def test_super_simple_norm(self):
out_features = spaces.Categorical(12, 24, 36)
bias = spaces.Categorical(True, False)
model = super_core.SuperSequential(
super_core.SuperSimpleNorm(5, 0.5),
super_core.SuperLinear(10, out_features, bias=bias),
)
print("The simple super module is:\n{:}".format(model))
model.apply_verbose(True)
print(model.super_run_type)
self.assertTrue(model[1].bias)
inputs = torch.rand(20, 10)
print("Input shape: {:}".format(inputs.shape))
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), (20, 36))
abstract_space = model.abstract_search_space
abstract_space.clean_last()
abstract_child = abstract_space.random()
print("The abstract searc space:\n{:}".format(abstract_space))
print("The abstract child program:\n{:}".format(abstract_child))
model.set_super_run_type(super_core.SuperRunMode.Candidate)
model.apply_candidate(abstract_child)
output_shape = (20, abstract_child["1"]["_out_features"].value)
outputs = model(inputs)
self.assertEqual(tuple(outputs.shape), output_shape)
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,
),
)
def __init__(
self,
d_feat: int = 6,
embed_dim: List[
super_core.IntSpaceType] = DefaultSearchSpace["embed_dim"],
num_heads: List[
super_core.IntSpaceType] = DefaultSearchSpace["num_heads"],
mlp_hidden_multipliers: List[
super_core.
IntSpaceType] = DefaultSearchSpace["mlp_hidden_multipliers"],
qkv_bias: bool = DefaultSearchSpace["qkv_bias"],
pos_drop: float = DefaultSearchSpace["pos_drop"],
other_drop: float = DefaultSearchSpace["other_drop"],
max_seq_len: int = 65,
):
super(SuperTransformer, self).__init__()
self._embed_dim = embed_dim
self._num_heads = num_heads
self._mlp_hidden_multipliers = mlp_hidden_multipliers
# the stem part
self.input_embed = super_core.SuperAlphaEBDv1(d_feat, embed_dim)
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))
self.pos_embed = super_core.SuperPositionalEncoder(
d_model=embed_dim, max_seq_len=max_seq_len, dropout=pos_drop)
# build the transformer encode layers -->> check params
_assert_types(num_heads, (tuple, list))
_assert_types(mlp_hidden_multipliers, (tuple, list))
assert len(num_heads) == len(
mlp_hidden_multipliers), "{:} vs {:}".format(
len(num_heads), len(mlp_hidden_multipliers))
# build the transformer encode layers -->> backbone
layers = []
for num_head, mlp_hidden_multiplier in zip(num_heads,
mlp_hidden_multipliers):
layer = super_core.SuperTransformerEncoderLayer(
embed_dim,
num_head,
qkv_bias,
mlp_hidden_multiplier,
other_drop,
)
layers.append(layer)
self.backbone = super_core.SuperSequential(*layers)
# the regression head
self.head = super_core.SuperSequential(
super_core.SuperLayerNorm1D(embed_dim),
super_core.SuperLinear(embed_dim, 1))
trunc_normal_(self.cls_token, std=0.02)
self.apply(self._init_weights)
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)
def test_transformer_encoder(self, input_dim):
output_dim = spaces.Categorical(12, 24, 36)
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)
inputs = torch.rand(4, 20, input_dim)
abstract_child, outputs = self._internal_func(inputs, model)
output_shape = (
4,
20,
output_dim.abstract(reuse_last=True).random(reuse_last=True).value,
)
self.assertEqual(tuple(outputs.shape), output_shape)