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

### 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>

tests/ut/sample/logits_processor/test_builtin.py

@@ -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

vllm_ascend/distributed/mooncake_layerwise_connector.py

@@ -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"
 
 

vllm_ascend/ops/triton/mamba/causal_conv1d.py

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

vllm_ascend/patch/worker/patch_bert.py

@@ -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
 

vllm_ascend/sample/logits_processor/__init__.py

@@ -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))

vllm_ascend/sample/logits_processor/builtin.py

@@ -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

vllm_ascend/sample/rejection_sampler.py

@@ -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]

vllm_ascend/spec_decode/eagle_proposer.py

@@ -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
 

vllm_ascend/spec_decode/mtp_proposer.py

@@ -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):
 

vllm_ascend/worker/model_runner_v1.py

@@ -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