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Update deprecated type hinting in models (#18132)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-05-15 06:06:50 +01:00
committed by GitHub
parent 83f74c698f
commit 26d0419309
130 changed files with 971 additions and 901 deletions
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19
vllm/model_executor/models/internlm2.py
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@@ -1,7 +1,8 @@
# SPDX-License-Identifier: Apache-2.0
from collections.abc import Iterable
from functools import partial
from typing import Any, Dict, Iterable, Optional, Set, Tuple, Type, Union
from typing import Any, Optional, Union
import torch
from torch import nn
@@ -81,7 +82,7 @@ class InternLM2Attention(nn.Module):
num_heads: int,
num_kv_heads: int,
rope_theta: float = 10000,
rope_scaling: Optional[Dict[str, Any]] = None,
rope_scaling: Optional[dict[str, Any]] = None,
max_position_embeddings: int = 8192,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
@@ -225,7 +226,7 @@ class InternLMDecoderLayer(nn.Module):
positions: torch.Tensor,
hidden_states: torch.Tensor,
residual: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
) -> tuple[torch.Tensor, torch.Tensor]:
# Self Attention
if residual is None:
residual = hidden_states
@@ -252,7 +253,7 @@ class InternLM2Model(nn.Module):
*,
vllm_config: VllmConfig,
prefix: str = "",
layer_type: Type[InternLMDecoderLayer] = InternLMDecoderLayer):
layer_type: type[InternLMDecoderLayer] = InternLMDecoderLayer):
super().__init__()
config = vllm_config.model_config.hf_config
@@ -316,7 +317,7 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
*,
vllm_config: VllmConfig,
prefix: str = "",
model_type: Type[InternLM2Model] = InternLM2Model):
model_type: type[InternLM2Model] = InternLM2Model):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
@@ -361,15 +362,15 @@ class InternLM2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
sampling_metadata)
return logits
def load_weights(self, weights: Iterable[Tuple[str,
torch.Tensor]]) -> Set[str]:
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("gate_up_proj", "w1", 0),
("gate_up_proj", "w3", 1),
]
params_dict = dict(self.named_parameters())
loaded_params: Set[str] = set()
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if "rotary_emb.inv_freq" in name:
continue
@@ -407,7 +408,7 @@ class InternLM2ForRewardModel(InternLM2ForCausalLM):
*,
vllm_config: VllmConfig,
prefix: str = "",
model_type: Type[InternLM2Model] = InternLM2Model,
model_type: type[InternLM2Model] = InternLM2Model,
):
super().__init__(vllm_config=vllm_config,
prefix=prefix,