[Pallas] Set a better tiling for gmm (#7119)

Summary:
Set a better tiling to make gmm run faster according to http://shortn/_e9u7jnMlYK.

Test Plan:
python test/test_gmm.py

torch_xla/experimental/custom_kernel.py

@@ -709,15 +709,20 @@ def repeat_with_fixed_output_size(input: torch.Tensor, repeats: torch.Tensor,
   return res
 
 
-def gmm(lhs: torch.Tensor, rhs: torch.Tensor,
-        group_sizes: torch.Tensor) -> torch.Tensor:
+def gmm(
+    lhs: torch.Tensor,
+    rhs: torch.Tensor,
+    group_sizes: torch.Tensor,
+    tiling: tuple[int, int, int] = (512, 512, 512)
+) -> torch.Tensor:
   """Compute lhs[sizes[i-1]:sizes[i], :] @ rhs for each group 'i'.
 
   Args:
     lhs: A 2d, jnp.ndarray with shape [m, k].
     rhs: A 3d, jnp.ndarray with shape [num_groups, k, n].
     group_sizes: A 1d, jnp.ndarray with shape [num_groups] and jnp.int32 dtype.
     preferred_element_type: jnp.dtype, the element type for the output matrix.
+    tiling: 3-tuple of ints. The m, k and n-dimension tile sizes.
 
   Returns:
     A 2d, jnp.ndarray with shape [m, n].
@@ -727,17 +732,24 @@ def gmm(lhs: torch.Tensor, rhs: torch.Tensor,
   jax_import_guard()
   from jax.experimental.pallas.ops.tpu.megablox.gmm import gmm
 
-  payload, _ = trace_pallas(gmm, lhs, rhs, group_sizes)
+  m, k, n = lhs.shape[0], lhs.shape[1], rhs.shape[2]
+  tm, tk, tn = min(tiling[0], m), min(tiling[1], k), min(tiling[2], n)
+  payload, _ = trace_pallas(
+      gmm,
+      lhs,
+      rhs,
+      group_sizes,
+      static_argnames=["tiling"],
+      tiling=(tm, tk, tn))
 
-  m, n = lhs.shape[0], rhs.shape[2]
   # Create the metadata we need for computation.
   # TODO (alanwaketan): The following assuumes groups_sizes is a cpu tensor.
   # That means we need to materialize this input in order to use this gmm
   # kernel, and that will introduce graph breaks in the computation.
   group_offsets, group_ids, m_tile_ids, num_tiles = _make_group_metadata(
       group_sizes=group_sizes,
-      m=lhs.shape[0],
-      tm=128  # TODO (alanwaketan): Tune this later.
+      m=m,
+      tm=tm,
   )
   group_offset_torch = torch.tensor([0], dtype=torch.int32).to("xla")