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colossalai/shardformer/modeling/command.py

@@ -1,10 +1,10 @@
 import math
-import warnings
-from typing import List, Optional, Tuple, Union, Callable
+from typing import List, Optional, Tuple, Union
 
 import torch
 from torch import nn
 from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
 from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from transformers.models.cohere.modeling_cohere import (
     CohereForCausalLM,
@@ -13,13 +13,8 @@ from transformers.models.cohere.modeling_cohere import (
     apply_rotary_pos_emb,
     repeat_kv,
 )
-from transformers.utils import logging
 from transformers.processing_utils import Unpack
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.models.cohere.modeling_cohere import eager_attention_forward
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-from functools import partial
+from transformers.utils import logging
 
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer.layer._operation import all_to_all_comm, split_forward_gather_backward
@@ -34,6 +29,7 @@ _SUPPORTED_SP_MODE = ["all_to_all", "split_gather", "ring", "ring_attn"]
 
 logger = logging.get_logger(__name__)
 
+
 class CommandPipelineForwards:
     """
     This class serves as a micro library for forward function substitution of Command models
@@ -200,7 +196,7 @@ class CommandPipelineForwards:
                     output_attentions,
                     use_cache,
                     cache_position,
-                    position_embeddings
+                    position_embeddings,
                 )
             else:
                 layer_outputs = decoder_layer(
@@ -211,7 +207,7 @@ class CommandPipelineForwards:
                     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]
@@ -348,7 +344,6 @@ class CommandPipelineForwards:
             return {"hidden_states": hidden_states}
 
 
-
 def get_command_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, sp_size=None, sp_group=None):
     def forward(
         self,
@@ -457,6 +452,7 @@ def get_command_flash_attention_forward(shard_config: ShardConfig, sp_mode=None,
 
     return forward
 
+
 def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode=None, sp_size=None, sp_group=None):
     logger = logging.get_logger(__name__)
 
@@ -553,7 +549,7 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
                     output_attentions,
                     use_cache,
                     cache_position,
-                    position_embeddings
+                    position_embeddings,
                 )
 
             else:
@@ -565,7 +561,7 @@ def get_command_flash_attention_model_forward(shard_config: ShardConfig, sp_mode
                     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/command.py

@@ -6,8 +6,6 @@ from torch import Tensor
 from torch.nn import Module
 
 from colossalai.shardformer.layer import (
-    FusedLayerNorm,
-    LayerNorm,
     Linear1D_Col,
     Linear1D_Row,
     LinearWithGradAccum,
@@ -38,11 +36,7 @@ class CommandPolicy(Policy):
         return self.model
 
     def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        from transformers.models.cohere.modeling_cohere import (
-            CohereAttention,
-            CohereDecoderLayer,
-            CohereModel,
-        )
+        from transformers.models.cohere.modeling_cohere import CohereAttention, CohereDecoderLayer, CohereModel
 
         ATTN_IMPLEMENTATION = {
             "eager": CohereAttention,
@@ -62,7 +56,7 @@ class CommandPolicy(Policy):
         sp_mode = self.shard_config.sequence_parallelism_mode or None
         sp_size = self.shard_config.sequence_parallel_size or None
         sp_group = self.shard_config.sequence_parallel_process_group or None
-        sp_partial_derived = sp_mode in ["split_gather", "ring"]
+        sp_mode in ["split_gather", "ring"]
         if sp_mode == "ring_attn" and not self.is_causal:
             raise ValueError("Ring attention is only meant for causal language modeling.")
 

tests/test_shardformer/test_model/test_shard_command.py

@@ -213,7 +213,6 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
             "precision": "fp16",
             "initial_scale": 1,
         },
-        
         {
             "tp_size": 1,
             "pp_size": 1,