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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Defines Transformer model parameters."""
import collections
BASE_PARAMS = collections.defaultdict(
lambda: None, # Set default value to None.
# Input params
default_batch_size=2048, # Maximum number of tokens per batch of examples.
default_batch_size_tpu=32768,
max_length=256, # Maximum number of tokens per example.
# Model params
initializer_gain=1.0, # Used in trainable variable initialization.
vocab_size=33708, # Number of tokens defined in the vocabulary file.
hidden_size=512, # Model dimension in the hidden layers.
num_hidden_layers=6, # Number of layers in the encoder and decoder stacks.
num_heads=8, # Number of heads to use in multi-headed attention.
filter_size=2048, # Inner layer dimension in the feedforward network.
# Dropout values (only used when training)
layer_postprocess_dropout=0.1,
attention_dropout=0.1,
relu_dropout=0.1,
# Training params
label_smoothing=0.1,
learning_rate=2.0,
learning_rate_decay_rate=1.0,
learning_rate_warmup_steps=16000,
# Optimizer params
optimizer_adam_beta1=0.9,
optimizer_adam_beta2=0.997,
optimizer_adam_epsilon=1e-09,
# Default prediction params
extra_decode_length=50,
beam_size=4,
alpha=0.6, # used to calculate length normalization in beam search
# TPU specific parameters
use_tpu=False,
static_batch=False,
allow_ffn_pad=True,
)
BIG_PARAMS = BASE_PARAMS.copy()
BIG_PARAMS.update(
default_batch_size=4096,
# default batch size is smaller than for BASE_PARAMS due to memory limits.
default_batch_size_tpu=16384,
hidden_size=1024,
filter_size=4096,
num_heads=16,
)
# Parameters for running the model in multi gpu. These should not change the
# params that modify the model shape (such as the hidden_size or num_heads).
BASE_MULTI_GPU_PARAMS = BASE_PARAMS.copy()
BASE_MULTI_GPU_PARAMS.update(
learning_rate_warmup_steps=8000
)
BIG_MULTI_GPU_PARAMS = BIG_PARAMS.copy()
BIG_MULTI_GPU_PARAMS.update(
layer_postprocess_dropout=0.3,
learning_rate_warmup_steps=8000
)
# Parameters for testing the model
TINY_PARAMS = BASE_PARAMS.copy()
TINY_PARAMS.update(
default_batch_size=1024,
default_batch_size_tpu=1024,
hidden_size=32,
num_heads=4,
filter_size=256,
)
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