hypothesis-torch 0.7.4

Hypothesis strategies for various Pytorch structures, including tensors and modules.

hypothesis-torch

Hypothesis strategies for various Pytorch structures (including tensors and modules).

Hypothesis is a powerful property-based testing library for Python. It lacks built-in support for Pytorch tensors and modules, so this library provides strategies for generating them.

Installation

hypothesis-torch can be installed via pip:

pip install hypothesis-torch

Optionally, you can also install the huggingface extra to also install the transformers library:

pip install hypothesis-torch[huggingface]

Strategies for generating Hugging Face transformer models are provided in the hypothesis_torch.huggingface module. If and only if transformers is installed when hypothesis-torch is imported, these strategies will be available from the root hypothesis_torch module.

What you can generate

Tensors

Tensors can be generated with the tensor_strategy function. This function takes in optional arguments for the shape, dtype, device, and other properties of the desired tensors. Each property can be specified as a fixed value or as a strategy. For example, to generate a tensor with a shape of 3x3, a dtype of torch.float32, and values between 0 and 1,

import hypothesis_torch
from hypothesis import strategies as st
import torch
hypothesis_torch.tensor_strategy(dtype=torch.float32, shape=(3, 3), elements=st.floats(0, 1))

Note that specifying other hypothesis strategies that return the same type as an argument will sample from that strategy while generating the tensor. For example, to generate a tensor with any dtype, specify a strategy that returns a dtype:

import hypothesis_torch
from hypothesis import strategies as st
import torch
hypothesis_torch.tensor_strategy(dtype=st.sampled_from([torch.float32, torch.float64]), shape=(3, 3), elements=st.floats(0, 1))

Dtypes

Dtypes can be generated with the dtype_strategy function. If no arguments are provided, this function will default to sampling from the set of all Pytorch dtypes.

import hypothesis_torch
hypothesis_torch.dtype_strategy()

If a set of dtypes is provided, the function will sample from that set.

import hypothesis_torch
import torch
hypothesis_torch.dtype_strategy(dtypes={torch.float32, torch.float64})

Devices

Devices can be generated with the device_strategy function. If no arguments are provided, this function will default to sampling from the set of all available, physical devices.

import hypothesis_torch
hypothesis_torch.device_strategy()

If a set of devices is provided, the function will sample from that set.

import hypothesis_torch
import torch
hypothesis_torch.device_strategy(devices={torch.device('cuda:0'), torch.device('cpu')})

If allow_meta_device is set to True, the strategy may also return meta devices, i.e. torch.device('meta').

import hypothesis_torch
hypothesis_torch.device_strategy(allow_meta_device=True)

Modules

Various types of PyTorch modules have their own strategies.

Activation functions

Activation functions can be generated with the same_shape_activation_strategy function.

import hypothesis_torch
hypothesis_torch.same_shape_activation_strategy()

Fully-connected/Feed forward neural networks

Fully-connected neural networks can be generated with the linear_network_strategy function. This function takes in optional arguments for the input size, output size, and number of hidden layers. Each of these arguments can be specified as a fixed value or as a strategy. For example, to generate a fully-connected neural network with an input size of 10, an output size of 5, and 3 hidden layers with sizes between 5 and 10:

import hypothesis_torch
from hypothesis import strategies as st
hypothesis_torch.linear_network_strategy(input_shape=(1,10), output_shape=(1,5), hidden_layer_size=st.integers(5, 10), num_hidden_layers=3)

Hugging Face Transformer Models

Hugging Face transformer models can be generated with the transformer_strategy function. This function takes in any Hugging Face PreTrainedModel subclass (or a strategy that generates references PreTrainedModel subclasses) and returns an instance of that model. For example, to generate an arbitrary Llama2 model:

import hypothesis_torch
import transformers
hypothesis_torch.transformer_strategy(transformers.LlamaForCausalLM)

The strategy also accepts kwargs to pass to the model constructor. These can be either fixed values or strategies to generate those corresponding values. For example, to generate an arbitrary Llama2 model with a hidden size between 64 and 128, but a fixed vocabulary size of 1000:

import hypothesis_torch
import transformers
from hypothesis import strategies as st
hypothesis_torch.transformer_strategy(transformers.LlamaForCausalLM, hidden_size=st.integers(64, 128), vocab_size=1000)

[! Note] Currently, the transformer_strategy only accepts kwargs that can be passed to the constructor of the model's config class. Thus, it cannot currently replicate all the behavior of calling from_pretrained on a model class.