This tiny model is for debugging. It is randomly initialized with the config adapted from meituan-longcat/LongCat-Flash-Chat.
Example usage:
- vLLM
vllm serve tiny-random/longcat-flash \
--trust-remote-code \
--enable-expert-parallel \
--tensor-parallel-size 1 \
--speculative_config '{"model": "tiny-random/longcat-flash", "num_speculative_tokens": 1, "method":"longcat_flash_mtp"}'
- SGLang
python3 -m sglang.launch_server \
--model tiny-random/longcat-flash \
--trust-remote-code \
--attention-backend flashinfer \
--enable-ep-moe \
--tp 1 \
--speculative-draft-model-path tiny-random/longcat-flash \
--speculative-algorithm NEXTN \
--speculative-num-draft-tokens 2 \
--speculative-num-steps 1 \
--speculative-eagle-topk 1
- Transformers
import torch
import transformers
model_id = "tiny-random/longcat-flash"
pipe = transformers.pipelines.pipeline(
'text-generation',
model=model_id,
trust_remote_code=True,
device_map='cuda',
torch_dtype=torch.bfloat16,
)
past_key_values = transformers.DynamicCache(config=None) # set config to None
r = pipe('Hello, world!', past_key_values=past_key_values, max_new_tokens=32)
print(r)
Codes to create this repo:
import json
from copy import deepcopy
from pathlib import Path
import torch
import torch.nn as nn
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
GenerationConfig,
set_seed,
)
from transformers.models.glm4_moe.modeling_glm4_moe import Glm4MoeRMSNorm
source_model_id = "meituan-longcat/LongCat-Flash-Chat"
save_folder = "/tmp/tiny-random/longcat-flash"
Path(save_folder).mkdir(parents=True, exist_ok=True)
tokenizer = AutoTokenizer.from_pretrained(source_model_id, trust_remote_code=True)
tokenizer.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.update({
'num_layers': 2,
'hidden_size': 8,
'ffn_hidden_size': 64,
'expert_ffn_hidden_size': 64,
'num_attention_heads': 4,
'kv_lora_rank': 384,
'n_routed_experts': 32,
'q_lora_rank': 32,
'qk_nope_head_dim': 64,
'qk_rope_head_dim': 192, # vllm mla kernel supports 576 only, FA supports head dim <= 256
'v_head_dim': 64,
'moe_topk': 12,
'zero_expert_num': 16,
})
# del config_json['quantization_config']
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 = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
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,
)
model = model.cpu()
# MTP
model.model.mtp = nn.ModuleDict({
"layers": nn.ModuleList([nn.ModuleDict(dict(
eh_proj=nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False),
enorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
hnorm=nn.ModuleDict({"m": nn.RMSNorm(config.hidden_size)}),
input_layernorm=nn.RMSNorm(config.hidden_size),
post_attention_layernorm=nn.RMSNorm(config.hidden_size),
self_attn=deepcopy(model.model.layers[0].self_attn[0]),
transformer_layer=nn.ModuleDict({"mlp": deepcopy(model.model.layers[0].mlps[0])}),
))]),
"norm": nn.RMSNorm(config.hidden_size),
})
for i in range(config.num_layers):
model.model.layers[i].mlp.router = model.model.layers[i].mlp.router.float()
# model.model.layers[i].mlp.router.e_score_correction_bias = torch.zeros((config.n_routed_experts + config.zero_expert_num)).float()
set_seed(42)
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)
model.model.mtp.embed_tokens = deepcopy(model.model.embed_tokens)
model.save_pretrained(save_folder)
torch.set_default_dtype(torch.float32)
for n, m in model.named_modules():
if 'LongcatFlashMLA' in str(type(m)):
print(n, m.layer_idx)
with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f:
config_json = json.load(f)
config_json['auto_map'] = {k: v.split('--')[-1] for k, v in config_json['auto_map'].items()}
with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f:
json.dump(config_json, f, indent=2)
Printing the model:
LongcatFlashForCausalLM(
(model): LongcatFlashModel(
(embed_tokens): Embedding(131072, 8)
(layers): ModuleList(
(0-1): 2 x LongcatFlashDecoderLayer(
(mlp): LongcatFlashMoE(
(experts): ModuleList(
(0-31): 32 x LongcatFlashMLP(
(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): SiLU()
)
)
(router): LongcatFlashTopkRouter(
(classifier): Linear(in_features=8, out_features=48, bias=False)
)
)
(self_attn): ModuleList(
(0-1): 2 x LongcatFlashMLA(
(q_a_proj): Linear(in_features=8, out_features=32, bias=False)
(q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
(q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
(kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
)
(mlps): ModuleList(
(0-1): 2 x LongcatFlashMLP(
(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): SiLU()
)
)
(input_layernorm): ModuleList(
(0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
)
(post_attention_layernorm): ModuleList(
(0-1): 2 x LongcatFlashRMSNorm((8,), eps=1e-05)
)
)
)
(norm): LongcatFlashRMSNorm((8,), eps=1e-05)
(rotary_emb): LongcatFlashRotaryEmbedding()
(mtp): ModuleDict(
(layers): ModuleList(
(0): ModuleDict(
(eh_proj): Linear(in_features=16, out_features=8, bias=False)
(enorm): ModuleDict(
(m): RMSNorm((8,), eps=None, elementwise_affine=True)
)
(hnorm): ModuleDict(
(m): RMSNorm((8,), eps=None, elementwise_affine=True)
)
(input_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(post_attention_layernorm): RMSNorm((8,), eps=None, elementwise_affine=True)
(self_attn): LongcatFlashMLA(
(q_a_proj): Linear(in_features=8, out_features=32, bias=False)
(q_a_layernorm): LongcatFlashRMSNorm((32,), eps=1e-06)
(q_b_proj): Linear(in_features=32, out_features=1024, bias=False)
(kv_a_proj_with_mqa): Linear(in_features=8, out_features=576, bias=False)
(kv_a_layernorm): LongcatFlashRMSNorm((384,), eps=1e-06)
(kv_b_proj): Linear(in_features=384, out_features=512, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
)
(transformer_layer): ModuleDict(
(mlp): LongcatFlashMLP(
(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): SiLU()
)
)
)
)
(norm): RMSNorm((8,), eps=None, elementwise_affine=True)
(embed_tokens): Embedding(131072, 8)
)
)
(lm_head): Linear(in_features=8, out_features=131072, bias=False)
)