doc: Add metrics documentation and add a 'Reference' section (#2230)
* doc: Add metrics documentation and add a 'Reference' section * doc: Add API reference * doc: Refactor API reference * fix: Message API link * Bad rebase * Moving the docs. --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
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Hugo Larcher
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@ -11,7 +11,7 @@ concurrency:
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jobs:
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build:
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uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yaml@main
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uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
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with:
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commit_sha: ${{ github.event.pull_request.head.sha }}
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pr_number: ${{ github.event.number }}
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@ -5,7 +5,7 @@ repos:
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- id: check-yaml
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- id: end-of-file-fixer
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- id: trailing-whitespace
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exclude: docs/source/basic_tutorials/launcher.md
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exclude: docs/source/reference/launcher.md
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- repo: https://github.com/psf/black
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rev: 24.2.0
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hooks:
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@ -17,8 +17,6 @@
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title: Installation from source
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- local: supported_models
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title: Supported Models and Hardware
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- local: messages_api
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title: Messages API
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- local: architecture
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title: Internal Architecture
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- local: usage_statistics
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@ -33,8 +31,6 @@
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title: Serving Private & Gated Models
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- local: basic_tutorials/using_cli
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title: Using TGI CLI
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- local: basic_tutorials/launcher
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title: All TGI CLI options
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- local: basic_tutorials/non_core_models
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title: Non-core Model Serving
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- local: basic_tutorials/safety
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@ -48,6 +44,14 @@
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- local: basic_tutorials/train_medusa
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title: Train Medusa
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title: Tutorials
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- sections:
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- local: reference/launcher
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title: All TGI CLI options
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- local: reference/metrics
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title: Exported Metrics
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- local: reference/api_reference
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title: API Reference
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title: Reference
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- sections:
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- local: conceptual/streaming
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title: Streaming
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@ -64,7 +68,7 @@
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- local: conceptual/speculation
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title: Speculation (Medusa, ngram)
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- local: conceptual/guidance
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title: How Guidance Works (via outlines
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title: How Guidance Works (via outlines)
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- local: conceptual/lora
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title: LoRA (Low-Rank Adaptation)
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@ -1,11 +1,9 @@
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# Messages API
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Text Generation Inference (TGI) now supports the Messages API, which is fully compatible with the OpenAI Chat Completion API. This feature is available starting from version 1.4.0. You can use OpenAI's client libraries or third-party libraries expecting OpenAI schema to interact with TGI's Messages API. Below are some examples of how to utilize this compatibility.
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> **Note:** The Messages API is supported from TGI version 1.4.0 and above. Ensure you are using a compatible version to access this feature.
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# HTTP API Reference
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#### Table of Contents
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- [Text Generation Inference custom API](#text-generation-inference-custom-api)
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- [OpenAI Messages API](#openai-messages-api)
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- [Making a Request](#making-a-request)
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- [Streaming](#streaming)
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- [Synchronous](#synchronous)
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@ -13,6 +11,21 @@ Text Generation Inference (TGI) now supports the Messages API, which is fully co
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- [Cloud Providers](#cloud-providers)
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- [Amazon SageMaker](#amazon-sagemaker)
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The HTTP API is a RESTful API that allows you to interact with the text-generation-inference component. Two endpoints are available:
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* Text Generation Inference [custom API](https://huggingface.github.io/text-generation-inference/)
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* OpenAI's [Messages API](#openai-messages-api)
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## Text Generation Inference custom API
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Check the [API documentation](https://huggingface.github.io/text-generation-inference/) for more information on how to interact with the Text Generation Inference API.
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## OpenAI Messages API
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Text Generation Inference (TGI) now supports the Messages API, which is fully compatible with the OpenAI Chat Completion API. This feature is available starting from version 1.4.0. You can use OpenAI's client libraries or third-party libraries expecting OpenAI schema to interact with TGI's Messages API. Below are some examples of how to utilize this compatibility.
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> **Note:** The Messages API is supported from TGI version 1.4.0 and above. Ensure you are using a compatible version to access this feature.
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## Making a Request
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You can make a request to TGI's Messages API using `curl`. Here's an example:
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@ -0,0 +1,30 @@
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# Metrics
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TGI exposes multiple metrics that can be collected via the `/metrics` Prometheus endpoint.
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These metrics can be used to monitor the performance of TGI, autoscale deployment and to help identify bottlenecks.
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The following metrics are exposed:
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| Metric Name | Description | Type | Unit |
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|--------------------------------------------|------------------------------------------------------------------------------------------|-----------|---------|
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| `tgi_batch_current_max_tokens` | Maximum tokens for the current batch | Gauge | Count |
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| `tgi_batch_current_size` | Current batch size | Gauge | Count |
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| `tgi_batch_decode_duration` | Time spent decoding a batch per method (prefill or decode) | Histogram | Seconds |
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| `tgi_batch_filter_duration` | Time spent filtering batches and sending generated tokens per method (prefill or decode) | Histogram | Seconds |
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| `tgi_batch_forward_duration` | Batch forward duration per method (prefill or decode) | Histogram | Seconds |
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| `tgi_batch_inference_count` | Inference calls per method (prefill or decode) | Counter | Count |
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| `tgi_batch_inference_duration` | Batch inference duration | Histogram | Seconds |
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| `tgi_batch_inference_success` | Number of successful inference calls per method (prefill or decode) | Counter | Count |
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| `tgi_batch_next_size` | Batch size of the next batch | Histogram | Count |
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| `tgi_queue_size` | Current queue size | Gauge | Count |
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| `tgi_request_count` | Total number of requests | Counter | Count |
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| `tgi_request_duration` | Total time spent processing the request (e2e latency) | Histogram | Seconds |
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| `tgi_request_generated_tokens` | Generated tokens per request | Histogram | Count |
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| `tgi_request_inference_duration` | Request inference duration | Histogram | Seconds |
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| `tgi_request_input_length` | Input token length per request | Histogram | Count |
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| `tgi_request_max_new_tokens` | Maximum new tokens per request | Histogram | Count |
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| `tgi_request_mean_time_per_token_duration` | Mean time per token per request (inter-token latency) | Histogram | Seconds |
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| `tgi_request_queue_duration` | Time spent in the queue per request | Histogram | Seconds |
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| `tgi_request_skipped_tokens` | Speculated tokens per request | Histogram | Count |
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| `tgi_request_success` | Number of successful requests | Counter | |
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| `tgi_request_validation_duration` | Time spent validating the request | Histogram | Seconds |
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@ -2003,6 +2003,120 @@ async fn start(
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.install_recorder()
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.expect("failed to install metrics recorder");
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// Metrics descriptions
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metrics::describe_counter!("tgi_request_success", "Number of successful requests");
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metrics::describe_histogram!(
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"tgi_request_duration",
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metrics::Unit::Seconds,
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"Request duration"
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);
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metrics::describe_histogram!(
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"tgi_request_validation_duration",
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metrics::Unit::Seconds,
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"Request validation duration"
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);
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metrics::describe_histogram!(
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"tgi_request_queue_duration",
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metrics::Unit::Seconds,
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"Request queue duration"
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);
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metrics::describe_histogram!(
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"tgi_request_inference_duration",
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metrics::Unit::Seconds,
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"Request inference duration"
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);
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metrics::describe_histogram!(
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"tgi_request_mean_time_per_token_duration",
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metrics::Unit::Seconds,
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"Mean time per token per request"
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);
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metrics::describe_histogram!(
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"tgi_request_generated_tokens",
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metrics::Unit::Count,
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"Generated tokens per request"
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);
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metrics::describe_counter!(
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"tgi_batch_inference_count",
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metrics::Unit::Count,
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"Inference calls per method (prefill or decode)"
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);
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metrics::describe_counter!(
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"tgi_request_count",
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metrics::Unit::Count,
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"Total number of requests"
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);
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metrics::describe_counter!(
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"tgi_batch_inference_success",
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metrics::Unit::Count,
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"Number of successful inference calls per method (prefill or decode)"
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);
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metrics::describe_gauge!(
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"tgi_batch_current_size",
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metrics::Unit::Count,
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"Current batch size"
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);
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metrics::describe_gauge!("tgi_queue_size", metrics::Unit::Count, "Current queue size");
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metrics::describe_gauge!(
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"tgi_batch_current_max_tokens",
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metrics::Unit::Count,
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"Maximum tokens for the current batch"
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);
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metrics::describe_histogram!(
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"tgi_request_max_new_tokens",
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metrics::Unit::Count,
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"Maximum new tokens per request"
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);
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metrics::describe_histogram!(
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"tgi_batch_inference_duration",
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metrics::Unit::Seconds,
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"Batch inference duration"
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);
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metrics::describe_histogram!(
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"tgi_batch_forward_duration",
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metrics::Unit::Seconds,
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"Batch forward duration per method (prefill or decode)"
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);
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metrics::describe_histogram!(
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"tgi_request_skipped_tokens",
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metrics::Unit::Count,
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"Speculated tokens per request"
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);
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metrics::describe_histogram!(
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"tgi_batch_filter_duration",
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metrics::Unit::Seconds,
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"Time spent filtering batches and sending generated tokens per method (prefill or decode)"
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);
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metrics::describe_histogram!(
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"tgi_request_queue_duration",
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metrics::Unit::Seconds,
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"Time spent in the queue per request"
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);
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metrics::describe_histogram!(
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"tgi_request_validation_duration",
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metrics::Unit::Seconds,
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"Time spent validating the request"
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);
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metrics::describe_histogram!(
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"tgi_request_duration",
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metrics::Unit::Seconds,
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"Total time spent processing the request"
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);
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metrics::describe_histogram!(
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"tgi_batch_decode_duration",
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metrics::Unit::Seconds,
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"Time spent decoding a batch per method (prefill or decode)"
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);
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metrics::describe_histogram!(
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"tgi_request_input_length",
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metrics::Unit::Count,
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"Input token length per request"
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);
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metrics::describe_histogram!(
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"tgi_batch_next_size",
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metrics::Unit::Count,
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"Batch size of the next batch"
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);
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// CORS layer
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let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
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let cors_layer = CorsLayer::new()
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@ -63,7 +63,7 @@ def check_cli(check: bool):
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final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
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block = []
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filename = "docs/source/basic_tutorials/launcher.md"
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filename = "docs/source/reference/launcher.md"
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if check:
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with open(filename, "r") as f:
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doc = f.read()
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