[BugFix] Require kv producer for layer sharding (#8563)

### What this PR does / why we need it?

This PR introduce stricter Ascend `additional_config.layer_sharding`
validation to the 0.18 release branch so it is only accepted on
PD-disaggregated P nodes with `kv_role="kv_producer"`.


### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

E2E test

---------

Signed-off-by: chenchuw886 <chenchuw@huawei.com>
Co-authored-by: chenchuw886 <chenchuw@huawei.com>

docs/source/developer_guide/Design_Documents/cpu_binding.md

@@ -20,7 +20,7 @@ On multi‑socket ARM systems, the OS scheduler may place vLLM threads on CPUs f
   | Device type | Default mode | Description |
   | ----------- | ------------ | ------------ |
   | A3 (No Affinity) | `global_slice` | Splits the allowed CPU list evenly based on the **total number of global logical NPUs**, ensuring each NPU is assigned a contiguous segment of CPU cores. This prevents CPU core overlap across multiple process groups. |
-  | A2 / 310P / Others | `topo_affinity` | Allocates CPUs based on NPU topology affinity (`npu‑smi info -t topo`). If multiple NPUs are assigned to a single NUMA node (which may cause bandwidth contention), the CPU allocation extends to adjacent NUMA nodes. |
+  | A2 / Atlas 300 inference products / Others | `topo_affinity` | Allocates CPUs based on NPU topology affinity (`npu‑smi info -t topo`). If multiple NPUs are assigned to a single NUMA node (which may cause bandwidth contention), the CPU allocation extends to adjacent NUMA nodes. |
 
     - **Default**: enabled (enable_cpu_binding = true).
     - **Fallback**: If NPU topo affinity is unavailable, global_slice is used.
@@ -156,7 +156,7 @@ With the current `global_slice` strategy, some CPU/NPU layouts cannot avoid cros
 |2|10-14|`IRQ`: 10-11, `Main`: 12, `ACL`: 13, `Release`: 14|
 |3|15-19|`IRQ`: 15-16, `Main`: 17, `ACL`: 18, `Release`: 19|
 
-### Example 5: A2/310P topo_affinity with NUMA extension
+### Example 5: A2/Atlas 300 inference products topo_affinity with NUMA extension
 
 **Inputs**:
 

tests/e2e/multicard/2-cards/test_offline_inference_distributed.py

@@ -26,6 +26,7 @@ from unittest.mock import patch
 
 import pytest
 from vllm import SamplingParams
+from vllm.config import KVTransferConfig
 
 from tests.e2e.conftest import VllmRunner, wait_until_npu_memory_free
 from tests.e2e.model_utils import check_outputs_equal
@@ -180,6 +181,7 @@ def test_qwen3_moe_fc2_oshard_tp2() -> None:
         enable_expert_parallel=True,
         enforce_eager=True,  # TODO(Levi-JQ): support graph mode for fc2 in Qwen
         additional_config={"layer_sharding": ["o_proj"]},
+        kv_transfer_config=KVTransferConfig(kv_role="kv_producer"),
     ) as vllm_model:
         vllm_model.generate(example_prompts, sampling_params)
 
@@ -261,7 +263,6 @@ def test_deepseek3_2_w8a8_pruning_mtp_tp2_ep():
         max_model_len=163840,
         compilation_config={"cudagraph_capture_sizes": [2, 4, 6, 8, 10, 12], "cudagraph_mode": "FULL_DECODE_ONLY"},
         speculative_config={"num_speculative_tokens": 1, "method": "deepseek_mtp"},
-        additional_config={"layer_sharding": ["q_b_proj", "o_proj"]},
         reasoning_parser="deepseek_v3",
         tokenizer_mode="deepseek_v32",
         gpu_memory_utilization=0.8,
@@ -290,7 +291,7 @@ def test_deepseek3_2_w8a8c8_pruning_mtp_tp2_ep():
         max_model_len=163840,
         compilation_config={"cudagraph_capture_sizes": [2, 4, 6, 8, 10, 12], "cudagraph_mode": "FULL_DECODE_ONLY"},
         speculative_config={"num_speculative_tokens": 1, "method": "deepseek_mtp"},
-        additional_config={"layer_sharding": ["q_b_proj", "o_proj"], "enable_sparse_c8": True},
+        additional_config={"enable_sparse_c8": True},
         reasoning_parser="deepseek_v3",
         tokenizer_mode="deepseek_v32",
         gpu_memory_utilization=0.8,

tests/ut/test_platform.py

@@ -689,12 +689,13 @@ class TestNPUPlatform(TestBase):
 
         self.assertEqual(vllm_config.cache_config.block_size, 512)
 
-    def test_validate_layer_sharding_config_accepts_single_node(self):
+    def test_validate_layer_sharding_config_rejects_missing_kv_transfer_config(self):
         vllm_config = TestNPUPlatform.mock_vllm_config()
         vllm_config.additional_config = {"layer_sharding": ["q_b_proj", "o_proj"]}
         vllm_config.kv_transfer_config = None
 
-        self.platform._validate_layer_sharding_config(vllm_config)
+        with pytest.raises(ValueError, match="layer_sharding is only supported on P nodes"):
+            self.platform._validate_layer_sharding_config(vllm_config)
 
     def test_validate_layer_sharding_config_accepts_kv_producer(self):
         vllm_config = TestNPUPlatform.mock_vllm_config()

vllm_ascend/platform.py

@@ -241,7 +241,7 @@ class NPUPlatform(Platform):
             return
 
         kv_transfer_config = vllm_config.kv_transfer_config
-        if kv_transfer_config is not None and kv_transfer_config.kv_role != "kv_producer":
+        if kv_transfer_config is None or kv_transfer_config.kv_role != "kv_producer":
             raise ValueError(
                 "additional_config.layer_sharding is only supported on P nodes "
                 "(kv_role='kv_producer') when KV transfer is enabled."