mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 14:20:04 +06:00
02d0e0355c
* diverse beam search
* bug fixes
* bug fixes
* bug fix
* separate out diverse_beam_search function
* separate out diverse_beam_search function
* bug fix
* improve code quality
* bug fix
* bug fix
* separate out diverse beam search scorer
* code format
* code format
* code format
* code format
* add test
* code format
* documentation changes
* code quality
* add slow integration tests
* more general name
* refactor into logits processor
* add test
* avoid too much copy paste
* refactor
* add to docs
* fix-copies
* bug fix
* Revert "bug fix"
This reverts commit c99eb5a8dc.
* improve comment
* implement sylvains feedback
Co-authored-by: Ayush Jain <a.jain@sprinklr.com>
Co-authored-by: ayushtiku5 <40797286+ayushtiku5@users.noreply.github.com>
659 lines
28 KiB
Python
659 lines
28 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a clone of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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if is_torch_available():
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import torch
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from transformers import BartForConditionalGeneration, BartTokenizer, top_k_top_p_filtering
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from transformers.generation_beam_search import BeamSearchScorer
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from transformers.generation_logits_process import (
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HammingDiversityLogitsProcessor,
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LogitsProcessorList,
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MinLengthLogitsProcessor,
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NoBadWordsLogitsProcessor,
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NoRepeatNGramLogitsProcessor,
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RepetitionPenaltyLogitsProcessor,
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TemperatureLogitsWarper,
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TopKLogitsWarper,
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TopPLogitsWarper,
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)
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class GenerationTesterMixin:
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model_tester = None
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all_generative_model_classes = ()
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def _get_input_ids_and_config(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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input_ids = inputs_dict["input_ids"]
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attention_mask = torch.ones_like(input_ids)
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# cut to half length & take max batch_size 3
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max_batch_size = 2
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sequence_length = input_ids.shape[-1] // 2
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input_ids = input_ids[:max_batch_size, :sequence_length]
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attention_mask = attention_mask[:max_batch_size, :sequence_length]
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# generate max 3 tokens
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max_length = input_ids.shape[-1] + 3
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if config.eos_token_id is not None and config.pad_token_id is None:
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# hack to allow generate for models such as GPT2 as is done in `generate()`
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config.pad_token_id = config.eos_token_id
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return config, input_ids, attention_mask, max_length
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@staticmethod
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def _get_logits_processor_and_kwargs(input_length, eos_token_id, diversity_penalty=None):
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process_kwargs = {
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"min_length": input_length + 1,
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"bad_words_ids": [[1, 0]],
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"no_repeat_ngram_size": 2,
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"repetition_penalty": 1.2,
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}
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logits_processor = LogitsProcessorList(
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(
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[
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HammingDiversityLogitsProcessor(diversity_penalty, num_beams=2, num_beam_groups=2),
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]
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if diversity_penalty is not None
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else []
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)
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+ (
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[
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MinLengthLogitsProcessor(process_kwargs["min_length"], eos_token_id),
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]
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if eos_token_id is not None
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else []
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)
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+ [
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NoBadWordsLogitsProcessor(process_kwargs["bad_words_ids"], eos_token_id),
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NoRepeatNGramLogitsProcessor(process_kwargs["no_repeat_ngram_size"]),
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RepetitionPenaltyLogitsProcessor(process_kwargs["repetition_penalty"]),
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]
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)
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return process_kwargs, logits_processor
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@staticmethod
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def _get_warper_and_kwargs(num_beams):
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warp_kwargs = {"top_k": 10, "top_p": 0.7, "temperature": 0.7}
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logits_warper = LogitsProcessorList(
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[
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TemperatureLogitsWarper(warp_kwargs["temperature"]),
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TopKLogitsWarper(top_k=warp_kwargs["top_k"], min_tokens_to_keep=(2 if num_beams > 1 else 1)),
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TopPLogitsWarper(top_p=warp_kwargs["top_p"], min_tokens_to_keep=(2 if num_beams > 1 else 1)),
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]
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)
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return warp_kwargs, logits_warper
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@staticmethod
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def _get_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1):
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beam_kwargs = {
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"early_stopping": False,
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"length_penalty": 2.0,
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"num_beams": 2,
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"num_return_sequences": num_return_sequences,
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}
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beam_scorer = BeamSearchScorer(
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batch_size=batch_size,
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max_length=max_length,
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num_beams=beam_kwargs["num_beams"],
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device=torch_device,
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length_penalty=beam_kwargs["length_penalty"],
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do_early_stopping=beam_kwargs["early_stopping"],
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num_beam_hyps_to_keep=num_return_sequences,
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)
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return beam_kwargs, beam_scorer
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@staticmethod
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def _get_diverse_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1):
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beam_kwargs = {
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"early_stopping": False,
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"length_penalty": 2.0,
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"num_beams": 2,
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"num_return_sequences": num_return_sequences,
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"num_beam_groups": 2, # one beam per group
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"diversity_penalty": 2.0,
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}
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beam_scorer = BeamSearchScorer(
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batch_size=batch_size,
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max_length=max_length,
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num_beams=beam_kwargs["num_beams"],
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device=torch_device,
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length_penalty=beam_kwargs["length_penalty"],
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do_early_stopping=beam_kwargs["early_stopping"],
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num_beam_hyps_to_keep=num_return_sequences,
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num_beam_groups=beam_kwargs["num_beam_groups"],
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)
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return beam_kwargs, beam_scorer
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@staticmethod
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def _get_encoder_outputs(model, input_ids, attention_mask, num_interleave=1):
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encoder = model.get_encoder()
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encoder_outputs = encoder(input_ids, attention_mask=attention_mask)
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encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
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num_interleave, dim=0
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)
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input_ids = torch.zeros_like(input_ids[:, :1]) + model._get_decoder_start_token_id()
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attention_mask = None
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return encoder_outputs, input_ids, attention_mask
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def test_greedy_generate(self):
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for model_class in self.all_generative_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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input_ids.shape[-1], config.eos_token_id
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)
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model = model_class(config).to(torch_device)
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model.eval()
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# check `generate()` and `greedy_search()` are equal
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kwargs = {}
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if model.config.is_encoder_decoder:
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max_length = 4
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output_ids_generate = model.generate(
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input_ids,
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attention_mask=attention_mask,
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do_sample=False,
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num_beams=1,
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max_length=max_length,
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**logits_process_kwargs,
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)
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if model.config.is_encoder_decoder:
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encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
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model, input_ids, attention_mask
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)
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kwargs["encoder_outputs"] = encoder_outputs
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with torch.no_grad():
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output_ids_greedy = model.greedy_search(
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input_ids,
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max_length=max_length,
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attention_mask=attention_mask,
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logits_processor=logits_processor,
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**kwargs,
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)
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self.assertListEqual(output_ids_generate.tolist(), output_ids_greedy.tolist())
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def test_sample_generate(self):
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for model_class in self.all_generative_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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input_ids.shape[-1], config.eos_token_id
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)
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logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
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model = model_class(config).to(torch_device)
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model.eval()
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# check `generate()` and `sample()` are equal
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if model.config.is_encoder_decoder:
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max_length = 4
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torch.manual_seed(0)
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output_ids_generate = model.generate(
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input_ids,
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do_sample=True,
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num_beams=1,
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max_length=max_length,
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attention_mask=attention_mask,
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**logits_warper_kwargs,
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**process_kwargs,
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)
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torch.manual_seed(0)
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kwargs = {}
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if model.config.is_encoder_decoder:
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encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
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model, input_ids, attention_mask
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)
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kwargs["encoder_outputs"] = encoder_outputs
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else:
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attention_mask_clone = attention_mask
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input_ids_clone = input_ids
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with torch.no_grad():
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output_ids_sample = model.sample(
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input_ids_clone,
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attention_mask=attention_mask_clone,
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max_length=max_length,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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**kwargs,
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)
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self.assertListEqual(output_ids_generate.tolist(), output_ids_sample.tolist())
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# check `generate()` and `sample()` yield equal results for `num_return_sequences`
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num_return_sequences = 3
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if model.config.is_encoder_decoder:
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max_length = 4
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torch.manual_seed(0)
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output_ids_generate = model.generate(
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input_ids,
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do_sample=True,
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num_beams=1,
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max_length=max_length,
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num_return_sequences=num_return_sequences,
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attention_mask=attention_mask,
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**logits_warper_kwargs,
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**process_kwargs,
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)
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torch.manual_seed(0)
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kwargs = {}
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if model.config.is_encoder_decoder:
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encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
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model, input_ids, attention_mask, num_interleave=num_return_sequences
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)
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kwargs["encoder_outputs"] = encoder_outputs
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input_ids_clone = input_ids_clone.repeat_interleave(num_return_sequences, dim=0)
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else:
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attention_mask_clone = attention_mask.repeat_interleave(num_return_sequences, dim=0)
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input_ids_clone = input_ids.repeat_interleave(num_return_sequences, dim=0)
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with torch.no_grad():
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output_ids_sample = model.sample(
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input_ids_clone,
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attention_mask=attention_mask_clone,
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max_length=max_length,
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logits_processor=logits_processor,
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logits_warper=logits_warper,
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**kwargs,
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)
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self.assertListEqual(output_ids_generate.tolist(), output_ids_sample.tolist())
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def test_beam_search_generate(self):
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for model_class in self.all_generative_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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input_ids.shape[-1], config.eos_token_id
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)
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model = model_class(config).to(torch_device)
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model.eval()
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# check `generate()` and `beam_search()` are equal
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if model.config.is_encoder_decoder:
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max_length = 4
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beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
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output_ids_generate = model.generate(
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input_ids,
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attention_mask=attention_mask,
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do_sample=False,
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max_length=max_length,
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**beam_kwargs,
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**logits_process_kwargs,
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)
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# beam_search does not automatically interleave `batch_size` dim for `num_beams`
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kwargs = {}
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if model.config.is_encoder_decoder:
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encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
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model, input_ids, attention_mask, num_interleave=beam_scorer.num_beams
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)
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kwargs["encoder_outputs"] = encoder_outputs
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input_ids_clone = input_ids_clone.repeat_interleave(beam_scorer.num_beams, dim=0)
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else:
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attention_mask_clone = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
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input_ids_clone = input_ids.repeat_interleave(beam_scorer.num_beams, dim=0)
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with torch.no_grad():
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output_ids_beam_search = model.beam_search(
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input_ids_clone,
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beam_scorer,
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max_length=max_length,
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attention_mask=attention_mask_clone,
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logits_processor=logits_processor,
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**kwargs,
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)
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self.assertListEqual(output_ids_generate.tolist(), output_ids_beam_search.tolist())
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# check `generate()` and `beam_search()` are equal for `num_return_sequences`
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num_return_sequences = 2
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if model.config.is_encoder_decoder:
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max_length = 4
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beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
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input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
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)
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output_ids_generate = model.generate(
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input_ids,
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attention_mask=attention_mask,
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do_sample=False,
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max_length=max_length,
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**beam_kwargs,
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**logits_process_kwargs,
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)
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# beam_search does not automatically interleave `batch_size` dim for `num_beams`
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kwargs = {}
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if model.config.is_encoder_decoder:
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encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
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model, input_ids, attention_mask, num_interleave=beam_scorer.num_beams
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)
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kwargs["encoder_outputs"] = encoder_outputs
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input_ids_clone = input_ids_clone.repeat_interleave(beam_scorer.num_beams, dim=0)
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else:
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attention_mask_clone = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
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input_ids_clone = input_ids.repeat_interleave(beam_scorer.num_beams, dim=0)
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with torch.no_grad():
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output_ids_beam_search = model.beam_search(
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input_ids_clone,
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beam_scorer,
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max_length=max_length,
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attention_mask=attention_mask_clone,
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logits_processor=logits_processor,
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**kwargs,
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)
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self.assertListEqual(output_ids_generate.tolist(), output_ids_beam_search.tolist())
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def test_beam_sample_generate(self):
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for model_class in self.all_generative_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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print("Return dict", config.return_dict)
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logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
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model = model_class(config).to(torch_device)
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model.eval()
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# check `generate()` and `beam_search()` are equal
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# change `num_return_sequences = 2` but not for `beam_scorer`
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num_return_sequences = 2
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if model.config.is_encoder_decoder:
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max_length = 4
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beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
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input_ids.shape[0] * num_return_sequences, max_length
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)
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beam_kwargs["num_return_sequences"] = num_return_sequences
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torch.manual_seed(0)
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output_ids_generate = model.generate(
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input_ids,
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attention_mask=attention_mask,
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do_sample=True,
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max_length=max_length,
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**beam_kwargs,
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**logits_warper_kwargs,
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)
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# beam_search does not automatically interleave `batch_size` dim for `num_beams * num_return_sequences`
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kwargs = {}
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if model.config.is_encoder_decoder:
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encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
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model, input_ids, attention_mask, num_interleave=beam_scorer.num_beams * num_return_sequences
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)
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kwargs["encoder_outputs"] = encoder_outputs
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else:
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attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0)
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torch.manual_seed(0)
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with torch.no_grad():
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output_ids_beam_sample = model.beam_sample(
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input_ids.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0),
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beam_scorer,
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max_length=max_length,
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attention_mask=attention_mask,
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logits_warper=logits_warper,
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**kwargs,
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)
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self.assertListEqual(output_ids_generate.tolist(), output_ids_beam_sample.tolist())
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def test_generate_without_input_ids(self):
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config, _, _, max_length = self._get_input_ids_and_config()
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# if no bos token id => cannot generate from None
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if config.bos_token_id is None:
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return
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for model_class in self.all_generative_model_classes:
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model = model_class(config).to(torch_device)
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model.eval()
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output_ids_generate = model.generate(
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do_sample=False,
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max_length=max_length,
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)
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self.assertIsNotNone(output_ids_generate)
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def test_group_beam_search_generate(self):
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for model_class in self.all_generative_model_classes:
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
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input_ids.shape[-1], config.eos_token_id, diversity_penalty=2.0
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)
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model = model_class(config).to(torch_device)
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model.eval()
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# check `generate()` and `group_beam_search()` are equal
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|
if model.config.is_encoder_decoder:
|
|
max_length = 4
|
|
beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
|
|
output_ids_generate = model.generate(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
do_sample=False,
|
|
max_length=max_length,
|
|
**beam_kwargs,
|
|
**logits_process_kwargs,
|
|
)
|
|
|
|
# group_beam_search does not automatically interleave `batch_size` dim for `num_beams`
|
|
kwargs = {}
|
|
if model.config.is_encoder_decoder:
|
|
encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
|
|
model, input_ids, attention_mask, num_interleave=beam_scorer.num_beams
|
|
)
|
|
kwargs["encoder_outputs"] = encoder_outputs
|
|
input_ids_clone = input_ids_clone.repeat_interleave(beam_scorer.num_beams, dim=0)
|
|
else:
|
|
attention_mask_clone = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
|
|
input_ids_clone = input_ids.repeat_interleave(beam_scorer.num_beams, dim=0)
|
|
|
|
with torch.no_grad():
|
|
output_ids_group_beam_search = model.group_beam_search(
|
|
input_ids_clone,
|
|
beam_scorer,
|
|
max_length=max_length,
|
|
attention_mask=attention_mask_clone,
|
|
logits_processor=logits_processor,
|
|
**kwargs,
|
|
)
|
|
self.assertListEqual(output_ids_generate.tolist(), output_ids_group_beam_search.tolist())
|
|
|
|
# check `generate()` and `group_beam_search()` are equal for `num_return_sequences`
|
|
num_return_sequences = 2
|
|
if model.config.is_encoder_decoder:
|
|
max_length = 4
|
|
beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(
|
|
input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
|
|
)
|
|
|
|
output_ids_generate = model.generate(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
do_sample=False,
|
|
max_length=max_length,
|
|
**beam_kwargs,
|
|
**logits_process_kwargs,
|
|
)
|
|
# group_beam_search does not automatically interleave `batch_size` dim for `num_beams`
|
|
kwargs = {}
|
|
if model.config.is_encoder_decoder:
|
|
encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
|
|
model, input_ids, attention_mask, num_interleave=beam_scorer.num_beams
|
|
)
|
|
kwargs["encoder_outputs"] = encoder_outputs
|
|
input_ids_clone = input_ids_clone.repeat_interleave(beam_scorer.num_beams, dim=0)
|
|
else:
|
|
attention_mask_clone = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
|
|
input_ids_clone = input_ids.repeat_interleave(beam_scorer.num_beams, dim=0)
|
|
|
|
with torch.no_grad():
|
|
output_ids_beam_search = model.group_beam_search(
|
|
input_ids_clone,
|
|
beam_scorer,
|
|
max_length=max_length,
|
|
attention_mask=attention_mask_clone,
|
|
logits_processor=logits_processor,
|
|
**kwargs,
|
|
)
|
|
self.assertListEqual(output_ids_generate.tolist(), output_ids_beam_search.tolist())
|
|
|
|
|
|
@require_torch
|
|
class UtilsFunctionsTest(unittest.TestCase):
|
|
|
|
# tests whether the top_k_top_p function behaves as expected
|
|
def test_top_k_top_p_filtering(self):
|
|
logits = torch.tensor(
|
|
[
|
|
[
|
|
8.2220991, # 3rd highest value; idx. 0
|
|
-0.5620044,
|
|
5.23229752,
|
|
4.0386393,
|
|
-6.8798378,
|
|
-0.54785802,
|
|
-3.2012153,
|
|
2.92777176,
|
|
1.88171953,
|
|
7.35341276,
|
|
8.43207833, # 2nd highest value; idx. 10
|
|
-9.85711836,
|
|
-5.96209236,
|
|
-1.13039161,
|
|
-7.1115294,
|
|
-0.8369633,
|
|
-5.3186408,
|
|
7.06427407,
|
|
0.81369344,
|
|
-0.82023817,
|
|
-5.9179796,
|
|
0.58813443,
|
|
-6.99778438,
|
|
4.71551189,
|
|
-0.18771637,
|
|
7.44020759, # 4th highest value; idx. 25
|
|
9.38450987, # 1st highest value; idx. 26
|
|
2.12662941,
|
|
-9.32562038,
|
|
2.35652522,
|
|
], # cummulative prob of 4 highest values <= 0.6
|
|
[
|
|
0.58425518,
|
|
4.53139238,
|
|
-5.57510464,
|
|
-6.28030699,
|
|
-7.19529503,
|
|
-4.02122551,
|
|
1.39337037,
|
|
-6.06707057,
|
|
1.59480517,
|
|
-9.643119,
|
|
0.03907799,
|
|
0.67231762,
|
|
-8.88206726,
|
|
6.27115922, # 4th highest value; idx. 13
|
|
2.28520723,
|
|
4.82767506,
|
|
4.30421368,
|
|
8.8275313, # 2nd highest value; idx. 17
|
|
5.44029958,
|
|
-4.4735794,
|
|
7.38579536, # 3rd highest value; idx. 20
|
|
-2.91051663,
|
|
2.61946077,
|
|
-2.5674762,
|
|
-9.48959302,
|
|
-4.02922645,
|
|
-1.35416918,
|
|
9.67702323, # 1st highest value; idx. 27
|
|
-5.89478553,
|
|
1.85370467,
|
|
], # cummulative prob of 4 highest values <= 0.6
|
|
],
|
|
dtype=torch.float,
|
|
device=torch_device,
|
|
)
|
|
|
|
non_inf_expected_idx = torch.tensor(
|
|
[[0, 0], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 20], [1, 27]],
|
|
dtype=torch.long,
|
|
device=torch_device,
|
|
) # expected non filtered idx as noted above
|
|
|
|
non_inf_expected_output = torch.tensor(
|
|
[
|
|
8.2221,
|
|
8.4321,
|
|
7.4402,
|
|
9.3845,
|
|
6.2712,
|
|
8.8275,
|
|
7.3858,
|
|
9.6770,
|
|
], # expected non filtered values as noted above
|
|
dtype=torch.float,
|
|
device=torch_device,
|
|
)
|
|
|
|
output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
|
|
non_inf_output = output[output != -float("inf")].to(device=torch_device)
|
|
non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)
|
|
|
|
self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
|
|
self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))
|
|
|
|
|
|
@require_torch
|
|
class GenerationIntegrationTests(unittest.TestCase):
|
|
@slow
|
|
def test_diverse_beam_search(self):
|
|
article = """Justin Timberlake and Jessica Biel, welcome to parenthood.
|
|
The celebrity couple announced the arrival of their son, Silas Randall Timberlake, in statements to People.
|
|
"Silas was the middle name of Timberlake's maternal grandfather Bill Bomar, who died in 2012, while Randall is the musician's own middle name, as well as his father's first," People reports.
|
|
The couple announced the pregnancy in January, with an Instagram post. It is the first baby for both."""
|
|
|
|
bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
|
|
bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device)
|
|
input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
|
|
|
|
outputs = bart_model.generate(
|
|
input_ids, num_beams=4, num_return_sequences=2, num_beam_groups=4, diversity_penalty=2.0
|
|
)
|
|
|
|
generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
|
|
|
self.assertListEqual(
|
|
generated_text,
|
|
[
|
|
"The couple announced the birth of their son, Silas Randall Timberlake, in a statement. Silas was the middle name of Timberlake's maternal grandfather Bill Bomar. Randall is the musician's own middle name, as well as his father's first. It is the first baby for both of them.",
|
|
"Justin Timberlake and Jessica Biel have a son. The baby is named Silas Randall Timberlake. It is the first child for both. The couple announced the pregnancy in January. The name Silas is the middle name of Timberlake's maternal grandfather. It's also his own middle name.",
|
|
],
|
|
)
|