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Merged
copybara-service merged 3 commits into from
May 8, 2026
Merged

Add demo scripts #4

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c734b57
Add demo scripts.
junjiang-lab May 7, 2026
0043c95
Add demo_utils.sh
junjiang-lab May 7, 2026
c7a7dfe
Move to folder test_scripts/models
junjiang-lab May 7, 2026
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120 changes: 120 additions & 0 deletions test_scripts/demo_efficientnet.sh
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#!/bin/bash
# LiteRT CLI EfficientNet Demo & Test Script
set -e


echo -e "${BLUE}${BOLD}==================================================================${NC}"
echo -e "${BLUE}${BOLD}>>> LiteRT CLI EfficientNet Demo Script${NC}"
echo -e "${BLUE}${BOLD}==================================================================${NC}"

# --- Environment Setup ---
export SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
export LITERT_CLI_ROOT="/tmp/litert_cli_efficientnet"

# Source shared utilities
source "$SCRIPT_DIR/demo_utils.sh"


# Clean up and create work directory
echo -e "\n${YELLOW}Setting up workspace at: $LITERT_CLI_ROOT...${NC}"
rm -rf "$LITERT_CLI_ROOT"
mkdir -p "$LITERT_CLI_ROOT"
cd "$LITERT_CLI_ROOT"

# Create Python virtual environment
echo -e "${YELLOW}Creating Python virtual environment...${NC}"
python3 -m venv venv_efficientnet
source venv_efficientnet/bin/activate

# Create output directories
export MODEL_DIR="$LITERT_CLI_ROOT/models"
mkdir -p "$MODEL_DIR"

# Test data directory
export TEST_DATA_DIR="$REPO_ROOT/litert_cli/test_data"

# Install litert-cli from source
echo -e "${YELLOW}Installing litert-cli from source...${NC}"
pip install -e "$REPO_ROOT"




# --- 1. Download EfficientNet-B1 model ---
run_case "Download: EfficientNet-B1 from HuggingFace" \
litert download litert-community/efficientnet_b1 --file "*.tflite" --output "$MODEL_DIR/efficientnet"

# Verify the downloaded model exists
EFFICIENTNET_TFLITE="$MODEL_DIR/efficientnet/efficientnet_b1.tflite"
if [ ! -f "$EFFICIENTNET_TFLITE" ]; then
echo -e "${RED}Error: Downloaded model not found at $EFFICIENTNET_TFLITE${NC}"
exit 1
fi

# --- 2. Quantize the EfficientNet model ---
run_case "Quantize: EfficientNet Dynamic Range INT8" \
litert quantize "$EFFICIENTNET_TFLITE" --type int8_dynamic --output "$MODEL_DIR/efficientnet/efficientnet_b1_int8_dynamic.tflite"

run_case "Quantize: EfficientNet Weight-Only INT8" \
litert quantize "$EFFICIENTNET_TFLITE" --type int8_weight_only --output "$MODEL_DIR/efficientnet/efficientnet_b1_int8_weight_only.tflite"

# --- 3. Run Inference (Desktop & Android) ---
run_case "Run: EfficientNet FP32 on Desktop (CPU)" \
litert run "$EFFICIENTNET_TFLITE" --desktop --cpu --iterations 1

if has_desktop_gpu "$EFFICIENTNET_TFLITE"; then
run_case "Run: EfficientNet FP32 on Desktop (GPU)" \
litert run "$EFFICIENTNET_TFLITE" --desktop --gpu --iterations 1
else
echo -e "\n${YELLOW}Desktop GPU delegate is not supported. Skipping Desktop GPU run.${NC}"
fi


run_case "Run: EfficientNet Dynamic INT8 on Desktop (CPU)" \
litert run "$MODEL_DIR/efficientnet/efficientnet_b1_int8_dynamic.tflite" --desktop --cpu --iterations 1

if has_android_device; then
echo -e "\n${GREEN}Android device detected. Running Android inference...${NC}"
run_case "Run: EfficientNet FP32 on Android (CPU)" \
litert run "$EFFICIENTNET_TFLITE" --android --cpu --iterations 1

run_case "Run: EfficientNet FP32 on Android (GPU)" \
litert run "$EFFICIENTNET_TFLITE" --android --gpu --iterations 1

run_case "Run: EfficientNet Dynamic INT8 on Android (CPU)" \
litert run "$MODEL_DIR/efficientnet/efficientnet_b1_int8_dynamic.tflite" --android --cpu --iterations 1
fi

# --- 4. Benchmark (Android) ---
if has_android_device; then
echo -e "\n${GREEN}Android device detected. Running Android benchmarks...${NC}"
run_case "Benchmark: EfficientNet FP32 on Android (CPU)" \
litert benchmark "$EFFICIENTNET_TFLITE" --android

run_case "Benchmark: EfficientNet FP32 on Android (GPU)" \
litert benchmark "$EFFICIENTNET_TFLITE" --android --gpu

run_case "Benchmark: EfficientNet Dynamic INT8 on Android" \
litert benchmark "$MODEL_DIR/efficientnet/efficientnet_b1_int8_dynamic.tflite" --android
else
echo -e "\n${YELLOW}No Android device detected. Skipping benchmarks (litert benchmark only supports Android/GCP).${NC}"
fi


# --- 5. Compile (AOT Compilation) ---
# TODO: Add this back when we fix the NPU compile issue.
# run_case "Compile: EfficientNet FP32 for Qualcomm sm8750 NPU" \
# litert compile "$EFFICIENTNET_TFLITE" --target sm8750 --output-dir "$MODEL_DIR/efficientnet"

# --- 6. Visualize (Model Explorer) ---
run_case "Visualize: Launch Model Explorer in the background" \
litert visualize "$EFFICIENTNET_TFLITE"

run_case "Visualize: Stop all Model Explorer servers" \
litert visualize --stop-all


# --- Summary Report ---
print_summary_report "EfficientNet"

61 changes: 61 additions & 0 deletions test_scripts/demo_gemma4.sh
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#!/bin/bash
# LiteRT CLI Gemma4 LLM Demo & Test Script
set -e


echo -e "${BLUE}${BOLD}==================================================================${NC}"
echo -e "${BLUE}${BOLD}>>> LiteRT CLI Gemma4 LLM Demo Script${NC}"
echo -e "${BLUE}${BOLD}==================================================================${NC}"

# --- Environment Setup ---
export SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
export LITERT_CLI_ROOT="/tmp/litert_cli_gemma4"

# Source shared utilities
source "$SCRIPT_DIR/demo_utils.sh"


# Clean up and create work directory
echo -e "\n${YELLOW}Setting up workspace at: $LITERT_CLI_ROOT...${NC}"
rm -rf "$LITERT_CLI_ROOT"
mkdir -p "$LITERT_CLI_ROOT"
cd "$LITERT_CLI_ROOT"

# Create output directories
export MODEL_DIR="$LITERT_CLI_ROOT/models"
mkdir -p "$MODEL_DIR"


# Create Python virtual environment
echo -e "${YELLOW}Creating Python virtual environment...${NC}"
python3 -m venv venv_gemma4
source venv_gemma4/bin/activate

# Install litert-cli from source
echo -e "${YELLOW}Installing litert-cli from source...${NC}"
pip install -e "$REPO_ROOT"




# --- 1. Convert HuggingFace Model google/gemma-4-E2B-it ---
# TODO: Bring this back when we add support for --externalize_embedder in CLI convert command.
# run_case "Convert: HuggingFace google/gemma-4-E2B-it" \
# litert convert google/gemma-4-E2B-it --output "$MODEL_DIR/gemma4"


# --- 2. Run Gemma4 Generative LLM Model ---
run_case "Run Gemma4: Generative inference with custom prompt" \
litert lm run gemma-4-E2B-it.litertlm --prompt "Explain machine learning in one sentence."

@smilingday smilingday May 7, 2026

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This might be not working, if you don't have models above.

But this should work:
litert lm run
--from-huggingface-repo=litert-community/gemma-4-E2B-it-litert-lm
gemma-4-E2B-it.litertlm
--prompt="What is the capital of France?"

Basically we can replace the examples below from "litert-lm", to "litert lm":
https://ai.google.dev/edge/litert-lm/cli

@junjiang-lab junjiang-lab May 7, 2026

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DONE



# --- 3. Benchmark Gemma4 LLM Model ---
run_case "Benchmark Gemma4: Local benchmark of LLM generation" \
litert lm benchmark gemma-4-E2B-it.litertlm



# --- Summary Report ---
print_summary_report "Gemma4"

119 changes: 119 additions & 0 deletions test_scripts/demo_resnet.sh
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#!/bin/bash
# LiteRT CLI ResNet Demo & Test Script
set -e

echo -e "${BLUE}${BOLD}==================================================================${NC}"
echo -e "${BLUE}${BOLD}>>> LiteRT CLI ResNet Demo Script${NC}"
echo -e "${BLUE}${BOLD}==================================================================${NC}"

# --- Environment Setup ---
export SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
export LITERT_CLI_ROOT="/tmp/litert_cli_resnet"

# Source shared utilities
source "$SCRIPT_DIR/demo_utils.sh"


# Clean up and create work directory
echo -e "\n${YELLOW}Setting up workspace at: $LITERT_CLI_ROOT...${NC}"
rm -rf "$LITERT_CLI_ROOT"
mkdir -p "$LITERT_CLI_ROOT"
cd "$LITERT_CLI_ROOT"

# Create Python virtual environment
echo -e "${YELLOW}Creating Python virtual environment...${NC}"
python3 -m venv venv_resnet
source venv_resnet/bin/activate

# Create output directories
export MODEL_DIR="$LITERT_CLI_ROOT/models"
mkdir -p "$MODEL_DIR"

# Test data directory
export TEST_DATA_DIR="$REPO_ROOT/litert_cli/test_data"

# Install litert-cli from source
echo -e "${YELLOW}Installing litert-cli from source...${NC}"
pip install -e "$REPO_ROOT"




# --- 1. Convert PyTorch ResNet18 model to LiteRT ---
run_case "Convert: PyTorch ResNet18 to LiteRT" \
litert convert "$TEST_DATA_DIR/resnet18.py" --output "$MODEL_DIR/resnet18"

# Verify the converted model exists
RESNET_TFLITE="$MODEL_DIR/resnet18/resnet18.tflite"
if [ ! -f "$RESNET_TFLITE" ]; then
echo -e "${RED}Error: Converted model not found at $RESNET_TFLITE${NC}"
exit 1
fi

# --- 2. Quantize the ResNet18 model ---
run_case "Quantize: ResNet18 Dynamic Range INT8" \
litert quantize "$RESNET_TFLITE" --type int8_dynamic --output "$MODEL_DIR/resnet18/resnet18_int8_dynamic.tflite"

run_case "Quantize: ResNet18 Weight-Only INT8" \
litert quantize "$RESNET_TFLITE" --type int8_weight_only --output "$MODEL_DIR/resnet18/resnet18_int8_weight_only.tflite"

# --- 3. Run Inference (Desktop & Android) ---
run_case "Run: ResNet18 FP32 on Desktop (CPU)" \
litert run "$RESNET_TFLITE" --desktop --cpu --iterations 1

if has_desktop_gpu "$RESNET_TFLITE"; then
run_case "Run: ResNet18 FP32 on Desktop (GPU)" \
litert run "$RESNET_TFLITE" --desktop --gpu --iterations 1
else
echo -e "\n${YELLOW}Desktop GPU delegate is not supported. Skipping Desktop GPU run.${NC}"
fi


run_case "Run: ResNet18 Dynamic INT8 on Desktop (CPU)" \
litert run "$MODEL_DIR/resnet18/resnet18_int8_dynamic.tflite" --desktop --cpu --iterations 1

if has_android_device; then
echo -e "\n${GREEN}Android device detected. Running Android inference...${NC}"
run_case "Run: ResNet18 FP32 on Android (CPU)" \
litert run "$RESNET_TFLITE" --android --cpu --iterations 1

run_case "Run: ResNet18 FP32 on Android (GPU)" \
litert run "$RESNET_TFLITE" --android --gpu --iterations 1

run_case "Run: ResNet18 Dynamic INT8 on Android (CPU)" \
litert run "$MODEL_DIR/resnet18/resnet18_int8_dynamic.tflite" --android --cpu --iterations 1
fi

# --- 4. Benchmark (Android) ---
if has_android_device; then
echo -e "\n${GREEN}Android device detected. Running Android benchmarks...${NC}"
run_case "Benchmark: ResNet18 FP32 on Android (CPU)" \
litert benchmark "$RESNET_TFLITE" --android

run_case "Benchmark: ResNet18 FP32 on Android (GPU)" \
litert benchmark "$RESNET_TFLITE" --android --gpu

run_case "Benchmark: ResNet18 Dynamic INT8 on Android" \
litert benchmark "$MODEL_DIR/resnet18/resnet18_int8_dynamic.tflite" --android
else
echo -e "\n${YELLOW}No Android device detected. Skipping benchmarks (litert benchmark only supports Android/GCP).${NC}"
fi


# --- 5. Compile (AOT Compilation) ---
# TODO: Add this back when we fix the NPU compile issue.
# run_case "Compile: ResNet18 FP32 for Qualcomm sm8750 NPU" \
# litert compile "$RESNET_TFLITE" --target sm8750 --output-dir "$MODEL_DIR/resnet18"

# --- 6. Visualize (Model Explorer) ---
run_case "Visualize: Launch Model Explorer in the background" \
litert visualize "$RESNET_TFLITE"

run_case "Visualize: Stop all Model Explorer servers" \
litert visualize --stop-all


# --- Summary Report ---
print_summary_report "ResNet"

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