lib/BERTalt.py

from __future__ import absolute_import

import torch
from torch import nn
import torch.nn.functional as F
import math
from BERT_explainability.modules.layers_ours import *

import transformers

from transformers import BertConfig
from transformers.modeling_outputs import BaseModelOutputWithPooling, BaseModelOutput
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 RPBertEmbeddings(BertEmbeddings):
    def __init__(self, config):
        super().__init__()

        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 RPBertEncoder(transformers.modeling_bert.BertEncoder):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])

    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 RPBertPooler(transformers.modeling_bert.BertPooler):
    def __init__(self, config):
        super().__init__()
        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(transformers.modeling_bert.BertAttention):
    def __init__(self, config):
        super().__init__()
        self.clone = Clone()

    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(transformers.modeling_bert.BertSelfAttention):
    def __init__(self, config):
        super().__init__()

        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_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(transformers.modeling_bert.BertSelfOutput):
    def __init__(self, config):
        super().__init__()
        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(transformers.modeling_bert.BertIntermediate):
    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(transformers.modeling_bert.BertOutput):
    def __init__(self, config):
        super().__init__()
        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 RPBertLayer(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


transformers.modeling_bert.BertEmbeddings = RPBertEmbeddings
transformers.modeling_bert.BertEncoder = RPBertEncoder

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)