ColossalAI/colossalai/shardformer/layer/embedding.py

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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from typing import Callable, List, Optional, Union
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.distributed import ProcessGroup
from colossalai.lazy import LazyInitContext
from colossalai.nn import init as init
from colossalai.nn.layer.utils import divide
from colossalai.tensor.d_tensor.api import (
is_distributed_tensor,
shard_colwise,
shard_rowwise,
sharded_tensor_to_existing_param,
)
from ._operation import gather_forward_split_backward, reduce_forward
from .parallel_module import ParallelModule
from .utils import create_randomizer_with_offset
__all__ = ['Embedding1D', 'VocabParallelEmbedding1D']
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: Optional[nn.Parameter] = None,
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.padding_idx = padding_idx
self.embed_args = args
self.embed_kwargs = kwargs
self.gather_output = gather_output
# 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)
# Parameters.
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_colwise(self.weight.data, process_group)
sharded_tensor_to_existing_param(sharded_weight, self.weight)
if weight is None:
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.
"""
LazyInitContext.materialize(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,
weight=module.weight,
*args,
**kwargs)
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:
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,
weight: Optional[nn.Parameter] = None,
weight_initializer: Callable = init.normal_(),
*args,
**kwargs):
super().__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.embed_args = args
self.embed_kwargs = kwargs
self.process_group = process_group
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
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
# 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)
# 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)
if weight is None:
self.reset_parameters(weight_initializer)
@staticmethod
def from_native_module(module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
**kwargs) -> ParallelModule:
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
# ensure only one process group is used
if isinstance(process_group, (list, tuple)):
assert len(process_group) == 1, \
f'Expected only one process group, got {len(process_group)}.'
process_group = process_group[0]
# create the parallel module
vocab_embedding_1d = VocabParallelEmbedding1D(num_embeddings=num_embeddings,
embedding_dim=embedding_dim,
padding_idx=padding_idx,
device=device,
process_group=process_group,
weight=module.weight,
*args,
**kwargs)
return vocab_embedding_1d
def reset_parameters(self, weight_initializer) -> None:
with self.randomizer.fork_rng(enable_cpu=True):
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 and \
self.padding_idx >= self.vocab_start_index and self.padding_idx < self.vocab_end_index:
with torch.no_grad():
self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
def _select_padding_idx(self, padding_idx: int):
# select padding index according to the rank
if padding_idx is None:
return None
elif padding_idx < self.vocab_end_index and padding_idx >= self.vocab_start_index:
return padding_idx - self.vocab_start_index
else:
return None
def forward(self, input_: Tensor) -> Tensor:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
# 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.
output_parallel[input_mask, :] = 0.
# Reduce across all the model parallel GPUs.
output = reduce_forward(output_parallel, self.process_group)
return output