support kit use for bert/gpt test

colossalai/shardformer/policies/autopolicy.py

@@ -25,17 +25,19 @@ class PolicyLocation:
 _POLICY_LIST = {
     # BERT
     "transformers.models.bert.modeling_bert.BertModel":
-        PolicyLocation(file_name="bert", class_name="BertPolicy"),
+        PolicyLocation(file_name="bert", class_name="BertModelPolicy"),
     "transformers.models.bert.modeling_bert.BertForPreTraining":
         PolicyLocation(file_name="bert", class_name="BertForPretrainingPolicy"),
-    "transformers.models.bert.modeling_bert.BertForMaskedLM":
-        PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"),
     "transformers.models.bert.modeling_bert.BertLMHeadModel":
         PolicyLocation(file_name="bert", class_name="BertLMHeadModelPolicy"),
-    "transformers.models.bert.modeling_bert.BertForNextSentencePrediction":
-        PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"),
+    "transformers.models.bert.modeling_bert.BertForMaskedLM":
+        PolicyLocation(file_name="bert", class_name="BertForMaskedLMPolicy"),
     "transformers.models.bert.modeling_bert.BertForSequenceClassification":
         PolicyLocation(file_name="bert", class_name="BertForSequenceClassificationPolicy"),
+    "transformers.models.bert.modeling_bert.BertForTokenClassification":
+        PolicyLocation(file_name="bert", class_name="BertForTokenClassificationPolicy"),
+    "transformers.models.bert.modeling_bert.BertForNextSentencePrediction":
+        PolicyLocation(file_name="bert", class_name="BertForNextSentencePredictionPolicy"),
     "transformers.models.bert.modeling_bert.BertForMultipleChoice":
         PolicyLocation(file_name="bert", class_name="BertForMultipleChoicePolicy"),
 
@@ -58,6 +60,14 @@ class PolicyLocation:
     # GPT2
     "transformers.models.gpt2.modeling_gpt2.GPT2Model":
         PolicyLocation(file_name="gpt2", class_name="GPT2ModelPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel":
+        PolicyLocation(file_name="gpt2", class_name="GPT2LMHeadModelPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModel":
+        PolicyLocation(file_name="gpt2", class_name="GPT2DoubleHeadsModelPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2ForTokenClassification":
+        PolicyLocation(file_name="gpt2", class_name="GPT2ForTokenClassificationPolicy"),
+    "transformers.models.gpt2.modeling_gpt2.GPT2ForSequenceClassification":
+        PolicyLocation(file_name="gpt2", class_name="GPT2ForSequenceClassificationPolicy"),
 }
 
 

colossalai/shardformer/policies/bert.py

@@ -131,8 +131,8 @@ def postprocess(self):
         return self.model
 
 
-# BertForMaskedLM
-class BertForMaskedLMPolicy(BertPolicy):
+# BertLMHeadModel
+class BertLMHeadModelPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
@@ -162,8 +162,8 @@ def postprocess(self):
         return self.model
 
 
-# BertLMHeadModel
-class BertLMHeadModelPolicy(BertPolicy):
+# BertForMaskedLM
+class BertForMaskedLMPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
@@ -193,15 +193,22 @@ def postprocess(self):
         return self.model
 
 
-# BertForNextSentencePrediction
-class BertForNextSentencePredictionPolicy(BertPolicy):
+# BertForSequenceClassification
+class BertForSequenceClassificationPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()
 
 
-# BertForSequenceClassification
-class BertForSequenceClassificationPolicy(BertPolicy):
+# BertForTokenClassification
+class BertForTokenClassificationPolicy(BertPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+# BertForNextSentencePrediction
+class BertForNextSentencePredictionPolicy(BertPolicy):
 
     def __init__(self) -> None:
         super().__init__()

colossalai/shardformer/policies/gpt2.py

@@ -1,7 +1,9 @@
-from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model
+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
 
 
@@ -82,7 +84,6 @@ def module_policy(self):
         }
 
     def new_model_class(self):
-
         return self.model
 
     def postprocess(self):
@@ -94,3 +95,79 @@ class GPT2ModelPolicy(GPT2Policy):
 
     def __init__(self) -> None:
         super().__init__()
+
+
+# GPT2LMHeadModel
+class GPT2LMHeadModelPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            GPT2LMHeadModel:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(suffix="lm_head",
+                                                                            target_module=col_nn.Linear1D_Col,
+                                                                            kwargs={"gather_output": True})
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy
+
+    def postprocess(self):
+        binding_map = {"transformer.wte.weight": "lm_head.weight"}
+        for k, v in binding_map.items():
+            param = getattr_(self.model, k)
+            param = nn.Parameter(param)
+            setattr_(self.model, k, param)
+            setattr_(self.model, v, param)
+        return self.model
+
+
+# GPT22DoubleHeadsModel
+class GPT2DoubleHeadsModelPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        module_policy = super().module_policy()
+        addon_module = {
+            GPT2DoubleHeadsModel:
+                ModulePolicyDescription(attribute_replacement={},
+                                        param_replacement=[],
+                                        sub_module_replacement=[
+                                            SubModuleReplacementDescription(suffix="lm_head",
+                                                                            target_module=col_nn.Linear1D_Col,
+                                                                            kwargs={"gather_output": True})
+                                        ])
+        }
+        module_policy.update(addon_module)
+        return module_policy
+
+    def postprocess(self):
+        binding_map = {"transformer.wte.weight": "lm_head.weight"}
+        for k, v in binding_map.items():
+            param = getattr_(self.model, k)
+            param = nn.Parameter(param)
+            setattr_(self.model, k, param)
+            setattr_(self.model, v, param)
+        return self.model
+
+
+# GPT2ForTokenClassification
+class GPT2ForTokenClassificationPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+
+# GPT2ForSequenceClassification
+class GPT2ForSequenceClassificationPolicy(GPT2Policy):
+
+    def __init__(self) -> None:
+        super().__init__()

tests/kit/model_zoo/transformers/bert.py

@@ -6,83 +6,147 @@
 # ===============================
 # Register single-sentence BERT
 # ===============================
-BATCH_SIZE = 2
-SEQ_LENGTH = 16
 
 
-def data_gen_fn():
-    input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    token_type_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
-    attention_mask = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
+# define data gen function
+def data_gen():
+    # Generated from following code snippet
+    #
+    # from transformers import BertTokenizer
+    # input = 'Hello, my dog is cute'
+    # tokenized_input = tokenizer(input, return_tensors='pt')
+    # input_ids = tokenized_input['input_ids']
+    # attention_mask = tokenized_input['attention_mask']
+    # token_type_ids = tokenized_input['token_type_ids']
+    input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]], dtype=torch.int64)
+    token_type_ids = torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
+    attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
     return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
 
 
+def data_gen_for_lm():
+    # LM data gen
+    # the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
+    data = data_gen()
+    data['labels'] = data['input_ids'].clone()
+    return data
+
+
+def data_gen_for_pretraining():
+    # pretraining data gen
+    # `next_sentence_label` is the label for next sentence prediction, 0 or 1
+    data = data_gen_for_lm()
+    data['next_sentence_label'] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_sequence_classification():
+    # sequence classification data gen
+    # `labels` is the label for sequence classification, 0 or 1
+    data = data_gen()
+    data['labels'] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_token_classification():
+    # token classification data gen
+    # `labels` is the type not the token id for token classification, 0 or 1
+    data = data_gen()
+    data['labels'] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_mcq():
+    # multiple choice question data gen
+    # Generated from following code snippet
+    #
+    # tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
+    # prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
+    # choice0 = "It is eaten with a fork and a knife."
+    # choice1 = "It is eaten while held in the hand."
+    # data = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
+    # data = {k: v.unsqueeze(0) for k, v in encoding.items()}
+    # data['labels'] = torch.tensor([0], dtype=torch.int64)
+    input_ids = torch.tensor([[[
+        101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591,
+        4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102
+    ],
+                               [
+                                   101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037,
+                                   4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096,
+                                   2218, 1999, 1996, 2192, 1012, 102, 0
+                               ]]])
+    token_type_ids = torch.tensor(
+        [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]])
+    attention_mask = torch.tensor(
+        [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+          [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]]])
+    labels = torch.tensor([0], dtype=torch.int64)
+
+    return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)
+
+
+# define output transform function
 output_transform_fn = lambda x: x
 
-config = transformers.BertConfig(hidden_size=128, num_hidden_layers=2, num_attention_heads=4, intermediate_size=256)
+# define loss funciton
+loss_fn_for_bert_model = lambda x: x.pooler_output.mean()
+loss_fn = lambda x: x.loss
+
+config = transformers.BertConfig(hidden_size=128,
+                                 num_hidden_layers=2,
+                                 num_attention_heads=4,
+                                 intermediate_size=256,
+                                 hidden_dropout_prob=0,
+                                 attention_probs_dropout_prob=0)
 
 # register the BERT variants
 model_zoo.register(name='transformers_bert',
                    model_fn=lambda: transformers.BertModel(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_for_bert_model,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_pretraining',
                    model_fn=lambda: transformers.BertForPreTraining(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_pretraining,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_lm_head_model',
                    model_fn=lambda: transformers.BertLMHeadModel(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_masked_lm',
                    model_fn=lambda: transformers.BertForMaskedLM(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_lm,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_sequence_classification',
                    model_fn=lambda: transformers.BertForSequenceClassification(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_sequence_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_token_classification',
                    model_fn=lambda: transformers.BertForTokenClassification(config),
-                   data_gen_fn=data_gen_fn,
+                   data_gen_fn=data_gen_for_token_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
-
-
-# ===============================
-# Register multi-sentence BERT
-# ===============================
-def data_gen_for_next_sentence():
-    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
-    prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-    next_sentence = "The sky is blue due to the shorter wavelength of blue light."
-    encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
-    return encoding
-
-
-def data_gen_for_mcq():
-    tokenizer = transformers.BertTokenizer.from_pretrained("bert-base-uncased")
-    prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
-    choice0 = "It is eaten with a fork and a knife."
-    choice1 = "It is eaten while held in the hand."
-    encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
-    encoding = {k: v.unsqueeze(0) for k, v in encoding.items()}
-    return encoding
-
-
-# register the following models
 model_zoo.register(name='transformers_bert_for_next_sentence',
                    model_fn=lambda: transformers.BertForNextSentencePrediction(config),
-                   data_gen_fn=data_gen_for_next_sentence,
+                   data_gen_fn=data_gen_for_sequence_classification,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))
 model_zoo.register(name='transformers_bert_for_mcq',
                    model_fn=lambda: transformers.BertForMultipleChoice(config),
                    data_gen_fn=data_gen_for_mcq,
                    output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
                    model_attribute=ModelAttribute(has_control_flow=True))