[shardformer] add gpt2 policy and modify shard and slicer to support (#3883)

* add gpt2 policy and modify shard and slicer to support

* remove unused code

* polish code

colossalai/shardformer/policies/autopolicy.py

@@ -10,16 +10,26 @@ def build_policies():
     """
     auto_policy_dict = {}
 
-    from transformers.models.bert.modeling_bert import BertForMaskedLM
+    from transformers import BertForMaskedLM
 
     from .bert import BertForMaskedLMPolicy
     auto_policy_dict[BertForMaskedLM] = BertForMaskedLMPolicy
 
-    from transformers.models.bert.modeling_bert import BertForSequenceClassification
+    from transformers import BertForSequenceClassification
 
     from .bert import BertForSequenceClassificationPolicy
     auto_policy_dict[BertForSequenceClassification] = BertForSequenceClassificationPolicy
 
+    from transformers import GPT2Model
+
+    from .gpt2 import GPT2Policy
+    auto_policy_dict[GPT2Model] = GPT2Policy
+
+    from transformers import GPT2LMHeadModel
+
+    from .gpt2 import GPT2LMHeadModelPolicy
+    auto_policy_dict[GPT2LMHeadModel] = GPT2LMHeadModelPolicy
+
     return auto_policy_dict
 
 

colossalai/shardformer/policies/basepolicy.py

@@ -1,11 +1,9 @@
 # part of code modified from https://github.com/tunib-ai/parallelformers
 
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Tuple, Type
 
-import torch
 import torch.nn as nn
-from transformers import AutoConfig
 
 
 @dataclass
@@ -31,11 +29,18 @@ class Layer:
         bias (str): The bias suffix of the layer
         replace_layer (:class:`colosalai.nn`): The layer to replace the original layer
         ignore (bool): Whether to ignore this layer if it is not in the model
+        reversed (bool): Whether the weight in layer is reversed, commonly the weight in `torch.nn.Linear` is [out, in],
+                        but in GPT2 `Conv1D` layer is [in, out] which is reversed.
+        n_cast (int): The number of weight will cast to, like q, k, v in attention layer, n_cast should be 3. commonly in TP, we just chunk the weight with the number of devices,
+                        but in multi-head attention, we need to chunk the weight with the number of devices * n_head, and
+                        each device should have a part of Q, K and V weight.
     """
     weight: str = None
     bias: str = None
     replace_layer: Any = None
     ignore: bool = False
+    reversed: bool = False
+    n_cast: int = None
 
 
 @dataclass
@@ -131,7 +136,7 @@ class Policy():
             (OrignModel, CustomModel)
             in `CustomModel`, we can overwrite the forward and backward process
         """
-        return ()
+        return None
 
     @staticmethod
     def binding_policy() -> Dict:
@@ -146,7 +151,7 @@ class Policy():
                 "bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight",
             }
         """
-        return NotImplementedError
+        return None
 
     @staticmethod
     def attn_in() -> List:
@@ -209,4 +214,4 @@ class Policy():
         Return:
             List[Layer]: List of layer object
         """
-        return NotImplementedError
+        return None

colossalai/shardformer/policies/bert.py

@@ -1,4 +1,3 @@
-from dataclasses import dataclass
 from typing import Any, Callable, Dict, List, Tuple, Type
 
 import torch.nn as nn

colossalai/shardformer/policies/gpt2.py

@@ -0,0 +1,118 @@
+from typing import Any, Callable, Dict, List, Tuple, Type
+
+import torch.nn as nn
+from transformers.models.gpt2.modeling_gpt2 import GPT2Block, GPT2Model
+
+import colossalai.shardformer.layer.layers as col_nn
+
+from .basepolicy import Argument, Col_Layer, Layer, Policy, Row_Layer
+
+
+class GPT2Policy(Policy):
+
+    @staticmethod
+    def argument_policy(config, world_size):
+        return {
+            GPT2Model:
+                Argument(attr_dict={}, param_funcs=[
+                    GPT2Policy.embedding,
+                ]),
+            GPT2Block:
+                Argument(
+                    attr_dict={
+        # 1. reduce hidden size
+                        "attn.embed_dim": config.hidden_size // world_size,
+                        "attn.split_size": config.hidden_size // world_size,
+                        "crossattention.embed_dim": config.hidden_size // world_size,
+                        "crossattention.split_size": config.hidden_size // world_size,
+        # 2. reduce number of heads
+                        "attn.num_heads": config.num_attention_heads // world_size,
+                        "crossattention.num_heads": config.num_attention_heads // world_size,
+                    },
+                    param_funcs=[
+                        GPT2Policy.attn_in,
+                        GPT2Policy.attn_out,
+                        GPT2Policy.mlp_in,
+                        GPT2Policy.mlp_out,
+                    ]),
+        }
+
+    @staticmethod
+    def attn_in() -> List:
+        return [
+            Col_Layer(weight="attn.c_attn.weight",
+                      bias="attn.c_attn.bias",
+                      n_cast=3,
+                      reversed=True,
+                      replace_layer=col_nn.Linear1D_Col),
+            Col_Layer(weight="crossattention.c_attn.weight",
+                      bias="crossattention.c_attn.bias",
+                      n_cast=2,
+                      reversed=True,
+                      ignore=True,
+                      replace_layer=col_nn.Linear1D_Col),
+            Col_Layer(weight="crossattention.q_attn.weight",
+                      bias="crossattention.q_attn.bias",
+                      reversed=True,
+                      ignore=True,
+                      replace_layer=col_nn.Linear1D_Col)
+        ]
+
+    @staticmethod
+    def attn_out() -> List:
+        return [
+            Row_Layer(weight="attn.c_proj.weight",
+                      bias="attn.c_proj.bias",
+                      reversed=True,
+                      replace_layer=col_nn.Linear1D_Row),
+            Row_Layer(weight="crossattention.c_proj.weight",
+                      bias="crossattention.c_proj.bias",
+                      reversed=True,
+                      ignore=True,
+                      replace_layer=col_nn.Linear1D_Row)
+        ]
+
+    @staticmethod
+    def mlp_in() -> List:
+        return [
+            Col_Layer(weight="mlp.c_fc.weight", bias="mlp.c_fc.bias", reversed=True, replace_layer=col_nn.Linear1D_Col),
+        ]
+
+    @staticmethod
+    def mlp_out() -> List:
+        return [
+            Row_Layer(weight="mlp.c_proj.weight",
+                      bias="mlp.c_proj.bias",
+                      reversed=True,
+                      replace_layer=col_nn.Linear1D_Row)
+        ]
+
+    @staticmethod
+    def embedding() -> List:
+        return [Col_Layer(weight="wte.weight", replace_layer=col_nn.VocabParallelEmbedding1D)]
+
+
+from transformers import GPT2LMHeadModel
+
+
+class GPT2LMHeadModelPolicy(GPT2Policy):
+
+    @staticmethod
+    def argument_policy(config, world_size):
+        base_argument = GPT2Policy.argument_policy(config, world_size)
+        argument = {
+            GPT2LMHeadModel: Argument(attr_dict={}, param_funcs=[
+                GPT2LMHeadModelPolicy.unembedding,
+            ]),
+        }
+        argument.update(base_argument)
+        return argument
+
+    @staticmethod
+    def unembedding() -> List:
+        return [
+            Col_Layer(weight="lm_head.weight",
+                      bias="lm_head.bias",
+                      replace_layer=col_nn.Linear1D_Col,
+                      gather_output=True)
+        ]

colossalai/shardformer/shard/sharder.py

@@ -2,6 +2,7 @@ from typing import Any, Callable, Dict, List
 
 import torch
 import torch.nn as nn
+from transformers.pytorch_utils import Conv1D
 
 from ..policies.autopolicy import get_autopolicy
 from ..policies.basepolicy import Policy
@@ -35,10 +36,22 @@ class ModelSharder(object):
         self.model_config = self.model.config
 
     def shard(self) -> None:
+        self.reshape_embedding()
         self.inject_model(self.model)
         self.replace_layer(self.model)
         self.bind_layer(self.model)
 
+    def reshape_embedding(self,) -> None:
+        r"""
+        Reshape the Embedding layer to make the embedding dimension divisible by world_size
+        """
+        vocab_size = self.model_config.vocab_size
+        world_size = self.shard_config.world_size
+        if vocab_size % world_size != 0:
+            new_vocab_size = vocab_size + world_size - vocab_size % world_size
+            self.model.resize_token_embeddings(new_vocab_size)
+            self.model_config = self.model.config
+
     def inject_model(
         self,
         model: nn.Module,
@@ -53,6 +66,8 @@ class ModelSharder(object):
         """
         inject_policy = self.policy.inject_policy()
 
+        if inject_policy is None:
+            return
         org_model_cls = inject_policy[0]
         shard_model_cls = inject_policy[1]
 
@@ -82,9 +97,9 @@ class ModelSharder(object):
             origin_layer_cls = argument_policy[0]
             attr_dict = argument_policy[1].attr_dict
             param_funcs = argument_policy[1].param_funcs
-            self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs)
+            self.traverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs)
 
-    def reverse_replace_layer(
+    def traverse_replace_layer(
         self,
         layer: nn.Module,
         origin_cls: nn.Module,
@@ -100,17 +115,12 @@ class ModelSharder(object):
             attr_dict (Dict): The attribute dict to modify
             policy_cls (:class:`Policy`): The policy class
         """
+        if layer.__class__ == origin_cls:
+            for k, v in attr_dict.items():
+                setattr_(layer, k, v, ignore=True)
+            self.shard_one_layer(layer, param_funcs)
         for name, child in layer.named_children():
-            if child.__class__ == origin_cls:
-                # replac_layer = child
-                for k, v in attr_dict.items():
-                    setattr_(child, k, v, ignore=True)
-                # print(f"Sharding {name} layer", replac_layer.attention.self.__dict__)
-                # setattr_(layer, name, self.shard_one_layer(child, policy_cls))
-                self.shard_one_layer(child, param_funcs)
-                continue
-
-            self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs)
+            self.traverse_replace_layer(child, origin_cls, attr_dict, param_funcs)
         return layer
 
     def shard_one_layer(
@@ -126,7 +136,6 @@ class ModelSharder(object):
             param_funcs (:class:`List[typing.Callable]`): The function list to get shard information in policy class
 
         """
-        # print(org_layer)
         for func in param_funcs:
             policy_layers = func()
             for policy_layer in policy_layers:
@@ -136,9 +145,10 @@ class ModelSharder(object):
                 bias_attr = policy_layer.bias
                 replace_layer_cls = policy_layer.replace_layer
                 ignore = policy_layer.ignore
+                n_cast = policy_layer.n_cast
+                reversed = policy_layer.reversed
                 if policy_layer.__class__.__name__ == "Col_Layer":
                     gather_output = policy_layer.gather_output
-                    # print(gather_output)
 
                 if weight_attr is not None:
                     if hasattr_(org_layer, weight_attr):
@@ -161,13 +171,11 @@ class ModelSharder(object):
                 layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr)
 
                 # slice weight and bias
-                weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__)
-                # print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None)
+                weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__, n_cast, reversed)
 
                 # create new object to replace the origin layer
                 if replace_layer_cls is not None:
-                    # print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}")
-                    if isinstance(getattr_(org_layer, layer_attr), nn.Linear):
+                    if isinstance(getattr_(org_layer, layer_attr), (nn.Linear, Conv1D)):
                         if replace_layer_cls.__name__ == "Linear1D_Row":
                             replace_layer = replace_layer_cls(weight.shape[1],
                                                               weight.shape[0],
@@ -235,6 +243,8 @@ class ModelSharder(object):
             model (:class:`torch.nn.Module`): The shard model
         """
         binding_map = self.policy.binding_policy()
+        if binding_map is None:
+            return
         for k, v in binding_map.items():
             param = getattr_(model, k)
             param = nn.Parameter(param)

colossalai/shardformer/shard/slicer.py

@@ -19,6 +19,8 @@ class Slicer():
         weight: torch.Tensor,
         bias: torch.Tensor,
         policy_layer_cls: Layer,
+        n_cast: int = None,
+        reversed: bool = False,
     ):
         r"""
         Slice the weight and bias according to policy layer cls
@@ -33,13 +35,18 @@ class Slicer():
         """
         if policy_layer_cls == Layer:
             return weight, bias
-        elif policy_layer_cls == Col_Layer:
-            weight = self.slice_tensor(weight, 1, False)
+
+        dim = dim_mapping[policy_layer_cls] if not reversed else (1 - dim_mapping[policy_layer_cls])
+        # print(weight.shape, dim)
+        if policy_layer_cls == Col_Layer:
+            weight = self.slice_tensor(weight, dim, False, n_cast)
             bias = self.slice_tensor(bias, 0, True)
         elif policy_layer_cls == Row_Layer:
-            weight = self.slice_tensor(weight, 0, False)
+            weight = self.slice_tensor(weight, dim, False, n_cast)
         else:
             raise NotImplementedError(f"The policy layer class {policy_layer_cls} is not supported")
+        if reversed:
+            weight = weight.transpose(0, 1).contiguous()
         return weight, bias
 
     def slice_tensor(
@@ -47,6 +54,7 @@ class Slicer():
         tensor_in: torch.Tensor,
         dim: int,
         is_bias: bool,
+        n_cast: int = None,
     ) -> torch.Tensor:
         r"""
         Slice tensor according to the config
@@ -59,14 +67,15 @@ class Slicer():
         if tensor_in is None:
             return None
         if not is_bias:
-            return self.slice_2d(tensor_in, dim)
+            return self.slice_2d(tensor_in, dim, n_cast)
         else:
-            return self.slice_1d(tensor_in)
+            return self.slice_1d(tensor_in, n_cast)
 
     def slice_2d(
         self,
         tensor: torch.Tensor,
         dim: int,
+        n_cast: int = None,
     ) -> torch.Tensor:
         r"""
         Slice the 2D tensor
@@ -77,13 +86,14 @@ class Slicer():
         """
         assert dim in [0, 1], f"Only support 2D tensor, but got {dim}D tensor"
         if dim == 0:
-            return self.slice_row(tensor)
+            return self.slice_row(tensor, n_cast)
         elif dim == 1:
-            return self.slice_col(tensor)
+            return self.slice_col(tensor, n_cast)
 
     def slice_1d(
         self,
         tensor: torch.Tensor,
+        n_cast: int = None,
     ) -> torch.Tensor:
         r"""
         Slice the 1D tensor
@@ -94,11 +104,19 @@ class Slicer():
         Returns:
             :class:`torch.Tensor`: The sliced tensor
         """
-        return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous()
+        if n_cast is None:
+            return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous()
+        else:
+            tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0)
+            chunk_list = [
+                tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size)
+            ]
+            return torch.cat(chunk_list, dim=0).contiguous()
 
     def slice_col(
         self,
         tensor: torch.Tensor,
+        n_cast: int = None,
     ) -> torch.Tensor:
         r"""
         Slice the tensor in column
@@ -110,11 +128,19 @@ class Slicer():
             :class:`torch.Tensor`: The sliced tensor
 
         """
-        return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous()
+        if n_cast is None:
+            return tensor.chunk(self.shardconfig.world_size, dim=0)[self.shardconfig.rank].contiguous()
+        else:
+            tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=0)
+            chunk_list = [
+                tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size)
+            ]
+            return torch.cat(chunk_list, dim=0).contiguous()
 
     def slice_row(
         self,
         tensor: torch.Tensor,
+        n_cast: int = None,
     ) -> torch.Tensor:
         r"""
         Slice the tensor in column
@@ -125,4 +151,11 @@ class Slicer():
         Returns:
             :class:`torch.Tensor`: The sliced tensor
         """
-        return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous()
+        if n_cast is None:
+            return tensor.chunk(self.shardconfig.world_size, dim=1)[self.shardconfig.rank].contiguous()
+        else:
+            tensor_chunks = tensor.chunk(self.shardconfig.world_size * n_cast, dim=1)
+            chunk_list = [
+                tensor_chunks[i] for i in range(self.shardconfig.rank, len(tensor_chunks), self.shardconfig.world_size)
+            ]
+            return torch.cat(chunk_list, dim=1).contiguous()

colossalai/shardformer/test/test.py

@@ -6,24 +6,28 @@ import torch.nn as nn
 from datasets import load_dataset
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
-from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, get_scheduler
+from transformers import AutoTokenizer, BertForMaskedLM, DataCollatorForLanguageModeling, GPT2LMHeadModel, get_scheduler
 
 import colossalai
 from colossalai.shardformer.shard import ShardConfig, shard_model
 from colossalai.utils import get_current_device, print_rank_0
 
 os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 
 
 def get_args():
     parser = colossalai.get_default_parser()
     parser.add_argument("--mode", type=str, default='inference')
     parser.add_argument("--save_model", action='store_true')
+    parser.add_argument("--model", type=str, default='bert-base-uncased')
     return parser.parse_args()
 
 
-def load_data():
+def load_data(args):
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    if tokenizer.pad_token is None:
+        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        # tokenizer.pad_token_id = 0
     datasets = load_dataset('wikitext', 'wikitext-2-raw-v1')
     # datasets=load_dataset("yelp_review_full")
     tokenized_datasets = datasets.map(
@@ -42,18 +46,23 @@ def load_data():
 
 
 def inference(model: nn.Module, args):
-    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+    print(model)
+    # print(model.wte.weight.shape)
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
+    if tokenizer.pad_token is None:
+        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        tokenizer.pad_token_id = 0
     token = "Hello, my dog is cute"
     inputs = tokenizer(token, return_tensors="pt")
     inputs.to("cuda")
     model.eval()
     model.to("cuda")
     outputs = model(**inputs)
-    print(outputs)
+    print(outputs[0])
 
 
 def train(model: nn.Module, args, num_epoch: int = 3):
-    train_dataloader, eval_dataloader = load_data()
+    train_dataloader, eval_dataloader = load_data(args)
     optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
     num_training = num_epoch * len(train_dataloader)
     progress_bar = tqdm(range(num_training))
@@ -94,8 +103,13 @@ def train(model: nn.Module, args, num_epoch: int = 3):
 
 if __name__ == "__main__":
     args = get_args()
-    model = BertForMaskedLM.from_pretrained("bert-base-uncased")
     colossalai.launch_from_torch(config=args.config)
+    if args.model == 'bert-base-uncased':
+        model = BertForMaskedLM.from_pretrained("bert-base-uncased")
+    elif args.model == 'gpt2':
+        model = GPT2LMHeadModel.from_pretrained("gpt2")
+    else:
+        raise AttributeError("model not supported")
     shard_config = ShardConfig(
         rank=int(str(get_current_device()).split(':')[-1]),
         world_size=int(os.environ['WORLD_SIZE']),