Updating the self check (#2209)

* Updating the self check

* Fix.

* Revert the CLI .

* cli.

* Space.

* Revert cargo update.
This commit is contained in:
Nicolas Patry 2024-07-09 17:23:48 +02:00 committed by GitHub
parent f5ba9bfd52
commit 4c976fb406
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7 changed files with 123 additions and 13 deletions

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@ -41,5 +41,5 @@ jobs:
- name: Check that documentation is up-to-date
run: |
npm install -g swagger-ui
npm install -g swagger-cli
python update_doc.py --check

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@ -492,12 +492,12 @@
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Completion"
"$ref": "#/components/schemas/CompletionFinal"
}
},
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/CompletionCompleteChunk"
"$ref": "#/components/schemas/Chunk"
}
}
}
@ -1324,6 +1324,17 @@
}
}
},
"FunctionName": {
"type": "object",
"required": [
"name"
],
"properties": {
"name": {
"type": "string"
}
}
},
"GenerateParameters": {
"type": "object",
"properties": {
@ -1764,6 +1775,16 @@
}
]
},
"OutputMessage": {
"oneOf": [
{
"$ref": "#/components/schemas/TextMessage"
},
{
"$ref": "#/components/schemas/ToolCallMessage"
}
]
},
"PrefillToken": {
"type": "object",
"required": [
@ -1890,6 +1911,23 @@
}
}
},
"TextMessage": {
"type": "object",
"required": [
"role",
"content"
],
"properties": {
"content": {
"type": "string",
"example": "My name is David and I"
},
"role": {
"type": "string",
"example": "user"
}
}
},
"Token": {
"type": "object",
"required": [
@ -1962,6 +2000,41 @@
}
}
},
"ToolCallDelta": {
"type": "object",
"required": [
"role",
"tool_calls"
],
"properties": {
"role": {
"type": "string",
"example": "assistant"
},
"tool_calls": {
"$ref": "#/components/schemas/DeltaToolCall"
}
}
},
"ToolCallMessage": {
"type": "object",
"required": [
"role",
"tool_calls"
],
"properties": {
"role": {
"type": "string",
"example": "assistant"
},
"tool_calls": {
"type": "array",
"items": {
"$ref": "#/components/schemas/ToolCall"
}
}
}
},
"ToolType": {
"oneOf": [
{
@ -1985,6 +2058,17 @@
}
]
},
"Url": {
"type": "object",
"required": [
"url"
],
"properties": {
"url": {
"type": "string"
}
}
},
"Usage": {
"type": "object",
"required": [

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@ -62,7 +62,9 @@ Options:
Possible values:
- awq: 4 bit quantization. Requires a specific AWQ quantized model: <https://hf.co/models?search=awq>. Should replace GPTQ models wherever possible because of the better latency
- eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from <https://github.com/NetEase-FuXi/EETQ.git>
- exl2: Variable bit quantization. Requires a specific EXL2 quantized model: <https://hf.co/models?search=exl2>. Requires exllama2 kernels and does not support tensor parallelism (num_shard > 1)
- gptq: 4 bit quantization. Requires a specific GTPQ quantized model: <https://hf.co/models?search=gptq>. text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels
- marlin: 4 bit quantization. Requires a specific Marlin quantized model: <https://hf.co/models?search=marlin>
- bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
@ -124,7 +126,7 @@ Options:
## MAX_TOP_N_TOKENS
```shell
--max-top-n-tokens <MAX_TOP_N_TOKENS>
This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking
This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens` is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking
[env: MAX_TOP_N_TOKENS=]
[default: 5]
@ -334,6 +336,13 @@ Options:
--otlp-endpoint <OTLP_ENDPOINT>
[env: OTLP_ENDPOINT=]
```
## OTLP_SERVICE_NAME
```shell
--otlp-service-name <OTLP_SERVICE_NAME>
[env: OTLP_SERVICE_NAME=]
[default: text-generation-inference.router]
```
## CORS_ALLOW_ORIGIN
```shell
@ -407,6 +416,14 @@ Options:
[env: MAX_CLIENT_BATCH_SIZE=]
[default: 4]
```
## LORA_ADAPTERS
```shell
--lora-adapters <LORA_ADAPTERS>
Lora Adapters a list of adapter ids i.e. `repo/adapter1,repo/adapter2` to load during startup that will be available to callers via the `adapter_id` field in a request
[env: LORA_ADAPTERS=]
```
## HELP
```shell

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@ -848,7 +848,7 @@ pub enum ToolType {
Function { function: FunctionName },
}
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize, ToSchema)]
pub struct FunctionName {
pub name: String,
}

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@ -11,10 +11,11 @@ use crate::kserve::{
};
use crate::validation::ValidationError;
use crate::{
BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Info,
Message, MessageChunk, MessageContent, PrefillToken, SimpleToken, StreamDetails,
StreamResponse, Token, TokenizeResponse, Usage, Validation,
BestOfSequence, Details, ErrorResponse, FinishReason, FunctionName, GenerateParameters,
GenerateRequest, GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig,
HubTokenizerConfig, Info, Message, MessageChunk, MessageContent, OutputMessage, PrefillToken,
SimpleToken, StreamDetails, StreamResponse, TextMessage, Token, TokenizeResponse,
ToolCallDelta, ToolCallMessage, Url, Usage, Validation,
};
use crate::{
ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete,
@ -562,8 +563,8 @@ request_body = CompletionRequest,
responses(
(status = 200, description = "Generated Chat Completion",
content(
("application/json" = Completion),
("text/event-stream" = CompletionCompleteChunk),
("application/json" = CompletionFinal),
("text/event-stream" = Chunk),
)),
(status = 424, description = "Generation Error", body = ErrorResponse,
example = json ! ({"error": "Request failed during generation"})),
@ -1448,6 +1449,12 @@ pub async fn run(
Message,
MessageContent,
MessageChunk,
Url,
FunctionName,
OutputMessage,
TextMessage,
ToolCallMessage,
ToolCallDelta,
ChatCompletionComplete,
ChatCompletionChoice,
ChatCompletionDelta,

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@ -177,7 +177,9 @@ def check_openapi(check: bool):
],
capture_output=True,
).stderr.decode("utf-8")
if errors:
# The openapi specs fails on `exclusive_minimum` which is expected to be a boolean where
# utoipa outputs a value instead: https://github.com/juhaku/utoipa/issues/969
if not errors.startswith("Swagger schema validation failed."):
print(errors)
raise Exception(
f"OpenAPI documentation is invalid, `swagger-cli validate` showed some error:\n {errors}"