[pre-commit.ci] auto fixes from pre-commit.com hooks

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colossalai/shardformer/modeling/llama.py

@@ -4,7 +4,6 @@ from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.distributed
-import torch.nn.functional as F
 import torch.utils.checkpoint
 from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
@@ -94,7 +93,6 @@ class LlamaPipelineForwards:
             batch_size, seq_length = input_shape
             device = hidden_states.device
 
-            
         # Support SP + PP
         sp_mode = shard_config.sequence_parallelism_mode
         sp_group = shard_config.sequence_parallel_process_group
@@ -113,7 +111,6 @@ class LlamaPipelineForwards:
                 raise ValueError("cache_position is a required argument when using StaticCache.")
             cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_length, device=device)
 
-
         seq_length_with_past = seq_length + past_seen_tokens
 
         if output_attentions:
@@ -143,7 +140,9 @@ class LlamaPipelineForwards:
                 invert=(sp_mode != "ring_attn"),
             )
         else:
-            attn_kwargs: torch.Tensor = self._update_causal_mask(attention_mask, hidden_states, cache_position, past_key_values)
+            attn_kwargs: torch.Tensor = self._update_causal_mask(
+                attention_mask, hidden_states, cache_position, past_key_values
+            )
 
         # Support SP + PP. Later stages have already received the split input.
         split_input = disable_pp or stage_manager.is_first_stage()
@@ -207,7 +206,7 @@ class LlamaPipelineForwards:
                     output_attentions,
                     use_cache,
                     cache_position,
-                    position_embeddings
+                    position_embeddings,
                 )
             else:
                 layer_outputs = decoder_layer(
@@ -218,7 +217,7 @@ class LlamaPipelineForwards:
                     output_attentions=output_attentions,
                     use_cache=use_cache,
                     cache_position=cache_position,
-                    position_embeddings=position_embeddings
+                    position_embeddings=position_embeddings,
                 )
             hidden_states = layer_outputs[0]
 

colossalai/shardformer/policies/llama.py

@@ -33,11 +33,7 @@ class LlamaPolicy(Policy):
         return self.model
 
     def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from transformers.models.llama.modeling_llama import (
-            LlamaAttention,
-            LlamaDecoderLayer,
-            LlamaModel,
-        )
+        from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel
 
         policy = {}
         embedding_cls = None