Add torchao conversion

.ci/scripts/test_torchao_huggingface_checkpoints.sh

@@ -5,6 +5,7 @@ set -euxo pipefail
 # Args / flags
 # -------------------------
 TEST_WITH_RUNNER=0
+USE_TORCHAO_KERNELS=0
 MODEL_NAME=""
 
 # Parse args
@@ -22,10 +23,14 @@ while [[ $# -gt 0 ]]; do
     --test_with_runner)
       TEST_WITH_RUNNER=1
       ;;
+    --use_torchao_kernels)
+      USE_TORCHAO_KERNELS=1
+      ;;
     -h|--help)
-      echo "Usage: $0 <model_name> [--test_with_runner]"
+      echo "Usage: $0 <model_name> [--test_with_runner] [--use_torchao_kernels]"
       echo "  model_name: qwen3_4b | phi_4_mini"
       echo "  --test_with_runner: build ET + run llama_main to sanity-check the export"
+      echo "  --use_torchao_kernels: use torchao kernels for linear and tied embedding"
       exit 0
       ;;
     *)
@@ -42,6 +47,13 @@ fi
 
 MODEL_OUT=model.pte
 
+
+# Default to XNNPACK
+BACKEND_ARGS="-X --xnnpack-extended-ops"
+if [[ "$USE_TORCHAO_KERNELS" -eq 1 ]]; then
+  BACKEND_ARGS="--torchao-kernels"
+fi
+
 case "$MODEL_NAME" in
   qwen3_4b)
     echo "Running Qwen3-4B export..."
@@ -58,12 +70,12 @@ case "$MODEL_NAME" in
       --output_name $MODEL_OUT \
       -kv \
       --use_sdpa_with_kv_cache \
-      -X \
-      --xnnpack-extended-ops \
       --max_context_length 1024 \
       --max_seq_length 1024 \
+      --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}' \
+      --verbose \
       --dtype fp32 \
-      --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}'
+      ${BACKEND_ARGS}
     ;;
 
   phi_4_mini)
@@ -81,12 +93,12 @@ case "$MODEL_NAME" in
       --output_name $MODEL_OUT \
       -kv \
       --use_sdpa_with_kv_cache \
-      -X \
-      --xnnpack-extended-ops \
       --max_context_length 1024 \
       --max_seq_length 1024 \
+      --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}' \
+      --verbose \
       --dtype fp32 \
-      --metadata '{"get_bos_id":199999, "get_eos_ids":[200020,199999]}'
+      ${BACKEND_ARGS}
     ;;
 
   *)
@@ -104,6 +116,10 @@ if [[ $MODEL_SIZE -gt $EXPECTED_MODEL_SIZE_UPPER_BOUND ]]; then
 fi
 
 # Install ET with CMake
+EXECUTORCH_BUILD_KERNELS_TORCHAO="OFF"
+if [[ "$USE_TORCHAO_KERNELS" -eq 1 ]]; then
+  EXECUTORCH_BUILD_KERNELS_TORCHAO="ON"
+fi
 if [[ "$TEST_WITH_RUNNER" -eq 1 ]]; then
   echo "[runner] Building and testing llama_main ..."
     cmake -DPYTHON_EXECUTABLE=python \
@@ -120,6 +136,7 @@ if [[ "$TEST_WITH_RUNNER" -eq 1 ]]; then
         -DEXECUTORCH_BUILD_EXTENSION_LLM_RUNNER=ON \
         -DEXECUTORCH_BUILD_EXTENSION_LLM=ON \
         -DEXECUTORCH_BUILD_KERNELS_LLM=ON \
+        -DEXECUTORCH_BUILD_KERNELS_TORCHAO=${EXECUTORCH_BUILD_KERNELS_TORCHAO} \
         -Bcmake-out .
     cmake --build cmake-out -j16 --config Release --target install
 

.github/workflows/trunk.yml

@@ -594,15 +594,22 @@ jobs:
     strategy:
       matrix:
         model: [qwen3_4b, phi_4_mini]
+        runner: [linux.2xlarge]
+        docker-image: [executorch-ubuntu-22.04-clang12]
+        backend: [xnnpack]
         include:
           - model: qwen3_4b
-            test_with_runner: true
+            runner: linux.arm64.2xlarge
+            docker-image: executorch-ubuntu-22.04-gcc11-aarch64
+            backend: torchao
           - model: phi_4_mini
-            test_with_runner: false
+            runner: linux.arm64.2xlarge
+            docker-image: executorch-ubuntu-22.04-gcc11-aarch64
+            backend: torchao
       fail-fast: false
     with:
-      runner: linux.2xlarge
-      docker-image: ci-image:executorch-ubuntu-22.04-clang12
+      runner: ${{ matrix.runner }}
+      docker-image: ci-image:${{ matrix.docker-image }}
       submodules: 'recursive'
       ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
       timeout: 900
@@ -612,9 +619,14 @@ jobs:
         conda activate "${CONDA_ENV}"
 
         PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh --build-tool cmake
+
+        if [[ "${{ matrix.backend }}" == "torchao" ]]; then
+          BUILD_TORCHAO_EXPERIMENTAL=1 TORCHAO_BUILD_CPU_AARCH64=1 TORCHAO_BUILD_KLEIDIAI=1 TORCHAO_ENABLE_ARM_NEON_DOT=1 TORCHAO_PARALLEL_BACKEND=OPENMP pip install third-party/ao
+        fi
+
         pip install -U "huggingface_hub[cli]"
 
-        bash .ci/scripts/test_torchao_huggingface_checkpoints.sh ${{ matrix.model }} ${{ matrix.test_with_runner && '--test_with_runner' || '' }}
+        bash .ci/scripts/test_torchao_huggingface_checkpoints.sh ${{ matrix.model }} ${{ matrix.model != 'phi_4_mini' && '--test_with_runner' || '' }}  ${{ matrix.backend == 'torchao' && '--use_torchao_kernels' || '' }}
 
   test-multimodal-macos:
     if: ${{ !github.event.pull_request.head.repo.fork }}

examples/models/llama/export_llama_lib.py

@@ -417,6 +417,21 @@ def build_args_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Delegate more operators beyond DQLinear to the xnnpack backend. Requires -X or --xnnpack to be set.",
     )
+    parser.add_argument(
+        "--torchao-kernels",
+        action="store_true",
+        help="Delegate tied-embedding and quantized linear ops to torchao kernels",
+    )
+    parser.add_argument(
+        "--torchao-kernels-tied-embedding",
+        action="store_true",
+        help="Delegate tied-embedding ops to torchao kernels",
+    )
+    parser.add_argument(
+        "--torchao-kernels-linear",
+        action="store_true",
+        help="Delegate linear ops to torchao kernels",
+    )
     parser.add_argument("-V", "--vulkan", action="store_true")
     parser.add_argument("--vulkan-force-fp16", action="store_true")
     parser.add_argument("--mps", action="store_true")
@@ -741,6 +756,8 @@ def _prepare_for_llama_export(llm_config: LlmConfig) -> LLMEdgeManager:
             preq_group_size=llm_config.base.preq_group_size,
             preq_embedding_quantize=llm_config.base.preq_embedding_quantize,
             local_global_attention=llm_config.model.local_global_attention,
+            use_torchao_kernels_linear=llm_config.backend.torchao.convert_linear,
+            use_torchao_kernels_tied_embedding=llm_config.backend.torchao.convert_tied_embedding,
         )
     )
 
@@ -1303,6 +1320,8 @@ def _get_source_transforms(  # noqa
     preq_group_size: Optional[int] = None,
     preq_embedding_quantize: Optional[str] = None,
     local_global_attention: Optional[List[int]] = None,
+    use_torchao_kernels_linear: bool = False,
+    use_torchao_kernels_tied_embedding: bool = False,
 ) -> List[Callable[[torch.nn.Module], torch.nn.Module]]:
     """
     Return a list of functions that transform a graph.
@@ -1475,6 +1494,17 @@ def _get_source_transforms(  # noqa
             )
         )
 
+    if any([use_torchao_kernels_linear, use_torchao_kernels_tied_embedding]):
+        from torchao.prototype.tensor_conversion.api import _convert_model_for_aarch64
+
+        transforms.append(
+            partial(
+                _convert_model_for_aarch64,
+                convert_linear=use_torchao_kernels_linear,
+                convert_tied_embedding=use_torchao_kernels_tied_embedding,
+            )
+        )
+
     return transforms
 
 

extension/llm/export/config/llm_config.py

@@ -452,6 +452,16 @@ class MPSConfig:
     enabled: bool = False
 
 
+@dataclass
+class TorchAOKernelsConfig:
+    """
+    Configures the torchao-kernels backend.
+    """
+
+    convert_linear: bool = False
+    convert_tied_embedding: bool = False
+
+
 @dataclass
 class BackendConfig:
     """
@@ -464,6 +474,7 @@ class BackendConfig:
     vulkan: VulkanConfig = field(default_factory=VulkanConfig)
     qnn: QNNConfig = field(default_factory=QNNConfig)
     mps: MPSConfig = field(default_factory=MPSConfig)
+    torchao: TorchAOKernelsConfig = field(default_factory=TorchAOKernelsConfig)
 
 
 ################################################################################
@@ -632,6 +643,28 @@ def from_args(cls, args: argparse.Namespace) -> "LlmConfig":  # noqa: C901
         if hasattr(args, "mps"):
             llm_config.backend.mps.enabled = args.mps
 
+        # TorchAoKernels
+        if any(
+            hasattr(args, a)
+            for a in [
+                "torchao_kernels",
+                "torchao_kernels_linear",
+                "torchao_kernels_tied_embedding",
+            ]
+        ):
+            if hasattr(args, "torchao_kernels") and args.torchao_kernels:
+                # Enable all conversions if torchao_kernels is specified
+                llm_config.backend.torchao.convert_linear = True
+                llm_config.backend.torchao.convert_tied_embedding = True
+            else:
+                # Otherwise, only enable the conversions that are specified
+                llm_config.backend.torchao.convert_linear = getattr(
+                    args, "torchao_kernels_linear", False
+                )
+                llm_config.backend.torchao.convert_tied_embedding = getattr(
+                    args, "torchao_kernels_tied_embedding", False
+                )
+
         # DebugConfig
         if hasattr(args, "profile_memory"):
             llm_config.debug.profile_memory = args.profile_memory