Merge branch 'v0.7.2-dev' of http://10.6.10.68/dcutoolkit/deeplearing/vllm into v0.7.2-dev

vllm/attention/backends/mla/utils.py

-# SPDX-License-Identifier: Apache-2.0
-
+import os
 from abc import abstractmethod
 from dataclasses import dataclass
 from typing import Any, Dict, Generic, List, Optional, Tuple
@@ -181,6 +180,8 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
         self.kv_b_proj = kv_b_proj
         self.o_proj = o_proj
 
+        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
+
     def _v_up_proj_and_o_proj(self, x):
         if envs.VLLM_MLA_PERFORM_MATRIX_ABSORPTION:
             if is_fp8(self.W_UV_O):
@@ -301,10 +302,21 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
         assert self.o_proj.weight.dtype == weight_dtype
         assert self.q_proj.weight.dtype == weight_dtype
 
-        kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
-        assert kv_b_proj_weight.shape == (
-            self.kv_lora_rank,
-            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
+        if self.use_llama_nn and self.kv_b_proj.quant_method is None:
+            kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj)
+            assert kv_b_proj_weight.shape == (
+                self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+                self.kv_lora_rank), (
+                f"{kv_b_proj_weight.shape=}, "
+                f"{self.kv_lora_rank=}, "
+                f"{self.num_heads=}, "
+                f"{self.qk_nope_head_dim=}, "
+                f"{self.v_head_dim=}")
+        else:
+            kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
+            assert kv_b_proj_weight.shape == (
+                self.kv_lora_rank,
+                self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
                 f"{kv_b_proj_weight.shape=}, "
                 f"{self.kv_lora_rank=}, "
                 f"{self.num_heads=}, "
@@ -319,8 +331,12 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
         W_UK, W_UV = kv_b_proj_weight.split(
             [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
-        q_proj_weight = get_and_maybe_dequant_weights(self.q_proj).T\
-                .view(-1, self.num_heads, self.qk_head_dim)
+        if self.use_llama_nn and self.q_proj.quant_method is None:
+            q_proj_weight = get_and_maybe_dequant_weights(self.q_proj)\
+                    .view(-1, self.num_heads, self.qk_head_dim)
+        else:
+            q_proj_weight = get_and_maybe_dequant_weights(self.q_proj).T\
+                    .view(-1, self.num_heads, self.qk_head_dim)
 
         # can be W_Q or W_UQ depending q_lora_rank, the former if
         # q_lora_rank is None, the latter otherwise. From the Attention backend
@@ -376,8 +392,12 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
             else:
                 self.W_Q_UK = W_Q_UK.to(act_dtype)
 
-            W_O = get_and_maybe_dequant_weights(self.o_proj)\
-                .view(-1, self.num_heads, self.v_head_dim)
+            if self.use_llama_nn and self.o_proj.quant_method is None:
+                W_O = get_and_maybe_dequant_weights(self.o_proj).T\
+                    .view(-1, self.num_heads, self.v_head_dim)
+            else:
+                W_O = get_and_maybe_dequant_weights(self.o_proj)\
+                    .view(-1, self.num_heads, self.v_head_dim)
             W_UV_O = torch.einsum("lnd,hnd -> nlh", W_UV, W_O)\
                 .flatten(start_dim=0, end_dim=1).contiguous()
 

vllm/model_executor/models/deepseek_v3.py

@@ -821,26 +821,18 @@ class DeepseekV3ForCausalLM(nn.Module, SupportsPP):
                     "mlp.gate_up_proj.weight",
                     "mlp.down_proj",
                     "shared_experts.gate_up_proj",
-                    "shared_experts.down_proj"
+                    "shared_experts.down_proj",
+                    "self_attn.q_proj.weight",
+                    "self_attn.q_b_proj.weight",
+                    "self_attn.kv_b_proj.weight",
+                    "self_attn.o_proj.weight",
+                    "lm_head.weight"
                 ]
-                if not self.use_mla:
-                    lay_key_words.extend([
-                        "self_attn.q_proj.weight",
-                        "self_attn.q_b_proj.weight",
-                        "self_attn.kv_b_proj.weight",
-                        "self_attn.o_proj.weight",
-                    ])
+
                 combined_words = "|".join(lay_key_words)
                 
                 for layername in loaded_params:
                     weight = params_dict[layername]
-                    if "lm_head.weight" in layername:
-                        lay_key_words.append("lm_head.weight")
-                        combined_words = "|".join(lay_key_words)
-                        os.environ['LM_NN'] = '1'
-                    else:
-                        os.environ['LM_NN'] = '0'
-                        
                     matches = re.findall(combined_words, layername)
                     if matches:
                         _weight = torch.zeros_like(weight.data)