[shardformer] import huggingface implicitly (#4101)

colossalai/shardformer/policies/autopolicy.py

@@ -5,6 +5,8 @@ import torch.nn as nn
 
 from .basepolicy import Policy
 
+__all__ = ["PolicyLocation", "get_autopolicy", "import_policy"]
+
 
 @dataclass
 class PolicyLocation:

colossalai/shardformer/policies/basepolicy.py

@@ -8,6 +8,8 @@ import torch.nn as nn
 
 from ..shard.shard_config import ShardConfig
 
+__all__ = ["ParallelModule", "SubModuleReplacementDescription", "ModulePolicyDescription", "Policy"]
+
 
 class ParallelModule():
 

colossalai/shardformer/policies/bert.py

@@ -1,18 +1,16 @@
 import torch.nn as nn
-from transformers.models.bert.modeling_bert import (
-    BertEmbeddings,
-    BertForMultipleChoice,
-    BertForSequenceClassification,
-    BertForTokenClassification,
-    BertLayer,
-    BertLMPredictionHead,
-)
 
 import colossalai.shardformer.layer as col_nn
 
 from .._utils import getattr_, setattr_
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
+__all__ = [
+    'BertPolicy', 'BertModelPolicy', 'BertForPretrainingPolicy', 'BertLMHeadModelPolicy', 'BertForMaskedLMPolicy',
+    'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy',
+    'BertForMultipleChoicePolicy'
+]
+
 
 class BertPolicy(Policy):
 
@@ -33,6 +31,8 @@ class BertPolicy(Policy):
         return self.model
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer
+
         base_policy = {
             BertLayer:
                 ModulePolicyDescription(
@@ -123,7 +123,7 @@ class BertPolicy(Policy):
 
     def new_model_class(self):
         # do nothing
-        return self.model
+        return None
 
     def postprocess(self):
         return self.model
@@ -143,6 +143,8 @@ class BertForPretrainingPolicy(BertPolicy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertLMPredictionHead
+
         module_policy = super().module_policy()
         addon_module = {
             BertLMPredictionHead:
@@ -184,6 +186,8 @@ class BertLMHeadModelPolicy(BertPolicy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertLMPredictionHead
+
         module_policy = super().module_policy()
         addon_module = {
             BertLMPredictionHead:
@@ -221,6 +225,8 @@ class BertForMaskedLMPolicy(BertPolicy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertLMPredictionHead
+
         module_policy = super().module_policy()
         addon_module = {
             BertLMPredictionHead:
@@ -261,6 +267,8 @@ class BertForSequenceClassificationPolicy(BertPolicy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertForSequenceClassification
+
         module_policy = super().module_policy()
         addon_module = {
             BertForSequenceClassification:
@@ -284,6 +292,8 @@ class BertForTokenClassificationPolicy(BertPolicy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertForTokenClassification
+
         module_policy = super().module_policy()
         addon_module = {
             BertForTokenClassification:
@@ -314,6 +324,8 @@ class BertForMultipleChoicePolicy(BertPolicy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.bert.modeling_bert import BertForMultipleChoice
+
         module_policy = super().module_policy()
         addon_module = {
             BertForMultipleChoice:

colossalai/shardformer/policies/gpt2.py

@@ -1,11 +1,15 @@
 import torch.nn as nn
-from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2DoubleHeadsModel, GPT2LMHeadModel, GPT2Model
 
 import colossalai.shardformer.layer as col_nn
 
 from .._utils import getattr_, setattr_
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
+__all__ = [
+    'GPT2Policy', 'GPT2ModelPolicy', 'GPT2LMHeadModelPolicy', 'GPT2DoubleHeadsModelPolicy',
+    'GPT2ForTokenClassificationPolicy', 'GPT2ForSequenceClassificationPolicy'
+]
+
 
 class GPT2Policy(Policy):
 
@@ -25,7 +29,9 @@ class GPT2Policy(Policy):
         return self.model
 
     def module_policy(self):
-        base_policy = {
+        from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model
+
+        return {
             GPT2Model:
                 ModulePolicyDescription(attribute_replacement={},
                                         param_replacement=[],
@@ -125,6 +131,8 @@ class GPT2LMHeadModelPolicy(GPT2Policy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
+
         module_policy = super().module_policy()
         addon_module = {
             GPT2LMHeadModel:
@@ -156,6 +164,8 @@ class GPT2DoubleHeadsModelPolicy(GPT2Policy):
         super().__init__()
 
     def module_policy(self):
+        from transformers.models.gpt2.modeling_gpt2 import GPT2DoubleHeadsModel
+
         module_policy = super().module_policy()
         addon_module = {
             GPT2DoubleHeadsModel:

colossalai/shardformer/policies/llama.py

@@ -1,13 +1,13 @@
 from typing import Dict, Union
 
 import torch.nn as nn
-from transformers import LlamaForCausalLM, LlamaForSequenceClassification
-from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel
 
 from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
 
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
+__all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy']
+
 
 class LlamaPolicy(Policy):
 
@@ -26,7 +26,9 @@ class LlamaPolicy(Policy):
         return self.model
 
     def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        base_policy = {
+        from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel
+
+        return {
             LlamaDecoderLayer:
                 ModulePolicyDescription(
                     attribute_replacement={
@@ -109,6 +111,8 @@ class LlamaPolicy(Policy):
 class LlamaForCausalLMPolicy(LlamaPolicy):
 
     def module_policy(self):
+        from transformers import LlamaForCausalLM
+
         policy = super().module_policy()
         # add a new item for casual lm
         new_item = {
@@ -128,6 +132,8 @@ class LlamaForCausalLMPolicy(LlamaPolicy):
 class LlamaForSequenceClassificationPolicy(LlamaPolicy):
 
     def module_policy(self):
+        from transformers import LlamaForSequenceClassification
+
         policy = super().module_policy()
 
         # add a new item for sequence classification

colossalai/shardformer/policies/opt.py

@@ -1,15 +1,12 @@
-from transformers.models.opt.modeling_opt import (
-    OPTAttention,
-    OPTDecoder,
-    OPTDecoderLayer,
-    OPTForCausalLM,
-    OPTForSequenceClassification,
-)
-
 from colossalai.shardformer.layer import Embedding1D, FusedLayerNorm, Linear1D_Col, Linear1D_Row
 
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
+__all__ = [
+    'OPTPolicy', 'OPTModelPolicy', 'OPTForCausalLMPolicy', 'OPTForSequenceClassificationPolicy',
+    'OPTForQuestionAnsweringPolicy'
+]
+
 
 class OPTPolicy(Policy):
 
@@ -29,6 +26,8 @@ class OPTPolicy(Policy):
         return self.model
 
     def module_policy(self):
+        from transformers.models.opt.modeling_opt import OPTAttention, OPTDecoder, OPTDecoderLayer
+
         base_policy = {
             OPTDecoder:
                 ModulePolicyDescription(attribute_replacement={},
@@ -111,6 +110,8 @@ class OPTModelPolicy(OPTPolicy):
 class OPTForCausalLMPolicy(OPTPolicy):
 
     def module_policy(self):
+        from transformers.models.opt.modeling_opt import OPTForCausalLM
+
         policy = super().module_policy()
         new_item = {
             OPTForCausalLM:

colossalai/shardformer/policies/t5.py

@@ -1,15 +1,4 @@
-from transformers import T5ForConditionalGeneration
-from transformers.models.t5.modeling_t5 import (
-    T5Attention,
-    T5DenseActDense,
-    T5DenseGatedActDense,
-    T5LayerCrossAttention,
-    T5LayerFF,
-    T5LayerSelfAttention,
-    T5Stack,
-)
-
-from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, FusedRMSNorm, Linear1D_Col, Linear1D_Row
+from colossalai.shardformer.layer import DropoutForParallelInput, Embedding1D, Linear1D_Col, Linear1D_Row
 
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
@@ -34,7 +23,17 @@ class T5ModelPolicy(Policy):
         return self.model
 
     def module_policy(self):
-        base_policy = {
+        from transformers.models.t5.modeling_t5 import (
+            T5Attention,
+            T5DenseActDense,
+            T5DenseGatedActDense,
+            T5LayerCrossAttention,
+            T5LayerFF,
+            T5LayerSelfAttention,
+            T5Stack,
+        )
+
+        return {
             T5Stack:
                 ModulePolicyDescription(attribute_replacement={},
                                         param_replacement=[],
@@ -165,6 +164,8 @@ class T5ModelPolicy(Policy):
 class T5ForConditionalGenerationPolicy(T5ModelPolicy):
 
     def module_policy(self):
+        from transformers import T5ForConditionalGeneration
+
         policy = super().module_policy()
 
         new_item = {

colossalai/shardformer/policies/vit.py

@@ -1,12 +1,13 @@
 from typing import Dict, Union
 
 import torch.nn as nn
-from transformers.models.vit.modeling_vit import ViTAttention, ViTEmbeddings, ViTLayer, ViTModel
 
 from colossalai.shardformer.layer import DropoutForReplicatedInput, FusedLayerNorm, Linear1D_Col, Linear1D_Row
 
 from .basepolicy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
+__all__ = ['ViTPolicy']
+
 
 class ViTPolicy(Policy):
 
@@ -25,7 +26,9 @@ class ViTPolicy(Policy):
         return self.model
 
     def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
-        base_policy = {
+        from transformers.models.vit.modeling_vit import ViTEmbeddings, ViTLayer
+
+        return {
             ViTEmbeddings:
                 ModulePolicyDescription(attribute_replacement={},
                                         param_replacement=[],

colossalai/shardformer/shard/shard_config.py

@@ -19,6 +19,7 @@ class ShardConfig:
     """
     tensor_parallel_process_group: int = None
     enable_fused_normalization: bool = False
+    enable_all_optimization: bool = False
 
     # TODO: add support for tensor parallel
     # pipeline_parallel_size: int
@@ -27,6 +28,21 @@ class ShardConfig:
     # inference_only: bool = True
     # gather_output: bool = True
 
+    @property
+    def tensor_parallel_size(self):
+        return self._tensor_parallel_size
+
     def __post_init__(self):
         # get the parallel size
-        self.tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group)
+        self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group)
+
+        # turn on all optimization if all_optimization is set to True
+        if self.enable_all_optimization:
+            self._turn_on_all_optimization()
+
+    def _turn_on_all_optimization(self):
+        """
+        Turn on all optimization.
+        """
+        # you can add all the optimization flag here
+        self.fused_layernorm = True