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 import mesh_tensorflow.transformer.transformer_layers import mesh_tensorflow.transformer.utils import t5.models.mesh_transformer # Macros: # ============================================================================== d_ff = 2048 d_kv = 64 d_model = 512 dropout_rate = 0.0 inputs_length = 512 mean_noise_span_length = 3.0 MIXTURE_NAME = 'c4_v220_unsupervised' noise_density = 0.15 num_heads = 8 num_layers = 6 # Parameters for adafactor_decay_rate_pow: # ============================================================================== adafactor_decay_rate_pow.offset = 0 # 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: # ============================================================================== denoise.inputs_fn = @preprocessors.noise_span_to_unique_sentinel denoise.noise_density = %noise_density denoise.noise_mask_fn = @preprocessors.random_spans_noise_mask denoise.targets_fn = @preprocessors.nonnoise_span_to_unique_sentinel # Parameters for decoder/DenseReluDense: # ============================================================================== decoder/DenseReluDense.activation = 'relu' decoder/DenseReluDense.dropout_rate = %dropout_rate decoder/DenseReluDense.hidden_size = %d_ff decoder/DenseReluDense.use_bias = False # Parameters for encoder/DenseReluDense: # ============================================================================== encoder/DenseReluDense.activation = 'relu' encoder/DenseReluDense.dropout_rate = %dropout_rate encoder/DenseReluDense.hidden_size = %d_ff encoder/DenseReluDense.use_bias = False # Parameters for enc_dec_attention: # ============================================================================== # None. # Parameters for enc_dec_attention_bias: # ============================================================================== # None. # Parameters for decoder/EncDecAttention: # ============================================================================== decoder/EncDecAttention.relative_attention_type = None # Parameters for get_variable_dtype: # ============================================================================== get_variable_dtype.activation_dtype = 'bfloat16' # Parameters for get_vocab_embedding_cls: # ============================================================================== # None. # Parameters for get_vocabulary: # ============================================================================== get_vocabulary.mixture_or_task_name = %MIXTURE_NAME # Parameters for decoder/LayerStack: # ============================================================================== decoder/LayerStack.dropout_rate = None decoder/LayerStack.norm_epsilon = None decoder/LayerStack.recompute_grads = False decoder/LayerStack.sublayers_final = \ [@transformer.sublayer_rms_norm, @transformer.sublayer_dropout] decoder/LayerStack.sublayers_initial = [@transformer.sublayer_dropout] decoder/LayerStack.sublayers_per_layer = \ [@transformer.sublayer_rms_norm, @transformer.sublayer_call_layer, @transformer.sublayer_dropout, @transformer.sublayer_residual] # Parameters for encoder/LayerStack: # ============================================================================== encoder/LayerStack.dropout_rate = None encoder/LayerStack.norm_epsilon = None encoder/LayerStack.recompute_grads = False encoder/LayerStack.sublayers_final = \ [@transformer.sublayer_rms_norm, @transformer.sublayer_dropout] encoder/LayerStack.sublayers_initial = [@transformer.sublayer_dropout] encoder/LayerStack.sublayers_per_layer = \ [@transformer.sublayer_rms_norm, @transformer.sublayer_call_layer, @transformer.sublayer_dropout, @transformer.sublayer_residual] # Parameters for learning_rate_schedule_noam: # ============================================================================== learning_rate_schedule_noam.linear_decay_fraction = 0.0 learning_rate_schedule_noam.multiplier = 1.0 learning_rate_schedule_noam.offset = 0 learning_rate_schedule_noam.warmup_steps = 10000 # Parameters for make_bitransformer: # ============================================================================== make_bitransformer.decoder_name = 'decoder' make_bitransformer.encoder_name = 'decoder' # Parameters for decoder/make_layer_stack: # ============================================================================== decoder/make_layer_stack.block_scope = True decoder/make_layer_stack.layers = \ [('self_attention', @mesh_tensorflow.transformer.transformer_layers.SelfAttention), ('enc_dec_attention', @mesh_tensorflow.transformer.transformer_layers.EncDecAttention), ('dense_relu_dense', @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 = \ [('self_attention', @mesh_tensorflow.transformer.transformer_layers.SelfAttention), ('dense_relu_dense', @mesh_tensorflow.transformer.transformer_layers.DenseReluDense)] encoder/make_layer_stack.num_layers = %num_layers # Parameters for mesh_train_dataset_fn: # ============================================================================== mesh_train_dataset_fn.mixture_or_task_name = %MIXTURE_NAME mesh_train_dataset_fn.pack = True mesh_train_dataset_fn.seed = None mesh_train_dataset_fn.shuffle = True mesh_train_dataset_fn.use_cached = 1 # Parameters for noise_span_to_unique_sentinel: # ============================================================================== # None. # Parameters for nonnoise_span_to_unique_sentinel: # ============================================================================== # None. # Parameters for pack_dataset: # ============================================================================== pack_dataset.use_custom_ops = True # Parameters for pack_or_pad: # ============================================================================== # None. # Parameters for random_spans_helper: # ============================================================================== random_spans_helper.extra_tokens_per_span_inputs = 1 random_spans_helper.extra_tokens_per_span_targets = 1 random_spans_helper.inputs_length = %inputs_length random_spans_helper.mean_noise_span_length = %mean_noise_span_length random_spans_helper.noise_density = %noise_density random_spans_helper.verbose = False # Parameters for random_spans_noise_mask: # ============================================================================== random_spans_noise_mask.mean_noise_span_length = %mean_noise_span_length # Parameters for random_spans_tokens_length: # ============================================================================== # None. # Parameters for reduce_concat_tokens: # ============================================================================== reduce_concat_tokens.batch_size = 128 reduce_concat_tokens.feature_key = 'targets' # Parameters for rewrite_stack_variables: # ============================================================================== rewrite_stack_variables.max_combined_variable_size = 536870912 # Parameters for run: # ============================================================================== run.autostack = True run.batch_size = ('tokens_per_batch', 65536) run.checkpoint_input_pipeline = False run.dataset_split = 'train' run.ensemble_inputs = None run.eval_checkpoint_step = None run.eval_dataset_fn = None run.eval_summary_dir = None run.export_checkpoint_step = None run.export_path = '' run.init_checkpoint = None run.iterations_per_loop = 100 run.keep_checkpoint_max = None run.layout_rules = \ 'ensemble:ensemble,batch:batch,d_ff:model,heads:model,vocab:model,experts:batch' run.learning_rate_schedule = @learning_rate_schedules.learning_rate_schedule_noam run.mesh_devices = None run.mesh_shape = @mesh_tensorflow.transformer.utils.tpu_mesh_shape() run.mode = 'train' run.model_type = 'bitransformer' run.optimizer = @optimize.AdafactorOptimizer run.output_eval_examples = True run.perplexity_eval_steps = 100 run.predict_fn = None run.save_checkpoints_steps = 5000 run.seen_data_init_step = 0 run.sequence_length = {'inputs': 512, 'targets': 128} run.skip_seen_data = False run.total_run_steps = None run.train_dataset_fn = @t5.models.mesh_transformer.mesh_train_dataset_fn run.train_steps = 524288 run.variable_filter = None # Parameters for select_random_chunk: # ============================================================================== select_random_chunk.additional_feature_keys = None select_random_chunk.additional_passthrough_keys = None select_random_chunk.feature_key = 'targets' select_random_chunk.max_length = 65536 select_random_chunk.uniform_random_start = False # 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.fold_scaling_into_initializer = True 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.fold_scaling_into_initializer = True 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 serialize_num_microbatches: # ============================================================================== serialize_num_microbatches.tokens_per_microbatch_per_replica = 8192 # Parameters for SimdMeshImpl: # ============================================================================== SimdMeshImpl.allreduce_in_bfloat16_max_group_size = 8 # Parameters for split_tokens: # ============================================================================== split_tokens.additional_feature_keys = None split_tokens.feature_key = 'targets' split_tokens.max_tokens_per_segment = @preprocessors.random_spans_tokens_length() split_tokens.min_tokens_per_segment = None split_tokens.passthrough_feature_keys = None # Parameters for sublayer_call_layer: # ============================================================================== # None. # Parameters for sublayer_dropout: # ============================================================================== sublayer_dropout.dropout_rate = %dropout_rate # Parameters for sublayer_mask_padding: # ============================================================================== # None. # Parameters for sublayer_residual: # ============================================================================== # None. # Parameters for sublayer_rms_norm: # ============================================================================== sublayer_rms_norm.epsilon = 1e-06 sublayer_rms_norm.name = 'rms_norm' # Parameters for tpu_estimator_model_fn: # ============================================================================== tpu_estimator_model_fn.hierarchical_tiling_spec = None tpu_estimator_model_fn.init_variable_filter = '' tpu_estimator_model_fn.model_info_file = '' 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 tpu_mesh_shape.model_parallelism = 1 tpu_mesh_shape.tpu_topology = '4x4' # Parameters for unit_scaling_convention: # ============================================================================== unit_scaling_convention.value = False # 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 = None 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 unsupervised: # ============================================================================== unsupervised.preprocessors = \ [@preprocessors.select_random_chunk, @preprocessors.reduce_concat_tokens, @preprocessors.split_tokens, @preprocessors.denoise] # Parameters for VarianceScalingInitializer: # ============================================================================== VarianceScalingInitializer.distribution = 'normal' VarianceScalingInitializer.mode = 'fan_in' VarianceScalingInitializer.scale = 1.0 # Parameters for VocabEmbedding: # ============================================================================== VocabEmbedding.scale_variable_like_classifier_weights = False