backprop.models

Submodules

backprop.models.auto_model

class AutoModel[source]

Bases: object

static from_pretrained(model_name: str, aliases: Optional[Dict] = None, device: Optional[str] = None)[source]

Loads a model by name

Parameters

  • model_name – unique name of the model

  • aliases – dictionary that maps aliases to model_name

  • device – device to use model on. Defaults to “cuda” if available

Returns

Initialised model object

Example:

import backprop

model = backprop.models.AutoModel.from_pretrained("t5_small")
static list_models(task=None, return_dict=False, display=False, limit=None, aliases: Optional[Dict] = None)[source]

Lists available models

Parameters

  • task – filter by task identifier

  • return_dict – whether to return dictionary instead of a list

  • display – print instead of returning

  • limit – maximum number of models to include

  • aliases – dict that maps aliases to model_name

Example:

import backprop
backprop.models.AutoModel.list_models(task="text-vectorisation", display=True)

> Name         clip
  Description  Model by OpenAI
  ...

backprop.models.generic_models

class BaseModel(model, name: Optional[str] = None, description: Optional[str] = None, tasks: Optional[List[str]] = None, details: Optional[Dict] = None)[source]

Bases: torch.nn.modules.module.Module

The base class for a model.

model

Your model that takes some args, kwargs and returns an output. Must be callable.

name

string identifier for the model. Lowercase letters and numbers. No spaces/special characters except dashes.

description

String description of the model.

tasks

List of supported task strings

details

Dictionary of additional details about the model

eval()[source]

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

This is equivalent with self.train(False).

Returns

self

Return type

Module

finetune(*args, **kwargs)[source]
num_parameters()[source]
to(device)[source]

Moves and/or casts the parameters and buffers.

This can be called as

to(device=None, dtype=None, non_blocking=False)[source]
to(dtype, non_blocking=False)[source]
to(tensor, non_blocking=False)[source]
to(memory_format=torch.channels_last)[source]

Its signature is similar to torch.Tensor.to(), but only accepts floating point desired dtype s. In addition, this method will only cast the floating point parameters and buffers to dtype (if given). The integral parameters and buffers will be moved device, if that is given, but with dtypes unchanged. When non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.

See below for examples.

Note

This method modifies the module in-place.

Parameters

  • device (torch.device) – the desired device of the parameters and buffers in this module

  • dtype (torch.dtype) – the desired floating point type of the floating point parameters and buffers in this module

  • tensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module

  • memory_format (torch.memory_format) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)

Returns

self

Return type

Module

Example:

>>> linear = nn.Linear(2, 2)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]])
>>> linear.to(torch.double)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1913, -0.3420],
        [-0.5113, -0.2325]], dtype=torch.float64)
>>> gpu1 = torch.device("cuda:1")
>>> linear.to(gpu1, dtype=torch.half, non_blocking=True)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')
>>> cpu = torch.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight
Parameter containing:
tensor([[ 0.1914, -0.3420],
        [-0.5112, -0.2324]], dtype=torch.float16)
train(mode: bool = True)[source]

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc.

Parameters

mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.

Returns

self

Return type

Module

training: bool
class HFModel(model_path, tokenizer_path=None, name: Optional[str] = None, description: Optional[str] = None, tasks: Optional[List[str]] = None, details: Optional[Dict] = None, model_class=<class 'transformers.models.auto.modeling_auto.AutoModelForPreTraining'>, tokenizer_class=<class 'transformers.models.auto.tokenization_auto.AutoTokenizer'>, device=None)[source]

Bases: backprop.models.generic_models.PathModel

Class for huggingface models

model_path

Local or huggingface.co path to the model

name

string identifier for the model. Lowercase letters and numbers. No spaces/special characters except dashes.

description

String description of the model.

tasks

List of supported task strings

details

Dictionary of additional details about the model

init_model

Callable to initialise model from path Defaults to AutoModelForPreTraining from huggingface

tokenizer_path

Path to the tokenizer

Type

optional

init_tokenizer

Callable to initialise tokenizer from path Defaults to AutoTokenizer from huggingface.

Type

optional

device

Device for inference. Defaults to “cuda” if available.

Type

optional

training: bool
class HFTextGenerationModel(model_path, tokenizer_path=None, name: Optional[str] = None, description: Optional[str] = None, tasks: Optional[List[str]] = None, details: Optional[Dict] = None, model_class=<class 'transformers.models.auto.modeling_auto.AutoModelForPreTraining'>, tokenizer_class=<class 'transformers.models.auto.tokenization_auto.AutoTokenizer'>, device=None)[source]

Bases: backprop.models.generic_models.HFModel

Class for huggingface models that implement the .generate method.

\*args and **kwargs are passed to HFModel's __init__
generate(text, variant='seq2seq', **kwargs)[source]

Generate according to the model’s generate method.

training: bool
class PathModel(model_path, init_model, name: Optional[str] = None, description: Optional[str] = None, tasks: Optional[List[str]] = None, details: Optional[Dict] = None, tokenizer_path=None, init_tokenizer=None, device=None)[source]

Bases: backprop.models.generic_models.BaseModel

Class for models which are initialised from a path.

model_path

Path to the model

name

string identifier for the model. Lowercase letters and numbers. No spaces/special characters except dashes.

description

String description of the model.

tasks

List of supported task strings

details

Dictionary of additional details about the model

init_model

Callable to initialise model from path

tokenizer_path

Path to the tokenizer

Type

optional

init_tokenizer

Callable to initialise tokenizer from path

Type

optional

device

Device for inference. Defaults to “cuda” if available.

Type

optional

training: bool

Module contents