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Significant performance improvement on MoE block of SwitchTransformer #30490

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Significant performance improvement on MoE block of SwitchTransformer #30490

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performance moe for switchtransformer
ranggihwang Apr 25, 2024
0b75517
Update src/transformers/models/switch_transformers/modeling_switch_tr…
ranggihwang Apr 26, 2024
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style by fixup
ranggihwang Apr 27, 2024
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19 changes: 16 additions & 3 deletions src/transformers/models/switch_transformers/modeling_switch_transformers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -297,9 +297,22 @@ def forward(self, hidden_states):
# can be unchanged from one layer to another. That is why the hidden states are cloned before updating only the seleced ones.

next_states = hidden_states.clone()
for idx, expert in enumerate(self.experts.values()):
token_indices = router_mask[:, :, idx].bool()
next_states[token_indices] = expert(hidden_states[token_indices]).to(next_states.dtype)
# for idx, expert in enumerate(self.experts.values()):
# token_indices = router_mask[:, :, idx].bool()
# next_states[token_indices] = expert(hidden_states[token_indices]).to(next_states.dtype)
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# Preformance improvement version of Switch Transformer
# It utilized sparse tensor and only access the activated experts
# This significantly reduces latency proprotional to the number of experts.
router_mask = router_mask.bool()
idx_mask = router_mask.transpose(1,2)
idx_mask = torch.cat(torch.split(idx_mask, 1, dim=0), dim=2)
idx_mask = idx_mask.sum(dim=2)
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idx_mask = idx_mask.sum(dim=2)
idx_mask = idx_mask.sum(dim=1)

idx_mask = idx_mask.squeeze() # length: number of experts / value: number of tokens
idx_mask = torch.nonzero(idx_mask, as_tuple=True)[0].tolist() # length: number of "activated" expert / value: index
for idx in idx_mask:
next_states[router_mask[:, :, idx]] = getattr(self.experts, "expert_{}".format(idx)) \
(hidden_states[router_mask[:, :, idx]])

hidden_states = router_probs * next_states
return hidden_states, (router_logits, expert_index)
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