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