mirror of https://github.com/hpcaitech/ColossalAI
158 lines
6.3 KiB
Python
158 lines
6.3 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Callable, List, 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 torch.nn.parameter import Parameter
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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 shard_colwise
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from colossalai.utils.cuda import get_current_device
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from ._operation import gather_forward_split_backward
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from .parallelmodule import ParallelModule
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from .utils import create_randomizer_with_offset
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Fast_LN = None
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try:
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from apex.contrib.layer_norm.layer_norm import FastLayerNorm
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Fast_LN = FastLayerNorm
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except ImportError:
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pass
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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__(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_initializer: Callable = init.normal_(),
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*args,
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**kwargs):
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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.num_partitions = dist.get_world_size(process_group)
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self.embed_dim_per_partition = divide(embedding_dim, self.num_partitions)
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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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if device is None:
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device = get_current_device()
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self.weight = Parameter(torch.empty((num_embeddings, self.embed_dim_per_partition), device=device, dtype=dtype))
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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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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(module: nn.Embedding,
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process_group: Union[ProcessGroup, List[ProcessGroup]] = None,
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*args,
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**kwargs) -> "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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# 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(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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*args,
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**kwargs)
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# copy the weight
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with torch.no_grad():
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sharded_weight = shard_colwise(module.weight.data, process_group)
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embedding.weight.copy_(sharded_weight)
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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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