Use Tokamax's representative group sizes.

PiperOrigin-RevId: 885246948

Author: niyatic21

src/maxtext/layers/moe.py

@@ -914,10 +914,7 @@ def gmm(
       elif self.config.attention == "vllm_rpa":
         tokamax_group_sizes = group_sizes
       else:
-        tokamax_group_sizes = tokamax.RaggedDotGroupSizes(
-            group_sizes,
-            max_utils.generate_representative_group_sizes(inputs.shape[0], kernel.shape[0]),
-        )
+        tokamax_group_sizes = tokamax.RaggedDotGroupSizes(group_sizes, len(inputs))
       pad_length = self.config.wi_tile_fwd_batch_seq
       hs_shape = inputs.shape
       # pad length is the 1st dimension of tiling size in gmm call

src/maxtext/models/deepseek_batchsplit.py

@@ -27,7 +27,6 @@
 from maxtext.layers import attention_op
 from maxtext.layers import moe as moe_lib
 from maxtext.layers import quantizations
-from maxtext.utils import max_utils
 import qwix.pallas as qpl
 import tokamax
 
@@ -970,10 +969,7 @@ def gmm(
       output = tokamax.ragged_dot(
           lhs=inputs,
           rhs=kernel,
-          group_sizes=tokamax.RaggedDotGroupSizes(
-              group_sizes,
-              max_utils.generate_representative_group_sizes(inputs.shape[0], kernel.shape[0]),
-          ),
+          group_sizes=tokamax.RaggedDotGroupSizes(group_sizes, len(inputs)),
           precision=jax.lax.Precision.DEFAULT,
           preferred_element_type=preferred_element_type,
           implementation="mosaic",

src/maxtext/utils/max_utils.py

@@ -1118,13 +1118,3 @@ def transformer_engine_context():
       yield
   except (ImportError, AttributeError):
     yield
-
-
-def generate_representative_group_sizes(target_m: int, g: int) -> tuple[int, ...]:
-  """Generate group sizes for a given target m."""
-  np.random.seed(0)
-  repr_val = np.random.uniform(size=(g,))
-  repr_val = np.random.binomial(1, 0.9, (g,)) * repr_val
-  repr_val = np.int32((repr_val / np.sum(repr_val)) * target_m)
-  repr_val[0] += target_m - np.sum(repr_val)
-  return tuple(map(int, repr_val))