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Generate: deprecate default max_length (#18018)

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This commit is contained in:
Joao Gante
2022-07-23 18:02:03 +01:00
committed by GitHub
parent 8e8384663d
commit 7e44226fc7
4 changed files with 166 additions and 93 deletions
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53
src/transformers/generation_flax_utils.py
View File

@@ -15,6 +15,7 @@
# limitations under the License.
import warnings
from functools import partial
from typing import Dict, Optional
@@ -163,6 +164,7 @@ class FlaxGenerationMixin:
self,
input_ids: jnp.ndarray,
max_length: Optional[int] = None,
max_new_tokens: Optional[int] = None,
pad_token_id: Optional[int] = None,
bos_token_id: Optional[int] = None,
eos_token_id: Optional[int] = None,
@@ -209,8 +211,12 @@ class FlaxGenerationMixin:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
do_sample (`bool`, *optional*, defaults to `False`):
Whether or not to use sampling ; use greedy decoding otherwise.
temperature (`float`, *optional*, defaults to 1.0):
@@ -258,8 +264,6 @@ class FlaxGenerationMixin:
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
```"""
# set init values
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
@@ -270,11 +274,6 @@ class FlaxGenerationMixin:
if decoder_start_token_id is None and self.config.is_encoder_decoder:
raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.")
if min_length is not None and min_length > max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
f"length ({max_length})"
)
if self.config.is_encoder_decoder:
# add encoder_outputs to model_kwargs
@@ -283,6 +282,42 @@ class FlaxGenerationMixin:
# prepare decoder_input_ids for generation
input_ids = jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
# Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
if max_length is None and max_new_tokens is None:
warnings.warn(
"Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to "
f"{self.config.max_length} (`self.config.max_length`). Controlling `max_length` via the config is "
"deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend "
"using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif max_length is None and max_new_tokens is not None:
max_length = max_new_tokens + input_ids_seq_length
elif max_length is not None and max_new_tokens is not None:
raise ValueError(
"Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
" limit to the generated output length. Remove one of those arguments. Please refer to the"
" documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
# default to config if still None
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
if min_length is not None and min_length > max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
f"length ({max_length})"
)
if input_ids_seq_length >= max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {max_length}. This can lead to unexpected behavior. You should consider increasing"
"`max_new_tokens`."
)
do_sample = do_sample if do_sample is not None else self.config.do_sample
num_beams = num_beams if num_beams is not None else self.config.num_beams

124
src/transformers/generation_tf_utils.py
View File

@@ -15,6 +15,7 @@
# limitations under the License.
import inspect
import warnings
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
@@ -53,8 +54,8 @@ class TFGreedySearchDecoderOnlyOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-input_ids.shape[-1],)`-shaped tuple of `tf.Tensor` with each tensor
of shape `(batch_size, config.vocab_size)`).
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
@@ -83,8 +84,8 @@ class TFGreedySearchEncoderDecoderOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-1,)`-shaped tuple of `tf.Tensor` with each tensor of shape
`(batch_size, config.vocab_size)`).
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
@@ -123,8 +124,8 @@ class TFSampleDecoderOnlyOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-input_ids.shape[-1],)`-shaped tuple of `tf.Tensor` with each tensor
of shape `(batch_size*num_return_sequences, config.vocab_size)`).
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length, sequence_length)`.
@@ -153,8 +154,8 @@ class TFSampleEncoderDecoderOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-1,)`-shaped tuple of `tf.Tensor` with each tensor of shape
`(batch_size*num_return_sequences, config.vocab_size)`).
at each generation step. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each
generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size*num_return_sequences,
num_heads, sequence_length, sequence_length)`.
@@ -194,9 +195,9 @@ class TFBeamSearchDecoderOnlyOutput(ModelOutput):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. `(max_length-input_ids.shape[-1],)`-shaped tuple of `tf.Tensor` with each tensor of shape
`(batch_size*num_beams*num_return_sequences, config.vocab_size)`).
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
@@ -227,9 +228,9 @@ class TFBeamSearchEncoderDecoderOutput(ModelOutput):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. `(max_length-1,)`-shaped tuple of `tf.Tensor` with each tensor of shape `(batch_size*num_beams,
config.vocab_size)`).
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. `Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
@@ -272,9 +273,9 @@ class TFBeamSampleDecoderOnlyOutput(ModelOutput):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. `(max_length-input_ids.shape[-1],)`-shaped tuple of `tf.Tensor` with each tensor of shape
`(batch_size*num_beams*num_return_sequences, config.vocab_size)`).
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(tf.Tensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`tf.Tensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
@@ -305,9 +306,9 @@ class TFBeamSampleEncoderDecoderOutput(ModelOutput):
Final beam scores of the generated `sequences`.
scores (`tuple(tf.Tensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed beam scores for each vocabulary token at each generation step. Beam scores consisting of log
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this beam
. `(max_length-1,)`-shaped tuple of `tf.Tensor` with each tensor of shape `(batch_size*num_beams,
config.vocab_size)`).
softmax scores for each vocabulary token and sum of log softmax of previously generated tokens in this
beam. Tuple of `tf.Tensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
encoder_attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer of the decoder) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
@@ -375,6 +376,7 @@ class TFGenerationMixin:
self,
input_ids=None,
max_length=None,
max_new_tokens=None,
min_length=None,
do_sample=None,
early_stopping=None,
@@ -423,8 +425,12 @@ class TFGenerationMixin:
method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
@@ -577,6 +583,7 @@ class TFGenerationMixin:
return self._generate(
input_ids=input_ids,
max_length=max_length,
max_new_tokens=max_new_tokens,
min_length=min_length,
do_sample=do_sample,
early_stopping=early_stopping,
@@ -1286,6 +1293,7 @@ class TFGenerationMixin:
self,
input_ids=None,
max_length=None,
max_new_tokens=None,
min_length=None,
do_sample=None,
early_stopping=None,
@@ -1332,8 +1340,12 @@ class TFGenerationMixin:
input_ids (`tf.Tensor` of `dtype=tf.int32` and shape `(batch_size, sequence_length)`, *optional*):
The sequence used as a prompt for the generation. If `None` the method initializes it with
`bos_token_id` and a batch size of 1.
max_length (`int`, *optional*, defaults to 20):
The maximum length of the sequence to be generated.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
@@ -1474,8 +1486,6 @@ class TFGenerationMixin:
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)
```"""
# 1. Set generation parameters if not already defined
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
@@ -1514,12 +1524,6 @@ class TFGenerationMixin:
logger.warning(f"Setting `pad_token_id` to {eos_token_id} (first `eos_token_id`) to generate sequence")
pad_token_id = eos_token_id
if min_length is not None and min_length > max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
f"length ({max_length})"
)
use_xla = not tf.executing_eagerly()
if use_xla and not self.supports_xla_generation:
raise ValueError(
@@ -1561,21 +1565,49 @@ class TFGenerationMixin:
model_kwargs=model_kwargs,
)
if input_ids.shape[-1] >= max_length:
# 5. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
if max_length is None and max_new_tokens is None:
warnings.warn(
"Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to "
f"{self.config.max_length} (`self.config.max_length`). Controlling `max_length` via the config is "
"deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend "
"using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif max_length is None and max_new_tokens is not None:
max_length = max_new_tokens + input_ids_seq_length
elif max_length is not None and max_new_tokens is not None:
raise ValueError(
f"The context has {input_ids.shape[-1]} number of tokens, "
f"but `max_length` is only {max_length}. "
"Please make sure that `max_length` is bigger than the number of tokens, "
"by setting either `generate(max_length=...,...)` or `config.max_length = ...`"
"Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
" limit to the generated output length. Remove one of those arguments. Please refer to the"
" documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
# default to config if still None
max_length = max_length if max_length is not None else self.config.max_length
min_length = min_length if min_length is not None else self.config.min_length
if min_length is not None and min_length > max_length:
raise ValueError(
f"Unfeasable length constraints: the minimum length ({min_length}) is larger than the maximum "
f"length ({max_length})"
)
if input_ids_seq_length >= max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {max_length}. This can lead to unexpected behavior. You should consider increasing"
"`max_new_tokens`."
)
# 5. determine generation mode
# 6. determine generation mode
# TODO(Matt, Joao, Patrick) - add more use cases here
is_greedy_gen_mode = (num_beams == 1) and do_sample is False
is_sample_gen_mode = (num_beams == 1) and do_sample is True
is_beam_gen_mode = (num_beams > 1) and do_sample is False
# 6. prepare distribution pre_processing samplers
# 7. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
@@ -1587,13 +1619,13 @@ class TFGenerationMixin:
forced_eos_token_id=forced_eos_token_id,
)
# 7. go into different generation modes
# 8. go into different generation modes
if is_greedy_gen_mode:
if num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {num_return_sequences} when doing greedy search."
)
# 8. run greedy search
# 9. run greedy search
return self.greedy_search(
input_ids,
max_length=max_length,
@@ -1605,10 +1637,10 @@ class TFGenerationMixin:
**model_kwargs,
)
elif is_sample_gen_mode:
# 8. prepare logits warper
# 9. prepare logits warper
logits_warper = self._get_logits_warper(top_k=top_k, top_p=top_p, temperature=temperature)
# 9. expand input_ids with `num_return_sequences` additional sequences per batch
# 10. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids,
expand_size=num_return_sequences,
@@ -1616,7 +1648,7 @@ class TFGenerationMixin:
**model_kwargs,
)
# 10. run sample
# 11. run sample
return self.sample(
input_ids,
logits_processor=logits_processor,
@@ -1637,7 +1669,7 @@ class TFGenerationMixin:
f"num_beams >= num_return_sequences, got {num_beams} and {num_return_sequences} (respectivelly)"
)
# 8. broadcast inputs to the desired number of beams
# 9. broadcast inputs to the desired number of beams
input_ids = self._expand_to_num_beams(input_ids, num_beams=num_beams)
if "encoder_outputs" in model_kwargs:
@@ -1650,7 +1682,7 @@ class TFGenerationMixin:
model_kwargs["attention_mask"], num_beams=num_beams
)
# 9. run beam search
# 10. run beam search
return self.beam_search(
input_ids,
max_length=max_length,

72
src/transformers/generation_utils.py
View File

@@ -70,8 +70,8 @@ class GreedySearchDecoderOnlyOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-input_ids.shape[-1],)`-shaped tuple of `torch.FloatTensor` with each
tensor of shape `(batch_size, config.vocab_size)`).
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
@@ -100,8 +100,8 @@ class GreedySearchEncoderDecoderOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-1,)`-shaped tuple of `torch.FloatTensor` with each tensor of shape
`(batch_size, config.vocab_size)`).
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
sequence_length, sequence_length)`.
@@ -140,8 +140,8 @@ class SampleDecoderOnlyOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-input_ids.shape[-1],)`-shaped tuple of `torch.FloatTensor` with each
tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`).
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
`torch.FloatTensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length,
@@ -171,8 +171,8 @@ class SampleEncoderDecoderOutput(ModelOutput):
if all batches finished early due to the `eos_token_id`.
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
at each generation step. `(max_length-1,)`-shaped tuple of `torch.FloatTensor` with each tensor of shape
`(batch_size*num_return_sequences, config.vocab_size)`).
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape
`(batch_size*num_return_sequences, num_heads, sequence_length, sequence_length)`.
@@ -214,8 +214,8 @@ class BeamSearchDecoderOnlyOutput(ModelOutput):
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
`(max_length-input_ids.shape[-1],)`-shaped tuple of `torch.FloatTensor` with each tensor of shape
`(batch_size*num_beams*num_return_sequences, config.vocab_size)`).
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`tuple(tuple(torch.LongTensor))`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, input_ids.shape[-1])`.
@@ -251,8 +251,8 @@ class BeamSearchEncoderDecoderOutput(ModelOutput):
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
`(max_length-1,)`-shaped tuple of `torch.FloatTensor` with each tensor of shape `(batch_size*num_beams,
config.vocab_size)`).
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
beam_indices (`tuple(tuple(torch.LongTensor))`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, max_length-1)`.
@@ -300,8 +300,8 @@ class BeamSampleDecoderOnlyOutput(ModelOutput):
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
`(max_length-input_ids.shape[-1],)`-shaped tuple of `torch.FloatTensor` with each tensor of shape
`(batch_size*num_beams*num_return_sequences, config.vocab_size)`).
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
beam_indices (`tuple(tuple(torch.LongTensor))`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, input_ids.shape[-1])`.
@@ -337,8 +337,8 @@ class BeamSampleEncoderDecoderOutput(ModelOutput):
scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
`(max_length-1,)`-shaped tuple of `torch.FloatTensor` with each tensor of shape `(batch_size*num_beams,
config.vocab_size)`).
Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
with each tensor of shape `(batch_size*num_beams, config.vocab_size)`).
beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
`(batch_size*num_return_sequences, max_length-1)`.
@@ -923,10 +923,11 @@ class GenerationMixin:
should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
`input_ids`, `input_values`, `input_features`, or `pixel_values`.
max_length (`int`, *optional*, defaults to `model.config.max_length`):
The maximum length of the sequence to be generated.
max_new_tokens (`int`, *optional*, defaults to None):
The maximum numbers of tokens to generate, ignore the current number of tokens. Use either
`max_new_tokens` or `max_length` but not both, they serve the same purpose.
The maximum length the generated tokens can have. Corresponds to the length of the input prompt +
`max_new_tokens`. In general, prefer the use of `max_new_tokens`, which ignores the number of tokens in
the prompt.
max_new_tokens (`int`, *optional*):
The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
min_length (`int`, *optional*, defaults to 10):
The minimum length of the sequence to be generated.
do_sample (`bool`, *optional*, defaults to `False`):
@@ -974,7 +975,7 @@ class GenerationMixin:
where one can allow different forms of each word.
num_return_sequences(`int`, *optional*, defaults to 1):
The number of independently computed returned sequences for each element in the batch.
max_time(`float`, *optional*, defaults to None):
max_time(`float`, *optional*):
The maximum amount of time you allow the computation to run for in seconds. generation will still
finish the current pass after allocated time has been passed.
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -1195,19 +1196,24 @@ class GenerationMixin:
# if decoder-only then inputs_tensor has to be `input_ids`
input_ids = inputs_tensor
# 5. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
# 5. Prepare `max_length` depending on other stopping criteria
# if `max_new_tokens` is passed, but not `max_length` -> set `max_length = max_new_tokens`
if max_length is None and max_new_tokens is not None:
if max_length is None and max_new_tokens is None:
warnings.warn(
"Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to "
f"{self.config.max_length} (`self.config.max_length`). Controlling `max_length` via the config is "
"deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend "
"using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif max_length is None and max_new_tokens is not None:
max_length = max_new_tokens + input_ids_seq_length
elif max_length is not None and max_new_tokens is not None:
# Both are set, this is odd, raise a warning
warnings.warn(
"Both `max_length` and `max_new_tokens` have been set "
f"but they serve the same purpose. `max_length` {max_length} "
f"will take priority over `max_new_tokens` {max_new_tokens}.",
UserWarning,
raise ValueError(
"Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
" limit to the generated output length. Remove one of those arguments. Please refer to the"
" documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
)
# default to config if still None
max_length = max_length if max_length is not None else self.config.max_length
@@ -1221,9 +1227,9 @@ class GenerationMixin:
if input_ids_seq_length >= max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but ``max_length`` is set to"
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {max_length}. This can lead to unexpected behavior. You should consider increasing "
" ``config.max_length`` or ``max_length``."
"`max_new_tokens`."
)
# 6. determine generation mode

8
tests/generation/test_generation_utils.py
View File

@@ -2023,8 +2023,8 @@ class GenerationIntegrationTests(unittest.TestCase):
# 1 BOS + 20 + 3 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
# max_new_tokens and max_length serve the same purpose and should not be used together.
with self.assertWarns(UserWarning):
# max_new_tokens and max_length serve the same purpose and must not be used together.
with self.assertRaises(ValueError):
bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=10, max_length=20)
def test_max_new_tokens_decoder_only(self):
@@ -2050,8 +2050,8 @@ class GenerationIntegrationTests(unittest.TestCase):
# 1 BOS token + 23 new tokens
self.assertEqual(list(outputs.shape), [1, 24])
# max_new_tokens and max_length serve the same purpose and should not be used together.
with self.assertWarns(UserWarning):
# max_new_tokens and max_length serve the same purpose and must not be used together.
with self.assertRaises(ValueError):
gpt2_model.generate(decoder_input_ids=input_ids, max_new_tokens=10, max_length=20)
def test_encoder_decoder_generate_with_inputs_embeds(self):