[10/N] MoE Refactor: reorganize deepgemm runner in DeepEPMoE

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

@@ -839,7 +839,7 @@ def forward(self, hidden_states: torch.Tensor, topk_output: TopKOutput, **kwargs
             dispatch_output=dispatch_output,
             **kwargs,
         )
-        final_hidden_states = self.dispatcher.combine(combine_input)
+        final_hidden_states = self.dispatcher.combine(combine_input=combine_input)
 
         # TODO: should we add some conditions here?
         final_hidden_states = final_hidden_states[

python/sglang/srt/layers/moe/moe_runner/deep_gemm.py

@@ -5,6 +5,7 @@
 
 import torch
 
+from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.moe.moe_runner.base import (
     MoeQuantInfo,
     MoeRunnerConfig,
@@ -15,14 +16,28 @@
     register_pre_permute,
 )
 from sglang.srt.layers.moe.utils import MoeRunnerBackend
-from sglang.srt.utils import dispose_tensor
+from sglang.srt.utils import ceil_div, dispose_tensor, get_bool_env_var, is_hip, is_npu
+from sglang.srt.utils.offloader import get_offloader
 
 if TYPE_CHECKING:
+    from sglang.srt.layers.moe.token_dispatcher.deepep import (
+        DeepEPLLCombineInput,
+        DeepEPLLDispatchOutput,
+        DeepEPNormalCombineInput,
+        DeepEPNormalDispatchOutput,
+    )
     from sglang.srt.layers.moe.token_dispatcher.standard import (
         StandardCombineInput,
         StandardDispatchOutput,
     )
 
+_is_hip = is_hip()
+_is_npu = is_npu()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+
+if not (_is_npu or _is_hip):
+    from sgl_kernel import silu_and_mul
+
 
 # TODO(kaixih@nvidia): ideally we should merge this logic into
 # `fill_gateup_input_triton_kernel` to directly generate e8m0 scale.
@@ -40,13 +55,23 @@ def _cast_to_e8m0_with_rounding_up(x: torch.Tensor) -> torch.Tensor:
     return new_x.transpose(1, 2).contiguous().transpose(1, 2)
 
 
+def copy_list_to_gpu_no_ce(arr: List[int]):
+    from sgl_kernel.elementwise import copy_to_gpu_no_ce
+
+    tensor_cpu = torch.tensor(arr, dtype=torch.int32, device="cpu")
+    tensor_gpu = torch.empty_like(tensor_cpu, device="cuda")
+    copy_to_gpu_no_ce(tensor_cpu, tensor_gpu)
+    return tensor_gpu
+
+
 @dataclass
 class DeepGemmRunnerInput(RunnerInput):
     hidden_states: torch.Tensor
     hidden_states_scale: torch.Tensor
-    masked_m: torch.Tensor
-    expected_m: int
     use_masked_gemm: bool
+    masked_m: Optional[torch.Tensor] = None
+    expected_m: Optional[int] = None
+    m_indices: Optional[torch.Tensor] = None
 
     @property
     def runner_backend(self) -> MoeRunnerBackend:
@@ -84,20 +109,100 @@ def run(
         running_state: dict,
     ) -> DeepGemmRunnerOutput:
 
-        if runner_input.use_masked_gemm:
-            hidden_states = self._run_masked_gemm(
-                runner_input,
-                quant_info,
-                running_state,
+        if not runner_input.use_masked_gemm:
+            hidden_states = self._run_contiguous_gemm(
+                runner_input, quant_info, running_state
             )
         else:
-            hidden_states = self._run_contiguous_gemm(
-                runner_input,
-                quant_info,
-                running_state,
+            hidden_states = self._run_masked_gemm(
+                runner_input, quant_info, running_state
             )
         return DeepGemmRunnerOutput(hidden_states=hidden_states)
 
+    def _run_contiguous_gemm(
+        self,
+        runner_input: DeepGemmRunnerInput,
+        quant_info: DeepGemmMoeQuantInfo,
+        running_state: dict,
+    ) -> torch.Tensor:
+
+        from sglang.srt.layers.moe.ep_moe.kernels import tma_align_input_scale
+        from sglang.srt.layers.quantization.fp8_kernel import (
+            sglang_per_token_group_quant_fp8,
+        )
+
+        hidden_states = runner_input.hidden_states
+        hidden_states_scale = runner_input.hidden_states_scale
+        all_tokens = running_state["all_tokens"]
+        hidden_states_device = running_state["hidden_states_device"]
+        hidden_states_dtype = running_state["hidden_states_dtype"]
+        hidden_states_shape = running_state["hidden_states_shape"]
+        m_indices = runner_input.m_indices
+
+        N = quant_info.w13_weight.size(1)
+        K = hidden_states_shape[1]
+        scale_block_size = 128
+
+        w13_weight_fp8 = (
+            quant_info.w13_weight,
+            quant_info.w13_scale,
+        )
+        w2_weight_fp8 = (quant_info.w2_weight, quant_info.w2_scale)
+
+        gateup_output = torch.empty(
+            (all_tokens, N),
+            device=hidden_states_device,
+            dtype=torch.bfloat16,
+        )
+        if not deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
+            hidden_states_scale = tma_align_input_scale(hidden_states_scale)
+        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig(
+            (hidden_states, hidden_states_scale),
+            w13_weight_fp8,
+            gateup_output,
+            m_indices,
+        )
+
+        dispose_tensor(hidden_states)
+        dispose_tensor(hidden_states_scale)
+
+        down_input = torch.empty(
+            (
+                all_tokens,
+                N // 2,
+            ),
+            device=gateup_output.device,
+            dtype=torch.bfloat16,
+        )
+        silu_and_mul(gateup_output.view(-1, N), down_input)
+        del gateup_output
+
+        down_input_fp8, down_input_scale = sglang_per_token_group_quant_fp8(
+            down_input,
+            scale_block_size,
+            column_major_scales=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+            scale_tma_aligned=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+            scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+        )
+        del down_input
+
+        down_output = torch.empty(
+            (all_tokens, K),
+            device=hidden_states_device,
+            dtype=torch.bfloat16,
+        )
+        if not deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
+            down_input_scale = tma_align_input_scale(down_input_scale)
+
+        deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_contig(
+            (down_input_fp8, down_input_scale),
+            w2_weight_fp8,
+            down_output,
+            m_indices,
+        )
+
+        return down_output
+
     def _run_masked_gemm(
         self,
         runner_input: DeepGemmRunnerInput,
@@ -149,6 +254,7 @@ def _run_masked_gemm(
             expected_m,
         )
         dispose_tensor(hidden_states)
+        dispose_tensor(hidden_states_scale)
 
         # Act
         down_input = torch.empty(
@@ -198,18 +304,9 @@ def _run_masked_gemm(
             masked_m,
             expected_m,
         )
-        del down_input
 
         return down_output
 
-    def _run_contiguous_gemm(
-        self,
-        runner_input: DeepGemmRunnerInput,
-        quant_info: DeepGemmMoeQuantInfo,
-        running_state: dict,
-    ) -> torch.Tensor:
-        pass
-
     @property
     def runner_backend(self) -> MoeRunnerBackend:
         return MoeRunnerBackend.DEEP_GEMM
@@ -222,6 +319,7 @@ def pre_permute_standard_to_deep_gemm(
     runner_config: MoeRunnerConfig,
     running_state: dict,
 ) -> DeepGemmRunnerInput:
+
     from sglang.srt.layers.moe.ep_moe.kernels import moe_ep_deepgemm_preprocess
 
     hidden_states, topk_output = dispatch_output
@@ -257,9 +355,9 @@ def pre_permute_standard_to_deep_gemm(
     return DeepGemmRunnerInput(
         hidden_states=hidden_states,
         hidden_states_scale=hidden_states_scale,
+        use_masked_gemm=True,
         masked_m=masked_m,
         expected_m=expected_m,
-        use_masked_gemm=True,
     )
 
 
@@ -302,3 +400,170 @@ def post_permute_deep_gemm_to_standard(
     return StandardCombineInput(
         hidden_states=output,
     )
+
+
+@register_pre_permute("deepep_ll", "deep_gemm")
+def pre_permute_deepep_ll_to_deep_gemm(
+    dispatch_output: DeepEPLLDispatchOutput,
+    quant_info: DeepGemmMoeQuantInfo,
+    runner_config: MoeRunnerConfig,
+    running_state: dict,
+) -> DeepGemmRunnerInput:
+
+    hidden_states, hidden_states_scale, topk_ids, topk_weights, masked_m, expected_m = (
+        dispatch_output
+    )
+
+    running_state["topk_ids"] = topk_ids
+    running_state["topk_weights"] = topk_weights
+    running_state["hidden_states_shape"] = hidden_states.shape
+    running_state["hidden_states_dtype"] = hidden_states.dtype
+    running_state["hidden_states_device"] = hidden_states.device
+
+    return DeepGemmRunnerInput(
+        hidden_states=hidden_states,
+        hidden_states_scale=hidden_states_scale,
+        use_masked_gemm=True,
+        masked_m=masked_m,
+        expected_m=expected_m,
+    )
+
+
+@register_post_permute("deep_gemm", "deepep_ll")
+def post_permute_deep_gemm_to_deepep_ll(
+    runner_output: DeepGemmRunnerOutput,
+    quant_info: DeepGemmMoeQuantInfo,
+    runner_config: MoeRunnerConfig,
+    running_state: dict,
+) -> DeepEPLLCombineInput:
+
+    from sglang.srt.layers.moe.token_dispatcher.deepep import DeepEPLLCombineInput
+
+    return DeepEPLLCombineInput(
+        hidden_states=runner_output.hidden_states,
+        topk_ids=running_state["topk_ids"],
+        topk_weights=running_state["topk_weights"],
+    )
+
+
+@register_pre_permute("deepep_normal", "deep_gemm")
+def pre_permute_deepep_normal_to_deep_gemm(
+    dispatch_output: DeepEPNormalDispatchOutput,
+    quant_info: DeepGemmMoeQuantInfo,
+    runner_config: MoeRunnerConfig,
+    running_state: dict,
+) -> DeepGemmRunnerInput:
+
+    from sglang.srt.layers.moe.ep_moe.kernels import ep_scatter
+
+    (
+        hidden_states,
+        hidden_states_scale,
+        topk_ids,
+        topk_weights,
+        num_recv_tokens_per_expert,
+    ) = dispatch_output
+    assert runner_config.activation == "silu"
+
+    all_tokens = sum(num_recv_tokens_per_expert)
+    running_state["all_tokens"] = all_tokens
+
+    K = hidden_states.shape[1]
+
+    hidden_states_shape = hidden_states.shape
+    hidden_states_device = hidden_states.device
+    hidden_states_dtype = hidden_states.dtype
+
+    running_state["hidden_states_shape"] = hidden_states_shape
+    running_state["hidden_states_device"] = hidden_states_device
+    running_state["hidden_states_dtype"] = hidden_states_dtype
+    running_state["topk_ids"] = topk_ids
+    running_state["topk_weights"] = topk_weights
+
+    input_tensor = torch.empty(
+        (all_tokens, K),
+        device=hidden_states.device,
+        dtype=hidden_states.dtype,
+    )
+    if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
+        # TODO check whether need `zeros`
+        input_tensor_scale = torch.zeros(
+            (ceil_div(K // 128, 4), all_tokens),
+            device=hidden_states.device,
+            dtype=torch.int,
+        ).transpose(0, 1)
+    else:
+        input_tensor_scale = torch.empty(
+            (all_tokens, K // 128),
+            device=hidden_states.device,
+            dtype=torch.float32,
+        )
+    m_indices = torch.empty(all_tokens, device=hidden_states.device, dtype=torch.int32)
+    output_index = torch.empty_like(topk_ids)
+
+    if get_offloader().forbid_copy_engine_usage:
+        num_recv_tokens_per_expert_gpu = copy_list_to_gpu_no_ce(
+            num_recv_tokens_per_expert
+        )
+    else:
+        num_recv_tokens_per_expert_gpu = torch.tensor(
+            num_recv_tokens_per_expert,
+            dtype=torch.int32,
+            pin_memory=True,
+            device="cpu",
+        ).cuda(non_blocking=True)
+    expert_start_loc = torch.empty_like(num_recv_tokens_per_expert_gpu)
+
+    ep_scatter(
+        hidden_states,
+        hidden_states_scale,
+        topk_ids,
+        num_recv_tokens_per_expert_gpu,
+        expert_start_loc,
+        input_tensor,
+        input_tensor_scale,
+        m_indices,
+        output_index,
+        scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
+    )
+    dispose_tensor(hidden_states)
+    dispose_tensor(hidden_states_scale)
+
+    running_state["output_index"] = output_index
+
+    return DeepGemmRunnerInput(
+        hidden_states=input_tensor,
+        hidden_states_scale=input_tensor_scale,
+        use_masked_gemm=False,
+        m_indices=m_indices,
+    )
+
+
+@register_post_permute("deep_gemm", "deepep_normal")
+def post_permute_deep_gemm_to_deepep_normal(
+    runner_output: DeepGemmRunnerOutput,
+    quant_info: DeepGemmMoeQuantInfo,
+    runner_config: MoeRunnerConfig,
+    running_state: dict,
+) -> DeepEPNormalCombineInput:
+
+    from sglang.srt.layers.moe.ep_moe.kernels import ep_gather
+    from sglang.srt.layers.moe.token_dispatcher.deepep import DeepEPNormalCombineInput
+
+    hidden_states = runner_output.hidden_states
+    topk_ids = running_state["topk_ids"]
+    topk_weights = running_state["topk_weights"]
+    output_index = running_state["output_index"]
+
+    gather_out = torch.empty(
+        running_state["hidden_states_shape"],
+        device=running_state["hidden_states_device"],
+        dtype=torch.bfloat16,
+    )
+    ep_gather(hidden_states, topk_ids, topk_weights, output_index, gather_out)
+
+    return DeepEPNormalCombineInput(
+        hidden_states=gather_out,
+        topk_ids=running_state["topk_ids"],
+        topk_weights=running_state["topk_weights"],
+    )