From b16f509bd4b570fb4587c639f460e79eb15d6283 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kacper=20=C5=81ukawski?= Date: Wed, 15 Feb 2023 17:56:34 +0100 Subject: [PATCH] Add UniXcoder embeddings provider --- backend/encoder.py | 24 + backend/unixcoder.py | 293 +++++++++++ poetry.lock | 1142 ++++++++++++++++++++++++++++++++++++++---- pyproject.toml | 5 +- 4 files changed, 1363 insertions(+), 101 deletions(-) create mode 100644 backend/encoder.py create mode 100644 backend/unixcoder.py diff --git a/backend/encoder.py b/backend/encoder.py new file mode 100644 index 0000000..8c1dbe6 --- /dev/null +++ b/backend/encoder.py @@ -0,0 +1,24 @@ +from typing import Optional, List +from unixcoder import UniXcoder + +import torch + + +class UniXcoderEmbeddingsProvider: + def __init__(self, device: Optional[str] = None): + default_device = "cuda" if torch.cuda.is_available() else "cpu" + self.device = torch.device(default_device if device is None else device) + self.model = UniXcoder("microsoft/unixcoder-base") + self.model.to(self.device) + self.model_name = "microsoft/unixcoder-base" + + def embed_code( + self, code: Optional[str] = None, docstring: Optional[str] = None + ) -> List[float]: + tokens_ids = self.model.tokenize( + [f"{docstring or ''} {code or ''}"], max_length=512, mode="" + ) + source_ids = torch.tensor(tokens_ids).to(self.device) + _, func_embedding = self.model(source_ids) + vector = func_embedding.detach().cpu().numpy()[0] + return vector.tolist() diff --git a/backend/unixcoder.py b/backend/unixcoder.py new file mode 100644 index 0000000..f8c6901 --- /dev/null +++ b/backend/unixcoder.py @@ -0,0 +1,293 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT license. + +import torch +import torch.nn as nn +from transformers import RobertaTokenizer, RobertaModel, RobertaConfig + + +class UniXcoder(nn.Module): + def __init__(self, model_name): + """ + Build UniXcoder. + + Parameters: + + * `model_name`- huggingface model card name. e.g. microsoft/unixcoder-base + """ + super(UniXcoder, self).__init__() + self.tokenizer = RobertaTokenizer.from_pretrained(model_name) + self.config = RobertaConfig.from_pretrained(model_name) + self.config.is_decoder = True + self.model = RobertaModel.from_pretrained(model_name, config=self.config) + + self.register_buffer( + "bias", + torch.tril(torch.ones((1024, 1024), dtype=torch.uint8)).view(1, 1024, 1024), + ) + self.lm_head = nn.Linear( + self.config.hidden_size, self.config.vocab_size, bias=False + ) + self.lm_head.weight = self.model.embeddings.word_embeddings.weight + self.lsm = nn.LogSoftmax(dim=-1) + + self.tokenizer.add_tokens([""], special_tokens=True) + + def tokenize(self, inputs, mode="", max_length=512, padding=False): + """ + Convert string to token ids + + Parameters: + + * `inputs`- list of input strings. + * `max_length`- The maximum total source sequence length after tokenization. + * `padding`- whether to pad source sequence length to max_length. + * `mode`- which mode the sequence will use. i.e. , , + """ + assert mode in ["", "", ""] + assert max_length < 1024 + + tokenizer = self.tokenizer + + tokens_ids = [] + for x in inputs: + tokens = tokenizer.tokenize(x) + if mode == "": + tokens = tokens[: max_length - 4] + tokens = ( + [tokenizer.cls_token, mode, tokenizer.sep_token] + + tokens + + [tokenizer.sep_token] + ) + elif mode == "": + tokens = tokens[-(max_length - 3) :] + tokens = [tokenizer.cls_token, mode, tokenizer.sep_token] + tokens + else: + tokens = tokens[: max_length - 5] + tokens = ( + [tokenizer.cls_token, mode, tokenizer.sep_token] + + tokens + + [tokenizer.sep_token] + ) + + tokens_id = tokenizer.convert_tokens_to_ids(tokens) + if padding: + tokens_id = tokens_id + [self.config.pad_token_id] * ( + max_length - len(tokens_id) + ) + tokens_ids.append(tokens_id) + return tokens_ids + + def decode(self, source_ids): + """Convert token ids to string""" + predictions = [] + for x in source_ids: + prediction = [] + for y in x: + t = y.cpu().numpy() + t = list(t) + if 0 in t: + t = t[: t.index(0)] + text = self.tokenizer.decode(t, clean_up_tokenization_spaces=False) + prediction.append(text) + predictions.append(prediction) + return predictions + + def forward(self, source_ids): + """Obtain token embeddings and sentence embeddings""" + mask = source_ids.ne(self.config.pad_token_id) + token_embeddings = self.model( + source_ids, attention_mask=mask.unsqueeze(1) * mask.unsqueeze(2) + )[0] + sentence_embeddings = (token_embeddings * mask.unsqueeze(-1)).sum(1) / mask.sum( + -1 + ).unsqueeze(-1) + return token_embeddings, sentence_embeddings + + def generate( + self, source_ids, decoder_only=True, eos_id=None, beam_size=5, max_length=64 + ): + """Generate sequence given context (source_ids)""" + + # Set encoder mask attention matrix: bidirectional for , unirectional for + if decoder_only: + mask = self.bias[:, : source_ids.size(-1), : source_ids.size(-1)] + else: + mask = source_ids.ne(self.config.pad_token_id) + mask = mask.unsqueeze(1) * mask.unsqueeze(2) + + if eos_id is None: + eos_id = self.config.eos_token_id + + device = source_ids.device + + # Decoding using beam search + preds = [] + zero = torch.LongTensor(1).fill_(0).to(device) + source_len = list(source_ids.ne(1).sum(-1).cpu().numpy()) + length = source_ids.size(-1) + encoder_output = self.model(source_ids, attention_mask=mask) + for i in range(source_ids.shape[0]): + context = [ + [x[i : i + 1, :, : source_len[i]].repeat(beam_size, 1, 1, 1) for x in y] + for y in encoder_output.past_key_values + ] + beam = Beam(beam_size, eos_id, device) + input_ids = beam.getCurrentState().clone() + context_ids = source_ids[i : i + 1, : source_len[i]].repeat(beam_size, 1) + out = encoder_output.last_hidden_state[i : i + 1, : source_len[i]].repeat( + beam_size, 1, 1 + ) + for _ in range(max_length): + if beam.done(): + break + if _ == 0: + hidden_states = out[:, -1, :] + out = self.lsm(self.lm_head(hidden_states)).data + beam.advance(out) + input_ids.data.copy_( + input_ids.data.index_select(0, beam.getCurrentOrigin()) + ) + input_ids = beam.getCurrentState().clone() + else: + length = context_ids.size(-1) + input_ids.size(-1) + out = self.model( + input_ids, + attention_mask=self.bias[ + :, context_ids.size(-1) : length, :length + ], + past_key_values=context, + ).last_hidden_state + hidden_states = out[:, -1, :] + out = self.lsm(self.lm_head(hidden_states)).data + beam.advance(out) + input_ids.data.copy_( + input_ids.data.index_select(0, beam.getCurrentOrigin()) + ) + input_ids = torch.cat( + (input_ids, beam.getCurrentState().clone()), -1 + ) + hyp = beam.getHyp(beam.getFinal()) + pred = beam.buildTargetTokens(hyp)[:beam_size] + pred = [ + torch.cat( + [x.view(-1) for x in p] + [zero] * (max_length - len(p)) + ).view(1, -1) + for p in pred + ] + preds.append(torch.cat(pred, 0).unsqueeze(0)) + + preds = torch.cat(preds, 0) + + return preds + + +class Beam(object): + def __init__(self, size, eos, device): + self.size = size + self.device = device + # The score for each translation on the beam. + self.scores = torch.FloatTensor(size).zero_().to(device) + # The backpointers at each time-step. + self.prevKs = [] + # The outputs at each time-step. + self.nextYs = [torch.LongTensor(size).fill_(0).to(device)] + # Has EOS topped the beam yet. + self._eos = eos + self.eosTop = False + # Time and k pair for finished. + self.finished = [] + + def getCurrentState(self): + "Get the outputs for the current timestep." + batch = self.nextYs[-1].view(-1, 1) + return batch + + def getCurrentOrigin(self): + "Get the backpointers for the current timestep." + return self.prevKs[-1] + + def advance(self, wordLk): + """ + Given prob over words for every last beam `wordLk` and attention + `attnOut`: Compute and update the beam search. + + Parameters: + + * `wordLk`- probs of advancing from the last step (K x words) + * `attnOut`- attention at the last step + + Returns: True if beam search is complete. + """ + numWords = wordLk.size(1) + + # Sum the previous scores. + if len(self.prevKs) > 0: + beamLk = wordLk + self.scores.unsqueeze(1).expand_as(wordLk) + + # Don't let EOS have children. + for i in range(self.nextYs[-1].size(0)): + if self.nextYs[-1][i] == self._eos: + beamLk[i] = -1e20 + else: + beamLk = wordLk[0] + flatBeamLk = beamLk.view(-1) + bestScores, bestScoresId = flatBeamLk.topk(self.size, 0, True, True) + + self.scores = bestScores + + # bestScoresId is flattened beam x word array, so calculate which + # word and beam each score came from + prevK = torch.div(bestScoresId, numWords, rounding_mode="floor") + self.prevKs.append(prevK) + self.nextYs.append((bestScoresId - prevK * numWords)) + + for i in range(self.nextYs[-1].size(0)): + if self.nextYs[-1][i] == self._eos: + s = self.scores[i] + self.finished.append((s, len(self.nextYs) - 1, i)) + + # End condition is when top-of-beam is EOS and no global score. + if self.nextYs[-1][0] == self._eos: + self.eosTop = True + + def done(self): + return self.eosTop and len(self.finished) >= self.size + + def getFinal(self): + if len(self.finished) == 0: + self.finished.append((self.scores[0], len(self.nextYs) - 1, 0)) + self.finished.sort(key=lambda a: -a[0]) + if len(self.finished) != self.size: + unfinished = [] + for i in range(self.nextYs[-1].size(0)): + if self.nextYs[-1][i] != self._eos: + s = self.scores[i] + unfinished.append((s, len(self.nextYs) - 1, i)) + unfinished.sort(key=lambda a: -a[0]) + self.finished += unfinished[: self.size - len(self.finished)] + return self.finished[: self.size] + + def getHyp(self, beam_res): + """ + Walk back to construct the full hypothesis. + """ + hyps = [] + for _, timestep, k in beam_res: + hyp = [] + for j in range(len(self.prevKs[:timestep]) - 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