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[bugfix] Fix tensor device in Idefics2, Idefics3, and SmolVLM #39975

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Merged
merged 4 commits into from
Aug 13, 2025
Merged

[bugfix] Fix tensor device in Idefics2, Idefics3, and SmolVLM #39975

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ab27606
[bugfix] ensure correct tensor device in Idefics2, Idefics3, and Smol…
qgallouedec Aug 7, 2025
36371b0
to cuda
qgallouedec Aug 12, 2025
6ac95c6
Merge branch 'main' into fix-smolvlm-idefics
qgallouedec Aug 12, 2025
b5aa571
Merge branch 'main' into fix-smolvlm-idefics
qgallouedec Aug 12, 2025
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11 changes: 7 additions & 4 deletions src/transformers/models/idefics2/modeling_idefics2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,8 +141,12 @@ def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.B
embeddings = patch_embeds.flatten(2).transpose(1, 2)

max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
position_ids = torch.full(size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)
boundaries = torch.arange(
1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side, device=pixel_values.device
)
position_ids = torch.full(
size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0, device=pixel_values.device
)

for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
nb_patches_h = p_attn_mask[:, 0].sum()
Expand All @@ -158,9 +162,8 @@ def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.B
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)

pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids[batch_idx][p_attn_mask.view(-1)] = pos_ids

position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings = embeddings + self.position_embedding(position_ids)
return embeddings

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11 changes: 7 additions & 4 deletions src/transformers/models/idefics3/modeling_idefics3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,8 +140,12 @@ def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.B
embeddings = patch_embeds.flatten(2).transpose(1, 2)

max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
position_ids = torch.full(size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)
boundaries = torch.arange(
1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side, device=pixel_values.device
)
position_ids = torch.full(
size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0, device=pixel_values.device
)

for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
nb_patches_h = p_attn_mask[:, 0].sum()
Expand All @@ -157,9 +161,8 @@ def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.B
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)

pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids[batch_idx][p_attn_mask.view(-1)] = pos_ids

position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings = embeddings + self.position_embedding(position_ids)
return embeddings

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11 changes: 7 additions & 4 deletions src/transformers/models/smolvlm/modeling_smolvlm.py
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@ArthurZucker ArthurZucker Aug 12, 2025

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test_export_smolvlm_vision_encoder does not seem to have been triggered

Original file line number Diff line number Diff line change
Expand Up @@ -135,8 +135,12 @@ def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.B
embeddings = patch_embeds.flatten(2).transpose(1, 2)

max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size
boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side)
position_ids = torch.full(size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0)
boundaries = torch.arange(
1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side, device=pixel_values.device
)
position_ids = torch.full(
size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0, device=pixel_values.device
)

for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
nb_patches_h = p_attn_mask[:, 0].sum()
Expand All @@ -152,9 +156,8 @@ def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.B
bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)

pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten()
position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids
position_ids[batch_idx][p_attn_mask.view(-1)] = pos_ids

position_ids = position_ids.to(self.position_embedding.weight.device)
embeddings = embeddings + self.position_embedding(position_ids)
return embeddings

Expand Down