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_latency(self, latency): self.latency = latency def get_latency(self): return self.latency class OpenAIModelGPT4(BaseTCOModel): def __init__(self): self.set_name("(SaaS) OpenAI GPT4") self.set_latency("15s") #Default value for GPT4 super().__init__() def render(self): def define_cost_per_token(context_length): if context_length == "8K": cost_per_1k_input_tokens = 0.03 cost_per_1k_output_tokens = 0.06 else: cost_per_1k_input_tokens = 0.06 cost_per_1k_output_tokens = 0.12 return cost_per_1k_input_tokens, cost_per_1k_output_tokens 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_token = gr.Number(0.03, visible=False, label="($) Price/1K input prompt tokens", interactive=False ) self.output_tokens_cost_per_token = gr.Number(0.06, visible=False, label="($) 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](https://openai.com/pricing)", interactive=False, visible=False) self.context_length.change(define_cost_per_token, inputs=self.context_length, outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token]) self.labor = gr.Number(0, visible=False, label="($) Labor cost per month", info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model", interactive=True ) def compute_cost_per_token(self, input_tokens_cost_per_token, output_tokens_cost_per_token, labor): cost_per_input_token = (input_tokens_cost_per_token / 1000) cost_per_output_token = (output_tokens_cost_per_token / 1000) return cost_per_input_token, cost_per_output_token, labor class OpenAIModelGPT3_5(BaseTCOModel): def __init__(self): self.set_name("(SaaS) OpenAI GPT3.5 Turbo") self.set_latency("5s") #Average latency value for GPT3.5 Turbo super().__init__() def render(self): def define_cost_per_token(context_length): if context_length == "4K": cost_per_1k_input_tokens = 0.0015 cost_per_1k_output_tokens = 0.002 else: cost_per_1k_input_tokens = 0.003 cost_per_1k_output_tokens = 0.004 return cost_per_1k_input_tokens, cost_per_1k_output_tokens self.context_length = gr.Dropdown(choices=["4K", "16K"], value="4K", interactive=True, label="Context size", visible=False, info="Number of tokens the model considers when processing text") self.input_tokens_cost_per_token = gr.Number(0.0015, visible=False, label="($) Price/1K input prompt tokens", interactive=False ) self.output_tokens_cost_per_token = gr.Number(0.002, visible=False, label="($) Price/1K output prompt tokens", interactive=False ) self.info = gr.Markdown("The cost per input and output tokens values are from OpenAI's [pricing web page](https://openai.com/pricing)", interactive=False, visible=False) self.context_length.change(define_cost_per_token, inputs=self.context_length, outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token]) self.labor = gr.Number(0, visible=False, label="($) Labor cost per month", info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model", interactive=True ) def compute_cost_per_token(self, input_tokens_cost_per_token, output_tokens_cost_per_token, labor): cost_per_input_token = (input_tokens_cost_per_token / 1000) cost_per_output_token = (output_tokens_cost_per_token / 1000) return cost_per_input_token, cost_per_output_token, labor class DIYLlama2Model(BaseTCOModel): def __init__(self): self.set_name("(Deploy yourself) Llama 2 70B") self.set_latency("27s") super().__init__() def render(self): def on_maxed_out_change(maxed_out, input_tokens_cost_per_token, output_tokens_cost_per_token): output_tokens_cost_per_token = 0.06656 input_tokens_cost_per_token = 0.00052 r = maxed_out / 100 return input_tokens_cost_per_token * 0.65 / r, output_tokens_cost_per_token * 0.65/ r self.source = gr.Markdown("""Source""", visible=False) self.info = gr.Markdown("The cost per input and output tokens values below are from [these benchmark results](https://www.cursor.so/blog/llama-inference#user-content-fn-llama-paper) that were obtained using the following initial configurations.", interactive=False, visible=False) self.vm = gr.Textbox(value="2x A100 80GB NVLINK", visible=False, label="Instance of VM with GPU", ) self.vm_cost_per_hour = gr.Number(4.42, label="Instance cost ($) per hour", interactive=False, visible=False) self.info_vm = gr.Markdown("This price above is from [CoreWeave's pricing web page](https://www.coreweave.com/gpu-cloud-pricing)", interactive=False, visible=False) self.maxed_out = gr.Slider(minimum=1, maximum=100, value=65, step=1, label="Maxed out", info="Estimated average percentage of total GPU memory that is used. The instantaneous value can go from very high when many users are using the service to very low when no one does.", visible=False) self.info_maxed_out = gr.Markdown(r"""This percentage influences the input and output cost/token values, and more precisely the number of token/s. Here is the formula used:
$CT = \frac{VM_C}{TS}$ where $TS = TS_{max} * \frac{MO}{100}$
with:
$CT$ = Cost per Token (Input or output),
$VM_C$ = VM Cost per second,
$TS$ = Tokens per second (Input or output),
$TS_{max}$ = Tokens per second when the GPU is maxed out at 100%,
$MO$ = Maxed Out,
""", interactive=False, visible=False) self.input_tokens_cost_per_token = gr.Number(0.00052, visible=False, label="($) Price/1K input prompt tokens", interactive=False ) self.output_tokens_cost_per_token = gr.Number(0.06656, visible=False, label="($) Price/1K output prompt tokens", interactive=False ) self.maxed_out.change(on_maxed_out_change, inputs=[self.maxed_out, self.input_tokens_cost_per_token, self.output_tokens_cost_per_token], outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token]) self.labor = gr.Number(5000, visible=False, label="($) Labor cost per month", info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model", interactive=True ) def compute_cost_per_token(self, input_tokens_cost_per_token, output_tokens_cost_per_token, labor): cost_per_input_token = (input_tokens_cost_per_token / 1000) cost_per_output_token = (output_tokens_cost_per_token / 1000) return cost_per_input_token, cost_per_output_token, labor class CohereModel(BaseTCOModel): def __init__(self): self.set_name("(SaaS) Cohere") self.set_latency("Not available") super().__init__() def render(self): def on_model_change(model): if model == "Default": cost_per_1M_tokens = 15 else: cost_per_1M_tokens = 30 cost_per_1K_tokens = cost_per_1M_tokens / 1000 return gr.update(value=cost_per_1K_tokens), gr.update(value=cost_per_1K_tokens) self.model = gr.Dropdown(["Default", "Custom"], value="Default", label="Model", interactive=True, visible=False) self.input_tokens_cost_per_token = gr.Number(0.015, visible=False, label="($) Price/1K input prompt tokens", interactive=False ) self.output_tokens_cost_per_token = gr.Number(0.015, visible=False, label="($) Price/1K output prompt tokens", interactive=False ) self.info = gr.Markdown("The cost per input and output tokens value is from Cohere's [pricing web page](https://cohere.com/pricing?utm_term=&utm_campaign=Cohere+Brand+%26+Industry+Terms&utm_source=adwords&utm_medium=ppc&hsa_acc=4946693046&hsa_cam=20368816223&hsa_grp=154209120409&hsa_ad=666081801359&hsa_src=g&hsa_tgt=dsa-19959388920&hsa_kw=&hsa_mt=&hsa_net=adwords&hsa_ver=3&gad=1&gclid=CjwKCAjww7KmBhAyEiwA5-PUSlyO7pq0zxeVrhViXMd8WuILW6uY-cfP1-SVuUfs-leUAz14xHlOHxoCmfkQAvD_BwE)", interactive=False, visible=False) self.model.change(on_model_change, inputs=self.model, outputs=[self.input_tokens_cost_per_token, self.output_tokens_cost_per_token]) self.labor = gr.Number(0, visible=False, label="($) Labor cost per month", info="This is an estimate of the labor cost of the AI engineer in charge of deploying the model", interactive=True ) def compute_cost_per_token(self, input_tokens_cost_per_token, output_tokens_cost_per_token, labor): cost_per_input_token = input_tokens_cost_per_token / 1000 cost_per_output_token = output_tokens_cost_per_token / 1000 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 value 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.value + cost_per_output_token * current_output_tokens.value latency = model.get_latency() return model_tco, latency, labor_cost begin = begin+model_n_args