Repository: huggingface/transformers
Pull request: Fix Qwen2.5 Omni `SinusoidsPositionEmbedding` precision

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fixes https://github.com/QwenLM/Qwen2.5-Omni/issues/271


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Changed files:
filename: src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
patch: @@ -794,7 +794,7 @@ def __init__(self, length, channels, max_timescale=10000):
         if channels % 2 != 0:
             raise ValueError("SinusoidsPositionEmbedding needs even channels input")
         log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
-        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)).float()
+        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
         scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
         self.register_buffer(
             "positional_embedding",

filename: src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
patch: @@ -1710,7 +1710,7 @@ def __init__(self, length, channels, max_timescale=10000):
         if channels % 2 != 0:
             raise ValueError("SinusoidsPositionEmbedding needs even channels input")
         log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
-        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)).float()
+        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
         scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
         self.register_buffer(
             "positional_embedding",