Qualcomm AI Engine Direct - GA CvT (#11036)

Summary:

- Add CvT example script
- Add the test for CvT
- Fix missing quant config for torch.ops.aten.split_with_sizes.default

Author: shewu-quic

backends/qualcomm/tests/test_qnn_delegate.py

@@ -3902,6 +3902,42 @@ def test_conv_former(self):
                 self.assertGreaterEqual(msg["top_1"], 60)
                 self.assertGreaterEqual(msg["top_5"], 80)
 
+    def test_cvt(self):
+        if not self.required_envs([self.image_dataset]):
+            self.skipTest("missing required envs")
+
+        cmds = [
+            "python",
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/cvt.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"], 90)
+
     def test_deit(self):
         if not self.required_envs([self.image_dataset]):
             self.skipTest("missing required envs")

examples/qualcomm/oss_scripts/cvt.py

@@ -0,0 +1,208 @@
+# 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
+import types
+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
+from transformers.models.cvt.modeling_cvt import CvtSelfAttention
+
+
+# Copy from transformers/models/cvt/modeling_cvt.py in transformers 4.47.1
+# torch.einsum("bhlk,bhtk->bhlt", [query, key]) will result in prepare failed due to 5D tensor with decompose_einsum.
+# TODO: once HTP fixed, this workaround can be removed
+def attention_forward_without_einsum(self, hidden_state, height, width):
+    if self.with_cls_token:
+        cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
+    batch_size, hidden_size, num_channels = hidden_state.shape
+    # rearrange "b (h w) c -> b c h w"
+    hidden_state = hidden_state.permute(0, 2, 1).view(
+        batch_size, num_channels, height, width
+    )
+
+    key = self.convolution_projection_key(hidden_state)
+    query = self.convolution_projection_query(hidden_state)
+    value = self.convolution_projection_value(hidden_state)
+
+    if self.with_cls_token:
+        query = torch.cat((cls_token, query), dim=1)
+        key = torch.cat((cls_token, key), dim=1)
+        value = torch.cat((cls_token, value), dim=1)
+
+    head_dim = self.embed_dim // self.num_heads
+
+    query = self.rearrange_for_multi_head_attention(self.projection_query(query))
+    key = self.rearrange_for_multi_head_attention(self.projection_key(key))
+    value = self.rearrange_for_multi_head_attention(self.projection_value(value))
+    # ====================Qualcomm Changed=================================
+    attention_score = query @ key.transpose(-1, -2)
+    attention_score = attention_score * self.scale
+    # attention_score = torch.einsum("bhlk,bhtk->bhlt", [query, key]) * self.scale
+    # =====================================================================
+    attention_probs = torch.nn.functional.softmax(attention_score, dim=-1)
+    attention_probs = self.dropout(attention_probs)
+    # ====================Qualcomm Changed=================================
+    context = attention_probs @ value
+    # context = torch.einsum("bhlt,bhtv->bhlv", [attention_probs, value])
+    # =====================================================================
+    # rearrange"b h t d -> b t (h d)"
+    _, _, hidden_size, _ = context.shape
+    context = (
+        context.permute(0, 2, 1, 3)
+        .contiguous()
+        .view(batch_size, hidden_size, self.num_heads * head_dim)
+    )
+    return context
+
+
+def _replace_attention(
+    module: torch.nn.Module,
+):
+    for _, child in module.named_children():
+        if isinstance(child, CvtSelfAttention):
+            child.forward = types.MethodType(  # pyre-ignore
+                attention_forward_without_einsum, child
+            )
+        else:
+            _replace_attention(child)
+    return module
+
+
+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/cvt-13")
+        .eval()
+        .to("cpu")
+    )
+    # Fix prepare failed due to einsum
+    module = _replace_attention(module)
+    pte_filename = "cvt_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 ./cvt",
+        default="./cvt",
+        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)