glm-4.6v-tiny-random

This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from zai-org/GLM-4.6V.

Example usage:

import torch
from transformers import AutoProcessor, Glm4vMoeForConditionalGeneration

model_id = "yujiepan/glm-4.6v-tiny-random"
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"
            },
            {
                "type": "text",
                "text": "describe this image"
            }
        ],
    }
]
processor = AutoProcessor.from_pretrained(model_id)
model = Glm4vMoeForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
)
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=16)
output_text = processor.decode(
    generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)

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,
    GenerationConfig,
    Glm4vForConditionalGeneration,
    Glm4vMoeForConditionalGeneration,
    set_seed,
)
from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeTextTopkRouter

source_model_id = "zai-org/GLM-4.6V"
save_folder = "/tmp/yujiepan/glm-4.6v-tiny-random"
processor = AutoProcessor.from_pretrained(
    source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)

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)
config_json['text_config'].update({
    "hidden_size": 8,
    "head_dim": 32,
    "intermediate_size": 64,
    "first_k_dense_replace": 1,
    "moe_intermediate_size": 64,
    "num_attention_heads": 8,
    "num_key_value_heads": 4,
    "num_hidden_layers": 2,  # one dense, one moe
    "tie_word_embeddings": True,
})
config_json['text_config']['rope_parameters']['mrope_section'] = [2, 2, 4]
config_json['vision_config']['hidden_size'] = 64
config_json['vision_config']['depth'] = 2
config_json['vision_config']['num_heads'] = 2
config_json['vision_config']['intermediate_size'] = 64
config_json['vision_config']['out_hidden_size'] = config_json['text_config']['hidden_size']

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)
torch.set_default_dtype(torch.bfloat16)
model = Glm4vMoeForConditionalGeneration(config)
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
num_params = sum(p.numel() for p in model.parameters())
with torch.no_grad():
    for name, p in sorted(model.named_parameters()):
        torch.nn.init.normal_(p, 0, 0.1)
        print(name, p.shape, p.dtype, p.device,
              f'{p.numel() / num_params * 100: .2f}%')
for _, m in sorted(model.named_modules()):
    if isinstance(m, Glm4vMoeTextTopkRouter):
        assert 'e_score_correction_bias' in m.state_dict()
        torch.nn.init.normal_(m.e_score_correction_bias, 0, 1)
model.save_pretrained(save_folder)
print(model)

Printing the model:

Glm4vMoeForConditionalGeneration(
  (model): Glm4vMoeModel(
    (visual): Glm4vMoeVisionModel(
      (embeddings): Glm4vMoeVisionEmbeddings(
        (position_embedding): Embedding(576, 64)
      )
      (patch_embed): Glm4vMoeVisionPatchEmbed(
        (proj): Conv3d(3, 64, kernel_size=(2, 14, 14), stride=(2, 14, 14))
      )
      (rotary_pos_emb): Glm4vMoeVisionRotaryEmbedding()
      (blocks): ModuleList(
        (0-1): 2 x Glm4vMoeVisionBlock(
          (norm1): Glm4vMoeRMSNorm((64,), eps=1e-05)
          (norm2): Glm4vMoeRMSNorm((64,), eps=1e-05)
          (attn): Glm4vMoeVisionAttention(
            (qkv): Linear(in_features=64, out_features=192, bias=False)
            (proj): Linear(in_features=64, out_features=64, bias=False)
          )
          (mlp): Glm4vMoeisionMlp(
            (gate_proj): Linear(in_features=64, out_features=8, bias=False)
            (up_proj): Linear(in_features=64, out_features=8, bias=False)
            (down_proj): Linear(in_features=8, out_features=64, bias=False)
            (act_fn): SiLUActivation()
          )
        )
      )
      (merger): Glm4vMoeVisionPatchMerger(
        (proj): Linear(in_features=8, out_features=8, bias=False)
        (post_projection_norm): LayerNorm((8,), eps=1e-05, elementwise_affine=True)
        (gate_proj): Linear(in_features=8, out_features=64, bias=False)
        (up_proj): Linear(in_features=8, out_features=64, bias=False)
        (down_proj): Linear(in_features=64, out_features=8, bias=False)
        (act1): GELU(approximate='none')
        (act_fn): SiLUActivation()
      )
      (post_conv_layernorm): Glm4vMoeRMSNorm((64,), eps=1e-05)
      (downsample): Conv2d(64, 8, kernel_size=(2, 2), stride=(2, 2))
      (post_layernorm): Glm4vMoeRMSNorm((64,), eps=1e-05)
    )
    (language_model): Glm4vMoeTextModel(
      (embed_tokens): Embedding(151552, 8, padding_idx=151329)
      (layers): ModuleList(
        (0): Glm4vMoeTextDecoderLayer(
          (self_attn): Glm4vMoeTextAttention(
            (q_proj): Linear(in_features=8, out_features=256, bias=True)
            (k_proj): Linear(in_features=8, out_features=128, bias=True)
            (v_proj): Linear(in_features=8, out_features=128, bias=True)
            (o_proj): Linear(in_features=256, out_features=8, bias=False)
          )
          (mlp): Glm4vMoeTextMLP(
            (gate_proj): Linear(in_features=8, out_features=64, bias=False)
            (up_proj): Linear(in_features=8, out_features=64, bias=False)
            (down_proj): Linear(in_features=64, out_features=8, bias=False)
            (act_fn): SiLUActivation()
          )
          (input_layernorm): Glm4vMoeTextRMSNorm((8,), eps=1e-05)
          (post_attention_layernorm): Glm4vMoeTextRMSNorm((8,), eps=1e-05)
        )
        (1): Glm4vMoeTextDecoderLayer(
          (self_attn): Glm4vMoeTextAttention(
            (q_proj): Linear(in_features=8, out_features=256, bias=True)
            (k_proj): Linear(in_features=8, out_features=128, bias=True)
            (v_proj): Linear(in_features=8, out_features=128, bias=True)
            (o_proj): Linear(in_features=256, out_features=8, bias=False)
          )
          (mlp): Glm4vMoeTextMoE(
            (experts): Glm4vMoeTextNaiveMoe(
              (act_fn): SiLUActivation()
            )
            (gate): Glm4vMoeTextTopkRouter()
            (shared_experts): Glm4vMoeTextMLP(
              (gate_proj): Linear(in_features=8, out_features=64, bias=False)
              (up_proj): Linear(in_features=8, out_features=64, bias=False)
              (down_proj): Linear(in_features=64, out_features=8, bias=False)
              (act_fn): SiLUActivation()
            )
          )
          (input_layernorm): Glm4vMoeTextRMSNorm((8,), eps=1e-05)
          (post_attention_layernorm): Glm4vMoeTextRMSNorm((8,), eps=1e-05)
        )
      )
      (norm): Glm4vMoeRMSNorm((8,), eps=1e-05)
      (rotary_emb): Glm4vMoeTextRotaryEmbedding()
    )
  )
  (lm_head): Linear(in_features=8, out_features=151552, bias=False)
)