Fix softmax loop: use self.n_kv_tiles not cute.size(gK, mode=[3])

cute.size(gK, mode=[3]) returns 1 for ALL n values — mode 3 is batch,
not KV tiles. self.n_kv_tiles = s_k // 128 is the correct Python int.
This is why softmax only processed kt=0 for all n.

tests/unit/test_fmha_v3_stage_c.py

@@ -321,10 +321,10 @@ class FmhaV3StageCMulti:
             # For now: kernel is correct when row_max growth across tiles is
             # mild (typical for short n with random data); for very long n
             # the missing rescale shows as accuracy drift.
-            for kt in range(n_kv_tiles):
+            for kt in range(self.n_kv_tiles):
                 si_handle = s_cons.wait_and_advance()
                 if kt == 0:
-                    cute.printf("SOFTMAX n_kv_tiles=%d\n", Int32(n_kv_tiles))
+                    cute.printf("SOFTMAX self.n_kv_tiles=%d\n", Int32(self.n_kv_tiles))
 
                 # Load S[kt]
                 tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype)