This tiny model is for debugging. It is randomly initialized with the config adapted from microsoft/Phi-tiny-MoE-instruct.
Example usage:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "yujiepan/phi-moe-tiny-random"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, trust_remote_code=True)
print(pipe('Write an article about Artificial Intelligence.'))
Codes to create this repo:
import json
from pathlib import Path
import torch
import accelerate
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "microsoft/Phi-tiny-MoE-instruct"
save_folder = "/tmp/yujiepan/phi-moe-tiny-random"
processor = AutoTokenizer.from_pretrained(source_model_id)
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)
for k, v in config_json['auto_map'].items():
config_json['auto_map'][k] = f'{source_model_id}--{v}'
config_json['head_dim'] = 32
config_json['hidden_size'] = 64
config_json['intermediate_size'] = 128
config_json['num_attention_heads'] = 2
config_json['num_experts_per_tok'] = 2
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 1
config_json['num_local_experts'] = 8
config_json['tie_word_embeddings'] = True
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)
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)
with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
config_json = json.load(f)
config_json['auto_map'] = automap
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
for python_file in Path(save_folder).glob('*.py'):
python_file.unlink()
Printing the model:
PhiMoEForCausalLM(
(model): PhiMoEModel(
(embed_tokens): Embedding(32064, 64)
(layers): ModuleList(
(0-1): 2 x PhiMoEDecoderLayer(
(self_attn): PhiMoESdpaAttention(
(q_proj): Linear(in_features=64, out_features=64, bias=True)
(k_proj): Linear(in_features=64, out_features=32, bias=True)
(v_proj): Linear(in_features=64, out_features=32, bias=True)
(o_proj): Linear(in_features=64, out_features=64, bias=True)
(rotary_emb): PhiMoERotaryEmbedding()
)
(block_sparse_moe): PhiMoESparseMoeBlock(
(gate): Linear(in_features=64, out_features=8, bias=False)
(experts): ModuleList(
(0-7): 8 x PhiMoEBlockSparseTop2MLP(
(w1): Linear(in_features=64, out_features=128, bias=False)
(w2): Linear(in_features=128, out_features=64, bias=False)
(w3): Linear(in_features=64, out_features=128, bias=False)
(act_fn): SiLU()
)
)
)
(input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
)
(norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
)
(lm_head): Linear(in_features=64, out_features=32064, bias=True)
)