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github-python

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else:
return score
class PolyEncoder(BertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.bert = kwargs['bert']
self.poly_m = kwargs['poly_m']
self.poly_code_embeddings = nn.Embedding(self.poly_m, config.hidden_size)
torch.nn.init.normal_(self.poly_code_embeddings.weight, config.hidden_size ** -0.5)
def dot_attention(self, q, k, v):
# q: [bs, poly_m, dim] or [bs, res_cnt, dim]
# k=v: [bs, length, dim] or [bs, poly_m, dim]
attn_weights = torch.matmul(q, k.transpose(2, 1)) # [bs, poly_m, length]
attn_weights = F.softmax(attn_weights, -1)
output = torch.matmul(attn_weights, v) # [bs, poly_m, dim]
return output
def forward(self, context_input_ids, context_input_masks,
responses_input_ids, responses_input_masks, labels=None):
temperature = 0.05
# during training, only select the first response; using other instances in a batch as negative examples
if labels is not None:
responses_input_ids = responses_input_ids[:, 0, :].unsqueeze(1)
responses_input_masks = responses_input_masks[:, 0, :].unsqueeze(1)
batch_size, res_cnt, seq_length = responses_input_ids.shape # res_cnt is 1 during training
# context encoder
ctx_out = self.bert(context_input_ids, context_input_masks)[0] # [bs, length, dim]
poly_code_ids = torch.arange(self.poly_m, dtype=torch.long).to(context_input_ids.device)
poly_code_ids = poly_code_ids.unsqueeze(0).expand(batch_size, self.poly_m)
poly_codes = self.poly_code_embeddings(poly_code_ids) # [bs, poly_m, dim]
embs = self.dot_attention(poly_codes, ctx_out, ctx_out) # [bs, poly_m, dim]
# response encoder
responses_input_ids = responses_input_ids.view(-1, seq_length)
responses_input_masks = responses_input_masks.view(-1, seq_length)
cand_emb = self.bert(responses_input_ids, responses_input_masks)[0][:,0,:] # [bs, dim]
cand_emb = cand_emb.view(batch_size, res_cnt, -1) # [bs, res_cnt, dim]
# merge
if labels is not None:
# we are recycling responses for faster training
# we repeat responses for batch_size times to simulate test phase
# so that every context is paired with batch_size responses
cand_emb = cand_emb.permute(1, 0, 2) # [1, bs, dim]
cand_emb = cand_emb.expand(batch_size, batch_size, cand_emb.shape[2]) # [bs, bs, dim]
ctx_emb = self.dot_attention(cand_emb, embs, embs).squeeze() # [bs, bs, dim], or [dim] is bs=1
cand_emb = F.normalize(cand_emb, dim=-1)
ctx_emb = F.normalize(ctx_emb, dim=-1)
dot_product = (ctx_emb*cand_emb).sum(-1) / temperature # [bs, bs]
mask = torch.eye(batch_size).to(context_input_ids.device) # [bs, bs]
loss = F.log_softmax(dot_product, dim=-1) * mask
loss = (-loss.sum(dim=1)).mean()
return loss
else:
ctx_emb = self.dot_attention(cand_emb, embs, embs) # [bs, res_cnt, dim]
cand_emb = F.normalize(cand_emb, dim=2)
ctx_emb = F.normalize(ctx_emb, dim=2)
dot_product = (ctx_emb*cand_emb).sum(-1)
return dot_product
# <FILESEP>
#!/usr/bin/env python
"""
MIT License
Copyright (c) 2021 Michael Alonge <malonge11@gmail.com>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import os
import sys
import argparse
from collections import defaultdict