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317 lines
13 KiB
317 lines
13 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Callable, List, Optional, Union
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from torch.distributed import ProcessGroup
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from colossalai.lazy import LazyInitContext
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from colossalai.nn import init as init
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from colossalai.nn.layer.utils import divide
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from colossalai.tensor.d_tensor.api import (
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is_distributed_tensor,
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shard_colwise,
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shard_rowwise,
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sharded_tensor_to_existing_param,
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)
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from ._operation import gather_forward_split_backward, reduce_forward
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from .parallel_module import ParallelModule
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from .utils import create_randomizer_with_offset
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__all__ = ["Embedding1D", "VocabParallelEmbedding1D"]
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class Embedding1D(ParallelModule):
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r"""Embedding for 1D parallelism.
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Args:
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num_embeddings (int): number of embeddings.
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embedding_dim (int): dimension of embedding.
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padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
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therefore, the embedding vector at padding_idx is not updated during training,
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i.e. it remains as a fixed “pad”, defaults to None.
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dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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weight_initializer (:class:`typing.Callable`, optional):
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he initializer of weight, defaults to normal initializer.
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The ``args`` and ``kwargs`` used in :class:`torch.nn.functional.embedding` should contain:
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::
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max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
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renormalized to have norm max_norm. Note: this will modify weight in-place.
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norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
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scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
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of frequency of the words in the mini-batch. Default False.
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sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
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More details about ``args`` and ``kwargs`` could be found in
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`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
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More details about ``initializer`` please refer to
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`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_
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"""
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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padding_idx: int = None,
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dtype: torch.dtype = None,
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device: torch.device = None,
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process_group: ProcessGroup = None,
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gather_output: bool = True,
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weight: Optional[nn.Parameter] = None,
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weight_initializer: Callable = init.normal_(),
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*args,
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**kwargs,
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):
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super().__init__()
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self.num_embeddings = num_embeddings
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self.embedding_dim = embedding_dim
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self.process_group = process_group
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self.padding_idx = padding_idx
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self.embed_args = args
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self.embed_kwargs = kwargs
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self.gather_output = gather_output
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# offset the seed with randomizer index and rank
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seed = torch.random.initial_seed()
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self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
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# Parameters.
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if weight is None:
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factory_kwargs = {"device": device, "dtype": dtype}
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self.weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
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else:
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weight.data = weight.data.to(device=device, dtype=dtype)
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self.weight = weight
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if not is_distributed_tensor(self.weight):
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sharded_weight = shard_colwise(self.weight.data, process_group)
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sharded_tensor_to_existing_param(sharded_weight, self.weight)
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if weight is None:
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with self.randomizer.fork_rng(enable_cpu=True):
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self.reset_parameters(weight_initializer)
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@staticmethod
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def from_native_module(
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module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]] = None, *args, **kwargs
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) -> "Embedding1D":
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r"""
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Build a 1D parallelized Embedding from a native nn.Embedding module.
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"""
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LazyInitContext.materialize(module)
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# get the attributes
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num_embedding = module.num_embeddings
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embedding_dim = module.embedding_dim
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padding_idx = module.padding_idx
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max_norm = module.max_norm
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norm_type = module.norm_type
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scale_grad_by_freq = module.scale_grad_by_freq
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sparse = module.sparse
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dtype = module.weight.dtype
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device = module.weight.device
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# sparse is not support yet
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if sparse:
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raise NotImplementedError("The Embedding1D module does not support sparse embedding yet.")
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embedding = Embedding1D(
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num_embeddings=num_embedding,
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embedding_dim=embedding_dim,
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padding_idx=padding_idx,
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process_group=process_group,
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dtype=dtype,
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device=device,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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sparse=sparse,
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weight=module.weight,
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*args,
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**kwargs,
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)
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return embedding
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def reset_parameters(self, weight_initializer) -> None:
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fan_in, fan_out = self.num_embeddings, self.embedding_dim
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weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
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self._fill_padding_idx_with_zero()
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def _fill_padding_idx_with_zero(self) -> None:
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if self.padding_idx is not None:
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with torch.no_grad():
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self.weight[self.padding_idx].fill_(0)
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def forward(self, input_: Tensor) -> Tensor:
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output_parallel = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs)
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if self.gather_output:
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output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group)
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return output
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else:
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return output_parallel
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class VocabParallelEmbedding1D(ParallelModule):
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r"""Embedding parallelized in the vocabulary dimension.
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Args:
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num_embeddings (int): number of embeddings.
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embedding_dim (int): dimension of embedding.
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padding_idx (int, optional): If specified, the entries at padding_idx do not contribute to the gradient;
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therefore, the embedding vector at padding_idx is not updated during training,
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i.e. it remains as a fixed “pad”, defaults to None.
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dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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weight_initializer (:class:`typing.Callable`, optional):
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he initializer of weight, defaults to normal initializer.
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The ``args`` and ``kwargs`` used in :class:``torch.nn.functional.embedding`` should contain:
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::
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max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is
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renormalized to have norm max_norm. Note: this will modify weight in-place.
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norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
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scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse
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of frequency of the words in the mini-batch. Default False.
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sparse (bool, optional): If True, gradient w.r.t. weight will be a sparse tensor. Default False.
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More details about ``args`` and ``kwargs`` could be found in
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`Embedding <https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding>`_.
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More details about initializer please refer to
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`init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
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"""
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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padding_idx: int = None,
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dtype: torch.dtype = None,
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device: torch.device = None,
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process_group: ProcessGroup = None,
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weight: Optional[nn.Parameter] = None,
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weight_initializer: Callable = init.normal_(),
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*args,
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**kwargs,
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):
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super().__init__()
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self.num_embeddings = num_embeddings
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self.embedding_dim = embedding_dim
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self.embed_args = args
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self.embed_kwargs = kwargs
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self.process_group = process_group
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tensor_parallel_size = dist.get_world_size(group=process_group)
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tensor_parallel_rank = dist.get_rank(group=process_group)
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self.num_embeddings_per_partition = divide(num_embeddings, tensor_parallel_size)
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self.num_embeddings = self.num_embeddings_per_partition
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self.vocab_start_index = tensor_parallel_rank * self.num_embeddings_per_partition
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self.vocab_end_index = self.vocab_start_index + self.num_embeddings_per_partition
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# padding index
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self.padding_idx = self._select_padding_idx(padding_idx)
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# offset the seed with randomizer index and rank
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seed = torch.random.initial_seed()
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self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
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# parameter
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if weight is None:
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factory_kwargs = {"device": device, "dtype": dtype}
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self.weight = nn.Parameter(torch.empty((num_embeddings, self.embedding_dim), **factory_kwargs))
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else:
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weight.data = weight.data.to(device=device, dtype=dtype)
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self.weight = weight
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if not is_distributed_tensor(self.weight):
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sharded_weight = shard_rowwise(self.weight.data, process_group)
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sharded_tensor_to_existing_param(sharded_weight, self.weight)
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if weight is None:
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self.reset_parameters(weight_initializer)
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@staticmethod
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def from_native_module(
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module: nn.Embedding, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
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) -> ParallelModule:
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r"""
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Convert a native pytorch embedding module to a parallel module.
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"""
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LazyInitContext.materialize(module)
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# get the origin attributes
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num_embeddings = module.num_embeddings
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embedding_dim = module.embedding_dim
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padding_idx = module.padding_idx
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device = module.weight.device
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# ensure only one process group is used
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if isinstance(process_group, (list, tuple)):
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assert len(process_group) == 1, f"Expected only one process group, got {len(process_group)}."
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process_group = process_group[0]
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# create the parallel module
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vocab_embedding_1d = VocabParallelEmbedding1D(
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num_embeddings=num_embeddings,
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embedding_dim=embedding_dim,
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padding_idx=padding_idx,
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device=device,
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process_group=process_group,
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weight=module.weight,
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*args,
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**kwargs,
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)
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return vocab_embedding_1d
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def reset_parameters(self, weight_initializer) -> None:
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with self.randomizer.fork_rng(enable_cpu=True):
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fan_in, fan_out = self.num_embeddings, self.embedding_dim
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weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
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self._fill_padding_idx_with_zero()
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def _fill_padding_idx_with_zero(self) -> None:
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if (
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self.padding_idx is not None
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and self.padding_idx >= self.vocab_start_index
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and self.padding_idx < self.vocab_end_index
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):
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with torch.no_grad():
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self.weight[self.padding_idx - self.vocab_start_index].fill_(0)
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def _select_padding_idx(self, padding_idx: int):
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# select padding index according to the rank
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if padding_idx is None:
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return None
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elif padding_idx < self.vocab_end_index and padding_idx >= self.vocab_start_index:
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return padding_idx - self.vocab_start_index
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else:
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return None
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def forward(self, input_: Tensor) -> Tensor:
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# Build the mask.
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input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
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# Mask the input.
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masked_input = input_.clone() - self.vocab_start_index
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masked_input[input_mask] = 0
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output_parallel = F.embedding(
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masked_input, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs
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)
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# Mask the output embedding.
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embedding_output = output_parallel.clone()
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embedding_output[input_mask, :] = 0.0
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# Reduce across all the model parallel GPUs.
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output = reduce_forward(embedding_output, self.process_group)
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return output
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