Merge pull request #616 from NVIDIA/example_tensors

Example tensors

cpp/include/trtorch/trtorch.h

@@ -427,7 +427,7 @@ struct TRTORCH_API CompileSpec {
     Input(c10::ArrayRef<int64_t> shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous);
 
     /**
-     * @brief Construct a new Input Range object dynamic input size from
+     * @brief Construct a new Input spec object dynamic input size from
      * c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max
      * supported sizes. dtype (Expected data type for the input) defaults to PyTorch
      * / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
@@ -462,7 +462,7 @@ struct TRTORCH_API CompileSpec {
         TensorFormat format = TensorFormat::kContiguous);
 
     /**
-     * @brief Construct a new Input Range object dynamic input size from
+     * @brief Construct a new Input spec object dynamic input size from
      * c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max
      * supported sizes. dtype (Expected data type for the input) defaults to PyTorch
      * / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8)
@@ -479,7 +479,7 @@ struct TRTORCH_API CompileSpec {
         TensorFormat format = TensorFormat::kContiguous);
 
     /**
-     * @brief Construct a new Input Range object dynamic input size from
+     * @brief Construct a new Input spec object dynamic input size from
      * c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max
      * supported sizes
      *
@@ -496,6 +496,16 @@ struct TRTORCH_API CompileSpec {
         DataType dtype,
         TensorFormat format = TensorFormat::kContiguous);
 
+    /**
+     * @brief Construct a new Input spec object using a torch tensor as an example
+     * The tensor's shape, type and layout inform the spec's values
+     *
+     * Note: You cannot set dynamic shape through this method, you must use an alternative constructor
+     *
+     * @param tensor Reference tensor to set shape, type and layout
+     */
+    Input(at::Tensor tensor);
+
     bool get_explicit_set_dtype() {
       return explicit_set_dtype;
     }

cpp/src/compile_spec.cpp

@@ -287,6 +287,26 @@ CompileSpec::Input::Input(
   this->input_is_dynamic = true;
 }
 
+CompileSpec::Input::Input(at::Tensor tensor) {
+  this->opt_shape = tensor.sizes().vec();
+  this->min_shape = tensor.sizes().vec();
+  this->max_shape = tensor.sizes().vec();
+  this->shape = tensor.sizes().vec();
+  this->dtype = tensor.scalar_type();
+  this->explicit_set_dtype = true;
+  TRTORCH_ASSERT(
+      tensor.is_contiguous(at::MemoryFormat::ChannelsLast) || tensor.is_contiguous(at::MemoryFormat::Contiguous),
+      "Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last");
+  at::MemoryFormat frmt;
+  if (tensor.is_contiguous(at::MemoryFormat::Contiguous)) {
+    frmt = at::MemoryFormat::Contiguous;
+  } else {
+    frmt = at::MemoryFormat::ChannelsLast;
+  }
+  this->format = frmt;
+  this->input_is_dynamic = false;
+}
+
 /* ==========================================*/
 
 core::ir::Input to_internal_input(CompileSpec::InputRange& i) {

py/trtorch/Input.py

@@ -196,3 +196,16 @@ def _parse_format(format: Any) -> _types.TensorFormat:
         else:
             raise TypeError(
                 "Tensor format needs to be specified with either torch.memory_format or trtorch.TensorFormat")
+
+    @classmethod
+    def _from_tensor(cls, t: torch.Tensor):
+        if not any([
+                t.is_contiguous(memory_format=torch.contiguous_format),
+                t.is_contiguous(memory_format=torch.channels_last)
+        ]):
+            raise ValueError(
+                "Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last"
+            )
+        frmt = torch.contiguous_format if t.is_contiguous(
+            memory_format=torch.contiguous_format) else torch.channels_last
+        return cls(shape=t.shape, dtype=t.dtype, format=frmt)

py/trtorch/_compile_spec.py

@@ -174,7 +174,12 @@ def _parse_compile_spec(compile_spec: Dict[str, Any]) -> trtorch._C.CompileSpec:
         info.inputs = _parse_input_ranges(compile_spec["input_shapes"])
 
     if "inputs" in compile_spec:
-        info.inputs = [i._to_internal() for i in compile_spec["inputs"]]
+        if not all([isinstance(i, torch.Tensor) or isinstance(i, trtorch.Input) for i in compile_spec["inputs"]]):
+            raise KeyError("Input specs should be either trtorch.Input or torch.Tensor, found types: {}".format(
+                [typeof(i) for i in compile_spec["inputs"]]))
+
+        inputs = [trtorch.Input._from_tensor(i) if isinstance(i, torch.Tensor) else i for i in compile_spec["inputs"]]
+        info.inputs = [i._to_internal() for i in inputs]
 
     if "op_precision" in compile_spec and "enabled_precisions" in compile_spec:
         raise KeyError(

tests/cpp/BUILD

@@ -16,7 +16,8 @@ test_suite(
         ":test_modules_as_engines",
         ":test_multiple_registered_engines",
         ":test_serialization",
-        ":test_module_fallback"
+        ":test_module_fallback",
+        ":test_example_tensors"
     ],
 )
 
@@ -28,7 +29,8 @@ test_suite(
         ":test_modules_as_engines",
         ":test_multiple_registered_engines",
         ":test_serialization",
-        ":test_module_fallback"
+        ":test_module_fallback",
+        ":test_example_tensors"
     ],
 )
 
@@ -43,6 +45,17 @@ cc_test(
     ],
 )
 
+cc_test(
+    name = "test_example_tensors",
+    srcs = ["test_example_tensors.cpp"],
+    data = [
+        "//tests/modules:jit_models",
+    ],
+    deps = [
+        ":cpp_api_test",
+    ],
+)
+
 cc_test(
     name = "test_serialization",
     srcs = ["test_serialization.cpp"],

tests/cpp/test_example_tensors.cpp

@@ -0,0 +1,23 @@
+#include "cpp_api_test.h"
+
+TEST_P(CppAPITests, InputsFromTensors) {
+  std::vector<torch::jit::IValue> jit_inputs_ivalues;
+  std::vector<torch::jit::IValue> trt_inputs_ivalues;
+  for (auto in_shape : input_shapes) {
+    auto in = at::randn(in_shape, {at::kCUDA});
+    jit_inputs_ivalues.push_back(in.clone());
+    trt_inputs_ivalues.push_back(in.clone());
+  }
+
+  auto spec = trtorch::CompileSpec({trt_inputs_ivalues[0].toTensor()});
+
+  auto trt_mod = trtorch::CompileGraph(mod, spec);
+  torch::jit::IValue trt_results_ivalues = trtorch::tests::util::RunModuleForward(trt_mod, trt_inputs_ivalues);
+  std::vector<at::Tensor> trt_results;
+  trt_results.push_back(trt_results_ivalues.toTensor());
+}
+
+INSTANTIATE_TEST_SUITE_P(
+    CompiledModuleForwardIsCloseSuite,
+    CppAPITests,
+    testing::Values(PathAndInSize({"tests/modules/resnet18_traced.jit.pt", {{1, 3, 224, 224}}, 2e-5})));

tests/py/test_api.py

@@ -73,6 +73,27 @@ def test_compile_script(self):
         same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
         self.assertTrue(same < 2e-2)
 
+    def test_from_torch_tensor(self):
+        compile_spec = {
+            "inputs": [self.input],
+            "device": {
+                "device_type": trtorch.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float}
+        }
+
+        trt_mod = trtorch.compile(self.scripted_model, compile_spec)
+        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
+        self.assertTrue(same < 2e-2)
+
+    def test_device(self):
+        compile_spec = {"inputs": [self.input], "device": trtorch.Device("gpu:0"), "enabled_precisions": {torch.float}}
+
+        trt_mod = trtorch.compile(self.scripted_model, compile_spec)
+        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
+        self.assertTrue(same < 2e-2)
+
 
 class TestCompileHalf(ModelTestCase):