[shardformer] refactored the shardformer layer structure

colossalai/shardformer/utils/utils.py → colossalai/shardformer/_utils.py

@@ -2,6 +2,9 @@
 
 
 def get_obj_list_element(obj, a):
+    r"""
+    Get the element of the list in the object
+    """
     re_pattern = r'\[\d+\]'
     prog = re.compile(re_pattern)
     result = prog.search(a)

colossalai/shardformer/layer/__init__.py

@@ -1,17 +1,10 @@
 from .dropout import Dropout1D
-from .embedding1d import Embedding1D
-from .layernorm1d import LayerNorm1D
-from .linear1d import Linear1D_Col, Linear1D_Row
-from .linearconv1d import LinearConv1D_Col, LinearConv1D_Row
-from .vocabparallelembedding1d import VocabParallelEmbedding1D
+from .embedding import Embedding1D, VocabParallelEmbedding1D
+from .linear import Linear1D_Col, Linear1D_Row
+from .linear_conv import LinearConv1D_Col, LinearConv1D_Row
+from .loss import cross_entropy_1d
 
 __all__ = [
-    "Embedding1D",
-    "VocabParallelEmbedding1D",
-    "Linear1D_Col",
-    "Linear1D_Row",
-    "LinearConv1D_Col",
-    "LinearConv1D_Row",
-    "LayerNorm1D",
-    "Dropout1D",
+    "Embedding1D", "VocabParallelEmbedding1D", "Linear1D_Col", "Linear1D_Row", "LinearConv1D_Col", "LinearConv1D_Row",
+    "Dropout1D", "cross_entropy_1d"
 ]

colossalai/shardformer/layer/_operation.py

@@ -1,8 +1,6 @@
 import torch
 import torch.distributed as dist
 
-from colossalai.core import global_context as gpc
-
 try:
     import fused_mix_prec_layer_norm_cuda
 except:

colossalai/shardformer/layer/dropout.py

@@ -4,9 +4,11 @@
 import torch.nn as nn
 from torch.distributed import ProcessGroup
 
-from .parallelmodule import ParallelModule
+from .parallel_module import ParallelModule
 from .utils import create_randomizer_with_offset
 
+__all__ = ['Dropout1D']
+
 
 class Dropout1D(ParallelModule, nn.Dropout):
     """

colossalai/shardformer/layer/vocabparallelembedding1d.py → colossalai/shardformer/layer/embedding.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
-from collections import OrderedDict
 from typing import Callable, List, Union
 
 import torch
@@ -12,26 +11,148 @@
 from torch.distributed import ProcessGroup
 from torch.nn.parameter import Parameter
 
-from colossalai.context import ParallelMode, seed
 from colossalai.nn import init as init
-from colossalai.nn.layer.base_layer import ParallelLayer
 from colossalai.nn.layer.utils import divide
-from colossalai.tensor.d_tensor.api import shard_rowwise
-from colossalai.utils.checkpointing import gather_tensor_parallel_state_dict
+from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise
+from colossalai.utils.cuda import get_current_device
 
-from ._operation import reduce_input
-from .parallelmodule import ParallelModule
+from ._operation import gather_forward_split_backward, reduce_input
+from .parallel_module import ParallelModule
 from .utils import create_randomizer_with_offset
 
-Fast_LN = None
-try:
-    from apex.contrib.layer_norm.layer_norm import FastLayerNorm
-    Fast_LN = FastLayerNorm
-except ImportError:
-    pass
+__all__ = ['Embedding1D', 'VocabParallelEmbedding1D']
 
 
-class VocabParallelEmbedding1D(ParallelLayer):
+class Embedding1D(ParallelModule):
+    r"""Embedding for 1D parallelism.
+
+    Args:
+        num_embeddings (int): number of embeddings.
+        embedding_dim (int): dimension of embedding.
+        padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
+            therefore, the embedding vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed “pad”, defaults to None.
+        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
+        weight_initializer (:class:`typing.Callable`, optional):
+            he initializer of weight, defaults to normal initializer.
+
+    The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain:
+    ::
+
+        max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
+                    renormalized to have norm max_norm. Note: this will modify weight in-place.
+        norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
+        scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
+                    of frequency of the words in the mini-batch. Default False.
+        sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
+
+    More details about ``args`` and ``kwargs`` could be found in
+    `Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
+
+    More details about ``initializer`` please refer to
+    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
+    """
+
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 padding_idx: int = None,
+                 dtype: torch.dtype = None,
+                 device: torch.device = None,
+                 process_group: ProcessGroup = None,
+                 gather_output: bool = True,
+                 weight_initializer: Callable = init.normal_(),
+                 *args,
+                 **kwargs):
+        super().__init__()
+
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.process_group = process_group
+        self.num_partitions = dist.get_world_size(process_group)
+        self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions)
+
+        self.padding_idx = padding_idx
+        self.embed_args = args
+        self.embed_kwargs = kwargs
+        self.gather_output = gather_output
+
+        if device is None:
+            device = get_current_device()
+
+        self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype))
+
+        # offset the seed with randomizer index and rank
+        seed = torch.random.initial_seed()
+        self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
+
+        with self.randomizer.fork_rng(enable_cpu=True):
+            self.reset_parameters(weight_initializer)
+
+    @staticmethod
+    def from_native_module(module: nn.Embedding,
+                           process_group: Union[ProcessGroup, List[ProcessGroup]] = None,
+                           *args,
+                           **kwargs) -> "Embedding1D":
+        r"""
+        Build a 1D parallelized Embedding from a native nn.Embedding module.
+        """
+        # get the attributes
+        num_embedding = module.num_embeddings
+        embedding_dim = module.embedding_dim
+        padding_idx = module.padding_idx
+        max_norm = module.max_norm
+        norm_type = module.norm_type
+        scale_grad_by_freq = module.scale_grad_by_freq
+        sparse = module.sparse
+        dtype = module.weight.dtype
+        device = module.weight.device
+
+        # sparse is not support yet
+        if sparse:
+            raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.")
+
+        embedding = Embedding1D(num_embeddings=num_embedding,
+                                embedding_dim=embedding_dim,
+                                padding_idx=padding_idx,
+                                process_group=process_group,
+                                dtype=dtype,
+                                device=device,
+                                max_norm=max_norm,
+                                norm_type=norm_type,
+                                scale_grad_by_freq=scale_grad_by_freq,
+                                sparse=sparse,
+                                *args,
+                                **kwargs)
+
+        # copy the weight
+        with torch.no_grad():
+            sharded_weight = shard_colwise(module.weight.data, process_group)
+            embedding.weight.copy_(sharded_weight)
+
+        return embedding
+
+    def reset_parameters(self, weight_initializer) -> None:
+        fan_in, fan_out = self.num_embeddings, self.embedding_dim
+        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+        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:
+        output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
+
+        if self.gather_output:
+            output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group)
+            return output
+        else:
+            return output_parallel
+
+
+class VocabParallelEmbedding1D(ParallelModule):
     r"""Embedding parallelized in the vocabulary dimension.
 
     Args:
@@ -93,9 +214,7 @@ def __init__(self,
         # offset the seed with randomizer index and rank
         seed = torch.random.initial_seed()
         self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
-
-        with self.randomizer.fork_rng(enable_cpu=True):
-            self.reset_parameters(weight_initializer)
+        self.reset_parameters(weight_initializer)
 
     @staticmethod
     def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
@@ -132,7 +251,7 @@ def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup,
         return vocab_embedding_1d
 
     def reset_parameters(self, weight_initializer) -> None:
-        with seed(ParallelMode.TENSOR):
+        with self.randomizer.fork_rng(enable_cpu=True):
             fan_in, fan_out = self.num_embeddings, self.embed_dim
             weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
             self._fill_padding_idx_with_zero()
@@ -143,16 +262,6 @@ def _fill_padding_idx_with_zero(self) -> None:
             with torch.no_grad():
                 self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
 
-    def _save_to_global_state_dict(self, destination, prefix, keep_vars):
-        weight_key = prefix + 'weight'
-        local_state = OrderedDict({weight_key: self.weight})
-        local_state = gather_tensor_parallel_state_dict(local_state,
-                                                        ParallelMode.PARALLEL_1D,
-                                                        dims={weight_key: 0},
-                                                        partition_states={weight_key: True},
-                                                        keep_vars=keep_vars)
-        destination.update(local_state)
-
     def forward(self, input_: Tensor) -> Tensor:
         # Build the mask.
         input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)