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from typing import TypeVar
# Model packages
import torch.cuda
from transformers import pipeline
import time
import spaces
torch.cuda.empty_cache()
PandasDataFrame = TypeVar('pd.core.frame.DataFrame')
model_type = None # global variable setup
full_text = "" # Define dummy source text (full text) just to enable highlight function to load
model = [] # Define empty list for model functions to run
tokenizer = [] # Define empty list for model functions to run
# Currently set gpu_layers to 0 even with cuda due to persistent bugs in implementation with cuda
if torch.cuda.is_available():
torch_device = "cuda"
gpu_layers = -1
else:
torch_device = "cpu"
gpu_layers = 0
print("Running on device:", torch_device)
threads = torch.get_num_threads() # 8
print("CPU threads:", threads)
temperature: float = 0.1
top_k: int = 3
top_p: float = 1
repetition_penalty: float = 1.2 # Mild repetition penalty to prevent repeating table rows
last_n_tokens: int = 512
max_new_tokens: int = 4096 # 200
seed: int = 42
reset: bool = True
stream: bool = False
threads: int = threads
batch_size:int = 256
context_length:int = 12288
sample = True
class llama_cpp_init_config_gpu:
def __init__(self,
last_n_tokens=last_n_tokens,
seed=seed,
n_threads=threads,
n_batch=batch_size,
n_ctx=context_length,
n_gpu_layers=gpu_layers):
self.last_n_tokens = last_n_tokens
self.seed = seed
self.n_threads = n_threads
self.n_batch = n_batch
self.n_ctx = n_ctx
self.n_gpu_layers = n_gpu_layers
# self.stop: list[str] = field(default_factory=lambda: [stop_string])
def update_gpu(self, new_value):
self.n_gpu_layers = new_value
def update_context(self, new_value):
self.n_ctx = new_value
class llama_cpp_init_config_cpu(llama_cpp_init_config_gpu):
def __init__(self):
super().__init__()
self.n_gpu_layers = gpu_layers
self.n_ctx=context_length
gpu_config = llama_cpp_init_config_gpu()
cpu_config = llama_cpp_init_config_cpu()
class CtransGenGenerationConfig:
def __init__(self, temperature=temperature,
top_k=top_k,
top_p=top_p,
repeat_penalty=repetition_penalty,
seed=seed,
stream=stream,
max_tokens=max_new_tokens
):
self.temperature = temperature
self.top_k = top_k
self.top_p = top_p
self.repeat_penalty = repeat_penalty
self.seed = seed
self.max_tokens=max_tokens
self.stream = stream
def update_temp(self, new_value):
self.temperature = new_value
def llama_cpp_streaming(history, full_prompt, temperature=temperature):
gen_config = CtransGenGenerationConfig()
gen_config.update_temp(temperature)
print(vars(gen_config))
# Pull the generated text from the streamer, and update the model output.
start = time.time()
NUM_TOKENS=0
print('-'*4+'Start Generation'+'-'*4)
output = model(
full_prompt, **vars(gen_config))
history[-1][1] = ""
for out in output:
if "choices" in out and len(out["choices"]) > 0 and "text" in out["choices"][0]:
history[-1][1] += out["choices"][0]["text"]
NUM_TOKENS+=1
yield history
else:
print(f"Unexpected output structure: {out}")
time_generate = time.time() - start
print('\n')
print('-'*4+'End Generation'+'-'*4)
print(f'Num of generated tokens: {NUM_TOKENS}')
print(f'Time for complete generation: {time_generate}s')
print(f'Tokens per secound: {NUM_TOKENS/time_generate}')
print(f'Time per token: {(time_generate/NUM_TOKENS)*1000}ms')
@spaces.GPU
def call_llama_cpp_model(formatted_string, gen_config):
"""
Calls your generation model with parameters from the CtransGenGenerationConfig object.
Args:
formatted_string (str): The formatted input text for the model.
gen_config (CtransGenGenerationConfig): An object containing generation parameters.
"""
# Extracting parameters from the gen_config object
temperature = gen_config.temperature
top_k = gen_config.top_k
top_p = gen_config.top_p
repeat_penalty = gen_config.repeat_penalty
seed = gen_config.seed
max_tokens = gen_config.max_tokens
stream = gen_config.stream
# Now you can call your model directly, passing the parameters:
output = model(
formatted_string,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repeat_penalty=repeat_penalty,
seed=seed,
max_tokens=max_tokens,
stream=stream#,
#stop=["<|eot_id|>", "\n\n"]
)
return output |