[refactor] Remove unnecessary attributes from set_ascend_forward_context (#5204)

### What this PR does / why we need it?
Remove unnecessary attributes from set_ascend_forward_context
1.prefetch_stream
2.weight_prefetch_method
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: Wang Kunpeng <1289706727@qq.com>

tests/e2e/nightly/ops/test_fused_moe.py

@@ -286,8 +286,8 @@ def test_select_experts(
 
     with patch("vllm_ascend.ops.fused_moe.experts_selector._native_grouped_topk"
                ) as mock_native_grouped_topk, \
-            patch('vllm_ascend.ops.fused_moe.experts_selector.get_forward_context',
-                  return_value=MagicMock(weight_prefetch_method=MagicMock())):
+            patch('vllm_ascend.ops.fused_moe.experts_selector.get_weight_prefetch_method',
+                  return_value=MagicMock()):
         mock_native_grouped_topk.side_effect = lambda x, num_groups, k: torch.randn_like(
             x)
 
@@ -323,8 +323,8 @@ def test_select_experts(
 
 @pytest.mark.parametrize("device", DEVICE)
 def test_select_experts_invalid_scoring_func(device: str):
-    with patch('vllm_ascend.ops.fused_moe.experts_selector.get_forward_context',
-                  return_value=MagicMock(weight_prefetch_method=MagicMock())), \
+    with patch('vllm_ascend.ops.fused_moe.experts_selector.get_weight_prefetch_method',
+                  return_value=MagicMock()), \
             pytest.raises(ValueError,
                        match="Unsupported scoring function: invalid"):
         select_experts(hidden_states=torch.randn(1, 128, device=device),
@@ -336,17 +336,3 @@ def test_select_experts_invalid_scoring_func(device: str):
     gc.collect()
     torch.npu.empty_cache()
     torch.npu.reset_peak_memory_stats()
-
-
-@pytest.mark.parametrize("device", DEVICE)
-def test_select_experts_missing_group_params(device: str):
-    with pytest.raises(AssertionError):
-        select_experts(hidden_states=torch.randn(1, 128, device=device),
-                       router_logits=torch.randn(1, 64, device=device),
-                       top_k=2,
-                       use_grouped_topk=True,
-                       renormalize=False,
-                       scoring_func="softmax")
-    gc.collect()
-    torch.npu.empty_cache()
-    torch.npu.reset_peak_memory_stats()