Expert Parallelism (EP) Support for DeepSeek V3/R1 (#3602)

Co-authored-by: laixin <xielx@shanghaitech.edu.cn>
Co-authored-by: HandH1998 <1335248067@qq.com>
Co-authored-by: laixin <q865809639@gmail.com>

python/sglang/srt/layers/moe/ep_moe/kernels.py

 import logging
-from typing import Optional
+from typing import List, Optional
 
 import torch
 import triton
 import triton.language as tl
 
+from sglang.srt.layers.quantization.fp8_kernel import per_token_group_quant_fp8
+
+_is_cuda = torch.cuda.is_available() and torch.version.cuda
+if _is_cuda:
+    from sglang.srt.layers.quantization.fp8_kernel import (
+        sglang_per_token_group_quant_fp8,
+    )
 logger = logging.getLogger(__name__)
 
 
@@ -218,12 +225,19 @@ def grouped_gemm_triton_kernel(
     seg_indptr,
     weight_indices,
     m_num_tiles_indptr,
-    use_fp8_w8a8,
     scale_a,
     scale_b,
+    use_fp8_w8a8: tl.constexpr,
+    group_n: tl.constexpr,
+    group_k: tl.constexpr,
     a_stride_0: tl.constexpr,
     b_stride_0: tl.constexpr,
     b_stride_1: tl.constexpr,
+    as_stride_0: tl.constexpr,
+    as_stride_1: tl.constexpr,
+    bs_stride_0: tl.constexpr,
+    bs_stride_2: tl.constexpr,
+    bs_stride_1: tl.constexpr,
     BLOCK_SIZE_M: tl.constexpr,
     BLOCK_SIZE_N: tl.constexpr,
     BLOCK_SIZE_K: tl.constexpr,
@@ -260,6 +274,12 @@ def grouped_gemm_triton_kernel(
         + (n_range_start + offs_bn[:, None]) * b_stride_1
         + offs_k[None, :]
     )
+
+    if group_k > 0 and group_n > 0:
+        a_scale_ptrs = scale_a + (m_range_start + offs_am[:, None]) * as_stride_0
+        offs_bsn = (n_range_start + offs_bn) // group_n
+        b_scale_ptrs = scale_b + (expert_id * bs_stride_0) + offs_bsn * bs_stride_1
+
     accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
     for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
         a_tile = tl.load(
@@ -268,14 +288,23 @@ def grouped_gemm_triton_kernel(
         b_tile = tl.load(
             b_ptr, mask=offs_k[None, :] < (K - k * BLOCK_SIZE_K), other=0.0
         )
-        accumulator = tl.dot(a_tile, b_tile.T, accumulator)
+
+        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 * as_stride_1)
+            b_scale = tl.load(b_scale_ptrs + offs_ks * bs_stride_2)
+            accumulator += tl.dot(a_tile, b_tile.T) * a_scale * b_scale[None, :]
+        else:
+            accumulator = tl.dot(a_tile, b_tile.T, accumulator)
         a_ptr += BLOCK_SIZE_K
         b_ptr += BLOCK_SIZE_K
 
-    if use_fp8_w8a8:
+    if use_fp8_w8a8 and not (group_k > 0 and group_n > 0):
         scale_a_value = tl.load(scale_a + expert_id)
         scale_b_value = tl.load(scale_b + expert_id)
         accumulator *= scale_a_value * scale_b_value
+
     c_tile = accumulator.to(c_dtype)
 
     offs_cm = m_range_start + tl.arange(0, BLOCK_SIZE_M)
@@ -307,14 +336,29 @@ def grouped_gemm_triton(
     use_fp8_w8a8: bool = False,
     scale_a: torch.Tensor = None,
     scale_b: torch.Tensor = None,
+    block_shape: Optional[List[int]] = None,
 ):
     assert weight_column_major == True  # TODO: more
-    if use_fp8_w8a8:
+    if use_fp8_w8a8 and block_shape is None:
         assert scale_a is not None and scale_b is not None
 
+    if block_shape is not None:
+        assert len(block_shape) == 2
+        block_n, block_k = block_shape[0], block_shape[1]
+        if _is_cuda:
+            a, scale_a = sglang_per_token_group_quant_fp8(a, block_k)
+        else:
+            a, scale_a = per_token_group_quant_fp8(a, block_k)
+
+        assert triton.cdiv(a.shape[-1], block_k) == scale_a.shape[-1]
+        assert triton.cdiv(b.shape[-2], block_n) == scale_b.shape[-2]
+        assert triton.cdiv(b.shape[-1], block_k) == scale_b.shape[-1]
+
+    # TODO: adjust config or tune kernel
+    # Reduce block size to prevent L40 shared memory overflow.
     config = {
-        "BLOCK_SIZE_M": 128,
-        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 32,
         "BLOCK_SIZE_K": 128,
     }
 
@@ -338,12 +382,19 @@ def grouped_gemm_triton(
         seg_indptr,
         weight_indices,
         m_num_tiles_indptr,
-        use_fp8_w8a8,
         scale_a,
         scale_b,
+        use_fp8_w8a8,
+        0 if block_shape is None else block_shape[0],
+        0 if block_shape is None else block_shape[1],
         a.stride(0),
         b.stride(0),
         b.stride(1),
+        scale_a.stride(0) if scale_a is not None and scale_a.ndim == 2 else 0,
+        scale_a.stride(1) if scale_a is not None and scale_a.ndim == 2 else 0,
+        scale_b.stride(0) if scale_b is not None and scale_b.ndim >= 2 else 0,
+        scale_b.stride(2) if scale_b is not None and scale_b.ndim == 3 else 0,
+        scale_b.stride(1) if scale_b is not None and scale_b.ndim >= 2 else 0,
         **config,
     )
     return c

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

@@ -17,6 +17,7 @@ from sglang.srt.layers.moe.ep_moe.kernels import (
     run_moe_ep_preproess,
     silu_and_mul_triton_kernel,
 )
+from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
 from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoEMethodBase
 from sglang.srt.layers.moe.topk import select_experts
 from sglang.srt.layers.quantization.base_config import (
@@ -61,6 +62,7 @@ class GroupedGemmRunner(torch.nn.Module):
         use_fp8_w8a8: bool = False,
         scale_a: torch.Tensor = None,
         scale_b: torch.Tensor = None,
+        block_shape: Optional[List[int]] = None,
     ):
         if self.use_flashinfer:
             # TODO: flashinfer
@@ -87,6 +89,7 @@ class GroupedGemmRunner(torch.nn.Module):
                 use_fp8_w8a8,
                 scale_a,
                 scale_b,
+                block_shape=block_shape,
             )
         return c
 
@@ -147,12 +150,20 @@ class EPMoE(torch.nn.Module):
         if quant_config is None:
             self.quant_method: Optional[QuantizeMethodBase] = UnquantizedEPMoEMethod()
             self.use_fp8_w8a8 = False
+            self.use_block_quant = False
+            self.block_shape = None
             self.activation_scheme = None
         else:
             self.quant_method: Optional[QuantizeMethodBase] = Fp8EPMoEMethod(
                 quant_config
             )
             self.use_fp8_w8a8 = True
+            self.use_block_quant = getattr(self.quant_method, "block_quant", False)
+            self.block_shape = (
+                self.quant_method.quant_config.weight_block_size
+                if self.use_block_quant
+                else None
+            )
             self.fp8_dtype = torch.float8_e4m3fn
             self.activation_scheme = quant_config.activation_scheme
 
@@ -173,7 +184,8 @@ class EPMoE(torch.nn.Module):
 
         if self.grouped_gemm_runner is None:
             self.grouped_gemm_runner = GroupedGemmRunner(
-                hidden_states.device, use_flashinfer=False  # TODO: use flashinfer
+                hidden_states.device,
+                use_flashinfer=False,  # TODO: use flashinfer
             )
 
         topk_weights, topk_ids = select_experts(
@@ -195,9 +207,13 @@ class EPMoE(torch.nn.Module):
         gateup_input = torch.empty(
             (int(hidden_states.shape[0] * self.top_k), hidden_states.shape[1]),
             device=hidden_states.device,
-            dtype=self.fp8_dtype if self.use_fp8_w8a8 else hidden_states.dtype,
+            dtype=(
+                self.fp8_dtype
+                if (self.use_fp8_w8a8 and not self.use_block_quant)
+                else hidden_states.dtype
+            ),
         )
-        if self.activation_scheme == "dynamic":
+        if self.activation_scheme == "dynamic" and not self.use_block_quant:
             max_value = (
                 torch.max(hidden_states)
                 .repeat(self.num_experts_per_partition)
@@ -243,7 +259,12 @@ class EPMoE(torch.nn.Module):
             weight_indices=weight_indices_cur_rank,
             use_fp8_w8a8=self.use_fp8_w8a8,
             scale_a=self.w13_input_scale,
-            scale_b=self.w13_weight_scale,
+            scale_b=(
+                self.w13_weight_scale_inv
+                if self.use_block_quant
+                else self.w13_weight_scale
+            ),
+            block_shape=self.block_shape,
         )
 
         # Act
@@ -251,9 +272,13 @@ class EPMoE(torch.nn.Module):
             gateup_output.shape[0],
             gateup_output.shape[1] // 2,
             device=gateup_output.device,
-            dtype=self.fp8_dtype if self.use_fp8_w8a8 else hidden_states.dtype,
+            dtype=(
+                self.fp8_dtype
+                if (self.use_fp8_w8a8 and not self.use_block_quant)
+                else hidden_states.dtype
+            ),
         )
-        if self.w2_input_scale is None:
+        if self.w2_input_scale is None and not self.use_block_quant:
             self.w2_input_scale = torch.ones(
                 self.num_experts_per_partition,
                 dtype=torch.float32,
@@ -291,7 +316,12 @@ class EPMoE(torch.nn.Module):
             weight_indices=weight_indices_cur_rank,
             use_fp8_w8a8=self.use_fp8_w8a8,
             scale_a=self.w2_input_scale,
-            scale_b=self.w2_weight_scale,
+            scale_b=(
+                self.w2_weight_scale_inv
+                if self.use_block_quant
+                else self.w2_weight_scale
+            ),
+            block_shape=self.block_shape,
         )
 
         # PostReorder
@@ -358,7 +388,11 @@ class EPMoE(torch.nn.Module):
         # Special case for fp8 scales.
         if "scale" in weight_name:
             self._load_fp8_scale(
-                param.data, loaded_weight, weight_name, shard_id, expert_id
+                param.data,
+                loaded_weight,
+                weight_name,
+                shard_id,
+                expert_id,
             )
             return
 
@@ -395,18 +429,33 @@ class EPMoE(torch.nn.Module):
             param_data[expert_id] = loaded_weight
         # Weight scales
         elif "weight_scale" in weight_name:
+            if self.use_block_quant:
+                block_n, block_k = self.block_shape[0], self.block_shape[1]
+                if shard_id == "w1":
+                    param_data[expert_id][
+                        : (self.intermediate_size + block_n - 1) // block_n, :
+                    ] = loaded_weight
+                elif shard_id == "w3":
+                    param_data[expert_id][
+                        (self.intermediate_size + block_n - 1) // block_n :, :
+                    ] = loaded_weight
+                else:  # w2
+                    param_data[expert_id] = loaded_weight
             # If we are in merged column case (gate_up_proj)
-            if shard_id in ("w1", "w3"):
-                # We have to keep the weight scales of w1 and w3 because
-                # we need to re-quantize w1/w3 weights after weight loading.
-                idx = 0 if shard_id == "w1" else 1
-                param_data[expert_id][idx] = loaded_weight
-            # If we are in the row parallel case (down_proj)
             else:
-                param_data[expert_id] = loaded_weight
+                if shard_id in ("w1", "w3"):
+                    # We have to keep the weight scales of w1 and w3 because
+                    # we need to re-quantize w1/w3 weights after weight loading.
+                    idx = 0 if shard_id == "w1" else 1
+                    param_data[expert_id][idx] = loaded_weight
+
+                # If we are in the row parallel case (down_proj)
+                else:
+                    param_data[expert_id] = loaded_weight
 
 
 class UnquantizedEPMoEMethod(FusedMoEMethodBase, CustomOp):
+
     def create_weights(
         self,
         layer: torch.nn.Module,
@@ -498,6 +547,7 @@ class Fp8EPMoEMethod(Fp8MoEMethod):
 
     def __init__(self, quant_config: Fp8Config):
         self.quant_config = quant_config
+        self.block_quant = self.quant_config.weight_block_size is not None
 
     def create_weights(
         self,
@@ -512,6 +562,29 @@ class Fp8EPMoEMethod(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(
             torch.empty(
@@ -538,21 +611,49 @@ class Fp8EPMoEMethod(Fp8MoEMethod):
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
         # WEIGHT_SCALES
-        # Allocate 2 scales for w1 and w3 respectively.
-        w13_weight_scale = torch.nn.Parameter(
-            torch.ones(num_experts_per_partition, 2, dtype=torch.float32),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        if self.block_quant:
+            w13_weight_scale = torch.nn.Parameter(
+                torch.ones(
+                    num_experts_per_partition,
+                    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_per_partition,
+                    (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:
+            # WEIGHT_SCALES
+            # Allocate 2 scales for w1 and w3 respectively.
+            w13_weight_scale = torch.nn.Parameter(
+                torch.ones(num_experts_per_partition, 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_per_partition, dtype=torch.float32),
-            requires_grad=False,
-        )
-        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            w2_weight_scale = torch.nn.Parameter(
+                torch.ones(num_experts_per_partition, dtype=torch.float32),
+                requires_grad=False,
+            )
+            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": "tensor"})
+        extra_weight_attrs.update(
+            {"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
         #   process_weights_after_loading()

python/sglang/test/test_block_fp8_ep.py

+import itertools
+import random
+import unittest
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import torch
+
+from sglang.srt.layers.moe.ep_moe.kernels import (
+    grouped_gemm_triton,
+    post_reorder_triton_kernel,
+    pre_reorder_triton_kernel,
+    run_moe_ep_preproess,
+    silu_and_mul_triton_kernel,
+)
+from sglang.srt.layers.moe.topk import select_experts
+
+
+# For test
+def ep_moe(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    router_logits: torch.Tensor,
+    top_k: int,
+    renormalize: bool,
+    # ep config
+    num_experts: int = 256,
+    fp8_dtype: torch.types = torch.float8_e4m3fn,
+    num_experts_per_partition: int = 128,
+    start_expert_id: int = 0,
+    end_expert_id: int = 127,
+    use_grouped_topk: bool = False,
+    num_expert_group: Optional[int] = None,
+    topk_group: Optional[int] = None,
+    custom_routing_function: Optional[Callable] = None,
+    use_fp8_w8a8: bool = False,
+    w1_scale_inv: Optional[torch.Tensor] = None,
+    w2_scale_inv: Optional[torch.Tensor] = None,
+    block_shape: Optional[List[int]] = None,
+):
+    use_blockwise_fp8 = block_shape is not None
+    topk_weights, topk_ids = select_experts(
+        hidden_states=hidden_states,
+        router_logits=router_logits,
+        top_k=top_k,
+        use_grouped_topk=use_grouped_topk,
+        renormalize=renormalize,
+        topk_group=topk_group,
+        num_expert_group=num_expert_group,
+        # correction_bias=correction_bias, #skip this in test
+        custom_routing_function=custom_routing_function,
+    )
+
+    reorder_topk_ids, src2dst, seg_indptr = run_moe_ep_preproess(topk_ids, num_experts)
+
+    gateup_input = torch.empty(
+        (int(hidden_states.shape[0] * top_k), hidden_states.shape[1]),
+        device=hidden_states.device,
+        dtype=(
+            fp8_dtype
+            if (use_fp8_w8a8 and not use_blockwise_fp8)
+            else hidden_states.dtype
+        ),
+    )
+
+    if use_fp8_w8a8 and not use_blockwise_fp8:
+        max_value = (
+            torch.max(hidden_states).repeat(num_experts_per_partition).to(torch.float32)
+        )
+        w1_input_scale = max_value / torch.finfo(fp8_dtype).max
+    else:
+        w1_input_scale = None
+
+    # PreReorder
+    pre_reorder_triton_kernel[(hidden_states.shape[0],)](
+        hidden_states,
+        gateup_input,
+        src2dst,
+        topk_ids,
+        w1_input_scale,
+        start_expert_id,
+        end_expert_id,
+        top_k,
+        hidden_states.shape[1],
+        BLOCK_SIZE=512,
+    )
+
+    seg_indptr_cur_rank = seg_indptr[start_expert_id : end_expert_id + 2]
+    weight_indices_cur_rank = torch.arange(
+        0,
+        num_experts_per_partition,
+        device=hidden_states.device,
+        dtype=torch.int64,
+    )
+
+    # GroupGemm-0
+    gateup_output = torch.empty(
+        gateup_input.shape[0],
+        w1.shape[1],
+        device=hidden_states.device,
+        dtype=hidden_states.dtype,
+    )
+
+    gateup_output = grouped_gemm_triton(
+        a=gateup_input,
+        b=w1,
+        c=gateup_output,
+        batch_size=num_experts_per_partition,
+        weight_column_major=True,
+        seg_indptr=seg_indptr_cur_rank,
+        weight_indices=weight_indices_cur_rank,
+        use_fp8_w8a8=use_fp8_w8a8,
+        scale_a=w1_input_scale,
+        scale_b=w1_scale_inv,
+        block_shape=block_shape,
+    )
+
+    # Act
+    down_input = torch.empty(
+        gateup_output.shape[0],
+        gateup_output.shape[1] // 2,
+        device=gateup_output.device,
+        dtype=(
+            fp8_dtype
+            if (use_fp8_w8a8 and not use_blockwise_fp8)
+            else hidden_states.dtype
+        ),
+    )
+    if use_fp8_w8a8 and not use_blockwise_fp8:
+        w2_input_scale = torch.ones(
+            num_experts_per_partition,
+            dtype=torch.float32,
+            device=hidden_states.device,
+        )
+    else:
+        w2_input_scale = None
+
+    silu_and_mul_triton_kernel[(gateup_output.shape[0],)](
+        gateup_output,
+        down_input,
+        gateup_output.shape[1],
+        reorder_topk_ids,
+        w2_input_scale,
+        start_expert_id,
+        end_expert_id,
+        BLOCK_SIZE=512,
+    )
+
+    # GroupGemm-1
+    down_output = torch.empty(
+        down_input.shape[0],
+        w2.shape[1],
+        device=hidden_states.device,
+        dtype=hidden_states.dtype,
+    )
+
+    down_output = grouped_gemm_triton(
+        a=down_input,
+        b=w2,
+        c=down_output,
+        batch_size=num_experts_per_partition,
+        weight_column_major=True,
+        seg_indptr=seg_indptr_cur_rank,
+        weight_indices=weight_indices_cur_rank,
+        use_fp8_w8a8=use_fp8_w8a8,
+        scale_a=w2_input_scale,
+        scale_b=w2_scale_inv,
+        block_shape=block_shape,
+    )
+
+    # PostReorder
+    output = torch.empty_like(hidden_states)
+    post_reorder_triton_kernel[(hidden_states.size(0),)](
+        down_output,
+        output,
+        src2dst,
+        topk_ids,
+        topk_weights,
+        start_expert_id,
+        end_expert_id,
+        top_k,
+        hidden_states.size(1),
+        BLOCK_SIZE=512,
+    )
+    return output
+
+
+# test util
+def block_dequant(
+    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.
+    """
+
+    # process 3D tensor
+    if x_q_block.dim() == 3:
+        batch_size = x_q_block.size(0)
+        return torch.stack(
+            [block_dequant(x_q_block[b], x_s[b], block_size) for b in range(batch_size)]
+        )
+
+    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]
+
+    return x_dq_block
+
+
+class TestW8A8BlockFP8EPMoE(unittest.TestCase):
+    DTYPES = [torch.half, torch.bfloat16]
+    M = [1, 222, 1024, 2048]
+    N = [128, 1024, 2048]
+    K = [256, 4096, 5120]
+    E = [8, 16]
+    ep_size = [2, 4]
+    TOP_KS = [2, 4]
+    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_ep_moe(
+        self, M, N, K, E, ep_size, topk, block_size, dtype, seed
+    ):
+        torch.manual_seed(seed)
+        random.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=dtype) - 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=dtype) - 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
+        )
+
+        w1_ref = block_dequant(w1, w1_s, block_size).to(dtype)
+        w2_ref = block_dequant(w2, w2_s, block_size).to(dtype)
+
+        score = torch.randn((M, E), dtype=dtype)
+        num_experts_per_partition = E // ep_size
+        cur_rank = random.randint(0, ep_size - 1)
+        start_id = cur_rank * num_experts_per_partition
+        end_id = start_id + num_experts_per_partition - 1
+
+        with torch.inference_mode():
+            out = ep_moe(
+                hidden_states=a,
+                w1=w1,
+                w2=w2,
+                router_logits=score,
+                top_k=topk,
+                renormalize=False,
+                use_fp8_w8a8=True,
+                w1_scale_inv=w1_s,
+                w2_scale_inv=w2_s,
+                block_shape=block_size,
+                num_experts=E,
+                num_experts_per_partition=num_experts_per_partition,
+                start_expert_id=start_id,
+                end_expert_id=end_id,
+            )
+            ref_out = ep_moe(
+                hidden_states=a,
+                w1=w1_ref,
+                w2=w2_ref,
+                router_logits=score,
+                top_k=topk,
+                renormalize=False,
+                use_fp8_w8a8=False,
+                w1_scale_inv=None,
+                w2_scale_inv=None,
+                block_shape=None,
+                num_experts=E,
+                num_experts_per_partition=num_experts_per_partition,
+                start_expert_id=start_id,
+                end_expert_id=end_id,
+            )
+        self.assertTrue(
+            torch.mean(torch.abs(out.to(torch.float32) - ref_out.to(torch.float32)))
+            / (torch.mean(torch.abs(ref_out.to(torch.float32))) + 1e-6)
+            < 0.06
+        )
+
+    def test_w8a8_block_fp8_ep_moe(self):
+        for params in itertools.product(
+            self.M,
+            self.N,
+            self.K,
+            self.E,
+            self.ep_size,
+            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],
+                ep_size=params[4],
+                topk=params[5],
+                block_size=params[6],
+                dtype=params[7],
+                seed=params[8],
+            ):
+                self._w8a8_block_fp8_ep_moe(*params)
+            torch.cuda.empty_cache()
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)