支持w8a8 compile，注册自定义算子解决部分断图问题

python/sglang/srt/layers/attention/dcu_mla_backend.py

@@ -362,7 +362,7 @@ class DCUMLABackend(AttentionBackend):
         )
         return o
 
-    @torch._dynamo.disable()
+    @torch._dynamo.disable()  # NOTE: FP8 cache decode不支持compile
     def forward_decode(
         self,
         q: torch.Tensor,
@@ -417,7 +417,7 @@ class DCUMLABackend(AttentionBackend):
 
         return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
 
-    @torch._dynamo.disable()  # NOTE: untested
+    @torch._dynamo.disable()
     def forward_extend(
         self,
         q: torch.Tensor,

python/sglang/srt/layers/attention/lightop_concat.py

@@ -4,7 +4,7 @@ import warnings
 
 import torch
 
-from sglang.srt.utils import get_bool_env_var
+from sglang.srt.utils import get_bool_env_var, direct_register_custom_op
 
 _USE_OPT_CAT = get_bool_env_var("SGLANG_USE_OPT_CAT")
 
@@ -20,13 +20,48 @@ else:
     ds_cat = None
     
 
-def concat_decode_opt(A:torch.Tensor, B:torch.Tensor, dim:int):
-    assert dim==2 , "tensor dim must be 3 and concat dim must be 2"
+
+
+
+# TODO: 单独注册有些问题
+def ds_cat_wrapper(A: torch.Tensor,
+                   B: torch.Tensor,
+                   dim: int,
+                   mode: int) -> torch.Tensor:
     output_shape = list(A.shape)
     output_shape[dim] = A.shape[dim] + B.shape[dim]
     C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
-    mode = 0
-    if dim!=0 :
-        ds_cat( A, B, C, mode)
+    ds_cat(A, B, C, mode)
     return C
+    
+def ds_cat_fake(A: torch.Tensor,
+                B: torch.Tensor,
+                dim: int,
+                mode: int) -> torch.Tensor:
+    # 使用标准cat作为fake实现
+    return torch.cat([A, B], dim=dim)
+
+direct_register_custom_op(
+    op_name="ds_cat",
+    op_func=ds_cat_wrapper,
+    mutates_args=[],  # 没有修改参数，只有返回值
+    fake_impl=ds_cat_fake
+)
+
+def concat_decode_opt(A: torch.Tensor, B: torch.Tensor, dim: int):
+    assert dim == 2, "tensor dim must be 3 and concat dim must be 2"
+    mode = 0
+    if dim != 0:
+        return torch.ops.sglang.ds_cat(A, B, dim, mode)
     assert False, "not support"
+
+# def concat_decode_opt(A:torch.Tensor, B:torch.Tensor, dim:int):
+#     assert dim==2 , "tensor dim must be 3 and concat dim must be 2"
+#     output_shape = list(A.shape)
+#     output_shape[dim] = A.shape[dim] + B.shape[dim]  
+#     C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
+#     mode=0
+#     if dim!=0 :
+#         ds_cat(A, B, C, mode)
+#         return C
+#     assert False, "not support"

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

@@ -31,7 +31,7 @@ from sglang.srt.layers.quantization.fp8_kernel import (
 )
 from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config, W4AFp8MoEMethod
 from sglang.srt.single_batch_overlap import DownGemmOverlapArgs
-from sglang.srt.utils import ceil_div, dispose_tensor, get_bool_env_var, is_hip, is_npu
+from sglang.srt.utils import ceil_div, dispose_tensor, get_bool_env_var, is_hip, is_npu, direct_register_custom_op
 from sglang.srt.utils.offloader import get_offloader
 
 if TYPE_CHECKING:
@@ -57,6 +57,105 @@ if _use_aiter:
 
 logger = logging.getLogger(__name__)
 
+#------ custom op for lightop
+def m_grouped_w4a8_gemm_nt_masked_wrapper(
+    a0: torch.Tensor, a1: torch.Tensor,
+    b0: torch.Tensor, b1: torch.Tensor,
+    d: torch.Tensor,
+    masked_m: torch.Tensor,
+    expected_m_per_group: int
+    ) -> torch.Tensor:
+    return m_grouped_w4a8_gemm_nt_masked(
+            (a0, a1),
+            (b0, b1),
+            d,
+            masked_m,
+            expected_m_per_group,
+            config={"MODE": 1000,}
+        )
+def m_grouped_w4a8_gemm_nt_masked_fake(
+    a0: torch.Tensor, a1: torch.Tensor,
+    b0: torch.Tensor, b1: torch.Tensor,
+    d: torch.Tensor,
+    masked_m: torch.Tensor,
+    expected_m_per_group: int
+    ) -> torch.Tensor:
+    return d
+def m_grouped_w8a8_gemm_nt_masked_wrapper(
+    a0: torch.Tensor, a1: torch.Tensor,
+    b0: torch.Tensor, b1: torch.Tensor,
+    d: torch.Tensor,
+    masked_m: torch.Tensor,
+    expected_m_per_group: int
+    ) -> torch.Tensor:
+    return m_grouped_w8a8_gemm_nt_masked(
+            (a0, a1),
+            (b0, b1),
+            d,
+            masked_m,
+            expected_m_per_group,
+            config={"MODE": 1000,}
+        )
+def m_grouped_w8a8_gemm_nt_masked_fake(
+    a0: torch.Tensor, a1: torch.Tensor,
+    b0: torch.Tensor, b1: torch.Tensor,
+    d: torch.Tensor,
+    masked_m: torch.Tensor,
+    expected_m_per_group: int
+    ) -> torch.Tensor:
+    return d
+
+def fuse_silu_mul_quant_ep_wrapper(
+    input: torch.Tensor,
+    tokens_per_expert: Optional[torch.Tensor] = None,
+    num_local_tokens_tensor: Optional[torch.Tensor] = None,
+    topk:int=1,
+    expect_m:int=-1) -> tuple[torch.Tensor, torch.Tensor]:
+    return fuse_silu_mul_quant_ep(
+        input,
+        tokens_per_expert,
+        num_local_tokens_tensor,
+        topk,
+        expect_m
+    )
+    
+    
+def fuse_silu_mul_quant_ep_fake(
+    input: torch.Tensor,
+    tokens_per_expert: Optional[torch.Tensor] = None,
+    num_local_tokens_tensor: Optional[torch.Tensor] = None,
+    topk:int=1,
+    expect_m:int=-1) -> tuple[torch.Tensor, torch.Tensor]:
+    E, T, H = input.shape
+    d = H // 2
+    output = torch.empty(E, T, d, dtype=torch.int8, device=input.device)
+    scales = torch.empty((E, T, 1),
+                        device=input.device,
+                        dtype=torch.float32)
+    return output, scales
+
+
+direct_register_custom_op(
+    op_name="m_grouped_w4a8_gemm_nt_masked",
+    op_func=m_grouped_w4a8_gemm_nt_masked_wrapper,
+    mutates_args=[],
+    fake_impl=m_grouped_w4a8_gemm_nt_masked_fake
+)
+direct_register_custom_op(
+    op_name="m_grouped_w8a8_gemm_nt_masked",
+    op_func=m_grouped_w8a8_gemm_nt_masked_wrapper,
+    mutates_args=[],
+    fake_impl=m_grouped_w8a8_gemm_nt_masked_fake
+)
+direct_register_custom_op(
+    op_name="fuse_silu_mul_quant_ep",
+    op_func=fuse_silu_mul_quant_ep_wrapper,
+    mutates_args=[],
+    fake_impl=fuse_silu_mul_quant_ep_fake
+)
+
+#------
+
 
 # TODO(kaixih@nvidia): ideally we should merge this logic into
 # `fill_gateup_input_triton_kernel` to directly generate e8m0 scale.
@@ -815,23 +914,23 @@ class DeepEPMoE(EPMoE):
         gateup_output = torch.empty((num_groups, m, n1), device=hidden_states.device, dtype=torch.bfloat16)
 
         # ---- first GEMM ----
-        m_grouped_w4a8_gemm_nt_masked(
-            (q_a1_all, q_a1_scale),
-            (w13_weight, w13_scales),
+        torch.ops.sglang.m_grouped_w4a8_gemm_nt_masked(
+            q_a1_all, q_a1_scale,
+            w13_weight, w13_scales,
             gateup_output,
             masked_m,
             expected_m,
         )
 
-        q_a2_all, q_a2_scale = fuse_silu_mul_quant_ep(gateup_output, masked_m)
+        q_a2_all, q_a2_scale = torch.ops.sglang.fuse_silu_mul_quant_ep(gateup_output, masked_m)
 
         # ---- second GEMM ----
         n2 = w2_scales.size(1)
         down_output = torch.empty((num_groups, m, n2), device=q_a2_all.device, dtype=torch.bfloat16)
 
-        m_grouped_w4a8_gemm_nt_masked(
-            (q_a2_all, q_a2_scale),
-            (w2_weight, w2_scales),
+        torch.ops.sglang.m_grouped_w4a8_gemm_nt_masked(
+            q_a2_all, q_a2_scale,
+            w2_weight, w2_scales,
             down_output,
             masked_m,
             expected_m,
@@ -865,23 +964,23 @@ class DeepEPMoE(EPMoE):
         gateup_output = torch.empty((num_groups, m, n1), device=hidden_states.device, dtype=torch.bfloat16)
 
         # ---- first GEMM ----
-        m_grouped_w8a8_gemm_nt_masked(
-            (q_a1_all, q_a1_scale),
-            (w13_weight, w13_scales),
+        torch.ops.sglang.m_grouped_w8a8_gemm_nt_masked(
+            q_a1_all, q_a1_scale,
+            w13_weight, w13_scales,
             gateup_output,
             masked_m,
             expected_m,
         )
 
-        q_a2_all, q_a2_scale = fuse_silu_mul_quant_ep(gateup_output, masked_m)
+        q_a2_all, q_a2_scale = torch.ops.sglang.fuse_silu_mul_quant_ep(gateup_output, masked_m)
 
         # ---- second GEMM ----
         n2 = w2_scales.size(1)
         down_output = torch.empty((num_groups, m, n2), device=q_a2_all.device, dtype=torch.bfloat16)
 
-        m_grouped_w8a8_gemm_nt_masked(
-            (q_a2_all, q_a2_scale),
-            (w2_weight, w2_scales),
+        torch.ops.sglang.m_grouped_w8a8_gemm_nt_masked(
+            q_a2_all, q_a2_scale,
+            w2_weight, w2_scales,
             down_output,
             masked_m,
             expected_m,

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe_marlin.py

+      
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from __future__ import annotations
@@ -15,6 +16,7 @@ from sglang.srt.layers.quantization.base_config import FusedMoEMethodBase
 
 from sglang.srt.utils import set_weight_attrs
 from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
+from sglang.srt.layers.moe.utils import get_moe_a2a_backend
 try:
     from lmslim.layers.fused_moe.fuse_moe_int8_marlin import fused_experts_impl_int8_marlin
 except Exception:
@@ -77,7 +79,7 @@ class CompressedTensorsW8A8Int8MarlinMoEMethod(CompressedTensorsMarlinMoEMethod)
             "weights")
         self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
             "input_activations")
-        self.use_deepep = True
+        self.use_deepep = get_moe_a2a_backend().is_deepep()
         per_channel = (
             self.weight_quant.strategy == QuantizationStrategy.CHANNEL
             and self.input_quant.strategy == QuantizationStrategy.TOKEN)

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -163,12 +163,14 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                 )
                 layer.register_parameter("input_zero_point", input_zero_point)
 
+    @torch._dynamo.disable()
     def apply_weights(
         self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor]
     ) -> torch.Tensor:
         # TODO: add cutlass_scaled_mm_azp support
         x_q, x_scale = per_token_quant_int8(x)
 
+        # TODO: fix with lmslim/lightop
         return quant_ops.triton_scaled_mm(
             x_q, layer.weight, x_scale, layer.weight_scale, out_dtype=x.dtype, bias=bias
         )

python/sglang/srt/layers/quantization/slimquant_w4a8.py

@@ -157,7 +157,7 @@ class SlimQuantW4A8Int8LinearMethod(LinearMethodBase):
         )
         layer.register_parameter("weight_scale", weight_scale)
 
-    @torch._dynamo.disable()
+    @torch._dynamo.disable()  # TODO: 性能优化需要lmslim/lightop配合
     def apply(
         self,
         layer: torch.nn.Module,

python/sglang/srt/layers/quantization/slimquant_w4a8_marlin.py

@@ -214,7 +214,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
         self.moe_runner_config = moe_runner_config
         self.runner = MoeRunner(MoeRunnerBackend.TRITON, moe_runner_config)
         
-    @torch._dynamo.disable()
+    @torch._dynamo.disable()  # TODO: 性能优化需lmslim/lightop配合
     def apply(
         self,
         layer: torch.nn.Module,
@@ -253,6 +253,7 @@ class SlimQuantW4A8Int8MarlinMoEMethod:
         )
         return StandardCombineInput(hidden_states=output)
 
+    @torch._dynamo.disable()  # TODO: 性能优化需lmslim/lightop配合
     def apply_with_shared_output(
         self,
         layer: torch.nn.Module,