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#7502: models 2 - Convert most models in models 2 to use file path, e…
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…xcept for some falcon and bert tests, converting soon
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@tt-rkim
tt-rkim committed Apr 17, 2024
1 parent 13f0411 commit 570a2dc
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38 changes: 32 additions & 6 deletions models/experimental/efficientnet/tests/test_efficientnet_model.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

# SPDX-License-Identifier: Apache-2.0

import pytest
import tt_lib
import torch
from loguru import logger
Expand Down Expand Up @@ -43,9 +44,7 @@ def make_input_tensor(imagenet_sample_input, resize=256, crop=224):
[
torchvision.transforms.Resize(resize),
torchvision.transforms.CenterCrop(crop),
torchvision.transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)

Expand Down Expand Up @@ -76,9 +75,7 @@ def run_efficientnet_model_test(

tt_model = tt_model_class(device)

test_input = torch2tt_tensor(
test_input, tt_device=device, tt_layout=tt_lib.tensor.Layout.ROW_MAJOR
)
test_input = torch2tt_tensor(test_input, tt_device=device, tt_layout=tt_lib.tensor.Layout.ROW_MAJOR)

with torch.no_grad():
tt_model.eval()
Expand All @@ -96,6 +93,7 @@ def run_efficientnet_model_test(
assert does_pass


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b0_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -105,6 +103,7 @@ def test_efficientnet_b0_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b1_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -115,6 +114,7 @@ def test_efficientnet_b1_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b2_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -124,6 +124,7 @@ def test_efficientnet_b2_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b3_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -133,6 +134,7 @@ def test_efficientnet_b3_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b4_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -142,6 +144,7 @@ def test_efficientnet_b4_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b5_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -151,6 +154,7 @@ def test_efficientnet_b5_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b6_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -160,6 +164,7 @@ def test_efficientnet_b6_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b7_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -169,6 +174,7 @@ def test_efficientnet_b7_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_v2_s_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -178,6 +184,7 @@ def test_efficientnet_v2_s_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_v2_m_model_synt(imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -187,6 +194,7 @@ def test_efficientnet_v2_m_model_synt(imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_v2_l_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -196,6 +204,7 @@ def test_efficientnet_v2_l_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite0_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -207,6 +216,7 @@ def test_efficientnet_lite0_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite1_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -220,6 +230,7 @@ def test_efficientnet_lite1_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite2_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -233,6 +244,7 @@ def test_efficientnet_lite2_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite3_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -246,6 +258,7 @@ def test_efficientnet_lite3_model_synt(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite4_model_synt(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -270,6 +283,7 @@ def test_efficientnet_b0_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b1_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -281,6 +295,7 @@ def test_efficientnet_b1_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b2_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -292,6 +307,7 @@ def test_efficientnet_b2_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b3_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -303,6 +319,7 @@ def test_efficientnet_b3_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b4_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -314,6 +331,7 @@ def test_efficientnet_b4_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b5_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -325,6 +343,7 @@ def test_efficientnet_b5_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b6_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -336,6 +355,7 @@ def test_efficientnet_b6_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_b7_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -358,6 +378,7 @@ def test_efficientnet_v2_s_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_v2_m_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -369,6 +390,7 @@ def test_efficientnet_v2_m_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_v2_l_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -391,6 +413,7 @@ def test_efficientnet_lite0_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite1_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -404,6 +427,7 @@ def test_efficientnet_lite1_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite2_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -417,6 +441,7 @@ def test_efficientnet_lite2_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite3_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand All @@ -430,6 +455,7 @@ def test_efficientnet_lite3_model_real(device, imagenet_sample_input):
)


@pytest.mark.skip(reason="Not tested")
def test_efficientnet_lite4_model_real(device, imagenet_sample_input):
run_efficientnet_model_test(
device,
Expand Down
2 changes: 1 addition & 1 deletion models/experimental/synthetic_gradients/tests/test_batchnorm1d_forward.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -347,7 +347,7 @@ def run_batchnorm_forward(device, bn_size):
print("\n\n", "atol/rtol:", test_results, "| pcc:", output, "\n\n")

pcc = comp_pcc(output_bn_torch[0], output_bn_tt_untilized)
assert float(pcc[1][5:]) > 0.99, f"pcc is lower than 0.99: {float(pcc[1][5:])}"
assert float(pcc[1]) > 0.99, f"pcc is lower than 0.99: {float(pcc[1])}"


def test_batchnorm_inference(device):
Expand Down
52 changes: 24 additions & 28 deletions tests/scripts/nightly/run_models_2.sh
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
env pytest models/experimental/whisper -k whisper_attention
env pytest models/experimental/whisper -k WhipserDecoderLayer_inference
env pytest models/experimental/whisper/tests/test_whisper_attention.py
env pytest models/experimental/whisper/tests/test_whisper_decoder_layer.py

env pytest models/experimental/deit/tests/test_deit_for_image_classification_with_teacher.py

Expand All @@ -18,43 +18,39 @@ env pytest models/experimental/roberta/tests/test_roberta_pooler.py
env pytest models/experimental/roberta/tests/test_roberta_lm_head.py
env pytest models/experimental/roberta/tests/test_roberta_classification_head.py

env pytest models/experimental/bloom/tests -k baddbmm
env pytest models/experimental/bloom/tests -k bloom_attention
env pytest models/experimental/bloom/tests -k bloom_gelu_forward
env pytest models/experimental/bloom/tests -k bloom_merge_heads
env pytest models/experimental/bloom/tests -k bloom_mlp
env pytest models/experimental/bloom/tests/test_baddbmm.py
env pytest models/experimental/bloom/tests/test_bloom_attention.py
env pytest models/experimental/bloom/tests/test_bloom_gelu_forward.py
env pytest models/experimental/bloom/tests/test_bloom_merge_heads.py
env pytest models/experimental/bloom/tests/test_bloom_mlp.py

env pytest models/demos/metal_BERT_large_11/tests/test_bert_batch_dram.py -k batch_7-BFLOAT8_B-SHARDED
env pytest models/demos/metal_BERT_large_11/tests/test_demo.py::test_demo -k batch_7
env pytest models/demos/metal_BERT_large_11/tests/test_demo.py::test_demo_squadv2 -k batch_7

env pytest models/experimental/synthetic_gradients/tests -k test_batchnorm1d
env pytest models/experimental/synthetic_gradients/tests -k test_linear
env pytest models/experimental/synthetic_gradients/tests -k test_block
env pytest models/experimental/synthetic_gradients/tests -k test_full_inference
env pytest models/experimental/synthetic_gradients/tests/

env pytest models/experimental/lenet/tests -k test_lenet_inference
env pytest models/experimental/lenet/tests/test_lenet.py

env pytest models/experimental/convnet_mnist/tests -k mnist_inference
env pytest models/experimental/convnet_mnist/tests/test_convnet_mnist.py

env pytest models/experimental/yolov5/tests -k Yolov5_detection_model
env pytest models/experimental/yolov3 -k conv2d_module
env pytest models/experimental/yolov3 -k conv_module
env pytest models/experimental/yolov3 -k concat_module
env pytest models/experimental/yolov3 -k bottleneck_module
env pytest models/experimental/yolov3 -k detect_module
env pytest models/experimental/yolov3 -k detection_model
env pytest models/experimental/yolov3 -k upsample_module
env pytest models/experimental/yolov5/tests/test_yolov5_detection_model.py

env pytest models/experimental/efficientnet/tests -k efficientnet_b0_model_real
env pytest models/experimental/efficientnet/tests -k efficientnet_v2_s_model_real
env pytest models/experimental/efficientnet/tests -k efficientnet_lite0_model_real
env pytest models/experimental/yolov3/tests/test_yolov3_upsample.py
env pytest models/experimental/yolov3/tests/test_yolov3_concat.py
env pytest models/experimental/yolov3/tests/test_yolov3_conv2d.py
env pytest models/experimental/yolov3/tests/test_yolov3_bottleneck.py
env pytest models/experimental/yolov3/tests/test_yolov3_detection_model.py
env pytest models/experimental/yolov3/tests/test_yolov3_detect.py
env pytest models/experimental/yolov3/tests/test_yolov3_conv.py

env pytest models/experimental/efficientnet/tests/test_efficientnet_model.py

env pytest models/demos/falcon7b/tests/test_falcon_end_to_end.py::test_FalconCausalLM_end_to_end_with_program_cache[BFLOAT16-L1-falcon_7b-layers_32-prefill_seq128]

env pytest models/experimental/stable_diffusion/tests/test_embedding.py

env pytest models/demos/ttnn_falcon7b/tests -k falcon_mlp
env pytest models/demos/ttnn_falcon7b/tests -k falcon_rotary_embeddings
env pytest models/demos/ttnn_falcon7b/tests -k falcon_attention
env pytest models/demos/ttnn_falcon7b/tests -k falcon_decoder
env pytest models/demos/ttnn_falcon7b/tests/test_falcon_mlp.py
env pytest models/demos/ttnn_falcon7b/tests/test_falcon_rotary_embedding.py
env pytest models/demos/ttnn_falcon7b/tests/test_falcon_attention.py
env pytest models/demos/ttnn_falcon7b/tests/test_falcon_decoder.py

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