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D-X-Y
2021-03-24 05:33:52 -07:00
parent 379b904203
commit 15dda79e3b
6 changed files with 60 additions and 58 deletions
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12
tests/test_super_att.py
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@@ -53,11 +53,13 @@ class TestSuperAttention(unittest.TestCase):
@parameterized.expand([[6], [12], [24], [48]])
def test_transformer_encoder(self, input_dim):
output_dim = spaces.Categorical(12, 24, 36)
model = super_core.SuperTransformerEncoderLayer(
input_dim,
output_dim=output_dim,
num_heads=spaces.Categorical(2, 4, 6),
mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),
model = 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),
),
)
print(model)
model.apply_verbose(True)

26
tests/test_super_container.py
View File

@@ -36,25 +36,31 @@ def _internal_func(inputs, model):
return abstract_child, outputs
def _create_stel(input_dim, output_dim):
return super_core.SuperTransformerEncoderLayer(
input_dim,
output_dim,
num_heads=spaces.Categorical(2, 4, 6),
mlp_hidden_multiplier=spaces.Categorical(1, 2, 4),
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))
def test_super_sequential(batch, seq_dim, input_dim):
@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)
layer2 = _create_stel(out1_dim, out2_dim)
layer3 = _create_stel(out2_dim, out3_dim)
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)