123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778 |
- """
- Copyright 2020 The Microsoft DeepSpeed Team
- """
- from torch import nn
- from deepspeed.ops.sparse_attention import SparseSelfAttention, FixedSparsityConfig
- class BertSparseSelfAttention(nn.Module):
- """Implements Sparse Self Attention layer of Bert model based on https://github.com/microsoft/DeepSpeedExamples/blob/master/bing_bert/nvidia/modelingpreln.py#L373
- For more information please see, TODO DeepSpeed Sparse Transformer.
- For usage example please see, TODO DeepSpeed Sparse Transformer Tutorial.
- """
- def __init__(
- self,
- config,
- # SparsityConfig parameters needs to be set accordingly
- sparsity_config=FixedSparsityConfig(num_heads=4)):
- """Initialize the bert sparse self attention layer.
- Note) you can use any of the provided sparsity configs or simply add yours!
- Arguments:
- config: required: Bert model config
- sparsity_config: optional: this parameter determines sparsity pattern configuration; it is based on FixedSparsityConfig class.
- """
- super(BertSparseSelfAttention, self).__init__()
- if config.hidden_size % config.num_attention_heads != 0:
- raise ValueError(
- "The hidden size (%d) is not a multiple of the number of attention "
- "heads (%d)" % (config.hidden_size,
- config.num_attention_heads))
- self.num_attention_heads = config.num_attention_heads
- self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
- self.all_head_size = self.num_attention_heads * self.attention_head_size
- self.query = nn.Linear(config.hidden_size, self.all_head_size)
- self.key = nn.Linear(config.hidden_size, self.all_head_size)
- self.value = nn.Linear(config.hidden_size, self.all_head_size)
- self.sparse_self_attention = SparseSelfAttention(sparsity_config)
- def transpose_for_scores(self, x):
- new_x_shape = x.size()[:-1] + (self.num_attention_heads,
- self.attention_head_size)
- x = x.view(*new_x_shape)
- return x.permute(0, 2, 1, 3)
- def forward(self, hidden_states, attention_mask):
- """Applies forward phase of bert sparse self attention
- Arguments:
- hidden_states: required: hidden_states tensor of the bert model
- attn_mask: required: a mask tensor of size (SequenceLength X SequenceLength); currently only 2D is supported
- Return:
- context_layer: a dense tensor containing attention context
- """
- mixed_query_layer = self.query(hidden_states)
- mixed_key_layer = self.key(hidden_states)
- mixed_value_layer = self.value(hidden_states)
- query_layer = self.transpose_for_scores(mixed_query_layer)
- key_layer = self.transpose_for_scores(mixed_key_layer)
- value_layer = self.transpose_for_scores(mixed_value_layer)
- context_layer = self.sparse_self_attention(query_layer,
- key_layer,
- value_layer,
- key_padding_mask=attention_mask)
- context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
- new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size, )
- context_layer = context_layer.view(*new_context_layer_shape)
- return context_layer
|