|
from gradio.components import Component |
|
import gradio as gr |
|
from abc import ABC, abstractclassmethod |
|
import inspect |
|
|
|
class BaseTCOModel(ABC): |
|
|
|
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): |
|
|
|
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): |
|
|
|
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 |
