This is a miniature PyTorch implementation of the paper Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity. Our implementation only has a few million parameters and doesn't do model parallel distributed training. It does single GPU training, but we implement the concept of switching as described in the paper.
The Switch Transformer uses different parameters for each token by switching among parameters based on the token. Therefore, only a fraction of parameters are chosen for each token. So you can have more parameters but less computational cost.
The switching happens at the Position-wise Feedforward network (FFN) of each transformer block. Position-wise feedforward network consists of two sequentially fully connected layers. In switch transformer we have multiple FFNs (multiple experts), and we chose which one to use based on a router. The output is a set of probabilities for picking a FFN, and we pick the one with the highest probability and only evaluate that. So essentially the computational cost is the same as having a single FFN. In our implementation this doesn't parallelize well when you have many or large FFNs since it's all happening on a single GPU. In a distributed setup you would have each FFN (each very large) on a different device.
The paper introduces another loss term to balance load among the experts (FFNs) and discusses dropping tokens when routing is not balanced.
Here's the training code and a notebook for training a switch transformer on Tiny Shakespeare dataset.
40import torch
41from torch import nn
42
43from labml_helpers.module import Module
44from labml_nn.transformers.feed_forward import FeedForward
45from labml_nn.transformers.mha import MultiHeadAttention
46from labml_nn.utils import clone_module_list
49class SwitchFeedForward(Module):
capacity_factor
is the capacity of each expert as a factor relative to ideally balanced load drop_tokens
specifies whether to drop tokens if more tokens are routed to an expert than the capacity is_scale_prob
specifies whether to multiply the input to the FFN by the routing probability n_experts
is the number of experts expert
is the expert layer, a FFN module d_model
is the number of features in a token embedding d_ff
is the number of features in the hidden layer of the FFN dropout
is dropout probability in the FFN54 def __init__(self, *,
55 capacity_factor: float,
56 drop_tokens: bool,
57 is_scale_prob: bool,
58 n_experts: int,
59 expert: FeedForward,
60 d_model: int):
71 super().__init__()
72
73 self.capacity_factor = capacity_factor
74 self.is_scale_prob = is_scale_prob
75 self.n_experts = n_experts
76 self.drop_tokens = drop_tokens
make copies of the FFNs
79 self.experts = clone_module_list(expert, n_experts)
Routing layer and softmax
81 self.switch = nn.Linear(d_model, n_experts)
82 self.softmax = nn.Softmax(dim=-1)
x
is the input to the switching module with shape [seq_len, batch_size, d_model]
84 def forward(self, x: torch.Tensor):
Capture the shape to change shapes later
90 seq_len, batch_size, d_model = x.shape
Flatten the sequence and batch dimensions
92 x = x.view(-1, d_model)
Get routing probabilities for each of the tokens. where is the number of experts n_experts
and is the linear transformation of token embeddings.
98 route_prob = self.softmax(self.switch(x))
Get the maximum routing probabilities and the routes. We route to the expert with highest probability
102 route_prob_max, routes = torch.max(route_prob, dim=-1)
Get indexes of tokens going to each expert
105 indexes_list = [torch.eq(routes, i).nonzero(as_tuple=True)[0] for i in range(self.n_experts)]
Initialize an empty tensor to store outputs
108 final_output = x.new_zeros(x.shape)
Capacity of each expert.
114 capacity = int(self.capacity_factor * len(x) / self.n_experts)
Number of tokens routed to each expert.
116 counts = x.new_tensor([len(indexes_list[i]) for i in range(self.n_experts)])
Initialize an empty list of dropped tokens
119 dropped = []
Only drop tokens if drop_tokens
is True
.
121 if self.drop_tokens:
Drop tokens in each of the experts
123 for i in range(self.n_experts):
Ignore if the expert is not over capacity
125 if len(indexes_list[i]) <= capacity:
126 continue
Shuffle indexes before dropping
128 indexes_list[i] = indexes_list[i][torch.randperm(len(indexes_list[i]))]
Collect the tokens over capacity as dropped tokens
130 dropped.append(indexes_list[i][capacity:])
Keep only the tokens upto the capacity of the expert
132 indexes_list[i] = indexes_list[i][:capacity]
Get outputs of the expert FFNs
135 expert_output = [self.experts[i](x[indexes_list[i], :]) for i in range(self.n_experts)]
Assign to final output
138 for i in range(self.n_experts):
139 final_output[indexes_list[i], :] = expert_output[i]
Pass through the dropped tokens
142 if dropped:
143 dropped = torch.cat(dropped)
144 final_output[dropped, :] = x[dropped, :]
145
146 if self.is_scale_prob:
Multiply by the expert outputs by the probabilities
148 final_output = final_output * route_prob_max.view(-1, 1)
149 else:
Don't scale the values but multiply by so that the gradients flow (this is something we experimented with).
152 final_output = final_output * (route_prob_max / route_prob_max.detach()).view(-1, 1)
Change the shape of the final output back to [seq_len, batch_size, d_model]
155 final_output = final_output.view(seq_len, batch_size, d_model)
Return
These are used for the load balancing loss and logging
166 return final_output, counts, route_prob.sum(0), len(dropped), route_prob_max
This is the same as normal transformer block with handling extra outputs of switch feedforward module.
169class SwitchTransformerLayer(Module):
d_model
is the token embedding size attn
is the attention module feed_forward
is the feed forward module (which is the switching module in this case) dropout_prob
is the probability of dropping out after self attention and FFN177 def __init__(self, *,
178 d_model: int,
179 attn: MultiHeadAttention,
180 feed_forward: SwitchFeedForward,
181 dropout_prob: float):
188 super().__init__()
189 self.size = d_model
190 self.attn = attn
191 self.feed_forward = feed_forward
192 self.dropout = nn.Dropout(dropout_prob)
193 self.norm_self_attn = nn.LayerNorm([d_model])
194 self.norm_ff = nn.LayerNorm([d_model])
196 def forward(self, *,
197 x: torch.Tensor,
198 mask: torch.Tensor):
Normalize the vectors before doing self attention
200 z = self.norm_self_attn(x)
Run through self attention, i.e. keys and values are from self
202 self_attn = self.attn(query=z, key=z, value=z, mask=mask)
Add the self attention results
204 x = x + self.dropout(self_attn)
Normalize for feed-forward
207 z = self.norm_ff(x)
Pass through the switching feed-forward network
209 ff, counts, route_prob, n_dropped, route_prob_max = self.feed_forward(z)
Add the feed-forward results back
211 x = x + self.dropout(ff)
212
213 return x, counts, route_prob, n_dropped, route_prob_max
216class SwitchTransformer(Module):
221 def __init__(self, layer: SwitchTransformerLayer, n_layers: int):
222 super().__init__()
Make copies of the transformer layer
224 self.layers = clone_module_list(layer, n_layers)
Final normalization layer
226 self.norm = nn.LayerNorm([layer.size])
228 def forward(self, x: torch.Tensor, mask: torch.Tensor):
Run through each transformer layer
230 counts, route_prob, n_dropped, route_prob_max = [], [], [], []
231 for layer in self.layers:
232 x, f, p, n_d, p_max = layer(x=x, mask=mask)
233 counts.append(f)
234 route_prob.append(p)
235 n_dropped.append(n_d)
236 route_prob_max.append(p_max)
Finally, normalize the vectors
238 x = self.norm(x)
240 return x, torch.stack(counts), torch.stack(route_prob), n_dropped, torch.stack(route_prob_max)