Improve GEMM performance of shape 4096x8x128x16384 (#2646)

This change (`grid` order adjustment to improve cache hit) originating
from #2600.
Batched gemm only.
~99% of XeTLA for `4096x8x128x16384`.

![image](https://github.com/user-attachments/assets/ef7e9750-b3f7-4adc-aa66-5be704383e40)

benchmarks/triton_kernels_benchmark/gemm_benchmark.py

@@ -129,8 +129,8 @@ def matmul_kernel_with_block_pointers_batched(
         stride_cz: tl.constexpr, stride_cm: tl.constexpr, stride_cn: tl.constexpr,
         # Meta-parameters
         BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
-    bid = tl.program_id(axis=0)
-    pid = tl.program_id(axis=1)
+    bid = tl.program_id(axis=1)
+    pid = tl.program_id(axis=0)
     num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
     num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
     num_pid_in_group = GROUP_SIZE_M * num_pid_n
@@ -186,8 +186,8 @@ def matmul(a, b, c, transpose_a=False, transpose_b=False):
         B = a.shape[0]
         # 1D launch kernel where each block gets its own program.
         grid = lambda META: (
-            B,
             triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
+            B,
         )
         matmul_kernel_with_block_pointers_batched[grid](
             a, b, c,  #