[Model][1/N] Delete deepseek v2/v3 modeling codes.

tests/ut/attention/test_mla_v1.py

@@ -302,6 +302,7 @@ def setUp(self, ascend_config, get_current_vllm_config, mock_get_tp_size,
             "v_head_dim": 128,
             "rotary_emb": MagicMock(),
             "q_proj": MagicMock(),
+            "q_b_proj": MagicMock(),
             "kv_b_proj": MagicMock(),
             "o_proj": MagicMock(),
             "kv_a_proj_with_mqa": MagicMock(),

tests/ut/models/conftest.py

@@ -90,25 +90,11 @@ def mock_distributed():
     mock_vllm_config.scheduler_config = Mock(max_num_seqs=256)
     mock_vllm_config.model_config = Mock(max_model_len=2048, quant_config=None)
 
-    with patch("vllm_ascend.models.deepseek_v2.get_tensor_model_parallel_rank", return_value=0), \
-            patch("vllm_ascend.models.deepseek_v2.get_tensor_model_parallel_world_size", return_value=1), \
-            patch("vllm_ascend.models.deepseek_v2.get_tp_group", return_value=tp_group), \
-            patch("vllm_ascend.models.deepseek_v2.get_pp_group", return_value=pp_group), \
-            patch("vllm_ascend.models.deepseek_v2.get_pp_group",
-                  return_value=Mock(is_first_rank=False, is_last_rank=False)), \
-            patch("vllm_ascend.ops.common_fused_moe.get_current_vllm_config", return_value=mock_vllm_config), \
+    with patch("vllm_ascend.ops.common_fused_moe.get_current_vllm_config", return_value=mock_vllm_config), \
             patch("vllm_ascend.ops.moe.token_dispatcher.torch.distributed.get_rank", return_value=0), \
             patch("vllm_ascend.ops.moe.token_dispatcher.get_ascend_soc_version", return_value=None), \
             patch.dict("vllm.distributed.parallel_state.__dict__", _TP=tp_group, _EP=ep_group, _DP=dp_group,
                        _PP=pp_group), \
             patch.dict("vllm_ascend.distributed.parallel_state.__dict__", _MC2=ep_group), \
             patch("torch.npu.current_device", return_value=0):
         yield
-
-
-@pytest.fixture
-def mock_forward_context():
-    forward_context = Mock(in_profile_run=False, with_prefill=False)
-    with patch("vllm_ascend.models.deepseek_v2.get_forward_context",
-               return_value=forward_context):
-        yield

tests/ut/models/test_deepseek_mtp.py

@@ -37,8 +37,6 @@ def setup_mtp_layer(self, mocker: MockerFixture, vllm_config: VllmConfig,
         mocker.patch(
             "vllm_ascend.ops.vocab_parallel_embedding.AscendVocabParallelEmbedding.__init__",
             return_value=None)
-        mocker.patch("vllm_ascend.models.deepseek_v2.get_ascend_config",
-                     return_value=mocker.Mock())
 
         mtp_layer = CustomDeepSeekMultiTokenPredictorLayer(config, "0", None)
         mocker_deepseek_v2_decode_layer.assert_called_once()

tests/ut/torchair/test_torchair_mla.py

@@ -525,6 +525,7 @@ def setUp(self, ascend_config, vllm_config, mock_get_tp_size, mock_tp):
             "v_head_dim": 128,
             "rotary_emb": MagicMock(),
             "q_proj": MagicMock(),
+            "q_b_proj": MagicMock(),
             "kv_b_proj": MagicMock(),
             "o_proj": MagicMock(),
             "kv_a_proj_with_mqa": MagicMock(),

vllm_ascend/attention/mla_v1.py

@@ -536,12 +536,13 @@ def __init__(
         self.qk_head_dim = kwargs['qk_head_dim']
         self.v_head_dim = kwargs['v_head_dim']
         self.rotary_emb = kwargs['rotary_emb']
-        self.q_proj = kwargs['q_proj']
+        self.fused_qkv_a_proj = kwargs.get('fused_qkv_a_proj', None)
+        self.q_proj = kwargs['q_proj'] if self.q_lora_rank is None else kwargs[
+            'q_b_proj']
         self.kv_b_proj = kwargs['kv_b_proj']
         self.o_proj = kwargs['o_proj']
         self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
         self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
-        self.q_a_proj = kwargs.get('q_a_proj', None)
         self.q_a_layernorm = kwargs.get('q_a_layernorm', None)
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
         self.tp_size = get_tensor_model_parallel_world_size()
@@ -648,36 +649,46 @@ def get_and_maybe_dequant_weights(layer: LinearBase):
             self._process_weights_for_fused_mlapo(act_dtype)
 
     def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
-        kv_a_proj_wt = self.kv_a_proj_with_mqa.weight.data
-        kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
+        kv_a_proj_wt = self.fused_qkv_a_proj.weight.data[
+            ..., self.q_lora_rank:].contiguous()
+        q_a_proj_wt = self.fused_qkv_a_proj.weight.data[
+            ..., :self.q_lora_rank].contiguous()
+        kv_a_proj_wt = kv_a_proj_wt.contiguous()
         kv_a_proj_wt = trans_rope_weight(kv_a_proj_wt, self.qk_rope_head_dim)
-        kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
-        wd_qkv = torch.cat((kv_a_proj_wt, self.q_a_proj.weight.data), dim=-1)
+        kv_a_proj_wt = kv_a_proj_wt.contiguous()
+        wd_qkv = torch.cat((kv_a_proj_wt, q_a_proj_wt), dim=-1)
         wd_qkv = wd_qkv.t().contiguous()
         wd_qkv = transdata(wd_qkv,
                            block_size=(16, 32)).unsqueeze(0).contiguous()
         self.wd_qkv = torch_npu.npu_format_cast(wd_qkv, 29)
 
-        kv_a_proj_deq_scl = self.kv_a_proj_with_mqa.deq_scale
+        kv_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[
+            self.q_lora_rank:].contiguous()
+        q_a_proj_deq_scl = self.fused_qkv_a_proj.deq_scale[:self.
+                                                           q_lora_rank].contiguous(
+                                                           )
         kv_a_proj_deq_scl = kv_a_proj_deq_scl.reshape(
             self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
         kv_a_proj_deq_scl = trans_rope_weight(kv_a_proj_deq_scl,
                                               self.qk_rope_head_dim)
         kv_a_proj_deq_scl = kv_a_proj_deq_scl.view(
             self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
-        self.deq_scale_qkv = torch.cat(
-            (kv_a_proj_deq_scl, self.q_a_proj.deq_scale), dim=-1).contiguous()
-
-        kv_a_proj_qt_bias = self.kv_a_proj_with_mqa.quant_bias
+        self.deq_scale_qkv = torch.cat((kv_a_proj_deq_scl, q_a_proj_deq_scl),
+                                       dim=-1).contiguous()
+
+        kv_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[
+            self.q_lora_rank:].contiguous()
+        q_a_proj_qt_bias = self.fused_qkv_a_proj.quant_bias[:self.
+                                                            q_lora_rank].contiguous(
+                                                            )
         kv_a_proj_qt_bias = kv_a_proj_qt_bias.reshape(
             self.kv_lora_rank + self.qk_rope_head_dim, -1).contiguous()
         kv_a_proj_qt_bias = trans_rope_weight(kv_a_proj_qt_bias,
                                               self.qk_rope_head_dim)
         kv_a_proj_qt_bias = kv_a_proj_qt_bias.view(
             self.kv_lora_rank + self.qk_rope_head_dim).contiguous()
-        self.quant_bias_qkv = torch.cat(
-            (kv_a_proj_qt_bias, self.q_a_proj.quant_bias),
-            dim=-1).contiguous()
+        self.quant_bias_qkv = torch.cat((kv_a_proj_qt_bias, q_a_proj_qt_bias),
+                                        dim=-1).contiguous()
 
         wu_q = self.q_proj.weight.data
         wu_q = wu_q.t().reshape(self.num_heads,
@@ -704,22 +715,22 @@ def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
         self.qb_qt_bias = qb_qt_bias.reshape(
             self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))
 
-        device = self.q_a_proj.weight.device
+        device = self.q_proj.weight.device
         self.gamma0 = torch.ones(
-            [self.q_a_proj.weight.shape[-1]],
+            [self.fused_qkv_a_proj.weight.shape[-1]],
             dtype=act_dtype,
             device=device,
         )
         self.beta0 = torch.zeros(
-            [self.q_a_proj.weight.shape[-1]],
+            [self.fused_qkv_a_proj.weight.shape[-1]],
             dtype=act_dtype,
             device=device,
         )
         self.gamma1 = self.q_a_layernorm.weight.data
         self.beta1 = self.q_a_layernorm.bias.data
         self.gamma2 = self.kv_a_layernorm.weight.data
-        self.quant_scale0 = self.q_a_proj.input_scale.data
-        self.quant_offset0 = self.q_a_proj.input_offset.data
+        self.quant_scale0 = self.fused_qkv_a_proj.input_scale.data
+        self.quant_offset0 = self.fused_qkv_a_proj.input_offset.data
         self.quant_scale1 = self.q_proj.input_scale.data
         self.quant_offset1 = self.q_proj.input_offset.data
         self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
@@ -1122,21 +1133,26 @@ def _mla_preprocess(self, layer_name, hidden_states, kv_cache,
         has_prefill = attn_metadata.num_prefills > 0
         num_decode_tokens = attn_metadata.num_decode_tokens
         num_actual_tokens = attn_metadata.num_actual_tokens
-        if self.q_a_proj is not None:
-            maybe_npu_prefetch(inputs=self.q_a_proj.weight,
+        if self.fused_qkv_a_proj is not None:
+            maybe_npu_prefetch(inputs=self.fused_qkv_a_proj.weight,
                                dependency=hidden_states,
                                enabled=self.enable_prefetch)
-            ckq = self.q_a_proj(hidden_states)[0]
-            q_c = self.q_a_layernorm(ckq)
+            qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
+            q_c, kv_no_split = qkv_lora.split(
+                [self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
+                dim=-1,
+            )
+            q_c = self.q_a_layernorm(q_c)
         else:
             q_c = hidden_states
+            kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0]
 
-        kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0]
         # Process for Flash Comm V1
         q_c = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
             q_c, need_gather_q_kv)
         kv_no_split = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
             kv_no_split, need_gather_q_kv)
+
         decode_preprocess_res = None
         prefill_preprocess_res = None
         if has_prefill:
@@ -1264,14 +1280,18 @@ def forward(
                                max_size=MAX_O_PROJ_PREFETCH_SIZE,
                                enabled=self.enable_prefetch)
 
-            output[...] = self.o_proj(o_proj_input)[0]
+            output[...] = self.o_proj(o_proj_input,
+                                      is_prefill=prefill_preprocess_res
+                                      is not None)[0]
         else:
             with torch.npu.stream(current_ms_metadata.comm_stream):
                 maybe_npu_prefetch(inputs=self.o_proj.weight,
                                    dependency=o_proj_input,
                                    max_size=MAX_O_PROJ_PREFETCH_SIZE,
                                    enabled=self.enable_prefetch)
-                output[...] = self.o_proj(o_proj_input)[0]
+                output[...] = self.o_proj(o_proj_input,
+                                          is_prefill=prefill_preprocess_res
+                                          is not None)[0]
                 current_ms_metadata.after_comm_event.record()
         del o_proj_input
 

vllm_ascend/models/__init__.py

@@ -29,14 +29,6 @@ def register_model():
             "vllm_ascend.models.qwen2_5_vl_without_padding:AscendQwen2_5_VLForConditionalGeneration_Without_Padding"
         )
 
-    ModelRegistry.register_model(
-        "DeepseekV2ForCausalLM",
-        "vllm_ascend.models.deepseek_v2:CustomDeepseekV2ForCausalLM")
-
-    ModelRegistry.register_model(
-        "DeepseekV3ForCausalLM",
-        "vllm_ascend.models.deepseek_v2:CustomDeepseekV3ForCausalLM")
-
     ModelRegistry.register_model(
         "DeepseekV32ForCausalLM",
         "vllm_ascend.models.deepseek_v3_2:CustomDeepseekV3ForCausalLM")