fix: setting the rotary base from the config for the grouped query models.

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -1,19 +1,19 @@
+from typing import List, Optional, Tuple
+
 import torch
 import torch.distributed
-
 from torch import nn
-from transformers.modeling_utils import PreTrainedModel
 from transformers.configuration_utils import PretrainedConfig
-from typing import Optional, List, Tuple
+from transformers.modeling_utils import PreTrainedModel
 
-from text_generation_server.utils import paged_attention, flash_attn
+from text_generation_server.utils import flash_attn, paged_attention
 from text_generation_server.utils.layers import (
-    TensorParallelRowLinear,
-    TensorParallelColumnLinear,
-    TensorParallelEmbedding,
-    SpeculativeHead,
     FastLayerNorm,
     PositionRotaryEmbedding,
+    SpeculativeHead,
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
     get_linear,
 )
 
@@ -134,7 +134,10 @@ class FlashRWAttention(torch.nn.Module):
         self.rope_theta = config.rope_theta
 
         self.rotary_emb = PositionRotaryEmbedding.static(
-            config=config, dim=self.head_size, base=self.rope_theta, device=weights.device
+            config=config,
+            dim=self.head_size,
+            base=self.rope_theta,
+            device=weights.device,
         )
         self.softmax_scale = self.head_size ** (-0.5)
 
@@ -247,7 +250,10 @@ class FlashRWLargeAttention(torch.nn.Module):
         self.rope_theta = config.rope_theta
 
         self.rotary_emb = PositionRotaryEmbedding.static(
-            config=config, dim=self.head_size, base=10000.0, device=weights.device
+            config=config,
+            dim=self.head_size,
+            base=self.rope_theta,
+            device=weights.device,
         )
         self.softmax_scale = self.head_size ** (-0.5)
 
@@ -469,6 +475,7 @@ class FlashRWLayer(nn.Module):
 
             return mlp_output, residual
 
+
 class FlashRWLayerNorm(nn.Module):
     def __init__(self, config, prefix, weights):
         super().__init__()