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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/exaone.py
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@@ -24,7 +24,8 @@
# limitations under the License.
"""Inference-only Exaone model compatible with HuggingFace weights."""
from typing import Any, Dict, Iterable, Optional, Set, Tuple, Union
from collections.abc import Iterable
from typing import Any, Optional, Union
import torch
from torch import nn
@@ -102,7 +103,7 @@ class ExaoneAttention(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,
quant_config: Optional[QuantizationConfig] = None,
bias: bool = False,
@@ -196,7 +197,7 @@ class ExaoneBlockAttention(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,
quant_config: Optional[QuantizationConfig] = None,
bias: bool = False,
@@ -282,7 +283,7 @@ class ExaoneDecoderLayer(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
@@ -384,8 +385,8 @@ class ExaoneModel(nn.Module):
hidden_states, _ = self.ln_f(hidden_states, residual)
return hidden_states
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)
(".qkv_proj", ".q_proj", "q"),
@@ -395,7 +396,7 @@ class ExaoneModel(nn.Module):
(".gate_up_proj", ".c_fc_1", 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
@@ -535,8 +536,8 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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]:
loader = AutoWeightsLoader(
self,
# With tie_word_embeddings, we can skip lm_head.weight