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[SwitchTransformer] Significant performance improvement on MoE blocks #31173

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Merged
ArthurZucker merged 4 commits into from
Jun 6, 2024
Merged

[SwitchTransformer] Significant performance improvement on MoE blocks #31173

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4965da6
SwitchTransformer MoE layer performance improvement
ranggihwang Jun 1, 2024
8e5bb3d
make fixup
ranggihwang Jun 3, 2024
3459299
comments about shapes
ranggihwang Jun 4, 2024
590cd13
make fixup
ranggihwang Jun 4, 2024
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14 changes: 11 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 @@ -294,9 +294,17 @@ 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)

router_mask = router_mask.bool()
batch_size, seq_len, num_experts = router_mask.shape
idx_mask = router_mask.transpose(1, 2).reshape(batch_size * seq_len, num_experts).sum(dim=0)
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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