from gradio.components import Component
import gradio as gr
import pandas as pd
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", [])
self.use_case = None
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
def set_formula(self, formula):
self.formula = formula
def get_formula(self):
return self.formula
def set_latency(self, latency):
self.latency = latency
def get_latency(self):
return self.latency
class OpenAIModel(BaseTCOModel):
def __init__(self):
self.set_name("(SaaS) OpenAI")
self.set_formula(r"""$CR = \frac{CIT\_1K \times IT + COT\_1K \times OT}{1000}$ <br>
with: <br>
CR = Cost per Request <br>
CIT_1K = Cost per 1000 Input Tokens <br>
COT_1K = Cost per 1000 Output Tokens <br>
IT = Input Tokens <br>
OT = Output Tokens
""")
self.latency = "15s" #Default value for GPT4
super().__init__()
def render(self):
def on_model_change(model):
if model == "GPT-4":
self.latency = "15s"
return gr.Dropdown.update(choices=["8K", "32K"])
else:
self.latency = "5s"
return gr.Dropdown.update(choices=["4K", "16K"], value="4K")
def define_cost_per_token(model, context_length):
if model == "GPT-4" and context_length == "8K":
cost_per_1k_input_tokens = 0.03
cost_per_1k_output_tokens = 0.06
elif model == "GPT-4" and context_length == "32K":
cost_per_1k_input_tokens = 0.06
cost_per_1k_output_tokens = 0.12
elif model == "GPT-3.5" and context_length == "4K":
cost_per_1k_input_tokens = 0.0015
cost_per_1k_output_tokens = 0.002
else:
cost_per_1k_input_tokens = 0.003
cost_per_1k_output_tokens = 0.004
return cost_per_1k_input_tokens, cost_per_1k_output_tokens
self.model = gr.Dropdown(["GPT-4", "GPT-3.5 Turbo"], value="GPT-4",
label="OpenAI models",
interactive=True, visible=False)
self.context_length = gr.Dropdown(["8K", "32K"], value="8K", interactive=True,
label="Context size",
visible=False, info="Number of tokens the model considers when processing text")
self.input_tokens_cost_per_second = gr.Number(0.03, visible=False,
label="($) Price/1K input prompt tokens",
interactive=False
)
self.output_tokens_cost_per_second = gr.Number(0.06, visible=False,
label="($) Price/1K output prompt tokens",
interactive=False
)
self.info = gr.Markdown("The cost per input and output tokens values are from OpenAI's pricing web page [here](https://openai.com/pricing)", interactive=False, visible=False)
self.model.change(on_model_change, inputs=self.model, outputs=self.context_length).then(define_cost_per_token, inputs=[self.model, self.context_length], outputs=[self.input_tokens_cost_per_second, self.output_tokens_cost_per_second])
self.context_length.change(define_cost_per_token, inputs=[self.model, self.context_length], outputs=[self.input_tokens_cost_per_second, self.output_tokens_cost_per_second])
self.labor = gr.Number(0, visible=False,
label="($) Labor cost per month",
interactive=True
)
def compute_cost_per_token(self, input_tokens_cost_per_second, output_tokens_cost_per_second, labor):
cost_per_input_token = (input_tokens_cost_per_second / 1000)
cost_per_output_token = (output_tokens_cost_per_second / 1000)
return cost_per_input_token, cost_per_output_token, labor
class OpenSourceLlama2Model(BaseTCOModel):
def __init__(self):
self.set_name("(Open source) Llama 2 70B")
self.set_formula(r"""$CR = \frac{CIT\_1K \times IT + COT\_1K \times OT}{1000}$ <br>
with: <br>
CR = Cost per Request <br>
CIT_1K = Cost per 1000 Input Tokens <br>
COT_1K = Cost per 1000 Output Tokens <br>
IT = Input Tokens <br>
OT = Output Tokens
""")
self.set_latency("27s")
super().__init__()
def render(self):
self.vm = gr.Textbox(value="2x A100 80GB NVLINK",
visible=False,
label="Instance of VM with GPU",
)
self.vm_cost_per_hour = gr.Number(2.21, label="VM instance cost ($) per hour", info="Note that this is the cost for a single VM instance, it is doubled in our case since two GPUs are needed",
interactive=False, visible=False)
self.input_tokens_cost_per_second = gr.Number(0.00052, visible=False,
label="($) Price/1K input prompt tokens",
interactive=False
)
self.output_tokens_cost_per_second = gr.Number(0.06656, visible=False,
label="($) Price/1K output prompt tokens",
interactive=False
)
self.info = gr.Markdown("For the Llama2-70B model, we took the cost per input and output tokens values from the benchmark results [here](https://www.cursor.so/blog/llama-inference#user-content-fn-llama-paper)", interactive=False, visible=False)
self.labor = gr.Number(1000, visible=False,
label="($) Labor cost per month",
interactive=True
)
# self.used = gr.Slider(minimum=0.01, value=30., step=0.01, label="% used",
# info="Percentage of time the GPU is used",
# interactive=True,
# visible=False)
def compute_cost_per_token(self, input_tokens_cost_per_second, output_tokens_cost_per_second, labor):
cost_per_input_token = (input_tokens_cost_per_second / 1000)
cost_per_output_token = (output_tokens_cost_per_second / 1000)
return cost_per_input_token, cost_per_output_token, labor
class CohereModel(BaseTCOModel):
def __init__(self):
self.set_name("(SaaS) Cohere")
self.set_formula(r"""$CR = \frac{CT\_1M \times (IT + OT)}{1000000}$ <br>
with: <br>
CR = Cost per Request <br>
CT_1M = Cost per one million Tokens (from Cohere's pricing web page) <br>
IT = Input Tokens <br>
OT = Output Tokens
""")
self.set_latency("")
super().__init__()
def render(self):
self.model = gr.Dropdown(["Default", "Custom"], value="Default",
label="Model",
interactive=True, visible=False)
if self.use_case == "Summarize":
self.model: gr.Dropdown.update(choices=["Default"])
elif self.use_case == "Question-answering":
self.model: gr.Dropdown.update(choices=["Default", "Custom"])
else:
self.model: gr.Dropdown.update(choices=["Default", "Custom"])
self.labor = gr.Number(0, visible=False,
label="($) Labor cost per month",
interactive=True
)
def compute_cost_per_token(self, model, labor):
"""Cost per token = """
use_case = self.use_case
if use_case == "Generate":
if model == "Default":
cost_per_1M_tokens = 15
else:
cost_per_1M_tokens = 30
elif use_case == "Summarize":
cost_per_1M_tokens = 15
else:
cost_per_1M_tokens = 200
cost_per_input_token = cost_per_1M_tokens / 1000000
cost_per_output_token = cost_per_1M_tokens / 1000000
return cost_per_input_token, cost_per_output_token, labor
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, use_case: gr.Dropdown):
# First decide which indexes
output = []
for model in self.models:
if model.get_name() == name:
output+= [gr.update(visible=True)] * len(model.get_components())
# Set use_case and num_users values in the model
model.use_case = use_case
else:
output+= [gr.update(visible=False)] * len(model.get_components())
return output
def compute_cost_per_token(self, *args):
begin=0
current_model = args[-3]
current_input_tokens = args[-2]
current_output_tokens = 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]
cost_per_input_token, cost_per_output_token, labor_cost = model.compute_cost_per_token(*model_args)
model_tco = cost_per_input_token * current_input_tokens + cost_per_output_token * current_output_tokens
formula = model.get_formula()
latency = model.get_latency()
return f"Model {current_model} has a cost/request of: ${model_tco}", model_tco, formula, f"The average latency of this model is {latency}", labor_cost
begin = begin+model_n_args