OPT · Hugging Face (original) (raw)

This model was released on 2022-05-02 and added to Hugging Face Transformers on 2022-05-12.

OPT is a suite of open-source decoder-only pre-trained transformers whose parameters range from 125M to 175B. OPT models are designed for causal language modeling and aim to enable responsible and reproducible research at scale. OPT-175B is comparable in performance to GPT-3 with only 1/7th the carbon footprint.

You can find all the original OPT checkpoints under the OPT collection.

This model was contributed by ArthurZ, ybelkada, and patrickvonplaten.

Click on the OPT models in the right sidebar for more examples of how to apply OPT to different language tasks.

The example below demonstrates how to generate text with Pipeline, AutoModel, and from the command line.

from transformers import pipeline

pipeline = pipeline(task="text-generation", model="facebook/opt-125m", device=0) pipeline("Once upon a time, in a land far, far away,", max_length=50, num_return_sequences=1)

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the weights to 8-bits.

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(load_in_8bit=True) model = AutoModelForCausalLM.from_pretrained("facebook/opt-13b", attn_implementation="sdpa", quantization_config=bnb_config, device_map="auto") tokenizer = AutoTokenizer.from_pretrained("facebook/opt-13b")

prompt = ("Once upon a time, in a land far, far away, ")

model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)

generated_ids = model.generate(**model_inputs, max_new_tokens=30, do_sample=False) tokenizer.batch_decode(generated_ids)[0]

Notes

• OPT adds an EOS token </s> to the beginning of every prompt.

Resources

• Refer to this notebook for an example of fine-tuning OPT with PEFT, bitsandbytes, and Transformers.
• The How 🤗 Accelerate runs very large models thanks to PyTorch blog post demonstrates how to run OPT for inference.

OPTConfig

class transformers.OPTConfig

< source >

( transformers_version: str | None = None architectures: list[str] | None = None output_hidden_states: bool | None = False return_dict: bool | None = True dtype: typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None chunk_size_feed_forward: int = 0 is_encoder_decoder: bool = False id2label: dict[int, str] | dict[str, str] | None = None label2id: dict[str, int] | dict[str, str] | None = None problem_type: typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None vocab_size: int = 50272 hidden_size: int = 768 num_hidden_layers: int = 12 ffn_dim: int = 3072 max_position_embeddings: int = 2048 do_layer_norm_before: bool = True _remove_final_layer_norm: bool = False word_embed_proj_dim: int | None = None dropout: float | int = 0.1 attention_dropout: float | int = 0.0 num_attention_heads: int = 12 activation_function: str = 'relu' layerdrop: float | int = 0.0 init_std: float = 0.02 use_cache: bool = True pad_token_id: int | None = 1 bos_token_id: int | None = 2 eos_token_id: int | list[int] | None = 2 enable_bias: bool = True layer_norm_elementwise_affine: bool = True tie_word_embeddings: bool = True )

Parameters

• vocab_size (int, optional, defaults to 50272) — Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.
• hidden_size (int, optional, defaults to 768) — Dimension of the hidden representations.
• num_hidden_layers (int, optional, defaults to 12) — Number of hidden layers in the Transformer decoder.
• ffn_dim (int, optional, defaults to 3072) — Dimension of the MLP representations.
• max_position_embeddings (int, optional, defaults to 2048) — The maximum sequence length that this model might ever be used with.
• do_layer_norm_before (bool, optional, defaults to True) — Whether to perform layer normalization before the attention block.
• word_embed_proj_dim (int, optional) —word_embed_proj_dim can be set to down-project word embeddings, e.g. opt-350m. Defaults tohidden_size.
• dropout (Union[float, int], optional, defaults to 0.1) — The ratio for all dropout layers.
• attention_dropout (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
• num_attention_heads (int, optional, defaults to 12) — Number of attention heads for each attention layer in the Transformer decoder.
• activation_function (str, optional, defaults to relu) — The non-linear activation function (function or string) in the decoder. For example, "gelu","relu", "silu", etc.
• layerdrop (Union[float, int], optional, defaults to 0.0) — The LayerDrop probability. See the [LayerDrop paper](see https://huggingface.co/papers/1909.11556) for more details.
• init_std (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
• use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True or when the model is a decoder-only generative model.
• pad_token_id (int, optional, defaults to 1) — Token id used for padding in the vocabulary.
• bos_token_id (int, optional, defaults to 2) — Token id used for beginning-of-stream in the vocabulary.
• eos_token_id (Union[int, list[int]], optional, defaults to 2) — Token id used for end-of-stream in the vocabulary.
• enable_bias (bool, optional, defaults to True) — Whether or not if the linear layers in the attention blocks should use the bias term.
• layer_norm_elementwise_affine (bool, optional, defaults to True) — Whether or not if the layer norms should have learnable parameters.
• tie_word_embeddings (bool, optional, defaults to True) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.

This is the configuration class to store the configuration of a OPTModel. It is used to instantiate a Opt model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the facebook/opt-350m

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

Example:

from transformers import OPTConfig, OPTModel

configuration = OPTConfig()

model = OPTModel(configuration)

configuration = model.config

OPTModel

class transformers.OPTModel

< source >

( config: OPTConfig )

Parameters

• config (OPTConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.

The bare Opt Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None use_cache: bool | None = None position_ids: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → BaseModelOutputWithPast or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_valuesreturned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.
  Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.
  The model will output the same cache format that is fed as input.
  If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_idsof shape (batch_size, sequence_length).
• inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?

Returns

BaseModelOutputWithPast or tuple(torch.FloatTensor)

A BaseModelOutputWithPast or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (OPTConfig) and inputs.

The OPTModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
  If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.
  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally ifconfig.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_valuesinput) to speed up sequential decoding.
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

OPTForCausalLM

class transformers.OPTForCausalLM

< source >

( config )

forward

< source >

( input_ids: torch.LongTensor | None = None attention_mask: torch.Tensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None use_cache: bool | None = None position_ids: torch.LongTensor | None = None logits_to_keep: int | torch.Tensor = 0 **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → CausalLMOutputWithPast or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_valuesreturned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.
  Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.
  The model will output the same cache format that is fed as input.
  If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_idsof shape (batch_size, sequence_length).
• inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
• labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?
• logits_to_keep (Union[int, torch.Tensor], optional, defaults to 0) — If an int, compute logits for the last logits_to_keep tokens. If 0, calculate logits for allinput_ids (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

CausalLMOutputWithPast or tuple(torch.FloatTensor)

A CausalLMOutputWithPast or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (OPTConfig) and inputs.

The OPTForCausalLM forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).
• logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.
  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (seepast_key_values input) to speed up sequential decoding.
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

from transformers import AutoTokenizer, OPTForCausalLM

model = OPTForCausalLM.from_pretrained("facebook/opt-350m") tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")

prompt = "Hey, are you conscious? Can you talk to me?" inputs = tokenizer(prompt, return_tensors="pt")

generate_ids = model.generate(inputs.input_ids, max_length=30) tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."

OPTForSequenceClassification

class transformers.OPTForSequenceClassification

< source >

( config: OPTConfig )

Parameters

• config (OPTConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.

The OPT Model transformer with a sequence classification head on top (linear layer).

OPTForSequenceClassification uses the last token in order to do the classification, as other causal models (e.g. GPT-2) do.

Since it does classification on the last token, it requires to know the position of the last token. If apad_token_id is defined in the configuration, it finds the last token that is not a padding token in each row. If no pad_token_id is defined, it simply takes the last value in each row of the batch. Since it cannot guess the padding tokens when inputs_embeds are passed instead of input_ids, it does the same (take the last value in each row of the batch).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None labels: torch.LongTensor | None = None use_cache: bool | None = None position_ids: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_valuesreturned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.
  Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.
  The model will output the same cache format that is fed as input.
  If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_idsof shape (batch_size, sequence_length).
• inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
• labels (torch.LongTensor of shape (batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1 a classification loss is computed (Cross-Entropy).
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?

Returns

SequenceClassifierOutputWithPast or tuple(torch.FloatTensor)

A SequenceClassifierOutputWithPast or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (OPTConfig) and inputs.

The OPTForSequenceClassification forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Classification (or regression if config.num_labels==1) loss.
• logits (torch.FloatTensor of shape (batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax).
• past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.
  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (seepast_key_values input) to speed up sequential decoding.
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example of single-label classification:

import torch from transformers import AutoTokenizer, OPTForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = OPTForSequenceClassification.from_pretrained("facebook/opt-350m")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

with torch.no_grad(): ... logits = model(**inputs).logits

predicted_class_id = logits.argmax().item() model.config.id2label[predicted_class_id] ...

num_labels = len(model.config.id2label) model = OPTForSequenceClassification.from_pretrained("facebook/opt-350m", num_labels=num_labels)

labels = torch.tensor([1]) loss = model(**inputs, labels=labels).loss round(loss.item(), 2) ...

Example of multi-label classification:

import torch from transformers import AutoTokenizer, OPTForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") model = OPTForSequenceClassification.from_pretrained("facebook/opt-350m", problem_type="multi_label_classification")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")

with torch.no_grad(): ... logits = model(**inputs).logits

predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

num_labels = len(model.config.id2label) model = OPTForSequenceClassification.from_pretrained( ... "facebook/opt-350m", num_labels=num_labels, problem_type="multi_label_classification" ... )

labels = torch.sum( ... torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1 ... ).to(torch.float) loss = model(**inputs, labels=labels).loss

OPTForQuestionAnswering

class transformers.OPTForQuestionAnswering

< source >

( config: OPTConfig )

Parameters

• config (OPTConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out thefrom_pretrained() method to load the model weights.

The Opt transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< source >

( input_ids: torch.LongTensor | None = None attention_mask: torch.FloatTensor | None = None past_key_values: transformers.cache_utils.Cache | None = None inputs_embeds: torch.FloatTensor | None = None start_positions: torch.LongTensor | None = None end_positions: torch.LongTensor | None = None use_cache: bool | None = None position_ids: torch.LongTensor | None = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.
  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.
  What are input IDs?
• attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
  • 1 for tokens that are not masked,
  • 0 for tokens that are masked.
    What are attention masks?
• past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_valuesreturned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.
  Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.
  The model will output the same cache format that is fed as input.
  If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_idsof shape (batch_size, sequence_length).
• inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
• start_positions (torch.LongTensor of shape (batch_size,), optional) — Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
• end_positions (torch.LongTensor of shape (batch_size,), optional) — Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.
• use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].
  What are position IDs?

Returns

QuestionAnsweringModelOutput or tuple(torch.FloatTensor)

A QuestionAnsweringModelOutput or a tuple oftorch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (OPTConfig) and inputs.

The OPTForQuestionAnswering forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Moduleinstance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
• start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-start scores (before SoftMax).
• end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) — Span-end scores (before SoftMax).
• hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
• attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

Example:

from transformers import AutoTokenizer, OPTForQuestionAnswering import torch

torch.manual_seed(4) tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")

model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"

inputs = tokenizer(question, text, return_tensors="pt") with torch.no_grad(): ... outputs = model(**inputs)

answer_start_index = outputs.start_logits.argmax() answer_end_index = outputs.end_logits.argmax()

answer_offset = len(tokenizer(question)[0])

predict_answer_tokens = inputs.input_ids[ ... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1 ... ] predicted = tokenizer.decode(predict_answer_tokens) predicted ' a nice puppet'

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