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[Chore] Remove unused sampler in models (#25324)

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
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Woosuk Kwon 2025-09-20 19:53:20 -07:00 committed by GitHub
parent 86647d1cd0
commit 572ddf83ce
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5 changed files with 0 additions and 49 deletions
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3
tests/lora/conftest.py
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@ -17,7 +17,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.models.interfaces import SupportsLoRA
from vllm.platforms import current_platform
@ -97,7 +96,6 @@ def dummy_model() -> nn.Module:
# Special handling for lm_head & sampler
("lm_head", ParallelLMHead(512, 10)),
("logits_processor", LogitsProcessor(512)),
("sampler", Sampler())
]))
model.config = MagicMock()
model.embedding_modules = {"lm_head": "lm_head"}
@ -125,7 +123,6 @@ def dummy_model_gate_up() -> nn.Module:
# Special handling for lm_head & sampler
("lm_head", ParallelLMHead(512, 10)),
("logits_processor", LogitsProcessor(512)),
("sampler", Sampler())
]))
model.config = MagicMock()
model.packed_modules_mapping = {

10
vllm/model_executor/models/ernie_mtp.py
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@ -33,7 +33,6 @@ from vllm.config import CacheConfig, ModelConfig, VllmConfig
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@ -160,7 +159,6 @@ class ErnieMTP(nn.Module, SupportsPP):
self.lm_head = ParallelLMHead(self.config.vocab_size,
self.config.hidden_size,
prefix=maybe_prefix(prefix, "lm_head"))
self.sampler = get_sampler()
if self.config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
@ -188,14 +186,6 @@ class ErnieMTP(nn.Module, SupportsPP):
return self.model.compute_logits(hidden_states, self.lm_head,
sampling_metadata, spec_step_idx)
def sample(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [

10
vllm/model_executor/models/plamo2.py
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@ -41,7 +41,6 @@ from vllm.model_executor.layers.mamba.ops.ssd_combined import (
mamba_chunk_scan_combined)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import (
@ -932,7 +931,6 @@ class Plamo2ForCausalLM(torch.nn.Module, HasInnerState, SupportsPP, IsHybrid):
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
self.config.vocab_size)
self.sampler = get_sampler()
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
@ -1030,14 +1028,6 @@ class Plamo2ForCausalLM(torch.nn.Module, HasInnerState, SupportsPP, IsHybrid):
sampling_metadata)
return logits
def sample(
self,
logits: Optional[torch.Tensor],
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
params_dict = dict(self.named_parameters())
for name, loaded_weight in weights:

10
vllm/model_executor/models/step3_text.py
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@ -26,7 +26,6 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@ -391,7 +390,6 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
)
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size)
self.sampler = get_sampler()
else:
self.lm_head = PPMissingLayer()
@ -413,14 +411,6 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
sampling_metadata)
return logits
def sample(
self,
logits: Optional[torch.Tensor],
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
qkv_params_mapping = [

16
vllm/model_executor/models/step3_vl.py
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@ -2,7 +2,6 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import math
from collections.abc import Iterable, Mapping, Sequence
from functools import cached_property
from itertools import product
from math import ceil, sqrt
from typing import Any, Literal, Optional, TypedDict, Union
@ -24,7 +23,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
@ -897,13 +895,6 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors)
@cached_property
def sampler(self):
if hasattr(self.language_model, "sampler"):
return self.language_model.sampler
return get_sampler()
@property
def device(self):
return next(self.parameters()).device
@ -1069,13 +1060,6 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
return self.language_model.compute_logits(hidden_states,
sampling_metadata)
def sample(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
return self.language_model.sample(logits, sampling_metadata)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
skip_prefixes = []