forked from jiuyuan/CPM-9G-8B
659 lines
25 KiB
Python
659 lines
25 KiB
Python
from typing import Any
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from typing import Dict
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from typing import List
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from typing import Tuple
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import numpy as np
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import torch
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import torch.nn.functional as F
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from ...generation import apply_repetition_penalty
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from ...generation import BeamHypotheses
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from ...generation import top_k_top_p_filtering
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from ...utils import pad
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from ..models import CPM9GTorch
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from ..tokenizers.cpm9g import CPM9GTokenizer
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class CPM9GGeneration:
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def __init__(
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self, model: CPM9GTorch, tokenizer: CPM9GTokenizer, max_in_len=1024, use_nbce: bool = False
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):
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model.eval()
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self.model = model
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self.tokenizer = tokenizer
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self.max_in_len = max_in_len
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def _convert_to_tensors(self, data: Any):
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input_ids = self.tokenizer.encode(
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data["input"]
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) # [self.tokenizer.bos_token_id] + self.tokenizer.encode(data["input"])
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model_input = {}
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model_input["input_ids"] = torch.tensor(input_ids[: self.max_in_len], dtype=torch.int32).unsqueeze(0)
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model_input["context"] = torch.zeros(
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(model_input["input_ids"].shape[0], model_input["input_ids"].shape[1]), dtype=torch.int16
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)
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model_input["span"] = torch.ones((model_input["input_ids"].shape[1],), dtype=torch.int16).unsqueeze(0)
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model_input["length"] = torch.tensor([model_input["input_ids"].shape[1]], dtype=torch.int16).unsqueeze(0)
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return model_input
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def _process_list(self, data_list: List[Any]):
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input_tensors = list(map(self._convert_to_tensors, data_list))
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keys = set(input_tensors[0].keys())
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padded = {}
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for key in keys:
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padded[key] = pad(input_tensors, key, padding_side="left").cuda()
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return padded
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def generate(self, data_list, **kwargs):
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origin_data_list = data_list.copy()
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model_inputs = self._process_list(data_list)
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with torch.inference_mode():
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result_ids = self._decode(model_inputs, **kwargs)
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return result_ids
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def _decode(self, model_inputs, **kwargs):
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raise NotImplementedError("_decode is not implemented.")
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class CPM9GBeamSearch(CPM9GGeneration):
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def _decode(
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self,
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model_inputs,
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beam_size=4,
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max_length=100,
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repetition_penalty=1.2,
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repetition_window=None,
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):
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"""
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Beam search
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Args:
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model_inputs (dict): input ids.
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beam_size (int, optional, defaults to 3): beam size of beam search.
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generate_length (int, optional, defaults to 100): maximum generation length.
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repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
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repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
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""" # noqa: E501
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# generate_length + 1 for EOS token
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max_length += 1
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# expand dimmension
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batch_size = model_inputs["input_ids"].size(0)
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input: torch.Tensor = (
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model_inputs["input_ids"]
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.unsqueeze(1)
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.expand(batch_size, beam_size, -1)
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.contiguous()
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.view(batch_size * beam_size, -1)
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)
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length = (
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model_inputs["length"]
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.squeeze(1)
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.unsqueeze(1)
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.expand(batch_size, beam_size)
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.contiguous()
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.view(
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batch_size * beam_size,
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)
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)
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span: torch.Tensor = (
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model_inputs["span"]
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.unsqueeze(1)
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.expand(batch_size, beam_size, -1)
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.contiguous()
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.view(batch_size * beam_size, -1)
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)
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context: torch.Tensor = (
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model_inputs["context"]
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.unsqueeze(1)
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.expand(batch_size, beam_size, -1)
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.contiguous()
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.view(batch_size * beam_size, -1)
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)
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done = [False for _ in range(batch_size)]
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beam_scores = torch.zeros((batch_size, beam_size), dtype=torch.float, device=input.device)
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beam_scores[:, 1:] = -1e9
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beam_scores = beam_scores.view(-1)
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# generated hypotheses
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generated_hyps = [
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BeamHypotheses(beam_size, max_length, length_penalty=1, early_stopping=False) for _ in range(batch_size)
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]
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pred_start_index = input.size(-1)
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past_key_values = None
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for i in range(max_length + 1):
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if i == 0:
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logits, _, past_key_values = self.model.inference(
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input=input,
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context=context,
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span=span,
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length=length,
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past_key_values=past_key_values,
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)
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else:
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logits, _, past_key_values = self.model.inference(
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input=input[:, -1:],
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context=context,
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span=span,
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length=length,
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past_key_values=past_key_values,
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)
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# skip all steps when we are done with each sentence
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if all(done):
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break
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# (batch * beam, seqlen, model_dim)
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logits = logits[:, -1, :]
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if i == 0:
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logits[:, self.tokenizer.bos_token_id] = -float("inf")
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# logits[:, self.tokenizer.newline_id] = -float("inf")
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apply_repetition_penalty(
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logits,
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batch_size,
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beam_size,
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input,
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repetition_penalty,
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pred_start_index,
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input.size(-1) - 1,
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repetition_window,
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)
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scores = F.log_softmax(logits, dim=-1)
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next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * beam_size, vocab_size)
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# re-organize to group the beam together (we are keeping top hypothesis accross beams)
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next_scores = next_scores.view(batch_size, -1) # (batch_size, beam_size * vocab_size)
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next_scores, next_words = torch.topk(next_scores, 2 * beam_size, dim=1, largest=True, sorted=True)
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assert next_scores.size() == next_words.size() == (batch_size, 2 * beam_size)
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next_batch_beam = []
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for sent_id in range(batch_size):
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# if we are done with this sentence
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done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item(), i)
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if done[sent_id]:
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next_batch_beam.extend([(0, 0, 0)] * beam_size) # pad the batch
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continue
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# next sentence beam content
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next_sent_beam = []
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# next words for this sentence
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for idx, value in zip(next_words[sent_id], next_scores[sent_id]):
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# get beam and word IDs
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beam_id = torch.div(idx, scores.size(-1), rounding_mode="floor")
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word_id = idx % scores.size(-1)
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# end of sentence, or next word
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if word_id == self.tokenizer.bos_token_id or i == max_length:
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generated_hyps[sent_id].add(
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input[sent_id * beam_size + beam_id, pred_start_index:].clone().cpu().tolist(),
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value.item(),
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)
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else:
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next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))
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# the beam for next step is full
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if len(next_sent_beam) == beam_size:
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break
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# update next beam content
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assert len(next_sent_beam) == 0 if i == max_length else beam_size
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if len(next_sent_beam) == 0:
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next_sent_beam = [(0, 0, 0)] * beam_size # pad the batch
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next_batch_beam.extend(next_sent_beam)
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assert len(next_batch_beam) == beam_size * (sent_id + 1)
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# we have reached the last step
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if i == max_length:
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break
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# sanity check / prepare next batch
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assert len(next_batch_beam) == batch_size * beam_size
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beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
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beam_words = input.new([x[1] for x in next_batch_beam])
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beam_idx = torch.tensor([x[2] for x in next_batch_beam], device=input.device).long()
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# re-order batch and internal states
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input = input[beam_idx, :]
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past_key_values["buffer"] = [list(each) if each is not None else each for each in past_key_values["buffer"]] # type: ignore # noqa: E501
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for key_value_layer in past_key_values["buffer"]:
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if key_value_layer is not None:
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key_value_layer[0] = key_value_layer[0][beam_idx]
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key_value_layer[1] = key_value_layer[1][beam_idx]
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input = torch.cat([input, beam_words.unsqueeze(1)], dim=-1)
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context = torch.cat(
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[context, context[:, -1:]],
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dim=-1,
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)
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length += 1
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span = torch.cat([span, span[:, -1:]], dim=-1)
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# select the best hypotheses
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results = []
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for i, hypotheses in enumerate(generated_hyps):
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best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]
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results.append(best_hyp)
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result_text = list(map(self.tokenizer.decode, results))
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return result_text
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class CPM9GRandomSampling(CPM9GGeneration):
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def _decode(
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self,
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model_inputs,
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max_length=100,
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top_k=0,
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top_p=0.9,
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temperature=0.9,
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repetition_penalty=1.0,
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repetition_window=None,
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**kwargs,
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):
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"""
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Top-k and top-p sampling.
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Args:
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model_inputs (dict): input ids
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generate_length (int, optional, defaults to 100): maximum generation length
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top_k (int, optional, defaults to 0): keep only top k tokens with highest probability. 0 means keeping all tokens.
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top_p (int, optional, defaults to 0.9): keep the top tokens with cumulative probability >= top_p.
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temperature (int, optional, defaults to 0.9): the value that can cool down the logits distribution.
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repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
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repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
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""" # noqa: E501
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# generate_length + 1 for EOS token
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max_length += 1
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input = model_inputs["input_ids"]
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context = model_inputs["context"]
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length = model_inputs["length"].squeeze(1)
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span = model_inputs["span"]
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batch_size = input.size(0)
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pred_start_index = input.size(-1)
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past_key_values = None
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done = [False for _ in range(batch_size)]
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results = [None for _ in range(batch_size)]
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for i in range(max_length):
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if i == 0:
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logits, _, past_key_values = self.model.inference(
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input=input,
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context=context,
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length=length,
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span=span,
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past_key_values=past_key_values,
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)
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else:
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logits, _, past_key_values = self.model.inference(
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input=input[:, -1:],
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context=context,
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length=length,
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span=span,
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past_key_values=past_key_values,
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)
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logits = logits[:, -1, :]
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if i == 0:
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logits[:, self.tokenizer.bos_token_id] = -float("inf")
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# logits[:, self.tokenizer.newline_id] = -float("inf")
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apply_repetition_penalty(
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logits,
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batch_size,
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1,
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input,
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repetition_penalty,
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pred_start_index,
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input.size(-1) - 1,
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repetition_window,
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)
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logits = logits / temperature
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logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
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probs = F.softmax(logits, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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for idx in range(batch_size):
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if not done[idx] and (next_token[idx].item() == self.tokenizer.bos_token_id or i == max_length - 1):
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done[idx] = True
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results[idx] = input[idx, pred_start_index:].clone().cpu().tolist() # type: ignore # noqa: E501
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if sum(done) == batch_size:
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break
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# update input ids
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input = torch.cat([input, next_token], dim=-1)
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length += 1
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context = torch.cat(
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[context, context[:, -1:]],
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dim=-1,
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)
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span = torch.cat(
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[span, span[:, -1:]],
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dim=-1,
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)
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result_text = list(map(self.tokenizer.decode, results))
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return result_text
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class CPM9GBeamSearchNBCE(CPM9GGeneration):
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def _decode(
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self,
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model_inputs,
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beam_size=5,
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max_length=100,
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repetition_penalty=1.0,
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repetition_window=None,
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):
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"""
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Beam search
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Args:
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model_inputs (dict): input ids.
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beam_size (int, optional, defaults to 3): beam size of beam search.
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generate_length (int, optional, defaults to 100): maximum generation length.
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repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
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repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
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""" # noqa: E501
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# generate_length + 1 for EOS token
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max_length += 1
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# expand dimmension
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batch_size = model_inputs["input_ids"].size(0)
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input: torch.Tensor = (
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model_inputs["input_ids"]
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.unsqueeze(1)
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.expand(batch_size, beam_size, -1)
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.contiguous()
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.view(batch_size * beam_size, -1)
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)
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length = (
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model_inputs["length"]
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.squeeze(1)
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.unsqueeze(1)
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.expand(batch_size, beam_size)
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.contiguous()
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.view(
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batch_size * beam_size,
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)
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)
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span: torch.Tensor = (
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model_inputs["span"]
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.unsqueeze(1)
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.expand(batch_size, beam_size, -1)
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.contiguous()
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.view(batch_size * beam_size, -1)
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)
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context: torch.Tensor = (
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model_inputs["context"]
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.unsqueeze(1)
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.expand(batch_size, beam_size, -1)
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.contiguous()
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.view(batch_size * beam_size, -1)
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)
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done = [False]
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beam_scores = torch.zeros((1, beam_size), dtype=torch.float, device=input.device)
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beam_scores[:, 1:] = -1e9
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beam_scores = beam_scores.view(-1)
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# generated hypotheses
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generated_hyps = [
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BeamHypotheses(beam_size, max_length, length_penalty=1, early_stopping=False) for _ in range(1)
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]
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pred_start_index = input.size(-1)
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past_key_values = None
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for i in range(max_length + 1):
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if i == 0:
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logits, _, past_key_values = self.model.inference(
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input=input,
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context=context,
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span=span,
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length=length,
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past_key_values=past_key_values,
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)
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else:
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logits, _, past_key_values = self.model.inference(
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input=input[:, -1:],
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context=context,
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span=span,
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length=length,
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past_key_values=past_key_values,
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)
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# skip all steps when we are done with each sentence
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if all(done):
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break
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# (batch * beam, seqlen, model_dim)
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assert logits.size(0) > 1, "nbce needs to ensure that the length of logits 0 is greater than 1"
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logits = NBCE(logits) # [vocab_size]
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logits = logits.tile(beam_size, 1)
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if i == 0:
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logits[:, self.tokenizer.bos_token_id] = -float("inf")
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apply_repetition_penalty(
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logits,
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1,
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beam_size,
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input,
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repetition_penalty,
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pred_start_index,
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input.size(-1) - 1,
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repetition_window,
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)
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scores = F.log_softmax(logits, dim=-1)
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next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * beam_size, vocab_size)
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# re-organize to group the beam together (we are keeping top hypothesis accross beams)
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next_scores = next_scores.view(1, -1) # (batch_size, beam_size * vocab_size)
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next_scores, next_words = torch.topk(next_scores, 2 * beam_size, dim=1, largest=True, sorted=True)
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assert next_scores.size() == next_words.size() == (1, 2 * beam_size)
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next_batch_beam = []
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for sent_id in range(1):
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# if we are done with this sentence
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done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item(), i)
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if done[sent_id]:
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next_batch_beam.extend([(0, 0, 0)] * beam_size) # pad the batch
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continue
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# next sentence beam content
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next_sent_beam = []
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# next words for this sentence
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for idx, value in zip(next_words[sent_id], next_scores[sent_id]):
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# get beam and word IDs
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beam_id = torch.div(idx, scores.size(-1), rounding_mode="floor")
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word_id = idx % scores.size(-1)
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# end of sentence, or next word
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if word_id == self.tokenizer.bos_token_id or i == max_length:
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generated_hyps[sent_id].add(
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input[sent_id * beam_size + beam_id, pred_start_index:].clone().cpu().tolist(),
|
|
value.item(),
|
|
)
|
|
else:
|
|
next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))
|
|
|
|
# the beam for next step is full
|
|
if len(next_sent_beam) == beam_size:
|
|
break
|
|
|
|
# update next beam content
|
|
assert len(next_sent_beam) == 0 if i == max_length else beam_size
|
|
if len(next_sent_beam) == 0:
|
|
next_sent_beam = [(0, 0, 0)] * beam_size # pad the batch
|
|
next_batch_beam.extend(next_sent_beam)
|
|
assert len(next_batch_beam) == beam_size * (sent_id + 1)
|
|
|
|
# we have reached the last step
|
|
if i == max_length:
|
|
break
|
|
|
|
# sanity check / prepare next batch
|
|
assert len(next_batch_beam) == batch_size * beam_size
|
|
beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
|
|
beam_words = input.new([x[1] for x in next_batch_beam])
|
|
beam_idx = torch.tensor([x[2] for x in next_batch_beam], device=input.device).long()
|
|
beam_idx *= batch_size
|
|
# re-order batch and internal states
|
|
input = input[beam_idx, :]
|
|
|
|
past_key_values["buffer"] = [list(each) if each is not None else each for each in past_key_values["buffer"]] # type: ignore # noqa: E501
|
|
for key_value_layer in past_key_values["buffer"]:
|
|
if key_value_layer is not None:
|
|
key_value_layer[0] = key_value_layer[0][beam_idx]
|
|
key_value_layer[1] = key_value_layer[1][beam_idx]
|
|
|
|
input = torch.cat([input, beam_words.unsqueeze(1)], dim=-1)
|
|
context = torch.cat(
|
|
[context, torch.zeros((context.size(0), 1), dtype=torch.int16, device=context.device)],
|
|
dim=-1,
|
|
)
|
|
length = past_key_values["buffer_length"]
|
|
length = torch.cat(
|
|
[length, torch.ones((length.size(0), 1), dtype=torch.int16, device=length.device)],
|
|
dim=-1,
|
|
)
|
|
span = torch.cat([span, span[:, -1:]], dim=-1)
|
|
|
|
# select the best hypotheses
|
|
results = []
|
|
for i, hypotheses in enumerate(generated_hyps):
|
|
best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]
|
|
results.append(best_hyp)
|
|
|
|
result_text = list(map(self.tokenizer.decode, results))
|
|
return result_text
|
|
|
|
|
|
class CPM9GRandomSamplingNBCE(CPM9GGeneration):
|
|
def _decode(
|
|
self,
|
|
model_inputs,
|
|
max_length=100,
|
|
top_k=0,
|
|
top_p=0.9,
|
|
temperature=0.9,
|
|
repetition_penalty=1.0,
|
|
repetition_window=None,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Top-k and top-p sampling.
|
|
Args:
|
|
model_inputs (dict): input ids
|
|
generate_length (int, optional, defaults to 100): maximum generation length
|
|
top_k (int, optional, defaults to 0): keep only top k tokens with highest probability. 0 means keeping all tokens.
|
|
top_p (int, optional, defaults to 0.9): keep the top tokens with cumulative probability >= top_p.
|
|
temperature (int, optional, defaults to 0.9): the value that can cool down the logits distribution.
|
|
repetition_penalty (float, optional, defaults to 1.0): repetition penalty coefficient, 1.0 means no penalty.
|
|
repetition_window (int, optional, defaults to None): window size of repetition penalty, None means that all output tokens are penalized.
|
|
""" # noqa: E501
|
|
# generate_length + 1 for EOS token
|
|
max_length += 1
|
|
|
|
input = model_inputs["input_ids"]
|
|
context = model_inputs["context"]
|
|
|
|
length = model_inputs["length"].squeeze(1)
|
|
span = model_inputs["span"]
|
|
batch_size = input.size(0)
|
|
|
|
pred_start_index = input.size(-1)
|
|
past_key_values = None
|
|
done = [False]
|
|
results = [None]
|
|
for i in range(max_length):
|
|
if i == 0:
|
|
logits, _, past_key_values = self.model.inference(
|
|
input=input,
|
|
context=context,
|
|
length=length,
|
|
span=span,
|
|
past_key_values=past_key_values,
|
|
)
|
|
else:
|
|
logits, _, past_key_values = self.model.inference(
|
|
input=input[:, -1:],
|
|
context=context,
|
|
length=length,
|
|
span=span,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
assert logits.size(0) > 1, "nbce needs to ensure that the length of logits 0 is greater than 1"
|
|
logits = NBCE(logits) # [vocab_size]
|
|
logits = logits[None]
|
|
|
|
if i == 0:
|
|
logits[:, self.tokenizer.bos_token_id] = -float("inf")
|
|
# logits[:, self.tokenizer.newline_id] = -float("inf")
|
|
|
|
apply_repetition_penalty(
|
|
logits,
|
|
1,
|
|
1,
|
|
input,
|
|
repetition_penalty,
|
|
pred_start_index,
|
|
input.size(-1) - 1,
|
|
repetition_window,
|
|
)
|
|
|
|
logits = logits / temperature
|
|
logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
|
|
|
|
probs = F.softmax(logits, dim=-1)
|
|
next_token = torch.multinomial(probs, num_samples=1)
|
|
|
|
for idx in range(1):
|
|
if not done[idx] and (next_token[idx].item() == self.tokenizer.bos_token_id or i == max_length - 1):
|
|
done[idx] = True
|
|
results[idx] = input[idx, pred_start_index:].clone().cpu().tolist() # type: ignore # noqa: E501
|
|
|
|
if sum(done) == 1:
|
|
break
|
|
next_token = next_token.tile(batch_size, 1)
|
|
# update input ids
|
|
input = torch.cat([input, next_token], dim=-1)
|
|
length = past_key_values["buffer_length"]
|
|
length = torch.cat(
|
|
[length, torch.ones((length.size(0), 1), dtype=torch.int32, device=length.device)],
|
|
dim=-1,
|
|
)
|
|
# length += 1
|
|
context = torch.cat(
|
|
[context, torch.zeros((context.size(0), 1), dtype=torch.int16, device=context.device)],
|
|
dim=-1,
|
|
)
|
|
span = torch.cat(
|
|
[span, torch.zeros((span.size(0), 1), dtype=torch.int32, device=span.device)],
|
|
dim=-1,
|
|
)
|
|
|
|
result_text = list(map(self.tokenizer.decode, results))
|
|
return result_text
|