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fix: Add support for truncate_long_and_double in FX #1865

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fix: Add support for truncate_long_and_double in FX #1865

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5382916
fix: Add support for `torch.int64` inputs in FXTRT
gs-olive Apr 27, 2023
6e53805
fix: Add support for `truncate_long_and_double` in FX
gs-olive Apr 28, 2023
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6 changes: 5 additions & 1 deletion py/torch_tensorrt/fx/fx2trt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,7 @@ def __init__(
explicit_batch_dimension: bool = False,
explicit_precision: bool = False,
logger_level=None,
truncate_long_and_double=False,
):
super().__init__(module)

Expand Down Expand Up @@ -70,6 +71,7 @@ def __init__(

self.optimization_profiles: Optional[List] = None
self.input_specs = input_specs
self.truncate_long_and_double = truncate_long_and_double
self.input_specs_iter = 0
self.validate_input_specs()
self._cur_node_name: Optional[str] = None
Expand Down Expand Up @@ -306,7 +308,9 @@ def placeholder(self, target, args, kwargs):
self.optimization_profiles[i].set_shape(target, *shape_range)

return self.network.add_input(
name=target, shape=tuple(shape), dtype=torch_dtype_to_trt(dtype)
name=target,
shape=tuple(shape),
dtype=torch_dtype_to_trt(dtype, self.truncate_long_and_double),
)

def call_module(self, target, args, kwargs):
Expand Down
4 changes: 4 additions & 0 deletions py/torch_tensorrt/fx/lower.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@ def compile(
use_experimental_fx_rt=False,
correctness_atol=1e-1,
correctness_rtol=1e-1,
truncate_long_and_double=False,
) -> nn.Module:
"""
Takes in original module, input and lowering setting, run lowering workflow to turn module
Expand All @@ -62,6 +63,7 @@ def compile(
cuda_graph_batch_size: Cuda graph batch size, default to be -1.
dynamic_batch: batch dimension (dim=0) is dynamic.
use_experimental_fx_rt: Uses the next generation TRTModule which supports both Python and TorchScript based execution (including in C++).
truncate_long_and_double: Whether to automatically truncate long and double-type tensor inputs to TRT Engines
Returns:
A torch.nn.Module lowered by TensorRT.
"""
Expand All @@ -85,6 +87,7 @@ def compile(
use_experimental_rt=use_experimental_fx_rt,
correctness_atol=correctness_atol,
correctness_rtol=correctness_rtol,
truncate_long_and_double=truncate_long_and_double,
)
lowerer = Lowerer.create(lower_setting=lower_setting)
return lowerer(module, input)
Expand Down Expand Up @@ -129,6 +132,7 @@ def __call__(self, mod, input, split_name) -> TRTInterpreterResult:
logger_level=trt.Logger.VERBOSE
if self.lower_setting.verbose_log
else trt.Logger.WARNING,
truncate_long_and_double=self.lower_setting.truncate_long_and_double,
)

interp_result: TRTInterpreterResult = interpreter.run(
Expand Down
1 change: 1 addition & 0 deletions py/torch_tensorrt/fx/lower_setting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,3 +101,4 @@ class LowerSetting(LowerSettingBasic):
correctness_atol: float = 0.1
correctness_rtol: float = 0.1
use_experimental_rt: bool = False
truncate_long_and_double: bool = False
287 changes: 202 additions & 85 deletions py/torch_tensorrt/fx/test/core/test_trt_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,8 +10,8 @@
from torch.testing._internal.common_utils import run_tests, TestCase
from torch_tensorrt.fx import InputTensorSpec, TRTInterpreter, TRTModule

# from torch_tensorrt import TRTModuleNext
# from torch_tensorrt import Device
from torch_tensorrt import TRTModuleNext
from torch_tensorrt import Device
from torch_tensorrt.fx.utils import LowerPrecision


Expand Down Expand Up @@ -58,89 +58,206 @@ def forward(self, x):
)


# TODO add unittest.skip later
# class TestTRTModuleNext(TestCase):
# def test_save_and_load_trt_module(self):
# class TestModule(torch.nn.Module):
# def forward(self, x):
# return x + x

# inputs = [torch.randn(1, 1)]
# mod = TestModule().eval()
# ref_output = mod(*inputs)

# mod = acc_tracer.trace(mod, inputs)

# interp = TRTInterpreter(
# mod,
# input_specs=InputTensorSpec.from_tensors(inputs),
# explicit_batch_dimension=True,
# )
# interp_res = interp.run(lower_precision=LowerPrecision.FP32)

# with io.BytesIO() as engine_bytes:
# engine_bytes.write(interp_res.engine.serialize())
# engine_str = engine_bytes.getvalue()

# trt_mod = TRTModuleNext(
# name="TestModule",
# serialized_engine=engine_str,
# input_binding_names=interp_res.input_names,
# output_binding_names=interp_res.output_names,
# target_device=Device(f"cuda:{torch.cuda.current_device()}"),
# )

# torch.save(trt_mod, "trt.pt")
# reload_trt_mod = torch.load("trt.pt")

# torch.testing.assert_allclose(
# reload_trt_mod(inputs[0].cuda()).cpu().reshape_as(ref_output),
# ref_output,
# rtol=1e-04,
# atol=1e-04,
# )
# os.remove(f"{os.getcwd()}/trt.pt")

# def test_save_and_load_state_dict(self):
# class TestModule(torch.nn.Module):
# def forward(self, x):
# return x + x

# inputs = [torch.randn(1, 1)]
# mod = TestModule().eval()
# ref_output = mod(*inputs)

# mod = acc_tracer.trace(mod, inputs)
# interp = TRTInterpreter(
# mod,
# input_specs=InputTensorSpec.from_tensors(inputs),
# explicit_batch_dimension=True,
# )
# interp_res = interp.run(lower_precision=LowerPrecision.FP32)

# with io.BytesIO() as engine_bytes:
# engine_bytes.write(interp_res.engine.serialize())
# engine_str = engine_bytes.getvalue()

# trt_mod = TRTModuleNext(
# name="TestModule",
# serialized_engine=engine_str,
# input_binding_names=interp_res.input_names,
# output_binding_names=interp_res.output_names,
# target_device=Device(f"cuda:{torch.cuda.current_device()}"),
# )

# st = trt_mod.state_dict()

# new_trt_mod = TRTModuleNext()
# new_trt_mod.load_state_dict(st)

# torch.testing.assert_allclose(
# new_trt_mod(inputs[0].cuda()).cpu().reshape_as(ref_output),
# ref_output,
# rtol=1e-04,
# atol=1e-04,
# )
class TestTRTModuleInt64Input(TestCase):
def test_save_and_load_trt_module(self):
class TestModule(torch.nn.Module):
def forward(self, x):
return x + x

inputs = [torch.randn(5, 5).long()]
mod = TestModule().eval()
ref_output = mod(*inputs)

mod = acc_tracer.trace(mod, inputs)
interp = TRTInterpreter(
mod,
input_specs=InputTensorSpec.from_tensors(inputs),
truncate_long_and_double=True,
)
trt_mod = TRTModule(*interp.run(lower_precision=LowerPrecision.FP32))
torch.save(trt_mod, "trt.pt")
reload_trt_mod = torch.load("trt.pt")

torch.testing.assert_close(
reload_trt_mod(inputs[0].cuda()).cpu(),
ref_output,
rtol=1e-04,
atol=1e-04,
check_dtype=False,
)
os.remove(f"{os.getcwd()}/trt.pt")

def test_save_and_load_state_dict(self):
class TestModule(torch.nn.Module):
def forward(self, x):
return x + x

inputs = [torch.randn(5, 5).long()]
mod = TestModule().eval()
ref_output = mod(*inputs)

mod = acc_tracer.trace(mod, inputs)
interp = TRTInterpreter(
mod,
input_specs=InputTensorSpec.from_tensors(inputs),
truncate_long_and_double=True,
)
trt_mod = TRTModule(*interp.run(lower_precision=LowerPrecision.FP32))
st = trt_mod.state_dict()

new_trt_mod = TRTModule()
new_trt_mod.load_state_dict(st)

torch.testing.assert_close(
new_trt_mod(inputs[0].cuda()).cpu(),
ref_output,
rtol=1e-04,
atol=1e-04,
check_dtype=False,
)


class TestTRTModuleFloat64Input(TestCase):
def test_save_and_load_trt_module(self):
class TestModule(torch.nn.Module):
def forward(self, x):
return x + x

inputs = [torch.randn(5, 5).double()]
mod = TestModule().eval()
ref_output = mod(*inputs)

mod = acc_tracer.trace(mod, inputs)
interp = TRTInterpreter(
mod,
input_specs=InputTensorSpec.from_tensors(inputs),
truncate_long_and_double=True,
)
trt_mod = TRTModule(*interp.run(lower_precision=LowerPrecision.FP32))
torch.save(trt_mod, "trt.pt")
reload_trt_mod = torch.load("trt.pt")

torch.testing.assert_close(
reload_trt_mod(inputs[0].cuda()).cpu(),
ref_output,
rtol=1e-04,
atol=1e-04,
check_dtype=False,
)
os.remove(f"{os.getcwd()}/trt.pt")

def test_save_and_load_state_dict(self):
class TestModule(torch.nn.Module):
def forward(self, x):
return x + x

inputs = [torch.randn(5, 5).double()]
mod = TestModule().eval()
ref_output = mod(*inputs)

mod = acc_tracer.trace(mod, inputs)
interp = TRTInterpreter(
mod,
input_specs=InputTensorSpec.from_tensors(inputs),
truncate_long_and_double=True,
)
trt_mod = TRTModule(*interp.run(lower_precision=LowerPrecision.FP32))
st = trt_mod.state_dict()

new_trt_mod = TRTModule()
new_trt_mod.load_state_dict(st)

torch.testing.assert_close(
new_trt_mod(inputs[0].cuda()).cpu(),
ref_output,
rtol=1e-04,
atol=1e-04,
check_dtype=False,
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The output data type will be different, since TRT cannot output int64 types

)


class TestTRTModuleNext(TestCase):
def test_save_and_load_trt_module(self):
class TestModule(torch.nn.Module):
def forward(self, x):
return x + x

inputs = [torch.randn(1, 1)]
mod = TestModule().eval()
ref_output = mod(*inputs)

mod = acc_tracer.trace(mod, inputs)

interp = TRTInterpreter(
mod,
input_specs=InputTensorSpec.from_tensors(inputs),
explicit_batch_dimension=True,
)
interp_res = interp.run(lower_precision=LowerPrecision.FP32)

with io.BytesIO() as engine_bytes:
engine_bytes.write(interp_res.engine.serialize())
engine_str = engine_bytes.getvalue()

trt_mod = TRTModuleNext(
name="TestModule",
serialized_engine=engine_str,
input_binding_names=interp_res.input_names,
output_binding_names=interp_res.output_names,
target_device=Device(f"cuda:{torch.cuda.current_device()}"),
)

torch.save(trt_mod, "trt.pt")
reload_trt_mod = torch.load("trt.pt")

torch.testing.assert_allclose(
reload_trt_mod(inputs[0].cuda()).cpu().reshape_as(ref_output),
ref_output,
rtol=1e-04,
atol=1e-04,
)
os.remove(f"{os.getcwd()}/trt.pt")

def test_save_and_load_state_dict(self):
class TestModule(torch.nn.Module):
def forward(self, x):
return x + x

inputs = [torch.randn(1, 1)]
mod = TestModule().eval()
ref_output = mod(*inputs)

mod = acc_tracer.trace(mod, inputs)
interp = TRTInterpreter(
mod,
input_specs=InputTensorSpec.from_tensors(inputs),
explicit_batch_dimension=True,
)
interp_res = interp.run(lower_precision=LowerPrecision.FP32)

with io.BytesIO() as engine_bytes:
engine_bytes.write(interp_res.engine.serialize())
engine_str = engine_bytes.getvalue()

trt_mod = TRTModuleNext(
name="TestModule",
serialized_engine=engine_str,
input_binding_names=interp_res.input_names,
output_binding_names=interp_res.output_names,
target_device=Device(f"cuda:{torch.cuda.current_device()}"),
)

st = trt_mod.state_dict()

new_trt_mod = TRTModuleNext()
new_trt_mod.load_state_dict(st)

torch.testing.assert_allclose(
new_trt_mod(inputs[0].cuda()).cpu().reshape_as(ref_output),
ref_output,
rtol=1e-04,
atol=1e-04,
)


if __name__ == "__main__":
Expand Down
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