test: Add a test for the global compile function #700
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Signed-off-by: Naren Dasan <[email protected]> Signed-off-by: Naren Dasan <[email protected]>
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There are some changes that do not conform to Python style guidelines:
--- /workspace/tests/py/test_ptq_to_backend.py (original)
+++ /workspace/tests/py/test_ptq_to_backend.py (reformatted)
@@ -26,25 +26,27 @@
batch_size=1,
shuffle=False,
num_workers=1)
- self.calibrator = torchtrt.ptq.DataLoaderCalibrator(self.testing_dataloader,
- cache_file='./calibration.cache',
- use_cache=False,
- algo_type=torchtrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
- device=torch.device('cuda:0'))
+ self.calibrator = torchtrt.ptq.DataLoaderCalibrator(
+ self.testing_dataloader,
+ cache_file='./calibration.cache',
+ use_cache=False,
+ algo_type=torchtrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
+ device=torch.device('cuda:0'))
self.spec = {
"forward":
- torchtrt.ts.TensorRTCompileSpec(**{
- "inputs": [torchtrt.Input([1, 3, 32, 32])],
- "enabled_precisions": {torch.float, torch.half, torch.int8},
- "calibrator": self.calibrator,
- "device": {
- "device_type": torchtrt.DeviceType.GPU,
- "gpu_id": 0,
- "dla_core": 0,
- "allow_gpu_fallback": False,
- }
- })
+ torchtrt.ts.TensorRTCompileSpec(
+ **{
+ "inputs": [torchtrt.Input([1, 3, 32, 32])],
+ "enabled_precisions": {torch.float, torch.half, torch.int8},
+ "calibrator": self.calibrator,
+ "device": {
+ "device_type": torchtrt.DeviceType.GPU,
+ "gpu_id": 0,
+ "dla_core": 0,
+ "allow_gpu_fallback": False,
+ }
+ })
}
def compute_accuracy(self, testing_dataloader, model):
--- /workspace/tests/py/test_api.py (original)
+++ /workspace/tests/py/test_api.py (reformatted)
@@ -31,26 +31,25 @@
def test_compile_script(self):
trt_mod = torchtrt.ts.compile(self.scripted_model,
- inputs=[self.input],
- device=torchtrt.Device(gpu_id=0),
- enabled_precisions={torch.float})
+ inputs=[self.input],
+ device=torchtrt.Device(gpu_id=0),
+ enabled_precisions={torch.float})
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
self.assertTrue(same < 2e-2)
-
def test_compile_global(self):
trt_mod = torchtrt.compile(self.scripted_model,
- inputs=[self.input],
- device=torchtrt.Device(gpu_id=0),
- enabled_precisions={torch.float})
+ inputs=[self.input],
+ device=torchtrt.Device(gpu_id=0),
+ enabled_precisions={torch.float})
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
self.assertTrue(same < 2e-2)
def test_compile_global_nn_mod(self):
trt_mod = torchtrt.compile(self.model,
- inputs=[self.input],
- device=torchtrt.Device(gpu_id=0),
- enabled_precisions={torch.float})
+ inputs=[self.input],
+ device=torchtrt.Device(gpu_id=0),
+ enabled_precisions={torch.float})
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
self.assertTrue(same < 2e-2)
@@ -224,16 +223,16 @@
def test_input_use_default_fp32(self):
ts_model = torch.jit.script(self.model)
trt_mod = torchtrt.ts.compile(ts_model,
- inputs=[torchtrt.Input(self.input.shape)],
- enabled_precisions={torch.float, torch.half})
+ inputs=[torchtrt.Input(self.input.shape)],
+ enabled_precisions={torch.float, torch.half})
trt_mod(self.input)
def test_input_respect_user_setting_fp32_weights_fp16_in(self):
ts_model = torch.jit.script(self.model)
trt_mod = torchtrt.ts.compile(ts_model,
- inputs=[self.input.half()],
- require_full_compilation=True,
- enabled_precisions={torch.float, torch.half})
+ inputs=[self.input.half()],
+ require_full_compilation=True,
+ enabled_precisions={torch.float, torch.half})
trt_mod(self.input.half())
def test_input_respect_user_setting_fp32_weights_fp16_in_non_constructor(self):
@@ -242,9 +241,9 @@
input_spec.dtype = torch.half
trt_mod = torchtrt.ts.compile(ts_model,
- inputs=[input_spec],
- require_full_compilation=True,
- enabled_precisions={torch.float, torch.half})
+ inputs=[input_spec],
+ require_full_compilation=True,
+ enabled_precisions={torch.float, torch.half})
trt_mod(self.input.half())
@@ -258,8 +257,8 @@
half_mod.half()
trt_mod = torchtrt.ts.compile(half_mod,
- inputs=[torchtrt.Input(self.input.shape)],
- enabled_precisions={torch.float, torch.half})
+ inputs=[torchtrt.Input(self.input.shape)],
+ enabled_precisions={torch.float, torch.half})
trt_mod(self.input.half())
def test_input_use_default_fp16_without_fp16_enabled(self):
@@ -274,9 +273,9 @@
half_mod.half()
trt_mod = torchtrt.ts.compile(half_mod,
- inputs=[self.input],
- require_full_compilation=True,
- enabled_precisions={torch.float, torch.half})
+ inputs=[self.input],
+ require_full_compilation=True,
+ enabled_precisions={torch.float, torch.half})
trt_mod(self.input)
def test_input_respect_user_setting_fp16_weights_fp32_in_non_constuctor(self):
@@ -287,9 +286,9 @@
input_spec.dtype = torch.float
trt_mod = torchtrt.ts.compile(half_mod,
- inputs=[input_spec],
- require_full_compilation=True,
- enabled_precisions={torch.float, torch.half})
+ inputs=[input_spec],
+ require_full_compilation=True,
+ enabled_precisions={torch.float, torch.half})
trt_mod(self.input)
@@ -369,14 +368,15 @@
self.assertEqual(device.device_type, torchtrt.DeviceType.GPU)
self.assertEqual(device.gpu_id, 0)
+
class TestInput(unittest.TestCase):
def _verify_correctness(self, struct: torchtrt.Input, target: Dict) -> bool:
internal = struct._to_internal()
- list_eq = lambda al, bl: all([a == b for (a, b) in zip (al, bl)])
-
- eq = lambda a, b : a == b
+ list_eq = lambda al, bl: all([a == b for (a, b) in zip(al, bl)])
+
+ eq = lambda a, b: a == b
def field_is_correct(field, equal_fn, a1, a2):
equal = equal_fn(a1, a2)
@@ -388,12 +388,12 @@
opt_ = field_is_correct("opt", list_eq, internal.opt, target["opt"])
max_ = field_is_correct("max", list_eq, internal.max, target["max"])
is_dynamic_ = field_is_correct("is_dynamic", eq, internal.input_is_dynamic, target["input_is_dynamic"])
- explicit_set_dtype_ = field_is_correct("explicit_dtype", eq, internal._explicit_set_dtype, target["explicit_set_dtype"])
+ explicit_set_dtype_ = field_is_correct("explicit_dtype", eq, internal._explicit_set_dtype,
+ target["explicit_set_dtype"])
dtype_ = field_is_correct("dtype", eq, int(internal.dtype), int(target["dtype"]))
format_ = field_is_correct("format", eq, int(internal.format), int(target["format"]))
- return all([min_,opt_,max_,is_dynamic_,explicit_set_dtype_,dtype_,format_])
-
+ return all([min_, opt_, max_, is_dynamic_, explicit_set_dtype_, dtype_, format_])
def test_infer_from_example_tensor(self):
shape = [1, 3, 255, 255]
@@ -410,7 +410,6 @@
example_tensor = torch.randn(shape).half()
i = torchtrt.Input._from_tensor(example_tensor)
self.assertTrue(self._verify_correctness(i, target))
-
def test_static_shape(self):
shape = [1, 3, 255, 255]
@@ -499,8 +498,11 @@
self.assertTrue(self._verify_correctness(i, target))
tensor_shape = lambda shape: torch.randn(shape).shape
- i = torchtrt.Input(min_shape=tensor_shape(min_shape), opt_shape=tensor_shape(opt_shape), max_shape=tensor_shape(max_shape))
- self.assertTrue(self._verify_correctness(i, target))
+ i = torchtrt.Input(min_shape=tensor_shape(min_shape),
+ opt_shape=tensor_shape(opt_shape),
+ max_shape=tensor_shape(max_shape))
+ self.assertTrue(self._verify_correctness(i, target))
+
def test_suite():
suite = unittest.TestSuite()
--- /workspace/tests/py/test_to_backend_api.py (original)
+++ /workspace/tests/py/test_to_backend_api.py (reformatted)
@@ -13,24 +13,25 @@
self.scripted_model = torch.jit.script(self.model)
self.spec = {
"forward":
- torchtrt.ts.TensorRTCompileSpec(**{
- "inputs": [torchtrt.Input([1, 3, 300, 300])],
- "enabled_precisions": {torch.float},
- "refit": False,
- "debug": False,
- "strict_types": False,
- "device": {
- "device_type": torchtrt.DeviceType.GPU,
- "gpu_id": 0,
- "dla_core": 0,
- "allow_gpu_fallback": True
- },
- "capability": torchtrt.EngineCapability.default,
- "num_min_timing_iters": 2,
- "num_avg_timing_iters": 1,
- "max_batch_size": 0,
- "disable_tf32": False,
- })
+ torchtrt.ts.TensorRTCompileSpec(
+ **{
+ "inputs": [torchtrt.Input([1, 3, 300, 300])],
+ "enabled_precisions": {torch.float},
+ "refit": False,
+ "debug": False,
+ "strict_types": False,
+ "device": {
+ "device_type": torchtrt.DeviceType.GPU,
+ "gpu_id": 0,
+ "dla_core": 0,
+ "allow_gpu_fallback": True
+ },
+ "capability": torchtrt.EngineCapability.default,
+ "num_min_timing_iters": 2,
+ "num_avg_timing_iters": 1,
+ "max_batch_size": 0,
+ "disable_tf32": False,
+ })
}
def test_to_backend_lowering(self):
Reformatting /workspace/tests/modules/hub.py
Reformatting /workspace/tests/py/test_api_dla.py
Reformatting /workspace/tests/py/test_multi_gpu.py
Reformatting /workspace/tests/py/test_qat_trt_accuracy.py
Reformatting /workspace/tests/py/test_to_backend_api.py
--- /workspace/tests/py/test_ptq_dataloader_calibrator.py (original)
+++ /workspace/tests/py/test_ptq_dataloader_calibrator.py (reformatted)
@@ -26,11 +26,12 @@
batch_size=1,
shuffle=False,
num_workers=1)
- self.calibrator = torchtrt.ptq.DataLoaderCalibrator(self.testing_dataloader,
- cache_file='./calibration.cache',
- use_cache=False,
- algo_type=torchtrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
- device=torch.device('cuda:0'))
+ self.calibrator = torchtrt.ptq.DataLoaderCalibrator(
+ self.testing_dataloader,
+ cache_file='./calibration.cache',
+ use_cache=False,
+ algo_type=torchtrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
+ device=torch.device('cuda:0'))
def compute_accuracy(self, testing_dataloader, model):
total = 0
Reformatting /workspace/tests/py/test_trt_intercompatability.py
Reformatting /workspace/tests/py/test_ptq_trt_calibrator.py
Reformatting /workspace/tests/py/test_ptq_to_backend.py
Reformatting /workspace/tests/py/test_api.py
Reformatting /workspace/tests/py/model_test_case.py
Reformatting /workspace/tests/py/test_ptq_dataloader_calibrator.py
ERROR: Some files do not conform to style guidelinesSigned-off-by: Naren Dasan <[email protected]> Signed-off-by: Naren Dasan <[email protected]>
… into test_global_compile
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Description
Add an additional test for the top level compilation api
Type of change
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