Add pytest nightly marker for CI (#551)

forge/test/model_demos/high_prio/cnn/onnx/test_ddrnet.py

@@ -16,6 +16,7 @@
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_ddrnet(variant, test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -76,6 +77,7 @@ def test_ddrnet(variant, test_device):
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_ddrnet_semantic_segmentation_onnx(variant, test_device):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_dla.py

@@ -29,6 +29,7 @@
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_dla_onnx(test_device, variant):
     compiler_cfg = forge.config._get_global_compiler_config()
     compiler_cfg.balancer_policy = "Ribbon"

forge/test/model_demos/high_prio/cnn/onnx/test_fpn.py

@@ -9,6 +9,7 @@
 from forge import VerifyConfig
 
 
+@pytest.mark.nightly
 def test_fpn_onnx(test_device, test_kind):
     compiler_cfg = forge.config._get_global_compiler_config()
     compiler_cfg.balancer_policy = "Ribbon"

forge/test/model_demos/high_prio/cnn/onnx/test_hardnet.py

@@ -16,6 +16,7 @@
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_hardnet_onnx(variant, test_device):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_lstm_genom.py

@@ -14,6 +14,7 @@
 from test.utils import download_model
 
 
+@pytest.mark.nightly
 def test_lstm_genom_onnx(test_device):
     load_path = "third_party/confidential_customer_models/model_2/onnx/lstm_genom/lstm-genom-model.onnx"
     model = onnx.load(load_path)

forge/test/model_demos/high_prio/cnn/onnx/test_lstm_valence.py

@@ -13,6 +13,7 @@
 from forge.verify.config import TestKind
 
 
+@pytest.mark.nightly
 def test_lstm_valence_onnx(test_device):
     # Load model checkpoint from HuggingFace
     load_path = "third_party/confidential_customer_models/model_2/onnx/lstm_valence/lstm-valence-model.onnx"

forge/test/model_demos/high_prio/cnn/onnx/test_perceiverio.py

@@ -33,6 +33,7 @@ def get_sample_data(model_name):
         "deepmind/vision-perceiver-fourier",
     ],
 )
+@pytest.mark.nightly
 def test_perceiver_for_image_classification_onnx(test_device, model_name):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_retinanet.py

@@ -49,6 +49,7 @@ def img_preprocess(scal_val=1):
 #########
 
 
+@pytest.mark.nightly
 def test_retinanet_r101_640x480_onnx(test_device):
     os.environ["FORGE_DECOMPOSE_SIGMOID"] = "1"
     os.environ["FORGE_DISABLE_CONV_MULTI_OP_FRACTURE"] = "1"
@@ -120,6 +121,7 @@ def img_preprocessing():
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_retinanet_onnx(variant, test_device):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_segformer_imgcls.py

@@ -34,6 +34,7 @@ def get_sample_data(model_name):
 
 
 @pytest.mark.parametrize("variant", variants_img_classification)
+@pytest.mark.nightly
 def test_segformer_image_classification_onnx(test_device, variant):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_segformer_semseg.py

@@ -33,6 +33,7 @@ def get_sample_data(model_name):
 
 
 @pytest.mark.parametrize("variant", variants_semseg)
+@pytest.mark.nightly
 def test_segformer_semantic_segmentation_onnx(test_device, variant):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_yolo_v3.py

@@ -46,6 +46,7 @@ def preprocess(img):
 
 
 @pytest.mark.skip(reason="While loop in model, not supported yet")
+@pytest.mark.nightly
 def test_yolov3_tiny_onnx(test_device):
     # STEP 1: Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()  # load global compiler config object
@@ -78,6 +79,7 @@ def test_yolov3_tiny_onnx(test_device):
 
 
 @pytest.mark.skip(reason="While loop in model, not supported yet")
+@pytest.mark.nightly
 def test_yolov3_onnx(test_device):
     # STEP 1: Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()  # load global compiler config object

forge/test/model_demos/high_prio/cnn/onnx/test_yolo_v5.py

@@ -58,6 +58,7 @@ def data_preprocessing(ims: Image.Image, size: tuple) -> tuple:
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_yolo_v5_320x320_onnx(test_device, variant):
 
     # forge configuration parameters
@@ -103,6 +104,7 @@ def test_yolo_v5_320x320_onnx(test_device, variant):
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_yolo_v5_480x480_onnx(test_device, variant):
 
     # forge configuration parameters
@@ -183,6 +185,7 @@ def test_yolo_v5_480x480_onnx(test_device, variant):
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_yolo_v5_640x640_onnx(test_device, variant):
 
     # forge configuration parameters

forge/test/model_demos/high_prio/cnn/onnx/test_yolo_x.py

@@ -38,6 +38,7 @@ def preprocess(img, input_size, swap=(2, 0, 1)):
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_yolox_onnx(variant, test_device):
 
     # forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_alexnet.py

@@ -13,6 +13,7 @@
 import os
 
 
+@pytest.mark.nightly
 def test_alexnet_torchhub(test_device):
     # Configurations
     compiler_cfg = forge.config._get_global_compiler_config()
@@ -45,6 +46,7 @@ def test_alexnet_torchhub(test_device):
     compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name="pt_alexnet_torchhub")
 
 
+@pytest.mark.nightly
 def test_alexnet_osmr(test_device):
     # Configurations
     compiler_cfg = forge.config._get_global_compiler_config()

forge/test/model_demos/high_prio/cnn/pytorch/test_autoencoder.py

@@ -87,6 +87,7 @@ def forward(self, x):
         return act
 
 
+@pytest.mark.nightly
 def test_conv_ae_pytorch(test_device):
     # Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()
@@ -113,6 +114,7 @@ def test_conv_ae_pytorch(test_device):
     compiled_model = forge.compile(model, sample_inputs=[sample_tensor], module_name="pt_conv_ae")
 
 
+@pytest.mark.nightly
 def test_linear_ae_pytorch(test_device):
     # Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()

forge/test/model_demos/high_prio/cnn/pytorch/test_blazepose.py

@@ -20,6 +20,7 @@
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_blazepose_detector_pytorch(test_device):
     # Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()
@@ -41,6 +42,7 @@ def test_blazepose_detector_pytorch(test_device):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_blazepose_regressor_pytorch(test_device):
     # Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()
@@ -54,6 +56,7 @@ def test_blazepose_regressor_pytorch(test_device):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_blaze_palm_pytorch(test_device):
 
     # Set Forge configuration parameters
@@ -77,6 +80,7 @@ def test_blaze_palm_pytorch(test_device):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_blaze_hand_pytorch(test_device):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_bts.py

@@ -22,6 +22,7 @@
 
 @pytest.mark.skip(reason="dependent on CCM repo")
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_bts_pytorch(test_device, variant):
 
     # Set PyBuda configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_clip.py

@@ -99,6 +99,7 @@ def forward(self, input_ids, vision_outputs, last_hidden_state, *encoder_outputs
         return output
 
 
+@pytest.mark.nightly
 def test_clip_pytorch(test_device):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_ddrnet.py

@@ -20,6 +20,7 @@
 
 @pytest.mark.skip(reason="dependent on CCM repo")
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_ddrnet_pytorch(variant, test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -69,6 +70,7 @@ def test_ddrnet_pytorch(variant, test_device):
 
 @pytest.mark.skip(reason="dependent on CCM repo")
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_ddrnet_semantic_segmentation_pytorch(variant, test_device):
 
     # Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_deit.py

@@ -15,6 +15,7 @@
 
 
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_vit_base_classify_224_hf_pytorch(variant, test_device):
     model, inputs, _ = generate_model_deit_imgcls_hf_pytorch(
         variant,

forge/test/model_demos/high_prio/cnn/pytorch/test_densenet.py

@@ -82,6 +82,7 @@ def get_input_img_hf_xray():
 
 
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_densenet_121_pytorch(variant, test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -104,6 +105,7 @@ def test_densenet_121_pytorch(variant, test_device):
     compiled_model = forge.compile(model, sample_inputs=inputs, module_name=f"pt_{variant_name}")
 
 
+@pytest.mark.nightly
 def test_densenet_161_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -120,6 +122,7 @@ def test_densenet_161_pytorch(test_device):
     compiled_model = forge.compile(model, sample_inputs=inputs, module_name="pt_densenet_161")
 
 
+@pytest.mark.nightly
 def test_densenet_169_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -136,6 +139,7 @@ def test_densenet_169_pytorch(test_device):
     compiled_model = forge.compile(model, sample_inputs=inputs, module_name="pt_densenet_169")
 
 
+@pytest.mark.nightly
 def test_densenet_201_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_dla.py

@@ -37,6 +37,7 @@
 
 
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_dla_pytorch(variant, test_device):
     # Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()

forge/test/model_demos/high_prio/cnn/pytorch/test_efficientnet.py

@@ -32,6 +32,7 @@
 
 
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_efficientnet_timm(variant, test_device):
 
     # Configuration
@@ -81,6 +82,7 @@ def get_state_dict(self, *args, **kwargs):
 ]
 
 
+@pytest.mark.nightly
 @pytest.mark.parametrize("variant", variants)
 def test_efficientnet_torchvision(variant, test_device):
     # Configuration

forge/test/model_demos/high_prio/cnn/pytorch/test_efficientnet_lite.py

@@ -32,6 +32,7 @@ def get_image_tensor(wh):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_efficientnet_lite_0_pytorch():
     # STEP 1: Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()
@@ -50,6 +51,7 @@ def test_efficientnet_lite_0_pytorch():
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_efficientnet_lite_1_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -70,6 +72,7 @@ def test_efficientnet_lite_1_pytorch(test_device):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_efficientnet_lite_2_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -89,6 +92,7 @@ def test_efficientnet_lite_2_pytorch(test_device):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_efficientnet_lite_3_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters
@@ -108,6 +112,7 @@ def test_efficientnet_lite_3_pytorch(test_device):
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_efficientnet_lite_4_pytorch(test_device):
 
     # STEP 1: Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_fchardnet.py

@@ -15,6 +15,7 @@
 
 
 @pytest.mark.skip(reason="dependent on CCM repo")
+@pytest.mark.nightly
 def test_fchardnet(test_device):
     # STEP 1: Set PyBuda configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()

forge/test/model_demos/high_prio/cnn/pytorch/test_fpn.py

@@ -25,6 +25,7 @@ def forward(self, feat0, feat1, feat2):
         return self.fpn(x)
 
 
+@pytest.mark.nightly
 def test_fpn_pytorch(test_device):
     compiler_cfg = forge.config._get_global_compiler_config()
     compiler_cfg.compile_depth = forge.CompileDepth.SPLIT_GRAPH

forge/test/model_demos/high_prio/cnn/pytorch/test_ghostnet.py

@@ -9,6 +9,7 @@
 
 
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_ghostnet_timm(variant, test_device):
     model, inputs = generate_model_ghostnet_imgcls_timm(variant)
     compiled_model = forge.compile(model, sample_inputs=inputs, module_name=f"pt_{variant}")

forge/test/model_demos/high_prio/cnn/pytorch/test_googlenet.py

@@ -10,6 +10,7 @@
 import os
 
 
+@pytest.mark.nightly
 def test_googlenet_pytorch(test_device):
     # Set Forge configuration parameters
     compiler_cfg = forge.config._get_global_compiler_config()  # load global compiler config object

forge/test/model_demos/high_prio/cnn/pytorch/test_hardnet.py

@@ -14,6 +14,7 @@
 
 @pytest.mark.skip(reason="dependent on CCM repo")
 @pytest.mark.parametrize("variant", variants)
+@pytest.mark.nightly
 def test_hardnet_pytorch(test_device, variant):
 
     # STEP 1: Set Forge configuration parameters

forge/test/model_demos/high_prio/cnn/pytorch/test_hrnet.py

@@ -83,6 +83,7 @@ def generate_model_hrnet_imgcls_osmr_pytorch(variant):
 
 
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_hrnet_osmr_pytorch(test_device, variant):
     model, inputs, _ = generate_model_hrnet_imgcls_osmr_pytorch(
         variant,
@@ -147,6 +148,7 @@ def generate_model_hrnet_imgcls_timm_pytorch(variant):
 
 
 @pytest.mark.parametrize("variant", variants, ids=variants)
+@pytest.mark.nightly
 def test_hrnet_timm_pytorch(test_device, variant):
     model, inputs, _ = generate_model_hrnet_imgcls_timm_pytorch(
         variant,