Qualcomm AI Engine Direct - GA mobilevit

backends/qualcomm/aot/wrappers/OpWrapper.h

@@ -68,14 +68,13 @@ class OpWrapper final {
     std::unique_ptr<QuantizeParamsWrapper> quantize_param_wrapper =
         std::make_unique<UndefinedQuantizeParamsWrapper>();
     constexpr std::uint32_t kBytes = 0;
-    std::vector<uint8_t> dynamic_dims(rank, 0);
     std::shared_ptr<TensorWrapper> tensor_wrapper = CreateTensorWrapper(
         QNN_TENSOR_TYPE_STATIC,
         data_type,
         std::move(quantize_param_wrapper),
         rank,
         dims,
-        dynamic_dims.data(),
+        nullptr,
         kBytes,
         data,
         copy_data);

backends/qualcomm/builders/node_visitor.py

@@ -348,7 +348,7 @@ def get_dynamic_dimension(self, dims):
                 nominal_dims.append(dim)
                 dynamic_dims.append(0)
 
-        return dynamic_dims, nominal_dims
+        return dynamic_dims if any(dynamic_dims) else [], nominal_dims
 
     def define_custom_tensor_wrapper(
         self,

backends/qualcomm/tests/test_qnn_delegate.py

@@ -4180,6 +4180,42 @@ def test_pvt(self):
                 self.assertGreaterEqual(msg["top_1"], 65)
                 self.assertGreaterEqual(msg["top_5"], 85)
 
+    def test_mobilevit1(self):
+        if not self.required_envs([self.image_dataset]):
+            self.skipTest("missing required envs")
+
+        cmds = [
+            "python",
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/mobilevit1.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])
+
+        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"], 70)
+                self.assertGreaterEqual(msg["top_5"], 85)
+
     def test_regnet(self):
         if not self.required_envs([self.image_dataset]):
             self.skipTest("missing required envs")

examples/qualcomm/oss_scripts/mobilevit1.py

@@ -0,0 +1,173 @@
+# 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,
+    make_output_dir,
+    parse_skip_delegation_node,
+    setup_common_args_and_variables,
+    SimpleADB,
+    topk_accuracy,
+)
+from PIL import Image
+from torchvision import datasets
+from transformers import AutoModelForImageClassification, MobileViTFeatureExtractor
+
+
+def get_imagenet_dataset(dataset_path, data_size, shuffle=True):
+
+    def get_data_loader():
+        imagenet_data = datasets.ImageFolder(dataset_path)
+        return torch.utils.data.DataLoader(
+            imagenet_data,
+            shuffle=shuffle,
+        )
+
+    # prepare input data
+    inputs, targets, input_list = [], [], ""
+    data_loader = get_data_loader()
+    feature_extractor = MobileViTFeatureExtractor.from_pretrained(
+        "apple/mobilevit-xx-small"
+    )
+    for index, data in enumerate(data_loader.dataset.imgs):
+        if index >= data_size:
+            break
+        data_path, target = data
+        image = Image.open(data_path).convert("RGB")
+        feature = feature_extractor(images=image, return_tensors="pt")
+        inputs.append((feature["pixel_values"],))
+        targets.append(torch.tensor(target))
+        input_list += f"input_{index}_0.raw\n"
+
+    return inputs, targets, input_list
+
+
+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,
+        )
+
+    module = (
+        AutoModelForImageClassification.from_pretrained("apple/mobilevit-xx-small")
+        .eval()
+        .to("cpu")
+    )
+
+    pte_filename = "mobilevit1_qnn_q16"
+    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_16a16w,
+        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 ./mobilevit1",
+        default="./mobilevit1",
+        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)