# coding=utf-8 # Copyright 2020 The HuggingFace Team Inc. # Copyright (c) 2023 Graphcore Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a clone of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import unittest from typing import TYPE_CHECKING from transformers import is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from optimum.graphcore import IPUConfig, pipeline from optimum.graphcore.generation.utils import MODELS_SUPPORTING_KV_CACHE from optimum.graphcore.modeling_utils import to_pipelined from ..test_modeling_common import floats_tensor, ids_tensor from .test_framework_agnostic import GenerationIntegrationTestsMixin if TYPE_CHECKING: pass if is_torch_available(): import torch from transformers import ( AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSpeechSeq2Seq, AutoModelForVision2Seq, AutoTokenizer, BartForConditionalGeneration, BartTokenizer, GPT2LMHeadModel, GPT2Tokenizer, ImageGPTForCausalImageModeling, SpeechEncoderDecoderModel, ) from transformers.generation.beam_constraints import DisjunctiveConstraint, PhrasalConstraint from transformers.generation.beam_search import BeamSearchScorer, ConstrainedBeamSearchScorer from transformers.generation.logits_process import ( ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitsProcessorList, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, RepetitionPenaltyLogitsProcessor, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, ) from transformers.generation.stopping_criteria import MaxLengthCriteria, StoppingCriteria, StoppingCriteriaList from transformers.generation.utils import ( BeamSampleDecoderOnlyOutput, BeamSampleEncoderDecoderOutput, BeamSearchDecoderOnlyOutput, BeamSearchEncoderDecoderOutput, GreedySearchDecoderOnlyOutput, GreedySearchEncoderDecoderOutput, SampleDecoderOnlyOutput, SampleEncoderDecoderOutput, ) from ..utils import skip_unsupported class GenerationTesterMixin: model_tester = None all_generative_model_classes = () input_name = "input_ids" def _get_input_ids_and_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = inputs_dict[self.input_name] attention_mask = torch.ones_like(input_ids, dtype=torch.long) # cut to half length & take max batch_size 3 max_batch_size = 2 sequence_length = input_ids.shape[-1] // 2 input_ids = input_ids[:max_batch_size, :sequence_length] attention_mask = attention_mask[:max_batch_size, :sequence_length] # generate max 3 tokens max_length = input_ids.shape[-1] + 3 if config.eos_token_id is not None and config.pad_token_id is None: # hack to allow generate for models such as GPT2 as is done in `generate()` config.pad_token_id = config.eos_token_id return config, input_ids, attention_mask, max_length @staticmethod def _get_logits_processor_and_kwargs( input_length, eos_token_id, forced_bos_token_id=None, forced_eos_token_id=None, max_length=None, diversity_penalty=None, ): process_kwargs = { "min_length": input_length + 1, "bad_words_ids": [[1, 0]], "no_repeat_ngram_size": 2, "repetition_penalty": 1.2, } logits_processor = LogitsProcessorList( ( [ HammingDiversityLogitsProcessor(diversity_penalty, num_beams=2, num_beam_groups=2), ] if diversity_penalty is not None else [] ) + ( [ MinLengthLogitsProcessor(process_kwargs["min_length"], eos_token_id), ] if eos_token_id is not None else [] ) + ( [ ForcedBOSTokenLogitsProcessor(forced_bos_token_id), ] if forced_bos_token_id is not None else [] ) + ( [ForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id)] if forced_eos_token_id is not None else [] ) + [ NoBadWordsLogitsProcessor(process_kwargs["bad_words_ids"], eos_token_id), NoRepeatNGramLogitsProcessor(process_kwargs["no_repeat_ngram_size"]), RepetitionPenaltyLogitsProcessor(process_kwargs["repetition_penalty"]), ] ) return process_kwargs, logits_processor @staticmethod def _get_warper_and_kwargs(num_beams): warp_kwargs = {"top_k": 10, "top_p": 0.7, "temperature": 0.7} logits_warper = LogitsProcessorList( [ TemperatureLogitsWarper(warp_kwargs["temperature"]), TopKLogitsWarper(top_k=warp_kwargs["top_k"], min_tokens_to_keep=(2 if num_beams > 1 else 1)), TopPLogitsWarper(top_p=warp_kwargs["top_p"], min_tokens_to_keep=(2 if num_beams > 1 else 1)), ] ) return warp_kwargs, logits_warper @staticmethod def _get_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1): beam_kwargs = { "early_stopping": False, "length_penalty": 2.0, "num_beams": 2, "num_return_sequences": num_return_sequences, } beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=beam_kwargs["num_beams"], device=torch_device, length_penalty=beam_kwargs["length_penalty"], do_early_stopping=beam_kwargs["early_stopping"], num_beam_hyps_to_keep=num_return_sequences, ) return beam_kwargs, beam_scorer @staticmethod def _get_diverse_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1): beam_kwargs = { "early_stopping": False, "length_penalty": 2.0, "num_beams": 2, "num_return_sequences": num_return_sequences, "num_beam_groups": 2, # one beam per group "diversity_penalty": 2.0, } beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=beam_kwargs["num_beams"], device=torch_device, length_penalty=beam_kwargs["length_penalty"], do_early_stopping=beam_kwargs["early_stopping"], num_beam_hyps_to_keep=num_return_sequences, num_beam_groups=beam_kwargs["num_beam_groups"], ) return beam_kwargs, beam_scorer @staticmethod def _get_constrained_beam_scorer_and_kwargs(batch_size, max_length, constraints, num_return_sequences=1): beam_kwargs = { "early_stopping": False, "length_penalty": 2.0, "num_beams": num_return_sequences * 4, "num_return_sequences": num_return_sequences, } beam_scorer = ConstrainedBeamSearchScorer( batch_size=batch_size, constraints=constraints, num_beams=beam_kwargs["num_beams"], device=torch_device, length_penalty=beam_kwargs["length_penalty"], do_early_stopping=beam_kwargs["early_stopping"], num_beam_hyps_to_keep=num_return_sequences, ) return beam_kwargs, beam_scorer @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave( num_interleave, dim=0 ) input_ids = torch.zeros_like(input_ids[:, :1]) + model._get_decoder_start_token_id() attention_mask = None return encoder_outputs, input_ids, attention_mask def _greedy_generate( self, model, input_ids, attention_mask, max_length, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): if model.config.is_encoder_decoder: max_length = 4 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], eos_token_id=model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) kwargs = {} output_generate = model.generate( input_ids, attention_mask=attention_mask, do_sample=False, num_beams=1, max_length=max_length, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_scores=output_scores, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **logits_process_kwargs, ) if model.config.is_encoder_decoder: encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs( model, input_ids, attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) kwargs["encoder_outputs"] = encoder_outputs with torch.no_grad(): output_greedy = model.greedy_search( input_ids, max_length=max_length, attention_mask=attention_mask, logits_processor=logits_processor, output_attentions=output_attentions, output_hidden_states=output_hidden_states, output_scores=output_scores, return_dict_in_generate=return_dict_in_generate, **kwargs, ) return output_greedy, output_generate def _sample_generate( self, model, input_ids, attention_mask, max_length, num_return_sequences, logits_processor, logits_warper, logits_warper_kwargs, process_kwargs, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): torch.manual_seed(0) output_generate = model.generate( input_ids, do_sample=True, num_beams=1, max_length=max_length, num_return_sequences=num_return_sequences, attention_mask=attention_mask, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **logits_warper_kwargs, **process_kwargs, ) torch.manual_seed(0) 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=num_return_sequences, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) kwargs["encoder_outputs"] = encoder_outputs input_ids_clone = input_ids_clone.repeat_interleave(num_return_sequences, dim=0) else: attention_mask_clone = attention_mask.repeat_interleave(num_return_sequences, dim=0) input_ids_clone = input_ids.repeat_interleave(num_return_sequences, dim=0) # prevent flaky generation test failures logits_processor.append(InfNanRemoveLogitsProcessor()) with torch.no_grad(): output_sample = model.sample( input_ids_clone, attention_mask=attention_mask_clone, max_length=max_length, logits_processor=logits_processor, logits_warper=logits_warper, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, **kwargs, ) return output_sample, output_generate def _beam_search_generate( self, model, input_ids, attention_mask, max_length, beam_scorer, beam_kwargs, logits_processor, logits_process_kwargs, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): output_generate = model.generate( input_ids, attention_mask=attention_mask, do_sample=False, max_length=max_length, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **beam_kwargs, **logits_process_kwargs, ) # 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, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) 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_beam_search = model.beam_search( input_ids_clone, beam_scorer, max_length=max_length, attention_mask=attention_mask_clone, logits_processor=logits_processor, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, **kwargs, ) return output_generate, output_beam_search def _beam_sample_generate( self, model, input_ids, attention_mask, max_length, num_return_sequences, beam_scorer, beam_kwargs, logits_warper, logits_warper_kwargs, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): torch.manual_seed(0) output_generate = model.generate( input_ids, attention_mask=attention_mask, do_sample=True, max_length=max_length, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **beam_kwargs, **logits_warper_kwargs, ) # beam_search does not automatically interleave `batch_size` dim for `num_beams * num_return_sequences` kwargs = {} if model.config.is_encoder_decoder: encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs( model, input_ids, attention_mask, num_interleave=beam_scorer.num_beams * num_return_sequences, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) kwargs["encoder_outputs"] = encoder_outputs else: attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0) # prevent flaky generation test failures logits_processor = LogitsProcessorList() logits_processor.append(InfNanRemoveLogitsProcessor()) torch.manual_seed(0) with torch.no_grad(): output_beam_sample = model.beam_sample( input_ids.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0), beam_scorer, max_length=max_length, attention_mask=attention_mask, logits_warper=logits_warper, logits_processor=logits_processor, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, **kwargs, ) return output_generate, output_beam_sample def _group_beam_search_generate( self, model, input_ids, attention_mask, max_length, beam_scorer, beam_kwargs, logits_processor, logits_process_kwargs, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): output_generate = model.generate( input_ids, attention_mask=attention_mask, do_sample=False, max_length=max_length, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, **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, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) 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_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, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, **kwargs, ) return output_generate, output_group_beam_search def _constrained_beam_search_generate( self, model, input_ids, attention_mask, max_length, constrained_beam_scorer, constraints, beam_kwargs, logits_processor, logits_process_kwargs, output_scores=False, output_attentions=False, output_hidden_states=False, return_dict_in_generate=False, ): output_generate = model.generate( input_ids, attention_mask=attention_mask, do_sample=False, max_length=max_length, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, remove_invalid_values=True, constraints=constraints, **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=constrained_beam_scorer.num_beams, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) kwargs["encoder_outputs"] = encoder_outputs input_ids_clone = input_ids_clone.repeat_interleave(constrained_beam_scorer.num_beams, dim=0) else: attention_mask_clone = attention_mask.repeat_interleave(constrained_beam_scorer.num_beams, dim=0) input_ids_clone = input_ids.repeat_interleave(constrained_beam_scorer.num_beams, dim=0) with torch.no_grad(): output_group_beam_search = model.constrained_beam_search( input_ids_clone, constrained_beam_scorer, max_length=max_length, attention_mask=attention_mask_clone, logits_processor=logits_processor, output_scores=output_scores, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict_in_generate=return_dict_in_generate, **kwargs, ) return output_generate, output_group_beam_search def test_greedy_generate(self): # check `generate()` and `greedy_search()` are equal for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # test old generation output for backwards compatibility model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length ) self.assertListEqual(output_greedy.tolist(), output_generate.tolist()) def test_greedy_generate_dict_outputs(self): for model_class in self.all_generative_model_classes: # disable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() config.use_cache = False model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput) self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput) else: self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput) self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput) self.assertListEqual(output_generate.sequences.tolist(), output_greedy.sequences.tolist()) for output in (output_greedy, output_generate): self._check_outputs(output, input_ids, model.config) def test_greedy_generate_dict_outputs_use_cache(self): for model_class in self.all_generative_model_classes: # enable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() if not hasattr(config, "use_cache"): # only relevant if model has "use_cache" return config.use_cache = True config.is_decoder = True model = model_class(config).to(torch_device).eval() output_greedy, output_generate = self._greedy_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertListEqual(output_generate.sequences.tolist(), output_greedy.sequences.tolist()) for output in (output_greedy, output_generate): self._check_outputs(output, input_ids, model.config, use_cache=True) def test_sample_generate(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) # check `generate()` and `sample()` are equal output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=1, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, ) self.assertListEqual(output_sample.tolist(), output_generate.tolist()) # check `generate()` and `sample()` yield equal results for `num_return_sequences` output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=3, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, ) self.assertListEqual(output_sample.tolist(), output_generate.tolist()) def test_sample_generate_dict_output(self): for model_class in self.all_generative_model_classes: # disable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() config.use_cache = False model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], model.config.eos_token_id, forced_bos_token_id=model.config.forced_bos_token_id, forced_eos_token_id=model.config.forced_eos_token_id, max_length=max_length, ) logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) output_sample, output_generate = self._sample_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=2, logits_processor=logits_processor, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, process_kwargs=process_kwargs, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_sample, SampleEncoderDecoderOutput) self.assertIsInstance(output_generate, SampleEncoderDecoderOutput) else: self.assertIsInstance(output_sample, SampleDecoderOnlyOutput) self.assertIsInstance(output_generate, SampleDecoderOnlyOutput) self.assertListEqual(output_generate.sequences.tolist(), output_sample.sequences.tolist()) for output in (output_sample, output_generate): self._check_outputs(output, input_ids, model.config, num_return_sequences=2) def test_beam_search_generate(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, ) beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length) # check `generate()` and `beam_search()` are equal output_generate, output_beam_search = self._beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_process_kwargs=logits_process_kwargs, logits_processor=logits_processor, ) self.assertListEqual(output_generate.tolist(), output_beam_search.tolist()) # check `generate()` and `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_beam_scorer_and_kwargs( input_ids.shape[0], max_length, num_return_sequences=num_return_sequences ) output_generate, output_beam_search = self._beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_process_kwargs=logits_process_kwargs, logits_processor=logits_processor, ) self.assertListEqual(output_generate.tolist(), output_beam_search.tolist()) def test_beam_search_generate_dict_output(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # disable cache config.use_cache = False # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, ) beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length) output_generate, output_beam_search = self._beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_process_kwargs=logits_process_kwargs, logits_processor=logits_processor, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_beam_search, BeamSearchEncoderDecoderOutput) self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput) else: self.assertIsInstance(output_beam_search, BeamSearchDecoderOnlyOutput) self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput) self.assertListEqual(output_generate.sequences.tolist(), output_beam_search.sequences.tolist()) self.assertTrue( torch.allclose(output_generate["sequences_scores"], output_beam_search["sequences_scores"], atol=1e-3) ) self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],)) self.assertTrue((output_generate["sequences_scores"] < 0).all().item()) for output in (output_beam_search, output_generate): self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams) def test_beam_search_generate_dict_outputs_use_cache(self): for model_class in self.all_generative_model_classes: # enable cache config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None if not hasattr(config, "use_cache"): # only relevant if model has "use_cache" return model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, ) beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length) config.use_cache = True config.is_decoder = True model = model_class(config).to(torch_device).eval() output_beam, output_generate = self._beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_process_kwargs=logits_process_kwargs, logits_processor=logits_processor, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) self.assertListEqual(output_generate.sequences.tolist(), output_beam.sequences.tolist()) for output in (output_beam, output_generate): self._check_outputs( output, input_ids, model.config, use_cache=True, num_return_sequences=beam_scorer.num_beams ) def test_beam_sample_generate(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) model = model_class(config).to(torch_device).eval() # check `generate()` and `beam_search()` are equal # change `num_return_sequences = 2` but not for `beam_scorer` num_return_sequences = 2 if model.config.is_encoder_decoder: max_length = 4 beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs( input_ids.shape[0] * num_return_sequences, max_length ) beam_kwargs["num_return_sequences"] = num_return_sequences output_generate, output_beam_sample = self._beam_sample_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=num_return_sequences, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, ) self.assertListEqual(output_generate.tolist(), output_beam_sample.tolist()) def test_beam_sample_generate_dict_output(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # disable cache config.use_cache = False # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1) num_return_sequences = 2 if model.config.is_encoder_decoder: max_length = 4 beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs( input_ids.shape[0] * num_return_sequences, max_length ) beam_kwargs["num_return_sequences"] = num_return_sequences output_beam_sample, output_generate = self._beam_sample_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, num_return_sequences=num_return_sequences, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_warper=logits_warper, logits_warper_kwargs=logits_warper_kwargs, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_beam_sample, BeamSampleEncoderDecoderOutput) self.assertIsInstance(output_generate, BeamSampleEncoderDecoderOutput) else: self.assertIsInstance(output_beam_sample, BeamSampleDecoderOnlyOutput) self.assertIsInstance(output_generate, BeamSampleDecoderOnlyOutput) self.assertListEqual(output_generate.sequences.tolist(), output_beam_sample.sequences.tolist()) self.assertTrue( torch.allclose(output_generate["sequences_scores"], output_beam_sample["sequences_scores"], atol=1e-3) ) self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],)) self.assertTrue((output_generate["sequences_scores"] < 0).all().item()) for output in (output_beam_sample, output_generate): self._check_outputs( output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams ) def test_generate_without_input_ids(self): config, _, _, max_length = self._get_input_ids_and_config() # if no bos token id => cannot generate from None if config.bos_token_id is None: return for model_class in self.all_generative_model_classes: model = model_class(config).to(torch_device) model.eval() output_ids_generate = model.generate( do_sample=False, max_length=max_length, remove_invalid_values=True, ) self.assertIsNotNone(output_ids_generate) def test_group_beam_search_generate(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, diversity_penalty=2.0, ) # check `generate()` and `group_beam_search()` are equal beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(input_ids.shape[0], max_length) output_generate, output_group_beam_search = self._group_beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_processor=logits_processor, logits_process_kwargs=logits_process_kwargs, ) self.assertListEqual(output_generate.tolist(), output_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_generate, output_group_beam_search = self._group_beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_processor=logits_processor, logits_process_kwargs=logits_process_kwargs, ) self.assertListEqual(output_generate.tolist(), output_group_beam_search.tolist()) def test_group_beam_search_generate_dict_output(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() config.use_cache = False # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 4 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, diversity_penalty=2.0, ) num_return_sequences = 1 beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs( input_ids.shape[0], max_length, num_return_sequences=num_return_sequences ) output_generate, output_group_beam_search = self._group_beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, beam_scorer=beam_scorer, beam_kwargs=beam_kwargs, logits_processor=logits_processor, logits_process_kwargs=logits_process_kwargs, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_group_beam_search, BeamSearchEncoderDecoderOutput) self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput) else: self.assertIsInstance(output_group_beam_search, BeamSearchDecoderOnlyOutput) self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput) self.assertListEqual(output_generate.sequences.tolist(), output_group_beam_search.sequences.tolist()) self.assertTrue( torch.allclose( output_generate["sequences_scores"], output_group_beam_search["sequences_scores"], atol=1e-3 ) ) self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],)) self.assertTrue((output_generate["sequences_scores"] < 0).all().item()) for output in (output_group_beam_search, output_generate): self._check_outputs( output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams ) def test_constrained_beam_search_generate(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() max_length = 20 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, ) # check `generate()` and `constrained_beam_search()` are equal # Sample constraints if not input_ids.dtype == torch.float32: min_id = torch.min(input_ids) + 3 max_id = torch.max(input_ids) else: # otherwise this throws an error for Speech2TextModel since its inputs are floating points min_id = 3 max_id = 100 force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0] constraints = [ PhrasalConstraint(force_tokens), ] beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs( input_ids.shape[0], max_length, constraints, num_return_sequences=1 ) output_generate, output_beam_search = self._constrained_beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, constrained_beam_scorer=beam_scorer, constraints=constraints, beam_kwargs=beam_kwargs, logits_processor=logits_processor, logits_process_kwargs=logits_process_kwargs, ) self.assertListEqual(output_generate.tolist(), output_beam_search.tolist()) for generation_output in output_generate: self._check_sequence_inside_sequence(force_tokens, generation_output) # check `generate()` and `constrained_beam_search()` are equal for `num_return_sequences` # Sample constraints force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0] constraints = [ PhrasalConstraint(force_tokens), ] num_return_sequences = 2 max_length = 20 beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs( input_ids.shape[0], max_length, constraints, num_return_sequences=num_return_sequences ) output_generate, output_beam_search = self._constrained_beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, constrained_beam_scorer=beam_scorer, constraints=constraints, beam_kwargs=beam_kwargs, logits_processor=logits_processor, logits_process_kwargs=logits_process_kwargs, ) self.assertListEqual(output_generate.tolist(), output_beam_search.tolist()) for generation_output in output_generate: self._check_sequence_inside_sequence(force_tokens, generation_output) def test_constrained_beam_search_generate_dict_output(self): for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() # disable cache config.use_cache = False # It is important set set the eos_token_id to None to ensure that no sequences # shorter than `max_length` can be generated which could lead to flaky circle ci # failures if the top `num_return_sequences` beams are all shorter than the longest beam config.eos_token_id = None config.forced_eos_token_id = None model = model_class(config).to(torch_device).eval() if model.config.is_encoder_decoder: max_length = 20 logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs( input_ids.shape[-1], config.eos_token_id, config.forced_bos_token_id, config.forced_eos_token_id, max_length, ) # Sample constraints if not input_ids.dtype == torch.float32: min_id = torch.min(input_ids) + 3 max_id = torch.max(input_ids) else: # otherwise this throws an error for Speech2TextModel since its inputs are floating points min_id = 3 max_id = 100 force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0] constraints = [ PhrasalConstraint(force_tokens), ] beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs( input_ids.shape[0], max_length, constraints, num_return_sequences=1 ) output_generate, output_beam_search = self._constrained_beam_search_generate( model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length, constrained_beam_scorer=beam_scorer, constraints=constraints, beam_kwargs=beam_kwargs, logits_processor=logits_processor, logits_process_kwargs=logits_process_kwargs, output_scores=True, output_hidden_states=True, output_attentions=True, return_dict_in_generate=True, ) if model.config.is_encoder_decoder: self.assertIsInstance(output_beam_search, BeamSearchEncoderDecoderOutput) self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput) else: self.assertIsInstance(output_beam_search, BeamSearchDecoderOnlyOutput) self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput) self.assertListEqual(output_generate.sequences.tolist(), output_beam_search.sequences.tolist()) self.assertTrue( torch.allclose(output_generate["sequences_scores"], output_beam_search["sequences_scores"], atol=1e-3) ) self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],)) self.assertTrue((output_generate["sequences_scores"] < 0).all().item()) for output in (output_beam_search, output_generate): self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams) def test_generate_with_head_masking(self): """Test designed for encoder-decoder models to ensure the attention head masking is used.""" attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"] for model_class in self.all_generative_model_classes: config, input_ids, attention_mask, max_length = self._get_input_ids_and_config() model = model_class(config).to(torch_device) # We want to test only encoder-decoder models if not config.is_encoder_decoder: continue head_masking = { "head_mask": torch.zeros(config.encoder_layers, config.encoder_attention_heads, device=torch_device), "decoder_head_mask": torch.zeros( config.decoder_layers, config.decoder_attention_heads, device=torch_device ), "cross_attn_head_mask": torch.zeros( config.decoder_layers, config.decoder_attention_heads, device=torch_device ), } signature = inspect.signature(model.forward) # We want to test only models where encoder/decoder head masking is implemented if not set(head_masking.keys()) < {*signature.parameters.keys()}: continue for attn_name, (name, mask) in zip(attention_names, head_masking.items()): out = model.generate( input_ids, attention_mask=attention_mask, num_beams=1, output_attentions=True, return_dict_in_generate=True, remove_invalid_values=True, **{name: mask}, ) # We check the state of decoder_attentions and cross_attentions just from the last step attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0) def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, seq_length = input_ids.shape num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions if config.is_encoder_decoder: # encoder self._check_encoder_attention_for_generate(output.encoder_attentions, batch_size, config, seq_length) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) else: # if use_cache first input is equal to no use_cache, so skip here attentions = output.attentions if not use_cache else output.attentions[1:] min_length = seq_length if not use_cache else seq_length + 1 self._check_attentions_for_generate( num_sequences_in_output, attentions=attentions, min_length=min_length, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States if config.is_encoder_decoder: # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) else: # if use_cache first input is equal to no use_cache, so skip here hidden_states = output.hidden_states if not use_cache else output.hidden_states[1:] min_length = seq_length if not use_cache else seq_length + 1 self._check_hidden_states_for_generate( num_sequences_in_output, hidden_states, min_length=min_length, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) def _check_scores(self, batch_size, scores, length, config): expected_shape = (batch_size, config.vocab_size) self.assertIsInstance(scores, tuple) self.assertEqual(len(scores), length) self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores)) def _check_attentions_for_generate( self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(attentions, tuple) self.assertListEqual( [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions) ) self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups) for idx, iter_attentions in enumerate(attentions): tgt_len = min_length + idx if not use_cache else 1 src_len = min_length + idx expected_shape = ( batch_size * num_beam_groups, config.num_attention_heads, tgt_len, src_len, ) # check attn size self.assertListEqual( [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions) ) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length): encoder_expected_shape = (batch_size, config.num_attention_heads, seq_length, seq_length) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions.shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) def _check_hidden_states_for_generate( self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1 ): self.assertIsInstance(hidden_states, tuple) self.assertListEqual( [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states], [True] * len(hidden_states), ) self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups) for idx, iter_hidden_states in enumerate(hidden_states): seq_len = min_length + idx if not use_cache else 1 expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size) # check hidden size self.assertListEqual( [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states], [expected_shape] * len(iter_hidden_states), ) def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length): encoder_expected_shape = (batch_size, seq_length, config.hidden_size) self.assertIsInstance(hidden_states, tuple) self.assertListEqual( [layer_hidden_states.shape for layer_hidden_states in hidden_states], [encoder_expected_shape] * len(hidden_states), ) def _check_sequence_inside_sequence(self, tensor_1, tensor_2): # check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1. # set to same device. we don't care what device. if not isinstance(tensor_1, list): tensor_1 = tensor_1.cpu().tolist() if not isinstance(tensor_2, list): tensor_2 = tensor_2.cpu().tolist() in_order = len(tensor_1) <= len(tensor_2) longer = tensor_2 if in_order else tensor_1 shorter = tensor_1 if in_order else tensor_2 flag = False chunk_size = len(shorter) for chunk_idx in range(len(longer) - chunk_size + 1): subseq = longer[chunk_idx : chunk_idx + chunk_size] if subseq == shorter: flag = True break self.assertTrue(flag) # Test below does not test anything IPU specific # @pytest.mark.skip(reason="Generation not supported yet") # @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, GenerationIntegrationTestsMixin): # setting framework_dependent_parameters needs to be gated, just like its contents' imports if is_torch_available(): framework_dependent_parameters = { "AutoModelForCausalLM": AutoModelForCausalLM, "AutoModelForSpeechSeq2Seq": AutoModelForSpeechSeq2Seq, "AutoModelForSeq2SeqLM": AutoModelForSeq2SeqLM, "AutoModelForVision2Seq": AutoModelForVision2Seq, "LogitsProcessorList": LogitsProcessorList, "MinLengthLogitsProcessor": MinLengthLogitsProcessor, "create_tensor_fn": torch.tensor, "floats_tensor": floats_tensor, "return_tensors": "pt", } default_ipu_config = IPUConfig.from_pretrained( "Graphcore/internal-testing-tiny-ipu", inference_layers_per_ipu=[-1], embedding_serialization_factor=1, inference_embedding_serialization_factor=1, ).eval() @classmethod def tearDown(cls): # detach from IPUs and destroy poplar executables if hasattr(GenerationIntegrationTests, "test_model") and hasattr( GenerationIntegrationTests.test_model, "destroy" ): GenerationIntegrationTests.test_model.destroy() @classmethod def parallelize_model(cls, model: torch.nn.Module, ipu_config: IPUConfig = None, **generation_kwargs): if ipu_config is None: ipu_config = cls.default_ipu_config model = to_pipelined(model, ipu_config) GenerationIntegrationTests.test_model = model def reparallelize(self, **generation_kwargs): self.deparallelize() self.parallelize(for_generation=True, use_cache=self.config.use_cache, **generation_kwargs) return self type(model).reparallelize = reparallelize if type(model) in MODELS_SUPPORTING_KV_CACHE and model.config.use_cache: model.parallelize(for_generation=True, use_cache=model.config.use_cache, **generation_kwargs) else: model.config.use_cache = False model.parallelize(for_generation=True) return model @skip_unsupported("Group beam search") @slow def test_diverse_beam_search(self): # PT-only test: TF doesn't have a diverse beam search implementation 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") ipu_config = IPUConfig(inference_layers_per_ipu=[-1], inference_ipus_per_replica=4).eval() num_beams = 4 bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn"), ipu_config=ipu_config, num_beams=num_beams, ) input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) outputs = bart_model.generate( input_ids, num_beams=num_beams, num_return_sequences=2, num_beam_groups=4, diversity_penalty=2.0, remove_invalid_values=True, ) 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.", ], ) def test_max_length_backward_compat_greedy(self): # PT-only test: TF doesn't have StoppingCriteria article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) input_ids = input_ids.expand(2, -1) max_length = 20 bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"), max_length=max_length, batch_size=input_ids.shape[0], ) model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {}) # required if making direct calls to generation methods model_kwargs["use_cache"] = bart_model.config.use_cache input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation( batch_size=input_ids.shape[0], model_input_name=bart_model.main_input_name, model_kwargs=model_kwargs, decoder_start_token_id=bart_model.config.decoder_start_token_id, bos_token_id=bart_model.config.bos_token_id, ) with self.assertWarns(UserWarning): bart_model.greedy_search( input_ids, max_length=max_length, pad_token_id=bart_model.config.pad_token_id, eos_token_id=bart_model.config.eos_token_id, **model_kwargs, ) def test_max_length_backward_compat_sample(self): # PT-only test: TF doesn't have StoppingCriteria article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) input_ids = input_ids.expand(2, -1) max_length = 20 bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"), max_length=max_length, batch_size=input_ids.shape[0], ) model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {}) input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation( batch_size=input_ids.shape[0], model_input_name=bart_model.main_input_name, model_kwargs=model_kwargs, decoder_start_token_id=bart_model.config.decoder_start_token_id, bos_token_id=bart_model.config.bos_token_id, ) # required if making direct calls to generation methods model_kwargs["use_cache"] = bart_model.config.use_cache with torch.no_grad(): with self.assertWarns(UserWarning): bart_model.sample( input_ids, max_length=max_length, pad_token_id=bart_model.config.pad_token_id, eos_token_id=bart_model.config.eos_token_id, **model_kwargs, ) def test_max_length_backward_compat_beam_search(self): # PT-only test: TF doesn't have StoppingCriteria article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) input_ids = input_ids.expand(2, -1) batch_size = 1 max_length = 20 num_beams = 2 bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"), batch_size=batch_size, max_length=max_length, num_beams=num_beams, ) model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {}) # required if making direct calls to generation methods model_kwargs["use_cache"] = bart_model.config.use_cache input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation( batch_size=input_ids.shape[0], model_input_name=bart_model.main_input_name, model_kwargs=model_kwargs, decoder_start_token_id=bart_model.config.decoder_start_token_id, bos_token_id=bart_model.config.bos_token_id, ) beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=num_beams, device=torch_device, ) with self.assertWarns(UserWarning): _ = bart_model.beam_search( input_ids, num_beams=num_beams, max_length=max_length, beam_scorer=beam_scorer, **model_kwargs ) @skip_unsupported("Group beam search") def test_max_length_backward_compat_group_beam_search(self): # PT-only test: TF doesn't have StoppingCriteria & group beam search article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"), ) input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) batch_size = 1 max_length = 20 num_beams = 6 num_beam_groups = 3 num_return_sequences = num_beams * batch_size input_ids = input_ids.expand(6, -1) model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {}) input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation( batch_size=input_ids.shape[0], model_input_name=bart_model.main_input_name, model_kwargs=model_kwargs, decoder_start_token_id=bart_model.config.decoder_start_token_id, bos_token_id=bart_model.config.bos_token_id, ) diverse_beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=num_beams, device=torch_device, num_beam_hyps_to_keep=num_return_sequences, num_beam_groups=num_beam_groups, ) with self.assertWarns(UserWarning): bart_model.group_beam_search( input_ids, diverse_beam_scorer, num_beams=num_beams, max_length=max_length, **model_kwargs ) def test_max_length_warning_if_different(self): # PT-only test: TF doesn't have StoppingCriteria article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) input_ids = input_ids.expand(6, -1) batch_size = 1 max_length = 20 num_beams = 6 # for all generation strategies except for beam search below the batch size is 6 bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"), batch_size=input_ids.shape[0], max_length=max_length, ) stopping_criteria_max_length = 18 stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)]) # Greedy model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {}) input_ids, model_kwargs = bart_model._prepare_decoder_input_ids_for_generation( batch_size=input_ids.shape[0], model_input_name=bart_model.main_input_name, model_kwargs=model_kwargs, decoder_start_token_id=bart_model.config.decoder_start_token_id, bos_token_id=bart_model.config.bos_token_id, ) model_kwargs["use_cache"] = bart_model.config.use_cache with self.assertWarns(UserWarning): bart_model.greedy_search( input_ids, max_length=max_length, pad_token_id=bart_model.config.pad_token_id, stopping_criteria=stopping_criteria, eos_token_id=bart_model.config.eos_token_id, **model_kwargs, ) bart_model.destroy() # Sample with self.assertWarns(UserWarning): with torch.no_grad(): bart_model.sample( input_ids, max_length=max_length, stopping_criteria=stopping_criteria, pad_token_id=bart_model.config.pad_token_id, eos_token_id=bart_model.config.eos_token_id, **model_kwargs, ) bart_model.destroy() # Beam beam_scorer = BeamSearchScorer( batch_size=batch_size, num_beams=num_beams, device=torch_device, ) with self.assertWarns(UserWarning): with torch.no_grad(): bart_model.reparallelize(num_beams=num_beams, max_length=max_length, batch_size=batch_size) bart_model.beam_search( input_ids, num_beams=num_beams, stopping_criteria=stopping_criteria, max_length=max_length, beam_scorer=beam_scorer, **model_kwargs, ) bart_model.destroy() # Not yet supported # Grouped beam search # diverse_beam_scorer = BeamSearchScorer( # batch_size=batch_size, # num_beams=num_beams, # device=torch_device, # num_beam_hyps_to_keep=num_return_sequences, # num_beam_groups=num_beam_groups, # ) # with self.assertWarns(UserWarning): # bart_model.group_beam_search( # input_ids, # diverse_beam_scorer, # stopping_criteria=stopping_criteria, # num_beams=num_beams, # max_length=max_length, # **model_kwargs, # ) def test_custom_stopping_criteria_overload_error(self): # PT-only test: TF doesn't have StoppingCriteria article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random") max_length = 42 input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random"), max_length=max_length, batch_size=input_ids.shape[0], ) stopping_criteria = StoppingCriteriaList() stopping_criteria.append(MaxLengthCriteria(max_length=max_length)) with self.assertRaises(ValueError): bart_model.generate(input_ids, stopping_criteria=stopping_criteria) bart_model.destroy() with self.assertRaises(ValueError): max_length = 32 bart_model.reparallelize(max_length=max_length, batch_size=input_ids.shape[0]) bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=max_length) def test_custom_stopping_criteria(self): # PT-only test: TF doesn't have StoppingCriteria article = """Justin Timberlake and Jessica Biel, welcome to parenthood.""" bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random") input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) max_length = 22 bart_model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random"), batch_size=input_ids.shape[0], max_length=22, ) class DummyCriteria(StoppingCriteria): def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: return input_ids.shape[-1] >= 20 stopping_criteria = StoppingCriteriaList() stopping_criteria.append(DummyCriteria()) self.assertEqual( list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=max_length).shape), [1, 20], ) bart_model.destroy() max_length = 18 bart_model.reparallelize(max_length=max_length, batch_size=input_ids.shape[0]) self.assertEqual( list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=max_length).shape), [1, 18], ) @skip_unsupported("BartForCausalLM") def test_stop_sequence_stopping_criteria(self): # PT-only test: TF doesn't have StoppingCriteria prompt = """Hello I believe in""" generator = pipeline( "text-generation", model="hf-internal-testing/tiny-random-bart", ipu_config=self.default_ipu_config ) output = generator(prompt) self.assertEqual( output, [ { "generated_text": ( "Hello I believe in in in number number number number number number number number number" ) } ], ) output = generator(prompt, stop_sequence=" number") self.assertEqual(output, [{"generated_text": "Hello I believe in in in number"}]) @skip_unsupported("ImageGPT") def test_generate_non_nlp_input_ids_as_kwarg(self): # PT-only test: AFAIK there's no non-NLP model architecture in TF that supports `input_ids` as its only input model = ImageGPTForCausalImageModeling.from_pretrained( "hf-internal-testing/tiny-random-imagegpt", max_length=10 ).to(torch_device) input_ids = ids_tensor((3, 5), vocab_size=10) output_sequences_kwargs = model.generate(input_ids=input_ids).cpu() output_sequences = model.generate(input_ids).cpu() self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist()) self.assertEqual(output_sequences.shape, (3, 10)) @skip_unsupported("SpeechEncoderDecoderModel") def test_generate_input_values_as_encoder_kwarg(self): # PT-only test: AFAIK there's no generate-capable architecture in TF that supports `input_values` as its input input_values = floats_tensor((2, 250)) model = SpeechEncoderDecoderModel.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder") model = model.to(torch_device) output_sequences_kwargs = model.generate(input_values=input_values, max_length=5).cpu() output_sequences = model.generate(input_values, max_length=5).cpu() self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist()) self.assertEqual(output_sequences.shape, (2, 5)) @skip_unsupported("Group beam search") def test_transition_scores_group_beam_search_encoder_decoder(self): # PT-only test: TF doesn't have group beam search articles = [ "Justin Timberlake and Jessica Biel, welcome to parenthood.", "Michael Phelps is arguably the most decorated Olympian of all time.", ] tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") model = self.parallelize_model( BartForConditionalGeneration.from_pretrained( "hf-internal-testing/tiny-random-bart", max_length=10, num_beams=2, num_beam_groups=2, num_return_sequences=2, eos_token_id=None, return_dict_in_generate=True, output_scores=True, length_penalty=0.0, ) ) input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device) outputs = model.generate(input_ids=input_ids) transition_scores = model.compute_transition_scores(outputs.sequences, outputs.scores, outputs.beam_indices) transition_scores_sum = transition_scores.sum(-1) self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3)) @slow def test_beam_search_example_integration(self): # PT-only test: TF doesn't have a BeamSearchScorer # exactly the example provided in the docstrings of beam search, which previously # failed after directly copying from it. Refer to PR #15555 tokenizer = AutoTokenizer.from_pretrained("t5-base") encoder_input_str = "translate English to German: How old are you?" encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids # lets run beam search using 3 beams num_beams = 3 max_length = 20 # define decoder start token ids input_ids = torch.ones((num_beams, 1), dtype=torch.long) ipu_config = IPUConfig(inference_layers_per_ipu=[-1], inference_ipus_per_replica=4).eval() model = self.parallelize_model( AutoModelForSeq2SeqLM.from_pretrained("t5-base"), ipu_config=ipu_config, # batch_size = input_ids.shape[0], batch_size=1, num_beams=num_beams, max_length=max_length, ) input_ids = input_ids * model.config.decoder_start_token_id # add encoder_outputs to model keyword arguments model_kwargs = { "encoder_outputs": model.get_encoder()( encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True ), "use_cache": model.config.use_cache, } # instantiate beam scorer beam_scorer = BeamSearchScorer( batch_size=1, num_beams=num_beams, device=model.device, ) # instantiate logits processors logits_processor = LogitsProcessorList( [ MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id), ] ) outputs = model.beam_search( input_ids, beam_scorer, logits_processor=logits_processor, max_length=max_length, **model_kwargs ) outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual(outputs, ["Wie alt bist du?"]) @skip_unsupported("Constrained beam search") @slow def test_constrained_beam_search(self): # PT-only test: TF doesn't have constrained beam search model = self.parallelize_model( GPT2LMHeadModel.from_pretrained("gpt2").to(torch_device), ) tokenizer = GPT2Tokenizer.from_pretrained("gpt2") force_tokens = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids force_tokens_2 = tokenizer("big weapons", add_prefix_space=True, add_special_tokens=False).input_ids constraints = [ PhrasalConstraint(force_tokens), PhrasalConstraint(force_tokens_2), ] starting_text = ["The soldiers were not prepared and"] input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device) outputs = model.generate( input_ids, constraints=constraints, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, max_length=30, remove_invalid_values=True, ) generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "The soldiers were not prepared and didn't know what to do. They had no idea how they would react if" " the enemy attacked them, big weapons scared" ], ) @skip_unsupported("Constrained beam search") @slow def test_constrained_beam_search_mixed(self): # PT-only test: TF doesn't have constrained beam search model = self.parallelize_model( GPT2LMHeadModel.from_pretrained("gpt2").to(torch_device), ) tokenizer = GPT2Tokenizer.from_pretrained("gpt2") force_phrase = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids flexible_phrases = tokenizer( ["scream", "screams", "screaming", "screamed"], add_prefix_space=True, add_special_tokens=False ).input_ids constraints = [ PhrasalConstraint(force_phrase), DisjunctiveConstraint(flexible_phrases), ] starting_text = ["The soldiers", "The child"] input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device) outputs = model.generate( input_ids, constraints=constraints, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, # max_length=20, remove_invalid_values=True, ) generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "The soldiers, who had been stationed at the base for more than a year before being evacuated" " screaming scared", "The child was taken to a local hospital where he died.\n 'I don't think screaming scared", ], ) @skip_unsupported("Constrained beam search") @slow def test_constrained_beam_search_mixed_mixin(self): # PT-only test: TF doesn't have constrained beam search model = self.parallelize_model( GPT2LMHeadModel.from_pretrained("gpt2"), ) tokenizer = GPT2Tokenizer.from_pretrained("gpt2") force_word = "scared" force_flexible = ["scream", "screams", "screaming", "screamed"] force_words_ids = [ tokenizer([force_word], add_prefix_space=True, add_special_tokens=False).input_ids, tokenizer(force_flexible, add_prefix_space=True, add_special_tokens=False).input_ids, ] starting_text = ["The soldiers", "The child"] input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device) outputs = model.generate( input_ids, force_words_ids=force_words_ids, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "The soldiers, who had been stationed at the base for more than a year before being evacuated" " screaming scared", "The child was taken to a local hospital where he died.\n 'I don't think screaming scared", ], ) @skip_unsupported("Constrained beam search") @slow def test_constrained_beam_search_example_translation_mixin(self): # PT-only test: TF doesn't have constrained beam search tokenizer = AutoTokenizer.from_pretrained("t5-base") ipu_config = IPUConfig(inference_layers_per_ipu=[-1], inference_ipus_per_replica=4).eval() model = self.parallelize_model( AutoModelForSeq2SeqLM.from_pretrained("t5-base"), ipu_config=ipu_config, ) encoder_input_str = "translate English to German: How old are you?" force_words = ["sind"] input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids outputs = model.generate( input_ids, force_words_ids=force_words_ids, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual(outputs, ["Wie alt sind Sie?"]) @skip_unsupported("Constrained beam search") @slow def test_constrained_beam_search_example_integration(self): # PT-only test: TF doesn't have constrained beam search tokenizer = AutoTokenizer.from_pretrained("t5-base") ipu_config = IPUConfig(inference_layers_per_ipu=[-1], inference_ipus_per_replica=4).eval() model = self.parallelize_model( AutoModelForSeq2SeqLM.from_pretrained("t5-base"), ipu_config=ipu_config, ) encoder_input_str = "translate English to German: How old are you?" encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids # lets run beam search using 5 beams num_beams = 5 # define decoder start token ids input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long) input_ids = input_ids * model.config.decoder_start_token_id # add encoder_outputs to model keyword arguments model_kwargs = { "encoder_outputs": model.get_encoder()( encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True ) } constraint_str = "sind" constraint_token_ids = tokenizer.encode(constraint_str)[:-1] # remove eos token constraints = [PhrasalConstraint(token_ids=constraint_token_ids)] # instantiate beam scorer beam_scorer = ConstrainedBeamSearchScorer( batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints ) # instantiate logits processors logits_processor = LogitsProcessorList( [ MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id), ] ) outputs = model.constrained_beam_search( input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs ) outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual(outputs, ["Wie alt sind Sie?"]) @skip_unsupported("Constrained beam search") def test_constrained_beam_search_mixin_type_checks(self): # PT-only test: TF doesn't have constrained beam search tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/t5-tiny-random") model = self.parallelize_model( AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/t5-tiny-random"), ) encoder_input_str = "translate English to German: How old are you?" input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids with self.assertRaises(ValueError): force_words = ["sind"] force_words_ids = tokenizer(force_words, return_tensors="pt").input_ids model.generate( input_ids, force_words_ids=force_words_ids, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) with self.assertRaises(ValueError): force_words = ["sind"] force_words_ids = [tokenizer(force_words, return_tensors="pt").input_ids] model.generate( input_ids, force_words_ids=force_words_ids, num_beams=10, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) with self.assertRaises(ValueError): model.generate(input_ids, force_words_ids=[]) with self.assertRaises(ValueError): model.generate(input_ids, force_words_ids=[[-1]]) with self.assertRaises(ValueError): model.generate(input_ids, force_words_ids=[[[-1]]]) @skip_unsupported("Contrastive search") def test_contrastive_search_batched(self): # PT-only test: TF doesn't have constrained beam search # Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs) articles = ["Foo", "Bar Baz"] tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") model = self.parallelize_model( BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"), ) model.config.eos_token_id = None input_ids_batched = tokenizer(articles, padding=True, return_tensors="pt").input_ids.to(torch_device) input_ids = tokenizer(articles[1], return_tensors="pt").input_ids.to(torch_device) output_sequences_batched = model.generate( input_ids=input_ids_batched, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True ) # model needs to be recompiled model.destroy() output_sequences = model.generate( input_ids=input_ids, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True ) batched_out = tokenizer.decode(output_sequences_batched.sequences[1], skip_special_tokens=True) out = tokenizer.decode(output_sequences.sequences[0], skip_special_tokens=True) self.assertEqual(batched_out, out) # output_sequences_batched.scores[0][1] -> 1st set of logits, 2nd sequence max_score_diff = (output_sequences_batched.scores[0][1] - output_sequences.scores[0][0]).abs().max() self.assertTrue(max_score_diff < 1e-5) def test_eos_token_id_int_and_list_top_k_top_sampling(self): # Has TF equivalent: this test relies on random sampling generation_kwargs = { "do_sample": True, "num_beams": 1, "top_p": 0.7, "top_k": 10, "temperature": 0.7, } expectation = 20 tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2") text = """Hello, my dog is cute and""" tokens = tokenizer(text, return_tensors="pt").to(torch_device) model = self.parallelize_model( AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"), batch_size=tokens.input_ids.shape[0], **generation_kwargs, ) # Only some seeds will work both on CPU/GPU for a fixed `expectation` value. # The selected seed is not guaranteed to work on all torch versions. torch.manual_seed(1) eos_token_id = 846 generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs) self.assertTrue(expectation == len(generated_tokens[0])) torch.manual_seed(1) eos_token_id = [846, 198] generated_tokens = model.generate(**tokens, eos_token_id=eos_token_id, **generation_kwargs) self.assertTrue(expectation == len(generated_tokens[0])) @skip_unsupported("Dynamic input shapes") def test_generate_from_inputs_embeds_decoder_only(self): # PT-only test: TF doesn't have a model with support to generate from input embeds (yet ;)) # Note: the model must support generation from input embeddings tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2") text = "Hello world" tokenized_inputs = tokenizer([text, text], return_tensors="pt") input_ids = tokenized_inputs.input_ids.to(torch_device) model = self.parallelize_model( AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device), batch_size=input_ids.shape[0], ) model.config.pad_token_id = tokenizer.eos_token_id # Traditional way of generating text outputs_from_ids = model.generate(input_ids) self.assertEqual(outputs_from_ids.shape, (2, 20)) # model needs to be recompiled model.destroy() # Input shapes will be different on subsequent calls to # _call_generate since inputs_embeds is only used for the first generation step # this will only work if we use inputs_embeds throughput the generation process # Same thing, but from input embeddings inputs_embeds = model.transformer.wte(input_ids) outputs_from_embeds = model.generate(input_ids, inputs_embeds=inputs_embeds) self.assertListEqual(outputs_from_ids.tolist(), outputs_from_embeds.tolist()) # But if we pass different inputs_embeds, we should get different outputs torch.manual_seed(0) random_embeds = torch.rand_like(inputs_embeds) outputs_from_rand_embeds = model.generate(input_ids, inputs_embeds=random_embeds) with self.assertRaises(AssertionError): self.assertListEqual(outputs_from_rand_embeds.tolist(), outputs_from_embeds.tolist()) model.destroy() # input_ids is not a required input -- if we don't pass it, the newly generated tokens will be the same outputs_from_embeds_wo_ids = model.generate( inputs_embeds=inputs_embeds, max_new_tokens=20 - inputs_embeds.shape[1] ) self.assertListEqual( outputs_from_embeds[:, inputs_embeds.shape[1] :].tolist(), outputs_from_embeds_wo_ids[:, 1:].tolist(), ) # def test_model_kwarg_encoder_signature_filtering(self): # # Has TF equivalent: ample use of framework-specific code # bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart") # article = """Hugging Face is a technology company based in New York and Paris.""" # input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) # bart_model = self.parallelize_model( # BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device) # ) # output = bart_model.generate(input_ids).cpu().numpy() # # Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an # # argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of # # the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and # # saves the day. # class FakeBart(BartForConditionalGeneration): # def forward(self, input_ids, foo=None, **kwargs): # return super().forward(input_ids, **kwargs) # bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device) # fake_output = bart_model.generate(input_ids, foo="bar").cpu().numpy() # bart_model.destroy() # self.assertTrue(np.array_equal(output, fake_output)) # # TODO: need to recreate PipelinedBARTForConditionalGeneration() # # Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail # # because it doesn't do signature filtering. # class FakeEncoder(bart_model.model.encoder.__class__): # def forward(self, input_ids, **kwargs): # return super().forward(input_ids, **kwargs) # fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared).to(torch_device) # bart_model.model.encoder = fake_encoder # # Normal generation still works (the output will be different because the encoder weights are different) # fake_output = bart_model.generate(input_ids).cpu().numpy() # with self.assertRaises(TypeError): # # FakeEncoder.forward() accepts **kwargs -> no filtering -> type error due to unexpected input "foo" # bart_model.generate(input_ids, foo="bar")