File size: 7,148 Bytes
c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d c194fcd 829e19d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
from gradio.components import Component
import gradio as gr
from abc import ABC, abstractclassmethod
import inspect
class BaseTCOModel(ABC):
# TO DO: Find way to specify which component should be used for computing cost
def __setattr__(self, name, value):
if isinstance(value, Component):
self._components.append(value)
self.__dict__[name] = value
def __init__(self):
super(BaseTCOModel, self).__setattr__("_components", [])
def get_components(self) -> list[Component]:
return self._components
def get_components_for_cost_computing(self):
return self.components_for_cost_computing
def get_name(self):
return self.name
def register_components_for_cost_computing(self):
args = inspect.getfullargspec(self.compute_cost_per_token)[0][1:]
self.components_for_cost_computing = [self.__getattribute__(arg) for arg in args]
@abstractclassmethod
def compute_cost_per_token(self):
pass
@abstractclassmethod
def render(self):
pass
def set_name(self, name):
self.name = name
class OpenAIModel(BaseTCOModel):
def __init__(self):
self.set_name("(SaaS) OpenAI")
super().__init__()
def render(self):
def on_model_change(model):
if model == "GPT-4":
print("GPT4")
return gr.Dropdown.update(choices=["8K", "32K"])
else:
print("GPT3.5")
return gr.Dropdown.update(choices=["4K", "16K"])
self.model = gr.Dropdown(["GPT-4", "GPT-3.5 Turbo"], value="GPT-4",
label="OpenAI model",
interactive=True, visible=False)
self.context_length = gr.Dropdown(["8K", "32K"], value="8K", interactive=True,
label="Context size",
visible=False)
self.model.change(on_model_change, inputs=self.model, outputs=self.context_length)
self.input_length = gr.Number(350, label="Average number of input tokens",
interactive=True, visible=False)
def compute_cost_per_token(self, model, context_length, input_length):
"""Cost per token = """
model = model[0]
context_length = context_length[0]
if model == "GPT-4" and context_length == "8K":
cost_per_1k_input_tokens = 0.03
elif model == "GPT-4" and context_length == "32K":
cost_per_1k_input_tokens = 0.06
elif model == "GPT-3.5" and context_length == "4K":
cost_per_1k_input_tokens = 0.0015
else:
cost_per_1k_input_tokens = 0.003
cost_per_output_token = cost_per_1k_input_tokens * input_length / 1000
return cost_per_output_token
class OpenSourceLlama2Model(BaseTCOModel):
def __init__(self):
self.set_name("(Open source) Llama 2")
super().__init__()
def render(self):
vm_choices = ["1x Nvidia A100 (Azure NC24ads A100 v4)",
"2x Nvidia A100 (Azure NC48ads A100 v4)"]
def on_model_change(model):
if model == "Llama 2 7B":
return gr.Dropdown.update(choices=vm_choices)
else:
not_supported_vm = ["1x Nvidia A100 (Azure NC24ads A100 v4)"]
choices = [x for x in vm_choices if x not in not_supported_vm]
return gr.Dropdown.update(choices=choices)
def on_vm_change(model, vm):
# TO DO: load info from CSV
if model == "Llama 2 7B" and vm == "1x Nvidia A100 (Azure NC24ads A100 v4)":
return gr.Number.update(value=900)
elif model == "Llama 2 7B" and vm == "2x Nvidia A100 (Azure NC48ads A100 v4)":
return gr.Number.update(value=1800)
self.model = gr.Dropdown(["Llama 2 7B", "Llama 2 70B"], value="Llama 2 7B", visible=False)
self.vm = gr.Dropdown(vm_choices,
visible=False,
label="Instance of VM with GPU"
)
self.vm_cost_per_hour = gr.Number(3.5, label="VM instance cost per hour",
interactive=True, visible=False)
self.tokens_per_second = gr.Number(900, visible=False,
label="Number of tokens per second for this specific model and VM instance",
interactive=False
)
self.input_length = gr.Number(350, label="Average number of input tokens",
interactive=True, visible=False)
self.model.change(on_model_change, inputs=self.model, outputs=self.vm)
self.vm.change(on_vm_change, inputs=[self.model, self.vm], outputs=self.tokens_per_second)
self.maxed_out = gr.Slider(minimum=0.01, value=1., step=0.01, label="% maxed out",
info="How much the GPU is fully used.",
interactive=True,
visible=False)
def compute_cost_per_token(self, vm_cost_per_hour, tokens_per_second, maxed_out):
cost_per_token = vm_cost_per_hour / (tokens_per_second * 3600 * maxed_out)
return cost_per_token
class ModelPage:
def __init__(self, Models: BaseTCOModel):
self.models: list[BaseTCOModel] = []
for Model in Models:
model = Model()
self.models.append(model)
def render(self):
for model in self.models:
model.render()
model.register_components_for_cost_computing()
def get_all_components(self) -> list[Component]:
output = []
for model in self.models:
output += model.get_components()
return output
def get_all_components_for_cost_computing(self) -> list[Component]:
output = []
for model in self.models:
output += model.get_components_for_cost_computing()
return output
def make_model_visible(self, name:str):
# First decide which indexes
output = []
for model in self.models:
if model.get_name() == name:
output+= [gr.update(visible=True)] * len(model.get_components())
else:
output+= [gr.update(visible=False)] * len(model.get_components())
return output
def compute_cost_per_token(self, *args):
begin=0
current_model = args[-1]
for model in self.models:
model_n_args = len(model.get_components_for_cost_computing())
if current_model == model.get_name():
model_args = args[begin:begin+model_n_args]
print("Model args: ",model_args)
model_tco = model.compute_cost_per_token(*model_args)
return f"Model {current_model} has TCO {model_tco}"
begin = begin+model_n_args |