EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

support deepseek quant & mix-parallel with graphmode (#585)

Browse Source 
### What this PR does / why we need it?
1. support deepseek with w8a8 quant;
2. support deepseek with mix-parallel(multi-DP, EP+TP);
3. support deepseek with graphmode.
---------

Signed-off-by: wen-jie666 <wenjie39@huawei.com>
Signed-off-by: Yizhou Liu <liuyizhou5@h-partners.com>
Signed-off-by: libaokui <libaokui@huawei.com>
Signed-off-by: linfeng-yuan <1102311262@qq.com>
Co-authored-by: wen-jie666 <wenjie39@huawei.com>
This commit is contained in:
zzzzwwjj
2025-04-23 16:23:25 +08:00
committed by GitHub
parent e74331a1ed
commit 5c6d05a59e
13 changed files with 520 additions and 221 deletions
Download Patch File Download Diff File Expand all files Collapse all files

73
vllm_ascend/worker/cache_engine.py
View File

@@ -17,53 +17,66 @@
# limitations under the License.
#
from typing import List, Tuple
from typing import Any, List
import torch
from vllm.config import get_current_vllm_config
from vllm.utils import is_pin_memory_available
from vllm.worker.cache_engine import CacheEngine
from vllm_ascend.utils import VLLM_ENABLE_GRAPH_MODE
def allocate_kv_cache(
self,
num_blocks: int,
device: str,
) -> List[Tuple]:
) -> List[Any]:
"""Allocates KV cache on the specified device."""
kv_cache_shape = self.attn_backend.get_kv_cache_shape(
num_blocks, self.block_size, self.num_kv_heads, self.head_size)
pin_memory = is_pin_memory_available() if device == "cpu" else False
kv_cache: List[Tuple] = []
kv_cache: List[Any] = []
# Align entries so they are 256 byte aligned for better performance
# Primarily targets MLA as this typically only ends up having entries
# be 128 byte aligned.
alloc_shape = kv_cache_shape
additional_config = get_current_vllm_config().additional_config
if additional_config and additional_config.get("enable_graph_mode", False):
# Align entries so they are 256 byte aligned for better performance
# Primarily targets MLA as this typically only ends up having entries
# be 128 byte aligned.
alloc_shape = kv_cache_shape
for _ in range(self.num_attention_layers):
# null block in CpuGpuBlockAllocator requires at least that
# block to be zeroed-out.
# We zero-out everything for simplicity.
layer_kv_cache_nope = torch.zeros(
alloc_shape[:-1] +
(self.model_config.hf_text_config.kv_lora_rank, ),
dtype=self.dtype,
pin_memory=pin_memory,
device=device)
layer_kv_cache_pe = torch.zeros(
alloc_shape[:-1] +
(self.model_config.hf_text_config.qk_rope_head_dim, ),
dtype=self.dtype,
pin_memory=pin_memory,
device=device)
for _ in range(self.num_attention_layers):
# null block in CpuGpuBlockAllocator requires at least that
# block to be zeroed-out.
# We zero-out everything for simplicity.
layer_kv_cache_nope = torch.zeros(
alloc_shape[:-1] +
(self.model_config.hf_text_config.kv_lora_rank, ),
dtype=self.dtype,
pin_memory=pin_memory,
device=device)
layer_kv_cache_pe = torch.zeros(
alloc_shape[:-1] +
(self.model_config.hf_text_config.qk_rope_head_dim, ),
dtype=self.dtype,
pin_memory=pin_memory,
device=device)
# view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
# when entry_shape is higher than 1D
kv_cache.append((layer_kv_cache_nope, layer_kv_cache_pe))
# view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
# when entry_shape is higher than 1D
kv_cache.append((layer_kv_cache_nope, layer_kv_cache_pe))
else:
for _ in range(self.num_attention_layers):
# null block in CpuGpuBlockAllocator requires at least that
# block to be zeroed-out.
# We zero-out everything for simplicity.
layer_kv_cache = torch.zeros(kv_cache_shape,
dtype=self.dtype,
pin_memory=pin_memory,
device=device)
# view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
# when entry_shape is higher than 1D
kv_cache.append(layer_kv_cache)
return kv_cache
if VLLM_ENABLE_GRAPH_MODE == '1':
CacheEngine._allocate_kv_cache = allocate_kv_cache
CacheEngine._allocate_kv_cache = allocate_kv_cache