File size: 8,722 Bytes
a8a0682 fbde720 a8a0682 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model:
- stepfun-ai/step3
---
This tiny model is for debugging. It is randomly initialized with the config adapted from [stepfun-ai/step3](https://huggingface.co/stepfun-ai/step3).
Note: For vLLM supported version, see [tiny-random/step3-vllm](https://huggingface.co/tiny-random/step3-vllm).
### Example usage:
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_id = "tiny-random/step3"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda", torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
{"type": "text", "text": "What's in this picture?"}
]
},
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device)
generate_ids = model.generate(**inputs, max_new_tokens=32, do_sample=False)
decoded = processor.decode(generate_ids[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=False)
print(decoded)
```
### Codes to create this repo:
```python
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "stepfun-ai/step3"
save_folder = "/tmp/tiny-random/step3"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
def rewrite_automap(filepath: str, source_model_id: str, overrides: dict = None):
import json
with open(filepath, 'r', encoding='utf-8') as f:
config = json.load(f)
for k, v in config['auto_map'].items():
v = v.split('--')[-1]
config['auto_map'][k] = f'{source_model_id}--{v}'
if overrides is not None:
config.update(overrides)
with open(filepath, 'w', encoding='utf - 8') as f:
json.dump(config, f, indent=2)
rewrite_automap(f'{save_folder}/processor_config.json', source_model_id)
rewrite_automap(f'{save_folder}/tokenizer_config.json', source_model_id)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
for k, v in config_json['auto_map'].items():
config_json['auto_map'][k] = f'{source_model_id}--{v}'
config_json['architectures'] = ["Step3VLForConditionalGeneration"]
config_json['text_config'].update({
"hidden_size": 32,
"intermediate_size": 64,
"num_hidden_layers": 2,
"num_attention_heads": 2,
"num_attention_groups": 1,
"head_dim": 256,
"share_q_dim": 512,
"moe_layers_enum": "1",
"moe_num_experts": 8,
"moe_top_k": 3,
"moe_intermediate_size": 64,
"share_expert_dim": 64,
# "tie_word_embeddings": True,
})
config_json['vision_config'].update({
"hidden_size": 64,
"output_hidden_size": 64,
"intermediate_size": 128,
"num_hidden_layers": 2,
"num_attention_heads": 2
})
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
config = AutoConfig.from_pretrained(
save_folder,
trust_remote_code=True,
)
print(config)
# key_mapping = {
# "^vision_model": "model.vision_model",
# r"^model(?!\.(language_model|vision_model))": "model.language_model",
# "vit_downsampler": "model.vit_downsampler",
# "vit_downsampler2": "model.vit_downsampler2",
# "vit_large_projector": "model.vit_large_projector",
# }
automap = config_json['auto_map']
torch.set_default_dtype(torch.bfloat16)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
torch.set_default_dtype(torch.float32)
if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'):
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
model = model.cpu() # cpu is more stable for random initialization across machines
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.2)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
rewrite_automap(f'{save_folder}/config.json', source_model_id)
for python_file in Path(save_folder).glob('*.py'):
if python_file.name.startswith('modeling_') or python_file.name.startswith('configuration_') or python_file.name.endswith('.py'):
python_file.unlink()
```
### Printing the model:
```text
Step3vForConditionalGeneration(
(model): Step3vModel(
(vision_model): StepCLIPVisionTransformer(
(embeddings): StepCLIPVisionEmbeddings(
(patch_embedding): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14))
(position_embedding): Embedding(2705, 64)
)
(transformer): StepCLIPEncoder(
(layers): ModuleList(
(0-1): 2 x StepCLIPEncoderLayer(
(layer_norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(layer_norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True)
(self_attn): StepCLIPAttention(
(qkv_proj): Linear(in_features=64, out_features=192, bias=True)
(out_proj): Linear(in_features=64, out_features=64, bias=True)
)
(mlp): StepCLIPMLP(
(fc1): Linear(in_features=64, out_features=128, bias=True)
(act): QuickGELUActivation()
(fc2): Linear(in_features=128, out_features=64, bias=True)
)
)
)
)
)
(language_model): Step3Model(
(embed_tokens): Embedding(128815, 32)
(layers): ModuleList(
(0): Step3vDecoderLayer(
(self_attn): Step3vAttention(
(q_proj): Linear(in_features=32, out_features=512, bias=False)
(k_proj): Linear(in_features=32, out_features=256, bias=False)
(v_proj): Linear(in_features=32, out_features=256, bias=False)
(o_proj): Linear(in_features=512, out_features=32, bias=False)
(inter_norm): Step3vRMSNorm((512,), eps=1e-05)
(wq): Linear(in_features=512, out_features=512, bias=False)
)
(mlp): Step3vMLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Step3vRMSNorm((32,), eps=1e-05)
(post_attention_layernorm): Step3vRMSNorm((32,), eps=1e-05)
)
(1): Step3vDecoderLayer(
(self_attn): Step3vAttention(
(q_proj): Linear(in_features=32, out_features=512, bias=False)
(k_proj): Linear(in_features=32, out_features=256, bias=False)
(v_proj): Linear(in_features=32, out_features=256, bias=False)
(o_proj): Linear(in_features=512, out_features=32, bias=False)
(inter_norm): Step3vRMSNorm((512,), eps=1e-05)
(wq): Linear(in_features=512, out_features=512, bias=False)
)
(moe): Step3vMoEMLP(
(gate): Linear(in_features=32, out_features=8, bias=False)
(up_proj): MoELinear()
(gate_proj): MoELinear()
(down_proj): MoELinear()
(act_fn): SiLU()
)
(share_expert): Step3vMLP(
(gate_proj): Linear(in_features=32, out_features=64, bias=False)
(up_proj): Linear(in_features=32, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=32, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Step3vRMSNorm((32,), eps=1e-05)
(post_attention_layernorm): Step3vRMSNorm((32,), eps=1e-05)
)
)
(norm): Step3vRMSNorm((32,), eps=1e-05)
(rotary_emb): Step3vRotaryEmbedding()
)
(vit_downsampler): Conv2d(64, 64, kernel_size=(2, 2), stride=(2, 2))
(vit_downsampler2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(vit_large_projector): Linear(in_features=128, out_features=32, bias=False)
)
(lm_head): Linear(in_features=32, out_features=128815, bias=False)
)
``` |