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Remove unused kwargs from model definitions (#13555)

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This commit is contained in:
Harry Mellor
2025-02-25 01:13:52 +00:00
committed by GitHub
parent f61528d46d
commit cdc1fa12eb
104 changed files with 436 additions and 1654 deletions
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19
vllm/model_executor/models/deepseek_mtp.py
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@@ -1,11 +1,10 @@
# SPDX-License-Identifier: Apache-2.0
from typing import Iterable, List, Optional, Set, Tuple
from typing import Iterable, Optional, Set, Tuple
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.attention.backends.abstract import AttentionMetadata
from vllm.config import CacheConfig, ModelConfig, VllmConfig
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
@@ -69,8 +68,6 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: AttentionMetadata,
previous_hidden_states: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
spec_step_index: int = 0,
@@ -88,8 +85,6 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
hidden_states, residual = self.mtp_block(positions=positions,
hidden_states=hidden_states,
kv_cache=kv_cache,
attn_metadata=attn_metadata,
residual=None)
hidden_states = residual + hidden_states
return self.shared_head(hidden_states)
@@ -122,8 +117,6 @@ class DeepSeekMultiTokenPredictor(nn.Module):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
previous_hidden_states: torch.Tensor,
inputs_embeds: Optional[torch.Tensor] = None,
spec_step_idx: int = 0,
@@ -131,8 +124,6 @@ class DeepSeekMultiTokenPredictor(nn.Module):
return self.layers[str(self.mtp_start_layer_idx + spec_step_idx)](
input_ids,
positions,
kv_caches[spec_step_idx],
attn_metadata,
previous_hidden_states,
inputs_embeds,
spec_step_idx,
@@ -165,16 +156,14 @@ class DeepSeekMTP(nn.Module):
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
previous_hidden_states: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
spec_step_idx: int = 0,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, previous_hidden_states,
inputs_embeds, spec_step_idx)
hidden_states = self.model(input_ids, positions,
previous_hidden_states, inputs_embeds,
spec_step_idx)
return hidden_states
def compute_logits(