Update base for Update on "[Exutorch] Add broadcast support for le op"

For refactored hf repro requires this to support mask generation

Differential Revision: [D76456398](https://our.internmc.facebook.com/intern/diff/D76456398/)

[ghstack-poisoned]

Author: kimishpatel

.ci/scripts/build-mediatek-sdk.sh

@@ -0,0 +1,22 @@
+#!/bin/bash
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+set -eux
+
+build_neuron_backend() {
+  echo "Start building neuron backend."
+  export ANDROID_NDK=/opt/ndk
+  export MEDIATEK_SDK_ROOT=/tmp/neuropilot
+  export NEURON_BUFFER_ALLOCATOR_LIB=${MEDIATEK_SDK_ROOT}/libneuron_buffer_allocator.so
+  export EXECUTORCH_ROOT="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../.." && pwd)"
+
+
+  cd ${EXECUTORCH_ROOT}
+  ./backends/mediatek/scripts/mtk_build.sh
+}
+
+build_neuron_backend

.ci/scripts/gather_benchmark_configs.py

@@ -32,7 +32,8 @@
 BENCHMARK_CONFIGS = {
     "xplat": [
         "xnnpack_q8",
-        "hf_xnnpack_fp32",
+        "hf_xnnpack_custom_spda_kv_cache_8da4w",
+        "et_xnnpack_custom_spda_kv_cache_8da4w",
         "llama3_fb16",
         "llama3_spinquant",
         "llama3_qlora",
@@ -129,25 +130,25 @@ def generate_compatible_configs(model_name: str, target_os=None) -> List[str]:
     """
     configs = []
     if is_valid_huggingface_model_id(model_name):
+        configs.append("hf_xnnpack_custom_spda_kv_cache_8da4w")
         if model_name.startswith("meta-llama/"):
-            # LLaMA models
+            # etLLM recipes for Llama
             repo_name = model_name.split("meta-llama/")[1]
             if "qlora" in repo_name.lower():
-                configs.append("llama3_qlora")
+                configs = ["llama3_qlora"]
             elif "spinquant" in repo_name.lower():
-                configs.append("llama3_spinquant")
+                configs = ["llama3_spinquant"]
             else:
-                configs.append("llama3_fb16")
+                configs.extend(["llama3_fb16", "et_xnnpack_custom_spda_kv_cache_8da4w"])
                 configs.extend(
                     [
                         config
                         for config in BENCHMARK_CONFIGS.get(target_os, [])
                         if config.startswith("llama")
                     ]
                 )
-        else:
-            # Non-LLaMA models
-            configs.append("hf_xnnpack_fp32")
+        if model_name.startswith("Qwen/Qwen3"):
+            configs.append("et_xnnpack_custom_spda_kv_cache_8da4w")
     elif model_name in MODEL_NAME_TO_MODEL:
         # ExecuTorch in-tree non-GenAI models
         configs.append("xnnpack_q8")

.ci/scripts/setup-mediatek-deps.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+set -eux
+
+MEDIATEK_INSTALLATION_DIR=/tmp/neuropilot
+EXECUTORCH_ROOT="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../.." && pwd)"
+
+install_neuropilot() {
+  echo "Start installing neuropilot."
+  mkdir -p "${MEDIATEK_INSTALLATION_DIR}"
+
+  curl -Lo /tmp/neuropilot-express.tar.gz "https://s3.ap-southeast-1.amazonaws.com/mediatek.neuropilot.com/06302508-4c94-4bf2-9789-b0ee44e83e27.gz"
+  echo "Finishing downloading neuropilot sdk."
+  tar zxvf /tmp/neuropilot-express.tar.gz --strip-components=1 --directory "${MEDIATEK_INSTALLATION_DIR}"
+  echo "Finishing unzip neuropilot sdk."
+
+  # Copy NP header
+  cp ${MEDIATEK_INSTALLATION_DIR}/api/NeuronAdapter.h ${EXECUTORCH_ROOT}/backends/mediatek/runtime/include/api/
+
+  # Print the content for manual verification
+  ls -lah "${MEDIATEK_INSTALLATION_DIR}"
+}
+
+setup_neuropilot() {
+  pip3 install -r ${EXECUTORCH_ROOT}/backends/mediatek/requirements.txt
+  pip3 install ${MEDIATEK_INSTALLATION_DIR}/mtk_neuron-8.2.19-py3-none-linux_x86_64.whl
+  pip3 install ${MEDIATEK_INSTALLATION_DIR}/mtk_converter-8.13.0_public_packages/mtk_converter-8.13.0+public-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+}
+
+setup_calibration_data() {
+  curl -Lo /tmp/imagenette2-160.tgz https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz
+  tar zxvf /tmp/imagenette2-160.tgz --strip-components=1 --directory "${MEDIATEK_INSTALLATION_DIR}"
+}
+
+install_neuropilot
+setup_neuropilot
+setup_calibration_data

.ci/scripts/test_llava.sh

@@ -147,7 +147,7 @@ run_and_verify() {
 
     # verify result.txt
     RESULT=$(cat result.txt)
-    EXPECTED_PREFIX="ASSISTANT: image captures a basketball game in progress, with several players on the court. "
+    EXPECTED_PREFIX="ASSISTANT: image captures a basketball game in progress, with"
 
     if [[ "${RESULT}" == *"${EXPECTED_PREFIX}"* ]]; then
         echo "Expected result prefix: ${EXPECTED_PREFIX}"

.ci/scripts/test_model.sh

@@ -244,6 +244,24 @@ test_model_with_mps() {
   EXPORTED_MODEL=$(find "." -type f -name "${MODEL_NAME}*.pte" -print -quit)
 }
 
+test_model_with_mediatek() {
+  if [[ "${MODEL_NAME}" == "dl3" ]]; then
+    EXPORT_SCRIPT=deeplab_v3
+  elif [[ "${MODEL_NAME}" == "mv3" ]]; then
+    EXPORT_SCRIPT=mobilenet_v3
+  elif [[ "${MODEL_NAME}" == "mv2" ]]; then
+    EXPORT_SCRIPT=mobilenet_v2
+  elif [[ "${MODEL_NAME}" == "ic4" ]]; then
+    EXPORT_SCRIPT=inception_v4
+  elif [[ "${MODEL_NAME}" == "ic3" ]]; then
+    EXPORT_SCRIPT=inception_v3
+  fi
+
+  PYTHONPATH=examples/mediatek/ "${PYTHON_EXECUTABLE}" -m examples.mediatek.model_export_scripts.${EXPORT_SCRIPT} -d /tmp/neuropilot/train -a ${EXPORT_SCRIPT}
+  EXPORTED_MODEL=$(find "./${EXPORT_SCRIPT}" -type f -name "*.pte" -print -quit)
+}
+
+
 if [[ "${BACKEND}" == "portable" ]]; then
   echo "Testing ${MODEL_NAME} with portable kernels..."
   test_model
@@ -281,6 +299,12 @@ elif [[ "${BACKEND}" == *"xnnpack"* ]]; then
   if [[ $? -eq 0 ]]; then
     prepare_artifacts_upload
   fi
+elif [[ "${BACKEND}" == "mediatek" ]]; then
+  echo "Testing ${MODEL_NAME} with mediatek..."
+  test_model_with_mediatek
+  if [[ $? -eq 0 ]]; then
+    prepare_artifacts_upload
+  fi
 else
   set +e
   if [[ "${BACKEND}" == *"quantization"* ]]; then

.ci/scripts/tests/test_gather_benchmark_configs.py

@@ -112,15 +112,24 @@ def test_generate_compatible_configs_llama_model(self):
         result = self.gather_benchmark_configs.generate_compatible_configs(
             model_name, target_os
         )
-        expected = ["llama3_fb16", "llama3_coreml_ane"]
-        self.assertEqual(result, expected)
+        expected = [
+            "llama3_fb16",
+            "llama3_coreml_ane",
+            "et_xnnpack_custom_spda_kv_cache_8da4w",
+            "hf_xnnpack_custom_spda_kv_cache_8da4w",
+        ]
+        self.assertCountEqual(result, expected)
 
         target_os = "android"
         result = self.gather_benchmark_configs.generate_compatible_configs(
             model_name, target_os
         )
-        expected = ["llama3_fb16"]
-        self.assertEqual(result, expected)
+        expected = [
+            "llama3_fb16",
+            "et_xnnpack_custom_spda_kv_cache_8da4w",
+            "hf_xnnpack_custom_spda_kv_cache_8da4w",
+        ]
+        self.assertCountEqual(result, expected)
 
     def test_generate_compatible_configs_quantized_llama_model(self):
         model_name = "meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8"

.ci/scripts/unittest-buck2.sh

@@ -15,8 +15,10 @@ buck2 query "//backends/apple/... + //backends/example/... + \
 //kernels/optimized/... + //kernels/portable/... + //kernels/quantized/... + \
 //kernels/test/... + //runtime/... + //schema/... + //test/... + //util/..."
 
+# TODO: optimized ops are unbuildable because they now use ATen; put
+# them back after we can use PyTorch in OSS buck.
 UNBUILDABLE_OPTIMIZED_OPS_REGEX="_elu|gelu|fft|log_softmax"
-BUILDABLE_OPTIMIZED_OPS=$(buck2 query //kernels/optimized/cpu/... | grep -E -v $UNBUILDABLE_OPTIMIZED_OPS_REGEX)
+BUILDABLE_OPTIMIZED_OPS= #$(buck2 query //kernels/optimized/cpu/... | grep -E -v $UNBUILDABLE_OPTIMIZED_OPS_REGEX)
 
 # TODO: build prim_ops_test_cpp again once supported_features works in
 # OSS buck.

.github/workflows/android-perf-private-device-experiment.yml

@@ -18,7 +18,7 @@ on:
         description: Models to be benchmarked
         required: false
         type: string
-        default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8
+        default: google/gemma-3-1b-it,Qwen/Qwen3-0.6B,HuggingFaceTB/SmolLM2-135M,meta-llama/Llama-3.2-1B,allenai/OLMo-1B-hf
       devices:
         description: Target devices to run benchmark
         required: false
@@ -34,7 +34,7 @@ on:
         description: Models to be benchmarked
         required: false
         type: string
-        default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8
+        default: google/gemma-3-1b-it,Qwen/Qwen3-0.6B,HuggingFaceTB/SmolLM2-135M,meta-llama/Llama-3.2-1B,allenai/OLMo-1B-hf
       devices:
         description: Target devices to run benchmark
         required: false
@@ -57,6 +57,6 @@ jobs:
       id-token: write
       contents: read
     with:
-      models: ${{ inputs.models || 'mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8' }}
+      models: ${{ inputs.models || 'Qwen/Qwen3-0.6B' }}
       devices: samsung_galaxy_s22_private
       benchmark_configs: ${{ inputs.benchmark_configs }}

.github/workflows/android-perf.yml

@@ -6,12 +6,14 @@ on:
   pull_request:
     paths:
       - .github/workflows/android-perf.yml
+      - .ci/scripts/gather_benchmark_configs.py
       - extension/benchmark/android/benchmark/android-llm-device-farm-test-spec.yml.j2
   push:
     branches:
       - main
     paths:
       - .github/workflows/android-perf.yml
+      - .ci/scripts/gather_benchmark_configs.py
       - extension/benchmark/android/benchmark/android-llm-device-farm-test-spec.yml.j2
   # Note: GitHub has an upper limit of 10 inputs
   workflow_dispatch:
@@ -70,7 +72,7 @@ jobs:
           # Separate default values from the workflow dispatch. To ensure defaults are accessible
           # during scheduled runs and to provide flexibility for different defaults between
           # on-demand and periodic benchmarking.
-          CRON_DEFAULT_MODELS: ${{ github.event_name == 'schedule' && 'llama,mv3,mv2,ic4,ic3,resnet50,edsr,mobilebert,w2l,meta-llama/Llama-3.2-1B,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8' || 'llama' }}
+          CRON_DEFAULT_MODELS: ${{ github.event_name == 'schedule' && 'llama,mv3,mv2,ic4,ic3,resnet50,edsr,mobilebert,w2l,meta-llama/Llama-3.2-1B,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8,google/gemma-3-1b-it,Qwen/Qwen3-0.6B,HuggingFaceTB/SmolLM2-135M,allenai/OLMo-1B-hf' || 'llama' }}
           CRON_DEFAULT_DEVICES: samsung_galaxy_s22
         run: |
           set -eux
@@ -201,8 +203,8 @@ jobs:
             HF_MODEL_REPO=${{ matrix.model }}
             OUT_ET_MODEL_NAME="$(echo "$HF_MODEL_REPO" | awk -F'/' '{print $2}' | sed 's/_/-/g' | tr '[:upper:]' '[:lower:]')_${{ matrix.config }}"
 
+            # Convert HF checkpoint to ET via etLLM path
             if [[ "$HF_MODEL_REPO" == meta-llama/* ]]; then
-                # Llama models on Hugging Face
                 if [[ ${{ matrix.config }} == "llama3_spinquant" ]]; then
                     # SpinQuant
                     # Download prequantized chceckpoint from Hugging Face
@@ -272,6 +274,21 @@ jobs:
                       --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' \
                       --output_name="${OUT_ET_MODEL_NAME}.pte"
                     ls -lh "${OUT_ET_MODEL_NAME}.pte"
+                elif [[ ${{ matrix.config }} == "et_xnnpack_custom_spda_kv_cache_8da4w" ]]; then
+                    DOWNLOADED_PATH=$(bash .ci/scripts/download_hf_hub.sh --model_id "${HF_MODEL_REPO}" --subdir "original" --files "tokenizer.model" "params.json" "consolidated.00.pth")
+                    python -m examples.models.llama.export_llama \
+                      --model llama3_2 \
+                      --checkpoint "${DOWNLOADED_PATH}/consolidated.00.pth" \
+                      --params "${DOWNLOADED_PATH}/params.json" \
+                      -kv \
+                      --use_sdpa_with_kv_cache \
+                      -d fp32 \
+                      -X \
+                      --xnnpack-extended-ops \
+                      -qmode 8da4w -G 32 -E 8,0 \
+                      --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' \
+                      --output_name="${OUT_ET_MODEL_NAME}.pte"
+                    ls -lh "${OUT_ET_MODEL_NAME}.pte"
                 elif [[ ${{ matrix.config }} == "llama3_qnn_htp" ]]; then
                     export QNN_SDK_ROOT=/tmp/qnn/2.28.0.241029
                     export LD_LIBRARY_PATH=$QNN_SDK_ROOT/lib/x86_64-linux-clang/
@@ -292,21 +309,75 @@ jobs:
                     OUT_ET_MODEL_NAME="llama3_2_qnn" # Qualcomm hard-coded it in their script
                     find . -name "${OUT_ET_MODEL_NAME}.pte" -not -path "./${OUT_ET_MODEL_NAME}.pte" -exec mv {} ./ \;
                     ls -lh "${OUT_ET_MODEL_NAME}.pte"
-                else
-                    # By default, test with the Hugging Face model and the xnnpack recipe
-                    DOWNLOADED_PATH=$(bash .ci/scripts/download_hf_hub.sh --model_id "${HF_MODEL_REPO}" --subdir "original" --files "tokenizer.model")
-                    python -m extension.export_util.export_hf_model -hfm="$HF_MODEL_REPO" -o "$OUT_ET_MODEL_NAME"
-                    ls -lh "${OUT_ET_MODEL_NAME}.pte"
                 fi
-            else
-                echo "Unsupported model ${{ matrix.model }}"
-                exit 1
+            elif [[ "$HF_MODEL_REPO" == "Qwen/Qwen3-0.6B" ]]; then
+              if [[ ${{ matrix.config }} == "et_xnnpack_custom_spda_kv_cache_8da4w" ]]; then
+                DOWNLOADED_PATH=$(bash .ci/scripts/download_hf_hub.sh --model_id "${HF_MODEL_REPO}" --subdir "." --files "tokenizer.json")
+                python -m examples.models.llama.export_llama \
+                  --model qwen3-0_6b \
+                  --params examples/models/qwen3/0_6b_config.json \
+                  -kv \
+                  --use_sdpa_with_kv_cache \
+                  -d fp32 \
+                  -X \
+                  --xnnpack-extended-ops \
+                  -qmode 8da4w \
+                  -G 32 \
+                  -E 8,0 \
+                  --metadata '{"get_bos_id": 151644, "get_eos_ids":[151645]}' \
+                  --output_name="${OUT_ET_MODEL_NAME}.pte"
+                ls -lh "${OUT_ET_MODEL_NAME}.pte"
+              fi
+            fi
+
+            if [[ ${{ matrix.config }} == "hf_xnnpack_custom_spda_kv_cache_8da4w" ]]; then
+              DOWNLOADED_PATH=$(
+                bash .ci/scripts/download_hf_hub.sh \
+                  --model_id "${HF_MODEL_REPO}" \
+                  --files "tokenizer.json"
+              )
+              echo "tokenizer.json is downloaded to $DOWNLOADED_PATH"
+
+              # Install optimum-executorch
+              git clone https://github.com/huggingface/optimum-executorch
+              pushd optimum-executorch
+              # There is no release yet, for CI stability, always test from the same commit on main
+              git checkout 1c653dc49812fc431a22312c7295d97005d22e12
+              python install_dev.py
+              pip list
+
+              ARGS=(
+                "--model" "${HF_MODEL_REPO}"
+                "--task" "text-generation"
+                "--recipe" "xnnpack"
+                "--use_custom_sdpa"
+                "--qlinear"
+                "--qembedding"
+                "--output_dir" ".."
+              )
+
+              # Add conditional arguments based on model
+              case "${HF_MODEL_REPO}" in
+                *"google/gemma-3-1b-it"*)
+                  echo "--use_custom_kv_cache can not be used for HybridCache"
+                  ;;
+                *)
+                  ARGS+=("--use_custom_kv_cache")
+                  ;;
+              esac
+
+              optimum-cli export executorch "${ARGS[@]}"
+              popd
+
+              mv model.pte ${OUT_ET_MODEL_NAME}.pte
+              ls -lh "${OUT_ET_MODEL_NAME}.pte"
             fi
 
-            zip -j model.zip "${OUT_ET_MODEL_NAME}.pte" "${DOWNLOADED_PATH}/tokenizer.model"
+            zip -j model.zip ${OUT_ET_MODEL_NAME}.pte ${DOWNLOADED_PATH}/tokenizer.*
             ls -lh model.zip
-            mkdir -p "${ARTIFACTS_DIR_NAME}"
-            mv model.zip "${ARTIFACTS_DIR_NAME}"
+            mkdir -p ${ARTIFACTS_DIR_NAME}
+            mv model.zip ${ARTIFACTS_DIR_NAME}
+            ls -lh ${ARTIFACTS_DIR_NAME}
         elif [[ ${{ matrix.model }} == "llama" ]]; then
             # Install requirements for export_llama
             PYTHON_EXECUTABLE=python bash examples/models/llama/install_requirements.sh

.github/workflows/apple-perf-private-device-experiment.yml

@@ -18,7 +18,7 @@ on:
         description: Models to be benchmarked
         required: false
         type: string
-        default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8
+        default: google/gemma-3-1b-it,Qwen/Qwen3-0.6B,HuggingFaceTB/SmolLM2-135M,meta-llama/Llama-3.2-1B,allenai/OLMo-1B-hf
       devices:
         description: Target devices to run benchmark
         required: false
@@ -34,7 +34,7 @@ on:
         description: Models to be benchmarked
         required: false
         type: string
-        default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8
+        default: Qwen/Qwen3-0.6B,HuggingFaceTB/SmolLM2-135M,meta-llama/Llama-3.2-1B,allenai/OLMo-1B-hf
       devices:
         description: Target devices to run benchmark
         required: false
@@ -57,6 +57,6 @@ jobs:
       id-token: write
       contents: read
     with:
-      models: ${{ inputs.models || 'mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8' }}
+      models: ${{ inputs.models || 'Qwen/Qwen3-0.6B' }}
       devices: apple_iphone_15_private
       benchmark_configs: ${{ inputs.benchmark_configs }}