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Fix Qwen2.5-Omni get_chunked_index chunking functionality #37631

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Merged
merged 5 commits into from
Apr 22, 2025
Merged

Fix Qwen2.5-Omni get_chunked_index chunking functionality #37631

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fd88731
fix: qwen2.5 omni modular get_rope_index
imkero Apr 19, 2025
3ce6737
test: add test for qwen2.5 omni rope index (video with audio input)
imkero Apr 19, 2025
bb41afa
style
imkero Apr 19, 2025
092716d
expected_position_ids readability
imkero Apr 20, 2025
ad926cc
fix: use spatial_merge_size = 1 in unit test
imkero Apr 21, 2025
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11 changes: 6 additions & 5 deletions src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
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Original file line number Diff line number Diff line change
Expand Up @@ -244,7 +244,8 @@ def get_chunked_index(
- the second chunk contains values >= 1000 and < 2000, and so on.

Parameters:
token_indices (`List[int]`): A monotonically increasing list of token index values.
token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of
token index values.
t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
remove_index (`int`) An index id to subtract from `token_indices` before chunking

Expand All @@ -257,12 +258,12 @@ def _iter():
i, start_idx = 0, 0 # skip bos token
current_chunk = 1
while i < len(token_indices): # skip eos token
if token_indices[0][i] - remove_index >= current_chunk * tokens_per_chunk:
if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk:
yield (start_idx, i)
start_idx = i
current_chunk += 1
i += 1
yield (start_idx, token_indices.shape[1])
yield (start_idx, len(token_indices))

return list(_iter())

Expand Down Expand Up @@ -499,8 +500,8 @@ def get_rope_index(
)

t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk)
video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids, t_ntoken_per_chunk, st_idx)
audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids, t_ntoken_per_chunk, st_idx)
video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
sub_len = 0
for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))):
video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None
Expand Down
11 changes: 6 additions & 5 deletions src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1145,7 +1145,8 @@ def get_chunked_index(
- the second chunk contains values >= 1000 and < 2000, and so on.

Parameters:
token_indices (`List[int]`): A monotonically increasing list of token index values.
token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of
token index values.
t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
remove_index (`int`) An index id to subtract from `token_indices` before chunking

Expand All @@ -1158,12 +1159,12 @@ def _iter():
i, start_idx = 0, 0 # skip bos token
current_chunk = 1
while i < len(token_indices): # skip eos token
if token_indices[0][i] - remove_index >= current_chunk * tokens_per_chunk:
if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk:
yield (start_idx, i)
start_idx = i
current_chunk += 1
i += 1
yield (start_idx, token_indices.shape[1])
yield (start_idx, len(token_indices))

return list(_iter())

Expand Down Expand Up @@ -1400,8 +1401,8 @@ def get_rope_index(
)

t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk)
video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids, t_ntoken_per_chunk, st_idx)
audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids, t_ntoken_per_chunk, st_idx)
video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx)
sub_len = 0
for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))):
video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None
Expand Down
2 changes: 1 addition & 1 deletion src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py
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Original file line number Diff line number Diff line change
Expand Up @@ -289,7 +289,7 @@ def get_chunked_index(self, token_indices: np.ndarray, tokens_per_chunk: int) ->
- the second chunk contains values >= 1000 and < 2000, and so on.

Parameters:
token_indices (`List[int]`): A monotonically increasing list of token index values.
token_indices (`np.ndarray`): A monotonically increasing list of token index values.
t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).

Returns:
Expand Down
137 changes: 137 additions & 0 deletions tests/models/qwen2_5_omni/test_modeling_qwen2_5_omni.py
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Original file line number Diff line number Diff line change
Expand Up @@ -381,6 +381,143 @@ def test_generate_with_static_cache(self):
def test_custom_4d_attention_mask(self):
pass

def test_get_rope_index_video_with_audio(self):
image_grid_thw = torch.empty((0, 3), dtype=torch.long)

# 3 * 2 * 2 = 12 video tokens
video_grid_thw = torch.tensor([[3, 2, 2]], dtype=torch.long)

# num_audio_tokens = ((audio_seqlen - 1) // 2 + 1 - 2) // 2 + 1
# i.e.: 300 audio_seqlen -> 75 audio tokens
audio_seqlens = torch.tensor([300], dtype=torch.long)

second_per_grids = torch.tensor([1.0], dtype=torch.float)

use_audio_in_video = True

# fmt: off
expected_position_ids = torch.tensor([
[[
0, 1, # text
2, 2, # vision_bos + audio_bos

# video chunk
3, 3, 3, 3,
28, 28, 28, 28,

# audio chunk
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52,

# video chunk
53, 53, 53, 53,

# audio chunk
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,

78, 78, # audio_eos + vision_eos
79, 80, # text
]],
[[
0, 1, # text
2, 2, # vision_bos + audio_bos

# video chunk
3, 3, 4, 4,
3, 3, 4, 4,

# audio chunk
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52,

# video chunk
3, 3, 4, 4,

# audio chunk
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,

78, 78, # audio_eos + vision_eos
79, 80, # text
]],
[[
0, 1, # text
2, 2, # vision_bos + audio_bos

# video chunk
3, 4, 3, 4,
3, 4, 3, 4,

# audio chunk
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44,
45, 46, 47, 48, 49, 50, 51, 52,

# video chunk
3, 4, 3, 4,

# audio chunk
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,

78, 78, # audio_eos + vision_eos
79, 80, # text
]],
], dtype=torch.long)
# fmt: on

for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

input_ids = torch.tensor(
[
[
100,
101,
]
+ [
config.vision_start_token_id,
config.audio_start_token_id,
]
# 1st chunk: 8 video tokens, 50 audio tokens
+ [config.video_token_id] * 2 * 2 * 2
+ [config.audio_token_id] * 50
+
# 2nd chunk: 4 video tokens, 25 audio tokens
[config.video_token_id] * 1 * 2 * 2
+ [config.audio_token_id] * 25
+ [
config.audio_end_token_id,
config.vision_end_token_id,
]
+ [
102,
103,
]
],
dtype=torch.long,
)

model = model_class(config)

position_ids, mrope_position_deltas = model.get_rope_index(
input_ids=input_ids,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
attention_mask=None,
use_audio_in_video=use_audio_in_video,
audio_seqlens=audio_seqlens,
second_per_grids=second_per_grids,
)

self.assertTrue(torch.equal(position_ids, expected_position_ids))


@require_torch
class Qwen2_5OmniModelIntegrationTest(unittest.TestCase):
Expand Down