[Bugfix][kvcache] revert multiple kv cache groups (#923)

Revert multiple kv cache groups related changes as this feature is
reverted in vllm vllm-project/vllm#18459

---------

Signed-off-by: MengqingCao <cmq0113@163.com>

Author: MengqingCao

vllm_ascend/attention/attention_v1.py

@@ -30,7 +30,6 @@
 from vllm.v1.worker.gpu_input_batch import InputBatch
 
 from vllm_ascend.ops.attention import vanilla_chunked_prefill
-from vllm_ascend.utils import vllm_version_is
 
 
 class AscendAttentionBackend(AttentionBackend):
@@ -141,14 +140,8 @@ def reorder_batch(self, input_batch: "InputBatch",
 
     def build(self, num_reqs, num_actual_tokens, max_query_len,
               common_prefix_len):
-        if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
-            block_table = (self.runner.input_batch.block_table.
-                           get_device_tensor()[:num_reqs])
-        else:
-            block_table = self.runner.input_batch.block_table[
-                0].get_device_tensor()
-            block_table[:num_reqs, :self.runner.max_num_blocks_per_req] = (
-                block_table[:num_reqs])
+        block_table = (
+            self.runner.input_batch.block_table.get_device_tensor()[:num_reqs])
 
         query_lens = self.runner.query_lens
         seq_lens = self.runner.seq_lens_cpu[:num_reqs]

vllm_ascend/attention/mla_v1.py

@@ -16,7 +16,6 @@
 
 from vllm_ascend.attention.attention_v1 import AscendAttentionState
 from vllm_ascend.ops.attention import vanilla_chunked_prefill_mla
-from vllm_ascend.utils import vllm_version_is
 from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
 
 if TYPE_CHECKING:
@@ -239,12 +238,8 @@ def build(self,
         # function. We should avoid GPU -> CPU sync as much as possible because
         # it blocks on all previous kernels.
         device = self.runner.device
-        if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
-            block_table = (self.runner.input_batch.block_table.
-                           get_device_tensor()[:num_reqs])
-        else:
-            block_table = (self.runner.input_batch.block_table[0].
-                           get_device_tensor()[:num_reqs])
+        block_table = (
+            self.runner.input_batch.block_table.get_device_tensor()[:num_reqs])
         slot_mapping = self.runner.slot_mapping_cpu[:num_actual_tokens].to(
             device, non_blocking=True)
         input_positions = self.runner.positions_cpu[:num_actual_tokens].to(

vllm_ascend/worker/model_runner_v1.py

@@ -164,6 +164,24 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
             raise NotImplementedError(
                 "Non-Attention backend is not supported by V1 NPUModelRunner.")
 
+        self.attn_backend = get_attn_backend(
+            self.head_size,
+            self.dtype,
+            self.kv_cache_dtype,
+            self.block_size,
+            self.model_config.is_attention_free,
+            use_mla=self.model_config.use_mla,
+        )
+        if self.attn_backend is None:
+            error_msg = (
+                f"Error with get_att_backend: {self.head_size=}, "
+                f"{self.dtype=}, {self.kv_cache_dtype=}, {self.block_size=}, "
+                f"{self.model_config.is_attention_free=}, "
+                f"{self.model_config.use_mla=}")
+            logger.error(error_msg)
+            raise NotImplementedError(
+                "Non-Attention backend is not supported by V1 GPUModelRunner.")
+
         self.attn_metadata_builder = self.attn_backend.get_builder_cls()(
             weakref.proxy(self))
 
@@ -196,6 +214,17 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
                 pin_memory=True,
                 vocab_size=self.model_config.get_vocab_size(),
             )
+        else:
+            self.input_batch = InputBatch(
+                max_num_reqs=self.max_num_reqs,
+                max_model_len=self.model_config.max_model_len,
+                max_num_blocks_per_req=self.max_num_blocks_per_req,
+                max_num_batched_tokens=self.max_num_tokens,
+                device=self.device,
+                pin_memory=True,
+                vocab_size=self.model_config.get_vocab_size(),
+            )
+
         self.input_ids = torch.zeros(self.max_num_tokens,
                                      dtype=torch.int32,
                                      device=self.device)
@@ -542,10 +571,7 @@ def _process_reqs(
 
         block_table_indices = (req_indices * self.max_num_blocks_per_req +
                                positions_np // self.block_size)
-        if vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1"):
-            block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
-        else:
-            block_table_cpu = self.input_batch.block_table[0].get_cpu_tensor()
+        block_table_cpu = self.input_batch.block_table.get_cpu_tensor()
         block_numbers = block_table_cpu.flatten()[block_table_indices].numpy()
         block_offsets = positions_np % self.block_size
         np.add(block_numbers * self.block_size,
@@ -960,16 +986,6 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         """
         import torch_npu
         kv_caches: Dict[str, torch.Tensor] = {}
-        if not (vllm_version_is("0.8.5") or vllm_version_is("0.8.5.post1")):
-            self.input_batch = InputBatch(
-                max_num_reqs=self.max_num_reqs,
-                max_model_len=self.model_config.max_model_len,
-                max_num_batched_tokens=self.max_num_tokens,
-                device=self.device,
-                pin_memory=True,
-                vocab_size=self.model_config.get_vocab_size(),
-                kv_cache_config=kv_cache_config,
-            )
 
         for kv_cache_group in kv_cache_config.kv_cache_groups:
             kv_cache_spec = kv_cache_group.kv_cache_spec