[AMD] Fix Llama 4 FP8 accuracy issues on MI300X (#7699)

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

@@ -52,7 +52,6 @@ if not (_is_npu or _is_hip):
 if _use_aiter:
     from aiter import ActivationType, QuantType
     from aiter.fused_moe import fused_moe
-    from aiter.ops.shuffle import shuffle_weight
 
 logger = logging.getLogger(__name__)
 

python/sglang/srt/layers/moe/rocm_moe_utils.py

+# Adapted from https://github.com/vllm-project/vllm/blob/v0.9.1rc2/vllm/model_executor/layers/fused_moe/rocm_aiter_fused_moe.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from enum import IntEnum
+from functools import cache
+from typing import Optional
+
+import torch
+
+from sglang.srt.utils import direct_register_custom_op, get_bool_env_var, is_hip
+
+_is_hip = is_hip()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+
+
+class ActivationMethod(IntEnum):
+    # This allows interfacing with AITER ActivationType enum
+    # without importing the ActivationType enum from AITER globally.
+    SILU = 0
+    GELU = 1
+
+
+def rocm_aiter_asm_moe_tkw1_impl(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    fc1_scale: Optional[torch.Tensor] = None,
+    fc2_scale: Optional[torch.Tensor] = None,
+    fc1_smooth_scale: Optional[torch.Tensor] = None,
+    fc2_smooth_scale: Optional[torch.Tensor] = None,
+    a16: bool = False,
+    per_tensor_quant_scale: Optional[torch.Tensor] = None,
+    expert_mask: Optional[torch.Tensor] = None,
+    activation_method: int = ActivationMethod.SILU.value,
+) -> torch.Tensor:
+
+    from aiter import ActivationType
+    from aiter.fused_moe_bf16_asm import asm_moe_tkw1
+
+    activation = ActivationType(activation_method)
+
+    return asm_moe_tkw1(
+        hidden_states,
+        w1,
+        w2,
+        topk_weights,
+        topk_ids,
+        fc1_scale=fc1_scale,
+        fc2_scale=fc2_scale,
+        fc1_smooth_scale=fc1_smooth_scale,
+        fc2_smooth_scale=fc2_smooth_scale,
+        a16=a16,
+        per_tensor_quant_scale=per_tensor_quant_scale,
+        expert_mask=expert_mask,
+        activation=activation,
+    )
+
+
+def rocm_aiter_asm_moe_tkw1_fake(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    fc1_scale: Optional[torch.Tensor] = None,
+    fc2_scale: Optional[torch.Tensor] = None,
+    fc1_smooth_scale: Optional[torch.Tensor] = None,
+    fc2_smooth_scale: Optional[torch.Tensor] = None,
+    a16: bool = False,
+    per_tensor_quant_scale: Optional[torch.Tensor] = None,
+    expert_mask: Optional[torch.Tensor] = None,
+    activation_method: int = ActivationMethod.SILU.value,
+) -> torch.Tensor:
+    return torch.empty_like(hidden_states)
+
+
+if _use_aiter:
+
+    direct_register_custom_op(
+        op_name="rocm_aiter_asm_moe_tkw1",
+        op_func=rocm_aiter_asm_moe_tkw1_impl,
+        mutates_args=[],
+        fake_impl=rocm_aiter_asm_moe_tkw1_fake,
+    )
+
+
+def rocm_fused_experts_tkw1(
+    hidden_states: torch.Tensor,
+    w1: torch.Tensor,
+    w2: torch.Tensor,
+    topk_weights: torch.Tensor,
+    topk_ids: torch.Tensor,
+    activation: str = "silu",
+    apply_router_weight_on_input: bool = False,
+    use_fp8_w8a8: bool = False,
+    per_channel_quant: bool = False,
+    w1_scale: Optional[torch.Tensor] = None,
+    w2_scale: Optional[torch.Tensor] = None,
+    a1_scale: Optional[torch.Tensor] = None,
+    a2_scale: Optional[torch.Tensor] = None,
+    block_shape: Optional[list[int]] = None,
+) -> torch.Tensor:
+
+    activation_method = (
+        ActivationMethod.SILU if activation == "silu" else ActivationMethod.GELU
+    )
+    # All AITER Fused MoE kernels are expecting the following datatypes
+    topk_weights = topk_weights.to(torch.float32)
+    topk_ids = topk_ids.to(torch.int32)
+
+    # w8a8 per-channel quantization
+    if per_channel_quant and apply_router_weight_on_input and use_fp8_w8a8:
+        # AITER tkw1 kernel for FP8 models with `apply_router_weight_on_input`
+        # This applies topk_weights on the GEMM output of the first FC layer
+        #  rather than the second FC.
+        assert (
+            topk_weights.dim() == 2
+        ), "`topk_weights` should be in shape (num_tokens, topk)"
+        assert topk_weights.shape[-1] == 1, (
+            "Only support topk=1 when" " `apply_router_weight_on_input` is True"
+        )
+
+        return torch.ops.sglang.rocm_aiter_asm_moe_tkw1(
+            hidden_states,
+            w1,
+            w2,
+            topk_weights,
+            topk_ids,
+            fc1_scale=w1_scale,
+            fc2_scale=w2_scale,
+            fc1_smooth_scale=None,
+            fc2_smooth_scale=None,
+            a16=False,
+            per_tensor_quant_scale=None,
+            expert_mask=None,
+            activation_method=activation_method,
+        )
+    else:
+        assert False, "This should not be called."

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

@@ -19,7 +19,14 @@ from sglang.srt.layers.quantization.utils import (
     per_tensor_dequantize,
     replace_parameter,
 )
-from sglang.srt.utils import is_cpu, is_cuda, is_hip, is_npu, set_weight_attrs
+from sglang.srt.utils import (
+    get_bool_env_var,
+    is_cpu,
+    is_cuda,
+    is_hip,
+    is_npu,
+    set_weight_attrs,
+)
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
@@ -29,6 +36,13 @@ if TYPE_CHECKING:
         CompressedTensorsConfig,
     )
 
+_is_hip = is_hip()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+
+if _use_aiter:
+    from aiter.ops.shuffle import shuffle_weight
+
+    from sglang.srt.layers.moe.rocm_moe_utils import rocm_fused_experts_tkw1
 
 try:
     import vllm
@@ -265,6 +279,20 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
                 max_w13_scales, requires_grad=False
             )
 
+        if _use_aiter:
+            with torch.no_grad():
+                # Pre-shuffle weights
+                layer.w13_weight = torch.nn.Parameter(
+                    shuffle_weight(layer.w13_weight.data, (16, 16)),
+                    requires_grad=False,
+                )
+                torch.cuda.empty_cache()
+                layer.w2_weight = torch.nn.Parameter(
+                    shuffle_weight(layer.w2_weight.data, (16, 16)),
+                    requires_grad=False,
+                )
+                torch.cuda.empty_cache()
+
     def apply(
         self,
         layer: torch.nn.Module,
@@ -274,20 +302,43 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
     ) -> torch.Tensor:
         from sglang.srt.layers.moe.fused_moe_triton import fused_experts
 
-        return fused_experts(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_output=topk_output,
-            moe_runner_config=moe_runner_config,
-            use_fp8_w8a8=True,
-            per_channel_quant=self.weight_quant.strategy
-            == QuantizationStrategy.CHANNEL,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
-            a1_scale=layer.w13_input_scale,
-            a2_scale=layer.w2_input_scale,
-        )
+        if (
+            _use_aiter
+            and self.weight_quant.strategy == QuantizationStrategy.CHANNEL
+            and moe_runner_config.apply_router_weight_on_input
+        ):
+            topk_weights, topk_ids, _ = topk_output
+            return rocm_fused_experts_tkw1(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                activation=moe_runner_config.activation,
+                apply_router_weight_on_input=moe_runner_config.apply_router_weight_on_input,
+                use_fp8_w8a8=True,
+                per_channel_quant=self.weight_quant.strategy
+                == QuantizationStrategy.CHANNEL,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                a1_scale=layer.w13_input_scale,
+                a2_scale=layer.w2_input_scale,
+            )
+        else:
+            return fused_experts(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                topk_output=topk_output,
+                moe_runner_config=moe_runner_config,
+                use_fp8_w8a8=True,
+                per_channel_quant=self.weight_quant.strategy
+                == QuantizationStrategy.CHANNEL,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                a1_scale=layer.w13_input_scale,
+                a2_scale=layer.w2_input_scale,
+            )
 
 
 class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):

python/sglang/srt/layers/quantization/fp8.py

@@ -966,6 +966,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
                 requires_grad=False,
             )
             torch.cuda.empty_cache()
+
             # ROCm (_use_aiter): using column-wise scaling
             layer.w13_weight_scale1 *= layer.w13_weight_scale.unsqueeze(-1)
             layer.w2_weight_scale1 *= layer.w2_weight_scale.unsqueeze(-1)

python/sglang/srt/server_args.py

@@ -2228,7 +2228,10 @@ class ServerArgs:
                 # use bf16 for mxfp4 triton kernels
                 self.dtype = "bfloat16"
         elif "Llama4" in model_arch:
-            assert self.attention_backend == "fa3", "fa3 is required for Llama4 model"
+            assert self.attention_backend in {
+                "fa3",
+                "aiter",
+            }, "fa3 or aiter is required for Llama4 model"
         elif model_arch in [
             "Gemma2ForCausalLM",
             "Gemma3ForCausalLM",