[AMD] Fuse RMSNorm + FP8 per-token quant for GLM-4.7-FP8

python/sglang/srt/layers/communicator.py

@@ -80,14 +80,66 @@
 _is_npu = is_npu()
 _use_ag_after_qlora = envs.SGLANG_USE_AG_AFTER_QLORA.get()
 
-if _use_aiter and _is_gfx95_supported:
-    from aiter.ops.triton.fused_fp8_quant import fused_rms_fp8_group_quant
+if _use_aiter:
+    from aiter.ops.rmsnorm import add_rmsnorm_quant as _aiter_add_rmsnorm_quant
+    from aiter.ops.rmsnorm import rmsnorm_quant as _aiter_rmsnorm_quant
 
-    from sglang.srt.layers.quantization.rocm_mxfp4_utils import fused_rms_mxfp4_quant
+    from sglang.srt.layers.quantization.fp8_kernel import fp8_dtype as _aiter_fp8_dtype
+
+    if _is_gfx95_supported:
+        from aiter.ops.triton.fused_fp8_quant import fused_rms_fp8_group_quant
+
+        from sglang.srt.layers.quantization.rocm_mxfp4_utils import (
+            fused_rms_mxfp4_quant,
+        )
 elif _is_npu:
     from sglang.srt.hardware_backend.npu.cmo import prepare_weight_cache
 
 
+def _fused_rmsnorm_fp8_per_token_quant(
+    hidden_states: torch.Tensor,
+    weight: torch.Tensor,
+    epsilon: float,
+    residual: Optional[torch.Tensor] = None,
+):
+    """Fused (optional residual-add +) RMSNorm + FP8 per-token quantization.
+
+    Args:
+        residual: if provided, computes hidden_states + residual before RMSNorm
+                  and returns updated residual_out as second element.
+
+    Returns:
+        If residual is None:  (out_fp8, scale)
+        If residual provided: ((out_fp8, scale), residual_out)
+    """
+    M, N = hidden_states.shape
+    out_fp8 = torch.empty((M, N), dtype=_aiter_fp8_dtype, device=hidden_states.device)
+    scale = torch.empty(M, dtype=torch.float32, device=hidden_states.device)
+    if residual is not None:
+        residual_out = torch.empty_like(hidden_states)
+        _aiter_add_rmsnorm_quant(
+            out_fp8,
+            hidden_states,
+            residual,
+            residual_out,
+            scale,
+            weight,
+            epsilon,
+            0,  # group_size=0 → per-token
+        )
+        return (out_fp8, scale.unsqueeze(1)), residual_out
+    else:
+        _aiter_rmsnorm_quant(
+            out_fp8,
+            hidden_states,
+            scale,
+            weight,
+            epsilon,
+            0,  # group_size=0 → per-token
+        )
+        return (out_fp8, scale.unsqueeze(1))
+
+
 # TODO: According to the discussion in https://github.com/flashinfer-ai/flashinfer/issues/1223#issuecomment-3047256465
 # We set the max token num to 128 for allreduce fusion with min-latency case(use_oneshot=True).
 FUSE_ALLREDUCE_MAX_BATCH_SIZE = 2048
@@ -147,7 +199,6 @@ def model_input_output():
 
 
 class AttentionInputs:
-
     def __init__(
         self,
         hidden_states: torch.Tensor,
@@ -309,8 +360,8 @@ def _compute_mlp_mode(cls, context: _LayerModeComputationContext):
         if context.is_layer_sparse:
             return (
                 ScatterMode.SCATTERED
+                # Token dispatch/combine will be handled outside of LayerCommunicator for these modes.
                 if (
-                    # Token dispatch/combine will be handled outside of LayerCommunicator for these modes.
                     not get_moe_a2a_backend().is_none()
                     or should_use_flashinfer_cutlass_moe_fp4_allgather()
                 )
@@ -482,8 +533,7 @@ def prepare_attn(
                             None,
                             None,
                         )
-                    elif _use_aiter and _is_gfx95_supported and ("fp8" in quant_format):
-
+                    elif _use_aiter and _is_gfx95_supported and (quant_format == "fp8"):
                         hidden_states, _, _, _res = fused_rms_fp8_group_quant(
                             hidden_states,
                             self.input_layernorm.weight,
@@ -497,10 +547,16 @@ def prepare_attn(
                             output_unquantized_inp1=False,
                         )
 
+                    elif _use_aiter and (quant_format == "fp8_per_token"):
+                        hidden_states = _fused_rmsnorm_fp8_per_token_quant(
+                            hidden_states,
+                            self.input_layernorm.weight.data,
+                            self.input_layernorm.variance_epsilon,
+                        )
+
                     else:
                         hidden_states = self.input_layernorm(hidden_states)
                 else:
-
                     if _use_aiter and _is_gfx95_supported and ("mxfp4" in quant_format):
                         hidden_states, *_, residual = fused_rms_mxfp4_quant(
                             hidden_states,
@@ -511,7 +567,7 @@ def prepare_attn(
                             None,
                             residual,
                         )
-                    elif _use_aiter and _is_gfx95_supported and ("fp8" in quant_format):
+                    elif _use_aiter and _is_gfx95_supported and (quant_format == "fp8"):
                         # RMSNorm + FP8 per-group quant
                         # return hidden_states：
                         #   out_fp8  : FP8 activation →  a8w8 GEMM
@@ -528,6 +584,15 @@ def prepare_attn(
                             res1=residual,
                             output_unquantized_inp1=False,
                         )
+                    elif _use_aiter and (quant_format == "fp8_per_token"):
+                        if post_residual_addition is not None:
+                            residual = residual + post_residual_addition
+                        hidden_states, residual = _fused_rmsnorm_fp8_per_token_quant(
+                            hidden_states,
+                            self.input_layernorm.weight.data,
+                            self.input_layernorm.variance_epsilon,
+                            residual=residual,
+                        )
                     else:
                         hidden_states, residual = self.input_layernorm(
                             hidden_states,

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

@@ -3,7 +3,7 @@
 import logging
 from enum import Enum
 from functools import lru_cache
-from typing import TYPE_CHECKING, Callable, List, Optional, Tuple
+from typing import TYPE_CHECKING, Callable, List, Optional, Tuple, Union
 
 import torch
 
@@ -1523,7 +1523,7 @@ def can_auto_enable_marlin_fp8() -> bool:
 
 
 def apply_fp8_ptpc_linear(
-    input: torch.Tensor,
+    input: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
     weight: torch.Tensor,
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
@@ -1534,6 +1534,18 @@ def apply_fp8_ptpc_linear(
     pad_output: Optional[bool] = None,
     compressed_tensor_quant: bool = False,
 ) -> torch.Tensor:
+    # Handle pre-quantized (fp8_tensor, scale) tuple from fused RMSNorm+Quant
+    if isinstance(input, tuple):
+        q_input, x_scale = input
+        q_input = q_input.view(-1, q_input.shape[-1])
+        output_shape = [*q_input.shape[:-1], weight.shape[0]]
+        output = aiter.gemm_a8w8_bpreshuffle(
+            q_input, weight, x_scale, weight_scale, None, torch.bfloat16
+        )
+        if bias is not None:
+            output = output + bias
+        return output.view(*output_shape)
+
     # View input as 2D matrix for fp8 methods
     input_2d = input.view(-1, input.shape[-1])
 

python/sglang/srt/models/glm4_moe.py

@@ -275,7 +275,9 @@ def forward_prepare(
         hidden_states: torch.Tensor,
         forward_batch: ForwardBatch,
     ):
-        if hidden_states.shape[0] == 0:
+        # hidden_states can be a (fp8_tensor, scale) tuple from fused RMSNorm+Quant
+        hs = hidden_states[0] if isinstance(hidden_states, tuple) else hidden_states
+        if hs.shape[0] == 0:
             return hidden_states, forward_batch, None
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
@@ -772,6 +774,51 @@ def __init__(
             ),
         )
 
+        # Detect if QKV uses aiter FP8 per-token quant so we can fuse
+        # RMSNorm + FP8 quant into a single kernel in prepare_attn
+        self.attn_quant_format = ""
+        self._detect_attn_quant_format()
+
+    def _detect_fp8_per_token_quant(self, linear_layer, label: str) -> str:
+        """Check if a linear layer uses aiter FP8 per-token quantization."""
+        from sglang.srt.utils import get_bool_env_var, is_hip
+
+        if not (get_bool_env_var("SGLANG_USE_AITER") and is_hip()):
+            return ""
+        if not hasattr(linear_layer, "quant_method"):
+            return ""
+        scheme = getattr(linear_layer, "scheme", None) or getattr(
+            linear_layer.quant_method, "scheme", None
+        )
+        if scheme is not None:
+            from compressed_tensors.quantization import QuantizationStrategy
+
+            from sglang.srt.layers.quantization.compressed_tensors.schemes.compressed_tensors_w8a8_fp8 import (
+                CompressedTensorsW8A8Fp8,
+            )
+
+            if (
+                isinstance(scheme, CompressedTensorsW8A8Fp8)
+                and scheme.strategy == QuantizationStrategy.CHANNEL
+            ):
+                logger.info(
+                    "layer_%d Fused RMSNorm+Quant %s: ENABLED (fp8_per_token)",
+                    self.layer_id,
+                    label,
+                )
+                return "fp8_per_token"
+        logger.info(
+            "layer_%d Fused RMSNorm+Quant %s: skipped",
+            self.layer_id,
+            label,
+        )
+        return ""
+
+    def _detect_attn_quant_format(self):
+        self.attn_quant_format = self._detect_fp8_per_token_quant(
+            self.self_attn.qkv_proj, "attn"
+        )
+
     def _is_layer_sparse(self, layer_id: int, is_nextn: bool) -> bool:
         return is_nextn or (
             self.config.n_routed_experts is not None
@@ -787,7 +834,10 @@ def forward(
     ) -> torch.Tensor:
 
         hidden_states, residual = self.layer_communicator.prepare_attn(
-            hidden_states, residual, forward_batch
+            hidden_states,
+            residual,
+            forward_batch,
+            quant_format=self.attn_quant_format,
         )
 
         hidden_states = self.self_attn(
@@ -834,7 +884,12 @@ def op_comm_prepare_attn(
         tbo_subbatch_index: Optional[int] = None,
     ):
         state.hidden_states_after_comm_pre_attn, state.residual_after_input_ln = (
-            self.layer_communicator.prepare_attn(hidden_states, residual, forward_batch)
+            self.layer_communicator.prepare_attn(
+                hidden_states,
+                residual,
+                forward_batch,
+                quant_format=self.attn_quant_format,
+            )
         )
         state.update(
             dict(