Qualcomm AI Engine Direct - CI for Non-LLM GA model Part1

Author: winskuo-quic

.ci/scripts/test_model.sh

@@ -188,6 +188,24 @@ test_model_with_qnn() {
     EXPORT_SCRIPT=edsr
     # Additional deps for edsr
     pip install piq
+  elif [[ "${MODEL_NAME}" == "cvt" ]]; then
+    EXPORT_SCRIPT=cvt
+  elif [[ "${MODEL_NAME}" == "deit" ]]; then
+    EXPORT_SCRIPT=deit
+  elif [[ "${MODEL_NAME}" == "dit" ]]; then
+    EXPORT_SCRIPT=dit
+  elif [[ "${MODEL_NAME}" == "efficientnet" ]]; then
+    EXPORT_SCRIPT=efficientnet
+  elif [[ "${MODEL_NAME}" == "focalnet" ]]; then
+    EXPORT_SCRIPT=focalnet
+  elif [[ "${MODEL_NAME}" == "mobilevit_v1" ]]; then
+    EXPORT_SCRIPT=mobilevit_v1
+  elif [[ "${MODEL_NAME}" == "mobilevit_v2" ]]; then
+    EXPORT_SCRIPT=mobilevit_v2
+  elif [[ "${MODEL_NAME}" == "pvt" ]]; then
+    EXPORT_SCRIPT=pvt
+  elif [[ "${MODEL_NAME}" == "swin" ]]; then
+    EXPORT_SCRIPT=swin_transformer
   elif [[ "${MODEL_NAME}" == "albert" ]]; then
     EXPORT_SCRIPT=albert
   elif [[ "${MODEL_NAME}" == "bert" ]]; then
@@ -196,6 +214,8 @@ test_model_with_qnn() {
     EXPORT_SCRIPT=distilbert
   elif [[ "${MODEL_NAME}" == "eurobert" ]]; then
     EXPORT_SCRIPT=eurobert
+  elif [[ "${MODEL_NAME}" == "roberta" ]]; then
+    EXPORT_SCRIPT=roberta
   else
     echo "Unsupported model $MODEL_NAME"
     exit 1
@@ -210,7 +230,10 @@ test_model_with_qnn() {
     "dl3"|"mv3"|"mv2"|"ic4"|"ic3"|"vit"|"mb"|"w2l")
         SCRIPT_FOLDER=scripts
         ;;
-    "albert"|"bert"|"distilbert")
+    "cvt"|"deit"|"dit"|"efficientnet"|"focalnet"|"mobilevit_v1"|"mobilevit_v2"|"pvt"|"swin")
+        SCRIPT_FOLDER=oss_scripts
+        ;;
+    "albert"|"bert"|"distilbert"|"roberta")
         pip install evaluate
         SCRIPT_FOLDER=oss_scripts
         # Bert models running in 16bit will encounter op validation fail on some operations,

.github/workflows/trunk.yml

@@ -489,7 +489,7 @@ jobs:
     strategy:
       matrix:
         dtype: [fp32]
-        model: [albert, bert, distilbert] # eurobert requires transfomer >= 4.48.0, skip for now
+        model: [cvt, deit, dit, efficientnet, focalnet, mobilevit_v1, mobilevit_v2, pvt, swin, albert, bert, distilbert, roberta] # eurobert requires transfomer >= 4.48.0, skip for now
       fail-fast: false
     with:
       runner: linux.2xlarge

backends/qualcomm/builders/op_slice_copy.py

@@ -56,7 +56,7 @@ def define_node(
         if start < 0:
             start = start % input_tensor.shape[dim]
 
-        if len(node.args) > 3:
+        if len(node.args) > 3 and node.args[3] is not None:
             end = min(cast(int, node.args[3]), input_tensor.shape[dim])
             if end < 0:
                 end = end % input_tensor.shape[dim]

backends/qualcomm/tests/test_qnn_delegate.py

@@ -4646,13 +4646,13 @@ def test_pvt(self):
                 self.assertGreaterEqual(msg["top_1"], 65)
                 self.assertGreaterEqual(msg["top_5"], 85)
 
-    def test_mobilevit1(self):
+    def test_mobilevit_v1(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"
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/mobilevit_v1.py"
             "--dataset",
             self.image_dataset,
             "--artifact",

examples/qualcomm/oss_scripts/deit.py

@@ -6,10 +6,12 @@
 
 import getpass
 import json
+import logging
 import os
 from multiprocessing.connection import Client
 
 import numpy as np
+import torch
 from executorch.backends.qualcomm._passes.qnn_pass_manager import (
     get_capture_program_passes,
 )
@@ -46,12 +48,19 @@ def main(args):
     data_num = 100
     height = config.image_size
     width = config.image_size
-    inputs, targets, input_list = get_imagenet_dataset(
-        dataset_path=f"{args.dataset}",
-        data_size=data_num,
-        image_shape=(height, width),
-        crop_size=(height, width),
-    )
+
+    if args.ci:
+        inputs = [(torch.rand(1, 3, height, width),)]
+        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=(height, width),
+            crop_size=(height, width),
+        )
 
     # Get the Deit model.
     model = get_instance()
@@ -134,7 +143,7 @@ def main(args):
             "for https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000)"
         ),
         type=str,
-        required=True,
+        required=False,
     )
 
     args = parser.parse_args()

examples/qualcomm/oss_scripts/mobilevit1.py renamed to examples/qualcomm/oss_scripts/mobilevit_v1.py

@@ -84,7 +84,7 @@ def main(args):
         .to("cpu")
     )
 
-    pte_filename = "mobilevit1_qnn_q16"
+    pte_filename = "mobilevit_v1_qnn_q16"
     build_executorch_binary(
         module.eval(),
         inputs[0],

examples/qualcomm/oss_scripts/roberta.py

@@ -6,6 +6,7 @@
 
 import getpass
 import json
+import logging
 import os
 from multiprocessing.connection import Client
 
@@ -38,16 +39,29 @@ def main(args):
     skip_node_id_set, skip_node_op_set = parse_skip_delegation_node(args)
 
     os.makedirs(args.artifact, exist_ok=True)
-    data_size = 100
 
     tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
-    inputs, targets, input_list = get_masked_language_model_dataset(
-        args.dataset, tokenizer, data_size
-    )
+    data_size = 100
+    if args.ci:
+        random_ids = torch.randint(low=0, high=100, size=(1, 100), dtype=torch.int32)
+        attention_mask = torch.ones((1, 100), dtype=torch.float32)
+        inputs = [
+            (
+                random_ids,
+                attention_mask,
+            )
+        ]
+        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_masked_language_model_dataset(
+            args.dataset, tokenizer, data_size
+        )
 
     # Get the Roberta model.
     model = get_instance(args)
-    pte_filename = "roberta_qnn"
+    pte_filename = "roberta_qnn_q16"
 
     # lower to QNN
     passes_job = get_capture_program_passes()
@@ -137,7 +151,7 @@ def main(args):
         "-a",
         "--artifact",
         help="path for storing generated artifacts and output by this example. Default ./Roberta_qnn",
-        default="./Roberta_qnn",
+        default="./roberta",
         type=str,
     )
     parser.add_argument(
@@ -149,7 +163,7 @@ def main(args):
             "for https://www.kaggle.com/datasets/mikeortman/wikipedia-sentences"
         ),
         type=str,
-        required=True,
+        required=False,
     )
 
     args = parser.parse_args()

examples/qualcomm/oss_scripts/swin_transformer.py

@@ -89,7 +89,7 @@ def main(args):
 
     data_num = 100
     if args.ci:
-        inputs = [torch.rand(1, 3, 224, 224)]
+        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."
         )
@@ -181,8 +181,9 @@ def main(args):
     parser.add_argument(
         "-a",
         "--artifact",
-        help="path for storing generated artifacts by this example. " "Default ./swin",
-        default="./swin",
+        help="path for storing generated artifacts by this example. "
+        "Default ./swin_transformer",
+        default="./swin_transformer",
         type=str,
     )