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[Bugfix] [NPU] bugfixes for running deepseek w4a8 quantization #14542

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[Bugfix] [NPU] bugfixes for running deepseek w4a8 quantization #14542

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96c15c7
fix w4a8 moe and ds bmm kernel use condition
iforgetmyname Dec 6, 2025
768b452
fix bmm shape
iforgetmyname Dec 6, 2025
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18 changes: 10 additions & 8 deletions python/sglang/srt/hardware_backend/npu/modules/deepseek_v2_attention_mla_npu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -229,19 +229,17 @@ def forward_mla_core_npu(
k_rope=k_pe,
**(dict(topk_indices=topk_indices) if topk_indices is not None else {}),
)

attn_output = attn_output.view(-1, m.num_local_heads, m.kv_lora_rank)

attn_bmm_output = torch.empty(
(attn_output.shape[0], m.num_local_heads * m.v_head_dim),
(attn_output.shape[0], m.num_local_heads, m.v_head_dim),
dtype=attn_output.dtype,
device=attn_output.device,
)
torch.bmm(
attn_output.transpose(0, 1),
m.w_vc,
out=attn_bmm_output.view(-1, m.num_local_heads, m.v_head_dim).transpose(0, 1),
)
torch.ops.npu.batch_matmul_transpose(attn_output, m.w_vc, attn_bmm_output)

attn_bmm_output = attn_bmm_output.reshape(-1, m.num_local_heads * m.v_head_dim)

output, _ = m.o_proj(attn_bmm_output)
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@gemini-code-assist gemini-code-assist bot Dec 6, 2025

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critical

The attn_bmm_output tensor has a 3D shape of (num_tokens, num_local_heads, v_head_dim), but m.o_proj (a RowParallelLinear layer) expects a 2D input where the last dimension is m.num_local_heads * m.v_head_dim. You should reshape attn_bmm_output before passing it to m.o_proj. This is consistent with how it's handled in forward_dsa_core_npu and forward_mha_core_npu.

Suggested change
output, _ = m.o_proj(attn_bmm_output)
attn_bmm_output = attn_bmm_output.reshape(-1, m.num_local_heads * m.v_head_dim)
output, _ = m.o_proj(attn_bmm_output)


return output
Expand Down Expand Up @@ -358,7 +356,11 @@ def forward_dsa_core_npu(
device=attn_output.device,
)

if not forward_batch.forward_mode.is_decode():
if (
not forward_batch.forward_mode.is_decode()
and not forward_batch.forward_mode.is_draft_extend(include_v2=True)
and not forward_batch.forward_mode.is_target_verify()
):
attn_output = attn_output.transpose(0, 1)
torch.bmm(
attn_output,
Expand Down
12 changes: 6 additions & 6 deletions python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -569,9 +569,9 @@ def apply_without_routing_weights(
):
hidden_states = torch.ops.npu.npu_grouped_matmul(
x=[hidden_states],
weight=[self.w13_weight],
scale=[self.w13_weight_scale],
bias=[self.w13_scale_bias],
weight=[layer.w13_weight],
scale=[layer.w13_weight_scale],
bias=[layer.w13_scale_bias],
per_token_scale=[hidden_states_scale],
group_list=group_list,
split_item=2,
Expand All @@ -586,9 +586,9 @@ def apply_without_routing_weights(

hidden_states = torch.ops.npu.npu_grouped_matmul(
x=[hidden_states],
weight=[self.w2_weight],
scale=[self.w2_weight_scale],
bias=[self.w2_scale_bias],
weight=[layer.w2_weight],
scale=[layer.w2_weight_scale],
bias=[layer.w2_scale_bias],
per_token_scale=[swiglu_out_scale],
group_list=group_list,
split_item=2,
Expand Down
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