style

src/transformers/models/cohere/modular_cohere.py

@@ -28,17 +28,23 @@
 import torch.utils.checkpoint
 from torch import nn
 
+from ...cache_utils import Cache
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
-from ...cache_utils import Cache, DynamicCache, StaticCache
 from ...modeling_outputs import CausalLMOutputWithPast
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
 from ...processing_utils import Unpack
 from ...pytorch_utils import ALL_LAYERNORM_LAYERS
-from ...utils import logging, LossKwargs
+from ...utils import LossKwargs, logging
+from ..llama.modeling_llama import (
+    LlamaAttention,
+    LlamaForCausalLM,
+    LlamaMLP,
+    LlamaModel,
+    LlamaRotaryEmbedding,
+    eager_attention_forward,
+)
 from .configuration_cohere import CohereConfig
 
-from ..llama.modeling_llama import LlamaRotaryEmbedding, LlamaAttention, LlamaMLP, LlamaModel, LlamaForCausalLM, eager_attention_forward
-
 
 logger = logging.get_logger(__name__)
 
@@ -144,7 +150,9 @@ def __init__(self, config: CohereConfig, layer_idx: Optional[int] = None):
         self.use_qk_norm = config.use_qk_norm
         if self.use_qk_norm:
             # When sharding the model using Tensor Parallelism, need to be careful to use n_local_heads
-            self.q_norm = CohereLayerNorm(hidden_size=(config.num_attention_heads, self.head_dim), eps=config.layer_norm_eps)
+            self.q_norm = CohereLayerNorm(
+                hidden_size=(config.num_attention_heads, self.head_dim), eps=config.layer_norm_eps
+            )
             self.k_norm = CohereLayerNorm(
                 hidden_size=(config.num_key_value_heads, self.head_dim), eps=config.layer_norm_eps
             )