#26426 fixed (#28715)

Summary:
This is the fix for reverted https://github.com/pytorch/pytorch/issues/26426
houseroad bddppq soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28715

Reviewed By: hl475

Differential Revision: D18146731

Pulled By: houseroad

fbshipit-source-id: 247366451a6334e84df82d00339521f797b33130

.jenkins/caffe2/test.sh

@@ -124,6 +124,7 @@ pip install --user pytest-sugar
   --ignore "$caffe2_pypath/python/operator_test/matmul_op_test.py" \
   --ignore "$caffe2_pypath/python/operator_test/pack_ops_test.py" \
   --ignore "$caffe2_pypath/python/mkl/mkl_sbn_speed_test.py" \
+  --ignore "$caffe2_pypath/python/trt/test_pt_onnx_trt.py" \
   ${rocm_ignore_test[@]} \
   "$caffe2_pypath/python" \
   "${EXTRA_TESTS[@]}"

CMakeLists.txt

@@ -15,6 +15,10 @@ if(NOT CMAKE_VERSION VERSION_LESS 3.15.0)
   cmake_policy(SET CMP0092 NEW)
 endif()
 
+if(NOT CMAKE_VERSION VERSION_LESS 3.10)
+  set(FIND_CUDA_MODULE_DEPRECATED ON)
+endif()
+
 # ---[ Project and semantic versioning.
 project(Caffe2 CXX C)
 

caffe2/contrib/tensorrt/trt_utils.cc

@@ -11,7 +11,15 @@ std::shared_ptr<nvinfer1::ICudaEngine> BuildTrtEngine(
     size_t max_workspace_size,
     bool debug_builder) {
   auto trt_builder = TrtObject(nvinfer1::createInferBuilder(*logger));
+#if defined(TENSORRT_VERSION_MAJOR) && (TENSORRT_VERSION_MAJOR >= 6)
+  auto trt_builder_cfg = TrtObject(trt_builder->createBuilderConfig());
+  // TensorRTOp doesn't support dynamic shapes yet
+  auto trt_network = TrtObject(trt_builder->createNetworkV2(
+      1U << static_cast<uint32_t>(nvinfer1::
+      NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)));
+#else
   auto trt_network = TrtObject(trt_builder->createNetwork());
+#endif
   auto trt_parser =
       TrtObject(nvonnxparser::createParser(*trt_network, *logger));
   auto status = trt_parser->parse(onnx_model_str.data(), onnx_model_str.size());
@@ -36,9 +44,19 @@ std::shared_ptr<nvinfer1::ICudaEngine> BuildTrtEngine(
     }
   }
   trt_builder->setMaxBatchSize(max_batch_size);
+#if defined(TENSORRT_VERSION_MAJOR) && (TENSORRT_VERSION_MAJOR >= 6)
+  trt_builder_cfg->setMaxWorkspaceSize(max_workspace_size);
+  if (debug_builder) {
+    trt_builder_cfg->setFlag(nvinfer1::BuilderFlag::kDEBUG);
+  }
+  trt_builder_cfg->setDefaultDeviceType(nvinfer1::DeviceType::kGPU);
+  return TrtObject(trt_builder->
+      buildEngineWithConfig(*trt_network.get(), *trt_builder_cfg));
+#else
   trt_builder->setMaxWorkspaceSize(max_workspace_size);
   trt_builder->setDebugSync(debug_builder);
   return TrtObject(trt_builder->buildCudaEngine(*trt_network.get()));
+#endif
 }
 } // namespace tensorrt
 } // namespace caffe2

caffe2/python/trt/test_pt_onnx_trt.py

@@ -0,0 +1,190 @@
+###################################################################################################
+# ATTENTION! This test will most probably fail if you install TensorRT 6.0.1 only.
+# That's because it's shipped with older version of ONNX parser not supporting some
+# required features. To make it work please use new version: https://github.com/onnx/onnx-tensorrt
+# Just clone it and do something like this:
+#
+# ~/pt/third_party/onnx-tensorrt$ mkdir build/
+# ~/pt/third_party/onnx-tensorrt$ cd build/
+# ~/pt/third_party/onnx-tensorrt/build$ cmake ..
+# ~/pt/third_party/onnx-tensorrt/build$ make
+# ~/pt/third_party/onnx-tensorrt/build$ sudo cp libnvonnxparser.so.6.0.1 /usr/lib/x86_64-linux-gnu
+#
+# This note is valid for 6.0.1 release only. September 18th, 2019.
+###################################################################################################
+
+import os
+import unittest
+from typing import List, Any
+
+from PIL import Image
+import numpy as np
+import torch
+from torch.onnx import OperatorExportTypes
+import torchvision.models as models
+
+import pycuda.driver as cuda
+# This import causes pycuda to automatically manage CUDA context creation and cleanup.
+import pycuda.autoinit
+
+import tensorrt as trt
+TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
+
+def allocate_buffers(engine):
+    h_input = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(0)),
+                                    dtype=trt.nptype(trt.float32))
+    h_output = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(1)),
+                                     dtype=trt.nptype(trt.float32))
+    d_input = cuda.mem_alloc(h_input.nbytes)
+    d_output = cuda.mem_alloc(h_output.nbytes)
+    stream = cuda.Stream()
+    return h_input, d_input, h_output, d_output, stream
+
+def load_normalized_test_case(input_shape, test_image, pagelocked_buffer, normalization_hint):
+    def normalize_image(image):
+        c, h, w = input_shape
+        image_arr = np.asarray(image.resize((w, h), Image.ANTIALIAS)).transpose([2, 0, 1])\
+            .astype(trt.nptype(trt.float32)).ravel()
+        if (normalization_hint == 0):
+            return (image_arr / 255.0 - 0.45) / 0.225
+        elif (normalization_hint == 1):
+            return (image_arr / 256.0 - 0.5)
+    np.copyto(pagelocked_buffer, normalize_image(Image.open(test_image)))
+    return test_image
+
+class Test_PT_ONNX_TRT(unittest.TestCase):
+    def __enter__(self):
+        return self
+
+    def setUp(self):
+        data_path = os.path.join(os.path.dirname(__file__), 'data')
+        self.image_files=["binoculars.jpeg", "reflex_camera.jpeg", "tabby_tiger_cat.jpg"]
+        for index, f in enumerate(self.image_files):
+            self.image_files[index] = os.path.abspath(os.path.join(data_path, f))
+            if not os.path.exists(self.image_files[index]):
+                raise FileNotFoundError(self.image_files[index] + " does not exist.")
+        self.labels = open(os.path.abspath(os.path.join(data_path, "class_labels.txt")), 'r').read().split('\n')
+
+    def build_engine_onnx(self, model_file):
+        with trt.Builder(TRT_LOGGER) as builder, builder.create_network(flags = 1) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
+            builder.max_workspace_size = 1 << 33
+            with open(model_file, 'rb') as model:
+                if not parser.parse(model.read()):
+                    for error in range(parser.num_errors):
+                        self.fail("ERROR: {}".format(parser.get_error(error)))
+            return builder.build_cuda_engine(network)
+
+    def _test_model(self, model_name, input_shape = (3, 224, 224), normalization_hint = 0):
+
+        model = getattr(models, model_name)(pretrained=True)
+
+        shape = (1,) + input_shape
+        dummy_input  = (torch.randn(shape),)
+        onnx_name = model_name + ".onnx"
+
+        torch.onnx.export(model,
+                          dummy_input,
+                          onnx_name,
+                          input_names = [],
+                          output_names = [],
+                          verbose=False,
+                          export_params=True,
+                          opset_version=9)
+
+        with self.build_engine_onnx(onnx_name) as engine:
+            h_input, d_input, h_output, d_output, stream = allocate_buffers(engine)
+            with engine.create_execution_context() as context:
+                err_count = 0
+                for index, f in enumerate(self.image_files):
+                    test_case = load_normalized_test_case(input_shape, f,\
+                        h_input, normalization_hint)
+                    cuda.memcpy_htod_async(d_input, h_input, stream)
+
+                    context.execute_async_v2(bindings=[d_input, d_output],
+                                             stream_handle=stream.handle)
+                    cuda.memcpy_dtoh_async(h_output, d_output, stream)
+                    stream.synchronize()
+
+                    amax = np.argmax(h_output)
+                    pred = self.labels[amax]
+                    if "_".join(pred.split()) not in\
+                            os.path.splitext(os.path.basename(test_case))[0]:
+                        err_count = err_count + 1
+                self.assertLessEqual(err_count, 1, "Too many recognition errors")
+
+    def test_alexnet(self):
+        self._test_model("alexnet", (3, 227, 227))
+
+    def test_resnet18(self):
+        self._test_model("resnet18")
+    def test_resnet34(self):
+        self._test_model("resnet34")
+    def test_resnet50(self):
+        self._test_model("resnet50")
+    def test_resnet101(self):
+        self._test_model("resnet101")
+    @unittest.skip("Takes 2m")
+    def test_resnet152(self):
+        self._test_model("resnet152")
+
+    def test_resnet50_2(self):
+        self._test_model("wide_resnet50_2")
+    @unittest.skip("Takes 2m")
+    def test_resnet101_2(self):
+        self._test_model("wide_resnet101_2")
+
+    def test_squeezenet1_0(self):
+        self._test_model("squeezenet1_0")
+    def test_squeezenet1_1(self):
+        self._test_model("squeezenet1_1")
+
+    def test_googlenet(self):
+        self._test_model("googlenet")
+    def test_inception_v3(self):
+        self._test_model("inception_v3")
+
+    def test_mnasnet0_5(self):
+        self._test_model("mnasnet0_5", normalization_hint = 1)
+    def test_mnasnet1_0(self):
+        self._test_model("mnasnet1_0", normalization_hint = 1)
+
+    def test_mobilenet_v2(self):
+        self._test_model("mobilenet_v2", normalization_hint = 1)
+
+    def test_shufflenet_v2_x0_5(self):
+        self._test_model("shufflenet_v2_x0_5")
+    def test_shufflenet_v2_x1_0(self):
+        self._test_model("shufflenet_v2_x1_0")
+
+    def test_vgg11(self):
+        self._test_model("vgg11")
+    def test_vgg11_bn(self):
+        self._test_model("vgg11_bn")
+    def test_vgg13(self):
+        self._test_model("vgg13")
+    def test_vgg13_bn(self):
+        self._test_model("vgg13_bn")
+    def test_vgg16(self):
+        self._test_model("vgg16")
+    def test_vgg16_bn(self):
+        self._test_model("vgg16_bn")
+    def test_vgg19(self):
+        self._test_model("vgg19")
+    def test_vgg19_bn(self):
+        self._test_model("vgg19_bn")
+
+    @unittest.skip("Takes 13m")
+    def test_densenet121(self):
+        self._test_model("densenet121")
+    @unittest.skip("Takes 25m")
+    def test_densenet161(self):
+        self._test_model("densenet161")
+    @unittest.skip("Takes 27m")
+    def test_densenet169(self):
+        self._test_model("densenet169")
+    @unittest.skip("Takes 44m")
+    def test_densenet201(self):
+        self._test_model("densenet201")
+
+if __name__ == '__main__':
+    unittest.main()

cmake/Dependencies.cmake

@@ -1171,7 +1171,13 @@ function (add_onnx_tensorrt_subdir)
   # We pass the paths we found to onnx tensorrt.
   set(CUDNN_INCLUDE_DIR "${CUDNN_INCLUDE_PATH}")
   set(CUDNN_LIBRARY "${CUDNN_LIBRARY_PATH}")
+  set(CMAKE_VERSION_ORIG "{CMAKE_VERSION}")
+  if (FIND_CUDA_MODULE_DEPRECATED)
+    # TODO: this WAR is for https://github.com/pytorch/pytorch/issues/18524
+    set(CMAKE_VERSION "3.9.0")
+  endif()
   add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/../third_party/onnx-tensorrt EXCLUDE_FROM_ALL)
+  set(CMAKE_VERSION "{CMAKE_VERSION_ORIG}")
 endfunction()
 if (CAFFE2_CMAKE_BUILDING_WITH_MAIN_REPO)
   if (USE_TENSORRT)

cmake/public/cuda.cmake

@@ -123,9 +123,23 @@ if(CAFFE2_USE_TENSORRT)
     PATH_SUFFIXES lib lib64 lib/x64)
   find_package_handle_standard_args(
     TENSORRT DEFAULT_MSG TENSORRT_INCLUDE_DIR TENSORRT_LIBRARY)
-  if(NOT TENSORRT_FOUND)
+  if(TENSORRT_FOUND)
+    execute_process(COMMAND /bin/sh -c "[ -r \"${TENSORRT_INCLUDE_DIR}/NvInferVersion.h\" ] && awk '/^\#define NV_TENSORRT_MAJOR/ {print $3}' \"${TENSORRT_INCLUDE_DIR}/NvInferVersion.h\"" OUTPUT_VARIABLE TENSORRT_VERSION_MAJOR)
+    execute_process(COMMAND /bin/sh -c "[ -r \"${TENSORRT_INCLUDE_DIR}/NvInferVersion.h\" ] && awk '/^\#define NV_TENSORRT_MINOR/ {print $3}' \"${TENSORRT_INCLUDE_DIR}/NvInferVersion.h\"" OUTPUT_VARIABLE TENSORRT_VERSION_MINOR)
+    if(TENSORRT_VERSION_MAJOR)
+      string(STRIP ${TENSORRT_VERSION_MAJOR} TENSORRT_VERSION_MAJOR)
+      string(STRIP ${TENSORRT_VERSION_MINOR} TENSORRT_VERSION_MINOR)
+      set(TENSORRT_VERSION "${TENSORRT_VERSION_MAJOR}.${TENSORRT_VERSION_MINOR}")
+      #CAFFE2_USE_TRT is set in Dependencies
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DTENSORRT_VERSION_MAJOR=${TENSORRT_VERSION_MAJOR}")
+      set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DTENSORRT_VERSION_MINOR=${TENSORRT_VERSION_MINOR}")
+    else()
+      message(WARNING "Caffe2: Cannot find ${TENSORRT_INCLUDE_DIR}/NvInferVersion.h. Assuming TRT 5.0 which is no longer supported. Turning the option off.")
+      set(CAFFE2_USE_TENSORRT OFF)
+    endif()
+  else()
     message(WARNING
-      "Caffe2: Cannot find TensorRT library. Turning the option off")
+      "Caffe2: Cannot find TensorRT library. Turning the option off.")
     set(CAFFE2_USE_TENSORRT OFF)
   endif()
 endif()

torch/onnx/symbolic_helper.py

@@ -77,8 +77,8 @@ def _parse_arg(value, desc):
         if desc == 'is':
             for v in value.node().inputs():
                 if v.node().kind() != 'onnx::Constant':
-                    raise RuntimeError("Failed to export an ONNX attribute, "
-                                       "since it's not constant, please try to make "
+                    raise RuntimeError("Failed to export an ONNX attribute '" + v.node().kind() +
+                                       "', since it's not constant, please try to make "
                                        "things (e.g., kernel size) static if possible")
             return [int(v.node()['value']) for v in value.node().inputs()]
         else: