[shardformer] fix pipeline forward error if custom layer distribution is used (#5189)

* Use self.[distribute_layers|get_stage_index] to exploit custom layer distribution

* Change static methods for t5 layer distribution to member functions

* Change static methods for whisper layer distribution to member functions

* Replace whisper policy usage with self one

* Fix test case to use non-static layer distribution methods

* fix: fix typo

---------
Co-authored-by: Wenhao Chen <cwher@outlook.com>

applications/ColossalMoE/colossal_moe/models/mixtral_policy.py

@@ -110,7 +110,7 @@ class MixtralPolicy(Policy):
                 module = self.model.model
 
             layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {"forward": partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
             self.append_or_create_method_replacement(
                 description=method_replacement, policy=policy, target_key=model_cls

colossalai/shardformer/policies/base_policy.py

@@ -197,8 +197,7 @@ class Policy(ABC):
         """
         return []
 
-    @staticmethod
-    def distribute_layers(num_layers: int, num_stages: int) -> List[int]:
+    def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]:
         """Divide layers into stages"""
         quotient = num_layers // num_stages
         remainder = num_layers % num_stages
@@ -213,8 +212,8 @@ class Policy(ABC):
                 layers_per_stage[i] += 1
         return layers_per_stage
 
-    @staticmethod
     def get_stage_index(
+        self,
         layers_per_stage: List[int],
         stage: int,
         num_model_chunks: int = 1,
@@ -242,4 +241,4 @@ class Policy(ABC):
             end_idx = num_layers_per_stage_accumulated[stage + model_chunk * num_stages + 1]
             stage_indices.append([start_idx, end_idx])
 
-        return stage_indices[0] if num_model_chunks == 1 else stage_indices
+        return stage_indices[0] if num_model_chunks == 1 else stage_indices
\ No newline at end of file

colossalai/shardformer/policies/bert.py

@@ -84,17 +84,26 @@ class BertPolicy(Policy):
                     SubModuleReplacementDescription(
                         suffix="attention.self.query",
                         target_module=col_nn.Linear1D_Col,
-                        kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
+                        kwargs={
+                            "seq_parallel": use_sequence_parallel,
+                            "overlap": overlap,
+                        },
                     ),
                     SubModuleReplacementDescription(
                         suffix="attention.self.key",
                         target_module=col_nn.Linear1D_Col,
-                        kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
+                        kwargs={
+                            "seq_parallel": use_sequence_parallel,
+                            "overlap": overlap,
+                        },
                     ),
                     SubModuleReplacementDescription(
                         suffix="attention.self.value",
                         target_module=col_nn.Linear1D_Col,
-                        kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
+                        kwargs={
+                            "seq_parallel": use_sequence_parallel,
+                            "overlap": overlap,
+                        },
                     ),
                     SubModuleReplacementDescription(
                         suffix="attention.self.dropout",
@@ -112,7 +121,10 @@ class BertPolicy(Policy):
                     SubModuleReplacementDescription(
                         suffix="intermediate.dense",
                         target_module=col_nn.Linear1D_Col,
-                        kwargs={"seq_parallel": use_sequence_parallel, "overlap": overlap},
+                        kwargs={
+                            "seq_parallel": use_sequence_parallel,
+                            "overlap": overlap,
+                        },
                     ),
                     SubModuleReplacementDescription(
                         suffix="output.dense",
@@ -214,7 +226,9 @@ class BertPolicy(Policy):
         if self.shard_config.enable_tensor_parallelism:
             self.append_or_create_submodule_replacement(
                 description=SubModuleReplacementDescription(
-                    suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}
+                    suffix="decoder",
+                    target_module=col_nn.Linear1D_Col,
+                    kwargs={"gather_output": True},
                 ),
                 policy=base_policy,
                 target_key=BertLMPredictionHead,
@@ -241,7 +255,9 @@ class BertPolicy(Policy):
             "_load_from_state_dict": col_nn.ParallelModule._load_from_state_dict,
         }
         self.append_or_create_method_replacement(
-            description=method_replacement, policy=base_policy, target_key=BertLMPredictionHead
+            description=method_replacement,
+            policy=base_policy,
+            target_key=BertLMPredictionHead,
         )
         return base_policy
 
@@ -264,24 +280,32 @@ class BertPolicy(Policy):
 
         if stage_manager.is_interleave:
             layers_per_stage = self.distribute_layers(
-                len(module.encoder.layer), stage_manager.num_stages * stage_manager.num_model_chunks
+                len(module.encoder.layer),
+                stage_manager.num_stages * stage_manager.num_model_chunks,
             )
-            stage_manager.stage_indices = Policy.get_stage_index(
+            stage_manager.stage_indices = self.get_stage_index(
                 layers_per_stage,
                 stage_manager.stage,
                 num_model_chunks=stage_manager.num_model_chunks,
                 num_stages=stage_manager.num_stages,
             )
             method_replacement = {
-                "forward": partial(new_forward, stage_manager=stage_manager, shard_config=self.shard_config)
+                "forward": partial(
+                    new_forward,
+                    stage_manager=stage_manager,
+                    shard_config=self.shard_config,
+                )
             }
 
         else:
-            layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {
                 "forward": partial(
-                    new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
+                    new_forward,
+                    stage_manager=stage_manager,
+                    stage_index=stage_index,
+                    shard_config=self.shard_config,
                 )
             }
 
@@ -301,9 +325,10 @@ class BertPolicy(Policy):
         if stage_manager.is_interleave:
             assert stage_manager.num_model_chunks is not None
             layers_per_stage = self.distribute_layers(
-                len(module.encoder.layer), stage_manager.num_stages * stage_manager.num_model_chunks
+                len(module.encoder.layer),
+                stage_manager.num_stages * stage_manager.num_model_chunks,
             )
-            stage_indices = Policy.get_stage_index(
+            stage_indices = self.get_stage_index(
                 layers_per_stage,
                 stage_manager.stage,
                 num_model_chunks=stage_manager.num_model_chunks,
@@ -320,7 +345,7 @@ class BertPolicy(Policy):
             layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
             if stage_manager.is_first_stage():
                 held_layers.append(module.embeddings)
-            start_idx, end_idx = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
             held_layers.extend(module.encoder.layer[start_idx:end_idx])
             if stage_manager.is_last_stage():
                 held_layers.append(module.pooler)
@@ -336,7 +361,9 @@ class BertModelPolicy(BertPolicy):
 
         if self.pipeline_stage_manager:
             self.set_pipeline_forward(
-                model_cls=BertModel, new_forward=BertPipelineForwards.bert_model_forward, policy=policy
+                model_cls=BertModel,
+                new_forward=BertPipelineForwards.bert_model_forward,
+                policy=policy,
             )
         return policy
 
@@ -399,7 +426,9 @@ class BertLMHeadModelPolicy(BertPolicy):
 
         if self.pipeline_stage_manager:
             self.set_pipeline_forward(
-                model_cls=BertLMHeadModel, new_forward=BertPipelineForwards.bert_lm_head_model_forward, policy=policy
+                model_cls=BertLMHeadModel,
+                new_forward=BertPipelineForwards.bert_lm_head_model_forward,
+                policy=policy,
             )
         return policy
 
@@ -437,7 +466,9 @@ class BertForMaskedLMPolicy(BertPolicy):
 
         if self.pipeline_stage_manager:
             self.set_pipeline_forward(
-                model_cls=BertForMaskedLM, new_forward=BertPipelineForwards.bert_for_masked_lm_forward, policy=policy
+                model_cls=BertForMaskedLM,
+                new_forward=BertPipelineForwards.bert_for_masked_lm_forward,
+                policy=policy,
             )
         return policy
 

colossalai/shardformer/policies/bloom.py

@@ -203,8 +203,8 @@ class BloomPolicy(Policy):
             else:
                 module = self.model.transformer
 
-            layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {
                 "forward": partial(
                     new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config

colossalai/shardformer/policies/chatglm2.py

@@ -204,8 +204,8 @@ class ChatGLMPolicy(Policy):
         else:
             module = self.model.transformer
 
-        layers_per_stage = Policy.distribute_layers(module.num_layers, stage_manager.num_stages)
-        stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+        layers_per_stage = self.distribute_layers(module.num_layers, stage_manager.num_stages)
+        stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
         method_replacement = {
             "forward": partial(
                 new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config

colossalai/shardformer/policies/falcon.py

@@ -161,8 +161,8 @@ class FalconPolicy(Policy):
             else:
                 module = self.model.transformer
 
-            layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {
                 "forward": partial(
                     new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config

colossalai/shardformer/policies/gpt2.py

@@ -188,7 +188,7 @@ class GPT2Policy(Policy):
             layers_per_stage = self.distribute_layers(
                 len(module.h), stage_manager.num_stages * stage_manager.num_model_chunks
             )
-            stage_indices = Policy.get_stage_index(
+            stage_indices = self.get_stage_index(
                 layers_per_stage,
                 stage_manager.stage,
                 num_model_chunks=stage_manager.num_model_chunks,
@@ -229,7 +229,7 @@ class GPT2Policy(Policy):
             layers_per_stage = self.distribute_layers(
                 len(module.h), stage_manager.num_stages * stage_manager.num_model_chunks
             )
-            stage_manager.stage_indices = Policy.get_stage_index(
+            stage_manager.stage_indices = self.get_stage_index(
                 layers_per_stage,
                 stage_manager.stage,
                 num_model_chunks=stage_manager.num_model_chunks,
@@ -243,8 +243,8 @@ class GPT2Policy(Policy):
                 )
             }
         else:
-            layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {
                 "forward": partial(
                     new_forward,

colossalai/shardformer/policies/gptj.py

@@ -200,8 +200,8 @@ class GPTJPolicy(Policy):
         else:
             module = self.model.transformer
 
-        layers_per_stage = Policy.distribute_layers(len(module.h), stage_manager.num_stages)
-        stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+        layers_per_stage = self.distribute_layers(len(module.h), stage_manager.num_stages)
+        stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
         method_replacement = {
             "forward": partial(
                 new_forward,

colossalai/shardformer/policies/llama.py

@@ -167,7 +167,7 @@ class LlamaPolicy(Policy):
             layers_per_stage = self.distribute_layers(
                 len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
             )
-            stage_manager.stage_indices = Policy.get_stage_index(
+            stage_manager.stage_indices = self.get_stage_index(
                 layers_per_stage,
                 stage_manager.stage,
                 num_model_chunks=stage_manager.num_model_chunks,
@@ -178,8 +178,8 @@ class LlamaPolicy(Policy):
             }
 
         else:
-            layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {
                 "forward": partial(
                     new_forward, stage_manager=stage_manager, stage_index=stage_index, shard_config=self.shard_config
@@ -207,7 +207,7 @@ class LlamaPolicy(Policy):
             layers_per_stage = self.distribute_layers(
                 len(module.layers), stage_manager.num_stages * stage_manager.num_model_chunks
             )
-            stage_indices = Policy.get_stage_index(
+            stage_indices = self.get_stage_index(
                 layers_per_stage,
                 stage_manager.stage,
                 num_model_chunks=stage_manager.num_model_chunks,

colossalai/shardformer/policies/opt.py

@@ -208,8 +208,8 @@ class OPTPolicy(Policy):
             else:
                 module = self.model.model.decoder
 
-            layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {
                 "forward": partial(
                     new_forward,

colossalai/shardformer/policies/t5.py

+from __future__ import annotations
+
 import warnings
 from functools import partial
 from typing import Callable, Dict, List, Tuple
@@ -241,9 +243,8 @@ class T5BasePolicy(Policy):
     def postprocess(self):
         return self.model
 
-    @staticmethod
     def distribute_t5_layers(
-        num_encoder_layers: int, num_decoder_layers: int, num_stages: int
+        self, num_encoder_layers: int, num_decoder_layers: int, num_stages: int
     ) -> Tuple[List[int], int]:
         """
         Distribute t5 layers into stages when pipeline parallel is used.
@@ -261,7 +262,7 @@ class T5BasePolicy(Policy):
 
         # in the case of T5EncoderModel, set decoder starting stage to num_stages since it doesn't exist
         if num_decoder_layers == 0:
-            return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages
+            return self.distribute_layers(num_encoder_layers, num_stages), num_stages
 
         # the number of stages distributed between encoder and decoder is optimized in this way:
         # num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages))
@@ -272,22 +273,21 @@ class T5BasePolicy(Policy):
         num_encoder_stages = np.argmin([objective(i) for i in range(1, num_stages)]) + 1
         num_decoder_stages = num_stages - num_encoder_stages
 
-        encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages)
-        decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages)
+        encoder_distribution = self.distribute_layers(num_encoder_layers, num_encoder_stages)
+        decoder_distribution = self.distribute_layers(num_decoder_layers, num_decoder_stages)
         return encoder_distribution + decoder_distribution, num_encoder_stages
 
-    @staticmethod
     def get_t5_stage_index(
-        layers_per_stage: List[int], stage: int, decoder_starting_stage: int
+        self, layers_per_stage: List[int], stage: int, decoder_starting_stage: int
     ) -> Tuple[bool, int, int]:
         """
         Input the distribution of layers among stages, the current stage and the first stage of decoder.
         Return the starting/ending idx of layers in encoder/decoder
         """
         if stage < decoder_starting_stage:
-            return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
+            return self.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
         else:
-            return Policy.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage)
+            return self.get_stage_index(layers_per_stage[decoder_starting_stage:], stage - decoder_starting_stage)
 
     def get_held_layers(self) -> List[nn.Module]:
         """Get pipeline layers for current stage."""
@@ -302,12 +302,10 @@ class T5BasePolicy(Policy):
         num_decoder_layers = len(decoder.block) if decoder else 0
 
         held_layers = []
-        layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
+        layers_per_stage, decoder_starting_stage = self.distribute_t5_layers(
             num_encoder_layers, num_decoder_layers, stage_manager.num_stages
         )
-        start_idx, end_idx = T5BasePolicy.get_t5_stage_index(
-            layers_per_stage, stage_manager.stage, decoder_starting_stage
-        )
+        start_idx, end_idx = self.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
 
         if stage_manager.stage < decoder_starting_stage:
             # current stage is in t5's encoder
@@ -343,10 +341,10 @@ class T5BasePolicy(Policy):
         num_encoder_layers = len(encoder.block)
         num_decoder_layers = len(decoder.block) if decoder else 0
 
-        layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
+        layers_per_stage, decoder_starting_stage = self.distribute_t5_layers(
             num_encoder_layers, num_decoder_layers, stage_manager.num_stages
         )
-        stage_index = T5BasePolicy.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
+        stage_index = self.get_t5_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
 
         method_replacement = {
             "forward": partial(
@@ -386,7 +384,7 @@ class T5ModelPolicy(T5BasePolicy):
         module = self.model
         stage_manager = self.pipeline_stage_manager
         if stage_manager is not None and stage_manager.num_stages > 1:
-            _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
+            _, decoder_starting_stage = self.distribute_t5_layers(
                 len(module.encoder.block), len(module.decoder.block), stage_manager.num_stages
             )
 
@@ -434,7 +432,7 @@ class T5ForConditionalGenerationPolicy(T5BasePolicy):
         module = self.model
         stage_manager = self.pipeline_stage_manager
         if stage_manager is not None and stage_manager.num_stages > 1:
-            _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
+            _, decoder_starting_stage = self.distribute_t5_layers(
                 len(module.encoder.block), len(module.decoder.block), stage_manager.num_stages
             )
 

colossalai/shardformer/policies/vit.py

@@ -149,8 +149,8 @@ class ViTPolicy(Policy):
             else:
                 module = self.model.vit
 
-            layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {"forward": pipeline_forward(stage_manager=stage_manager, stage_index=stage_index)}
             self.append_or_create_method_replacement(
                 description=method_replacement, policy=policy, target_key=model_cls

colossalai/shardformer/policies/whisper.py

@@ -292,9 +292,8 @@ class WhisperPolicy(Policy):
     def postprocess(self):
         return self.model
 
-    @staticmethod
     def distribute_whisper_layers(
-        num_encoder_layers: int, num_decoder_layers: int, num_stages: int
+        self, num_encoder_layers: int, num_decoder_layers: int, num_stages: int
     ) -> Tuple[List[int], int]:
         """
         Distribute whisper layers into stages when pipeline parallel is used.
@@ -312,7 +311,7 @@ class WhisperPolicy(Policy):
 
         # in the case of whisperEncoderModel, set decoder starting stage to num_stages since it doesn't exist
         if num_decoder_layers == 0:
-            return Policy.distribute_layers(num_encoder_layers, num_stages), num_stages
+            return self.distribute_layers(num_encoder_layers, num_stages), num_stages
 
         # the number of stages distributed between encoder and decoder is optimized in this way:
         # num_encoder_stages = argmin(abs(num_encoder_layers / encoder_stages - num_decoder_layers / decoder_stages))
@@ -323,22 +322,21 @@ class WhisperPolicy(Policy):
         num_encoder_stages = np.argmin([objective(i) for i in range(1, num_stages)]) + 1
         num_decoder_stages = num_stages - num_encoder_stages
 
-        encoder_distribution = Policy.distribute_layers(num_encoder_layers, num_encoder_stages)
-        decoder_distribution = Policy.distribute_layers(num_decoder_layers, num_decoder_stages)
+        encoder_distribution = self.distribute_layers(num_encoder_layers, num_encoder_stages)
+        decoder_distribution = self.distribute_layers(num_decoder_layers, num_decoder_stages)
         return encoder_distribution + decoder_distribution, num_encoder_stages
 
-    @staticmethod
     def get_whisper_stage_index(
-        layers_per_stage: List[int], stage: int, decoder_starting_stage: int
+        self, layers_per_stage: List[int], stage: int, decoder_starting_stage: int
     ) -> Tuple[bool, int, int]:
         """
         Input the distribution of layers among stages, the current stage and the first stage of decoder.
         Return the starting/ending idx of layers in encoder/decoder
         """
         if stage < decoder_starting_stage:
-            return Policy.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
+            return self.get_stage_index(layers_per_stage[:decoder_starting_stage], stage)
         else:
-            return Policy.get_stage_index(
+            return self.get_stage_index(
                 layers_per_stage[decoder_starting_stage:],
                 stage - decoder_starting_stage,
             )
@@ -369,12 +367,10 @@ class WhisperPolicy(Policy):
             num_decoder_layers = 0
 
         held_layers = []
-        layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
+        layers_per_stage, decoder_starting_stage = self.distribute_whisper_layers(
             num_encoder_layers, num_decoder_layers, stage_manager.num_stages
         )
-        start_idx, end_idx = WhisperPolicy.get_whisper_stage_index(
-            layers_per_stage, stage_manager.stage, decoder_starting_stage
-        )
+        start_idx, end_idx = self.get_whisper_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
 
         if stage_manager.stage < decoder_starting_stage:
             # current stage is in whisper's encoder
@@ -424,12 +420,10 @@ class WhisperPolicy(Policy):
         else:
             num_decoder_layers = 0
 
-        layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
+        layers_per_stage, decoder_starting_stage = self.distribute_whisper_layers(
             num_encoder_layers, num_decoder_layers, stage_manager.num_stages
         )
-        stage_index = WhisperPolicy.get_whisper_stage_index(
-            layers_per_stage, stage_manager.stage, decoder_starting_stage
-        )
+        stage_index = self.get_whisper_stage_index(layers_per_stage, stage_manager.stage, decoder_starting_stage)
 
         method_replacement = {
             "forward": partial(
@@ -511,7 +505,7 @@ class WhisperForConditionalGenerationPolicy(WhisperPolicy):
 
         stage_manager = self.pipeline_stage_manager
         if stage_manager is not None and stage_manager.num_stages > 1:
-            _, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
+            _, decoder_starting_stage = self.distribute_whisper_layers(
                 num_encoder_layers, num_decoder_layers, stage_manager.num_stages
             )
             shared_params = []

examples/language/openmoe/model/openmoe_policy.py

@@ -98,11 +98,11 @@ class OpenMoePolicy(Policy):
                 module = self.model.model
 
             layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
-            stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
+            stage_index = self.get_stage_index(layers_per_stage, stage_manager.stage)
             method_replacement = {"forward": partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
-            self.append_or_create_method_replacement(description=method_replacement,
-                                                     policy=policy,
-                                                     target_key=model_cls)
+            self.append_or_create_method_replacement(
+                description=method_replacement, policy=policy, target_key=model_cls
+            )
 
         return
 
@@ -126,12 +126,9 @@ class OpenMoePolicy(Policy):
             held_layers.append(module.norm)
 
         return held_layers
-    
-    @staticmethod
-    def distribute_layers(num_layers: int, num_stages: int) -> List[int]:
-        """Divide layers into stages
 
-        """
+    def distribute_layers(self, num_layers: int, num_stages: int) -> List[int]:
+        """Divide layers into stages"""
         if num_layers == 24 and num_stages == 4:
             return [7, 7, 7, 3]
         elif num_layers == 24 and num_stages == 2:
@@ -142,7 +139,7 @@ class OpenMoePolicy(Policy):
             return [8, 4]
         else:
             print(f"num_layers: {num_layers}, num_stages: {num_stages} not optimized, use origin pp policy")
-            return Policy.distribute_layers(num_layers, num_stages)
+            return super().distribute_layers(num_layers, num_stages)
 
 
 class OpenMoeModelPolicy(OpenMoePolicy):

tests/test_booster/test_plugin/test_3d_plugin.py

@@ -83,7 +83,7 @@ def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn) -> Optional[
 
 @parameterize("init_method", ["none", "lazy"])
 def check_3d_plugin(init_method: str = "none", early_stop: bool = True):
-    """check gemini plugin over model zoo
+    """check hybrid plugin over model zoo
 
     Args:
         early_stop (bool, optional): Whether to stop when getting the first error. Defaults to True.
@@ -260,7 +260,7 @@ def run_grad_acc_test(test_args):
         origin_model, origin_optimizer, dataloader=dataloader
     )
     for p1, p2 in zip(model.unwrap().parameters(), origin_model.unwrap().parameters()):
-        assert_close(p1.to(p2.dtype), p2, atol=1e-2, rtol=1e-2)      
+        assert_close(p1.to(p2.dtype), p2, atol=1e-2, rtol=1e-2)
 
 
 def run_dist(rank, world_size, port, early_stop: bool = True):
@@ -271,9 +271,9 @@ def run_dist(rank, world_size, port, early_stop: bool = True):
 
 
 @rerun_if_address_is_in_use()
-def test_gemini_plugin(early_stop: bool = True):
+def test_3d_plugin(early_stop: bool = True):
     spawn(run_dist, 4, early_stop=early_stop)
 
 
 if __name__ == "__main__":
-    test_gemini_plugin(early_stop=False)
+    test_3d_plugin(early_stop=False)
\ No newline at end of file

tests/test_pipeline/test_pipeline_utils/test_t5_pipeline_utils.py

@@ -10,9 +10,12 @@ def test_t5_pipeline_distribution():
         "decoder_starting_stage": [1, 1, 2, 2, 3, 1, 5, 2],
     }
 
+    policy = T5BasePolicy()
     for i in range(num_test_cases):
-        _, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
-            test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
+        _, decoder_starting_stage = policy.distribute_t5_layers(
+            test_dict["num_encoder_layers"][i],
+            test_dict["num_decoder_layers"][i],
+            test_dict["num_stages"][i],
         )
         assert test_dict["decoder_starting_stage"][i] == decoder_starting_stage
 
@@ -32,14 +35,15 @@ def test_t5_pipeline_layers():
     }
 
     for i in range(num_test_cases):
-        layers_per_stage, decoder_starting_stage = T5BasePolicy.distribute_t5_layers(
-            test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
+        policy = T5BasePolicy()
+        layers_per_stage, decoder_starting_stage = policy.distribute_t5_layers(
+            test_dict["num_encoder_layers"][i],
+            test_dict["num_decoder_layers"][i],
+            test_dict["num_stages"][i],
         )
 
         for stage in range(test_dict["num_stages"][i]):
             start_idx, end_idx = test_dict["layers_per_stage"][i][stage]
-            predicted_start, predicted_end = T5BasePolicy.get_t5_stage_index(
-                layers_per_stage, stage, decoder_starting_stage
-            )
+            predicted_start, predicted_end = policy.get_t5_stage_index(layers_per_stage, stage, decoder_starting_stage)
             assert start_idx == predicted_start
             assert end_idx == predicted_end

tests/test_pipeline/test_pipeline_utils/test_whisper_pipeline_utils.py

@@ -10,9 +10,12 @@ def test_whisper_pipeline_distribution():
         "decoder_starting_stage": [1, 1, 2, 2, 3, 1, 5, 2],
     }
 
+    policy = WhisperPolicy()
     for i in range(num_test_cases):
-        _, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
-            test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
+        _, decoder_starting_stage = policy.distribute_whisper_layers(
+            test_dict["num_encoder_layers"][i],
+            test_dict["num_decoder_layers"][i],
+            test_dict["num_stages"][i],
         )
         assert test_dict["decoder_starting_stage"][i] == decoder_starting_stage
 
@@ -31,14 +34,17 @@ def test_whisper_pipeline_layers():
         ],
     }
 
+    policy = WhisperPolicy()
     for i in range(num_test_cases):
-        layers_per_stage, decoder_starting_stage = WhisperPolicy.distribute_whisper_layers(
-            test_dict["num_encoder_layers"][i], test_dict["num_decoder_layers"][i], test_dict["num_stages"][i]
+        layers_per_stage, decoder_starting_stage = policy.distribute_whisper_layers(
+            test_dict["num_encoder_layers"][i],
+            test_dict["num_decoder_layers"][i],
+            test_dict["num_stages"][i],
         )
 
         for stage in range(test_dict["num_stages"][i]):
             start_idx, end_idx = test_dict["layers_per_stage"][i][stage]
-            predicted_start, predicted_end = WhisperPolicy.get_whisper_stage_index(
+            predicted_start, predicted_end = policy.get_whisper_stage_index(
                 layers_per_stage, stage, decoder_starting_stage
             )
             assert start_idx == predicted_start

tests/test_shardformer/test_layer/test_dist_crossentropy.py

@@ -38,9 +38,10 @@ def check_dist_crossentropy(rank, world_size, port, ignore_index):
         org_loss, dist_loss, atol=1e-5
     ), f"dist cross entropy loss is not equal to orgin loss\n{org_loss}\n{dist_loss}"
 
-
     target_grad = torch.chunk(pred.grad, world_size, dim=-1)[rank]
-    assert torch.allclose(target_grad, dist_pred.grad), f"dist grad is not equal to orgin grad\n{target_grad}\n{dist_pred.grad}"
+    assert torch.allclose(
+        target_grad, dist_pred.grad
+    ), f"dist grad is not equal to orgin grad\n{target_grad}\n{dist_pred.grad}"
 
 
 @pytest.mark.dist