def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
cls.layer = MultiHeadAttention(
n_attention_heads=N_ATTENTION_HEADS,
token_representation_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB)
cls.forward_propagation_kwargs = {
'query_tokens':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH - 1,
REPRESENTATION_DIMENSION),
dtype=torch_float),
'key_or_value_tokens':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION),
dtype=torch_float),
'mask':
torch_rand(size=(MINI_BATCH_SIZE, 1, MAX_SEQUENCE_LENGTH),
dtype=torch_float)
}
cls.expected_output_shapes = [
(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH - 1,
REPRESENTATION_DIMENSION)
]
cls.expected_output_dtypes = [torch_float]
def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
feedforward_layer = PositionWiseFeedForward(
token_representation_dimension=REPRESENTATION_DIMENSION,
feedforward_dimension=FEEDFORWARD_DIMENSION,
dropout_prob=DROPOUT_PROB)
multi_head_attention_later = MultiHeadAttention(
n_attention_heads=N_ATTENTION_HEADS,
token_representation_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB)
cls.layer = EncoderBlock(building_blocks=EncoderBlockBuildingBlocks(
self_multi_head_attention_layer=deepcopy(
multi_head_attention_later),
fully_connected_layer=feedforward_layer),
feature_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB)
cls.forward_propagation_kwargs = {
'src_features':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION),
dtype=torch_float),
'src_mask':
torch_rand(size=(MINI_BATCH_SIZE, 1, MAX_SEQUENCE_LENGTH),
dtype=torch_float)
}
cls.expected_output_shapes = [(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION)]
cls.expected_output_dtypes = [torch_float]
def test_pong_duel_converter(self):
mapper = lambda actions: torch_round(actions * 1.5 + 1).flatten(
).tolist()
action_tensor = torch_rand((2, )) * 2 - 1
action_list = mapper(action_tensor)
self.assertEqual(len(action_list), 2,
"the action list is not of dim 2")
self.assertTrue(all([0 <= i <= 2 for i in action_list]),
"the action list is not mapped to [0, 2]")
print("Origin:", action_tensor)
print("Action:", action_list)
def test_agent_step(self):
wrapper = VectorizedMultiAgentEnvWrapper.MultiAgentEnvWrapper(
env="PongDuel-v0",
mapper=lambda actions: torch_round(actions * 1.5 + 1).flatten(
).tolist())
wrapper.reset()
mock_action = torch_rand(size=(2, ))
states, reward, done, _ = wrapper.step(mock_action)
self.assertTrue(isinstance(states, Tensor))
self.assertTrue(isinstance(reward, Tensor))
self.assertTrue(isinstance(done, list))
self.assertEqual(states.shape, (2, 10))
self.assertEqual(reward.shape, (1, 2))
def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
cls.layer = LayerNorm(feature_dimension=REPRESENTATION_DIMENSION)
cls.forward_propagation_kwargs = {
'features':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION),
dtype=torch_float)
}
cls.expected_output_shapes = [(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION)]
cls.expected_output_dtypes = [torch_float]
def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
cls.layer = LogSoftmax(
token_representation_dimension=REPRESENTATION_DIMENSION,
vocabulary_dimension=TGT_VOCABULARY_DIMENSION)
cls.forward_propagation_kwargs = {
'logits':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION),
dtype=torch_float)
}
cls.expected_output_shapes = [(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
TGT_VOCABULARY_DIMENSION)]
cls.expected_output_dtypes = [torch_float]
def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
cls.layer = PositionWiseFeedForward(
token_representation_dimension=REPRESENTATION_DIMENSION,
feedforward_dimension=FEEDFORWARD_DIMENSION,
dropout_prob=DROPOUT_PROB)
cls.forward_propagation_kwargs = {
'features':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION),
dtype=torch_float)
}
cls.expected_output_shapes = [(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION)]
cls.expected_output_dtypes = [torch_float]
def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
cls.layer = PositionalEncoding(
token_representation_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB,
max_sequence_length=MAX_SEQUENCE_LENGTH)
cls.forward_propagation_kwargs = {
'token_embeddings':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION),
dtype=torch_float)
}
cls.expected_output_shapes = [(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH,
REPRESENTATION_DIMENSION)]
cls.expected_output_dtypes = [torch_float]
def setUpClass(cls):
"""
Avoid redundant, time-consuming, equivalent setups when testing across
the different methods, that can use common instantiations.
"""
positional_encoding_layer = PositionalEncoding(
token_representation_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB,
max_sequence_length=MAX_SEQUENCE_LENGTH)
src_embedder = Sequential(
Embedder(token_representation_dimension=REPRESENTATION_DIMENSION,
vocabulary_dimension=SRC_VOCABULARY_DIMENSION),
deepcopy(positional_encoding_layer))
tgt_embedder = Sequential(
Embedder(token_representation_dimension=REPRESENTATION_DIMENSION,
vocabulary_dimension=TGT_VOCABULARY_DIMENSION),
deepcopy(positional_encoding_layer))
feedforward_layer = PositionWiseFeedForward(
token_representation_dimension=REPRESENTATION_DIMENSION,
feedforward_dimension=FEEDFORWARD_DIMENSION,
dropout_prob=DROPOUT_PROB)
multi_head_attention_later = MultiHeadAttention(
n_attention_heads=N_ATTENTION_HEADS,
token_representation_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB)
encoder = Encoder(base_block=EncoderBlock(
building_blocks=EncoderBlockBuildingBlocks(
self_multi_head_attention_layer=deepcopy(
multi_head_attention_later),
fully_connected_layer=feedforward_layer),
feature_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB),
n_clones=N_ENCODER_BLOCKS)
decoder = Decoder(base_block=DecoderBlock(
building_blocks=DecoderBlockBuildingBlocks(
self_multi_head_attention_layer=deepcopy(
multi_head_attention_later),
source_multi_head_attention_layer=deepcopy(
multi_head_attention_later),
fully_connected_layer=feedforward_layer),
feature_dimension=REPRESENTATION_DIMENSION,
dropout_prob=DROPOUT_PROB),
n_clones=N_DECODER_BLOCKS)
log_softmax_layer = LogSoftmax(
token_representation_dimension=REPRESENTATION_DIMENSION,
vocabulary_dimension=TGT_VOCABULARY_DIMENSION)
building_blocks = Seq2SeqBuildingBlocks(
encoder=encoder,
decoder=decoder,
src_embedder=src_embedder,
tgt_embedder=tgt_embedder,
log_softmax_layer=log_softmax_layer)
cls.layer = Seq2Seq(building_blocks=building_blocks)
cls.forward_propagation_kwargs = {
'src_tokens':
torch_randint(low=0,
high=SRC_VOCABULARY_DIMENSION,
size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH),
dtype=torch_long),
'tgt_tokens':
torch_randint(low=0,
high=TGT_VOCABULARY_DIMENSION,
size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH - 1),
dtype=torch_long),
'src_mask':
torch_rand(size=(MINI_BATCH_SIZE, 1, MAX_SEQUENCE_LENGTH),
dtype=torch_float),
'tgt_mask':
torch_rand(size=(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH - 1,
MAX_SEQUENCE_LENGTH - 1),
dtype=torch_float)
}
cls.expected_output_shapes = [
(MINI_BATCH_SIZE, MAX_SEQUENCE_LENGTH - 1,
REPRESENTATION_DIMENSION)
]
cls.expected_output_dtypes = [torch_float]
