Rollback to `ChatRequest` for Vertex AI Chat instead of `VertexChat` (#2651)
As spotted by @philschmid, the payload was compliant with Vertex AI, but just partially, since ideally the most compliant version would be with the generation kwargs flattened to be on the same level as the `messages`; meaning that Vertex AI would still expect a list of instances, but each instance would be an OpenAI-compatible instance, which is more clear; and more aligned with the SageMaker integration too, so kudos to him for spotting that; and sorry from my end for any inconvenience @Narsil.
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Alvaro Bartolome
GitHub
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ce7e356561
commit
ffe05ccd05
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@ -1,9 +1,6 @@
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use crate::infer::Infer;
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use crate::server::{generate_internal, ComputeType};
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use crate::{
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ChatRequest, ErrorResponse, GenerateParameters, GenerateRequest, GrammarType, Message,
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StreamOptions, Tool, ToolChoice,
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};
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use crate::{ChatRequest, ErrorResponse, GenerateParameters, GenerateRequest};
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use axum::extract::Extension;
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use axum::http::{HeaderMap, StatusCode};
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use axum::response::{IntoResponse, Response};
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@ -21,162 +18,12 @@ pub(crate) struct GenerateVertexInstance {
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pub parameters: Option<GenerateParameters>,
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}
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#[derive(Clone, Deserialize, ToSchema)]
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#[cfg_attr(test, derive(Debug, PartialEq))]
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pub(crate) struct VertexChat {
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messages: Vec<Message>,
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// Messages is ignored there.
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#[serde(default)]
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parameters: VertexParameters,
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}
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#[derive(Clone, Deserialize, ToSchema, Serialize, Default)]
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#[cfg_attr(test, derive(Debug, PartialEq))]
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pub(crate) struct VertexParameters {
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#[schema(example = "mistralai/Mistral-7B-Instruct-v0.2")]
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/// [UNUSED] ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
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pub model: Option<String>,
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/// Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,
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/// decreasing the model's likelihood to repeat the same line verbatim.
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#[serde(default)]
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#[schema(example = "1.0")]
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pub frequency_penalty: Option<f32>,
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/// UNUSED
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/// Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens
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/// (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically,
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/// the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model,
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/// but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should
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/// result in a ban or exclusive selection of the relevant token.
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#[serde(default)]
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pub logit_bias: Option<Vec<f32>>,
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/// Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each
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/// output token returned in the content of message.
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#[serde(default)]
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#[schema(example = "false")]
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pub logprobs: Option<bool>,
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/// An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with
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/// an associated log probability. logprobs must be set to true if this parameter is used.
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#[serde(default)]
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#[schema(example = "5")]
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pub top_logprobs: Option<u32>,
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/// The maximum number of tokens that can be generated in the chat completion.
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#[serde(default)]
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#[schema(example = "32")]
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pub max_tokens: Option<u32>,
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/// UNUSED
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/// How many chat completion choices to generate for each input message. Note that you will be charged based on the
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/// number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
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#[serde(default)]
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#[schema(nullable = true, example = "2")]
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pub n: Option<u32>,
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/// Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,
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/// increasing the model's likelihood to talk about new topics
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#[serde(default)]
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#[schema(nullable = true, example = 0.1)]
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pub presence_penalty: Option<f32>,
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/// Up to 4 sequences where the API will stop generating further tokens.
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#[serde(default)]
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#[schema(nullable = true, example = "null")]
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pub stop: Option<Vec<String>>,
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#[serde(default = "bool::default")]
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pub stream: bool,
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#[schema(nullable = true, example = 42)]
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pub seed: Option<u64>,
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/// What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while
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/// lower values like 0.2 will make it more focused and deterministic.
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///
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/// We generally recommend altering this or `top_p` but not both.
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#[serde(default)]
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#[schema(nullable = true, example = 1.0)]
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pub temperature: Option<f32>,
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/// An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the
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/// tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
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#[serde(default)]
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#[schema(nullable = true, example = 0.95)]
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pub top_p: Option<f32>,
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/// A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of
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/// functions the model may generate JSON inputs for.
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#[serde(default)]
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#[schema(nullable = true, example = "null")]
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pub tools: Option<Vec<Tool>>,
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/// A prompt to be appended before the tools
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#[serde(default)]
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#[schema(
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nullable = true,
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example = "Given the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables."
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)]
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pub tool_prompt: Option<String>,
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/// A specific tool to use. If not provided, the model will default to use any of the tools provided in the tools parameter.
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#[serde(default)]
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#[schema(nullable = true, example = "null")]
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pub tool_choice: ToolChoice,
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/// Response format constraints for the generation.
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///
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/// NOTE: A request can use `response_format` OR `tools` but not both.
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#[serde(default)]
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#[schema(nullable = true, default = "null", example = "null")]
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pub response_format: Option<GrammarType>,
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/// A guideline to be used in the chat_template
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#[serde(default)]
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#[schema(nullable = true, default = "null", example = "null")]
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pub guideline: Option<String>,
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/// Options for streaming response. Only set this when you set stream: true.
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#[serde(default)]
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#[schema(nullable = true, example = "null")]
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pub stream_options: Option<StreamOptions>,
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}
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impl From<VertexChat> for ChatRequest {
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fn from(val: VertexChat) -> Self {
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Self {
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messages: val.messages,
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frequency_penalty: val.parameters.frequency_penalty,
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guideline: val.parameters.guideline,
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logit_bias: val.parameters.logit_bias,
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logprobs: val.parameters.logprobs,
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max_tokens: val.parameters.max_tokens,
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model: val.parameters.model,
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n: val.parameters.n,
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presence_penalty: val.parameters.presence_penalty,
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response_format: val.parameters.response_format,
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seed: val.parameters.seed,
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stop: val.parameters.stop,
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stream_options: val.parameters.stream_options,
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stream: val.parameters.stream,
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temperature: val.parameters.temperature,
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tool_choice: val.parameters.tool_choice,
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tool_prompt: val.parameters.tool_prompt,
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tools: val.parameters.tools,
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top_logprobs: val.parameters.top_logprobs,
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top_p: val.parameters.top_p,
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}
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}
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}
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#[derive(Clone, Deserialize, ToSchema)]
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#[cfg_attr(test, derive(Debug, PartialEq))]
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#[serde(untagged)]
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pub(crate) enum VertexInstance {
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Generate(GenerateVertexInstance),
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Chat(VertexChat),
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Chat(ChatRequest),
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}
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#[derive(Deserialize, ToSchema)]
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@ -257,9 +104,8 @@ pub(crate) async fn vertex_compatibility(
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},
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},
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VertexInstance::Chat(instance) => {
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let chat_request: ChatRequest = instance.into();
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let (generate_request, _using_tools): (GenerateRequest, bool) =
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chat_request.try_into_generate(&infer)?;
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instance.try_into_generate(&infer)?;
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generate_request
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}
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};
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@ -305,34 +151,14 @@ mod tests {
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#[test]
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fn vertex_deserialization() {
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let string = serde_json::json!({
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"messages": [{"role": "user", "content": "What's Deep Learning?"}],
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"parameters": {
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"max_tokens": 128,
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"top_p": 0.95,
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"temperature": 0.7
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}
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});
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let _request: VertexChat = serde_json::from_value(string).expect("Can deserialize");
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let string = serde_json::json!({
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"messages": [{"role": "user", "content": "What's Deep Learning?"}],
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});
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let _request: VertexChat = serde_json::from_value(string).expect("Can deserialize");
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let string = serde_json::json!({
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"instances": [
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{
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"messages": [{"role": "user", "content": "What's Deep Learning?"}],
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"parameters": {
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"max_tokens": 128,
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"top_p": 0.95,
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"temperature": 0.7
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}
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"max_tokens": 128,
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"top_p": 0.95,
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"temperature": 0.7
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}
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]
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@ -341,18 +167,16 @@ mod tests {
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assert_eq!(
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request,
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VertexRequest {
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instances: vec![VertexInstance::Chat(VertexChat {
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instances: vec![VertexInstance::Chat(ChatRequest {
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messages: vec![Message {
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role: "user".to_string(),
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content: MessageContent::SingleText("What's Deep Learning?".to_string()),
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name: None,
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},],
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parameters: VertexParameters {
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max_tokens: Some(128),
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top_p: Some(0.95),
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temperature: Some(0.7),
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..Default::default()
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}
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max_tokens: Some(128),
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top_p: Some(0.95),
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temperature: Some(0.7),
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..Default::default()
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})]
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}
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);
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