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[SwitchTransformer] Significant performance improvement on MoE blocks #31173

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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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16 changes: 13 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 +294,19 @@ 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()
idx_mask = router_mask.transpose(1, 2) # Batch * experts * tokens
idx_mask = torch.cat(torch.split(idx_mask, 1, dim=0), dim=2) # 1 * experts * (batch * tokens)
idx_mask = idx_mask.sum(dim=2)
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
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Suggested change
router_mask = router_mask.bool()
idx_mask = router_mask.transpose(1, 2) # Batch * experts * tokens
idx_mask = torch.cat(torch.split(idx_mask, 1, dim=0), dim=2) # 1 * experts * (batch * tokens)
idx_mask = idx_mask.sum(dim=2)
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
idx_mask = router_mask.reshape(batch*seq_len, num_experts).transpose(0,1).sum(dim=1)
idx_mask = torch.nonzero(idx_mask, as_tuple=True)[0].tolist()
  • the comment about shapes! 🤗

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The batch_size, seq_len, and num_experts are not defined in the funciton.
So, I've defined it with the router_mask and reflected your suggestions.

Thank you @ArthurZucker !

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thanks!

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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