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[Refactor] Import global var form vllm instead of overwirte it (#5469)

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### What this PR does / why we need it?
Import global var form vllm instead of overwirte it, so that we could
use the correct global variant value

- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
This commit is contained in:
Mengqing Cao
2026-01-07 18:41:45 +08:00
committed by GitHub
parent 380f089fbf
commit 3f4f2b4ae6
10 changed files with 7 additions and 157 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
tests/ut/sample/logits_processor/test_builtin.py
View File

@@ -1,40 +0,0 @@
import torch
from pytest_mock import MockerFixture
from vllm.config import SchedulerConfig, VllmConfig
from tests.ut.base import PytestBase
from vllm_ascend.sample.logits_processor import AscendMinPLogitsProcessor
class TestMinPLogitsProcessorInitFunc(PytestBase):
def test_init_func_with_decode_max_num_seqs(self, mocker: MockerFixture):
device_cpu = torch.device("cpu")
device_npu = torch.device("npu")
is_pin_memory = False
mock_vllm_config = mocker.MagicMock(spec=VllmConfig)
mock_scheduler_config = mocker.MagicMock(spec=SchedulerConfig)
mock_scheduler_config.decode_max_num_seqs = 0
mock_scheduler_config.max_num_seqs = 128
mock_vllm_config.scheduler_config = mock_scheduler_config
# torch.zeros/torch.empty returns error on online ut machine, so mock it
mock_tensor = torch.zeros((256, ),
dtype=torch.float32,
pin_memory=False)
mocker.patch("torch.zeros", return_value=mock_tensor)
mock_empty_tensor = torch.empty((256, ), dtype=torch.float32)
mocker.patch("torch.empty", return_value=mock_empty_tensor)
processor_cpu = AscendMinPLogitsProcessor(mock_vllm_config, device_cpu,
is_pin_memory)
assert processor_cpu.min_p is not None
assert processor_cpu.use_double_tensor is False
assert processor_cpu.min_p_cpu.shape[0] == 256
processor_cpu = AscendMinPLogitsProcessor(mock_vllm_config, device_npu,
is_pin_memory)
assert processor_cpu.min_p is not None
assert processor_cpu.use_double_tensor is True
assert processor_cpu.min_p_cpu.shape[0] == 256

2
vllm_ascend/distributed/mooncake_layerwise_connector.py
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@@ -32,6 +32,7 @@ from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.distributed.mooncake_connector import GET_META_MSG
from vllm_ascend.distributed.mooncake_transfer_engine import global_te
from vllm_ascend.distributed.utils import (align_memory,
get_transfer_timeout_value,
@@ -44,7 +45,6 @@ if TYPE_CHECKING:
from vllm.v1.core.kv_cache_manager import KVCacheBlocks
from vllm.v1.request import Request
GET_META_MSG = b"get_meta_msg"
DONE_SENDING_MSG = b"done_sending_msg"

3
vllm_ascend/ops/triton/mamba/causal_conv1d.py
View File

@@ -13,8 +13,7 @@ import torch
import torch.nn.functional as F
import triton
import triton.language as tl
PAD_SLOT_ID = -1
from vllm.attention.backends.utils import PAD_SLOT_ID
def causal_conv1d_ref(

1
vllm_ascend/patch/worker/patch_bert.py
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@@ -20,7 +20,6 @@ from vllm.model_executor.models import bert
# aclgraph does not support shift operator for now
# TODO: revert me when aclgraph supports shift operator
TOKEN_TYPE_SHIFT = 30
TOKEN_TYPE_MULTIPLIER = 1 << 30
TOKEN_MASK = TOKEN_TYPE_MULTIPLIER - 1

50
vllm_ascend/sample/logits_processor/__init__.py
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@@ -1,50 +0,0 @@
import itertools
from collections.abc import Sequence
from typing import TYPE_CHECKING, Union
import torch
from vllm.logger import init_logger
from vllm.v1.sample import logits_processor
from vllm.v1.sample.logits_processor.builtin import (LogitBiasLogitsProcessor,
MinTokensLogitsProcessor)
from vllm.v1.sample.logits_processor.interface import LogitsProcessor
from vllm.v1.sample.logits_processor.state import LogitsProcessors
from vllm_ascend.sample.logits_processor.builtin import \
AscendMinPLogitsProcessor
if TYPE_CHECKING:
from vllm.config import VllmConfig
logger = init_logger(__name__)
# Error message when the user tries to initialize vLLM with a pooling model
# and custom logitsproces
STR_POOLING_REJECTS_LOGITSPROCS = ("Pooling models do not support custom"
" logits processors.")
BUILTIN_LOGITS_PROCESSORS: list[type[LogitsProcessor]] = [
MinTokensLogitsProcessor,
LogitBiasLogitsProcessor,
AscendMinPLogitsProcessor,
]
def build_logitsprocs(
vllm_config: "VllmConfig",
device: torch.device,
is_pin_memory: bool,
is_pooling_model: bool,
custom_logitsprocs: Sequence[Union[str, type[LogitsProcessor]]] = (),
) -> LogitsProcessors:
if is_pooling_model:
if custom_logitsprocs:
raise ValueError(STR_POOLING_REJECTS_LOGITSPROCS)
logger.debug("Skipping logits processor loading because pooling models"
" do not support logits processors.")
return LogitsProcessors()
custom_logitsprocs_classes = logits_processor._load_custom_logitsprocs(
custom_logitsprocs)
return LogitsProcessors(
ctor(vllm_config, device, is_pin_memory) for ctor in itertools.chain(
BUILTIN_LOGITS_PROCESSORS, custom_logitsprocs_classes))

52
vllm_ascend/sample/logits_processor/builtin.py
View File

@@ -1,52 +0,0 @@
import torch
from vllm.config import VllmConfig
from vllm.v1.sample.logits_processor import MinPLogitsProcessor
class AscendMinPLogitsProcessor(MinPLogitsProcessor):
def __init__(self, vllm_config: "VllmConfig", device: torch.device,
is_pin_memory: bool):
super().__init__(vllm_config, device, is_pin_memory)
decode_max_num_seqs = getattr(vllm_config.scheduler_config,
'decode_max_num_seqs', 0)
if decode_max_num_seqs != 0:
max_num_reqs = max(vllm_config.scheduler_config.max_num_seqs,
decode_max_num_seqs)
self.min_p_count: int = 0
self.min_p_cpu_tensor = torch.zeros((max_num_reqs, ),
dtype=torch.float32,
device="cpu",
pin_memory=is_pin_memory)
self.min_p_cpu = self.min_p_cpu_tensor.numpy()
self.use_double_tensor = torch.device(device).type != "cpu"
if self.use_double_tensor:
# Pre-allocated device tensor
self.min_p_device: torch.Tensor = torch.empty(
(max_num_reqs, ), dtype=torch.float32, device=device)
else:
self.min_p_device = self.min_p_cpu_tensor
# Current slice of the device tensor
self.min_p: torch.Tensor = self.min_p_device[:0]
def apply(self, logits: torch.Tensor) -> torch.Tensor:
if not self.min_p_count:
return logits
# Convert logits to probability distribution
probability_values = torch.nn.functional.softmax(logits, dim=-1)
# Calculate maximum probabilities per sequence
max_probabilities = torch.amax(probability_values,
dim=-1,
keepdim=True)
# Adjust min_p
adjusted_min_p = max_probabilities.mul_(self.min_p)
# Identify valid tokens using threshold comparison
invalid_token_mask = probability_values < adjusted_min_p
# Apply mask using boolean indexing
logits.masked_fill_(invalid_token_mask, -float('inf'))
return logits

8
vllm_ascend/sample/rejection_sampler.py
View File

@@ -4,7 +4,8 @@ from typing import Optional
import torch
from vllm.triton_utils import HAS_TRITON, triton
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.rejection_sampler import (GREEDY_TEMPERATURE,
from vllm.v1.sample.rejection_sampler import (GREEDY_TEMPERATURE, MAX_SPEC_LEN,
PLACEHOLDER_TOKEN_ID,
generate_uniform_probs)
from vllm_ascend.ops.triton.reject_sample import (
@@ -13,11 +14,6 @@ from vllm_ascend.ops.triton.reject_sample import (
sample_recovered_tokens_kernel)
from vllm_ascend.sample.sampler import apply_top_k_top_p
PLACEHOLDER_TOKEN_ID = -1
# Maximum number of speculative draft tokens allowed per request in a single
# step. This value is chosen to be large enough to handle typical use cases.
MAX_SPEC_LEN = 32
def apply_sampling_constraints(
logits: torch.Tensor, # [num_tokens, vocab_size]

3
vllm_ascend/spec_decode/eagle_proposer.py
View File

@@ -21,6 +21,7 @@ from vllm.utils.platform_utils import is_pin_memory_available
from vllm.v1.attention.backends.utils import CommonAttentionMetadata
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID
from vllm.v1.spec_decode.eagle import EagleProposer as VllmEagleProposer
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
@@ -40,8 +41,6 @@ from vllm_ascend.ops.triton.spec_decode.utils import \
from vllm_ascend.ops.triton.triton_utils import get_vectorcore_num
from vllm_ascend.utils import shared_expert_dp_enabled
PADDING_SLOT_ID = -1
# Currently we will fix block size to a small one since `num_reqs` can't be too large
_PREPARE_INPUTS_BLOCK_SIZE = 4

3
vllm_ascend/spec_decode/mtp_proposer.py
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@@ -9,6 +9,7 @@ from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
from vllm.v1.attention.backends.utils import CommonAttentionMetadata
from vllm.v1.core.sched.output import SchedulerOutput
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.spec_decode.eagle import PADDING_SLOT_ID
from vllm_ascend.ascend_forward_context import set_ascend_forward_context
from vllm_ascend.attention.attention_v1 import AscendAttentionState
@@ -18,8 +19,6 @@ from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla
from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
from vllm_ascend.utils import ProfileExecuteDuration, lmhead_tp_enable
PADDING_SLOT_ID = -1
class MtpProposer(EagleProposer):

2
vllm_ascend/worker/model_runner_v1.py
View File

@@ -65,6 +65,7 @@ from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput,
LogprobsLists, LogprobsTensors, ModelRunnerOutput,
SamplerOutput,
make_empty_encoder_model_runner_output)
from vllm.v1.sample.logits_processor import build_logitsprocs
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.rejection_sampler import RejectionSampler
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
@@ -98,7 +99,6 @@ from vllm_ascend.eplb.eplb_updator import EplbUpdator
from vllm_ascend.eplb.utils import model_register
from vllm_ascend.ops.rotary_embedding import set_cos_and_sin, update_cos_sin
from vllm_ascend.patch.worker.patch_module import patch_torch_npu_argsort
from vllm_ascend.sample.logits_processor import build_logitsprocs
from vllm_ascend.sample.sampler import AscendSampler
from vllm_ascend.spec_decode import get_spec_decode_method
from vllm_ascend.spec_decode.eagle_proposer import EagleProposer