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from __future__ import absolute_import
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import torch
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from torch import nn
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import torch.nn.functional as F
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import math
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from transformers import BertConfig
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from transformers.modeling_outputs import BaseModelOutputWithPooling, BaseModelOutput
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from BERT_explainability.modules.layers_ours import *
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from transformers import (
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BertPreTrainedModel,
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PreTrainedModel,
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)
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ACT2FN = {
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"relu": ReLU,
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"tanh": Tanh,
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"gelu": GELU,
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}
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def get_activation(activation_string):
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if activation_string in ACT2FN:
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return ACT2FN[activation_string]
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else:
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raise KeyError("function {} not found in ACT2FN mapping {}".format(activation_string, list(ACT2FN.keys())))
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def compute_rollout_attention(all_layer_matrices, start_layer=0):
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num_tokens = all_layer_matrices[0].shape[1]
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batch_size = all_layer_matrices[0].shape[0]
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eye = torch.eye(num_tokens).expand(batch_size, num_tokens, num_tokens).to(all_layer_matrices[0].device)
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all_layer_matrices = [all_layer_matrices[i] + eye for i in range(len(all_layer_matrices))]
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all_layer_matrices = [all_layer_matrices[i] / all_layer_matrices[i].sum(dim=-1, keepdim=True)
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for i in range(len(all_layer_matrices))]
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joint_attention = all_layer_matrices[start_layer]
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for i in range(start_layer+1, len(all_layer_matrices)):
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joint_attention = all_layer_matrices[i].bmm(joint_attention)
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return joint_attention
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class BertEmbeddings(nn.Module):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config):
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super().__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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self.LayerNorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = Dropout(config.hidden_dropout_prob)
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self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
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self.add1 = Add()
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self.add2 = Add()
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
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if input_ids is not None:
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input_shape = input_ids.size()
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else:
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input_shape = inputs_embeds.size()[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if token_type_ids is None:
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = self.add1([token_type_embeddings, position_embeddings])
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embeddings = self.add2([embeddings, inputs_embeds])
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embeddings = self.LayerNorm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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def relprop(self, cam, **kwargs):
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cam = self.dropout.relprop(cam, **kwargs)
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cam = self.LayerNorm.relprop(cam, **kwargs)
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(cam) = self.add2.relprop(cam, **kwargs)
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return cam
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class BertEncoder(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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output_attentions=False,
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output_hidden_states=False,
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return_dict=False,
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):
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all_hidden_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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for i, layer_module in enumerate(self.layer):
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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layer_head_mask = head_mask[i] if head_mask is not None else None
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if getattr(self.config, "gradient_checkpointing", False):
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def create_custom_forward(module):
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def custom_forward(*inputs):
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return module(*inputs, output_attentions)
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return custom_forward
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layer_outputs = torch.utils.checkpoint.checkpoint(
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create_custom_forward(layer_module),
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hidden_states,
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attention_mask,
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layer_head_mask,
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)
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else:
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layer_outputs = layer_module(
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hidden_states,
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attention_mask,
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layer_head_mask,
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output_attentions,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_attentions = all_attentions + (layer_outputs[1],)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
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)
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def relprop(self, cam, **kwargs):
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for layer_module in reversed(self.layer):
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cam = layer_module.relprop(cam, **kwargs)
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return cam
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class BertPooler(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = Linear(config.hidden_size, config.hidden_size)
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self.activation = Tanh()
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self.pool = IndexSelect()
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def forward(self, hidden_states):
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self._seq_size = hidden_states.shape[1]
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first_token_tensor = self.pool(hidden_states, 1, torch.tensor(0, device=hidden_states.device))
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first_token_tensor = first_token_tensor.squeeze(1)
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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def relprop(self, cam, **kwargs):
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cam = self.activation.relprop(cam, **kwargs)
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cam = self.dense.relprop(cam, **kwargs)
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cam = cam.unsqueeze(1)
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cam = self.pool.relprop(cam, **kwargs)
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return cam
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class BertAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.self = BertSelfAttention(config)
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self.output = BertSelfOutput(config)
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self.pruned_heads = set()
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self.clone = Clone()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
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)
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self.self.query = prune_linear_layer(self.self.query, index)
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self.self.key = prune_linear_layer(self.self.key, index)
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self.self.value = prune_linear_layer(self.self.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
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self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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output_attentions=False,
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):
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h1, h2 = self.clone(hidden_states, 2)
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self_outputs = self.self(
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h1,
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attention_mask,
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head_mask,
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encoder_hidden_states,
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encoder_attention_mask,
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output_attentions,
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)
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attention_output = self.output(self_outputs[0], h2)
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outputs = (attention_output,) + self_outputs[1:]
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return outputs
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def relprop(self, cam, **kwargs):
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(cam1, cam2) = self.output.relprop(cam, **kwargs)
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cam1 = self.self.relprop(cam1, **kwargs)
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return self.clone.relprop((cam1, cam2), **kwargs)
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class BertSelfAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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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, config.num_attention_heads)
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)
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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 = Linear(config.hidden_size, self.all_head_size)
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self.key = Linear(config.hidden_size, self.all_head_size)
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self.value = Linear(config.hidden_size, self.all_head_size)
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self.dropout = Dropout(config.attention_probs_dropout_prob)
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self.matmul1 = MatMul()
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self.matmul2 = MatMul()
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self.softmax = Softmax(dim=-1)
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self.add = Add()
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self.mul = Mul()
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self.head_mask = None
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self.attention_mask = None
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self.clone = Clone()
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self.attn_cam = None
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self.attn = None
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self.attn_gradients = None
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def get_attn(self):
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return self.attn
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def save_attn(self, attn):
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self.attn = attn
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def save_attn_cam(self, cam):
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self.attn_cam = cam
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def get_attn_cam(self):
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return self.attn_cam
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def save_attn_gradients(self, attn_gradients):
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self.attn_gradients = attn_gradients
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def get_attn_gradients(self):
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return self.attn_gradients
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def transpose_for_scores(self, x):
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, 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 transpose_for_scores_relprop(self, x):
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return x.permute(0, 2, 1, 3).flatten(2)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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encoder_hidden_states=None,
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encoder_attention_mask=None,
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output_attentions=False,
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):
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self.head_mask = head_mask
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self.attention_mask = attention_mask
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h1, h2, h3 = self.clone(hidden_states, 3)
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mixed_query_layer = self.query(h1)
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if encoder_hidden_states is not None:
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mixed_key_layer = self.key(encoder_hidden_states)
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mixed_value_layer = self.value(encoder_hidden_states)
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attention_mask = encoder_attention_mask
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else:
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mixed_key_layer = self.key(h2)
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mixed_value_layer = self.value(h3)
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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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attention_scores = self.matmul1([query_layer, key_layer.transpose(-1, -2)])
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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attention_scores = self.add([attention_scores, attention_mask])
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attention_probs = self.softmax(attention_scores)
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self.save_attn(attention_probs)
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attention_probs.register_hook(self.save_attn_gradients)
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attention_probs = self.dropout(attention_probs)
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = self.matmul2([attention_probs, value_layer])
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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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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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def relprop(self, cam, **kwargs):
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cam = self.transpose_for_scores(cam)
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(cam1, cam2) = self.matmul2.relprop(cam, **kwargs)
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cam1 /= 2
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cam2 /= 2
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if self.head_mask is not None:
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(cam1, _)= self.mul.relprop(cam1, **kwargs)
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self.save_attn_cam(cam1)
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cam1 = self.dropout.relprop(cam1, **kwargs)
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cam1 = self.softmax.relprop(cam1, **kwargs)
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if self.attention_mask is not None:
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(cam1, _) = self.add.relprop(cam1, **kwargs)
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(cam1_1, cam1_2) = self.matmul1.relprop(cam1, **kwargs)
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cam1_1 /= 2
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cam1_2 /= 2
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cam1_1 = self.transpose_for_scores_relprop(cam1_1)
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cam1_1 = self.query.relprop(cam1_1, **kwargs)
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cam1_2 = self.transpose_for_scores_relprop(cam1_2.transpose(-1, -2))
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cam1_2 = self.key.relprop(cam1_2, **kwargs)
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cam2 = self.transpose_for_scores_relprop(cam2)
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cam2 = self.value.relprop(cam2, **kwargs)
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cam = self.clone.relprop((cam1_1, cam1_2, cam2), **kwargs)
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return cam
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class BertSelfOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = Linear(config.hidden_size, config.hidden_size)
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self.LayerNorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = Dropout(config.hidden_dropout_prob)
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self.add = Add()
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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add = self.add([hidden_states, input_tensor])
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hidden_states = self.LayerNorm(add)
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return hidden_states
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def relprop(self, cam, **kwargs):
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cam = self.LayerNorm.relprop(cam, **kwargs)
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(cam1, cam2) = self.add.relprop(cam, **kwargs)
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cam1 = self.dropout.relprop(cam1, **kwargs)
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cam1 = self.dense.relprop(cam1, **kwargs)
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return (cam1, cam2)
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class BertIntermediate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = Linear(config.hidden_size, config.intermediate_size)
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if isinstance(config.hidden_act, str):
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self.intermediate_act_fn = ACT2FN[config.hidden_act]()
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else:
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self.intermediate_act_fn = config.hidden_act
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.intermediate_act_fn(hidden_states)
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return hidden_states
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def relprop(self, cam, **kwargs):
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cam = self.intermediate_act_fn.relprop(cam, **kwargs)
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cam = self.dense.relprop(cam, **kwargs)
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return cam
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class BertOutput(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.dense = Linear(config.intermediate_size, config.hidden_size)
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self.LayerNorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.dropout = Dropout(config.hidden_dropout_prob)
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self.add = Add()
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def forward(self, hidden_states, input_tensor):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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add = self.add([hidden_states, input_tensor])
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hidden_states = self.LayerNorm(add)
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return hidden_states
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def relprop(self, cam, **kwargs):
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cam = self.LayerNorm.relprop(cam, **kwargs)
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|
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(cam1, cam2)= self.add.relprop(cam, **kwargs)
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cam1 = self.dropout.relprop(cam1, **kwargs)
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cam1 = self.dense.relprop(cam1, **kwargs)
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return (cam1, cam2)
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|
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class BertLayer(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.attention = BertAttention(config)
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self.intermediate = BertIntermediate(config)
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self.output = BertOutput(config)
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self.clone = Clone()
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|
|
def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
|
|
output_attentions=False,
|
|
):
|
|
self_attention_outputs = self.attention(
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hidden_states,
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attention_mask,
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head_mask,
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output_attentions=output_attentions,
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)
|
|
attention_output = self_attention_outputs[0]
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outputs = self_attention_outputs[1:]
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ao1, ao2 = self.clone(attention_output, 2)
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intermediate_output = self.intermediate(ao1)
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layer_output = self.output(intermediate_output, ao2)
|
|
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|
outputs = (layer_output,) + outputs
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|
return outputs
|
|
|
|
def relprop(self, cam, **kwargs):
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|
(cam1, cam2) = self.output.relprop(cam, **kwargs)
|
|
|
|
cam1 = self.intermediate.relprop(cam1, **kwargs)
|
|
|
|
cam = self.clone.relprop((cam1, cam2), **kwargs)
|
|
|
|
cam = self.attention.relprop(cam, **kwargs)
|
|
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|
return cam
|
|
|
|
|
|
class BertModel(BertPreTrainedModel):
|
|
def __init__(self, config):
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|
super().__init__(config)
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|
self.config = config
|
|
|
|
self.embeddings = BertEmbeddings(config)
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|
self.encoder = BertEncoder(config)
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|
self.pooler = BertPooler(config)
|
|
|
|
self.init_weights()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def forward(
|
|
self,
|
|
input_ids=None,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
encoder_hidden_states=None,
|
|
encoder_attention_mask=None,
|
|
output_attentions=None,
|
|
output_hidden_states=None,
|
|
return_dict=None,
|
|
):
|
|
r"""
|
|
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
|
|
if the model is configured as a decoder.
|
|
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
|
|
Mask to avoid performing attention on the padding token indices of the encoder input. This mask
|
|
is used in the cross-attention if the model is configured as a decoder.
|
|
Mask values selected in ``[0, 1]``:
|
|
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(input_shape, device=device)
|
|
if token_type_ids is None:
|
|
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
|
|
|
|
|
|
|
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device)
|
|
|
|
|
|
|
|
if self.config.is_decoder and encoder_hidden_states is not None:
|
|
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
|
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
|
|
if encoder_attention_mask is None:
|
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
|
|
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
|
|
else:
|
|
encoder_extended_attention_mask = None
|
|
|
|
|
|
|
|
|
|
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
attention_mask=extended_attention_mask,
|
|
head_mask=head_mask,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_extended_attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output)
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
def relprop(self, cam, **kwargs):
|
|
cam = self.pooler.relprop(cam, **kwargs)
|
|
|
|
cam = self.encoder.relprop(cam, **kwargs)
|
|
|
|
|
|
return cam
|
|
|
|
|
|
if __name__ == '__main__':
|
|
class Config:
|
|
def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):
|
|
self.hidden_size = hidden_size
|
|
self.num_attention_heads = num_attention_heads
|
|
self.attention_probs_dropout_prob = attention_probs_dropout_prob
|
|
|
|
model = BertSelfAttention(Config(1024, 4, 0.1))
|
|
x = torch.rand(2, 20, 1024)
|
|
x.requires_grad_()
|
|
|
|
model.eval()
|
|
|
|
y = model.forward(x)
|
|
|
|
relprop = model.relprop(torch.rand(2, 20, 1024), (torch.rand(2, 20, 1024),))
|
|
|
|
print(relprop[1][0].shape)
|
|
|
