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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #6) (#6001)

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### What this PR does / why we need it?
| File Path |
| :--- |
| ` vllm_ascend/eplb/adaptor/abstract_adaptor.py` |
| ` vllm_ascend/eplb/adaptor/vllm_adaptor.py` |
| ` vllm_ascend/eplb/core/eplb_device_transfer_loader.py` |
| ` vllm_ascend/eplb/core/eplb_utils.py` |
| ` vllm_ascend/eplb/core/eplb_worker.py` |
| ` vllm_ascend/eplb/core/policy/policy_abstract.py` |
| ` vllm_ascend/eplb/core/policy/policy_default_eplb.py` |
| ` vllm_ascend/eplb/core/policy/policy_factory.py` |
| ` vllm_ascend/eplb/core/policy/policy_flashlb.py` |
| ` vllm_ascend/eplb/core/policy/policy_random.py` |
| ` vllm_ascend/eplb/core/policy/policy_swift_balancer.py` |
| ` vllm_ascend/eplb/eplb_updator.py` |
| ` vllm_ascend/eplb/utils.py` |
| ` vllm_ascend/model_loader/netloader/executor/elastic_load.py` |
| ` vllm_ascend/model_loader/netloader/executor/netloader_pg.py` |
| ` vllm_ascend/model_loader/netloader/interaction/elastic.py` |
| ` vllm_ascend/model_loader/netloader/load.py` |
| ` vllm_ascend/model_loader/netloader/netloader.py` |
| ` vllm_ascend/model_loader/netloader/utils.py` |
| ` vllm_ascend/patch/platform/__init__.py` |
| ` vllm_ascend/patch/platform/patch_balance_schedule.py` |
| ` vllm_ascend/patch/platform/patch_ec_connector.py` |
| ` vllm_ascend/patch/platform/patch_mamba_config.py` |
| ` vllm_ascend/patch/platform/patch_multiproc_executor.py` |
| ` vllm_ascend/patch/platform/patch_sched_yield.py` |


- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-01-24 22:08:33 +08:00
committed by GitHub
parent 153da1a669
commit 4e53c1d900
26 changed files with 894 additions and 1148 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
vllm_ascend/eplb/core/eplb_utils.py
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@@ -25,7 +25,7 @@ from vllm.logger import logger
def expert_file_to_tensor(expert_map_path, layer_id):
with open(expert_map_path, "r") as f:
with open(expert_map_path) as f:
data = json.load(f)
physical_count = 0
device_data = []
@@ -61,38 +61,32 @@ def init_eplb_config(eplb_config, layer_id, moe_config):
eplb_enable = eplb_config.dynamic_eplb
n_redundant = eplb_config.num_redundant_experts if eplb_enable else 0
if expert_map_path:
if not (os.path.exists(expert_map_path)
and os.access(expert_map_path, os.R_OK)):
if not (os.path.exists(expert_map_path) and os.access(expert_map_path, os.R_OK)):
raise ValueError("Invalid EPLB path")
eplb_enable = True
global_placement, physical_count = expert_file_to_tensor(
expert_map_path, layer_id)
global_placement, physical_count = expert_file_to_tensor(expert_map_path, layer_id)
if physical_count is not None:
n_redundant = physical_count - n_experts
if not moe_config.supports_eplb:
raise ValueError(
"Eplb supports only w8a8_dynamic quantization.")
raise ValueError("Eplb supports only w8a8_dynamic quantization.")
else:
eplb_enable = False
if global_placement is None:
global_placement = generate_global_placement(n_experts, ep_size,
n_redundant)
global_placement = generate_global_placement(n_experts, ep_size, n_redundant)
if ep_size == 1:
assert not eplb_enable, "EPLB must used in expert parallelism."
return None, None, None, n_redundant
global_expert_map = []
for rankid in range(ep_size):
expert_map = torch.full((n_experts, ), -1, dtype=torch.int32)
expert_map = torch.full((n_experts,), -1, dtype=torch.int32)
local_placement = global_placement[rankid]
expert_map[local_placement] = torch.arange(local_placement.shape[0],
dtype=torch.int32)
expert_map[local_placement] = torch.arange(local_placement.shape[0], dtype=torch.int32)
global_expert_map.append(expert_map)
if rankid == moe_config.ep_rank:
local_expert_map = expert_map.npu()
log2phy = generate_log2phy_map(
global_expert_map, moe_config.ep_rank).npu() if eplb_enable else None
log2phy = generate_log2phy_map(global_expert_map, moe_config.ep_rank).npu() if eplb_enable else None
return torch.stack(global_expert_map), local_expert_map, log2phy, n_redundant
@@ -106,13 +100,15 @@ def generate_log2phy_map(global_expert_map, ep_rank):
if val != -1:
log2phy_map[idx].append(val + rankid * valid_count)
for key in log2phy_map.keys():
for key in log2phy_map:
num_of_duplications = len(log2phy_map[key])
log2phy_map[key] = log2phy_map[key][ep_rank % num_of_duplications]
log2phy_map = torch.scatter(
torch.zeros(len(log2phy_map.keys()), dtype=torch.int32), 0,
torch.tensor(list(log2phy_map.keys()), dtype=torch.int64),
torch.tensor(list(log2phy_map.values()), dtype=torch.int32))
torch.zeros(len(log2phy_map), dtype=torch.int32),
0,
torch.tensor(list(log2phy_map), dtype=torch.int64),
torch.tensor(list(log2phy_map.values()), dtype=torch.int32),
)
return log2phy_map