Merge branch 'v0.15.1-dev-wm' into 'v0.15.1-dev'

适配w8a8 deepep,接入lightop版deepgemm

See merge request dcutoolkit/deeplearing/vllm!418

vllm/distributed/device_communicators/all2all.py

@@ -259,7 +259,7 @@ class DeepEPAll2AllManagerBase(All2AllManagerBase):
 
         # This is the DeepEP default. Stick to it till we can establish
         # reasonable defaults based on profiling.
-        self.num_sms = 20
+        self.num_sms = 30
 
     def get_handle(self, kwargs):
         raise NotImplementedError
@@ -292,16 +292,21 @@ class DeepEPHTAll2AllManager(DeepEPAll2AllManagerBase):
 
     def _make_all2all_kwargs(self) -> dict[Any, Any]:
         # Defaults for internode and intranode are taken from DeepEP tests.
-        num_nvl_bytes = envs.VLLM_DEEPEP_BUFFER_SIZE_MB * 1024 * 1024
+        #num_nvl_bytes = envs.VLLM_DEEPEP_BUFFER_SIZE_MB * 1024 * 1024
+        num_nvl_bytes = int(2e9/2)#1024 * 1024 * 1024
         num_rdma_bytes = None
         num_qps_per_rank = None
 
         if self.internode and not envs.VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE:
-            num_rdma_bytes = envs.VLLM_DEEPEP_BUFFER_SIZE_MB * 1024 * 1024
-            num_qps_per_rank = self.num_sms // 2
+            # num_rdma_bytes = envs.VLLM_DEEPEP_BUFFER_SIZE_MB * 1024 * 1024
+            # num_qps_per_rank = self.num_sms // 2
+            num_rdma_bytes = int(1e9/2) #1024 * 1024 * 1024
+            num_qps_per_rank = 30 #self.num_sms // 2
+            self.num_sms = 30
         else:
             num_rdma_bytes = 0
             num_qps_per_rank = 1
+            self.num_sms = 60
 
         assert num_rdma_bytes is not None
         assert num_qps_per_rank is not None

vllm/model_executor/layers/fused_moe/all2all_utils.py

@@ -162,6 +162,8 @@ def maybe_make_prepare_finalize(
             and quant_config.block_shape == DEEPEP_QUANT_BLOCK_SHAPE
         )
 
+        use_int8_dispatch = quant_config.quant_dtype == torch.int8
+
         prepare_finalize = DeepEPLLPrepareAndFinalize(
             handle,
             max_tokens_per_rank=moe.max_num_tokens,
@@ -170,6 +172,7 @@ def maybe_make_prepare_finalize(
             global_to_physical=global_to_physical,
             physical_to_global=physical_to_global,
             local_expert_global_ids=local_expert_global_ids,
+			use_int8_dispatch=use_int8_dispatch,
         )
     elif moe.use_mori_kernels:
         assert quant_config is not None

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -32,6 +32,16 @@ from vllm.utils.deep_gemm import (
 )
 from vllm.utils.math_utils import cdiv, round_up
 
+from vllm.utils.import_utils import has_deep_gemm
+
+from lightop import fuse_silu_mul_quant_ep
+if has_deep_gemm():
+    from deep_gemm import m_grouped_w8a8_gemm_nt_masked
+else:
+    from lightop import m_grouped_w8a8_gemm_nt_masked
+
+
+
 logger = init_logger(__name__)
 
 
@@ -267,6 +277,8 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         quant_config: FusedMoEQuantConfig,
         max_num_tokens: int,
         num_dispatchers: int,
+        N: int = -1,
+        K: int = -1,
     ):
         """
         max_num_tokens: Maximum number of tokens from a DP Rank
@@ -279,8 +291,12 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
             max_num_tokens=max_num_tokens,
             num_dispatchers=num_dispatchers,
         )
-        assert self.block_shape == get_mk_alignment_for_contiguous_layout()
-        assert self.quant_config.use_fp8_w8a8
+
+        if quant_config.use_fp8_w8a8:
+            assert self.block_shape == get_mk_alignment_for_contiguous_layout()
+
+        self.N = N
+        self.K = K
 
     @staticmethod
     def activation_format() -> mk.FusedMoEActivationFormat:
@@ -398,6 +414,7 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         workspace2: torch.Tensor,
         expert_tokens_meta: mk.ExpertTokensMetadata | None,
         apply_router_weight_on_input: bool,
+        use_nn_moe: bool | None = False,
     ):
         assert expert_tokens_meta is not None
         expert_num_tokens = expert_tokens_meta.expert_num_tokens
@@ -408,12 +425,15 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         a1q = hidden_states
         _, N, K = w1.size()
 
-        assert w2.size(1) == K
+        #assert w2.size(1) == K
 
         E, max_num_tokens, N, K, _ = self.moe_problem_size(
             hidden_states, w1, w2, topk_ids
         )
 
+        if self.N > 0:
+            N = self.N
+
         workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
 
         expected_m = self.estimate_expected_m(
@@ -422,25 +442,44 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
             topk=topk_ids.size(-1),
         )
 
-        fp8_m_grouped_gemm_nt_masked(
-            (a1q, a1q_scale),
-            (w1, self.w1_scale),
-            workspace1,
-            expert_num_tokens,
-            expected_m,
-        )
+        if self.quant_config.use_fp8_w8a16 or self.quant_config.use_fp8_w8a8:
+            fp8_m_grouped_gemm_nt_masked(
+                (a1q, a1q_scale),
+                (w1, self.w1_scale),
+                workspace1,
+                expert_num_tokens,
+                expected_m,
+            )
 
-        quant_scale_fmt = DeepGemmQuantScaleFMT.from_oracle()
-        a2q, a2q_scale = persistent_masked_m_silu_mul_quant(
-            workspace1,
-            expert_num_tokens,
-            quant_scale_fmt=quant_scale_fmt,
-        )
+            quant_scale_fmt = DeepGemmQuantScaleFMT.from_oracle()
+            a2q, a2q_scale = persistent_masked_m_silu_mul_quant(
+                workspace1,
+                expert_num_tokens,
+                quant_scale_fmt=quant_scale_fmt,
+            )
 
-        fp8_m_grouped_gemm_nt_masked(
-            (a2q, a2q_scale),
-            (w2, self.w2_scale),
-            output,
-            expert_num_tokens,
-            expected_m,
-        )
+            fp8_m_grouped_gemm_nt_masked(
+                (a2q, a2q_scale),
+                (w2, self.w2_scale),
+                output,
+                expert_num_tokens,
+                expected_m,
+            )
+        elif self.quant_config.use_int8_w8a8:
+            m_grouped_w8a8_gemm_nt_masked((a1q, a1q_scale), 
+                                  (w1, self.w1_scale),
+                                    workspace1,
+                                    expert_num_tokens, 
+                                    expected_m,
+                                    )
+
+            assert expert_num_tokens is not None
+
+            a2q, a2q_scale = fuse_silu_mul_quant_ep(workspace1, expert_num_tokens)
+            m_grouped_w8a8_gemm_nt_masked((a2q, a2q_scale),
+                                          (w2, self.w2_scale),
+                                          output,
+                                          expert_num_tokens,
+                                          expected_m)
+        else:
+            raise ValueError(f"Unsupported dtype {self.quant_config.quant_dtype}")

vllm/model_executor/layers/fused_moe/config.py

@@ -87,7 +87,7 @@ def _quant_flags_to_group_shape(
     """
     a_shape: GroupShape | None
     w_shape: GroupShape | None
-    if block_shape is not None:
+    if block_shape is not None and quant_dtype!=torch.int8:
         assert not per_act_token_quant
         assert not per_out_ch_quant
         # TODO(bnell): this is not quite right for activations since first
@@ -211,10 +211,10 @@ class FusedMoEQuantConfig:
     _w1: FusedMoEQuantDesc
     _w2: FusedMoEQuantDesc
 
-    def __post_init__(self):
-        assert not self.per_act_token_quant or self.block_shape is None, (
-            "illegal quantization"
-        )
+    # def __post_init__(self):
+    #     assert not self.per_act_token_quant or self.block_shape is None, (
+    #         "illegal quantization"
+    #     )
 
     #
     # Convenience accessors for various properties.
@@ -246,6 +246,9 @@ class FusedMoEQuantConfig:
 
     @property
     def block_shape(self) -> list[int] | None:
+        if self.use_int8_w8a8:
+            return [256, 256]
+
         if (
             self._a1.shape is not None
             and self._a1.shape != GroupShape.PER_TENSOR
@@ -569,7 +572,7 @@ def int8_w8a8_moe_quant_config(
         a2_scale=a2_scale,
         per_act_token_quant=per_act_token_quant,
         per_out_ch_quant=False,
-        block_shape=None,
+        block_shape=[256, 256],
     )
 
 

vllm/model_executor/layers/fused_moe/deep_gemm_moe.py

@@ -37,6 +37,12 @@ from vllm.utils.deep_gemm import (
 )
 from vllm.utils.import_utils import has_deep_gemm
 
+from lightop import fuse_silu_mul_quant
+if has_deep_gemm():
+    from deep_gemm import m_grouped_i8_gemm_nt_contiguous
+else:
+    from lightop import m_grouped_w8a8_gemm_nt_contig_asm as m_grouped_i8_gemm_nt_contiguous
+
 logger = init_logger(__name__)
 
 
@@ -113,12 +119,22 @@ def _valid_deep_gemm(
 
 
 class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
-    def __init__(self, moe_config: FusedMoEConfig, quant_config: FusedMoEQuantConfig):
+    def __init__(self, 
+                 moe_config: FusedMoEConfig, 
+                 quant_config: FusedMoEQuantConfig,
+                 N: int = -1,
+                 K: int = -1,):
         super().__init__(moe_config=moe_config, quant_config=quant_config)
-        assert quant_config.block_shape == get_mk_alignment_for_contiguous_layout()
-        assert quant_config.quant_dtype == torch.float8_e4m3fn
-        assert not quant_config.per_act_token_quant
-        assert not quant_config.per_out_ch_quant
+
+        if quant_config.use_fp8_w8a8 or quant_config.use_fp8_w8a16:
+            assert quant_config.block_shape == get_mk_alignment_for_contiguous_layout()
+            assert quant_config.quant_dtype == torch.float8_e4m3fn
+            assert not quant_config.per_act_token_quant
+            assert not quant_config.per_out_ch_quant
+
+        self.N = N
+        self.K = K
+
 
     @staticmethod
     def activation_format() -> mk.FusedMoEActivationFormat:
@@ -241,6 +257,7 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         workspace2: torch.Tensor,
         expert_tokens_meta: mk.ExpertTokensMetadata | None,
         apply_router_weight_on_input: bool,
+        use_nn_moe: bool | None = False,
     ):
         assert a1q_scale is not None
         assert a2_scale is None
@@ -255,19 +272,24 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         if global_num_experts == -1:
             global_num_experts = local_num_experts
 
-        assert w2.size(1) == K
+        #assert w2.size(1) == K
+        if self.N > 0:
+            N = self.N
+            K = self.K
 
+        use_fp8 = self.quant_config.use_fp8_w8a16 or self.quant_config.use_fp8_w8a8
         M_sum = compute_aligned_M(
             M=topk_ids.size(0),
             num_topk=topk_ids.size(1),
             local_num_experts=local_num_experts,
-            alignment=get_mk_alignment_for_contiguous_layout()[0],
+            alignment=get_mk_alignment_for_contiguous_layout()[0] if use_fp8 else self.block_shape[0],
             expert_tokens_meta=expert_tokens_meta,
         )
 
         a1q_perm = _resize_cache(
-            workspace13.view(dtype=torch.float8_e4m3fn), (M_sum, K)
+            workspace13.view(dtype=torch.float8_e4m3fn if use_fp8 else a1q.dtype), (M_sum, K)
         )
+
         a1q, a1q_scale, expert_ids, inv_perm = deepgemm_moe_permute(
             aq=a1q,
             aq_scale=a1q_scale,
@@ -280,22 +302,37 @@ class DeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         assert a1q.size(0) == M_sum
 
         mm1_out = _resize_cache(workspace2, (M_sum, N))
-        m_grouped_fp8_gemm_nt_contiguous(
-            (a1q, a1q_scale), (w1, self.w1_scale), mm1_out, expert_ids
-        )
 
-        activation_out_dim = self.adjust_N_for_activation(N, activation)
-        quant_out = _resize_cache(
-            workspace13.view(dtype=torch.float8_e4m3fn), (M_sum, activation_out_dim)
-        )
-        a2q, a2q_scale = self._act_mul_quant(
-            input=mm1_out.view(-1, N), output=quant_out, activation=activation
-        )
+        if use_fp8:
+            m_grouped_fp8_gemm_nt_contiguous(
+                (a1q, a1q_scale), (w1, self.w1_scale), mm1_out, expert_ids
+            )
+
+            activation_out_dim = self.adjust_N_for_activation(N, activation)
+            quant_out = _resize_cache(
+                workspace13.view(dtype=torch.float8_e4m3fn), (M_sum, activation_out_dim)
+            )
+            a2q, a2q_scale = self._act_mul_quant(
+                input=mm1_out.view(-1, N), output=quant_out, activation=activation
+            )
+
+            mm2_out = _resize_cache(workspace2, (M_sum, K))
+            m_grouped_fp8_gemm_nt_contiguous(
+                (a2q, a2q_scale), (w2, self.w2_scale), mm2_out, expert_ids
+            )
+        elif self.quant_config.use_int8_w8a8:
+            m_grouped_i8_gemm_nt_contiguous(
+            (a1q, a1q_scale), (w1, self.w1_scale), mm1_out, expert_ids)
+
+            a2q, a2q_scale = fuse_silu_mul_quant(mm1_out)
+            #a2q, a2q_scale = fuse_silu_mul_quant(input=mm1_out, expert_ids=expert_ids)
+            mm2_out = _resize_cache(workspace2, (M_sum, K))
+
+            m_grouped_i8_gemm_nt_contiguous(
+                (a2q, a2q_scale), (w2, self.w2_scale), mm2_out, expert_ids)
+        else:
+            raise ValueError(f"Unsupported dtype {self.quant_config.quant_dtype}")
 
-        mm2_out = _resize_cache(workspace2, (M_sum, K))
-        m_grouped_fp8_gemm_nt_contiguous(
-            (a2q, a2q_scale), (w2, self.w2_scale), mm2_out, expert_ids
-        )
 
         if apply_router_weight_on_input:
             topk_weights = torch.ones_like(topk_weights)

vllm/model_executor/layers/fused_moe/deep_gemm_utils.py

@@ -13,6 +13,8 @@ from vllm.triton_utils import tl, triton
 from vllm.utils.deep_gemm import get_mk_alignment_for_contiguous_layout
 from vllm.utils.math_utils import round_up
 
+from lightop import op
+
 
 def expert_num_tokens_round_up_and_sum(
     expert_num_tokens: torch.Tensor, alignment: int
@@ -57,6 +59,12 @@ def round_up_128(x: int) -> int:
     return ((x + y - 1) // y) * y
 
 
+@triton.jit
+def round_up_256(x: int) -> int:
+    y = 256
+    return ((x + y - 1) // y) * y
+
+
 @triton.jit
 def _fwd_kernel_ep_scatter_1(
     num_recv_tokens_per_expert,
@@ -74,26 +82,27 @@ def _fwd_kernel_ep_scatter_1(
         mask=offset_cumsum < num_experts,
         other=0,
     )
-    tokens_per_expert = round_up_128(tokens_per_expert)
+    #tokens_per_expert = round_up_128(tokens_per_expert)
+    tokens_per_expert = round_up_256(tokens_per_expert)
     cumsum = tl.cumsum(tokens_per_expert) - tokens_per_expert
+
+    #if cur_expert == 0:
     tl.store(expert_start_loc + offset_cumsum, cumsum, mask=offset_cumsum < num_experts)
 
+    tl.debug_barrier()
+
+    #cur_expert_start = cumsum[cur_expert]
     cur_expert_start = tl.load(expert_start_loc + cur_expert)
     cur_expert_token_num = tl.load(num_recv_tokens_per_expert + cur_expert)
 
     m_indices_start_ptr = m_indices + cur_expert_start
     off_expert = tl.arange(0, BLOCK_E)
 
-    # any rows in the per-expert aligned region that do not correspond to
-    # real tokens are left untouched here and should remain initialized to
-    # -1 so DeepGEMM can skip them
     for start_m in tl.range(0, cur_expert_token_num, BLOCK_E, num_stages=4):
-        offs = start_m + off_expert
-        mask = offs < cur_expert_token_num
         tl.store(
-            m_indices_start_ptr + offs,
+            m_indices_start_ptr + start_m + off_expert,
             cur_expert,
-            mask=mask,
+            mask=start_m + off_expert < cur_expert_token_num
         )
 
 
@@ -133,26 +142,32 @@ def _fwd_kernel_ep_scatter_2(
     offset_in = tl.arange(0, HIDDEN_SIZE_PAD)
     mask = offset_in < HIDDEN_SIZE
 
-    offset_in_s = tl.arange(0, SCALE_HIDDEN_SIZE_PAD)
-    mask_s = offset_in_s < SCALE_HIDDEN_SIZE
+    index_in_s = tl.arange(0, SCALE_HIDDEN_SIZE_PAD)
+    mask_s = index_in_s < SCALE_HIDDEN_SIZE
 
-    for token_id in range(start_token_id, total_token_num, grid_num):
+    for token_id_int32 in range(start_token_id, total_token_num, grid_num):
+        token_id = token_id_int32.to(tl.int64)
         to_copy = tl.load(recv_x + token_id * recv_x_stride0 + offset_in, mask=mask)
         to_copy_s = tl.load(
-            recv_x_scale + token_id * recv_x_scale_stride0 + offset_in_s, mask=mask_s
+            recv_x_scale
+            + token_id * recv_x_scale_stride0
+            + index_in_s * recv_x_scale_stride1,
+            mask=mask_s,
         )
 
-        for topk_index in tl.range(0, topk_num, 1, num_stages=4):
+        for topk_idx_int32 in tl.range(0, topk_num, 1, num_stages=4):
+            topk_index = topk_idx_int32.to(tl.int64)
             expert_id = tl.load(recv_topk + token_id * recv_topk_stride0 + topk_index)
 
             if HAS_EXPERT_MAP:
                 expert_id = apply_expert_map(expert_id, expert_map)
-
             if expert_id >= 0:
-                dest_token_index = tl.atomic_add(expert_start_loc + expert_id, 1)
+                dest_token_index_int32 = tl.atomic_add(expert_start_loc + expert_id, 1)
+                dest_token_index = dest_token_index_int32.to(tl.int64)
+
                 tl.store(
                     output_index + token_id * output_index_stride0 + topk_index,
-                    dest_token_index,
+                    dest_token_index_int32,
                 )
                 output_tensor_ptr = (
                     output_tensor + dest_token_index * output_tensor_stride0
@@ -161,7 +176,11 @@ def _fwd_kernel_ep_scatter_2(
                     output_tensor_scale + dest_token_index * output_tensor_scale_stride0
                 )
                 tl.store(output_tensor_ptr + offset_in, to_copy, mask=mask)
-                tl.store(output_tensor_scale_ptr + offset_in_s, to_copy_s, mask=mask_s)
+                tl.store(
+                    output_tensor_scale_ptr + index_in_s * output_tensor_scale_stride1,
+                    to_copy_s,
+                    mask=mask_s,
+                )
 
 
 @torch.no_grad()
@@ -177,58 +196,71 @@ def ep_scatter(
     m_indices: torch.Tensor,
     output_index: torch.Tensor,
 ):
-    BLOCK_E = 128  # token num of per expert is aligned to 128
-    BLOCK_D = 128  # block size of quantization
+    # BLOCK_E = 128  # token num of per expert is aligned to 128
+    # BLOCK_D = 128  # block size of quantization
+    BLOCK_E = 256  # token num of per expert is aligned to 256
     num_warps = 8
     num_experts = num_recv_tokens_per_expert.shape[0]
     hidden_size = recv_x.shape[1]
+    scale_hidden_size = recv_x_scale.shape[-1]
     # grid = (triton.cdiv(hidden_size, BLOCK_D), num_experts)
     grid = num_experts
 
     assert m_indices.shape[0] % BLOCK_E == 0
 
-    _fwd_kernel_ep_scatter_1[(grid,)](
-        num_recv_tokens_per_expert,
-        expert_start_loc,
-        m_indices,
-        num_experts=num_experts,
-        num_warps=num_warps,
-        BLOCK_E=BLOCK_E,
-        BLOCK_EXPERT_NUM=triton.next_power_of_2(num_experts),
-    )
+    if hasattr(op, "ep_scatter"):
+        op.ep_scatter(
+            recv_x, recv_x_scale,
+            recv_topk, expert_map,
+            num_recv_tokens_per_expert, 
+            output_tensor, output_tensor_scale, m_indices, output_index,
+            num_experts, BLOCK_E
+        )
+    else:
+        _fwd_kernel_ep_scatter_1[(grid,)](
+            num_recv_tokens_per_expert,
+            expert_start_loc,
+            m_indices,
+            num_experts=num_experts,
+            num_warps=num_warps,
+            BLOCK_E=BLOCK_E,
+            BLOCK_EXPERT_NUM=triton.next_power_of_2(num_experts),
+        )
 
-    grid = min(recv_topk.shape[0], 1024 * 8)
-
-    _fwd_kernel_ep_scatter_2[(grid,)](
-        recv_topk.shape[0],
-        expert_start_loc,
-        recv_x,
-        recv_x.stride(0),
-        recv_x.stride(1),
-        recv_x_scale,
-        recv_x_scale.stride(0),
-        recv_x_scale.stride(1),
-        recv_topk,
-        recv_topk.stride(0),
-        recv_topk.stride(1),
-        output_tensor,
-        output_tensor.stride(0),
-        output_tensor.stride(1),
-        output_tensor_scale,
-        output_tensor_scale.stride(0),
-        output_tensor_scale.stride(1),
-        output_index,
-        output_index.stride(0),
-        output_index.stride(1),
-        topk_num=recv_topk.shape[1],
-        expert_map=expert_map,
-        HAS_EXPERT_MAP=expert_map is not None,
-        num_warps=num_warps,
-        HIDDEN_SIZE=hidden_size,
-        HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size),
-        SCALE_HIDDEN_SIZE=hidden_size // BLOCK_D,
-        SCALE_HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size // BLOCK_D),
-    )
+        grid = min(recv_topk.shape[0], 1024 * 8)
+
+        _fwd_kernel_ep_scatter_2[(grid,)](
+            recv_topk.shape[0],
+            expert_start_loc,
+            recv_x,
+            recv_x.stride(0),
+            recv_x.stride(1),
+            recv_x_scale,
+            recv_x_scale.stride(0),
+            recv_x_scale.stride(1),
+            recv_topk,
+            recv_topk.stride(0),
+            recv_topk.stride(1),
+            output_tensor,
+            output_tensor.stride(0),
+            output_tensor.stride(1),
+            output_tensor_scale,
+            output_tensor_scale.stride(0),
+            output_tensor_scale.stride(1),
+            output_index,
+            output_index.stride(0),
+            output_index.stride(1),
+            topk_num=recv_topk.shape[1],
+            expert_map=expert_map,
+            HAS_EXPERT_MAP=expert_map is not None,
+            num_warps=num_warps,
+            HIDDEN_SIZE=hidden_size,
+            HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size),
+            # SCALE_HIDDEN_SIZE=hidden_size // BLOCK_D,
+            # SCALE_HIDDEN_SIZE_PAD=triton.next_power_of_2(hidden_size // BLOCK_D),
+            SCALE_HIDDEN_SIZE=scale_hidden_size,
+            SCALE_HIDDEN_SIZE_PAD=triton.next_power_of_2(scale_hidden_size),
+        )
     return
 
 
@@ -255,25 +287,34 @@ def _fwd_kernel_ep_gather(
     HAS_EXPERT_MAP: tl.constexpr,
     BLOCK_D: tl.constexpr,
 ):
-    cur_block = tl.program_id(0)
-    start_cur_token = tl.program_id(1)
+    cur_block_int32 = tl.program_id(0)
+    cur_block = cur_block_int32.to(tl.int64)
+
+    start_cur_token_int32 = tl.program_id(1)
+
     grid_num = tl.num_programs(1)
 
-    for cur_token in range(start_cur_token, total_token_num, grid_num):
+    for cur_token_int32 in range(start_cur_token_int32, total_token_num, grid_num):
+        cur_token = cur_token_int32.to(tl.int64)
+
         off_d = tl.arange(0, BLOCK_D)
         accumulator = tl.zeros([BLOCK_D], dtype=tl.float32)
-        for topk_index in range(0, topk_num):
+
+        for topk_index_int32 in range(0, topk_num):
+            topk_index = topk_index_int32.to(tl.int64)
+
             expert_id = tl.load(
                 recv_topk_ids + cur_token * recv_topk_ids_stride0 + topk_index
             )
-
             if HAS_EXPERT_MAP:
                 expert_id = apply_expert_map(expert_id, expert_map)
 
             if expert_id >= 0:
-                source_token_index = tl.load(
+                source_token_index_int32 = tl.load(
                     input_index + cur_token * input_index_stride0 + topk_index
                 )
+                source_token_index = source_token_index_int32.to(tl.int64)
+
                 acc_weight = tl.load(
                     recv_topk_weight + cur_token * recv_topk_weight_stride0 + topk_index
                 )
@@ -350,7 +391,8 @@ def deepgemm_moe_permute(
     H = aq.size(1)
     device = aq.device
 
-    block_m, block_k = get_mk_alignment_for_contiguous_layout()
+    #block_m, block_k = get_mk_alignment_for_contiguous_layout()
+    block_m = 256
 
     M_sum = compute_aligned_M(
         M=topk_ids.size(0),
@@ -368,8 +410,11 @@ def deepgemm_moe_permute(
     if aq_out is None:
         aq_out = torch.empty((M_sum, H), device=device, dtype=aq.dtype)
 
+    # aq_scale_out = torch.empty(
+    #     (M_sum, H // block_k), device=device, dtype=torch.float32
+    # )
     aq_scale_out = torch.empty(
-        (M_sum, H // block_k), device=device, dtype=torch.float32
+        (M_sum, aq_scale.shape[-1]), device=device, dtype=torch.float32
     )
 
     # DeepGEMM uses negative values in m_indices (here expert_ids) to mark

vllm/model_executor/layers/fused_moe/deepep_ht_prepare_finalize.py

@@ -225,7 +225,7 @@ class DeepEPHTPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         # DeepEP kernels only support dispatching block-quantized
         # activation scales.
         # Dispatch in bfloat16 and quantize afterwards
-        if not quant_config.is_block_quantized:
+        if not quant_config.is_block_quantized and not quant_config.is_per_act_token:
             # Quantize after dispatch.
             expert_x_scale = None
             if expert_x.numel() != 0:
@@ -266,7 +266,7 @@ class DeepEPHTPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
             )
             a1 = a1 * topk_weights.to(a1.dtype)
 
-        if quant_config.is_block_quantized:
+        if quant_config.is_block_quantized or quant_config.is_per_act_token:
             # Quant and Dispatch
             a1q, a1q_scale = moe_kernel_quantize_input(
                 a1,

vllm/model_executor/layers/fused_moe/deepep_ll_prepare_finalize.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Callable
+from typing import Callable, Optional
 
 import deep_ep
 import torch
@@ -91,12 +92,14 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         global_to_physical: torch.Tensor | None = None,
         physical_to_global: torch.Tensor | None = None,
         local_expert_global_ids: torch.Tensor | None = None,
+        use_int8_dispatch: bool = False
     ):
         super().__init__()
 
         self.buffer = buffer
         self.max_tokens_per_rank = max_tokens_per_rank
         self.use_fp8_dispatch = use_fp8_dispatch
+        self.use_int8_dispatch = use_int8_dispatch
         # The dispatch function returns a handle that the combine function
         # requires. We store the handle here so it is available to the
         # combine function.
@@ -168,6 +171,7 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         x: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
         a1_dtype: torch.dtype,
         quant_config: FusedMoEQuantConfig,
+        expert_num_tokens: Optional[torch.Tensor]= None,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         if self.use_fp8_dispatch:
             block_k = (
@@ -183,6 +187,9 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
             # Dequant to get back the tokens in the datatype we dispatched in.
             x_fp8, x_scales = x
             x = dequant_fp8(x_fp8, x_scales).to(dtype=a1_dtype)
+        elif self.use_int8_dispatch:
+            x, x_scales = x
+            return x, x_scales
 
         assert isinstance(x, (torch.Tensor, tuple))
         q_dtype = quant_config.quant_dtype
@@ -214,7 +221,8 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
             num_experts, max_tokens, hidden_dim = x.size()
 
             # TODO (varun): Optimization - Use a batched version of quant
-            x = x.view((-1, hidden_dim))
+            if expert_num_tokens is None:
+                x = x.view((-1, hidden_dim))
             x, x_scales = moe_kernel_quantize_input(
                 x,
                 quant_config.a1_scale,
@@ -294,7 +302,8 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
             dispatch_topk_ids,
             self.max_tokens_per_rank,
             num_experts,
-            use_fp8=self.use_fp8_dispatch,
+            use_fp8=self.use_fp8_dispatch or self.use_int8_dispatch,
+            use_int8=self.use_int8_dispatch,
             round_scale=self.use_ue8m0_dispatch,
             use_ue8m0=self.use_ue8m0_dispatch,
             **(dict(use_nvfp4=True) if use_nvfp4 else dict()),
@@ -327,7 +336,7 @@ class DeepEPLLPrepareAndFinalize(mk.FusedMoEPrepareAndFinalize):
         a1_dtype: torch.dtype,
         quant_config: FusedMoEQuantConfig,
     ) -> mk.PrepareResultType:
-        expert_x, expert_x_scale = self._do_quant(expert_x, a1_dtype, quant_config)
+        expert_x, expert_x_scale = self._do_quant(expert_x, a1_dtype, quant_config, expert_num_tokens)
 
         expert_tokens_meta = mk.ExpertTokensMetadata(
             expert_num_tokens=expert_num_tokens, expert_num_tokens_cpu=None

vllm/model_executor/layers/fused_moe/fused_moe_modular_method.py

@@ -54,6 +54,8 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
                 old_quant_method.select_gemm_impl(prepare_finalize, moe_layer),
                 shared_experts,
                 moe_parallel_config=moe_layer.moe_parallel_config,
+                N=old_quant_method.N if hasattr(old_quant_method, "N") else -1,
+                K=old_quant_method.K if hasattr(old_quant_method, "K") else -1,
             ),
         )
 
@@ -95,6 +97,7 @@ class FusedMoEModularMethod(FusedMoEMethodBase, CustomOp):
         x: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
+        use_nn_moe: bool | None = False,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         return self.fused_experts(
             hidden_states=x,

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -785,6 +785,21 @@ def _slice_scales(
     return None
 
 
+_alt_stream: torch.cuda.Stream | None = None
+
+def alt_stream() -> torch.cuda.Stream | None:
+    """
+    Ensures aux_stream is initialized only once
+    """
+    global _alt_stream
+
+    # TODO: validate this works properly on ROCm platform.
+    if _alt_stream is None:
+        _alt_stream = torch.cuda.Stream()
+
+    return _alt_stream
+
+
 @final
 class FusedMoEModularKernel(torch.nn.Module):
     """
@@ -805,6 +820,8 @@ class FusedMoEModularKernel(torch.nn.Module):
         fused_experts: FusedMoEPermuteExpertsUnpermute,
         shared_experts: torch.nn.Module | None = None,
         moe_parallel_config: FusedMoEParallelConfig | None = None,
+        N: int = -1,
+        K: int = -1,
     ):
         super().__init__()
         self.prepare_finalize = prepare_finalize
@@ -831,6 +848,12 @@ class FusedMoEModularKernel(torch.nn.Module):
             f"{fused_experts.__class__.__name__}."
             f"{fused_experts.activation_format()}"
         )
+        self.N = N
+        self.K = K
+
+        if self.shared_experts is not None:
+          self.alt_stream = alt_stream()
+          self.alt_event = torch.cuda.Event()
 
     def _post_init_setup(self):
         """
@@ -1136,10 +1159,10 @@ class FusedMoEModularKernel(torch.nn.Module):
         _, M_full, N, K, top_k = self.fused_experts.moe_problem_size(
             a1q, w1, w2, topk_ids
         )
+        if self.N > 0:
+            N = self.N
+            K = self.K
 
-        if use_nn_moe:
-            N = w1.size(2)
-            
         num_chunks, CHUNK_SIZE = self._chunk_info(M_full)
 
         def input_chunk_range(chunk_idx: int) -> tuple[int, int]:
@@ -1244,37 +1267,46 @@ class FusedMoEModularKernel(torch.nn.Module):
             if self.shared_experts is not None:
                 shared_output = self.shared_experts(hidden_states)
         else:
-            finalize_ret = self.prepare_finalize.finalize_async(
-                output,
-                fused_out,
-                topk_weights,
-                topk_ids,
-                apply_router_weight_on_input,
-                self.fused_experts.finalize_weight_and_reduce_impl(),
-            )
+            self.alt_event.record()
             if self.shared_experts is not None:
                 shared_output = self.shared_experts(hidden_states)
 
-            # TODO(lucas): refactor this in the alternative schedules followup
-            # currently unpack if we have hook + receiver pair or just
-            # receiver (see finalize_async docstring)
-            hook, receiver = (
-                finalize_ret
-                if isinstance(finalize_ret, tuple)
-                else (None, finalize_ret)
-            )
+            current_stream = torch.cuda.current_stream()
+            with torch.cuda.stream(self.alt_stream):
+                self.alt_stream.wait_event(self.alt_event)
+
+                finalize_ret = self.prepare_finalize.finalize_async(
+                    output,
+                    fused_out,
+                    topk_weights,
+                    topk_ids,
+                    apply_router_weight_on_input,
+                    self.fused_experts.finalize_weight_and_reduce_impl(),
+                )
 
-            if hook is not None:
-                if dbo_enabled():
-                    # If DBO is being used, register the hook with the ubatch
-                    # context and call it in dbo_maybe_run_recv_hook instead of
-                    #  passing it to the receiver.
-                    dbo_register_recv_hook(hook)
-                    dbo_yield()
-                else:
-                    hook()
+                # TODO(lucas): refactor this in the alternative schedules followup
+                # currently unpack if we have hook + receiver pair or just
+                # receiver (see finalize_async docstring)
+                hook, receiver = (
+                    finalize_ret
+                    if isinstance(finalize_ret, tuple)
+                    else (None, finalize_ret)
+                )
+
+                if hook is not None:
+                    if dbo_enabled():
+                        # If DBO is being used, register the hook with the ubatch
+                        # context and call it in dbo_maybe_run_recv_hook instead of
+                        #  passing it to the receiver.
+                        dbo_register_recv_hook(hook)
+                        dbo_yield()
+                    else:
+                        hook()
+
+                receiver()
 
-            receiver()
+                self.alt_event.record()
+            current_stream.wait_event(self.alt_event)
 
         if self.shared_experts is None:
             return output

vllm/model_executor/layers/fused_moe/utils.py

@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import functools
 from math import prod
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
+from triton.language.extra import libdevice
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
@@ -154,11 +156,147 @@ def _fp8_quantize(
     return A, A_scale
 
 
+@triton.jit
+def _per_token_quant_int8_one_kernel_opt(
+    x_ptr,
+    xq_ptr,
+    scale_ptr,
+    stride_x,
+    stride_xq,
+    N, 
+    T_dim, 
+    has_tokens_per_expert: tl.constexpr,
+    tokens_per_expert_ptr, 
+    BLOCK: tl.constexpr
+):
+    row_id = tl.program_id(0)
+
+    if has_tokens_per_expert:
+        e = row_id // T_dim
+        t = row_id % T_dim
+        
+        num_valid_tokens_for_e = tl.load(tokens_per_expert_ptr + e)
+        
+        if t >= num_valid_tokens_for_e:
+            return
+
+    cols = tl.arange(0, BLOCK)
+    mask = cols < N
+
+    x = tl.load(x_ptr + row_id * stride_x + cols, mask=mask,
+                other=0.0).to(tl.float32)
+    absmax = tl.maximum(tl.max(tl.abs(x)), 1e-10)
+    scale_x = absmax / 127
+    x_q = x * (127 / absmax)
+    x_q = libdevice.nearbyint(x_q).to(tl.int8)
+
+    tl.store(xq_ptr + row_id * stride_xq + cols, x_q, mask=mask)
+    tl.store(scale_ptr + row_id, scale_x)
+
+@triton.jit
+def _per_token_quant_int8_kernel_opt(
+    x_ptr,
+    xq_ptr,
+    scale_ptr,
+    stride_x,
+    stride_xq,
+    N, 
+    E_dim,
+    T_dim,
+    has_tokens_per_expert: tl.constexpr,
+    tokens_per_expert_ptr, 
+    BLOCK: tl.constexpr
+):
+    token_idx_start = tl.program_id(0)
+    grid_size = tl.num_programs(0) 
+    num_total_tokens = E_dim * T_dim
+
+    for token_idx in range(token_idx_start, num_total_tokens, grid_size):
+        
+        is_valid_token = True
+        if has_tokens_per_expert:
+            e = token_idx // T_dim
+            t = token_idx % T_dim
+            
+            num_valid_tokens_for_e = tl.load(tokens_per_expert_ptr + e)
+            
+            if t >= num_valid_tokens_for_e:
+                is_valid_token = False 
+
+        if is_valid_token:
+            cols = tl.arange(0, BLOCK)
+            mask = cols < N
+
+            x = tl.load(x_ptr + token_idx * stride_x + cols, mask=mask,
+                        other=0.0).to(tl.float32)
+            absmax = tl.maximum(tl.max(tl.abs(x)), 1e-10)
+            scale_x = absmax / 127
+            x_q = x * (127 / absmax)
+            x_q = libdevice.nearbyint(x_q).to(tl.int8)
+
+            tl.store(xq_ptr + token_idx * stride_xq + cols, x_q, mask=mask)
+            tl.store(scale_ptr + token_idx, scale_x)
+
+
+def per_token_quant_int8_triton_opt(x: torch.Tensor,
+                                    tokens_per_expert: Optional[torch.Tensor] = None):
+    if x.dim() != 3:
+        raise ValueError(f"Input must be 3D [E, T, H], but got {x.shape}")
+    E, T, H = x.shape
+    N = H
+    
+    x_q = torch.empty_like(x, device=x.device, dtype=torch.int8)
+    scales = torch.empty(x.shape[:-1] + (1, ),
+                         device=x.device,
+                         dtype=torch.float32)
+    BLOCK = triton.next_power_of_2(N)
+    num_warps = min(max(BLOCK // 256, 1), 8)
+    if T >= 4096:
+        num_warps = 1
+    
+    num_tokens = E * T
+    grid_opt = num_tokens
+    if E == 16 and T >= 1024 :
+        grid_opt = max(1, num_tokens // (T // 256))
+        _per_token_quant_int8_kernel_opt[(grid_opt, )](
+            x,
+            x_q,
+            scales,
+            stride_x=x.stride(-2),
+            stride_xq=x_q.stride(-2),
+            N=N,
+            E_dim=E,
+            T_dim=T,
+            has_tokens_per_expert=tokens_per_expert is not None,
+            tokens_per_expert_ptr=tokens_per_expert,
+            BLOCK=BLOCK,
+            num_warps=num_warps,
+            num_stages=1,
+        )
+    else:
+        _per_token_quant_int8_one_kernel_opt[(grid_opt, )](
+            x,
+            x_q,
+            scales,
+            stride_x=x.stride(-2),
+            stride_xq=x_q.stride(-2),
+            N=N,
+            T_dim=T,
+            has_tokens_per_expert=tokens_per_expert is not None,
+            tokens_per_expert_ptr=tokens_per_expert,
+            BLOCK=BLOCK,
+            num_warps=num_warps,
+            num_stages=1,
+        )
+    return x_q, scales
+
+
 def _int8_quantize(
     A: torch.Tensor,
     A_scale: torch.Tensor | None,
     per_act_token: bool,
     block_shape: list[int] | None = None,
+    expert_num_tokens: Optional[torch.Tensor] = None,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
     Perform int8 quantization on the inputs.  If a block_shape
@@ -168,9 +306,12 @@ def _int8_quantize(
     # If weights are per-channel (per_channel_quant=True), then
     # activations apply per-token quantization. Otherwise, assume
     # activation tensor-wise fp8/int8 quantization, dynamic or static
-    if block_shape is None:
+    if block_shape is None or per_act_token:
         assert per_act_token, "int8 quantization only supports block or channel-wise"
-        A, A_scale = per_token_quant_int8(A)
+        if expert_num_tokens is None:   
+            A, A_scale = per_token_quant_int8(A)
+        else:
+            A, A_scale = per_token_quant_int8_triton_opt(A, expert_num_tokens)
     else:
         assert not per_act_token
         assert len(block_shape) == 2

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py

@@ -6,15 +6,26 @@ from enum import Enum
 from typing import Callable, Optional
 import torch
 from compressed_tensors.quantization import (QuantizationStrategy)
+
+import vllm.envs as envs
+from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from torch.nn.parameter import Parameter
+from vllm.distributed import get_ep_group, get_dp_group
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE, FusedMoEActivationFormat, FusedMoEMethodBase,
     FusedMoeWeightScaleSupported, FusedMoEConfig)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import(
-    get_w8a8_int8_marlin_weights)
-from vllm.model_executor.layers.fused_moe.config import (FusedMoEQuantConfig)
+    get_w8a8_int8_marlin_weights, w8a8_nt_kpack2_marlin_weight)
+from vllm.model_executor.layers.fused_moe.config import (FusedMoEQuantConfig, int8_w8a8_moe_quant_config)
+from vllm.model_executor.layers.fused_moe import (
+    FusedMoE,
+    FusedMoEMethodBase,
+    FusedMoEPermuteExpertsUnpermute,
+    FusedMoEPrepareAndFinalize,
+    FusedMoeWeightScaleSupported,
+)
 try:
     from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin
 except Exception:
@@ -74,14 +85,38 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
             raise ValueError(
                 "For INT8 Fused MoE layers, we require channelwise, "
                 "dynamic per token quantization. Found static input scales.")
-
+        
+        vllm_config = get_current_vllm_config()
+        parallel_config = vllm_config.parallel_config
+        self.dp_size = get_dp_group().world_size
+        self.use_deepep = self.dp_size > 1 and parallel_config.enable_expert_parallel and \
+            (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput" or \
+             envs.VLLM_ALL2ALL_BACKEND == "deepep_low_latency" or \
+             envs.VLLM_ALL2ALL_BACKEND == "deepep_auto")
+        
+        if self.use_deepep:
+            all2all_manager = get_ep_group().device_communicator.all2all_manager
+            assert all2all_manager is not None
+            self.num_dispatchers = all2all_manager.world_size
+    
     def get_fused_moe_quant_config(
-            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
-        return None
+        self, layer: torch.nn.Module
+    ) -> FusedMoEQuantConfig | None:
+        return int8_w8a8_moe_quant_config(
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            per_act_token_quant=True,
+        )
         
     def create_weights(self, layer: torch.nn.Module, num_experts: int,
                        hidden_size: int, intermediate_size_per_partition: int,
                        params_dtype: torch.dtype, **extra_weight_attrs):
+        
+        if self.use_deepep:
+            self.N = 2 * intermediate_size_per_partition
+            self.K = hidden_size
 
         params_dtype = torch.int8
 
@@ -133,14 +168,20 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         w1_marlin_list = []
         for ii in range(layer.w13_weight.shape[0]):
-            w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
+            if not self.use_deepep:
+                w1_marlin_in = get_w8a8_int8_marlin_weights(layer.w13_weight[ii])
+            else:
+                w1_marlin_in = w8a8_nt_kpack2_marlin_weight(layer.w13_weight[ii])
             w1_marlin_list.append(w1_marlin_in)
         w1_marlin = torch.stack(w1_marlin_list, dim=0)
 
         del w1_marlin_list
         w2_marlin_list = []
         for ii in range(layer.w2_weight.shape[0]):
-            w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
+            if not self.use_deepep:
+                w2_marlin_in = get_w8a8_int8_marlin_weights(layer.w2_weight[ii])
+            else:
+                w2_marlin_in = w8a8_nt_kpack2_marlin_weight(layer.w2_weight[ii])
             w2_marlin_list.append(w2_marlin_in)
         w2_marlin = torch.stack(w2_marlin_list, dim=0)
 
@@ -176,4 +217,40 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
             a1_scale=layer.w13_input_scale,
             a2_scale=layer.w2_input_scale,
             use_nn_moe=False,
-        )
\ No newline at end of file
+        )
+    
+    def select_gemm_impl(
+        self,
+        prepare_finalize: FusedMoEPrepareAndFinalize,
+        layer: torch.nn.Module,
+    ) -> FusedMoEPermuteExpertsUnpermute:
+        from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+            BatchedDeepGemmExperts,
+        )
+        from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
+            DeepGemmExperts,
+        )
+
+        if (
+            prepare_finalize.activation_format
+            == FusedMoEActivationFormat.BatchedExperts
+        ):
+            max_num_tokens_per_rank = prepare_finalize.max_num_tokens_per_rank()
+            assert max_num_tokens_per_rank is not None
+
+            logger.debug("BatchedDeepGemmExperts(%s)", self.__class__.__name__)
+            return BatchedDeepGemmExperts(
+                moe_config=self.moe,
+                max_num_tokens=max_num_tokens_per_rank,
+                num_dispatchers=prepare_finalize.num_dispatchers(),
+                quant_config=self.moe_quant_config,
+                N=self.N,
+                K=self.K
+            )
+
+        else:
+            logger.debug("DeepGemmExperts(%s)", self.__class__.__name__)
+            return DeepGemmExperts(moe_config=self.moe,
+                                   quant_config=self.moe_quant_config,
+                                   N=self.N,
+                                   K=self.K)
\ No newline at end of file

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -30,6 +30,19 @@ def get_w8a8_int8_marlin_weights(
     return weight
 
 
+def w8a8_nt_kpack2_marlin_weight(w8a8_w, # [size_n, size_k// 2 ]
+                                k_tile=16,
+                                n_tile=16, ):
+    assert w8a8_w.dtype == torch.int8, "w8a8_w 必须是 int8 类型"
+    size_n, size_k = w8a8_w.shape
+    assert size_n % k_tile == 0 and size_k % n_tile == 0, "k_tile / n_tile 必须能整除对应维度"
+
+    w8a8_w = w8a8_w.reshape((size_n // n_tile,  n_tile, size_k // k_tile, k_tile))
+    w8a8_w = w8a8_w.permute((0, 2, 1, 3)).contiguous()
+    w8a8_w = w8a8_w.reshape((size_n // k_tile, size_k * k_tile))
+    return w8a8_w
+
+
 def sparse_cutlass_supported() -> bool:
     if not current_platform.is_cuda():
         return False