pyvene.models.backpack_gpt2.modelings_backpack_gpt2.BackpackGPT2LMHeadModel

class BackpackGPT2LMHeadModel(config)

Bases: BackpackGPT2PreTrainedModel

__init__(config)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(config)	Initialize internal Module state, shared by both nn.Module and ScriptModule.
active_adapter()
active_adapters()	If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
add_adapter(adapter_config[, adapter_name])	If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
add_memory_hooks()	Add a memory hook before and after each sub-module forward pass to record increase in memory consumption.
add_model_tags(tags)	Add custom tags into the model that gets pushed to the Hugging Face Hub.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
can_generate()	Returns whether this model can generate sequences with .generate().
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
compute_transition_scores(sequences, scores)	Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was used).
cpu()	Move all model parameters and buffers to the CPU.
create_extended_attention_mask_for_decoder(...)
cuda([device])	Move all model parameters and buffers to the GPU.
dequantize()	Potentially dequantize the model in case it has been quantized by a quantization method that support dequantization.
disable_adapters()	If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
disable_input_require_grads()	Removes the _require_grads_hook.
double()	Casts all floating point parameters and buffers to double datatype.
enable_adapters()	If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
enable_input_require_grads()	Enables the gradients for the input embeddings.
estimate_tokens(input_dict)	Helper function to estimate the total number of tokens from the model inputs.
eval()	Set the module in evaluation mode.
extra_repr()	Set the extra representation of the module.
float(*args)	Casts all floating point parameters and buffers to float datatype.
floating_point_ops(input_dict[, ...])	Get number of (optionally, non-embeddings) floating-point operations for the forward and backward passes of a batch with this transformer model.
forward(input_ids[, position_ids])	Define the computation performed at every call.
from_pretrained(...[, config, cache_dir, ...])	Instantiate a pretrained pytorch model from a pre-trained model configuration.
generate([inputs, generation_config, ...])	Generates sequences of token ids for models with a language modeling head.
get_adapter_state_dict([adapter_name])	If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extended_attention_mask(attention_mask, ...)	Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_head_mask(head_mask, num_hidden_layers)	Prepare the head mask if needed.
get_input_embeddings()	Returns the model's input embeddings.
get_lm_head()
get_memory_footprint([return_buffers])	Get the memory footprint of a model.
get_output_embeddings()	Returns the model's output embeddings.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_position_embeddings()
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
gradient_checkpointing_disable()	Deactivates gradient checkpointing for the current model.
gradient_checkpointing_enable([...])	Activates gradient checkpointing for the current model.
half(*args)	Casts all floating point parameters and buffers to half datatype.
heal_tokens(input_ids[, tokenizer])	Generates sequences of token ids for models with a language modeling head. Parameters: input_ids (torch.LongTensor): The sequence used as a prompt for the generation. tokenizer (PreTrainedTokenizerBase, optional): The tokenizer used to decode the input ids. Return: torch.LongTensor where each sequence has its tail token replaced with its appropriate extension.
init_weights()	If needed prunes and maybe initializes weights.
invert_attention_mask(encoder_attention_mask)	Invert an attention mask (e.g., switches 0.
ipu([device])	Move all model parameters and buffers to the IPU.
load_adapter([peft_model_id, adapter_name, ...])	Load adapter weights from file or remote Hub folder.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
load_tf_weights(config, gpt2_checkpoint_path)	Load tf checkpoints in a pytorch model
modules()	Return an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
num_parameters([only_trainable, ...])	Get number of (optionally, trainable or non-embeddings) parameters in the module.
parameters([recurse])	Return an iterator over module parameters.
post_init()	A method executed at the end of each Transformer model initialization, to execute code that needs the model's modules properly initialized (such as weight initialization).
prepare_inputs_for_generation(*args, **kwargs)
prune_heads(heads_to_prune)	Prunes heads of the base model.
push_to_hub(repo_id[, use_temp_dir, ...])	Upload the model file to the 🤗 Model Hub.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_for_auto_class([auto_class])	Register this class with a given auto class.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
reset_memory_hooks_state()	Reset the mem_rss_diff attribute of each module (see [~modeling_utils.ModuleUtilsMixin.add_memory_hooks]).
resize_position_embeddings(...)
resize_token_embeddings([new_num_tokens, ...])	Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
retrieve_modules_from_names(names[, ...])
reverse_bettertransformer()	Reverts the transformation from [~PreTrainedModel.to_bettertransformer] so that the original modeling is used, for example in order to save the model.
run_with_custom_contextualization(input_ids, ...)
save_pretrained(save_directory[, ...])	Save a model and its configuration file to a directory, so that it can be re-loaded using the [~PreTrainedModel.from_pretrained] class method.
set_adapter(adapter_name)	If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_input_embeddings(value)	Set model's input embeddings.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
tie_weights()	Tie the weights between the input embeddings and the output embeddings.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_bettertransformer()	Converts the model to use [PyTorch's native attention implementation](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html), integrated to Transformers through [Optimum library](https://huggingface.co/docs/optimum/bettertransformer/overview).
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
warn_if_padding_and_no_attention_mask(...)	Shows a one-time warning if the input_ids appear to contain padding and no attention mask was given.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
base_model	torch.nn.Module: The main body of the model.
base_model_prefix
call_super_init
device	torch.device: The device on which the module is (assuming that all the module parameters are on the same device).
dtype	torch.dtype: The dtype of the module (assuming that all the module parameters have the same dtype).
dummy_inputs	Dict[str, torch.Tensor]: Dummy inputs to do a forward pass in the network.
dump_patches
framework	str: Identifies that this is a PyTorch model.
is_gradient_checkpointing	Whether gradient checkpointing is activated for this model or not.
is_parallelizable
main_input_name
model_tags
supports_gradient_checkpointing
training

forward(input_ids, position_ids=None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

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 registered hooks while the latter silently ignores them.

tie_weights()

Tie the weights between the input embeddings and the output embeddings.

If the torchscript flag is set in the configuration, can’t handle parameter sharing so we are cloning the weights instead.