# coding=utf-8 # Copyright 2022, Google and HuggingFace Inc. # # 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. """ Switch Transformers model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json", } class SwitchTransformersConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SwitchTransformersModel`]. It is used to instantiate a SwitchTransformers model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the SwitchTransformers [google/switch-base-8](https://huggingface.co/google/switch-base-8) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 32128): Vocabulary size of the SwitchTransformers model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SwitchTransformersModel`]. d_model (`int`, *optional*, defaults to 768): Size of the encoder layers and the pooler layer. d_kv (`int`, *optional*, defaults to 64): Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`. d_ff (`int`, *optional*, defaults to 2048): Size of the intermediate feed forward layer in each `SwitchTransformersBlock`. expert_capacity (`int`, *optional*, defaults to 64): Number of tokens that can be stored in each expert. If set to 1, the model will behave like a regular Transformer. num_layers (`int`, *optional*, defaults to 12): Number of dense hidden layers in the Transformer encoder layer. num_sparse_encoder_layers (`int`, *optional*, defaults to 3): Number of sparse (MoE) dense hidden layers in the Transformer encoder layer. num_decoder_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. num_sparse_decoder_layers (`int`, *optional*, defaults to 3): Number of sparse (MoE) dense hidden layers in the Transformer decoder layer. num_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. num_experts (`int`, *optional*, defaults to 8): Number of experts for each SwitchTransformer layer. router_bias (`bool`, *optional*, defaults to `False`): Whether to add a bias to the router. router_jitter_noise (`float`, *optional*, defaults to 0.01): Amount of noise to add to the router. router_dtype (`str`, *optional*, default to `"float32"`): The `dtype` used for the routers. It is preferable to keep the `dtype` to `"float32"` as specified in the *selective precision* discussion in [the paper](https://arxiv.org/abs/2101.03961). router_ignore_padding_tokens (`bool`, *optional*, defaults to `False`): Whether to ignore padding tokens when routing. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. dropout_rate (`float`, *optional*, defaults to 0.1): The ratio for all dropout layers. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. layer_norm_eps (`float`, *optional*, defaults to 1e-6): The epsilon used by the layer normalization layers. router_z_loss_coef (`float`, *optional*, defaults to 0.001): The z loss factor for the total loss. router_aux_loss_coef (`float`, *optional*, defaults to 0.001): The aux loss factor for the total loss. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). dense_act_fn (`string`, *optional*, defaults to `"relu"`): Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. SwitchTransformersv1.1 uses the `"gated-gelu"` feed forward projection. Original SwitchTransformers uses `"relu"`. add_router_probs (`bool`, *optional*, defaults to `False`): Whether to output router probabilities to compute router auxiliary loss. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ model_type = "switch_transformers" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} def __init__( self, vocab_size=32128, d_model=768, d_kv=64, d_ff=2048, expert_capacity=64, num_layers=12, num_sparse_encoder_layers=3, num_decoder_layers=12, num_sparse_decoder_layers=3, num_heads=12, num_experts=8, router_bias=False, router_jitter_noise=0.01, router_dtype="float32", router_ignore_padding_tokens=False, relative_attention_num_buckets=32, relative_attention_max_distance=128, dropout_rate=0.1, classifier_dropout=0.0, layer_norm_epsilon=1e-6, router_z_loss_coef=0.001, router_aux_loss_coef=0.001, initializer_factor=1.0, dense_act_fn="relu", is_encoder_decoder=True, add_router_probs=False, use_cache=True, pad_token_id=0, eos_token_id=1, **kwargs, ): self.vocab_size = vocab_size self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff self.num_sparse_encoder_layers = num_sparse_encoder_layers self.num_layers = num_layers self.num_decoder_layers = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry self.num_sparse_decoder_layers = num_sparse_decoder_layers # This tells us, each how many encoder layer we'll have to set a sparse layer. if self.num_sparse_encoder_layers > 0: self.encoder_sparse_step = self.num_layers // self.num_sparse_encoder_layers else: self.encoder_sparse_step = self.num_layers # HACK: this will create 0 sparse layers # This tells us, each how many encoder layer we'll have to set a sparse layer. if self.num_sparse_decoder_layers > 0: self.decoder_sparse_step = self.num_decoder_layers // self.num_sparse_decoder_layers else: self.decoder_sparse_step = self.num_decoder_layers # HACK: this will create 0 sparse layers self.num_heads = num_heads self.num_experts = num_experts self.expert_capacity = expert_capacity self.router_bias = router_bias self.router_jitter_noise = router_jitter_noise if router_dtype not in ["float32", "float16", "bfloat16"]: raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}") self.router_dtype = router_dtype self.router_ignore_padding_tokens = router_ignore_padding_tokens self.relative_attention_num_buckets = relative_attention_num_buckets self.relative_attention_max_distance = relative_attention_max_distance self.dropout_rate = dropout_rate if classifier_dropout is not None: self.classifier_dropout = classifier_dropout else: self.classifier_dropout = 0.0 self.layer_norm_epsilon = layer_norm_epsilon self.initializer_factor = initializer_factor self.use_cache = use_cache self.add_router_probs = add_router_probs self.router_z_loss_coef = router_z_loss_coef self.router_aux_loss_coef = router_aux_loss_coef self.dense_act_fn = dense_act_fn super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, **kwargs, )