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Generate: add config-level validation (#25381)
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Joao Gante
GitHub
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9e57e0c063
commit
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@ -332,12 +332,121 @@ class GenerationConfig(PushToHubMixin):
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def validate(self):
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"""
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Validates the values of the attributes of the GenerationConfig instance, and raises a `ValueError` if any of
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the values are invalid.
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Validates the values of the attributes of the [`GenerationConfig`] instance. Raises exceptions in the presence
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of parameterization that can be detected as incorrect from the configuration instance alone.
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Note that some parameters are best validated at generate runtime, as they may depend on other inputs and/or the
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model, such as parameters related to the generation length.
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"""
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# Validation of individual attributes
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if self.early_stopping not in {True, False, "never"}:
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raise ValueError(f"`early_stopping` must be a boolean or 'never', but is {self.early_stopping}.")
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# Validation of attribute relations:
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# 1. detect sampling-only parameterization when not in sampling mode
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if self.do_sample is False:
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greedy_wrong_parameter_msg = (
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"`do_sample` is set to `False`. However, {flag_name} is set to {flag_value} -- this flag is only used "
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"in sample-based generation modes. Set `do_sample=True` or unset {flag_name} to continue."
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)
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if self.temperature != 1.0:
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raise ValueError(
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greedy_wrong_parameter_msg.format(flag_name="temperature", flag_value=self.temperature)
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)
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if self.top_p != 1.0:
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raise ValueError(greedy_wrong_parameter_msg.format(flag_name="top_p", flag_value=self.top_p))
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if self.typical_p != 1.0:
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raise ValueError(greedy_wrong_parameter_msg.format(flag_name="typical_p", flag_value=self.typical_p))
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if self.top_k != 50 and self.penalty_alpha is None: # contrastive search uses top_k
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raise ValueError(greedy_wrong_parameter_msg.format(flag_name="top_k", flag_value=self.top_k))
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if self.epsilon_cutoff != 0.0:
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raise ValueError(
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greedy_wrong_parameter_msg.format(flag_name="epsilon_cutoff", flag_value=self.epsilon_cutoff)
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)
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if self.eta_cutoff != 0.0:
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raise ValueError(greedy_wrong_parameter_msg.format(flag_name="eta_cutoff", flag_value=self.eta_cutoff))
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# 2. detect beam-only parameterization when not in beam mode
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if self.num_beams == 1:
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single_beam_wrong_parameter_msg = (
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"`num_beams` is set to 1. However, {flag_name} is set to {flag_value} -- this flag is only used in "
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"beam-based generation modes. Set `num_beams>1` or unset {flag_name} to continue."
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)
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if self.early_stopping is not False:
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raise ValueError(
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single_beam_wrong_parameter_msg.format(flag_name="early_stopping", flag_value=self.early_stopping)
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)
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if self.num_beam_groups != 1:
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raise ValueError(
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single_beam_wrong_parameter_msg.format(
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flag_name="num_beam_groups", flag_value=self.num_beam_groups
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)
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)
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if self.diversity_penalty != 0.0:
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raise ValueError(
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single_beam_wrong_parameter_msg.format(
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flag_name="diversity_penalty", flag_value=self.diversity_penalty
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)
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)
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if self.length_penalty != 1.0:
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raise ValueError(
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single_beam_wrong_parameter_msg.format(flag_name="length_penalty", flag_value=self.length_penalty)
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)
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if self.constraints is not None:
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raise ValueError(
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single_beam_wrong_parameter_msg.format(flag_name="constraints", flag_value=self.constraints)
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)
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# 3. detect incorrect paramaterization specific to advanced beam modes
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else:
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# constrained beam search
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if self.constraints is not None:
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constrained_wrong_parameter_msg = (
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"`constraints` is not `None`, triggering constrained beam search. However, {flag_name} is set to "
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"{flag_value}, which is incompatible with this generation mode. Set `constraints=None` or unset "
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"{flag_name} to continue."
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)
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if self.do_sample is True:
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raise ValueError(
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constrained_wrong_parameter_msg.format(flag_name="do_sample", flag_value=self.do_sample)
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)
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if self.num_beam_groups != 1:
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raise ValueError(
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constrained_wrong_parameter_msg.format(
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flag_name="num_beam_groups", flag_value=self.num_beam_groups
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)
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)
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# group beam search
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if self.diversity_penalty != 0.0 or self.num_beam_groups != 1:
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group_error_prefix = (
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"`diversity_penalty` is not 0.0 or `num_beam_groups` is not 1, triggering group beam search. In "
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"this generation mode, "
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)
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if self.do_sample is True:
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raise ValueError(group_error_prefix + "`do_sample` must be set to `False`")
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if self.num_beams % self.num_beam_groups != 0:
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raise ValueError(group_error_prefix + "`num_beams` should be divisible by `num_beam_groups`")
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if self.diversity_penalty == 0.0:
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raise ValueError(
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group_error_prefix
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+ "`diversity_penalty` should be greater than `0.0`, otherwise your groups will be identical."
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)
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# 4. check `num_return_sequences`
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if self.num_return_sequences != 1:
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if self.num_beams == 1:
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if self.do_sample is False:
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raise ValueError(
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"Greedy methods without beam search do not support `num_return_sequences` different than 1 "
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f"(got {self.num_return_sequences})."
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)
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elif self.num_return_sequences > self.num_beams:
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raise ValueError(
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f"`num_return_sequences` ({self.num_return_sequences}) has to be smaller or equal to `num_beams` "
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f"({self.num_beams})."
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)
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def save_pretrained(
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self,
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save_directory: Union[str, os.PathLike],
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@ -1493,13 +1493,6 @@ class GenerationMixin:
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# 7. determine generation mode
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generation_mode = self._get_generation_mode(generation_config, assistant_model)
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if generation_config.num_beam_groups > generation_config.num_beams:
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raise ValueError("`num_beam_groups` has to be smaller or equal to `num_beams`")
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if generation_mode == GenerationMode.GROUP_BEAM_SEARCH and generation_config.do_sample is True:
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raise ValueError(
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"Diverse beam search cannot be used in sampling mode. Make sure that `do_sample` is set to `False`."
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)
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if streamer is not None and (generation_config.num_beams > 1):
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raise ValueError(
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"`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
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@ -1572,12 +1565,6 @@ class GenerationMixin:
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**model_kwargs,
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)
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if generation_mode == GenerationMode.GREEDY_SEARCH:
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if generation_config.num_return_sequences > 1:
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raise ValueError(
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"num_return_sequences has to be 1 when doing greedy search, "
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f"but is {generation_config.num_return_sequences}."
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)
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# 11. run greedy search
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return self.greedy_search(
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input_ids,
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@ -1593,11 +1580,6 @@ class GenerationMixin:
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)
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elif generation_mode == GenerationMode.CONTRASTIVE_SEARCH:
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if generation_config.num_return_sequences > 1:
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raise ValueError(
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"num_return_sequences has to be 1 when doing contrastive search, "
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f"but is {generation_config.num_return_sequences}."
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)
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if not model_kwargs["use_cache"]:
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raise ValueError("Contrastive search requires `use_cache=True`")
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@ -1645,12 +1627,6 @@ class GenerationMixin:
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)
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elif generation_mode == GenerationMode.BEAM_SEARCH:
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if generation_config.num_return_sequences > generation_config.num_beams:
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raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
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if stopping_criteria.max_length is None:
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raise ValueError("`max_length` needs to be a stopping_criteria for now.")
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# 11. prepare beam search scorer
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beam_scorer = BeamSearchScorer(
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batch_size=batch_size,
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@ -1686,8 +1662,6 @@ class GenerationMixin:
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# 11. prepare logits warper
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logits_warper = self._get_logits_warper(generation_config)
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if stopping_criteria.max_length is None:
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raise ValueError("`max_length` needs to be a stopping_criteria for now.")
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# 12. prepare beam search scorer
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beam_scorer = BeamSearchScorer(
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batch_size=batch_size * generation_config.num_return_sequences,
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@ -1722,24 +1696,6 @@ class GenerationMixin:
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)
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elif generation_mode == GenerationMode.GROUP_BEAM_SEARCH:
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if generation_config.num_return_sequences > generation_config.num_beams:
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raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
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if generation_config.num_beams % generation_config.num_beam_groups != 0:
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raise ValueError("`num_beams` should be divisible by `num_beam_groups` for group beam search.")
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if generation_config.diversity_penalty == 0.0:
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raise ValueError(
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"`diversity_penalty` should be greater than `0.0`, otherwise your beam groups will be identical."
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)
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if stopping_criteria.max_length is None:
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raise ValueError("`max_length` needs to be a stopping_criteria for now.")
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has_default_typical_p = kwargs.get("typical_p") is None and generation_config.typical_p == 1.0
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if not has_default_typical_p:
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raise ValueError("Decoder argument `typical_p` is not supported with beam groups.")
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# 11. prepare beam search scorer
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beam_scorer = BeamSearchScorer(
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batch_size=batch_size,
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@ -1773,21 +1729,6 @@ class GenerationMixin:
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)
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elif generation_mode == GenerationMode.CONSTRAINED_BEAM_SEARCH:
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if generation_config.num_return_sequences > generation_config.num_beams:
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raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
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if stopping_criteria.max_length is None:
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raise ValueError("`max_length` needs to be a stopping_criteria for now.")
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if generation_config.num_beams <= 1:
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raise ValueError("`num_beams` needs to be greater than 1 for constrained generation.")
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if generation_config.do_sample:
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raise ValueError("`do_sample` needs to be false for constrained generation.")
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if generation_config.num_beam_groups is not None and generation_config.num_beam_groups > 1:
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raise ValueError("`num_beam_groups` not supported yet for constrained generation.")
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final_constraints = []
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if generation_config.constraints is not None:
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final_constraints = generation_config.constraints
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