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79f7d56b7d
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03b593621b |
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@ -1,5 +1,7 @@
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import os, math, torch, soundfile as sf, librosa, warnings, numpy as np, onnxruntime as ort, logging, contextlib, io
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import os, math, torch, soundfile as sf, librosa, warnings, numpy as np, onnxruntime as ort, logging, contextlib, io
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from types import SimpleNamespace
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from types import SimpleNamespace
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from dataclasses import dataclass
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from typing import Optional
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from torch import nn
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from torch import nn
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from torch.nn import functional as F
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from torch.nn import functional as F
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from transformers.modeling_outputs import MoeCausalLMOutputWithPast
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from transformers.modeling_outputs import MoeCausalLMOutputWithPast
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@ -28,6 +30,11 @@ class OmniConfig(MiniMindConfig):
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self.image_token_len = kwargs.get("image_token_len", 64)
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self.image_token_len = kwargs.get("image_token_len", 64)
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self.bridge_layer = kwargs.get("bridge_layer", self.num_hidden_layers // 2 - 1)
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self.bridge_layer = kwargs.get("bridge_layer", self.num_hidden_layers // 2 - 1)
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# 避免 DDP + compile 重建 OmniCausalLMOutputWithPast(不支持 audio_logits 参数)
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@dataclass
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class OmniCausalLMOutputWithPast(MoeCausalLMOutputWithPast):
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audio_logits: Optional[list[torch.FloatTensor]] = None
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class MMAudioProjector(nn.Module):
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class MMAudioProjector(nn.Module):
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def __init__(self, in_dim, out_dim):
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def __init__(self, in_dim, out_dim):
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super().__init__()
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super().__init__()
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@ -311,9 +318,12 @@ class MiniMindOmni(MiniMindForCausalLM):
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text_logits = self.thinker.lm_head(h_thinker[:, slice_indices, :])
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text_logits = self.thinker.lm_head(h_thinker[:, slice_indices, :])
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audio_logits = self.talker.lm_head(h_talker[:, slice_indices, :])
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audio_logits = self.talker.lm_head(h_talker[:, slice_indices, :])
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out = MoeCausalLMOutputWithPast(aux_loss=aux_loss, logits=text_logits, past_key_values=presents)
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return OmniCausalLMOutputWithPast(
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out.audio_logits = audio_logits
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aux_loss=aux_loss,
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return out
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logits=text_logits,
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past_key_values=presents,
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audio_logits=audio_logits,
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)
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@torch.inference_mode()
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@torch.inference_mode()
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def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
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def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
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