Cleanup: remove flat 4b_stock/4b_v9_1 (moved into S2) + mistaken flat 32f dirs
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- ckpts/4b_stock/args.json +0 -376
- ckpts/4b_stock/chat_template.jinja +0 -61
- ckpts/4b_stock/chat_template.json +0 -3
- ckpts/4b_stock/config.json +0 -262
- ckpts/4b_stock/generation_config.json +0 -12
- ckpts/4b_stock/latest +0 -1
- ckpts/4b_stock/model-00001-of-00002.safetensors +0 -3
- ckpts/4b_stock/model-00002-of-00002.safetensors +0 -3
- ckpts/4b_stock/model.safetensors.index.json +0 -705
- ckpts/4b_stock/modeling_qwen3_5vit_qwen3.py +0 -351
- ckpts/4b_stock/processor_config.json +0 -203
- ckpts/4b_stock/tokenizer.json +0 -3
- ckpts/4b_stock/tokenizer_config.json +0 -19
- ckpts/4b_stock/zero_to_fp32.py +0 -760
- ckpts/4b_v9_1/args.json +0 -376
- ckpts/4b_v9_1/chat_template.jinja +0 -61
- ckpts/4b_v9_1/chat_template.json +0 -3
- ckpts/4b_v9_1/config.json +0 -262
- ckpts/4b_v9_1/generation_config.json +0 -12
- ckpts/4b_v9_1/latest +0 -1
- ckpts/4b_v9_1/model-00001-of-00002.safetensors +0 -3
- ckpts/4b_v9_1/model-00002-of-00002.safetensors +0 -3
- ckpts/4b_v9_1/model.safetensors.index.json +0 -705
- ckpts/4b_v9_1/modeling_qwen3_5vit_qwen3.py +0 -351
- ckpts/4b_v9_1/processor_config.json +0 -203
- ckpts/4b_v9_1/tokenizer.json +0 -3
- ckpts/4b_v9_1/tokenizer_config.json +0 -19
- ckpts/4b_v9_1/zero_to_fp32.py +0 -760
- ckpts/stock_32f/args.json +0 -376
- ckpts/stock_32f/chat_template.jinja +0 -61
- ckpts/stock_32f/chat_template.json +0 -3
- ckpts/stock_32f/config.json +0 -262
- ckpts/stock_32f/generation_config.json +0 -12
- ckpts/stock_32f/latest +0 -1
- ckpts/stock_32f/model-00001-of-00002.safetensors +0 -3
- ckpts/stock_32f/model-00002-of-00002.safetensors +0 -3
- ckpts/stock_32f/model.safetensors.index.json +0 -705
- ckpts/stock_32f/modeling_qwen3_5vit_qwen3.py +0 -405
- ckpts/stock_32f/processor_config.json +0 -203
- ckpts/stock_32f/tokenizer.json +0 -3
- ckpts/stock_32f/tokenizer_config.json +0 -19
- ckpts/stock_32f/zero_to_fp32.py +0 -760
- ckpts/v10_2_32f/args.json +0 -376
- ckpts/v10_2_32f/chat_template.jinja +0 -61
- ckpts/v10_2_32f/chat_template.json +0 -3
- ckpts/v10_2_32f/config.json +0 -264
- ckpts/v10_2_32f/generation_config.json +0 -12
- ckpts/v10_2_32f/latest +0 -1
- ckpts/v10_2_32f/model-00001-of-00002.safetensors +0 -3
- ckpts/v10_2_32f/model-00002-of-00002.safetensors +0 -3
ckpts/4b_stock/args.json
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galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)"
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| 376 |
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ckpts/4b_stock/chat_template.jinja
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@@ -1,61 +0,0 @@
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| 1 |
-
{%- if tools %}
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| 2 |
-
{{- '<|im_start|>system\n' }}
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| 3 |
-
{%- if messages[0].role == 'system' %}
|
| 4 |
-
{{- messages[0].content + '\n\n' }}
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| 5 |
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{%- endif %}
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| 6 |
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 7 |
-
{%- for tool in tools %}
|
| 8 |
-
{{- "\n" }}
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| 9 |
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{{- tool | tojson }}
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| 10 |
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{%- endfor %}
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| 11 |
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 12 |
-
{%- else %}
|
| 13 |
-
{%- if messages[0].role == 'system' %}
|
| 14 |
-
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 15 |
-
{%- endif %}
|
| 16 |
-
{%- endif %}
|
| 17 |
-
{%- for message in messages %}
|
| 18 |
-
{%- if message.content is string %}
|
| 19 |
-
{%- set content = message.content %}
|
| 20 |
-
{%- else %}
|
| 21 |
-
{%- set content = '' %}
|
| 22 |
-
{%- endif %}
|
| 23 |
-
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 24 |
-
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 25 |
-
{%- elif message.role == "assistant" %}
|
| 26 |
-
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 27 |
-
{%- if message.tool_calls %}
|
| 28 |
-
{%- for tool_call in message.tool_calls %}
|
| 29 |
-
{%- if (loop.first and content) or (not loop.first) %}
|
| 30 |
-
{{- '\n' }}
|
| 31 |
-
{%- endif %}
|
| 32 |
-
{%- if tool_call.function %}
|
| 33 |
-
{%- set tool_call = tool_call.function %}
|
| 34 |
-
{%- endif %}
|
| 35 |
-
{{- '<tool_call>\n{"name": "' }}
|
| 36 |
-
{{- tool_call.name }}
|
| 37 |
-
{{- '", "arguments": ' }}
|
| 38 |
-
{%- if tool_call.arguments is string %}
|
| 39 |
-
{{- tool_call.arguments }}
|
| 40 |
-
{%- else %}
|
| 41 |
-
{{- tool_call.arguments | tojson }}
|
| 42 |
-
{%- endif %}
|
| 43 |
-
{{- '}\n</tool_call>' }}
|
| 44 |
-
{%- endfor %}
|
| 45 |
-
{%- endif %}
|
| 46 |
-
{{- '<|im_end|>\n' }}
|
| 47 |
-
{%- elif message.role == "tool" %}
|
| 48 |
-
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 49 |
-
{{- '<|im_start|>user' }}
|
| 50 |
-
{%- endif %}
|
| 51 |
-
{{- '\n<tool_response>\n' }}
|
| 52 |
-
{{- content }}
|
| 53 |
-
{{- '\n</tool_response>' }}
|
| 54 |
-
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 55 |
-
{{- '<|im_end|>\n' }}
|
| 56 |
-
{%- endif %}
|
| 57 |
-
{%- endif %}
|
| 58 |
-
{%- endfor %}
|
| 59 |
-
{%- if add_generation_prompt %}
|
| 60 |
-
{{- '<|im_start|>assistant\n' }}
|
| 61 |
-
{%- endif %}
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ckpts/4b_stock/chat_template.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + ' '}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ '\n' + content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ '\n' + content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
| 3 |
-
}
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ckpts/4b_stock/config.json
DELETED
|
@@ -1,262 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"architectures": [
|
| 3 |
-
"LlavaQwen3_5ViTForConditionalGeneration"
|
| 4 |
-
],
|
| 5 |
-
"bos_token_id": null,
|
| 6 |
-
"dtype": "bfloat16",
|
| 7 |
-
"eos_token_id": 151645,
|
| 8 |
-
"hidden_size": 2560,
|
| 9 |
-
"image_grid_pinpoints": [
|
| 10 |
-
[
|
| 11 |
-
384,
|
| 12 |
-
384
|
| 13 |
-
],
|
| 14 |
-
[
|
| 15 |
-
384,
|
| 16 |
-
768
|
| 17 |
-
],
|
| 18 |
-
[
|
| 19 |
-
384,
|
| 20 |
-
1152
|
| 21 |
-
],
|
| 22 |
-
[
|
| 23 |
-
384,
|
| 24 |
-
1536
|
| 25 |
-
],
|
| 26 |
-
[
|
| 27 |
-
384,
|
| 28 |
-
1920
|
| 29 |
-
],
|
| 30 |
-
[
|
| 31 |
-
384,
|
| 32 |
-
2304
|
| 33 |
-
],
|
| 34 |
-
[
|
| 35 |
-
768,
|
| 36 |
-
384
|
| 37 |
-
],
|
| 38 |
-
[
|
| 39 |
-
768,
|
| 40 |
-
768
|
| 41 |
-
],
|
| 42 |
-
[
|
| 43 |
-
768,
|
| 44 |
-
1152
|
| 45 |
-
],
|
| 46 |
-
[
|
| 47 |
-
768,
|
| 48 |
-
1536
|
| 49 |
-
],
|
| 50 |
-
[
|
| 51 |
-
768,
|
| 52 |
-
1920
|
| 53 |
-
],
|
| 54 |
-
[
|
| 55 |
-
768,
|
| 56 |
-
2304
|
| 57 |
-
],
|
| 58 |
-
[
|
| 59 |
-
1152,
|
| 60 |
-
384
|
| 61 |
-
],
|
| 62 |
-
[
|
| 63 |
-
1152,
|
| 64 |
-
768
|
| 65 |
-
],
|
| 66 |
-
[
|
| 67 |
-
1152,
|
| 68 |
-
1152
|
| 69 |
-
],
|
| 70 |
-
[
|
| 71 |
-
1152,
|
| 72 |
-
1536
|
| 73 |
-
],
|
| 74 |
-
[
|
| 75 |
-
1152,
|
| 76 |
-
1920
|
| 77 |
-
],
|
| 78 |
-
[
|
| 79 |
-
1152,
|
| 80 |
-
2304
|
| 81 |
-
],
|
| 82 |
-
[
|
| 83 |
-
1536,
|
| 84 |
-
384
|
| 85 |
-
],
|
| 86 |
-
[
|
| 87 |
-
1536,
|
| 88 |
-
768
|
| 89 |
-
],
|
| 90 |
-
[
|
| 91 |
-
1536,
|
| 92 |
-
1152
|
| 93 |
-
],
|
| 94 |
-
[
|
| 95 |
-
1536,
|
| 96 |
-
1536
|
| 97 |
-
],
|
| 98 |
-
[
|
| 99 |
-
1536,
|
| 100 |
-
1920
|
| 101 |
-
],
|
| 102 |
-
[
|
| 103 |
-
1536,
|
| 104 |
-
2304
|
| 105 |
-
],
|
| 106 |
-
[
|
| 107 |
-
1920,
|
| 108 |
-
384
|
| 109 |
-
],
|
| 110 |
-
[
|
| 111 |
-
1920,
|
| 112 |
-
768
|
| 113 |
-
],
|
| 114 |
-
[
|
| 115 |
-
1920,
|
| 116 |
-
1152
|
| 117 |
-
],
|
| 118 |
-
[
|
| 119 |
-
1920,
|
| 120 |
-
1536
|
| 121 |
-
],
|
| 122 |
-
[
|
| 123 |
-
1920,
|
| 124 |
-
1920
|
| 125 |
-
],
|
| 126 |
-
[
|
| 127 |
-
1920,
|
| 128 |
-
2304
|
| 129 |
-
],
|
| 130 |
-
[
|
| 131 |
-
2304,
|
| 132 |
-
384
|
| 133 |
-
],
|
| 134 |
-
[
|
| 135 |
-
2304,
|
| 136 |
-
768
|
| 137 |
-
],
|
| 138 |
-
[
|
| 139 |
-
2304,
|
| 140 |
-
1152
|
| 141 |
-
],
|
| 142 |
-
[
|
| 143 |
-
2304,
|
| 144 |
-
1536
|
| 145 |
-
],
|
| 146 |
-
[
|
| 147 |
-
2304,
|
| 148 |
-
1920
|
| 149 |
-
],
|
| 150 |
-
[
|
| 151 |
-
2304,
|
| 152 |
-
2304
|
| 153 |
-
]
|
| 154 |
-
],
|
| 155 |
-
"image_token_index": 151669,
|
| 156 |
-
"model_type": "llava_qwen3_5vit_qwen3",
|
| 157 |
-
"multimodal_projector_bias": true,
|
| 158 |
-
"pad_token_id": 151643,
|
| 159 |
-
"projector_hidden_act": "gelu",
|
| 160 |
-
"text_config": {
|
| 161 |
-
"_name_or_path": "/share/m2v_intern_v3/wangjunjie09/model_cache/huggingface/Qwen/Qwen3-4B-Instruct-2507",
|
| 162 |
-
"architectures": [
|
| 163 |
-
"Qwen3ForCausalLM"
|
| 164 |
-
],
|
| 165 |
-
"attention_bias": false,
|
| 166 |
-
"attention_dropout": 0.0,
|
| 167 |
-
"bos_token_id": 151643,
|
| 168 |
-
"dtype": "bfloat16",
|
| 169 |
-
"eos_token_id": 151645,
|
| 170 |
-
"head_dim": 128,
|
| 171 |
-
"hidden_act": "silu",
|
| 172 |
-
"hidden_size": 2560,
|
| 173 |
-
"initializer_range": 0.02,
|
| 174 |
-
"intermediate_size": 9728,
|
| 175 |
-
"layer_types": [
|
| 176 |
-
"full_attention",
|
| 177 |
-
"full_attention",
|
| 178 |
-
"full_attention",
|
| 179 |
-
"full_attention",
|
| 180 |
-
"full_attention",
|
| 181 |
-
"full_attention",
|
| 182 |
-
"full_attention",
|
| 183 |
-
"full_attention",
|
| 184 |
-
"full_attention",
|
| 185 |
-
"full_attention",
|
| 186 |
-
"full_attention",
|
| 187 |
-
"full_attention",
|
| 188 |
-
"full_attention",
|
| 189 |
-
"full_attention",
|
| 190 |
-
"full_attention",
|
| 191 |
-
"full_attention",
|
| 192 |
-
"full_attention",
|
| 193 |
-
"full_attention",
|
| 194 |
-
"full_attention",
|
| 195 |
-
"full_attention",
|
| 196 |
-
"full_attention",
|
| 197 |
-
"full_attention",
|
| 198 |
-
"full_attention",
|
| 199 |
-
"full_attention",
|
| 200 |
-
"full_attention",
|
| 201 |
-
"full_attention",
|
| 202 |
-
"full_attention",
|
| 203 |
-
"full_attention",
|
| 204 |
-
"full_attention",
|
| 205 |
-
"full_attention",
|
| 206 |
-
"full_attention",
|
| 207 |
-
"full_attention",
|
| 208 |
-
"full_attention",
|
| 209 |
-
"full_attention",
|
| 210 |
-
"full_attention",
|
| 211 |
-
"full_attention"
|
| 212 |
-
],
|
| 213 |
-
"max_position_embeddings": 262144,
|
| 214 |
-
"max_window_layers": 36,
|
| 215 |
-
"model_type": "qwen3",
|
| 216 |
-
"num_attention_heads": 32,
|
| 217 |
-
"num_hidden_layers": 36,
|
| 218 |
-
"num_key_value_heads": 8,
|
| 219 |
-
"pad_token_id": 151643,
|
| 220 |
-
"rms_norm_eps": 1e-06,
|
| 221 |
-
"rope_parameters": {
|
| 222 |
-
"rope_theta": 5000000,
|
| 223 |
-
"rope_type": "default"
|
| 224 |
-
},
|
| 225 |
-
"sliding_window": null,
|
| 226 |
-
"tie_word_embeddings": true,
|
| 227 |
-
"use_cache": false,
|
| 228 |
-
"use_sliding_window": false,
|
| 229 |
-
"vocab_size": 151936
|
| 230 |
-
},
|
| 231 |
-
"tie_word_embeddings": true,
|
| 232 |
-
"transformers_version": "5.5.4",
|
| 233 |
-
"use_cache": false,
|
| 234 |
-
"video_token_index": 151670,
|
| 235 |
-
"vision_aspect_ratio": "anyres_max_9",
|
| 236 |
-
"vision_config": {
|
| 237 |
-
"deepstack_visual_indexes": [],
|
| 238 |
-
"depth": 24,
|
| 239 |
-
"dtype": "bfloat16",
|
| 240 |
-
"hidden_act": "gelu_pytorch_tanh",
|
| 241 |
-
"hidden_size": 1024,
|
| 242 |
-
"image_size": 384,
|
| 243 |
-
"in_channels": 3,
|
| 244 |
-
"initializer_range": 0.02,
|
| 245 |
-
"intermediate_size": 4096,
|
| 246 |
-
"model_type": "qwen3_5",
|
| 247 |
-
"num_heads": 16,
|
| 248 |
-
"num_position_embeddings": 2304,
|
| 249 |
-
"out_hidden_size": 2048,
|
| 250 |
-
"patch_size": 16,
|
| 251 |
-
"spatial_merge_size": 2,
|
| 252 |
-
"temporal_patch_size": 2
|
| 253 |
-
},
|
| 254 |
-
"vision_feature_layer": -1,
|
| 255 |
-
"vision_feature_select_strategy": "full",
|
| 256 |
-
"auto_map": {
|
| 257 |
-
"AutoConfig": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTConfig",
|
| 258 |
-
"AutoModel": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTForConditionalGeneration",
|
| 259 |
-
"AutoModelForCausalLM": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTForConditionalGeneration",
|
| 260 |
-
"AutoModelForImageTextToText": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTForConditionalGeneration"
|
| 261 |
-
}
|
| 262 |
-
}
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|
ckpts/4b_stock/modeling_qwen3_5vit_qwen3.py
DELETED
|
@@ -1,351 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
modeling_qwen3_5vit_qwen3.py — Qwen3.5 Vision as SigLIP-compat vision_tower in LlavaOnevision.
|
| 3 |
-
|
| 4 |
-
设计：与 `modeling_qwen3vlvit_qwen3.py` 严格同构，仅换 vision backbone 源：
|
| 5 |
-
- Qwen3VLVisionModel → Qwen3_5VisionModel（继承关系：Qwen3_5VisionModel(Qwen3VLVisionModel) 去 DeepStack）
|
| 6 |
-
- Qwen3VLVisionConfig → Qwen3_5VisionConfig（父类用 AttributeError 哨兵屏蔽 deepstack_visual_indexes）
|
| 7 |
-
|
| 8 |
-
其余（Adapter 契约翻译、MLP projector + pre_norm、LlavaOnevision 继承 wire class）与
|
| 9 |
-
Qwen3-VL ViT pipeline 完全一致。两条 pipeline 并存意义：DeepStack ablation 天然实验组。
|
| 10 |
-
|
| 11 |
-
类层级：
|
| 12 |
-
Qwen3_5ViTBackbone(Qwen3_5VisionModel) — 去 merger，保持 NaViT 契约
|
| 13 |
-
Qwen3_5ViTAsSiglipAdapter(nn.Module) — 持有 Backbone，做 SigLIP ↔ NaViT 契约翻译
|
| 14 |
-
|
| 15 |
-
三方对比公平性：定 384×384 AnyRes tile + 同款 projector 骨架 + 同款 Qwen3-1.7B LLM。
|
| 16 |
-
"""
|
| 17 |
-
|
| 18 |
-
import math
|
| 19 |
-
import os
|
| 20 |
-
import sys
|
| 21 |
-
from typing import Optional
|
| 22 |
-
|
| 23 |
-
import torch
|
| 24 |
-
import torch.nn as nn
|
| 25 |
-
import torch.nn.functional as F
|
| 26 |
-
from transformers import (
|
| 27 |
-
AutoConfig,
|
| 28 |
-
AutoModel,
|
| 29 |
-
AutoModelForCausalLM,
|
| 30 |
-
LlavaOnevisionConfig,
|
| 31 |
-
LlavaOnevisionForConditionalGeneration,
|
| 32 |
-
LlavaOnevisionModel,
|
| 33 |
-
LlavaOnevisionPreTrainedModel,
|
| 34 |
-
Qwen3Config,
|
| 35 |
-
)
|
| 36 |
-
from transformers.activations import ACT2FN
|
| 37 |
-
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
|
| 38 |
-
from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5VisionConfig
|
| 39 |
-
from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5VisionModel
|
| 40 |
-
|
| 41 |
-
# Shared layout-permutation utility lives in declip_qwenvit (single source of
|
| 42 |
-
# truth — same code path runs in declip-training-side qk_cosine reorder).
|
| 43 |
-
# Add VisionEncoder repo root to sys.path so this modeling file is importable
|
| 44 |
-
# even when the package isn't pip-installed (ms-swift integration loads it
|
| 45 |
-
# via dynamic plugin path).
|
| 46 |
-
_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
|
| 47 |
-
if _REPO_ROOT not in sys.path:
|
| 48 |
-
sys.path.append(_REPO_ROOT)
|
| 49 |
-
from declip_qwenvit.model.qwen3vit_qk import block_merge_to_row_major_permutation # noqa: E402
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
class LlavaQwen3_5ViTConfig(LlavaOnevisionConfig):
|
| 53 |
-
"""LlavaOnevisionConfig 子类，vision_config 类型换成 Qwen3_5VisionConfig。
|
| 54 |
-
|
| 55 |
-
与 Qwen3-VL ViT 版的差异：
|
| 56 |
-
- sub_configs.vision_config 用 Qwen3_5VisionConfig
|
| 57 |
-
- 不再设置 deepstack_visual_indexes（Qwen3_5VisionConfig 用 AttributeError 哨兵屏蔽此字段）
|
| 58 |
-
|
| 59 |
-
其余同 LlavaQwen3VLViTConfig（tile_size 默认 384；vision_feature_select_strategy='full'
|
| 60 |
-
必须 override，Qwen3.5 ViT 无 CLS token）。
|
| 61 |
-
"""
|
| 62 |
-
|
| 63 |
-
model_type = "llava_qwen3_5vit_qwen3"
|
| 64 |
-
sub_configs = {"vision_config": Qwen3_5VisionConfig, "text_config": Qwen3Config}
|
| 65 |
-
|
| 66 |
-
def __init__(
|
| 67 |
-
self,
|
| 68 |
-
vision_config=None,
|
| 69 |
-
text_config=None,
|
| 70 |
-
tile_size: int = 384,
|
| 71 |
-
**kwargs,
|
| 72 |
-
):
|
| 73 |
-
if isinstance(vision_config, dict):
|
| 74 |
-
vision_config = Qwen3_5VisionConfig(**vision_config)
|
| 75 |
-
elif vision_config is None:
|
| 76 |
-
vision_config = Qwen3_5VisionConfig()
|
| 77 |
-
# WHY 无 `vision_config.deepstack_visual_indexes = []`（对比 Qwen3-VL ViT 版）：
|
| 78 |
-
# Qwen3_5VisionConfig 父类用 AttributeError() 哨兵显式屏蔽此字段，设置会报 AttributeError
|
| 79 |
-
# LlavaOnevision.pack_image_features 用这个作为 tile 像素大小（不是 patch_size）
|
| 80 |
-
vision_config.image_size = tile_size
|
| 81 |
-
|
| 82 |
-
if isinstance(text_config, dict):
|
| 83 |
-
text_config = Qwen3Config(**text_config)
|
| 84 |
-
elif text_config is None:
|
| 85 |
-
text_config = Qwen3Config()
|
| 86 |
-
|
| 87 |
-
# 父类默认 select_strategy='default' 会跳首 token (CLS) — Qwen3.5 ViT 无 CLS 必须用 'full'
|
| 88 |
-
kwargs.setdefault("vision_feature_select_strategy", "full")
|
| 89 |
-
super().__init__(vision_config=vision_config, text_config=text_config, **kwargs)
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
class Qwen3_5ViTBackbone(Qwen3_5VisionModel):
|
| 93 |
-
"""Qwen3.5 Vision 去除原生 patch_merger 的 backbone 版本（V6 final_layernorm fix (2026-05-16): append final_layernorm）。
|
| 94 |
-
|
| 95 |
-
构造时把 merger.norm 的预训练权重抠到 final_layernorm，然后 `del self.merger`
|
| 96 |
-
释放 ~37M 参数（保留 norm 的 LN 焊到末端做 post_layernorm 角色，丢弃 spatial
|
| 97 |
-
shuffle + linear_fc1/fc2，那对应 LlavaOV projector 的职责）。
|
| 98 |
-
|
| 99 |
-
架构对称（V6 final_layernorm fix (2026-05-16) 修复）：
|
| 100 |
-
SigLIP2: encoder → post_layernorm → last_hidden_state → LlavaOV MLP → LLM
|
| 101 |
-
V6 final_layernorm fix (2026-05-16): encoder → final_layernorm → last_hidden_state → LlavaOV MLP → LLM
|
| 102 |
-
|
| 103 |
-
forward 跑完 transformer blocks 后过 final_layernorm，再返回。下游 LlavaOnevision
|
| 104 |
-
pack_image_features 的 AnyRes 2×2 pool 接管原 merger 的空间合并职责。
|
| 105 |
-
|
| 106 |
-
输入输出契约与父类 Qwen3_5VisionModel 一致（NaViT flat）：
|
| 107 |
-
forward(hidden_states=[L, patch_dim], grid_thw=[N, 3])
|
| 108 |
-
→ BaseModelOutput(last_hidden_state=[L, hidden_size])
|
| 109 |
-
|
| 110 |
-
forward 主体 1:1 对照 `Qwen3_5VisionModel.forward`（已无 deepstack loop，比
|
| 111 |
-
Qwen3VLVisionModel.forward 更短），仅跳过末尾 `self.merger(x)`，改为 final_layernorm。
|
| 112 |
-
"""
|
| 113 |
-
|
| 114 |
-
def __init__(self, config):
|
| 115 |
-
super().__init__(config)
|
| 116 |
-
# V6 final_layernorm fix (2026-05-16): extract merger.norm pretrained weights into final_layernorm.
|
| 117 |
-
# Default init: even if ckpt 阶段没 inject final_layernorm.* (e.g. stock
|
| 118 |
-
# bootstrap path that filters merger.*), final_layernorm 仍持有 merger.norm
|
| 119 |
-
# 的预训练值, 不是 random — 这是防止 silent corruption 的兜底.
|
| 120 |
-
ln_w = self.merger.norm.weight.detach().clone()
|
| 121 |
-
ln_b = self.merger.norm.bias.detach().clone()
|
| 122 |
-
del self.merger
|
| 123 |
-
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=1e-6)
|
| 124 |
-
self.final_layernorm.weight.data.copy_(ln_w)
|
| 125 |
-
self.final_layernorm.bias.data.copy_(ln_b)
|
| 126 |
-
|
| 127 |
-
def forward(self, hidden_states, grid_thw, **kwargs):
|
| 128 |
-
hidden_states = self.patch_embed(hidden_states)
|
| 129 |
-
|
| 130 |
-
pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
|
| 131 |
-
hidden_states = hidden_states + pos_embeds
|
| 132 |
-
|
| 133 |
-
rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
| 134 |
-
seq_len, _ = hidden_states.size()
|
| 135 |
-
rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
|
| 136 |
-
emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
|
| 137 |
-
position_embeddings = (emb.cos(), emb.sin())
|
| 138 |
-
|
| 139 |
-
cu_seqlens = torch.repeat_interleave(
|
| 140 |
-
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
| 141 |
-
).cumsum(dim=0, dtype=torch.int32)
|
| 142 |
-
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
|
| 143 |
-
|
| 144 |
-
for blk in self.blocks:
|
| 145 |
-
hidden_states = blk(
|
| 146 |
-
hidden_states,
|
| 147 |
-
cu_seqlens=cu_seqlens,
|
| 148 |
-
position_embeddings=position_embeddings,
|
| 149 |
-
**kwargs,
|
| 150 |
-
)
|
| 151 |
-
|
| 152 |
-
# V6 final_layernorm fix (2026-05-16): appended final LayerNorm — mirrors SigLIP2's post_layernorm.
|
| 153 |
-
# Per-token affine; layout-invariant (reorder happens in adapter).
|
| 154 |
-
hidden_states = self.final_layernorm(hidden_states)
|
| 155 |
-
|
| 156 |
-
return BaseModelOutput(last_hidden_state=hidden_states)
|
| 157 |
-
|
| 158 |
-
|
| 159 |
-
class Qwen3_5ViTAsSiglipAdapter(nn.Module):
|
| 160 |
-
"""SigLIP 契约 → NaViT 契约的翻译层。持有 Qwen3_5ViTBackbone。
|
| 161 |
-
|
| 162 |
-
对外暴露 SigLIP 式 forward(pixel_values=[N,3,H,W]) → BaseModelOutputWithPooling，
|
| 163 |
-
供 LlavaOnevision 消费；对内按官方 _preprocess 的 reshape 链把 pixel_values
|
| 164 |
-
转成 NaViT flat + grid_thw 喂给 Backbone。
|
| 165 |
-
|
| 166 |
-
reshape 链 1:1 照抄 transformers 官方 Qwen2VLImageProcessorFast._preprocess
|
| 167 |
-
(video_processing_qwen3_vl.py L227-252) —— Qwen3.5 无独立 image_processor，复用 Qwen3-VL 格式。
|
| 168 |
-
"""
|
| 169 |
-
|
| 170 |
-
def __init__(self, vision_config: Qwen3_5VisionConfig):
|
| 171 |
-
super().__init__()
|
| 172 |
-
self.vision = Qwen3_5ViTBackbone(vision_config)
|
| 173 |
-
self.config = vision_config
|
| 174 |
-
|
| 175 |
-
@property
|
| 176 |
-
def dtype(self):
|
| 177 |
-
return next(self.parameters()).dtype
|
| 178 |
-
|
| 179 |
-
@property
|
| 180 |
-
def device(self):
|
| 181 |
-
return next(self.parameters()).device
|
| 182 |
-
|
| 183 |
-
def _flatten_navit(self, pixel_values: torch.Tensor):
|
| 184 |
-
"""[N, 3, H, W] → (flat=[N*L, patch_dim], grid_thw=[N, 3], shape=(N, L)).
|
| 185 |
-
|
| 186 |
-
L = grid_t * grid_h * grid_w = 1 * (H/16) * (W/16)
|
| 187 |
-
patch_dim = C * temporal_patch_size * patch_size^2 = 3 * 2 * 16 * 16 = 1536
|
| 188 |
-
"""
|
| 189 |
-
pixel_values = pixel_values.to(dtype=self.dtype)
|
| 190 |
-
tps = self.config.temporal_patch_size
|
| 191 |
-
ps = self.config.patch_size
|
| 192 |
-
ms = self.config.spatial_merge_size
|
| 193 |
-
|
| 194 |
-
patches = pixel_values.unsqueeze(1)
|
| 195 |
-
# 对单帧图像 T=1, pad=1 → expand 一帧使 T 整除 temporal_patch_size，
|
| 196 |
-
# Conv3d 在复制帧上退化为等效 2D Conv（数学无损）
|
| 197 |
-
T = patches.shape[1]
|
| 198 |
-
pad = -T % tps
|
| 199 |
-
if pad:
|
| 200 |
-
repeats = patches[:, -1:].expand(-1, pad, -1, -1, -1)
|
| 201 |
-
patches = torch.cat((patches, repeats), dim=1)
|
| 202 |
-
|
| 203 |
-
batch_size, t, channel, H, W = patches.shape
|
| 204 |
-
grid_t = t // tps
|
| 205 |
-
grid_h = H // ps
|
| 206 |
-
grid_w = W // ps
|
| 207 |
-
|
| 208 |
-
patches = patches.view(
|
| 209 |
-
batch_size, grid_t, tps, channel,
|
| 210 |
-
grid_h // ms, ms, ps,
|
| 211 |
-
grid_w // ms, ms, ps,
|
| 212 |
-
)
|
| 213 |
-
patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
|
| 214 |
-
flatten_patches = patches.reshape(
|
| 215 |
-
batch_size,
|
| 216 |
-
grid_t * grid_h * grid_w,
|
| 217 |
-
channel * tps * ps * ps,
|
| 218 |
-
)
|
| 219 |
-
|
| 220 |
-
seq_len = grid_t * grid_h * grid_w
|
| 221 |
-
flat = flatten_patches.reshape(batch_size * seq_len, -1)
|
| 222 |
-
# on-device 构造小 tensor 再 expand，host→GPU 同步量 O(3) 而非 O(N*3)
|
| 223 |
-
grid_unit = torch.tensor(
|
| 224 |
-
[grid_t, grid_h, grid_w], dtype=torch.int32, device=pixel_values.device,
|
| 225 |
-
)
|
| 226 |
-
grid_thw = grid_unit.unsqueeze(0).expand(batch_size, -1).contiguous()
|
| 227 |
-
return flat, grid_thw, (batch_size, seq_len)
|
| 228 |
-
|
| 229 |
-
def forward(
|
| 230 |
-
self,
|
| 231 |
-
pixel_values: torch.Tensor,
|
| 232 |
-
output_hidden_states: Optional[bool] = None,
|
| 233 |
-
return_dict: Optional[bool] = None,
|
| 234 |
-
**kwargs,
|
| 235 |
-
) -> BaseModelOutputWithPooling:
|
| 236 |
-
flat, grid_thw, (N, S) = self._flatten_navit(pixel_values)
|
| 237 |
-
vision_out = self.vision(flat, grid_thw=grid_thw)
|
| 238 |
-
hidden = vision_out.last_hidden_state.view(N, S, -1)
|
| 239 |
-
|
| 240 |
-
# Block-merge → row-major reorder before handing to LlavaOnevision.
|
| 241 |
-
# Internally the ViT runs in Qwen NaViT block-merge layout (pretrained
|
| 242 |
-
# pos_embed + RoPE contract); downstream LlavaOV `pack_image_features`
|
| 243 |
-
# (multi-tile AnyRes path, view(num_patch_h, num_patch_w, h, w, -1))
|
| 244 |
-
# and `apply_pooling` (video path, view(B, h, w, -1) + bilinear) BOTH
|
| 245 |
-
# assume row-major. Without this reorder, the multi-tile/video spatial
|
| 246 |
-
# pool pulls together tokens that are NOT spatially adjacent — silent
|
| 247 |
-
# corruption that doesn't fire on S1 single-tile path (line 348-351 of
|
| 248 |
-
# modeling_llava_onevision.py just flattens [N,D] verbatim) but kills
|
| 249 |
-
# S2 / eval quality.
|
| 250 |
-
grid_h = int(grid_thw[0, 1].item())
|
| 251 |
-
grid_w = int(grid_thw[0, 2].item())
|
| 252 |
-
ms = getattr(self.config, "spatial_merge_size", 2)
|
| 253 |
-
layout_perm = block_merge_to_row_major_permutation(
|
| 254 |
-
grid_h, grid_w, ms=ms, device=hidden.device,
|
| 255 |
-
)
|
| 256 |
-
hidden = hidden[:, layout_perm, :]
|
| 257 |
-
|
| 258 |
-
return BaseModelOutputWithPooling(
|
| 259 |
-
last_hidden_state=hidden,
|
| 260 |
-
# LlavaOnevision 索引 hidden_states[vision_feature_layer=-1]；tuple 长度 1 足够
|
| 261 |
-
hidden_states=(hidden,),
|
| 262 |
-
pooler_output=None,
|
| 263 |
-
)
|
| 264 |
-
|
| 265 |
-
|
| 266 |
-
class LlavaQwen3_5ViTMultiModalProjector(nn.Module):
|
| 267 |
-
"""标准 LlavaOnevision projector（V6 final_layernorm fix (2026-05-16): pre_norm → Identity）。
|
| 268 |
-
|
| 269 |
-
V6 final_layernorm fix (2026-05-16) 修复后, encoder 末端已自带 final_layernorm（与 SigLIP2 post_layernorm 对称），
|
| 270 |
-
projector 不再需要补 LN — pre_norm 改为 nn.Identity，对齐 SigLIP2 plugin 的
|
| 271 |
-
LlavaOnevision stock projector 结构（裸 linear_1 → GELU → linear_2），
|
| 272 |
-
保证 SigLIP2 / Qwen3.5 / Qwen3-VL 三个 backbone 在 LlavaOV 设定下公平对比。
|
| 273 |
-
|
| 274 |
-
（历史：V6.0.0~V6.0.4 时期 encoder 无 final LN，projector pre_norm 是补丁；
|
| 275 |
-
现在补丁回到 encoder 内部，projector 回归 stock 形态。）
|
| 276 |
-
"""
|
| 277 |
-
|
| 278 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 279 |
-
super().__init__()
|
| 280 |
-
num_feature_layers = (
|
| 281 |
-
1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
|
| 282 |
-
)
|
| 283 |
-
vision_dim = config.vision_config.hidden_size * num_feature_layers
|
| 284 |
-
text_dim = config.text_config.hidden_size
|
| 285 |
-
bias = getattr(config, "multimodal_projector_bias", True)
|
| 286 |
-
|
| 287 |
-
# V6 final_layernorm fix (2026-05-16): pre_norm = Identity (encoder 已自带 final_layernorm).
|
| 288 |
-
self.pre_norm = nn.Identity()
|
| 289 |
-
self.linear_1 = nn.Linear(vision_dim, text_dim, bias=bias)
|
| 290 |
-
self.act = ACT2FN[config.projector_hidden_act]
|
| 291 |
-
self.linear_2 = nn.Linear(text_dim, text_dim, bias=bias)
|
| 292 |
-
|
| 293 |
-
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 294 |
-
return self.linear_2(self.act(self.linear_1(self.pre_norm(x))))
|
| 295 |
-
|
| 296 |
-
|
| 297 |
-
class LlavaQwen3_5ViTModel(LlavaOnevisionModel):
|
| 298 |
-
"""继承 LlavaOnevisionModel 但绕过其 __init__ 手动装配。
|
| 299 |
-
|
| 300 |
-
父类 __init__ 调 `AutoModel.from_config(config.vision_config)` 会对 Qwen3_5VisionConfig
|
| 301 |
-
抛 "Unrecognized configuration"（Qwen3.5 vision 没注册到 AutoModel）。手动装配避开
|
| 302 |
-
这一步，同时省掉"先构造 Qwen3_5VisionModel 再被替换"的双重开销（~1.3GB init-peak）。
|
| 303 |
-
|
| 304 |
-
装配顺序与父类一致：vision_tower / projector / image_newline / language_model / post_init。
|
| 305 |
-
"""
|
| 306 |
-
|
| 307 |
-
config_class = LlavaQwen3_5ViTConfig
|
| 308 |
-
|
| 309 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 310 |
-
# 跳过 LlavaOnevisionModel.__init__（AutoModel 不识别 Qwen3_5VisionConfig）
|
| 311 |
-
LlavaOnevisionPreTrainedModel.__init__(self, config)
|
| 312 |
-
self.vision_tower = Qwen3_5ViTAsSiglipAdapter(config.vision_config)
|
| 313 |
-
self.multi_modal_projector = LlavaQwen3_5ViTMultiModalProjector(config)
|
| 314 |
-
embed_std = 1 / math.sqrt(config.text_config.hidden_size)
|
| 315 |
-
self.image_newline = nn.Parameter(
|
| 316 |
-
torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std
|
| 317 |
-
)
|
| 318 |
-
self.vocab_size = config.text_config.vocab_size
|
| 319 |
-
self.language_model = AutoModel.from_config(config.text_config)
|
| 320 |
-
self.post_init()
|
| 321 |
-
|
| 322 |
-
|
| 323 |
-
class LlavaQwen3_5ViTForConditionalGeneration(LlavaOnevisionForConditionalGeneration):
|
| 324 |
-
"""继承 LlavaOnevisionForConditionalGeneration，只换 self.model。
|
| 325 |
-
|
| 326 |
-
同样跳过父类 __init__（避免重复构造 LlavaOnevisionModel，根因见 LlavaQwen3_5ViTModel）。
|
| 327 |
-
"""
|
| 328 |
-
|
| 329 |
-
config_class = LlavaQwen3_5ViTConfig
|
| 330 |
-
|
| 331 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 332 |
-
LlavaOnevisionPreTrainedModel.__init__(self, config)
|
| 333 |
-
self.model = LlavaQwen3_5ViTModel(config)
|
| 334 |
-
self.lm_head = nn.Linear(
|
| 335 |
-
config.text_config.hidden_size, config.text_config.vocab_size, bias=False
|
| 336 |
-
)
|
| 337 |
-
self.post_init()
|
| 338 |
-
|
| 339 |
-
|
| 340 |
-
AutoConfig.register(LlavaQwen3_5ViTConfig.model_type, LlavaQwen3_5ViTConfig)
|
| 341 |
-
AutoModelForCausalLM.register(LlavaQwen3_5ViTConfig, LlavaQwen3_5ViTForConditionalGeneration)
|
| 342 |
-
|
| 343 |
-
|
| 344 |
-
__all__ = [
|
| 345 |
-
"LlavaQwen3_5ViTConfig",
|
| 346 |
-
"Qwen3_5ViTBackbone",
|
| 347 |
-
"Qwen3_5ViTAsSiglipAdapter",
|
| 348 |
-
"LlavaQwen3_5ViTMultiModalProjector",
|
| 349 |
-
"LlavaQwen3_5ViTModel",
|
| 350 |
-
"LlavaQwen3_5ViTForConditionalGeneration",
|
| 351 |
-
]
|
|
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|
ckpts/4b_stock/processor_config.json
DELETED
|
@@ -1,203 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"image_processor": {
|
| 3 |
-
"do_convert_rgb": true,
|
| 4 |
-
"do_normalize": true,
|
| 5 |
-
"do_pad": true,
|
| 6 |
-
"do_rescale": true,
|
| 7 |
-
"do_resize": true,
|
| 8 |
-
"image_grid_pinpoints": [
|
| 9 |
-
[
|
| 10 |
-
384,
|
| 11 |
-
384
|
| 12 |
-
],
|
| 13 |
-
[
|
| 14 |
-
384,
|
| 15 |
-
768
|
| 16 |
-
],
|
| 17 |
-
[
|
| 18 |
-
384,
|
| 19 |
-
1152
|
| 20 |
-
],
|
| 21 |
-
[
|
| 22 |
-
384,
|
| 23 |
-
1536
|
| 24 |
-
],
|
| 25 |
-
[
|
| 26 |
-
384,
|
| 27 |
-
1920
|
| 28 |
-
],
|
| 29 |
-
[
|
| 30 |
-
384,
|
| 31 |
-
2304
|
| 32 |
-
],
|
| 33 |
-
[
|
| 34 |
-
768,
|
| 35 |
-
384
|
| 36 |
-
],
|
| 37 |
-
[
|
| 38 |
-
768,
|
| 39 |
-
768
|
| 40 |
-
],
|
| 41 |
-
[
|
| 42 |
-
768,
|
| 43 |
-
1152
|
| 44 |
-
],
|
| 45 |
-
[
|
| 46 |
-
768,
|
| 47 |
-
1536
|
| 48 |
-
],
|
| 49 |
-
[
|
| 50 |
-
768,
|
| 51 |
-
1920
|
| 52 |
-
],
|
| 53 |
-
[
|
| 54 |
-
768,
|
| 55 |
-
2304
|
| 56 |
-
],
|
| 57 |
-
[
|
| 58 |
-
1152,
|
| 59 |
-
384
|
| 60 |
-
],
|
| 61 |
-
[
|
| 62 |
-
1152,
|
| 63 |
-
768
|
| 64 |
-
],
|
| 65 |
-
[
|
| 66 |
-
1152,
|
| 67 |
-
1152
|
| 68 |
-
],
|
| 69 |
-
[
|
| 70 |
-
1152,
|
| 71 |
-
1536
|
| 72 |
-
],
|
| 73 |
-
[
|
| 74 |
-
1152,
|
| 75 |
-
1920
|
| 76 |
-
],
|
| 77 |
-
[
|
| 78 |
-
1152,
|
| 79 |
-
2304
|
| 80 |
-
],
|
| 81 |
-
[
|
| 82 |
-
1536,
|
| 83 |
-
384
|
| 84 |
-
],
|
| 85 |
-
[
|
| 86 |
-
1536,
|
| 87 |
-
768
|
| 88 |
-
],
|
| 89 |
-
[
|
| 90 |
-
1536,
|
| 91 |
-
1152
|
| 92 |
-
],
|
| 93 |
-
[
|
| 94 |
-
1536,
|
| 95 |
-
1536
|
| 96 |
-
],
|
| 97 |
-
[
|
| 98 |
-
1536,
|
| 99 |
-
1920
|
| 100 |
-
],
|
| 101 |
-
[
|
| 102 |
-
1536,
|
| 103 |
-
2304
|
| 104 |
-
],
|
| 105 |
-
[
|
| 106 |
-
1920,
|
| 107 |
-
384
|
| 108 |
-
],
|
| 109 |
-
[
|
| 110 |
-
1920,
|
| 111 |
-
768
|
| 112 |
-
],
|
| 113 |
-
[
|
| 114 |
-
1920,
|
| 115 |
-
1152
|
| 116 |
-
],
|
| 117 |
-
[
|
| 118 |
-
1920,
|
| 119 |
-
1536
|
| 120 |
-
],
|
| 121 |
-
[
|
| 122 |
-
1920,
|
| 123 |
-
1920
|
| 124 |
-
],
|
| 125 |
-
[
|
| 126 |
-
1920,
|
| 127 |
-
2304
|
| 128 |
-
],
|
| 129 |
-
[
|
| 130 |
-
2304,
|
| 131 |
-
384
|
| 132 |
-
],
|
| 133 |
-
[
|
| 134 |
-
2304,
|
| 135 |
-
768
|
| 136 |
-
],
|
| 137 |
-
[
|
| 138 |
-
2304,
|
| 139 |
-
1152
|
| 140 |
-
],
|
| 141 |
-
[
|
| 142 |
-
2304,
|
| 143 |
-
1536
|
| 144 |
-
],
|
| 145 |
-
[
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| 146 |
-
2304,
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-
1920
|
| 148 |
-
],
|
| 149 |
-
[
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| 150 |
-
2304,
|
| 151 |
-
2304
|
| 152 |
-
]
|
| 153 |
-
],
|
| 154 |
-
"image_mean": [
|
| 155 |
-
0.5,
|
| 156 |
-
0.5,
|
| 157 |
-
0.5
|
| 158 |
-
],
|
| 159 |
-
"image_processor_type": "LlavaOnevisionImageProcessor",
|
| 160 |
-
"image_std": [
|
| 161 |
-
0.5,
|
| 162 |
-
0.5,
|
| 163 |
-
0.5
|
| 164 |
-
],
|
| 165 |
-
"resample": 3,
|
| 166 |
-
"rescale_factor": 0.00392156862745098,
|
| 167 |
-
"size": {
|
| 168 |
-
"height": 384,
|
| 169 |
-
"width": 384
|
| 170 |
-
}
|
| 171 |
-
},
|
| 172 |
-
"image_token": "<image>",
|
| 173 |
-
"num_image_tokens": 576,
|
| 174 |
-
"processor_class": "LlavaOnevisionProcessor",
|
| 175 |
-
"video_processor": {
|
| 176 |
-
"do_convert_rgb": true,
|
| 177 |
-
"do_normalize": true,
|
| 178 |
-
"do_rescale": true,
|
| 179 |
-
"do_resize": true,
|
| 180 |
-
"do_sample_frames": false,
|
| 181 |
-
"image_mean": [
|
| 182 |
-
0.5,
|
| 183 |
-
0.5,
|
| 184 |
-
0.5
|
| 185 |
-
],
|
| 186 |
-
"image_std": [
|
| 187 |
-
0.5,
|
| 188 |
-
0.5,
|
| 189 |
-
0.5
|
| 190 |
-
],
|
| 191 |
-
"resample": 3,
|
| 192 |
-
"rescale_factor": 0.00392156862745098,
|
| 193 |
-
"return_metadata": false,
|
| 194 |
-
"size": {
|
| 195 |
-
"height": 384,
|
| 196 |
-
"width": 384
|
| 197 |
-
},
|
| 198 |
-
"video_processor_type": "LlavaOnevisionVideoProcessor"
|
| 199 |
-
},
|
| 200 |
-
"video_token": "<video>",
|
| 201 |
-
"vision_aspect_ratio": "anyres_max_9",
|
| 202 |
-
"vision_feature_select_strategy": null
|
| 203 |
-
}
|
|
|
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|
ckpts/4b_stock/tokenizer.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:9024318c850eaadf26be79389d21b07a7afd8f1af749b89f9109b06c0d12173c
|
| 3 |
-
size 11423018
|
|
|
|
|
|
|
|
|
|
|
|
ckpts/4b_stock/tokenizer_config.json
DELETED
|
@@ -1,19 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"add_prefix_space": false,
|
| 3 |
-
"backend": "tokenizers",
|
| 4 |
-
"bos_token": null,
|
| 5 |
-
"clean_up_tokenization_spaces": false,
|
| 6 |
-
"eos_token": "<|im_end|>",
|
| 7 |
-
"errors": "replace",
|
| 8 |
-
"extra_special_tokens": [
|
| 9 |
-
"<image>",
|
| 10 |
-
"<video>"
|
| 11 |
-
],
|
| 12 |
-
"is_local": true,
|
| 13 |
-
"model_max_length": 1010000,
|
| 14 |
-
"pad_token": "<|endoftext|>",
|
| 15 |
-
"processor_class": "LlavaOnevisionProcessor",
|
| 16 |
-
"split_special_tokens": false,
|
| 17 |
-
"tokenizer_class": "Qwen2Tokenizer",
|
| 18 |
-
"unk_token": null
|
| 19 |
-
}
|
|
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|
|
|
|
ckpts/4b_stock/zero_to_fp32.py
DELETED
|
@@ -1,760 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
|
| 3 |
-
# Copyright (c) Microsoft Corporation.
|
| 4 |
-
# SPDX-License-Identifier: Apache-2.0
|
| 5 |
-
|
| 6 |
-
# DeepSpeed Team
|
| 7 |
-
|
| 8 |
-
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
| 9 |
-
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
| 10 |
-
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
-
# application.
|
| 12 |
-
#
|
| 13 |
-
# example:
|
| 14 |
-
# python zero_to_fp32.py . output_dir/
|
| 15 |
-
# or
|
| 16 |
-
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
| 17 |
-
|
| 18 |
-
import argparse
|
| 19 |
-
import torch
|
| 20 |
-
import glob
|
| 21 |
-
import math
|
| 22 |
-
import os
|
| 23 |
-
import re
|
| 24 |
-
import gc
|
| 25 |
-
import json
|
| 26 |
-
import numpy as np
|
| 27 |
-
from tqdm import tqdm
|
| 28 |
-
from collections import OrderedDict
|
| 29 |
-
from dataclasses import dataclass
|
| 30 |
-
|
| 31 |
-
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
| 32 |
-
# DeepSpeed data structures it has to be available in the current python environment.
|
| 33 |
-
from deepspeed.utils import logger
|
| 34 |
-
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
| 35 |
-
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
| 36 |
-
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
@dataclass
|
| 40 |
-
class zero_model_state:
|
| 41 |
-
buffers: dict()
|
| 42 |
-
param_shapes: dict()
|
| 43 |
-
shared_params: list
|
| 44 |
-
ds_version: int
|
| 45 |
-
frozen_param_shapes: dict()
|
| 46 |
-
frozen_param_fragments: dict()
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
debug = 0
|
| 50 |
-
|
| 51 |
-
# load to cpu
|
| 52 |
-
device = torch.device('cpu')
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def atoi(text):
|
| 56 |
-
return int(text) if text.isdigit() else text
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
def natural_keys(text):
|
| 60 |
-
'''
|
| 61 |
-
alist.sort(key=natural_keys) sorts in human order
|
| 62 |
-
http://nedbatchelder.com/blog/200712/human_sorting.html
|
| 63 |
-
(See Toothy's implementation in the comments)
|
| 64 |
-
'''
|
| 65 |
-
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def get_model_state_file(checkpoint_dir, zero_stage):
|
| 69 |
-
if not os.path.isdir(checkpoint_dir):
|
| 70 |
-
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
| 71 |
-
|
| 72 |
-
# there should be only one file
|
| 73 |
-
if zero_stage <= 2:
|
| 74 |
-
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
| 75 |
-
elif zero_stage == 3:
|
| 76 |
-
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
| 77 |
-
|
| 78 |
-
if not os.path.exists(file):
|
| 79 |
-
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
| 80 |
-
|
| 81 |
-
return file
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
| 85 |
-
# XXX: need to test that this simple glob rule works for multi-node setup too
|
| 86 |
-
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
| 87 |
-
|
| 88 |
-
if len(ckpt_files) == 0:
|
| 89 |
-
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
| 90 |
-
|
| 91 |
-
return ckpt_files
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def get_optim_files(checkpoint_dir):
|
| 95 |
-
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def get_model_state_files(checkpoint_dir):
|
| 99 |
-
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def parse_model_states(files):
|
| 103 |
-
zero_model_states = []
|
| 104 |
-
for file in files:
|
| 105 |
-
state_dict = torch.load(file, map_location=device, weights_only=False)
|
| 106 |
-
|
| 107 |
-
if BUFFER_NAMES not in state_dict:
|
| 108 |
-
raise ValueError(f"{file} is not a model state checkpoint")
|
| 109 |
-
buffer_names = state_dict[BUFFER_NAMES]
|
| 110 |
-
if debug:
|
| 111 |
-
print("Found buffers:", buffer_names)
|
| 112 |
-
|
| 113 |
-
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
| 114 |
-
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
| 115 |
-
param_shapes = state_dict[PARAM_SHAPES]
|
| 116 |
-
|
| 117 |
-
# collect parameters that are included in param_shapes
|
| 118 |
-
param_names = []
|
| 119 |
-
for s in param_shapes:
|
| 120 |
-
for name in s.keys():
|
| 121 |
-
param_names.append(name)
|
| 122 |
-
|
| 123 |
-
# update with frozen parameters
|
| 124 |
-
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
| 125 |
-
if frozen_param_shapes is not None:
|
| 126 |
-
if debug:
|
| 127 |
-
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
| 128 |
-
param_names += list(frozen_param_shapes.keys())
|
| 129 |
-
|
| 130 |
-
# handle shared params
|
| 131 |
-
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
| 132 |
-
|
| 133 |
-
ds_version = state_dict.get(DS_VERSION, None)
|
| 134 |
-
|
| 135 |
-
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
| 136 |
-
|
| 137 |
-
z_model_state = zero_model_state(buffers=buffers,
|
| 138 |
-
param_shapes=param_shapes,
|
| 139 |
-
shared_params=shared_params,
|
| 140 |
-
ds_version=ds_version,
|
| 141 |
-
frozen_param_shapes=frozen_param_shapes,
|
| 142 |
-
frozen_param_fragments=frozen_param_fragments)
|
| 143 |
-
zero_model_states.append(z_model_state)
|
| 144 |
-
|
| 145 |
-
return zero_model_states
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
def parse_optim_states(files, ds_checkpoint_dir):
|
| 149 |
-
total_files = len(files)
|
| 150 |
-
state_dicts = []
|
| 151 |
-
for f in tqdm(files, desc='Loading checkpoint shards'):
|
| 152 |
-
state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
|
| 153 |
-
# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
|
| 154 |
-
# and also handle the case where it was already removed by another helper script
|
| 155 |
-
state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
|
| 156 |
-
state_dicts.append(state_dict)
|
| 157 |
-
|
| 158 |
-
if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
| 159 |
-
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
| 160 |
-
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
| 161 |
-
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
| 162 |
-
|
| 163 |
-
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
| 164 |
-
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
| 165 |
-
# use the max of the partition_count to get the dp world_size.
|
| 166 |
-
|
| 167 |
-
if type(world_size) is list:
|
| 168 |
-
world_size = max(world_size)
|
| 169 |
-
|
| 170 |
-
if world_size != total_files:
|
| 171 |
-
raise ValueError(
|
| 172 |
-
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
| 173 |
-
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
| 174 |
-
)
|
| 175 |
-
|
| 176 |
-
# the groups are named differently in each stage
|
| 177 |
-
if zero_stage <= 2:
|
| 178 |
-
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
| 179 |
-
elif zero_stage == 3:
|
| 180 |
-
fp32_groups_key = FP32_FLAT_GROUPS
|
| 181 |
-
else:
|
| 182 |
-
raise ValueError(f"unknown zero stage {zero_stage}")
|
| 183 |
-
|
| 184 |
-
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
| 185 |
-
return zero_stage, world_size, fp32_flat_groups
|
| 186 |
-
|
| 187 |
-
|
| 188 |
-
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
|
| 189 |
-
"""
|
| 190 |
-
Returns fp32 state_dict reconstructed from ds checkpoint
|
| 191 |
-
|
| 192 |
-
Args:
|
| 193 |
-
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
| 194 |
-
|
| 195 |
-
"""
|
| 196 |
-
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
| 197 |
-
|
| 198 |
-
optim_files = get_optim_files(ds_checkpoint_dir)
|
| 199 |
-
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
| 200 |
-
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
| 201 |
-
|
| 202 |
-
model_files = get_model_state_files(ds_checkpoint_dir)
|
| 203 |
-
|
| 204 |
-
zero_model_states = parse_model_states(model_files)
|
| 205 |
-
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
| 206 |
-
|
| 207 |
-
if zero_stage <= 2:
|
| 208 |
-
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 209 |
-
exclude_frozen_parameters)
|
| 210 |
-
elif zero_stage == 3:
|
| 211 |
-
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 212 |
-
exclude_frozen_parameters)
|
| 213 |
-
|
| 214 |
-
|
| 215 |
-
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
| 216 |
-
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 217 |
-
return
|
| 218 |
-
|
| 219 |
-
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 220 |
-
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
| 221 |
-
|
| 222 |
-
if debug:
|
| 223 |
-
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
| 224 |
-
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 225 |
-
|
| 226 |
-
wanted_params = len(frozen_param_shapes)
|
| 227 |
-
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 228 |
-
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
| 229 |
-
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 230 |
-
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 231 |
-
|
| 232 |
-
total_params = 0
|
| 233 |
-
total_numel = 0
|
| 234 |
-
for name, shape in frozen_param_shapes.items():
|
| 235 |
-
total_params += 1
|
| 236 |
-
unpartitioned_numel = shape.numel()
|
| 237 |
-
total_numel += unpartitioned_numel
|
| 238 |
-
|
| 239 |
-
state_dict[name] = frozen_param_fragments[name]
|
| 240 |
-
|
| 241 |
-
if debug:
|
| 242 |
-
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 243 |
-
|
| 244 |
-
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 245 |
-
|
| 246 |
-
|
| 247 |
-
def _has_callable(obj, fn):
|
| 248 |
-
attr = getattr(obj, fn, None)
|
| 249 |
-
return callable(attr)
|
| 250 |
-
|
| 251 |
-
|
| 252 |
-
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 253 |
-
param_shapes = zero_model_states[0].param_shapes
|
| 254 |
-
|
| 255 |
-
# Reconstruction protocol:
|
| 256 |
-
#
|
| 257 |
-
# XXX: document this
|
| 258 |
-
|
| 259 |
-
if debug:
|
| 260 |
-
for i in range(world_size):
|
| 261 |
-
for j in range(len(fp32_flat_groups[0])):
|
| 262 |
-
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
| 263 |
-
|
| 264 |
-
# XXX: memory usage doubles here (zero2)
|
| 265 |
-
num_param_groups = len(fp32_flat_groups[0])
|
| 266 |
-
merged_single_partition_of_fp32_groups = []
|
| 267 |
-
for i in range(num_param_groups):
|
| 268 |
-
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
| 269 |
-
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
| 270 |
-
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
| 271 |
-
avail_numel = sum(
|
| 272 |
-
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
| 273 |
-
|
| 274 |
-
if debug:
|
| 275 |
-
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
| 276 |
-
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
| 277 |
-
# not asserting if there is a mismatch due to possible padding
|
| 278 |
-
print(f"Have {avail_numel} numels to process.")
|
| 279 |
-
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
| 280 |
-
|
| 281 |
-
# params
|
| 282 |
-
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 283 |
-
# out-of-core computing solution
|
| 284 |
-
total_numel = 0
|
| 285 |
-
total_params = 0
|
| 286 |
-
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
| 287 |
-
offset = 0
|
| 288 |
-
avail_numel = full_single_fp32_vector.numel()
|
| 289 |
-
for name, shape in shapes.items():
|
| 290 |
-
|
| 291 |
-
unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
|
| 292 |
-
total_numel += unpartitioned_numel
|
| 293 |
-
total_params += 1
|
| 294 |
-
|
| 295 |
-
if debug:
|
| 296 |
-
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 297 |
-
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
| 298 |
-
offset += unpartitioned_numel
|
| 299 |
-
|
| 300 |
-
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
| 301 |
-
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
| 302 |
-
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
| 303 |
-
# live optimizer object, so we are checking that the numbers are within the right range
|
| 304 |
-
align_to = 2 * world_size
|
| 305 |
-
|
| 306 |
-
def zero2_align(x):
|
| 307 |
-
return align_to * math.ceil(x / align_to)
|
| 308 |
-
|
| 309 |
-
if debug:
|
| 310 |
-
print(f"original offset={offset}, avail_numel={avail_numel}")
|
| 311 |
-
|
| 312 |
-
offset = zero2_align(offset)
|
| 313 |
-
avail_numel = zero2_align(avail_numel)
|
| 314 |
-
|
| 315 |
-
if debug:
|
| 316 |
-
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
| 317 |
-
|
| 318 |
-
# Sanity check
|
| 319 |
-
if offset != avail_numel:
|
| 320 |
-
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 321 |
-
|
| 322 |
-
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
| 323 |
-
|
| 324 |
-
|
| 325 |
-
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 326 |
-
exclude_frozen_parameters):
|
| 327 |
-
state_dict = OrderedDict()
|
| 328 |
-
|
| 329 |
-
# buffers
|
| 330 |
-
buffers = zero_model_states[0].buffers
|
| 331 |
-
state_dict.update(buffers)
|
| 332 |
-
if debug:
|
| 333 |
-
print(f"added {len(buffers)} buffers")
|
| 334 |
-
|
| 335 |
-
if not exclude_frozen_parameters:
|
| 336 |
-
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
| 337 |
-
|
| 338 |
-
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 339 |
-
|
| 340 |
-
# recover shared parameters
|
| 341 |
-
for pair in zero_model_states[0].shared_params:
|
| 342 |
-
if pair[1] in state_dict:
|
| 343 |
-
state_dict[pair[0]] = state_dict[pair[1]]
|
| 344 |
-
|
| 345 |
-
return state_dict
|
| 346 |
-
|
| 347 |
-
|
| 348 |
-
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
| 349 |
-
remainder = unpartitioned_numel % world_size
|
| 350 |
-
padding_numel = (world_size - remainder) if remainder else 0
|
| 351 |
-
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
| 352 |
-
return partitioned_numel, padding_numel
|
| 353 |
-
|
| 354 |
-
|
| 355 |
-
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
| 356 |
-
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 357 |
-
return
|
| 358 |
-
|
| 359 |
-
if debug:
|
| 360 |
-
for i in range(world_size):
|
| 361 |
-
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
| 362 |
-
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 363 |
-
|
| 364 |
-
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 365 |
-
wanted_params = len(frozen_param_shapes)
|
| 366 |
-
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 367 |
-
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
| 368 |
-
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 369 |
-
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 370 |
-
|
| 371 |
-
total_params = 0
|
| 372 |
-
total_numel = 0
|
| 373 |
-
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
| 374 |
-
total_params += 1
|
| 375 |
-
unpartitioned_numel = shape.numel()
|
| 376 |
-
total_numel += unpartitioned_numel
|
| 377 |
-
|
| 378 |
-
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
| 379 |
-
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
| 380 |
-
|
| 381 |
-
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 382 |
-
|
| 383 |
-
if debug:
|
| 384 |
-
print(
|
| 385 |
-
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 386 |
-
)
|
| 387 |
-
|
| 388 |
-
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 389 |
-
|
| 390 |
-
|
| 391 |
-
class GatheredTensor:
|
| 392 |
-
"""
|
| 393 |
-
A pseudo tensor that collects partitioned weights.
|
| 394 |
-
It is more memory efficient when there are multiple groups.
|
| 395 |
-
"""
|
| 396 |
-
|
| 397 |
-
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
| 398 |
-
self.flat_groups = flat_groups
|
| 399 |
-
self.flat_groups_offset = flat_groups_offset
|
| 400 |
-
self.offset = offset
|
| 401 |
-
self.partitioned_numel = partitioned_numel
|
| 402 |
-
self.shape = shape
|
| 403 |
-
self.dtype = self.flat_groups[0][0].dtype
|
| 404 |
-
|
| 405 |
-
def contiguous(self):
|
| 406 |
-
"""
|
| 407 |
-
Merge partitioned weights from flat_groups into a single tensor.
|
| 408 |
-
"""
|
| 409 |
-
end_idx = self.offset + self.partitioned_numel
|
| 410 |
-
world_size = len(self.flat_groups)
|
| 411 |
-
pad_flat_param_chunks = []
|
| 412 |
-
|
| 413 |
-
for rank_i in range(world_size):
|
| 414 |
-
# for each rank, we need to collect weights from related group/groups
|
| 415 |
-
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
| 416 |
-
start_group_id = None
|
| 417 |
-
end_group_id = None
|
| 418 |
-
for group_id in range(len(self.flat_groups_offset)):
|
| 419 |
-
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
| 420 |
-
start_group_id = group_id
|
| 421 |
-
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
| 422 |
-
end_group_id = group_id
|
| 423 |
-
break
|
| 424 |
-
# collect weights from related group/groups
|
| 425 |
-
for group_id in range(start_group_id, end_group_id + 1):
|
| 426 |
-
flat_tensor = flat_groups_at_rank_i[group_id]
|
| 427 |
-
start_offset = self.offset - self.flat_groups_offset[group_id]
|
| 428 |
-
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
| 429 |
-
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
| 430 |
-
|
| 431 |
-
# collect weights from all ranks
|
| 432 |
-
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
| 433 |
-
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
| 434 |
-
return param
|
| 435 |
-
|
| 436 |
-
|
| 437 |
-
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 438 |
-
param_shapes = zero_model_states[0].param_shapes
|
| 439 |
-
avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
|
| 440 |
-
|
| 441 |
-
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
| 442 |
-
# param, re-consolidating each param, while dealing with padding if any
|
| 443 |
-
|
| 444 |
-
# merge list of dicts, preserving order
|
| 445 |
-
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
| 446 |
-
|
| 447 |
-
if debug:
|
| 448 |
-
for i in range(world_size):
|
| 449 |
-
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
| 450 |
-
|
| 451 |
-
wanted_params = len(param_shapes)
|
| 452 |
-
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
| 453 |
-
# not asserting if there is a mismatch due to possible padding
|
| 454 |
-
avail_numel = fp32_flat_groups[0].numel() * world_size
|
| 455 |
-
print(f"Trainable params: Have {avail_numel} numels to process.")
|
| 456 |
-
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
| 457 |
-
|
| 458 |
-
# params
|
| 459 |
-
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 460 |
-
# out-of-core computing solution
|
| 461 |
-
offset = 0
|
| 462 |
-
total_numel = 0
|
| 463 |
-
total_params = 0
|
| 464 |
-
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
|
| 465 |
-
for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
|
| 466 |
-
unpartitioned_numel = shape.numel()
|
| 467 |
-
total_numel += unpartitioned_numel
|
| 468 |
-
total_params += 1
|
| 469 |
-
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 470 |
-
|
| 471 |
-
if debug:
|
| 472 |
-
print(
|
| 473 |
-
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 474 |
-
)
|
| 475 |
-
|
| 476 |
-
# memory efficient tensor
|
| 477 |
-
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
| 478 |
-
state_dict[name] = tensor
|
| 479 |
-
offset += partitioned_numel
|
| 480 |
-
|
| 481 |
-
offset *= world_size
|
| 482 |
-
|
| 483 |
-
# Sanity check
|
| 484 |
-
if offset != avail_numel:
|
| 485 |
-
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 486 |
-
|
| 487 |
-
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
| 488 |
-
|
| 489 |
-
|
| 490 |
-
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 491 |
-
exclude_frozen_parameters):
|
| 492 |
-
state_dict = OrderedDict()
|
| 493 |
-
|
| 494 |
-
# buffers
|
| 495 |
-
buffers = zero_model_states[0].buffers
|
| 496 |
-
state_dict.update(buffers)
|
| 497 |
-
if debug:
|
| 498 |
-
print(f"added {len(buffers)} buffers")
|
| 499 |
-
|
| 500 |
-
if not exclude_frozen_parameters:
|
| 501 |
-
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
| 502 |
-
|
| 503 |
-
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 504 |
-
|
| 505 |
-
# recover shared parameters
|
| 506 |
-
for pair in zero_model_states[0].shared_params:
|
| 507 |
-
if pair[1] in state_dict:
|
| 508 |
-
state_dict[pair[0]] = state_dict[pair[1]]
|
| 509 |
-
|
| 510 |
-
return state_dict
|
| 511 |
-
|
| 512 |
-
|
| 513 |
-
def to_torch_tensor(state_dict, return_empty_tensor=False):
|
| 514 |
-
"""
|
| 515 |
-
Convert state_dict of GatheredTensor to torch tensor
|
| 516 |
-
"""
|
| 517 |
-
torch_state_dict = {}
|
| 518 |
-
converted_tensors = {}
|
| 519 |
-
for name, tensor in state_dict.items():
|
| 520 |
-
tensor_id = id(tensor)
|
| 521 |
-
if tensor_id in converted_tensors: # shared tensors
|
| 522 |
-
shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
|
| 523 |
-
torch_state_dict[name] = shared_tensor
|
| 524 |
-
else:
|
| 525 |
-
converted_tensors[tensor_id] = name
|
| 526 |
-
if return_empty_tensor:
|
| 527 |
-
torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
| 528 |
-
else:
|
| 529 |
-
torch_state_dict[name] = tensor.contiguous()
|
| 530 |
-
return torch_state_dict
|
| 531 |
-
|
| 532 |
-
|
| 533 |
-
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 534 |
-
tag=None,
|
| 535 |
-
exclude_frozen_parameters=False,
|
| 536 |
-
lazy_mode=False):
|
| 537 |
-
"""
|
| 538 |
-
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
| 539 |
-
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
| 540 |
-
via a model hub.
|
| 541 |
-
|
| 542 |
-
Args:
|
| 543 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder
|
| 544 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
| 545 |
-
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 546 |
-
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
| 547 |
-
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
| 548 |
-
|
| 549 |
-
Returns:
|
| 550 |
-
- pytorch ``state_dict``
|
| 551 |
-
|
| 552 |
-
A typical usage might be ::
|
| 553 |
-
|
| 554 |
-
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 555 |
-
# do the training and checkpoint saving
|
| 556 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
| 557 |
-
model = model.cpu() # move to cpu
|
| 558 |
-
model.load_state_dict(state_dict)
|
| 559 |
-
# submit to model hub or save the model to share with others
|
| 560 |
-
|
| 561 |
-
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
| 562 |
-
application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 563 |
-
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 564 |
-
|
| 565 |
-
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
| 566 |
-
|
| 567 |
-
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
| 568 |
-
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
| 569 |
-
the checkpoint. Or you can load state_dict in lazy mode ::
|
| 570 |
-
|
| 571 |
-
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 572 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
| 573 |
-
for name, lazy_tensor in state_dict.item():
|
| 574 |
-
tensor = lazy_tensor.contiguous() # to cpu
|
| 575 |
-
print(name, tensor)
|
| 576 |
-
# del tensor to release memory if it no longer in use
|
| 577 |
-
"""
|
| 578 |
-
if tag is None:
|
| 579 |
-
latest_path = os.path.join(checkpoint_dir, 'latest')
|
| 580 |
-
if os.path.isfile(latest_path):
|
| 581 |
-
with open(latest_path, 'r') as fd:
|
| 582 |
-
tag = fd.read().strip()
|
| 583 |
-
else:
|
| 584 |
-
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
| 585 |
-
|
| 586 |
-
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
| 587 |
-
|
| 588 |
-
if not os.path.isdir(ds_checkpoint_dir):
|
| 589 |
-
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
| 590 |
-
|
| 591 |
-
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
| 592 |
-
if lazy_mode:
|
| 593 |
-
return state_dict
|
| 594 |
-
else:
|
| 595 |
-
return to_torch_tensor(state_dict)
|
| 596 |
-
|
| 597 |
-
|
| 598 |
-
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
| 599 |
-
output_dir,
|
| 600 |
-
max_shard_size="5GB",
|
| 601 |
-
safe_serialization=False,
|
| 602 |
-
tag=None,
|
| 603 |
-
exclude_frozen_parameters=False):
|
| 604 |
-
"""
|
| 605 |
-
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
| 606 |
-
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
| 607 |
-
|
| 608 |
-
Args:
|
| 609 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 610 |
-
- ``output_dir``: directory to the pytorch fp32 state_dict output files
|
| 611 |
-
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
| 612 |
-
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
| 613 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 614 |
-
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 615 |
-
"""
|
| 616 |
-
|
| 617 |
-
# Dependency pre-check
|
| 618 |
-
if safe_serialization:
|
| 619 |
-
try:
|
| 620 |
-
from safetensors.torch import save_file
|
| 621 |
-
except ImportError:
|
| 622 |
-
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
| 623 |
-
raise
|
| 624 |
-
if max_shard_size is not None:
|
| 625 |
-
try:
|
| 626 |
-
from huggingface_hub import split_torch_state_dict_into_shards
|
| 627 |
-
except ImportError:
|
| 628 |
-
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
| 629 |
-
raise
|
| 630 |
-
|
| 631 |
-
# Convert zero checkpoint to state_dict
|
| 632 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 633 |
-
tag,
|
| 634 |
-
exclude_frozen_parameters,
|
| 635 |
-
lazy_mode=True)
|
| 636 |
-
|
| 637 |
-
# Shard the model if it is too big.
|
| 638 |
-
weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
|
| 639 |
-
if max_shard_size is not None:
|
| 640 |
-
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
| 641 |
-
# an memory-efficient approach for sharding
|
| 642 |
-
empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
|
| 643 |
-
state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
|
| 644 |
-
filename_pattern=filename_pattern,
|
| 645 |
-
max_shard_size=max_shard_size)
|
| 646 |
-
else:
|
| 647 |
-
from collections import namedtuple
|
| 648 |
-
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
| 649 |
-
state_dict_split = StateDictSplit(is_sharded=False,
|
| 650 |
-
filename_to_tensors={weights_name: list(state_dict.keys())})
|
| 651 |
-
|
| 652 |
-
# Save the model by shard
|
| 653 |
-
os.makedirs(output_dir, exist_ok=True)
|
| 654 |
-
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
| 655 |
-
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
| 656 |
-
shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
|
| 657 |
-
shard_state_dict = to_torch_tensor(shard_state_dict)
|
| 658 |
-
output_path = os.path.join(output_dir, shard_file)
|
| 659 |
-
if safe_serialization:
|
| 660 |
-
save_file(shard_state_dict, output_path, metadata={"format": "pt"})
|
| 661 |
-
else:
|
| 662 |
-
torch.save(shard_state_dict, output_path)
|
| 663 |
-
# release the memory of current shard
|
| 664 |
-
for tensor_name in list(shard_state_dict.keys()):
|
| 665 |
-
del state_dict[tensor_name]
|
| 666 |
-
del shard_state_dict[tensor_name]
|
| 667 |
-
del shard_state_dict
|
| 668 |
-
gc.collect()
|
| 669 |
-
|
| 670 |
-
# Save index if sharded
|
| 671 |
-
if state_dict_split.is_sharded:
|
| 672 |
-
index = {
|
| 673 |
-
"metadata": state_dict_split.metadata,
|
| 674 |
-
"weight_map": state_dict_split.tensor_to_filename,
|
| 675 |
-
}
|
| 676 |
-
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
| 677 |
-
save_index_file = os.path.join(output_dir, save_index_file)
|
| 678 |
-
with open(save_index_file, "w", encoding="utf-8") as f:
|
| 679 |
-
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
| 680 |
-
f.write(content)
|
| 681 |
-
|
| 682 |
-
|
| 683 |
-
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
| 684 |
-
"""
|
| 685 |
-
1. Put the provided model to cpu
|
| 686 |
-
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
| 687 |
-
3. Load it into the provided model
|
| 688 |
-
|
| 689 |
-
Args:
|
| 690 |
-
- ``model``: the model object to update
|
| 691 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 692 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 693 |
-
|
| 694 |
-
Returns:
|
| 695 |
-
- ``model`: modified model
|
| 696 |
-
|
| 697 |
-
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
| 698 |
-
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
| 699 |
-
conveniently placed for you in the checkpoint folder.
|
| 700 |
-
|
| 701 |
-
A typical usage might be ::
|
| 702 |
-
|
| 703 |
-
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
| 704 |
-
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
| 705 |
-
# submit to model hub or save the model to share with others
|
| 706 |
-
|
| 707 |
-
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
| 708 |
-
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 709 |
-
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 710 |
-
|
| 711 |
-
"""
|
| 712 |
-
logger.info("Extracting fp32 weights")
|
| 713 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
| 714 |
-
|
| 715 |
-
logger.info("Overwriting model with fp32 weights")
|
| 716 |
-
model = model.cpu()
|
| 717 |
-
model.load_state_dict(state_dict, strict=False)
|
| 718 |
-
|
| 719 |
-
return model
|
| 720 |
-
|
| 721 |
-
|
| 722 |
-
if __name__ == "__main__":
|
| 723 |
-
parser = argparse.ArgumentParser()
|
| 724 |
-
parser.add_argument("checkpoint_dir",
|
| 725 |
-
type=str,
|
| 726 |
-
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
| 727 |
-
parser.add_argument("output_dir",
|
| 728 |
-
type=str,
|
| 729 |
-
help="directory to the pytorch fp32 state_dict output files"
|
| 730 |
-
"(e.g. path/checkpoint-12-output/)")
|
| 731 |
-
parser.add_argument(
|
| 732 |
-
"--max_shard_size",
|
| 733 |
-
type=str,
|
| 734 |
-
default="5GB",
|
| 735 |
-
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
| 736 |
-
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
| 737 |
-
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
| 738 |
-
"without CPU OOM issues.")
|
| 739 |
-
parser.add_argument(
|
| 740 |
-
"--safe_serialization",
|
| 741 |
-
default=False,
|
| 742 |
-
action='store_true',
|
| 743 |
-
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
| 744 |
-
parser.add_argument("-t",
|
| 745 |
-
"--tag",
|
| 746 |
-
type=str,
|
| 747 |
-
default=None,
|
| 748 |
-
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
| 749 |
-
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
| 750 |
-
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
| 751 |
-
args = parser.parse_args()
|
| 752 |
-
|
| 753 |
-
debug = args.debug
|
| 754 |
-
|
| 755 |
-
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
| 756 |
-
args.output_dir,
|
| 757 |
-
max_shard_size=args.max_shard_size,
|
| 758 |
-
safe_serialization=args.safe_serialization,
|
| 759 |
-
tag=args.tag,
|
| 760 |
-
exclude_frozen_parameters=args.exclude_frozen_parameters)
|
|
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|
ckpts/4b_v9_1/args.json
DELETED
|
@@ -1,376 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"output_dir": "/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/video_mllm_swift/4b/s2_v9_1_a800_10pct/v0-20260602-181529",
|
| 3 |
-
"per_device_train_batch_size": 1,
|
| 4 |
-
"num_train_epochs": 3.0,
|
| 5 |
-
"max_steps": 505,
|
| 6 |
-
"learning_rate": 1e-05,
|
| 7 |
-
"lr_scheduler_type": "cosine",
|
| 8 |
-
"lr_scheduler_kwargs": null,
|
| 9 |
-
"warmup_steps": 0,
|
| 10 |
-
"optim": "adamw_torch_fused",
|
| 11 |
-
"optim_args": null,
|
| 12 |
-
"weight_decay": 0.1,
|
| 13 |
-
"adam_beta1": 0.9,
|
| 14 |
-
"adam_beta2": 0.95,
|
| 15 |
-
"adam_epsilon": 1e-08,
|
| 16 |
-
"optim_target_modules": null,
|
| 17 |
-
"gradient_accumulation_steps": 8,
|
| 18 |
-
"average_tokens_across_devices": true,
|
| 19 |
-
"max_grad_norm": 1.0,
|
| 20 |
-
"label_smoothing_factor": 0.0,
|
| 21 |
-
"bf16": true,
|
| 22 |
-
"fp16": false,
|
| 23 |
-
"bf16_full_eval": false,
|
| 24 |
-
"fp16_full_eval": false,
|
| 25 |
-
"tf32": null,
|
| 26 |
-
"gradient_checkpointing": true,
|
| 27 |
-
"gradient_checkpointing_kwargs": "{\"use_reentrant\": false}",
|
| 28 |
-
"torch_compile": false,
|
| 29 |
-
"torch_compile_backend": null,
|
| 30 |
-
"torch_compile_mode": null,
|
| 31 |
-
"use_liger_kernel": false,
|
| 32 |
-
"liger_kernel_config": null,
|
| 33 |
-
"use_cache": false,
|
| 34 |
-
"neftune_noise_alpha": null,
|
| 35 |
-
"torch_empty_cache_steps": null,
|
| 36 |
-
"auto_find_batch_size": false,
|
| 37 |
-
"logging_strategy": "steps",
|
| 38 |
-
"logging_steps": 1,
|
| 39 |
-
"logging_first_step": true,
|
| 40 |
-
"log_on_each_node": true,
|
| 41 |
-
"logging_nan_inf_filter": true,
|
| 42 |
-
"include_num_input_tokens_seen": false,
|
| 43 |
-
"log_level": "passive",
|
| 44 |
-
"log_level_replica": "warning",
|
| 45 |
-
"disable_tqdm": null,
|
| 46 |
-
"report_to": [
|
| 47 |
-
"none"
|
| 48 |
-
],
|
| 49 |
-
"run_name": "/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/video_mllm_swift/4b/s2_v9_1_a800_10pct/v0-20260602-181529",
|
| 50 |
-
"project": "huggingface",
|
| 51 |
-
"trackio_space_id": "trackio",
|
| 52 |
-
"eval_strategy": "no",
|
| 53 |
-
"eval_steps": 200.0,
|
| 54 |
-
"eval_delay": 0,
|
| 55 |
-
"per_device_eval_batch_size": 1,
|
| 56 |
-
"prediction_loss_only": false,
|
| 57 |
-
"eval_on_start": false,
|
| 58 |
-
"eval_do_concat_batches": true,
|
| 59 |
-
"eval_use_gather_object": false,
|
| 60 |
-
"eval_accumulation_steps": null,
|
| 61 |
-
"include_for_metrics": [],
|
| 62 |
-
"batch_eval_metrics": false,
|
| 63 |
-
"save_only_model": false,
|
| 64 |
-
"save_strategy": "steps",
|
| 65 |
-
"save_steps": 200.0,
|
| 66 |
-
"save_on_each_node": false,
|
| 67 |
-
"save_total_limit": 1,
|
| 68 |
-
"enable_jit_checkpoint": false,
|
| 69 |
-
"push_to_hub": false,
|
| 70 |
-
"hub_token": null,
|
| 71 |
-
"hub_private_repo": null,
|
| 72 |
-
"hub_model_id": null,
|
| 73 |
-
"hub_strategy": "every_save",
|
| 74 |
-
"hub_always_push": false,
|
| 75 |
-
"hub_revision": null,
|
| 76 |
-
"load_best_model_at_end": false,
|
| 77 |
-
"metric_for_best_model": "loss",
|
| 78 |
-
"greater_is_better": false,
|
| 79 |
-
"ignore_data_skip": false,
|
| 80 |
-
"restore_callback_states_from_checkpoint": false,
|
| 81 |
-
"full_determinism": false,
|
| 82 |
-
"seed": 42,
|
| 83 |
-
"data_seed": 42,
|
| 84 |
-
"use_cpu": false,
|
| 85 |
-
"accelerator_config": "{\"dispatch_batches\": false}",
|
| 86 |
-
"parallelism_config": null,
|
| 87 |
-
"dataloader_drop_last": false,
|
| 88 |
-
"dataloader_num_workers": 6,
|
| 89 |
-
"dataloader_pin_memory": true,
|
| 90 |
-
"dataloader_persistent_workers": true,
|
| 91 |
-
"dataloader_prefetch_factor": 4,
|
| 92 |
-
"remove_unused_columns": true,
|
| 93 |
-
"label_names": null,
|
| 94 |
-
"train_sampling_strategy": "random",
|
| 95 |
-
"length_column_name": "length",
|
| 96 |
-
"ddp_find_unused_parameters": null,
|
| 97 |
-
"ddp_bucket_cap_mb": null,
|
| 98 |
-
"ddp_broadcast_buffers": null,
|
| 99 |
-
"ddp_backend": null,
|
| 100 |
-
"ddp_timeout": 7200,
|
| 101 |
-
"fsdp": [],
|
| 102 |
-
"fsdp_config": null,
|
| 103 |
-
"deepspeed": {
|
| 104 |
-
"fp16": {
|
| 105 |
-
"enabled": "auto",
|
| 106 |
-
"loss_scale": 0,
|
| 107 |
-
"loss_scale_window": 1000,
|
| 108 |
-
"initial_scale_power": 16,
|
| 109 |
-
"hysteresis": 2,
|
| 110 |
-
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hub_private_repo=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_always_push=False, hub_revision=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, restore_callback_states_from_checkpoint=False, full_determinism=False, seed=42, data_seed=42, use_cpu=False, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), parallelism_config=None, dataloader_drop_last=False, dataloader_num_workers=6, dataloader_pin_memory=True, dataloader_persistent_workers=True, dataloader_prefetch_factor=4, remove_unused_columns=False, label_names=None, train_sampling_strategy='random', length_column_name='length', ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, ddp_backend=None, ddp_timeout=7200, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, deepspeed={'fp16': {'enabled': 'auto', 'loss_scale': 0, 'loss_scale_window': 1000, 'initial_scale_power': 16, 'hysteresis': 2, 'min_loss_scale': 1}, 'bf16': {'enabled': 'auto'}, 'zero_optimization': {'stage': 2, 'offload_optimizer': {'device': 'none', 'pin_memory': True}, 'allgather_partitions': True, 'allgather_bucket_size': 200000000.0, 'overlap_comm': False, 'reduce_scatter': True, 'reduce_bucket_size': 200000000.0, 'contiguous_gradients': True}, 'gradient_accumulation_steps': 'auto', 'gradient_clipping': 'auto', 'steps_per_print': 2000, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'wall_clock_breakdown': False}, debug=[], skip_memory_metrics=True, do_train=False, do_eval=False, do_predict=False, resume_from_checkpoint=None, warmup_ratio=0.05, logging_dir='/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/video_mllm_swift/4b/s2_v9_1_a800_10pct/v0-20260602-181529/runs', local_rank=0, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, tuner_backend='peft', vit_gradient_checkpointing=True, router_aux_loss_coef=0.0, enable_dft_loss=False, enable_channel_loss=False, safe_serialization=True, max_shard_size='5GB', check_model=True, acc_strategy='token', train_dataloader_shuffle=True, group_by_length=False, max_epochs=None, aligner_lr=None, vit_lr=1e-06, use_logits_to_keep=None, ds3_gather_for_generation=True, resume_only_model=False, optimizer='multimodal', loss_type=None, eval_metric=None, callbacks=[], early_stop_interval=None, eval_use_evalscope=False, eval_dataset=[], eval_dataset_args=None, eval_limit=None, eval_generation_config=None, extra_eval_args=None, tuner_type='full', use_galore=False, galore_target_modules=None, galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)"
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-
}
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ckpts/4b_v9_1/chat_template.jinja
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
{%- if tools %}
|
| 2 |
-
{{- '<|im_start|>system\n' }}
|
| 3 |
-
{%- if messages[0].role == 'system' %}
|
| 4 |
-
{{- messages[0].content + '\n\n' }}
|
| 5 |
-
{%- endif %}
|
| 6 |
-
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 7 |
-
{%- for tool in tools %}
|
| 8 |
-
{{- "\n" }}
|
| 9 |
-
{{- tool | tojson }}
|
| 10 |
-
{%- endfor %}
|
| 11 |
-
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 12 |
-
{%- else %}
|
| 13 |
-
{%- if messages[0].role == 'system' %}
|
| 14 |
-
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 15 |
-
{%- endif %}
|
| 16 |
-
{%- endif %}
|
| 17 |
-
{%- for message in messages %}
|
| 18 |
-
{%- if message.content is string %}
|
| 19 |
-
{%- set content = message.content %}
|
| 20 |
-
{%- else %}
|
| 21 |
-
{%- set content = '' %}
|
| 22 |
-
{%- endif %}
|
| 23 |
-
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 24 |
-
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 25 |
-
{%- elif message.role == "assistant" %}
|
| 26 |
-
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 27 |
-
{%- if message.tool_calls %}
|
| 28 |
-
{%- for tool_call in message.tool_calls %}
|
| 29 |
-
{%- if (loop.first and content) or (not loop.first) %}
|
| 30 |
-
{{- '\n' }}
|
| 31 |
-
{%- endif %}
|
| 32 |
-
{%- if tool_call.function %}
|
| 33 |
-
{%- set tool_call = tool_call.function %}
|
| 34 |
-
{%- endif %}
|
| 35 |
-
{{- '<tool_call>\n{"name": "' }}
|
| 36 |
-
{{- tool_call.name }}
|
| 37 |
-
{{- '", "arguments": ' }}
|
| 38 |
-
{%- if tool_call.arguments is string %}
|
| 39 |
-
{{- tool_call.arguments }}
|
| 40 |
-
{%- else %}
|
| 41 |
-
{{- tool_call.arguments | tojson }}
|
| 42 |
-
{%- endif %}
|
| 43 |
-
{{- '}\n</tool_call>' }}
|
| 44 |
-
{%- endfor %}
|
| 45 |
-
{%- endif %}
|
| 46 |
-
{{- '<|im_end|>\n' }}
|
| 47 |
-
{%- elif message.role == "tool" %}
|
| 48 |
-
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 49 |
-
{{- '<|im_start|>user' }}
|
| 50 |
-
{%- endif %}
|
| 51 |
-
{{- '\n<tool_response>\n' }}
|
| 52 |
-
{{- content }}
|
| 53 |
-
{{- '\n</tool_response>' }}
|
| 54 |
-
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 55 |
-
{{- '<|im_end|>\n' }}
|
| 56 |
-
{%- endif %}
|
| 57 |
-
{%- endif %}
|
| 58 |
-
{%- endfor %}
|
| 59 |
-
{%- if add_generation_prompt %}
|
| 60 |
-
{{- '<|im_start|>assistant\n' }}
|
| 61 |
-
{%- endif %}
|
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|
ckpts/4b_v9_1/chat_template.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + ' '}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ '\n' + content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ '\n' + content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
| 3 |
-
}
|
|
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|
|
ckpts/4b_v9_1/config.json
DELETED
|
@@ -1,262 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"architectures": [
|
| 3 |
-
"LlavaQwen3_5ViTForConditionalGeneration"
|
| 4 |
-
],
|
| 5 |
-
"bos_token_id": null,
|
| 6 |
-
"dtype": "bfloat16",
|
| 7 |
-
"eos_token_id": 151645,
|
| 8 |
-
"hidden_size": 2560,
|
| 9 |
-
"image_grid_pinpoints": [
|
| 10 |
-
[
|
| 11 |
-
384,
|
| 12 |
-
384
|
| 13 |
-
],
|
| 14 |
-
[
|
| 15 |
-
384,
|
| 16 |
-
768
|
| 17 |
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ckpts/4b_v9_1/modeling_qwen3_5vit_qwen3.py
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"""
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modeling_qwen3_5vit_qwen3.py — Qwen3.5 Vision as SigLIP-compat vision_tower in LlavaOnevision.
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设计：与 `modeling_qwen3vlvit_qwen3.py` 严格同构，仅换 vision backbone 源：
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- Qwen3VLVisionModel → Qwen3_5VisionModel（继承关系：Qwen3_5VisionModel(Qwen3VLVisionModel) 去 DeepStack）
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- Qwen3VLVisionConfig → Qwen3_5VisionConfig（父类用 AttributeError 哨兵屏蔽 deepstack_visual_indexes）
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其余（Adapter 契约翻译、MLP projector + pre_norm、LlavaOnevision 继承 wire class）与
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Qwen3-VL ViT pipeline 完全一致。两条 pipeline 并存意义：DeepStack ablation 天然实验组。
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类层级：
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Qwen3_5ViTBackbone(Qwen3_5VisionModel) — 去 merger，保持 NaViT 契约
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Qwen3_5ViTAsSiglipAdapter(nn.Module) — 持有 Backbone，做 SigLIP ↔ NaViT 契约翻译
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三方对比公平性：定 384×384 AnyRes tile + 同款 projector 骨架 + 同款 Qwen3-1.7B LLM。
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"""
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import math
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import os
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import sys
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoModelForCausalLM,
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LlavaOnevisionConfig,
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LlavaOnevisionForConditionalGeneration,
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LlavaOnevisionModel,
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LlavaOnevisionPreTrainedModel,
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Qwen3Config,
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)
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
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from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5VisionConfig
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from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5VisionModel
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# Shared layout-permutation utility lives in declip_qwenvit (single source of
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# truth — same code path runs in declip-training-side qk_cosine reorder).
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# Add VisionEncoder repo root to sys.path so this modeling file is importable
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# even when the package isn't pip-installed (ms-swift integration loads it
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# via dynamic plugin path).
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_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
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if _REPO_ROOT not in sys.path:
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sys.path.append(_REPO_ROOT)
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from declip_qwenvit.model.qwen3vit_qk import block_merge_to_row_major_permutation # noqa: E402
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class LlavaQwen3_5ViTConfig(LlavaOnevisionConfig):
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"""LlavaOnevisionConfig 子类，vision_config 类型换成 Qwen3_5VisionConfig。
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与 Qwen3-VL ViT 版的差异：
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- sub_configs.vision_config 用 Qwen3_5VisionConfig
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- 不再设置 deepstack_visual_indexes（Qwen3_5VisionConfig 用 AttributeError 哨兵屏蔽此字段）
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其余同 LlavaQwen3VLViTConfig（tile_size 默认 384；vision_feature_select_strategy='full'
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必须 override，Qwen3.5 ViT 无 CLS token）。
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"""
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model_type = "llava_qwen3_5vit_qwen3"
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sub_configs = {"vision_config": Qwen3_5VisionConfig, "text_config": Qwen3Config}
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def __init__(
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self,
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vision_config=None,
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text_config=None,
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tile_size: int = 384,
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**kwargs,
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):
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if isinstance(vision_config, dict):
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vision_config = Qwen3_5VisionConfig(**vision_config)
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elif vision_config is None:
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vision_config = Qwen3_5VisionConfig()
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# WHY 无 `vision_config.deepstack_visual_indexes = []`（对比 Qwen3-VL ViT 版）：
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# Qwen3_5VisionConfig 父类用 AttributeError() 哨兵显式屏蔽此字段，设置会报 AttributeError
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# LlavaOnevision.pack_image_features 用这个作为 tile 像素大小（不是 patch_size）
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vision_config.image_size = tile_size
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if isinstance(text_config, dict):
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text_config = Qwen3Config(**text_config)
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elif text_config is None:
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text_config = Qwen3Config()
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# 父类默认 select_strategy='default' 会跳首 token (CLS) — Qwen3.5 ViT 无 CLS 必须用 'full'
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kwargs.setdefault("vision_feature_select_strategy", "full")
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super().__init__(vision_config=vision_config, text_config=text_config, **kwargs)
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class Qwen3_5ViTBackbone(Qwen3_5VisionModel):
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"""Qwen3.5 Vision 去除原生 patch_merger 的 backbone 版本（V6 final_layernorm fix (2026-05-16): append final_layernorm）。
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构造时把 merger.norm 的预训练权重抠到 final_layernorm，然后 `del self.merger`
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释放 ~37M 参数（保留 norm 的 LN 焊到末端做 post_layernorm 角色，丢弃 spatial
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shuffle + linear_fc1/fc2，那对应 LlavaOV projector 的职责）。
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架构对称（V6 final_layernorm fix (2026-05-16) 修复）：
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SigLIP2: encoder → post_layernorm → last_hidden_state → LlavaOV MLP → LLM
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V6 final_layernorm fix (2026-05-16): encoder → final_layernorm → last_hidden_state → LlavaOV MLP → LLM
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forward 跑完 transformer blocks 后过 final_layernorm，再返回。下游 LlavaOnevision
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pack_image_features 的 AnyRes 2×2 pool 接管原 merger 的空间合并职责。
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输入输出契约与父类 Qwen3_5VisionModel 一致（NaViT flat）：
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forward(hidden_states=[L, patch_dim], grid_thw=[N, 3])
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→ BaseModelOutput(last_hidden_state=[L, hidden_size])
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forward 主体 1:1 对照 `Qwen3_5VisionModel.forward`（已无 deepstack loop，比
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Qwen3VLVisionModel.forward 更短），仅跳过末尾 `self.merger(x)`，改为 final_layernorm。
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"""
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def __init__(self, config):
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super().__init__(config)
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# V6 final_layernorm fix (2026-05-16): extract merger.norm pretrained weights into final_layernorm.
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# Default init: even if ckpt 阶段没 inject final_layernorm.* (e.g. stock
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# bootstrap path that filters merger.*), final_layernorm 仍持有 merger.norm
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# 的预训练值, 不是 random — 这是防止 silent corruption 的兜底.
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ln_w = self.merger.norm.weight.detach().clone()
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ln_b = self.merger.norm.bias.detach().clone()
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del self.merger
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self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=1e-6)
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self.final_layernorm.weight.data.copy_(ln_w)
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self.final_layernorm.bias.data.copy_(ln_b)
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def forward(self, hidden_states, grid_thw, **kwargs):
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hidden_states = self.patch_embed(hidden_states)
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pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
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hidden_states = hidden_states + pos_embeds
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rotary_pos_emb = self.rot_pos_emb(grid_thw)
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seq_len, _ = hidden_states.size()
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rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
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emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
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position_embeddings = (emb.cos(), emb.sin())
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cu_seqlens = torch.repeat_interleave(
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grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
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).cumsum(dim=0, dtype=torch.int32)
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cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
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for blk in self.blocks:
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hidden_states = blk(
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hidden_states,
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cu_seqlens=cu_seqlens,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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# V6 final_layernorm fix (2026-05-16): appended final LayerNorm — mirrors SigLIP2's post_layernorm.
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# Per-token affine; layout-invariant (reorder happens in adapter).
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hidden_states = self.final_layernorm(hidden_states)
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return BaseModelOutput(last_hidden_state=hidden_states)
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class Qwen3_5ViTAsSiglipAdapter(nn.Module):
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"""SigLIP 契约 → NaViT 契约的翻译层。持有 Qwen3_5ViTBackbone。
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对外暴露 SigLIP 式 forward(pixel_values=[N,3,H,W]) → BaseModelOutputWithPooling，
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供 LlavaOnevision 消费；对内按官方 _preprocess 的 reshape 链把 pixel_values
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转成 NaViT flat + grid_thw 喂给 Backbone。
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reshape 链 1:1 照抄 transformers 官方 Qwen2VLImageProcessorFast._preprocess
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(video_processing_qwen3_vl.py L227-252) —— Qwen3.5 无独立 image_processor，复用 Qwen3-VL 格式。
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"""
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def __init__(self, vision_config: Qwen3_5VisionConfig):
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super().__init__()
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self.vision = Qwen3_5ViTBackbone(vision_config)
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self.config = vision_config
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@property
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def dtype(self):
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return next(self.parameters()).dtype
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@property
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def device(self):
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return next(self.parameters()).device
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def _flatten_navit(self, pixel_values: torch.Tensor):
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"""[N, 3, H, W] → (flat=[N*L, patch_dim], grid_thw=[N, 3], shape=(N, L)).
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L = grid_t * grid_h * grid_w = 1 * (H/16) * (W/16)
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patch_dim = C * temporal_patch_size * patch_size^2 = 3 * 2 * 16 * 16 = 1536
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"""
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pixel_values = pixel_values.to(dtype=self.dtype)
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tps = self.config.temporal_patch_size
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ps = self.config.patch_size
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ms = self.config.spatial_merge_size
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patches = pixel_values.unsqueeze(1)
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# 对单帧图像 T=1, pad=1 → expand 一帧使 T 整除 temporal_patch_size，
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# Conv3d 在复制帧上退化为等效 2D Conv（数学无损）
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T = patches.shape[1]
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pad = -T % tps
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if pad:
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repeats = patches[:, -1:].expand(-1, pad, -1, -1, -1)
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patches = torch.cat((patches, repeats), dim=1)
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batch_size, t, channel, H, W = patches.shape
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grid_t = t // tps
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grid_h = H // ps
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grid_w = W // ps
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patches = patches.view(
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batch_size, grid_t, tps, channel,
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grid_h // ms, ms, ps,
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grid_w // ms, ms, ps,
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)
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patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
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flatten_patches = patches.reshape(
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batch_size,
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grid_t * grid_h * grid_w,
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channel * tps * ps * ps,
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)
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seq_len = grid_t * grid_h * grid_w
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flat = flatten_patches.reshape(batch_size * seq_len, -1)
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# on-device 构造小 tensor 再 expand，host→GPU 同步量 O(3) 而非 O(N*3)
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grid_unit = torch.tensor(
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[grid_t, grid_h, grid_w], dtype=torch.int32, device=pixel_values.device,
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)
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grid_thw = grid_unit.unsqueeze(0).expand(batch_size, -1).contiguous()
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return flat, grid_thw, (batch_size, seq_len)
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def forward(
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self,
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pixel_values: torch.Tensor,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs,
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) -> BaseModelOutputWithPooling:
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flat, grid_thw, (N, S) = self._flatten_navit(pixel_values)
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vision_out = self.vision(flat, grid_thw=grid_thw)
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hidden = vision_out.last_hidden_state.view(N, S, -1)
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# Block-merge → row-major reorder before handing to LlavaOnevision.
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# Internally the ViT runs in Qwen NaViT block-merge layout (pretrained
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# pos_embed + RoPE contract); downstream LlavaOV `pack_image_features`
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# (multi-tile AnyRes path, view(num_patch_h, num_patch_w, h, w, -1))
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# and `apply_pooling` (video path, view(B, h, w, -1) + bilinear) BOTH
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# assume row-major. Without this reorder, the multi-tile/video spatial
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# pool pulls together tokens that are NOT spatially adjacent — silent
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# corruption that doesn't fire on S1 single-tile path (line 348-351 of
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# modeling_llava_onevision.py just flattens [N,D] verbatim) but kills
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# S2 / eval quality.
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grid_h = int(grid_thw[0, 1].item())
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grid_w = int(grid_thw[0, 2].item())
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ms = getattr(self.config, "spatial_merge_size", 2)
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layout_perm = block_merge_to_row_major_permutation(
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grid_h, grid_w, ms=ms, device=hidden.device,
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)
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hidden = hidden[:, layout_perm, :]
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return BaseModelOutputWithPooling(
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last_hidden_state=hidden,
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# LlavaOnevision 索引 hidden_states[vision_feature_layer=-1]；tuple 长度 1 足够
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hidden_states=(hidden,),
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pooler_output=None,
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)
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class LlavaQwen3_5ViTMultiModalProjector(nn.Module):
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"""标准 LlavaOnevision projector（V6 final_layernorm fix (2026-05-16): pre_norm → Identity）。
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V6 final_layernorm fix (2026-05-16) 修复后, encoder 末端已自带 final_layernorm（与 SigLIP2 post_layernorm 对称），
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projector 不再需要补 LN — pre_norm 改为 nn.Identity，对齐 SigLIP2 plugin 的
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LlavaOnevision stock projector 结构（裸 linear_1 → GELU → linear_2），
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保证 SigLIP2 / Qwen3.5 / Qwen3-VL 三个 backbone 在 LlavaOV 设定下公平对比。
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（历史：V6.0.0~V6.0.4 时期 encoder 无 final LN，projector pre_norm 是补丁；
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现在补丁回到 encoder 内部，projector 回归 stock 形态。）
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"""
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def __init__(self, config: LlavaQwen3_5ViTConfig):
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super().__init__()
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num_feature_layers = (
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1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
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)
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vision_dim = config.vision_config.hidden_size * num_feature_layers
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text_dim = config.text_config.hidden_size
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bias = getattr(config, "multimodal_projector_bias", True)
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# V6 final_layernorm fix (2026-05-16): pre_norm = Identity (encoder 已自带 final_layernorm).
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self.pre_norm = nn.Identity()
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self.linear_1 = nn.Linear(vision_dim, text_dim, bias=bias)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(text_dim, text_dim, bias=bias)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.linear_2(self.act(self.linear_1(self.pre_norm(x))))
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class LlavaQwen3_5ViTModel(LlavaOnevisionModel):
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"""继承 LlavaOnevisionModel 但绕过其 __init__ 手动装配。
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父类 __init__ 调 `AutoModel.from_config(config.vision_config)` 会对 Qwen3_5VisionConfig
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抛 "Unrecognized configuration"（Qwen3.5 vision 没注册到 AutoModel）。手动装配避开
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| 302 |
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这一步，同时省掉"先构造 Qwen3_5VisionModel 再被替换"的双重开销（~1.3GB init-peak）。
|
| 303 |
-
|
| 304 |
-
装配顺序与父类一致：vision_tower / projector / image_newline / language_model / post_init。
|
| 305 |
-
"""
|
| 306 |
-
|
| 307 |
-
config_class = LlavaQwen3_5ViTConfig
|
| 308 |
-
|
| 309 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 310 |
-
# 跳过 LlavaOnevisionModel.__init__（AutoModel 不识别 Qwen3_5VisionConfig）
|
| 311 |
-
LlavaOnevisionPreTrainedModel.__init__(self, config)
|
| 312 |
-
self.vision_tower = Qwen3_5ViTAsSiglipAdapter(config.vision_config)
|
| 313 |
-
self.multi_modal_projector = LlavaQwen3_5ViTMultiModalProjector(config)
|
| 314 |
-
embed_std = 1 / math.sqrt(config.text_config.hidden_size)
|
| 315 |
-
self.image_newline = nn.Parameter(
|
| 316 |
-
torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std
|
| 317 |
-
)
|
| 318 |
-
self.vocab_size = config.text_config.vocab_size
|
| 319 |
-
self.language_model = AutoModel.from_config(config.text_config)
|
| 320 |
-
self.post_init()
|
| 321 |
-
|
| 322 |
-
|
| 323 |
-
class LlavaQwen3_5ViTForConditionalGeneration(LlavaOnevisionForConditionalGeneration):
|
| 324 |
-
"""继承 LlavaOnevisionForConditionalGeneration，只换 self.model。
|
| 325 |
-
|
| 326 |
-
同样跳过父类 __init__（避免重复构造 LlavaOnevisionModel，根因见 LlavaQwen3_5ViTModel）。
|
| 327 |
-
"""
|
| 328 |
-
|
| 329 |
-
config_class = LlavaQwen3_5ViTConfig
|
| 330 |
-
|
| 331 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 332 |
-
LlavaOnevisionPreTrainedModel.__init__(self, config)
|
| 333 |
-
self.model = LlavaQwen3_5ViTModel(config)
|
| 334 |
-
self.lm_head = nn.Linear(
|
| 335 |
-
config.text_config.hidden_size, config.text_config.vocab_size, bias=False
|
| 336 |
-
)
|
| 337 |
-
self.post_init()
|
| 338 |
-
|
| 339 |
-
|
| 340 |
-
AutoConfig.register(LlavaQwen3_5ViTConfig.model_type, LlavaQwen3_5ViTConfig)
|
| 341 |
-
AutoModelForCausalLM.register(LlavaQwen3_5ViTConfig, LlavaQwen3_5ViTForConditionalGeneration)
|
| 342 |
-
|
| 343 |
-
|
| 344 |
-
__all__ = [
|
| 345 |
-
"LlavaQwen3_5ViTConfig",
|
| 346 |
-
"Qwen3_5ViTBackbone",
|
| 347 |
-
"Qwen3_5ViTAsSiglipAdapter",
|
| 348 |
-
"LlavaQwen3_5ViTMultiModalProjector",
|
| 349 |
-
"LlavaQwen3_5ViTModel",
|
| 350 |
-
"LlavaQwen3_5ViTForConditionalGeneration",
|
| 351 |
-
]
|
|
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|
ckpts/4b_v9_1/processor_config.json
DELETED
|
@@ -1,203 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"image_processor": {
|
| 3 |
-
"do_convert_rgb": true,
|
| 4 |
-
"do_normalize": true,
|
| 5 |
-
"do_pad": true,
|
| 6 |
-
"do_rescale": true,
|
| 7 |
-
"do_resize": true,
|
| 8 |
-
"image_grid_pinpoints": [
|
| 9 |
-
[
|
| 10 |
-
384,
|
| 11 |
-
384
|
| 12 |
-
],
|
| 13 |
-
[
|
| 14 |
-
384,
|
| 15 |
-
768
|
| 16 |
-
],
|
| 17 |
-
[
|
| 18 |
-
384,
|
| 19 |
-
1152
|
| 20 |
-
],
|
| 21 |
-
[
|
| 22 |
-
384,
|
| 23 |
-
1536
|
| 24 |
-
],
|
| 25 |
-
[
|
| 26 |
-
384,
|
| 27 |
-
1920
|
| 28 |
-
],
|
| 29 |
-
[
|
| 30 |
-
384,
|
| 31 |
-
2304
|
| 32 |
-
],
|
| 33 |
-
[
|
| 34 |
-
768,
|
| 35 |
-
384
|
| 36 |
-
],
|
| 37 |
-
[
|
| 38 |
-
768,
|
| 39 |
-
768
|
| 40 |
-
],
|
| 41 |
-
[
|
| 42 |
-
768,
|
| 43 |
-
1152
|
| 44 |
-
],
|
| 45 |
-
[
|
| 46 |
-
768,
|
| 47 |
-
1536
|
| 48 |
-
],
|
| 49 |
-
[
|
| 50 |
-
768,
|
| 51 |
-
1920
|
| 52 |
-
],
|
| 53 |
-
[
|
| 54 |
-
768,
|
| 55 |
-
2304
|
| 56 |
-
],
|
| 57 |
-
[
|
| 58 |
-
1152,
|
| 59 |
-
384
|
| 60 |
-
],
|
| 61 |
-
[
|
| 62 |
-
1152,
|
| 63 |
-
768
|
| 64 |
-
],
|
| 65 |
-
[
|
| 66 |
-
1152,
|
| 67 |
-
1152
|
| 68 |
-
],
|
| 69 |
-
[
|
| 70 |
-
1152,
|
| 71 |
-
1536
|
| 72 |
-
],
|
| 73 |
-
[
|
| 74 |
-
1152,
|
| 75 |
-
1920
|
| 76 |
-
],
|
| 77 |
-
[
|
| 78 |
-
1152,
|
| 79 |
-
2304
|
| 80 |
-
],
|
| 81 |
-
[
|
| 82 |
-
1536,
|
| 83 |
-
384
|
| 84 |
-
],
|
| 85 |
-
[
|
| 86 |
-
1536,
|
| 87 |
-
768
|
| 88 |
-
],
|
| 89 |
-
[
|
| 90 |
-
1536,
|
| 91 |
-
1152
|
| 92 |
-
],
|
| 93 |
-
[
|
| 94 |
-
1536,
|
| 95 |
-
1536
|
| 96 |
-
],
|
| 97 |
-
[
|
| 98 |
-
1536,
|
| 99 |
-
1920
|
| 100 |
-
],
|
| 101 |
-
[
|
| 102 |
-
1536,
|
| 103 |
-
2304
|
| 104 |
-
],
|
| 105 |
-
[
|
| 106 |
-
1920,
|
| 107 |
-
384
|
| 108 |
-
],
|
| 109 |
-
[
|
| 110 |
-
1920,
|
| 111 |
-
768
|
| 112 |
-
],
|
| 113 |
-
[
|
| 114 |
-
1920,
|
| 115 |
-
1152
|
| 116 |
-
],
|
| 117 |
-
[
|
| 118 |
-
1920,
|
| 119 |
-
1536
|
| 120 |
-
],
|
| 121 |
-
[
|
| 122 |
-
1920,
|
| 123 |
-
1920
|
| 124 |
-
],
|
| 125 |
-
[
|
| 126 |
-
1920,
|
| 127 |
-
2304
|
| 128 |
-
],
|
| 129 |
-
[
|
| 130 |
-
2304,
|
| 131 |
-
384
|
| 132 |
-
],
|
| 133 |
-
[
|
| 134 |
-
2304,
|
| 135 |
-
768
|
| 136 |
-
],
|
| 137 |
-
[
|
| 138 |
-
2304,
|
| 139 |
-
1152
|
| 140 |
-
],
|
| 141 |
-
[
|
| 142 |
-
2304,
|
| 143 |
-
1536
|
| 144 |
-
],
|
| 145 |
-
[
|
| 146 |
-
2304,
|
| 147 |
-
1920
|
| 148 |
-
],
|
| 149 |
-
[
|
| 150 |
-
2304,
|
| 151 |
-
2304
|
| 152 |
-
]
|
| 153 |
-
],
|
| 154 |
-
"image_mean": [
|
| 155 |
-
0.5,
|
| 156 |
-
0.5,
|
| 157 |
-
0.5
|
| 158 |
-
],
|
| 159 |
-
"image_processor_type": "LlavaOnevisionImageProcessor",
|
| 160 |
-
"image_std": [
|
| 161 |
-
0.5,
|
| 162 |
-
0.5,
|
| 163 |
-
0.5
|
| 164 |
-
],
|
| 165 |
-
"resample": 3,
|
| 166 |
-
"rescale_factor": 0.00392156862745098,
|
| 167 |
-
"size": {
|
| 168 |
-
"height": 384,
|
| 169 |
-
"width": 384
|
| 170 |
-
}
|
| 171 |
-
},
|
| 172 |
-
"image_token": "<image>",
|
| 173 |
-
"num_image_tokens": 576,
|
| 174 |
-
"processor_class": "LlavaOnevisionProcessor",
|
| 175 |
-
"video_processor": {
|
| 176 |
-
"do_convert_rgb": true,
|
| 177 |
-
"do_normalize": true,
|
| 178 |
-
"do_rescale": true,
|
| 179 |
-
"do_resize": true,
|
| 180 |
-
"do_sample_frames": false,
|
| 181 |
-
"image_mean": [
|
| 182 |
-
0.5,
|
| 183 |
-
0.5,
|
| 184 |
-
0.5
|
| 185 |
-
],
|
| 186 |
-
"image_std": [
|
| 187 |
-
0.5,
|
| 188 |
-
0.5,
|
| 189 |
-
0.5
|
| 190 |
-
],
|
| 191 |
-
"resample": 3,
|
| 192 |
-
"rescale_factor": 0.00392156862745098,
|
| 193 |
-
"return_metadata": false,
|
| 194 |
-
"size": {
|
| 195 |
-
"height": 384,
|
| 196 |
-
"width": 384
|
| 197 |
-
},
|
| 198 |
-
"video_processor_type": "LlavaOnevisionVideoProcessor"
|
| 199 |
-
},
|
| 200 |
-
"video_token": "<video>",
|
| 201 |
-
"vision_aspect_ratio": "anyres_max_9",
|
| 202 |
-
"vision_feature_select_strategy": null
|
| 203 |
-
}
|
|
|
|
|
|
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|
ckpts/4b_v9_1/tokenizer.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:9024318c850eaadf26be79389d21b07a7afd8f1af749b89f9109b06c0d12173c
|
| 3 |
-
size 11423018
|
|
|
|
|
|
|
|
|
|
|
|
ckpts/4b_v9_1/tokenizer_config.json
DELETED
|
@@ -1,19 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"add_prefix_space": false,
|
| 3 |
-
"backend": "tokenizers",
|
| 4 |
-
"bos_token": null,
|
| 5 |
-
"clean_up_tokenization_spaces": false,
|
| 6 |
-
"eos_token": "<|im_end|>",
|
| 7 |
-
"errors": "replace",
|
| 8 |
-
"extra_special_tokens": [
|
| 9 |
-
"<image>",
|
| 10 |
-
"<video>"
|
| 11 |
-
],
|
| 12 |
-
"is_local": true,
|
| 13 |
-
"model_max_length": 1010000,
|
| 14 |
-
"pad_token": "<|endoftext|>",
|
| 15 |
-
"processor_class": "LlavaOnevisionProcessor",
|
| 16 |
-
"split_special_tokens": false,
|
| 17 |
-
"tokenizer_class": "Qwen2Tokenizer",
|
| 18 |
-
"unk_token": null
|
| 19 |
-
}
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ckpts/4b_v9_1/zero_to_fp32.py
DELETED
|
@@ -1,760 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
|
| 3 |
-
# Copyright (c) Microsoft Corporation.
|
| 4 |
-
# SPDX-License-Identifier: Apache-2.0
|
| 5 |
-
|
| 6 |
-
# DeepSpeed Team
|
| 7 |
-
|
| 8 |
-
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
| 9 |
-
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
| 10 |
-
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
-
# application.
|
| 12 |
-
#
|
| 13 |
-
# example:
|
| 14 |
-
# python zero_to_fp32.py . output_dir/
|
| 15 |
-
# or
|
| 16 |
-
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
| 17 |
-
|
| 18 |
-
import argparse
|
| 19 |
-
import torch
|
| 20 |
-
import glob
|
| 21 |
-
import math
|
| 22 |
-
import os
|
| 23 |
-
import re
|
| 24 |
-
import gc
|
| 25 |
-
import json
|
| 26 |
-
import numpy as np
|
| 27 |
-
from tqdm import tqdm
|
| 28 |
-
from collections import OrderedDict
|
| 29 |
-
from dataclasses import dataclass
|
| 30 |
-
|
| 31 |
-
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
| 32 |
-
# DeepSpeed data structures it has to be available in the current python environment.
|
| 33 |
-
from deepspeed.utils import logger
|
| 34 |
-
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
| 35 |
-
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
| 36 |
-
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
@dataclass
|
| 40 |
-
class zero_model_state:
|
| 41 |
-
buffers: dict()
|
| 42 |
-
param_shapes: dict()
|
| 43 |
-
shared_params: list
|
| 44 |
-
ds_version: int
|
| 45 |
-
frozen_param_shapes: dict()
|
| 46 |
-
frozen_param_fragments: dict()
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
debug = 0
|
| 50 |
-
|
| 51 |
-
# load to cpu
|
| 52 |
-
device = torch.device('cpu')
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def atoi(text):
|
| 56 |
-
return int(text) if text.isdigit() else text
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
def natural_keys(text):
|
| 60 |
-
'''
|
| 61 |
-
alist.sort(key=natural_keys) sorts in human order
|
| 62 |
-
http://nedbatchelder.com/blog/200712/human_sorting.html
|
| 63 |
-
(See Toothy's implementation in the comments)
|
| 64 |
-
'''
|
| 65 |
-
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def get_model_state_file(checkpoint_dir, zero_stage):
|
| 69 |
-
if not os.path.isdir(checkpoint_dir):
|
| 70 |
-
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
| 71 |
-
|
| 72 |
-
# there should be only one file
|
| 73 |
-
if zero_stage <= 2:
|
| 74 |
-
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
| 75 |
-
elif zero_stage == 3:
|
| 76 |
-
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
| 77 |
-
|
| 78 |
-
if not os.path.exists(file):
|
| 79 |
-
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
| 80 |
-
|
| 81 |
-
return file
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
| 85 |
-
# XXX: need to test that this simple glob rule works for multi-node setup too
|
| 86 |
-
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
| 87 |
-
|
| 88 |
-
if len(ckpt_files) == 0:
|
| 89 |
-
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
| 90 |
-
|
| 91 |
-
return ckpt_files
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def get_optim_files(checkpoint_dir):
|
| 95 |
-
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def get_model_state_files(checkpoint_dir):
|
| 99 |
-
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def parse_model_states(files):
|
| 103 |
-
zero_model_states = []
|
| 104 |
-
for file in files:
|
| 105 |
-
state_dict = torch.load(file, map_location=device, weights_only=False)
|
| 106 |
-
|
| 107 |
-
if BUFFER_NAMES not in state_dict:
|
| 108 |
-
raise ValueError(f"{file} is not a model state checkpoint")
|
| 109 |
-
buffer_names = state_dict[BUFFER_NAMES]
|
| 110 |
-
if debug:
|
| 111 |
-
print("Found buffers:", buffer_names)
|
| 112 |
-
|
| 113 |
-
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
| 114 |
-
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
| 115 |
-
param_shapes = state_dict[PARAM_SHAPES]
|
| 116 |
-
|
| 117 |
-
# collect parameters that are included in param_shapes
|
| 118 |
-
param_names = []
|
| 119 |
-
for s in param_shapes:
|
| 120 |
-
for name in s.keys():
|
| 121 |
-
param_names.append(name)
|
| 122 |
-
|
| 123 |
-
# update with frozen parameters
|
| 124 |
-
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
| 125 |
-
if frozen_param_shapes is not None:
|
| 126 |
-
if debug:
|
| 127 |
-
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
| 128 |
-
param_names += list(frozen_param_shapes.keys())
|
| 129 |
-
|
| 130 |
-
# handle shared params
|
| 131 |
-
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
| 132 |
-
|
| 133 |
-
ds_version = state_dict.get(DS_VERSION, None)
|
| 134 |
-
|
| 135 |
-
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
| 136 |
-
|
| 137 |
-
z_model_state = zero_model_state(buffers=buffers,
|
| 138 |
-
param_shapes=param_shapes,
|
| 139 |
-
shared_params=shared_params,
|
| 140 |
-
ds_version=ds_version,
|
| 141 |
-
frozen_param_shapes=frozen_param_shapes,
|
| 142 |
-
frozen_param_fragments=frozen_param_fragments)
|
| 143 |
-
zero_model_states.append(z_model_state)
|
| 144 |
-
|
| 145 |
-
return zero_model_states
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
def parse_optim_states(files, ds_checkpoint_dir):
|
| 149 |
-
total_files = len(files)
|
| 150 |
-
state_dicts = []
|
| 151 |
-
for f in tqdm(files, desc='Loading checkpoint shards'):
|
| 152 |
-
state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
|
| 153 |
-
# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
|
| 154 |
-
# and also handle the case where it was already removed by another helper script
|
| 155 |
-
state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
|
| 156 |
-
state_dicts.append(state_dict)
|
| 157 |
-
|
| 158 |
-
if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
| 159 |
-
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
| 160 |
-
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
| 161 |
-
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
| 162 |
-
|
| 163 |
-
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
| 164 |
-
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
| 165 |
-
# use the max of the partition_count to get the dp world_size.
|
| 166 |
-
|
| 167 |
-
if type(world_size) is list:
|
| 168 |
-
world_size = max(world_size)
|
| 169 |
-
|
| 170 |
-
if world_size != total_files:
|
| 171 |
-
raise ValueError(
|
| 172 |
-
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
| 173 |
-
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
| 174 |
-
)
|
| 175 |
-
|
| 176 |
-
# the groups are named differently in each stage
|
| 177 |
-
if zero_stage <= 2:
|
| 178 |
-
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
| 179 |
-
elif zero_stage == 3:
|
| 180 |
-
fp32_groups_key = FP32_FLAT_GROUPS
|
| 181 |
-
else:
|
| 182 |
-
raise ValueError(f"unknown zero stage {zero_stage}")
|
| 183 |
-
|
| 184 |
-
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
| 185 |
-
return zero_stage, world_size, fp32_flat_groups
|
| 186 |
-
|
| 187 |
-
|
| 188 |
-
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
|
| 189 |
-
"""
|
| 190 |
-
Returns fp32 state_dict reconstructed from ds checkpoint
|
| 191 |
-
|
| 192 |
-
Args:
|
| 193 |
-
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
| 194 |
-
|
| 195 |
-
"""
|
| 196 |
-
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
| 197 |
-
|
| 198 |
-
optim_files = get_optim_files(ds_checkpoint_dir)
|
| 199 |
-
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
| 200 |
-
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
| 201 |
-
|
| 202 |
-
model_files = get_model_state_files(ds_checkpoint_dir)
|
| 203 |
-
|
| 204 |
-
zero_model_states = parse_model_states(model_files)
|
| 205 |
-
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
| 206 |
-
|
| 207 |
-
if zero_stage <= 2:
|
| 208 |
-
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 209 |
-
exclude_frozen_parameters)
|
| 210 |
-
elif zero_stage == 3:
|
| 211 |
-
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 212 |
-
exclude_frozen_parameters)
|
| 213 |
-
|
| 214 |
-
|
| 215 |
-
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
| 216 |
-
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 217 |
-
return
|
| 218 |
-
|
| 219 |
-
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 220 |
-
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
| 221 |
-
|
| 222 |
-
if debug:
|
| 223 |
-
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
| 224 |
-
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 225 |
-
|
| 226 |
-
wanted_params = len(frozen_param_shapes)
|
| 227 |
-
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 228 |
-
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
| 229 |
-
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 230 |
-
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 231 |
-
|
| 232 |
-
total_params = 0
|
| 233 |
-
total_numel = 0
|
| 234 |
-
for name, shape in frozen_param_shapes.items():
|
| 235 |
-
total_params += 1
|
| 236 |
-
unpartitioned_numel = shape.numel()
|
| 237 |
-
total_numel += unpartitioned_numel
|
| 238 |
-
|
| 239 |
-
state_dict[name] = frozen_param_fragments[name]
|
| 240 |
-
|
| 241 |
-
if debug:
|
| 242 |
-
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 243 |
-
|
| 244 |
-
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 245 |
-
|
| 246 |
-
|
| 247 |
-
def _has_callable(obj, fn):
|
| 248 |
-
attr = getattr(obj, fn, None)
|
| 249 |
-
return callable(attr)
|
| 250 |
-
|
| 251 |
-
|
| 252 |
-
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 253 |
-
param_shapes = zero_model_states[0].param_shapes
|
| 254 |
-
|
| 255 |
-
# Reconstruction protocol:
|
| 256 |
-
#
|
| 257 |
-
# XXX: document this
|
| 258 |
-
|
| 259 |
-
if debug:
|
| 260 |
-
for i in range(world_size):
|
| 261 |
-
for j in range(len(fp32_flat_groups[0])):
|
| 262 |
-
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
| 263 |
-
|
| 264 |
-
# XXX: memory usage doubles here (zero2)
|
| 265 |
-
num_param_groups = len(fp32_flat_groups[0])
|
| 266 |
-
merged_single_partition_of_fp32_groups = []
|
| 267 |
-
for i in range(num_param_groups):
|
| 268 |
-
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
| 269 |
-
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
| 270 |
-
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
| 271 |
-
avail_numel = sum(
|
| 272 |
-
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
| 273 |
-
|
| 274 |
-
if debug:
|
| 275 |
-
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
| 276 |
-
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
| 277 |
-
# not asserting if there is a mismatch due to possible padding
|
| 278 |
-
print(f"Have {avail_numel} numels to process.")
|
| 279 |
-
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
| 280 |
-
|
| 281 |
-
# params
|
| 282 |
-
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 283 |
-
# out-of-core computing solution
|
| 284 |
-
total_numel = 0
|
| 285 |
-
total_params = 0
|
| 286 |
-
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
| 287 |
-
offset = 0
|
| 288 |
-
avail_numel = full_single_fp32_vector.numel()
|
| 289 |
-
for name, shape in shapes.items():
|
| 290 |
-
|
| 291 |
-
unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
|
| 292 |
-
total_numel += unpartitioned_numel
|
| 293 |
-
total_params += 1
|
| 294 |
-
|
| 295 |
-
if debug:
|
| 296 |
-
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 297 |
-
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
| 298 |
-
offset += unpartitioned_numel
|
| 299 |
-
|
| 300 |
-
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
| 301 |
-
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
| 302 |
-
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
| 303 |
-
# live optimizer object, so we are checking that the numbers are within the right range
|
| 304 |
-
align_to = 2 * world_size
|
| 305 |
-
|
| 306 |
-
def zero2_align(x):
|
| 307 |
-
return align_to * math.ceil(x / align_to)
|
| 308 |
-
|
| 309 |
-
if debug:
|
| 310 |
-
print(f"original offset={offset}, avail_numel={avail_numel}")
|
| 311 |
-
|
| 312 |
-
offset = zero2_align(offset)
|
| 313 |
-
avail_numel = zero2_align(avail_numel)
|
| 314 |
-
|
| 315 |
-
if debug:
|
| 316 |
-
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
| 317 |
-
|
| 318 |
-
# Sanity check
|
| 319 |
-
if offset != avail_numel:
|
| 320 |
-
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 321 |
-
|
| 322 |
-
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
| 323 |
-
|
| 324 |
-
|
| 325 |
-
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 326 |
-
exclude_frozen_parameters):
|
| 327 |
-
state_dict = OrderedDict()
|
| 328 |
-
|
| 329 |
-
# buffers
|
| 330 |
-
buffers = zero_model_states[0].buffers
|
| 331 |
-
state_dict.update(buffers)
|
| 332 |
-
if debug:
|
| 333 |
-
print(f"added {len(buffers)} buffers")
|
| 334 |
-
|
| 335 |
-
if not exclude_frozen_parameters:
|
| 336 |
-
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
| 337 |
-
|
| 338 |
-
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 339 |
-
|
| 340 |
-
# recover shared parameters
|
| 341 |
-
for pair in zero_model_states[0].shared_params:
|
| 342 |
-
if pair[1] in state_dict:
|
| 343 |
-
state_dict[pair[0]] = state_dict[pair[1]]
|
| 344 |
-
|
| 345 |
-
return state_dict
|
| 346 |
-
|
| 347 |
-
|
| 348 |
-
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
| 349 |
-
remainder = unpartitioned_numel % world_size
|
| 350 |
-
padding_numel = (world_size - remainder) if remainder else 0
|
| 351 |
-
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
| 352 |
-
return partitioned_numel, padding_numel
|
| 353 |
-
|
| 354 |
-
|
| 355 |
-
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
| 356 |
-
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 357 |
-
return
|
| 358 |
-
|
| 359 |
-
if debug:
|
| 360 |
-
for i in range(world_size):
|
| 361 |
-
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
| 362 |
-
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 363 |
-
|
| 364 |
-
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 365 |
-
wanted_params = len(frozen_param_shapes)
|
| 366 |
-
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 367 |
-
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
| 368 |
-
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 369 |
-
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 370 |
-
|
| 371 |
-
total_params = 0
|
| 372 |
-
total_numel = 0
|
| 373 |
-
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
| 374 |
-
total_params += 1
|
| 375 |
-
unpartitioned_numel = shape.numel()
|
| 376 |
-
total_numel += unpartitioned_numel
|
| 377 |
-
|
| 378 |
-
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
| 379 |
-
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
| 380 |
-
|
| 381 |
-
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 382 |
-
|
| 383 |
-
if debug:
|
| 384 |
-
print(
|
| 385 |
-
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 386 |
-
)
|
| 387 |
-
|
| 388 |
-
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 389 |
-
|
| 390 |
-
|
| 391 |
-
class GatheredTensor:
|
| 392 |
-
"""
|
| 393 |
-
A pseudo tensor that collects partitioned weights.
|
| 394 |
-
It is more memory efficient when there are multiple groups.
|
| 395 |
-
"""
|
| 396 |
-
|
| 397 |
-
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
| 398 |
-
self.flat_groups = flat_groups
|
| 399 |
-
self.flat_groups_offset = flat_groups_offset
|
| 400 |
-
self.offset = offset
|
| 401 |
-
self.partitioned_numel = partitioned_numel
|
| 402 |
-
self.shape = shape
|
| 403 |
-
self.dtype = self.flat_groups[0][0].dtype
|
| 404 |
-
|
| 405 |
-
def contiguous(self):
|
| 406 |
-
"""
|
| 407 |
-
Merge partitioned weights from flat_groups into a single tensor.
|
| 408 |
-
"""
|
| 409 |
-
end_idx = self.offset + self.partitioned_numel
|
| 410 |
-
world_size = len(self.flat_groups)
|
| 411 |
-
pad_flat_param_chunks = []
|
| 412 |
-
|
| 413 |
-
for rank_i in range(world_size):
|
| 414 |
-
# for each rank, we need to collect weights from related group/groups
|
| 415 |
-
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
| 416 |
-
start_group_id = None
|
| 417 |
-
end_group_id = None
|
| 418 |
-
for group_id in range(len(self.flat_groups_offset)):
|
| 419 |
-
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
| 420 |
-
start_group_id = group_id
|
| 421 |
-
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
| 422 |
-
end_group_id = group_id
|
| 423 |
-
break
|
| 424 |
-
# collect weights from related group/groups
|
| 425 |
-
for group_id in range(start_group_id, end_group_id + 1):
|
| 426 |
-
flat_tensor = flat_groups_at_rank_i[group_id]
|
| 427 |
-
start_offset = self.offset - self.flat_groups_offset[group_id]
|
| 428 |
-
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
| 429 |
-
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
| 430 |
-
|
| 431 |
-
# collect weights from all ranks
|
| 432 |
-
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
| 433 |
-
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
| 434 |
-
return param
|
| 435 |
-
|
| 436 |
-
|
| 437 |
-
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 438 |
-
param_shapes = zero_model_states[0].param_shapes
|
| 439 |
-
avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
|
| 440 |
-
|
| 441 |
-
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
| 442 |
-
# param, re-consolidating each param, while dealing with padding if any
|
| 443 |
-
|
| 444 |
-
# merge list of dicts, preserving order
|
| 445 |
-
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
| 446 |
-
|
| 447 |
-
if debug:
|
| 448 |
-
for i in range(world_size):
|
| 449 |
-
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
| 450 |
-
|
| 451 |
-
wanted_params = len(param_shapes)
|
| 452 |
-
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
| 453 |
-
# not asserting if there is a mismatch due to possible padding
|
| 454 |
-
avail_numel = fp32_flat_groups[0].numel() * world_size
|
| 455 |
-
print(f"Trainable params: Have {avail_numel} numels to process.")
|
| 456 |
-
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
| 457 |
-
|
| 458 |
-
# params
|
| 459 |
-
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 460 |
-
# out-of-core computing solution
|
| 461 |
-
offset = 0
|
| 462 |
-
total_numel = 0
|
| 463 |
-
total_params = 0
|
| 464 |
-
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
|
| 465 |
-
for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
|
| 466 |
-
unpartitioned_numel = shape.numel()
|
| 467 |
-
total_numel += unpartitioned_numel
|
| 468 |
-
total_params += 1
|
| 469 |
-
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 470 |
-
|
| 471 |
-
if debug:
|
| 472 |
-
print(
|
| 473 |
-
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 474 |
-
)
|
| 475 |
-
|
| 476 |
-
# memory efficient tensor
|
| 477 |
-
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
| 478 |
-
state_dict[name] = tensor
|
| 479 |
-
offset += partitioned_numel
|
| 480 |
-
|
| 481 |
-
offset *= world_size
|
| 482 |
-
|
| 483 |
-
# Sanity check
|
| 484 |
-
if offset != avail_numel:
|
| 485 |
-
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 486 |
-
|
| 487 |
-
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
| 488 |
-
|
| 489 |
-
|
| 490 |
-
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 491 |
-
exclude_frozen_parameters):
|
| 492 |
-
state_dict = OrderedDict()
|
| 493 |
-
|
| 494 |
-
# buffers
|
| 495 |
-
buffers = zero_model_states[0].buffers
|
| 496 |
-
state_dict.update(buffers)
|
| 497 |
-
if debug:
|
| 498 |
-
print(f"added {len(buffers)} buffers")
|
| 499 |
-
|
| 500 |
-
if not exclude_frozen_parameters:
|
| 501 |
-
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
| 502 |
-
|
| 503 |
-
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 504 |
-
|
| 505 |
-
# recover shared parameters
|
| 506 |
-
for pair in zero_model_states[0].shared_params:
|
| 507 |
-
if pair[1] in state_dict:
|
| 508 |
-
state_dict[pair[0]] = state_dict[pair[1]]
|
| 509 |
-
|
| 510 |
-
return state_dict
|
| 511 |
-
|
| 512 |
-
|
| 513 |
-
def to_torch_tensor(state_dict, return_empty_tensor=False):
|
| 514 |
-
"""
|
| 515 |
-
Convert state_dict of GatheredTensor to torch tensor
|
| 516 |
-
"""
|
| 517 |
-
torch_state_dict = {}
|
| 518 |
-
converted_tensors = {}
|
| 519 |
-
for name, tensor in state_dict.items():
|
| 520 |
-
tensor_id = id(tensor)
|
| 521 |
-
if tensor_id in converted_tensors: # shared tensors
|
| 522 |
-
shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
|
| 523 |
-
torch_state_dict[name] = shared_tensor
|
| 524 |
-
else:
|
| 525 |
-
converted_tensors[tensor_id] = name
|
| 526 |
-
if return_empty_tensor:
|
| 527 |
-
torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
| 528 |
-
else:
|
| 529 |
-
torch_state_dict[name] = tensor.contiguous()
|
| 530 |
-
return torch_state_dict
|
| 531 |
-
|
| 532 |
-
|
| 533 |
-
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 534 |
-
tag=None,
|
| 535 |
-
exclude_frozen_parameters=False,
|
| 536 |
-
lazy_mode=False):
|
| 537 |
-
"""
|
| 538 |
-
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
| 539 |
-
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
| 540 |
-
via a model hub.
|
| 541 |
-
|
| 542 |
-
Args:
|
| 543 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder
|
| 544 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
| 545 |
-
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 546 |
-
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
| 547 |
-
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
| 548 |
-
|
| 549 |
-
Returns:
|
| 550 |
-
- pytorch ``state_dict``
|
| 551 |
-
|
| 552 |
-
A typical usage might be ::
|
| 553 |
-
|
| 554 |
-
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 555 |
-
# do the training and checkpoint saving
|
| 556 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
| 557 |
-
model = model.cpu() # move to cpu
|
| 558 |
-
model.load_state_dict(state_dict)
|
| 559 |
-
# submit to model hub or save the model to share with others
|
| 560 |
-
|
| 561 |
-
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
| 562 |
-
application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 563 |
-
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 564 |
-
|
| 565 |
-
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
| 566 |
-
|
| 567 |
-
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
| 568 |
-
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
| 569 |
-
the checkpoint. Or you can load state_dict in lazy mode ::
|
| 570 |
-
|
| 571 |
-
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 572 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
| 573 |
-
for name, lazy_tensor in state_dict.item():
|
| 574 |
-
tensor = lazy_tensor.contiguous() # to cpu
|
| 575 |
-
print(name, tensor)
|
| 576 |
-
# del tensor to release memory if it no longer in use
|
| 577 |
-
"""
|
| 578 |
-
if tag is None:
|
| 579 |
-
latest_path = os.path.join(checkpoint_dir, 'latest')
|
| 580 |
-
if os.path.isfile(latest_path):
|
| 581 |
-
with open(latest_path, 'r') as fd:
|
| 582 |
-
tag = fd.read().strip()
|
| 583 |
-
else:
|
| 584 |
-
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
| 585 |
-
|
| 586 |
-
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
| 587 |
-
|
| 588 |
-
if not os.path.isdir(ds_checkpoint_dir):
|
| 589 |
-
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
| 590 |
-
|
| 591 |
-
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
| 592 |
-
if lazy_mode:
|
| 593 |
-
return state_dict
|
| 594 |
-
else:
|
| 595 |
-
return to_torch_tensor(state_dict)
|
| 596 |
-
|
| 597 |
-
|
| 598 |
-
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
| 599 |
-
output_dir,
|
| 600 |
-
max_shard_size="5GB",
|
| 601 |
-
safe_serialization=False,
|
| 602 |
-
tag=None,
|
| 603 |
-
exclude_frozen_parameters=False):
|
| 604 |
-
"""
|
| 605 |
-
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
| 606 |
-
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
| 607 |
-
|
| 608 |
-
Args:
|
| 609 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 610 |
-
- ``output_dir``: directory to the pytorch fp32 state_dict output files
|
| 611 |
-
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
| 612 |
-
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
| 613 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 614 |
-
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 615 |
-
"""
|
| 616 |
-
|
| 617 |
-
# Dependency pre-check
|
| 618 |
-
if safe_serialization:
|
| 619 |
-
try:
|
| 620 |
-
from safetensors.torch import save_file
|
| 621 |
-
except ImportError:
|
| 622 |
-
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
| 623 |
-
raise
|
| 624 |
-
if max_shard_size is not None:
|
| 625 |
-
try:
|
| 626 |
-
from huggingface_hub import split_torch_state_dict_into_shards
|
| 627 |
-
except ImportError:
|
| 628 |
-
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
| 629 |
-
raise
|
| 630 |
-
|
| 631 |
-
# Convert zero checkpoint to state_dict
|
| 632 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 633 |
-
tag,
|
| 634 |
-
exclude_frozen_parameters,
|
| 635 |
-
lazy_mode=True)
|
| 636 |
-
|
| 637 |
-
# Shard the model if it is too big.
|
| 638 |
-
weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
|
| 639 |
-
if max_shard_size is not None:
|
| 640 |
-
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
| 641 |
-
# an memory-efficient approach for sharding
|
| 642 |
-
empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
|
| 643 |
-
state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
|
| 644 |
-
filename_pattern=filename_pattern,
|
| 645 |
-
max_shard_size=max_shard_size)
|
| 646 |
-
else:
|
| 647 |
-
from collections import namedtuple
|
| 648 |
-
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
| 649 |
-
state_dict_split = StateDictSplit(is_sharded=False,
|
| 650 |
-
filename_to_tensors={weights_name: list(state_dict.keys())})
|
| 651 |
-
|
| 652 |
-
# Save the model by shard
|
| 653 |
-
os.makedirs(output_dir, exist_ok=True)
|
| 654 |
-
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
| 655 |
-
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
| 656 |
-
shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
|
| 657 |
-
shard_state_dict = to_torch_tensor(shard_state_dict)
|
| 658 |
-
output_path = os.path.join(output_dir, shard_file)
|
| 659 |
-
if safe_serialization:
|
| 660 |
-
save_file(shard_state_dict, output_path, metadata={"format": "pt"})
|
| 661 |
-
else:
|
| 662 |
-
torch.save(shard_state_dict, output_path)
|
| 663 |
-
# release the memory of current shard
|
| 664 |
-
for tensor_name in list(shard_state_dict.keys()):
|
| 665 |
-
del state_dict[tensor_name]
|
| 666 |
-
del shard_state_dict[tensor_name]
|
| 667 |
-
del shard_state_dict
|
| 668 |
-
gc.collect()
|
| 669 |
-
|
| 670 |
-
# Save index if sharded
|
| 671 |
-
if state_dict_split.is_sharded:
|
| 672 |
-
index = {
|
| 673 |
-
"metadata": state_dict_split.metadata,
|
| 674 |
-
"weight_map": state_dict_split.tensor_to_filename,
|
| 675 |
-
}
|
| 676 |
-
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
| 677 |
-
save_index_file = os.path.join(output_dir, save_index_file)
|
| 678 |
-
with open(save_index_file, "w", encoding="utf-8") as f:
|
| 679 |
-
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
| 680 |
-
f.write(content)
|
| 681 |
-
|
| 682 |
-
|
| 683 |
-
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
| 684 |
-
"""
|
| 685 |
-
1. Put the provided model to cpu
|
| 686 |
-
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
| 687 |
-
3. Load it into the provided model
|
| 688 |
-
|
| 689 |
-
Args:
|
| 690 |
-
- ``model``: the model object to update
|
| 691 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 692 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 693 |
-
|
| 694 |
-
Returns:
|
| 695 |
-
- ``model`: modified model
|
| 696 |
-
|
| 697 |
-
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
| 698 |
-
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
| 699 |
-
conveniently placed for you in the checkpoint folder.
|
| 700 |
-
|
| 701 |
-
A typical usage might be ::
|
| 702 |
-
|
| 703 |
-
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
| 704 |
-
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
| 705 |
-
# submit to model hub or save the model to share with others
|
| 706 |
-
|
| 707 |
-
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
| 708 |
-
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 709 |
-
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 710 |
-
|
| 711 |
-
"""
|
| 712 |
-
logger.info("Extracting fp32 weights")
|
| 713 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
| 714 |
-
|
| 715 |
-
logger.info("Overwriting model with fp32 weights")
|
| 716 |
-
model = model.cpu()
|
| 717 |
-
model.load_state_dict(state_dict, strict=False)
|
| 718 |
-
|
| 719 |
-
return model
|
| 720 |
-
|
| 721 |
-
|
| 722 |
-
if __name__ == "__main__":
|
| 723 |
-
parser = argparse.ArgumentParser()
|
| 724 |
-
parser.add_argument("checkpoint_dir",
|
| 725 |
-
type=str,
|
| 726 |
-
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
| 727 |
-
parser.add_argument("output_dir",
|
| 728 |
-
type=str,
|
| 729 |
-
help="directory to the pytorch fp32 state_dict output files"
|
| 730 |
-
"(e.g. path/checkpoint-12-output/)")
|
| 731 |
-
parser.add_argument(
|
| 732 |
-
"--max_shard_size",
|
| 733 |
-
type=str,
|
| 734 |
-
default="5GB",
|
| 735 |
-
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
| 736 |
-
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
| 737 |
-
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
| 738 |
-
"without CPU OOM issues.")
|
| 739 |
-
parser.add_argument(
|
| 740 |
-
"--safe_serialization",
|
| 741 |
-
default=False,
|
| 742 |
-
action='store_true',
|
| 743 |
-
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
| 744 |
-
parser.add_argument("-t",
|
| 745 |
-
"--tag",
|
| 746 |
-
type=str,
|
| 747 |
-
default=None,
|
| 748 |
-
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
| 749 |
-
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
| 750 |
-
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
| 751 |
-
args = parser.parse_args()
|
| 752 |
-
|
| 753 |
-
debug = args.debug
|
| 754 |
-
|
| 755 |
-
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
| 756 |
-
args.output_dir,
|
| 757 |
-
max_shard_size=args.max_shard_size,
|
| 758 |
-
safe_serialization=args.safe_serialization,
|
| 759 |
-
tag=args.tag,
|
| 760 |
-
exclude_frozen_parameters=args.exclude_frozen_parameters)
|
|
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|
ckpts/stock_32f/args.json
DELETED
|
@@ -1,376 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"output_dir": "/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/video_mllm_swift/4b/s2_stock_32f_10pct/v0-20260606-044138",
|
| 3 |
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| 4 |
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|
| 5 |
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| 6 |
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| 7 |
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|
| 8 |
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| 9 |
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| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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| 17 |
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|
| 18 |
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| 19 |
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|
| 20 |
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|
| 21 |
-
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|
| 22 |
-
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|
| 23 |
-
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|
| 24 |
-
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|
| 25 |
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|
| 26 |
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|
| 27 |
-
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|
| 28 |
-
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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|
| 36 |
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| 37 |
-
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| 38 |
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|
| 39 |
-
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|
| 40 |
-
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|
| 41 |
-
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|
| 42 |
-
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|
| 43 |
-
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| 44 |
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| 45 |
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|
| 46 |
-
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|
| 47 |
-
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| 48 |
-
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| 49 |
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|
| 50 |
-
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|
| 51 |
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|
| 52 |
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| 53 |
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| 55 |
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| 58 |
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| 59 |
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| 60 |
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| 64 |
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| 65 |
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| 66 |
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| 81 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 94 |
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| 99 |
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| 100 |
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| 102 |
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| 103 |
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| 104 |
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| 105 |
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| 111 |
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| 112 |
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| 113 |
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|
| 114 |
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| 115 |
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| 116 |
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|
| 117 |
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| 118 |
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| 119 |
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| 120 |
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| 123 |
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| 124 |
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| 125 |
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| 126 |
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| 127 |
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| 128 |
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| 129 |
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| 130 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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| 137 |
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| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 145 |
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| 146 |
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| 147 |
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| 148 |
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| 149 |
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| 150 |
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| 151 |
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| 152 |
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| 153 |
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| 154 |
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| 155 |
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| 156 |
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|
| 157 |
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| 158 |
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| 159 |
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|
| 160 |
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|
| 161 |
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| 162 |
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| 163 |
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| 164 |
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| 165 |
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| 166 |
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 180 |
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| 181 |
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| 182 |
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| 183 |
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| 184 |
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| 185 |
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| 186 |
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|
| 187 |
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|
| 188 |
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| 189 |
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| 190 |
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| 191 |
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| 194 |
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| 195 |
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| 196 |
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| 197 |
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| 198 |
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| 199 |
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| 200 |
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| 201 |
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| 202 |
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|
| 203 |
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| 204 |
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| 205 |
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| 207 |
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| 215 |
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| 216 |
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| 224 |
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| 225 |
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| 226 |
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| 227 |
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| 228 |
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| 229 |
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| 232 |
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| 233 |
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galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)"
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ckpts/stock_32f/chat_template.jinja
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
{%- if tools %}
|
| 2 |
-
{{- '<|im_start|>system\n' }}
|
| 3 |
-
{%- if messages[0].role == 'system' %}
|
| 4 |
-
{{- messages[0].content + '\n\n' }}
|
| 5 |
-
{%- endif %}
|
| 6 |
-
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 7 |
-
{%- for tool in tools %}
|
| 8 |
-
{{- "\n" }}
|
| 9 |
-
{{- tool | tojson }}
|
| 10 |
-
{%- endfor %}
|
| 11 |
-
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 12 |
-
{%- else %}
|
| 13 |
-
{%- if messages[0].role == 'system' %}
|
| 14 |
-
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 15 |
-
{%- endif %}
|
| 16 |
-
{%- endif %}
|
| 17 |
-
{%- for message in messages %}
|
| 18 |
-
{%- if message.content is string %}
|
| 19 |
-
{%- set content = message.content %}
|
| 20 |
-
{%- else %}
|
| 21 |
-
{%- set content = '' %}
|
| 22 |
-
{%- endif %}
|
| 23 |
-
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 24 |
-
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 25 |
-
{%- elif message.role == "assistant" %}
|
| 26 |
-
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 27 |
-
{%- if message.tool_calls %}
|
| 28 |
-
{%- for tool_call in message.tool_calls %}
|
| 29 |
-
{%- if (loop.first and content) or (not loop.first) %}
|
| 30 |
-
{{- '\n' }}
|
| 31 |
-
{%- endif %}
|
| 32 |
-
{%- if tool_call.function %}
|
| 33 |
-
{%- set tool_call = tool_call.function %}
|
| 34 |
-
{%- endif %}
|
| 35 |
-
{{- '<tool_call>\n{"name": "' }}
|
| 36 |
-
{{- tool_call.name }}
|
| 37 |
-
{{- '", "arguments": ' }}
|
| 38 |
-
{%- if tool_call.arguments is string %}
|
| 39 |
-
{{- tool_call.arguments }}
|
| 40 |
-
{%- else %}
|
| 41 |
-
{{- tool_call.arguments | tojson }}
|
| 42 |
-
{%- endif %}
|
| 43 |
-
{{- '}\n</tool_call>' }}
|
| 44 |
-
{%- endfor %}
|
| 45 |
-
{%- endif %}
|
| 46 |
-
{{- '<|im_end|>\n' }}
|
| 47 |
-
{%- elif message.role == "tool" %}
|
| 48 |
-
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 49 |
-
{{- '<|im_start|>user' }}
|
| 50 |
-
{%- endif %}
|
| 51 |
-
{{- '\n<tool_response>\n' }}
|
| 52 |
-
{{- content }}
|
| 53 |
-
{{- '\n</tool_response>' }}
|
| 54 |
-
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 55 |
-
{{- '<|im_end|>\n' }}
|
| 56 |
-
{%- endif %}
|
| 57 |
-
{%- endif %}
|
| 58 |
-
{%- endfor %}
|
| 59 |
-
{%- if add_generation_prompt %}
|
| 60 |
-
{{- '<|im_start|>assistant\n' }}
|
| 61 |
-
{%- endif %}
|
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|
ckpts/stock_32f/chat_template.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + ' '}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ '\n' + content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ '\n' + content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
| 3 |
-
}
|
|
|
|
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|
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|
|
|
|
ckpts/stock_32f/config.json
DELETED
|
@@ -1,262 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"architectures": [
|
| 3 |
-
"LlavaQwen3_5ViTForConditionalGeneration"
|
| 4 |
-
],
|
| 5 |
-
"bos_token_id": null,
|
| 6 |
-
"dtype": "bfloat16",
|
| 7 |
-
"eos_token_id": 151645,
|
| 8 |
-
"hidden_size": 2560,
|
| 9 |
-
"image_grid_pinpoints": [
|
| 10 |
-
[
|
| 11 |
-
384,
|
| 12 |
-
384
|
| 13 |
-
],
|
| 14 |
-
[
|
| 15 |
-
384,
|
| 16 |
-
768
|
| 17 |
-
],
|
| 18 |
-
[
|
| 19 |
-
384,
|
| 20 |
-
1152
|
| 21 |
-
],
|
| 22 |
-
[
|
| 23 |
-
384,
|
| 24 |
-
1536
|
| 25 |
-
],
|
| 26 |
-
[
|
| 27 |
-
384,
|
| 28 |
-
1920
|
| 29 |
-
],
|
| 30 |
-
[
|
| 31 |
-
384,
|
| 32 |
-
2304
|
| 33 |
-
],
|
| 34 |
-
[
|
| 35 |
-
768,
|
| 36 |
-
384
|
| 37 |
-
],
|
| 38 |
-
[
|
| 39 |
-
768,
|
| 40 |
-
768
|
| 41 |
-
],
|
| 42 |
-
[
|
| 43 |
-
768,
|
| 44 |
-
1152
|
| 45 |
-
],
|
| 46 |
-
[
|
| 47 |
-
768,
|
| 48 |
-
1536
|
| 49 |
-
],
|
| 50 |
-
[
|
| 51 |
-
768,
|
| 52 |
-
1920
|
| 53 |
-
],
|
| 54 |
-
[
|
| 55 |
-
768,
|
| 56 |
-
2304
|
| 57 |
-
],
|
| 58 |
-
[
|
| 59 |
-
1152,
|
| 60 |
-
384
|
| 61 |
-
],
|
| 62 |
-
[
|
| 63 |
-
1152,
|
| 64 |
-
768
|
| 65 |
-
],
|
| 66 |
-
[
|
| 67 |
-
1152,
|
| 68 |
-
1152
|
| 69 |
-
],
|
| 70 |
-
[
|
| 71 |
-
1152,
|
| 72 |
-
1536
|
| 73 |
-
],
|
| 74 |
-
[
|
| 75 |
-
1152,
|
| 76 |
-
1920
|
| 77 |
-
],
|
| 78 |
-
[
|
| 79 |
-
1152,
|
| 80 |
-
2304
|
| 81 |
-
],
|
| 82 |
-
[
|
| 83 |
-
1536,
|
| 84 |
-
384
|
| 85 |
-
],
|
| 86 |
-
[
|
| 87 |
-
1536,
|
| 88 |
-
768
|
| 89 |
-
],
|
| 90 |
-
[
|
| 91 |
-
1536,
|
| 92 |
-
1152
|
| 93 |
-
],
|
| 94 |
-
[
|
| 95 |
-
1536,
|
| 96 |
-
1536
|
| 97 |
-
],
|
| 98 |
-
[
|
| 99 |
-
1536,
|
| 100 |
-
1920
|
| 101 |
-
],
|
| 102 |
-
[
|
| 103 |
-
1536,
|
| 104 |
-
2304
|
| 105 |
-
],
|
| 106 |
-
[
|
| 107 |
-
1920,
|
| 108 |
-
384
|
| 109 |
-
],
|
| 110 |
-
[
|
| 111 |
-
1920,
|
| 112 |
-
768
|
| 113 |
-
],
|
| 114 |
-
[
|
| 115 |
-
1920,
|
| 116 |
-
1152
|
| 117 |
-
],
|
| 118 |
-
[
|
| 119 |
-
1920,
|
| 120 |
-
1536
|
| 121 |
-
],
|
| 122 |
-
[
|
| 123 |
-
1920,
|
| 124 |
-
1920
|
| 125 |
-
],
|
| 126 |
-
[
|
| 127 |
-
1920,
|
| 128 |
-
2304
|
| 129 |
-
],
|
| 130 |
-
[
|
| 131 |
-
2304,
|
| 132 |
-
384
|
| 133 |
-
],
|
| 134 |
-
[
|
| 135 |
-
2304,
|
| 136 |
-
768
|
| 137 |
-
],
|
| 138 |
-
[
|
| 139 |
-
2304,
|
| 140 |
-
1152
|
| 141 |
-
],
|
| 142 |
-
[
|
| 143 |
-
2304,
|
| 144 |
-
1536
|
| 145 |
-
],
|
| 146 |
-
[
|
| 147 |
-
2304,
|
| 148 |
-
1920
|
| 149 |
-
],
|
| 150 |
-
[
|
| 151 |
-
2304,
|
| 152 |
-
2304
|
| 153 |
-
]
|
| 154 |
-
],
|
| 155 |
-
"image_token_index": 151669,
|
| 156 |
-
"model_type": "llava_qwen3_5vit_qwen3",
|
| 157 |
-
"multimodal_projector_bias": true,
|
| 158 |
-
"pad_token_id": 151643,
|
| 159 |
-
"projector_hidden_act": "gelu",
|
| 160 |
-
"text_config": {
|
| 161 |
-
"_name_or_path": "/share/m2v_intern_v3/wangjunjie09/model_cache/huggingface/Qwen/Qwen3-4B-Instruct-2507",
|
| 162 |
-
"architectures": [
|
| 163 |
-
"Qwen3ForCausalLM"
|
| 164 |
-
],
|
| 165 |
-
"attention_bias": false,
|
| 166 |
-
"attention_dropout": 0.0,
|
| 167 |
-
"bos_token_id": 151643,
|
| 168 |
-
"dtype": "bfloat16",
|
| 169 |
-
"eos_token_id": 151645,
|
| 170 |
-
"head_dim": 128,
|
| 171 |
-
"hidden_act": "silu",
|
| 172 |
-
"hidden_size": 2560,
|
| 173 |
-
"initializer_range": 0.02,
|
| 174 |
-
"intermediate_size": 9728,
|
| 175 |
-
"layer_types": [
|
| 176 |
-
"full_attention",
|
| 177 |
-
"full_attention",
|
| 178 |
-
"full_attention",
|
| 179 |
-
"full_attention",
|
| 180 |
-
"full_attention",
|
| 181 |
-
"full_attention",
|
| 182 |
-
"full_attention",
|
| 183 |
-
"full_attention",
|
| 184 |
-
"full_attention",
|
| 185 |
-
"full_attention",
|
| 186 |
-
"full_attention",
|
| 187 |
-
"full_attention",
|
| 188 |
-
"full_attention",
|
| 189 |
-
"full_attention",
|
| 190 |
-
"full_attention",
|
| 191 |
-
"full_attention",
|
| 192 |
-
"full_attention",
|
| 193 |
-
"full_attention",
|
| 194 |
-
"full_attention",
|
| 195 |
-
"full_attention",
|
| 196 |
-
"full_attention",
|
| 197 |
-
"full_attention",
|
| 198 |
-
"full_attention",
|
| 199 |
-
"full_attention",
|
| 200 |
-
"full_attention",
|
| 201 |
-
"full_attention",
|
| 202 |
-
"full_attention",
|
| 203 |
-
"full_attention",
|
| 204 |
-
"full_attention",
|
| 205 |
-
"full_attention",
|
| 206 |
-
"full_attention",
|
| 207 |
-
"full_attention",
|
| 208 |
-
"full_attention",
|
| 209 |
-
"full_attention",
|
| 210 |
-
"full_attention",
|
| 211 |
-
"full_attention"
|
| 212 |
-
],
|
| 213 |
-
"max_position_embeddings": 262144,
|
| 214 |
-
"max_window_layers": 36,
|
| 215 |
-
"model_type": "qwen3",
|
| 216 |
-
"num_attention_heads": 32,
|
| 217 |
-
"num_hidden_layers": 36,
|
| 218 |
-
"num_key_value_heads": 8,
|
| 219 |
-
"pad_token_id": 151643,
|
| 220 |
-
"rms_norm_eps": 1e-06,
|
| 221 |
-
"rope_parameters": {
|
| 222 |
-
"rope_theta": 5000000,
|
| 223 |
-
"rope_type": "default"
|
| 224 |
-
},
|
| 225 |
-
"sliding_window": null,
|
| 226 |
-
"tie_word_embeddings": true,
|
| 227 |
-
"use_cache": false,
|
| 228 |
-
"use_sliding_window": false,
|
| 229 |
-
"vocab_size": 151936
|
| 230 |
-
},
|
| 231 |
-
"tie_word_embeddings": true,
|
| 232 |
-
"transformers_version": "5.5.4",
|
| 233 |
-
"use_cache": false,
|
| 234 |
-
"video_token_index": 151670,
|
| 235 |
-
"vision_aspect_ratio": "anyres_max_9",
|
| 236 |
-
"vision_config": {
|
| 237 |
-
"deepstack_visual_indexes": [],
|
| 238 |
-
"depth": 24,
|
| 239 |
-
"dtype": "bfloat16",
|
| 240 |
-
"hidden_act": "gelu_pytorch_tanh",
|
| 241 |
-
"hidden_size": 1024,
|
| 242 |
-
"image_size": 384,
|
| 243 |
-
"in_channels": 3,
|
| 244 |
-
"initializer_range": 0.02,
|
| 245 |
-
"intermediate_size": 4096,
|
| 246 |
-
"model_type": "qwen3_5",
|
| 247 |
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|
ckpts/stock_32f/modeling_qwen3_5vit_qwen3.py
DELETED
|
@@ -1,405 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
modeling_qwen3_5vit_qwen3.py — Qwen3.5 Vision as SigLIP-compat vision_tower in LlavaOnevision.
|
| 3 |
-
|
| 4 |
-
设计：与 `modeling_qwen3vlvit_qwen3.py` 严格同构，仅换 vision backbone 源：
|
| 5 |
-
- Qwen3VLVisionModel → Qwen3_5VisionModel（继承关系：Qwen3_5VisionModel(Qwen3VLVisionModel) 去 DeepStack）
|
| 6 |
-
- Qwen3VLVisionConfig → Qwen3_5VisionConfig（父类用 AttributeError 哨兵屏蔽 deepstack_visual_indexes）
|
| 7 |
-
|
| 8 |
-
其余（Adapter 契约翻译、MLP projector + pre_norm、LlavaOnevision 继承 wire class）与
|
| 9 |
-
Qwen3-VL ViT pipeline 完全一致。两条 pipeline 并存意义：DeepStack ablation 天然实验组。
|
| 10 |
-
|
| 11 |
-
类层级：
|
| 12 |
-
Qwen3_5ViTBackbone(Qwen3_5VisionModel) — 去 merger，保持 NaViT 契约
|
| 13 |
-
Qwen3_5ViTAsSiglipAdapter(nn.Module) — 持有 Backbone，做 SigLIP ↔ NaViT 契约翻译
|
| 14 |
-
|
| 15 |
-
三方对比公平性：定 384×384 AnyRes tile + 同款 projector 骨架 + 同款 Qwen3-1.7B LLM。
|
| 16 |
-
"""
|
| 17 |
-
|
| 18 |
-
import math
|
| 19 |
-
import os
|
| 20 |
-
import sys
|
| 21 |
-
from typing import Optional
|
| 22 |
-
|
| 23 |
-
import torch
|
| 24 |
-
import torch.nn as nn
|
| 25 |
-
import torch.nn.functional as F
|
| 26 |
-
from transformers import (
|
| 27 |
-
AutoConfig,
|
| 28 |
-
AutoModel,
|
| 29 |
-
AutoModelForCausalLM,
|
| 30 |
-
LlavaOnevisionConfig,
|
| 31 |
-
LlavaOnevisionForConditionalGeneration,
|
| 32 |
-
LlavaOnevisionModel,
|
| 33 |
-
LlavaOnevisionPreTrainedModel,
|
| 34 |
-
Qwen3Config,
|
| 35 |
-
)
|
| 36 |
-
from transformers.activations import ACT2FN
|
| 37 |
-
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
|
| 38 |
-
from transformers.models.qwen3_5.configuration_qwen3_5 import Qwen3_5VisionConfig
|
| 39 |
-
from transformers.models.qwen3_5.modeling_qwen3_5 import Qwen3_5VisionModel
|
| 40 |
-
|
| 41 |
-
# Shared layout-permutation utility lives in declip_qwenvit (single source of
|
| 42 |
-
# truth — same code path runs in declip-training-side qk_cosine reorder).
|
| 43 |
-
# Add VisionEncoder repo root to sys.path so this modeling file is importable
|
| 44 |
-
# even when the package isn't pip-installed (ms-swift integration loads it
|
| 45 |
-
# via dynamic plugin path).
|
| 46 |
-
_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
|
| 47 |
-
if _REPO_ROOT not in sys.path:
|
| 48 |
-
sys.path.append(_REPO_ROOT)
|
| 49 |
-
from declip_qwenvit.model.qwen3vit_qk import block_merge_to_row_major_permutation # noqa: E402
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
class LlavaQwen3_5ViTConfig(LlavaOnevisionConfig):
|
| 53 |
-
"""LlavaOnevisionConfig 子类，vision_config 类型换成 Qwen3_5VisionConfig。
|
| 54 |
-
|
| 55 |
-
与 Qwen3-VL ViT 版的差异：
|
| 56 |
-
- sub_configs.vision_config 用 Qwen3_5VisionConfig
|
| 57 |
-
- 不再设置 deepstack_visual_indexes（Qwen3_5VisionConfig 用 AttributeError 哨兵屏蔽此字段）
|
| 58 |
-
|
| 59 |
-
其余同 LlavaQwen3VLViTConfig（tile_size 默认 384；vision_feature_select_strategy='full'
|
| 60 |
-
必须 override，Qwen3.5 ViT 无 CLS token）。
|
| 61 |
-
"""
|
| 62 |
-
|
| 63 |
-
model_type = "llava_qwen3_5vit_qwen3"
|
| 64 |
-
sub_configs = {"vision_config": Qwen3_5VisionConfig, "text_config": Qwen3Config}
|
| 65 |
-
|
| 66 |
-
def __init__(
|
| 67 |
-
self,
|
| 68 |
-
vision_config=None,
|
| 69 |
-
text_config=None,
|
| 70 |
-
tile_size: int = 384,
|
| 71 |
-
vit_register_ring: int = 0,
|
| 72 |
-
vit_skip_last_blocks: int = 0,
|
| 73 |
-
**kwargs,
|
| 74 |
-
):
|
| 75 |
-
if isinstance(vision_config, dict):
|
| 76 |
-
vision_config = Qwen3_5VisionConfig(**vision_config)
|
| 77 |
-
elif vision_config is None:
|
| 78 |
-
vision_config = Qwen3_5VisionConfig()
|
| 79 |
-
# UniRefiner deploy-side register ring width in tokens (V10.0.1+). The S0
|
| 80 |
-
# refinement optimizes image tokens INSIDE a register-ring canvas; >0 makes
|
| 81 |
-
# every tower forward pad a zero ring around each tile/frame and drop the
|
| 82 |
-
# ring tokens after encoding (LLM-side token contract unchanged). Persisted
|
| 83 |
-
# in config.json so S2-resume and eval inherit it automatically. 0 = stock
|
| 84 |
-
# behavior (all non-V10.0.1 chains).
|
| 85 |
-
self.vit_register_ring = int(vit_register_ring)
|
| 86 |
-
# -N layer ablation (stock_minus2 chains, 2026-06-10): physically DELETE the
|
| 87 |
-
# last N ViT blocks after construction (LLaVA-NeXT convention,
|
| 88 |
-
# `del encoder.layers[-1:]`), so last_hidden_state = hidden_states[-(N+1)]
|
| 89 |
-
# of the full tower, still through final_layernorm. Physical deletion (vs
|
| 90 |
-
# forward-skip) keeps the state_dict 1:1 with truncated declip S0 ckpts
|
| 91 |
-
# (missing=0 contract) and makes silently running the dropped block
|
| 92 |
-
# impossible. Persisted in config.json; S2-resume/eval inherit. 0 = stock.
|
| 93 |
-
# NOTE: vision_config.depth stays at the full count; truncation applies at
|
| 94 |
-
# adapter build, before any load_state_dict.
|
| 95 |
-
self.vit_skip_last_blocks = int(vit_skip_last_blocks)
|
| 96 |
-
# WHY 无 `vision_config.deepstack_visual_indexes = []`（对比 Qwen3-VL ViT 版）：
|
| 97 |
-
# Qwen3_5VisionConfig 父类用 AttributeError() 哨兵显式屏蔽此字段，设置会报 AttributeError
|
| 98 |
-
# LlavaOnevision.pack_image_features 用这个作为 tile 像素大小（不是 patch_size）
|
| 99 |
-
vision_config.image_size = tile_size
|
| 100 |
-
|
| 101 |
-
if isinstance(text_config, dict):
|
| 102 |
-
text_config = Qwen3Config(**text_config)
|
| 103 |
-
elif text_config is None:
|
| 104 |
-
text_config = Qwen3Config()
|
| 105 |
-
|
| 106 |
-
# 父类默认 select_strategy='default' 会跳首 token (CLS) — Qwen3.5 ViT 无 CLS 必须用 'full'
|
| 107 |
-
kwargs.setdefault("vision_feature_select_strategy", "full")
|
| 108 |
-
super().__init__(vision_config=vision_config, text_config=text_config, **kwargs)
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
class Qwen3_5ViTBackbone(Qwen3_5VisionModel):
|
| 112 |
-
"""Qwen3.5 Vision 去除原生 patch_merger 的 backbone 版本（V6 final_layernorm fix (2026-05-16): append final_layernorm）。
|
| 113 |
-
|
| 114 |
-
构造时把 merger.norm 的预训练权重抠到 final_layernorm，然后 `del self.merger`
|
| 115 |
-
释放 ~37M 参数（保留 norm 的 LN 焊到末端做 post_layernorm 角色，丢弃 spatial
|
| 116 |
-
shuffle + linear_fc1/fc2，那对应 LlavaOV projector 的职责）。
|
| 117 |
-
|
| 118 |
-
架构对称（V6 final_layernorm fix (2026-05-16) 修复）：
|
| 119 |
-
SigLIP2: encoder → post_layernorm → last_hidden_state → LlavaOV MLP → LLM
|
| 120 |
-
V6 final_layernorm fix (2026-05-16): encoder → final_layernorm → last_hidden_state → LlavaOV MLP → LLM
|
| 121 |
-
|
| 122 |
-
forward 跑完 transformer blocks 后过 final_layernorm，再返回。下游 LlavaOnevision
|
| 123 |
-
pack_image_features 的 AnyRes 2×2 pool 接管原 merger 的空间合并职责。
|
| 124 |
-
|
| 125 |
-
输入输出契约与父类 Qwen3_5VisionModel 一致（NaViT flat）：
|
| 126 |
-
forward(hidden_states=[L, patch_dim], grid_thw=[N, 3])
|
| 127 |
-
→ BaseModelOutput(last_hidden_state=[L, hidden_size])
|
| 128 |
-
|
| 129 |
-
forward 主体 1:1 对照 `Qwen3_5VisionModel.forward`（已无 deepstack loop，比
|
| 130 |
-
Qwen3VLVisionModel.forward 更短），仅跳过末尾 `self.merger(x)`，改为 final_layernorm。
|
| 131 |
-
"""
|
| 132 |
-
|
| 133 |
-
def __init__(self, config):
|
| 134 |
-
super().__init__(config)
|
| 135 |
-
# V6 final_layernorm fix (2026-05-16): extract merger.norm pretrained weights into final_layernorm.
|
| 136 |
-
# Default init: even if ckpt 阶段没 inject final_layernorm.* (e.g. stock
|
| 137 |
-
# bootstrap path that filters merger.*), final_layernorm 仍持有 merger.norm
|
| 138 |
-
# 的预训练值, 不是 random — 这是防止 silent corruption 的兜底.
|
| 139 |
-
ln_w = self.merger.norm.weight.detach().clone()
|
| 140 |
-
ln_b = self.merger.norm.bias.detach().clone()
|
| 141 |
-
del self.merger
|
| 142 |
-
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=1e-6)
|
| 143 |
-
self.final_layernorm.weight.data.copy_(ln_w)
|
| 144 |
-
self.final_layernorm.bias.data.copy_(ln_b)
|
| 145 |
-
|
| 146 |
-
def forward(self, hidden_states, grid_thw, **kwargs):
|
| 147 |
-
hidden_states = self.patch_embed(hidden_states)
|
| 148 |
-
|
| 149 |
-
pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
|
| 150 |
-
hidden_states = hidden_states + pos_embeds
|
| 151 |
-
|
| 152 |
-
rotary_pos_emb = self.rot_pos_emb(grid_thw)
|
| 153 |
-
seq_len, _ = hidden_states.size()
|
| 154 |
-
rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
|
| 155 |
-
emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
|
| 156 |
-
position_embeddings = (emb.cos(), emb.sin())
|
| 157 |
-
|
| 158 |
-
cu_seqlens = torch.repeat_interleave(
|
| 159 |
-
grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
|
| 160 |
-
).cumsum(dim=0, dtype=torch.int32)
|
| 161 |
-
cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
|
| 162 |
-
|
| 163 |
-
for blk in self.blocks:
|
| 164 |
-
hidden_states = blk(
|
| 165 |
-
hidden_states,
|
| 166 |
-
cu_seqlens=cu_seqlens,
|
| 167 |
-
position_embeddings=position_embeddings,
|
| 168 |
-
**kwargs,
|
| 169 |
-
)
|
| 170 |
-
|
| 171 |
-
# V6 final_layernorm fix (2026-05-16): appended final LayerNorm — mirrors SigLIP2's post_layernorm.
|
| 172 |
-
# Per-token affine; layout-invariant (reorder happens in adapter).
|
| 173 |
-
hidden_states = self.final_layernorm(hidden_states)
|
| 174 |
-
|
| 175 |
-
return BaseModelOutput(last_hidden_state=hidden_states)
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
class Qwen3_5ViTAsSiglipAdapter(nn.Module):
|
| 179 |
-
"""SigLIP 契约 → NaViT 契约的翻译层。持有 Qwen3_5ViTBackbone。
|
| 180 |
-
|
| 181 |
-
对外暴露 SigLIP 式 forward(pixel_values=[N,3,H,W]) → BaseModelOutputWithPooling，
|
| 182 |
-
供 LlavaOnevision 消费；对内按官方 _preprocess 的 reshape 链把 pixel_values
|
| 183 |
-
转成 NaViT flat + grid_thw 喂给 Backbone。
|
| 184 |
-
|
| 185 |
-
reshape 链 1:1 照抄 transformers 官方 Qwen2VLImageProcessorFast._preprocess
|
| 186 |
-
(video_processing_qwen3_vl.py L227-252) —— Qwen3.5 无独立 image_processor，复用 Qwen3-VL 格式。
|
| 187 |
-
"""
|
| 188 |
-
|
| 189 |
-
def __init__(self, vision_config: Qwen3_5VisionConfig, register_ring: int = 0,
|
| 190 |
-
skip_last_blocks: int = 0):
|
| 191 |
-
super().__init__()
|
| 192 |
-
self.vision = Qwen3_5ViTBackbone(vision_config)
|
| 193 |
-
self.config = vision_config
|
| 194 |
-
# Deploy-side UniRefiner register ring (tokens). See LlavaQwen3_5ViTConfig.
|
| 195 |
-
# Ring must keep the padded grid even for spatial_merge_size=2 NaViT
|
| 196 |
-
# flattening: 24 + 2*ring stays even for any integer ring.
|
| 197 |
-
self.register_ring = int(register_ring)
|
| 198 |
-
# -N layer ablation: physically delete the last N blocks (see
|
| 199 |
-
# LlavaQwen3_5ViTConfig.vit_skip_last_blocks). final_layernorm stays,
|
| 200 |
-
# applied to the new last block's output.
|
| 201 |
-
skip = int(skip_last_blocks)
|
| 202 |
-
if skip > 0:
|
| 203 |
-
self.vision.blocks = self.vision.blocks[:-skip]
|
| 204 |
-
self.skip_last_blocks = skip
|
| 205 |
-
|
| 206 |
-
@property
|
| 207 |
-
def dtype(self):
|
| 208 |
-
return next(self.parameters()).dtype
|
| 209 |
-
|
| 210 |
-
@property
|
| 211 |
-
def device(self):
|
| 212 |
-
return next(self.parameters()).device
|
| 213 |
-
|
| 214 |
-
def _flatten_navit(self, pixel_values: torch.Tensor):
|
| 215 |
-
"""[N, 3, H, W] → (flat=[N*L, patch_dim], grid_thw=[N, 3], shape=(N, L)).
|
| 216 |
-
|
| 217 |
-
L = grid_t * grid_h * grid_w = 1 * (H/16) * (W/16)
|
| 218 |
-
patch_dim = C * temporal_patch_size * patch_size^2 = 3 * 2 * 16 * 16 = 1536
|
| 219 |
-
"""
|
| 220 |
-
pixel_values = pixel_values.to(dtype=self.dtype)
|
| 221 |
-
tps = self.config.temporal_patch_size
|
| 222 |
-
ps = self.config.patch_size
|
| 223 |
-
ms = self.config.spatial_merge_size
|
| 224 |
-
|
| 225 |
-
patches = pixel_values.unsqueeze(1)
|
| 226 |
-
# 对单帧图像 T=1, pad=1 → expand 一帧使 T 整除 temporal_patch_size，
|
| 227 |
-
# Conv3d 在复制帧上退化为等效 2D Conv（数学无损）
|
| 228 |
-
T = patches.shape[1]
|
| 229 |
-
pad = -T % tps
|
| 230 |
-
if pad:
|
| 231 |
-
repeats = patches[:, -1:].expand(-1, pad, -1, -1, -1)
|
| 232 |
-
patches = torch.cat((patches, repeats), dim=1)
|
| 233 |
-
|
| 234 |
-
batch_size, t, channel, H, W = patches.shape
|
| 235 |
-
grid_t = t // tps
|
| 236 |
-
grid_h = H // ps
|
| 237 |
-
grid_w = W // ps
|
| 238 |
-
|
| 239 |
-
patches = patches.view(
|
| 240 |
-
batch_size, grid_t, tps, channel,
|
| 241 |
-
grid_h // ms, ms, ps,
|
| 242 |
-
grid_w // ms, ms, ps,
|
| 243 |
-
)
|
| 244 |
-
patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
|
| 245 |
-
flatten_patches = patches.reshape(
|
| 246 |
-
batch_size,
|
| 247 |
-
grid_t * grid_h * grid_w,
|
| 248 |
-
channel * tps * ps * ps,
|
| 249 |
-
)
|
| 250 |
-
|
| 251 |
-
seq_len = grid_t * grid_h * grid_w
|
| 252 |
-
flat = flatten_patches.reshape(batch_size * seq_len, -1)
|
| 253 |
-
# on-device 构造小 tensor 再 expand，host→GPU 同步量 O(3) 而非 O(N*3)
|
| 254 |
-
grid_unit = torch.tensor(
|
| 255 |
-
[grid_t, grid_h, grid_w], dtype=torch.int32, device=pixel_values.device,
|
| 256 |
-
)
|
| 257 |
-
grid_thw = grid_unit.unsqueeze(0).expand(batch_size, -1).contiguous()
|
| 258 |
-
return flat, grid_thw, (batch_size, seq_len)
|
| 259 |
-
|
| 260 |
-
def forward(
|
| 261 |
-
self,
|
| 262 |
-
pixel_values: torch.Tensor,
|
| 263 |
-
output_hidden_states: Optional[bool] = None,
|
| 264 |
-
return_dict: Optional[bool] = None,
|
| 265 |
-
**kwargs,
|
| 266 |
-
) -> BaseModelOutputWithPooling:
|
| 267 |
-
ring = self.register_ring
|
| 268 |
-
if ring > 0:
|
| 269 |
-
# UniRefiner deploy protocol: surround each tile/frame with a register
|
| 270 |
-
# ring, encode, drop the ring afterwards. zero-fill in normalized pixel
|
| 271 |
-
# space == the S0 training ring (register_fill: zero, gray patches).
|
| 272 |
-
pad_px = ring * self.config.patch_size
|
| 273 |
-
pixel_values = F.pad(pixel_values, (pad_px, pad_px, pad_px, pad_px), value=0.0)
|
| 274 |
-
|
| 275 |
-
flat, grid_thw, (N, S) = self._flatten_navit(pixel_values)
|
| 276 |
-
vision_out = self.vision(flat, grid_thw=grid_thw)
|
| 277 |
-
hidden = vision_out.last_hidden_state.view(N, S, -1)
|
| 278 |
-
|
| 279 |
-
# Block-merge → row-major reorder before handing to LlavaOnevision.
|
| 280 |
-
# Internally the ViT runs in Qwen NaViT block-merge layout (pretrained
|
| 281 |
-
# pos_embed + RoPE contract); downstream LlavaOV `pack_image_features`
|
| 282 |
-
# (multi-tile AnyRes path, view(num_patch_h, num_patch_w, h, w, -1))
|
| 283 |
-
# and `apply_pooling` (video path, view(B, h, w, -1) + bilinear) BOTH
|
| 284 |
-
# assume row-major. Without this reorder, the multi-tile/video spatial
|
| 285 |
-
# pool pulls together tokens that are NOT spatially adjacent — silent
|
| 286 |
-
# corruption that doesn't fire on S1 single-tile path (line 348-351 of
|
| 287 |
-
# modeling_llava_onevision.py just flattens [N,D] verbatim) but kills
|
| 288 |
-
# S2 / eval quality.
|
| 289 |
-
grid_h = int(grid_thw[0, 1].item())
|
| 290 |
-
grid_w = int(grid_thw[0, 2].item())
|
| 291 |
-
ms = getattr(self.config, "spatial_merge_size", 2)
|
| 292 |
-
layout_perm = block_merge_to_row_major_permutation(
|
| 293 |
-
grid_h, grid_w, ms=ms, device=hidden.device,
|
| 294 |
-
)
|
| 295 |
-
hidden = hidden[:, layout_perm, :]
|
| 296 |
-
|
| 297 |
-
if ring > 0:
|
| 298 |
-
# Drop the ring tokens after the row-major reorder: keep the inner
|
| 299 |
-
# (grid_h-2r)x(grid_w-2r) block. Downstream LlavaOV consumes the same
|
| 300 |
-
# row-major image-token count as the ring-off path.
|
| 301 |
-
keep = torch.ones(grid_h, grid_w, dtype=torch.bool, device=hidden.device)
|
| 302 |
-
keep[:ring, :] = False
|
| 303 |
-
keep[grid_h - ring:, :] = False
|
| 304 |
-
keep[:, :ring] = False
|
| 305 |
-
keep[:, grid_w - ring:] = False
|
| 306 |
-
hidden = hidden[:, keep.reshape(-1), :]
|
| 307 |
-
|
| 308 |
-
return BaseModelOutputWithPooling(
|
| 309 |
-
last_hidden_state=hidden,
|
| 310 |
-
# LlavaOnevision 索引 hidden_states[vision_feature_layer=-1]；tuple 长度 1 足够
|
| 311 |
-
hidden_states=(hidden,),
|
| 312 |
-
pooler_output=None,
|
| 313 |
-
)
|
| 314 |
-
|
| 315 |
-
|
| 316 |
-
class LlavaQwen3_5ViTMultiModalProjector(nn.Module):
|
| 317 |
-
"""标准 LlavaOnevision projector（V6 final_layernorm fix (2026-05-16): pre_norm → Identity）。
|
| 318 |
-
|
| 319 |
-
V6 final_layernorm fix (2026-05-16) 修复后, encoder 末端已自带 final_layernorm（与 SigLIP2 post_layernorm 对称），
|
| 320 |
-
projector 不再需要补 LN — pre_norm 改为 nn.Identity，对齐 SigLIP2 plugin 的
|
| 321 |
-
LlavaOnevision stock projector 结构（裸 linear_1 → GELU → linear_2），
|
| 322 |
-
保证 SigLIP2 / Qwen3.5 / Qwen3-VL 三个 backbone 在 LlavaOV 设定下公平对比。
|
| 323 |
-
|
| 324 |
-
（历史：V6.0.0~V6.0.4 时期 encoder 无 final LN，projector pre_norm 是补丁；
|
| 325 |
-
现在补丁回到 encoder 内部，projector 回归 stock 形态。）
|
| 326 |
-
"""
|
| 327 |
-
|
| 328 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 329 |
-
super().__init__()
|
| 330 |
-
num_feature_layers = (
|
| 331 |
-
1 if isinstance(config.vision_feature_layer, int) else len(config.vision_feature_layer)
|
| 332 |
-
)
|
| 333 |
-
vision_dim = config.vision_config.hidden_size * num_feature_layers
|
| 334 |
-
text_dim = config.text_config.hidden_size
|
| 335 |
-
bias = getattr(config, "multimodal_projector_bias", True)
|
| 336 |
-
|
| 337 |
-
# V6 final_layernorm fix (2026-05-16): pre_norm = Identity (encoder 已自带 final_layernorm).
|
| 338 |
-
self.pre_norm = nn.Identity()
|
| 339 |
-
self.linear_1 = nn.Linear(vision_dim, text_dim, bias=bias)
|
| 340 |
-
self.act = ACT2FN[config.projector_hidden_act]
|
| 341 |
-
self.linear_2 = nn.Linear(text_dim, text_dim, bias=bias)
|
| 342 |
-
|
| 343 |
-
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 344 |
-
return self.linear_2(self.act(self.linear_1(self.pre_norm(x))))
|
| 345 |
-
|
| 346 |
-
|
| 347 |
-
class LlavaQwen3_5ViTModel(LlavaOnevisionModel):
|
| 348 |
-
"""继承 LlavaOnevisionModel 但绕过其 __init__ 手动装配。
|
| 349 |
-
|
| 350 |
-
父类 __init__ 调 `AutoModel.from_config(config.vision_config)` 会对 Qwen3_5VisionConfig
|
| 351 |
-
抛 "Unrecognized configuration"（Qwen3.5 vision 没注册到 AutoModel）。手动装配避开
|
| 352 |
-
这一步，同时省掉"先构造 Qwen3_5VisionModel 再被替换"的双重开销（~1.3GB init-peak）。
|
| 353 |
-
|
| 354 |
-
装配顺序与父类一致：vision_tower / projector / image_newline / language_model / post_init。
|
| 355 |
-
"""
|
| 356 |
-
|
| 357 |
-
config_class = LlavaQwen3_5ViTConfig
|
| 358 |
-
|
| 359 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 360 |
-
# 跳过 LlavaOnevisionModel.__init__（AutoModel 不识别 Qwen3_5VisionConfig）
|
| 361 |
-
LlavaOnevisionPreTrainedModel.__init__(self, config)
|
| 362 |
-
self.vision_tower = Qwen3_5ViTAsSiglipAdapter(
|
| 363 |
-
config.vision_config,
|
| 364 |
-
register_ring=getattr(config, "vit_register_ring", 0),
|
| 365 |
-
skip_last_blocks=getattr(config, "vit_skip_last_blocks", 0),
|
| 366 |
-
)
|
| 367 |
-
self.multi_modal_projector = LlavaQwen3_5ViTMultiModalProjector(config)
|
| 368 |
-
embed_std = 1 / math.sqrt(config.text_config.hidden_size)
|
| 369 |
-
self.image_newline = nn.Parameter(
|
| 370 |
-
torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std
|
| 371 |
-
)
|
| 372 |
-
self.vocab_size = config.text_config.vocab_size
|
| 373 |
-
self.language_model = AutoModel.from_config(config.text_config)
|
| 374 |
-
self.post_init()
|
| 375 |
-
|
| 376 |
-
|
| 377 |
-
class LlavaQwen3_5ViTForConditionalGeneration(LlavaOnevisionForConditionalGeneration):
|
| 378 |
-
"""继承 LlavaOnevisionForConditionalGeneration，只换 self.model。
|
| 379 |
-
|
| 380 |
-
同样跳过父类 __init__（避免重复构造 LlavaOnevisionModel，根因见 LlavaQwen3_5ViTModel）。
|
| 381 |
-
"""
|
| 382 |
-
|
| 383 |
-
config_class = LlavaQwen3_5ViTConfig
|
| 384 |
-
|
| 385 |
-
def __init__(self, config: LlavaQwen3_5ViTConfig):
|
| 386 |
-
LlavaOnevisionPreTrainedModel.__init__(self, config)
|
| 387 |
-
self.model = LlavaQwen3_5ViTModel(config)
|
| 388 |
-
self.lm_head = nn.Linear(
|
| 389 |
-
config.text_config.hidden_size, config.text_config.vocab_size, bias=False
|
| 390 |
-
)
|
| 391 |
-
self.post_init()
|
| 392 |
-
|
| 393 |
-
|
| 394 |
-
AutoConfig.register(LlavaQwen3_5ViTConfig.model_type, LlavaQwen3_5ViTConfig)
|
| 395 |
-
AutoModelForCausalLM.register(LlavaQwen3_5ViTConfig, LlavaQwen3_5ViTForConditionalGeneration)
|
| 396 |
-
|
| 397 |
-
|
| 398 |
-
__all__ = [
|
| 399 |
-
"LlavaQwen3_5ViTConfig",
|
| 400 |
-
"Qwen3_5ViTBackbone",
|
| 401 |
-
"Qwen3_5ViTAsSiglipAdapter",
|
| 402 |
-
"LlavaQwen3_5ViTMultiModalProjector",
|
| 403 |
-
"LlavaQwen3_5ViTModel",
|
| 404 |
-
"LlavaQwen3_5ViTForConditionalGeneration",
|
| 405 |
-
]
|
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|
ckpts/stock_32f/processor_config.json
DELETED
|
@@ -1,203 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"image_processor": {
|
| 3 |
-
"do_convert_rgb": true,
|
| 4 |
-
"do_normalize": true,
|
| 5 |
-
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| 9 |
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384,
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1152
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|
| 26 |
-
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| 28 |
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|
| 30 |
-
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|
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2304
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| 32 |
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|
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|
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|
| 35 |
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| 36 |
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|
| 37 |
-
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|
| 38 |
-
768,
|
| 39 |
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|
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-
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|
| 43 |
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1152
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| 44 |
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|
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|
| 50 |
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|
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|
| 54 |
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|
| 55 |
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2304
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| 60 |
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|
| 61 |
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| 62 |
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| 80 |
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1536,
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| 83 |
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| 84 |
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| 86 |
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| 87 |
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| 90 |
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| 91 |
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| 92 |
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| 96 |
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1536,
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| 108 |
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-
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| 110 |
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| 114 |
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| 122 |
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| 124 |
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| 126 |
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|
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| 132 |
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| 133 |
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| 134 |
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| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
-
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|
| 159 |
-
"image_processor_type": "LlavaOnevisionImageProcessor",
|
| 160 |
-
"image_std": [
|
| 161 |
-
0.5,
|
| 162 |
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|
| 163 |
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0.5
|
| 164 |
-
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|
| 165 |
-
"resample": 3,
|
| 166 |
-
"rescale_factor": 0.00392156862745098,
|
| 167 |
-
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|
| 168 |
-
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|
| 169 |
-
"width": 384
|
| 170 |
-
}
|
| 171 |
-
},
|
| 172 |
-
"image_token": "<image>",
|
| 173 |
-
"num_image_tokens": 576,
|
| 174 |
-
"processor_class": "LlavaOnevisionProcessor",
|
| 175 |
-
"video_processor": {
|
| 176 |
-
"do_convert_rgb": true,
|
| 177 |
-
"do_normalize": true,
|
| 178 |
-
"do_rescale": true,
|
| 179 |
-
"do_resize": true,
|
| 180 |
-
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|
| 181 |
-
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|
| 182 |
-
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|
| 183 |
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0.5,
|
| 184 |
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0.5
|
| 185 |
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|
| 186 |
-
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
-
"resample": 3,
|
| 192 |
-
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|
| 193 |
-
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|
| 194 |
-
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|
| 195 |
-
"height": 384,
|
| 196 |
-
"width": 384
|
| 197 |
-
},
|
| 198 |
-
"video_processor_type": "LlavaOnevisionVideoProcessor"
|
| 199 |
-
},
|
| 200 |
-
"video_token": "<video>",
|
| 201 |
-
"vision_aspect_ratio": "anyres_max_9",
|
| 202 |
-
"vision_feature_select_strategy": null
|
| 203 |
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}
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|
ckpts/stock_32f/tokenizer.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:9024318c850eaadf26be79389d21b07a7afd8f1af749b89f9109b06c0d12173c
|
| 3 |
-
size 11423018
|
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|
ckpts/stock_32f/tokenizer_config.json
DELETED
|
@@ -1,19 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"add_prefix_space": false,
|
| 3 |
-
"backend": "tokenizers",
|
| 4 |
-
"bos_token": null,
|
| 5 |
-
"clean_up_tokenization_spaces": false,
|
| 6 |
-
"eos_token": "<|im_end|>",
|
| 7 |
-
"errors": "replace",
|
| 8 |
-
"extra_special_tokens": [
|
| 9 |
-
"<image>",
|
| 10 |
-
"<video>"
|
| 11 |
-
],
|
| 12 |
-
"is_local": true,
|
| 13 |
-
"model_max_length": 1010000,
|
| 14 |
-
"pad_token": "<|endoftext|>",
|
| 15 |
-
"processor_class": "LlavaOnevisionProcessor",
|
| 16 |
-
"split_special_tokens": false,
|
| 17 |
-
"tokenizer_class": "Qwen2Tokenizer",
|
| 18 |
-
"unk_token": null
|
| 19 |
-
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ckpts/stock_32f/zero_to_fp32.py
DELETED
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@@ -1,760 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
|
| 3 |
-
# Copyright (c) Microsoft Corporation.
|
| 4 |
-
# SPDX-License-Identifier: Apache-2.0
|
| 5 |
-
|
| 6 |
-
# DeepSpeed Team
|
| 7 |
-
|
| 8 |
-
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
| 9 |
-
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
| 10 |
-
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
-
# application.
|
| 12 |
-
#
|
| 13 |
-
# example:
|
| 14 |
-
# python zero_to_fp32.py . output_dir/
|
| 15 |
-
# or
|
| 16 |
-
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
| 17 |
-
|
| 18 |
-
import argparse
|
| 19 |
-
import torch
|
| 20 |
-
import glob
|
| 21 |
-
import math
|
| 22 |
-
import os
|
| 23 |
-
import re
|
| 24 |
-
import gc
|
| 25 |
-
import json
|
| 26 |
-
import numpy as np
|
| 27 |
-
from tqdm import tqdm
|
| 28 |
-
from collections import OrderedDict
|
| 29 |
-
from dataclasses import dataclass
|
| 30 |
-
|
| 31 |
-
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
| 32 |
-
# DeepSpeed data structures it has to be available in the current python environment.
|
| 33 |
-
from deepspeed.utils import logger
|
| 34 |
-
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
| 35 |
-
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
| 36 |
-
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
@dataclass
|
| 40 |
-
class zero_model_state:
|
| 41 |
-
buffers: dict()
|
| 42 |
-
param_shapes: dict()
|
| 43 |
-
shared_params: list
|
| 44 |
-
ds_version: int
|
| 45 |
-
frozen_param_shapes: dict()
|
| 46 |
-
frozen_param_fragments: dict()
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
debug = 0
|
| 50 |
-
|
| 51 |
-
# load to cpu
|
| 52 |
-
device = torch.device('cpu')
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def atoi(text):
|
| 56 |
-
return int(text) if text.isdigit() else text
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
def natural_keys(text):
|
| 60 |
-
'''
|
| 61 |
-
alist.sort(key=natural_keys) sorts in human order
|
| 62 |
-
http://nedbatchelder.com/blog/200712/human_sorting.html
|
| 63 |
-
(See Toothy's implementation in the comments)
|
| 64 |
-
'''
|
| 65 |
-
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def get_model_state_file(checkpoint_dir, zero_stage):
|
| 69 |
-
if not os.path.isdir(checkpoint_dir):
|
| 70 |
-
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
| 71 |
-
|
| 72 |
-
# there should be only one file
|
| 73 |
-
if zero_stage <= 2:
|
| 74 |
-
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
| 75 |
-
elif zero_stage == 3:
|
| 76 |
-
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
| 77 |
-
|
| 78 |
-
if not os.path.exists(file):
|
| 79 |
-
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
| 80 |
-
|
| 81 |
-
return file
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
| 85 |
-
# XXX: need to test that this simple glob rule works for multi-node setup too
|
| 86 |
-
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
| 87 |
-
|
| 88 |
-
if len(ckpt_files) == 0:
|
| 89 |
-
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
| 90 |
-
|
| 91 |
-
return ckpt_files
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def get_optim_files(checkpoint_dir):
|
| 95 |
-
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def get_model_state_files(checkpoint_dir):
|
| 99 |
-
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def parse_model_states(files):
|
| 103 |
-
zero_model_states = []
|
| 104 |
-
for file in files:
|
| 105 |
-
state_dict = torch.load(file, map_location=device, weights_only=False)
|
| 106 |
-
|
| 107 |
-
if BUFFER_NAMES not in state_dict:
|
| 108 |
-
raise ValueError(f"{file} is not a model state checkpoint")
|
| 109 |
-
buffer_names = state_dict[BUFFER_NAMES]
|
| 110 |
-
if debug:
|
| 111 |
-
print("Found buffers:", buffer_names)
|
| 112 |
-
|
| 113 |
-
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
| 114 |
-
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
| 115 |
-
param_shapes = state_dict[PARAM_SHAPES]
|
| 116 |
-
|
| 117 |
-
# collect parameters that are included in param_shapes
|
| 118 |
-
param_names = []
|
| 119 |
-
for s in param_shapes:
|
| 120 |
-
for name in s.keys():
|
| 121 |
-
param_names.append(name)
|
| 122 |
-
|
| 123 |
-
# update with frozen parameters
|
| 124 |
-
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
| 125 |
-
if frozen_param_shapes is not None:
|
| 126 |
-
if debug:
|
| 127 |
-
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
| 128 |
-
param_names += list(frozen_param_shapes.keys())
|
| 129 |
-
|
| 130 |
-
# handle shared params
|
| 131 |
-
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
| 132 |
-
|
| 133 |
-
ds_version = state_dict.get(DS_VERSION, None)
|
| 134 |
-
|
| 135 |
-
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
| 136 |
-
|
| 137 |
-
z_model_state = zero_model_state(buffers=buffers,
|
| 138 |
-
param_shapes=param_shapes,
|
| 139 |
-
shared_params=shared_params,
|
| 140 |
-
ds_version=ds_version,
|
| 141 |
-
frozen_param_shapes=frozen_param_shapes,
|
| 142 |
-
frozen_param_fragments=frozen_param_fragments)
|
| 143 |
-
zero_model_states.append(z_model_state)
|
| 144 |
-
|
| 145 |
-
return zero_model_states
|
| 146 |
-
|
| 147 |
-
|
| 148 |
-
def parse_optim_states(files, ds_checkpoint_dir):
|
| 149 |
-
total_files = len(files)
|
| 150 |
-
state_dicts = []
|
| 151 |
-
for f in tqdm(files, desc='Loading checkpoint shards'):
|
| 152 |
-
state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
|
| 153 |
-
# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
|
| 154 |
-
# and also handle the case where it was already removed by another helper script
|
| 155 |
-
state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
|
| 156 |
-
state_dicts.append(state_dict)
|
| 157 |
-
|
| 158 |
-
if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
| 159 |
-
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
| 160 |
-
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
| 161 |
-
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
| 162 |
-
|
| 163 |
-
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
| 164 |
-
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
| 165 |
-
# use the max of the partition_count to get the dp world_size.
|
| 166 |
-
|
| 167 |
-
if type(world_size) is list:
|
| 168 |
-
world_size = max(world_size)
|
| 169 |
-
|
| 170 |
-
if world_size != total_files:
|
| 171 |
-
raise ValueError(
|
| 172 |
-
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
| 173 |
-
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
| 174 |
-
)
|
| 175 |
-
|
| 176 |
-
# the groups are named differently in each stage
|
| 177 |
-
if zero_stage <= 2:
|
| 178 |
-
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
| 179 |
-
elif zero_stage == 3:
|
| 180 |
-
fp32_groups_key = FP32_FLAT_GROUPS
|
| 181 |
-
else:
|
| 182 |
-
raise ValueError(f"unknown zero stage {zero_stage}")
|
| 183 |
-
|
| 184 |
-
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
| 185 |
-
return zero_stage, world_size, fp32_flat_groups
|
| 186 |
-
|
| 187 |
-
|
| 188 |
-
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
|
| 189 |
-
"""
|
| 190 |
-
Returns fp32 state_dict reconstructed from ds checkpoint
|
| 191 |
-
|
| 192 |
-
Args:
|
| 193 |
-
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
| 194 |
-
|
| 195 |
-
"""
|
| 196 |
-
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
| 197 |
-
|
| 198 |
-
optim_files = get_optim_files(ds_checkpoint_dir)
|
| 199 |
-
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
| 200 |
-
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
| 201 |
-
|
| 202 |
-
model_files = get_model_state_files(ds_checkpoint_dir)
|
| 203 |
-
|
| 204 |
-
zero_model_states = parse_model_states(model_files)
|
| 205 |
-
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
| 206 |
-
|
| 207 |
-
if zero_stage <= 2:
|
| 208 |
-
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 209 |
-
exclude_frozen_parameters)
|
| 210 |
-
elif zero_stage == 3:
|
| 211 |
-
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 212 |
-
exclude_frozen_parameters)
|
| 213 |
-
|
| 214 |
-
|
| 215 |
-
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
| 216 |
-
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 217 |
-
return
|
| 218 |
-
|
| 219 |
-
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 220 |
-
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
| 221 |
-
|
| 222 |
-
if debug:
|
| 223 |
-
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
| 224 |
-
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 225 |
-
|
| 226 |
-
wanted_params = len(frozen_param_shapes)
|
| 227 |
-
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 228 |
-
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
| 229 |
-
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 230 |
-
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 231 |
-
|
| 232 |
-
total_params = 0
|
| 233 |
-
total_numel = 0
|
| 234 |
-
for name, shape in frozen_param_shapes.items():
|
| 235 |
-
total_params += 1
|
| 236 |
-
unpartitioned_numel = shape.numel()
|
| 237 |
-
total_numel += unpartitioned_numel
|
| 238 |
-
|
| 239 |
-
state_dict[name] = frozen_param_fragments[name]
|
| 240 |
-
|
| 241 |
-
if debug:
|
| 242 |
-
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 243 |
-
|
| 244 |
-
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 245 |
-
|
| 246 |
-
|
| 247 |
-
def _has_callable(obj, fn):
|
| 248 |
-
attr = getattr(obj, fn, None)
|
| 249 |
-
return callable(attr)
|
| 250 |
-
|
| 251 |
-
|
| 252 |
-
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 253 |
-
param_shapes = zero_model_states[0].param_shapes
|
| 254 |
-
|
| 255 |
-
# Reconstruction protocol:
|
| 256 |
-
#
|
| 257 |
-
# XXX: document this
|
| 258 |
-
|
| 259 |
-
if debug:
|
| 260 |
-
for i in range(world_size):
|
| 261 |
-
for j in range(len(fp32_flat_groups[0])):
|
| 262 |
-
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
| 263 |
-
|
| 264 |
-
# XXX: memory usage doubles here (zero2)
|
| 265 |
-
num_param_groups = len(fp32_flat_groups[0])
|
| 266 |
-
merged_single_partition_of_fp32_groups = []
|
| 267 |
-
for i in range(num_param_groups):
|
| 268 |
-
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
| 269 |
-
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
| 270 |
-
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
| 271 |
-
avail_numel = sum(
|
| 272 |
-
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
| 273 |
-
|
| 274 |
-
if debug:
|
| 275 |
-
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
| 276 |
-
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
| 277 |
-
# not asserting if there is a mismatch due to possible padding
|
| 278 |
-
print(f"Have {avail_numel} numels to process.")
|
| 279 |
-
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
| 280 |
-
|
| 281 |
-
# params
|
| 282 |
-
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 283 |
-
# out-of-core computing solution
|
| 284 |
-
total_numel = 0
|
| 285 |
-
total_params = 0
|
| 286 |
-
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
| 287 |
-
offset = 0
|
| 288 |
-
avail_numel = full_single_fp32_vector.numel()
|
| 289 |
-
for name, shape in shapes.items():
|
| 290 |
-
|
| 291 |
-
unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
|
| 292 |
-
total_numel += unpartitioned_numel
|
| 293 |
-
total_params += 1
|
| 294 |
-
|
| 295 |
-
if debug:
|
| 296 |
-
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
| 297 |
-
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
| 298 |
-
offset += unpartitioned_numel
|
| 299 |
-
|
| 300 |
-
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
| 301 |
-
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
| 302 |
-
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
| 303 |
-
# live optimizer object, so we are checking that the numbers are within the right range
|
| 304 |
-
align_to = 2 * world_size
|
| 305 |
-
|
| 306 |
-
def zero2_align(x):
|
| 307 |
-
return align_to * math.ceil(x / align_to)
|
| 308 |
-
|
| 309 |
-
if debug:
|
| 310 |
-
print(f"original offset={offset}, avail_numel={avail_numel}")
|
| 311 |
-
|
| 312 |
-
offset = zero2_align(offset)
|
| 313 |
-
avail_numel = zero2_align(avail_numel)
|
| 314 |
-
|
| 315 |
-
if debug:
|
| 316 |
-
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
| 317 |
-
|
| 318 |
-
# Sanity check
|
| 319 |
-
if offset != avail_numel:
|
| 320 |
-
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 321 |
-
|
| 322 |
-
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
| 323 |
-
|
| 324 |
-
|
| 325 |
-
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 326 |
-
exclude_frozen_parameters):
|
| 327 |
-
state_dict = OrderedDict()
|
| 328 |
-
|
| 329 |
-
# buffers
|
| 330 |
-
buffers = zero_model_states[0].buffers
|
| 331 |
-
state_dict.update(buffers)
|
| 332 |
-
if debug:
|
| 333 |
-
print(f"added {len(buffers)} buffers")
|
| 334 |
-
|
| 335 |
-
if not exclude_frozen_parameters:
|
| 336 |
-
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
| 337 |
-
|
| 338 |
-
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 339 |
-
|
| 340 |
-
# recover shared parameters
|
| 341 |
-
for pair in zero_model_states[0].shared_params:
|
| 342 |
-
if pair[1] in state_dict:
|
| 343 |
-
state_dict[pair[0]] = state_dict[pair[1]]
|
| 344 |
-
|
| 345 |
-
return state_dict
|
| 346 |
-
|
| 347 |
-
|
| 348 |
-
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
| 349 |
-
remainder = unpartitioned_numel % world_size
|
| 350 |
-
padding_numel = (world_size - remainder) if remainder else 0
|
| 351 |
-
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
| 352 |
-
return partitioned_numel, padding_numel
|
| 353 |
-
|
| 354 |
-
|
| 355 |
-
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
| 356 |
-
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 357 |
-
return
|
| 358 |
-
|
| 359 |
-
if debug:
|
| 360 |
-
for i in range(world_size):
|
| 361 |
-
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
| 362 |
-
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 363 |
-
|
| 364 |
-
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 365 |
-
wanted_params = len(frozen_param_shapes)
|
| 366 |
-
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 367 |
-
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
| 368 |
-
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 369 |
-
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 370 |
-
|
| 371 |
-
total_params = 0
|
| 372 |
-
total_numel = 0
|
| 373 |
-
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
| 374 |
-
total_params += 1
|
| 375 |
-
unpartitioned_numel = shape.numel()
|
| 376 |
-
total_numel += unpartitioned_numel
|
| 377 |
-
|
| 378 |
-
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
| 379 |
-
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
| 380 |
-
|
| 381 |
-
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 382 |
-
|
| 383 |
-
if debug:
|
| 384 |
-
print(
|
| 385 |
-
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 386 |
-
)
|
| 387 |
-
|
| 388 |
-
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 389 |
-
|
| 390 |
-
|
| 391 |
-
class GatheredTensor:
|
| 392 |
-
"""
|
| 393 |
-
A pseudo tensor that collects partitioned weights.
|
| 394 |
-
It is more memory efficient when there are multiple groups.
|
| 395 |
-
"""
|
| 396 |
-
|
| 397 |
-
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
| 398 |
-
self.flat_groups = flat_groups
|
| 399 |
-
self.flat_groups_offset = flat_groups_offset
|
| 400 |
-
self.offset = offset
|
| 401 |
-
self.partitioned_numel = partitioned_numel
|
| 402 |
-
self.shape = shape
|
| 403 |
-
self.dtype = self.flat_groups[0][0].dtype
|
| 404 |
-
|
| 405 |
-
def contiguous(self):
|
| 406 |
-
"""
|
| 407 |
-
Merge partitioned weights from flat_groups into a single tensor.
|
| 408 |
-
"""
|
| 409 |
-
end_idx = self.offset + self.partitioned_numel
|
| 410 |
-
world_size = len(self.flat_groups)
|
| 411 |
-
pad_flat_param_chunks = []
|
| 412 |
-
|
| 413 |
-
for rank_i in range(world_size):
|
| 414 |
-
# for each rank, we need to collect weights from related group/groups
|
| 415 |
-
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
| 416 |
-
start_group_id = None
|
| 417 |
-
end_group_id = None
|
| 418 |
-
for group_id in range(len(self.flat_groups_offset)):
|
| 419 |
-
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
| 420 |
-
start_group_id = group_id
|
| 421 |
-
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
| 422 |
-
end_group_id = group_id
|
| 423 |
-
break
|
| 424 |
-
# collect weights from related group/groups
|
| 425 |
-
for group_id in range(start_group_id, end_group_id + 1):
|
| 426 |
-
flat_tensor = flat_groups_at_rank_i[group_id]
|
| 427 |
-
start_offset = self.offset - self.flat_groups_offset[group_id]
|
| 428 |
-
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
| 429 |
-
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
| 430 |
-
|
| 431 |
-
# collect weights from all ranks
|
| 432 |
-
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
| 433 |
-
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
| 434 |
-
return param
|
| 435 |
-
|
| 436 |
-
|
| 437 |
-
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 438 |
-
param_shapes = zero_model_states[0].param_shapes
|
| 439 |
-
avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
|
| 440 |
-
|
| 441 |
-
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
| 442 |
-
# param, re-consolidating each param, while dealing with padding if any
|
| 443 |
-
|
| 444 |
-
# merge list of dicts, preserving order
|
| 445 |
-
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
| 446 |
-
|
| 447 |
-
if debug:
|
| 448 |
-
for i in range(world_size):
|
| 449 |
-
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
| 450 |
-
|
| 451 |
-
wanted_params = len(param_shapes)
|
| 452 |
-
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
| 453 |
-
# not asserting if there is a mismatch due to possible padding
|
| 454 |
-
avail_numel = fp32_flat_groups[0].numel() * world_size
|
| 455 |
-
print(f"Trainable params: Have {avail_numel} numels to process.")
|
| 456 |
-
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
| 457 |
-
|
| 458 |
-
# params
|
| 459 |
-
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 460 |
-
# out-of-core computing solution
|
| 461 |
-
offset = 0
|
| 462 |
-
total_numel = 0
|
| 463 |
-
total_params = 0
|
| 464 |
-
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
|
| 465 |
-
for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
|
| 466 |
-
unpartitioned_numel = shape.numel()
|
| 467 |
-
total_numel += unpartitioned_numel
|
| 468 |
-
total_params += 1
|
| 469 |
-
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 470 |
-
|
| 471 |
-
if debug:
|
| 472 |
-
print(
|
| 473 |
-
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 474 |
-
)
|
| 475 |
-
|
| 476 |
-
# memory efficient tensor
|
| 477 |
-
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
| 478 |
-
state_dict[name] = tensor
|
| 479 |
-
offset += partitioned_numel
|
| 480 |
-
|
| 481 |
-
offset *= world_size
|
| 482 |
-
|
| 483 |
-
# Sanity check
|
| 484 |
-
if offset != avail_numel:
|
| 485 |
-
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 486 |
-
|
| 487 |
-
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
| 488 |
-
|
| 489 |
-
|
| 490 |
-
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 491 |
-
exclude_frozen_parameters):
|
| 492 |
-
state_dict = OrderedDict()
|
| 493 |
-
|
| 494 |
-
# buffers
|
| 495 |
-
buffers = zero_model_states[0].buffers
|
| 496 |
-
state_dict.update(buffers)
|
| 497 |
-
if debug:
|
| 498 |
-
print(f"added {len(buffers)} buffers")
|
| 499 |
-
|
| 500 |
-
if not exclude_frozen_parameters:
|
| 501 |
-
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
| 502 |
-
|
| 503 |
-
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 504 |
-
|
| 505 |
-
# recover shared parameters
|
| 506 |
-
for pair in zero_model_states[0].shared_params:
|
| 507 |
-
if pair[1] in state_dict:
|
| 508 |
-
state_dict[pair[0]] = state_dict[pair[1]]
|
| 509 |
-
|
| 510 |
-
return state_dict
|
| 511 |
-
|
| 512 |
-
|
| 513 |
-
def to_torch_tensor(state_dict, return_empty_tensor=False):
|
| 514 |
-
"""
|
| 515 |
-
Convert state_dict of GatheredTensor to torch tensor
|
| 516 |
-
"""
|
| 517 |
-
torch_state_dict = {}
|
| 518 |
-
converted_tensors = {}
|
| 519 |
-
for name, tensor in state_dict.items():
|
| 520 |
-
tensor_id = id(tensor)
|
| 521 |
-
if tensor_id in converted_tensors: # shared tensors
|
| 522 |
-
shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
|
| 523 |
-
torch_state_dict[name] = shared_tensor
|
| 524 |
-
else:
|
| 525 |
-
converted_tensors[tensor_id] = name
|
| 526 |
-
if return_empty_tensor:
|
| 527 |
-
torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
| 528 |
-
else:
|
| 529 |
-
torch_state_dict[name] = tensor.contiguous()
|
| 530 |
-
return torch_state_dict
|
| 531 |
-
|
| 532 |
-
|
| 533 |
-
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 534 |
-
tag=None,
|
| 535 |
-
exclude_frozen_parameters=False,
|
| 536 |
-
lazy_mode=False):
|
| 537 |
-
"""
|
| 538 |
-
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
| 539 |
-
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
| 540 |
-
via a model hub.
|
| 541 |
-
|
| 542 |
-
Args:
|
| 543 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder
|
| 544 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
| 545 |
-
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 546 |
-
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
| 547 |
-
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
| 548 |
-
|
| 549 |
-
Returns:
|
| 550 |
-
- pytorch ``state_dict``
|
| 551 |
-
|
| 552 |
-
A typical usage might be ::
|
| 553 |
-
|
| 554 |
-
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 555 |
-
# do the training and checkpoint saving
|
| 556 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
| 557 |
-
model = model.cpu() # move to cpu
|
| 558 |
-
model.load_state_dict(state_dict)
|
| 559 |
-
# submit to model hub or save the model to share with others
|
| 560 |
-
|
| 561 |
-
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
| 562 |
-
application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 563 |
-
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 564 |
-
|
| 565 |
-
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
| 566 |
-
|
| 567 |
-
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
| 568 |
-
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
| 569 |
-
the checkpoint. Or you can load state_dict in lazy mode ::
|
| 570 |
-
|
| 571 |
-
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 572 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
| 573 |
-
for name, lazy_tensor in state_dict.item():
|
| 574 |
-
tensor = lazy_tensor.contiguous() # to cpu
|
| 575 |
-
print(name, tensor)
|
| 576 |
-
# del tensor to release memory if it no longer in use
|
| 577 |
-
"""
|
| 578 |
-
if tag is None:
|
| 579 |
-
latest_path = os.path.join(checkpoint_dir, 'latest')
|
| 580 |
-
if os.path.isfile(latest_path):
|
| 581 |
-
with open(latest_path, 'r') as fd:
|
| 582 |
-
tag = fd.read().strip()
|
| 583 |
-
else:
|
| 584 |
-
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
| 585 |
-
|
| 586 |
-
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
| 587 |
-
|
| 588 |
-
if not os.path.isdir(ds_checkpoint_dir):
|
| 589 |
-
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
| 590 |
-
|
| 591 |
-
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
| 592 |
-
if lazy_mode:
|
| 593 |
-
return state_dict
|
| 594 |
-
else:
|
| 595 |
-
return to_torch_tensor(state_dict)
|
| 596 |
-
|
| 597 |
-
|
| 598 |
-
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
| 599 |
-
output_dir,
|
| 600 |
-
max_shard_size="5GB",
|
| 601 |
-
safe_serialization=False,
|
| 602 |
-
tag=None,
|
| 603 |
-
exclude_frozen_parameters=False):
|
| 604 |
-
"""
|
| 605 |
-
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
| 606 |
-
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
| 607 |
-
|
| 608 |
-
Args:
|
| 609 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 610 |
-
- ``output_dir``: directory to the pytorch fp32 state_dict output files
|
| 611 |
-
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
| 612 |
-
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
| 613 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 614 |
-
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 615 |
-
"""
|
| 616 |
-
|
| 617 |
-
# Dependency pre-check
|
| 618 |
-
if safe_serialization:
|
| 619 |
-
try:
|
| 620 |
-
from safetensors.torch import save_file
|
| 621 |
-
except ImportError:
|
| 622 |
-
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
| 623 |
-
raise
|
| 624 |
-
if max_shard_size is not None:
|
| 625 |
-
try:
|
| 626 |
-
from huggingface_hub import split_torch_state_dict_into_shards
|
| 627 |
-
except ImportError:
|
| 628 |
-
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
| 629 |
-
raise
|
| 630 |
-
|
| 631 |
-
# Convert zero checkpoint to state_dict
|
| 632 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 633 |
-
tag,
|
| 634 |
-
exclude_frozen_parameters,
|
| 635 |
-
lazy_mode=True)
|
| 636 |
-
|
| 637 |
-
# Shard the model if it is too big.
|
| 638 |
-
weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
|
| 639 |
-
if max_shard_size is not None:
|
| 640 |
-
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
| 641 |
-
# an memory-efficient approach for sharding
|
| 642 |
-
empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
|
| 643 |
-
state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
|
| 644 |
-
filename_pattern=filename_pattern,
|
| 645 |
-
max_shard_size=max_shard_size)
|
| 646 |
-
else:
|
| 647 |
-
from collections import namedtuple
|
| 648 |
-
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
| 649 |
-
state_dict_split = StateDictSplit(is_sharded=False,
|
| 650 |
-
filename_to_tensors={weights_name: list(state_dict.keys())})
|
| 651 |
-
|
| 652 |
-
# Save the model by shard
|
| 653 |
-
os.makedirs(output_dir, exist_ok=True)
|
| 654 |
-
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
| 655 |
-
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
| 656 |
-
shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
|
| 657 |
-
shard_state_dict = to_torch_tensor(shard_state_dict)
|
| 658 |
-
output_path = os.path.join(output_dir, shard_file)
|
| 659 |
-
if safe_serialization:
|
| 660 |
-
save_file(shard_state_dict, output_path, metadata={"format": "pt"})
|
| 661 |
-
else:
|
| 662 |
-
torch.save(shard_state_dict, output_path)
|
| 663 |
-
# release the memory of current shard
|
| 664 |
-
for tensor_name in list(shard_state_dict.keys()):
|
| 665 |
-
del state_dict[tensor_name]
|
| 666 |
-
del shard_state_dict[tensor_name]
|
| 667 |
-
del shard_state_dict
|
| 668 |
-
gc.collect()
|
| 669 |
-
|
| 670 |
-
# Save index if sharded
|
| 671 |
-
if state_dict_split.is_sharded:
|
| 672 |
-
index = {
|
| 673 |
-
"metadata": state_dict_split.metadata,
|
| 674 |
-
"weight_map": state_dict_split.tensor_to_filename,
|
| 675 |
-
}
|
| 676 |
-
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
| 677 |
-
save_index_file = os.path.join(output_dir, save_index_file)
|
| 678 |
-
with open(save_index_file, "w", encoding="utf-8") as f:
|
| 679 |
-
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
| 680 |
-
f.write(content)
|
| 681 |
-
|
| 682 |
-
|
| 683 |
-
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
| 684 |
-
"""
|
| 685 |
-
1. Put the provided model to cpu
|
| 686 |
-
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
| 687 |
-
3. Load it into the provided model
|
| 688 |
-
|
| 689 |
-
Args:
|
| 690 |
-
- ``model``: the model object to update
|
| 691 |
-
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 692 |
-
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 693 |
-
|
| 694 |
-
Returns:
|
| 695 |
-
- ``model`: modified model
|
| 696 |
-
|
| 697 |
-
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
| 698 |
-
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
| 699 |
-
conveniently placed for you in the checkpoint folder.
|
| 700 |
-
|
| 701 |
-
A typical usage might be ::
|
| 702 |
-
|
| 703 |
-
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
| 704 |
-
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
| 705 |
-
# submit to model hub or save the model to share with others
|
| 706 |
-
|
| 707 |
-
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
| 708 |
-
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 709 |
-
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 710 |
-
|
| 711 |
-
"""
|
| 712 |
-
logger.info("Extracting fp32 weights")
|
| 713 |
-
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
| 714 |
-
|
| 715 |
-
logger.info("Overwriting model with fp32 weights")
|
| 716 |
-
model = model.cpu()
|
| 717 |
-
model.load_state_dict(state_dict, strict=False)
|
| 718 |
-
|
| 719 |
-
return model
|
| 720 |
-
|
| 721 |
-
|
| 722 |
-
if __name__ == "__main__":
|
| 723 |
-
parser = argparse.ArgumentParser()
|
| 724 |
-
parser.add_argument("checkpoint_dir",
|
| 725 |
-
type=str,
|
| 726 |
-
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
| 727 |
-
parser.add_argument("output_dir",
|
| 728 |
-
type=str,
|
| 729 |
-
help="directory to the pytorch fp32 state_dict output files"
|
| 730 |
-
"(e.g. path/checkpoint-12-output/)")
|
| 731 |
-
parser.add_argument(
|
| 732 |
-
"--max_shard_size",
|
| 733 |
-
type=str,
|
| 734 |
-
default="5GB",
|
| 735 |
-
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
| 736 |
-
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
| 737 |
-
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
| 738 |
-
"without CPU OOM issues.")
|
| 739 |
-
parser.add_argument(
|
| 740 |
-
"--safe_serialization",
|
| 741 |
-
default=False,
|
| 742 |
-
action='store_true',
|
| 743 |
-
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
| 744 |
-
parser.add_argument("-t",
|
| 745 |
-
"--tag",
|
| 746 |
-
type=str,
|
| 747 |
-
default=None,
|
| 748 |
-
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
| 749 |
-
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
| 750 |
-
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
| 751 |
-
args = parser.parse_args()
|
| 752 |
-
|
| 753 |
-
debug = args.debug
|
| 754 |
-
|
| 755 |
-
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
| 756 |
-
args.output_dir,
|
| 757 |
-
max_shard_size=args.max_shard_size,
|
| 758 |
-
safe_serialization=args.safe_serialization,
|
| 759 |
-
tag=args.tag,
|
| 760 |
-
exclude_frozen_parameters=args.exclude_frozen_parameters)
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ckpts/v10_2_32f/args.json
DELETED
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@@ -1,376 +0,0 @@
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|
| 321 |
-
"boft_block_num": 0,
|
| 322 |
-
"boft_n_butterfly_factor": 1,
|
| 323 |
-
"boft_dropout": 0.0,
|
| 324 |
-
"vera_rank": 256,
|
| 325 |
-
"vera_projection_prng_key": 0,
|
| 326 |
-
"vera_dropout": 0.0,
|
| 327 |
-
"vera_d_initial": 0.1,
|
| 328 |
-
"adapter_act": "gelu",
|
| 329 |
-
"adapter_length": 128,
|
| 330 |
-
"adalora_target_r": 8,
|
| 331 |
-
"adalora_init_r": 12,
|
| 332 |
-
"adalora_tinit": 0,
|
| 333 |
-
"adalora_tfinal": 0,
|
| 334 |
-
"adalora_deltaT": 1,
|
| 335 |
-
"adalora_beta1": 0.85,
|
| 336 |
-
"adalora_beta2": 0.85,
|
| 337 |
-
"adalora_orth_reg_weight": 0.5,
|
| 338 |
-
"llamapro_num_new_blocks": 4,
|
| 339 |
-
"llamapro_num_groups": null,
|
| 340 |
-
"reft_layer_key": null,
|
| 341 |
-
"reft_layers": null,
|
| 342 |
-
"reft_rank": 4,
|
| 343 |
-
"reft_intervention_type": "LoreftIntervention",
|
| 344 |
-
"reft_args": null,
|
| 345 |
-
"swanlab_token": null,
|
| 346 |
-
"swanlab_project": "ms-swift",
|
| 347 |
-
"swanlab_workspace": null,
|
| 348 |
-
"swanlab_exp_name": null,
|
| 349 |
-
"swanlab_notification_method": null,
|
| 350 |
-
"swanlab_webhook_url": null,
|
| 351 |
-
"swanlab_secret": null,
|
| 352 |
-
"swanlab_sender_email": null,
|
| 353 |
-
"swanlab_receiver_email": null,
|
| 354 |
-
"swanlab_smtp_server": null,
|
| 355 |
-
"swanlab_smtp_port": null,
|
| 356 |
-
"swanlab_email_language": "zh",
|
| 357 |
-
"swanlab_mode": "cloud",
|
| 358 |
-
"add_version": true,
|
| 359 |
-
"create_checkpoint_symlink": false,
|
| 360 |
-
"zero_hpz_partition_size": null,
|
| 361 |
-
"deepspeed_autotp_size": null,
|
| 362 |
-
"swift_version": "4.1.1",
|
| 363 |
-
"ckpt_dir": "/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/S1/4b/qwen3_5_2b/v10_2/v0-20260610-091722/checkpoint-2181",
|
| 364 |
-
"rank": 0,
|
| 365 |
-
"global_world_size": 8,
|
| 366 |
-
"local_world_size": 8,
|
| 367 |
-
"model_suffix": "checkpoint-2181",
|
| 368 |
-
"model_info": "ModelInfo(model_type='llava_qwen3_5vit_qwen3', model_dir='/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/S1/4b/qwen3_5_2b/v10_2/v0-20260610-091722/checkpoint-2181', torch_dtype=torch.bfloat16, max_model_len=262144, quant_method=None, quant_bits=None, rope_scaling=None, is_moe_model=False, is_multimodal=True, config=None, task_type='causal_lm', num_labels=None)",
|
| 369 |
-
"model_meta": "ModelMeta(model_type='llava_qwen3_5vit_qwen3', model_groups=[ModelGroup(models=[Model(ms_model_id='Qwen/Qwen3-0.6B', hf_model_id='Qwen/Qwen3-0.6B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3-1.7B', hf_model_id='Qwen/Qwen3-1.7B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3-4B', hf_model_id='Qwen/Qwen3-4B', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen3-8B', hf_model_id='Qwen/Qwen3-8B', model_path=None, ms_revision=None, hf_revision=None)], template=None, ignore_patterns=None, requires=None, tags=[])], loader=<class 'model_plugin_qwen3_5vit.Qwen3_5ViTQwen3Loader'>, template='llava_qwen3_5vit_qwen3', model_arch=MultiModelKeys(arch_name='llava_hf', embedding=None, module_list=None, lm_head=None, q_proj=None, k_proj=None, v_proj=None, o_proj=None, attention=None, mlp=None, down_proj=None, qkv_proj=None, qk_proj=None, qa_proj=None, qb_proj=None, kv_proj=None, kva_proj=None, kvb_proj=None, language_model=['model.language_model', 'lm_head'], aligner=['model.multi_modal_projector'], vision_tower=['model.vision_tower'], generator=[]), mcore_model_type=None, architectures=['LlavaQwen3_5ViTForConditionalGeneration'], additional_saved_files=[], torch_dtype=None, is_multimodal=True, is_reward=False, task_type=None, ignore_patterns=None, requires=[], tags=['vision', 'video'])",
|
| 370 |
-
"model_dir": "/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/S1/4b/qwen3_5_2b/v10_2/v0-20260610-091722/checkpoint-2181",
|
| 371 |
-
"template_meta": "QwenTemplateMeta(template_type='llava_qwen3_5vit_qwen3', prefix=[], prompt=['<|im_start|>user\\n{{QUERY}}<|im_end|>\\n<|im_start|>assistant\\n'], chat_sep=['<|im_end|>\\n'], suffix=['<|im_end|>\\n'], template_cls=<class 'model_plugin_qwen3_5vit.Qwen3_5ViTLlavaTemplate'>, system_prefix=['<|im_start|>system\\n{{SYSTEM}}<|im_end|>\\n'], default_system=None, auto_add_bos=False, stop_words=['<|endoftext|>'], agent_template='hermes', is_thinking=False, thinking_prefix='', non_thinking_prefix='', history_thinking_prefix='')",
|
| 372 |
-
"_val_dataset_exists": false,
|
| 373 |
-
"hub": "<class 'swift.hub.hub.HFHub'>",
|
| 374 |
-
"evaluation_strategy": "steps",
|
| 375 |
-
"training_args": "Seq2SeqTrainingArguments(output_dir='/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/S2/4b/qwen3_5_2b/v10_2_32f_10pct/v0-20260610-154054', per_device_train_batch_size=1, num_train_epochs=1.0, max_steps=-1, learning_rate=1e-05, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs=None, warmup_steps=0.05, optim=<OptimizerNames.ADAMW_TORCH_FUSED: 'adamw_torch_fused'>, optim_args=None, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, optim_target_modules=None, gradient_accumulation_steps=8, average_tokens_across_devices=None, max_grad_norm=1.0, label_smoothing_factor=0.0, bf16=True, fp16=False, bf16_full_eval=False, fp16_full_eval=False, tf32=None, gradient_checkpointing=True, gradient_checkpointing_kwargs={'use_reentrant': False}, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, use_liger_kernel=False, liger_kernel_config=None, use_cache=False, neftune_noise_alpha=None, torch_empty_cache_steps=None, auto_find_batch_size=False, logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_steps=1, logging_first_step=True, log_on_each_node=True, logging_nan_inf_filter=True, include_num_input_tokens_seen=None, log_level='passive', log_level_replica='warning', disable_tqdm=False, report_to=[], run_name='/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/S2/4b/qwen3_5_2b/v10_2_32f_10pct/v0-20260610-154054', project='huggingface', trackio_space_id='trackio', eval_strategy=<IntervalStrategy.NO: 'no'>, eval_steps=200.0, eval_delay=0, per_device_eval_batch_size=1, prediction_loss_only=False, eval_on_start=False, eval_do_concat_batches=True, eval_use_gather_object=False, eval_accumulation_steps=None, include_for_metrics=[], batch_eval_metrics=False, save_only_model=False, save_strategy=<SaveStrategy.STEPS: 'steps'>, save_steps=200, save_on_each_node=False, save_total_limit=1, enable_jit_checkpoint=False, push_to_hub=False, hub_token=None, hub_private_repo=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_always_push=False, hub_revision=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, restore_callback_states_from_checkpoint=False, full_determinism=False, seed=42, data_seed=42, use_cpu=False, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), parallelism_config=None, dataloader_drop_last=False, dataloader_num_workers=6, dataloader_pin_memory=True, dataloader_persistent_workers=True, dataloader_prefetch_factor=4, remove_unused_columns=False, label_names=None, train_sampling_strategy='random', length_column_name='length', ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, ddp_backend=None, ddp_timeout=7200, fsdp=[], fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, deepspeed={'fp16': {'enabled': 'auto', 'loss_scale': 0, 'loss_scale_window': 1000, 'initial_scale_power': 16, 'hysteresis': 2, 'min_loss_scale': 1}, 'bf16': {'enabled': 'auto'}, 'zero_optimization': {'stage': 2, 'offload_optimizer': {'device': 'none', 'pin_memory': True}, 'allgather_partitions': True, 'allgather_bucket_size': 200000000.0, 'overlap_comm': False, 'reduce_scatter': True, 'reduce_bucket_size': 200000000.0, 'contiguous_gradients': True}, 'gradient_accumulation_steps': 'auto', 'gradient_clipping': 'auto', 'steps_per_print': 2000, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'wall_clock_breakdown': False}, debug=[], skip_memory_metrics=True, do_train=False, do_eval=False, do_predict=False, resume_from_checkpoint=None, warmup_ratio=0.05, logging_dir='/share/m2v_intern_v3/wangjunjie09/VisionEncoder/exps/S2/4b/qwen3_5_2b/v10_2_32f_10pct/v0-20260610-154054/runs', local_rank=0, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, tuner_backend='peft', vit_gradient_checkpointing=True, router_aux_loss_coef=0.0, enable_dft_loss=False, enable_channel_loss=False, safe_serialization=True, max_shard_size='5GB', check_model=True, acc_strategy='token', train_dataloader_shuffle=True, group_by_length=False, max_epochs=None, aligner_lr=None, vit_lr=1e-06, use_logits_to_keep=None, ds3_gather_for_generation=True, resume_only_model=False, optimizer='multimodal', loss_type=None, eval_metric=None, callbacks=[], early_stop_interval=None, eval_use_evalscope=False, eval_dataset=[], eval_dataset_args=None, eval_limit=None, eval_generation_config=None, extra_eval_args=None, tuner_type='full', use_galore=False, galore_target_modules=None, galore_rank=128, galore_update_proj_gap=50, galore_scale=1.0, galore_proj_type='std', galore_optim_per_parameter=False, galore_with_embedding=False, galore_quantization=False, galore_proj_quant=False, galore_proj_bits=4, galore_proj_group_size=256, galore_cos_threshold=0.4, galore_gamma_proj=2, galore_queue_size=5, lisa_activated_layers=0, lisa_step_interval=20, use_flash_ckpt=False)"
|
| 376 |
-
}
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|
ckpts/v10_2_32f/chat_template.jinja
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
{%- if tools %}
|
| 2 |
-
{{- '<|im_start|>system\n' }}
|
| 3 |
-
{%- if messages[0].role == 'system' %}
|
| 4 |
-
{{- messages[0].content + '\n\n' }}
|
| 5 |
-
{%- endif %}
|
| 6 |
-
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 7 |
-
{%- for tool in tools %}
|
| 8 |
-
{{- "\n" }}
|
| 9 |
-
{{- tool | tojson }}
|
| 10 |
-
{%- endfor %}
|
| 11 |
-
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 12 |
-
{%- else %}
|
| 13 |
-
{%- if messages[0].role == 'system' %}
|
| 14 |
-
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
| 15 |
-
{%- endif %}
|
| 16 |
-
{%- endif %}
|
| 17 |
-
{%- for message in messages %}
|
| 18 |
-
{%- if message.content is string %}
|
| 19 |
-
{%- set content = message.content %}
|
| 20 |
-
{%- else %}
|
| 21 |
-
{%- set content = '' %}
|
| 22 |
-
{%- endif %}
|
| 23 |
-
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
| 24 |
-
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 25 |
-
{%- elif message.role == "assistant" %}
|
| 26 |
-
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 27 |
-
{%- if message.tool_calls %}
|
| 28 |
-
{%- for tool_call in message.tool_calls %}
|
| 29 |
-
{%- if (loop.first and content) or (not loop.first) %}
|
| 30 |
-
{{- '\n' }}
|
| 31 |
-
{%- endif %}
|
| 32 |
-
{%- if tool_call.function %}
|
| 33 |
-
{%- set tool_call = tool_call.function %}
|
| 34 |
-
{%- endif %}
|
| 35 |
-
{{- '<tool_call>\n{"name": "' }}
|
| 36 |
-
{{- tool_call.name }}
|
| 37 |
-
{{- '", "arguments": ' }}
|
| 38 |
-
{%- if tool_call.arguments is string %}
|
| 39 |
-
{{- tool_call.arguments }}
|
| 40 |
-
{%- else %}
|
| 41 |
-
{{- tool_call.arguments | tojson }}
|
| 42 |
-
{%- endif %}
|
| 43 |
-
{{- '}\n</tool_call>' }}
|
| 44 |
-
{%- endfor %}
|
| 45 |
-
{%- endif %}
|
| 46 |
-
{{- '<|im_end|>\n' }}
|
| 47 |
-
{%- elif message.role == "tool" %}
|
| 48 |
-
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 49 |
-
{{- '<|im_start|>user' }}
|
| 50 |
-
{%- endif %}
|
| 51 |
-
{{- '\n<tool_response>\n' }}
|
| 52 |
-
{{- content }}
|
| 53 |
-
{{- '\n</tool_response>' }}
|
| 54 |
-
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 55 |
-
{{- '<|im_end|>\n' }}
|
| 56 |
-
{%- endif %}
|
| 57 |
-
{%- endif %}
|
| 58 |
-
{%- endfor %}
|
| 59 |
-
{%- if add_generation_prompt %}
|
| 60 |
-
{{- '<|im_start|>assistant\n' }}
|
| 61 |
-
{%- endif %}
|
|
|
|
|
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|
|
ckpts/v10_2_32f/chat_template.json
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + ' '}}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>' }}{% endfor %}{# Render all video then #}{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}{{ '<video>' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ '\n' + content['text'] }}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ '\n' + content['text'] }}{% endgeneration %}{% endfor %}{% endif %}{{'<|im_end|>'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
|
| 3 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
ckpts/v10_2_32f/config.json
DELETED
|
@@ -1,264 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"architectures": [
|
| 3 |
-
"LlavaQwen3_5ViTForConditionalGeneration"
|
| 4 |
-
],
|
| 5 |
-
"bos_token_id": null,
|
| 6 |
-
"dtype": "bfloat16",
|
| 7 |
-
"eos_token_id": 151645,
|
| 8 |
-
"hidden_size": 2560,
|
| 9 |
-
"image_grid_pinpoints": [
|
| 10 |
-
[
|
| 11 |
-
384,
|
| 12 |
-
384
|
| 13 |
-
],
|
| 14 |
-
[
|
| 15 |
-
384,
|
| 16 |
-
768
|
| 17 |
-
],
|
| 18 |
-
[
|
| 19 |
-
384,
|
| 20 |
-
1152
|
| 21 |
-
],
|
| 22 |
-
[
|
| 23 |
-
384,
|
| 24 |
-
1536
|
| 25 |
-
],
|
| 26 |
-
[
|
| 27 |
-
384,
|
| 28 |
-
1920
|
| 29 |
-
],
|
| 30 |
-
[
|
| 31 |
-
384,
|
| 32 |
-
2304
|
| 33 |
-
],
|
| 34 |
-
[
|
| 35 |
-
768,
|
| 36 |
-
384
|
| 37 |
-
],
|
| 38 |
-
[
|
| 39 |
-
768,
|
| 40 |
-
768
|
| 41 |
-
],
|
| 42 |
-
[
|
| 43 |
-
768,
|
| 44 |
-
1152
|
| 45 |
-
],
|
| 46 |
-
[
|
| 47 |
-
768,
|
| 48 |
-
1536
|
| 49 |
-
],
|
| 50 |
-
[
|
| 51 |
-
768,
|
| 52 |
-
1920
|
| 53 |
-
],
|
| 54 |
-
[
|
| 55 |
-
768,
|
| 56 |
-
2304
|
| 57 |
-
],
|
| 58 |
-
[
|
| 59 |
-
1152,
|
| 60 |
-
384
|
| 61 |
-
],
|
| 62 |
-
[
|
| 63 |
-
1152,
|
| 64 |
-
768
|
| 65 |
-
],
|
| 66 |
-
[
|
| 67 |
-
1152,
|
| 68 |
-
1152
|
| 69 |
-
],
|
| 70 |
-
[
|
| 71 |
-
1152,
|
| 72 |
-
1536
|
| 73 |
-
],
|
| 74 |
-
[
|
| 75 |
-
1152,
|
| 76 |
-
1920
|
| 77 |
-
],
|
| 78 |
-
[
|
| 79 |
-
1152,
|
| 80 |
-
2304
|
| 81 |
-
],
|
| 82 |
-
[
|
| 83 |
-
1536,
|
| 84 |
-
384
|
| 85 |
-
],
|
| 86 |
-
[
|
| 87 |
-
1536,
|
| 88 |
-
768
|
| 89 |
-
],
|
| 90 |
-
[
|
| 91 |
-
1536,
|
| 92 |
-
1152
|
| 93 |
-
],
|
| 94 |
-
[
|
| 95 |
-
1536,
|
| 96 |
-
1536
|
| 97 |
-
],
|
| 98 |
-
[
|
| 99 |
-
1536,
|
| 100 |
-
1920
|
| 101 |
-
],
|
| 102 |
-
[
|
| 103 |
-
1536,
|
| 104 |
-
2304
|
| 105 |
-
],
|
| 106 |
-
[
|
| 107 |
-
1920,
|
| 108 |
-
384
|
| 109 |
-
],
|
| 110 |
-
[
|
| 111 |
-
1920,
|
| 112 |
-
768
|
| 113 |
-
],
|
| 114 |
-
[
|
| 115 |
-
1920,
|
| 116 |
-
1152
|
| 117 |
-
],
|
| 118 |
-
[
|
| 119 |
-
1920,
|
| 120 |
-
1536
|
| 121 |
-
],
|
| 122 |
-
[
|
| 123 |
-
1920,
|
| 124 |
-
1920
|
| 125 |
-
],
|
| 126 |
-
[
|
| 127 |
-
1920,
|
| 128 |
-
2304
|
| 129 |
-
],
|
| 130 |
-
[
|
| 131 |
-
2304,
|
| 132 |
-
384
|
| 133 |
-
],
|
| 134 |
-
[
|
| 135 |
-
2304,
|
| 136 |
-
768
|
| 137 |
-
],
|
| 138 |
-
[
|
| 139 |
-
2304,
|
| 140 |
-
1152
|
| 141 |
-
],
|
| 142 |
-
[
|
| 143 |
-
2304,
|
| 144 |
-
1536
|
| 145 |
-
],
|
| 146 |
-
[
|
| 147 |
-
2304,
|
| 148 |
-
1920
|
| 149 |
-
],
|
| 150 |
-
[
|
| 151 |
-
2304,
|
| 152 |
-
2304
|
| 153 |
-
]
|
| 154 |
-
],
|
| 155 |
-
"image_token_index": 151669,
|
| 156 |
-
"model_type": "llava_qwen3_5vit_qwen3",
|
| 157 |
-
"multimodal_projector_bias": true,
|
| 158 |
-
"pad_token_id": 151643,
|
| 159 |
-
"projector_hidden_act": "gelu",
|
| 160 |
-
"text_config": {
|
| 161 |
-
"_name_or_path": "/share/m2v_intern_v3/wangjunjie09/model_cache/huggingface/Qwen/Qwen3-4B-Instruct-2507",
|
| 162 |
-
"architectures": [
|
| 163 |
-
"Qwen3ForCausalLM"
|
| 164 |
-
],
|
| 165 |
-
"attention_bias": false,
|
| 166 |
-
"attention_dropout": 0.0,
|
| 167 |
-
"bos_token_id": 151643,
|
| 168 |
-
"dtype": "bfloat16",
|
| 169 |
-
"eos_token_id": 151645,
|
| 170 |
-
"head_dim": 128,
|
| 171 |
-
"hidden_act": "silu",
|
| 172 |
-
"hidden_size": 2560,
|
| 173 |
-
"initializer_range": 0.02,
|
| 174 |
-
"intermediate_size": 9728,
|
| 175 |
-
"layer_types": [
|
| 176 |
-
"full_attention",
|
| 177 |
-
"full_attention",
|
| 178 |
-
"full_attention",
|
| 179 |
-
"full_attention",
|
| 180 |
-
"full_attention",
|
| 181 |
-
"full_attention",
|
| 182 |
-
"full_attention",
|
| 183 |
-
"full_attention",
|
| 184 |
-
"full_attention",
|
| 185 |
-
"full_attention",
|
| 186 |
-
"full_attention",
|
| 187 |
-
"full_attention",
|
| 188 |
-
"full_attention",
|
| 189 |
-
"full_attention",
|
| 190 |
-
"full_attention",
|
| 191 |
-
"full_attention",
|
| 192 |
-
"full_attention",
|
| 193 |
-
"full_attention",
|
| 194 |
-
"full_attention",
|
| 195 |
-
"full_attention",
|
| 196 |
-
"full_attention",
|
| 197 |
-
"full_attention",
|
| 198 |
-
"full_attention",
|
| 199 |
-
"full_attention",
|
| 200 |
-
"full_attention",
|
| 201 |
-
"full_attention",
|
| 202 |
-
"full_attention",
|
| 203 |
-
"full_attention",
|
| 204 |
-
"full_attention",
|
| 205 |
-
"full_attention",
|
| 206 |
-
"full_attention",
|
| 207 |
-
"full_attention",
|
| 208 |
-
"full_attention",
|
| 209 |
-
"full_attention",
|
| 210 |
-
"full_attention",
|
| 211 |
-
"full_attention"
|
| 212 |
-
],
|
| 213 |
-
"max_position_embeddings": 262144,
|
| 214 |
-
"max_window_layers": 36,
|
| 215 |
-
"model_type": "qwen3",
|
| 216 |
-
"num_attention_heads": 32,
|
| 217 |
-
"num_hidden_layers": 36,
|
| 218 |
-
"num_key_value_heads": 8,
|
| 219 |
-
"pad_token_id": 151643,
|
| 220 |
-
"rms_norm_eps": 1e-06,
|
| 221 |
-
"rope_parameters": {
|
| 222 |
-
"rope_theta": 5000000,
|
| 223 |
-
"rope_type": "default"
|
| 224 |
-
},
|
| 225 |
-
"sliding_window": null,
|
| 226 |
-
"tie_word_embeddings": true,
|
| 227 |
-
"use_cache": false,
|
| 228 |
-
"use_sliding_window": false,
|
| 229 |
-
"vocab_size": 151936
|
| 230 |
-
},
|
| 231 |
-
"tie_word_embeddings": true,
|
| 232 |
-
"transformers_version": "5.5.4",
|
| 233 |
-
"use_cache": false,
|
| 234 |
-
"video_token_index": 151670,
|
| 235 |
-
"vision_aspect_ratio": "anyres_max_9",
|
| 236 |
-
"vision_config": {
|
| 237 |
-
"deepstack_visual_indexes": [],
|
| 238 |
-
"depth": 24,
|
| 239 |
-
"dtype": "bfloat16",
|
| 240 |
-
"hidden_act": "gelu_pytorch_tanh",
|
| 241 |
-
"hidden_size": 1024,
|
| 242 |
-
"image_size": 384,
|
| 243 |
-
"in_channels": 3,
|
| 244 |
-
"initializer_range": 0.02,
|
| 245 |
-
"intermediate_size": 4096,
|
| 246 |
-
"model_type": "qwen3_5",
|
| 247 |
-
"num_heads": 16,
|
| 248 |
-
"num_position_embeddings": 2304,
|
| 249 |
-
"out_hidden_size": 2048,
|
| 250 |
-
"patch_size": 16,
|
| 251 |
-
"spatial_merge_size": 2,
|
| 252 |
-
"temporal_patch_size": 2
|
| 253 |
-
},
|
| 254 |
-
"vision_feature_layer": -1,
|
| 255 |
-
"vision_feature_select_strategy": "full",
|
| 256 |
-
"vit_register_ring": 0,
|
| 257 |
-
"vit_skip_last_blocks": 0,
|
| 258 |
-
"auto_map": {
|
| 259 |
-
"AutoConfig": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTConfig",
|
| 260 |
-
"AutoModel": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTForConditionalGeneration",
|
| 261 |
-
"AutoModelForCausalLM": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTForConditionalGeneration",
|
| 262 |
-
"AutoModelForImageTextToText": "modeling_qwen3_5vit_qwen3.LlavaQwen3_5ViTForConditionalGeneration"
|
| 263 |
-
}
|
| 264 |
-
}
|
|
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ckpts/v10_2_32f/generation_config.json
DELETED
|
@@ -1,12 +0,0 @@
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| 1 |
-
{
|
| 2 |
-
"_from_model_config": true,
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| 3 |
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| 5 |
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ckpts/v10_2_32f/latest
DELETED
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global_step785
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ckpts/v10_2_32f/model-00001-of-00002.safetensors
DELETED
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DELETED
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