[AMD] Perf Kimi-K3 MoE optimization

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

@@ -16,7 +16,7 @@
 
 import logging
 import math
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from enum import IntEnum, auto
 from typing import (
     TYPE_CHECKING,
@@ -230,6 +230,32 @@ class TopKConfig:
     # Draft-side MoE blocks set this False so they never write the target's
     # process-global routed-experts capture buffer.
     allow_routed_experts_capture: bool = True
+    _correction_bias_dtype_cache: Optional[torch.Tensor] = field(
+        default=None, init=False, repr=False, compare=False
+    )
+    _correction_bias_cache_key: Optional[Tuple] = field(
+        default=None, init=False, repr=False, compare=False
+    )
+
+    def correction_bias_for_dtype(self, dtype: torch.dtype) -> Optional[torch.Tensor]:
+        """Return correction bias in ``dtype``, reusing a per-TopK lazy copy."""
+        correction_bias = self.correction_bias
+        if correction_bias is None or correction_bias.dtype == dtype:
+            return correction_bias
+
+        # Weight loaders update parameters in place. Including the version in
+        # the key prevents an early access from retaining pre-load contents.
+        cache_key = (
+            correction_bias.data_ptr(),
+            correction_bias._version,
+            correction_bias.device,
+            correction_bias.dtype,
+            dtype,
+        )
+        if self._correction_bias_cache_key != cache_key:
+            self._correction_bias_dtype_cache = correction_bias.to(dtype=dtype)
+            self._correction_bias_cache_key = cache_key
+        return self._correction_bias_dtype_cache
 
 
 # -------------------------------- TopKOutput ---------------------------------------
@@ -2134,6 +2160,9 @@ def select_experts(
         info=expert_location_dispatch_info,
     )
 
+    if _use_aiter and use_grouped_topk and correction_bias is not None:
+        correction_bias = topk_config.correction_bias_for_dtype(router_logits.dtype)
+
     # DeepSeek V2/V3/R1 series models use grouped_top_k
     # remove num_fused_shared_experts from grouped_topk/biased_grouped_topk
     num_routed_topk = top_k - num_fused_shared_experts

python/sglang/srt/layers/quantization/mxfp4.py

@@ -107,6 +107,13 @@
 ] = {}
 
 
+def _aiter_situ_uses_gu_interleaved_weights() -> bool:
+    """Match AITER's SiTU activation-mode precedence when choosing weight layout."""
+    a8w4 = get_bool_env_var("AITER_SITUV2_A8W4", "false")
+    a4w4 = get_bool_env_var("AITER_SITUV2_A4W4", "false")
+    return a8w4 or not a4w4
+
+
 def _get_flashinfer_mxfp4_device_permute_indices(
     x: torch.Tensor,
     epilogue_tile_m: int,
@@ -878,12 +885,17 @@ def swap_every_two_rows(x, axis=-1):
                         .view(-1, n)
                     )
 
-            k3_situ_a8w4 = (
-                os.environ.get("AITER_SITUV2_A8W4", "0") == "1"
-                and getattr(layer.moe_runner_config, "activation", None) == "situ"
-            )
-            use_aiter_gu_interleave = k3_situ_a8w4 or (
-                envs.SGLANG_USE_AITER_MOE_GU_ITLV.get() and gate_up_interleaved
+            # AITER selects the activation dtype at runtime. A8W4 takes precedence
+            # and, together with A16W4, uses the preshuffled GU-interleaved layout.
+            # A4W4 uses the generic separated layout instead; feeding it the
+            # A16/A8 layout makes real-checkpoint MoE outputs nearly orthogonal.
+            k3_situ = getattr(layer.moe_runner_config, "activation", None) == "situ"
+            use_aiter_gu_interleave = (
+                k3_situ and _aiter_situ_uses_gu_interleaved_weights()
+            ) or (
+                not k3_situ
+                and envs.SGLANG_USE_AITER_MOE_GU_ITLV.get()
+                and gate_up_interleaved
             )
             if use_aiter_gu_interleave:
                 layer.w13_weight.data = shuffle_weight_a16w4(layer.w13_weight, 16, True)
@@ -1614,9 +1626,14 @@ def apply(
                             expanded_idx_to_permuted_idx=expanded_idx,
                             top_k=packed_topk.shape[1],
                         )
-                    else:
-                        result = result[0]
-                    return StandardCombineInput(hidden_states=result)
+                        return StandardCombineInput(hidden_states=result)
+                    # The finalized kernel writes to its explicit output
+                    # argument. Do not propagate the FFI return tensor: some
+                    # SiTU runner versions return a distinct wrapper/allocation
+                    # even though symm_output contains the published result.
+                    # Returning the destination makes the pointer contract
+                    # explicit for K3's zero-copy latent buffer.
+                    return StandardCombineInput(hidden_states=symm_output)
 
                 # Bypassed topk: route from logits inside the op.
                 correction_bias = topk_output.topk_config.correction_bias

test/registered/unit/layers/moe/test_topk_correction_bias_cache.py

@@ -0,0 +1,72 @@
+import unittest
+
+import torch
+
+from sglang.srt.layers.moe.topk import TopKConfig
+from sglang.test.ci.ci_register import (
+    register_amd_ci,
+    register_cpu_ci,
+    register_cuda_ci,
+)
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=5, suite="base-a-test-cpu")
+register_cuda_ci(est_time=5, stage="base-a", runner_config="1-gpu-small")
+register_amd_ci(est_time=5, stage="stage-a", runner_config="1-gpu-small-amd")
+
+
+class CorrectionBiasCacheTestMixin:
+    device: str
+
+    def test_lazy_cache_uses_loaded_value_and_reuses_pointer(self):
+        correction_bias = torch.empty(8, dtype=torch.bfloat16, device=self.device)
+        config = TopKConfig(top_k=2, correction_bias=correction_bias)
+
+        loaded_value = torch.linspace(-1, 1, 8, dtype=torch.float32).to(
+            device=self.device, dtype=torch.bfloat16
+        )
+        with torch.no_grad():
+            correction_bias.copy_(loaded_value)
+
+        converted = config.correction_bias_for_dtype(torch.float32)
+        converted_again = config.correction_bias_for_dtype(torch.float32)
+
+        torch.testing.assert_close(converted, loaded_value.float())
+        self.assertEqual(converted.data_ptr(), converted_again.data_ptr())
+        self.assertNotEqual(converted.data_ptr(), correction_bias.data_ptr())
+
+    def test_cache_refreshes_if_weight_is_reloaded(self):
+        correction_bias = torch.zeros(8, dtype=torch.bfloat16, device=self.device)
+        config = TopKConfig(top_k=2, correction_bias=correction_bias)
+        converted = config.correction_bias_for_dtype(torch.float32)
+
+        reloaded_value = torch.arange(8, dtype=torch.float32).to(
+            device=self.device, dtype=torch.bfloat16
+        )
+        with torch.no_grad():
+            correction_bias.copy_(reloaded_value)
+        converted_after_reload = config.correction_bias_for_dtype(torch.float32)
+
+        torch.testing.assert_close(converted_after_reload, reloaded_value.float())
+        self.assertNotEqual(converted.data_ptr(), converted_after_reload.data_ptr())
+
+    def test_matching_dtype_returns_original_tensor(self):
+        correction_bias = torch.randn(8, dtype=torch.float32, device=self.device)
+        config = TopKConfig(top_k=2, correction_bias=correction_bias)
+
+        result = config.correction_bias_for_dtype(torch.float32)
+
+        self.assertEqual(result.data_ptr(), correction_bias.data_ptr())
+
+
+class TestCorrectionBiasCacheCPU(CorrectionBiasCacheTestMixin, CustomTestCase):
+    device = "cpu"
+
+
+@unittest.skipUnless(torch.cuda.is_available(), "needs a GPU")
+class TestCorrectionBiasCacheGPU(CorrectionBiasCacheTestMixin, CustomTestCase):
+    device = "cuda"
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/layers/quantization/test_mxfp4_situ_output.py

@@ -0,0 +1,112 @@
+import sys
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import pytest
+import torch
+
+from sglang.srt.layers import zero_copy_context
+from sglang.srt.layers.moe.token_dispatcher.standard import StandardDispatchOutput
+from sglang.srt.layers.moe.topk import StandardTopKOutput
+from sglang.test.ci.ci_register import register_amd_ci
+
+register_amd_ci(est_time=5, stage="stage-b", runner_config="1-gpu-small-amd")
+
+pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a GPU")
+
+
+def test_situ_routed_moe_returns_published_output_buffer():
+    from sglang.srt.layers.moe import route_quant_handoff
+    from sglang.srt.layers.quantization import mxfp4 as mxfp4_module
+    from sglang.srt.layers.quantization.mxfp4 import Mxfp4MoEMethod
+
+    tokens, hidden, top_k = 3, 128, 2
+    x = torch.randn(tokens, hidden, dtype=torch.bfloat16, device="cuda")
+    x_quant = torch.zeros(tokens, hidden, dtype=torch.uint8, device="cuda")
+    x_scale = torch.zeros(tokens, hidden // 32, dtype=torch.uint8, device="cuda")
+    packed_topk = torch.zeros(tokens, top_k, dtype=torch.int32, device="cuda")
+    topk_output = StandardTopKOutput(
+        topk_weights=torch.full(
+            (tokens, top_k), 0.5, dtype=torch.float32, device="cuda"
+        ),
+        topk_ids=torch.zeros(tokens, top_k, dtype=torch.int32, device="cuda"),
+        router_logits=torch.empty(tokens, 0, dtype=torch.float32, device="cuda"),
+    )
+    dispatch_output = StandardDispatchOutput(
+        hidden_states=x,
+        hidden_states_scale=None,
+        topk_output=topk_output,
+    )
+
+    method = Mxfp4MoEMethod.__new__(Mxfp4MoEMethod)
+    method.use_deep_gemm = False
+    method.use_marlin = False
+    method.use_flashinfer = True
+    method._fi_kernel = None
+    method.flashinfer_mxfp4_moe_precision = "default"
+    method.hidden_size = hidden
+    method.intermediate_size_per_partition = 128
+    method.moe_runner_config = SimpleNamespace(activation="situ")
+
+    dummy = torch.empty(1, dtype=torch.uint8, device="cuda")
+    layer = SimpleNamespace(
+        moe_ep_rank=0,
+        num_local_experts=1,
+        num_experts=1,
+        w13_weight=dummy,
+        w13_weight_scale=dummy,
+        gemm1_alpha=None,
+        gemm1_clamp_limit=None,
+        w2_weight=dummy,
+        w2_weight_scale=dummy,
+    )
+    expected = (
+        torch.arange(tokens * hidden, dtype=torch.float32, device="cuda")
+        .reshape(tokens, hidden)
+        .to(torch.bfloat16)
+    )
+    returned_ptr = None
+
+    def fake_routed_moe(**kwargs):
+        nonlocal returned_ptr
+        kwargs["output"].copy_(expected)
+        ffi_result = kwargs["output"].clone()
+        returned_ptr = ffi_result.data_ptr()
+        return ffi_result
+
+    latent = torch.empty_like(x)
+    with (
+        patch.object(
+            route_quant_handoff,
+            "take",
+            return_value=(packed_topk, x_quant, x_scale),
+        ),
+        patch(
+            "sglang.srt.layers.quantization.mxfp4.trtllm_fp4_block_scale_routed_moe",
+            side_effect=fake_routed_moe,
+            create=True,
+        ),
+        patch.object(
+            mxfp4_module,
+            "RoutingMethodType",
+            SimpleNamespace(TopK=SimpleNamespace(value=0)),
+            create=True,
+        ),
+        patch.object(
+            mxfp4_module,
+            "ActivationType",
+            SimpleNamespace(Situ=SimpleNamespace(value=0)),
+            create=True,
+        ),
+        zero_copy_context.set_moe_output(latent),
+    ):
+        combine_input = method.apply(layer, dispatch_output)
+
+    assert returned_ptr is not None
+    assert returned_ptr != latent.data_ptr()
+    assert combine_input.hidden_states.data_ptr() == latent.data_ptr()
+    torch.testing.assert_close(combine_input.hidden_states, expected, rtol=0, atol=0)
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__, "-v"]))

test/registered/unit/layers/quantization/test_mxfp4_situ_weight_layout.py

@@ -0,0 +1,37 @@
+import sys
+
+import pytest
+
+from sglang.test.ci.ci_register import register_amd_ci
+
+register_amd_ci(est_time=5, stage="stage-b", runner_config="1-gpu-small-amd")
+
+
+@pytest.mark.parametrize(
+    ("a8w4", "a4w4", "expected"),
+    [
+        (None, None, True),
+        ("0", "1", False),
+        ("1", "0", True),
+        ("1", "1", True),
+    ],
+)
+def test_aiter_situ_weight_layout_matches_activation_mode_precedence(
+    monkeypatch, a8w4, a4w4, expected
+):
+    from sglang.srt.layers.quantization.mxfp4 import (
+        _aiter_situ_uses_gu_interleaved_weights,
+    )
+
+    monkeypatch.delenv("AITER_SITUV2_A8W4", raising=False)
+    monkeypatch.delenv("AITER_SITUV2_A4W4", raising=False)
+    if a8w4 is not None:
+        monkeypatch.setenv("AITER_SITUV2_A8W4", a8w4)
+    if a4w4 is not None:
+        monkeypatch.setenv("AITER_SITUV2_A4W4", a4w4)
+
+    assert _aiter_situ_uses_gu_interleaved_weights() is expected
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__, "-v"]))