
 
SwitchTransformers
	
Overview
	
The SwitchTransformers model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity by William Fedus, Barret Zoph, Noam Shazeer.
The Switch Transformer model uses a sparse T5 encoder-decoder architecture, where the MLP are replaced by a Mixture of Experts (MoE). A routing mechanism (top 1 in this case) associates each token to one of the expert, where each expert is a dense MLP. While switch transformers have a lot more weights than their equivalent dense models, the sparsity allows better scaling and better finetuning performance at scale.
During a forward pass, only a fraction of the weights are used. The routing mechanism allows the model to select relevant weights on the fly which increases the model capacity without increasing the number of operations.
The abstract from the paper is the following:
In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model — with outrageous numbers of parameters — but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability — we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the “Colossal Clean Crawled Corpus” and achieve a 4x speedup over the T5-XXL model.
Tips:
SwitchTransformers uses the T5Tokenizer, which can be loaded directly from each model’s repository.
The released weights are pretrained on English Masked Language Modeling task, and should be finetuned.
This model was contributed by Younes Belkada and Arthur Zucker .
The original code can be found here.
Resources
	
Translation task guide
Summarization task guide
SwitchTransformersConfig
	
class transformers.SwitchTransformersConfig
<
source
>
(
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
layer_norm_epsilon = 1e-06
router_z_loss_coef = 0.001
router_aux_loss_coef = 0.001
initializer_factor = 1.0
feed_forward_proj = 'relu'
is_encoder_decoder = True
add_router_probs = False
use_cache = True
pad_token_id = 0
eos_token_id = 1
**kwargs
)
Parameters 
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 512) —
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 6) —
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 12) —
Number of sparse (MoE) dense hidden layers in the Transformer decoder layer.
num_heads (int, optional, defaults to 8) —
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_type (str, optional, defaults to "tokens_masked") —
Router type - choose between "tokens_masked", “tokens_scatter”and“experts_masked”`.
router_bias (bool, optional, defaults to True) —
Whether to add a bias to the router.
router_jitter_noise (float, optional, defaults to 0.1) —
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.
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.
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) —
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
testing).
feed_forward_proj (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).
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 architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
SwitchTransformersTop1Router
	
class transformers.SwitchTransformersTop1Router
<
source
>
(
config: SwitchTransformersConfig
)
Router using tokens choose top-1 experts assignment.
This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE
(https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_probs and then
routed to their choice of expert until the expert’s expert_capacity is reached. There is no guarantee that each
token is processed by an expert, or that each expert receives at least one token.
_compute_router_probabilities
<
source
>
(
hidden_states: Tensor
)
→
router_probabilities (torch.Tensor)
Parameters 
hidden_states (torch.Tensor) —
(batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
Returns
router_probabilities (torch.Tensor)
Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each
token and expert. Used for routing tokens to experts.
router_logits (torch.Tensor):
Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits.
This is used later for computing router z-loss.
Computes router probabilities from input hidden states.
forward
<
source
>
(
hidden_states: Tensor
)
Parameters 
hidden_states (torch.Tensor)  —
[num_groups, tokens_per_group, hidden_dim] inputs to send to experts.
Generic forward function for every Router class. Each Router expects to have the same input hidden states
(hidden_states) corresponding to the hidden states for each token, the expert_capacity corresponding to the
number of tokens the Router will send to each expert, some Routers can send up to few tokens to each expert.
Each Router works as the following: it expects the hidden states for each token, gets the router_probs and
router_logits from the router_weights. This will assign for each token, the raw probability to be assigned
to an expert. Then each Router class will have to define its own _compute_routing_instructions.
SwitchTransformersSparseMLP
	
class transformers.SwitchTransformersSparseMLP
<
source
>
(
config: SwitchTransformersConfig
expert_class: Module = <class 'transformers.models.switch_transformers.modeling_switch_transformers.SwitchTransformersDenseActDense'>
)
Implementation of the Switch Transformers Sparse MLP module.
forward
<
source
>
(
hidden_states
)
Hold on, this will be slightly tricky to understand In the correct order, a MoE layer does the following:
1- Gets the router_mask from the router. The shape of the mask is (batch_size, sequence_length, num_expert)
and corresponds to the argmax of the router_probs. The probabilities are needed in the computation of the
hidden states : they are broadcasted to the hidden states values (can be interpreted as a scaling factor).
2- Dispatch the tokens to its associated experts. We do a classic for loop over the experts and assign for each
expert the corresponding hidden states.
SwitchTransformersModel
	
class transformers.SwitchTransformersModel
<
source
>
(
config: SwitchTransformersConfig
)
Parameters 
config (SwitchTransformersConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.
The bare SWITCH_TRANSFORMERS Model transformer outputting raw hidden-states without any specific head on top.
The SWITCH_TRANSFORMERS model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with
Simple and Efficient Sparsity by William
Fedus, Barret
Zoph, and Noam
Shazeer. It’s an encoder-decoder T5-like model
with sparse Feed Forward that stands for Mixture of Experts (MoE) architecture.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
decoder_head_mask: typing.Optional[torch.FloatTensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None
inputs_embeds: typing.Optional[torch.Tensor] = None
decoder_inputs_embeds: typing.Optional[torch.Tensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
output_router_logits: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.Seq2SeqMoEModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
embeddings so you should be able to pad the inputs on both the right and the left.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for detail.
What are input IDs?
To know more on how to prepare input_ids for pretraining take a look a SWITCH_TRANSFORMERS
Training.
attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
SWITCH_TRANSFORMERS uses the pad_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
To know more on how to prepare decoder_input_ids for pretraining take a look at SWITCH_TRANSFORMERS
Training.
decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
decoder_head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
[0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size) is a sequence of hidden states at
the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
output_router_logits (bool, optional) —
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.Seq2SeqMoEModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqMoEModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (SwitchTransformersConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the decoder of the model.
If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
modules.
The SwitchTransformersModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
	Copied
 from transformers import AutoTokenizer, SwitchTransformersModel
 tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
 model = SwitchTransformersModel.from_pretrained("google/switch-base-8")
 input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids  # Batch size 1
 decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
 # preprocess: Prepend decoder_input_ids with start token which is pad token for SwitchTransformersModel.
 # This is not needed for torch's SwitchTransformersForConditionalGeneration as it does this internally using labels arg.
 decoder_input_ids = model._shift_right(decoder_input_ids)
 # forward pass
 outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
 last_hidden_states = outputs.last_hidden_state
SwitchTransformersForConditionalGeneration
	
class transformers.SwitchTransformersForConditionalGeneration
<
source
>
(
config: SwitchTransformersConfig
)
Parameters 
config (SwitchTransformersConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.
SWITCH_TRANSFORMERS Model with a language modeling head on top.
The SWITCH_TRANSFORMERS model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with
Simple and Efficient Sparsity by William
Fedus, Barret
Zoph, and Noam
Shazeer. It’s an encoder-decoder T5-like model
with sparse Feed Forward that stands for Mixture of Experts (MoE) architecture.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.BoolTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
decoder_head_mask: typing.Optional[torch.FloatTensor] = None
cross_attn_head_mask: typing.Optional[torch.Tensor] = None
encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None
past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
output_router_logits: typing.Optional[bool] = True
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.Seq2SeqMoEOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
embeddings so you should be able to pad the inputs on both the right and the left.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for detail.
What are input IDs?
To know more on how to prepare input_ids for pretraining take a look a SWITCH_TRANSFORMERS
Training.
attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
SWITCH_TRANSFORMERS uses the pad_token_id as the starting token for decoder_input_ids generation. If
past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
To know more on how to prepare decoder_input_ids for pretraining take a look at SWITCH_TRANSFORMERS
Training.
decoder_attention_mask (torch.BoolTensor of shape (batch_size, target_sequence_length), optional) —
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also
be used by default.
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
decoder_head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
cross_attn_head_mask (torch.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
[0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size) is a sequence of hidden states at
the output of the last layer of the encoder. Used in the cross-attention of the decoder.
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.
use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
output_router_logits (bool, optional) —
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the sequence classification/regression loss. Indices should be in [-100, 0, ..., config.vocab_size - 1]. All labels set to -100 are ignored (masked), the loss is only computed for
labels in [0, ..., config.vocab_size]
Returns
transformers.modeling_outputs.Seq2SeqMoEOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.Seq2SeqMoEOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (SwitchTransformersConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
decoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
encoder_router_logits (tuple(torch.FloatTensor), optional, returned when output_router_logits=True is passed or when config.add_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts
models.
The SwitchTransformersForConditionalGeneration forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
	Copied
 from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration
 tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
 model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-8")
 # training
 input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
 labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
 outputs = model(input_ids=input_ids, labels=labels)
 loss = outputs.loss
 logits = outputs.logits
 # inference
 input_ids = tokenizer(
...     "summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids  # Batch size 1
 outputs = model.generate(input_ids)
 # . To, let’s say you have a dog. To summarize:
 # Since the model has been trained on MLM, this will output gibberish
SwitchTransformersEncoderModel
	
class transformers.SwitchTransformersEncoderModel
<
source
>
(
config: SwitchTransformersConfig
)
Parameters 
config (SwitchTransformersConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.
The bare SWITCH_TRANSFORMERS Model transformer outputting encoder’s raw hidden-states without any specific head on top.
The SWITCH_TRANSFORMERS model was proposed in Switch Transformers: Scaling to Trillion Parameter Models with
Simple and Efficient Sparsity by William
Fedus, Barret
Zoph, and Noam
Shazeer. It’s an encoder-decoder T5-like model
with sparse Feed Forward that stands for Mixture of Experts (MoE) architecture.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch torch.nn.Module subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
head_mask: typing.Optional[torch.FloatTensor] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
output_router_logits: typing.Optional[bool] = True
return_dict: typing.Optional[bool] = None
)
→
transformers.modeling_outputs.MoEModelOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. SWITCH_TRANSFORMERS is a model with relative position
embeddings so you should be able to pad the inputs on both the right and the left.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for detail.
To know more on how to prepare input_ids for pretraining take a look a SWITCH_TRANSFORMERS
Training.
attention_mask (torch.FloatTensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?
head_mask (torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) —
Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.
output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.
output_router_logits (bool, optional) —
Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
should not be returned during inference.
return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.
Returns
transformers.modeling_outputs.MoEModelOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.MoEModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (SwitchTransformersConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
router_probs (tuple(torch.FloatTensor), optional, returned when output_router_probs=True and config.add_router_probs=True is passed or when config.output_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).
Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary
loss and the z_loss for Mixture of Experts models.
The SwitchTransformersEncoderModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Example:
	Copied
 from transformers import AutoTokenizer, SwitchTransformersEncoderModel
 tokenizer = AutoTokenizer.from_pretrained("google/switch-base-8")
 model = SwitchTransformersEncoderModel.from_pretrained("google/switch-base-8")
 input_ids = tokenizer(
...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
... ).input_ids  # Batch size 1
 outputs = model(input_ids=input_ids)
 last_hidden_states = outputs.last_hidden_state
←SqueezeBERT
T5→
SwitchTransformers
Overview
Resources
SwitchTransformersConfig
SwitchTransformersTop1Router
SwitchTransformersSparseMLP
SwitchTransformersModel
SwitchTransformersForConditionalGeneration
SwitchTransformersEncoderModel
