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[Model] Remove model sampler (#21059)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2025-07-17 03:03:37 +08:00
committed by GitHub
parent a931b4cdcf
commit ac2bf41e53
6 changed files with 0 additions and 45 deletions
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10
vllm/model_executor/models/bailing_moe.py
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@ -47,7 +47,6 @@ 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 (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@ -485,7 +484,6 @@ class BailingMoeForCausalLM(nn.Module, SupportsPP):
else:
self.lm_head = PPMissingLayer()
self.sampler = get_sampler()
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
@ -512,14 +510,6 @@ class BailingMoeForCausalLM(nn.Module, SupportsPP):
sampling_metadata)
return logits
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]:
loader = AutoWeightsLoader(

2
vllm/model_executor/models/granite_speech.py
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@ -36,7 +36,6 @@ from vllm.config import CacheConfig, VllmConfig
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.sampler import get_sampler
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
@ -549,7 +548,6 @@ class GraniteSpeechForConditionalGeneration(
self.config = config
self.quant_config = quant_config
self.cache_config = cache_config
self.sampler = get_sampler()
# The language model is typically a Granite LLM
self.language_model = init_vllm_registered_model(

10
vllm/model_executor/models/hunyuan_v1_moe.py
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@ -49,7 +49,6 @@ 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 (
@ -661,7 +660,6 @@ class HunYuanMoEV1ForCausalLM(nn.Module):
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
logit_scale)
self.sampler = get_sampler()
else:
self.lm_head = PPMissingLayer()
@ -685,14 +683,6 @@ class HunYuanMoEV1ForCausalLM(nn.Module):
sampling_metadata)
return logits
def sample(
self,
logits: torch.Tensor,
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def make_empty_intermediate_tensors(
self, batch_size: int, dtype: torch.dtype,
device: torch.device) -> IntermediateTensors:

2
vllm/model_executor/models/mimo.py
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@ -36,7 +36,6 @@ from vllm.config import VllmConfig
from vllm.distributed import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.sampler import get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
from vllm.model_executor.model_loader.weight_utils import (
default_weight_loader, maybe_remap_kv_scale_name)
@ -176,7 +175,6 @@ class MiMoForCausalLM(Qwen2ForCausalLM, nn.Module):
self.lm_head = PPMissingLayer()
self.logits_processor = LogitsProcessor(config.vocab_size)
self.sampler = get_sampler()
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)

11
vllm/model_executor/models/mimo_mtp.py
View File

@ -30,7 +30,6 @@ 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
@ -161,8 +160,6 @@ class MiMoMTP(nn.Module):
self.lm_head = ParallelLMHead(self.config.vocab_size,
self.config.hidden_size)
self.sampler = get_sampler()
def forward(
self,
input_ids: torch.Tensor,
@ -187,14 +184,6 @@ class MiMoMTP(nn.Module):
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/phi4flash.py
View File

@ -23,7 +23,6 @@ from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
causal_conv1d_fn, causal_conv1d_update)
from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
selective_scan_fn, selective_state_update)
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.models.interfaces import (HasInnerState, IsHybrid,
@ -641,7 +640,6 @@ class Phi4FlashForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsV0Only):
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
logits_as_input=False)
self.sampler = get_sampler()
def forward(
self,
@ -709,14 +707,6 @@ class Phi4FlashForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsV0Only):
prune_hidden_states=prune_hidden_states)
return processed_logits
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]],