From ff648221bdf25d3168828d8b11a5916f67ae03cc Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 6 Mar 2020 11:31:19 +0100 Subject: [PATCH] fix conflicts --- src/transformers/configuration_utils.py | 1 + src/transformers/modeling_bart.py | 1 - src/transformers/modeling_utils.py | 129 +++++++++++++++++------- tests/test_modeling_bart.py | 116 ++++++++++----------- tests/test_modeling_common.py | 58 +++++------ 5 files changed, 174 insertions(+), 131 deletions(-) diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py index 09ef959dd3..3cc1c5c212 100644 --- a/src/transformers/configuration_utils.py +++ b/src/transformers/configuration_utils.py @@ -81,6 +81,7 @@ class PretrainedConfig(object): self.pad_token_id = kwargs.pop("pad_token_id", None) self.eos_token_ids = kwargs.pop("eos_token_ids", None) self.length_penalty = kwargs.pop("length_penalty", 1.0) + self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0) self.num_return_sequences = kwargs.pop("num_return_sequences", 1) # Fine-tuning task arguments diff --git a/src/transformers/modeling_bart.py b/src/transformers/modeling_bart.py index 0da7da1343..dff240c809 100644 --- a/src/transformers/modeling_bart.py +++ b/src/transformers/modeling_bart.py @@ -16,7 +16,6 @@ import logging import math import random -import ipdb from typing import Dict, List, Optional, Tuple import torch diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py index 93347b4525..51f3b15f68 100644 --- a/src/transformers/modeling_utils.py +++ b/src/transformers/modeling_utils.py @@ -173,7 +173,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): if getattr(output_embeddings, "bias", None) is not None: output_embeddings.bias.data = torch.nn.functional.pad( output_embeddings.bias.data, - (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0]), + (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],), "constant", 0, ) @@ -411,7 +411,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): else: raise EnvironmentError( "Error no file named {} found in directory {} or `from_tf` set to False".format( - [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"], pretrained_model_name_or_path + [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index",], + pretrained_model_name_or_path, ) ) elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path): @@ -425,7 +426,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): archive_file = pretrained_model_name_or_path + ".index" else: archive_file = hf_bucket_url( - pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME) + pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME), ) # redirect to the cache, if necessary @@ -520,7 +521,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): def load(module: nn.Module, prefix=""): local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {}) module._load_from_state_dict( - state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs + state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs, ) for name, child in module._modules.items(): if child is not None: @@ -620,6 +621,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): pad_token_id=None, eos_token_ids=None, length_penalty=None, + no_repeat_ngram_size=None, num_return_sequences=None, ): r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling @@ -725,6 +727,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_ids = eos_token_ids if eos_token_ids is not None else self.config.eos_token_ids length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty + no_repeat_ngram_size = ( + no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size + ) num_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) @@ -754,6 +759,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): isinstance(eos_token_ids, (list, tuple)) and ((isinstance(e, int) and e >= 0) for e in eos_token_ids) ), "`eos_token_ids` should be a positive integer or a list/tuple of positive integers." assert length_penalty > 0, "`length_penalty` should be strictly positive." + assert ( + isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0 + ), "`no_repeat_ngram_size` should be a positive integer." assert ( isinstance(num_return_sequences, int) and num_return_sequences > 0 ), "`num_return_sequences` should be a strictly positive integer." @@ -764,7 +772,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): "or a `bos_token_id` (integer >= 0) as a first token to start the generation." ) input_ids = torch.full( - (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device + (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device, ) else: assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)." @@ -811,23 +819,17 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): # TODO (PVP): check eos_token_id # TODO (PVP): probably not the best way to check whether model is encoder decoder is_encoder_decoder = ( - hasattr(self, "model") - and hasattr(self.model, "decoder") - and hasattr(self.model, "encoder") + hasattr(self, "model") and hasattr(self.model, "decoder") and hasattr(self.model, "encoder") ) if is_encoder_decoder: eos_token_id = eos_token_ids[0] - assert ( - bos_token_id is not None - ), "Encoder Decoder Models need to have a bos_token_id" - assert ( - eos_token_id is not None - ), "Encoder Decoder Models need to have a eos_token_id" + assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id" + assert eos_token_id is not None, "Encoder Decoder Models need to have a eos_token_id" # encoder decoder need to start with empty input_ids and copy the input_ids to encoder_inputs encoder_inputs = input_ids input_ids = torch.full( (effective_batch_size * num_beams, 1), -# eos_token_id, # Why eos_token_id here? bos_token_id makes more sense no? + # eos_token_id, # Why eos_token_id here? bos_token_id makes more sense no? bos_token_id, dtype=torch.long, device=next(self.parameters()).device, @@ -849,6 +851,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): top_k, top_p, repetition_penalty, + no_repeat_ngram_size, pad_token_id, eos_token_ids, effective_batch_size, @@ -869,6 +872,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): top_k, top_p, repetition_penalty, + no_repeat_ngram_size, pad_token_id, eos_token_ids, effective_batch_size, @@ -888,6 +892,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): top_k, top_p, repetition_penalty, + no_repeat_ngram_size, pad_token_id, eos_token_ids, batch_size, @@ -902,9 +907,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): past = None while cur_len < max_length: - model_inputs = self.prepare_inputs_for_generation( - input_ids, past=past, encoder_inputs=encoder_inputs - ) + model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, encoder_inputs=encoder_inputs) outputs = self(**model_inputs) next_token_logits = outputs[0][:, -1, :] @@ -917,9 +920,20 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): if repetition_penalty != 1.0: self.enforce_repetition_penalty_(next_token_logits, batch_size, 1, input_ids, repetition_penalty) + # calculate a list of banned tokens to prevent repetitively generating the same ngrams + if no_repeat_ngram_size > 0: + # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345 + banned_tokens = calc_banned_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len) + for batch_idx in range(batch_size): + next_token_logits[ + batch_idx, banned_tokens[batch_idx] + ] = -10000.0 # set eos token prob to 0 as is done for attention masks + if eos_token_ids is not None and cur_len < min_length: for eos_token_id in eos_token_ids: - next_token_logits[:, eos_token_id] = -10000.0 # set eos token prob to 0 as is done for attention masks + next_token_logits[ + :, eos_token_id + ] = -10000.0 # set eos token prob to 0 as is done for attention masks if do_sample: # Temperature (higher temperature => more likely to sample low probability tokens) @@ -981,6 +995,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): top_k, top_p, repetition_penalty, + no_repeat_ngram_size, pad_token_id, eos_token_ids, batch_size, @@ -993,9 +1008,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): """ Generate sequences for each example with beam search. """ is_encoder_decoder = ( - hasattr(self, "model") - and hasattr(self.model, "decoder") - and hasattr(self.model, "encoder") + hasattr(self, "model") and hasattr(self.model, "decoder") and hasattr(self.model, "encoder") ) # generated hypotheses @@ -1017,9 +1030,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): done = [False for _ in range(batch_size)] while cur_len < max_length: - model_inputs = self.prepare_inputs_for_generation( - input_ids, past=past, encoder_inputs=encoder_inputs - ) + model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, encoder_inputs=encoder_inputs) outputs = self(**model_inputs) # (batch_size * num_beams, cur_len, vocab_size) next_token_logits = outputs[0][:, -1, :] # (batch_size * num_beams, vocab_size) @@ -1030,12 +1041,23 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858) if repetition_penalty != 1.0: self.enforce_repetition_penalty_( - next_token_logits, batch_size, num_beams, input_ids, repetition_penalty + next_token_logits, batch_size, num_beams, input_ids, repetition_penalty, ) + # calculate a list of banned tokens to prevent repetitively generating the same ngrams + if no_repeat_ngram_size > 0: + # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345 + banned_tokens = calc_banned_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len) + for batch_idx in range(batch_size): + next_token_logits[ + batch_idx, banned_tokens[batch_idx] + ] = -10000.0 # set eos token prob to 0 as is done for attention masks + if eos_token_ids is not None and cur_len < min_length: for eos_token_id in eos_token_ids: - next_token_logits[:, eos_token_id] = -10000.0 # set eos token prob to 0 as is done for attention masks + next_token_logits[ + :, eos_token_id + ] = -10000.0 # set eos token prob to 0 as is done for attention masks if do_sample: # Temperature (higher temperature => more likely to sample low probability tokens) @@ -1070,14 +1092,18 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): # do greedy beam search scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size) - if is_encoder_decoder: # TODO(PVP) to be refactored later -# scores[scores != scores] = -math.inf # block nans => seems very hacky here -# scores[:, pad_token_id] = -math.inf => seems very hacky here + if is_encoder_decoder: # TODO(PVP) to be refactored later - do we need this boolean flag here? + # scores[scores != scores] = -math.inf # block nans => seems very hacky here + # scores[:, pad_token_id] = -math.inf => seems very hacky here # TODO(SS): fairseq also takes out every step, and has unk at slot 3 -# if cur_len == 0: # Force BOS to be chosen => also very hacky ... seems also to work without this line -# scores[:, self.config.bos_token_id + 1 :] = -math.inf + # if cur_len == 0: # Force BOS to be chosen => also very hacky ... seems also to work without this line + # scores[:, self.config.bos_token_id + 1 :] = -math.inf if cur_len == max_length - 1: # FORCE EOS to be chosen - all_but_eos_mask = torch.tensor([x for x in range(vocab_size) if x not in eos_token_ids], dtype=torch.long, device=next(self.parameters()).device) + all_but_eos_mask = torch.tensor( + [x for x in range(vocab_size) if x not in eos_token_ids], + dtype=torch.long, + device=next(self.parameters()).device, + ) scores[:, all_but_eos_mask] = -10000.0 assert scores.size() == (batch_size * num_beams, vocab_size) @@ -1175,7 +1201,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): assert torch.all( next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx] ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format( - next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx] + next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx], ) # need to add best num_beams hypotheses to generated hyps @@ -1218,7 +1244,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): assert (len(hypo) == max_length for hypo in best) decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device) - return decoded[:, 1:] + if is_encoder_decoder: + # do not return first token + return decoded[:, 1:] + return decoded @staticmethod def _reorder_cache(past, beam_idx): @@ -1235,6 +1264,30 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin): return past +def calc_banned_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, step): + # Copied from fairseq for no_repeat_ngram in beam_search""" + if step + 2 < no_repeat_ngram_size: + # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet + return [[] for _ in range(num_hypos)] + generated_ngrams = [{} for _ in range(num_hypos)] + for idx in range(num_hypos): + gen_tokens = prev_input_ids[idx].tolist() + generated_ngram = generated_ngrams[idx] + for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]): + prev_ngram_tuple = tuple(ngram[:-1]) + generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]] + + def _get_generated_ngrams(hypo_idx): + # Before decoding the next token, prevent decoding of ngrams that have already appeared + start_idx = step + 2 - no_repeat_ngram_size + end_idx = step + 1 + ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:end_idx].tolist()) + return generated_ngrams[hypo_idx].get(ngram_idx, []) + + banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)] + return banned_tokens + + def top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: @@ -1508,7 +1561,7 @@ class SQuADHead(nn.Module): self.answer_class = PoolerAnswerClass(config) def forward( - self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None + self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None, ): outputs = () @@ -1567,7 +1620,7 @@ class SQuADHead(nn.Module): start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs) cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index) - outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs + outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits,) + outputs # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits # or (if labels are provided) (total_loss,) @@ -1636,7 +1689,7 @@ class SequenceSummary(nn.Module): output = hidden_states.mean(dim=1) elif self.summary_type == "cls_index": if cls_index is None: - cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long) + cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long,) else: cls_index = cls_index.unsqueeze(-1).unsqueeze(-1) cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),)) diff --git a/tests/test_modeling_bart.py b/tests/test_modeling_bart.py index ffc5a004f7..34298d8134 100644 --- a/tests/test_modeling_bart.py +++ b/tests/test_modeling_bart.py @@ -16,7 +16,6 @@ import tempfile import unittest -import ipdb from transformers import is_torch_available @@ -87,7 +86,7 @@ class ModelTester: eos_token_ids=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, - output_past=self.output_past + output_past=self.output_past, ) inputs_dict = prepare_bart_inputs_dict(config, input_ids) return config, inputs_dict @@ -107,14 +106,8 @@ def prepare_bart_inputs_dict( @require_torch class BARTModelTest(ModelTesterMixin, unittest.TestCase): -<<<<<<< HEAD - all_model_classes = ( - (BartModel, BartForConditionalGeneration, BartForSequenceClassification) if is_torch_available() else () - ) -======= - all_model_classes = (BartModel, BartForMaskedLM, BartForSequenceClassification) if is_torch_available() else () - all_generative_model_classes = (BartForMaskedLM,) if is_torch_available() else () ->>>>>>> add to pass first tests + all_model_classes = (BartModel, BartForConditionalGeneration, BartForSequenceClassification) if is_torch_available() else () + all_generative_model_classes = (BartForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True # TODO(SS): fix the below in a separate PR test_pruning = False @@ -126,10 +119,10 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase): self.model_tester = ModelTester(self) self.config_tester = ConfigTester(self, config_class=BartConfig) - def _A_test_config(self): + def test_config(self): self.config_tester.run_common_tests() - def _A_test_advanced_inputs(self): + def test_advanced_inputs(self): # (config, input_ids, token_type_ids, input_mask, *unused) = \ config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(config, inputs_dict["input_ids"]) @@ -168,7 +161,7 @@ class BARTModelTest(ModelTesterMixin, unittest.TestCase): )[0] _assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask) - def _A_test_save_load_strict(self): + def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) @@ -224,7 +217,7 @@ class BartHeadTests(unittest.TestCase): ) return config, input_ids, batch_size - def _A_test_sequence_classification_forward(self): + def test_sequence_classification_forward(self): config, input_ids, batch_size = self._get_config_and_data() labels = _long_tensor([2] * batch_size).to(torch_device) model = BartForSequenceClassification(config) @@ -236,7 +229,7 @@ class BartHeadTests(unittest.TestCase): loss = outputs[0] self.assertIsInstance(loss.item(), float) - def _A_test_lm_forward(self): + def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data(output_past=False) decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size).to(torch_device) lm_model = BartForConditionalGeneration(config) @@ -248,7 +241,7 @@ class BartHeadTests(unittest.TestCase): self.assertEqual(logits.shape, expected_shape) self.assertIsInstance(loss.item(), float) - def _A_test_lm_uneven_forward(self): + def test_lm_uneven_forward(self): config = BartConfig( vocab_size=self.vocab_size, d_model=24, @@ -267,13 +260,8 @@ class BartHeadTests(unittest.TestCase): expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(logits.shape, expected_shape) -<<<<<<< HEAD def test_generate_beam_search(self): input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long().to(torch_device) -======= - def _A_test_generate_beam_search(self): - input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long() ->>>>>>> add to pass first tests config = BartConfig( vocab_size=self.vocab_size, d_model=24, @@ -287,21 +275,19 @@ class BartHeadTests(unittest.TestCase): output_past=True, eos_token_ids=2, pad_token_id=1, - bos_token_id=0 + bos_token_id=0, ) lm_model = BartForConditionalGeneration(config).to(torch_device) lm_model.eval() -# new_input_ids = lm_model.generate( -# input_ids.clone(), num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=5 -# ) + max_length = 5 new_input_ids = lm_model.generate( - input_ids.clone(), num_return_sequences=1, num_beams=2, max_length=5 + input_ids.clone(), num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=max_length ) - self.assertEqual(new_input_ids.shape, (input_ids.shape[0], 5)) + self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length - 1)) # TODO(SS): uneven length batches, empty inputs - def _A_test_shift_tokens_right(self): + def test_shift_tokens_right(self): input_ids = torch.Tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]).long() shifted = shift_tokens_right(input_ids, 1) n_pad_before = input_ids.eq(1).float().sum() @@ -311,7 +297,7 @@ class BartHeadTests(unittest.TestCase): self.assertTrue(torch.eq(shifted[:, 0], 2).all()) @slow - def _A_test_tokenization(self): + def test_tokenization(self): tokenizer = BartTokenizer.from_pretrained("bart-large") examples = [" Hello world", " DomDramg"] # need leading spaces for equality fairseq_results = [ @@ -387,7 +373,7 @@ TOLERANCE = 1e-4 @require_torch class BartModelIntegrationTest(unittest.TestCase): @slow - def _A_test_inference_no_head(self): + def test_inference_no_head(self): model = BartModel.from_pretrained("bart-large").to(torch_device) input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) inputs_dict = prepare_bart_inputs_dict(model.config, input_ids) @@ -401,7 +387,7 @@ class BartModelIntegrationTest(unittest.TestCase): self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) @slow - def _A_test_mnli_inference(self): + def test_mnli_inference(self): example_b = [0, 31414, 232, 328, 740, 1140, 69, 46078, 1588, 2, 1] input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2], example_b]) @@ -428,30 +414,34 @@ class BartModelIntegrationTest(unittest.TestCase): _assert_tensors_equal(expected_slice, logits_arr, atol=TOLERANCE) @unittest.skip("This is just too slow") - def _A_test_model_from_pretrained(self): + def test_model_from_pretrained(self): # Forces 1.6GB download from S3 for each model for model_name in list(BART_PRETRAINED_MODEL_ARCHIVE_MAP.keys()): model = BartModel.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model) @slow - def test_cnn_summarization_same_as_fairseq(self): + def test_cnn_summarization_same_as_fairseq_easy(self): hf = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(torch_device) tok = BartTokenizer.from_pretrained("bart-large") text = " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian" tokens = tok.encode(text, return_tensors="pt").to(torch_device) extra_len = 20 - gen_tokens_1 = hf.generate_1(tokens, num_beams=4, max_length=extra_len,) # repetition_penalty=10., - gen_tokens = hf.generate(tokens, num_beams=4, max_length=extra_len + 2, do_sample=False) # repetition_penalty=10., - print("1: {}".format(gen_tokens_1)) - print("2: {}".format(gen_tokens)) - ipdb.set_trace() + gen_tokens_bart = hf.generate_1(tokens, num_beams=4, max_length=extra_len,) # repetition_penalty=10., + gen_tokens = hf.generate( + tokens, num_beams=4, max_length=extra_len + 2, do_sample=False + ) # repetition_penalty=10., expected_result = "The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday." - generated_1 = [tok.decode(g,) for g in gen_tokens_1] + generated_bart = [tok.decode(g,) for g in gen_tokens_bart] generated = [tok.decode(g,) for g in gen_tokens] + self.assertEqual(expected_result, generated_bart[0]) self.assertEqual(expected_result, generated[0]) - # Harder cases with batching + @slow + def test_cnn_summarization_same_as_fairseq_hard(self): + hf = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(torch_device) + tok = BartTokenizer.from_pretrained("bart-large") + FRANCE_ARTICLE = ' Marseille, France (CNN)The French prosecutor leading an investigation into the crash of Germanwings Flight 9525 insisted Wednesday that he was not aware of any video footage from on board the plane. Marseille prosecutor Brice Robin told CNN that "so far no videos were used in the crash investigation." He added, "A person who has such a video needs to immediately give it to the investigators." Robin\'s comments follow claims by two magazines, German daily Bild and French Paris Match, of a cell phone video showing the harrowing final seconds from on board Germanwings Flight 9525 as it crashed into the French Alps. All 150 on board were killed. Paris Match and Bild reported that the video was recovered from a phone at the wreckage site. The two publications described the supposed video, but did not post it on their websites. The publications said that they watched the video, which was found by a source close to the investigation. "One can hear cries of \'My God\' in several languages," Paris Match reported. "Metallic banging can also be heard more than three times, perhaps of the pilot trying to open the cockpit door with a heavy object. Towards the end, after a heavy shake, stronger than the others, the screaming intensifies. Then nothing." "It is a very disturbing scene," said Julian Reichelt, editor-in-chief of Bild online. An official with France\'s accident investigation agency, the BEA, said the agency is not aware of any such video. Lt. Col. Jean-Marc Menichini, a French Gendarmerie spokesman in charge of communications on rescue efforts around the Germanwings crash site, told CNN that the reports were "completely wrong" and "unwarranted." Cell phones have been collected at the site, he said, but that they "hadn\'t been exploited yet." Menichini said he believed the cell phones would need to be sent to the Criminal Research Institute in Rosny sous-Bois, near Paris, in order to be analyzed by specialized technicians working hand-in-hand with investigators. But none of the cell phones found so far have been sent to the institute, Menichini said. Asked whether staff involved in the search could have leaked a memory card to the media, Menichini answered with a categorical "no." Reichelt told "Erin Burnett: Outfront" that he had watched the video and stood by the report, saying Bild and Paris Match are "very confident" that the clip is real. He noted that investigators only revealed they\'d recovered cell phones from the crash site after Bild and Paris Match published their reports. "That is something we did not know before. ... Overall we can say many things of the investigation weren\'t revealed by the investigation at the beginning," he said. What was mental state of Germanwings co-pilot? German airline Lufthansa confirmed Tuesday that co-pilot Andreas Lubitz had battled depression years before he took the controls of Germanwings Flight 9525, which he\'s accused of deliberately crashing last week in the French Alps. Lubitz told his Lufthansa flight training school in 2009 that he had a "previous episode of severe depression," the airline said Tuesday. Email correspondence between Lubitz and the school discovered in an internal investigation, Lufthansa said, included medical documents he submitted in connection with resuming his flight training. The announcement indicates that Lufthansa, the parent company of Germanwings, knew of Lubitz\'s battle with depression, allowed him to continue training and ultimately put him in the cockpit. Lufthansa, whose CEO Carsten Spohr previously said Lubitz was 100% fit to fly, described its statement Tuesday as a "swift and seamless clarification" and said it was sharing the information and documents -- including training and medical records -- with public prosecutors. Spohr traveled to the crash site Wednesday, where recovery teams have been working for the past week to recover human remains and plane debris scattered across a steep mountainside. He saw the crisis center set up in Seyne-les-Alpes, laid a wreath in the village of Le Vernet, closer to the crash site, where grieving families have left flowers at a simple stone memorial. Menichini told CNN late Tuesday that no visible human remains were left at the site but recovery teams would keep searching. French President Francois Hollande, speaking Tuesday, said that it should be possible to identify all the victims using DNA analysis by the end of the week, sooner than authorities had previously suggested. In the meantime, the recovery of the victims\' personal belongings will start Wednesday, Menichini said. Among those personal belongings could be more cell phones belonging to the 144 passengers and six crew on board. Check out the latest from our correspondents . The details about Lubitz\'s correspondence with the flight school during his training were among several developments as investigators continued to delve into what caused the crash and Lubitz\'s possible motive for downing the jet. A Lufthansa spokesperson told CNN on Tuesday that Lubitz had a valid medical certificate, had passed all his examinations and "held all the licenses required." Earlier, a spokesman for the prosecutor\'s office in Dusseldorf, Christoph Kumpa, said medical records reveal Lubitz suffered from suicidal tendencies at some point before his aviation career and underwent psychotherapy before he got his pilot\'s license. Kumpa emphasized there\'s no evidence suggesting Lubitz was suicidal or acting aggressively before the crash. Investigators are looking into whether Lubitz feared his medical condition would cause him to lose his pilot\'s license, a European government official briefed on the investigation told CNN on Tuesday. While flying was "a big part of his life," the source said, it\'s only one theory being considered. Another source, a law enforcement official briefed on the investigation, also told CNN that authorities believe the primary motive for Lubitz to bring down the plane was that he feared he would not be allowed to fly because of his medical problems. Lubitz\'s girlfriend told investigators he had seen an eye doctor and a neuropsychologist, both of whom deemed him unfit to work recently and concluded he had psychological issues, the European government official said. But no matter what details emerge about his previous mental health struggles, there\'s more to the story, said Brian Russell, a forensic psychologist. "Psychology can explain why somebody would turn rage inward on themselves about the fact that maybe they weren\'t going to keep doing their job and they\'re upset about that and so they\'re suicidal," he said. "But there is no mental illness that explains why somebody then feels entitled to also take that rage and turn it outward on 149 other people who had nothing to do with the person\'s problems." Germanwings crash compensation: What we know . Who was the captain of Germanwings Flight 9525? CNN\'s Margot Haddad reported from Marseille and Pamela Brown from Dusseldorf, while Laura Smith-Spark wrote from London. CNN\'s Frederik Pleitgen, Pamela Boykoff, Antonia Mortensen, Sandrine Amiel and Anna-Maja Rappard contributed to this report.' # @noqa EXPECTED_SUMMARY_FRANCE = 'French prosecutor says he\'s not aware of any video footage from on board the plane. German daily Bild and French Paris Match claim to have found a cell phone video of the crash. A French Gendarmerie spokesman calls the reports "completely wrong" and "unwarranted" German airline Lufthansa confirms co-pilot Andreas Lubitz had battled depression.' @@ -465,28 +455,28 @@ class BartModelIntegrationTest(unittest.TestCase): ARTICLE_SUBWAY = ' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County, New York. A year later, she got married again in Westchester County, but to a different man and without divorcing her first husband. Only 18 days after that marriage, she got hitched yet again. Then, Barrientos declared "I do" five more times, sometimes only within two weeks of each other. In 2010, she married once more, this time in the Bronx. In an application for a marriage license, she stated it was her "first and only" marriage. Barrientos, now 39, is facing two criminal counts of "offering a false instrument for filing in the first degree," referring to her false statements on the 2010 marriage license application, according to court documents. Prosecutors said the marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx, according to her attorney, Christopher Wright, who declined to comment further. After leaving court, Barrientos was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the New York subway through an emergency exit, said Detective Annette Markowski, a police spokeswoman. In total, Barrientos has been married 10 times, with nine of her marriages occurring between 1999 and 2002. All occurred either in Westchester County, Long Island, New Jersey or the Bronx. She is believed to still be married to four men, and at one time, she was married to eight men at once, prosecutors say. Prosecutors said the immigration scam involved some of her husbands, who filed for permanent residence status shortly after the marriages. Any divorces happened only after such filings were approved. It was unclear whether any of the men will be prosecuted. The case was referred to the Bronx District Attorney\'s Office by Immigration and Customs Enforcement and the Department of Homeland Security\'s Investigation Division. Seven of the men are from so-called "red-flagged" countries, including Egypt, Turkey, Georgia, Pakistan and Mali. Her eighth husband, Rashid Rajput, was deported in 2006 to his native Pakistan after an investigation by the Joint Terrorism Task Force. If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.' EXPECTED_SUMMARY_SUBWAY = "Liana Barrientos has been married 10 times, sometimes within two weeks of each other. Prosecutors say the marriages were part of an immigration scam. On Friday, she pleaded not guilty at State Supreme Court in the Bronx. She was arrested and charged with theft of service and criminal trespass for allegedly sneaking into the subway." -# dct = tok.batch_encode_plus( -# [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], -# max_length=1024, -# pad_to_max_length=True, -# return_tensors="pt", -# ) -# self.assertEqual(1024, dct["input_ids"].shape[1]) -# hypotheses_batch = hf.generate( -# input_ids=dct["input_ids"].to(torch_device), -# attention_mask=dct["attention_mask"].to(torch_device), -# num_beams=4, -# length_penalty=2.0, -# max_length=140, -# min_len=55, -# no_repeat_ngram_size=3, -# ) -# decoded = [ -# tok.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in hypotheses_batch -# ] -# self.assertListEqual( -# [EXPECTED_SUMMARY_FRANCE, EXPECTED_SUMMARY_SHORTER, EXPECTED_SUMMARY_IRAN, EXPECTED_SUMMARY_SUBWAY], -# decoded, -# ) + dct = tok.batch_encode_plus( + [FRANCE_ARTICLE, SHORTER_ARTICLE, IRAN_ARTICLE, ARTICLE_SUBWAY], + max_length=1024, + pad_to_max_length=True, + return_tensors="pt", + ) + self.assertEqual(1024, dct["input_ids"].shape[1]) + hypotheses_batch = hf.generate( + input_ids=dct["input_ids"].to(torch_device), + attention_mask=dct["attention_mask"].to(torch_device), + num_beams=4, + length_penalty=2.0, + max_length=140, + min_len=55, + no_repeat_ngram_size=3, + ) + decoded = [ + tok.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in hypotheses_batch + ] + self.assertListEqual( + [EXPECTED_SUMMARY_FRANCE, EXPECTED_SUMMARY_SHORTER, EXPECTED_SUMMARY_IRAN, EXPECTED_SUMMARY_SUBWAY], + decoded, + ) # TODO(SS): run fairseq again with num_beams=2, min_len=20. # TODO(SS): add test case that hits max_length diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 4abc183218..a52d746947 100644 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -54,13 +54,13 @@ class ModelTesterMixin: model_tester = None all_model_classes = () all_generative_model_classes = () - _A_test_torchscript = True - _A_test_pruning = True - _A_test_resize_embeddings = True - _A_test_head_masking = True + test_torchscript = True + test_pruning = True + test_resize_embeddings = True + test_head_masking = True is_encoder_decoder = False - def _A_test_save_load(self): + def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: @@ -85,7 +85,7 @@ class ModelTesterMixin: max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) - def _A_test_initialization(self): + def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) @@ -99,7 +99,7 @@ class ModelTesterMixin: msg="Parameter {} of model {} seems not properly initialized".format(name, model_class), ) - def _A_test_determinism(self): + def test_determinism(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: @@ -116,7 +116,7 @@ class ModelTesterMixin: max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) - def _A_test_attention_outputs(self): + def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) @@ -179,25 +179,25 @@ class ModelTesterMixin: [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) - def _A_test_torchscript(self): + def test_torchscript(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() self._create_and_check_torchscript(config, inputs_dict) - def _A_test_torchscript_output_attentions(self): + def test_torchscript_output_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_attentions = True self._create_and_check_torchscript(config, inputs_dict) - def _A_test_torchscript_output_hidden_state(self): + def test_torchscript_output_hidden_state(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True self._create_and_check_torchscript(config, inputs_dict) def _create_and_check_torchscript(self, config, inputs_dict): - if not self._A_test_torchscript: + if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan @@ -245,8 +245,8 @@ class ModelTesterMixin: self.assertTrue(models_equal) - def _A_test_headmasking(self): - if not self._A_test_head_masking: + def test_headmasking(self): + if not self.test_head_masking: return global_rng.seed(42) @@ -299,8 +299,8 @@ class ModelTesterMixin: self.assertAlmostEqual(attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0) self.assertNotEqual(attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0) - def _A_test_head_pruning(self): - if not self._A_test_pruning: + def test_head_pruning(self): + if not self.test_pruning: return for model_class in self.all_model_classes: @@ -328,8 +328,8 @@ class ModelTesterMixin: self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) - def _A_test_head_pruning_save_load_from_pretrained(self): - if not self._A_test_pruning: + def test_head_pruning_save_load_from_pretrained(self): + if not self.test_pruning: return for model_class in self.all_model_classes: @@ -361,8 +361,8 @@ class ModelTesterMixin: self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) - def _A_test_head_pruning_save_load_from_config_init(self): - if not self._A_test_pruning: + def test_head_pruning_save_load_from_config_init(self): + if not self.test_pruning: return for model_class in self.all_model_classes: @@ -392,8 +392,8 @@ class ModelTesterMixin: self.assertEqual(attentions[1].shape[-3], self.model_tester.num_attention_heads) self.assertEqual(attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1) - def _A_test_head_pruning_integration(self): - if not self._A_test_pruning: + def test_head_pruning_integration(self): + if not self.test_pruning: return for model_class in self.all_model_classes: @@ -449,7 +449,7 @@ class ModelTesterMixin: self.assertDictEqual(model.config.pruned_heads, {0: [0], 1: [1, 2], 2: [1, 2]}) - def _A_test_hidden_states_output(self): + def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: @@ -474,9 +474,9 @@ class ModelTesterMixin: ], ) - def _A_test_resize_tokens_embeddings(self): + def test_resize_tokens_embeddings(self): (original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common() - if not self._A_test_resize_embeddings: + if not self.test_resize_embeddings: return for model_class in self.all_model_classes: @@ -516,7 +516,7 @@ class ModelTesterMixin: self.assertTrue(models_equal) - def _A_test_model_common_attributes(self): + def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: @@ -594,7 +594,7 @@ class ModelTesterMixin: # self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape) # self.assertTrue(check_same_values(model.transformer.wte, model.lm_head)) - def _A_test_inputs_embeds(self): + def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.is_encoder_decoder: @@ -711,7 +711,7 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None): @require_torch class ModelUtilsTest(unittest.TestCase): @slow - def _A_test_model_from_pretrained(self): + def test_model_from_pretrained(self): logging.basicConfig(level=logging.INFO) for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: config = BertConfig.from_pretrained(model_name) @@ -736,7 +736,7 @@ class ModelUtilsTest(unittest.TestCase): class UtilsFunctionsTest(unittest.TestCase): # tests whether the top_k_top_p function behaves as expected - def _A_test_top_k_top_p_filtering(self): + def test_top_k_top_p_filtering(self): logits = torch.tensor( [ [