Android Multimodal Embedding Quantization and Benchmarking Suite

Role: Staff Android AI Infrastructure & Performance Engineer

Context

• Source PyTorch or ONNX multimodal model path: {{onnx_source_model}}
• Quantization scheme and precision target: {{quantization_precision}}
• Native Android Architecture ABI target: {{target_android_abi}}
• Physical test device identifier: {{benchmark_device_serial}}
• Multimodal visual token vector dimension: {{clip_embedding_dimension}}
• Maximum inference latency ceiling: {{max_latency_budget_ms}}

Task

Author an automated Python and Bash optimization script that converts multimodal vision-language prompt encoders into an optimized Android LiteRT/NNAPI artifact, benchmarks inference on connected hardware, and produces integration manifests.

Method

1. Parse the input model {{onnx_source_model}} and isolate the visual prompt projection heads and text tokenizer graph.
2. Apply representative calibration datasets matching {{clip_embedding_dimension}} to calculate tensor activation dynamic ranges.
3. Execute static INT8/FP16 quantization based on {{quantization_precision}} using LiteRT model optimizer flags.
4. Package the compiled artifact targeting native libraries for {{target_android_abi}} with fused operator kernels.
5. Deploy the compiled binary and test harness via ADB to device {{benchmark_device_serial}}.
6. Run 100 warmup cycles followed by 500 timed inference passes measuring latency against {{max_latency_budget_ms}}.
7. Extract per-layer NPU, GPU, and CPU execution splits via Android Systrace and generate a hardware affinity matrix.
8. Validate that cosine similarity between fp32 golden outputs and quantized on-device embeddings exceeds 0.985.

Constraints

• The script MUST abort pipeline execution if median device latency exceeds {{max_latency_budget_ms}}.
• The output model MUST NOT produce NaN or Inf values when handling out-of-distribution visual tokens.
• Include automated ADB device state verification before executing benchmarks (battery level > 50%, thermal status normal).
• Ensure all generated model metadata headers conform to Android LiteRT schema standards.

Output format

A structured executable automation script and report generator containing:

1. Environment Setup & Dependency Verification (Bash)
2. Optimization & Quantization Script (quantize_multimodal.py)
3. ADB Remote Benchmark Runner (benchmark_android.sh)
4. JSON Telemetry Schema for latency, memory RSS, and cosine accuracy.

Self-review

• Are ADB shell commands parameterized safely against device disconnects?
• Does the quantization logic correctly preserve multi-head attention precision?
• Is cosine similarity validation executed against real tensor outputs?