AI/MiniCPM-o-2_6
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Replace the inplace operation

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Hongji Zhu 2025-01-20 11:56:10 +08:00
parent 2710956711
commit 161f4b7db3
1 changed files with 13 additions and 10 deletions
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modeling_minicpmo.py
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@ -377,6 +377,8 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
else: else:
vllm_embedding = self.llm.model.embed_tokens(data["input_ids"]) vllm_embedding = self.llm.model.embed_tokens(data["input_ids"])
new_vllm_embedding = vllm_embedding.clone()
vision_hidden_states = [ vision_hidden_states = [
i.type(vllm_embedding.dtype) if isinstance(i, torch.Tensor) else i for i in vision_hidden_states i.type(vllm_embedding.dtype) if isinstance(i, torch.Tensor) else i for i in vision_hidden_states
] ]
@ -392,15 +394,16 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
[torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound] [torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound]
).to(vllm_embedding.device) ).to(vllm_embedding.device)
cur_vllm_emb.scatter_( new_vllm_embedding[i] = cur_vllm_emb.scatter(
0, 0,
image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]), image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]),
cur_vs_hs.view(-1, cur_vs_hs.shape[-1]), cur_vs_hs.view(-1, cur_vs_hs.shape[-1]),
) )
elif self.training:
cur_vllm_emb += cur_vs_hs[0].mean() * 0
return vllm_embedding, vision_hidden_states elif self.training:
new_vllm_embedding[i] += cur_vs_hs[0].mean() * 0
return new_vllm_embedding, vision_hidden_states
def get_audio_embedding_streaming(self, data): def get_audio_embedding_streaming(self, data):
r""" r"""
@ -595,7 +598,7 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
elif self.training: elif self.training:
for i in range(bs): for i in range(bs):
# dummy audio_embeddings # dummy audio_embeddings
input_embeddings += audio_embeddings[0].mean() * 0 input_embeddings = input_embeddings + audio_embeddings[0].mean() * 0
return input_embeddings return input_embeddings
@ -668,9 +671,9 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
mode: mode:
"default": default system prompt and not refer to any task "default": default system prompt and not refer to any task
"omni": input video and audio simultaneously "omni": input video and audio simultaneously
"audio_assistant": Default voice-only mode, the model will use the ref_audio's voice to reply user as a helpful assistant. "audio_assistant": Default voice-only mode, the model will use the ref_audio's voice to reply user's question as a helpful assistant.
"audio_roleplay": Roleplay voice-only model, the model will use the ref_audio's voice to reply, and also role-play the character based on the audio prompt. "audio_roleplay": Roleplay voice-only mode, the model will use the ref_audio's voice to reply, and also role-play the character based on the audio prompt.
"voice_cloning": TTS mode, the model will clone the voice of ref_audio "voice_cloning": TTS mode, the model will clone the voice of ref_audio.
language: prompts language, the model has the ability to automatically select the response language language: prompts language, the model has the ability to automatically select the response language
based on the question language based on the question language
Returns: Returns:
@ -751,7 +754,7 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
input_ids=None, input_ids=None,
pixel_values=None, pixel_values=None,
tgt_sizes=None, tgt_sizes=None,
audio_features=None, audio_features=[],
audio_feature_lens=None, audio_feature_lens=None,
image_bound=None, image_bound=None,
audio_bounds=None, audio_bounds=None,
@ -2982,7 +2985,7 @@ class ConditionalChatTTS(PreTrainedModel):
inputs_embeds = torch.stack(code_emb, 3).sum(3) inputs_embeds = torch.stack(code_emb, 3).sum(3)
position_ids = torch.tensor( position_ids = torch.tensor(
[past_key_values[0][0].shape[2] + 1], dtype=torch.long, device=self.device [past_key_values[0][0].shape[2]], dtype=torch.long, device=self.device
).unsqueeze(0) ).unsqueeze(0)
cache_position = position_ids.clone() cache_position = position_ids.clone()