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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) | |