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[ROCM][RL] Shuffle Weight In-Place to Preserve Parameter Attributes (#21825)

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This commit is contained in:
Yuzhen Zhou
2026-04-02 23:43:55 -07:00
committed by GitHub
parent 75de479680
commit 6b876a7710
1 changed files with 5 additions and 7 deletions
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python/sglang/srt/layers/quantization/unquant.py
+5 -7
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@@ -22,7 +22,7 @@ from sglang.srt.layers.quantization.base_config import (
LinearMethodBase,
QuantizeMethodBase,
)
from sglang.srt.layers.utils import MultiPlatformOp
from sglang.srt.layers.utils import MultiPlatformOp, copy_or_rebind_param
from sglang.srt.utils import (
cpu_has_amx_support,
get_bool_env_var,
@@ -233,14 +233,12 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, MultiPlatformOp):
# because aiter CK kernels don't support all GEMM dimensions
_should_use_aiter_moe = _use_aiter and get_moe_runner_backend().is_auto()
if _should_use_aiter_moe:
layer.w13_weight = torch.nn.Parameter(
shuffle_weight(layer.w13_weight.data, (16, 16)),
requires_grad=False,
copy_or_rebind_param(
layer, "w13_weight", shuffle_weight(layer.w13_weight.data, (16, 16))
)
torch.cuda.empty_cache()
layer.w2_weight = torch.nn.Parameter(
shuffle_weight(layer.w2_weight.data, (16, 16)),
requires_grad=False,
copy_or_rebind_param(
layer, "w2_weight", shuffle_weight(layer.w2_weight.data, (16, 16))
)
torch.cuda.empty_cache()