Improve special_token_id logic in run_generation.py and add tests (#2885)
* improving generation * finalized special token behaviour for no_beam_search generation * solved modeling_utils merge conflict * solve merge conflicts in modeling_utils.py * add run_generation improvements from PR #2749 * adapted language generation to not use hardcoded -1 if no padding token is available * remove the -1 removal as hard coded -1`s are not necessary anymore * add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown * add slow language generation tests for pretrained models using hardcoded output with pytorch seed * delete ipdb * check that all generated tokens are valid * renaming * renaming Generation -> Generate * make style * updated so that generate_beam_search has same token behavior than generate_no_beam_search * consistent return format for run_generation.py * deleted pretrain lm generate tests -> will be added in another PR * cleaning of unused if statements and renaming * run_generate will always return an iterable * make style * consistent renaming * improve naming, make sure generate function always returns the same tensor, add docstring * add slow tests for all lmhead models * make style and improve example comments modeling_utils * better naming and refactoring in modeling_utils * improving generation * finalized special token behaviour for no_beam_search generation * solved modeling_utils merge conflict * solve merge conflicts in modeling_utils.py * add run_generation improvements from PR #2749 * adapted language generation to not use hardcoded -1 if no padding token is available * remove the -1 removal as hard coded -1`s are not necessary anymore * add lightweight language generation testing for randomely initialized models - just checking whether no errors are thrown * add slow language generation tests for pretrained models using hardcoded output with pytorch seed * delete ipdb * check that all generated tokens are valid * renaming * renaming Generation -> Generate * make style * updated so that generate_beam_search has same token behavior than generate_no_beam_search * consistent return format for run_generation.py * deleted pretrain lm generate tests -> will be added in another PR * cleaning of unused if statements and renaming * run_generate will always return an iterable * make style * consistent renaming * improve naming, make sure generate function always returns the same tensor, add docstring * add slow tests for all lmhead models * make style and improve example comments modeling_utils * better naming and refactoring in modeling_utils * changed fast random lm generation testing design to more general one * delete in old testing design in gpt2 * correct old variable name * temporary fix for encoder_decoder lm generation tests - has to be updated when t5 is fixed * adapted all fast random generate tests to new design * better warning description in modeling_utils * better comment * better comment and error message Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
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@@ -37,6 +37,9 @@ if is_torch_available():
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class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
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all_generative_model_classes = (
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(GPT2LMHeadModel,) if is_torch_available() else ()
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) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
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class GPT2ModelTester(object):
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def __init__(
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@@ -88,6 +91,8 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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@@ -122,9 +127,11 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
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# hidden_dropout_prob=self.hidden_dropout_prob,
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# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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n_positions=self.max_position_embeddings,
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n_ctx=self.max_position_embeddings
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n_ctx=self.max_position_embeddings,
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# type_vocab_size=self.type_vocab_size,
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# initializer_range=self.initializer_range
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bos_token_id=self.bos_token_id,
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eos_token_ids=self.eos_token_id,
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)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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