Refactor MoE (#2575)

Co-authored-by: zhyncs <me@zhyncs.com>

python/sglang/srt/configs/model_config.py

@@ -94,7 +94,10 @@ class ModelConfig:
         )
 
         # FIXME: temporary special judge for MLA architecture
-        if "DeepseekV2ForCausalLM" in self.hf_config.architectures:
+        if (
+            "DeepseekV2ForCausalLM" in self.hf_config.architectures
+            or "DeepseekV3ForCausalLM" in self.hf_config.architectures
+        ):
             self.head_dim = 256
             self.attention_arch = AttentionArch.MLA
             self.kv_lora_rank = self.hf_config.kv_lora_rank

python/sglang/srt/layers/linear.py

@@ -30,6 +30,7 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
+from sglang.srt.layers.quantization.fp8_utils import BlockQuantScaleParameter
 from sglang.srt.utils import set_weight_attrs
 
 logger = logging.getLogger(__name__)
@@ -628,6 +629,17 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
         assert loaded_shard_id < len(self.output_sizes)
 
         tp_size = get_tensor_model_parallel_world_size()
+
+        if isinstance(param, BlockQuantScaleParameter):
+            weight_block_size = self.quant_method.quant_config.weight_block_size
+            block_n, _ = weight_block_size[0], weight_block_size[1]
+            shard_offset = (
+                (sum(self.output_sizes[:loaded_shard_id]) + block_n - 1) // block_n
+            ) // tp_size
+            shard_size = (
+                (self.output_sizes[loaded_shard_id] + block_n - 1) // block_n // tp_size
+            )
+        else:
             shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
             shard_size = self.output_sizes[loaded_shard_id] // tp_size
 
@@ -795,6 +807,12 @@ class QKVParallelLinear(ColumnParallelLinear):
         shard_offset = self._get_shard_offset_mapping(loaded_shard_id)
         shard_size = self._get_shard_size_mapping(loaded_shard_id)
 
+        if isinstance(param, BlockQuantScaleParameter):
+            weight_block_size = self.quant_method.quant_config.weight_block_size
+            block_n, _ = weight_block_size[0], weight_block_size[1]
+            shard_offset = (shard_offset + block_n - 1) // block_n
+            shard_size = (shard_size + block_n - 1) // block_n
+
         param.load_qkv_weight(
             loaded_weight=loaded_weight,
             num_heads=self.num_kv_head_replicas,

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

@@ -6,7 +6,7 @@ import functools
 import json
 import logging
 import os
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
 import triton
@@ -14,6 +14,7 @@ import triton.language as tl
 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
 
 logger = logging.getLogger(__name__)
@@ -48,8 +49,14 @@ def fused_moe_kernel(
     stride_bn,
     stride_cm,
     stride_cn,
+    stride_asm,
+    stride_ask,
     stride_bse,
+    stride_bsk,
     stride_bsn,
+    # Block size for block-wise quantization
+    group_n: tl.constexpr,
+    group_k: tl.constexpr,
     # Meta-parameters
     BLOCK_SIZE_M: tl.constexpr,
     BLOCK_SIZE_N: tl.constexpr,
@@ -133,6 +140,13 @@ def fused_moe_kernel(
         b_scale = tl.load(b_scale_ptrs)
 
     if use_fp8_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
+            b_scale_ptrs = (
+                b_scale_ptr + off_experts * stride_bse + offs_bsn * stride_bsn
+            )
+        else:
             a_scale = tl.load(a_scale_ptr)
             b_scale = tl.load(b_scale_ptr + off_experts)
 
@@ -165,6 +179,16 @@ def fused_moe_kernel(
         if use_int8_w8a16:
             accumulator = tl.dot(a, b.to(compute_type), acc=accumulator)
         elif use_fp8_w8a8:
+            if group_k > 0 and group_n > 0:
+                k_start = k * BLOCK_SIZE_K
+                offs_ks = k_start // group_k
+                a_scale = tl.load(
+                    a_scale_ptrs + offs_ks * stride_ask, mask=token_mask, other=0.0
+                )
+                b_scale = tl.load(b_scale_ptrs + offs_ks * stride_bsk)
+
+                accumulator += tl.dot(a, b) * a_scale[:, None] * b_scale[None, :]
+            else:
                 accumulator = tl.dot(a, b, acc=accumulator)
         else:
             accumulator += tl.dot(a, b)
@@ -178,6 +202,9 @@ def fused_moe_kernel(
     if use_int8_w8a16:
         accumulator = (accumulator * b_scale).to(compute_type)
     elif use_fp8_w8a8:
+        if group_k > 0 and group_n > 0:
+            accumulator = accumulator.to(compute_type)
+        else:
             accumulator = (accumulator * a_scale * b_scale).to(compute_type)
     else:
         accumulator = accumulator.to(compute_type)
@@ -262,6 +289,7 @@ def invoke_fused_moe_kernel(
     compute_type: tl.dtype,
     use_fp8_w8a8: bool,
     use_int8_w8a16: bool,
+    block_shape: Optional[List[int]] = None,
 ) -> None:
     assert topk_weights.stride(1) == 1
     assert sorted_token_ids.stride(0) == 1
@@ -269,8 +297,16 @@ def invoke_fused_moe_kernel(
     padded_size = 0
     if use_fp8_w8a8:
         padded_size = padding_size
-        A, A_scale = ops.scaled_fp8_quant(A, A_scale)
         assert B_scale is not None
+        if block_shape is None:
+            A, A_scale = ops.scaled_fp8_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_fp8(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:
@@ -309,8 +345,13 @@ def invoke_fused_moe_kernel(
         B.stride(1),
         C.stride(1),
         C.stride(2),
-        B_scale.stride(0) if B_scale is not None and use_int8_w8a16 else 0,
-        B_scale.stride(1) if B_scale is not None and use_int8_w8a16 else 0,
+        A_scale.stride(0) if A_scale is not None and A_scale.ndim == 2 else 0,
+        A_scale.stride(1) if A_scale is not None and A_scale.ndim == 2 else 0,
+        B_scale.stride(0) if B_scale is not None and B_scale.ndim >= 2 else 0,
+        B_scale.stride(2) if B_scale is not None and B_scale.ndim == 3 else 0,
+        B_scale.stride(1) if B_scale is not None and B_scale.ndim >= 2 else 0,
+        0 if block_shape is None else block_shape[0],
+        0 if block_shape is None else block_shape[1],
         MUL_ROUTED_WEIGHT=mul_routed_weight,
         top_k=top_k,
         compute_type=compute_type,
@@ -415,6 +456,7 @@ def try_get_optimal_moe_config(
     dtype: Optional[str],
     M: int,
     is_marlin: bool = False,
+    block_shape: Optional[List[int]] = None,
 ):
     from sglang.srt.layers.moe.fused_moe_triton import get_config
 
@@ -433,6 +475,13 @@ def try_get_optimal_moe_config(
         else:
             # Else use the default config
             config = get_default_config(M, E, N, w1_shape[2], top_k, dtype, is_marlin)
+    # TODO(HandH1998): Optimize the configs of block-wise quant.
+    # NOTE(HandH1998): For block-wise quant,
+    # BLOCK_K must be divisable by block_shape[1]
+    # BLOCK_N and BLOCK_M has no requirements
+    if block_shape is not None:
+        config["BLOCK_SIZE_N"] = block_shape[0]
+        config["BLOCK_SIZE_K"] = block_shape[1]
     return config
 
 
@@ -464,6 +513,7 @@ def inplace_fused_experts(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ) -> None:
     fused_experts_impl(
         hidden_states,
@@ -478,6 +528,7 @@ def inplace_fused_experts(
         w2_scale,
         a1_scale,
         a2_scale,
+        block_shape,
     )
 
 
@@ -493,6 +544,7 @@ def inplace_fused_experts_fake(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ) -> None:
     pass
 
@@ -517,6 +569,7 @@ def outplace_fused_experts(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ) -> torch.Tensor:
     return fused_experts_impl(
         hidden_states,
@@ -531,6 +584,7 @@ def outplace_fused_experts(
         w2_scale,
         a1_scale,
         a2_scale,
+        block_shape,
     )
 
 
@@ -546,6 +600,7 @@ def outplace_fused_experts_fake(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ) -> torch.Tensor:
     return torch.empty_like(hidden_states)
 
@@ -571,6 +626,7 @@ def fused_experts(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ):
     if inplace:
         torch.ops.sglang.inplace_fused_experts(
@@ -585,6 +641,7 @@ def fused_experts(
             w2_scale,
             a1_scale,
             a2_scale,
+            block_shape,
         )
         return hidden_states
     else:
@@ -600,6 +657,7 @@ def fused_experts(
             w2_scale,
             a1_scale,
             a2_scale,
+            block_shape,
         )
 
 
@@ -616,6 +674,7 @@ def fused_experts_impl(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ):
     padded_size = padding_size
     if not use_fp8_w8a8:
@@ -647,6 +706,7 @@ def fused_experts_impl(
         (w2.shape[0], w2.shape[1], w2.shape[2] - padded_size),
         topk_ids.shape[1],
         config_dtype,
+        block_shape=block_shape,
     )
 
     config = get_config_func(M)
@@ -719,6 +779,7 @@ def fused_experts_impl(
             compute_type=compute_type,
             use_fp8_w8a8=use_fp8_w8a8,
             use_int8_w8a16=use_int8_w8a16,
+            block_shape=block_shape,
         )
 
         ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
@@ -740,6 +801,7 @@ def fused_experts_impl(
             compute_type=compute_type,
             use_fp8_w8a8=use_fp8_w8a8,
             use_int8_w8a16=use_int8_w8a16,
+            block_shape=block_shape,
         )
 
         torch.sum(
@@ -768,6 +830,7 @@ def fused_moe(
     w2_scale: Optional[torch.Tensor] = None,
     a1_scale: Optional[torch.Tensor] = None,
     a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
 ) -> torch.Tensor:
     """
     This function computes a Mixture of Experts (MoE) layer using two sets of
@@ -795,6 +858,12 @@ def fused_moe(
         w1.
     - w2_scale (Optional[torch.Tensor]): Optional scale to be used for
         w2.
+    - a1_scale (Optional[torch.Tensor]): Optional scale to be used for
+        a1.
+    - a2_scale (Optional[torch.Tensor]): Optional scale to be used for
+        a2.
+    - block_shape: (Optional[List[int]]): Optional block size for block-wise
+        quantization.
 
     Returns:
     - torch.Tensor: The output tensor after applying the MoE layer.
@@ -826,4 +895,5 @@ def fused_moe(
         w2_scale=w2_scale,
         a1_scale=a1_scale,
         a2_scale=a2_scale,
+        block_shape=block_shape,
     )

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

@@ -34,6 +34,7 @@ class FusedMoeWeightScaleSupported(Enum):
     TENSOR = "tensor"
     CHANNEL = "channel"
     GROUP = "group"
+    BLOCK = "block"
 
 
 class FusedMoEMethodBase(QuantizeMethodBase):
@@ -214,6 +215,7 @@ class FusedMoE(torch.nn.Module):
         )
         self.top_k = top_k
         self.num_experts = num_experts
+        assert intermediate_size % self.tp_size == 0
         self.intermediate_size_per_partition = intermediate_size // self.tp_size
         self.reduce_results = reduce_results
         self.renormalize = renormalize
@@ -470,7 +472,10 @@ class FusedMoE(torch.nn.Module):
                     expert_data=expert_data,
                     tp_rank=tp_rank,
                 )
-            elif quant_method == FusedMoeWeightScaleSupported.GROUP.value:
+            elif quant_method in [
+                FusedMoeWeightScaleSupported.GROUP.value,
+                FusedMoeWeightScaleSupported.BLOCK.value,
+            ]:
                 self._load_model_weight_or_group_weight_scale(
                     shard_id=shard_id,
                     shard_dim=shard_dim,

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

@@ -9,6 +9,7 @@ import torch.nn.functional as F
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 from vllm import _custom_ops as ops
+from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.linear import LinearBase
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
@@ -32,7 +33,11 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
-from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
+from sglang.srt.layers.quantization.fp8_utils import (
+    BlockQuantScaleParameter,
+    apply_w8a8_block_fp8_linear,
+    normalize_e4m3fn_to_e4m3fnuz,
+)
 from sglang.srt.utils import (
     get_bool_env_var,
     is_hip,
@@ -53,6 +58,7 @@ class Fp8Config(QuantizationConfig):
         is_checkpoint_fp8_serialized: bool = False,
         activation_scheme: str = "dynamic",
         ignored_layers: Optional[List[str]] = None,
+        weight_block_size: List[int] = None,
     ) -> None:
         self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
         if is_checkpoint_fp8_serialized:
@@ -64,6 +70,20 @@ class Fp8Config(QuantizationConfig):
             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_fp8_serialized:
+                raise ValueError(
+                    f"The block-wise quantization only supports fp8-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:
@@ -87,10 +107,12 @@ class Fp8Config(QuantizationConfig):
         is_checkpoint_fp8_serialized = "fp8" 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_fp8_serialized=is_checkpoint_fp8_serialized,
             activation_scheme=activation_scheme,
             ignored_layers=ignored_layers,
+            weight_block_size=weight_block_size,
         )
 
     def get_quant_method(
@@ -143,6 +165,11 @@ class Fp8LinearMethod(LinearMethodBase):
         if is_hip():
             self.use_marlin = False
 
+        self.block_quant = self.quant_config.weight_block_size is not None
+        if self.block_quant:
+            # Marlin doesn't support block-wise fp8
+            self.use_marlin = False
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -153,10 +180,35 @@ class Fp8LinearMethod(LinearMethodBase):
         params_dtype: torch.dtype,
         **extra_weight_attrs,
     ):
-        del input_size, output_size
         output_size_per_partition = sum(output_partition_sizes)
         weight_loader = extra_weight_attrs.get("weight_loader")
 
+        tp_size = get_tensor_model_parallel_world_size()
+        if self.block_quant:
+            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
@@ -184,11 +236,25 @@ class Fp8LinearMethod(LinearMethodBase):
         # Otherwise, wait until process_weights_after_loading.
         if self.quant_config.is_checkpoint_fp8_serialized:
             # WEIGHT SCALE
+            if self.block_quant:
+                assert self.quant_config.activation_scheme == "dynamic"
+                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)
+            else:
                 scale = PerTensorScaleParameter(
                     data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
                     weight_loader=weight_loader,
                 )
-
                 scale[:] = torch.finfo(torch.float32).min
                 layer.register_parameter("weight_scale", scale)
 
@@ -205,6 +271,9 @@ class Fp8LinearMethod(LinearMethodBase):
                 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
+        if self.block_quant:
+            return
         layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
         # If checkpoint not serialized fp8, quantize the weights.
         if not self.quant_config.is_checkpoint_fp8_serialized:
@@ -295,6 +364,16 @@ class Fp8LinearMethod(LinearMethodBase):
                 bias=bias,
             )
 
+        if self.block_quant:
+            return apply_w8a8_block_fp8_linear(
+                input=x,
+                weight=layer.weight,
+                block_size=self.quant_config.weight_block_size,
+                weight_scale=layer.weight_scale_inv,
+                input_scale=layer.input_scale,
+                bias=bias,
+            )
+
         return apply_fp8_linear(
             input=x,
             weight=layer.weight,
@@ -339,6 +418,7 @@ class Fp8MoEMethod:
 
     def __init__(self, quant_config):
         self.quant_config = quant_config
+        self.block_quant = self.quant_config.weight_block_size is not None
 
     def create_weights(
         self,
@@ -353,6 +433,28 @@ class Fp8MoEMethod:
 
         if self.quant_config.is_checkpoint_fp8_serialized:
             params_dtype = torch.float8_e4m3fn
+        tp_size = get_tensor_model_parallel_world_size()
+        if self.block_quant:
+            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(
@@ -374,21 +476,45 @@ class Fp8MoEMethod:
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
         # WEIGHT_SCALES
+        if self.block_quant:
+            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)
+            assert self.quant_config.activation_scheme == "dynamic"
+        else:
             # Allocate 2 scales for w1 and w3 respectively.
             # They will be combined to a single scale after weight loading.
             w13_weight_scale = torch.nn.Parameter(
                 torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
             )
-        layer.register_parameter("w13_weight_scale", w13_weight_scale)
-
             w2_weight_scale = torch.nn.Parameter(
                 torch.ones(num_experts, dtype=torch.float32), requires_grad=False
             )
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
             layer.register_parameter("w2_weight_scale", w2_weight_scale)
         # Add the quantization method used (per tensor/grouped/channel)
         # to ensure the weight scales are loaded in properly
         extra_weight_attrs.update(
-            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
+            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
+            if self.block_quant
+            else {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
         )
         # If loading fp8 checkpoint, pass the weight loaders.
         # If loading an fp16 checkpoint, do not (we will quantize in
@@ -422,7 +548,9 @@ class Fp8MoEMethod:
             layer.w2_input_scale = None
 
     def process_weights_after_loading(self, layer: Module) -> None:
-
+        # Block quant doesn't need to process weights after loading
+        if self.block_quant:
+            return
         # If checkpoint is fp16 or bfloat16, quantize in place.
         if not self.quant_config.is_checkpoint_fp8_serialized:
             # If ROCm, use float8_e4m3fnuz instead (MI300x HW)
@@ -519,7 +647,6 @@ class Fp8MoEMethod:
                     layer.w2_input_scale = torch.nn.Parameter(
                         w2_input_scale, requires_grad=False
                     )
-
             # Fp8 moe kernel needs single weight scale for w13 per expert.
             # We take the max then dequant and requant each expert.
             assert layer.w13_weight_scale is not None
@@ -594,10 +721,17 @@ class Fp8MoEMethod:
             topk_ids=topk_ids,
             inplace=True,
             use_fp8_w8a8=True,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
+            w1_scale=(
+                layer.w13_weight_scale_inv
+                if self.block_quant
+                else layer.w13_weight_scale
+            ),
+            w2_scale=(
+                layer.w2_weight_scale_inv if self.block_quant else layer.w2_weight_scale
+            ),
             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/fp8_kernel.py

+from typing import List, Tuple
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def _per_token_group_quant_fp8(
+    # 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 float8
+    fp8_min,
+    fp8_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 float8 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 / fp8_max
+    y_q = tl.clamp(y / y_s, fp8_min, fp8_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_fp8(
+    x: torch.Tensor,
+    group_size: int,
+    eps: float = 1e-10,
+    dtype: torch.dtype = torch.float8_e4m3fn,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Function to perform per-token-group quantization on an input tensor `x`.
+
+    It converts the tensor values into signed float8 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.float8_e4m3fn` 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"
+
+    finfo = torch.finfo(dtype)
+    fp8_min = finfo.min
+    fp8_max = finfo.max
+
+    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_fp8[(M,)](
+        x,
+        x_q,
+        x_s,
+        group_size,
+        N,
+        eps,
+        fp8_min=fp8_min,
+        fp8_max=fp8_max,
+        BLOCK=BLOCK,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+
+    return x_q, x_s
+
+
+@triton.jit
+def _w8a8_block_fp8_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) * 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)
+
+
+def w8a8_block_fp8_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)
+
+    # TODO(HandH1998):
+    # BLOCK_SIZE_M, BLOCK_SIZE_K, BLOCK_SIZE_N can be optimized.
+    # BLOCK_SIZE_K must be divisable by block_k
+    # BLOCK_SIZE_N and BLOCK_SIZE_M has no requirements
+    BLOCK_SIZE_M = 128
+    if M < BLOCK_SIZE_M:
+        BLOCK_SIZE_M = triton.next_power_of_2(M)
+        BLOCK_SIZE_M = max(BLOCK_SIZE_M, 16)
+    BLOCK_SIZE_K = block_k
+    assert block_k % BLOCK_SIZE_K == 0
+    BLOCK_SIZE_N = block_n
+
+    def grid(META):
+        return (
+            triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
+        )
+
+    _w8a8_block_fp8_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),
+        BLOCK_SIZE_M=BLOCK_SIZE_M,
+        BLOCK_SIZE_N=BLOCK_SIZE_N,
+        BLOCK_SIZE_K=BLOCK_SIZE_K,
+        GROUP_SIZE_M=8,
+    )
+
+    return C

python/sglang/srt/layers/quantization/fp8_utils.py

-from typing import Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
+from vllm.model_executor.parameter import RowvLLMParameter, _ColumnvLLMParameter
+
+from sglang.srt.layers.quantization.fp8_kernel import (
+    per_token_group_quant_fp8,
+    w8a8_block_fp8_matmul,
+)
 
 
 def normalize_e4m3fn_to_e4m3fnuz(
@@ -25,3 +31,86 @@ def normalize_e4m3fn_to_e4m3fnuz(
     if input_scale is not None:
         input_scale = input_scale * 2.0
     return weight, weight_scale, input_scale
+
+
+def apply_w8a8_block_fp8_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_fp8(input_2d, block_size[1])
+    output = w8a8_block_fp8_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_float8(
+    x: torch.Tensor, dtype: torch.dtype = torch.float8_e4m3fn
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """This function quantizes input values to float8 values with tensor-wise quantization."""
+    finfo = torch.finfo(dtype)
+    min_val, max_val = x.aminmax()
+    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
+    scale = finfo.max / amax
+    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
+    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
+
+
+def block_quant_to_tensor_quant(
+    x_q_block: torch.Tensor,
+    x_s: torch.Tensor,
+    block_size: List[int],
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """This function converts block-wise quantization to tensor-wise quantization.
+    The inputs are block-wise quantization tensor `x_q_block`, block-wise quantization scale
+    and the block size.
+    The outputs are tensor-wise quantization tensor and tensor-wise quantization scale.
+    Note only float8 is supported for now.
+    """
+    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)
+
+    x_dq_block_tiles = [
+        [
+            x_dq_block[
+                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)
+    ]
+
+    for i in range(k_tiles):
+        for j in range(n_tiles):
+            x_dq_block_tiles[j][i][:, :] = x_dq_block_tiles[j][i] * x_s[j][i]
+
+    x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)
+    return x_q_tensor, scale
+
+
+class BlockQuantScaleParameter(_ColumnvLLMParameter, RowvLLMParameter):
+    """
+    Parameter class for weight scales loaded for weights with
+    block-wise quantization. Uses both column and row parallelism.
+    """
+
+    pass

python/sglang/srt/models/deepseek_v2.py

@@ -43,6 +43,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.ep_moe.layer import EPMoE
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.fp8_utils import (
+    block_quant_to_tensor_quant,
+    input_to_float8,
+)
 from sglang.srt.layers.radix_attention import RadixAttention
 from sglang.srt.layers.vocab_parallel_embedding import (
     ParallelLMHead,
@@ -186,15 +190,6 @@ def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
     return 0.1 * mscale * math.log(scale) + 1.0
 
 
-def input_to_float8(x, dtype=torch.float8_e4m3fn):
-    finfo = torch.finfo(dtype)
-    min_val, max_val = x.aminmax()
-    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
-    scale = finfo.max / amax
-    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
-    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
-
-
 class DeepseekV2Attention(nn.Module):
 
     def __init__(
@@ -869,6 +864,16 @@ class DeepseekV2ForCausalLM(nn.Module):
 
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in weights:
+            # TODO(HandH1998): Modify it when nextn is supported.
+            if hasattr(self.config, "num_nextn_predict_layers"):
+                num_nextn_layers = self.config.num_nextn_predict_layers
+                if num_nextn_layers > 0 and name.startswith("model.layers"):
+                    name_list = name.split(".")
+                    if (
+                        len(name_list) >= 3
+                        and int(name_list[2]) >= self.config.num_hidden_layers
+                    ):
+                        continue
             if "rotary_emb.inv_freq" in name:
                 continue
             for param_name, weight_name, shard_id in stacked_params_mapping:
@@ -933,13 +938,33 @@ class DeepseekV2ForCausalLM(nn.Module):
                     ).T
                 else:
                     w = self_attn.kv_b_proj.weight
+                # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
+                # This may affect the accuracy of fp8 model.
+                if (
+                    hasattr(self.quant_config, "weight_block_size")
+                    and w.dtype == torch.float8_e4m3fn
+                ):
+                    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")
+                        w, scale = block_quant_to_tensor_quant(
+                            w, self_attn.kv_b_proj.weight_scale_inv, weight_block_size
+                        )
+                        self_attn.w_scale = scale
                 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)
                 self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
                 self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
-                if hasattr(self_attn.kv_b_proj, "weight_scale"):
+                if (
+                    hasattr(self_attn.kv_b_proj, "weight_scale")
+                    and self_attn.w_scale is None
+                ):
                     self_attn.w_scale = self_attn.kv_b_proj.weight_scale
 
 
-EntryClass = DeepseekV2ForCausalLM
+class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
+    pass
+
+
+EntryClass = [DeepseekV2ForCausalLM, DeepseekV3ForCausalLM]

python/sglang/test/test_block_fp8.py

+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
+from sglang.srt.layers.quantization.fp8_kernel import (
+    per_token_group_quant_fp8,
+    w8a8_block_fp8_matmul,
+)
+
+
+# For test
+def native_per_token_group_quant_fp8(
+    x, group_size, eps=1e-10, dtype=torch.float8_e4m3fn
+):
+    """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"
+
+    finfo = torch.finfo(dtype)
+    fp8_min = finfo.min
+    fp8_max = finfo.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 / fp8_max
+    x_q = (x_ / x_s).clamp(min=fp8_min, max=fp8_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
+
+
+class TestPerTokenGroupQuantFP8(unittest.TestCase):
+    DTYPES = [torch.half, torch.bfloat16, torch.float32]
+    NUM_TOKENS = [7, 83, 2048]
+    D = [512, 4096, 5120, 13824]
+    GROUP_SIZE = [64, 128, 256, 512]
+    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 _per_token_group_quant_fp8(self, num_tokens, d, dtype, group_size, seed):
+        torch.manual_seed(seed)
+
+        x = torch.rand(num_tokens, d, dtype=dtype)
+
+        with torch.inference_mode():
+            ref_out, ref_scale = native_per_token_group_quant_fp8(x, group_size)
+            out, scale = per_token_group_quant_fp8(x, group_size)
+
+        self.assertTrue(
+            torch.allclose(out.to(torch.float32), ref_out.to(torch.float32), rtol=0.15)
+        )
+        self.assertTrue(torch.allclose(scale, ref_scale))
+
+    def test_per_token_group_quant_fp8(self):
+        for params in itertools.product(
+            self.NUM_TOKENS,
+            self.D,
+            self.DTYPES,
+            self.GROUP_SIZE,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                num_tokens=params[0],
+                d=params[1],
+                dtype=params[2],
+                group_size=params[3],
+                seed=params[4],
+            ):
+                self._per_token_group_quant_fp8(*params)
+
+
+# For test
+def native_w8a8_block_fp8_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
+
+
+class TestW8A8BlockFP8Matmul(unittest.TestCase):
+    OUT_DTYPES = [torch.float32, torch.half, torch.bfloat16]
+    M = [1, 7, 83, 512, 2048]
+    N = [128, 512, 1024, 4096, 7748, 13824]
+    K = [256, 4096, 5120, 3884, 13824]
+    # 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_fp8_matmul(self, M, N, K, block_size, out_dtype, seed):
+        torch.manual_seed(seed)
+        # NOTE(HandH1998): to avoid overflow when out_dtype = torch.half
+        factor_for_scale = 1e-2
+        fp8_info = torch.finfo(torch.float8_e4m3fn)
+        fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+        A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
+        A_fp8 = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
+        B_fp8 = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        block_n, block_k = block_size[0], block_size[1]
+        n_tiles = (N + block_n - 1) // block_n
+        k_tiles = (K + block_k - 1) // block_k
+
+        As = torch.rand(M, k_tiles, dtype=torch.float32) * factor_for_scale
+        Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
+
+        with torch.inference_mode():
+            ref_out = native_w8a8_block_fp8_matmul(
+                A_fp8, B_fp8, As, Bs, block_size, out_dtype
+            )
+            out = w8a8_block_fp8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
+
+        self.assertTrue(
+            torch.mean(torch.abs(out.to(torch.float32) - ref_out.to(torch.float32)))
+            / torch.mean(torch.abs(ref_out.to(torch.float32)))
+            < 0.001
+        )
+
+    def test_w8a8_block_fp8_matmul(self):
+        for params in itertools.product(
+            self.M,
+            self.N,
+            self.K,
+            self.BLOCK_SIZE,
+            self.OUT_DTYPES,
+            self.SEEDS,
+        ):
+            with self.subTest(
+                M=params[0],
+                N=params[1],
+                K=params[2],
+                block_size=params[3],
+                out_dtype=params[4],
+                seed=params[5],
+            ):
+                self._w8a8_block_fp8_matmul(*params)
+
+
+# For test
+def torch_w8a8_block_fp8_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_fp8(a, block_k)
+    # NOTE(HandH1998): Since "index_cuda" not implemented for 'Float8_e4m3fn', we need to cast `float8`` to `float32``.
+    a_q = a_q.to(torch.float32)
+    for i in range(w1.shape[0]):
+        mask = topk_ids == i
+        if mask.sum():
+            inter_out = native_w8a8_block_fp8_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_fp8(act_out, block_k)
+            act_out = act_out.to(torch.float32)
+            out[mask] = native_w8a8_block_fp8_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 TestW8A8BlockFP8FusedMoE(unittest.TestCase):
+    DTYPES = [torch.float32, torch.half, torch.bfloat16]
+    M = [1, 33, 64, 222, 1024 * 128]
+    N = [128, 1024, 2048]
+    K = [256, 4096, 5120]
+    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_fp8_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
+        fp8_info = torch.finfo(torch.float8_e4m3fn)
+        fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+        a = torch.randn((M, K), dtype=dtype) / 10
+
+        w1_fp32 = (torch.rand((E, 2 * N, K), dtype=torch.float32) - 0.5) * 2 * fp8_max
+        w1 = w1_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        w2_fp32 = (torch.rand((E, K, N), dtype=torch.float32) - 0.5) * 2 * fp8_max
+        w2 = w2_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        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_fp8_w8a8=True,
+                w1_scale=w1_s,
+                w2_scale=w2_s,
+                block_shape=block_size,
+            )
+            ref_out = torch_w8a8_block_fp8_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_fp8_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_fp8_fused_moe(*params)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)