This tiny model is for debugging. It is randomly initialized with the config adapted from stepfun-ai/step3.
Note: For vLLM supported version, see yujiepan/step3-tiny-random-vllm.
Example usage:
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_id = "yujiepan/step3-tiny-random"
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:
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/yujiepan/step3-tiny-random"
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:
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)
)