Remove files that shouldn't have been committed

lib/BERTalt.py

@@ -1,551 +0,0 @@
-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)

lib/roberta2.py.rej

@@ -1,63 +0,0 @@
---- modeling_roberta.py	2022-06-28 11:59:19.974278244 +0200
-+++ roberta2.py	2022-06-28 14:13:05.765050058 +0200
-@@ -23,14 +23,14 @@
- from torch import nn
- from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
- 
--from ...activations import ACT2FN, gelu
--from ...file_utils import (
-+from transformers.activations import ACT2FN, gelu
-+from transformers.file_utils import (
-     add_code_sample_docstrings,
-     add_start_docstrings,
-     add_start_docstrings_to_model_forward,
-     replace_return_docstrings,
- )
--from ...modeling_outputs import (
-+from transformers.modeling_outputs import (
-     BaseModelOutputWithPastAndCrossAttentions,
-     BaseModelOutputWithPoolingAndCrossAttentions,
-     CausalLMOutputWithCrossAttentions,
-@@ -40,14 +40,14 @@
-     SequenceClassifierOutput,
-     TokenClassifierOutput,
- )
--from ...modeling_utils import (
-+from transformers.modeling_utils import (
-     PreTrainedModel,
-     apply_chunking_to_forward,
-     find_pruneable_heads_and_indices,
-     prune_linear_layer,
- )
--from ...utils import logging
--from .configuration_roberta import RobertaConfig
-+from transformers.utils import logging
-+from transformers.models.roberta.configuration_roberta import RobertaConfig
- 
- 
- logger = logging.get_logger(__name__)
-@@ -183,6 +183,24 @@
- 
-         self.is_decoder = config.is_decoder
- 
-+    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)