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ptt5-large-t5-vocab

Text2Text Generation
Transformers
PyTorch
TensorFlow
Portuguese
t5
tensorflow
pt-br
text-generation-inference
Model card Files Community
2
ptt5-large-t5-vocab
File size: 9,856 Bytes 
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import mesh_tensorflow.optimize
import mesh_tensorflow.transformer.dataset
import mesh_tensorflow.transformer.learning_rate_schedules
import mesh_tensorflow.transformer.t2t_vocabulary
import mesh_tensorflow.transformer.transformer_layers
import mesh_tensorflow.transformer.utils
import t5.data.sentencepiece_vocabulary
import t5.models.mesh_transformer

# Macros:
# ==============================================================================
d_ff = 4096
d_kv = 64
d_model = 1024
dropout_rate = 0.1
num_heads = 16
num_layers = 24

# Parameters for AdafactorOptimizer:
# ==============================================================================
AdafactorOptimizer.beta1 = 0.0
AdafactorOptimizer.clipping_threshold = 1.0
AdafactorOptimizer.decay_rate = None
AdafactorOptimizer.epsilon1 = 1e-30
AdafactorOptimizer.epsilon2 = 0.001
AdafactorOptimizer.factored = True
AdafactorOptimizer.min_dim_size_to_factor = 128
AdafactorOptimizer.multiply_by_parameter_scale = True

# Parameters for Bitransformer:
# ==============================================================================
Bitransformer.shared_embedding = True

# Parameters for denoise:
# ==============================================================================
# None.

# Parameters for decoder/DenseReluDense:
# ==============================================================================
decoder/DenseReluDense.activation = 'relu'
decoder/DenseReluDense.dropout_rate = %dropout_rate
decoder/DenseReluDense.hidden_size = %d_ff

# Parameters for encoder/DenseReluDense:
# ==============================================================================
encoder/DenseReluDense.activation = 'relu'
encoder/DenseReluDense.dropout_rate = %dropout_rate
encoder/DenseReluDense.hidden_size = %d_ff

# Parameters for decoder/EncDecAttention:
# ==============================================================================
# None.

# Parameters for get_variable_dtype:
# ==============================================================================
get_variable_dtype.activation_dtype = 'bfloat16'

# Parameters for get_vocab_embedding_cls:
# ==============================================================================
# None.

# Parameters for get_vocabulary:
# ==============================================================================
# None.

# Parameters for iid_noise_mask:
# ==============================================================================
# None.

# Parameters for decoder/LayerStack:
# ==============================================================================
decoder/LayerStack.dropout_rate = %dropout_rate
decoder/LayerStack.norm_epsilon = 1e-06
decoder/LayerStack.recompute_grads = False

# Parameters for encoder/LayerStack:
# ==============================================================================
encoder/LayerStack.dropout_rate = %dropout_rate
encoder/LayerStack.norm_epsilon = 1e-06
encoder/LayerStack.recompute_grads = False

# Parameters for make_bitransformer:
# ==============================================================================
make_bitransformer.decoder_name = 'decoder'
make_bitransformer.encoder_name = 'encoder'

# Parameters for decoder/make_layer_stack:
# ==============================================================================
decoder/make_layer_stack.block_scope = True
decoder/make_layer_stack.layers = \
    [@mesh_tensorflow.transformer.transformer_layers.SelfAttention,
     @mesh_tensorflow.transformer.transformer_layers.EncDecAttention,
     @mesh_tensorflow.transformer.transformer_layers.DenseReluDense]
decoder/make_layer_stack.num_layers = %num_layers

# Parameters for encoder/make_layer_stack:
# ==============================================================================
encoder/make_layer_stack.block_scope = True
encoder/make_layer_stack.layers = \
    [@mesh_tensorflow.transformer.transformer_layers.SelfAttention,
     @mesh_tensorflow.transformer.transformer_layers.DenseReluDense]
encoder/make_layer_stack.num_layers = %num_layers

# Parameters for maybe_print_dataset:
# ==============================================================================
maybe_print_dataset.should_print = False

# Parameters for mesh_train_dataset_fn:
# ==============================================================================
mesh_train_dataset_fn.use_cached = False

# Parameters for MtfModel:
# ==============================================================================
MtfModel.autostack = True
MtfModel.ensemble_inputs = None
MtfModel.gcp_project = None
MtfModel.layout_rules = \
    'ensemble:ensemble,batch:batch,d_ff:model,heads:model,vocab:model,experts:batch'
MtfModel.mesh_devices = None
MtfModel.mesh_shape = None
MtfModel.model_type = 'bitransformer'
MtfModel.optimizer = None
MtfModel.predict_fn = None
MtfModel.tpu_job_name = None
MtfModel.tpu_zone = None
MtfModel.variable_filter = None

# Parameters for noise_token_to_sentinel:
# ==============================================================================
# None.

# Parameters for num_parallel_calls:
# ==============================================================================
num_parallel_calls.deterministic = False

# Parameters for pack_dataset:
# ==============================================================================
pack_dataset.use_custom_ops = False

# Parameters for pack_or_pad:
# ==============================================================================
# None.

# Parameters for decoder/SelfAttention:
# ==============================================================================
decoder/SelfAttention.attention_func = None
decoder/SelfAttention.attention_kwargs = None
decoder/SelfAttention.combine_dims = True
decoder/SelfAttention.dropout_rate = %dropout_rate
decoder/SelfAttention.keep_query_heads_dims = False
decoder/SelfAttention.key_value_size = %d_kv
decoder/SelfAttention.num_heads = %num_heads
decoder/SelfAttention.num_memory_heads = 0
decoder/SelfAttention.relative_attention_num_buckets = 32
decoder/SelfAttention.relative_attention_type = 'bias_shared'
decoder/SelfAttention.shared_kv = False

# Parameters for encoder/SelfAttention:
# ==============================================================================
encoder/SelfAttention.attention_func = None
encoder/SelfAttention.attention_kwargs = None
encoder/SelfAttention.combine_dims = True
encoder/SelfAttention.dropout_rate = %dropout_rate
encoder/SelfAttention.keep_query_heads_dims = False
encoder/SelfAttention.key_value_size = %d_kv
encoder/SelfAttention.num_heads = %num_heads
encoder/SelfAttention.num_memory_heads = 0
encoder/SelfAttention.relative_attention_num_buckets = 32
encoder/SelfAttention.relative_attention_type = 'bias_shared'
encoder/SelfAttention.shared_kv = False

# Parameters for SentencePieceVocabulary:
# ==============================================================================
# None.

# Parameters for sentinel_id:
# ==============================================================================
sentinel_id.return_value = None

# Parameters for serialize_num_microbatches:
# ==============================================================================
serialize_num_microbatches.tokens_per_microbatch_per_replica = 8192

# Parameters for shift_targets:
# ==============================================================================
shift_targets.bos_id = 0
shift_targets.eos_id = 1

# Parameters for tpu_estimator_model_fn:
# ==============================================================================
tpu_estimator_model_fn.model_info_file = None
tpu_estimator_model_fn.outer_batch_size = 1
tpu_estimator_model_fn.tpu_summaries = False

# Parameters for tpu_mesh_shape:
# ==============================================================================
tpu_mesh_shape.ensemble_parallelism = None

# Parameters for decoder/Unitransformer:
# ==============================================================================
decoder/Unitransformer.d_model = %d_model
decoder/Unitransformer.ensemble = None
decoder/Unitransformer.input_full_attention = False
decoder/Unitransformer.label_smoothing = 0.0
decoder/Unitransformer.loss_denominator = 233472
decoder/Unitransformer.loss_fn = None
decoder/Unitransformer.loss_on_targets_only = False
decoder/Unitransformer.max_length = 512
decoder/Unitransformer.positional_embedding = False
decoder/Unitransformer.shared_embedding_and_softmax_weights = True
decoder/Unitransformer.sinusoid_positional_embedding = False
decoder/Unitransformer.token_dropout_rate = 0.0
decoder/Unitransformer.vocab_divisor = 128
decoder/Unitransformer.z_loss = 0.0001

# Parameters for encoder/Unitransformer:
# ==============================================================================
encoder/Unitransformer.d_model = %d_model
encoder/Unitransformer.ensemble = None
encoder/Unitransformer.input_full_attention = False
encoder/Unitransformer.label_smoothing = 0.0
encoder/Unitransformer.loss_denominator = None
encoder/Unitransformer.loss_fn = None
encoder/Unitransformer.loss_on_targets_only = False
encoder/Unitransformer.max_length = 512
encoder/Unitransformer.positional_embedding = False
encoder/Unitransformer.shared_embedding_and_softmax_weights = True
encoder/Unitransformer.sinusoid_positional_embedding = False
encoder/Unitransformer.token_dropout_rate = 0.0
encoder/Unitransformer.vocab_divisor = 128
encoder/Unitransformer.z_loss = 0.0001

# Parameters for VarianceScalingInitializer:
# ==============================================================================
VarianceScalingInitializer.distribution = 'normal'
VarianceScalingInitializer.mode = 'fan_in'
VarianceScalingInitializer.scale = 1.0

# Parameters for VocabEmbedding:
# ==============================================================================
# None.

# Parameters for Vocabulary:
# ==============================================================================
# None.