Qualcomm AI Engine Direct - GA FocalNet

backends/qualcomm/CMakeLists.txt

@@ -252,7 +252,7 @@ if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64")
 
   pybind11_extension(PyQnnManagerAdaptor)
   pybind11_extension(PyQnnWrapperAdaptor)
-  if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES Debug|RelWithDebInfo)
+  if(NOT MSVC AND NOT ${CMAKE_BUILD_TYPE} MATCHES RelWithDebInfo)
     # Strip unnecessary sections of the binary
     pybind11_strip(PyQnnManagerAdaptor)
     pybind11_strip(PyQnnWrapperAdaptor)

backends/qualcomm/_passes/annotate_adaptive_avg_pool1d.py

@@ -19,8 +19,6 @@ class AnnotateAdaptiveAvgPool1D(ExportPass):
     adaptive_avg_pool1d got decomposed to unsqueeze -> adaptive_avg_pool2d -> squeeze
     """
 
-    decomp_ops = [torch.ops.aten.adaptive_avg_pool2d.default]
-
     def __init__(self, edge_program: torch.export.ExportedProgram):
         super(AnnotateAdaptiveAvgPool1D, self).__init__()
         self.edge_program = edge_program

backends/qualcomm/_passes/annotate_stack.py

@@ -28,7 +28,7 @@ def _annotate_stack(self, graph_module: torch.fx.GraphModule):
         partitions = get_source_partitions(
             graph_module.graph, [torch.stack, torch.ops.aten.stack.default, "stack"]
         )
-        for _, src_partitions in partitions.items():
+        for src_partitions in partitions.values():
             for src_partition in src_partitions:
                 output = src_partition.output_nodes[0]
                 if (list(output.users)[0].target) in q_ops:

backends/qualcomm/_passes/annotate_unbind.py

@@ -28,7 +28,7 @@ def _annotate_unbind(self, graph_module: torch.fx.GraphModule):
         partitions = get_source_partitions(
             graph_module.graph, [torch.unbind, torch.ops.aten.unbind.int, "unbind"]
         )
-        for _, src_partitions in partitions.items():
+        for src_partitions in partitions.values():
             for src_partition in src_partitions:
                 if src_partition.input_nodes[0].target in dq_ops:
                     q_node = src_partition.input_nodes[0].args[0]

backends/qualcomm/quantizer/annotators.py

@@ -1193,7 +1193,13 @@ def annotate_unbind(node: Node, quantization_config: QuantizationConfig) -> None
         )
 
 
-@register_annotator([torch.ops.aten.split.Tensor, torch.ops.aten.chunk.default])
+@register_annotator(
+    [
+        torch.ops.aten.split_with_sizes.default,
+        torch.ops.aten.split.Tensor,
+        torch.ops.aten.chunk.default,
+    ]
+)
 def annotate_chunk(node: Node, quantization_config: QuantizationConfig) -> None:
     if _is_annotated([node]):
         return

backends/qualcomm/scripts/build.sh

@@ -30,7 +30,7 @@ CMAKE_X86_64="build-x86"
 BUILD_AARCH64="true"
 CMAKE_AARCH64="build-android"
 CLEAN="true"
-BUILD_TYPE="Debug"
+BUILD_TYPE="RelWithDebInfo"
 BUILD_JOB_NUMBER="16"
 
 if [ -z PYTHON_EXECUTABLE ]; then
@@ -71,7 +71,7 @@ if [ "$BUILD_AARCH64" = true ]; then
         rm -rf $BUILD_ROOT && mkdir $BUILD_ROOT
     else
         # Force rebuild flatccrt for the correct platform
-        cd $BUILD_ROOT/devtools && make clean
+        cd $BUILD_ROOT/third-party/flatcc && make clean
     fi
 
     cd $BUILD_ROOT
@@ -116,7 +116,7 @@ if [ "$BUILD_X86_64" = true ]; then
         rm -rf $BUILD_ROOT && mkdir $BUILD_ROOT
     else
         # Force rebuild flatccrt for the correct platform
-        cd $BUILD_ROOT/devtools && make clean
+        cd $BUILD_ROOT/third-party/flatcc && make clean
     fi
 
     cd $BUILD_ROOT

backends/qualcomm/tests/test_qnn_delegate.py

@@ -4108,6 +4108,44 @@ def test_fbnet(self):
                 self.assertGreaterEqual(msg["top_1"], 60)
                 self.assertGreaterEqual(msg["top_5"], 90)
 
+    def test_focalnet(self):
+        if not self.required_envs([self.image_dataset]):
+            self.skipTest("missing required envs")
+
+        cmds = [
+            "python",
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/focalnet.py",
+            "--dataset",
+            self.image_dataset,
+            "--artifact",
+            self.artifact_dir,
+            "--build_folder",
+            self.build_folder,
+            "--device",
+            self.device,
+            "--model",
+            self.model,
+            "--ip",
+            self.ip,
+            "--port",
+            str(self.port),
+        ]
+        if self.host:
+            cmds.extend(["--host", self.host])
+        if self.shared_buffer:
+            cmds.extend(["--shared_buffer"])
+
+        p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
+        with Listener((self.ip, self.port)) as listener:
+            conn = listener.accept()
+            p.communicate()
+            msg = json.loads(conn.recv())
+            if "Error" in msg:
+                self.fail(msg["Error"])
+            else:
+                self.assertGreaterEqual(msg["top_1"], 55)
+                self.assertGreaterEqual(msg["top_5"], 80)
+
     def test_gMLP(self):
         if not self.required_envs([self.image_dataset]):
             self.skipTest("missing required envs")

examples/qualcomm/CMakeLists.txt

@@ -23,7 +23,7 @@ if(NOT PYTHON_EXECUTABLE)
 endif()
 
 if(NOT CMAKE_BUILD_TYPE)
-  set(CMAKE_BUILD_TYPE Debug)
+  set(CMAKE_BUILD_TYPE RelWithDebInfo)
 endif()
 
 # Find prebuilt libraries. executorch package should contain portable_ops_lib,

examples/qualcomm/oss_scripts/focalnet.py

@@ -0,0 +1,145 @@
+# Copyright (c) Qualcomm Innovation Center, Inc.
+# 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.
+
+import json
+import logging
+import os
+from multiprocessing.connection import Client
+
+import numpy as np
+
+import torch
+from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype
+from executorch.examples.qualcomm.utils import (
+    build_executorch_binary,
+    get_imagenet_dataset,
+    make_output_dir,
+    parse_skip_delegation_node,
+    setup_common_args_and_variables,
+    SimpleADB,
+    topk_accuracy,
+)
+from transformers import AutoModelForImageClassification
+
+
+def main(args):
+    skip_node_id_set, skip_node_op_set = parse_skip_delegation_node(args)
+
+    # ensure the working directory exist.
+    os.makedirs(args.artifact, exist_ok=True)
+
+    if not args.compile_only and args.device is None:
+        raise RuntimeError(
+            "device serial is required if not compile only. "
+            "Please specify a device serial by -s/--device argument."
+        )
+
+    data_num = 100
+    if args.ci:
+        inputs = [(torch.rand(1, 3, 224, 224),)]
+        logging.warning(
+            "This option is for CI to verify the export flow. It uses random input and will result in poor accuracy."
+        )
+    else:
+        inputs, targets, input_list = get_imagenet_dataset(
+            dataset_path=f"{args.dataset}",
+            data_size=data_num,
+            image_shape=(256, 256),
+            crop_size=224,
+        )
+
+    module = (
+        AutoModelForImageClassification.from_pretrained("microsoft/focalnet-tiny")
+        .eval()
+        .to("cpu")
+    )
+    pte_filename = "focalnet_qnn_q8"
+    build_executorch_binary(
+        module.eval(),
+        inputs[0],
+        args.model,
+        f"{args.artifact}/{pte_filename}",
+        inputs,
+        skip_node_id_set=skip_node_id_set,
+        skip_node_op_set=skip_node_op_set,
+        quant_dtype=QuantDtype.use_8a8w,
+        shared_buffer=args.shared_buffer,
+    )
+
+    if args.compile_only:
+        return
+
+    adb = SimpleADB(
+        qnn_sdk=os.getenv("QNN_SDK_ROOT"),
+        build_path=f"{args.build_folder}",
+        pte_path=f"{args.artifact}/{pte_filename}.pte",
+        workspace=f"/data/local/tmp/executorch/{pte_filename}",
+        device_id=args.device,
+        host_id=args.host,
+        soc_model=args.model,
+        shared_buffer=args.shared_buffer,
+    )
+    adb.push(inputs=inputs, input_list=input_list)
+    adb.execute()
+
+    # collect output data
+    output_data_folder = f"{args.artifact}/outputs"
+    make_output_dir(output_data_folder)
+
+    adb.pull(output_path=args.artifact)
+
+    # top-k analysis
+    predictions = []
+    for i in range(data_num):
+        predictions.append(
+            np.fromfile(
+                os.path.join(output_data_folder, f"output_{i}_0.raw"), dtype=np.float32
+            )
+        )
+
+    k_val = [1, 5]
+    topk = [topk_accuracy(predictions, targets, k).item() for k in k_val]
+    if args.ip and args.port != -1:
+        with Client((args.ip, args.port)) as conn:
+            conn.send(json.dumps({f"top_{k}": topk[i] for i, k in enumerate(k_val)}))
+    else:
+        for i, k in enumerate(k_val):
+            print(f"top_{k}->{topk[i]}%")
+
+
+if __name__ == "__main__":
+    parser = setup_common_args_and_variables()
+
+    parser.add_argument(
+        "-d",
+        "--dataset",
+        help=(
+            "path to the validation folder of ImageNet dataset. "
+            "e.g. --dataset imagenet-mini/val "
+            "for https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000)"
+        ),
+        type=str,
+        required=False,
+    )
+
+    parser.add_argument(
+        "-a",
+        "--artifact",
+        help="path for storing generated artifacts by this example. "
+        "Default ./focalnet",
+        default="./focalnet",
+        type=str,
+    )
+
+    args = parser.parse_args()
+    try:
+        main(args)
+    except Exception as e:
+        if args.ip and args.port != -1:
+            with Client((args.ip, args.port)) as conn:
+                conn.send(json.dumps({"Error": str(e)}))
+        else:
+            raise Exception(e)