiles = [A[:, i * block_k : min((i + 1) * block_k, K)] for i in range(k_tiles)]
+    B_tiles = [
+        [
+            B[
+                j * block_n : min((j + 1) * block_n, N),
+                i * block_k : min((i + 1) * block_k, K),
+            ]
+            for i in range(k_tiles)
+        ]
+        for j in range(n_tiles)
+    ]
+    C_tiles = [C[:, j * block_n : min((j + 1) * block_n, N)] for j in range(n_tiles)]
+    As_tiles = [As[:, i : i + 1] for i in range(k_tiles)]
+
+    for i in range(k_tiles):
+        for j in range(n_tiles):
+            a = A_tiles[i]
+            b = B_tiles[j][i]
+            c = C_tiles[j]
+            s = As_tiles[i] * Bs[j][i]
+            c[:, :] += torch.matmul(a, b.t()) * s
+
+    C = C.reshape(origin_C_shape).to(output_dtype)
+    return C
+
+
+# For test
+def torch_w8a8_block_int8_moe(a, w1, w2, w1_s, w2_s, score, topk, block_shape):
+    """This function performs fused moe with block-wise quantization using native torch."""
+
+    B, D = a.shape
+    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
+    out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
+    score = torch.softmax(score, dim=-1, dtype=torch.float32)
+    topk_weight, topk_ids = torch.topk(score, topk)
+    topk_weight = topk_weight.view(-1)
+    topk_ids = topk_ids.view(-1)
+
+    _, block_k = block_shape[0], block_shape[1]
+    a_q, a_s = native_per_token_group_quant_int8(a, block_k)
+    for i in range(w1.shape[0]):
+        mask = topk_ids == i
+        if mask.sum():
+            inter_out = native_w8a8_block_int8_matmul(
+                a_q[mask], w1[i], a_s[mask], w1_s[i], block_shape, output_dtype=a.dtype
+            )
+            act_out = SiluAndMul().forward_native(inter_out)
+            act_out_q, act_out_s = native_per_token_group_quant_int8(act_out, block_k)
+            act_out = act_out.to(torch.float32)
+            out[mask] = native_w8a8_block_int8_matmul(
+                act_out_q, w2[i], act_out_s, w2_s[i], block_shape, output_dtype=a.dtype
+            )
+    return (
+        out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
+    ).sum(dim=1)
+
+
+class TestW8A8BlockINT8FusedMoE(unittest.TestCase):
+    DTYPES = [torch.half, torch.bfloat16]
+    M = [1, 33, 64, 222]
+    N = [128, 1024]
+    K = [256, 4096]
+    E = [8, 24]
+    TOP_KS = [2, 6]
+    # BLOCK_SIZE = [[64, 64], [64, 128], [128, 64], [128, 128]]
+    BLOCK_SIZE = [[128, 128]]
+    SEEDS = [0]
+
+    @classmethod
+    def setUpClass(cls):
+        if not torch.cuda.is_available():
+            raise unittest.SkipTest("CUDA is not available")
+        torch.set_default_device("cuda")
+
+    def _w8a8_block_int8_fused_moe(self, M, N, K, E, topk, block_size, dtype, seed):
+        torch.manual_seed(seed)
+        # NOTE(HandH1998): to avoid overflow when out_dtype = torch.half
+        factor_for_scale = 1e-2
+        int8_info = torch.iinfo(torch.int8)
+        int8_max, int8_min = int8_info.max, int8_info.min
+
+        a = torch.randn((M, K), dtype=dtype) / 10
+
+        w1_fp32 = (torch.rand((E, 2 * N, K), dtype=torch.float32) - 0.5) * 2 * int8_max
+        w1 = w1_fp32.clamp(min=int8_min, max=int8_max).to(torch.int8)
+
+        w2_fp32 = (torch.rand((E, K, N), dtype=torch.float32) - 0.5) * 2 * int8_max
+        w2 = w2_fp32.clamp(min=int8_min, max=int8_max).to(torch.int8)
+
+        block_n, block_k = block_size[0], block_size[1]
+        n_tiles_w1 = (2 * N + block_n - 1) // block_n
+        n_tiles_w2 = (K + block_n - 1) // block_n
+        k_tiles_w1 = (K + block_k - 1) // block_k
+        k_tiles_w2 = (N + block_k - 1) // block_k
+
+        w1_s = (
+            torch.rand((E, n_tiles_w1, k_tiles_w1), dtype=torch.float32)
+            * factor_for_scale
+        )
+        w2_s = (
+            torch.rand((E, n_tiles_w2, k_tiles_w2), dtype=torch.float32)
+            * factor_for_scale
+        )
+
+        score = torch.randn((M, E), dtype=dtype)
+
+        with torch.inference_mode():
+            out = fused_moe(
+                a,
+                w1,
+                w2,
+                score,
+                topk,
+                renormalize=False,
+                use_int8_w8a8=True,
+                w1_scale=w1_s,
+                w2_scale=w2_s,
+                block_shape=block_size,
+            )
+            ref_out = torch_w8a8_block_int8_moe(
+                a, w1, w2, w1_s, w2_s, score, topk, block_size
+            )
+
+        self.assertTrue(
+            torch.mean(torch.abs(out.to(torch.float32) - ref_out.to(torch.float32)))
+            / torch.mean(torch.abs(ref_out.to(torch.float32)))
+            < 0.02
+        )
+
+    def test_w8a8_block_int8_fused_moe(self):
+        for params in itertools.product(
+            self.M,
+            self.N,
+            self.K,
+            self.E,
+            self.TOP_KS,
+            self.BLOCK_SIZE,
+            self.DTYPES,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                M=params[0],
+                N=params[1],
+                K=params[2],
+                E=params[3],
+                topk=params[4],
+                block_size=params[5],
+                dtype=params[6],
+                seed=params[7],
+            ):
+                self._w8a8_block_int8_fused_moe(*params)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)