mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2025-09-06 20:49:55 +00:00
local chat for cicd test
This commit is contained in:
SkqLiao
parent
12949c8acd
commit
0f1684c28d
1 changed files with 171 additions and 0 deletions
171
ktransformers/local_chat_test.py
Normal file
171
ktransformers/local_chat_test.py
Normal file
|
|
@ -0,0 +1,171 @@
|
|||
"""
|
||||
Description :
|
||||
Author : Boxin Zhang, Azure-Tang
|
||||
Version : 0.1.0
|
||||
Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
|
||||
"""
|
||||
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
|
||||
project_dir = os.path.dirname(os.path.dirname(__file__))
|
||||
sys.path.insert(0, project_dir)
|
||||
import torch
|
||||
import logging
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoConfig,
|
||||
AutoModelForCausalLM,
|
||||
GenerationConfig,
|
||||
TextStreamer,
|
||||
)
|
||||
import json
|
||||
import fire
|
||||
from ktransformers.optimize.optimize import optimize_and_load_gguf
|
||||
from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM
|
||||
from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
|
||||
from ktransformers.models.modeling_deepseek_v3 import DeepseekV3ForCausalLM
|
||||
from ktransformers.models.modeling_llama import LlamaForCausalLM
|
||||
from ktransformers.models.modeling_mixtral import MixtralForCausalLM
|
||||
from ktransformers.util.utils import prefill_and_generate, get_compute_capability
|
||||
from ktransformers.server.config.config import Config
|
||||
from ktransformers.operators.flashinfer_wrapper import flashinfer_enabled
|
||||
|
||||
custom_models = {
|
||||
"DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
|
||||
"DeepseekV3ForCausalLM": DeepseekV3ForCausalLM,
|
||||
"Qwen2MoeForCausalLM": Qwen2MoeForCausalLM,
|
||||
"LlamaForCausalLM": LlamaForCausalLM,
|
||||
"MixtralForCausalLM": MixtralForCausalLM,
|
||||
}
|
||||
|
||||
ktransformer_rules_dir = (
|
||||
os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/"
|
||||
)
|
||||
default_optimize_rules = {
|
||||
"DeepseekV2ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml",
|
||||
"DeepseekV3ForCausalLM": ktransformer_rules_dir + "DeepSeek-V3-Chat.yaml",
|
||||
"Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct.yaml",
|
||||
"LlamaForCausalLM": ktransformer_rules_dir + "Internlm2_5-7b-Chat-1m.yaml",
|
||||
"MixtralForCausalLM": ktransformer_rules_dir + "Mixtral.yaml",
|
||||
}
|
||||
|
||||
|
||||
def local_chat(
|
||||
model_path: str | None = None,
|
||||
optimize_config_path: str = None,
|
||||
gguf_path: str | None = None,
|
||||
max_new_tokens: int = 1000,
|
||||
cpu_infer: int = Config().cpu_infer,
|
||||
use_cuda_graph: bool = True,
|
||||
prompt_file : str | None = None,
|
||||
mode: str = "normal",
|
||||
force_think: bool = False,
|
||||
chunk_prefill_size: int = 8192
|
||||
):
|
||||
|
||||
torch.set_grad_enabled(False)
|
||||
|
||||
Config().cpu_infer = cpu_infer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
|
||||
if mode == 'long_context':
|
||||
assert config.architectures[0] == "LlamaForCausalLM", "only LlamaForCausalLM support long_context mode"
|
||||
torch.set_default_dtype(torch.float16)
|
||||
else:
|
||||
torch.set_default_dtype(config.torch_dtype)
|
||||
|
||||
with torch.device("meta"):
|
||||
if config.architectures[0] in custom_models:
|
||||
print("using custom modeling_xxx.py.")
|
||||
if (
|
||||
"Qwen2Moe" in config.architectures[0]
|
||||
): # Qwen2Moe must use flash_attention_2 to avoid overflow.
|
||||
config._attn_implementation = "flash_attention_2"
|
||||
if "Llama" in config.architectures[0]:
|
||||
config._attn_implementation = "eager"
|
||||
if "Mixtral" in config.architectures[0]:
|
||||
config._attn_implementation = "flash_attention_2"
|
||||
|
||||
model = custom_models[config.architectures[0]](config)
|
||||
else:
|
||||
model = AutoModelForCausalLM.from_config(
|
||||
config, trust_remote_code=True, attn_implementation="flash_attention_2"
|
||||
)
|
||||
|
||||
if optimize_config_path is None:
|
||||
if config.architectures[0] in default_optimize_rules:
|
||||
print("using default_optimize_rule for", config.architectures[0])
|
||||
optimize_config_path = default_optimize_rules[config.architectures[0]]
|
||||
else:
|
||||
optimize_config_path = input(
|
||||
"please input the path of your rule file(yaml file containing optimize rules):"
|
||||
)
|
||||
|
||||
if gguf_path is None:
|
||||
gguf_path = input(
|
||||
"please input the path of your gguf file(gguf file in the dir containing input gguf file must all belong to current model):"
|
||||
)
|
||||
optimize_and_load_gguf(model, optimize_config_path, gguf_path, config)
|
||||
|
||||
try:
|
||||
model.generation_config = GenerationConfig.from_pretrained(model_path)
|
||||
except Exception as e:
|
||||
print(f"generation config can't auto create, make default. Message: {e}")
|
||||
gen_config = GenerationConfig(
|
||||
temperature=0.6,
|
||||
top_p=0.95,
|
||||
do_sample=True
|
||||
)
|
||||
model.generation_config = gen_config
|
||||
# model.generation_config = GenerationConfig.from_pretrained(model_path)
|
||||
if model.generation_config.pad_token_id is None:
|
||||
model.generation_config.pad_token_id = model.generation_config.eos_token_id
|
||||
model.eval()
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
system = platform.system()
|
||||
if system == "Windows":
|
||||
os.system("cls")
|
||||
else:
|
||||
os.system("clear")
|
||||
|
||||
if prompt_file != None:
|
||||
assert os.path.isfile(prompt_file), "prompt file not exist"
|
||||
print(f"prompt file is {prompt_file}")
|
||||
content = open(prompt_file, "r").read()
|
||||
else:
|
||||
content = "Please write a piece of quicksort code in C++."
|
||||
|
||||
print('Start Testing...(1 round)')
|
||||
print('Prompt:', content)
|
||||
|
||||
while True:
|
||||
messages = [{"role": "user", "content": content}]
|
||||
input_tensor = tokenizer.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt"
|
||||
)
|
||||
if force_think:
|
||||
token_thinks = torch.tensor([tokenizer.encode("<think>\\n",add_special_tokens=False)],device=input_tensor.device)
|
||||
input_tensor = torch.cat(
|
||||
[input_tensor, token_thinks], dim=1
|
||||
)
|
||||
if mode == 'long_context':
|
||||
assert Config().long_context_config['max_seq_len'] > input_tensor.shape[1] + max_new_tokens, \
|
||||
"please change max_seq_len in ~/.ktransformers/config.yaml"
|
||||
|
||||
if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8:
|
||||
generated = prefill_and_generate(
|
||||
model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_prefill_size = chunk_prefill_size,
|
||||
use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim
|
||||
)
|
||||
else:
|
||||
generated = prefill_and_generate(
|
||||
model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_prefill_size = chunk_prefill_size,
|
||||
)
|
||||
break
|
||||
|
||||
if __name__ == "__main__":
|
||||
fire.Fire(local_chat)
|
||||
Loading…
Add table
Reference in a new issue