Improves API parameter naming consistency

Renames q/k/v parameters to query/key/value in flash attention functions for better readability and standardization.

Updates parameter documentation to reflect the new naming convention and fixes GQA condition description to use <= instead of <.

Removes outdated footer reference to integration docs.

Author: LoserCheems

docs/api_reference.md

@@ -68,22 +68,23 @@ Main attention function. Supports multi-head and grouped-query attention (when t
 
 ```python
 def flash_dmattn_func(
-    q: torch.Tensor,                               # (batch, seqlen_q, num_heads, head_dim)
-    k: torch.Tensor,                               # (batch, seqlen_k, num_kv_heads, head_dim)
-    v: torch.Tensor,                               # (batch, seqlen_k, num_kv_heads, head_dim)
-    attn_mask: Optional[torch.Tensor] = None,      # (batch, num_heads, seqlen_q, seqlen_k)
-    attn_bias: Optional[torch.Tensor] = None,      # (batch, num_heads, seqlen_q, seqlen_k)
-    scale: Optional[float] = None,                 # score scaling, defaults to 1/sqrt(head_dim)
-    is_causal: Optional[bool] = None,              # causal mask
-    softcap: Optional[float] = None,               # CUDA-only
-    deterministic: Optional[bool] = None,          # CUDA-only
+    query: torch.Tensor,                            # (batch, seqlen_q, num_heads, head_dim)
+    key: torch.Tensor,                              # (batch, seqlen_k, num_kv_heads, head_dim)
+    value: torch.Tensor,                            # (batch, seqlen_k, num_kv_heads, head_dim)
+    attn_mask: Optional[torch.Tensor] = None,       # (batch, num_heads, seqlen_q, seqlen_k)
+    attn_bias: Optional[torch.Tensor] = None,       # (batch, num_heads, seqlen_q, seqlen_k)
+    scale: Optional[float] = None,                  # score scaling, defaults to 1/sqrt(head_dim)
+    is_causal: Optional[bool] = None,               # causal mask
+    softcap: Optional[float] = None,                # CUDA-only
+    deterministic: Optional[bool] = None,           # CUDA-only
 ) -> torch.Tensor
 ```
 
 #### Parameters
 
-- q: (B, Q, H, D). CUDA tensor, fp16/bf16, last dim contiguous
-- k, v: (B, K, H_kv, D). Same dtype/device as q; GQA when H_kv < H
+- query: (B, Q, H, D). CUDA tensor, fp16/bf16, last dim contiguous
+- key: (B, K, H_kv, D). Same dtype/device as query; GQA when H_kv <= H
+- value: (B, K, H_kv, D). Same dtype/device as query; GQA when H_kv <= H
 - attn_mask: (B, H, Q, K). 1.0 = visible, 0.0 = masked. None to disable
 - attn_bias: (B, H, Q, K). Added to scores before softmax. None to disable
 - scale: score scaling; default 1/sqrt(D)
@@ -137,20 +138,20 @@ Variable length attention for batches with mixed sequence lengths.
 
 ```python
 def flash_dmattn_varlen_func(
-    q: torch.Tensor,                               # (total_q, H, D) or (B, Q, H, D)
-    k: torch.Tensor,                               # same layout as q
-    v: torch.Tensor,                               # same layout as q
-    attn_mask: Optional[torch.Tensor] = None,      # (B, H, Q, K)
-    attn_bias: Optional[torch.Tensor] = None,      # (B, H, Q, K)
-    cu_seqlens_q: torch.Tensor = None,             # (B+1,)
-    cu_seqlens_k: torch.Tensor = None,             # (B+1,)
+    query: torch.Tensor,                            # (total_q, H, D) or (B, Q, H, D)
+    key: torch.Tensor,                              # same layout as query
+    value: torch.Tensor,                            # same layout as query
+    attn_mask: Optional[torch.Tensor] = None,       # (B, H, Q, K)
+    attn_bias: Optional[torch.Tensor] = None,       # (B, H, Q, K)
+    cu_seqlens_q: torch.Tensor = None,              # (B+1,)
+    cu_seqlens_k: torch.Tensor = None,              # (B+1,)
     max_seqlen_q: int = None,
     max_seqlen_k: int = None,
     scale: Optional[float] = None,
     is_causal: Optional[bool] = None,
-    softcap: Optional[float] = None,               # CUDA-only
-    deterministic: Optional[bool] = None,          # CUDA-only
-    block_table: Optional[torch.Tensor] = None,    # experimental: paged attention
+    softcap: Optional[float] = None,                # CUDA-only
+    deterministic: Optional[bool] = None,           # CUDA-only
+    block_table: Optional[torch.Tensor] = None,     # experimental: paged attention
 ) -> torch.Tensor
 ```
 
@@ -386,6 +387,3 @@ print_memory_stats()
 torch.cuda.empty_cache()
 ```
 
----
-
-See also: `docs/integration.md` and `benchmarks/`.