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https://www.modelscope.cn/OpenBMB/MiniCPM-o-2_6.git
synced 2025-08-14 12:46:02 +08:00
Replace the inplace operation
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2710956711
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@ -377,6 +377,8 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
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else:
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vllm_embedding = self.llm.model.embed_tokens(data["input_ids"])
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new_vllm_embedding = vllm_embedding.clone()
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vision_hidden_states = [
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i.type(vllm_embedding.dtype) if isinstance(i, torch.Tensor) else i for i in vision_hidden_states
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]
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@ -392,15 +394,16 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
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[torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound]
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).to(vllm_embedding.device)
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cur_vllm_emb.scatter_(
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new_vllm_embedding[i] = cur_vllm_emb.scatter(
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0,
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image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]),
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cur_vs_hs.view(-1, cur_vs_hs.shape[-1]),
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)
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elif self.training:
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cur_vllm_emb += cur_vs_hs[0].mean() * 0
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return vllm_embedding, vision_hidden_states
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elif self.training:
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new_vllm_embedding[i] += cur_vs_hs[0].mean() * 0
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return new_vllm_embedding, vision_hidden_states
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def get_audio_embedding_streaming(self, data):
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r"""
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@ -595,7 +598,7 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
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elif self.training:
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for i in range(bs):
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# dummy audio_embeddings
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input_embeddings += audio_embeddings[0].mean() * 0
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input_embeddings = input_embeddings + audio_embeddings[0].mean() * 0
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return input_embeddings
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@ -668,9 +671,9 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
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mode:
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"default": default system prompt and not refer to any task
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"omni": input video and audio simultaneously
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"audio_assistant": Default voice-only mode, the model will use the ref_audio's voice to reply user as a helpful assistant.
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"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.
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"voice_cloning": TTS mode, the model will clone the voice of ref_audio
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"audio_assistant": Default voice-only mode, the model will use the ref_audio's voice to reply user's question as a helpful assistant.
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"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.
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"voice_cloning": TTS mode, the model will clone the voice of ref_audio.
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language: prompts language, the model has the ability to automatically select the response language
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based on the question language
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Returns:
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@ -751,7 +754,7 @@ class MiniCPMO(MiniCPMOPreTrainedModel):
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input_ids=None,
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pixel_values=None,
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tgt_sizes=None,
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audio_features=None,
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audio_features=[],
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audio_feature_lens=None,
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image_bound=None,
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audio_bounds=None,
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@ -2982,7 +2985,7 @@ class ConditionalChatTTS(PreTrainedModel):
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inputs_embeds = torch.stack(code_emb, 3).sum(3)
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position_ids = torch.tensor(
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[past_key_values[0][0].shape[2] + 1], dtype=torch.long, device=self.device
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[past_key_values[0][0].shape[2]], dtype=torch.long, device=self.device
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).unsqueeze(0)
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cache_position = position_ids.clone()
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