refactor(//tests) : Refactor the test suite #1329
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…y models and restructuring Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
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--- noxfile.py 2022-09-01 00:21:41.224247 +0000
+++ noxfile.py 2022-09-01 00:22:02.734123 +0000
@@ -29,17 +29,18 @@
USE_HOST_DEPS = 0 if not "USE_HOST_DEPS" in os.environ else os.environ["USE_HOST_DEPS"]
if USE_HOST_DEPS:
print("Using dependencies from host python")
# Set epochs to train VGG model for accuracy tests
-EPOCHS=25
+EPOCHS = 25
SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
nox.options.sessions = [
"l0_api_tests-" + "{}.{}".format(sys.version_info.major, sys.version_info.minor)
]
+
def install_deps(session):
print("Installing deps")
session.install("-r", os.path.join(TOP_DIR, "py", "requirements.txt"))
session.install("-r", os.path.join(TOP_DIR, "tests", "py", "requirements.txt"))
@@ -108,11 +109,13 @@
"vgg16_ckpts",
"--epochs",
str(EPOCHS),
)
- session.run_always("python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth")
+ session.run_always(
+ "python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth"
+ )
def finetune_model(session):
# Install pytorch-quantization dependency
session.install(
@@ -133,19 +136,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
env={"PYTHONPATH": PYT_PATH},
)
# Export model
session.run_always(
"python",
"export_qat.py",
- "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
env={"PYTHONPATH": PYT_PATH},
)
else:
session.run_always(
"python",
@@ -159,15 +162,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
)
# Export model
- session.run_always("python", "export_qat.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth")
+ session.run_always(
+ "python",
+ "export_qat.py",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
+ )
def cleanup(session):
target = [
"examples/int8/training/vgg16/*.jit.pt",
@@ -193,10 +200,11 @@
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
session.run_always("pytest", test)
+
def run_model_tests(session):
print("Running model tests")
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"models",
@@ -256,11 +264,11 @@
print("Running TensorRT compatibility tests")
copy_model(session)
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"integrations/test_trt_intercompatibility.py",
- #"ptq/test_ptq_trt_calibrator.py",
+ # "ptq/test_ptq_trt_calibrator.py",
]
for test in tests:
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
@@ -308,17 +316,19 @@
install_torch_trt(session)
download_models(session)
run_base_tests(session)
cleanup(session)
+
def run_l1_model_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
download_models(session)
run_model_tests(session)
cleanup(session)
+
def run_l1_int8_accuracy_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
@@ -350,20 +360,23 @@
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_api_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l0_api_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_dla_tests(session):
"""When a developer needs to check basic api functionality using host dependencies"""
run_l0_dla_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_model_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l1_model_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_int8_accuracy_tests(session):
"""Checking accuracy performance on various usecases"""
run_l1_int8_accuracy_tests(session)
--- tests/py/api/test_e2e_behavior.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/test_e2e_behavior.py 2022-09-01 00:22:05.266917 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import copy
from typing import Dict
+
class TestInputTypeDefaultsFP32Model(unittest.TestCase):
def test_input_use_default_fp32(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
--- tests/py/api/test_collections.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/test_collections.py 2022-09-01 00:22:05.303130 +0000
@@ -39,12 +39,17 @@
"device": torchtrt.Device("gpu:0"),
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model(self.input, self.input), trt_mod(self.input, self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model(self.input, self.input), trt_mod(self.input, self.input)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInput(unittest.TestCase):
def test_compile(self):
@@ -63,12 +68,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model((self.input, self.input)), trt_mod((self.input, self.input)))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model((self.input, self.input)), trt_mod((self.input, self.input))
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInput(unittest.TestCase):
def test_compile(self):
@@ -85,12 +95,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model([self.input, self.input]), trt_mod([self.input, self.input]))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model([self.input, self.input]), trt_mod([self.input, self.input])
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInputOutput(unittest.TestCase):
def test_compile(self):
@@ -113,11 +128,14 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputOutput(unittest.TestCase):
def test_compile(self):
@@ -141,11 +159,14 @@
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputTupleOutput(unittest.TestCase):
def test_compile(self):
@@ -168,10 +189,13 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_embed_engines.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/test_embed_engines.py 2022-09-01 00:22:05.311558 +0000
@@ -4,10 +4,11 @@
import torchvision.models as models
import copy
import timm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -24,17 +25,24 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -47,13 +55,19 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_operator_fallback.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/test_operator_fallback.py 2022-09-01 00:22:05.347708 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestFallbackModels(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::add"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -43,10 +47,13 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::hardtanh"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_module_fallback.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/test_module_fallback.py 2022-09-01 00:22:05.351341 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModuleFallback(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_modules": ["torchvision.models.resnet.BasicBlock"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -39,15 +43,20 @@
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
"enabled_precisions": {torch.float},
- "torch_executed_modules": ["torchvision.models.mobilenetv2.ConvBNActivation"],
+ "torch_executed_modules": [
+ "torchvision.models.mobilenetv2.ConvBNActivation"
+ ],
"min_block_size": 5,
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/utils.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/utils.py 2022-09-01 00:22:05.368533 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/hw/test_api_dla.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/hw/test_api_dla.py 2022-09-01 00:22:05.419583 +0000
@@ -39,11 +39,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
"device": {
@@ -55,11 +58,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/api/test_ts_backend.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/api/test_ts_backend.py 2022-09-01 00:22:05.445275 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestCompile(unittest.TestCase):
def test_compile_traced(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -25,11 +26,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -39,11 +43,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_global(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -52,11 +59,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_from_torch_tensor(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -69,11 +79,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -83,21 +96,28 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_default_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
compile_spec = {"inputs": [self.input], "enabled_precisions": {torch.float}}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
class TestCheckMethodOpSupport(unittest.TestCase):
def test_check_support(self):
module = models.alexnet(pretrained=True).eval().to("cuda")
self.module = torch.jit.trace(module, torch.ones((1, 3, 224, 224)).to("cuda"))
--- tests/py/integrations/test_to_backend_api.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/integrations/test_to_backend_api.py 2022-09-01 00:22:05.468738 +0000
@@ -1,10 +1,11 @@
import unittest
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestToBackendLowering(unittest.TestCase):
def setUp(self):
self.input = torch.randn((1, 3, 300, 300)).to("cuda")
self.model = models.resnet18(pretrained=True).eval().to("cuda")
@@ -30,11 +31,13 @@
}
def test_to_backend_lowering(self):
trt_mod = torch._C._jit_to_backend("tensorrt", self.scripted_model, self.spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:22:05.496769 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import tensorrt as trt
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestPyTorchToTRTEngine(unittest.TestCase):
def test_pt_to_trt(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda:0")
self.input = torch.randn((1, 3, 224, 224)).to("cuda:0")
@@ -41,10 +42,13 @@
stream_handle=torch.cuda.current_stream(
device="cuda:0"
).cuda_stream,
)
cos_sim = cosine_similarity(self.model(self.input), out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/hw/test_multi_gpu.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/hw/test_multi_gpu.py 2022-09-01 00:22:05.500810 +0000
@@ -35,11 +35,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -54,12 +57,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestMultiGpuSerializeDeserializeSwitching(ModelTestCase):
def setUp(self):
if torch.cuda.device_count() < 2:
@@ -89,11 +94,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -108,11 +116,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/models/custom_models.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/models/custom_models.py 2022-09-01 00:22:05.520342 +0000
@@ -1,7 +1,8 @@
import torch
from transformers import BertModel, BertTokenizer, BertConfig
+
def BertModule():
model_name = "bert-base-uncased"
enc = BertTokenizer.from_pretrained(model_name)
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
--- tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:22:05.555033 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_multiple_engines(self):
self.resnet18 = models.resnet18(pretrained=True).eval().to("cuda")
self.resnet50 = models.resnet50(pretrained=True).eval().to("cuda")
@@ -28,13 +29,24 @@
"enabled_precisions": {torch.float},
}
rn18_trt_mod = torchtrt.compile(self.resnet18, **compile_spec)
rn50_trt_mod = torchtrt.compile(self.resnet50, **compile_spec)
- cos_sim = cosine_similarity(self.resnet18(self.input1), rn18_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet18(self.input1), rn18_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
- cos_sim = cosine_similarity(self.resnet50(self.input1), rn50_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet50(self.input1), rn50_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/models/utils.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/models/utils.py 2022-09-01 00:22:05.569169 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/models/test_models.py 2022-09-01 00:21:41.236247 +0000
+++ tests/py/models/test_models.py 2022-09-01 00:22:05.613332 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModels(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -26,11 +27,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -47,14 +51,19 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -68,20 +77,31 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_bert_base_uncased(self):
self.model = cm.BertModule().cuda()
self.input = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
compile_spec = {
"inputs": [
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format),
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format)
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
],
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
@@ -93,11 +113,14 @@
model_outputs = self.model(self.input, self.input)
trt_model_outputs = trt_mod(self.input, self.input)
for out, trt_out in zip(model_outputs, trt_model_outputs):
cos_sim = cosine_similarity(out, trt_out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_resnet50_half(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -115,10 +138,16 @@
},
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.compile(self.scripted_model, **compile_spec)
- cos_sim = cosine_similarity(self.model.half()(self.input.half()), trt_mod(self.input.half()))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model.half()(self.input.half()), trt_mod(self.input.half())
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
There was a problem hiding this comment.
There are some changes that do not conform to Python style guidelines:
--- noxfile.py 2022-09-01 00:21:41.226375 +0000
+++ noxfile.py 2022-09-01 00:22:03.508531 +0000
@@ -29,17 +29,18 @@
USE_HOST_DEPS = 0 if not "USE_HOST_DEPS" in os.environ else os.environ["USE_HOST_DEPS"]
if USE_HOST_DEPS:
print("Using dependencies from host python")
# Set epochs to train VGG model for accuracy tests
-EPOCHS=25
+EPOCHS = 25
SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
nox.options.sessions = [
"l0_api_tests-" + "{}.{}".format(sys.version_info.major, sys.version_info.minor)
]
+
def install_deps(session):
print("Installing deps")
session.install("-r", os.path.join(TOP_DIR, "py", "requirements.txt"))
session.install("-r", os.path.join(TOP_DIR, "tests", "py", "requirements.txt"))
@@ -108,11 +109,13 @@
"vgg16_ckpts",
"--epochs",
str(EPOCHS),
)
- session.run_always("python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth")
+ session.run_always(
+ "python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth"
+ )
def finetune_model(session):
# Install pytorch-quantization dependency
session.install(
@@ -133,19 +136,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
env={"PYTHONPATH": PYT_PATH},
)
# Export model
session.run_always(
"python",
"export_qat.py",
- "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
env={"PYTHONPATH": PYT_PATH},
)
else:
session.run_always(
"python",
@@ -159,15 +162,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
)
# Export model
- session.run_always("python", "export_qat.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth")
+ session.run_always(
+ "python",
+ "export_qat.py",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
+ )
def cleanup(session):
target = [
"examples/int8/training/vgg16/*.jit.pt",
@@ -193,10 +200,11 @@
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
session.run_always("pytest", test)
+
def run_model_tests(session):
print("Running model tests")
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"models",
@@ -256,11 +264,11 @@
print("Running TensorRT compatibility tests")
copy_model(session)
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"integrations/test_trt_intercompatibility.py",
- #"ptq/test_ptq_trt_calibrator.py",
+ # "ptq/test_ptq_trt_calibrator.py",
]
for test in tests:
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
@@ -308,17 +316,19 @@
install_torch_trt(session)
download_models(session)
run_base_tests(session)
cleanup(session)
+
def run_l1_model_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
download_models(session)
run_model_tests(session)
cleanup(session)
+
def run_l1_int8_accuracy_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
@@ -350,20 +360,23 @@
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_api_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l0_api_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_dla_tests(session):
"""When a developer needs to check basic api functionality using host dependencies"""
run_l0_dla_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_model_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l1_model_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_int8_accuracy_tests(session):
"""Checking accuracy performance on various usecases"""
run_l1_int8_accuracy_tests(session)
--- tests/py/api/test_e2e_behavior.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/test_e2e_behavior.py 2022-09-01 00:22:06.192658 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import copy
from typing import Dict
+
class TestInputTypeDefaultsFP32Model(unittest.TestCase):
def test_input_use_default_fp32(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
--- tests/py/api/test_collections.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/test_collections.py 2022-09-01 00:22:06.233654 +0000
@@ -39,12 +39,17 @@
"device": torchtrt.Device("gpu:0"),
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model(self.input, self.input), trt_mod(self.input, self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model(self.input, self.input), trt_mod(self.input, self.input)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInput(unittest.TestCase):
def test_compile(self):
@@ -63,12 +68,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model((self.input, self.input)), trt_mod((self.input, self.input)))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model((self.input, self.input)), trt_mod((self.input, self.input))
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInput(unittest.TestCase):
def test_compile(self):
@@ -85,12 +95,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model([self.input, self.input]), trt_mod([self.input, self.input]))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model([self.input, self.input]), trt_mod([self.input, self.input])
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInputOutput(unittest.TestCase):
def test_compile(self):
@@ -113,11 +128,14 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputOutput(unittest.TestCase):
def test_compile(self):
@@ -141,11 +159,14 @@
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputTupleOutput(unittest.TestCase):
def test_compile(self):
@@ -168,10 +189,13 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_embed_engines.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/test_embed_engines.py 2022-09-01 00:22:06.236038 +0000
@@ -4,10 +4,11 @@
import torchvision.models as models
import copy
import timm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -24,17 +25,24 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -47,13 +55,19 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_module_fallback.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/test_module_fallback.py 2022-09-01 00:22:06.276775 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModuleFallback(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_modules": ["torchvision.models.resnet.BasicBlock"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -39,15 +43,20 @@
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
"enabled_precisions": {torch.float},
- "torch_executed_modules": ["torchvision.models.mobilenetv2.ConvBNActivation"],
+ "torch_executed_modules": [
+ "torchvision.models.mobilenetv2.ConvBNActivation"
+ ],
"min_block_size": 5,
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_operator_fallback.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/test_operator_fallback.py 2022-09-01 00:22:06.294315 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestFallbackModels(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::add"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -43,10 +47,13 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::hardtanh"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/utils.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/utils.py 2022-09-01 00:22:06.307217 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/hw/test_api_dla.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/hw/test_api_dla.py 2022-09-01 00:22:06.355138 +0000
@@ -39,11 +39,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
"device": {
@@ -55,11 +58,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/api/test_ts_backend.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/api/test_ts_backend.py 2022-09-01 00:22:06.380324 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestCompile(unittest.TestCase):
def test_compile_traced(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -25,11 +26,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -39,11 +43,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_global(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -52,11 +59,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_from_torch_tensor(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -69,11 +79,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -83,21 +96,28 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_default_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
compile_spec = {"inputs": [self.input], "enabled_precisions": {torch.float}}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
class TestCheckMethodOpSupport(unittest.TestCase):
def test_check_support(self):
module = models.alexnet(pretrained=True).eval().to("cuda")
self.module = torch.jit.trace(module, torch.ones((1, 3, 224, 224)).to("cuda"))
--- tests/py/integrations/test_to_backend_api.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/integrations/test_to_backend_api.py 2022-09-01 00:22:06.408752 +0000
@@ -1,10 +1,11 @@
import unittest
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestToBackendLowering(unittest.TestCase):
def setUp(self):
self.input = torch.randn((1, 3, 300, 300)).to("cuda")
self.model = models.resnet18(pretrained=True).eval().to("cuda")
@@ -30,11 +31,13 @@
}
def test_to_backend_lowering(self):
trt_mod = torch._C._jit_to_backend("tensorrt", self.scripted_model, self.spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/hw/test_multi_gpu.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/hw/test_multi_gpu.py 2022-09-01 00:22:06.437269 +0000
@@ -35,11 +35,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -54,12 +57,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestMultiGpuSerializeDeserializeSwitching(ModelTestCase):
def setUp(self):
if torch.cuda.device_count() < 2:
@@ -89,11 +94,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -108,11 +116,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:22:06.449372 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import tensorrt as trt
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestPyTorchToTRTEngine(unittest.TestCase):
def test_pt_to_trt(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda:0")
self.input = torch.randn((1, 3, 224, 224)).to("cuda:0")
@@ -41,10 +42,13 @@
stream_handle=torch.cuda.current_stream(
device="cuda:0"
).cuda_stream,
)
cos_sim = cosine_similarity(self.model(self.input), out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/models/test_models.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/models/test_models.py 2022-09-01 00:22:06.540177 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModels(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -26,11 +27,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -47,14 +51,19 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -68,20 +77,31 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_bert_base_uncased(self):
self.model = cm.BertModule().cuda()
self.input = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
compile_spec = {
"inputs": [
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format),
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format)
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
],
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
@@ -93,11 +113,14 @@
model_outputs = self.model(self.input, self.input)
trt_model_outputs = trt_mod(self.input, self.input)
for out, trt_out in zip(model_outputs, trt_model_outputs):
cos_sim = cosine_similarity(out, trt_out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_resnet50_half(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -115,10 +138,16 @@
},
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.compile(self.scripted_model, **compile_spec)
- cos_sim = cosine_similarity(self.model.half()(self.input.half()), trt_mod(self.input.half()))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model.half()(self.input.half()), trt_mod(self.input.half())
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/models/custom_models.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/models/custom_models.py 2022-09-01 00:22:06.547632 +0000
@@ -1,7 +1,8 @@
import torch
from transformers import BertModel, BertTokenizer, BertConfig
+
def BertModule():
model_name = "bert-base-uncased"
enc = BertTokenizer.from_pretrained(model_name)
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
--- tests/py/models/utils.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/models/utils.py 2022-09-01 00:22:06.560206 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:21:41.242376 +0000
+++ tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:22:06.574279 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_multiple_engines(self):
self.resnet18 = models.resnet18(pretrained=True).eval().to("cuda")
self.resnet50 = models.resnet50(pretrained=True).eval().to("cuda")
@@ -28,13 +29,24 @@
"enabled_precisions": {torch.float},
}
rn18_trt_mod = torchtrt.compile(self.resnet18, **compile_spec)
rn50_trt_mod = torchtrt.compile(self.resnet50, **compile_spec)
- cos_sim = cosine_similarity(self.resnet18(self.input1), rn18_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet18(self.input1), rn18_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
- cos_sim = cosine_similarity(self.resnet50(self.input1), rn50_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet50(self.input1), rn50_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp b/tmp/changes.txt
index 21670ac..430ce82 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp
+++ b/tmp/changes.txt
@@ -14,8 +14,8 @@ TEST_P(CppAPITests, ModuleAsEngineIsClose) {
jit_results.push_back(jit_results_ivalues.toTensor());
auto trt_results = torch_tensorrt::tests::util::RunModuleForwardAsEngine(mod, inputs);
- ASSERT_TRUE(
- torch_tensorrt::tests::util::cosineSimEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
+ ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+ jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
}
#ifndef DISABLE_TEST_IN_CI
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 3318aec..e3f0d91 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -192,7 +192,7 @@ TEST(CppAPITests, TestCollectionTupleInputOutput) {
auto trt_out = trt_mod.forward(complex_inputs);
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
- out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
+ out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor(), 1e-5));
}
diff --git a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp b/tmp/changes.txt
index 91004c0..8359d31 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp
+++ b/tmp/changes.txt
@@ -6,9 +6,9 @@ namespace torch_tensorrt {
namespace tests {
namespace util {
-bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f){
-
- torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
+bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f) {
+ torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(
+ computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
std::ostringstream ss;
ss << computed_tensor << std::endl << gt_tensor << std::endl;
LOG_GRAPH(ss.str());
ERROR: Some files do not conform to style guidelines
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp b/tmp/changes.txt
index 21670ac..430ce82 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp
+++ b/tmp/changes.txt
@@ -14,8 +14,8 @@ TEST_P(CppAPITests, ModuleAsEngineIsClose) {
jit_results.push_back(jit_results_ivalues.toTensor());
auto trt_results = torch_tensorrt::tests::util::RunModuleForwardAsEngine(mod, inputs);
- ASSERT_TRUE(
- torch_tensorrt::tests::util::cosineSimEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
+ ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+ jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
}
#ifndef DISABLE_TEST_IN_CI
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 3318aec..e3f0d91 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -192,7 +192,7 @@ TEST(CppAPITests, TestCollectionTupleInputOutput) {
auto trt_out = trt_mod.forward(complex_inputs);
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
- out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
+ out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor(), 1e-5));
}
diff --git a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp b/tmp/changes.txt
index 91004c0..8359d31 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp
+++ b/tmp/changes.txt
@@ -6,9 +6,9 @@ namespace torch_tensorrt {
namespace tests {
namespace util {
-bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f){
-
- torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
+bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f) {
+ torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(
+ computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
std::ostringstream ss;
ss << computed_tensor << std::endl << gt_tensor << std::endl;
LOG_GRAPH(ss.str());
ERROR: Some files do not conform to style guidelines
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp b/tmp/changes.txt
index 21670ac..430ce82 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp
+++ b/tmp/changes.txt
@@ -14,8 +14,8 @@ TEST_P(CppAPITests, ModuleAsEngineIsClose) {
jit_results.push_back(jit_results_ivalues.toTensor());
auto trt_results = torch_tensorrt::tests::util::RunModuleForwardAsEngine(mod, inputs);
- ASSERT_TRUE(
- torch_tensorrt::tests::util::cosineSimEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
+ ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+ jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
}
#ifndef DISABLE_TEST_IN_CI
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 3318aec..e3f0d91 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -192,7 +192,7 @@ TEST(CppAPITests, TestCollectionTupleInputOutput) {
auto trt_out = trt_mod.forward(complex_inputs);
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
- out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
+ out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor(), 1e-5));
}
diff --git a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp b/tmp/changes.txt
index 91004c0..8359d31 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp
+++ b/tmp/changes.txt
@@ -6,9 +6,9 @@ namespace torch_tensorrt {
namespace tests {
namespace util {
-bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f){
-
- torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
+bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f) {
+ torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(
+ computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
std::ostringstream ss;
ss << computed_tensor << std::endl << gt_tensor << std::endl;
LOG_GRAPH(ss.str());
ERROR: Some files do not conform to style guidelinesSigned-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
There was a problem hiding this comment.
There are some changes that do not conform to Python style guidelines:
--- noxfile.py 2022-09-01 00:21:46.784249 +0000
+++ noxfile.py 2022-09-01 00:22:23.009895 +0000
@@ -29,17 +29,18 @@
USE_HOST_DEPS = 0 if not "USE_HOST_DEPS" in os.environ else os.environ["USE_HOST_DEPS"]
if USE_HOST_DEPS:
print("Using dependencies from host python")
# Set epochs to train VGG model for accuracy tests
-EPOCHS=25
+EPOCHS = 25
SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
nox.options.sessions = [
"l0_api_tests-" + "{}.{}".format(sys.version_info.major, sys.version_info.minor)
]
+
def install_deps(session):
print("Installing deps")
session.install("-r", os.path.join(TOP_DIR, "py", "requirements.txt"))
session.install("-r", os.path.join(TOP_DIR, "tests", "py", "requirements.txt"))
@@ -108,11 +109,13 @@
"vgg16_ckpts",
"--epochs",
str(EPOCHS),
)
- session.run_always("python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth")
+ session.run_always(
+ "python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth"
+ )
def finetune_model(session):
# Install pytorch-quantization dependency
session.install(
@@ -133,19 +136,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
env={"PYTHONPATH": PYT_PATH},
)
# Export model
session.run_always(
"python",
"export_qat.py",
- "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
env={"PYTHONPATH": PYT_PATH},
)
else:
session.run_always(
"python",
@@ -159,15 +162,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
)
# Export model
- session.run_always("python", "export_qat.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth")
+ session.run_always(
+ "python",
+ "export_qat.py",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
+ )
def cleanup(session):
target = [
"examples/int8/training/vgg16/*.jit.pt",
@@ -193,10 +200,11 @@
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
session.run_always("pytest", test)
+
def run_model_tests(session):
print("Running model tests")
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"models",
@@ -256,11 +264,11 @@
print("Running TensorRT compatibility tests")
copy_model(session)
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"integrations/test_trt_intercompatibility.py",
- #"ptq/test_ptq_trt_calibrator.py",
+ # "ptq/test_ptq_trt_calibrator.py",
]
for test in tests:
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
@@ -308,17 +316,19 @@
install_torch_trt(session)
download_models(session)
run_base_tests(session)
cleanup(session)
+
def run_l1_model_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
download_models(session)
run_model_tests(session)
cleanup(session)
+
def run_l1_int8_accuracy_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
@@ -350,20 +360,23 @@
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_api_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l0_api_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_dla_tests(session):
"""When a developer needs to check basic api functionality using host dependencies"""
run_l0_dla_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_model_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l1_model_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_int8_accuracy_tests(session):
"""Checking accuracy performance on various usecases"""
run_l1_int8_accuracy_tests(session)
--- tests/py/api/test_e2e_behavior.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/test_e2e_behavior.py 2022-09-01 00:22:25.547298 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import copy
from typing import Dict
+
class TestInputTypeDefaultsFP32Model(unittest.TestCase):
def test_input_use_default_fp32(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
--- tests/py/api/test_embed_engines.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/test_embed_engines.py 2022-09-01 00:22:25.591593 +0000
@@ -4,10 +4,11 @@
import torchvision.models as models
import copy
import timm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -24,17 +25,24 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -47,13 +55,19 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_collections.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/test_collections.py 2022-09-01 00:22:25.598246 +0000
@@ -39,12 +39,17 @@
"device": torchtrt.Device("gpu:0"),
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model(self.input, self.input), trt_mod(self.input, self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model(self.input, self.input), trt_mod(self.input, self.input)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInput(unittest.TestCase):
def test_compile(self):
@@ -63,12 +68,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model((self.input, self.input)), trt_mod((self.input, self.input)))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model((self.input, self.input)), trt_mod((self.input, self.input))
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInput(unittest.TestCase):
def test_compile(self):
@@ -85,12 +95,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model([self.input, self.input]), trt_mod([self.input, self.input]))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model([self.input, self.input]), trt_mod([self.input, self.input])
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInputOutput(unittest.TestCase):
def test_compile(self):
@@ -113,11 +128,14 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputOutput(unittest.TestCase):
def test_compile(self):
@@ -141,11 +159,14 @@
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputTupleOutput(unittest.TestCase):
def test_compile(self):
@@ -168,10 +189,13 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_operator_fallback.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/test_operator_fallback.py 2022-09-01 00:22:25.643051 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestFallbackModels(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::add"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -43,10 +47,13 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::hardtanh"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_module_fallback.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/test_module_fallback.py 2022-09-01 00:22:25.643245 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModuleFallback(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_modules": ["torchvision.models.resnet.BasicBlock"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -39,15 +43,20 @@
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
"enabled_precisions": {torch.float},
- "torch_executed_modules": ["torchvision.models.mobilenetv2.ConvBNActivation"],
+ "torch_executed_modules": [
+ "torchvision.models.mobilenetv2.ConvBNActivation"
+ ],
"min_block_size": 5,
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/utils.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/utils.py 2022-09-01 00:22:25.661870 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/hw/test_api_dla.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/hw/test_api_dla.py 2022-09-01 00:22:25.709043 +0000
@@ -39,11 +39,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
"device": {
@@ -55,11 +58,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/api/test_ts_backend.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/api/test_ts_backend.py 2022-09-01 00:22:25.750559 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestCompile(unittest.TestCase):
def test_compile_traced(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -25,11 +26,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -39,11 +43,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_global(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -52,11 +59,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_from_torch_tensor(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -69,11 +79,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -83,21 +96,28 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_default_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
compile_spec = {"inputs": [self.input], "enabled_precisions": {torch.float}}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
class TestCheckMethodOpSupport(unittest.TestCase):
def test_check_support(self):
module = models.alexnet(pretrained=True).eval().to("cuda")
self.module = torch.jit.trace(module, torch.ones((1, 3, 224, 224)).to("cuda"))
--- tests/py/integrations/test_to_backend_api.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/integrations/test_to_backend_api.py 2022-09-01 00:22:25.783867 +0000
@@ -1,10 +1,11 @@
import unittest
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestToBackendLowering(unittest.TestCase):
def setUp(self):
self.input = torch.randn((1, 3, 300, 300)).to("cuda")
self.model = models.resnet18(pretrained=True).eval().to("cuda")
@@ -30,11 +31,13 @@
}
def test_to_backend_lowering(self):
trt_mod = torch._C._jit_to_backend("tensorrt", self.scripted_model, self.spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/hw/test_multi_gpu.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/hw/test_multi_gpu.py 2022-09-01 00:22:25.788870 +0000
@@ -35,11 +35,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -54,12 +57,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestMultiGpuSerializeDeserializeSwitching(ModelTestCase):
def setUp(self):
if torch.cuda.device_count() < 2:
@@ -89,11 +94,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -108,11 +116,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/models/custom_models.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/models/custom_models.py 2022-09-01 00:22:25.819554 +0000
@@ -1,7 +1,8 @@
import torch
from transformers import BertModel, BertTokenizer, BertConfig
+
def BertModule():
model_name = "bert-base-uncased"
enc = BertTokenizer.from_pretrained(model_name)
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
--- tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:22:25.877928 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import tensorrt as trt
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestPyTorchToTRTEngine(unittest.TestCase):
def test_pt_to_trt(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda:0")
self.input = torch.randn((1, 3, 224, 224)).to("cuda:0")
@@ -41,10 +42,13 @@
stream_handle=torch.cuda.current_stream(
device="cuda:0"
).cuda_stream,
)
cos_sim = cosine_similarity(self.model(self.input), out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/models/test_models.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/models/test_models.py 2022-09-01 00:22:25.909572 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModels(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -26,11 +27,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -47,14 +51,19 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -68,20 +77,31 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_bert_base_uncased(self):
self.model = cm.BertModule().cuda()
self.input = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
compile_spec = {
"inputs": [
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format),
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format)
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
],
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
@@ -93,11 +113,14 @@
model_outputs = self.model(self.input, self.input)
trt_model_outputs = trt_mod(self.input, self.input)
for out, trt_out in zip(model_outputs, trt_model_outputs):
cos_sim = cosine_similarity(out, trt_out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_resnet50_half(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -115,10 +138,16 @@
},
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.compile(self.scripted_model, **compile_spec)
- cos_sim = cosine_similarity(self.model.half()(self.input.half()), trt_mod(self.input.half()))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model.half()(self.input.half()), trt_mod(self.input.half())
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:22:25.910258 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_multiple_engines(self):
self.resnet18 = models.resnet18(pretrained=True).eval().to("cuda")
self.resnet50 = models.resnet50(pretrained=True).eval().to("cuda")
@@ -28,13 +29,24 @@
"enabled_precisions": {torch.float},
}
rn18_trt_mod = torchtrt.compile(self.resnet18, **compile_spec)
rn50_trt_mod = torchtrt.compile(self.resnet50, **compile_spec)
- cos_sim = cosine_similarity(self.resnet18(self.input1), rn18_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet18(self.input1), rn18_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
- cos_sim = cosine_similarity(self.resnet50(self.input1), rn50_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet50(self.input1), rn50_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/models/utils.py 2022-09-01 00:21:46.796249 +0000
+++ tests/py/models/utils.py 2022-09-01 00:22:25.932861 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp b/tmp/changes.txt
index 21670ac..430ce82 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_modules_as_engines.cpp
+++ b/tmp/changes.txt
@@ -14,8 +14,8 @@ TEST_P(CppAPITests, ModuleAsEngineIsClose) {
jit_results.push_back(jit_results_ivalues.toTensor());
auto trt_results = torch_tensorrt::tests::util::RunModuleForwardAsEngine(mod, inputs);
- ASSERT_TRUE(
- torch_tensorrt::tests::util::cosineSimEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
+ ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+ jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
}
#ifndef DISABLE_TEST_IN_CI
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp b/tmp/changes.txt
index 3318aec..e3f0d91 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_collections.cpp
+++ b/tmp/changes.txt
@@ -192,7 +192,7 @@ TEST(CppAPITests, TestCollectionTupleInputOutput) {
auto trt_out = trt_mod.forward(complex_inputs);
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
- out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
+ out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor(), 1e-5));
}
diff --git a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp b/tmp/changes.txt
index 91004c0..8359d31 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/util/util.cpp
+++ b/tmp/changes.txt
@@ -6,9 +6,9 @@ namespace torch_tensorrt {
namespace tests {
namespace util {
-bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f){
-
- torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
+bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f) {
+ torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(
+ computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
std::ostringstream ss;
ss << computed_tensor << std::endl << gt_tensor << std::endl;
LOG_GRAPH(ss.str());
ERROR: Some files do not conform to style guidelines
There was a problem hiding this comment.
There are some changes that do not conform to Python style guidelines:
--- noxfile.py 2022-09-01 00:22:49.981932 +0000
+++ noxfile.py 2022-09-01 00:23:19.392546 +0000
@@ -29,17 +29,18 @@
USE_HOST_DEPS = 0 if not "USE_HOST_DEPS" in os.environ else os.environ["USE_HOST_DEPS"]
if USE_HOST_DEPS:
print("Using dependencies from host python")
# Set epochs to train VGG model for accuracy tests
-EPOCHS=25
+EPOCHS = 25
SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
nox.options.sessions = [
"l0_api_tests-" + "{}.{}".format(sys.version_info.major, sys.version_info.minor)
]
+
def install_deps(session):
print("Installing deps")
session.install("-r", os.path.join(TOP_DIR, "py", "requirements.txt"))
session.install("-r", os.path.join(TOP_DIR, "tests", "py", "requirements.txt"))
@@ -108,11 +109,13 @@
"vgg16_ckpts",
"--epochs",
str(EPOCHS),
)
- session.run_always("python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth")
+ session.run_always(
+ "python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth"
+ )
def finetune_model(session):
# Install pytorch-quantization dependency
session.install(
@@ -133,19 +136,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
env={"PYTHONPATH": PYT_PATH},
)
# Export model
session.run_always(
"python",
"export_qat.py",
- "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
env={"PYTHONPATH": PYT_PATH},
)
else:
session.run_always(
"python",
@@ -159,15 +162,19 @@
"--ckpt-dir",
"vgg16_ckpts",
"--start-from",
str(EPOCHS),
"--epochs",
- str(EPOCHS+1),
+ str(EPOCHS + 1),
)
# Export model
- session.run_always("python", "export_qat.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS+1) + ".pth")
+ session.run_always(
+ "python",
+ "export_qat.py",
+ "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
+ )
def cleanup(session):
target = [
"examples/int8/training/vgg16/*.jit.pt",
@@ -193,10 +200,11 @@
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
session.run_always("pytest", test)
+
def run_model_tests(session):
print("Running model tests")
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"models",
@@ -256,11 +264,11 @@
print("Running TensorRT compatibility tests")
copy_model(session)
session.chdir(os.path.join(TOP_DIR, "tests/py"))
tests = [
"integrations/test_trt_intercompatibility.py",
- #"ptq/test_ptq_trt_calibrator.py",
+ # "ptq/test_ptq_trt_calibrator.py",
]
for test in tests:
if USE_HOST_DEPS:
session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
else:
@@ -308,17 +316,19 @@
install_torch_trt(session)
download_models(session)
run_base_tests(session)
cleanup(session)
+
def run_l1_model_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
download_models(session)
run_model_tests(session)
cleanup(session)
+
def run_l1_int8_accuracy_tests(session):
if not USE_HOST_DEPS:
install_deps(session)
install_torch_trt(session)
@@ -350,20 +360,23 @@
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_api_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l0_api_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l0_dla_tests(session):
"""When a developer needs to check basic api functionality using host dependencies"""
run_l0_dla_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_model_tests(session):
"""When a developer needs to check correctness for a PR or something"""
run_l1_model_tests(session)
+
@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
def l1_int8_accuracy_tests(session):
"""Checking accuracy performance on various usecases"""
run_l1_int8_accuracy_tests(session)
--- tests/py/api/test_e2e_behavior.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/test_e2e_behavior.py 2022-09-01 00:23:23.124313 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import copy
from typing import Dict
+
class TestInputTypeDefaultsFP32Model(unittest.TestCase):
def test_input_use_default_fp32(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
--- tests/py/api/test_collections.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/test_collections.py 2022-09-01 00:23:23.139462 +0000
@@ -39,12 +39,17 @@
"device": torchtrt.Device("gpu:0"),
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model(self.input, self.input), trt_mod(self.input, self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model(self.input, self.input), trt_mod(self.input, self.input)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInput(unittest.TestCase):
def test_compile(self):
@@ -63,12 +68,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model((self.input, self.input)), trt_mod((self.input, self.input)))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model((self.input, self.input)), trt_mod((self.input, self.input))
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInput(unittest.TestCase):
def test_compile(self):
@@ -85,12 +95,17 @@
"enabled_precisions": {torch.float},
"min_block_size": 1,
}
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
- cos_sim = cosine_similarity(self.model([self.input, self.input]), trt_mod([self.input, self.input]))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model([self.input, self.input]), trt_mod([self.input, self.input])
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestTupleInputOutput(unittest.TestCase):
def test_compile(self):
@@ -113,11 +128,14 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputOutput(unittest.TestCase):
def test_compile(self):
@@ -141,11 +159,14 @@
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestListInputTupleOutput(unittest.TestCase):
def test_compile(self):
@@ -168,10 +189,13 @@
trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
trt_out = trt_mod((self.input, self.input))
pyt_out = self.model((self.input, self.input))
for (t, p) in zip(trt_out, pyt_out):
cos_sim = cosine_similarity(t, p)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_embed_engines.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/test_embed_engines.py 2022-09-01 00:23:23.183675 +0000
@@ -4,10 +4,11 @@
import torchvision.models as models
import copy
import timm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -24,17 +25,24 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -47,13 +55,19 @@
},
"enabled_precisions": {torch.float},
}
self.scripted_model = torch.jit.script(self.model)
- trt_engine = torchtrt.ts.convert_method_to_trt_engine(self.scripted_model, "forward", **compile_spec)
+ trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+ self.scripted_model, "forward", **compile_spec
+ )
trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_module_fallback.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/test_module_fallback.py 2022-09-01 00:23:23.213252 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModuleFallback(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_modules": ["torchvision.models.resnet.BasicBlock"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -39,15 +43,20 @@
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
"enabled_precisions": {torch.float},
- "torch_executed_modules": ["torchvision.models.mobilenetv2.ConvBNActivation"],
+ "torch_executed_modules": [
+ "torchvision.models.mobilenetv2.ConvBNActivation"
+ ],
"min_block_size": 5,
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/test_operator_fallback.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/test_operator_fallback.py 2022-09-01 00:23:23.238160 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestFallbackModels(unittest.TestCase):
def test_fallback_resnet18(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -23,11 +24,14 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::add"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_fallback_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
@@ -43,10 +47,13 @@
"enabled_precisions": {torch.float},
"torch_executed_ops": ["aten::hardtanh"],
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/api/utils.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/utils.py 2022-09-01 00:23:23.272887 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/hw/test_api_dla.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/hw/test_api_dla.py 2022-09-01 00:23:23.343014 +0000
@@ -39,11 +39,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
"device": {
@@ -55,11 +58,14 @@
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/api/test_ts_backend.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/api/test_ts_backend.py 2022-09-01 00:23:23.368619 +0000
@@ -3,10 +3,11 @@
import torch
import torchvision.models as models
import copy
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestCompile(unittest.TestCase):
def test_compile_traced(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -25,11 +26,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -39,11 +43,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_global(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -52,11 +59,14 @@
inputs=[self.input],
device=torchtrt.Device(gpu_id=0),
enabled_precisions={torch.float},
)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_from_torch_tensor(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -69,11 +79,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
@@ -83,21 +96,28 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_default_device(self):
self.model = models.vgg16(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.traced_model = torch.jit.trace(self.model, [self.input])
compile_spec = {"inputs": [self.input], "enabled_precisions": {torch.float}}
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
class TestCheckMethodOpSupport(unittest.TestCase):
def test_check_support(self):
module = models.alexnet(pretrained=True).eval().to("cuda")
self.module = torch.jit.trace(module, torch.ones((1, 3, 224, 224)).to("cuda"))
--- tests/py/hw/utils.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/hw/utils.py 2022-09-01 00:23:23.395957 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/integrations/test_to_backend_api.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/integrations/test_to_backend_api.py 2022-09-01 00:23:23.431678 +0000
@@ -1,10 +1,11 @@
import unittest
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestToBackendLowering(unittest.TestCase):
def setUp(self):
self.input = torch.randn((1, 3, 300, 300)).to("cuda")
self.model = models.resnet18(pretrained=True).eval().to("cuda")
@@ -30,11 +31,13 @@
}
def test_to_backend_lowering(self):
trt_mod = torch._C._jit_to_backend("tensorrt", self.scripted_model, self.spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/hw/test_multi_gpu.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/hw/test_multi_gpu.py 2022-09-01 00:23:23.465978 +0000
@@ -35,11 +35,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -54,12 +57,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
torchtrt.set_device(self.target_gpu)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
torchtrt.set_device(0)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
-
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
class TestMultiGpuSerializeDeserializeSwitching(ModelTestCase):
def setUp(self):
if torch.cuda.device_count() < 2:
@@ -89,11 +94,14 @@
trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_compile_script(self):
torchtrt.set_device(0)
compile_spec = {
"inputs": [torchtrt.Input(self.input.shape)],
@@ -108,11 +116,14 @@
trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
# Changing the device ID deliberately. It should still run on correct device ID by context switching
torchtrt.set_device(1)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_suite():
suite = unittest.TestSuite()
suite.addTest(
--- tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/integrations/test_trt_intercompatibility.py 2022-09-01 00:23:23.481055 +0000
@@ -2,10 +2,11 @@
import torch_tensorrt as torchtrt
import torch
import torchvision.models as models
import tensorrt as trt
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestPyTorchToTRTEngine(unittest.TestCase):
def test_pt_to_trt(self):
self.model = models.resnet18(pretrained=True).eval().to("cuda:0")
self.input = torch.randn((1, 3, 224, 224)).to("cuda:0")
@@ -41,10 +42,13 @@
stream_handle=torch.cuda.current_stream(
device="cuda:0"
).cuda_stream,
)
cos_sim = cosine_similarity(self.model(self.input), out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
if __name__ == "__main__":
unittest.main()
--- tests/py/integrations/utils.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/integrations/utils.py 2022-09-01 00:23:23.486728 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/models/custom_models.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/models/custom_models.py 2022-09-01 00:23:23.515755 +0000
@@ -1,7 +1,8 @@
import torch
from transformers import BertModel, BertTokenizer, BertConfig
+
def BertModule():
model_name = "bert-base-uncased"
enc = BertTokenizer.from_pretrained(model_name)
text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
--- tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/models/test_multiple_registered_engines.py 2022-09-01 00:23:23.561105 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModelToEngineToModel(unittest.TestCase):
def test_multiple_engines(self):
self.resnet18 = models.resnet18(pretrained=True).eval().to("cuda")
self.resnet50 = models.resnet50(pretrained=True).eval().to("cuda")
@@ -28,13 +29,24 @@
"enabled_precisions": {torch.float},
}
rn18_trt_mod = torchtrt.compile(self.resnet18, **compile_spec)
rn50_trt_mod = torchtrt.compile(self.resnet50, **compile_spec)
- cos_sim = cosine_similarity(self.resnet18(self.input1), rn18_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet18(self.input1), rn18_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
- cos_sim = cosine_similarity(self.resnet50(self.input1), rn50_trt_mod(self.input1))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.resnet50(self.input1), rn50_trt_mod(self.input1)
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/models/utils.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/models/utils.py 2022-09-01 00:23:23.582299 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
--- tests/py/models/test_models.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/models/test_models.py 2022-09-01 00:23:23.653895 +0000
@@ -5,10 +5,11 @@
import copy
import timm
import custom_models as cm
from typing import Dict
from utils import cosine_similarity, COSINE_THRESHOLD
+
class TestModels(unittest.TestCase):
def test_resnet50(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -26,11 +27,14 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_mobilenet_v2(self):
self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
@@ -47,14 +51,19 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_efficientnet_b0(self):
- self.model = timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ self.model = (
+ timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+ )
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
"inputs": [
torchtrt.Input(
@@ -68,20 +77,31 @@
"enabled_precisions": {torch.float},
}
trt_mod = torchtrt.compile(self.model, **compile_spec)
cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_bert_base_uncased(self):
self.model = cm.BertModule().cuda()
self.input = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
compile_spec = {
"inputs": [
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format),
- torchtrt.Input(self.input.shape, dtype=self.input.dtype, format=torch.contiguous_format)
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
+ torchtrt.Input(
+ self.input.shape,
+ dtype=self.input.dtype,
+ format=torch.contiguous_format,
+ ),
],
"device": {
"device_type": torchtrt.DeviceType.GPU,
"gpu_id": 0,
},
@@ -93,11 +113,14 @@
model_outputs = self.model(self.input, self.input)
trt_model_outputs = trt_mod(self.input, self.input)
for out, trt_out in zip(model_outputs, trt_model_outputs):
cos_sim = cosine_similarity(out, trt_out)
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
def test_resnet50_half(self):
self.model = models.resnet50(pretrained=True).eval().to("cuda")
self.input = torch.randn((1, 3, 224, 224)).to("cuda")
self.scripted_model = torch.jit.script(self.model)
@@ -115,10 +138,16 @@
},
"enabled_precisions": {torch.half},
}
trt_mod = torchtrt.compile(self.scripted_model, **compile_spec)
- cos_sim = cosine_similarity(self.model.half()(self.input.half()), trt_mod(self.input.half()))
- self.assertTrue(cos_sim > COSINE_THRESHOLD, msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}")
+ cos_sim = cosine_similarity(
+ self.model.half()(self.input.half()), trt_mod(self.input.half())
+ )
+ self.assertTrue(
+ cos_sim > COSINE_THRESHOLD,
+ msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+ )
+
if __name__ == "__main__":
unittest.main()
--- tests/py/utils.py 2022-09-01 00:22:50.001933 +0000
+++ tests/py/utils.py 2022-09-01 00:23:23.709930 +0000
@@ -1,8 +1,9 @@
import torch
-COSINE_THRESHOLD=0.99
+COSINE_THRESHOLD = 0.99
+
def cosine_similarity(gt_tensor, pred_tensor):
gt_tensor = gt_tensor.flatten().to(torch.float32)
pred_tensor = pred_tensor.flatten().to(torch.float32)
if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
narendasan
reviewed
Sep 1, 2022
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
andi4191
suggested changes
Sep 1, 2022
| @@ -63,31 +65,6 @@ def install_torch_trt(session): | |||
| session.run("python", "setup.py", "develop") | |||
|
|
|||
|
|
|||
| def download_datasets(session): | |||
There was a problem hiding this comment.
@peri044: Why are we getting rid of download_datasets?
There was a problem hiding this comment.
This wasn't being used. The torch.datasets.CIFAR10 https://github.com/pytorch/TensorRT/blob/master/examples/int8/training/vgg16/main.py#L89-L103 is downloading the data automatically and the tests are basically downloading the data twice before.
noxfile.py
Outdated
| "test_trt_intercompatibility.py", | ||
| "test_ptq_trt_calibrator.py", | ||
| "integrations/test_trt_intercompatibility.py", | ||
| #"ptq/test_ptq_trt_calibrator.py", |
There was a problem hiding this comment.
Is this intentional change?
There was a problem hiding this comment.
Yeah we face the pybind issue with ptq_trt_calibrator. This issue has been there for a while now. However, this passes in DLFW CI. We need to address this in the future.
| # TODO: Remove deep copy once collections does not need partial compilation | ||
| compile_spec = deepcopy(compile_spec_) | ||
| # TODO: Use deepcopy to support partial compilation of collections | ||
| compile_spec = compile_spec_ |
There was a problem hiding this comment.
Is this from upstreaming change?
Let's keep this change as a separate PR
There was a problem hiding this comment.
This is needed for 1.2 to work. By not using deepcopy, I didn't see any issues with input_signature. However, with deepcopy, dataloadercalibrator object can't be pickled was the issue.
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@peri044 this is a result of not deepcopying https://app.circleci.com/pipelines/github/pytorch/TensorRT/882/workflows/942a4e63-8aeb-44da-a93b-3c2a18cc08f7/jobs/3848/tests
There was a problem hiding this comment.
We can add deepcopy support for PTQ here: https://pybind11.readthedocs.io/en/stable/advanced/classes.html#deepcopy-support
| @@ -301,7 +301,7 @@ def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec: | |||
| compile_spec["enabled_precisions"] | |||
| ) | |||
|
|
|||
| if "calibrator" in compile_spec: | |||
| if "calibrator" in compile_spec and compile_spec["calibrator"]: | |||
There was a problem hiding this comment.
This is needed for 1.2 to work
tests/cpp/BUILD
Outdated
| @@ -110,21 +89,6 @@ cc_test( | |||
| ], | |||
| ) | |||
|
|
|||
| cc_test( | |||
There was a problem hiding this comment.
Any particular reason, we are getting rid of this?
There was a problem hiding this comment.
I moved this test to python to compare with pytorch nn.Module directly using cosine sim metric
|
@peri044: Let's separate the upstreaming changes as a separate PR. |
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
There was a problem hiding this comment.
There are some changes that do not conform to C++ style guidelines:
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_compiled_modules.cpp b/tmp/changes.txt
index e1e923b..3a81f0a 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_compiled_modules.cpp
+++ b/tmp/changes.txt
@@ -41,7 +41,8 @@ TEST_P(CppAPITests, CompiledModuleIsClose) {
}
for (size_t i = 0; i < trt_results.size(); i++) {
- ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit_results[i], trt_results[i].reshape_as(jit_results[i]), 0.99));
+ ASSERT_TRUE(
+ torch_tensorrt::tests::util::cosineSimEqual(jit_results[i], trt_results[i].reshape_as(jit_results[i]), 0.99));
}
}
diff --git a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_multiple_registered_engines.cpp b/tmp/changes.txt
index 16ae4c8..658f59c 100644
--- a/home/runner/work/TensorRT/TensorRT/tests/cpp/test_multiple_registered_engines.cpp
+++ b/tmp/changes.txt
@@ -56,11 +56,13 @@ TEST(CppAPITest, CanRunMultipleEngines) {
trt2_results.push_back(trt2_results_ivalues.toTensor());
for (size_t i = 0; i < trt1_results.size(); i++) {
- ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit1_results[i], trt1_results[i].reshape_as(jit1_results[i]), 0.99));
+ ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+ jit1_results[i], trt1_results[i].reshape_as(jit1_results[i]), 0.99));
}
for (size_t i = 0; i < trt2_results.size(); i++) {
- ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit2_results[i], trt2_results[i].reshape_as(jit2_results[i]), 0.99));
+ ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+ jit2_results[i], trt2_results[i].reshape_as(jit2_results[i]), 0.99));
}
}
#endif
ERROR: Some files do not conform to style guidelinesSigned-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
There was a problem hiding this comment.
There are some changes that do not conform to Python style guidelines:
--- py/torch_tensorrt/ptq.py 2022-09-07 01:08:11.352285 +0000
+++ py/torch_tensorrt/ptq.py 2022-09-07 01:08:38.513738 +0000
@@ -53,15 +53,17 @@
with open(self.cache_file, "wb") as f:
f.write(cache)
else:
return b""
+
# deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.
# We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.
# This should be the object's local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__
def __reduce__(self):
return self.__class__.__name__
+
class DataLoaderCalibrator(object):
"""
Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
data which is passed during calibration.
@@ -117,21 +119,23 @@
"use_cache": use_cache,
"get_batch_size": get_batch_size,
"get_batch": get_cache_mode_batch if use_cache else get_batch,
"read_calibration_cache": read_calibration_cache,
"write_calibration_cache": write_calibration_cache,
- "__reduce__": __reduce__ # used when you deepcopy the DataLoaderCalibrator object
+ "__reduce__": __reduce__, # used when you deepcopy the DataLoaderCalibrator object
}
# Using type metaclass to construct calibrator class based on algorithm type
if algo_type == CalibrationAlgo.ENTROPY_CALIBRATION:
return type(
"Int8EntropyCalibrator", (_C.IInt8EntropyCalibrator,), attribute_mapping
)()
elif algo_type == CalibrationAlgo.ENTROPY_CALIBRATION_2:
return type(
- "Int8EntropyCalibrator2", (_C.IInt8EntropyCalibrator2,), attribute_mapping
+ "Int8EntropyCalibrator2",
+ (_C.IInt8EntropyCalibrator2,),
+ attribute_mapping,
)()
elif algo_type == CalibrationAlgo.LEGACY_CALIBRATION:
return type(
"Int8LegacyCalibrator", (_C.IInt8LegacyCalibrator,), attribute_mapping
)()Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
narendasan
reviewed
Sep 7, 2022
| "test_trt_intercompatibility.py", | ||
| "test_ptq_trt_calibrator.py", | ||
| "integrations/test_trt_intercompatibility.py", | ||
| # "ptq/test_ptq_trt_calibrator.py", |
There was a problem hiding this comment.
why is this test disabled?
There was a problem hiding this comment.
This is the test with pybind issue that has been there for a while. This used to pass in NGC containers though.
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