Rename layer to comply with deepseek

Signed-off-by: peaceh <[email protected]>

Author: peaceh-nv

tensorrt_llm/_torch/models/modeling_deepseekv3.py

@@ -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),
@@ -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:

tensorrt_llm/_torch/modules/attention.py

@@ -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,
@@ -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,
@@ -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),