from __future__ import absolute_import import torch from torch import nn import torch.nn.functional as F import math from transformers import BertConfig from transformers.modeling_outputs import BaseModelOutputWithPooling, BaseModelOutput from BERT_explainability.modules.layers_lrp import * from transformers import ( BertPreTrainedModel, PreTrainedModel, ) ACT2FN = { "relu": ReLU, "tanh": Tanh, "gelu": GELU, } def get_activation(activation_string): if activation_string in ACT2FN: return ACT2FN[activation_string] else: raise KeyError("function {} not found in ACT2FN mapping {}".format(activation_string, list(ACT2FN.keys()))) def compute_rollout_attention(all_layer_matrices, start_layer=0): # adding residual consideration num_tokens = all_layer_matrices[0].shape[1] batch_size = all_layer_matrices[0].shape[0] eye = torch.eye(num_tokens).expand(batch_size, num_tokens, num_tokens).to(all_layer_matrices[0].device) all_layer_matrices = [all_layer_matrices[i] + eye for i in range(len(all_layer_matrices))] all_layer_matrices = [all_layer_matrices[i] / all_layer_matrices[i].sum(dim=-1, keepdim=True) for i in range(len(all_layer_matrices))] joint_attention = all_layer_matrices[start_layer] for i in range(start_layer+1, len(all_layer_matrices)): joint_attention = all_layer_matrices[i].bmm(joint_attention) return joint_attention class BertEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.add1 = Add() self.add2 = Add() def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) # embeddings = inputs_embeds + position_embeddings + token_type_embeddings embeddings = self.add1([token_type_embeddings, position_embeddings]) embeddings = self.add2([embeddings, inputs_embeds]) embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def relprop(self, cam, **kwargs): cam = self.dropout.relprop(cam, **kwargs) cam = self.LayerNorm.relprop(cam, **kwargs) # [inputs_embeds, position_embeddings, token_type_embeddings] (cam) = self.add2.relprop(cam, **kwargs) return cam class BertEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)]) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=False, ): all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if getattr(self.config, "gradient_checkpointing", False): def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) def relprop(self, cam, **kwargs): # assuming output_hidden_states is False for layer_module in reversed(self.layer): cam = layer_module.relprop(cam, **kwargs) return cam # not adding relprop since this is only pooling at the end of the network, does not impact tokens importance class BertPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = Linear(config.hidden_size, config.hidden_size) self.activation = Tanh() self.pool = IndexSelect() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. self._seq_size = hidden_states.shape[1] # first_token_tensor = hidden_states[:, 0] first_token_tensor = self.pool(hidden_states, 1, torch.tensor(0, device=hidden_states.device)) first_token_tensor = first_token_tensor.squeeze(1) pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output def relprop(self, cam, **kwargs): cam = self.activation.relprop(cam, **kwargs) #print(cam.sum()) cam = self.dense.relprop(cam, **kwargs) #print(cam.sum()) cam = cam.unsqueeze(1) cam = self.pool.relprop(cam, **kwargs) #print(cam.sum()) return cam class BertAttention(nn.Module): def __init__(self, config): super().__init__() self.self = BertSelfAttention(config) self.output = BertSelfOutput(config) self.pruned_heads = set() self.clone = Clone() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=False, ): h1, h2 = self.clone(hidden_states, 2) self_outputs = self.self( h1, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions, ) attention_output = self.output(self_outputs[0], h2) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs def relprop(self, cam, **kwargs): # assuming that we don't ouput the attentions (outputs = (attention_output,)), self_outputs=(context_layer,) (cam1, cam2) = self.output.relprop(cam, **kwargs) #print(cam1.sum(), cam2.sum(), (cam1 + cam2).sum()) cam1 = self.self.relprop(cam1, **kwargs) #print(cam1.sum(), cam2.sum(), (cam1 + cam2).sum()) return self.clone.relprop((cam1, cam2), **kwargs) class BertSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): 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 = Linear(config.hidden_size, self.all_head_size) self.key = Linear(config.hidden_size, self.all_head_size) self.value = Linear(config.hidden_size, self.all_head_size) self.dropout = Dropout(config.attention_probs_dropout_prob) self.matmul1 = MatMul() self.matmul2 = MatMul() self.softmax = Softmax(dim=-1) self.add = Add() self.mul = Mul() self.head_mask = None self.attention_mask = None self.clone = Clone() self.attn_cam = None self.attn = None self.attn_gradients = None def get_attn(self): return self.attn def save_attn(self, attn): self.attn = attn def save_attn_cam(self, cam): self.attn_cam = cam def get_attn_cam(self): return self.attn_cam def save_attn_gradients(self, attn_gradients): self.attn_gradients = attn_gradients def get_attn_gradients(self): return self.attn_gradients 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 transpose_for_scores_relprop(self, x): return x.permute(0, 2, 1, 3).flatten(2) def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=False, ): self.head_mask = head_mask self.attention_mask = attention_mask h1, h2, h3 = self.clone(hidden_states, 3) mixed_query_layer = self.query(h1) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. if encoder_hidden_states is not None: mixed_key_layer = self.key(encoder_hidden_states) mixed_value_layer = self.value(encoder_hidden_states) attention_mask = encoder_attention_mask else: mixed_key_layer = self.key(h2) mixed_value_layer = self.value(h3) 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) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = self.matmul1([query_layer, key_layer.transpose(-1, -2)]) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = self.add([attention_scores, attention_mask]) # Normalize the attention scores to probabilities. attention_probs = self.softmax(attention_scores) self.save_attn(attention_probs) attention_probs.register_hook(self.save_attn_gradients) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = self.matmul2([attention_probs, value_layer]) 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) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs def relprop(self, cam, **kwargs): # Assume output_attentions == False cam = self.transpose_for_scores(cam) # [attention_probs, value_layer] (cam1, cam2) = self.matmul2.relprop(cam, **kwargs) cam1 /= 2 cam2 /= 2 if self.head_mask is not None: # [attention_probs, head_mask] (cam1, _)= self.mul.relprop(cam1, **kwargs) self.save_attn_cam(cam1) cam1 = self.dropout.relprop(cam1, **kwargs) cam1 = self.softmax.relprop(cam1, **kwargs) if self.attention_mask is not None: # [attention_scores, attention_mask] (cam1, _) = self.add.relprop(cam1, **kwargs) # [query_layer, key_layer.transpose(-1, -2)] (cam1_1, cam1_2) = self.matmul1.relprop(cam1, **kwargs) cam1_1 /= 2 cam1_2 /= 2 # query cam1_1 = self.transpose_for_scores_relprop(cam1_1) cam1_1 = self.query.relprop(cam1_1, **kwargs) # key cam1_2 = self.transpose_for_scores_relprop(cam1_2.transpose(-1, -2)) cam1_2 = self.key.relprop(cam1_2, **kwargs) # value cam2 = self.transpose_for_scores_relprop(cam2) cam2 = self.value.relprop(cam2, **kwargs) cam = self.clone.relprop((cam1_1, cam1_2, cam2), **kwargs) return cam class BertSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = Linear(config.hidden_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = Dropout(config.hidden_dropout_prob) self.add = Add() def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) add = self.add([hidden_states, input_tensor]) hidden_states = self.LayerNorm(add) return hidden_states def relprop(self, cam, **kwargs): cam = self.LayerNorm.relprop(cam, **kwargs) # [hidden_states, input_tensor] (cam1, cam2) = self.add.relprop(cam, **kwargs) cam1 = self.dropout.relprop(cam1, **kwargs) cam1 = self.dense.relprop(cam1, **kwargs) return (cam1, cam2) class BertIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]() else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def relprop(self, cam, **kwargs): cam = self.intermediate_act_fn.relprop(cam, **kwargs) # FIXME only ReLU #print(cam.sum()) cam = self.dense.relprop(cam, **kwargs) #print(cam.sum()) return cam class BertOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = Dropout(config.hidden_dropout_prob) self.add = Add() def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) add = self.add([hidden_states, input_tensor]) hidden_states = self.LayerNorm(add) return hidden_states def relprop(self, cam, **kwargs): # print("in", cam.sum()) cam = self.LayerNorm.relprop(cam, **kwargs) #print(cam.sum()) # [hidden_states, input_tensor] (cam1, cam2)= self.add.relprop(cam, **kwargs) # print("add", cam1.sum(), cam2.sum(), cam1.sum() + cam2.sum()) cam1 = self.dropout.relprop(cam1, **kwargs) #print(cam1.sum()) cam1 = self.dense.relprop(cam1, **kwargs) # print("dense", cam1.sum()) # print("out", cam1.sum() + cam2.sum(), cam1.sum(), cam2.sum()) return (cam1, cam2) class BertLayer(nn.Module): def __init__(self, config): super().__init__() self.attention = BertAttention(config) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) self.clone = Clone() def forward( self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, ): self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights ao1, ao2 = self.clone(attention_output, 2) intermediate_output = self.intermediate(ao1) layer_output = self.output(intermediate_output, ao2) outputs = (layer_output,) + outputs return outputs def relprop(self, cam, **kwargs): (cam1, cam2) = self.output.relprop(cam, **kwargs) # print("output", cam1.sum(), cam2.sum(), cam1.sum() + cam2.sum()) cam1 = self.intermediate.relprop(cam1, **kwargs) # print("intermediate", cam1.sum()) cam = self.clone.relprop((cam1, cam2), **kwargs) # print("clone", cam.sum()) cam = self.attention.relprop(cam, **kwargs) # print("attention", cam.sum()) return cam class BertModel(BertPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) 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) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, device) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] 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 # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] 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) # print("111111111111",cam.sum()) cam = self.encoder.relprop(cam, **kwargs) # print("222222222222222", cam.sum()) # print("conservation: ", cam.sum()) 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)