GPT-J
Overview
The GPT-J model was released in the kingoflolz/mesh-transformer-jax repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like causal language model trained on the Pile dataset.
This model was contributed by Stella Biderman.
Tips:
- To load GPT-J in float32 one would need at least 2x model size CPU
RAM: 1x for initial weights and another 1x to load the checkpoint. So for GPT-J it would take at least 48GB of CPU
RAM to just load the model. To reduce the CPU RAM usage there are a few options. The
torch_dtypeargument can be used to initialize the model in half-precision. And thelow_cpu_mem_usageargument can be used to keep the RAM usage to 1x. There is also a fp16 branch which stores the fp16 weights, which could be used to further minimize the RAM usage. Combining all this it should take roughly 12.1GB of CPU RAM to load the model.
>>> from transformers import GPTJForCausalLM
>>> import torch
>>> model = GPTJForCausalLM.from_pretrained(
... "EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True
... )The model should fit on 16GB GPU for inference. For training/fine-tuning it would take much more GPU RAM. Adam optimizer for example makes four copies of the model: model, gradients, average and squared average of the gradients. So it would need at least 4x model size GPU memory, even with mixed precision as gradient updates are in fp32. This is not including the activations and data batches, which would again require some more GPU RAM. So one should explore solutions such as DeepSpeed, to train/fine-tune the model. Another option is to use the original codebase to train/fine-tune the model on TPU and then convert the model to Transformers format for inference. Instructions for that could be found here
Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. These extra tokens are added for the sake of efficiency on TPUs. To avoid the mis-match between embedding matrix size and vocab size, the tokenizer for GPT-J contains 143 extra tokens
<|extratoken_1|>... <|extratoken_143|>, so thevocab_sizeof tokenizer also becomes 50400.
Generation
The generate() method can be used to generate text using GPT-J model.
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> prompt = (
... "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
... "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
... "researchers was the fact that the unicorns spoke perfect English."
... )
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
>>> gen_tokens = model.generate(
... input_ids,
... do_sample=True,
... temperature=0.9,
... max_length=100,
... )
>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]…or in float16 precision:
>>> from transformers import GPTJForCausalLM, AutoTokenizer
>>> import torch
>>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> prompt = (
... "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
... "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
... "researchers was the fact that the unicorns spoke perfect English."
... )
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
>>> gen_tokens = model.generate(
... input_ids,
... do_sample=True,
... temperature=0.9,
... max_length=100,
... )
>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]GPTJConfig
class transformers.GPTJConfig
< source >( vocab_size = 50400 n_positions = 2048 n_embd = 4096 n_layer = 28 n_head = 16 rotary_dim = 64 n_inner = None activation_function = 'gelu_new' resid_pdrop = 0.0 embd_pdrop = 0.0 attn_pdrop = 0.0 layer_norm_epsilon = 1e-05 initializer_range = 0.02 scale_attn_weights = True use_cache = True bos_token_id = 50256 eos_token_id = 50256 tie_word_embeddings = False **kwargs )
Parameters
-
vocab_size (
int, optional, defaults to 50400) — Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by theinputs_idspassed when calling GPTJModel. -
n_positions (
int, optional, defaults to 2048) — The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). -
n_embd (
int, optional, defaults to 4096) — Dimensionality of the embeddings and hidden states. -
n_layer (
int, optional, defaults to 28) — Number of hidden layers in the Transformer encoder. -
n_head (
int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder. -
rotary_dim (
int, optional, defaults to 64) — Number of dimensions in the embedding that Rotary Position Embedding is applied to. -
n_inner (
int, optional, defaults to None) — Dimensionality of the inner feed-forward layers.Nonewill set it to 4 times n_embd -
activation_function (
str, optional, defaults to"gelu_new") — Activation function, to be selected in the list["relu", "silu", "gelu", "tanh", "gelu_new"]. -
resid_pdrop (
float, optional, defaults to 0.1) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. -
embd_pdrop (
int, optional, defaults to 0.1) — The dropout ratio for the embeddings. -
attn_pdrop (
float, optional, defaults to 0.1) — The dropout ratio for the attention. -
layer_norm_epsilon (
float, optional, defaults to 1e-5) — The epsilon to use in the layer normalization layers. -
initializer_range (
float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices. -
scale_attn_weights (
bool, optional, defaults toTrue) — Scale attention weights by dividing by sqrt(hidden_size). -
use_cache (
bool, optional, defaults toTrue) — Whether or not the model should return the last key/values attentions (not used by all models).
This is the configuration class to store the configuration of a GPTJModel. It is used to instantiate a GPT-J 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 GPT-J gpt-j-6B architecture. 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 GPTJModel, GPTJConfig
>>> # Initializing a GPT-J 6B configuration
>>> configuration = GPTJConfig()
>>> # Initializing a model from the configuration
>>> model = GPTJModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.configGPTJModel
class transformers.GPTJModel
< source >( config )
Parameters
- config (GPTJConfig) — 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 the from_pretrained() method to load the model weights.
The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch torch.nn.Module sub-class. 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 = None
past_key_values = None
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
inputs_embeds = None
use_cache = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using
GPTJTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. -
attention_mask (
torch.FloatTensorof 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.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof 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]. -
head_mask (
torch.FloatTensorof shape(num_attention_heads,)or(n_layer, num_attention_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_dim), optional) — Optionally, instead of passinginput_idsyou 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. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.BaseModelOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.BaseModelOutputWithPast or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPTJConfig) and inputs.
-
last_hidden_state (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.If
past_key_valuesis used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size)is output. -
past_key_values (
tuple(tuple(torch.FloatTensor)), optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — Tuple oftuple(torch.FloatTensor)of lengthconfig.n_layers, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally ifconfig.is_encoder_decoder=True2 additional tensors of shape(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
config.is_encoder_decoder=Truein the cross-attention blocks) that can be used (seepast_key_valuesinput) to speed up sequential decoding. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings + 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 initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
The GPTJModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import GPT2Tokenizer, GPTJModel
>>> import torch
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> model = GPTJModel.from_pretrained("EleutherAI/gpt-j-6B")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_stateGPTJForCausalLM
class transformers.GPTJForCausalLM
< source >( config )
Parameters
- config (GPTJConfig) — 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 the from_pretrained() method to load the model weights.
The GPT-J Model transformer with a language modeling head on top.
This model is a PyTorch torch.nn.Module sub-class. 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 = None
past_key_values = None
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
inputs_embeds = None
labels = None
use_cache = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using
GPTJTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. -
attention_mask (
torch.FloatTensorof 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.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof 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]. -
head_mask (
torch.FloatTensorof shape(num_attention_heads,)or(n_layer, num_attention_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_dim), optional) — Optionally, instead of passinginput_idsyou 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. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
labels (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Labels for language modeling. Note that the labels are shifted inside the model, i.e. you can setlabels = input_idsIndices are selected in[-100, 0, ..., config.vocab_size]All labels set to-100are ignored (masked), the loss is only computed for labels in[0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.CausalLMOutputWithPast or tuple(torch.FloatTensor)
A transformers.modeling_outputs.CausalLMOutputWithPast or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPTJConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Language modeling loss (for next-token prediction). -
logits (
torch.FloatTensorof 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 (
tuple(tuple(torch.FloatTensor)), optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — Tuple oftuple(torch.FloatTensor)of lengthconfig.n_layers, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head))Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_valuesinput) to speed up sequential decoding. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings + 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 initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
The GPTJForCausalLM forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> import torch
>>> from transformers import GPT2Tokenizer, GPTJForCausalLM
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs, labels=inputs["input_ids"])
>>> loss = outputs.loss
>>> logits = outputs.logitsGPTJForSequenceClassification
class transformers.GPTJForSequenceClassification
< source >( config )
Parameters
- config (GPTJConfig) — 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 the from_pretrained() method to load the model weights.
The GPT-J Model transformer with a sequence classification head on top (linear layer).
GPTJForSequenceClassification uses the last token in order to do the classification, as other causal models (e.g. GPT, GPT-2, GPT-Neo) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
pad_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 is a PyTorch torch.nn.Module sub-class. 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 = None
past_key_values = None
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
inputs_embeds = None
labels = None
use_cache = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.modeling_outputs.SequenceClassifierOutputWithPastor tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using
GPTJTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. -
attention_mask (
torch.FloatTensorof 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.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof 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]. -
head_mask (
torch.FloatTensorof shape(num_attention_heads,)or(n_layer, num_attention_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_dim), optional) — Optionally, instead of passinginput_idsyou 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. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
labels (
torch.LongTensorof shape(batch_size,), optional) — Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]. Ifconfig.num_labels == 1a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1a classification loss is computed (Cross-Entropy).
Returns
transformers.modeling_outputs.SequenceClassifierOutputWithPastor tuple(torch.FloatTensor)
A transformers.modeling_outputs.SequenceClassifierOutputWithPastor a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPTJConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Classification (or regression if config.num_labels==1) loss. -
logits (
torch.FloatTensorof shape(batch_size, config.num_labels)) — Classification (or regression if config.num_labels==1) scores (before SoftMax). -
past_key_values (
tuple(tuple(torch.FloatTensor)), optional, returned whenuse_cache=Trueis passed or whenconfig.use_cache=True) — Tuple oftuple(torch.FloatTensor)of lengthconfig.n_layers, with each tuple having 2 tensors of shape(batch_size, num_heads, sequence_length, embed_size_per_head))Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
past_key_valuesinput) to speed up sequential decoding. -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings + 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 initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
The GPTJForSequenceClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example of single-label classification:
>>> from transformers import GPT2Tokenizer, GPTJForSequenceClassification
>>> import torch
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> model = GPTJForSequenceClassification.from_pretrained("EleutherAI/gpt-j-6B")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logitsExample of multi-label classification:
>>> from transformers import GPT2Tokenizer, GPTJForSequenceClassification
>>> import torch
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> model = GPTJForSequenceClassification.from_pretrained("EleutherAI/gpt-j-6B", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([[1, 1]], dtype=torch.float) # need dtype=float for BCEWithLogitsLoss
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logitsGPTJForQuestionAnswering
class transformers.GPTJForQuestionAnswering
< source >( config )
Parameters
- config (GPTJConfig) — 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 the from_pretrained() method to load the model weights.
The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).
This model is a PyTorch torch.nn.Module sub-class. 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 = None
attention_mask = None
token_type_ids = None
position_ids = None
head_mask = None
inputs_embeds = None
start_positions = None
end_positions = None
output_attentions = None
output_hidden_states = None
return_dict = None
)
→
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
torch.LongTensorof shape(batch_size, sequence_length)) — Indices of input sequence tokens in the vocabulary.Indices can be obtained using
GPTJTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. -
attention_mask (
torch.FloatTensorof 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.
-
token_type_ids (
torch.LongTensorof shape(batch_size, sequence_length), optional) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in[0, 1]:- 0 corresponds to a sentence A token,
- 1 corresponds to a sentence B token.
-
position_ids (
torch.LongTensorof 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]. -
head_mask (
torch.FloatTensorof shape(num_attention_heads,)or(n_layer, num_attention_heads), optional) — Mask to nullify selected heads of the self-attention modules. Mask values selected in[0, 1]:- 1 indicates the head is not masked,
- 0 indicates the head is masked.
-
inputs_embeds (
torch.FloatTensorof shape(batch_size, sequence_length, hidden_dim), optional) — Optionally, instead of passinginput_idsyou 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. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple. -
start_positions (
torch.LongTensorof 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.LongTensorof 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.
Returns
transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPTJConfig) and inputs.
-
loss (
torch.FloatTensorof shape(1,), optional, returned whenlabelsis provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. -
start_logits (
torch.FloatTensorof shape(batch_size, sequence_length)) — Span-start scores (before SoftMax). -
end_logits (
torch.FloatTensorof shape(batch_size, sequence_length)) — Span-end scores (before SoftMax). -
hidden_states (
tuple(torch.FloatTensor), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor(one for the output of the embeddings + 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 initial embedding outputs.
-
attentions (
tuple(torch.FloatTensor), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple oftorch.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.
The GPTJForQuestionAnswering forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import GPT2Tokenizer, GPTJForQuestionAnswering
>>> import torch
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
>>> model = GPTJForQuestionAnswering.from_pretrained("EleutherAI/gpt-j-6B")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])
>>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logitsFlaxGPTJModel
class transformers.FlaxGPTJModel
< source >( config: GPTJConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax._src.numpy.lax_numpy.float32'> **kwargs )
Parameters
- config (GPTJConfig) — 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 the from_pretrained() method to load the model weights.
-
dtype (
jax.numpy.dtype, optional, defaults tojax.numpy.float32) — The data type of the computation. Can be one ofjax.numpy.float32,jax.numpy.float16(on GPUs) andjax.numpy.bfloat16(on TPUs).This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given
dtype.Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and to_bf16().
The bare GPTJ Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. 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 Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__
< source >(
input_ids
attention_mask = None
position_ids = None
params: dict = None
past_key_values: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
numpy.ndarrayof shape(batch_size, input_ids_length)) —input_ids_length=sequence_length. Indices of input sequence tokens in the vocabulary.Indices can be obtained using
GPTJTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. -
attention_mask (
numpy.ndarrayof 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.
-
position_ids (
numpy.ndarrayof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
past_key_values (
Dict[str, np.ndarray], optional, returned byinit_cacheor when passing previouspast_key_values) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length]. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPTJConfig) and inputs.
-
logits (
jnp.ndarrayof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
hidden_states (
tuple(jnp.ndarray), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple ofjnp.ndarray(one for the output of the embeddings + 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 initial embedding outputs.
-
attentions (
tuple(jnp.ndarray), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple ofjnp.ndarray(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.
The FlaxGPTJPreTrainedModelforward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import GPTJTokenizer, FlaxGPTJModel
>>> tokenizer = GPTJTokenizer.from_pretrained("gptj")
>>> model = FlaxGPTJModel.from_pretrained("gptj")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_stateFlaxGPTJForCausalLM
class transformers.FlaxGPTJForCausalLM
< source >( config: GPTJConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax._src.numpy.lax_numpy.float32'> **kwargs )
Parameters
- config (GPTJConfig) — 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 the from_pretrained() method to load the model weights.
-
dtype (
jax.numpy.dtype, optional, defaults tojax.numpy.float32) — The data type of the computation. Can be one ofjax.numpy.float32,jax.numpy.float16(on GPUs) andjax.numpy.bfloat16(on TPUs).This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given
dtype.Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and to_bf16().
The GPTJ Model transformer with a language modeling head on top.
This model inherits from FlaxPreTrainedModel. 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 Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__
< source >(
input_ids
attention_mask = None
position_ids = None
params: dict = None
past_key_values: dict = None
dropout_rng: PRNGKey = None
train: bool = False
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
Parameters
-
input_ids (
numpy.ndarrayof shape(batch_size, input_ids_length)) —input_ids_length=sequence_length. Indices of input sequence tokens in the vocabulary.Indices can be obtained using
GPTJTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. -
attention_mask (
numpy.ndarrayof 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.
-
position_ids (
numpy.ndarrayof shape(batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.max_position_embeddings - 1]. -
past_key_values (
Dict[str, np.ndarray], optional, returned byinit_cacheor when passing previouspast_key_values) — Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length]. -
output_attentions (
bool, optional) — Whether or not to return the attentions tensors of all attention layers. Seeattentionsunder returned tensors for more detail. -
output_hidden_states (
bool, optional) — Whether or not to return the hidden states of all layers. Seehidden_statesunder returned tensors for more detail. -
return_dict (
bool, optional) — Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxMaskedLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.FlaxMaskedLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (GPTJConfig) and inputs.
-
logits (
jnp.ndarrayof shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). -
hidden_states (
tuple(jnp.ndarray), optional, returned whenoutput_hidden_states=Trueis passed or whenconfig.output_hidden_states=True) — Tuple ofjnp.ndarray(one for the output of the embeddings + 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 initial embedding outputs.
-
attentions (
tuple(jnp.ndarray), optional, returned whenoutput_attentions=Trueis passed or whenconfig.output_attentions=True) — Tuple ofjnp.ndarray(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.
The FlaxGPTJPreTrainedModelforward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
>>> from transformers import GPTJTokenizer, FlaxGPTJForCausalLM
>>> tokenizer = GPTJTokenizer.from_pretrained("gptj")
>>> model = FlaxGPTJForCausalLM.from_pretrained("gptj")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
>>> outputs = model(**inputs)
>>> # retrieve logts for next token
>>> next_token_logits = outputs.logits[:, -1]