diff --git a/vllm/model_executor/layers/attention/mla_attention.py b/vllm/model_executor/layers/attention/mla_attention.py
index b04edcc513c..b067cdd00e5 100644
--- a/vllm/model_executor/layers/attention/mla_attention.py
+++ b/vllm/model_executor/layers/attention/mla_attention.py
@@ -14,7 +14,7 @@ MLA has two possible ways of computing, a data-movement friendly approach and a
 compute friendly approach. We generally want to use the compute friendly
 approach for "prefill" (i.e. the ratio Sq / Skv is relatively large, often near
 1) and the data-movement friendly approach for "decode" (i.e. the ratio
-Sq / Skv is small).
+Sq / Skv is small, often near 0).
 
 NOTE what we deem small and large is currently determined by if it is labelled
 prefill or decode by the scheduler, but this is something we should probably
@@ -28,7 +28,7 @@ Deepseek's MLA attention works the following way:
 * For decode (i.e. the memory friendly approach) the attention "simulates" a
 multi-head attention, while the compute is similar to multi-query attention.
 
-Below is example of both paths assuming batchsize = 1
+Below is an example of both paths assuming batch size = 1
 
 ## More Extent Definitions:
 
@@ -77,13 +77,13 @@ v        = (kv_c @ W_UV.view(Lkv, N * V)).view(Skv, N, V)
 
 // MHA with QK headdim = P + R
 //           V headdim = V
-//      spda_o shape [Sq, N, V]
-spda_o = scaled_dot_product_attention(
+//      sdpa_o shape [Sq, N, V]
+sdpa_o = scaled_dot_product_attention(
     torch.cat([q_nope, q_pe], dim=-1),
     torch.cat([k_nope, k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
     v
 )
-return spda_o @ W_O
+return sdpa_o @ W_O
 
 NOTE: in the actual code,
     `kv_b_proj` is [W_UK; W_UV] concatenated per head
@@ -105,16 +105,16 @@ k_pe     = torch.cat([new_k_pe, cache_k_pe], dim=0)
 
 // MQA with QK headdim = Lkv + R
 //           V headdim = Lkv
-//      spda_o shape [Sq, N, Lkv]
+//      sdpa_o shape [Sq, N, Lkv]
 // NOTE: this is less compute-friendly since Lkv > P
 //       but is more data-movement friendly since its MQA vs MHA
-spda_o = scaled_dot_product_attention(
+sdpa_o = scaled_dot_product_attention(
     torch.cat([ql_nope, q_pe], dim=-1),
     torch.cat([kv_c, k_pe], dim=-1),
     kv_c
 )
 
-o = einsum("snl,lnv->snv", spda_o.reshape(-1, N, Lkv), W_UV)
+o = einsum("snl,lnv->snv", sdpa_o.reshape(-1, N, Lkv), W_UV)
 return o.view(-1, N * V) @ W_O
 
 
@@ -153,7 +153,7 @@ curr_o, curr_lse = scaled_dot_product_attention(
     torch.cat([q_nope, q_pe], dim=-1),
     torch.cat([new_k_nope, new_k_pe.unsqueeze(1).expand(-1, N, -1)], dim=-1),
     new_v,
-    casual=True,
+    causal=True,
     return_softmax_lse=True
 )
 
@@ -173,7 +173,7 @@ for chunk_idx in range(cdiv(C, MCC)):
                    cache_k_pe_chunk.unsqueeze(1).expand(-1, N, -1)],
                    dim=-1),
         cache_v_chunk,
-        casual=False,
+        causal=False,
         return_softmax_lse=True
     )