69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
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#####################################################
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# pytest ./tests/test_super_container.py -s #
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#####################################################
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import sys, random
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import unittest
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import pytest
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
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print("library path: {:}".format(lib_dir))
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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import torch
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from xlayers import super_core
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import spaces
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"""Test the super container layers."""
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def _internal_func(inputs, model):
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outputs = model(inputs)
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abstract_space = model.abstract_search_space
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print(
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"The abstract search space for SuperAttention is:\n{:}".format(abstract_space)
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)
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abstract_space.clean_last()
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abstract_child = abstract_space.random(reuse_last=True)
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print("The abstract child program is:\n{:}".format(abstract_child))
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model.set_super_run_type(super_core.SuperRunMode.Candidate)
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model.apply_candidate(abstract_child)
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outputs = model(inputs)
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return abstract_child, outputs
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def _create_stel(input_dim, output_dim):
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return super_core.SuperTransformerEncoderLayer(
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input_dim,
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output_dim,
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num_heads=spaces.Categorical(2, 4, 6),
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mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),
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)
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@pytest.mark.parametrize("batch", (1, 2, 4))
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@pytest.mark.parametrize("seq_dim", (1, 10, 30))
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@pytest.mark.parametrize("input_dim", (6, 12, 24, 27))
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def test_super_sequential(batch, seq_dim, input_dim):
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out1_dim = spaces.Categorical(12, 24, 36)
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out2_dim = spaces.Categorical(24, 36, 48)
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out3_dim = spaces.Categorical(36, 72, 100)
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layer1 = _create_stel(input_dim, out1_dim)
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layer2 = _create_stel(out1_dim, out2_dim)
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layer3 = _create_stel(out2_dim, out3_dim)
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model = super_core.SuperSequential(layer1, layer2, layer3)
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print(model)
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model.apply_verbose(True)
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inputs = torch.rand(batch, seq_dim, input_dim)
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abstract_child, outputs = _internal_func(inputs, model)
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output_shape = (
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batch,
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seq_dim,
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out3_dim.abstract(reuse_last=True).random(reuse_last=True).value,
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)
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assert tuple(outputs.shape) == output_shape
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