add weight_sum_fp32 config

update

update

Update base.yml

Update moe.py

Update moe.py

Author: suexu1025

src/MaxText/configs/base.yml

@@ -149,6 +149,7 @@ logits_dot_in_fp32: False  # whether to use fp32 in logits_dense or shared_embed
 cast_logits_to_fp32: True # whether to cast the logits to fp32. The higher precision is generally beneficial, but it can vary slightly.
 float32_qk_product: False # in dot_product attention, whether to cast to fp32 the inputs to qk product
 float32_logits: False # in dot_product attention, whether to cast to fp32 the inputs to softmax
+float32_weight_sum: True # whether to use full fp32 precision for weight_sum during final unpermute in moe
 
 # Multi-Token Prediction Configs
 # The number of auxiliary prediction layers to use for MTP.

src/MaxText/layers/moe.py

@@ -579,10 +579,13 @@ def unpermute(
       if self.config.decoder_block == ctypes.DecoderBlockType.LLAMA4:
         # For Llama4, combine using weights of 1 for selected experts
         reshaped_weights = jnp.ones_like(reshaped_weights)
+      if self.config.float32_weight_sum:
+        reshaped_intermediate = reshaped_intermediate.astype(jnp.float32)
+        reshaped_weights = reshaped_weights.astype(jnp.float32)
       output = jnp.einsum(
           "BKE,BK -> BE",
-          reshaped_intermediate.astype(jnp.float32),
-          reshaped_weights.astype(jnp.float32),
+          reshaped_intermediate,
+          reshaped_weights,
           precision=matmul_precision,
       )
     return output.reshape(batch_size, sequence_length, -1).astype(self.dtype)
@@ -1670,14 +1673,17 @@ def dense_matmul(
         if self.config.activations_in_float32:
           intermediate_layer = intermediate_layer.astype(jnp.float32)
         intermediate_layer = adc.checkpoint_name(intermediate_layer, "mlpwo")
-      with jax.named_scope("w_sum"):
+      with jax.named_scope("weight_sum"):
         if is_llama4_decoder_layer:
           weights = self.reshape_and_update_weights(jnp.ones_like(top_k_weights), top_k_indices)
+        if self.config.float32_weight_sum:
+          intermediate_layer = intermediate_layer.astype(jnp.float32)
+          weights = weights.astype(jnp.float32)
         # cast to f32 for sum up in einsum op
         output = jnp.einsum(
             "BSEM,BSE -> BSM",
-            intermediate_layer.astype(jnp.float32),
-            weights.astype(jnp.float32),  # pylint: disable=undefined-variable,possibly-used-before-assignment
+            intermediate_layer,
+            weights,
             precision=matmul_precision,
         ).astype(self.dtype)
       return output, None