[4/N][Refactor] torchair model runner refactor (#2208)

There is lot of torchair code in model runner leading the code hard for
maintenance. We'll create new torchair_model_runner to split torchair
related logic. Following the workflow #2203, this is the first PR.

What's this PR do:

create common function `_convert_torch_foramt`  for initialize_kv_cache


- vLLM version: v0.10.0
- vLLM main:
vllm-project/vllm@14a5d90

Signed-off-by: wangxiyuan <[email protected]>

Author: wangxiyuan

vllm_ascend/torchair/torchair_model_runner.py

@@ -20,10 +20,11 @@
 from typing import Optional
 
 import torch
+import torch_npu
 from vllm.config import VllmConfig
 from vllm.forward_context import get_forward_context
 
-from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ,
+from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
                                maybe_converting_weight_acl_format)
 from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
 
@@ -113,3 +114,7 @@ def _generate_dummy_run_hidden_states(self, with_prefill,
                 with_prefill, is_torchair_compile, input_ids, positions,
                 attn_metadata, num_tokens, intermediate_tensors, inputs_embeds)
         return hidden_states
+
+    def _convert_torch_format(self, kv_cache):
+        kv_cache = torch_npu.npu_format_cast(kv_cache, ACL_FORMAT_FRACTAL_ND)
+        return kv_cache

vllm_ascend/worker/model_runner_v1.py

@@ -110,6 +110,9 @@
 
 if is_310p():
     torch_npu.npu.set_compile_mode(jit_compile=False)
+    ACL_FORMAT = ACL_FORMAT_FRACTAL_NZ
+else:
+    ACL_FORMAT = ACL_FORMAT_FRACTAL_ND
 
 
 @dataclass
@@ -2047,8 +2050,8 @@ def load_model(self) -> None:
                     if isinstance(module,
                                   (MergedColumnParallelLinear,
                                    QKVParallelLinear, RowParallelLinear)):
-                        module.weight.data = torch_npu.npu_format_cast(
-                            module.weight.data, ACL_FORMAT_FRACTAL_NZ)
+                        module.weight.data = self._convert_torch_format(
+                            module.weight.data)
             if self.drafter:
                 logger.info("Loading drafter model...")
                 if isinstance(self.drafter, EagleProposer):
@@ -2133,6 +2136,10 @@ def _get_torchair_lazy_compiled_model(self, batch_size: int):
                     ge_cache=False)
             return self.torchair_compiled_models[batch_size]
 
+    def _convert_torch_format(self, tensor):
+        tensor = torch_npu.npu_format_cast(tensor, ACL_FORMAT)
+        return tensor
+
     def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         """
         Initialize KV cache based on `kv_cache_config`.
@@ -2141,9 +2148,6 @@ def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
             cache size of each layer
         """
         self.kv_cache_config = kv_cache_config
-        import torch_npu
-        acl_format = ACL_FORMAT_FRACTAL_NZ if is_310p(
-        ) and not self.torchair_graph_enabled else ACL_FORMAT_FRACTAL_ND
         kv_caches: Dict[str, torch.Tensor] = {}
 
         def align_memory(tensor: torch.Tensor, alignment: int) -> torch.Tensor:
@@ -2202,7 +2206,6 @@ def align_memory(tensor: torch.Tensor, alignment: int) -> torch.Tensor:
                             kv_cache_spec.head_size)
                     dtype = kv_cache_spec.dtype
                     if self.model_config.is_deepseek_mla:
-
                         num_blocks, block_size, num_kv_heads, head_size = kv_cache_shape
                         rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
                         nope_dim = head_size - rope_dim
@@ -2218,10 +2221,8 @@ def align_memory(tensor: torch.Tensor, alignment: int) -> torch.Tensor:
                             nope_cache = torch.zeros(nope_cache_shape,
                                                      dtype=dtype,
                                                      device=self.device)
-                            rope_cache = torch_npu.npu_format_cast(
-                                rope_cache, acl_format)
-                            nope_cache = torch_npu.npu_format_cast(
-                                nope_cache, acl_format)
+                            rope_cache = self._convert_torch_format(rope_cache)
+                            nope_cache = self._convert_torch_format(nope_cache)
                         else:
 
                             # In order to transfer kv cache through the reigster_memory api from llmdatadist, the memory
@@ -2259,8 +2260,7 @@ def align_memory(tensor: torch.Tensor, alignment: int) -> torch.Tensor:
                                 kv_cache = torch.zeros(cache_shape,
                                                        dtype=dtype,
                                                        device=self.device)
-                                kv_cache = torch_npu.npu_format_cast(
-                                    kv_cache, acl_format)
+                                kv_cache = self._convert_torch_format(kv_cache)
                             else:
                                 cache_size = math.prod(cache_shape)
                                 cache_size_aligned = cache_size + alignment