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ERNIE-4.5-VL-28B-A3B-Base-PT

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Safetensors
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ernie4_5_moe_vl
feature-extraction
ERNIE4.5
conversational
custom_code
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xet
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AntonV HF Staff commited on
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1 Parent(s): 497a404

hf files

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Files changed (13) hide show
  1. .gitattributes +1 -0
  2. added_tokens.json +0 -1
  3. chat_template.jinja +81 -0
  4. chat_template.json +0 -3
  5. config.json +75 -62
  6. configuration_ernie4_5_vl.py +0 -650
  7. modeling_ernie4_5_vl.py +0 -0
  8. preprocessor_config.json +0 -29
  9. processing_ernie4_5_vl.py +0 -1822
  10. processor_config.json +63 -0
  11. special_tokens_map.json +0 -1
  12. tokenizer.model → tokenizer.json +2 -2
  13. tokenizer_config.json +47 -20
.gitattributes CHANGED
@@ -1,2 +1,3 @@
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  *.safetensors filter=lfs diff=lfs merge=lfs -text
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  tokenizer.model filter=lfs diff=lfs merge=lfs -text
 
 
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  *.safetensors filter=lfs diff=lfs merge=lfs -text
2
  tokenizer.model filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
added_tokens.json DELETED
@@ -1 +0,0 @@
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"<|AUDIO_UNUSE:744|>": 103090, "<|AUDIO_UNUSE:745|>": 103091, "<|AUDIO_UNUSE:746|>": 103092, "<|AUDIO_UNUSE:747|>": 103093, "<|AUDIO_UNUSE:748|>": 103094, "<|AUDIO_UNUSE:749|>": 103095, "<|AUDIO_UNUSE:750|>": 103096, "<|AUDIO_UNUSE:751|>": 103097, "<|AUDIO_UNUSE:752|>": 103098, "<|AUDIO_UNUSE:753|>": 103099, "<|AUDIO_UNUSE:754|>": 103100, "<|AUDIO_UNUSE:755|>": 103101, "<|AUDIO_UNUSE:756|>": 103102, "<|AUDIO_UNUSE:757|>": 103103, "<|AUDIO_UNUSE:758|>": 103104, "<|AUDIO_UNUSE:759|>": 103105, "<|AUDIO_UNUSE:760|>": 103106, "<|AUDIO_UNUSE:761|>": 103107, "<|AUDIO_UNUSE:762|>": 103108, "<|AUDIO_UNUSE:763|>": 103109, "<|AUDIO_UNUSE:764|>": 103110, "<|AUDIO_UNUSE:765|>": 103111, "<|AUDIO_UNUSE:766|>": 103112, "<|AUDIO_UNUSE:767|>": 103113, "<|AUDIO_UNUSE:768|>": 103114, "<|AUDIO_UNUSE:769|>": 103115, "<|AUDIO_UNUSE:770|>": 103116, "<|AUDIO_UNUSE:771|>": 103117, "<|AUDIO_UNUSE:772|>": 103118, "<|AUDIO_UNUSE:773|>": 103119, "<|AUDIO_UNUSE:774|>": 103120, "<|AUDIO_UNUSE:775|>": 103121, "<|AUDIO_UNUSE:776|>": 103122, "<|AUDIO_UNUSE:777|>": 103123, "<|AUDIO_UNUSE:778|>": 103124, "<|AUDIO_UNUSE:779|>": 103125, "<|AUDIO_UNUSE:780|>": 103126, "<|AUDIO_UNUSE:781|>": 103127, "<|AUDIO_UNUSE:782|>": 103128, "<|AUDIO_UNUSE:783|>": 103129, "<|AUDIO_UNUSE:784|>": 103130, "<|AUDIO_UNUSE:785|>": 103131, "<|AUDIO_UNUSE:786|>": 103132, "<|AUDIO_UNUSE:787|>": 103133, "<|AUDIO_UNUSE:788|>": 103134, "<|AUDIO_UNUSE:789|>": 103135, "<|AUDIO_UNUSE:790|>": 103136, "<|AUDIO_UNUSE:791|>": 103137, "<|AUDIO_UNUSE:792|>": 103138, "<|AUDIO_UNUSE:793|>": 103139, "<|AUDIO_UNUSE:794|>": 103140, "<|AUDIO_UNUSE:795|>": 103141, "<|AUDIO_UNUSE:796|>": 103142, "<|AUDIO_UNUSE:797|>": 103143, "<|AUDIO_UNUSE:798|>": 103144, "<|AUDIO_UNUSE:799|>": 103145, "<|AUDIO_UNUSE:800|>": 103146, "<|AUDIO_UNUSE:801|>": 103147, "<|AUDIO_UNUSE:802|>": 103148, "<|AUDIO_UNUSE:803|>": 103149, "<|AUDIO_UNUSE:804|>": 103150, "<|AUDIO_UNUSE:805|>": 103151, "<|AUDIO_UNUSE:806|>": 103152, "<|AUDIO_UNUSE:807|>": 103153, "<|AUDIO_UNUSE:808|>": 103154, "<|AUDIO_UNUSE:809|>": 103155, "<|AUDIO_UNUSE:810|>": 103156, "<|AUDIO_UNUSE:811|>": 103157, "<|AUDIO_UNUSE:812|>": 103158, "<|AUDIO_UNUSE:813|>": 103159, "<|AUDIO_UNUSE:814|>": 103160, "<|AUDIO_UNUSE:815|>": 103161, "<|AUDIO_UNUSE:816|>": 103162, "<|AUDIO_UNUSE:817|>": 103163, "<|AUDIO_UNUSE:818|>": 103164, "<|AUDIO_UNUSE:819|>": 103165, "<|AUDIO_UNUSE:820|>": 103166, "<|AUDIO_UNUSE:821|>": 103167, "<|AUDIO_UNUSE:822|>": 103168, "<|AUDIO_UNUSE:823|>": 103169, "<|AUDIO_UNUSE:824|>": 103170, "<|AUDIO_UNUSE:825|>": 103171, "<|AUDIO_UNUSE:826|>": 103172, "<|AUDIO_UNUSE:827|>": 103173, "<|AUDIO_UNUSE:828|>": 103174, "<|AUDIO_UNUSE:829|>": 103175, "<|AUDIO_UNUSE:830|>": 103176, "<|AUDIO_UNUSE:831|>": 103177, "<|AUDIO_UNUSE:832|>": 103178, "<|AUDIO_UNUSE:833|>": 103179, "<|AUDIO_UNUSE:834|>": 103180, "<|AUDIO_UNUSE:835|>": 103181, "<|AUDIO_UNUSE:836|>": 103182, "<|AUDIO_UNUSE:837|>": 103183, "<|AUDIO_UNUSE:838|>": 103184, "<|AUDIO_UNUSE:839|>": 103185, "<|AUDIO_UNUSE:840|>": 103186, "<|AUDIO_UNUSE:841|>": 103187, "<|AUDIO_UNUSE:842|>": 103188, "<|AUDIO_UNUSE:843|>": 103189, "<|AUDIO_UNUSE:844|>": 103190, "<|AUDIO_UNUSE:845|>": 103191, "<|AUDIO_UNUSE:846|>": 103192, "<|AUDIO_UNUSE:847|>": 103193, "<|AUDIO_UNUSE:848|>": 103194, "<|AUDIO_UNUSE:849|>": 103195, "<|AUDIO_UNUSE:850|>": 103196, "<|AUDIO_UNUSE:851|>": 103197, "<|AUDIO_UNUSE:852|>": 103198, "<|AUDIO_UNUSE:853|>": 103199, "<|AUDIO_UNUSE:854|>": 103200, "<|AUDIO_UNUSE:855|>": 103201, "<|AUDIO_UNUSE:856|>": 103202, "<|AUDIO_UNUSE:857|>": 103203, "<|AUDIO_UNUSE:858|>": 103204, "<|AUDIO_UNUSE:859|>": 103205, "<|AUDIO_UNUSE:860|>": 103206, "<|AUDIO_UNUSE:861|>": 103207, "<|AUDIO_UNUSE:862|>": 103208, "<|AUDIO_UNUSE:863|>": 103209, "<|AUDIO_UNUSE:864|>": 103210, "<|AUDIO_UNUSE:865|>": 103211, "<|AUDIO_UNUSE:866|>": 103212, "<|AUDIO_UNUSE:867|>": 103213, "<|AUDIO_UNUSE:868|>": 103214, "<|AUDIO_UNUSE:869|>": 103215, "<|AUDIO_UNUSE:870|>": 103216, "<|AUDIO_UNUSE:871|>": 103217, "<|AUDIO_UNUSE:872|>": 103218, "<|AUDIO_UNUSE:873|>": 103219, "<|AUDIO_UNUSE:874|>": 103220, "<|AUDIO_UNUSE:875|>": 103221, "<|AUDIO_UNUSE:876|>": 103222, "<|AUDIO_UNUSE:877|>": 103223, "<|AUDIO_UNUSE:878|>": 103224, "<|AUDIO_UNUSE:879|>": 103225, "<|AUDIO_UNUSE:880|>": 103226, "<|AUDIO_UNUSE:881|>": 103227, "<|AUDIO_UNUSE:882|>": 103228, "<|AUDIO_UNUSE:883|>": 103229, "<|AUDIO_UNUSE:884|>": 103230, "<|AUDIO_UNUSE:885|>": 103231, "<|AUDIO_UNUSE:886|>": 103232, "<|AUDIO_UNUSE:887|>": 103233, "<|AUDIO_UNUSE:888|>": 103234, "<|AUDIO_UNUSE:889|>": 103235, "<|AUDIO_UNUSE:890|>": 103236, "<|AUDIO_UNUSE:891|>": 103237, "<|AUDIO_UNUSE:892|>": 103238, "<|AUDIO_UNUSE:893|>": 103239, "<|AUDIO_UNUSE:894|>": 103240, "<|AUDIO_UNUSE:895|>": 103241, "<|AUDIO_UNUSE:896|>": 103242, "<|AUDIO_UNUSE:897|>": 103243, "<|AUDIO_UNUSE:898|>": 103244, "<|AUDIO_UNUSE:899|>": 103245, "<|AUDIO_UNUSE:900|>": 103246, "<|AUDIO_UNUSE:901|>": 103247, "<|AUDIO_UNUSE:902|>": 103248, "<|AUDIO_UNUSE:903|>": 103249, "<|AUDIO_UNUSE:904|>": 103250, "<|AUDIO_UNUSE:905|>": 103251, "<|AUDIO_UNUSE:906|>": 103252, "<|AUDIO_UNUSE:907|>": 103253, "<|AUDIO_UNUSE:908|>": 103254, "<|AUDIO_UNUSE:909|>": 103255, "<|AUDIO_UNUSE:910|>": 103256, "<|AUDIO_UNUSE:911|>": 103257, "<|AUDIO_UNUSE:912|>": 103258, "<|AUDIO_UNUSE:913|>": 103259, "<|AUDIO_UNUSE:914|>": 103260, "<|AUDIO_UNUSE:915|>": 103261, "<|AUDIO_UNUSE:916|>": 103262, "<|AUDIO_UNUSE:917|>": 103263, "<|AUDIO_UNUSE:918|>": 103264, "<|AUDIO_UNUSE:919|>": 103265, "<|AUDIO_UNUSE:920|>": 103266, "<|AUDIO_UNUSE:921|>": 103267, "<|AUDIO_UNUSE:922|>": 103268, "<|AUDIO_UNUSE:923|>": 103269, "<|AUDIO_UNUSE:924|>": 103270, "<|AUDIO_UNUSE:925|>": 103271, "<|AUDIO_UNUSE:926|>": 103272, "<|AUDIO_UNUSE:927|>": 103273, "<|AUDIO_UNUSE:928|>": 103274, "<|AUDIO_UNUSE:929|>": 103275, "<|AUDIO_UNUSE:930|>": 103276, "<|AUDIO_UNUSE:931|>": 103277, "<|AUDIO_UNUSE:932|>": 103278, "<|AUDIO_UNUSE:933|>": 103279, "<|AUDIO_UNUSE:934|>": 103280, "<|AUDIO_UNUSE:935|>": 103281, "<|AUDIO_UNUSE:936|>": 103282, "<|AUDIO_UNUSE:937|>": 103283, "<|AUDIO_UNUSE:938|>": 103284, "<|AUDIO_UNUSE:939|>": 103285, "<|AUDIO_UNUSE:940|>": 103286, "<|AUDIO_UNUSE:941|>": 103287, "<|AUDIO_UNUSE:942|>": 103288, "<|AUDIO_UNUSE:943|>": 103289, "<|AUDIO_UNUSE:944|>": 103290, "<|AUDIO_UNUSE:945|>": 103291, "<|AUDIO_UNUSE:946|>": 103292, "<|AUDIO_UNUSE:947|>": 103293, "<|AUDIO_UNUSE:948|>": 103294, "<|AUDIO_UNUSE:949|>": 103295, "<|AUDIO_UNUSE:950|>": 103296, "<|AUDIO_UNUSE:951|>": 103297, "<|AUDIO_UNUSE:952|>": 103298, "<|AUDIO_UNUSE:953|>": 103299, "<|AUDIO_UNUSE:954|>": 103300, "<|AUDIO_UNUSE:955|>": 103301, "<|AUDIO_UNUSE:956|>": 103302, "<|AUDIO_UNUSE:957|>": 103303, "<|AUDIO_UNUSE:958|>": 103304, "<|AUDIO_UNUSE:959|>": 103305, "<|AUDIO_UNUSE:960|>": 103306, "<|AUDIO_UNUSE:961|>": 103307, "<|AUDIO_UNUSE:962|>": 103308, "<|AUDIO_UNUSE:963|>": 103309, "<|AUDIO_UNUSE:964|>": 103310, "<|AUDIO_UNUSE:965|>": 103311, "<|AUDIO_UNUSE:966|>": 103312, "<|AUDIO_UNUSE:967|>": 103313, "<|AUDIO_UNUSE:968|>": 103314, "<|AUDIO_UNUSE:969|>": 103315, "<|AUDIO_UNUSE:970|>": 103316, "<|AUDIO_UNUSE:971|>": 103317, "<|AUDIO_UNUSE:972|>": 103318, "<|AUDIO_UNUSE:973|>": 103319, "<|AUDIO_UNUSE:974|>": 103320, "<|AUDIO_UNUSE:975|>": 103321, "<|AUDIO_UNUSE:976|>": 103322, "<|AUDIO_UNUSE:977|>": 103323, "<|AUDIO_UNUSE:978|>": 103324, "<|AUDIO_UNUSE:979|>": 103325, "<|AUDIO_UNUSE:980|>": 103326, "<|AUDIO_UNUSE:981|>": 103327, "<|AUDIO_UNUSE:982|>": 103328, "<|AUDIO_UNUSE:983|>": 103329, "<|AUDIO_UNUSE:984|>": 103330, "<|AUDIO_UNUSE:985|>": 103331, "<|AUDIO_UNUSE:986|>": 103332, "<|AUDIO_UNUSE:987|>": 103333, "<|AUDIO_UNUSE:988|>": 103334, "<|AUDIO_UNUSE:989|>": 103335, "<|AUDIO_UNUSE:990|>": 103336, "<|AUDIO_UNUSE:991|>": 103337, "<|AUDIO_UNUSE:992|>": 103338, "<|AUDIO_UNUSE:993|>": 103339, "<|AUDIO_UNUSE:994|>": 103340, "<|AUDIO_UNUSE:995|>": 103341, "<|AUDIO_UNUSE:996|>": 103342, "<|AUDIO_UNUSE:997|>": 103343, "<|AUDIO_UNUSE:998|>": 103344, "<|AUDIO_UNUSE:999|>": 103345, "<|AUDIO_UNUSE:1000|>": 103346, "<|AUDIO_UNUSE:1001|>": 103347, "<|AUDIO_UNUSE:1002|>": 103348, "<|AUDIO_UNUSE:1003|>": 103349, "<|AUDIO_UNUSE:1004|>": 103350, "<|AUDIO_UNUSE:1005|>": 103351, "<|AUDIO_UNUSE:1006|>": 103352, "<|AUDIO_UNUSE:1007|>": 103353, "<|AUDIO_UNUSE:1008|>": 103354, "<|AUDIO_UNUSE:1009|>": 103355, "<|AUDIO_UNUSE:1010|>": 103356, "<|AUDIO_UNUSE:1011|>": 103357, "<|AUDIO_UNUSE:1012|>": 103358, "<|AUDIO_UNUSE:1013|>": 103359, "<|AUDIO_UNUSE:1014|>": 103360, "<|AUDIO_UNUSE:1015|>": 103361, "<|AUDIO_UNUSE:1016|>": 103362, "<|AUDIO_UNUSE:1017|>": 103363, "<|AUDIO_UNUSE:1018|>": 103364, "<|AUDIO_UNUSE:1019|>": 103365, "<|AUDIO_UNUSE:1020|>": 103366}
 
 
chat_template.jinja ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ {%- set image_count = namespace(value=0) -%}
3
+ {%- set video_count = namespace(value=0) -%}
4
+ {{- '<|begin_of_sentence|>' }}
5
+ {%- for message in messages -%}
6
+ {%- if message.role in ['system', 'user'] -%}
7
+ {%- if message.role == 'user' -%}
8
+ {{- 'User: ' -}}
9
+ {%- endif -%}
10
+ {%- if message.content is string -%}
11
+ {{- message.content -}}
12
+ {%- else -%}
13
+ {%- for content_item in message.content -%}
14
+ {%- if content_item.type == 'text' -%}
15
+ {{- content_item.text -}}
16
+ {%- elif content_item.type in ['image_url', 'image'] -%}
17
+ {%- set image_count.value = image_count.value + 1 -%}
18
+ Picture {{ image_count.value }}:<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>
19
+ {%- elif content_item.type in ['video_url', 'video'] -%}
20
+ {%- set video_count.value = video_count.value + 1 -%}
21
+ Video {{ video_count.value }}:<|VIDEO_START|><|VIDEO_PLACEHOLDER|><|VIDEO_END|>
22
+ {%- endif -%}
23
+ {%- endfor -%}
24
+ {%- endif -%}
25
+ {%- if message.role == 'system' -%}
26
+ {{- '
27
+ ' -}}
28
+ {%- endif -%}
29
+ {%- elif message.role == 'assistant' -%}
30
+ {%- macro extract_text_content(content_field) -%}
31
+ {%- if content_field is string -%}
32
+ {{- content_field -}}
33
+ {%- elif content_field is iterable and content_field is not string -%}
34
+ {%- set ns = namespace(text_parts=[]) -%}
35
+ {%- set text_parts = [] -%}
36
+ {%- for item in content_field -%}
37
+ {%- if item.type == 'text' -%}
38
+ {%- set ns.text_parts = ns.text_parts + [item.text] -%}
39
+ {%- endif -%}
40
+ {%- endfor -%}
41
+ {{- ns.text_parts | join("") -}}
42
+ {%- else -%}
43
+ {{- '' -}}
44
+ {%- endif -%}
45
+ {%- endmacro -%}
46
+ {%- set reasoning_content = extract_text_content(message.reasoning_content) -%}
47
+ {%- set content = extract_text_content(message.content) -%}
48
+ {%- if '</think>' in content %}
49
+ {%- set reasoning_content = content.split('</think>')[0].rstrip('
50
+ ').split('<think>')[-1].lstrip('
51
+ ') %}
52
+ {%- set content = content.split('</think>')[-1].lstrip('
53
+ ') %}
54
+ {%- endif %}
55
+ {%- if reasoning_content %}
56
+ {{- '
57
+ ' + 'Assistant: ' + '<think>
58
+ ' + reasoning_content.strip('
59
+ ') + '
60
+ </think>
61
+ ' + content.lstrip('
62
+ ') }}
63
+ {%- else %}
64
+ {{- '
65
+ ' + 'Assistant: ' + content }}
66
+ {%- endif %}
67
+ {{- '<|end_of_sentence |>' }}
68
+ {%- endif -%}
69
+ {%- endfor -%}
70
+ {%- if add_generation_prompt is not defined or add_generation_prompt is true %}
71
+ {{- '
72
+ Assistant: ' -}}
73
+ {%- if (enable_thinking is defined and enable_thinking is false) or enable_thinking is not defined %}
74
+ {{- '<think>
75
+
76
+ </think>
77
+
78
+ ' }}
79
+ {%- endif %}
80
+ {%- if enable_thinking is defined and enable_thinking is true %}{{- '<think>' }}{%- endif %}
81
+ {%- endif %}
chat_template.json DELETED
@@ -1,3 +0,0 @@
1
- {
2
- "chat_template": "\n{%- set image_count = namespace(value=0) -%}\n{%- set video_count = namespace(value=0) -%}\n{{- '<|begin_of_sentence|>' }}\n{%- for message in messages -%}\n {%- if message.role in ['system', 'user'] -%}\n {%- if message.role == 'user' -%}\n {{- 'User: ' -}}\n {%- endif -%}\n {%- if message.content is string -%}\n {{- message.content -}}\n {%- else -%}\n {%- for content_item in message.content -%}\n {%- if content_item.type == 'text' -%}\n {{- content_item.text -}}\n {%- elif content_item.type in ['image_url', 'image'] -%}\n {%- set image_count.value = image_count.value + 1 -%}\n Picture {{ image_count.value }}:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>\n {%- elif content_item.type in ['video_url', 'video'] -%}\n {%- set video_count.value = video_count.value + 1 -%}\n Video {{ video_count.value }}:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- if message.role == 'system' -%}\n {{- '\n' -}}\n {%- endif -%}\n {%- elif message.role == 'assistant' -%}\n {%- macro extract_text_content(content_field) -%}\n {%- if content_field is string -%}\n {{- content_field -}}\n {%- elif content_field is iterable and content_field is not string -%}\n {%- set ns = namespace(text_parts=[]) -%}\n {%- set text_parts = [] -%}\n {%- for item in content_field -%}\n {%- if item.type == 'text' -%}\n {%- set ns.text_parts = ns.text_parts + [item.text] -%}\n {%- endif -%}\n {%- endfor -%}\n {{- ns.text_parts | join('') -}}\n {%- else -%}\n {{- '' -}}\n {%- endif -%}\n {%- endmacro -%}\n {%- set reasoning_content = extract_text_content(message.reasoning_content) -%}\n {%- set content = extract_text_content(message.content) -%}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\n') %}\n {%- endif %}\n {%- if reasoning_content %}\n {{- '\n' + 'Assistant: ' + '<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}\n {%- else %}\n {{- '\n' + 'Assistant: ' + content }}\n {%- endif %}\n {{- '<|end_of_sentence|>' }}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt is not defined or add_generation_prompt is true %}\n {{- '\nAssistant: ' -}}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\n\n</think>\n\n' }}\n {%- endif %}\n {%- if enable_thinking is not defined or enable_thinking is true %}\n {{- '<think>' }}\n {%- endif %}\n{%- endif %}\n"
3
- }
 
 
 
 
config.json CHANGED
@@ -1,75 +1,88 @@
1
  {
2
- "architectures": [
3
- "Ernie4_5_VLMoeForConditionalGeneration"
4
- ],
5
- "auto_map": {
6
- "AutoConfig": "configuration_ernie4_5_vl.Ernie4_5_VLMoEConfig",
7
- "AutoModel": "modeling_ernie4_5_vl.Ernie4_5_VLMoeForConditionalGeneration",
8
- "AutoModelForCausalLM": "modeling_ernie4_5_vl.Ernie4_5_VLMoeForConditionalGeneration",
9
- "AutoProcessor": "processing_ernie4_5_vl.Ernie4_5_VLProcessor",
10
- "AutoImageProcessor": "processing_ernie4_5_vl.Ernie4_5_VLImageProcessor"
11
- },
12
- "pad_token_id": 0,
13
- "bos_token_id": 1,
14
- "eos_token_id": 2,
15
- "torch_dtype": "bfloat16",
16
- "hidden_act": "silu",
17
- "hidden_size": 2560,
18
- "intermediate_size": 12288,
19
- "im_patch_id": 100295,
20
- "image_start_token_id": 101304,
21
  "image_end_token_id": 101305,
22
- "video_start_token_id": 101306,
23
- "video_end_token_id": 101307,
24
- "max_position_embeddings": 131072,
25
- "model_type": "ernie4_5_moe_vl",
26
- "moe_capacity": [128, 128, 128],
27
- "moe_gate": "topk",
28
- "moe_intermediate_size": [1536, 512],
29
- "moe_k": 6,
30
- "moe_layer_end_index": [29, 28],
31
- "moe_layer_interval": 1,
32
- "moe_layer_start_index": [1, 1],
33
- "moe_multimodal_dispatch_use_allgather": "v2-alltoall-unpad-text",
34
- "moe_num_experts": [64, 64],
35
- "moe_num_shared_experts": 2,
36
- "moe_use_aux_free": true,
37
- "num_attention_heads": 20,
38
- "num_hidden_layers": 28,
39
- "num_key_value_heads": 4,
40
- "pixel_hidden_size": 1280,
41
- "rms_norm_eps": 1e-05,
42
- "rope_3d": true,
43
- "rope_theta": 500000,
44
- "spatial_conv_size": 2,
45
- "temporal_conv_size": 2,
46
- "vocab_size": 103424,
47
- "tie_word_embeddings": true,
48
- "use_cache": true,
49
- "use_rmsnorm": true,
50
- "use_bias": false,
51
- "rope_scaling": {
52
- "type": "default",
53
- "mrope_section": [
54
- 22,
55
- 22,
56
- 20
57
- ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58
  },
 
 
 
 
59
  "vision_config": {
60
- "attn_implementation": "eager",
61
  "depth": 32,
62
- "embed_dim": 1280,
63
  "hidden_act": "quick_gelu",
64
  "hidden_size": 1280,
65
  "in_channels": 3,
66
- "in_chans": 3,
67
- "mlp_ratio": 4,
 
68
  "num_heads": 16,
69
  "patch_size": 14,
 
70
  "spatial_merge_size": 2,
71
- "spatial_patch_size": 14,
72
- "vit_first_fwd_bsz": 128,
73
- "attn_sep": true
74
  }
75
  }
 
1
  {
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2
  "image_end_token_id": 101305,
3
+ "image_start_token_id": 101304,
4
+ "image_token_id": 100295,
5
+ "model_type": "ernie4_5_vl_moe",
6
+ "text_config": {
7
+ "hidden_act": "silu",
8
+ "hidden_size": 2560,
9
+ "initializer_range": 0.02,
10
+ "intermediate_size": 12288,
11
+ "max_position_embeddings": 131072,
12
+ "mlp_layer_types": [
13
+ "dense",
14
+ "sparse",
15
+ "sparse",
16
+ "sparse",
17
+ "sparse",
18
+ "sparse",
19
+ "sparse",
20
+ "sparse",
21
+ "sparse",
22
+ "sparse",
23
+ "sparse",
24
+ "sparse",
25
+ "sparse",
26
+ "sparse",
27
+ "sparse",
28
+ "sparse",
29
+ "sparse",
30
+ "sparse",
31
+ "sparse",
32
+ "sparse",
33
+ "sparse",
34
+ "sparse",
35
+ "sparse",
36
+ "sparse",
37
+ "sparse",
38
+ "sparse",
39
+ "sparse",
40
+ "sparse"
41
+ ],
42
+ "model_type": "ernie4_5_vl_moe_text",
43
+ "moe_intermediate_size": [
44
+ 1536,
45
+ 512
46
+ ],
47
+ "moe_k": 6,
48
+ "moe_norm_min": 1e-12,
49
+ "moe_num_experts": 64,
50
+ "moe_num_shared_experts": 2,
51
+ "num_attention_heads": 20,
52
+ "num_hidden_layers": 28,
53
+ "num_key_value_heads": 4,
54
+ "output_router_logits": false,
55
+ "rms_norm_eps": 1e-05,
56
+ "rope_parameters": {
57
+ "mrope_section": [
58
+ 22,
59
+ 22,
60
+ 20
61
+ ],
62
+ "rope_theta": 500000.0,
63
+ "rope_type": "default"
64
+ },
65
+ "router_aux_loss_coef": 0.001,
66
+ "use_bias": false,
67
+ "use_cache": true,
68
+ "vocab_size": 103424
69
  },
70
+ "transformers_version": "5.0.0.dev0",
71
+ "video_end_token_id": 101307,
72
+ "video_start_token_id": 101306,
73
+ "video_token_id": 103367,
74
  "vision_config": {
 
75
  "depth": 32,
 
76
  "hidden_act": "quick_gelu",
77
  "hidden_size": 1280,
78
  "in_channels": 3,
79
+ "initializer_range": 0.02,
80
+ "intermediate_size": 5120,
81
+ "model_type": "ernie4_5_vl_moe_vision",
82
  "num_heads": 16,
83
  "patch_size": 14,
84
+ "rms_norm_eps": 1e-06,
85
  "spatial_merge_size": 2,
86
+ "temporal_merge_size": 2
 
 
87
  }
88
  }
configuration_ernie4_5_vl.py DELETED
@@ -1,650 +0,0 @@
1
- # Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
2
- #
3
- # Licensed under the Apache License, Version 2.0 (the "License");
4
- # you may not use this file except in compliance with the License.
5
- # You may obtain a copy of the License at
6
- #
7
- # http://www.apache.org/licenses/LICENSE-2.0
8
- #
9
- # Unless required by applicable law or agreed to in writing, software
10
- # distributed under the License is distributed on an "AS IS" BASIS,
11
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
- # See the License for the specific language governing permissions and
13
- # limitations under the License.
14
-
15
- """Ernie model configuration"""
16
- import copy
17
-
18
- from typing import List, Optional, Tuple, Union
19
-
20
- from transformers import PretrainedConfig
21
-
22
-
23
- __all__ = [
24
- "ERNIE_PRETRAINED_INIT_CONFIGURATION",
25
- "Ernie4_5_Config",
26
- "Ernie4_5_MoEConfig",
27
- "Ernie4_5_VLMoEConfig",
28
- ]
29
-
30
-
31
- class DFNRopeVisionTransformerConfig(PretrainedConfig):
32
- """
33
- Configuration class for DFNRopeVisionTransformer model.
34
- This class inherits from [`PretrainedConfig`] and can be used to control the model outputs. Read the
35
- documentation from [`PretrainedConfig`] for more information.
36
- """
37
-
38
- model_type = "DFNRope_vision_transformer"
39
- base_model_tp_plan = {}
40
-
41
- def __init__(
42
- self,
43
- depth=32,
44
- embed_dim=1280,
45
- hidden_size=3584,
46
- hidden_act="quick_gelu",
47
- mlp_ratio=4,
48
- num_heads=16,
49
- in_channels=3,
50
- patch_size=14,
51
- spatial_merge_size=2,
52
- attn_implementation="eager", # new added
53
- pp_data_balance=False,
54
- recompute=False,
55
- attn_sep=False,
56
- vit_first_fwd_bsz=128,
57
- vit_num_recompute_layers=10000,
58
- **kwargs,
59
- ):
60
- """
61
- Initialize DFNRopeVisionTransformer model configuration with default or specified parameters.
62
-
63
- Args:
64
- depth (int): Number of transformer layers in the model.
65
- embed_dim (int): Dimensionality of the embedding layer.
66
- hidden_size (int): Dimensionality of the feedforward network.
67
- hidden_act (str): Activation function for the feedforward network.
68
- mlp_ratio (float): Ratio between the number of input features and
69
- the number of output features in the feedforward network.
70
- num_heads (int): Number of attention heads in each attention layer.
71
- in_channels (int): Number of channels in the input image.
72
- patch_size (int):
73
- Size of patches in the input image. Defaults to 14.
74
- spatial_merge_size (int):
75
- Spatial merge size for the spatial transformer module. Defaults to 2.
76
- attn_implementation (str): Attention implementation type. Defaults to "eager".
77
- pp_data_balance (bool): Whether to balance data during preprocessing. Defaults to False.
78
- recompute (bool): Whether to use recompute. Defaults to False.
79
- attn_sep (bool): Whether to separate attention computation into two stages. Defaults to False.
80
- vit_first_fwd_bsz (int): First forward batch size for ViT. Defaults to 128.
81
- vit_num_recompute_layers (int): Number of recomputed layers for ViT. Defaults to
82
- """
83
- super().__init__(**kwargs)
84
-
85
- self.depth = depth
86
- self.embed_dim = embed_dim
87
- self.hidden_size = hidden_size
88
- self.hidden_act = hidden_act
89
- self.mlp_ratio = mlp_ratio
90
- self.num_heads = num_heads
91
- self.in_channels = in_channels
92
- self.patch_size = patch_size
93
- self.spatial_merge_size = spatial_merge_size
94
- self.attn_implementation = attn_implementation
95
- self.pp_data_balance = pp_data_balance
96
- self.recompute = recompute
97
- self.attn_sep = attn_sep
98
- self.vit_first_fwd_bsz = vit_first_fwd_bsz
99
- self.vit_num_recompute_layers = vit_num_recompute_layers
100
-
101
- def get(self, key, default=None):
102
- """get config value by key"""
103
- if hasattr(self, key):
104
- return getattr(self, key)
105
- else:
106
- return default
107
-
108
-
109
- ERNIE_PRETRAINED_INIT_CONFIGURATION = {
110
- "ernie/tiny-random-ernie": {
111
- "hidden_size": 768,
112
- "initializer_range": 0.02,
113
- "intermediate_size": 11008,
114
- "max_position_embeddings": 2048,
115
- "model_type": "ernie",
116
- "num_attention_heads": 2,
117
- "num_hidden_layers": 2,
118
- "rms_norm_eps": 1e-06,
119
- "vocab_size": 32000,
120
- "bos_token_id": 1,
121
- "eos_token_id": 2,
122
- "pad_token_id": 0,
123
- "use_cache": False,
124
- "recompute": False,
125
- "use_flash_attn": True,
126
- "use_pure_fp16": False,
127
- },
128
- }
129
-
130
-
131
- class Ernie4_5_Config(PretrainedConfig):
132
- """
133
- Configuration class for ERNIE model.
134
-
135
- This class stores the configuration of an ERNIE model, defining the model architecture.
136
- It inherits from PretrainedConfig and can be used to control model outputs.
137
- """
138
-
139
- model_type = "ernie"
140
- pretrained_init_configuration = ERNIE_PRETRAINED_INIT_CONFIGURATION
141
- base_model_tp_plan = {}
142
-
143
- def __init__(
144
- self,
145
- vocab_size=32000,
146
- hidden_size=768,
147
- intermediate_size=11008,
148
- max_position_embeddings=32768,
149
- num_hidden_layers=2,
150
- num_attention_heads=2,
151
- initializer_range=0.02, # no use
152
- rms_norm_eps=1e-6,
153
- use_cache=False,
154
- use_flash_attention=True,
155
- use_sparse_flash_attn=True,
156
- use_var_len_flash_attn=False,
157
- recompute=False,
158
- recompute_granularity="core_attn",
159
- recompute_use_reentrant=False,
160
- use_rmsnorm=True,
161
- fuse_rms_norm=False,
162
- fuse_ln=False,
163
- pad_token_id=0,
164
- bos_token_id=1,
165
- eos_token_id=2,
166
- fuse_swiglu=False,
167
- use_bias=False,
168
- rope_theta=10000,
169
- fuse_rope=False,
170
- fuse_softmax_mask=False,
171
- use_fast_ln=False,
172
- weight_share_add_bias=True,
173
- fuse_linear=False,
174
- max_sequence_length=None,
175
- ignored_index=-100,
176
- add_tail_layers=False,
177
- use_recompute_lm_head=False,
178
- use_recompute_loss_fn=False,
179
- refined_recompute=dict(),
180
- attention_probs_dropout_prob=0.0,
181
- hidden_dropout_prob=0.0,
182
- compression_ratio: float = 1.0,
183
- num_key_value_heads=None,
184
- use_sparse_head_and_loss_fn=False,
185
- micro_batch_size=-1,
186
- use_ep_comm_overlap=False,
187
- use_fused_head_and_loss_fn=False,
188
- token_balance_loss=False,
189
- token_balance_seqlen=False, # calculated based on batchsize and seqlen
190
- cachekv_quant: bool = False,
191
- pp_seg_method="layer:ErnieDecoderLayer|EmptyLayer",
192
- **kwargs,
193
- ):
194
- """
195
- Initialize ERNIE model configuration with default or specified parameters.
196
-
197
- Args:
198
- vocab_size (int): Size of the vocabulary (number of unique tokens)
199
- hidden_size (int): Dimensionality of the encoder layers and the pooler layer
200
- intermediate_size (int): Dimensionality of the "intermediate" (feed-forward) layer
201
- max_position_embeddings (int): Maximum sequence length the model can handle
202
- num_hidden_layers (int): Number of hidden layers in the Transformer encoder
203
- num_attention_heads (int): Number of attention heads for each attention layer
204
- rms_norm_eps (float): The epsilon used by the RMS normalization layers
205
- use_cache (bool): Whether to use caching for faster generation (decoding)
206
- use_flash_attention (bool): Whether to use FlashAttention for optimized attention computation
207
- use_sparse_flash_attn (bool): Whether to use sparse FlashAttention
208
- use_var_len_flash_attn (bool): Whether to use variable-length FlashAttention
209
- recompute (bool): Whether to use gradient checkpointing to save memory
210
- recompute_granularity (str): Granularity of recomputation ("core_attn", "full", etc.)
211
- recompute_use_reentrant (bool): Whether to use reentrant checkpointing
212
- use_rmsnorm (bool): Whether to use RMSNorm instead of LayerNorm
213
- fuse_rms_norm (bool): Whether to fuse RMSNorm operations for optimization
214
- fuse_ln (bool): Whether to fuse LayerNorm operations
215
- pad_token_id (int): Token ID used for padding sequences
216
- bos_token_id (int): Token ID used for beginning-of-sequence
217
- eos_token_id (int): Token ID used for end-of-sequence
218
- fuse_swiglu (bool): Whether to fuse SwiGLU operations
219
- use_bias (bool): Whether to use bias terms in linear layers
220
- rope_theta (float): The base period of the RoPE embeddings
221
- fuse_rope (bool): Whether to fuse RoPE operations
222
- use_fast_ln (bool): Whether to use optimized LayerNorm implementation
223
- weight_share_add_bias (bool): Whether to share bias weights in certain layers
224
- fuse_linear (bool): Whether to fuse linear operations
225
- max_sequence_length (int): Maximum sequence length for positional embeddings
226
- ignored_index (int): Target value that is ignored during loss computation
227
- add_tail_layers (bool): Whether to add additional layers at the end
228
- use_recompute_lm_head (bool): Whether to recompute gradients for language model head
229
- use_recompute_loss_fn (bool): Whether to recompute gradients for loss function
230
- refined_recompute (dict): Dictionary specifying refined recomputation settings
231
- attention_probs_dropout_prob (float): Dropout probability for attention weights
232
- hidden_dropout_prob (float): Dropout probability for hidden layers
233
- compression_ratio (float): Ratio for KV cache compression (1.0 = no compression)
234
- num_key_value_heads (int): Number of key/value heads (for Grouped Query Attention)
235
- use_sparse_head_and_loss_fn (bool): Whether to use sparse attention head and loss function
236
- micro_batch_size (int): Size of micro batches (-1 for automatic)
237
- use_ep_comm_overlap (bool): Whether to overlap communication with computation
238
- use_fused_head_loss_fn (bool): Whether to use fused head and loss function
239
- token_balance_loss (bool): Whether to balance loss by token count
240
- token_balance_seqlen (bool): Whether to balance sequence lengths
241
- cachekv_quant (bool): Whether to quantize key-value cache
242
- pp_seg_method (str): Method for pipeline parallel segmentation
243
- **kwargs: Additional keyword arguments passed to parent class
244
- """
245
-
246
- # Set default for tied embeddings if not specified.
247
- if "tie_word_embeddings" not in kwargs:
248
- kwargs["tie_word_embeddings"] = False
249
- super().__init__(
250
- pad_token_id=pad_token_id,
251
- bos_token_id=bos_token_id,
252
- eos_token_id=eos_token_id,
253
- **kwargs,
254
- )
255
- self.vocab_size = vocab_size
256
- self.hidden_size = hidden_size
257
- self.intermediate_size = intermediate_size
258
- self.max_position_embeddings = max_position_embeddings
259
- self.num_hidden_layers = num_hidden_layers
260
- self.num_attention_heads = num_attention_heads
261
- self.initializer_range = initializer_range
262
- self.rms_norm_eps = rms_norm_eps
263
- self.use_cache = use_cache
264
- self.recompute = recompute
265
- self.recompute_granularity = recompute_granularity
266
- self.use_flash_attention = use_flash_attention
267
- self.use_sparse_flash_attn = use_sparse_flash_attn
268
- self.recompute_use_reentrant = recompute_use_reentrant
269
- self.use_var_len_flash_attn = use_var_len_flash_attn
270
- self.pad_token_id = pad_token_id
271
- self.bos_token_id = bos_token_id
272
- self.eos_token_id = eos_token_id
273
- self.fuse_swiglu = fuse_swiglu
274
- self.fuse_rms_norm = fuse_rms_norm
275
- self.fuse_ln = fuse_ln
276
- self.use_rmsnorm = use_rmsnorm
277
- self.micro_batch_size = micro_batch_size
278
-
279
- self.max_sequence_length = max_sequence_length
280
- self.use_bias = use_bias
281
- self.weight_share_add_bias = weight_share_add_bias
282
- self.rope_theta = rope_theta
283
- self.fuse_rope = fuse_rope
284
- self.fuse_softmax_mask = fuse_softmax_mask
285
- self.use_fast_ln = use_fast_ln
286
-
287
- self.fuse_linear = fuse_linear
288
- self.ignored_index = ignored_index
289
- self.add_tail_layers = add_tail_layers
290
- self.use_recompute_lm_head = use_recompute_lm_head
291
- self.use_recompute_loss_fn = use_recompute_loss_fn
292
-
293
- self.refined_recompute = refined_recompute
294
- self.skip_recompute_ops = dict()
295
- """
296
- `refined_recompute` is a dictionary that specifies fine-grained gradient recomputation settings,
297
- which currently only takes effect in Pipeline Parallel (PP) mode.
298
-
299
- In PP mode, this dictionary populates `self.skip_recompute_ops` with the following structure:
300
- - Key (`op_name`): The operation name to configure, with possible values:
301
- * "mlp_row_ln" - MLP row-wise layer normalization
302
- * "flash_attn" - Flash attention operation
303
- * "attention_row_ln" - Attention row-wise layer normalization
304
- * "attention_column_ln" - Attention column-wise layer normalization
305
- * "mlp_column_ln" - MLP column-wise layer normalization
306
-
307
- - Value (`skip_num`): Controls how many times to skip recomputation:
308
- * 0: Never skip recomputation (minimum memory usage)
309
- * -1: Always skip recomputation (maximum memory usage)
310
- * [0,1,...,12]: Skip recomputation for specified number of times
311
- * ≥12: Equivalent to -1 (always skip recomputation)
312
-
313
- This allows precise control over memory/computation tradeoffs for different operations.
314
- """
315
- self.attention_probs_dropout_prob = attention_probs_dropout_prob
316
- self.hidden_dropout_prob = hidden_dropout_prob
317
- self.compression_ratio = compression_ratio
318
- self.num_key_value_heads = num_key_value_heads
319
- self.use_sparse_head_and_loss_fn = use_sparse_head_and_loss_fn
320
- self.use_ep_comm_overlap = use_ep_comm_overlap
321
- self.use_fused_head_and_loss_fn = use_fused_head_and_loss_fn
322
- self.token_balance_loss = token_balance_loss
323
- self.token_balance_seqlen = token_balance_seqlen
324
- self.cachekv_quant = cachekv_quant
325
- self.pp_seg_method = pp_seg_method
326
-
327
- def get(self, key, default=None):
328
- """get config value by key"""
329
- if hasattr(self, key):
330
- return getattr(self, key)
331
- else:
332
- return default
333
-
334
-
335
- class Ernie4_5_MoEConfig(Ernie4_5_Config):
336
- r"""
337
- Configuration class for ErnieMoE model architecture.
338
-
339
- This class stores the configuration for a [`~ErnieModel`] and is used to instantiate
340
- an ErnieMoE model according to the specified arguments. Inherits from [`PretrainedConfig`]
341
- and can control model outputs.
342
-
343
- Attributes:
344
- Inherits all attributes from Ernie4_5_Config and adds MoE-specific configurations.
345
- """
346
-
347
- model_type = "ernie"
348
- attribute_map = {
349
- "n_positions": "max_position_embeddings",
350
- "n_embd": "hidden_size",
351
- "n_layer": "num_hidden_layers",
352
- "n_head": "num_attention_heads",
353
- "n_inner": "intermediate_size",
354
- "activation_function": "hidden_act",
355
- }
356
- pretrained_init_configuration = ERNIE_PRETRAINED_INIT_CONFIGURATION
357
- base_model_tp_plan = {}
358
-
359
- def __init__(
360
- self,
361
- moe_num_experts: Union[int, list] = 0,
362
- use_recompute_moe=False,
363
- moe_capacity=(),
364
- moe_layer_interval=2,
365
- moe_layer_start_index=0,
366
- moe_layer_end_index=-1,
367
- moe_aux_loss_lambda=1e-2,
368
- moe_z_loss_lambda=1e-4,
369
- moe_orthogonal_loss_lambda=1e-2,
370
- sinkhorn_2gate=True,
371
- sinkhorn_temp=3e-2,
372
- global_aux_loss=False,
373
- moe_dropout_prob=0.0,
374
- moe_group="world",
375
- moe_gate="top2",
376
- moe_intermediate_size: Union[int, list] = 0,
377
- moe_num_shared_experts: int = 0,
378
- moe_reverse_token_drop: bool = False,
379
- moe_gate_act: str = "softmax",
380
- moe_norm_gate_logits=True,
381
- moe_all_to_all_dropout: float = 0.0,
382
- moe_k=2,
383
- moe_use_aux_free: bool = False,
384
- # `moe_group_experts` must be used with `moe_use_hard_gate=True`
385
- moe_group_experts: bool = False,
386
- moe_group_orthogonal_loss: bool = True,
387
- enable_delay_scale_loss: bool = True,
388
- num_acc_steps: int = 1,
389
- fuse_gate_detach_matmul: bool = False,
390
- dpo_config=None,
391
- moe_multimodal_dispatch_use_allgather: str = "",
392
- moe_use_hard_gate=False,
393
- moe_dense_experts_token_type_id=3,
394
- **kwargs,
395
- ):
396
- """
397
- Initialize ErnieMoE configuration with MoE-specific parameters.
398
-
399
- Args:
400
- moe_num_experts: Number of experts in MoE layers
401
- use_recompute_moe: Whether to use recomputation for MoE layers
402
- moe_capacity: Capacity configuration for MoE layers
403
- moe_layer_interval: Interval between MoE layers
404
- moe_layer_start_index: Starting layer index for MoE
405
- moe_layer_end_index: Ending layer index for MoE (-1 means last layer)
406
- moe_aux_loss_lambda: Weight for auxiliary loss
407
- moe_z_loss_lambda: Weight for z-loss
408
- moe_orthogonal_loss_lambda: Weight for orthogonal loss
409
- sinkhorn_2gate: Whether to use sinkhorn 2-gate routing
410
- sinkhorn_temp: Temperature for sinkhorn routing
411
- global_aux_loss: Whether to use global auxiliary loss
412
- moe_dropout_prob: Dropout probability for MoE layers
413
- moe_group: Group configuration for MoE experts
414
- moe_gate: Type of gating mechanism ('top2', etc.)
415
- moe_intermediate_size: Intermediate size for MoE layers
416
- moe_num_shared_experts: Number of shared experts
417
- moe_reverse_token_drop: Whether to use reverse token dropping
418
- moe_gate_act: Activation function for gating
419
- moe_norm_gate_logits: Whether to normalize gate logits
420
- moe_all_to_all_dropout: Dropout for all-to-all communication
421
- moe_k: Number of experts to route to
422
- moe_use_aux_free: Whether to use auxiliary-free routing
423
- moe_group_experts: Whether to group experts (requires hard gating)
424
- moe_group_orthogonal_loss: Whether to use group orthogonal loss
425
- enable_delay_scale_loss: Whether to enable delayed loss scaling
426
- num_acc_steps: Number of accumulation steps
427
- fuse_gate_detach_matmul: Whether to fuse gate detach matmul
428
- **kwargs: Additional base model configuration parameters
429
-
430
- Note:
431
- When use_recompute_moe is True, recompute_granularity will be changed to full_attn.
432
- """
433
-
434
- if use_recompute_moe:
435
- logger.warning(
436
- "set `use_recompute_moe`=True, disabling `recompute_granularity=full`, change to full_attn."
437
- )
438
- if kwargs["recompute"] and kwargs["recompute_granularity"] == "full":
439
- kwargs["recompute_granularity"] = "full_attn"
440
- super().__init__(**kwargs)
441
-
442
- self.moe_num_experts = moe_num_experts
443
- self.use_recompute_moe = use_recompute_moe
444
- self.moe_capacity = moe_capacity
445
- self.moe_aux_loss_lambda = moe_aux_loss_lambda
446
- self.moe_z_loss_lambda = moe_z_loss_lambda
447
- self.moe_orthogonal_loss_lambda = moe_orthogonal_loss_lambda
448
- self.global_aux_loss = global_aux_loss
449
- self.sinkhorn_2gate = sinkhorn_2gate
450
- self.sinkhorn_temp = sinkhorn_temp
451
- self.moe_layer_interval = moe_layer_interval
452
- self.moe_dropout_prob = moe_dropout_prob
453
- self.moe_group = moe_group
454
- self.moe_gate = moe_gate
455
- self.moe_intermediate_size = moe_intermediate_size
456
- self.moe_num_shared_experts = moe_num_shared_experts
457
- self.moe_reverse_token_drop = moe_reverse_token_drop
458
- self.moe_k = moe_k
459
- self.moe_all_to_all_dropout = moe_all_to_all_dropout
460
- self.moe_group_experts = moe_group_experts
461
- self.moe_group_orthogonal_loss = moe_group_orthogonal_loss
462
- self.enable_delay_scale_loss = enable_delay_scale_loss
463
- self.num_acc_steps = num_acc_steps
464
- self.moe_layer_start_index = moe_layer_start_index
465
- self.moe_layer_end_index = (
466
- self.num_hidden_layers - 1
467
- if moe_layer_end_index == -1
468
- else moe_layer_end_index
469
- )
470
- self.moe_gate_act = moe_gate_act
471
- self.moe_norm_gate_logits = moe_norm_gate_logits
472
- self.moe_use_aux_free = moe_use_aux_free
473
- self.fuse_gate_detach_matmul = fuse_gate_detach_matmul
474
- self.dpo_config = dpo_config
475
- self.moe_multimodal_dispatch_use_allgather = (
476
- moe_multimodal_dispatch_use_allgather
477
- )
478
- self.moe_use_hard_gate = moe_use_hard_gate
479
- self.moe_dense_experts_token_type_id = moe_dense_experts_token_type_id
480
-
481
- @property
482
- def multimodel_experts(self) -> bool:
483
- """multimodel experts."""
484
- return (
485
- isinstance(self.moe_num_experts, (tuple, list))
486
- and len(self.moe_num_experts) > 1
487
- )
488
-
489
- @property
490
- def use_moe(self) -> bool:
491
- """
492
- Check if model is using MoE architecture.
493
-
494
- Returns:
495
- bool: True if moe_num_experts > 0, False otherwise
496
- """
497
- return self.moe_num_experts > 0
498
-
499
-
500
- class Ernie4_5_VLMoEConfig(Ernie4_5_MoEConfig):
501
- """
502
- This is the configuration class to store the configuration of a [`~ErnieModel`]. It is used to instantiate an Ernie
503
- model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
504
- defaults will yield a similar configuration to that of the Ernie-7B.
505
- Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
506
- documentation from [`PretrainedConfig`] for more information.
507
- Args:
508
- vocab_size (`int`, *optional*, defaults to 32000):
509
- Vocabulary size of the Ernie model. Defines the number of different tokens that can be represented by the
510
- `inputs_ids` passed when calling [`~ErnieModel`] or [`~TFErnieModel`].
511
- hidden_size (`int`, *optional*, defaults to 4096):
512
- Dimension of the hidden representations.
513
- intermediate_size (`int`, *optional*, defaults to 11008):
514
- Dimension of the MLP representations.
515
- num_hidden_layers (`int`, *optional*, defaults to 32):
516
- Number of hidden layers in the Transformer encoder.
517
- num_attention_heads (`int`, *optional*, defaults to 32):
518
- Number of attention heads for each attention layer in the Transformer encoder.
519
- hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
520
- The non-linear activation function (function or string) in the decoder.
521
- initializer_range (`float`, *optional*, defaults to 0.02):
522
- The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
523
- rms_norm_eps (`float`, *optional*, defaults to 1e-12):
524
- The epsilon used by the rms normalization layers.
525
- use_cache (`bool`, *optional*, defaults to `True`):
526
- Whether or not the model should return the last key/values attentions (not used by all models). Only
527
- relevant if `config.is_decoder=True`.
528
- tie_word_embeddings(`bool`, *optional*, defaults to `False`):
529
- Whether to tie weight embeddings
530
- """
531
-
532
- model_type = "ernie4_5_moe_vl"
533
- attribute_map = {
534
- "n_positions": "max_position_embeddings",
535
- "n_embd": "hidden_size",
536
- "n_layer": "num_hidden_layers",
537
- "n_head": "num_attention_heads",
538
- "n_inner": "intermediate_size",
539
- "activation_function": "hidden_act",
540
- }
541
- base_model_tp_plan = {
542
- "model.layers.*.self_attn.q_proj": "colwise_rep",
543
- "model.layers.*.self_attn.k_proj": "colwise_rep",
544
- "model.layers.*.self_attn.v_proj": "colwise_rep",
545
- "model.layers.*.self_attn.o_proj": "rowwise_rep",
546
- "model.layers.*.mlp.experts.*.gate_proj": "colwise",
547
- "model.layers.*.mlp.experts.*.up_proj": "colwise",
548
- "model.layers.*.mlp.experts.*.down_proj": "rowwise",
549
- "model.layers.*.mlp_text.experts.*.gate_proj": "colwise",
550
- "model.layers.*.mlp_text.experts.*.up_proj": "colwise",
551
- "model.layers.*.mlp_text.experts.*.down_proj": "rowwise",
552
- "model.layers.*.mlp.gate_proj": "colwise",
553
- "model.layers.*.mlp.up_proj": "colwise",
554
- "model.layers.*.mlp.down_proj": "rowwise"
555
- }
556
-
557
- def __init__(
558
- self,
559
- vision_config=None,
560
- im_patch_id=None,
561
- pixel_hidden_size=None,
562
- modality_detach=False,
563
- temporal_conv_size=2,
564
- spatial_conv_size=2,
565
- mm_vocab_size=0, # vocab for mm specialtokens
566
- max_text_id=None,
567
- use_temporal_conv=True,
568
- moe_use_size_all2all=False,
569
- moe_num_attn_experts=False,
570
- moe_dense_experts_token_type_id: int = 3,
571
- moe_use_hard_gate: bool = True,
572
- moe_fuse_experts: bool = False,
573
- moe_use_token_type_bias: bool = False,
574
- disable_ffn_model_parallel=False,
575
- fuse_attn_ffn=True,
576
- rope_3d=True,
577
- freq_allocation=20,
578
- using_precision_check=False,
579
- use_recompute_resampler=False,
580
- resampler_fuse_rms_norm=False,
581
- moe_layer_feed_fake_token=False,
582
- tensor_parallel_degree=1,
583
- **kwargs,
584
- ):
585
- super().__init__(**kwargs)
586
- if isinstance(vision_config, dict):
587
- self.vision_config = DFNRopeVisionTransformerConfig(**vision_config)
588
- else:
589
- self.vision_config = DFNRopeVisionTransformerConfig()
590
- self.im_patch_id = im_patch_id
591
- self.pixel_hidden_size = pixel_hidden_size
592
- self.modality_detach = modality_detach
593
- self.temporal_conv_size = temporal_conv_size
594
- self.spatial_conv_size = spatial_conv_size
595
- self.mm_vocab_size = mm_vocab_size
596
- self.max_text_id = max_text_id
597
- self.use_temporal_conv = use_temporal_conv
598
-
599
- self.moe_use_size_all2all = moe_use_size_all2all
600
- self.moe_num_attn_experts = moe_num_attn_experts
601
- self.moe_dense_experts_token_type_id = moe_dense_experts_token_type_id
602
- self.moe_use_hard_gate = moe_use_hard_gate
603
- self.moe_fuse_experts = moe_fuse_experts
604
- self.moe_use_token_type_bias = moe_use_token_type_bias
605
- self.disable_ffn_model_parallel = disable_ffn_model_parallel
606
-
607
- self.fuse_attn_ffn = fuse_attn_ffn
608
- self.rope_3d = rope_3d
609
- self.freq_allocation = freq_allocation
610
- self.using_precision_check = using_precision_check
611
- self.use_recompute_resampler = use_recompute_resampler
612
- self.resampler_fuse_rms_norm = resampler_fuse_rms_norm
613
- self.moe_layer_feed_fake_token = moe_layer_feed_fake_token
614
-
615
- self.tensor_parallel_degree = tensor_parallel_degree
616
-
617
- @property
618
- def multimodel_experts(self) -> bool:
619
- """Check if model is using more than 1 multimodel experts."""
620
- return (
621
- isinstance(self.moe_num_experts, (tuple, list))
622
- and len(self.moe_num_experts) > 1
623
- )
624
-
625
- @property
626
- def use_moe(self) -> bool:
627
- """
628
- Check if model is using MoE architecture.
629
-
630
- Returns:
631
- bool: True if moe_num_experts > 0, False otherwise
632
- """
633
- return (
634
- sum(self.moe_num_experts) > 0
635
- if self.multimodel_experts
636
- else self.moe_num_experts > 0
637
- )
638
-
639
- def to_dict(self, saving_file=False):
640
- """to_dict"""
641
- output = copy.deepcopy(self.__dict__)
642
- if self.vision_config:
643
- output["vision_config"] = (
644
- self.vision_config.to_dict()
645
- if isinstance(self.vision_config, (DFNRopeVisionTransformerConfig))
646
- else self.vision_config
647
- )
648
-
649
- output["model_type"] = self.__class__.model_type
650
- return output
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
modeling_ernie4_5_vl.py DELETED
The diff for this file is too large to render. See raw diff
 
preprocessor_config.json DELETED
@@ -1,29 +0,0 @@
1
- {
2
- "crop_size": {
3
- "height": 224,
4
- "width": 224
5
- },
6
- "do_center_crop": false,
7
- "do_convert_rgb": true,
8
- "do_normalize": true,
9
- "do_rescale": true,
10
- "do_resize": true,
11
- "image_mean": [
12
- 0.48145466,
13
- 0.4578275,
14
- 0.40821073
15
- ],
16
- "image_std": [
17
- 0.26862954,
18
- 0.26130258,
19
- 0.27577711
20
- ],
21
- "resample": 3,
22
- "rescale_factor": 0.00392156862745098,
23
- "size": {
24
- "height": 224,
25
- "width": 224
26
- },
27
- "min_pixels": 3136,
28
- "max_pixels": 4816896
29
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
processing_ernie4_5_vl.py DELETED
@@ -1,1822 +0,0 @@
1
- # Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
2
- #
3
- # Licensed under the Apache License, Version 2.0 (the "License");
4
- # you may not use this file except in compliance with the License.
5
- # You may obtain a copy of the License at
6
- #
7
- # http://www.apache.org/licenses/LICENSE-2.0
8
- #
9
- # Unless required by applicable law or agreed to in writing, software
10
- # distributed under the License is distributed on an "AS IS" BASIS,
11
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
- # See the License for the specific language governing permissions and
13
- # limitations under the License.
14
-
15
- """Tokenization classes and Image processor class, Processor class for Ernie_45T_VL."""
16
-
17
- import copy
18
- import io
19
- import os
20
- import math
21
- import random
22
- import requests
23
- import base64
24
- import datetime
25
- import hashlib
26
- import threading
27
- import uuid
28
- import decord
29
- from shutil import copyfile
30
- from typing import Any, Dict, List, Optional, Tuple, Union
31
-
32
- import numpy as np
33
- import torch
34
- from PIL import Image, ImageDraw, ImageFont
35
- from PIL.ExifTags import TAGS
36
- from collections import defaultdict
37
- from pathlib import Path
38
- from tempfile import NamedTemporaryFile as ntf
39
-
40
- try:
41
- # moviepy 1.0
42
- import moviepy.editor as mp
43
- except:
44
- # moviepy 2.0
45
- import moviepy as mp
46
-
47
- import sentencepiece as spm
48
- from transformers.tokenization_utils import PreTrainedTokenizer
49
- from transformers.tokenization_utils_base import (
50
- PaddingStrategy,
51
- TextInput,
52
- )
53
- from transformers.utils import TensorType, logging
54
- from transformers.video_utils import VideoInput
55
- from transformers.processing_utils import ProcessorMixin
56
- from transformers.feature_extraction_utils import BatchFeature
57
- from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
58
- from transformers.image_transforms import (
59
- convert_to_rgb,
60
- normalize,
61
- rescale,
62
- resize,
63
- to_channel_dimension_format,
64
- )
65
- from transformers.image_utils import (
66
- OPENAI_CLIP_MEAN,
67
- OPENAI_CLIP_STD,
68
- ChannelDimension,
69
- ImageInput,
70
- PILImageResampling,
71
- get_image_size,
72
- infer_channel_dimension_format,
73
- is_valid_image,
74
- make_list_of_images,
75
- to_numpy_array,
76
- valid_images,
77
- )
78
-
79
- logger = logging.get_logger(__name__)
80
-
81
-
82
- class Ernie4_5_VLTokenizer(PreTrainedTokenizer):
83
- """
84
- Ernie4_5_VLTokenizer
85
- """
86
-
87
- vocab_files_names = {
88
- "vocab_file": "tokenizer.model",
89
- }
90
- # Model input names expected by the tokenizer
91
- model_input_names = ["input_ids", "position_ids", "attention_mask", "labels"]
92
- # Padding side (where to add padding tokens)
93
- padding_side = "right"
94
-
95
- def __init__(
96
- self,
97
- vocab_file,
98
- bos_token="<s>",
99
- cls_token="<cls>",
100
- eos_token="</s>",
101
- mask_token="<mask:0>",
102
- pad_token="<pad>",
103
- sep_token="<sep>",
104
- unk_token="<unk>",
105
- additional_special_tokens=None,
106
- **kwargs,
107
- ):
108
- """
109
- Initialize the Ernie4_5_VLTokenizer
110
-
111
- Args:
112
- vocab_file (str): Path to the tokenizer vocabulary model.
113
- bos_token (str, optional): The beginning of sequence token. Defaults to `"<s>"`.
114
- cls_token (str, optional): The classifier token. Defaults to `"<cls>"`.
115
- eos_token (str, optional): The end of sequence token. Defaults to `"</s>"`.
116
- mask_token (str, optional): The masking token. Defaults to `"<mask:0>"`.
117
- pad_token (str, optional): The padding token. Defaults to `"<pad>"`.
118
- sep_token (str, optional): The separation token. Defaults to `"<sep>"`.
119
- unk_token (str, optional): The unknown tokens symbol. Defaults to `"<unk>"`.
120
- additional_special_tokens (List[str], optional): Additional special tokens to use.
121
- Defaults to `["<mask:1>", "<mask:7>"]`.
122
- **kwargs (dict): Additional keyword arguments passed along to the superclass.
123
- """
124
-
125
- # Store vocabulary file path
126
- self.vocab_file = vocab_file
127
- # Initialize SentencePiece processor
128
- self.sp_model = spm.SentencePieceProcessor()
129
- # Load the vocabulary model
130
- self.sp_model.Load(vocab_file)
131
-
132
- # Set default additional special tokens if none provided
133
- if additional_special_tokens is None:
134
- additional_special_tokens = ["<mask:1>", "<mask:7>"]
135
- super().__init__(
136
- bos_token=bos_token,
137
- cls_token=cls_token,
138
- eos_token=eos_token,
139
- mask_token=mask_token,
140
- pad_token=pad_token,
141
- sep_token=sep_token,
142
- unk_token=unk_token,
143
- additional_special_tokens=additional_special_tokens,
144
- **kwargs,
145
- )
146
-
147
- @property
148
- def space_token(self):
149
- """Return the space token"""
150
- return "<mask:1>"
151
-
152
- @property
153
- def space_token_id(self):
154
- """Return the ID of the space token"""
155
- return self.sp_model.piece_to_id("<mask:1>")
156
-
157
- @property
158
- def gend_token(self):
159
- """Return the gender token"""
160
- return "<mask:7>"
161
-
162
- @property
163
- def gend_token_id(self):
164
- """Return the ID of the gender token"""
165
- return self.sp_model.piece_to_id("<mask:7>")
166
-
167
- @property
168
- def im_start_id(self):
169
- """Return the ID of the image start token"""
170
- return self.sp_model.piece_to_id("<|im_start|>")
171
-
172
- @property
173
- def im_end_id(self):
174
- """Return the ID of the image end token"""
175
- return self.sp_model.piece_to_id("<|im_end|>")
176
-
177
- @property
178
- def vocab_size(self):
179
- """Return the size of the vocabulary"""
180
- return self.sp_model.vocab_size()
181
-
182
- def get_vocab(self):
183
- """Return the vocabulary as a dictionary mapping tokens to IDs"""
184
- vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
185
- vocab.update(self.added_tokens_encoder)
186
- return vocab
187
-
188
- def _tokenize(self, text):
189
- """Tokenize the input text into pieces"""
190
- return self.sp_model.encode_as_pieces(text)
191
-
192
- def _convert_token_to_id(self, token):
193
- """Convert a token to its corresponding ID"""
194
- return self.sp_model.piece_to_id(token)
195
-
196
- def _convert_id_to_token(self, id):
197
- """Convert an ID to its corresponding token"""
198
- return self.sp_model.id_to_piece(id)
199
-
200
- def convert_tokens_to_string(self, tokens):
201
- """Convert a sequence of tokens back to a string"""
202
- current_sub_tokens = []
203
- out_string = ""
204
-
205
- for token in tokens:
206
- # Handle special tokens differently
207
- if token in self.all_special_tokens:
208
- out_string += self.sp_model.decode(current_sub_tokens) + token
209
- current_sub_tokens = []
210
- else:
211
- current_sub_tokens.append(token)
212
-
213
- # Add any remaining sub-tokens
214
- out_string += self.sp_model.decode(current_sub_tokens)
215
- return out_string
216
-
217
- def prepare_for_model(self, *args, **kwargs):
218
- """Prepare the tokenized inputs for the model"""
219
- # Remove add_special_tokens if present (not supported)
220
- if "add_special_tokens" in kwargs:
221
- kwargs.pop("add_special_tokens")
222
- return super().prepare_for_model(*args, **kwargs)
223
-
224
- def save_vocabulary(
225
- self, save_directory, filename_prefix: Optional[str] = None
226
- ) -> Tuple[str]:
227
- """
228
- Save the vocabulary and special tokens file to a directory.
229
-
230
- Args:
231
- save_directory (`str`): The directory to save the vocabulary to
232
- filename_prefix (`str`, optional): Prefix to add to the filename
233
-
234
- Returns:
235
- `Tuple(str)`: Paths to the saved files
236
- """
237
- if not os.path.isdir(save_directory):
238
- logger.error(f"Vocabulary path ({save_directory}) should be a directory")
239
- return
240
-
241
- # Construct output vocabulary file path
242
- out_vocab_file = os.path.join(
243
- save_directory,
244
- (filename_prefix + "-" if filename_prefix else "")
245
- + self.vocab_files_names["vocab_file"],
246
- )
247
-
248
- # Copy or create vocabulary file
249
- if os.path.abspath(self.vocab_file) != os.path.abspath(
250
- out_vocab_file
251
- ) and os.path.isfile(self.vocab_file):
252
- copyfile(self.vocab_file, out_vocab_file)
253
- elif not os.path.isfile(self.vocab_file):
254
- with open(out_vocab_file, "wb") as fi:
255
- content_spiece_model = self.sp_model.serialized_model_proto()
256
- fi.write(content_spiece_model)
257
-
258
- return (out_vocab_file,)
259
-
260
- def _decode(self, *args, **kwargs):
261
- """Decode token_id back to text"""
262
- # Remove some parameters that aren't used
263
- kwargs.pop("clean_up_tokenization_spaces", None)
264
- kwargs.pop("spaces_between_special_tokens", None)
265
-
266
- # Call parent decode method with specific parameters
267
- return super()._decode(
268
- *args,
269
- **kwargs,
270
- clean_up_tokenization_spaces=False,
271
- spaces_between_special_tokens=False,
272
- )
273
-
274
- def _pad(
275
- self,
276
- encoded_inputs: Dict,
277
- max_length: Optional[int] = None,
278
- padding_strategy=PaddingStrategy.DO_NOT_PAD,
279
- pad_to_multiple_of: Optional[int] = None,
280
- return_attention_mask: Optional[bool] = None,
281
- **kwargs
282
- ) -> dict:
283
- """Pad the encoded inputs to the specified length"""
284
- if return_attention_mask is None:
285
- return_attention_mask = "attention_mask" in self.model_input_names
286
- if return_attention_mask:
287
- required_input = encoded_inputs[self.model_input_names[0]]
288
- if padding_strategy == PaddingStrategy.LONGEST:
289
- max_length = len(required_input)
290
-
291
- # Adjust max_length if needed for multiple of padding
292
- if (
293
- max_length is not None
294
- and pad_to_multiple_of is not None
295
- and (max_length % pad_to_multiple_of != 0)
296
- ):
297
- max_length = (
298
- (max_length // pad_to_multiple_of) + 1
299
- ) * pad_to_multiple_of
300
-
301
- # Check if padding is needed
302
- needs_to_be_padded = (
303
- padding_strategy != PaddingStrategy.DO_NOT_PAD
304
- and len(required_input) != max_length
305
- )
306
-
307
- # Handle attention mask if present
308
- if (
309
- "attention_mask" in encoded_inputs
310
- and encoded_inputs["attention_mask"] is not None
311
- ):
312
- attention_mask = encoded_inputs.pop("attention_mask")
313
- if isinstance(attention_mask, torch.Tensor):
314
- attention_mask = attention_mask.numpy()
315
- elif isinstance(attention_mask, list):
316
- attention_mask = np.array(attention_mask)
317
- elif not isinstance(attention_mask, np.ndarray):
318
- raise ValueError(
319
- f"Unexpected type {type(attention_mask)} of attention_mask, "
320
- )
321
- else:
322
- # Create default attention mask if none provided
323
- attention_mask = np.tril(
324
- np.ones((len(required_input), len(required_input)), dtype=np.int64)
325
- )
326
- attention_mask = np.expand_dims(attention_mask, axis=0)
327
-
328
- # Perform padding if needed
329
- if needs_to_be_padded:
330
- difference = max_length - len(required_input)
331
- if self.padding_side == "right":
332
- if attention_mask.ndim == 1:
333
- pad_width = [(0, difference)]
334
- else:
335
- pad_width = [(0, 0), (0, difference), (0, difference)]
336
- elif self.padding_side == "left":
337
- if attention_mask.ndim == 1:
338
- pad_width = [(difference, 0)]
339
- else:
340
- pad_width = [(0, 0), (difference, 0), (difference, 0)]
341
- else:
342
- raise ValueError(
343
- "Invalid padding strategy:" + str(self.padding_side)
344
- )
345
-
346
- attention_mask = np.pad(
347
- attention_mask,
348
- pad_width=pad_width,
349
- mode="constant",
350
- constant_values=0,
351
- )
352
-
353
- # Call parent padding method
354
- encoded_inputs = super()._pad(
355
- encoded_inputs,
356
- max_length,
357
- padding_strategy=padding_strategy,
358
- pad_to_multiple_of=pad_to_multiple_of,
359
- return_attention_mask=False,
360
- )
361
-
362
- # Add attention mask back if needed
363
- if return_attention_mask:
364
- encoded_inputs["attention_mask"] = attention_mask.tolist()
365
-
366
- return encoded_inputs
367
-
368
-
369
- def round_by_factor(number: int, factor: int) -> int:
370
- """Returns the closest integer to 'number' that is divisible by 'factor'."""
371
- return round(number / factor) * factor
372
-
373
-
374
- def ceil_by_factor(number: int, factor: int) -> int:
375
- """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
376
- return math.ceil(number / factor) * factor
377
-
378
-
379
- def floor_by_factor(number: int, factor: int) -> int:
380
- """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
381
- return math.floor(number / factor) * factor
382
-
383
-
384
- def smart_resize(
385
- height: int,
386
- width: int,
387
- factor: int = 28,
388
- min_pixels: int = 4 * 28 * 28,
389
- max_pixels: int = 16384 * 28 * 28,
390
- ):
391
- """
392
- Rescales the image so that the following conditions are met:
393
-
394
- 1. Both dimensions (height and width) are divisible by 'factor'.
395
-
396
- 2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
397
-
398
- 3. The aspect ratio of the image is maintained as closely as possible.
399
- """
400
- MAX_RATIO = 200
401
- if max(height, width) / min(height, width) > MAX_RATIO:
402
- if height > width:
403
- new_width = max(factor, round_by_factor(width, factor))
404
- new_height = floor_by_factor(new_width * MAX_RATIO, factor)
405
- else:
406
- new_height = max(factor, round_by_factor(height, factor))
407
- new_width = floor_by_factor(new_height * MAX_RATIO, factor)
408
-
409
- logger.info(
410
- f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)},\
411
- resize to {max(new_height, new_width) / min(new_height, new_width)}"
412
- )
413
-
414
- height = new_height
415
- width = new_width
416
-
417
- h_bar = max(factor, round_by_factor(height, factor))
418
- w_bar = max(factor, round_by_factor(width, factor))
419
- if h_bar * w_bar > max_pixels:
420
- beta = math.sqrt((height * width) / max_pixels)
421
- h_bar = floor_by_factor(height / beta, factor)
422
- w_bar = floor_by_factor(width / beta, factor)
423
- elif h_bar * w_bar < min_pixels:
424
- beta = math.sqrt(min_pixels / (height * width))
425
- h_bar = ceil_by_factor(height * beta, factor)
426
- w_bar = ceil_by_factor(width * beta, factor)
427
-
428
- if min_pixels > h_bar * w_bar or h_bar * w_bar > max_pixels:
429
- raise ValueError(f"encounter invalid h_bar: {h_bar}, w_bar: {w_bar}")
430
-
431
- return h_bar, w_bar
432
-
433
-
434
- def is_scaled_image(image: np.ndarray) -> bool:
435
- """
436
- Checks to see whether the pixel values have already been rescaled to [0, 1].
437
- """
438
- if image.dtype == np.uint8:
439
- return False
440
-
441
- # It's possible the image has pixel values in [0, 255] but is of floating type
442
- return np.min(image) >= 0 and np.max(image) <= 1
443
-
444
-
445
- def make_batched_images(images) -> List[List[ImageInput]]:
446
- """
447
- Accepts images in list or nested list format, and makes a list of images for preprocessing.
448
-
449
- Args:
450
- images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`):
451
- The input image.
452
-
453
- Returns:
454
- list: A list of images.
455
- """
456
- if (
457
- isinstance(images, (list, tuple))
458
- and isinstance(images[0], (list, tuple))
459
- and is_valid_image(images[0][0])
460
- ):
461
- return [img for img_list in images for img in img_list]
462
-
463
- elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
464
- return images
465
-
466
- elif is_valid_image(images):
467
- return [images]
468
-
469
- raise ValueError(f"Could not make batched images from {images}")
470
-
471
-
472
- # Copied from transformers.models.llava_next_video.image_processing_llava_next_video.make_batched_videos
473
- def make_batched_videos(videos) -> List[VideoInput]:
474
- """dummy"""
475
- if (
476
- isinstance(videos, (list, tuple))
477
- and isinstance(videos[0], (list, tuple))
478
- and is_valid_image(videos[0][0])
479
- ):
480
- return videos
481
-
482
- elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
483
- if isinstance(videos[0], Image.Image):
484
- return [videos]
485
- elif len(videos[0].shape) == 4:
486
- return [list(video) for video in videos]
487
-
488
- elif is_valid_image(videos) and len(videos.shape) == 4:
489
- return [list(videos)]
490
-
491
- raise ValueError(f"Could not make batched video from {videos}")
492
-
493
-
494
- class Ernie4_5_VLImageProcessor(BaseImageProcessor):
495
- r"""
496
- Constructs a adaptive image processor that dynamically resizes images based on the original images.
497
-
498
- Args:
499
- do_resize (`bool`, *optional*, defaults to `True`):
500
- Whether to resize the image's (height, width) dimensions.
501
- resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
502
- Resampling filter to use when resizing the image.
503
- do_rescale (`bool`, *optional*, defaults to `True`):
504
- Whether to rescale the image by the specified scale `rescale_factor`.
505
- rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
506
- Scale factor to use if rescaling the image.
507
- do_normalize (`bool`, *optional*, defaults to `True`):
508
- Whether to normalize the image.
509
- image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
510
- Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
511
- image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
512
- Standard deviation to use if normalizing the image. This is a float or list of floats for each channel
513
- in the image.
514
- do_convert_rgb (`bool`, *optional*, defaults to `True`):
515
- Whether to convert the image to RGB.
516
- min_pixels (`int`, *optional*, defaults to `56 * 56`):
517
- The min pixels of the image to resize the image.
518
- max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
519
- The max pixels of the image to resize the image.
520
- patch_size (`int`, *optional*, defaults to 14):
521
- The spacial patch size of the vision encoder.
522
- temporal_conv_size (`int`, *optional*, defaults to 2):
523
- The temporal conv size in resampler.
524
- merge_size (`int`, *optional*, defaults to 2):
525
- The merge size of the vision encoder to llm encoder.
526
- """
527
-
528
- model_input_names = [
529
- "pixel_values",
530
- "image_grid_thw",
531
- "pixel_values_videos",
532
- "video_grid_thw",
533
- ]
534
-
535
- def __init__(
536
- self,
537
- do_resize: bool = True,
538
- resample: PILImageResampling = PILImageResampling.BICUBIC,
539
- do_rescale: bool = True,
540
- rescale_factor: Union[float, List[float]] = 1 / 255,
541
- do_normalize: bool = True,
542
- image_mean: Optional[Union[float, List[float]]] = None,
543
- image_std: Optional[Union[float, List[float]]] = None,
544
- do_convert_rgb: bool = True,
545
- min_pixels: int = 56 * 56,
546
- max_pixels: int = 28 * 28 * 1280,
547
- patch_size: int = 14,
548
- temporal_conv_size: int = 2,
549
- merge_size: int = 2,
550
- **kwargs,
551
- ) -> None:
552
- """init"""
553
- super().__init__(**kwargs)
554
- self.do_resize = do_resize
555
- self.resample = resample
556
- self.do_rescale = do_rescale
557
- self.rescale_factor = rescale_factor
558
- self.do_normalize = do_normalize
559
- self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
560
- self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
561
- self.min_pixels = min_pixels
562
- self.max_pixels = max_pixels
563
- self.patch_size = patch_size
564
- self.temporal_conv_size = temporal_conv_size
565
- self.merge_size = merge_size
566
- self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}
567
- self.do_convert_rgb = do_convert_rgb
568
-
569
- def set_pixels(self, min_pixels=None, max_pixels=None, msg=""):
570
- """set_pixels"""
571
- if min_pixels is not None:
572
- assert (
573
- isinstance(min_pixels, int) and min_pixels >= 0
574
- ), "min_pixels must be positive int"
575
- logger.info(
576
- f"{msg} Ernie4_5_VLImageProcessor set min_pixels = {min_pixels}"
577
- )
578
- self.min_pixels = min_pixels
579
- self.size["min_pixels"] = int(min_pixels)
580
- if max_pixels is not None:
581
- assert (
582
- isinstance(max_pixels, int) and max_pixels > 0
583
- ), "max_pixels must be positive int"
584
- logger.info(
585
- f"{msg} Ernie4_5_VLImageProcessor set max_pixels = {max_pixels}"
586
- )
587
- self.max_pixels = max_pixels
588
- self.size["max_pixels"] = int(max_pixels)
589
-
590
- def get_smarted_resize(self, height, width, min_pixels=None, max_pixels=None):
591
- """dummy"""
592
- actual_min_pixels = min_pixels if min_pixels is not None else self.min_pixels
593
- actual_max_pixels = max_pixels if max_pixels is not None else self.max_pixels
594
- resized_height, resized_width = smart_resize(
595
- height,
596
- width,
597
- factor=self.patch_size * self.merge_size,
598
- min_pixels=actual_min_pixels,
599
- max_pixels=actual_max_pixels,
600
- )
601
- return (resized_height, resized_width), (
602
- resized_height // self.patch_size,
603
- resized_width // self.patch_size,
604
- )
605
-
606
- def _preprocess(
607
- self,
608
- images: Union[ImageInput, VideoInput],
609
- do_resize: bool = True,
610
- resample: PILImageResampling = None,
611
- do_rescale: bool = True,
612
- rescale_factor: float = 1 / 255,
613
- do_normalize: bool = True,
614
- image_mean: Optional[Union[float, List[float]]] = None,
615
- image_std: Optional[Union[float, List[float]]] = None,
616
- do_convert_rgb: bool = False,
617
- data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
618
- input_data_format: Optional[Union[str, ChannelDimension]] = None,
619
- predetermined_grid_thw=None,
620
- ):
621
- """
622
- Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
623
-
624
- Args:
625
- images (`ImageInput` or `VideoInput`):
626
- Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255.
627
- If pixel values range from 0 to 1, set `do_rescale=False`.
628
- do_resize (`bool`, *optional*, defaults to `self.do_resize`):
629
- Whether to resize the image.
630
- resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
631
- Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
632
- do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
633
- Whether to rescale the image.
634
- rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
635
- Scale factor to use if rescaling the image.
636
- do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
637
- Whether to normalize the image.
638
- image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
639
- Mean to use if normalizing the image.
640
- Can be a float or a list of floats corresponding to the number of channels in the image.
641
- image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
642
- Standard deviation to use if normalizing the image.
643
- Can be a float or a list of floats corresponding to the number of channels in the image.
644
- do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
645
- Whether to convert the image to RGB.
646
- data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
647
- The channel dimension format for the output image. Can be one of:
648
- - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
649
- - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
650
- - Unset: Use the channel dimension format of the input image.
651
- input_data_format (`ChannelDimension` or `str`, *optional*):
652
- The channel dimension format for the input image. Can be one of:
653
- - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
654
- - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
655
- - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
656
- - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
657
- """
658
- images = make_list_of_images(images)
659
-
660
- if do_convert_rgb:
661
- images = [convert_to_rgb(image) for image in images]
662
-
663
- # All transformations expect numpy arrays.
664
- images = [to_numpy_array(image) for image in images]
665
-
666
- if is_scaled_image(images[0]) and do_rescale:
667
- logger.warning_once(
668
- "It looks like you are trying to rescale already rescaled images. If the input"
669
- " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
670
- )
671
- if input_data_format is None:
672
- # We assume that all images have the same channel dimension format.
673
- input_data_format = infer_channel_dimension_format(images[0])
674
-
675
- height, width = get_image_size(images[0], channel_dim=input_data_format)
676
- resized_height, resized_width = height, width
677
- processed_images = []
678
-
679
- if predetermined_grid_thw is not None:
680
- assert len(predetermined_grid_thw) == len(
681
- images
682
- ), f"len(predetermined_grid_thw) {len(predetermined_grid_thw)} == len(images) {len(images)}"
683
-
684
- for img_idx, image in enumerate(images):
685
- if do_resize:
686
- if predetermined_grid_thw is not None:
687
- (resized_height, resized_width) = predetermined_grid_thw[img_idx]
688
- resized_height *= self.patch_size
689
- resized_width *= self.patch_size
690
- else:
691
- resized_height, resized_width = smart_resize(
692
- height,
693
- width,
694
- factor=self.patch_size * self.merge_size,
695
- min_pixels=self.min_pixels,
696
- max_pixels=self.max_pixels,
697
- )
698
-
699
- image = resize(
700
- image,
701
- size=(resized_height, resized_width),
702
- resample=resample,
703
- data_format=input_data_format,
704
- )
705
- if do_rescale:
706
- image = rescale(
707
- image, scale=rescale_factor, data_format=input_data_format
708
- )
709
-
710
- if do_normalize:
711
- image = normalize(
712
- image=image,
713
- mean=image_mean,
714
- std=image_std,
715
- data_format=input_data_format,
716
- )
717
-
718
- image = to_channel_dimension_format(
719
- image, data_format, input_channel_dim=input_data_format
720
- ) # [C, H, W]
721
-
722
- processed_images.append(image)
723
- patches = np.array(processed_images)
724
- if data_format == ChannelDimension.LAST:
725
- patches = patches.transpose([0, 3, 1, 2])
726
-
727
- channel = patches.shape[1] # [time, C, H, W]
728
- grid_t = patches.shape[0]
729
- grid_h, grid_w = (
730
- resized_height // self.patch_size,
731
- resized_width // self.patch_size,
732
- )
733
- patches = patches.reshape(
734
- [
735
- grid_t,
736
- channel,
737
- grid_h // self.merge_size,
738
- self.merge_size,
739
- self.patch_size,
740
- grid_w // self.merge_size,
741
- self.merge_size,
742
- self.patch_size,
743
- ]
744
- )
745
- # [grid_t, grid_h/merge_size, grid_w/merge_size, merge_size, merge_size, C, psz, psz]
746
- patches = patches.transpose([0, 2, 5, 3, 6, 1, 4, 7])
747
-
748
- flatten_patches = patches.reshape(
749
- [grid_t * grid_h * grid_w, channel * self.patch_size * self.patch_size]
750
- ) # [grid_t * grid_h * grid_w, C * psz * psz]
751
-
752
- return flatten_patches, (grid_t, grid_h, grid_w)
753
-
754
- def preprocess(
755
- self,
756
- images: ImageInput,
757
- videos: VideoInput = None,
758
- do_resize: bool = True,
759
- size: Optional[Union[int, List[int]]] = None,
760
- resample: PILImageResampling = None,
761
- do_rescale: bool = True,
762
- rescale_factor: float = 1 / 255,
763
- do_normalize: bool = True,
764
- image_mean: Optional[Union[float, List[float]]] = None,
765
- image_std: Optional[Union[float, List[float]]] = None,
766
- do_convert_rgb: bool = False,
767
- return_tensors: Optional[Union[str, TensorType]] = None,
768
- data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
769
- input_data_format: Optional[Union[str, ChannelDimension]] = None,
770
- predetermined_grid_thw=None,
771
- ):
772
- """
773
- Args:
774
- images (`ImageInput`):
775
- Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
776
- passing in images with pixel values between 0 and 1, set `do_rescale=False`.
777
- videos (`VideoInput`):
778
- Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
779
- passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
780
- do_resize (`bool`, *optional*, defaults to `self.do_resize`):
781
- Whether to resize the image.
782
- size (`Dict[str, int]`, *optional*, defaults to `self.size`):
783
- Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
784
- the longest edge resized to keep the input aspect ratio.
785
- resample (`int`, *optional*, defaults to `self.resample`):
786
- Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
787
- has an effect if `do_resize` is set to `True`.
788
- do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
789
- Whether to rescale the image.
790
- rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
791
- Rescale factor to rescale the image by if `do_rescale` is set to `True`.
792
- do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
793
- Whether to normalize the image.
794
- image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
795
- Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
796
- image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
797
- Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
798
- `True`.
799
- do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
800
- Whether to convert the image to RGB.
801
- return_tensors (`str` or `TensorType`, *optional*):
802
- The type of tensors to return. Can be one of:
803
- - Unset: Return a list of `np.ndarray`.
804
- - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
805
- - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
806
- data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
807
- The channel dimension format for the output image. Can be one of:
808
- - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
809
- - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
810
- - Unset: Use the channel dimension format of the input image.
811
- input_data_format (`ChannelDimension` or `str`, *optional*):
812
- The channel dimension format for the input image. If unset, the channel dimension format is inferred
813
- from the input image. Can be one of:
814
- - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
815
- - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
816
- - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
817
-
818
- """
819
- do_resize = do_resize if do_resize is not None else self.do_resize
820
- size = size if size is not None else self.size
821
- resample = resample if resample is not None else self.resample
822
- do_rescale = do_rescale if do_rescale is not None else self.do_rescale
823
- rescale_factor = (
824
- rescale_factor if rescale_factor is not None else self.rescale_factor
825
- )
826
- do_normalize = do_normalize if do_normalize is not None else self.do_normalize
827
- image_mean = image_mean if image_mean is not None else self.image_mean
828
- image_std = image_std if image_std is not None else self.image_std
829
- do_convert_rgb = (
830
- do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
831
- )
832
-
833
- if images is not None:
834
- images = make_batched_images(images)
835
-
836
- if images is not None and not valid_images(images):
837
- raise ValueError(
838
- "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
839
- "torch.Tensor."
840
- )
841
-
842
- data = {}
843
- if images is not None:
844
- pixel_values, vision_grid_thws = [], []
845
- for img_idx, image in enumerate(images):
846
- if predetermined_grid_thw is not None:
847
- predetermined_grid_thw_one = [predetermined_grid_thw[img_idx]]
848
- else:
849
- predetermined_grid_thw_one = None
850
- patches, image_grid_thw = self._preprocess(
851
- image,
852
- do_resize=do_resize,
853
- resample=resample,
854
- do_rescale=do_rescale,
855
- rescale_factor=rescale_factor,
856
- do_normalize=do_normalize,
857
- image_mean=image_mean,
858
- image_std=image_std,
859
- data_format=data_format,
860
- do_convert_rgb=do_convert_rgb,
861
- input_data_format=input_data_format,
862
- predetermined_grid_thw=predetermined_grid_thw_one,
863
- )
864
- pixel_values.extend(patches)
865
- vision_grid_thws.append(image_grid_thw)
866
- pixel_values = np.array(pixel_values)
867
- vision_grid_thws = np.array(vision_grid_thws)
868
- data.update(
869
- {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
870
- )
871
-
872
- if videos is not None:
873
- videos = make_batched_videos(videos)
874
- pixel_values, vision_grid_thws = [], []
875
- for images in videos:
876
- patches, video_grid_thw = self._preprocess(
877
- images,
878
- do_resize=do_resize,
879
- resample=resample,
880
- do_rescale=do_rescale,
881
- rescale_factor=rescale_factor,
882
- do_normalize=do_normalize,
883
- image_mean=image_mean,
884
- image_std=image_std,
885
- data_format=data_format,
886
- do_convert_rgb=do_convert_rgb,
887
- input_data_format=input_data_format,
888
- predetermined_grid_thw=predetermined_grid_thw,
889
- )
890
- pixel_values.extend(patches)
891
- vision_grid_thws.append(video_grid_thw)
892
- pixel_values = np.array(pixel_values)
893
- vision_grid_thws = np.array(vision_grid_thws)
894
-
895
- data.update(
896
- {
897
- "pixel_values_videos": pixel_values,
898
- "video_grid_thw": vision_grid_thws,
899
- }
900
- )
901
-
902
- return BatchFeature(data=data, tensor_type=return_tensors)
903
-
904
-
905
- RAW_VIDEO_DIR = "./download_tmp/raw_video/"
906
- RAW_IMAGE_DIR = "./download_tmp/raw_images/"
907
- EXTRACTED_FRAME_DIR = "./download_tmp/extracted_frames/"
908
- TMP_DIR = "./download_tmp/upload_tmp/"
909
-
910
- FONT_PATH = os.path.join(Path(__file__).parent.absolute(), "Roboto-Regular.ttf")
911
- if not os.path.exists(FONT_PATH):
912
- ttf = requests.get("https://paddlenlp.bj.bcebos.com/vision-language-models/materials/Roboto-Regular.ttf")
913
- open(FONT_PATH, "wb").write(ttf.content)
914
-
915
-
916
- def is_gif(data: bytes) -> bool:
917
- """
918
- check if a bytes is a gif based on the magic head
919
- """
920
- return data[:6] in (b"GIF87a", b"GIF89a")
921
-
922
-
923
- class VideoReaderWrapper(decord.VideoReader):
924
- """
925
- Solving memory leak bug
926
-
927
- https://github.com/dmlc/decord/issues/208
928
- """
929
-
930
- def __init__(self, video_path, *args, **kwargs):
931
- with ntf(delete=True, suffix=".gif") as gif_file:
932
- gif_input = None
933
- self.original_file = None
934
- if isinstance(video_path, str):
935
- self.original_file = video_path
936
- if video_path.lower().endswith(".gif"):
937
- gif_input = video_path
938
- elif isinstance(video_path, bytes):
939
- if is_gif(video_path):
940
- gif_file.write(video_path)
941
- gif_input = gif_file.name
942
- elif isinstance(video_path, io.BytesIO):
943
- video_path.seek(0)
944
- tmp_bytes = video_path.read()
945
- video_path.seek(0)
946
- if is_gif(tmp_bytes):
947
- gif_file.write(tmp_bytes)
948
- gif_input = gif_file.name
949
-
950
- if gif_input is not None:
951
- clip = mp.VideoFileClip(gif_input)
952
- mp4_file = ntf(delete=False, suffix=".mp4")
953
- clip.write_videofile(mp4_file.name, verbose=False, logger=None)
954
- clip.close()
955
- video_path = mp4_file.name
956
- self.original_file = video_path
957
-
958
- super().__init__(video_path, *args, **kwargs)
959
- self.seek(0)
960
-
961
- def __getitem__(self, key):
962
- frames = super().__getitem__(key)
963
- self.seek(0)
964
- return frames
965
-
966
- def __del__(self):
967
- if self.original_file and os.path.exists(self.original_file):
968
- os.remove(self.original_file)
969
-
970
-
971
- def get_filename(url=None):
972
- """
973
- Get Filename
974
- """
975
- if url is None:
976
- return str(uuid.uuid4()).replace("-", "")
977
- t = datetime.datetime.now()
978
- if not isinstance(url, bytes):
979
- url = url.encode("utf-8")
980
-
981
- md5_hash = hashlib.md5(url).hexdigest()
982
- pid = os.getpid()
983
- tid = threading.get_ident()
984
-
985
- # Remove the suffix to prevent save-jpg from reporting errors
986
- image_filname = f"{t.year}-{t.month:02d}-{t.day:02d}-{pid}-{tid}-{md5_hash}"
987
- return image_filname
988
-
989
-
990
- def file_download(url, download_dir, save_to_disk=False, retry=0, retry_interval=3):
991
- """
992
- Description: Download url, if url is PIL, return directly
993
- Args:
994
- url(str, PIL): http/local path/io.Bytes, note that io.Bytes is the image byte stream
995
- download_path: when save_to_disk=True, return the saved address
996
- save_to_disk: whether to save in the local path
997
- """
998
-
999
- if isinstance(url, Image.Image):
1000
- return url
1001
- elif isinstance(url, VideoReaderWrapper):
1002
- return url
1003
- elif url.startswith("http"):
1004
- response = requests.get(url)
1005
- bytes_data = response.content
1006
- elif os.path.isfile(url):
1007
- if save_to_disk:
1008
- return url
1009
- bytes_data = open(url, "rb").read()
1010
- else:
1011
- bytes_data = base64.b64decode(url)
1012
- if not save_to_disk:
1013
- return bytes_data
1014
-
1015
- download_path = os.path.join(download_dir, get_filename(url))
1016
- Path(download_path).parent.mkdir(parents=True, exist_ok=True)
1017
- with open(download_path, "wb") as f:
1018
- f.write(bytes_data)
1019
- return download_path
1020
-
1021
-
1022
- def get_downloadable(
1023
- url, download_dir=RAW_VIDEO_DIR, save_to_disk=False, retry=0, retry_interval=3
1024
- ):
1025
- """download video and store it in the disk
1026
-
1027
- return downloaded **path** if save_to_disk is set to true
1028
- return downloaded **bytes** if save_to_disk is set to false
1029
- """
1030
-
1031
- if not os.path.exists(download_dir):
1032
- os.makedirs(download_dir)
1033
- downloaded_path = file_download(
1034
- url,
1035
- download_dir,
1036
- save_to_disk=save_to_disk,
1037
- retry=retry,
1038
- retry_interval=retry_interval,
1039
- )
1040
- return downloaded_path
1041
-
1042
-
1043
- def get_downloadable_image(
1044
- download_path, need_exif_info, retry_max_time=0, retry_interval=3
1045
- ):
1046
- """
1047
- Get downloadable with exif info and image processing
1048
- """
1049
-
1050
- def get_image_exif(image):
1051
- exif_data = image._getexif()
1052
- exif_info = {}
1053
- if exif_data is not None:
1054
- for tag, value in exif_data.items():
1055
- tag_name = TAGS.get(tag, tag)
1056
- exif_info[tag_name] = value.strip()
1057
- return exif_info
1058
-
1059
- def has_transparent_background(img):
1060
- """has_transparent_background"""
1061
- if img.mode in ("RGBA", "LA") or (
1062
- img.mode == "P" and "transparency" in img.info
1063
- ):
1064
- # Check for any pixel with alpha channel less than 255 (fully opaque)
1065
- alpha = img.convert("RGBA").split()[-1]
1066
- if alpha.getextrema()[0] < 255:
1067
- return True
1068
- return False
1069
-
1070
- def add_white_background(img):
1071
- """
1072
- Add a white background to a transparent background image
1073
- """
1074
- if img.mode != "RGBA":
1075
- img = img.convert("RGBA")
1076
- # Create an image with a white background and the same size as the original image
1077
- img_white_background = Image.new("RGBA", img.size, (255, 255, 255))
1078
-
1079
- # Paste the original image onto a white background
1080
- img_white_background.paste(img, (0, 0), img)
1081
-
1082
- return img_white_background
1083
-
1084
- def change_I16_to_L(img):
1085
- """
1086
- Convert image from I;16 mode to L mode
1087
- """
1088
- # Since the point function in I mode only supports addition, subtraction, and multiplication,
1089
- # the following * (1 / 256) cannot be changed to division.
1090
- return img.point(lambda i: i * (1 / 256)).convert("L")
1091
-
1092
- image = get_downloadable(
1093
- download_path,
1094
- save_to_disk=False,
1095
- retry=retry_max_time,
1096
- retry_interval=retry_interval,
1097
- )
1098
- if isinstance(image, Image.Image):
1099
- pil_image = image
1100
- else:
1101
- pil_image = Image.open(io.BytesIO(image))
1102
- if need_exif_info:
1103
- try:
1104
- exif_info = get_image_exif(pil_image)
1105
- except Exception as why:
1106
- exif_info = {}
1107
- else:
1108
- exif_info = {}
1109
-
1110
- try:
1111
- if pil_image.mode == "I;16":
1112
- pil_image = change_I16_to_L(pil_image)
1113
- if has_transparent_background(pil_image):
1114
- pil_image = add_white_background(pil_image)
1115
- except Exception as e:
1116
- pass
1117
-
1118
- return pil_image.convert("RGB"), exif_info
1119
-
1120
-
1121
- def read_video_decord(video_path, save_to_disk):
1122
- """get reader and meta by decord"""
1123
- video_path = get_downloadable(video_path, save_to_disk=save_to_disk)
1124
- if isinstance(video_path, VideoReaderWrapper):
1125
- video_reader = video_path
1126
- else:
1127
- if isinstance(video_path, bytes):
1128
- video_path = io.BytesIO(video_path)
1129
- video_reader = VideoReaderWrapper(video_path, num_threads=1)
1130
- vlen = len(video_reader)
1131
- fps = video_reader.get_avg_fps()
1132
- duration = vlen / float(fps)
1133
-
1134
- video_meta = {"fps": fps, "duration": duration, "num_of_frame": vlen}
1135
-
1136
- return video_reader, video_meta, video_path
1137
-
1138
-
1139
- def get_frame_indices(
1140
- vlen,
1141
- target_frames=-1,
1142
- target_fps=-1,
1143
- frames_sample="middle",
1144
- fix_start=None,
1145
- input_fps=-1,
1146
- ):
1147
- """get_frame_indices"""
1148
- assert frames_sample in ["rand", "middle", "leading"]
1149
- if target_frames > 0:
1150
- assert target_fps <= 0, "target_fps must be negative if target_frames is given."
1151
- if target_frames > vlen:
1152
- acc_samples = vlen
1153
- logger.info(
1154
- f"target_frames={target_frames} is larger than video length {vlen}, "
1155
- f"will sample {acc_samples} frames."
1156
- )
1157
- else:
1158
- acc_samples = target_frames
1159
- logger.debug(
1160
- f"sampling at target_frames={target_frames}, frames_sample={frames_sample}"
1161
- )
1162
-
1163
- # split the video into `acc_samples` intervals, and sample from each interval.
1164
- intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int)
1165
- ranges = []
1166
- for idx, interv in enumerate(intervals[:-1]):
1167
- ranges.append((interv, intervals[idx + 1] - 1))
1168
- if frames_sample == "rand":
1169
- try:
1170
- frame_indices = [random.choice(range(x[0], x[1])) for x in ranges]
1171
- except Exception as e:
1172
- frame_indices = np.random.permutation(vlen)[:acc_samples]
1173
- frame_indices.sort()
1174
- frame_indices = list(frame_indices)
1175
- elif fix_start is not None:
1176
- frame_indices = [x[0] + fix_start for x in ranges]
1177
- elif frames_sample == "leading":
1178
- frame_indices = [x[0] for x in ranges]
1179
- elif frames_sample == "middle":
1180
- frame_indices = [(x[0] + x[1]) // 2 for x in ranges]
1181
- else:
1182
- raise NotImplementedError
1183
-
1184
- elif target_fps > 0:
1185
- assert (
1186
- target_frames <= 0
1187
- ), "target_frames must be negative if target_fps is given."
1188
- assert input_fps > 0, "input_fps must be provided if target_fps is given."
1189
- logger.info(f"sampling at fps={target_fps}, frames_sample={frames_sample}")
1190
- duration = float(vlen) / input_fps
1191
- delta = (
1192
- 1 / target_fps
1193
- ) # gap between frames, this is also the clip length each frame represents
1194
- if frames_sample == "middle":
1195
- frame_seconds = np.arange(0 + delta / 2, duration + delta / 2, delta)
1196
- elif frames_sample == "leading":
1197
- frame_seconds = np.arange(0, duration, delta)
1198
- if frames_sample == "rand":
1199
- frame_seconds = np.arange(0 + delta / 2, duration + delta / 2, delta)
1200
- rand_offset = np.random.rand(*(frame_seconds.shape)) - 0.5
1201
- frame_seconds += rand_offset * delta
1202
- frame_indices = np.around(frame_seconds * input_fps).astype(int)
1203
- frame_indices = [e for e in frame_indices if e < vlen]
1204
-
1205
- else:
1206
- raise ValueError(
1207
- "Must provide either positive target_fps or positive target_frames."
1208
- )
1209
-
1210
- return frame_indices
1211
-
1212
-
1213
- def read_frames_decord(
1214
- video_path,
1215
- video_reader,
1216
- video_meta,
1217
- target_frames=-1,
1218
- target_fps=-1,
1219
- frames_sample="middle",
1220
- fix_start=None,
1221
- save_to_disk=False,
1222
- cache_dir=EXTRACTED_FRAME_DIR,
1223
- frame_indices=None,
1224
- tol=10,
1225
- ):
1226
- """get frames by decord"""
1227
-
1228
- if frame_indices is None:
1229
- frame_indices = get_frame_indices(
1230
- video_meta["num_of_frame"],
1231
- target_frames=target_frames,
1232
- target_fps=target_fps,
1233
- frames_sample=frames_sample,
1234
- fix_start=fix_start,
1235
- input_fps=video_meta["fps"],
1236
- )
1237
-
1238
- frames = []
1239
- for frame_indice_index in range(0, len(frame_indices)):
1240
- frame_indice = frame_indices[frame_indice_index]
1241
- try:
1242
- frames.append(video_reader[frame_indice].asnumpy()) # (T, H, W, C)
1243
- except Exception as e:
1244
- logger.debug(f"encounter error when get frame: {frame_indice}, error: {e}")
1245
- previous_counter = 1
1246
- later_counter = 1
1247
- previous_after_flag = True
1248
- if frame_indice == 0 or frame_indice == len(video_reader) - 1:
1249
- cur_tol = tol * 2
1250
- else:
1251
- cur_tol = tol
1252
- while previous_counter < cur_tol or later_counter < cur_tol:
1253
- if previous_after_flag:
1254
- if frame_indice - previous_counter < 0:
1255
- previous_counter += 1
1256
- previous_after_flag = not previous_after_flag
1257
- continue
1258
- try:
1259
- frames.append(
1260
- video_reader[frame_indice - previous_counter].asnumpy()
1261
- )
1262
- logger.info(
1263
- f"replace {frame_indice}-th frame with {frame_indice-previous_counter}-th frame"
1264
- )
1265
- frame_indices[frame_indice_index] = (
1266
- frame_indice - previous_counter
1267
- )
1268
- break
1269
- except Exception as e:
1270
- previous_counter += 1
1271
- else:
1272
- if frame_indice + later_counter >= len(video_reader):
1273
- later_counter += 1
1274
- previous_after_flag = not previous_after_flag
1275
- continue
1276
- try:
1277
- frames.append(
1278
- video_reader[frame_indice + later_counter].asnumpy()
1279
- )
1280
- logger.info(
1281
- f"replace {frame_indice}-th frame with {frame_indice+later_counter}-th frame"
1282
- )
1283
- frame_indices[frame_indice_index] = frame_indice + later_counter
1284
- break
1285
- except Exception as e:
1286
- later_counter += 1
1287
- previous_after_flag = not previous_after_flag
1288
-
1289
- frames = np.stack(frames, axis=0)
1290
- assert len(frames) == len(
1291
- frame_indices
1292
- ), f"len(frames): {len(frames)} != len(frame_indices): {len(frame_indices)}"
1293
-
1294
- ret = []
1295
-
1296
- url_sha1 = get_filename()
1297
- for idx, frame in enumerate(frames):
1298
- tmp = Image.fromarray(frame, "RGB")
1299
- if save_to_disk:
1300
- save_path = os.path.join(cache_dir, f"{url_sha1}", f"{idx}.png")
1301
- if not os.path.exists(os.path.dirname(save_path)):
1302
- os.makedirs(os.path.dirname(save_path))
1303
- tmp.save(save_path)
1304
- tmp = save_path
1305
- ret.append(tmp)
1306
-
1307
- time_stamps = [
1308
- frame_idx * video_meta["duration"] / video_meta["num_of_frame"]
1309
- for frame_idx in frame_indices
1310
- ]
1311
-
1312
- return ret, frame_indices, time_stamps
1313
-
1314
-
1315
- def render_single_image_with_timestamp(
1316
- image: Image, number: str, rate: float, font_path: str = FONT_PATH
1317
- ):
1318
- """
1319
- Function: Renders a timestamp to the image of pil.image
1320
- The timestamp size is the rate of min(width, height)
1321
- The font color is black, the outline is white, and the outline size is 10% of the font
1322
- Returns an Image object
1323
- """
1324
- draw = ImageDraw.Draw(image)
1325
- width, height = image.size
1326
- font_size = int(min(width, height) * rate)
1327
- outline_size = int(font_size * 0.1)
1328
- font = ImageFont.truetype(font_path, font_size)
1329
- x = 0
1330
- y = 0
1331
-
1332
- # Draw a black timestamp with a white border
1333
- draw.text(
1334
- (x, y),
1335
- number,
1336
- font=font,
1337
- fill=(0, 0, 0),
1338
- stroke_width=outline_size,
1339
- stroke_fill=(255, 255, 255),
1340
- )
1341
-
1342
- return image
1343
-
1344
-
1345
- def timestamp_converting(time_stamp_in_seconds):
1346
- """
1347
- convert timestamp format from seconds to hr:min:sec
1348
- """
1349
- # get hours
1350
- hours = 0
1351
- while time_stamp_in_seconds >= 3600:
1352
- hours += 1
1353
- time_stamp_in_seconds -= 3600
1354
- # get minutes
1355
- mins = 0
1356
- while time_stamp_in_seconds >= 60:
1357
- mins += 1
1358
- time_stamp_in_seconds -= 60
1359
- time_hours = f"{int(hours):02d}"
1360
- time_mins = f"{int(mins):02d}"
1361
- time_secs = f"{time_stamp_in_seconds:05.02f}"
1362
- fi_time_stamp = time_hours + ":" + time_mins + ":" + time_secs
1363
-
1364
- return fi_time_stamp
1365
-
1366
-
1367
- def render_frame_timestamp(frame, timestamp, font_rate=0.1):
1368
- """
1369
- Function, given a frame, render the index in order
1370
- Logic: render the index to the upper left corner of the image
1371
- frame: frame, PIL.Image object
1372
- timestamp: timestamp, in seconds
1373
- font_rate: the ratio of font size to min(wi, hei)
1374
- """
1375
- time_stamp = "time: " + timestamp_converting(timestamp)
1376
- new_frame = render_single_image_with_timestamp(frame, time_stamp, font_rate)
1377
-
1378
- return new_frame
1379
-
1380
-
1381
- IDS_TYPE_FLAG = {"text": 0, "image": 1, "video": 2, "audio": 3}
1382
-
1383
-
1384
- class Ernie4_5_VLProcessor(ProcessorMixin):
1385
- """
1386
- Processes multimodal chat messages into model-ready inputs,
1387
- handling text, images, and videos with 3D positional embeddings.
1388
- """
1389
-
1390
- attributes = ["image_processor", "tokenizer"]
1391
- valid_kwargs = [
1392
- "chat_template",
1393
- "spatial_conv_size",
1394
- "temporal_conv_size",
1395
- "image_min_pixels",
1396
- "image_max_pixels",
1397
- "video_min_pixels",
1398
- "video_max_pixels",
1399
- "video_target_frames",
1400
- "video_frames_sample",
1401
- "video_max_frames",
1402
- "video_min_frames",
1403
- "video_fps",
1404
- ]
1405
- image_processor_class = "AutoImageProcessor"
1406
- tokenizer_class = "AutoTokenizer"
1407
-
1408
- CLS_TOKEN = "<|begin_of_sentence|>"
1409
- SEP_TOKEN = "<|end_of_sentence|>"
1410
- IMG_START = "<|IMAGE_START|>"
1411
- IMG_END = "<|IMAGE_END|>"
1412
- VID_START = "<|VIDEO_START|>"
1413
- VID_END = "<|VIDEO_END|>"
1414
-
1415
- def __init__(
1416
- self,
1417
- image_processor=None,
1418
- tokenizer=None,
1419
- chat_template=None,
1420
- spatial_conv_size: int = 2,
1421
- temporal_conv_size: int = 2,
1422
- image_min_pixels: int = 4 * 28 * 28,
1423
- image_max_pixels: int = 6177 * 28 * 28,
1424
- video_min_pixels: int = 299 * 28 * 28,
1425
- video_max_pixels: int = 1196 * 28 * 28,
1426
- video_target_frames: int = -1,
1427
- video_frames_sample: str = "leading",
1428
- video_max_frames: int = 180,
1429
- video_min_frames: int = 16,
1430
- video_fps: int = 2,
1431
- **kwargs,
1432
- ):
1433
- super().__init__(image_processor, tokenizer, chat_template=chat_template)
1434
- self.tokenizer.ignored_index = -100
1435
-
1436
- # Convolution sizes for patch aggregation
1437
- self.spatial_conv_size = spatial_conv_size
1438
- self.temporal_conv_size = temporal_conv_size
1439
-
1440
- # Pixel constraints
1441
- self.image_min_pixels = image_min_pixels
1442
- self.image_max_pixels = image_max_pixels
1443
- self.video_min_pixels = video_min_pixels
1444
- self.video_max_pixels = video_max_pixels
1445
-
1446
- # Video sampling parameters
1447
- self.target_frames = video_target_frames
1448
- self.frames_sample = video_frames_sample
1449
- self.max_frames = video_max_frames
1450
- self.min_frames = video_min_frames
1451
- self.fps = video_fps
1452
-
1453
- # Special tokens and IDs
1454
- self.cls_token = self.CLS_TOKEN
1455
- self.sep_token = self.SEP_TOKEN
1456
- self.image_start = self.IMG_START
1457
- self.image_end = self.IMG_END
1458
- self.video_start = self.VID_START
1459
- self.video_end = self.VID_END
1460
- self.image_patch_id = self.tokenizer.convert_tokens_to_ids(
1461
- "<|IMAGE_PLACEHOLDER|>"
1462
- )
1463
-
1464
- self.token_type_mapping = self._build_token_type_mapping()
1465
- self.is_training = True
1466
- self.role_prefixes = {"system": "", "user": "User: ", "bot": "Assistant: "}
1467
-
1468
- def _build_token_type_mapping(self) -> Dict[Any, int]:
1469
- mapping = defaultdict(lambda: IDS_TYPE_FLAG["text"])
1470
- for token in (self.IMG_START, self.IMG_END, self.VID_START, self.VID_END):
1471
- mapping[token] = IDS_TYPE_FLAG["image"]
1472
- mapping[self.image_patch_id] = IDS_TYPE_FLAG["image"]
1473
- return mapping
1474
-
1475
- def train(self) -> None:
1476
- """Enable training mode (produces labels)."""
1477
- self.is_training = True
1478
-
1479
- def eval(self) -> None:
1480
- """Enable evaluation mode (doesn't produce labels)."""
1481
- self.is_training = False
1482
-
1483
- def _download_image(
1484
- self,
1485
- item: Dict,
1486
- ):
1487
- """Download image from url and resize it to the specified size."""
1488
- url_info = item.get("image_url", {})
1489
- url = url_info.get("url")
1490
- w = url_info.get("image_width", None)
1491
- h = url_info.get("image_height", None)
1492
- data = get_downloadable(url, download_dir=RAW_IMAGE_DIR, save_to_disk=False)
1493
-
1494
- img = Image.open(io.BytesIO(data) if isinstance(data, bytes) else data)
1495
- if w and h:
1496
- img = img.resize((w, h))
1497
- return img
1498
-
1499
- def _download_video(self, item: Dict):
1500
- """Download video from url and resize it to the specified size."""
1501
- url_info = item.get("video_url", {})
1502
- url = url_info.get("url")
1503
-
1504
- frames = self._load_and_process_video(url, item)
1505
-
1506
- pixel_stack = np.stack([np.array(f.convert("RGB")) for f in frames], axis=0)
1507
- return pixel_stack
1508
-
1509
- def process_vision_info(self, messages: List[Dict[str, Any]]):
1510
- """Preprocess messages into lists of text, images, and videos."""
1511
- images = []
1512
- videos = []
1513
-
1514
- for msg in messages:
1515
- content_items = msg.get("content")
1516
- if not isinstance(content_items, list):
1517
- content_items = [content_items]
1518
-
1519
- for item in content_items:
1520
- if item.get("type") == "image_url":
1521
- img = self._download_image(item)
1522
- images.append(img)
1523
- elif item.get("type") == "video_url":
1524
- pixel_stack = self._download_video(item)
1525
- videos.append(pixel_stack)
1526
-
1527
- return images, videos
1528
-
1529
- def __call__(
1530
- self,
1531
- text: Union[str, List[str]],
1532
- images: List[Image.Image] = None,
1533
- videos: List[List[Image.Image]] = None,
1534
- **kwargs,
1535
- ) -> BatchFeature:
1536
- """
1537
- Convert chat messages into model inputs.
1538
- Returns a dict with input_ids, token_type_ids, position_ids, images, grid_thw, image_type_ids, labels.
1539
- """
1540
- outputs = {
1541
- "input_ids": [],
1542
- "token_type_ids": [],
1543
- "position_ids": [],
1544
- "images": [],
1545
- "grid_thw": [],
1546
- "image_type_ids": [],
1547
- "cur_position": 0,
1548
- "pic_cnt": 0,
1549
- "video_cnt": 0,
1550
- }
1551
- if images is None:
1552
- images = []
1553
- if videos is None:
1554
- videos = []
1555
- if not isinstance(text, list):
1556
- text = [text]
1557
-
1558
- texts = text[0]
1559
-
1560
- new_video_seg = True
1561
- for text_with_image in texts.split(self.VID_START + "<|video@placeholder|>" + self.VID_END):
1562
- new_text_seg = True
1563
- if not new_video_seg:
1564
- self._add_video(videos[outputs["video_cnt"]], outputs)
1565
- for text in text_with_image.split(self.IMG_START + "<|image@placeholder|>" + self.IMG_END):
1566
- if not new_text_seg:
1567
- self._add_image(images[outputs["pic_cnt"]], outputs)
1568
- self._add_text(text, outputs)
1569
- new_text_seg = False
1570
- new_video_seg = False
1571
-
1572
- for key in ["cur_position", "pic_cnt", "video_cnt"]:
1573
- outputs.pop(key, None)
1574
-
1575
- outputs = self._pack_outputs(outputs)
1576
- for key in outputs.keys():
1577
- if isinstance(outputs[key], np.ndarray):
1578
- if key in ["images", "grid_thw"]:
1579
- outputs[key] = torch.tensor(np.array(outputs[key]))
1580
- else:
1581
- outputs[key] = torch.tensor(np.array([outputs[key]]))
1582
-
1583
- return BatchFeature(data=outputs)
1584
-
1585
- def _add_special_token(self, token: Union[str, int], outputs: Dict) -> None:
1586
- """add special token to outputs"""
1587
- token_id = (
1588
- token
1589
- if isinstance(token, int)
1590
- else self.tokenizer.convert_tokens_to_ids(token)
1591
- )
1592
- outputs["input_ids"].append(token_id)
1593
- outputs["token_type_ids"].append(self.token_type_mapping[token])
1594
- pos = outputs["cur_position"]
1595
- outputs["position_ids"].append([pos] * 3)
1596
- outputs["cur_position"] += 1
1597
-
1598
- def _add_text(self, text: str, outputs: Dict) -> None:
1599
- """add text to outputs"""
1600
- tokens = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(text))
1601
- outputs["input_ids"].extend(tokens)
1602
- outputs["token_type_ids"].extend([IDS_TYPE_FLAG["text"]] * len(tokens))
1603
-
1604
- start = outputs["cur_position"]
1605
- for i in range(len(tokens)):
1606
- outputs["position_ids"].append([start + i] * 3)
1607
- outputs["cur_position"] += len(tokens)
1608
-
1609
- def _add_image(self, img: Image.Image, outputs: Dict) -> None:
1610
- """add image to outputs"""
1611
- outputs["pic_cnt"] += 1
1612
- self._add_special_token(self.IMG_START, outputs)
1613
-
1614
- patches_h, patches_w = self.image_processor.get_smarted_resize(
1615
- img.height,
1616
- img.width,
1617
- min_pixels=self.image_min_pixels,
1618
- max_pixels=self.image_max_pixels,
1619
- )[1]
1620
- num_tokens = (patches_h * patches_w) // (self.spatial_conv_size**2)
1621
-
1622
- outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
1623
- outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
1624
-
1625
- pos_ids = self._compute_3d_positions(
1626
- 1, patches_h, patches_w, outputs["cur_position"]
1627
- )
1628
- outputs["position_ids"].extend(pos_ids)
1629
- outputs["cur_position"] = np.max(pos_ids) + 1
1630
-
1631
- # Preprocess pixels
1632
- ret = self.image_processor.preprocess(
1633
- images=[img.convert("RGB")],
1634
- do_normalize=False,
1635
- do_rescale=False,
1636
- predetermined_grid_thw=np.array([[patches_h, patches_w]]),
1637
- do_convert_rgb=True,
1638
- input_data_format=ChannelDimension.LAST,
1639
- )
1640
- outputs["images"].append(ret["pixel_values"])
1641
- outputs["grid_thw"].append(ret["image_grid_thw"])
1642
- outputs["image_type_ids"].append(0)
1643
-
1644
- self._add_special_token(self.IMG_END, outputs)
1645
-
1646
- def _add_video(
1647
- self, pixel_stack: np.ndarray, outputs: Dict
1648
- ) -> None:
1649
- outputs["video_cnt"] += 1
1650
- self._add_special_token(self.VID_START, outputs)
1651
-
1652
- patches_h, patches_w = self.image_processor.get_smarted_resize(
1653
- pixel_stack.shape[1],
1654
- pixel_stack.shape[2],
1655
- min_pixels=self.video_min_pixels,
1656
- max_pixels=self.video_max_pixels,
1657
- )[1]
1658
- num_frames = pixel_stack.shape[0]
1659
- num_tokens = (num_frames * patches_h * patches_w) // (
1660
- self.spatial_conv_size**2 * self.temporal_conv_size
1661
- )
1662
-
1663
- ret = self.image_processor.preprocess(
1664
- images=None,
1665
- videos=pixel_stack,
1666
- do_normalize=False,
1667
- do_rescale=False,
1668
- predetermined_grid_thw=np.array([[patches_h, patches_w]] * num_frames),
1669
- do_convert_rgb=True,
1670
- input_data_format=ChannelDimension.LAST,
1671
- )
1672
- outputs["images"].append(ret["pixel_values_videos"])
1673
- outputs["grid_thw"].append(ret["video_grid_thw"])
1674
- outputs["image_type_ids"].extend([1] * num_frames)
1675
-
1676
- outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
1677
- outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
1678
-
1679
- pos_ids = self._compute_3d_positions(
1680
- num_frames, patches_h, patches_w, outputs["cur_position"]
1681
- )
1682
- outputs["position_ids"].extend(pos_ids)
1683
- outputs["cur_position"] = np.max(pos_ids) + 1
1684
-
1685
- self._add_special_token(self.VID_END, outputs)
1686
-
1687
- def _load_and_process_video(self, url: str, item: Dict) -> List[Image.Image]:
1688
- reader, meta, path = read_video_decord(url, save_to_disk=False)
1689
-
1690
- video_frame_args = dict()
1691
- video_frame_args["fps"] = item.get("fps", self.fps)
1692
- video_frame_args["min_frames"] = item.get("min_frames", self.min_frames)
1693
- video_frame_args["max_frames"] = item.get("max_frames", self.max_frames)
1694
- video_frame_args["target_frames"] = item.get(
1695
- "target_frames", self.target_frames
1696
- )
1697
- video_frame_args["frames_sample"] = item.get(
1698
- "frames_sample", self.frames_sample
1699
- )
1700
-
1701
- video_frame_args = self._set_video_frame_args(video_frame_args, meta)
1702
-
1703
- frames_data, _, timestamps = read_frames_decord(
1704
- path,
1705
- reader,
1706
- meta,
1707
- target_frames=video_frame_args["target_frames"],
1708
- target_fps=video_frame_args["fps"],
1709
- frames_sample=video_frame_args["frames_sample"],
1710
- save_to_disk=False,
1711
- )
1712
-
1713
- frames: List[Image.Image] = []
1714
- for img_array, ts in zip(frames_data, timestamps):
1715
- frames.append(render_frame_timestamp(img_array, ts))
1716
- # Ensure even number of frames for temporal conv
1717
- if len(frames) % 2 != 0:
1718
- frames.append(copy.deepcopy(frames[-1]))
1719
- return frames
1720
-
1721
- def _set_video_frame_args(self, video_frame_args, video_meta):
1722
- """
1723
- Set the final frame extraction parameters based on known parameters and priorities
1724
- """
1725
- # Priority: video_target_frames > (video_min_frames, video_max_frames) > video_fps
1726
- if video_frame_args["target_frames"] > 0:
1727
- if video_frame_args["fps"] >= 0:
1728
- raise ValueError("fps must be negative if target_frames is given")
1729
- if (
1730
- video_frame_args["min_frames"] > 0
1731
- and video_frame_args["target_frames"] < video_frame_args["min_frames"]
1732
- ):
1733
- raise ValueError("target_frames must be larger than min_frames")
1734
- if (
1735
- video_frame_args["max_frames"] > 0
1736
- and video_frame_args["target_frames"] > video_frame_args["max_frames"]
1737
- ):
1738
- raise ValueError("target_frames must be smaller than max_frames")
1739
- else:
1740
- if video_frame_args["fps"] < 0:
1741
- raise ValueError(
1742
- "Must provide either positive target_fps or positive target_frames."
1743
- )
1744
- # First calculate the number of frames extracted under video_fps
1745
- frames_to_extract = int(video_meta["duration"] * video_frame_args["fps"])
1746
- # Determine whether it is within the target range. If not, take target_frames as the upper or lower bound
1747
- if (
1748
- video_frame_args["min_frames"] > 0
1749
- and video_frame_args["max_frames"] > 0
1750
- and video_frame_args["min_frames"] > video_frame_args["max_frames"]
1751
- ):
1752
- raise ValueError("min_frames must be smaller than max_frames")
1753
- if (
1754
- video_frame_args["min_frames"] > 0
1755
- and frames_to_extract < video_frame_args["min_frames"]
1756
- ):
1757
- video_frame_args["target_frames"] = video_frame_args["min_frames"]
1758
- video_frame_args["fps"] = -1
1759
- if (
1760
- video_frame_args["max_frames"] > 0
1761
- and frames_to_extract > video_frame_args["max_frames"]
1762
- ):
1763
- video_frame_args["target_frames"] = video_frame_args["max_frames"]
1764
- video_frame_args["fps"] = -1
1765
-
1766
- return video_frame_args
1767
-
1768
- def _compute_3d_positions(
1769
- self, t: int, h: int, w: int, start_idx: int
1770
- ) -> List[List[int]]:
1771
- # Downsample time if needed
1772
- t_eff = t // self.temporal_conv_size if t != 1 else 1
1773
- gh, gw = h // self.spatial_conv_size, w // self.spatial_conv_size
1774
- time_idx = np.repeat(np.arange(t_eff), gh * gw)
1775
- h_idx = np.tile(np.repeat(np.arange(gh), gw), t_eff)
1776
- w_idx = np.tile(np.arange(gw), t_eff * gh)
1777
-
1778
- coords = list(zip(time_idx, h_idx, w_idx))
1779
- return [
1780
- [start_idx + ti, start_idx + hi, start_idx + wi] for ti, hi, wi in coords
1781
- ]
1782
-
1783
- def _pack_outputs(self, outs: Dict) -> Dict[str, Any]:
1784
- # Stack or nullify image-related fields
1785
- if not outs["images"]:
1786
- outs["images"] = None
1787
- outs["grid_thw"] = None
1788
- outs["image_type_ids"] = None
1789
- else:
1790
- outs["images"] = np.vstack(outs["images"])
1791
- outs["grid_thw"] = np.vstack(outs["grid_thw"])
1792
- outs["image_type_ids"] = np.array(outs["image_type_ids"])
1793
-
1794
- # Convert lists to arrays
1795
- outs["input_ids"] = np.array(outs["input_ids"], dtype=np.int64)
1796
- outs["token_type_ids"] = np.array(outs["token_type_ids"], dtype=np.int64)
1797
- outs["position_ids"] = np.array(outs["position_ids"], dtype=np.int64)
1798
- return outs
1799
-
1800
- def batch_decode(self, *args, **kwargs):
1801
- """
1802
- This method forwards all its arguments to Ernie4_5_VLTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
1803
- refer to the docstring of this method for more information.
1804
- """
1805
- return self.tokenizer.batch_decode(*args, **kwargs)
1806
-
1807
- def decode(self, *args, **kwargs):
1808
- """
1809
- This method forwards all its arguments to Ernie4_5_VLTokenizer's [`~PreTrainedTokenizer.decode`].
1810
- Please refer to the docstring of this method for more information.
1811
- """
1812
- return self.tokenizer.decode(*args, **kwargs)
1813
-
1814
- @property
1815
- def model_input_names(self):
1816
- """get model input names"""
1817
- tokenizer_input_names = self.tokenizer.model_input_names
1818
- image_processor_input_names = self.image_processor.model_input_names
1819
- return list(tokenizer_input_names) + list(image_processor_input_names)
1820
-
1821
-
1822
- __all__ = ["Ernie4_5_VLTokenizer", "Ernie4_5_VLImageProcessor", "Ernie4_5_VLProcessor"]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
processor_config.json ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "image_processor": {
3
+ "data_format": "channels_first",
4
+ "do_convert_rgb": true,
5
+ "do_normalize": true,
6
+ "do_rescale": true,
7
+ "do_resize": true,
8
+ "image_mean": [
9
+ 0.48145466,
10
+ 0.4578275,
11
+ 0.40821073
12
+ ],
13
+ "image_processor_type": "Ernie4_5_VL_MoeImageProcessorFast",
14
+ "image_std": [
15
+ 0.26862954,
16
+ 0.26130258,
17
+ 0.27577711
18
+ ],
19
+ "merge_size": 2,
20
+ "patch_size": 14,
21
+ "resample": 3,
22
+ "rescale_factor": 0.00392156862745098,
23
+ "size": {
24
+ "longest_edge": 4842768,
25
+ "shortest_edge": 3136
26
+ }
27
+ },
28
+ "processor_class": "Ernie4_5_VL_MoeProcessor",
29
+ "video_processor": {
30
+ "data_format": "channels_first",
31
+ "default_to_square": true,
32
+ "do_convert_rgb": true,
33
+ "do_normalize": true,
34
+ "do_rescale": true,
35
+ "do_resize": true,
36
+ "do_sample_frames": true,
37
+ "draw_on_frames": true,
38
+ "font": "Roboto-Regular.ttf",
39
+ "image_mean": [
40
+ 0.48145466,
41
+ 0.4578275,
42
+ 0.40821073
43
+ ],
44
+ "image_std": [
45
+ 0.26862954,
46
+ 0.26130258,
47
+ 0.27577711
48
+ ],
49
+ "max_frames": 180,
50
+ "merge_size": 2,
51
+ "min_frames": 16,
52
+ "patch_size": 14,
53
+ "resample": 3,
54
+ "rescale_factor": 0.00392156862745098,
55
+ "return_metadata": false,
56
+ "size": {
57
+ "longest_edge": 937664,
58
+ "shortest_edge": 234416
59
+ },
60
+ "temporal_patch_size": 2,
61
+ "video_processor_type": "Ernie4_5_VL_MoeVideoProcessor"
62
+ }
63
+ }
special_tokens_map.json DELETED
@@ -1 +0,0 @@
1
- {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "<|end_of_sentence|>", "pad_token": "<unk>", "cls_token": "<|begin_of_sentence|>", "mask_token": "<mask:1>", "sys_start_token": "<mask:4>", "sys_end_token": "<mask:5>", "header_start_token": "<mask:6>", "header_end_token": "<mask:7>", "additional_special_tokens": ["<|IMAGE_PLACEHOLDER|>", "<|AUDIO_PLACEHOLDER|>", "<|LOC_0|>", "<|LOC_1|>", "<|LOC_2|>", "<|LOC_3|>", "<|LOC_4|>", "<|LOC_5|>", "<|LOC_6|>", "<|LOC_7|>", "<|LOC_8|>", "<|LOC_9|>", "<|LOC_10|>", "<|LOC_11|>", "<|LOC_12|>", "<|LOC_13|>", "<|LOC_14|>", "<|LOC_15|>", "<|LOC_16|>", "<|LOC_17|>", "<|LOC_18|>", "<|LOC_19|>", "<|LOC_20|>", "<|LOC_21|>", "<|LOC_22|>", "<|LOC_23|>", "<|LOC_24|>", "<|LOC_25|>", "<|LOC_26|>", "<|LOC_27|>", "<|LOC_28|>", "<|LOC_29|>", "<|LOC_30|>", "<|LOC_31|>", "<|LOC_32|>", "<|LOC_33|>", "<|LOC_34|>", "<|LOC_35|>", "<|LOC_36|>", "<|LOC_37|>", "<|LOC_38|>", "<|LOC_39|>", "<|LOC_40|>", "<|LOC_41|>", "<|LOC_42|>", "<|LOC_43|>", "<|LOC_44|>", "<|LOC_45|>", "<|LOC_46|>", "<|LOC_47|>", "<|LOC_48|>", "<|LOC_49|>", "<|LOC_50|>", "<|LOC_51|>", "<|LOC_52|>", "<|LOC_53|>", "<|LOC_54|>", "<|LOC_55|>", "<|LOC_56|>", "<|LOC_57|>", "<|LOC_58|>", "<|LOC_59|>", "<|LOC_60|>", "<|LOC_61|>", "<|LOC_62|>", "<|LOC_63|>", "<|LOC_64|>", "<|LOC_65|>", "<|LOC_66|>", "<|LOC_67|>", "<|LOC_68|>", "<|LOC_69|>", "<|LOC_70|>", "<|LOC_71|>", "<|LOC_72|>", "<|LOC_73|>", "<|LOC_74|>", "<|LOC_75|>", "<|LOC_76|>", "<|LOC_77|>", "<|LOC_78|>", "<|LOC_79|>", "<|LOC_80|>", "<|LOC_81|>", "<|LOC_82|>", "<|LOC_83|>", "<|LOC_84|>", "<|LOC_85|>", "<|LOC_86|>", "<|LOC_87|>", "<|LOC_88|>", "<|LOC_89|>", "<|LOC_90|>", "<|LOC_91|>", "<|LOC_92|>", "<|LOC_93|>", "<|LOC_94|>", "<|LOC_95|>", "<|LOC_96|>", "<|LOC_97|>", "<|LOC_98|>", "<|LOC_99|>", "<|LOC_100|>", "<|LOC_101|>", "<|LOC_102|>", "<|LOC_103|>", "<|LOC_104|>", "<|LOC_105|>", "<|LOC_106|>", "<|LOC_107|>", "<|LOC_108|>", "<|LOC_109|>", "<|LOC_110|>", "<|LOC_111|>", "<|LOC_112|>", "<|LOC_113|>", 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"<|AUDIO_UNUSE:1007|>", "<|AUDIO_UNUSE:1008|>", "<|AUDIO_UNUSE:1009|>", "<|AUDIO_UNUSE:1010|>", "<|AUDIO_UNUSE:1011|>", "<|AUDIO_UNUSE:1012|>", "<|AUDIO_UNUSE:1013|>", "<|AUDIO_UNUSE:1014|>", "<|AUDIO_UNUSE:1015|>", "<|AUDIO_UNUSE:1016|>", "<|AUDIO_UNUSE:1017|>", "<|AUDIO_UNUSE:1018|>", "<|AUDIO_UNUSE:1019|>", "<|AUDIO_UNUSE:1020|>", "<think>", "</think>"]}
 
 
tokenizer.model → tokenizer.json RENAMED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:2ed2203974453df691287a0432c06737f1b17f20f5ab325fb33e31844d90ddb0
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- size 1614362
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:94718bd0ed3c1f3591a1cf2c517d6364283457f2fd7e95ae4d201138e922079d
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+ size 11590466
tokenizer_config.json CHANGED
@@ -1,22 +1,49 @@
1
  {
2
- "bos_token": "<s>",
3
- "eos_token": "</s>",
4
- "pad_token": "<unk>",
5
- "unk_token": "<unk>",
6
- "cls_token": "<|begin_of_sentence|>",
7
- "sep_token": "<|end_of_sentence|>",
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- "mask_token": "<mask:1>",
9
- "sys_start_token": "<mask:4>",
10
- "sys_end_token": "<mask:5>",
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- "header_start_token": "<mask:6>",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12
  "header_end_token": "<mask:7>",
13
- "additional_special_tokens": null,
14
- "tokenizer_class": "Ernie4_5_VLTokenizer",
15
- "auto_map": {
16
- "AutoTokenizer": [
17
- "processing_ernie4_5_vl.Ernie4_5_VLTokenizer",
18
- null
19
- ]
20
- },
21
- "chat_template": "\n{%- set image_count = namespace(value=0) -%}\n{%- set video_count = namespace(value=0) -%}\n{{- '<|begin_of_sentence|>' }}\n{%- for message in messages -%}\n {%- if message.role in ['system', 'user'] -%}\n {%- if message.role == 'user' -%}\n {{- 'User: ' -}}\n {%- endif -%}\n {%- if message.content is string -%}\n {{- message.content -}}\n {%- else -%}\n {%- for content_item in message.content -%}\n {%- if content_item.type == 'text' -%}\n {{- content_item.text -}}\n {%- elif content_item.type in ['image_url', 'image'] -%}\n {%- set image_count.value = image_count.value + 1 -%}\n Picture {{ image_count.value }}:<|IMAGE_START|><|image@placeholder|><|IMAGE_END|>\n {%- elif content_item.type in ['video_url', 'video'] -%}\n {%- set video_count.value = video_count.value + 1 -%}\n Video {{ video_count.value }}:<|VIDEO_START|><|video@placeholder|><|VIDEO_END|>\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- if message.role == 'system' -%}\n {{- '\n' -}}\n {%- endif -%}\n {%- elif message.role == 'assistant' -%}\n {%- macro extract_text_content(content_field) -%}\n {%- if content_field is string -%}\n {{- content_field -}}\n {%- elif content_field is iterable and content_field is not string -%}\n {%- set ns = namespace(text_parts=[]) -%}\n {%- set text_parts = [] -%}\n {%- for item in content_field -%}\n {%- if item.type == 'text' -%}\n {%- set ns.text_parts = ns.text_parts + [item.text] -%}\n {%- endif -%}\n {%- endfor -%}\n {{- ns.text_parts | join('') -}}\n {%- else -%}\n {{- '' -}}\n {%- endif -%}\n {%- endmacro -%}\n {%- set reasoning_content = extract_text_content(message.reasoning_content) -%}\n {%- set content = extract_text_content(message.content) -%}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\n') %}\n {%- endif %}\n {%- if reasoning_content %}\n {{- '\n' + 'Assistant: ' + '<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}\n {%- else %}\n {{- '\n' + 'Assistant: ' + content }}\n {%- endif %}\n {{- '<|end_of_sentence|>' }}\n {%- endif -%}\n{%- endfor -%}\n{%- if add_generation_prompt is not defined or add_generation_prompt is true %}\n {{- '\nAssistant: ' -}}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\n\n</think>\n\n' }}\n {%- endif %}\n {%- if enable_thinking is not defined or enable_thinking is true %}\n {{- '<think>' }}\n {%- endif %}\n{%- endif %}\n"
22
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  {
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+ "add_prefix_space": false,
3
+ "backend": "tokenizers",
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+ "bos_token": "<s>",
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+ "clean_up_tokenization_spaces": false,
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+ "cls_token": "<|begin_of_sentence|>",
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+ "eos_token": "</s>",
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+ "extra_special_tokens": [
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+ "<|IMAGE_PLACEHOLDER|>",
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+ "<|IMAGE_START|>",
11
+ "<|IMAGE_END|>",
12
+ "<|VIDEO_PLACEHOLDER|>",
13
+ "<|VIDEO_START|>",
14
+ "<|VIDEO_END|>",
15
+ "<think>",
16
+ "</think>"
17
+ ],
18
+ "header_end_token": "<mask:7>",
19
+ "header_start_token": "<mask:6>",
20
+ "image_end_token": "<|IMAGE_END|>",
21
+ "image_start_token": "<|IMAGE_START|>",
22
+ "image_token": "<|IMAGE_PLACEHOLDER|>",
23
+ "is_local": true,
24
+ "mask_token": "<mask:1>",
25
+ "model_max_length": 131072,
26
+ "model_specific_special_tokens": {
27
  "header_end_token": "<mask:7>",
28
+ "header_start_token": "<mask:6>",
29
+ "image_end_token": "<|IMAGE_END|>",
30
+ "image_start_token": "<|IMAGE_START|>",
31
+ "image_token": "<|IMAGE_PLACEHOLDER|>",
32
+ "sys_end_token": "<mask:5>",
33
+ "sys_start_token": "<mask:4>",
34
+ "video_end_token": "<|VIDEO_END|>",
35
+ "video_start_token": "<|VIDEO_START|>",
36
+ "video_token": "<|VIDEO_PLACEHOLDER|>"
37
+ },
38
+ "pad_token": "<unk>",
39
+ "processor_class": "Ernie4_5_VL_MoeProcessor",
40
+ "sep_token": "<|end_of_sentence|>",
41
+ "sys_end_token": "<mask:5>",
42
+ "sys_start_token": "<mask:4>",
43
+ "tokenizer_class": "LlamaTokenizer",
44
+ "unk_token": "<unk>",
45
+ "use_default_system_prompt": false,
46
+ "video_end_token": "<|VIDEO_END|>",
47
+ "video_start_token": "<|VIDEO_START|>",
48
+ "video_token": "<|VIDEO_PLACEHOLDER|>"
49
+ }