sycl: *glu flat path (#26354)

* tests: add SWIGLU perf cases

perf mode had no GLU coverage. Adds SWIGLU at 17408 columns, 512 and
2048 tokens, f16 and f32, with the operands both fused and split.

* sycl: consolidate fused-GLU kernels

They differed only in which op_* they called, so take the op as an argument and share a common launcher.
Their block sizes were all 256, so launch geometry is unchanged;
SYCL_GELU_BLOCK_SIZE and SYCL_SILU_BLOCK_SIZE lose their last users so are dropped.

* sycl: contiguous fast path for the fused GLU ops

o0 == n and o1 == n collapse the de-interleave index math to the
identity, so dispatch a flat kernel in that case. It fires for
ggml_glu_split with packed operands; a fused [gate|up] tensor keeps the
strided path. test-backend-ops perf -o SWIGLU on an Arc Pro B70: split
+14% f16 and +4% f32, fused unchanged.

tests/test-backend-ops.cpp

@@ -9747,6 +9747,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     std::vector<std::unique_ptr<test_case>> test_cases;
 
+    // SWIGLU at a 27B-class FFN width, fused [gate|up] vs split operands
+    // note: same bytes either way, so a backend that indexes them differently shows it here
+    for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        for (int64_t n_tokens : {512, 2048}) {
+            test_cases.emplace_back(new test_glu(GGML_GLU_OP_SWIGLU, type, { 2*17408, n_tokens, 1, 1 }, 0, false));
+            test_cases.emplace_back(new test_glu_split(GGML_GLU_OP_SWIGLU, type, { 17408, n_tokens, 1, 1 }, 0));
+        }
+    }
+
     // Conv2d: K=CRS=NPQ=4096 matmul performance
     uint32_t                        iwh_idx  = 0;
     uint32_t                        kwh_idx  = 1;