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Fix BOS token embedding: use embed_tokens() instead of raw integer #264

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Fix BOS token embedding: use embed_tokens() instead of raw integer #264

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5 changes: 4 additions & 1 deletion video_chat/models/videochat_it.py
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Original file line number Diff line number Diff line change
Expand Up @@ -265,7 +265,10 @@ def forward(self, image, text_input):
attention_mask = torch.zeros([batch_size, txt_len], dtype=torch.long).to(img_embeds.device)
targets = torch.ones([batch_size, txt_len], dtype=torch.long).to(img_embeds.device).fill_(-100)
# set bos_token
inputs_embeds[:, :1] = self.llama_tokenizer.bos_token_id
bos_embeds = self.llama_model.model.embed_tokens(
torch.tensor([[self.llama_tokenizer.bos_token_id]], device=inputs_embeds.device)
)
inputs_embeds[:, :1] = bos_embeds
for idx in range(batch_size):
input_len = min(input_embed_list[idx].shape[1], txt_len - 1)
# if less than txt_len, the input will be padding
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7 changes: 6 additions & 1 deletion video_chat2/models/videochat_mistra/videochat2_it_hd_mistral.py
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Original file line number Diff line number Diff line change
Expand Up @@ -368,7 +368,12 @@ def forward(self, image, text_input, instruction):
attention_mask = torch.zeros([batch_size, txt_len], dtype=torch.long).to(device)
targets = torch.ones([batch_size, txt_len], dtype=torch.long).to(device).fill_(-100)
# set bos_token
inputs_embeds[:, :1] = self.mistral_tokenizer.bos_token_id
bos_token_id = torch.tensor([[self.mistral_tokenizer.bos_token_id]], device=inputs_embeds.device)
if self.use_lora:
bos_embeds = self.mistral_model.base_model.model.model.embed_tokens(bos_token_id)
else:
bos_embeds = self.mistral_model.model.embed_tokens(bos_token_id)
inputs_embeds[:, :1] = bos_embeds

for idx in range(batch_size):
input_len = min(input_embed_list[idx].shape[1], txt_len - 1)
Expand Down
7 changes: 6 additions & 1 deletion video_chat2/models/videochat_mistra/videochat2_it_mistral.py
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Original file line number Diff line number Diff line change
Expand Up @@ -299,7 +299,12 @@ def forward(self, image, text_input, instruction):
attention_mask = torch.zeros([batch_size, txt_len], dtype=torch.long).to(img_embeds.device)
targets = torch.ones([batch_size, txt_len], dtype=torch.long).to(img_embeds.device).fill_(-100)
# set bos_token
inputs_embeds[:, :1] = self.mistral_tokenizer.bos_token_id
bos_token_id = torch.tensor([[self.mistral_tokenizer.bos_token_id]], device=inputs_embeds.device)
if self.use_lora:
bos_embeds = self.mistral_model.base_model.model.model.embed_tokens(bos_token_id)
else:
bos_embeds = self.mistral_model.model.embed_tokens(bos_token_id)
inputs_embeds[:, :1] = bos_embeds
for idx in range(batch_size):
input_len = min(input_embed_list[idx].shape[1], txt_len - 1)
# if less than txt_len, the input will be padding
Expand Down
7 changes: 6 additions & 1 deletion video_chat2/models/videochat_vicuna/videochat2_it_vicuna.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,7 +296,12 @@ def forward(self, image, text_input, instruction):
attention_mask = torch.zeros([batch_size, txt_len], dtype=torch.long).to(img_embeds.device)
targets = torch.ones([batch_size, txt_len], dtype=torch.long).to(img_embeds.device).fill_(-100)
# set bos_token
inputs_embeds[:, :1] = self.llama_tokenizer.bos_token_id
bos_token_id = torch.tensor([[self.llama_tokenizer.bos_token_id]], device=inputs_embeds.device)
if self.use_lora:
bos_embeds = self.llama_model.base_model.model.model.embed_tokens(bos_token_id)
else:
bos_embeds = self.llama_model.model.embed_tokens(bos_token_id)
inputs_embeds[:, :1] = bos_embeds
for idx in range(batch_size):
input_len = min(input_embed_list[idx].shape[1], txt_len - 1)
# if less than txt_len, the input will be padding
Expand Down