From d5f5de7a7db4e13ea9f94ce8662b55b6b5e97d02 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=84=8D=F0=9D=95=A0=F0=9D=95=9D=F0=9D=95=9D=F0=9D=95=A0?=
 =?UTF-8?q?=F0=9D=95=A8=20=F0=9D=95=84=F0=9D=95=92=F0=9D=95=9F?=
 <hollowman@opensuse.org>
Date: Tue, 18 Aug 2026 05:55:37 +0300
Subject: [PATCH] [Bugfix] Fix DeepSeek V4 mHC broadcast buffer for weight sync
 (#52626)

Signed-off-by: Hollow Man <hollowman@opensuse.org>
---
 tests/kernels/test_mhc_kernels.py       | 61 +++++++++++++++++++++++++
 vllm/models/deepseek_v4/nvidia/model.py |  6 ++-
 2 files changed, 66 insertions(+), 1 deletion(-)

diff --git a/tests/kernels/test_mhc_kernels.py b/tests/kernels/test_mhc_kernels.py
index 2bdce9f9c1..a2070dd595 100644
--- a/tests/kernels/test_mhc_kernels.py
+++ b/tests/kernels/test_mhc_kernels.py
@@ -1,7 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from types import SimpleNamespace
+
 import pytest
 import torch
+import torch.nn as nn
 
 import vllm.model_executor.kernels.mhc  # noqa: F401
 from vllm.model_executor.kernels.mhc.tilelang import (
@@ -9,6 +12,10 @@ from vllm.model_executor.kernels.mhc.tilelang import (
     _torch_hc_prenorm_gemm,
 )
 from vllm.model_executor.layers.mhc import HAS_TILELANG_MHC
+from vllm.models.deepseek_v4.nvidia.model import (
+    DeepseekV4DecoderLayer,
+    DeepseekV4Model,
+)
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
 
@@ -355,3 +362,57 @@ def test_hc_head_tilelang(num_tokens, hidden_size, hc_mult):
 
     out_ref = hc_head_ref(residual, fn, hc_scale, hc_base, rms_eps, hc_eps)
     torch.testing.assert_close(out, out_ref, atol=5e-2, rtol=1e-2)
+
+
+def _make_mhc_decoder_layer(hc_mult: int, hidden_size: int) -> DeepseekV4DecoderLayer:
+    layer = DeepseekV4DecoderLayer.__new__(DeepseekV4DecoderLayer)
+    nn.Module.__init__(layer)
+    layer.hc_mult = hc_mult
+    layer.hidden_size = hidden_size
+    mix_hc = (2 + hc_mult) * hc_mult
+    layer.hc_attn_fn = nn.Parameter(
+        torch.randn(mix_hc, hc_mult * hidden_size, dtype=torch.float32),
+        requires_grad=False,
+    )
+    layer.hc_attn_fn_broadcast = None
+    return layer
+
+
+def _patch_first_rank_pp_group(monkeypatch):
+    monkeypatch.setattr(
+        "vllm.models.deepseek_v4.nvidia.model.get_pp_group",
+        lambda: SimpleNamespace(is_first_rank=True),
+    )
+
+
+def test_deepseek_v4_mhc_broadcast_finalize_sums_hc_streams(monkeypatch):
+    """First finalize (at the end of load_weights) allocates
+    hc_attn_fn_broadcast as hc_attn_fn summed over hc streams."""
+    _patch_first_rank_pp_group(monkeypatch)
+    layer = _make_mhc_decoder_layer(hc_mult=2, hidden_size=8)
+    model = SimpleNamespace(start_layer=0, end_layer=1, layers=[layer])
+
+    DeepseekV4Model.finalize_mhc_broadcast_weights(model)
+
+    assert layer.hc_attn_fn_broadcast is not None
+    expected = layer.hc_attn_fn.detach().view(-1, 2, 8).sum(dim=1)
+    assert torch.equal(layer.hc_attn_fn_broadcast, expected)
+
+
+def test_deepseek_v4_mhc_broadcast_refit_refreshes_in_place(monkeypatch):
+    """Re-finalizing after a weight refit must copy into the existing
+    broadcast tensor so its address stays stable for captured CUDA graphs,
+    while picking up the new hc_attn_fn values."""
+    _patch_first_rank_pp_group(monkeypatch)
+    layer = _make_mhc_decoder_layer(hc_mult=2, hidden_size=8)
+    model = SimpleNamespace(start_layer=0, end_layer=1, layers=[layer])
+
+    DeepseekV4Model.finalize_mhc_broadcast_weights(model)
+    buffer = layer.hc_attn_fn_broadcast
+
+    layer.hc_attn_fn.add_(1.0)
+    DeepseekV4Model.finalize_mhc_broadcast_weights(model)
+
+    assert layer.hc_attn_fn_broadcast is buffer
+    expected = layer.hc_attn_fn.detach().view(-1, 2, 8).sum(dim=1)
+    assert torch.equal(layer.hc_attn_fn_broadcast, expected)
diff --git a/vllm/models/deepseek_v4/nvidia/model.py b/vllm/models/deepseek_v4/nvidia/model.py
index ed7ee11ce5..17f29f4a1a 100644
--- a/vllm/models/deepseek_v4/nvidia/model.py
+++ b/vllm/models/deepseek_v4/nvidia/model.py
@@ -1372,11 +1372,15 @@ class DeepseekV4Model(nn.Module, EagleModelMixin):
             return
         layer = self.layers[self.start_layer]
         if isinstance(layer, DeepseekV4DecoderLayer):
-            layer.hc_attn_fn_broadcast = (
+            broadcast = (
                 layer.hc_attn_fn.detach()
                 .view(-1, layer.hc_mult, layer.hidden_size)
                 .sum(dim=1)
             )
+            if layer.hc_attn_fn_broadcast is None:
+                layer.hc_attn_fn_broadcast = broadcast
+            else:
+                layer.hc_attn_fn_broadcast.copy_(broadcast)
 
 
 def _make_deepseek_v4_weights_mapper(expert_dtype: str) -> WeightsMapper: