[refactor] refactor deepseek-related files (#2849)

### What this PR does / why we need it?
This PR deletes ~2K lines of code about deepseek modeling. It falls back
CustomDeepseekV2 modules to original vllm implementations and adapts
some modifications in vllm about deepseek and moe.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
E2E  vllm serving with torchair graph mode and eager mode.

- vLLM version: v0.10.2
- vLLM main:
759ef49b15

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
Co-authored-by: yiz-liu <136800916+yiz-liu@users.noreply.github.com>
Co-authored-by: Yizhou Liu <liu_yizhou@outlook.com>

tests/ut/models/conftest.py

@@ -0,0 +1,114 @@
+from types import SimpleNamespace
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+import torch
+from transformers import PretrainedConfig
+from vllm.config import CacheConfig, EPLBConfig, ParallelConfig
+from vllm.distributed.parallel_state import GroupCoordinator
+
+
+@pytest.fixture
+def base_config():
+    config = PretrainedConfig(
+        hidden_size=128,
+        num_attention_heads=8,
+        num_hidden_layers=2,
+        intermediate_size=256,
+        hidden_act="silu",
+        rms_norm_eps=1e-6,
+        rope_theta=10000.0,
+        max_position_embeddings=2048,
+        n_routed_experts=4,
+        n_shared_experts=1,
+        moe_intermediate_size=256,
+        num_experts_per_tok=2,
+        routed_scaling_factor=1.0,
+        first_k_dense_replace=0,
+        moe_layer_freq=1,
+        kv_lora_rank=16,
+        qk_nope_head_dim=16,
+        qk_rope_head_dim=16,
+        v_head_dim=32,
+        topk_method="noaux_tc",
+        scoring_func="softmax",
+        norm_topk_prob=True,
+        n_group=1,
+        topk_group=1,
+        vocab_size=10000,
+    )
+    return config
+
+
+@pytest.fixture
+def vllm_config(base_config):
+    model_config = SimpleNamespace(
+        hf_config=base_config,
+        tensor_parallel_size=1,
+        dtype=torch.float32,
+        use_mla=True,
+        quant_config=None,
+        max_model_len=2048,
+    )
+    parallel_config = MagicMock(spec=ParallelConfig)
+    eplb_config = MagicMock(spec=EPLBConfig)
+    eplb_config.num_redundant_experts = 0
+    parallel_config.eplb_config = eplb_config
+
+    cache_config = CacheConfig()
+    vllm_config = Mock()
+    vllm_config.model_config = model_config
+    vllm_config.cache_config = cache_config
+    vllm_config.quant_config = None
+    vllm_config.parallel_config = parallel_config
+    return vllm_config
+
+
+@pytest.fixture
+def mock_distributed():
+    tp_group = Mock(spec=GroupCoordinator)
+    tp_group.rank_in_group = 0
+    tp_group.world_size = 1
+    tp_group.device_group = Mock()
+
+    dp_group = Mock(spec=GroupCoordinator)
+    dp_group.rank_in_group = 0
+    dp_group.world_size = 1
+
+    ep_group = Mock(spec=GroupCoordinator)
+    ep_group.rank_in_group = 0
+    ep_group.world_size = 1
+    ep_group.device_group = Mock()
+    ep_group.device_group.rank.return_value = 0
+    ep_group.device_group.size.return_value = 1
+
+    pp_group = Mock(spec=GroupCoordinator)
+    pp_group.rank_in_group = 0
+    pp_group.world_size = 1
+
+    mock_vllm_config = Mock()
+    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.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