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feat(//py)!: Porting forward the API to use kwargs
BREAKING CHANGE: This changes the API for compile settings from a dictionary of settings to a set of kwargs for the various compilation functions. This will break existing code. However there is simple guidance to port forward your code: Given a dict of valid TRTorch CompileSpec settings ```py spec = { "inputs": ... ... } ``` You can use this same dict with the new APIs by changing your code from: ```py trtorch.compile(mod, spec) ``` to: ```py trtorch.compile(mod, **spec) ``` which will unpack the dictionary as arguments to the function Signed-off-by: Naren Dasan <[email protected]> Signed-off-by: Naren Dasan <[email protected]>
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‎docsrc/py_api/trtorch.rst‎

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@@ -1,5 +1,8 @@
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.. _trtorch_py:
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.. automodule trtorch
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:undoc-members:
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trtorch
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===============
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‎py/trtorch/_compile_spec.py‎

Lines changed: 3 additions & 1 deletion
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@@ -64,7 +64,7 @@ def _parse_op_precision(precision: Any) -> _types.dtype:
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raise TypeError("Provided an unsupported dtype as operating precision (support: int8, half, float), got: " +
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str(precision))
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67-
elif isinstance(precision, _types.DataTypes):
67+
elif isinstance(precision, _types.dtype):
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return precision
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else:
@@ -170,6 +170,8 @@ def _parse_compile_spec(compile_spec: Dict[str, Any]) -> trtorch._C.CompileSpec:
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inputs = [trtorch.Input._from_tensor(i) if isinstance(i, torch.Tensor) else i for i in compile_spec["inputs"]]
171171
info.inputs = [i._to_internal() for i in inputs]
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173+
assert (len(info.inputs) > 0), "Require at least one input definition to compile model"
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if "op_precision" in compile_spec and "enabled_precisions" in compile_spec:
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raise KeyError(
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"Found both key \"op_precision\", and \"enabled_precisions\" in compile spec, please port forward to using only \"enabled_precisions\""

‎py/trtorch/_compiler.py‎

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‎py/trtorch/csrc/tensorrt_classes.cpp‎

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@@ -38,6 +38,13 @@ nvinfer1::DataType toTRTDataType(DataType value) {
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}
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}
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Device::Device(const core::runtime::CudaDevice& internal_dev) {
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device_type = DeviceType::kGPU;
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gpu_id = internal_dev.id;
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dla_core = -1;
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allow_gpu_fallback = false;
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}
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nvinfer1::TensorFormat toTRTTensorFormat(TensorFormat value) {
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switch (value) {
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case TensorFormat::kChannelLast:

‎py/trtorch/csrc/tensorrt_classes.h‎

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@@ -74,6 +74,8 @@ struct Device : torch::CustomClassHolder {
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allow_gpu_fallback(false) // allow_gpu_fallback
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{}
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Device(const core::runtime::CudaDevice& internal_dev);
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ADD_ENUM_GET_SET(device_type, DeviceType, static_cast<int64_t>(DeviceType::kDLA));
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ADD_FIELD_GET_SET(gpu_id, int64_t);
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ADD_FIELD_GET_SET(dla_core, int64_t);

‎py/trtorch/csrc/trtorch_py.cpp‎

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@@ -103,6 +103,10 @@ void set_device(const int device_id) {
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core::set_device(device_id);
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}
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Device get_current_device() {
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return Device(core::runtime::get_current_device());
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}
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torch::jit::Module CompileGraph(const torch::jit::Module& mod, CompileSpec& info) {
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py::gil_scoped_acquire gil;
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auto trt_mod = core::CompileGraph(mod, info.toInternalCompileSpec());
@@ -315,6 +319,8 @@ PYBIND11_MODULE(_C, m) {
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m.def("_set_is_colored_output_on", &logging::set_is_colored_output_on, "Set if the logging output should be colored");
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m.def("_log", &logging::log, "Add a message to the logger");
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m.def("set_device", &trtorch::pyapi::set_device, "Set CUDA device id");
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m.def("_get_current_device", &trtorch::pyapi::get_current_device, "Get the current active CUDA device");
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py::enum_<core::util::logging::LogLevel>(m, "LogLevel", py::arithmetic())
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.value("INTERNAL_ERROR", core::util::logging::LogLevel::kINTERNAL_ERROR)

‎tests/py/test_api.py‎

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@@ -23,44 +23,52 @@ def test_compile_traced(self):
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"enabled_precisions": {torch.float}
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}
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26-
trt_mod = trtorch.compile(self.traced_model, compile_spec)
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trt_mod = trtorch.compile(self.traced_model, **compile_spec)
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same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
2828
self.assertTrue(same < 2e-2)
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def test_compile_script(self):
31+
trt_mod = trtorch.compile(self.scripted_model, inputs=[self.input], device=trtorch.Device(gpu_id=0), enabled_precisions={torch.float})
32+
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
33+
self.assertTrue(same < 2e-2)
34+
35+
def test_from_torch_tensor(self):
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compile_spec = {
32-
"inputs": [trtorch.Input(shape=self.input.shape)],
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"inputs": [self.input],
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"device": {
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"device_type": trtorch.DeviceType.GPU,
3540
"gpu_id": 0,
3641
},
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"enabled_precisions": {torch.float}
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}
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40-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
45+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
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same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
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self.assertTrue(same < 2e-2)
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44-
def test_from_torch_tensor(self):
49+
def test_device(self):
50+
compile_spec = {"inputs": [self.input], "device": trtorch.Device("gpu:0"), "enabled_precisions": {torch.float}}
51+
52+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
53+
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
54+
self.assertTrue(same < 2e-2)
55+
56+
57+
def test_compile_script_from_dict(self):
4558
compile_spec = {
46-
"inputs": [self.input],
59+
"inputs": [trtorch.Input(shape=self.input.shape)],
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"device": {
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"device_type": trtorch.DeviceType.GPU,
4962
"gpu_id": 0,
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},
5164
"enabled_precisions": {torch.float}
5265
}
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trt_mod = trtorch.compile(self.scripted_model, compile_spec)
55-
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
67+
trt_mod = trtorch.compile(self.traced_model, **compile_spec)
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same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
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self.assertTrue(same < 2e-2)
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58-
def test_device(self):
59-
compile_spec = {"inputs": [self.input], "device": trtorch.Device("gpu:0"), "enabled_precisions": {torch.float}}
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trt_mod = trtorch.compile(self.scripted_model, compile_spec)
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same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
63-
self.assertTrue(same < 2e-2)
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class TestCompileHalf(ModelTestCase):
@@ -80,7 +88,7 @@ def test_compile_script_half(self):
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"enabled_precisions": {torch.half}
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}
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83-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
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trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
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same = (trt_mod(self.input.half()) - self.scripted_model(self.input.half())).abs().max()
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trtorch.logging.log(trtorch.logging.Level.Debug, "Max diff: " + str(same))
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self.assertTrue(same < 3e-2)
@@ -103,7 +111,7 @@ def test_compile_script_half_by_default(self):
103111
"enabled_precisions": {torch.float, torch.half}
104112
}
105113

106-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
114+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
107115
same = (trt_mod(self.input.half()) - self.scripted_model(self.input.half())).abs().max()
108116
trtorch.logging.log(trtorch.logging.Level.Debug, "Max diff: " + str(same))
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self.assertTrue(same < 3e-2)
@@ -132,7 +140,7 @@ def test_compile_script(self):
132140
}
133141
}
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135-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
143+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
136144
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
137145
self.assertTrue(same < 2e-3)
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@@ -160,7 +168,7 @@ def test_compile_script(self):
160168
}
161169
}
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163-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
171+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
164172
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
165173
self.assertTrue(same < 2e-3)
166174

@@ -183,7 +191,7 @@ def test_pt_to_trt_to_pt(self):
183191
}
184192
}
185193

186-
trt_engine = trtorch.convert_method_to_trt_engine(self.ts_model, "forward", compile_spec)
194+
trt_engine = trtorch.convert_method_to_trt_engine(self.ts_model, "forward", **compile_spec)
187195
trt_mod = trtorch.embed_engine_in_new_module(trt_engine, trtorch.Device("cuda:0"))
188196
same = (trt_mod(self.input) - self.ts_model(self.input)).abs().max()
189197
self.assertTrue(same < 2e-3)

‎tests/py/test_api_dla.py‎

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@@ -40,7 +40,7 @@ def test_compile_traced(self):
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"enabled_precisions": {torch.half}
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}
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43-
trt_mod = trtorch.compile(self.traced_model, compile_spec)
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trt_mod = trtorch.compile(self.traced_model, **compile_spec)
4444
same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
4545
self.assertTrue(same < 2e-2)
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@@ -56,7 +56,7 @@ def test_compile_script(self):
5656
"enabled_precisions": {torch.half}
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}
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59-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
59+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
6060
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
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self.assertTrue(same < 2e-2)
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‎tests/py/test_multi_gpu.py‎

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@@ -32,7 +32,7 @@ def test_compile_traced(self):
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}
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}
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35-
trt_mod = trtorch.compile(self.traced_model, compile_spec)
35+
trt_mod = trtorch.compile(self.traced_model, **compile_spec)
3636
trtorch.set_device(self.target_gpu)
3737
same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
3838
trtorch.set_device(0)
@@ -51,7 +51,7 @@ def test_compile_script(self):
5151
}
5252
}
5353

54-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
54+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
5555
trtorch.set_device(self.target_gpu)
5656
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
5757
trtorch.set_device(0)
@@ -84,7 +84,7 @@ def test_compile_traced(self):
8484
}
8585
}
8686

87-
trt_mod = trtorch.compile(self.traced_model, compile_spec)
87+
trt_mod = trtorch.compile(self.traced_model, **compile_spec)
8888
# Changing the device ID deliberately. It should still run on correct device ID by context switching
8989
trtorch.set_device(1)
9090
same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
@@ -103,7 +103,7 @@ def test_compile_script(self):
103103
}
104104
}
105105

106-
trt_mod = trtorch.compile(self.scripted_model, compile_spec)
106+
trt_mod = trtorch.compile(self.scripted_model, **compile_spec)
107107
# Changing the device ID deliberately. It should still run on correct device ID by context switching
108108
trtorch.set_device(1)
109109
same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()

‎tests/py/test_ptq_dataloader_calibrator.py‎

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@@ -72,7 +72,7 @@ def test_compile_script(self):
7272
}
7373
}
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75-
trt_mod = trtorch.compile(self.model, compile_spec)
75+
trt_mod = trtorch.compile(self.model, **compile_spec)
7676
int8_test_acc = self.compute_accuracy(self.testing_dataloader, trt_mod)
7777
log(Level.Info, "[TRT INT8] Test Acc: {:.2f}%".format(100 * int8_test_acc))
7878
acc_diff = fp32_test_acc - int8_test_acc

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