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utils.py
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utils.py
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# coding=utf-8
# Copyright 2022 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import warnings
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
from transformers.deepspeed import is_deepspeed_zero3_enabled
from transformers.generation.beam_constraints import DisjunctiveConstraint, PhrasalConstraint
from transformers.generation.beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
from transformers.generation.logits_process import LogitsProcessorList
from transformers.generation.stopping_criteria import (
StoppingCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
from transformers.generation.utils import (
BeamSampleOutput,
BeamSearchDecoderOnlyOutput,
BeamSearchEncoderDecoderOutput,
BeamSearchOutput,
ContrastiveSearchOutput,
GenerateOutput,
GenerationMixin,
GreedySearchDecoderOnlyOutput,
GreedySearchEncoderDecoderOutput,
GreedySearchOutput,
SampleDecoderOnlyOutput,
SampleEncoderDecoderOutput,
SampleOutput,
)
from transformers.utils import ModelOutput
from optimum.utils import logging
from ...utils import HabanaProfile
from .configuration_utils import GaudiGenerationConfig
if TYPE_CHECKING:
from .streamers import BaseStreamer
MODELS_OPTIMIZED_WITH_STATIC_SHAPES = ["bloom", "gpt2", "opt", "gptj", "gpt_neox", "llama"]
logger = logging.get_logger(__name__)
class StaticMaxLengthCriteria(StoppingCriteria):
def __init__(self, max_steps: int):
self.max_steps = max_steps
self.cur_step = 0
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
self.cur_step += 1
return self.cur_step >= self.max_steps
class GaudiGenerationMixin(GenerationMixin):
"""
This class enables to perform fast generation in lazy mode and with HPU graphs.
The only difference with GenerationMixin is that the various generation
methods will generate sequences whose size is max_length. Having constant
sizes allows to make the most of lazy mode and HPU graphs.
"""
@staticmethod
def _expand_inputs_for_generation(
expand_size: int = 1,
is_encoder_decoder: bool = False,
input_ids: Optional[torch.LongTensor] = None,
**model_kwargs,
) -> Tuple[torch.LongTensor, Dict[str, Any]]:
"""
Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...].
Copied from Transformers: https://github.com/huggingface/transformers/blob/527ab894e59b6582578008e3b47648a65063f73d/src/transformers/generation/utils.py#L704
The tensor `token_idx` is not expanded.
"""
def _expand_dict_for_generation(dict_to_expand):
for key in dict_to_expand:
if (
dict_to_expand[key] is not None
and key != "token_idx"
and isinstance(dict_to_expand[key], torch.Tensor)
):
dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
return dict_to_expand
if input_ids is not None:
input_ids = input_ids.repeat_interleave(expand_size, dim=0)
model_kwargs = _expand_dict_for_generation(model_kwargs)
if is_encoder_decoder:
if model_kwargs.get("encoder_outputs") is None:
raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
return input_ids, model_kwargs
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_kwargs: Dict[str, Any],
is_encoder_decoder: bool = False,
standardize_cache_format: bool = False,
) -> Dict[str, Any]:
"""
Copied from Transformers: https://github.com/huggingface/transformers/blob/527ab894e59b6582578008e3b47648a65063f73d/src/transformers/generation/utils.py#L745
Adds support for `token_idx`, which is necessary for using static shapes.
"""
# update past_key_values
model_kwargs["past_key_values"] = self._extract_past_from_model_output(
outputs, standardize_cache_format=standardize_cache_format
)
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
token_idx = model_kwargs.get("token_idx", None)
if not is_encoder_decoder:
# update attention mask
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
if token_idx is not None:
attention_mask.index_fill_(1, token_idx, 1)
else:
attention_mask = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
)
model_kwargs["attention_mask"] = attention_mask
else:
# update decoder attention mask
if "decoder_attention_mask" in model_kwargs:
decoder_attention_mask = model_kwargs["decoder_attention_mask"]
if token_idx is not None:
decoder_attention_mask.index_fill_(1, token_idx, 1)
else:
decoder_attention_mask = torch.cat(
[
decoder_attention_mask,
decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1)),
],
dim=-1,
)
model_kwargs["decoder_attention_mask"] = decoder_attention_mask
if token_idx is not None:
token_idx.add_(1)
return model_kwargs
# TODO: remove this method when Transformers v4.31 is released since it solves the issue with Llama
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
"""
Copied from Transformers: https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py
Remove `token_type_ids` from model_kwargs, which is not used for llama model
"""
if self.config.is_encoder_decoder:
for key in ["decoder_input_ids"]:
model_kwargs.pop(key, None)
if self.config.model_type == "llama":
for key in ["token_type_ids"]:
model_kwargs.pop(key, None)
unused_model_args = []
model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
# `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
# `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
if "kwargs" in model_args or "model_kwargs" in model_args:
model_args |= set(inspect.signature(self.forward).parameters)
for key, value in model_kwargs.items():
if value is not None and key not in model_args:
unused_model_args.append(key)
if unused_model_args:
raise ValueError(
f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
" generate arguments will also show up in this list)"
)
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
generation_config: Optional[GaudiGenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
synced_gpus: Optional[bool] = None,
streamer: Optional["BaseStreamer"] = None,
lazy_mode: Optional[bool] = False,
hpu_graphs: Optional[bool] = False,
profiling_warmup_steps: Optional[int] = 0,
profiling_steps: Optional[int] = 0,
**kwargs,
) -> Union[GenerateOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head.
<Tip warning={true}>
Most generation-controlling parameters are set in [`transformers.generation.generation_config`] which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate, e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).
</Tip>
Most of these parameters are explained in more detail in [this blog
post](https://huggingface.co/blog/how-to-generate).
Parameters:
inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
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`.
generation_config (`transformers.generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call. `**kwargs`
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
logits_processor (`LogitsProcessorList`, *optional*):
Custom logits processors that complement the default logits processors built from arguments and
generation config. If a logit processor is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
stopping_criteria (`StoppingCriteriaList`, *optional*):
Custom stopping criteria that complement the default stopping criteria built from arguments and a
generation config. If a stopping criteria is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
If provided, this function constraints the beam search to allowed tokens only at each step. If not
provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
`input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
Retrieval](https://arxiv.org/abs/2010.00904).
synced_gpus (`bool`, *optional*):
Whether to continue running the while loop until max_length. Unless overridden this flag will be set to
`True` under DeepSpeed ZeRO Stage 3 multiple GPUs environment to avoid hanging if one GPU finished
generating before other GPUs. Otherwise it'll be set to `False`.
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
lazy_mode (`bool`, *optional*, defaults to `False`):
Whether the run is executed in lazy mode or not (i.e. eager mode).
hpu_graphs (`bool`, *optional*, defaults to `False`):
Whether to use HPU graphs for inference.
profiling_warmup_steps (`int`, *optional*, defaults to 0):
Number of steps to ignore for profling.
profiling_steps (`int`, *optional*, defaults to 0):
Number of steps to be captured when enabling profiling.
kwargs:
Ad hoc parametrization of `generation_config` and/or additional model-specific kwargs that will be
forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
Return:
[`transformers.utils.ModelOutput`] or `torch.LongTensor`: A [`transformers.generationutils.ModelOutput`] (if `return_dict_in_generate=True`
or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
[`transformers.generationutils.ModelOutput`] types are:
- [`transformers.generation.GreedySearchDecoderOnlyOutput`],
- [`transformers.generation.SampleDecoderOnlyOutput`],
- [`transformers.generation.BeamSearchDecoderOnlyOutput`],
- [`transformers.generation.BeamSampleDecoderOnlyOutput`]
If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
[`transformers.generationutils.ModelOutput`] types are:
- [`transformers.generation.GreedySearchEncoderDecoderOutput`],
- [`transformers.generation.SampleEncoderDecoderOutput`],
- [`transformers.generation.BeamSearchEncoderDecoderOutput`],
- [`transformers.generation.BeamSampleEncoderDecoderOutput`]
"""
if synced_gpus is None:
if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
synced_gpus = True
else:
synced_gpus = False
# 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
self._validate_model_class()
if hpu_graphs and not lazy_mode:
raise ValueError(
"`hpu_graphs` is True but `lazy_mode` is False. HPU graphs require `lazy_mode` to be set to True."
)
# priority: `generation_config` argument > `model.generation_config` (the default generation config)
if generation_config is None:
# legacy: users may modify the model configuration to control generation -- update the generation config
# model attribute accordingly, if it was created from the model config
if self.generation_config._from_model_config:
new_generation_config = GaudiGenerationConfig.from_model_config(self.config)
if new_generation_config != self.generation_config:
warnings.warn(
"You have modified the pretrained model configuration to control generation. This is a"
" deprecated strategy to control generation and will be removed soon, in a future version."
" Please use a generation configuration file (see"
" https://huggingface.co/docs/transformers/main_classes/text_generation)"
)
self.generation_config = new_generation_config
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
if generation_config.static_shapes is None:
generation_config.static_shapes = self.config.model_type in MODELS_OPTIMIZED_WITH_STATIC_SHAPES
if generation_config.ignore_eos is None:
generation_config.ignore_eos = lazy_mode
generation_config.validate()
model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
self._validate_model_kwargs(model_kwargs.copy())
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
if model_kwargs.get("attention_mask", None) is None:
logger.warning(
"The attention mask and the pad token id were not set. As a consequence, you may observe "
"unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
)
eos_token_id = generation_config.eos_token_id
if isinstance(eos_token_id, list):
eos_token_id = eos_token_id[0]
logger.warning(
f"Setting `pad_token_id` to `eos_token_id`:{generation_config.eos_token_id} for open-end generation."
)
generation_config.pad_token_id = eos_token_id
# 3. Define model inputs
# inputs_tensor has to be defined
# model_input_name is defined if model-specific keyword input is passed
# otherwise model_input_name is None
# all model-specific keyword inputs are removed from `model_kwargs`
inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
inputs, generation_config.bos_token_id, model_kwargs
)
batch_size = inputs_tensor.shape[0]
# 4. Define other model kwargs
model_kwargs["output_attentions"] = generation_config.output_attentions
model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
model_kwargs["use_cache"] = generation_config.use_cache
accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
requires_attention_mask = "encoder_outputs" not in model_kwargs
if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
)
if generation_config.static_shapes:
# token_idx is the current index in the generation process, it is incremented each time a new token is generated
model_kwargs["token_idx"] = torch.tensor(inputs_tensor.shape[-1], device=inputs_tensor.device)
# Pad inputs to have static shapes during generation, this gives better performance than dynamic shapes on HPUs
inputs_tensor = torch.nn.functional.pad(
inputs_tensor, (0, generation_config.max_new_tokens), value=generation_config.pad_token_id
)
if model_kwargs["attention_mask"] is not None:
model_kwargs["attention_mask"] = torch.nn.functional.pad(
model_kwargs["attention_mask"], (0, generation_config.max_new_tokens), value=0
)
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder:
if generation_config.pad_token_id is not None:
position = model_kwargs["token_idx"] - 1 if "token_idx" in model_kwargs else -1
if torch.sum(inputs_tensor[:, position] == generation_config.pad_token_id) > 0:
logger.warning(
"A decoder-only architecture is being used, but right-padding was detected! For correct "
"generation results, please set `padding_side='left'` when initializing the tokenizer."
)
if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
# if model is encoder decoder encoder_outputs are created
# and added to `model_kwargs`
model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, model_kwargs, model_input_name
)
# 5. Prepare `input_ids` which will be used for auto-regressive generation
if self.config.is_encoder_decoder:
input_ids = self._prepare_decoder_input_ids_for_generation(
batch_size,
decoder_start_token_id=generation_config.decoder_start_token_id,
bos_token_id=generation_config.bos_token_id,
model_kwargs=model_kwargs,
device=inputs_tensor.device,
)
# conditional generation for multi-modal models.
if "input_ids" in model_kwargs and model_input_name == "pixel_values":
input_ids = torch.cat([input_ids, model_kwargs.pop("input_ids")], dim=-1)
else:
input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
if streamer is not None:
streamer.put(input_ids.cpu())
# 6. Prepare `max_length` depending on other stopping criteria.
input_ids_seq_length = input_ids.shape[-1]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and 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 generation_config.max_new_tokens is not None:
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
if not has_default_max_length:
logger.warn(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
UserWarning,
)
if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
raise ValueError(
f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
f" the maximum length ({generation_config.max_length})"
)
if input_ids_seq_length >= generation_config.max_length and "token_idx" not in model_kwargs:
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" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing `max_new_tokens`."
)
# 7. determine generation mode
is_constraint_gen_mode = (
generation_config.constraints is not None or generation_config.force_words_ids is not None
)
is_contrastive_search_gen_mode = (
(generation_config.num_beams == 1)
and generation_config.top_k is not None
and generation_config.top_k > 1
and generation_config.do_sample is False
and generation_config.penalty_alpha is not None
and generation_config.penalty_alpha > 0
)
is_greedy_gen_mode = (
(generation_config.num_beams == 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is False
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_sample_gen_mode = (
(generation_config.num_beams == 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is True
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_beam_gen_mode = (
(generation_config.num_beams > 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is False
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_beam_sample_gen_mode = (
(generation_config.num_beams > 1)
and (generation_config.num_beam_groups == 1)
and generation_config.do_sample is True
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
is_group_beam_gen_mode = (
(generation_config.num_beams > 1)
and (generation_config.num_beam_groups > 1)
and not is_constraint_gen_mode
and not is_contrastive_search_gen_mode
)
if generation_config.num_beam_groups > generation_config.num_beams:
raise ValueError("`num_beam_groups` has to be smaller or equal to `num_beams`")
if is_group_beam_gen_mode and generation_config.do_sample is True:
raise ValueError(
"Diverse beam search cannot be used in sampling mode. Make sure that `do_sample` is set to `False`."
)
if streamer is not None and (generation_config.num_beams > 1):
raise ValueError(
"`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
)
if self.device.type != input_ids.device.type:
warnings.warn(
(
"You are calling .generate() with the `input_ids` being on a device type different"
f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
" Please make sure that you have put `input_ids` to the"
f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
" running `.generate()`."
),
UserWarning,
)
# 8. prepare distribution pre_processing samplers
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=inputs_tensor,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
# 9. prepare stopping criteria
stopping_criteria = self._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
if "token_idx" in model_kwargs:
if generation_config.max_new_tokens is not None:
stopping_criteria.append(StaticMaxLengthCriteria(generation_config.max_new_tokens))
else:
raise ValueError(
"You need to set `max_new_tokens` in your generation configuration to use static shapes."
)
# In lazy mode, import Habana torch to be able to add mark_step()
if lazy_mode:
import habana_frameworks.torch.core as htcore
self.htcore_generation = htcore
# 10. go into different generation modes
if is_greedy_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
" greedy search."
)
# 11. run greedy search
return self.greedy_search(
input_ids,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
lazy_mode=lazy_mode,
ignore_eos=generation_config.ignore_eos,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
elif is_contrastive_search_gen_mode:
if generation_config.num_return_sequences > 1:
raise ValueError(
f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
" contrastive search."
)
return self.contrastive_search(
input_ids,
top_k=generation_config.top_k,
penalty_alpha=generation_config.penalty_alpha,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
elif is_sample_gen_mode:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
# 12. expand input_ids with `num_return_sequences` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run sample
return self.sample(
input_ids,
logits_processor=logits_processor,
logits_warper=logits_warper,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
streamer=streamer,
lazy_mode=lazy_mode,
ignore_eos=generation_config.ignore_eos,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
elif is_beam_gen_mode:
if generation_config.num_return_sequences > generation_config.num_beams:
raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.beam_search(
input_ids,
beam_scorer,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
lazy_mode=lazy_mode,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
elif is_beam_sample_gen_mode:
# 11. prepare logits warper
logits_warper = self._get_logits_warper(generation_config)
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
# 12. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size * generation_config.num_return_sequences,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
max_length=generation_config.max_length,
)
# 13. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams * generation_config.num_return_sequences,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 14. run beam sample
return self.beam_sample(
input_ids,
beam_scorer,
logits_processor=logits_processor,
logits_warper=logits_warper,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
lazy_mode=lazy_mode,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
elif is_group_beam_gen_mode:
if generation_config.num_return_sequences > generation_config.num_beams:
raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
if generation_config.num_beams % generation_config.num_beam_groups != 0:
raise ValueError("`num_beams` should be divisible by `num_beam_groups` for group beam search.")
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
has_default_typical_p = kwargs.get("typical_p") is None and generation_config.typical_p == 1.0
if not has_default_typical_p:
raise ValueError("Decoder argument `typical_p` is not supported with beam groups.")
# 11. prepare beam search scorer
beam_scorer = BeamSearchScorer(
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
num_beam_groups=generation_config.num_beam_groups,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.group_beam_search(
input_ids,
beam_scorer,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
lazy_mode=lazy_mode,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
elif is_constraint_gen_mode:
if generation_config.num_return_sequences > generation_config.num_beams:
raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
if stopping_criteria.max_length is None:
raise ValueError("`max_length` needs to be a stopping_criteria for now.")
if generation_config.num_beams <= 1:
raise ValueError("`num_beams` needs to be greater than 1 for constrained generation.")
if generation_config.do_sample:
raise ValueError("`do_sample` needs to be false for constrained generation.")
if generation_config.num_beam_groups is not None and generation_config.num_beam_groups > 1:
raise ValueError("`num_beam_groups` not supported yet for constrained generation.")
final_constraints = []
if generation_config.constraints is not None:
final_constraints = generation_config.constraints
if generation_config.force_words_ids is not None:
def typeerror():
raise ValueError(
"`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
f"of positive integers, but is {generation_config.force_words_ids}."
)
if (
not isinstance(generation_config.force_words_ids, list)
or len(generation_config.force_words_ids) == 0
):
typeerror()
for word_ids in generation_config.force_words_ids:
if isinstance(word_ids[0], list):
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any(not isinstance(token_ids, list) for token_ids in word_ids):
typeerror()
if any(
any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
for token_ids in word_ids
):
typeerror()
constraint = DisjunctiveConstraint(word_ids)
else:
if not isinstance(word_ids, list) or len(word_ids) == 0:
typeerror()
if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
typeerror()
constraint = PhrasalConstraint(word_ids)
final_constraints.append(constraint)
# 11. prepare beam search scorer
constrained_beam_scorer = ConstrainedBeamSearchScorer(
constraints=final_constraints,
batch_size=batch_size,
num_beams=generation_config.num_beams,
device=inputs_tensor.device,
length_penalty=generation_config.length_penalty,
do_early_stopping=generation_config.early_stopping,
num_beam_hyps_to_keep=generation_config.num_return_sequences,
max_length=generation_config.max_length,
)
# 12. interleave input_ids with `num_beams` additional sequences per batch
input_ids, model_kwargs = self._expand_inputs_for_generation(
input_ids=input_ids,
expand_size=generation_config.num_beams,
is_encoder_decoder=self.config.is_encoder_decoder,
**model_kwargs,
)
# 13. run beam search
return self.constrained_beam_search(
input_ids,
constrained_beam_scorer=constrained_beam_scorer,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
pad_token_id=generation_config.pad_token_id,
eos_token_id=generation_config.eos_token_id,
output_scores=generation_config.output_scores,
return_dict_in_generate=generation_config.return_dict_in_generate,
synced_gpus=synced_gpus,
lazy_mode=lazy_mode,
profiling_warmup_steps=profiling_warmup_steps,
profiling_steps=profiling_steps,
**model_kwargs,
)
@torch.no_grad()
def contrastive_search(
self,
input_ids: torch.LongTensor,
top_k: Optional[int] = 1,
penalty_alpha: Optional[float] = 0,
logits_processor: Optional[LogitsProcessorList] = None,
logits_warper: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: Optional[bool] = False,
streamer: Optional["BaseStreamer"] = None,
lazy_mode: Optional[bool] = False,
profiling_warmup_steps: Optional[int] = 0,
profiling_steps: Optional[int] = 0,
**model_kwargs,
) -> Union[ContrastiveSearchOutput, torch.LongTensor]:
r"""
Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
<Tip warning={true}>
In most cases, you do not need to call [`~generation.GenerationMixin.contrastive_search`] directly. Use
generate() instead. For an overview of generation strategies and code examples, check the [following
guide](../generation_strategies).
</Tip>
Parameters:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The sequence used as a prompt for the generation.
top_k (`int`, *optional*, defaults to 1):
The size of the candidate set that is used to re-rank for contrastive search
penalty_alpha (`float`, *optional*, defaults to 0):
The degeneration penalty for contrastive search; activate when it is larger than 0
logits_processor (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
logits_warper (`LogitsProcessorList`, *optional*):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
to warp the prediction score distribution of the language modeling head applied before multinomial
sampling at each generation step.
stopping_criteria (`StoppingCriteriaList`, *optional*):
An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
used to tell if the generation loop should stop.
pad_token_id (`int`, *optional*):
The id of the *padding* token.
eos_token_id (`Union[int, List[int]]`, *optional*):
The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
output_attentions (`bool`, *optional*, defaults to `False`):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more details.
output_hidden_states (`bool`, *optional*, defaults to `False`):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more details.
output_scores (`bool`, *optional*, defaults to `False`):
Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
Whether or not to return a [`transformers.generationutils.ModelOutput`] instead of a plain tuple.
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
streamer (`BaseStreamer`, *optional*):
Streamer object that will be used to stream the generated sequences. Generated tokens are passed
through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
lazy_mode (`bool`, *optional*, defaults to `False`):
Whether the run is executed in lazy mode or not (i.e. eager mode).
profiling_warmup_steps (`int`, *optional*, defaults to 0):
Number of steps to ignore for profling.
profiling_steps (`int`, *optional*, defaults to 0):
Number of steps to be captured when enabling profiling.
model_kwargs:
Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
Return:
[`transformers.generation.ContrastiveSearchDecoderOnlyOutput`],
[`transformers.generation.ContrastiveSearchEncoderDecoderOutput`] or `torch.LongTensor`: A `torch.LongTensor`
containing the generated tokens (default behaviour) or a
[`transformers.generation.ContrastiveSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
`return_dict_in_generate=True` or a [`transformers.generation.ContrastiveSearchEncoderDecoderOutput`] if
`model.config.is_encoder_decoder=True`.
Examples:
```python
>>> from transformers import (
... AutoTokenizer,
... AutoModelForCausalLM,
... StoppingCriteriaList,
... MaxLengthCriteria,
... )
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")
>>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
>>> model.config.pad_token_id = model.config.eos_token_id
>>> input_prompt = "DeepMind Company is"
>>> input_ids = tokenizer(input_prompt, return_tensors="pt")
>>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=64)])
>>> outputs = model.contrastive_search(
... **input_ids, penalty_alpha=0.6, top_k=4, stopping_criteria=stopping_criteria
... )
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['DeepMind Company is a company that focuses on the development and commercialization of artificial intelligence (AI). DeepMind鈥檚 mission is to help people understand and solve problems that are difficult to solve in the world today.\n\nIn this post, we talk about the benefits of deep learning in business and how it']
```"""
raise NotImplementedError("Contrastive search is not supported by optimum-habana yet.")
def greedy_search(
self,
input_ids: torch.LongTensor,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
max_length: Optional[int] = None,
pad_token_id: Optional[int] = None,
eos_token_id: Optional[Union[int, List[int]]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_scores: Optional[bool] = None,
return_dict_in_generate: Optional[bool] = None,
synced_gpus: Optional[bool] = False,
streamer: Optional["BaseStreamer"] = None,
lazy_mode: Optional[bool] = False,
ignore_eos: Optional[bool] = None,
profiling_warmup_steps: Optional[int] = 0,
profiling_steps: Optional[int] = 0,
**model_kwargs,
) -> Union[GreedySearchOutput, torch.LongTensor]: