xautodl/tests/test_super_container.py
2021-06-10 21:53:22 +08:00

86 lines
2.8 KiB
Python

#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
# pytest ./tests/test_super_container.py -s #
#####################################################
import random
import unittest
import pytest
import torch
from xautodl import spaces
from xautodl.xlayers import super_core
"""Test the super container layers."""
def _internal_func(inputs, model):
outputs = model(inputs)
abstract_space = model.abstract_search_space
print(
"The abstract search space for SuperAttention is:\n{:}".format(abstract_space)
)
abstract_space.clean_last()
abstract_child = abstract_space.random(reuse_last=True)
print("The abstract child program is:\n{:}".format(abstract_child))
model.enable_candidate()
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.apply_candidate(abstract_child)
outputs = model(inputs)
return abstract_child, outputs
def _create_stel(input_dim, output_dim, order):
return super_core.SuperSequential(
super_core.SuperLinear(input_dim, output_dim),
super_core.SuperTransformerEncoderLayer(
output_dim,
num_heads=spaces.Categorical(2, 4, 6),
mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),
order=order,
),
)
@pytest.mark.parametrize("batch", (1, 2, 4))
@pytest.mark.parametrize("seq_dim", (1, 10, 30))
@pytest.mark.parametrize("input_dim", (6, 12, 24, 27))
@pytest.mark.parametrize(
"order", (super_core.LayerOrder.PreNorm, super_core.LayerOrder.PostNorm)
)
def test_super_sequential(batch, seq_dim, input_dim, order):
out1_dim = spaces.Categorical(12, 24, 36)
out2_dim = spaces.Categorical(24, 36, 48)
out3_dim = spaces.Categorical(36, 72, 100)
layer1 = _create_stel(input_dim, out1_dim, order)
layer2 = _create_stel(out1_dim, out2_dim, order)
layer3 = _create_stel(out2_dim, out3_dim, order)
model = super_core.SuperSequential(layer1, layer2, layer3)
print(model)
model.apply_verbose(True)
inputs = torch.rand(batch, seq_dim, input_dim)
abstract_child, outputs = _internal_func(inputs, model)
output_shape = (
batch,
seq_dim,
out3_dim.abstract(reuse_last=True).random(reuse_last=True).value,
)
assert tuple(outputs.shape) == output_shape
def test_super_sequential_v1():
model = super_core.SuperSequential(
super_core.SuperSimpleNorm(1, 1),
torch.nn.ReLU(),
super_core.SuperLeakyReLU(),
super_core.SuperLinear(10, 10),
super_core.SuperReLU(),
)
inputs = torch.rand(10, 10)
print(model)
outputs = model(inputs)
abstract_search_space = model.abstract_search_space
print(abstract_search_space)