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| 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] | |
| def compute_cost_per_token(self): | |
| pass | |
| 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("""<span style="font-size: 16px; font-weight: 600; color: #212529;">Source</span>""") | |
| 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.") | |
| 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:<br> | |
| $CT = \frac{VM_C}{TS}$ where $TS = TS_{max} * \frac{MO}{100}$ <br> | |
| with: <br> | |
| $CT$ = Cost per Token (Input or output), <br> | |
| $VM_C$ = VM Cost per second, <br> | |
| $TS$ = Tokens per second (Input or output), <br> | |
| $TS_{max}$ = Tokens per second when the GPU is maxed out at 100%, <br> | |
| $MO$ = Maxed Out, <br> | |
| """, 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 |