add autodl

AutoDL-Projects/tests/test_super_container.py

@@ -0,0 +1,85 @@
+#####################################################
+# 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)