Repository: huggingface/transformers
Pull request: Fix inference bugs in Qwen2.5 Omni

# What does this PR do?

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Fixes some inference issues


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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
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Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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Changed files:
filename: src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
patch: @@ -815,7 +815,7 @@ 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))
+        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",
@@ -4560,12 +4560,44 @@ def generate(
             return thinker_result
 
         # 2. Generate speech tokens from talker module
+        embeds_to_talker = thinker_result.hidden_states[0][0].clone()
+        if thinker_kwargs.get("input_features", None) is not None:
+            audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index
+            audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device)
+            audio_mask_tensor = torch.zeros(
+                [audio_ids_mask.sum(), embeds_to_talker.shape[-1]],
+                dtype=embeds_to_talker.dtype,
+                device=self.talker.device,
+            )
+            embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor)
+        if thinker_kwargs.get("pixel_values", None) is not None:
+            image_ids_mask = input_ids == self.config.thinker_config.image_token_index
+            image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device)
+            image_mask_tensor = torch.zeros(
+                [image_ids_mask.sum(), embeds_to_talker.shape[-1]],
+                dtype=embeds_to_talker.dtype,
+                device=self.talker.device,
+            )
+            embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor)
+        if thinker_kwargs.get("pixel_values_videos", None) is not None:
+            video_ids_mask = input_ids == self.config.thinker_config.video_token_index
+            video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device)
+            video_mask_tensor = torch.zeros(
+                [video_ids_mask.sum(), embeds_to_talker.shape[-1]],
+                dtype=embeds_to_talker.dtype,
+                device=self.talker.device,
+            )
+            embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor)
+
+        processed_thinker_hidden = (
+            (embeds_to_talker,) + thinker_result.hidden_states[0][1:],
+        ) + thinker_result.hidden_states[1:]
         thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(self.talker.device)
         thinker_token_embeds = [
-            token_hidden_states[0].to(self.talker.device) for token_hidden_states in thinker_result.hidden_states
+            token_hidden_states[0].to(self.talker.device) for token_hidden_states in processed_thinker_hidden
         ]
         thinker_hidden_states = [
-            token_hidden_states[-1].to(self.talker.device) for token_hidden_states in thinker_result.hidden_states
+            token_hidden_states[-1].to(self.talker.device) for token_hidden_states in processed_thinker_hidden
         ]
 
         talker_text_bos_token = speaker_params["bos_token"]

filename: src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
patch: @@ -1711,7 +1711,7 @@ 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))
+        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",
@@ -4243,12 +4243,44 @@ def generate(
             return thinker_result
 
         # 2. Generate speech tokens from talker module
+        embeds_to_talker = thinker_result.hidden_states[0][0].clone()
+        if thinker_kwargs.get("input_features", None) is not None:
+            audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index
+            audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device)
+            audio_mask_tensor = torch.zeros(
+                [audio_ids_mask.sum(), embeds_to_talker.shape[-1]],
+                dtype=embeds_to_talker.dtype,
+                device=self.talker.device,
+            )
+            embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor)
+        if thinker_kwargs.get("pixel_values", None) is not None:
+            image_ids_mask = input_ids == self.config.thinker_config.image_token_index
+            image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device)
+            image_mask_tensor = torch.zeros(
+                [image_ids_mask.sum(), embeds_to_talker.shape[-1]],
+                dtype=embeds_to_talker.dtype,
+                device=self.talker.device,
+            )
+            embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor)
+        if thinker_kwargs.get("pixel_values_videos", None) is not None:
+            video_ids_mask = input_ids == self.config.thinker_config.video_token_index
+            video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker).to(embeds_to_talker.device)
+            video_mask_tensor = torch.zeros(
+                [video_ids_mask.sum(), embeds_to_talker.shape[-1]],
+                dtype=embeds_to_talker.dtype,
+                device=self.talker.device,
+            )
+            embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor)
+
+        processed_thinker_hidden = (
+            (embeds_to_talker,) + thinker_result.hidden_states[0][1:],
+        ) + thinker_result.hidden_states[1:]
         thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(self.talker.device)
         thinker_token_embeds = [
-            token_hidden_states[0].to(self.talker.device) for token_hidden_states in thinker_result.hidden_states
+            token_hidden_states[0].to(self.talker.device) for token_hidden_states in processed_thinker_hidden
         ]
         thinker_hidden_states = [
-            token_hidden_states[-1].to(self.talker.device) for token_hidden_states in thinker_result.hidden_states
+            token_hidden_states[-1].to(self.talker.device) for token_hidden_states in processed_thinker_hidden
         ]
 
         talker_text_bos_token = speaker_params["bos_token"]