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refactor(pi05): remove unused variables in embed_suffix method (#3263)

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* refactor(pi05): remove unused variables in embed_suffix method

* Refactor embed_suffix to streamline pad_masks handling

Removed unused pad_masks list and simplified its creation.

Signed-off-by: Duhyeon, Kim <49020301+dudududukim@users.noreply.github.com>

---------

Signed-off-by: Duhyeon, Kim <49020301+dudududukim@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
This commit is contained in:
Duhyeon, Kim
2026-07-23 21:37:57 +09:00
committed by GitHub
parent 9c82c39c7b
commit d6c605e8c5
1 changed files with 4 additions and 12 deletions
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src/lerobot/policies/pi05/modeling_pi05.py
+4 -12
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@@ -524,8 +524,6 @@ class PI05Pytorch(nn.Module): # see openpi `PI0Pytorch`
def embed_suffix(self, noisy_actions, timestep): def embed_suffix(self, noisy_actions, timestep):
"""Embed noisy_actions, timestep to prepare for Expert Gemma processing.""" """Embed noisy_actions, timestep to prepare for Expert Gemma processing."""
embs = []
pad_masks = []
att_masks = [] att_masks = []
# Embed timestep using sine-cosine positional encoding # Embed timestep using sine-cosine positional encoding
@@ -551,23 +549,17 @@ class PI05Pytorch(nn.Module): # see openpi `PI0Pytorch`
return F.silu(x) return F.silu(x)
time_emb = self._apply_checkpoint(time_mlp_func, time_emb) time_emb = self._apply_checkpoint(time_mlp_func, time_emb)
action_time_emb = action_emb
adarms_cond = time_emb adarms_cond = time_emb
embs.append(action_time_emb) bsize, action_time_dim = action_emb.shape[:2]
bsize, action_time_dim = action_time_emb.shape[:2] pad_masks = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device)
action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device)
pad_masks.append(action_time_mask)
# Set attention masks so that image, language and state inputs do not attend to action tokens # Set attention masks so that image, language and state inputs do not attend to action tokens
att_masks += [1] + ([0] * (self.config.chunk_size - 1)) att_masks += [1] + ([0] * (self.config.chunk_size - 1))
att_masks = torch.tensor(att_masks, dtype=action_emb.dtype, device=action_emb.device)
embs = torch.cat(embs, dim=1)
pad_masks = torch.cat(pad_masks, dim=1)
att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device)
att_masks = att_masks[None, :].expand(bsize, len(att_masks)) att_masks = att_masks[None, :].expand(bsize, len(att_masks))
return embs, pad_masks, att_masks, adarms_cond return action_emb, pad_masks, att_masks, adarms_cond
def forward(self, images, img_masks, tokens, masks, actions, noise, time) -> Tensor: def forward(self, images, img_masks, tokens, masks, actions, noise, time) -> Tensor:
"""Do a full training forward pass and compute the loss.""" """Do a full training forward pass and compute the loss."""