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Add a batched auto tune script (#25076)
Signed-off-by: Karan Goel <karangoel@google.com> Signed-off-by: Karan Goel <3261985+karan@users.noreply.github.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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Karan Goel
GitHub
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@ -149,3 +149,70 @@ The script follows a systematic process to find the optimal parameters:
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4. **Track Best Result**: Throughout the process, the script tracks the parameter combination that has yielded the highest valid throughput so far.
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5. **Profile Collection**: For the best-performing run, the script saves the vLLM profiler output, which can be used for deep-dive performance analysis with tools like TensorBoard.
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## Batched `auto_tune`
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The `batch_auto_tune.sh` script allows you to run multiple `auto_tune.sh` experiments sequentially from a single configuration file. It iterates through a list of parameter sets, executes `auto_tune.sh` for each, and records the results back into the input file.
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### Prerequisites
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- **jq**: This script requires `jq` to parse the JSON configuration file.
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- **gcloud**: If you plan to upload results to Google Cloud Storage, the `gcloud` CLI must be installed and authenticated.
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### How to Run
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1. **Create a JSON configuration file**: Create a file (e.g., `runs_config.json`) containing an array of JSON objects. Each object defines the parameters for a single `auto_tune.sh` run.
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2. **Execute the script**:
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```bash
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bash batch_auto_tune.sh <path_to_json_file> [gcs_upload_path]
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```
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- `<path_to_json_file>`: **Required.** Path to your JSON configuration file.
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- `[gcs_upload_path]`: **Optional.** A GCS path (e.g., `gs://my-bucket/benchmark-results`) where the detailed results and profiles for each run will be uploaded. If this is empty, the results will be available on the local filesystem (see the log for `RESULT_FILE=/path/to/results/file.txt`).
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### Configuration File
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The JSON configuration file should contain an array of objects. Each object's keys correspond to the configuration variables for `auto_tune.sh` (see the [Configuration table above](#configuration)). These keys will be converted to uppercase environment variables for each run.
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Here is an example `runs_config.json` with two benchmark configurations:
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```json
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[
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{
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"base": "/home/user",
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"model": "meta-llama/Llama-3.1-8B-Instruct",
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"system": "TPU", # OR GPU
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"tp": 8,
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"input_len": 128,
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"output_len": 2048,
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"max_model_len": 2300,
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"num_seqs_list": "128 256",
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"num_batched_tokens_list": "8192 16384"
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},
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{
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"base": "/home/user",
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"model": "meta-llama/Llama-3.1-70B-Instruct",
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"system": "TPU", # OR GPU
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"tp": 8,
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"input_len": 4000,
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"output_len": 16,
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"max_model_len": 4096,
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"num_seqs_list": "64 128",
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"num_batched_tokens_list": "4096 8192",
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"max_latency_allowed_ms": 500
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}
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]
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```
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### Output
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The script modifies the input JSON file in place, adding the results of each run to the corresponding object. The following fields are added:
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- `run_id`: A unique identifier for the run, derived from the timestamp.
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- `status`: The outcome of the run (`SUCCESS`, `FAILURE`, or `WARNING_NO_RESULT_FILE`).
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- `results`: The content of the `result.txt` file from the `auto_tune.sh` run.
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- `gcs_results`: The GCS URL where the run's artifacts are stored (if a GCS path was provided).
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A summary of successful and failed runs is also printed to the console upon completion.
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128
benchmarks/auto_tune/batch_auto_tune.sh
Executable file
128
benchmarks/auto_tune/batch_auto_tune.sh
Executable file
@ -0,0 +1,128 @@
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#!/bin/bash
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INPUT_JSON="$1"
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GCS_PATH="$2" # Optional GCS path for uploading results for each run
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SCRIPT_DIR=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd)
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AUTOTUNE_SCRIPT="$SCRIPT_DIR/auto_tune.sh"
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if [[ -z "$INPUT_JSON" ]]; then
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echo "Error: Input JSON file not provided."
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echo "Usage: $0 <path_to_json_file> [gcs_upload_path]"
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exit 1
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fi
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if [[ ! -f "$INPUT_JSON" ]]; then
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echo "Error: File not found at '$INPUT_JSON'"
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exit 1
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fi
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if ! command -v jq &> /dev/null; then
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echo "Error: 'jq' command not found. Please install jq to process the JSON input."
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exit 1
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fi
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if [[ -n "$GCS_PATH" ]] && ! command -v gcloud &> /dev/null; then
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echo "Error: 'gcloud' command not found, but a GCS_PATH was provided."
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exit 1
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fi
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SUCCESS_COUNT=0
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FAILURE_COUNT=0
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FAILED_RUNS=()
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SCRIPT_START_TIME=$(date +%s)
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json_content=$(cat "$INPUT_JSON")
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if ! num_runs=$(echo "$json_content" | jq 'length'); then
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echo "Error: Invalid JSON in $INPUT_JSON. 'jq' failed to get array length." >&2
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exit 1
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fi
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echo "Found $num_runs benchmark configurations in $INPUT_JSON."
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echo "Starting benchmark runs..."
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echo "--------------------------------------------------"
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for i in $(seq 0 $(($num_runs - 1))); do
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run_object=$(echo "$json_content" | jq ".[$i]")
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RUN_START_TIME=$(date +%s)
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ENV_VARS_ARRAY=()
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# Dynamically create env vars from the JSON object's keys
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for key in $(echo "$run_object" | jq -r 'keys_unsorted[]'); do
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value=$(echo "$run_object" | jq -r ".$key")
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var_name=$(echo "$key" | tr '[:lower:]' '[:upper:]' | tr -cd 'A-Z0-9_')
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ENV_VARS_ARRAY+=("${var_name}=${value}")
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done
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echo "Executing run #$((i+1))/$num_runs with parameters: ${ENV_VARS_ARRAY[*]}"
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# Execute auto_tune.sh and capture output
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RUN_OUTPUT_FILE=$(mktemp)
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if env "${ENV_VARS_ARRAY[@]}" bash "$AUTOTUNE_SCRIPT" > >(tee -a "$RUN_OUTPUT_FILE") 2>&1; then
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STATUS="SUCCESS"
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((SUCCESS_COUNT++))
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else
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STATUS="FAILURE"
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((FAILURE_COUNT++))
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FAILED_RUNS+=("Run #$((i+1)): $(echo $run_object | jq -c .)")
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fi
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RUN_OUTPUT=$(<"$RUN_OUTPUT_FILE")
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rm "$RUN_OUTPUT_FILE"
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# Parse results and optionally upload them to GCS
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RUN_ID=""
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RESULTS=""
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GCS_RESULTS_URL=""
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if [[ "$STATUS" == "SUCCESS" ]]; then
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RESULT_FILE_PATH=$(echo "$RUN_OUTPUT" | grep 'RESULT_FILE=' | tail -n 1 | cut -d'=' -f2 | tr -s '/' || true)
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if [[ -n "$RESULT_FILE_PATH" && -f "$RESULT_FILE_PATH" ]]; then
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RUN_ID=$(basename "$(dirname "$RESULT_FILE_PATH")")
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RESULT_DIR=$(dirname "$RESULT_FILE_PATH")
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RESULTS=$(cat "$RESULT_FILE_PATH")
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if [[ -n "$GCS_PATH" ]]; then
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GCS_RESULTS_URL="${GCS_PATH}/${RUN_ID}"
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echo "Uploading results to GCS..."
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if gcloud storage rsync --recursive "$RESULT_DIR/" "$GCS_RESULTS_URL"; then
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echo "GCS upload successful."
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else
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echo "Warning: GCS upload failed for RUN_ID $RUN_ID."
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fi
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fi
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else
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echo "Warning: Could not find result file for a successful run."
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STATUS="WARNING_NO_RESULT_FILE"
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fi
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fi
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# Add the results back into the JSON object for this run
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json_content=$(echo "$json_content" | jq --argjson i "$i" --arg run_id "$RUN_ID" --arg status "$STATUS" --arg results "$RESULTS" --arg gcs_results "$GCS_RESULTS_URL" \
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'.[$i] += {run_id: $run_id, status: $status, results: $results, gcs_results: $gcs_results}')
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RUN_END_TIME=$(date +%s)
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echo "Run finished in $((RUN_END_TIME - RUN_START_TIME)) seconds. Status: $STATUS"
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echo "--------------------------------------------------"
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# Save intermediate progress back to the file
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echo "$json_content" > "$INPUT_JSON.tmp" && mv "$INPUT_JSON.tmp" "$INPUT_JSON"
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done
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SCRIPT_END_TIME=$(date +%s)
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echo "All benchmark runs completed in $((SCRIPT_END_TIME - SCRIPT_START_TIME)) seconds."
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echo
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echo "====================== SUMMARY ======================"
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echo "Successful runs: $SUCCESS_COUNT"
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echo "Failed runs: $FAILURE_COUNT"
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echo "==================================================="
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if [[ $FAILURE_COUNT -gt 0 ]]; then
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echo "Details of failed runs (see JSON file for full parameters):"
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for failed in "${FAILED_RUNS[@]}"; do
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echo " - $failed"
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done
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fi
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echo "Updated results have been saved to '$INPUT_JSON'."
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