ver217
diff --git a/‎colossalai/booster/plugin/gemini_plugin.py‎
Lines changed: 3 additions & 3 deletions b/‎colossalai/booster/plugin/gemini_plugin.py‎
Lines changed: 3 additions & 3 deletions
diff --git a/‎colossalai/booster/plugin/hybrid_parallel_plugin.py‎
Lines changed: 5 additions & 6 deletions b/‎colossalai/booster/plugin/hybrid_parallel_plugin.py‎
Lines changed: 5 additions & 6 deletions
diff --git a/‎colossalai/checkpoint_io/hybrid_parallel_checkpoint_io.py‎
Lines changed: 28 additions & 1 deletion b/‎colossalai/checkpoint_io/hybrid_parallel_checkpoint_io.py‎
Lines changed: 28 additions & 1 deletion
diff --git a/‎colossalai/checkpoint_io/utils.py‎
Lines changed: 9 additions & 0 deletions b/‎colossalai/checkpoint_io/utils.py‎
Lines changed: 9 additions & 0 deletions
diff --git a/‎colossalai/shardformer/layer/__init__.py‎
Lines changed: 5 additions & 2 deletions b/‎colossalai/shardformer/layer/__init__.py‎
Lines changed: 5 additions & 2 deletions
diff --git a/‎colossalai/shardformer/layer/embedding.py‎
Lines changed: 95 additions & 16 deletions b/‎colossalai/shardformer/layer/embedding.py‎
Lines changed: 95 additions & 16 deletions
@@ -44,10 +44,10 @@
 def get_param_info(optim: Optimizer):
     # Get a backup of necessary information of parameters for future use, which includes:
     # 1. A mapping from integer param_id to param32 shape.
-
     if optim is None:
         return {}
     param_info = {"id2shape": {}}
+
     start_index = 0
     for group in optim.param_groups:
         for param_id, param in enumerate(group["params"], start_index):
@@ -527,7 +527,7 @@ def configure(
         dataloader: Optional[DataLoader] = None,
         lr_scheduler: Optional[LRScheduler] = None,
     ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
-        optimizer_params_info = get_param_info(optimizer)
+        params_info = get_param_info(optimizer)
         if not isinstance(model, ModelWrapper):
             # convert model to sync bn
             # FIXME(ver217): gemini does not support sync bn
@@ -558,7 +558,7 @@ def configure(
                 **self.zero_optim_config,
                 **self.optim_kwargs,
                 tp_group=self.tp_group,
-                optimizer_params_info=optimizer_params_info,
+                params_info=params_info,
                 verbose=self.verbose,
             )
 
 
@@ -213,12 +213,7 @@ def get_param_info(optim: Optimizer):
 
     if optim is None:
         return {}
-    param_info = {
-        "param_groups": [],
-        "param2id": {},
-        "id2param": {},
-        "param2shape": {},
-    }
+    param_info = {"param_groups": [], "param2id": {}, "id2param": {}, "param2shape": {}}
     start_index = 0
     for group in optim.param_groups:
         packed_group = {k: v for k, v in group.items() if k != "params"}
@@ -947,6 +942,8 @@ class HybridParallelPlugin(PipelinePluginBase):
         num_model_chunks (int, optional): The number of model chunks for interleaved pipeline parallelism. Defaults to 1.
         gradient_checkpoint_config (GradientCheckpointConfig, optional): Configuration for gradient checkpointing. Defaults to None.
         enable_metadata_cache (bool, optional): Whether to enable metadata cache for pipeline parallelism. Defaults to True.
+        make_vocab_size_divisible_by (int, optional): it's used when padding the vocabulary size, to make it choose an faster kenel. Default to 64.
+
     """
 
     def __init__(
@@ -989,6 +986,7 @@ def __init__(
         num_model_chunks: int = 1,
         gradient_checkpoint_config: Optional[GradientCheckpointConfig] = None,
         enable_metadata_cache: bool = True,
+        make_vocab_size_divisible_by: int = 64,
     ) -> None:
         super().__init__()
         assert (
@@ -1095,6 +1093,7 @@ def __init__(
             sequence_parallelism_mode=sequence_parallelism_mode,
             enable_sequence_overlap=enable_sequence_overlap,
             parallel_output=parallel_output,
+            make_vocab_size_divisible_by=make_vocab_size_divisible_by,
             gradient_checkpoint_config=gradient_checkpoint_config,
         )
         self.amp_config = dict(
 
@@ -14,6 +14,12 @@
 
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
+from colossalai.tensor.padded_tensor import (
+    init_as_padded_tensor,
+    is_padded_tensor,
+    to_padded_tensor,
+    to_unpadded_tensor,
+)
 from colossalai.utils import get_current_device
 
 from .general_checkpoint_io import GeneralCheckpointIO
@@ -32,6 +38,7 @@
     save_param_groups,
     save_state_dict,
     save_state_dict_shards,
+    search_padding_dim,
     search_tp_partition_dim,
     sharded_optimizer_loading_epilogue,
 )
@@ -89,6 +96,8 @@ def _model_sharder(
             if param is None:
                 continue
             # Gather tensor pieces when using tensor parallel.
+            if is_padded_tensor(param):
+                param = to_unpadded_tensor(param)
             param_ = gather_distributed_param(param, keep_vars=False)
             block, block_size = state_dict_sharder.append_param(prefix + name, param_)
             if block is not None:
@@ -231,7 +240,6 @@ def save_sharded_model(
             # When pipeline is used, each stage produces its own shard files and index files.
             # Index files belonging to each stage are saved under a temporary folder ./tmp_index_files/
             # After all the state_dicts have been saved, the master rank integrates all the index files into one final index file and deletes the tmp folder.
-
             final_index_file_path = copy.deepcopy(save_index_file)
             tmp_index_file_folder = os.path.join(checkpoint, "tmp_index_files")
             Path(tmp_index_file_folder).mkdir(parents=True, exist_ok=True)
@@ -251,6 +259,7 @@ def save_sharded_model(
                 use_safetensors=use_safetensors,
                 use_pp_format=True,
             )
+
             if control_saving:
                 assert (
                     self.dp_rank == 0 and self.tp_rank == 0
@@ -867,6 +876,11 @@ def gather_from_sharded_optimizer_state(
                     dist.all_gather(gather_tensor, v, group=tp_group)
                     v = torch.cat(gather_tensor, dim=partition_dim)
 
+                padding_dim = search_padding_dim(v.shape, original_shape)
+                if padding_dim is not None:
+                    v = init_as_padded_tensor(v, v.shape[padding_dim], original_shape[padding_dim], padding_dim)
+                    v = to_unpadded_tensor(v)
+
                 state_[k] = v.detach().clone().to(device)
 
         return state_
@@ -899,6 +913,19 @@ def shard_from_complete_optimizer_state(
             if isinstance(v, torch.Tensor) and k != "step":
                 # Shard state along tensor parallel group.
                 partition_dim = search_tp_partition_dim(current_shape, original_shape, self.tp_size)
+                global_shape = current_shape
+                if partition_dim is not None:
+                    # pad embedding params
+                    global_shape = (
+                        *current_shape[:partition_dim],
+                        current_shape[partition_dim] * self.tp_size,
+                        *current_shape[partition_dim + 1 :],
+                    )
+
+                padding_dim = search_padding_dim(global_shape, original_shape)
+                if padding_dim is not None:
+                    v = to_padded_tensor(v, global_shape[padding_dim], padding_dim)
+
                 if partition_dim is not None:
                     slice_size = current_shape[partition_dim]
                     v = v.split(slice_size, dim=partition_dim)[self.tp_rank]
 
@@ -120,6 +120,15 @@ def search_tp_partition_dim(current_shape: torch.Size, original_shape: torch.Siz
     return partition_dim
 
 
+def search_padding_dim(global_shape: torch.Size, original_shape: torch.Size) -> Optional[int]:
+    padding_dim = None
+    for dim, length in enumerate(global_shape):
+        if length > original_shape[dim]:
+            padding_dim = dim
+            break
+    return padding_dim
+
+
 # ======================================
 # Helper classes and functions for saving shard file
 # ======================================
 
@@ -1,8 +1,8 @@
 from ._operation import all_to_all_comm
 from .attn import AttnMaskType, ColoAttention
 from .dropout import DropoutForParallelInput, DropoutForReplicatedInput
-from .embedding import Embedding1D, VocabParallelEmbedding1D
-from .linear import Linear1D_Col, Linear1D_Row
+from .embedding import Embedding1D, PaddingEmbedding, VocabParallelEmbedding1D
+from .linear import Linear1D_Col, Linear1D_Row, PaddingLMHead, VocabParallelLMHead1D
 from .loss import cross_entropy_1d
 from .normalization import FusedLayerNorm, FusedRMSNorm, LayerNorm, RMSNorm
 from .parallel_module import ParallelModule
@@ -25,6 +25,9 @@
     "FusedRMSNorm",
     "FusedLinear1D_Col",
     "ParallelModule",
+    "PaddingEmbedding",
+    "PaddingLMHead",
+    "VocabParallelLMHead1D",
     "AttnMaskType",
     "ColoAttention",
     "all_to_all_comm",
 
@@ -21,10 +21,10 @@
 )
 
 from ._operation import gather_forward_split_backward, reduce_forward
-from .parallel_module import ParallelModule
+from .parallel_module import PaddingParallelModule, ParallelModule
 from .utils import create_randomizer_with_offset
 
-__all__ = ["Embedding1D", "VocabParallelEmbedding1D"]
+__all__ = ["Embedding1D", "VocabParallelEmbedding1D", "PaddingEmbedding"]
 
 
 class Embedding1D(ParallelModule):
@@ -161,7 +161,80 @@ def forward(self, input_: Tensor) -> Tensor:
             return output_parallel
 
 
-class VocabParallelEmbedding1D(ParallelModule):
+class PaddingEmbedding(PaddingParallelModule):
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int = None,
+        dtype: torch.dtype = None,
+        device: torch.device = None,
+        weight: Optional[nn.Parameter] = None,
+        make_vocab_size_divisible_by: int = 64,
+        *args,
+        **kwargs,
+    ):
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.embed_args = args
+        self.embed_kwargs = kwargs
+        self.padding_idx = padding_idx
+        if num_embeddings % make_vocab_size_divisible_by != 0:
+            self.num_embeddings = (
+                num_embeddings + make_vocab_size_divisible_by - (num_embeddings % make_vocab_size_divisible_by)
+            )
+        # create weight and bias
+        if weight is None:
+            factory_kwargs = {"device": device, "dtype": dtype}
+            weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
+        else:
+            weight.data = weight.data.to(device=device, dtype=dtype)
+
+        super().__init__(self.num_embeddings, num_embeddings, weight)
+
+        if weight is None:
+            self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        init.normal_(self.weight)
+        self._fill_padding_idx_with_zero()
+
+    def _fill_padding_idx_with_zero(self) -> None:
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.weight[self.padding_idx].fill_(0)
+
+    def forward(self, input: Tensor) -> Tensor:
+        return F.embedding(input, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
+
+    @staticmethod
+    def from_native_module(
+        module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
+    ) -> PaddingParallelModule:
+        r"""
+        Convert a native pytorch embedding module to a parallel module.
+        """
+        LazyInitContext.materialize(module)
+        # get the origin attributes
+        num_embeddings = module.num_embeddings
+        embedding_dim = module.embedding_dim
+        padding_idx = module.padding_idx
+        device = module.weight.device
+        # create the parallel module
+        padding_embedding = PaddingEmbedding(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            padding_idx=padding_idx,
+            device=device,
+            weight=module.weight,
+            *args,
+            **kwargs,
+        )
+
+        return padding_embedding
+
+
+class VocabParallelEmbedding1D(PaddingParallelModule):
     r"""Embedding parallelized in the vocabulary dimension.
 
     Args:
@@ -201,10 +274,10 @@ def __init__(
         process_group: ProcessGroup = None,
         weight: Optional[nn.Parameter] = None,
         weight_initializer: Callable = init.normal_(),
+        make_vocab_size_divisible_by: int = 64,
         *args,
         **kwargs,
     ):
-        super().__init__()
         self.num_embeddings = num_embeddings
         self.embedding_dim = embedding_dim
         self.embed_args = args
@@ -214,8 +287,23 @@ def __init__(
         tensor_parallel_size = dist.get_world_size(group=process_group)
         tensor_parallel_rank = dist.get_rank(group=process_group)
 
-        self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size)
-        self.num_embeddings = self.num_embeddings_per_partition
+        # generate weight and bias
+        if weight is None:
+            factory_kwargs = {"device": device, "dtype": dtype}
+            weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
+        else:
+            weight.data = weight.data.to(device=device, dtype=dtype)
+
+        # calculate new padding size
+        multiple = make_vocab_size_divisible_by * tensor_parallel_size
+        if num_embeddings % multiple != 0:
+            self.num_embeddings = num_embeddings + multiple - (num_embeddings % multiple)
+
+        # resize vocabulary size
+        super().__init__(self.num_embeddings, num_embeddings, weight)
+
+        # deal with tensor parallelism
+        self.num_embeddings_per_partition = divide(self.num_embeddings, tensor_parallel_size)
         self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
         self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
 
@@ -226,13 +314,6 @@ def __init__(
         seed = torch.random.initial_seed()
         self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
 
-        # parameter
-        if weight is None:
-            factory_kwargs = {"device": device, "dtype": dtype}
-            self.weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
-        else:
-            weight.data = weight.data.to(device=device, dtype=dtype)
-            self.weight = weight
         if not is_distributed_tensor(self.weight):
             sharded_weight = shard_rowwise(self.weight.data, process_group)
             sharded_tensor_to_existing_param(sharded_weight, self.weight)
@@ -243,7 +324,7 @@ def __init__(
     @staticmethod
     def from_native_module(
         module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
-    ) -> ParallelModule:
+    ) -> PaddingParallelModule:
         r"""
         Convert a native pytorch embedding module to a parallel module.
         """
@@ -303,11 +384,9 @@ def forward(self, input_: Tensor) -> Tensor:
         # Mask the input.
         masked_input = input_.clone() - self.vocab_start_index
         masked_input[input_mask] = 0
-
         output_parallel = F.embedding(
             masked_input, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs
         )
-
         # Mask the output embedding.
         embedding_output = output_parallel.clone()
         embedding_output[input_mask, :] = 0.0