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Rename layer to comply with deepseek #6393

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Merged
merged 1 commit into from
Jul 30, 2025
Merged

Rename layer to comply with deepseek #6393

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4 changes: 2 additions & 2 deletions tensorrt_llm/_torch/models/modeling_deepseekv3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -248,7 +248,7 @@ def __init__(
dtype=config.torch_dtype,
config=model_config,
aux_stream=aux_stream)
self.fused_a = DeepseekV3Linear(
self.kv_a_proj_with_mqa = DeepseekV3Linear(
config.hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim +
(self.q_lora_rank if not self.is_lite else 0),
Expand Down Expand Up @@ -1384,7 +1384,7 @@ def split_kv_b_proj(kv_b_proj: torch.Tensor,
attn_module.v_b_proj_scale = nn.Parameter(
v_b_proj_scale, requires_grad=False)

elif names[-1] == "fused_a":
elif names[-1] == "kv_a_proj_with_mqa":
fused_a = weights[
f"{'.'.join(names[:-1])}.kv_a_proj_with_mqa.weight"][:]
if not is_lite:
Expand Down
13 changes: 7 additions & 6 deletions tensorrt_llm/_torch/modules/attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -502,7 +502,7 @@ def __init__(
self.quant_config = quant_config

if not self.is_lite:
self.fused_a = Linear(
self.kv_a_proj_with_mqa = Linear(
hidden_size,
self.q_lora_rank + self.kv_lora_rank + self.qk_rope_head_dim,
bias=bias,
Expand All @@ -528,7 +528,7 @@ def __init__(
allreduce_strategy=config.allreduce_strategy,
force_dynamic_quantization=config.force_dynamic_quantization)
else:
self.fused_a = Linear(
self.kv_a_proj_with_mqa = Linear(
hidden_size,
self.kv_lora_rank + self.qk_rope_head_dim,
bias=bias,
Expand Down Expand Up @@ -743,14 +743,15 @@ def forward_impl(self,
torch.Tensor: The output tensor.
"""
if self.is_lite:
compressed_kv, k_pe = self.fused_a(hidden_states).split(
compressed_kv, k_pe = self.kv_a_proj_with_mqa(hidden_states).split(
[self.kv_lora_rank, self.qk_rope_head_dim], -1)
compressed_kv = self.kv_a_layernorm(compressed_kv)
q = hidden_states
else:
q, compressed_kv, k_pe = self.fused_a(hidden_states).split(
[self.q_lora_rank, self.kv_lora_rank, self.qk_rope_head_dim],
-1)
q, compressed_kv, k_pe = self.kv_a_proj_with_mqa(
hidden_states).split([
self.q_lora_rank, self.kv_lora_rank, self.qk_rope_head_dim
], -1)

q, compressed_kv = maybe_execute_in_parallel(
lambda: self.q_a_layernorm(q),
Expand Down