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WMT19-1.0.14
Commit 42676b59 by libei
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fix bugs in transformer_dla

parent a440c641
正在显示 包含 246 行增加 和 375 行删除
.idea/workspace.xml
...@@ -3,6 +3,8 @@ ...@@ -3,6 +3,8 @@
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...@@ -17,46 +19,38 @@ ...@@ -17,46 +19,38 @@
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...@@ -65,8 +59,8 @@ ...@@ -65,8 +59,8 @@
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...@@ -74,13 +68,55 @@ ...@@ -74,13 +68,55 @@
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...@@ -113,6 +159,9 @@ ...@@ -113,6 +159,9 @@
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......
tensor2tensor/models/__init__.py
...@@ -11,4 +11,3 @@ ...@@ -11,4 +11,3 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
tensor2tensor/models/models.py
...@@ -39,4 +39,5 @@ from tensor2tensor.models import transformer_mlrf ...@@ -39,4 +39,5 @@ from tensor2tensor.models import transformer_mlrf
#from tensor2tensor.models import transformer_fix #from tensor2tensor.models import transformer_fix
from tensor2tensor.models import transformer_relative_pos from tensor2tensor.models import transformer_relative_pos
from tensor2tensor.models import transformer_dropout from tensor2tensor.models import transformer_dropout
from tensor2tensor.models import transformer_dla
# pylint: enable=unused-import # pylint: enable=unused-import
tensor2tensor/models/transformer_dla.py
...@@ -33,6 +33,7 @@ from tensor2tensor.models import common_hparams ...@@ -33,6 +33,7 @@ from tensor2tensor.models import common_hparams
from tensor2tensor.models import common_layers from tensor2tensor.models import common_layers
from tensor2tensor.utils import registry from tensor2tensor.utils import registry
from tensor2tensor.utils import t2t_model from tensor2tensor.utils import t2t_model
from tensor2tensor.models import layer_history
import tensorflow as tf import tensorflow as tf
...@@ -374,7 +375,7 @@ def transformer_ffn_layer(x, hparams): ...@@ -374,7 +375,7 @@ def transformer_ffn_layer(x, hparams):
@registry.register_hparams @registry.register_hparams
def transformer_base(): def transformer_dla():
"""Set of hyperparameters.""" """Set of hyperparameters."""
hparams = common_hparams.basic_params1() hparams = common_hparams.basic_params1()
hparams.hidden_size = 512 hparams.hidden_size = 512
...@@ -427,326 +428,32 @@ def transformer_base(): ...@@ -427,326 +428,32 @@ def transformer_base():
hparams.add_hparam("relu_dropout_broadcast_dims", "1") # length hparams.add_hparam("relu_dropout_broadcast_dims", "1") # length
hparams.add_hparam("residual_dropout_broadcast_dims", "1") # length hparams.add_hparam("residual_dropout_broadcast_dims", "1") # length
hparams.add_hparam("normalize_before", False) hparams.add_hparam("normalize_before", False)
hparams.add_hparam("use_emb", True)
hparams.add_hparam("encoder_history_type", "learnable_dense")
hparams.add_hparam("decoder_history_type", "learnable_dense")
return hparams return hparams
@registry.register_hparams @registry.register_hparams
def transformer_big(): def transformer_dla_base():
"""HParams for transfomer big model on WMT.""" hparams = transformer_dla()
hparams = transformer_base()
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.batching_mantissa_bits = 2
hparams.residual_dropout = 0.3
return hparams
@registry.register_hparams
def transformer_before():
"""HParams for transfomer big model on WMT."""
hparams = transformer_base()
hparams.normalize_before = True hparams.normalize_before = True
hparams.relu_dropout = 0.1
hparams.attention_dropout = 0.1 hparams.attention_dropout = 0.1
hparams.learning_rate = 0.2
hparams.learning_rate_warmup_steps = 8000
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.997
return hparams
@registry.register_hparams
def transformer_before_big():
"""HParams for transfomer big model on WMT."""
hparams = transformer_before()
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.batching_mantissa_bits = 2
hparams.residual_dropout = 0.3
return hparams
@registry.register_hparams
def transformer_big_single_gpu():
"""HParams for transformer big model for single gpu."""
hparams = transformer_big()
hparams.residual_dropout = 0.1 hparams.residual_dropout = 0.1
hparams.learning_rate_warmup_steps = 16000 hparams.learning_rate = 0.4
hparams.optimizer_adam_beta2 = 0.998 hparams.learning_rate_warmup_steps = 8000
hparams.batching_mantissa_bits = 3 hparams.batch_size = 2048
return hparams hparams.optimizer = "MultistepAdam"
hparams.optimizer_multistep_accumulate_steps = 4
@registry.register_hparams
def transformer_base_single_gpu():
"""HParams for transformer base model for single gpu."""
hparams = transformer_base()
hparams.batch_size = 8192
hparams.learning_rate_warmup_steps = 16000
hparams.batching_mantissa_bits = 2
return hparams
@registry.register_hparams
def transformer_big_dr1():
hparams = transformer_base()
hparams.hidden_size = 1024
hparams.filter_size = 4096
hparams.num_heads = 16
hparams.residual_dropout = 0.1
hparams.batching_mantissa_bits = 2
return hparams
@registry.register_hparams
def transformer_big_enfr():
hparams = transformer_big_dr1()
hparams.shared_embedding_and_softmax_weights = int(False)
hparams.filter_size = 8192
hparams.residual_dropout = 0.1
return hparams
@registry.register_hparams
def transformer_big_dr2():
hparams = transformer_big_dr1()
hparams.residual_dropout = 0.2
return hparams
@registry.register_hparams
def transformer_base_ldcd():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.learning_rate_decay_scheme = "ld&cd"
hparams.learning_rate_ldcd_epoch = 5
hparams.learning_rate_warmup_steps = 4000
return hparams
@registry.register_hparams
def transformer_base_ldcd_n10():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.learning_rate_decay_scheme = "ld&cd"
hparams.learning_rate_ldcd_epoch = 10
hparams.learning_rate_warmup_steps = 4000
return hparams
@registry.register_hparams
def transformer_base_ldcd_n2():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.learning_rate_decay_scheme = "ld&cd"
hparams.learning_rate_ldcd_epoch = 2
hparams.learning_rate_warmup_steps = 4000
return hparams
@registry.register_hparams
def transformer_base_ldcd_n1():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.learning_rate_decay_scheme = "ld&cd"
hparams.learning_rate_ldcd_epoch = 1
hparams.learning_rate_warmup_steps = 4000
return hparams
@registry.register_hparams
def transformer_base_amsgrad():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "AMSGrad"
return hparams
@registry.register_hparams
def transformer_base_amsgrad_v2():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "AMSGrad"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.optimizer_adam_epsilon = 1e-8
return hparams
@registry.register_hparams
def transformer_base_amsgrad_v3():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "AMSGrad"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.99
hparams.optimizer_adam_epsilon = 1e-8
return hparams
@registry.register_hparams
def transformer_base_amsgrad_v4():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "AMSGrad"
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.99
hparams.optimizer_adam_epsilon = 1e-9
return hparams
@registry.register_hparams
def transformer_base_ldrestart_n3():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.learning_rate_decay_scheme = "ld&restart"
hparams.learning_rate_ldrestart_epoch = 3
hparams.learning_rate_warmup_steps = 4000
return hparams
@registry.register_hparams
def transformer_base_powersign():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "PowerSign"
hparams.optimizer_powersign_beta = 0.9
hparams.optimizer_powersign_decay = ""
return hparams
@registry.register_hparams
def transformer_base_powersign_ld():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "PowerSign"
hparams.optimizer_powersign_beta = 0.9
hparams.optimizer_powersign_decay = "linear"
return hparams
@registry.register_hparams
def transformer_base_powersign_cd():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "PowerSign"
hparams.optimizer_powersign_beta = 0.9
hparams.optimizer_powersign_decay = "cosine"
hparams.optimizer_powersign_period = 1
return hparams
@registry.register_hparams
def transformer_base_powersign_rd():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "PowerSign"
hparams.optimizer_powersign_beta = 0.9
hparams.optimizer_powersign_decay = "restart"
hparams.optimizer_powersign_period = 1
return hparams
@registry.register_hparams
def transformer_base_powersign_rd_n10():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "PowerSign"
hparams.optimizer_powersign_beta = 0.9
hparams.optimizer_powersign_decay = "restart"
hparams.optimizer_powersign_period = 10
return hparams
@registry.register_hparams
def transformer_base_powersign_rd_n20():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.optimizer = "PowerSign"
hparams.optimizer_powersign_beta = 0.9
hparams.optimizer_powersign_decay = "restart"
hparams.optimizer_powersign_period = 20
return hparams
@registry.register_hparams
def transformer_base_swish1():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.ffn_layer = "conv_hidden_swish"
hparams.swish_dropout = 0.0
hparams.swish_beta = 1.0
hparams.swish_beta_is_trainable = False
return hparams return hparams
@registry.register_hparams @registry.register_hparams
def transformer_base_swish_trainable(): def transformer_dla_big():
"""Set of hyperparameters."""
hparams = transformer_base()
hparams.ffn_layer = "conv_hidden_swish"
hparams.swish_dropout = 0.0
#hparams.swish_beta = 1.0
hparams.swish_beta_is_trainable = True
return hparams
@registry.register_hparams
def transformer_big_adafactor():
"""HParams for transfomer big model on WMT.""" """HParams for transfomer big model on WMT."""
hparams = transformer_base() hparams = transformer_dla_base()
hparams.hidden_size = 1024 hparams.hidden_size = 1024
hparams.filter_size = 4096 hparams.filter_size = 4096
hparams.num_heads = 16 hparams.num_heads = 16
hparams.batching_mantissa_bits = 2 hparams.batching_mantissa_bits = 2
hparams.residual_dropout = 0.3 hparams.residual_dropout = 0.3
hparams.optimizer = "Adafactor"
hparams.epsilon = 1e-8
hparams.learning_rate_warmup_steps = 16000
hparams.optimizer_adafactor_beta2 = 0.997
return hparams
@registry.register_hparams
def transformer_base_v2():
"""Set of hyperparameters.
set relu_dropout and attention_dropout as 0.1
"""
hparams = transformer_base()
hparams.attention_dropout = 0.1
hparams.relu_dropout = 0.1
return hparams
@registry.register_hparams
def transformer_base_rpr_dropout1():
hparams = transformer_base()
hparams.max_relative_length = 16
hparams.attention_type = "relative_dot_product"
hparams.relu_dropout = 0.1
hparams.attention_dropout = 0.1
return hparams
@registry.register_hparams
def transformer_base_v3():
"""Set of hyperparameters.
set filter as 4096
"""
hparams = transformer_base_v2()
hparams.filter_size = 4096
return hparams
@registry.register_hparams
def transformer_big_multistep2():
# new model use optimizer MultistepAdam
hparams = transformer_big()
hparams.optimizer = "MultistepAdam"
hparams.optimizer_multistep_accumulate_steps = 2
hparams.batch_size = 2048
#hparams.attention_dropout = 0.1
#hparams.relu_dropout = 0.1
return hparams
@registry.register_hparams
def transformer_big_adafactor_test():
# new model use optimizer MultistepAdam
hparams = transformer_big()
hparams.optimizer = "Adafactor"
hparams.learning_rate_warmup_steps = 8000
hparams.batch_size = 4096
hparams.optimizer_adafactor_beta2 = 0.999
return hparams return hparams
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