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

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Merged
wangxiyuan merged 1 commit into from
Aug 11, 2025
Merged

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

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7 changes: 6 additions & 1 deletion vllm_ascend/torchair/torchair_model_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand Down Expand Up @@ -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
24 changes: 12 additions & 12 deletions vllm_ascend/worker/model_runner_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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`.
Expand All @@ -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:
Expand Down Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down
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