fix some typos (#6209)

Co-authored-by: Brayden Zhong <b8zhong@uwaterloo.ca>

python/sglang/srt/layers/attention/base_attn_backend.py

@@ -20,7 +20,7 @@ class AttentionBackend(ABC):
         raise NotImplementedError()
 
     def init_cuda_graph_state(self, max_bs: int):
-        """Init the global shared states for cuda graph."""
+        """Init the global shared states for CUDA graph."""
         raise NotImplementedError()
 
     def init_forward_metadata_capture_cuda_graph(
@@ -33,7 +33,7 @@ class AttentionBackend(ABC):
         forward_mode: ForwardMode,
         spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
     ):
-        """Init the metadata for a forward pass for capturing a cuda graph."""
+        """Init the metadata for a forward pass for capturing a CUDA graph."""
         raise NotImplementedError()
 
     def init_forward_metadata_replay_cuda_graph(
@@ -47,7 +47,7 @@ class AttentionBackend(ABC):
         spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
         seq_lens_cpu: Optional[torch.Tensor],
     ):
-        """Init the metadata for a forward pass for replaying a cuda graph."""
+        """Init the metadata for a forward pass for replaying a CUDA graph."""
         raise NotImplementedError()
 
     def get_cuda_graph_seq_len_fill_value(self):

python/sglang/srt/layers/attention/double_sparsity_backend.py

@@ -15,7 +15,7 @@ if TYPE_CHECKING:
 
 class DoubleSparseAttnBackend(AttentionBackend):
     def __init__(self, model_runner: ModelRunner):
-        # Lazy import to avoid the initialization of cuda context
+        # Lazy import to avoid the initialization of CUDA context
         from sglang.srt.layers.attention.triton_ops.double_sparsity_attention import (
             extend_attention_fwd,
             flash_decode_attention_fwd,

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -664,7 +664,7 @@ class FlashInferIndicesUpdaterDecode:
             kv_indptr = kv_indptr[: bs + 1]
 
             if wrapper.is_cuda_graph_enabled:
-                # Directly write to the cuda graph input buffer
+                # Directly write to the CUDA graph input buffer
                 kv_indices = wrapper._paged_kv_indices_buf
             else:
                 kv_indices = torch.empty(
@@ -1173,7 +1173,7 @@ def fast_decode_plan(
     """
     A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for FlashInferMultiStepDraftBackend.
     Modifications:
-    - Remove unnecessary device-to-device copy for the cuda graph buffers.
+    - Remove unnecessary device-to-device copy for the CUDA graph buffers.
     - Remove unnecessary host-to-device copy for the metadata buffers.
     """
     batch_size = len(last_page_len)

python/sglang/srt/layers/attention/flashinfer_mla_backend.py

@@ -874,7 +874,7 @@ def fast_mla_decode_plan(
 ) -> None:
     """A faster version of BatchMLAPagedAttentionWrapper::plan,
     for skipping the stream synchronization in original plan function during
-    cuda graph replaying.
+    CUDA graph replaying.
     """
     self._causal = causal
     self._page_size = page_size

python/sglang/srt/layers/attention/triton_backend.py

@@ -92,7 +92,7 @@ class TritonAttnBackend(AttentionBackend):
         skip_prefill: bool = False,
         kv_indptr_buf: Optional[torch.Tensor] = None,
     ):
-        # Lazy import to avoid the initialization of cuda context
+        # Lazy import to avoid the initialization of CUDA context
         from sglang.srt.layers.attention.triton_ops.decode_attention import (
             decode_attention_fwd,
         )

python/sglang/srt/layers/attention/vision.py

@@ -257,7 +257,7 @@ class VisionFlash3Attention(nn.Module):
         **kwargs,
     ):
         if not _is_cuda:
-            raise Exception("VisionFlash3Attention is only available for cuda")
+            raise Exception("VisionFlash3Attention is only available for CUDA")
         super().__init__()
 
     def forward(