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Add CI test for CoreML LoRA multimethod export
Tests three export configurations with file size validation: 1. Base only (single method) - baseline size 2. Base + LoRA adapter (multimethod) - small overhead from lora_a/lora_b 3. Base + LoRA + multifunction - same overhead (POSITIONAL sharing) Uses stories110M with a synthetic zero-initialized LoRA adapter so base and adapter outputs match. Inference tests run on macOS only. Authored with Claude. ghstack-source-id: 79dd55a ghstack-comment-id: 4094191365 Pull-Request: #18354
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‎.ci/scripts/test_coreml_lora.sh‎

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#!/bin/bash
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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set -exu
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source "$(dirname "${BASH_SOURCE[0]}")/utils.sh"
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export EXECUTORCH_ROOT="$(dirname "${BASH_SOURCE[0]}")/../.."
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if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then
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PYTHON_EXECUTABLE=python3
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fi
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which "${PYTHON_EXECUTABLE}"
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EXPORT_SCRIPT="examples/apple/coreml/llama/export_static_llm_coreml.py"
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RUN_SCRIPT="examples/apple/coreml/llama/run_static_llm.py"
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RUN_MF_SCRIPT="examples/apple/coreml/llama/run_static_llm_multifunction.py"
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# Export parameters — small context for fast CI.
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MAX_CONTEXT_LEN=64
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INPUT_LEN=32
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CACHE_LEN=$((MAX_CONTEXT_LEN - INPUT_LEN))
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cleanup_files() {
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echo "Deleting generated files"
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rm -f base.pte lora.pte lora_mf.pte
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rm -f result_base*.txt result_lora*.txt
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rm -rf "${ADAPTER_DIR}"
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}
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### SETUP ###
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pushd "${EXECUTORCH_ROOT}/examples/apple/coreml/llama"
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# Download stories110M artifacts.
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download_stories_model_artifacts
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# Create a synthetic LoRA adapter for stories110M.
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ADAPTER_DIR=$(mktemp -d)
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${PYTHON_EXECUTABLE} - "${ADAPTER_DIR}" <<'PYEOF'
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import json
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import sys
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import torch
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from safetensors.torch import save_file
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adapter_dir = sys.argv[1]
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dim = 768
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n_heads = 12
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n_layers = 12
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rank = 8
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alpha = 16
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target_modules = ["q_proj", "v_proj"]
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config = {
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"r": rank,
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"lora_alpha": alpha,
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"target_modules": target_modules,
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}
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with open(f"{adapter_dir}/adapter_config.json", "w") as f:
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json.dump(config, f)
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# Create adapter weights in unsloth format.
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# lora_A: [rank, in_features], lora_B: [out_features, rank]
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# Initialize lora_B to zeros so the adapter is initially a no-op,
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# meaning base and lora outputs should match.
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tensors = {}
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for i in range(n_layers):
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for proj in target_modules:
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prefix = f"base_model.model.model.layers.{i}.self_attn.{proj}"
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tensors[f"{prefix}.lora_A.weight"] = torch.randn(rank, dim) * 0.01
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tensors[f"{prefix}.lora_B.weight"] = torch.zeros(dim, rank)
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save_file(tensors, f"{adapter_dir}/adapter_model.safetensors")
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print(f"Created synthetic adapter in {adapter_dir}")
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PYEOF
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ADAPTER_CHECKPOINT="${ADAPTER_DIR}/adapter_model.safetensors"
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ADAPTER_CONFIG="${ADAPTER_DIR}/adapter_config.json"
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popd
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### TEST 1: Base only (single method) ###
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echo "=== Test 1: Base only (single method) ==="
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${PYTHON_EXECUTABLE} "${EXPORT_SCRIPT}" \
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--checkpoint examples/apple/coreml/llama/stories110M.pt \
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--params examples/apple/coreml/llama/params.json \
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--output base.pte \
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--max_context_len ${MAX_CONTEXT_LEN} \
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--input_len ${INPUT_LEN}
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BASE_SIZE=$(stat -f%z base.pte 2>/dev/null || stat -c%s base.pte)
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echo "Test 1: base.pte size = ${BASE_SIZE} bytes"
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### TEST 2: Base + LoRA adapter (multimethod, no multifunction) ###
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echo "=== Test 2: Base + LoRA adapter ==="
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${PYTHON_EXECUTABLE} "${EXPORT_SCRIPT}" \
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--checkpoint examples/apple/coreml/llama/stories110M.pt \
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--params examples/apple/coreml/llama/params.json \
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--output lora.pte \
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--max_context_len ${MAX_CONTEXT_LEN} \
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--input_len ${INPUT_LEN} \
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--adapter lora "${ADAPTER_CHECKPOINT}" "${ADAPTER_CONFIG}"
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LORA_SIZE=$(stat -f%z lora.pte 2>/dev/null || stat -c%s lora.pte)
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echo "Test 2: lora.pte size = ${LORA_SIZE} bytes"
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### TEST 3: Base + LoRA + multifunction ###
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echo "=== Test 3: Base + LoRA + multifunction ==="
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${PYTHON_EXECUTABLE} "${EXPORT_SCRIPT}" \
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--checkpoint examples/apple/coreml/llama/stories110M.pt \
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--params examples/apple/coreml/llama/params.json \
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--output lora_mf.pte \
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--max_context_len ${MAX_CONTEXT_LEN} \
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--input_len ${INPUT_LEN} \
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--multifunction \
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--adapter lora "${ADAPTER_CHECKPOINT}" "${ADAPTER_CONFIG}"
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LORA_MF_SIZE=$(stat -f%z lora_mf.pte 2>/dev/null || stat -c%s lora_mf.pte)
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echo "Test 3: lora_mf.pte size = ${LORA_MF_SIZE} bytes"
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### FILE SIZE CHECKS ###
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echo ""
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echo "=== File size summary ==="
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echo " Base: ${BASE_SIZE} bytes"
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echo " Base + LoRA: ${LORA_SIZE} bytes"
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echo " Base + LoRA + MF: ${LORA_MF_SIZE} bytes"
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# LoRA overhead should be small relative to base size.
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# With lora_B initialized to zeros, the adapter weights are tiny.
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LORA_OVERHEAD=$((LORA_SIZE - BASE_SIZE))
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echo " LoRA overhead: ${LORA_OVERHEAD} bytes"
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# Multifunction should add negligible overhead over LoRA
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# (POSITIONAL sharing deduplicates base weights).
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MF_OVERHEAD=$((LORA_MF_SIZE - LORA_SIZE))
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echo " Multifunction overhead: ${MF_OVERHEAD} bytes"
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# LoRA PTE should be larger than base (adapter weights add some size).
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if [[ ${LORA_SIZE} -le ${BASE_SIZE} ]]; then
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echo "FAIL: lora.pte (${LORA_SIZE}) should be larger than base.pte (${BASE_SIZE})"
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cleanup_files
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exit 1
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fi
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# LoRA overhead should be less than 10% of base size.
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MAX_LORA_OVERHEAD=$((BASE_SIZE / 10))
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if [[ ${LORA_OVERHEAD} -gt ${MAX_LORA_OVERHEAD} ]]; then
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echo "FAIL: LoRA overhead ${LORA_OVERHEAD} exceeds 10% of base size ${BASE_SIZE}"
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cleanup_files
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exit 1
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fi
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# Multifunction overhead should be less than 5% of base size.
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MAX_MF_OVERHEAD=$((BASE_SIZE / 20))
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if [[ ${MF_OVERHEAD} -gt ${MAX_MF_OVERHEAD} ]]; then
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echo "FAIL: Multifunction overhead ${MF_OVERHEAD} exceeds 5% of base size ${BASE_SIZE}"
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cleanup_files
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exit 1
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fi
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echo "File size checks passed."
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### INFERENCE TESTS ###
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# These require CoreML runtime (macOS with ANE).
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# Skip if not on macOS or if explicitly disabled.
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if [[ "$(uname)" != "Darwin" ]] || [[ "${SKIP_INFERENCE:-0}" == "1" ]]; then
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echo "Skipping inference tests (not on macOS or SKIP_INFERENCE=1)"
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cleanup_files
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exit 0
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fi
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RUNNER_ARGS="--params examples/apple/coreml/llama/params.json --tokenizer examples/apple/coreml/llama/tokenizer.model --temperature 0 --max_new_tokens 20 --input_len ${INPUT_LEN} --cache_len ${CACHE_LEN}"
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PROMPT="Once upon a time,"
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# Test 1 inference: base only
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echo ""
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echo "=== Test 1 inference: base (single method) ==="
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${PYTHON_EXECUTABLE} "${RUN_SCRIPT}" \
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--model base.pte \
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--prompt "${PROMPT}" \
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${RUNNER_ARGS} > result_base.txt 2>&1 || true
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echo "Base output:"
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cat result_base.txt
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# Test 2 inference: base method from lora PTE
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echo ""
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echo "=== Test 2 inference: base method (from lora PTE) ==="
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# The base method is "forward" in the multimethod PTE.
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${PYTHON_EXECUTABLE} "${RUN_SCRIPT}" \
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--model lora.pte \
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--prompt "${PROMPT}" \
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${RUNNER_ARGS} > result_lora_base.txt 2>&1 || true
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echo "LoRA PTE base output:"
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cat result_lora_base.txt
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# Test 2 inference: lora method from lora PTE
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echo ""
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echo "=== Test 2 inference: lora method (from lora PTE) ==="
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# For multimethod without multifunction, the lora method name is "lora".
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# Need a runner that supports --method. For now, just verify export succeeded.
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echo "Skipping lora method inference (needs --method support in runner)"
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# Test 3 inference: multifunction lora PTE
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echo ""
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echo "=== Test 3 inference: multifunction ==="
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${PYTHON_EXECUTABLE} "${RUN_MF_SCRIPT}" \
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--model lora_mf.pte \
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--prompt "${PROMPT}" \
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--max_context_len ${MAX_CONTEXT_LEN} \
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--max_new_tokens 20 \
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--temperature 0 \
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--params examples/apple/coreml/llama/params.json \
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--tokenizer examples/apple/coreml/llama/tokenizer.model > result_lora_mf.txt 2>&1 || true
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echo "Multifunction output:"
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cat result_lora_mf.txt
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# Since lora_B is initialized to zeros, the LoRA adapter is a no-op.
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# Base output from Test 1 and base output from Test 2 should match.
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echo ""
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echo "=== Output comparison ==="
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echo "Base and LoRA-base outputs should match (zero adapter)."
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echo "Full verification requires --method support in the runner."
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echo ""
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echo "All CoreML LoRA export tests passed!"
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cleanup_files

‎.github/workflows/trunk.yml‎

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# Test ANE llama
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${CONDA_RUN} sh .ci/scripts/test_ane_static_llama.sh
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test-coreml-lora:
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name: test-coreml-lora
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uses: pytorch/test-infra/.github/workflows/macos_job.yml@main
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with:
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runner: macos-m1-stable
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python-version: '3.11'
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submodules: 'recursive'
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ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
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script: |
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set -eux
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bash .ci/scripts/setup-conda.sh
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eval "$(conda shell.bash hook)"
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# Install requirements
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${CONDA_RUN} sh install_requirements.sh
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${CONDA_RUN} sh backends/apple/coreml/scripts/install_requirements.sh
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${CONDA_RUN} python install_executorch.py
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${CONDA_RUN} sh examples/models/llama/install_requirements.sh
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# Test CoreML LoRA multimethod export
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SKIP_INFERENCE=1 ${CONDA_RUN} sh .ci/scripts/test_coreml_lora.sh
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test-llama-torchao-lowbit:
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name: test-llama-torchao-lowbit
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uses: pytorch/test-infra/.github/workflows/macos_job.yml@main

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