Merge branch 'pytorch:main' into add-profiling-to-xnn-executor-runner-2

Author: benkli01

.ci/scripts/gather_test_models.py

@@ -20,16 +20,16 @@
 CUSTOM_RUNNERS = {
     "linux": {
         # This one runs OOM on smaller runner, the root cause is unclear (T163016365)
-        "w2l": "linux.12xlarge",
-        "ic4": "linux.12xlarge",
-        "resnet50": "linux.12xlarge",
-        "llava": "linux.12xlarge",
-        "llama3_2_vision_encoder": "linux.12xlarge",
-        # "llama3_2_text_decoder": "linux.12xlarge",  # TODO: re-enable test when Huy's change is in / model gets smaller.
+        "w2l": "linux.4xlarge.memory",
+        "ic4": "linux.4xlarge.memory",
+        "resnet50": "linux.4xlarge.memory",
+        "llava": "linux.4xlarge.memory",
+        "llama3_2_vision_encoder": "linux.4xlarge.memory",
+        "llama3_2_text_decoder": "linux.4xlarge.memory",
         # This one causes timeout on smaller runner, the root cause is unclear (T161064121)
-        "dl3": "linux.12xlarge",
-        "emformer_join": "linux.12xlarge",
-        "emformer_predict": "linux.12xlarge",
+        "dl3": "linux.4xlarge.memory",
+        "emformer_join": "linux.4xlarge.memory",
+        "emformer_predict": "linux.4xlarge.memory",
     }
 }
 
@@ -39,10 +39,12 @@
     "linux": {
         "mobilebert": 90,
         "emformer_predict": 360,
+        "llama3_2_text_decoder": 360,
     },
     "macos": {
         "mobilebert": 90,
         "emformer_predict": 360,
+        "llama3_2_text_decoder": 360,
     },
 }
 

.ci/scripts/setup-macos.sh

@@ -49,6 +49,9 @@ install_buck() {
 
   rm "${BUCK2}"
   popd
+
+  # Kill all running buck2 daemon for a fresh start
+  buck2 killall || true
 }
 
 function write_sccache_stub() {

.ci/scripts/test_llama.sh

@@ -48,6 +48,12 @@ MODE=${MODE:-"xnnpack+custom"}
 # Default UPLOAD_DIR to empty string if not set
 UPLOAD_DIR="${UPLOAD_DIR:-}"
 
+# Default PT2E_QUANTIZE to empty string if not set
+PT2E_QUANTIZE="${PT2E_QUANTIZE:-}"
+
+# Default CMake Build Type to release mode
+CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release}
+
 if [[ $# -lt 4 ]]; then # Assuming 4 mandatory args
     echo "Expecting atleast 4 positional arguments"
     echo "Usage: [...]"
@@ -140,7 +146,7 @@ cmake_install_executorch_libraries() {
     rm -rf cmake-out
     retry cmake \
         -DCMAKE_INSTALL_PREFIX=cmake-out \
-        -DCMAKE_BUILD_TYPE=Debug \
+        -DCMAKE_BUILD_TYPE="$CMAKE_BUILD_TYPE" \
         -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
         -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
         -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \
@@ -154,22 +160,22 @@ cmake_install_executorch_libraries() {
         -DQNN_SDK_ROOT="$QNN_SDK_ROOT" \
         -DPYTHON_EXECUTABLE="$PYTHON_EXECUTABLE" \
         -Bcmake-out .
-    cmake --build cmake-out -j9 --target install --config Debug
+    cmake --build cmake-out -j9 --target install --config "$CMAKE_BUILD_TYPE"
 }
 
 cmake_build_llama_runner() {
     echo "Building llama runner"
     dir="examples/models/llama"
     retry cmake \
         -DCMAKE_INSTALL_PREFIX=cmake-out \
-        -DCMAKE_BUILD_TYPE=Debug \
+        -DCMAKE_BUILD_TYPE="$CMAKE_BUILD_TYPE" \
         -DEXECUTORCH_BUILD_KERNELS_CUSTOM="$CUSTOM" \
         -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \
         -DEXECUTORCH_BUILD_XNNPACK="$XNNPACK" \
         -DPYTHON_EXECUTABLE="$PYTHON_EXECUTABLE" \
         -Bcmake-out/${dir} \
         ${dir}
-    cmake --build cmake-out/${dir} -j9 --config Debug
+    cmake --build cmake-out/${dir} -j9 --config "$CMAKE_BUILD_TYPE"
 
 }
 

.ci/scripts/test_llava.sh

@@ -8,11 +8,11 @@
 set -exu
 # shellcheck source=/dev/null
 
-BUILD_TYPE=${1:-Debug}
 TARGET_OS=${2:-Native}
 BUILD_DIR=${3:-cmake-out}
+CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release}
 
-echo "Building with BUILD_TYPE: $BUILD_TYPE, TARGET_OS: $TARGET_OS, BUILD_DIR: $BUILD_DIR"
+echo "Building with CMAKE_BUILD_TYPE: $CMAKE_BUILD_TYPE, TARGET_OS: $TARGET_OS, BUILD_DIR: $BUILD_DIR"
 
 if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then
     PYTHON_EXECUTABLE=python3
@@ -32,7 +32,7 @@ if hash nproc &> /dev/null; then NPROC=$(nproc); fi
 
 EXECUTORCH_COMMON_CMAKE_ARGS="                      \
         -DCMAKE_INSTALL_PREFIX=${BUILD_DIR}         \
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE}            \
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}            \
         -DEXECUTORCH_ENABLE_LOGGING=ON              \
         -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON      \
         -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
@@ -49,7 +49,7 @@ cmake_install_executorch_libraries() {
         ${EXECUTORCH_COMMON_CMAKE_ARGS} \
         -B${BUILD_DIR} .
 
-    cmake --build ${BUILD_DIR} -j${NPROC} --target install --config ${BUILD_TYPE}
+    cmake --build ${BUILD_DIR} -j${NPROC} --target install --config ${CMAKE_BUILD_TYPE}
 }
 
 cmake_install_executorch_libraries_for_android() {
@@ -59,14 +59,14 @@ cmake_install_executorch_libraries_for_android() {
         ${EXECUTORCH_COMMON_CMAKE_ARGS}                                         \
         -B${BUILD_DIR} .
 
-    cmake --build ${BUILD_DIR} -j${NPROC} --target install --config ${BUILD_TYPE}
+    cmake --build ${BUILD_DIR} -j${NPROC} --target install --config ${CMAKE_BUILD_TYPE}
 }
 
 
 LLAVA_COMMON_CMAKE_ARGS="                        \
         -DPYTHON_EXECUTABLE="$PYTHON_EXECUTABLE" \
         -DCMAKE_INSTALL_PREFIX=${BUILD_DIR}      \
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE}         \
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}         \
         -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON     \
         -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON  \
         -DEXECUTORCH_BUILD_XNNPACK=ON"
@@ -81,7 +81,7 @@ cmake_build_llava_runner() {
         -B${BUILD_DIR}/${dir}             \
         ${dir}
 
-    cmake --build ${BUILD_DIR}/${dir} -j${NPROC} --config ${BUILD_TYPE}
+    cmake --build ${BUILD_DIR}/${dir} -j${NPROC} --config ${CMAKE_BUILD_TYPE}
 }
 
 
@@ -98,7 +98,7 @@ cmake_build_llava_runner_for_android() {
         -B${BUILD_DIR}/${dir}                                                   \
         ${dir}
 
-    cmake --build ${BUILD_DIR}/${dir} -j${NPROC} --config ${BUILD_TYPE}
+    cmake --build ${BUILD_DIR}/${dir} -j${NPROC} --config ${CMAKE_BUILD_TYPE}
 }
 
 # only export the one without custom op for now since it's

.github/scripts/extract_benchmark_results.py

@@ -310,6 +310,7 @@ def transform(
     workflow_run_attempt: int,
     job_name: str,
     job_id: int,
+    schema_version: str,
 ) -> List:
     """
     Transform the benchmark results into the format writable into the benchmark database
@@ -319,45 +320,91 @@ def transform(
     for r in benchmark_results:
         r["deviceInfo"]["device"] = job_name
 
-    # TODO (huydhn): This is the current schema of the database oss_ci_benchmark_v2,
-    # and I'm trying to fit ET benchmark results into it, which is kind of awkward.
-    # However, the schema is going to be updated soon
-    return [
-        {
-            # GH-info to identify where the benchmark is run
-            "repo": repo,
-            "head_branch": head_branch,
-            "workflow_id": workflow_run_id,
-            "run_attempt": workflow_run_attempt,
-            "job_id": job_id,
-            # The model
-            "name": f"{r['benchmarkModel']['name']} {r['benchmarkModel'].get('backend', '')}".strip(),
-            "dtype": (
-                r["benchmarkModel"]["quantization"]
-                if r["benchmarkModel"]["quantization"]
-                else "unknown"
-            ),
-            # The metric value
-            "metric": r["metric"],
-            "actual": r["actualValue"],
-            "target": r["targetValue"],
-            # The device
-            "device": r["deviceInfo"]["device"],
-            "arch": r["deviceInfo"].get("os", ""),
-            # Not used here, just set it to something unique here
-            "filename": workflow_name,
-            "test_name": app_type,
-            "runner": job_name,
-        }
-        for r in benchmark_results
-    ]
+    if schema_version == "v2":
+        # TODO (huydhn): Clean up this branch after ExecuTorch dashboard migrates to v3
+        return [
+            {
+                # GH-info to identify where the benchmark is run
+                "repo": repo,
+                "head_branch": head_branch,
+                "workflow_id": workflow_run_id,
+                "run_attempt": workflow_run_attempt,
+                "job_id": job_id,
+                # The model
+                "name": f"{r['benchmarkModel']['name']} {r['benchmarkModel'].get('backend', '')}".strip(),
+                "dtype": (
+                    r["benchmarkModel"]["quantization"]
+                    if r["benchmarkModel"]["quantization"]
+                    else "unknown"
+                ),
+                # The metric value
+                "metric": r["metric"],
+                "actual": r["actualValue"],
+                "target": r["targetValue"],
+                # The device
+                "device": r["deviceInfo"]["device"],
+                "arch": r["deviceInfo"].get("os", ""),
+                # Not used here, just set it to something unique here
+                "filename": workflow_name,
+                "test_name": app_type,
+                "runner": job_name,
+            }
+            for r in benchmark_results
+        ]
+    elif schema_version == "v3":
+        quantization = (
+            r["benchmarkModel"]["quantization"]
+            if r["benchmarkModel"]["quantization"]
+            else "unknown"
+        )
+        # From https://github.com/pytorch/pytorch/wiki/How-to-integrate-with-PyTorch-OSS-benchmark-database
+        return [
+            {
+                "benchmark": {
+                    "name": "ExecuTorch",
+                    "mode": "inference",
+                    "dtype": quantization,
+                    "extra_info": {
+                        "app_type": app_type,
+                    },
+                },
+                "model": {
+                    "name": r["benchmarkModel"]["name"],
+                    "type": "OSS model",
+                    "backend": r["benchmarkModel"].get("backend", ""),
+                    "extra_info": {
+                        "quantization": quantization,
+                    },
+                },
+                "metric": {
+                    "name": r["metric"],
+                    "benchmark_values": [r["actualValue"]],
+                    "target_value": r["targetValue"],
+                    "extra_info": {
+                        "method": r.get("method", ""),
+                    },
+                },
+                "runners": [
+                    {
+                        "name": r["deviceInfo"]["device"],
+                        "type": r["deviceInfo"]["os"],
+                        "avail_mem_in_gb": r["deviceInfo"].get("availMem", ""),
+                        "total_mem_in_gb": r["deviceInfo"].get("totalMem", ""),
+                    }
+                ],
+            }
+            for r in benchmark_results
+        ]
 
 
 def main() -> None:
     args = parse_args()
 
-    # Across all devices
-    all_benchmark_results = []
+    # Across all devices, keeping both schemas for now until ExecuTorch dashboard migrates to v3
+    all_benchmark_results = {
+        "v2": [],
+        "v3": [],
+    }
 
     with open(args.artifacts) as f:
         for artifact in json.load(f):
@@ -384,23 +431,31 @@ def main() -> None:
                 )
 
             if benchmark_results:
-                benchmark_results = transform(
-                    app_type,
-                    benchmark_results,
-                    args.repo,
-                    args.head_branch,
-                    args.workflow_name,
-                    args.workflow_run_id,
-                    args.workflow_run_attempt,
-                    job_name,
-                    extract_job_id(args.artifacts),
-                )
-                all_benchmark_results.extend(benchmark_results)
+                for schema in all_benchmark_results.keys():
+                    results = transform(
+                        app_type,
+                        benchmark_results,
+                        args.repo,
+                        args.head_branch,
+                        args.workflow_name,
+                        args.workflow_run_id,
+                        args.workflow_run_attempt,
+                        job_name,
+                        extract_job_id(args.artifacts),
+                        schema,
+                    )
+                    all_benchmark_results[schema].extend(results)
+
+    for schema in all_benchmark_results.keys():
+        if not all_benchmark_results.get(schema):
+            continue
+
+        output_dir = os.path.join(args.output_dir, schema)
+        os.mkdir(output_dir)
 
-    if all_benchmark_results:
         output_file = os.path.basename(args.artifacts)
-        with open(f"{args.output_dir}/{output_file}", "w") as f:
-            json.dump(all_benchmark_results, f)
+        with open(f"{output_dir}/{output_file}", "w") as f:
+            json.dump(all_benchmark_results[schema], f)
 
 
 if __name__ == "__main__":