Registry refactor

tensor2tensor/bin/t2t_attack.py

@@ -73,7 +73,7 @@ def create_attack_params():
 
 
 def create_attack(attack):
-  return registry.attacks(attack)
+  return registry.attack(attack)
 
 
 def create_surrogate_hparams():

tensor2tensor/bin/t2t_datagen.py

@@ -147,7 +147,7 @@ def main(_):
 
   # Calculate the list of problems to generate.
   problems = sorted(
-      list(_SUPPORTED_PROBLEM_GENERATORS) + registry.list_problems())
+      list(_SUPPORTED_PROBLEM_GENERATORS) + registry.list_base_problems())
   for exclude in FLAGS.exclude_problems.split(","):
     if exclude:
       problems = [p for p in problems if exclude not in p]
@@ -169,7 +169,8 @@ def main(_):
 
   if not problems:
     problems_str = "\n  * ".join(
-        sorted(list(_SUPPORTED_PROBLEM_GENERATORS) + registry.list_problems()))
+        sorted(list(_SUPPORTED_PROBLEM_GENERATORS) +
+               registry.list_base_problems()))
     error_msg = ("You must specify one of the supported problems to "
                  "generate data for:\n  * " + problems_str + "\n")
     error_msg += ("TIMIT and parsing need data_sets specified with "

tensor2tensor/bin/t2t_prune.py

@@ -54,7 +54,7 @@ def create_pruning_params():
 
 
 def create_pruning_strategy(name):
-  return registry.pruning_strategies(name)
+  return registry.pruning_strategy(name)
 
 
 def main(argv):

tensor2tensor/layers/common_hparams.py

@@ -55,7 +55,7 @@ def basic_params1():
       initializer="orthogonal",
       initializer_gain=1.5,
       label_smoothing=0.1,
-      optimizer="Adam",
+      optimizer="adam",
       optimizer_adam_epsilon=1e-6,
       optimizer_adam_beta1=0.85,
       optimizer_adam_beta2=0.997,
@@ -466,7 +466,7 @@ def basic_range1(ranged_hparams):
   rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
   rhp.set_categorical(
       "optimizer",
-      ["Adam", "Adagrad", "Momentum", "RMSProp", "SGD", "YellowFin"])
+      ["adam", "adagrad", "momentum", "rms_prop", "sgd", "yellow_fin"])
 
 
 @registry.register_ranged_hparams

tensor2tensor/models/mtf_transformer2.py

@@ -269,7 +269,7 @@ def sample(self, features, mesh):
     return self.combine_batch_dims(ret)
 
 
-layers_registry = registry.create_registry("layers")
+layers_registry = registry.Registries.layers
 
 
 # The following functions construct layers based on hyperparmeters

tensor2tensor/models/research/adafactor_experiments.py

@@ -30,16 +30,16 @@ def mimic_adam_with_adafactor(hparams):
   Some minor things may be different, like epsilon and beta1 correction.
 
   Args:
-    hparams: model hyperparameters where "Adam" in hparams.optimizer
+    hparams: model hyperparameters where "adam" in hparams.optimizer
   """
-  assert "Adam" in hparams.optimizer
-  hparams.optimizer = "Adafactor"
+  assert "adam" in hparams.optimizer
+  hparams.optimizer = "adafactor"
   hparams.optimizer_adafactor_beta1 = hparams.optimizer_adam_beta1
   hparams.optimizer_adafactor_beta2 = hparams.optimizer_adam_beta2
   hparams.optimizer_adafactor_multiply_by_parameter_scale = False
   hparams.optimizer_adafactor_factored = False
   hparams.optimizer_adafactor_clipping_threshold = None
-  hparams.optimizer_adafactor_decay_type = "Adam"
+  hparams.optimizer_adafactor_decay_type = "adam"
 
 
 @registry.register_hparams
@@ -50,7 +50,7 @@ def afx_adam():
   hparams.optimizer_adam_beta2 = 0.999
   hparams.symbol_modality_num_shards = 1
   hparams.batch_size = 2048
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.learning_rate_schedule = (
       "constant*rsqrt_decay*linear_warmup*rsqrt_hidden_size")
   hparams.learning_rate_constant = 2.0

tensor2tensor/models/research/autoencoders.py

@@ -1020,7 +1020,7 @@ def body(self, features):
 def autoencoder_basic():
   """Basic autoencoder model."""
   hparams = common_hparams.basic_params1()
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.learning_rate_constant = 0.0002
   hparams.learning_rate_warmup_steps = 500
   hparams.learning_rate_schedule = "constant * linear_warmup"

tensor2tensor/models/research/transformer_nat.py

@@ -392,7 +392,7 @@ def transformer_nat_small():
   hparams.filter_size = 2048
   hparams.label_smoothing = 0.0
   hparams.force_full_predict = True
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.optimizer_adam_epsilon = 1e-9
   hparams.optimizer_adam_beta1 = 0.9
   hparams.optimizer_adam_beta2 = 0.997

tensor2tensor/models/research/transformer_vae.py

@@ -767,7 +767,7 @@ def transformer_ae_small():
   hparams.filter_size = 2048
   hparams.add_hparam("compress_filter_size", 2048 * 2)
   hparams.label_smoothing = 0.0
-  hparams.optimizer = "Adam"  # Can be unstable, maybe try Adam.
+  hparams.optimizer = "adam"  # Can be unstable, maybe try Adam.
   hparams.optimizer_adam_epsilon = 1e-9
   hparams.optimizer_adam_beta1 = 0.9
   hparams.optimizer_adam_beta2 = 0.997  # Needs tuning, try 0.98 to 0.999.
@@ -941,7 +941,7 @@ def transformer_ae_a3():
 def transformer_ae_a6():
   """Best hparams for transformer with semhash."""
   hparams = transformer_ae_a3()
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.noise_dev = 0.5
   return hparams
 

tensor2tensor/models/research/vqa_attention.py

@@ -335,7 +335,7 @@ def vqa_attention_base():
   hparams = common_hparams.basic_params1()
   hparams.batch_size = 128
   hparams.use_fixed_batch_size = True,
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.optimizer_adam_beta1 = 0.9
   hparams.optimizer_adam_beta2 = 0.999
   hparams.optimizer_adam_epsilon = 1e-8

tensor2tensor/models/research/vqa_self_attention.py

@@ -684,7 +684,7 @@ def vqa_self_attention_base():
   hparams = common_hparams.basic_params1()
   hparams.batch_size = 128
   hparams.use_fixed_batch_size = True,
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.optimizer_adam_beta1 = 0.9
   hparams.optimizer_adam_beta2 = 0.997
   hparams.optimizer_adam_epsilon = 1e-9

tensor2tensor/models/shake_shake.py

@@ -189,7 +189,7 @@ def shakeshake_small():
 @registry.register_hparams
 def shake_shake_quick():
   hparams = shakeshake_small()
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.learning_rate_cosine_cycle_steps = 1000
   hparams.learning_rate = 0.5
   hparams.batch_size = 100

tensor2tensor/models/transformer.py

@@ -1694,7 +1694,7 @@ def transformer_tall_pretrain_lm():
   hparams.learning_rate_constant = 2e-4
   hparams.learning_rate_schedule = (
       "linear_warmup*constant*cosdecay")
-  hparams.optimizer = "AdamW"
+  hparams.optimizer = "adam_w"
   hparams.optimizer_adam_beta1 = 0.9
   hparams.optimizer_adam_beta2 = 0.999
   hparams.optimizer_adam_epsilon = 1e-8
@@ -1739,7 +1739,7 @@ def transformer_tall_pretrain_lm_tpu():
   # Optimizer gets reset in update_hparams_for_tpu so we set it again here.
   hparams.learning_rate_constant = 2e-4
   hparams.learning_rate_schedule = ("linear_warmup * constant * cosdecay")
-  hparams.optimizer = "AdamW"
+  hparams.optimizer = "adam_w"
   return hparams
 
 

tensor2tensor/models/vanilla_gan.py

@@ -199,7 +199,7 @@ def infer(self, *args, **kwargs):  # pylint: disable=arguments-differ
 def sliced_gan():
   """Basic parameters for a vanilla_gan."""
   hparams = common_hparams.basic_params1()
-  hparams.optimizer = "Adam"
+  hparams.optimizer = "adam"
   hparams.learning_rate_constant = 0.0002
   hparams.learning_rate_warmup_steps = 500
   hparams.learning_rate_schedule = "constant * linear_warmup"

tensor2tensor/problems.py

@@ -27,7 +27,7 @@ def problem(name):
 
 
 def available():
-  return sorted(registry.list_problems())
+  return registry.list_base_problems()
 
 
 all_problems.import_modules(all_problems.ALL_MODULES)

tensor2tensor/rl/datagen_with_agent.py

@@ -45,7 +45,7 @@ def main(_):
 
   # Create problem if not already defined
   problem_name = "gym_discrete_problem_with_agent_on_%s" % FLAGS.game
-  if problem_name not in registry.list_problems():
+  if problem_name not in registry.Registries.problems:
     gym_env.register_game(FLAGS.game)
 
   # Generate

tensor2tensor/test_data/transformer_test_ckpt/hparams.json

@@ -1 +1 @@
-{"daisy_chain_variables": true, "optimizer_adam_beta1": 0.9, "scheduled_sampling_prob": 0.0, "num_hidden_layers": 2, "moe_loss_coef": 0.01, "max_target_seq_length": 0, "clip_grad_norm": 0.0, "pos": "timing", "scheduled_sampling_gold_mixin_prob": 0.5, "initializer": "uniform_unit_scaling", "grad_noise_scale": 0.0, "optimizer_momentum_momentum": 0.9, "nbr_decoder_problems": 1, "attention_key_channels": 0, "eval_drop_long_sequences": false, "learning_rate_cosine_cycle_steps": 250000, "prepend_mode": "none", "weight_decay": 0.0, "symbol_modality_skip_top": false, "weight_noise": 0.0, "target_modality": "default", "attention_dropout": 0.1, "parameter_attention_value_channels": 0, "factored_logits": false, "relu_dropout": 0.1, "no_data_parallelism": false, "layer_preprocess_sequence": "n", "sampling_method": "argmax", "learning_rate": 0.2, "num_heads": 2, "max_length": 256, "summarize_grads": false, "attention_value_channels": 0, "num_encoder_layers": 0, "label_smoothing": 0.1, "use_fixed_batch_size": false, "optimizer": "Adam", "moe_k": 2, "self_attention_type": "dot_product", "learning_rate_decay_scheme": "noam", "sampling_temp": 1.0, "kernel_height": 3, "use_pad_remover": true, "batch_size": 4096, "max_relative_position": 0, "force_full_predict": false, "min_length_bucket": 8, "layer_prepostprocess_dropout": 0.1, "eval_run_autoregressive": false, "shared_embedding_and_softmax_weights": true, "symbol_modality_num_shards": 16, "dropout": 0.2, "compress_steps": 0, "parameter_attention_key_channels": 0, "length_bucket_step": 1.1, "kernel_width": 1, "hidden_size": 16, "num_decoder_layers": 0, "input_modalities": "default", "filter_size": 8, "optimizer_adam_beta2": 0.98, "scheduled_sampling_warmup_steps": 50000, "norm_type": "layer", "min_length": 0, "moe_num_experts": 64, "multiply_embedding_mode": "sqrt_depth", "max_input_seq_length": 0, "learning_rate_warmup_steps": 8000, "proximity_bias": false, "ffn_layer": "dense_relu_dense", "initializer_gain": 1.0, "layer_postprocess_sequence": "da", "moe_hidden_sizes": "2048", "optimizer_adam_epsilon": 1e-09, "norm_epsilon": 1e-06}
+{"daisy_chain_variables": true, "optimizer_adam_beta1": 0.9, "scheduled_sampling_prob": 0.0, "num_hidden_layers": 2, "moe_loss_coef": 0.01, "max_target_seq_length": 0, "clip_grad_norm": 0.0, "pos": "timing", "scheduled_sampling_gold_mixin_prob": 0.5, "initializer": "uniform_unit_scaling", "grad_noise_scale": 0.0, "optimizer_momentum_momentum": 0.9, "nbr_decoder_problems": 1, "attention_key_channels": 0, "eval_drop_long_sequences": false, "learning_rate_cosine_cycle_steps": 250000, "prepend_mode": "none", "weight_decay": 0.0, "symbol_modality_skip_top": false, "weight_noise": 0.0, "target_modality": "default", "attention_dropout": 0.1, "parameter_attention_value_channels": 0, "factored_logits": false, "relu_dropout": 0.1, "no_data_parallelism": false, "layer_preprocess_sequence": "n", "sampling_method": "argmax", "learning_rate": 0.2, "num_heads": 2, "max_length": 256, "summarize_grads": false, "attention_value_channels": 0, "num_encoder_layers": 0, "label_smoothing": 0.1, "use_fixed_batch_size": false, "optimizer": "adam", "moe_k": 2, "self_attention_type": "dot_product", "learning_rate_decay_scheme": "noam", "sampling_temp": 1.0, "kernel_height": 3, "use_pad_remover": true, "batch_size": 4096, "max_relative_position": 0, "force_full_predict": false, "min_length_bucket": 8, "layer_prepostprocess_dropout": 0.1, "eval_run_autoregressive": false, "shared_embedding_and_softmax_weights": true, "symbol_modality_num_shards": 16, "dropout": 0.2, "compress_steps": 0, "parameter_attention_key_channels": 0, "length_bucket_step": 1.1, "kernel_width": 1, "hidden_size": 16, "num_decoder_layers": 0, "input_modalities": "default", "filter_size": 8, "optimizer_adam_beta2": 0.98, "scheduled_sampling_warmup_steps": 50000, "norm_type": "layer", "min_length": 0, "moe_num_experts": 64, "multiply_embedding_mode": "sqrt_depth", "max_input_seq_length": 0, "learning_rate_warmup_steps": 8000, "proximity_bias": false, "ffn_layer": "dense_relu_dense", "initializer_gain": 1.0, "layer_postprocess_sequence": "da", "moe_hidden_sizes": "2048", "optimizer_adam_epsilon": 1e-09, "norm_epsilon": 1e-06}

tensor2tensor/utils/adafactor.py

@@ -326,7 +326,7 @@ def adafactor_optimizer_from_hparams(hparams, lr):
   Raises:
     ValueError: on illegal values
   """
-  if hparams.optimizer_adafactor_decay_type == "Adam":
+  if hparams.optimizer_adafactor_decay_type == "adam":
     decay_rate = adafactor_decay_rate_adam(
         hparams.optimizer_adafactor_beta2)
   elif hparams.optimizer_adafactor_decay_type == "pow":

tensor2tensor/utils/learning_rate.py

@@ -90,7 +90,7 @@ def legacy_learning_rate_schedule(hparams):
     warmup = _learning_rate_warmup(warmup_steps, hparams=hparams)
     decay = _learning_rate_decay(hparams, warmup_steps)
     ret = tf.where(step_num < warmup_steps, warmup, decay)
-  optimizer_correction = 0.002 if "Adam" in hparams.optimizer else 1.0
+  optimizer_correction = 0.002 if "adam" in hparams.optimizer else 1.0
   tf.logging.info("Base learning rate: %f", hparams.learning_rate)
   return ret * optimizer_correction * hparams.learning_rate
 

tensor2tensor/utils/optimize.py

@@ -94,7 +94,7 @@ def optimize(loss, learning_rate, hparams, use_tpu=False, variables=None):
   return train_op
 
 
-@registry.register_optimizer("Adam")
+@registry.register_optimizer
 def adam(learning_rate, hparams):
   # We change the default epsilon for Adam.
   # Using LazyAdam as it's much faster for large vocabulary embeddings.
@@ -105,7 +105,7 @@ def adam(learning_rate, hparams):
       epsilon=hparams.optimizer_adam_epsilon)
 
 
-@registry.register_optimizer("MultistepAdam")
+@registry.register_optimizer
 def multistep_adam(learning_rate, hparams):
   return multistep_optimizer.MultistepAdamOptimizer(
       learning_rate,
@@ -115,22 +115,22 @@ def multistep_adam(learning_rate, hparams):
       n=hparams.optimizer_multistep_accumulate_steps)
 
 
-@registry.register_optimizer("Momentum")
+@registry.register_optimizer
 def momentum(learning_rate, hparams):
   return tf.train.MomentumOptimizer(
       learning_rate,
       momentum=hparams.optimizer_momentum_momentum,
       use_nesterov=hparams.optimizer_momentum_nesterov)
 
 
-@registry.register_optimizer("YellowFin")
+@registry.register_optimizer
 def yellow_fin(learning_rate, hparams):
   return yellowfin.YellowFinOptimizer(
       learning_rate=learning_rate,
       momentum=hparams.optimizer_momentum_momentum)
 
 
-@registry.register_optimizer("TrueAdam")
+@registry.register_optimizer
 def true_adam(learning_rate, hparams):
   return tf.train.AdamOptimizer(
       learning_rate,
@@ -139,7 +139,7 @@ def true_adam(learning_rate, hparams):
       epsilon=hparams.optimizer_adam_epsilon)
 
 
-@registry.register_optimizer("AdamW")
+@registry.register_optimizer
 def adam_w(learning_rate, hparams):
   # Openai gpt used weight decay.
   # Given the internals of AdamW, weight decay dependent on the
@@ -156,7 +156,7 @@ def adam_w(learning_rate, hparams):
       epsilon=hparams.optimizer_adam_epsilon)
 
 
-@registry.register_optimizer("Adafactor")
+@registry.register_optimizer("adafactor")
 def register_adafactor(learning_rate, hparams):
   return adafactor.adafactor_optimizer_from_hparams(hparams, learning_rate)
 
@@ -169,8 +169,11 @@ def _register_base_optimizer(key, fn):
 
 
 for k in tf.contrib.layers.OPTIMIZER_CLS_NAMES:
-  if k not in registry._OPTIMIZERS:  # pylint: disable=protected-access
+  if k not in registry.Registries.optimizers and k not in ('SGD', 'RMSProp'):
     _register_base_optimizer(k, tf.contrib.layers.OPTIMIZER_CLS_NAMES[k])
+_register_base_optimizer('sgd', tf.contrib.layers.OPTIMIZER_CLS_NAMES['SGD'])
+_register_base_optimizer(
+    'rms_prop', tf.contrib.layers.OPTIMIZER_CLS_NAMES['RMSProp'])
 
 
 class ConditionalOptimizer(tf.train.Optimizer):