diff --git a/llmfoundry/models/layers/attention.py b/llmfoundry/models/layers/attention.py
index d441531d29..b3bb6e193d 100644
--- a/llmfoundry/models/layers/attention.py
+++ b/llmfoundry/models/layers/attention.py
@@ -5,7 +5,7 @@
 
 import math
 import warnings
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Optional
 
 import torch
 import torch.nn as nn
@@ -55,7 +55,7 @@ def scaled_multihead_dot_product_attention(
     value: torch.Tensor,
     n_heads: int,
     kv_n_heads: Optional[int] = None,
-    past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
     softmax_scale: Optional[float] = None,
     attn_bias: Optional[torch.Tensor] = None,
     key_padding_mask: Optional[torch.Tensor] = None,
@@ -64,7 +64,7 @@ def scaled_multihead_dot_product_attention(
     training: bool = False,
     needs_weights: bool = False,
     multiquery: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor,
+) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor,
                                                                 torch.Tensor]]]:
 
     if multiquery:
@@ -168,7 +168,7 @@ def scaled_multihead_dot_product_attention(
 
 
 def check_valid_inputs(*tensors: torch.Tensor,
-                       valid_dtypes: Optional[List[torch.dtype]] = None):
+                       valid_dtypes: Optional[list[torch.dtype]] = None):
     if valid_dtypes is None:
         valid_dtypes = [torch.float16, torch.bfloat16]
     for tensor in tensors:
@@ -184,7 +184,7 @@ def flash_attn_fn(
     value: torch.Tensor,
     n_heads: int,
     kv_n_heads: Optional[int] = None,
-    past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
     softmax_scale: Optional[float] = None,
     attn_bias: Optional[torch.Tensor] = None,
     key_padding_mask: Optional[torch.Tensor] = None,
@@ -193,7 +193,7 @@ def flash_attn_fn(
     training: bool = False,
     needs_weights: bool = False,
     multiquery: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor,
+) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor,
                                                                 torch.Tensor]]]:
     try:
         from flash_attn import bert_padding, flash_attn_interface  # type: ignore # yapf: disable # isort: skip
@@ -304,7 +304,7 @@ def triton_flash_attn_fn(
     value: torch.Tensor,
     n_heads: int,
     kv_n_heads: Optional[int] = None,
-    past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
     softmax_scale: Optional[float] = None,
     attn_bias: Optional[torch.Tensor] = None,
     key_padding_mask: Optional[torch.Tensor] = None,
@@ -313,7 +313,7 @@ def triton_flash_attn_fn(
     training: bool = False,
     needs_weights: bool = False,
     multiquery: bool = False,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor,
+) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor,
                                                                 torch.Tensor]]]:
     try:
         from llmfoundry.models.layers.flash_attn_triton import flash_attn_func
@@ -519,13 +519,13 @@ def __init__(
     def forward(
         self,
         x: torch.Tensor,
-        past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
         attn_bias: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        rotary_emb_w_offset_info: Optional[Dict] = None,
+        rotary_emb_w_offset_info: Optional[dict] = None,
         is_causal: bool = True,
         needs_weights: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[
             torch.Tensor, torch.Tensor]]]:
         qkv = self.Wqkv(x)
 
@@ -663,7 +663,7 @@ def __init__(
 def attn_bias_shape(
         attn_impl: str, n_heads: int, seq_len: int, alibi: bool,
         prefix_lm: bool, causal: bool,
-        use_sequence_id: bool) -> Optional[Tuple[int, int, int, int]]:
+        use_sequence_id: bool) -> Optional[tuple[int, int, int, int]]:
     if attn_impl == 'flash':
         return None
     elif attn_impl in ['torch', 'triton']:
diff --git a/llmfoundry/models/mpt/modeling_mpt.py b/llmfoundry/models/mpt/modeling_mpt.py
index 62c7da7cd5..18050da079 100644
--- a/llmfoundry/models/mpt/modeling_mpt.py
+++ b/llmfoundry/models/mpt/modeling_mpt.py
@@ -390,7 +390,6 @@ def forward(
         ), f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
 
         rotary_emb_w_offset_info = None
-        pos_emb = 0.0
         tok_emb = self.wte(input_ids)
         if self.learned_pos_emb or self.rope:
             past_position = 0
@@ -434,12 +433,10 @@ def forward(
                     'pos': pos,
                     'seq_len': S + past_position
                 }
-            x = tok_emb
-            if self.learned_pos_emb:
-                pos_emb = self.wpe(pos)
-                x = x + pos_emb
-
-        x = tok_emb + pos_emb
+        x = tok_emb
+        if self.learned_pos_emb:
+            pos_emb = self.wpe(pos)
+            x = x + pos_emb
 
         if self.embedding_fraction == 1:
             x = self.emb_drop(x)