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android / platform / external / tensorflow / a31025b53cf / . / tensorflow / compiler / tf2tensorrt / convert / convert_nodes_test.cc
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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/compiler/tf2tensorrt/convert/convert_nodes.h"
#include <algorithm>
#include <cmath>
#include <functional>
#include <memory>
#include <type_traits>
#include <unordered_map>
#include <vector>
#if GOOGLE_CUDA && GOOGLE_TENSORRT
#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "absl/algorithm/container.h"
#include "absl/strings/match.h"
#include "absl/strings/numbers.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/string_view.h"
#include "absl/types/span.h"
#include "third_party/gpus/cuda/include/cuda.h"
#include "third_party/gpus/cuda/include/cuda_runtime_api.h"
#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/cc/ops/nn_ops_internal.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/compiler/tf2tensorrt/common/datavec.h"
#include "tensorflow/compiler/tf2tensorrt/common/utils.h"
#include "tensorflow/compiler/tf2tensorrt/convert/utils.h"
#include "tensorflow/compiler/tf2tensorrt/utils/trt_engine_utils.h"
#include "tensorflow/compiler/tf2tensorrt/utils/trt_logger.h"
#include "tensorflow/compiler/tf2tensorrt/utils/trt_testutils.h"
#include "tensorflow/core/common_runtime/gpu/gpu_managed_allocator.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/node_def.pb.h" // NOLINT
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h" // NOLINT
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/grappler/costs/graph_properties.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/status_matchers.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/config.pb.h" // NOLINT
#include "tensorflow/core/public/session.h"
#include "third_party/tensorrt/NvInfer.h"
namespace tensorflow {
namespace tensorrt {
// TensorRT modes for testing. We define the following three modes:
// 1. Implicit batch mode: The tensors have static (known) input shape and the
// the batch dimension (first dim) is removed from the TRT tensor shape. In
// a loose notation: trt_shape = tf_shape[1:].
// 2. Explicit batch mode: static (known) input shape, but the batch dimension
// is part of the trt tensor shape. (trt_shape = tf_shape)
// 3. Dynamic shape mode allows unknown input shapes, and requires explicit
// batch size definition (trt_shape = tf_shape).
//
// Note that the Converter only distinguishes between two modes:
// - use_implicit_batch == true, this corresponds to kImplicitBatch,
// - use_implicit_batch == false which includes both kExplicitBatch and
// kDynamicShape.
//
// For the converter, the distinction between explicit batch or dynamic shape
// mode follows from the input tensors of the network: dynamic shape input
// implies dynamic shape mode, while static shape input tensors imply explicit
// batch mode. We want to test all these modes, therefore we define the
// TrtTestMode with the following three options.
enum class TrtTestMode {
kImplicitBatch = 0,
kExplicitBatch = 1,
kDynamicShape = 2
};
string DebugString(const TrtTestMode mode) {
switch (mode) {
case TrtTestMode::kImplicitBatch:
return "kImplicitBatch";
case TrtTestMode::kExplicitBatch:
return "kExplicitBatch";
case TrtTestMode::kDynamicShape:
return "kDynamicShape";
default:
return "Invalid TrtTestMode";
}
}
namespace convert {
using absl::StrCat;
using ::testing::ElementsAre;
using ::testing::ElementsAreArray;
using ::testing::HasSubstr;
using ::testing::Matcher;
using ::testing::PrintToString;
using ::tensorflow::testing::IsOk;
using ::tensorflow::testing::StatusIs;
constexpr std::array<TrtTestMode, 3> ValidTrtModes = {
TrtTestMode::kImplicitBatch, TrtTestMode::kExplicitBatch,
TrtTestMode::kDynamicShape};
bool TrtShapedWeightsEquals(const TRT_ShapedWeights& lhs,
const TRT_ShapedWeights& rhs) {
return lhs.shape_ == rhs.shape_ && lhs.TrtDType() == rhs.TrtDType() &&
lhs.GetPointer<int8>() == rhs.GetPointer<int8>();
}
template <typename T>
void ValidateWeights(const TRT_ShapedWeights& weights,
const std::vector<int>& expected_dims,
const std::vector<T>& expected_value) {
EXPECT_THAT(weights.shape_, DimsAreArray(expected_dims));
ASSERT_EQ(expected_value.size(), weights.count()) << weights.DebugString();
const T* actual_values = weights.GetPointer<T>();
for (int i = 0; i < expected_value.size(); ++i) {
EXPECT_EQ(expected_value[i], actual_values[i]);
}
}
TEST(TRT_ShapedWeights_Test, Basic) {
// Test constructor with no arguments.
{
TRT_ShapedWeights weights;
TRT_ShapedWeights copy(weights);
for (auto ptr : {&weights, &copy}) {
nvinfer1::Weights trt_weights = ptr->GetTrtWeights();
EXPECT_EQ(nvinfer1::DataType::kFLOAT, trt_weights.type);
EXPECT_EQ(nullptr, trt_weights.values);
EXPECT_EQ(0, trt_weights.count);
EXPECT_EQ(nullptr, ptr->GetPointer<int8>());
EXPECT_EQ(0, ptr->count());
EXPECT_EQ(0, ptr->size_bytes());
}
}
// Test constructor with DataType argument.
{
TRT_ShapedWeights weights(nvinfer1::DataType::kFLOAT);
TRT_ShapedWeights copy(weights);
for (auto ptr : {&weights, &copy}) {
nvinfer1::Weights trt_weights = ptr->GetTrtWeights();
EXPECT_EQ(nvinfer1::DataType::kFLOAT, trt_weights.type);
EXPECT_EQ(nullptr, trt_weights.values);
EXPECT_EQ(0, trt_weights.count);
EXPECT_EQ(nullptr, ptr->GetPointer<int8>());
EXPECT_EQ(0, ptr->count());
EXPECT_EQ(0, ptr->size_bytes());
}
}
// Test constructor with DataType and nvinfer1::Dims arguments.
{
TrtWeightStore store;
TRT_ShapedWeights weights =
store.GetTempWeights(nvinfer1::DataType::kFLOAT, CreateDims({2, 5}));
TRT_ShapedWeights copy(weights);
for (auto ptr : {&weights, &copy}) {
nvinfer1::Weights trt_weights = ptr->GetTrtWeights();
EXPECT_EQ(nvinfer1::DataType::kFLOAT, trt_weights.type);
EXPECT_NE(nullptr, trt_weights.values);
EXPECT_EQ(10, trt_weights.count);
EXPECT_EQ(trt_weights.values, ptr->GetPointer<int8>());
EXPECT_EQ(10, ptr->count());
EXPECT_EQ(40, ptr->size_bytes());
}
// Test that it doesn't copy the underlying buffer.
EXPECT_EQ(weights.GetPointer<int8>(), copy.GetPointer<int8>());
}
}
TEST(TRT_TensorOrWeights_Test, Basic) {
// Test constructor with no arguments.
{
TRT_TensorOrWeights tw;
TRT_TensorOrWeights copy(tw);
TRT_TensorOrWeights assigned;
assigned = tw;
for (auto ptr : {&tw, &copy, &assigned}) {
EXPECT_EQ(false, ptr->is_tensor());
EXPECT_EQ(false, ptr->is_weights());
EXPECT_EQ(-1, ptr->batch_size());
}
}
// Test constructor with ITensor and batch size argument.
{
nvinfer1::Dims dims;
dims.nbDims = 1;
dims.d[0] = 1;
ITensorProxyPtr itensor(dims);
TRT_TensorOrWeights tw(itensor);
TRT_TensorOrWeights tw1(itensor, /*batch_size=*/1);
for (auto original_ptr : {&tw, &tw1}) {
TRT_TensorOrWeights copy(*original_ptr);
TRT_TensorOrWeights assigned;
assigned = *original_ptr;
for (auto ptr : {original_ptr, &copy, &assigned}) {
ASSERT_TRUE(ptr->is_tensor());
EXPECT_EQ(false, ptr->is_weights());
if (original_ptr == &tw) {
EXPECT_EQ(-1, ptr->batch_size());
} else {
EXPECT_EQ(1, ptr->batch_size());
}
EXPECT_EQ(itensor->simple_tensor(), ptr->tensor()->simple_tensor());
EXPECT_THAT(ptr->GetTrtDims(), DimsAreArray({1}));
}
}
}
// Test constructor which creates and owns an ITensor.
{
nvinfer1::Dims dims;
dims.nbDims = 1;
dims.d[0] = 1;
TRT_TensorOrWeights tw(nvinfer1::DataType::kFLOAT, dims, /*batch_size=*/1);
TRT_TensorOrWeights copy(tw);
TRT_TensorOrWeights assigned;
assigned = tw;
for (auto ptr : {&tw, &copy, &assigned}) {
ASSERT_TRUE(ptr->is_tensor());
EXPECT_EQ(false, ptr->is_weights());
EXPECT_EQ(1, ptr->batch_size());
EXPECT_NE(nullptr, ptr->tensor()->simple_tensor());
EXPECT_THAT(ptr->GetTrtDims(), DimsAreArray({1}));
}
}
// Test constructor with TRT_ShapedWeights argument.
{
TRT_ShapedWeights weights;
TRT_TensorOrWeights tw(weights);
TRT_TensorOrWeights copy(tw);
TRT_TensorOrWeights assigned;
assigned = tw;
for (auto ptr : {&tw, &copy, &assigned}) {
EXPECT_EQ(false, ptr->is_tensor());
EXPECT_EQ(true, ptr->is_weights());
EXPECT_TRUE(TrtShapedWeightsEquals(weights, ptr->weights()));
std::vector<int> empty_dims;
EXPECT_THAT(ptr->GetTrtDims(), DimsAreArray(empty_dims));
}
}
}
class ValidatorTest : public ::testing::Test {
public:
std::unordered_map<string, OpConverter>& op_validators(
TrtNodeValidator* validator) {
return validator->op_validators_;
}
Status ConvertToTensorOrWeights(const Scope& scope, const Node* node,
int output_port,
TRT_TensorOrWeights* tensor_or_weights) {
grappler::GrapplerItem item;
TF_EXPECT_OK(scope.ToGraphDef(&item.graph));
grappler::GraphProperties graph_properties(item);
TF_EXPECT_OK(graph_properties.InferStatically(true));
TrtNodeValidator validator(graph_properties, TrtPrecisionMode::FP32,
/*use_calibration=*/false,
/*use_implicit_batch=*/true);
return validator.ConvertToTensorOrWeights(node->def(), output_port,
tensor_or_weights);
}
const std::set<string>* GetQuantizeOps(TrtNodeValidator* validator) {
return validator->quantize_ops;
}
};
TEST_F(ValidatorTest, QuantizeOpsAreRegistered) {
grappler::GrapplerItem item;
grappler::GraphProperties graph_properties(item);
TrtNodeValidator validator(graph_properties, TrtPrecisionMode::FP32,
/*use_calibration=*/false,
/*use_implicit_batch=*/true);
for (const string& quantize_op : *GetQuantizeOps(&validator)) {
QCHECK(op_validators(&validator).count(quantize_op));
}
}
TEST_F(ValidatorTest, ConvertToTensorOrWeights) {
// Convert Const.
{
Scope s = Scope::NewRootScope();
auto node =
ops::Const(s.WithOpName("my_const"), {1.0f, 2.0f}, TensorShape({2}));
TRT_TensorOrWeights output;
EXPECT_THAT(ConvertToTensorOrWeights(s, node.op().node(),
/*output_port=*/0, &output),
IsOk());
ValidateWeights<float>(output.weights(), {2}, {1.0, 2.0});
}
// Helper method to run ConvertToTensorOrWeights() with predefined parameters.
auto convert_to_tensor_or_weights = [this](const std::vector<int64>& dims,
TRT_TensorOrWeights* output) {
Scope s = Scope::NewRootScope();
const auto attrs = ops::Placeholder::Shape(PartialTensorShape{dims});
auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT, attrs);
auto add = ops::Add(s.WithOpName("add"), feed, feed);
return this->ConvertToTensorOrWeights(s, add.operation.node(),
/*output_port=*/0, output);
};
// Convert non-Const with #dims > nvinfer1::Dims::MAX_DIMS+1.
{
TRT_TensorOrWeights output;
EXPECT_THAT(
convert_to_tensor_or_weights(
std::vector<int64>(nvinfer1::Dims::MAX_DIMS + 2, 1), &output),
StatusIs(error::OUT_OF_RANGE,
HasSubstr("Input tensor rank is greater than 9")));
}
// Convert non-Const with #dims < 1.
{
TRT_TensorOrWeights output;
EXPECT_THAT(convert_to_tensor_or_weights({}, &output),
StatusIs(error::INVALID_ARGUMENT,
HasSubstr("Scalar input tensor is not supported since "
"the first dimension "
"is treated as batch dimension by TRT")));
}
// Convert non-Const. We test the case where the non-batch dimension is
// unknown as well, to make sure the validator allows that.
for (const int32 non_batch_dim : {-1, 2}) {
const int32 batch_size = 12;
TRT_TensorOrWeights output;
EXPECT_THAT(
convert_to_tensor_or_weights({batch_size, non_batch_dim}, &output),
IsOk());
ASSERT_TRUE(output.is_tensor());
EXPECT_EQ(batch_size, output.batch_size());
EXPECT_NE(nullptr, output.tensor()->simple_tensor());
EXPECT_THAT(output.GetTrtDims(), DimsAreArray({non_batch_dim}));
}
}
TEST_F(ValidatorTest, IsTensorRTCandidate_Basics) {
Scope s = Scope::NewRootScope();
auto input =
ops::Const(s.WithOpName("const"), {1.0f, 2.0f}, TensorShape({2}));
auto add = ops::Add(s.WithOpName("add"), input, input);
const Node* add_node = add.operation.node();
grappler::GrapplerItem item;
TF_EXPECT_OK(s.ToGraphDef(&item.graph));
grappler::GraphProperties graph_properties(item);
TF_EXPECT_OK(graph_properties.InferStatically(true));
TrtNodeValidator validator(graph_properties, TrtPrecisionMode::FP32,
/*use_calibration=*/false,
/*use_implicit_batch=*/true);
bool start_conversion = false;
bool should_fail = false;
auto op_converter = [&start_conversion,
&should_fail](OpConverterParams* params) -> Status {
if (should_fail) return errors::InvalidArgument("");
if (!params->validation_only) start_conversion = true;
return Status::OK();
};
// Validator not registered.
ASSERT_EQ(1, op_validators(&validator).erase("Add"));
EXPECT_THAT(validator.IsTensorRTCandidate(add_node),
StatusIs(error::UNIMPLEMENTED,
HasSubstr("Op type Add is not supported.")));
// Register validator.
op_validators(&validator)["Add"] = op_converter;
TF_EXPECT_OK(validator.IsTensorRTCandidate(add_node));
EXPECT_EQ(false, start_conversion);
// Let the converter return error.
should_fail = true;
EXPECT_THAT(validator.IsTensorRTCandidate(add_node),
StatusIs(error::INVALID_ARGUMENT));
}
TEST(TrtNodeValidator, IsTensorRTCandidate) {
// Create a graph containing both TRT-compatible and TRT-incompatible nodes
// and use it to test TrtNodeValidator::IsTensorRTCandidate().
const std::vector<int32> input_shape_array{2, 2};
TensorShape input_shape;
TF_EXPECT_OK(TensorShapeUtils::MakeShape(input_shape_array, &input_shape));
Scope s = Scope::NewRootScope();
ops::Placeholder::Attrs feed_attrs;
TF_EXPECT_OK(
TensorShapeUtils::MakeShape(input_shape_array, &feed_attrs.shape_));
// Compatible input.
auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT, feed_attrs);
auto const_1 = ops::Const(s.WithOpName("const_1"), 1.0f, input_shape);
// Compatible MatMul.
auto matmul = ops::MatMul(s.WithOpName("matmul"), feed, const_1);
// Incompatible MatMul.
ops::MatMul::Attrs matmul_attrs;
matmul_attrs.transpose_a_ = true;
auto incompatible_matmul = ops::MatMul(s.WithOpName("incompatible_matmul"),
feed, const_1, matmul_attrs);
// Unsupported op.
auto unsupported_op = ops::Erfc(s.WithOpName("sin"), feed);
// Incompatible input.
auto incompatible_feed = ops::Placeholder(s.WithOpName("feed"), DT_DOUBLE);
auto const_2 = ops::Const(s.WithOpName("const_2"), 1.0, input_shape);
// Compatible op with incompatible input.
auto matmul_with_incompatible_input =
ops::MatMul(s.WithOpName("matmul_with_incompatible_input"),
incompatible_feed, const_2);
// Quantize ops.
auto quantize_attrs = ops::FakeQuantWithMinMaxArgs::Min(-6.0f).Max(6.0f);
auto quantize = ops::FakeQuantWithMinMaxArgs(s.WithOpName("quantize"), feed,
quantize_attrs);
// Get GrapplerItem and GraphProperties.
grappler::GrapplerItem item;
TF_EXPECT_OK(s.ToGraphDef(&item.graph));
Tensor feed_tensor(DT_FLOAT, input_shape);
item.feed.push_back(std::make_pair("feed", feed_tensor));
grappler::GraphProperties graph_properties(item);
TF_EXPECT_OK(graph_properties.InferStatically(true));
for (const TrtPrecisionMode precision_mode :
{TrtPrecisionMode::FP32, TrtPrecisionMode::INT8}) {
TrtNodeValidator validator(graph_properties, precision_mode,
/*use_calibration=*/false,
/*use_implicit_batch=*/true);
TF_EXPECT_OK(validator.IsTensorRTCandidate(matmul.operation.node()));
EXPECT_THAT(
validator.IsTensorRTCandidate(incompatible_matmul.operation.node()),
StatusIs(error::INVALID_ARGUMENT,
HasSubstr("MatMul with 2D tensors requires explicit batch "
"mode, or that tensor A "
"is not transposed and B is a constant tensor.")));
EXPECT_THAT(validator.IsTensorRTCandidate(unsupported_op.operation.node()),
StatusIs(error::UNIMPLEMENTED,
HasSubstr("Op type Erfc is not supported")));
EXPECT_THAT(
validator.IsTensorRTCandidate(
matmul_with_incompatible_input.operation.node()),
StatusIs(
error::INTERNAL,
HasSubstr(
"Failed to convert input feed_1 to a TRT_TensorOrWeights")));
if (precision_mode == TrtPrecisionMode::INT8) {
TF_EXPECT_OK(validator.IsTensorRTCandidate(quantize.operation.node()));
} else {
EXPECT_THAT(
validator.IsTensorRTCandidate(quantize.operation.node()),
StatusIs(
error::UNIMPLEMENTED,
HasSubstr("Op type FakeQuantWithMinMaxArgs is not supported")));
}
}
}
class ConverterTest : public ::testing::Test {
public:
ConverterTest() { Reset(); }
void Reset() {
converter_ =
std::move(Converter::Create(TrtPrecisionMode::FP32,
/*use_calibration=*/false, &logger_,
/*use_implicit_batch=*/true,
/*engine_name=*/"TRTEngineOp_0_0")
.ValueOrDie());
weight_store_ = &converter_->weight_store_;
}
void AddOpConverter(const string& op_name, OpConverter op_converter) {
converter_->op_registry_[op_name] = op_converter;
}
// Below we expose private methods of Converter for testing.
Status MaybeUpdateBatchSize(int batch_size) {
return converter_->MaybeUpdateBatchSize(batch_size);
}
Status AddTensorOrWeights(const string& name, TRT_TensorOrWeights input) {
return converter_->AddTensorOrWeights(name, input);
}
Status GetTensorOrWeights(const string& name, TRT_TensorOrWeights* output) {
return converter_->GetTensorOrWeights(name, output);
}
Status GetInputs(const NodeDef& node_def,
std::vector<TRT_TensorOrWeights>* inputs) const {
return converter_->GetInputs(node_def, inputs);
}
Status GetWeightRange(const TRT_ShapedWeights& weights, float* out_min,
float* out_max) const {
return converter_->GetWeightRange(weights, out_min, out_max);
}
int batch_size() const { return converter_->batch_size_; }
std::unordered_map<ITensorProxyPtr*, float>& quantization_ranges_proxy() {
return converter_->quantization_ranges_proxy_;
}
std::unordered_map<nvinfer1::ITensor*, float>& quantization_ranges() {
return converter_->quantization_ranges_;
}
private:
Logger& logger_ = *Logger::GetLogger();
protected:
std::unique_ptr<Converter> converter_;
TrtWeightStore* weight_store_;
};
TEST_F(ConverterTest, ConvertNode) {
ITensorProxyPtr output_tensors[2];
auto op_converter = [&output_tensors](OpConverterParams* params) -> Status {
nvinfer1::Dims dims = params->inputs[0].tensor()->getDimensions();
for (int i = 0; i < 2; ++i) {
dims.d[0] += 1;
output_tensors[i]->setDimensions(dims);
params->outputs->push_back(TRT_TensorOrWeights(output_tensors[i]));
}
return Status::OK();
};
NodeDef node_def = MakeNodeDef("my_op", "MyOp", {"my_input"});
TF_EXPECT_OK(converter_->AddInputTensor(
"my_input", nvinfer1::DataType::kFLOAT, CreateDims({123}), 1));
// Converter not registered.
EXPECT_THAT(converter_->ConvertNode(node_def),
StatusIs(error::UNIMPLEMENTED,
HasSubstr("No converter registered for op: MyOp")));
// Register the converter and retry.
AddOpConverter("MyOp", op_converter);
TF_EXPECT_OK(converter_->ConvertNode(node_def));
TRT_TensorOrWeights actual_output_1;
TF_EXPECT_OK(GetTensorOrWeights("my_op", &actual_output_1));
EXPECT_EQ(output_tensors[0]->simple_tensor(),
actual_output_1.tensor()->simple_tensor());
EXPECT_EQ(124, actual_output_1.tensor()->getDimensions().d[0]);
TRT_TensorOrWeights actual_output_2;
TF_EXPECT_OK(GetTensorOrWeights("my_op:1", &actual_output_2));
EXPECT_EQ(output_tensors[1]->simple_tensor(),
actual_output_2.tensor()->simple_tensor());
EXPECT_EQ(125, actual_output_2.tensor()->getDimensions().d[0]);
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, AddAndGetInputs) {
NodeDef node_def;
node_def.add_input("^control_input");
node_def.add_input("input");
node_def.add_input("input:0");
node_def.add_input("input:1");
node_def.add_input("weird_input:2:3:4:0");
TF_EXPECT_OK(converter_->AddInputTensor("input", nvinfer1::DataType::kFLOAT,
CreateDims({1}), 1));
TF_EXPECT_OK(converter_->AddInputTensor("input:1", nvinfer1::DataType::kINT32,
CreateDims({2, 3}), 1));
TF_EXPECT_OK(converter_->AddInputTensor(
"weird_input:2:3:4", nvinfer1::DataType::kHALF, CreateDims({5, 3}), 1));
std::vector<TRT_TensorOrWeights> inputs;
TF_EXPECT_OK(GetInputs(node_def, &inputs));
EXPECT_EQ(4, inputs.size());
EXPECT_EQ(inputs[0].tensor()->trt_tensor(), inputs[1].tensor()->trt_tensor());
EXPECT_EQ(nvinfer1::DataType::kFLOAT, inputs[0].tensor()->getType());
EXPECT_EQ(nvinfer1::DataType::kINT32, inputs[2].tensor()->getType());
EXPECT_EQ(nvinfer1::DataType::kHALF, inputs[3].tensor()->getType());
EXPECT_THAT(inputs[0].tensor()->getDimensions(), DimsAreArray({1}));
EXPECT_THAT(inputs[2].tensor()->getDimensions(), DimsAreArray({2, 3}));
EXPECT_THAT(inputs[3].tensor()->getDimensions(), DimsAreArray({5, 3}));
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, RenameAndMarkOutputTensors) {
// Test that the tensor are actually named and marked as output after
// Converter::RenameAndMarkOutputTensors() is called.
// Register a custom converter which shuffles the input. We use it to build a
// TRT network whose output will be later marked.
std::vector<ITensorProxyPtr> output_tensors;
auto op_converter = [&output_tensors](OpConverterParams* params) -> Status {
nvinfer1::Permutation perm;
perm.order[0] = 1;
perm.order[1] = 0;
for (int i = 0; i < 2; ++i) {
ITensorProxyPtr input_tensor = params->inputs[0].tensor();
nvinfer1::IShuffleLayer* layer =
params->converter->network()->addShuffle(*input_tensor->trt_tensor());
layer->setFirstTranspose(perm);
ITensorProxyPtr output_tensor = layer->getOutput(0);
params->outputs->emplace_back(output_tensor);
output_tensors.push_back(output_tensor);
}
TRT_ShapedWeights output_weights(nvinfer1::DataType::kFLOAT);
params->outputs->emplace_back(output_weights);
return Status::OK();
};
AddOpConverter("MyOp", op_converter);
// Run the conversion.
NodeDef node_def = MakeNodeDef("my_op", "MyOp", {"my_input"});
TF_EXPECT_OK(converter_->AddInputTensor(
"my_input", nvinfer1::DataType::kFLOAT, CreateDims({1, 2}), 1));
TF_EXPECT_OK(converter_->ConvertNode(node_def));
// Mark a weight as output, should fail.
EXPECT_THAT(
converter_->RenameAndMarkOutputTensors({{"my_op:2", "my_output"}}),
StatusIs(error::INVALID_ARGUMENT,
HasSubstr("Output my_op:2 is weights not tensor")));
// Mark tensors as output, should pass.
TF_EXPECT_OK(converter_->RenameAndMarkOutputTensors(
{{"my_op", "my_output"}, {"my_op:1", "my_output_1"}}));
EXPECT_EQ(2, output_tensors.size());
for (auto output_tensor : output_tensors) {
EXPECT_THAT(output_tensor->getDimensions(), DimsAreArray({2, 1}));
}
EXPECT_EQ("my_output", string(output_tensors[0]->getName()));
EXPECT_EQ("my_output_1", string(output_tensors[1]->getName()));
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, TransposeTensor) {
ITensorProxyPtr input_tensor = converter_->network()->addInput(
"", nvinfer1::DataType::kFLOAT, CreateDims({2, 3, 5}));
ITensorProxyPtr output_tensor = nullptr;
NodeDef dummy_node_def = MakeNodeDef("dummy_op", "DummyOp", {});
// Rank doesn't match.
EXPECT_THAT(converter_->TransposeTensor(input_tensor, {0, 1}, &output_tensor,
dummy_node_def, "sub1"),
StatusIs(error::INVALID_ARGUMENT,
HasSubstr("Rank of perm for transpose does not match "
"with that of the input")));
// Transpose at batch dimension.
EXPECT_THAT(
converter_->TransposeTensor(input_tensor, {1, 0, 2, 3}, &output_tensor,
dummy_node_def, "sub2"),
StatusIs(error::UNIMPLEMENTED,
HasSubstr("Transpose at batch dimension is not supported.")));
// OK.
TF_EXPECT_OK(converter_->TransposeTensor(
input_tensor, {0, 3, 1, 2}, &output_tensor, dummy_node_def, "sub3"));
EXPECT_THAT(output_tensor->getDimensions(), DimsAreArray({5, 2, 3}));
EXPECT_THAT(converter_->network(),
LayerNamesAreArray({"TRTEngineOp_0_0/dummy_op-sub3:SHUFFLE"}));
}
void TestPrepareTensorForShape(
const std::vector<int>& input_dims, const std::vector<int>& reshape_dims,
const std::vector<int>& expected_tensor_dims, bool input_is_tensor,
Converter* converter, TrtWeightStore* weight_store,
error::Code expected_code = error::OK,
const char* expected_error_msg_substr = nullptr) {
TRT_TensorOrWeights input;
if (input_is_tensor) {
input = TRT_TensorOrWeights(converter->network()->addInput(
"", nvinfer1::DataType::kFLOAT, CreateDims(input_dims)));
} else {
input = TRT_TensorOrWeights(weight_store->GetTempWeights(
nvinfer1::DataType::kFLOAT, CreateDims(input_dims)));
}
ITensorProxyPtr output_tensor = nullptr;
NodeDef dummy_node_def = MakeNodeDef("dummy_op", "DummyOp", {});
for (bool validation_only : {false, true}) {
const Status status =
PrepareTensorForShape(converter, input, CreateDims(reshape_dims),
validation_only, &output_tensor, dummy_node_def);
if (expected_code == error::OK) {
TF_EXPECT_OK(status);
if (validation_only) {
EXPECT_EQ(nullptr, *output_tensor);
} else {
EXPECT_THAT(output_tensor->getDimensions(),
DimsAreArray(expected_tensor_dims));
}
} else {
EXPECT_THAT(status, StatusIs(expected_code,
HasSubstr(expected_error_msg_substr)));
}
}
}
TEST_F(ConverterTest, PrepareTensorForShape) {
for (bool input_is_tensor : {true, false}) {
// Shape size doesn't match.
Reset();
TestPrepareTensorForShape({2, 3, 5}, {2, 3, 6}, {}, input_is_tensor,
converter_.get(), weight_store_,
error::INVALID_ARGUMENT, "Incompatible shapes");
// Regular shape.
Reset();
TestPrepareTensorForShape({2, 3, 5}, {10, 3}, {10, 3}, input_is_tensor,
converter_.get(), weight_store_);
// Reshape to zero rank.
Reset();
TestPrepareTensorForShape({1, 1}, {}, {}, input_is_tensor, converter_.get(),
weight_store_);
}
// Tensor input with zero rank.
Reset();
TestPrepareTensorForShape({}, {1, 1}, {1, 1}, /*input_is_tensor=*/true,
converter_.get(), weight_store_);
// TODO(aaroey): we should check the case where uninferred dimensions are
// not an exact divisor of input dim ensions, e.g. for dims {-1, 7}.
// Infer tensor shape, ok.
Reset();
TestPrepareTensorForShape({2, 3, 5}, {-1, 2}, {15, 2},
/*input_is_tensor=*/true, converter_.get(),
weight_store_);
// Infer weight shape, should fail.
Reset();
TestPrepareTensorForShape({2, 3, 5}, {-1, 2}, {15, 2},
/*input_is_tensor=*/false, converter_.get(),
weight_store_, error::INVALID_ARGUMENT,
"Shape is not fully defined");
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, MaybeUpdateBatchSize) {
EXPECT_EQ(-1, batch_size());
TF_EXPECT_OK(MaybeUpdateBatchSize(-1));
EXPECT_EQ(-1, batch_size());
TF_EXPECT_OK(MaybeUpdateBatchSize(123));
EXPECT_EQ(123, batch_size());
TF_EXPECT_OK(MaybeUpdateBatchSize(123));
EXPECT_EQ(123, batch_size());
TF_EXPECT_OK(MaybeUpdateBatchSize(-1));
EXPECT_EQ(123, batch_size());
EXPECT_THAT(
MaybeUpdateBatchSize(124),
StatusIs(error::INVALID_ARGUMENT,
HasSubstr(
"Provided batch size does not match converter batch size")));
}
TEST_F(ConverterTest, AddAndGetTensorOrWeights) {
// Add a tensor.
ITensorProxyPtr simple_tensor;
TRT_TensorOrWeights tensor(simple_tensor);
EXPECT_EQ(-1, tensor.batch_size());
TF_EXPECT_OK(MaybeUpdateBatchSize(123));
TF_EXPECT_OK(AddTensorOrWeights("my_tensor", tensor));
// Get the added tensor.
TRT_TensorOrWeights added_tensor;
TF_EXPECT_OK(GetTensorOrWeights("my_tensor", &added_tensor));
EXPECT_EQ(123, added_tensor.batch_size());
// Add the same tensor again.
EXPECT_THAT(AddTensorOrWeights("my_tensor", tensor),
StatusIs(error::ALREADY_EXISTS,
HasSubstr("tensor/weights my_tensor already exist")));
}
template <typename T>
void TestGetWeightRange(ConverterTest* test, TrtWeightStore* weight_store) {
nvinfer1::DataType trt_type;
TF_ASSERT_OK(TfTypeToTrtType(DataTypeToEnum<T>::v(), &trt_type));
TRT_ShapedWeights weights =
weight_store->GetTempWeights(trt_type, CreateDims({2, 3}));
const std::vector<T> values = {T(3), T(1), T(2), T(6), T(5), T(4)};
memcpy(weights.GetPointer<int8>(), values.data(), weights.size_bytes());
float out_min = 0.0f;
float out_max = 0.0f;
TF_EXPECT_OK(test->GetWeightRange(weights, &out_min, &out_max));
EXPECT_EQ(1.0f, out_min);
EXPECT_EQ(6.0f, out_max);
}
TEST_F(ConverterTest, GetWeightRange) {
TestGetWeightRange<float>(this, weight_store_);
TestGetWeightRange<Eigen::half>(this, weight_store_);
TestGetWeightRange<int32>(this, weight_store_);
}
TEST_F(ConverterTest, ProvideQuantizationRange) {
ITensorProxyPtr simple_tensor;
// Asymmetric range
converter_->ProvideQuantizationRange(&simple_tensor, 0.0f, 6.0f);
EXPECT_EQ(6.0f, quantization_ranges_proxy()[&simple_tensor]);
converter_->ProvideQuantizationRange(&simple_tensor, 1.0f, 6.0f);
EXPECT_EQ(6.0f, quantization_ranges_proxy()[&simple_tensor]);
converter_->ProvideQuantizationRange(&simple_tensor, -8.0f, 6.0f);
EXPECT_EQ(8.0f, quantization_ranges_proxy()[&simple_tensor]);
converter_->ProvideQuantizationRange(&simple_tensor, -8.123f, -6.123f);
EXPECT_EQ(8.123f, quantization_ranges_proxy()[&simple_tensor]);
// Symmetric range
converter_->ProvideQuantizationRange(&simple_tensor, -6.123f, 6.123f);
EXPECT_EQ(6.123f, quantization_ranges_proxy()[&simple_tensor]);
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, MaybeApplyQuantizationRanges) {
ITensorProxyPtr input;
ITensorProxyPtr not_infer;
Logger& logger = *Logger::GetLogger();
auto int8_converter = Converter::Create(TrtPrecisionMode::INT8,
/*use_calibration=*/true, &logger,
/*use_implicit_batch=*/true,
/*engine_name=*/"")
.ValueOrDie();
int8_converter->ProvideQuantizationRange(&input, -5.0f, 5.0f);
int8_converter->ProvideQuantizationRange(&not_infer, -100.0f, 100.0f);
int8_converter->MaybeApplyQuantizationRanges();
EXPECT_EQ(input->getDynamicRangeMax(), 5.0f);
EXPECT_EQ(not_infer->getDynamicRangeMax(), 100.0f);
EXPECT_THAT(int8_converter->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, GetTrtBroadcastShape) {
const bool kIsTensor = true;
const bool kIsNotTensor = false;
auto symmetric_test = [this](const std::vector<int>& operand_1_shape,
const std::vector<int>& operand_2_shape,
const bool operand_1_is_tensor,
const bool operand_2_is_tensor,
const std::vector<int>& expected_operand_1_shape,
const std::vector<int>& expected_operand_2_shape,
error::Code expected_code = error::OK,
const char* expected_error_msg_substr = "",
const int operand_1_batch_size = -1,
const int operand_2_batch_size = -1) {
auto create_tensor_or_weights = [](const std::vector<int>& shape,
bool is_tensor, int batch_size = -1) {
if (is_tensor) {
return TRT_TensorOrWeights{nvinfer1::DataType::kFLOAT,
CreateDims(shape), batch_size};
}
TRT_ShapedWeights weights;
weights.shape_ = CreateDims(shape);
return TRT_TensorOrWeights(weights);
};
nvinfer1::Dims operand_1_new_dims, operand_2_new_dims;
TRT_TensorOrWeights operand_1 = create_tensor_or_weights(
operand_1_shape, operand_1_is_tensor, operand_1_batch_size);
TRT_TensorOrWeights operand_2 = create_tensor_or_weights(
operand_2_shape, operand_2_is_tensor, operand_2_batch_size);
// operand_1 broadcast operand_2
EXPECT_THAT(
GetTrtBroadcastShape(operand_1, operand_2, /*check_feasibility=*/true,
/*use_implicit_batch=*/true, &operand_1_new_dims,
&operand_2_new_dims),
StatusIs(expected_code, HasSubstr(expected_error_msg_substr)));
if (expected_code == error::OK) {
EXPECT_THAT(operand_1_new_dims, DimsAreArray(expected_operand_1_shape));
EXPECT_THAT(operand_2_new_dims, DimsAreArray(expected_operand_2_shape));
}
// operand_2 broadcast operand_1
EXPECT_THAT(
GetTrtBroadcastShape(operand_2, operand_1, /*check_feasibility=*/true,
/*use_implicit_batch=*/true, &operand_2_new_dims,
&operand_1_new_dims),
StatusIs(expected_code, HasSubstr(expected_error_msg_substr)));
if (expected_code == error::OK) {
EXPECT_THAT(operand_1_new_dims, DimsAreArray(expected_operand_1_shape));
EXPECT_THAT(operand_2_new_dims, DimsAreArray(expected_operand_2_shape));
}
};
// Both inputs are weights.
symmetric_test(
{1}, {1}, kIsNotTensor, kIsNotTensor, {}, {}, error::INVALID_ARGUMENT,
"Broadcasting requires at least one of the operands be tensors");
// One tensor and one weights.
symmetric_test({1, 1, 1}, {2}, kIsTensor, kIsNotTensor, {1, 1, 1}, {1, 1, 2});
symmetric_test({1, 1, 2}, {2}, kIsTensor, kIsNotTensor, {1, 1, 2}, {1, 1, 2});
symmetric_test({1, 3, 2}, {1}, kIsTensor, kIsNotTensor, {1, 3, 2}, {1, 1, 1});
symmetric_test({1, 1, 1}, {2, 3}, kIsTensor, kIsNotTensor, {1, 1, 1},
{1, 2, 3});
symmetric_test({1, 1, 1}, {2, 3, 4}, kIsTensor, kIsNotTensor, {1, 1, 1},
{2, 3, 4});
symmetric_test({1, 1, 1}, {1, 2, 3, 4}, kIsTensor, kIsNotTensor, {1, 1, 1},
{2, 3, 4});
symmetric_test({1, 3, 4}, {1, 2, 1, 4}, kIsTensor, kIsNotTensor, {1, 3, 4},
{2, 1, 4});
symmetric_test({1, 1, 1}, {2, 1, 1, 1}, kIsTensor, kIsNotTensor, {}, {},
error::INVALID_ARGUMENT, "Infeasible broadcast scheme");
symmetric_test({1, 1, 1}, {2, 1, 1, 1}, kIsTensor, kIsNotTensor, {}, {},
error::INVALID_ARGUMENT, "Infeasible broadcast scheme",
/*operand_1_batch_size=*/2);
symmetric_test({1, 1, 1}, {1, 1, 1, 1, 1}, kIsTensor, kIsNotTensor, {}, {},
error::INVALID_ARGUMENT,
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims 4 vs broadcast #dims 5)");
symmetric_test({3}, {1, 1, 3}, kIsTensor, kIsNotTensor, {}, {},
error::INVALID_ARGUMENT,
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims 2 vs broadcast #dims 3)",
/*operand_1_batch_size=*/2);
// Both inputs are tensors.
symmetric_test({1, 1, 1}, {1, 1}, kIsTensor, kIsTensor, {}, {},
error::INVALID_ARGUMENT,
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims 3 vs broadcast #dims 4)");
symmetric_test({1, 3}, {3}, kIsTensor, kIsTensor, {}, {},
error::INVALID_ARGUMENT,
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims 2 vs broadcast #dims 3)");
symmetric_test({1, 3, 4}, {2, 1, 4}, kIsTensor, kIsTensor, {1, 3, 4},
{2, 1, 4});
symmetric_test({1, 1, 1}, {1, 1, 1, 1}, kIsTensor, kIsTensor, {}, {},
error::INVALID_ARGUMENT,
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims 4 vs broadcast #dims 5)");
symmetric_test({2, 3}, {7, 5}, kIsTensor, kIsTensor, {}, {},
error::INVALID_ARGUMENT, "Infeasible broadcast scheme");
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
TEST_F(ConverterTest, CreateConstantLayer) {
for (auto dtype : {nvinfer1::DataType::kFLOAT, nvinfer1::DataType::kINT32}) {
TRT_ShapedWeights weights =
weight_store_->GetTempWeights(dtype, CreateDims({2, 3, 5}));
ITensorProxyPtr tensor =
converter_->CreateConstantLayer(weights, CreateDims({3, 10}));
ASSERT_NE(nullptr, tensor->trt_tensor());
EXPECT_EQ(dtype, tensor->getType())
<< "Expected " << DebugString(dtype) << " vs. actual "
<< DebugString(tensor->getType());
EXPECT_THAT(tensor->getDimensions(), DimsAreArray({3, 10}));
}
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
class ConvertGraphDefToEngineTest : public ::testing::Test {
public:
Status RunConvertGraphDefToEngine(Scope* s) {
GraphDef gdef;
TF_EXPECT_OK(s->ToGraphDef(&gdef));
std::vector<PartialTensorShape> input_shapes;
int batch_size = -1;
for (const NodeDef& node : gdef.node()) {
absl::string_view node_name(node.name());
if (absl::ConsumePrefix(&node_name, IONamePrefixes::kInputPHName)) {
int port = -1;
EXPECT_TRUE(absl::SimpleAtoi(node_name, &port)) << node.name();
if (input_shapes.size() < port + 1) input_shapes.resize(port + 1);
input_shapes[port] =
PartialTensorShape(node.attr().at("shape").shape());
if (batch_size == -1) {
batch_size = input_shapes[port].dim_size(0);
} else {
EXPECT_EQ(batch_size, input_shapes[port].dim_size(0));
}
}
}
// TODO(laigd): execute the engine and get outputs.
return ConvertGraphDefToEngine(
gdef, TrtPrecisionMode::FP32, /*max_batch_size=*/1,
/*max_workspace_size_bytes=*/64 << 20, input_shapes, &logger_,
/*allocator=*/nullptr, /*calibrator=*/nullptr, &engine_,
/*use_calibration=*/false, /*use_implicit_batch=*/true,
/*convert_successfully=*/nullptr, /*profiles=*/nullptr,
"TRTEngineOp_0_0");
}
protected:
TrtUniquePtrType<nvinfer1::ICudaEngine> engine_;
private:
Logger& logger_ = *Logger::GetLogger();
};
TEST_F(ConvertGraphDefToEngineTest, IdentityGraph) {
Scope s = Scope::NewRootScope();
auto input =
ops::Placeholder(s.WithOpName(StrCat(IONamePrefixes::kInputPHName, 0)),
DT_FLOAT, ops::Placeholder::Shape({1, 1}));
auto output = ops::Identity(s.WithOpName("identity1"), input);
output = ops::Identity(s.WithOpName("identity2"), output);
output = ops::Identity(s.WithOpName(StrCat(IONamePrefixes::kOutputPHName, 0)),
output);
// If the converter marks the input tensor as output tensor, the conversion
// below will fail with:
// > TensorRTOutputPH_0 cannot be both input and output
// > Network must have at least one output
TF_EXPECT_OK(RunConvertGraphDefToEngine(&s));
}
// Returns a vector of shapes from a vector of input tensors. This can be used
// to create optimization profiles.
Status GetShapeFromDataVec(DataVec input_data,
std::vector<TensorShape>* shape_vec) {
shape_vec->reserve(input_data.size());
std::transform(input_data.begin(), input_data.end(),
std::back_inserter(*shape_vec),
[](InputOutputData x) { return x.tensor.shape(); });
return Status::OK();
}
template <typename T>
inline absl::Span<const T> GetSpanForData(const InputOutputData& data) {
const auto& tensor_map = data.tensor.flat<T>();
return absl::Span<const T>(tensor_map.data(), tensor_map.size());
}
std::vector<float> GetDataAsFloat(InputOutputData& data) {
if (data.tensor.dtype() == DT_FLOAT) {
auto span = GetSpanForData<float>(data);
return std::vector<float>(span.begin(), span.end());
}
if (data.tensor.dtype() == DT_HALF) {
return CastVector<Eigen::half, float>(GetSpanForData<Eigen::half>(data));
}
if (data.tensor.dtype() == DT_INT32) {
return CastVector<int32, float>(GetSpanForData<int32>(data));
}
LOG(FATAL) << "DataType not supported for testing "
<< DataTypeString(data.tensor.dtype());
return {};
}
// Class to test various op converters, using both a TrtNodeValidator and
// Converter.
class OpConverterTest : public ::testing::Test {
public:
OpConverterTest()
: tensor_buffer_allocator_(new GpuManagedAllocator()),
scope_(Scope::NewRootScope()) {
QCHECK_EQ(0, cudaStreamCreate(&stream_));
Reset();
}
~OpConverterTest() noexcept override {
QCHECK_EQ(0, cudaStreamDestroy(stream_));
}
Status GetTensorOrWeights(const string& name, TRT_TensorOrWeights* output) {
return converter_->GetTensorOrWeights(name, output);
}
void Reset(TrtPrecisionMode precision_mode_to_test = TrtPrecisionMode::FP32,
TrtTestMode trt_mode = TrtTestMode::kImplicitBatch) {
// Destroy existing TRT objects in a proper order.
converter_.reset(nullptr);
engine_.reset(nullptr);
// Re-create them in proper order.
converter_ =
std::move(Converter::Create(precision_mode_to_test,
/*use_calibration=*/false, &logger_,
/*use_implicit_batch=*/trt_mode ==
TrtTestMode::kImplicitBatch,
/*engine_name=*/"")
.ValueOrDie());
// Reset other related artifacts.
scope_ = Scope::NewRootScope();
}
// Constructs a flat tensor with 'vals' in Unified Memory.
template <typename T>
Tensor AsTensor(gtl::ArraySlice<T> vals) { // non-absl ok
Tensor ret(tensor_buffer_allocator_.get(), DataTypeToEnum<T>::value,
{static_cast<int64>(vals.size())});
std::copy_n(vals.data(), vals.size(), ret.flat<T>().data());
return ret;
}
// Constructs a tensor of "shape" with values "vals" in Unified Memory.
template <typename T>
Tensor AsTensor(gtl::ArraySlice<T> vals, // non-absl ok
const TensorShape& shape) {
Tensor ret(tensor_buffer_allocator_.get(), DataTypeToEnum<T>::value,
{static_cast<int64>(vals.size())});
CHECK(ret.CopyFrom(AsTensor(vals), shape));
return ret;
}
// Constructs a tensor with given values (vals). The tensor type is defined by
// the tf_type argument, its shape is given by input_dims. The tensor is
// constructed using the allocator of OpConverterTest in Unified Memory.
template <typename T>
Tensor AsTensor(std::vector<T> vals, const std::vector<int> input_dims,
DataType tf_type) {
Tensor ret(tensor_buffer_allocator_.get(), tf_type,
{static_cast<int64>(vals.size())});
if (tf_type == DT_FLOAT) {
auto conv_vals = CastVector<T, float>(vals);
std::copy_n(conv_vals.data(), conv_vals.size(), ret.flat<float>().data());
} else if (tf_type == DT_HALF) {
auto conv_vals = CastVector<T, Eigen::half>(vals);
std::copy_n(conv_vals.data(), conv_vals.size(),
ret.flat<Eigen::half>().data());
} else if (tf_type == DT_INT32) {
auto conv_vals = CastVector<T, int32>(vals);
std::copy_n(conv_vals.data(), conv_vals.size(), ret.flat<int32>().data());
} else {
LOG(FATAL) << "Cannot create tensor with type "
<< DataTypeString(tf_type);
}
TensorShape shape;
TF_EXPECT_OK(TensorShapeUtils::MakeShape(input_dims, &shape));
CHECK(ret.CopyFrom(ret, shape));
return ret;
}
// Constructs a flat tensor in Unified Memory.
template <typename T>
Tensor ConstructTensor(int data_size, const T& value = T()) {
std::vector<T> values(data_size, value);
return AsTensor<T>(values);
}
// Constructs a flat tensor in Unified Memory.
template <typename T>
Tensor ConstructTensor(int data_size, const T& value, DataType tf_type) {
std::vector<T> values(data_size, value);
return AsTensor<T>(values, {data_size}, tf_type);
}
void CheckDataTypeMatches(const DataVec& datas) {
if (VLOG_IS_ON(2)) {
int nbBindings = engine_->getNbBindings();
VLOG(2) << "Number of engine bindings: " << nbBindings;
for (int i = 0; i < nbBindings; i++) {
VLOG(2) << "Binding " << i << " name: " << engine_->getBindingName(i);
}
}
for (const auto& data : datas) {
VLOG(2) << "Checking if data type matches for tensor " << data.name;
const int input_index = engine_->getBindingIndex(data.name.c_str());
ASSERT_NE(-1, input_index);
const nvinfer1::DataType trt_dtype =
engine_->getBindingDataType(input_index);
DataType tf_type;
TF_ASSERT_OK(TrtTypeToTfType(trt_dtype, &tf_type));
ASSERT_EQ(data.tensor.dtype(), tf_type)
<< DataTypeString(data.tensor.dtype()) << " vs. "
<< DataTypeString(tf_type);
}
}
Status BuildAndRun(const DataVec& input_data, DataVec* output_data,
const int batch_size = 1) {
// Mark the output tensor as TRT engine output.
std::vector<Converter::EngineOutputInfo> output_info;
for (const auto& data : *output_data) {
nvinfer1::DataType trt_type;
TF_RETURN_IF_ERROR(TfTypeToTrtType(data.tensor.dtype(), &trt_type));
output_info.push_back({data.name, data.name, trt_type});
}
TF_RETURN_IF_ERROR(converter_->RenameAndMarkOutputTensors(output_info));
// Build the TRT engine.
if (engine_.get() != nullptr) {
return errors::Internal("Engine already exists");
}
TrtShapeOptimizationProfile profiles;
if (!converter_->use_implicit_batch()) {
profiles.SetShapeTensorMask(converter_->network());
TF_RETURN_IF_ERROR(profiles.CollectShapeValues(input_data));
// Create a single optimization profile for explicit batch mode
std::vector<TensorShape> input_shapes;
TF_RETURN_IF_ERROR(GetShapeFromDataVec(input_data, &input_shapes));
profiles.AddShape(input_shapes);
std::vector<PartialTensorShape> input_partial_shapes;
TF_RETURN_IF_ERROR(
GetNetworkInputShapes(converter_->network(), &input_partial_shapes));
profiles.InitProfiles(input_partial_shapes,
ProfileStrategy::kImplicitBatchModeCompatible);
}
TF_RETURN_IF_ERROR(
converter_->BuildCudaEngine(&engine_,
/*max_batch_size=*/batch_size,
/*max_workspace_size_bytes=*/1 << 26,
/*allocator=*/nullptr,
/*calibrator=*/nullptr,
/*profiles=*/&profiles));
CHECK_NOTNULL(engine_.get());
CheckDataTypeMatches(input_data);
CheckDataTypeMatches(*output_data);
const int num_bindings = input_data.size() + output_data->size();
std::vector<void*> buffers(num_bindings);
if (engine_->getNbBindings() != num_bindings) {
return errors::Internal("Number of bindings do not match");
}
// Since we have only 1 optimization profile (which is enabled by default)
// it is fine to create execution context directly, instead of calling
// profiles.CreateExecutionContexts()
TrtUniquePtrType<nvinfer1::IExecutionContext> execution_context(
engine_->createExecutionContext());
// Prepare input bindings.
TF_RETURN_IF_ERROR(
SetTrtEngineInputs(engine_.get(), execution_context.get(), 0, buffers,
converter_->use_implicit_batch(), batch_size,
profiles, nullptr, &input_data));
// Prepare output bindings.
TF_RETURN_IF_ERROR(SetTrtEngineOutputs(
engine_.get(), execution_context.get(), 0, buffers,
converter_->use_implicit_batch(), batch_size, nullptr, output_data));
// Execute the TRT engine.
TF_RETURN_IF_ERROR(TrtEnqueue(execution_context.get(), buffers, stream_,
converter_->use_implicit_batch(),
batch_size));
cudaStreamSynchronize(stream_);
return Status::OK();
}
// Adds ITensor for both validation and conversion, assuming explicit batch
// dimension is included in dims (ie for an NCHW tensor dims = {N, C, H, W}).
void AddTestTensorWithTFDims(
const string& name, const std::vector<int32>& dims,
nvinfer1::DataType trt_type = nvinfer1::DataType::kFLOAT,
Status add_input_status = Status::OK()) {
DataType tf_type;
TF_ASSERT_OK(TrtTypeToTfType(trt_type, &tf_type));
ops::Placeholder::Attrs attrs;
TF_EXPECT_OK(TensorShapeUtils::MakeShape(dims, &attrs.shape_));
auto input = ops::Placeholder(scope_.WithOpName(name), tf_type, attrs);
node_inputs_[name] = input.output;
// Add a real ITensor for conversion conditionally.
nvinfer1::Dims trt_dims;
Status status = TensorShapeToTrtDims(
attrs.shape_, converter_->use_implicit_batch(), &trt_dims);
if (converter_->use_implicit_batch() && !status.ok()) {
ASSERT_EQ(add_input_status, status);
return;
} else {
TF_EXPECT_OK(status);
}
if (!converter_->use_implicit_batch() || HasStaticShape(trt_dims)) {
int batch_size = dims.size() > 0 ? dims[0] : 0;
Status status =
converter_->AddInputTensor(name, trt_type, trt_dims, batch_size);
ASSERT_EQ(add_input_status, status);
}
}
// Adds ITensor for both validation and conversion. The difference compared to
// AddTestTensorWithTFDims is in the meaning of the dims parameter. To define
// a tensor with NCHW shape, here we set dims = {C,H,W} and batch_size = N.
// TODO(tfeher) remove this function once all test are updated to use the
// other version of AddTestTensor (defined by
// ParameterizedOpConverterTestBase).
void AddTestTensor(
const string& name, const std::vector<int32>& dims, int batch_size = 1,
nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT) {
std::vector<int32> dims_with_batch(dims.size() + 1);
dims_with_batch[0] = batch_size;
std::copy(dims.begin(), dims.end(), dims_with_batch.begin() + 1);
AddTestTensorWithTFDims(name, dims_with_batch, trt_dtype);
if (HasStaticShape(dims)) {
ASSERT_EQ(batch_size, converter_->batch_size_);
}
}
// Add weights for both validation and conversion.
template <typename T>
void AddTestWeights(const string& name, const std::vector<int>& dims,
const std::vector<T>& values) {
// Add weights for validation.
TensorShape shape;
TF_EXPECT_OK(TensorShapeUtils::MakeShape(dims, &shape));
Tensor t = AsTensor<T>(values, shape);
node_inputs_[name] = ops::Const(scope_.WithOpName(name), t);
// Add weights for conversion.
nvinfer1::DataType dtype;
TF_ASSERT_OK(TfTypeToTrtType(DataTypeToEnum<T>::v(), &dtype));
const nvinfer1::Dims trt_dims = CreateDims(dims);
const int64_t num_elements = TRT_ShapedWeights::count(trt_dims);
QCHECK_EQ(num_elements, values.size())
<< num_elements << " vs " << values.size();
TRT_ShapedWeights weights(dtype);
if (num_elements) {
weights = converter_->weight_store_.GetTempWeights(dtype, trt_dims);
QCHECK_EQ(weights.size_bytes(), sizeof(T) * values.size())
<< weights.size_bytes() << " vs " << sizeof(T) * values.size();
memcpy(weights.GetPointer<int8>(), values.data(), weights.size_bytes());
}
TF_EXPECT_OK(
converter_->AddTensorOrWeights(name, TRT_TensorOrWeights{weights}));
}
template <typename T = int32>
void AddTestWeights(const string& name, const std::vector<int>& dims,
const std::vector<T>& values, DataType tf_type) {
if (tf_type == DT_FLOAT) {
AddTestWeights(name, dims, CastVector<T, float>(values));
} else if (tf_type == DT_HALF) {
AddTestWeights(name, dims, CastVector<T, Eigen::half>(values));
} else if (tf_type == DT_INT32) {
AddTestWeights(name, dims, CastVector<T, int32>(values));
} else {
FAIL() << "Cannot create test weights with type "
<< DataTypeString(tf_type);
}
}
// Test validation in validation-only mode.
Status RunValidation(const Node* node) {
grappler::GrapplerItem item;
TF_EXPECT_OK(scope_.ToGraphDef(&item.graph));
grappler::GraphProperties graph_properties(item);
TF_EXPECT_OK(graph_properties.InferStatically(true));
TrtNodeValidator validator(graph_properties, converter_->precision_mode(),
/*use_calibration=*/false,
converter_->use_implicit_batch());
return validator.IsTensorRTCandidate(node);
}
void RunConversion(const Node* node, error::Code expected_code = error::OK,
const std::string& expected_msg_substr = "") {
EXPECT_THAT(converter_->ConvertNode(node->def()),
StatusIs(expected_code, HasSubstr(expected_msg_substr)));
if (expected_code == error::OK) {
EXPECT_THAT(converter_->network(), LayerNamesNonEmpty());
}
}
// Helper method to run both validation and conversion, when the expected
// output are same.
void RunValidationAndConversion(const NodeDef& node_def,
error::Code expected_code = error::OK,
const std::string& expected_msg_substr = "",
bool should_run_conversion = true) {
// Add the node to the graph.
// TODO(laigd): we should accept a function that adds the node using
// `scope_`, so individual test case can reuse the scope object and we don't
// need to add the edges here by ourselves.
Graph* graph = scope_.graph();
Status status;
Node* node = graph->AddNode(std::move(node_def), &status);
TF_EXPECT_OK(status);
for (int i = 0; i < node_def.input().size(); ++i) {
const string& input_name = node_def.input(i);
const auto& itr = node_inputs_.find(input_name);
QCHECK(itr != node_inputs_.end());
const Output& input = itr->second;
graph->AddEdge(input.node(), input.index(), node, i);
}
status = RunValidation(node);
if (should_run_conversion && status.ok()) {
RunConversion(node, expected_code, expected_msg_substr);
} else {
EXPECT_THAT(status,
StatusIs(expected_code, HasSubstr(expected_msg_substr)));
}
}
// Helper method to run both validation and conversion, and check the output
// shapes.
void RunValidationAndConversion(
const NodeDef& node_def, const Status& status,
const std::string& output_name,
const std::vector<std::vector<int>>& exp_out_dims) {
RunValidationAndConversion(node_def, status.code(), status.error_message(),
true);
if (status.ok()) {
// TODO(tfeher): Enable this check in explicit_batch_mode.
// In dynamic shape mode the output dims cannot be tested here. In that
// case we need to wait for the concrate input shapes to be defined (by
// setBindingDimensions before enqueue) before we can check the output
// dims.
if (converter_->use_implicit_batch()) {
for (int i = 0; i < exp_out_dims.size(); i++) {
TRT_TensorOrWeights output;
string name = i == 0 ? output_name : StrCat(output_name, ":", i);
TF_EXPECT_OK(GetTensorOrWeights(name.c_str(), &output));
ASSERT_TRUE(output.is_tensor());
if (!exp_out_dims[i].empty()) {
// Removing batch dim.
auto out_dims = std::vector<int>(exp_out_dims[i].begin() + 1,
exp_out_dims[i].end());
VLOG(2) << "Testing output shape for tensor " << name;
EXPECT_THAT(output.tensor()->getDimensions(),
DimsAreArray(out_dims));
}
}
}
}
}
// Expose quantization_ranges_ for tests
std::unordered_map<ITensorProxyPtr*, float>& quantization_ranges_proxy() {
return converter_->quantization_ranges_proxy_;
}
// Expose quantization_ranges_ for tests
std::unordered_map<nvinfer1::ITensor*, float>& quantization_ranges() {
return converter_->quantization_ranges_;
}
std::unique_ptr<Converter> converter_;
private:
Logger& logger_ = *Logger::GetLogger();
TrtUniquePtrType<nvinfer1::ICudaEngine> engine_;
cudaStream_t stream_;
std::unique_ptr<Allocator> tensor_buffer_allocator_;
// The scope that contains the graph being converted. Because
// tensor_buffer_allocator_ provides the storage for tensor contents that are
// represented as attributes for graph nodes within scope_,
// tensor_buffer_allocator_ needs to be available when destructing scope_.
// Therefore, scope_ comes after tensor_buffer_allocator_ in the class member
// field list.
Scope scope_;
std::unordered_map<string, Output> node_inputs_;
};
// General test parameters to be used with ops that take a single input tensor.
struct TestParamBase {
// Concrete input dimensions for the test (including the batch dim)
std::vector<int> input_dims;
// Dimensions to define an input with PartialTensorShape. This can be used to
// define networks with dynamic input shape. It can be left empty, in that
// case AddTestTensor sets partial shapes that are appropriate to TrtTestMode.
std::vector<int> partial_input_dims;
// Concrete (static) output dimensions, including batch size as first dim
std::vector<int> expected_output_dims;
// Parameter vector, has converter specific meaning.
std::vector<int> param;
// Expected status of conversion (with concrete error message)
Status status;
// Expected status of BuildAndRun
Status runtime_status;
};
std::ostream& operator<<(std::ostream& os, const TestParamBase& p) {
os << "input_dims" << PrintToString(p.input_dims);
if (!p.partial_input_dims.empty()) {
os << ", partial_input_dims" << PrintToString(p.partial_input_dims);
}
if (!p.expected_output_dims.empty()) {
os << ", exp_out_dims" << PrintToString(p.expected_output_dims);
}
if (!p.param.empty()) {
os << ", param" << PrintToString(p.param);
}
os << ", " << p.status;
return os;
}
// Parameterized version of OpConverterTest. We have the following parameters:
// 1. TrtTestMode: implicit batch, explicit batch, dynamic shape modes
// 2. DataType of the input TF tensors: DT_FLOAT, DT_HALF, DT_INT32
// 3. TrtPrecisionMode argument for the Converter: FP32, FP16, INT8
// We will introduce subclasses that will be instantiated using different
// combinations of the DataType and TrtPrecisionMode parameters.
class ParameterizedOpConverterTestBase
: public OpConverterTest,
public ::testing::WithParamInterface<
std::tuple<TrtTestMode, DataType, TrtPrecisionMode>> {
public:
ParameterizedOpConverterTestBase()
: trt_mode_(std::get<0>(GetParam())),
tf_type_(std::get<1>(GetParam())),
converter_precision_(std::get<2>(GetParam())) {
LOG(INFO) << "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%";
LOG(INFO) << "tf_type_: " << DebugString(tf_type_);
LOG(INFO) << "trt_mode_: " << DebugString(trt_mode_);
LOG(INFO) << "converter_precision_: " << DebugString(converter_precision_);
LOG(INFO) << "%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%";
}
void Reset() {
OpConverterTest::Reset(converter_precision_, trt_mode_);
input_data_.clear();
}
void Reset(TrtPrecisionMode precision) {
OpConverterTest::Reset(precision, trt_mode_);
input_data_.clear();
}
// Getters of protected attributes
DataType get_tf_type() { return tf_type_; }
TrtTestMode get_trt_mode() { return trt_mode_; }
TrtPrecisionMode get_converter_precision() { return converter_precision_; }
// Adds an input ITensor for TRT network. Also creates the corresponding TF
// tensor, and stores it in the list of inputs (input_data_).
//
// The TF tensor is always created with concrete static input shape given by
// dims. The ITensor can have static or dynamic shape based on the trt_mode
// attribute. The ITensor shape is set automatically according to the trt_mode
// parameter, unless the user overrides it with an explicit
// partial_input_shape_dims argument.
//
// Parameters:
// - name of the input node
// - dims actual dimensions of the tensor that we will use during the test
// (including explicit batch dim)
// - values initial values for the TF tensor
// - dtype data type of the tensor
// - partial_input_shape dimensions which can include unknown shapes. This can
// be empty, in that case the partial_input_shape will be set automatically
// depending on the trt_mode argument. (This argument also includes explicit
// batch dim).
// - add_input_status adding ITensor to the network can fail in implicit batch
// mode if the batch size is inconsistent. Using the add_input_status arg we
// can test such errors.
//
template <typename T = int>
void AddTestTensor(const string& name, const std::vector<int32>& dims,
DataType tf_type, const std::vector<T>& values,
const std::vector<int32>& partial_input_shape_dims = {},
Status add_input_status = Status::OK()) {
if (!dims.empty()) {
const auto num_elements = std::accumulate(
std::begin(dims), std::end(dims), 1, std::multiplies<double>());
if (!values.empty() && num_elements != values.size()) {
// Note: for conversion only tests, it is valid to have empty values,
// otherwise the number of elements should match.
LOG(WARNING) << "Expected Test Tensor Shape: " << DebugString(dims)
<< ", Received Input Tensor: " << DebugString(values);
}
}
std::vector<int32> partial_shape;
if (!partial_input_shape_dims.empty()) {
partial_shape = partial_input_shape_dims;
} else {
if (trt_mode_ == TrtTestMode::kDynamicShape) {
// In dynamic shape mode we make all dims unknown.
partial_shape = std::vector<int32>(dims.size(), -1);
} else {
// Use static (known) input shapes.
partial_shape = dims;
}
}
nvinfer1::DataType trt_type;
TF_ASSERT_OK(TfTypeToTrtType(tf_type, &trt_type));
AddTestTensorWithTFDims(name, partial_shape, trt_type, add_input_status);
if (!values.empty()) {
VLOG(2) << "Adding test tensor: " << name << " "
<< DataTypeString(tf_type);
InputOutputData data{name, AsTensor(values, dims, tf_type)};
VLOG(2) << "Added tensor: " << data.name << " with dtype "
<< DataTypeString(data.tensor.dtype());
input_data_.push_back(data);
}
}
// Adds test tensor (same as above) but with the default tf_type defined by
// the test params.
template <typename T = int>
void AddTestTensor(const string& name, const std::vector<int32>& dims,
const std::vector<T>& values = {},
const std::vector<int32>& partial_input_shape_dims = {}) {
AddTestTensor<T>(name, dims, tf_type_, values, partial_input_shape_dims);
}
// Builds and runs the converted network. Checks output tensor shape. Tests
// output values using a matcher. The network can have multiple input and
// output tensors. The inputs are defined by the input_data_ member variable.
void BuildAndRun(const string& name,
const std::vector<std::vector<int>>& expected_output_dims,
const Status& expected_runtime_status,
const std::vector<Matcher<std::vector<float>>>& matcher,
const std::vector<DataType>& out_tf_types = {}) {
TensorShape shape;
const int n_output = expected_output_dims.size();
ASSERT_EQ(n_output, matcher.size());
DataVec output_data;
for (int i = 0; i < n_output; i++) {
TF_EXPECT_OK(
TensorShapeUtils::MakeShape(expected_output_dims[i], &shape));
string out_name = (i == 0) ? name : StrCat(name, ":", i);
DataType out_tf_type =
out_tf_types.size() > i ? out_tf_types[i] : tf_type_;
InputOutputData data{
out_name, ConstructTensor(shape.num_elements(), 0, out_tf_type)};
output_data.push_back(data);
}
const int batch_size =
input_data_.empty() ||
TensorShapeUtils::IsScalar(input_data_[0].tensor.shape())
? 1
: input_data_[0].tensor.shape().dim_size(0);
Status stat =
OpConverterTest::BuildAndRun(input_data_, &output_data, batch_size);
ASSERT_EQ(expected_runtime_status.ok(), stat.ok())
<< "expected status: " << expected_runtime_status
<< ", actual status: " << stat;
if (expected_runtime_status.ok() && stat.ok()) {
for (int i = 0; i < n_output; i++) {
// Check the shape of the actual output tensors
TF_EXPECT_OK(
TensorShapeUtils::MakeShape(expected_output_dims[i], &shape));
EXPECT_TRUE(output_data[i].tensor.shape() == shape)
<< "Expected shape: " << shape.DebugString() << ", actual shape: "
<< output_data[i].tensor.shape().DebugString();
EXPECT_THAT(GetDataAsFloat(output_data[i]), matcher[i]);
}
}
}
// Runs validation and conversion. If conversion is successfull then builds
// the TRT network, executes it and checks the output. Handles multiple output
// tensors.
void TestOpConverterMultiOut(
const string& name, const NodeDef node_def,
const std::vector<std::vector<int>>& expected_output_dims,
const Status& expected_conversion_status,
const Status& expected_runtime_status,
const std::vector<Matcher<std::vector<float>>>& matcher,
const std::vector<DataType>& out_tf_type = {}) {
RunValidationAndConversion(node_def, expected_conversion_status, name,
expected_output_dims);
if (expected_conversion_status.ok()) {
BuildAndRun(name, expected_output_dims, expected_runtime_status, matcher,
out_tf_type);
}
}
// Runs validation and conversion. If conversion is successfull then builds
// the TRT network, executes it and checks the output.
void TestOpConverter(const string& name, const NodeDef node_def,
const std::vector<int>& expected_output_dims,
const Status& expected_conversion_status,
const Status& expected_runtime_status,
const Matcher<std::vector<float>>& matcher,
const std::vector<DataType>& out_tf_types = {}) {
RunValidationAndConversion(
node_def, expected_conversion_status, name,
std::vector<std::vector<int>>({expected_output_dims}));
if (expected_conversion_status.ok()) {
BuildAndRun(name, std::vector<std::vector<int>>({expected_output_dims}),
expected_runtime_status,
std::vector<Matcher<std::vector<float>>>({matcher}),
out_tf_types);
}
}
protected:
const TrtTestMode trt_mode_;
const DataType tf_type_;
const TrtPrecisionMode converter_precision_;
DataVec input_data_;
};
// Op converter test in FP32 mode. While for debugging purposes it might make
// sense to run over all possible combinations, normally a subset of them
// would be sufficient:
// - All valid options to TrtTestMode (implicit, explicit, dynamic shape)
// - DataType: is the TF data type of the input tensors. This usually only
// influences the data type added by Converter::AddInputTensor. We test the
// valid combinations of input data types in AddAndGetInputs, therefore
// for most of the OpConverterTest its is sufficient to test for DT_FLOAT.
// - TrtPrecisionMode: valid options are FP32, FP16 and INT8. This influences
// how TRT handles the precision inside the TRT network, but should not matter
// for the TF -> TRT conversion. Therefore it should be sufficient to test
// for FP32.
class OpConverter_FP32_Test : public ParameterizedOpConverterTestBase {};
// Base class for tests that need to be tested for both FP32 and FP16.
class OpConverter_FP32_FP16_Test : public ParameterizedOpConverterTestBase {};
// Base class for tests that need to be tested for FP32, FP16, and INT32
class OpConverter_FP32_FP16_INT32_Test
: public ParameterizedOpConverterTestBase {};
// Instantiate parameter combinations to OpConverter_<DT_X...>_Test
INSTANTIATE_TEST_CASE_P(
OpConvTestInstantiation, OpConverter_FP32_Test,
::testing::Combine(::testing::ValuesIn(ValidTrtModes),
::testing::Values(DT_FLOAT),
::testing::Values(TrtPrecisionMode::FP32)));
INSTANTIATE_TEST_CASE_P(
OpConvTestInstantiation, OpConverter_FP32_FP16_Test,
::testing::Combine(::testing::ValuesIn(ValidTrtModes),
::testing::Values(DT_FLOAT, DT_HALF),
::testing::Values(TrtPrecisionMode::FP32)));
INSTANTIATE_TEST_CASE_P(
OpConvTestInstantiation, OpConverter_FP32_FP16_INT32_Test,
::testing::Combine(::testing::ValuesIn(ValidTrtModes),
::testing::Values(DT_FLOAT, DT_HALF, DT_INT32),
::testing::Values(TrtPrecisionMode::FP32)));
template <typename T>
void CopyTensorElements(const Tensor& tensor, protobuf::RepeatedField<T>* out) {
out->Clear();
if (tensor.NumElements() == 0) return;
// TensorProto does not need to have all the elements present and can truncate
// trailing elements with the same value for compressed representation. Such
// elements are derived based on the tensor shape.
const auto flat = tensor.flat<T>();
int64 last_index = 0;
for (int64 i = 0; i < tensor.NumElements(); ++i) {
if (flat(i) != flat(last_index)) {
last_index = i;
}
}
int num_out_elements = last_index + 1;
out->Reserve(num_out_elements);
out->AddNAlreadyReserved(num_out_elements);
const T* src = flat.data();
T* dst = out->mutable_data();
std::copy(src, src + num_out_elements, dst);
}
template <DataType dtype, typename InputCType, typename OutputCType>
void TestConvertConst(OpConverterTest* test) {
NodeDef node_def;
node_def.set_name("my_const");
node_def.set_op("Const");
auto reset_and_test = [&node_def, test](
const Tensor& tensor, const bool as_tensor_content,
const std::vector<int>& expected_dims,
const std::vector<OutputCType>& expected_value) {
test->Reset();
TensorProto* tensor_attr =
(*node_def.mutable_attr())["value"].mutable_tensor();
tensor_attr->Clear();
if (as_tensor_content) {
tensor.AsProtoTensorContent(tensor_attr);
} else {
tensor.shape().AsProto(tensor_attr->mutable_tensor_shape());
tensor_attr->set_dtype(tensor.dtype());
if (tensor.dtype() == DT_FLOAT) {
CopyTensorElements<float>(tensor, tensor_attr->mutable_float_val());
} else if (tensor.dtype() == DT_INT32) {
CopyTensorElements<int32>(tensor, tensor_attr->mutable_int_val());
} else {
tensor.AsProtoField(tensor_attr);
}
}
test->RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(test->GetTensorOrWeights("my_const", &output));
EXPECT_THAT(output.weights(), ShapedWeightsHasDimsAndValues<OutputCType>(
expected_dims, expected_value));
};
auto& attr = *node_def.mutable_attr();
attr["dtype"].set_type(dtype);
{
// By default empty tensor will pick DT_FLOAT as data type and we fix it
// here.
Tensor t(dtype); // Empty tensor.
reset_and_test(t, false, {}, {});
}
{
Tensor t = test::AsScalar<InputCType>(12);
std::vector<int> expected_dims{1};
// Scalars are represented as rank 0 tensors.
expected_dims.clear();
reset_and_test(t, false, expected_dims, {12});
reset_and_test(t, true, expected_dims, {12});
}
{
Tensor t = test->AsTensor<InputCType>({1, 2});
reset_and_test(t, false, {2}, {1, 2});
reset_and_test(t, true, {2}, {1, 2});
}
{
Tensor t =
test->AsTensor<InputCType>({1, 2, 3, 4, 5, 6}, TensorShape({2, 3}));
reset_and_test(t, false, {2, 3}, {1, 2, 3, 4, 5, 6});
reset_and_test(t, true, {2, 3}, {1, 2, 3, 4, 5, 6});
}
{
// Set all tensor elements to the same value. Such tensors are encoded
// using a single element list in tensor proto.
Tensor t =
test->AsTensor<InputCType>({1, 1, 1, 1, 1, 1}, TensorShape({2, 3}));
reset_and_test(t, false, {2, 3}, {1, 1, 1, 1, 1, 1});
reset_and_test(t, true, {2, 3}, {1, 1, 1, 1, 1, 1});
}
{
// Set trailing tensor elements to the same value. Such tensors are
// encoded by truncating all equal elements except the first one.
Tensor t =
test->AsTensor<InputCType>({2, 2, 1, 1, 1, 1}, TensorShape({2, 3}));
reset_and_test(t, false, {2, 3}, {2, 2, 1, 1, 1, 1});
reset_and_test(t, true, {2, 3}, {2, 2, 1, 1, 1, 1});
}
}
TEST_F(OpConverterTest, ConvertConst) {
{
Reset();
NodeDef node_def = MakeConstNodeDef<double>("my_const", {});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Unsupported tensorflow data type double");
}
{
Reset();
Tensor tensor = AsTensor<int64>({1, std::numeric_limits<int64>::max(), 1, 1,
1, std::numeric_limits<int64>::lowest()},
TensorShape({2, 3}));
NodeDef node_def;
node_def.set_name("my_const");
node_def.set_op("Const");
(*node_def.mutable_attr())["dtype"].set_type(DT_INT64);
TensorProto* tensor_attr =
(*node_def.mutable_attr())["value"].mutable_tensor();
tensor_attr->Clear();
tensor.AsProtoTensorContent(tensor_attr);
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"outside the range of int32");
}
TestConvertConst<DT_FLOAT, float, float>(this);
TestConvertConst<DT_INT8, int8, int32>(this);
TestConvertConst<DT_UINT8, uint8, int32>(this);
TestConvertConst<DT_INT16, int16, int32>(this);
TestConvertConst<DT_UINT16, uint16, int32>(this);
TestConvertConst<DT_INT32, int32, int32>(this);
TestConvertConst<DT_UINT32, uint32, int32>(this);
TestConvertConst<DT_INT64, int64, int32>(this);
TestConvertConst<DT_UINT64, uint64, int32>(this);
}
template <typename T>
NodeDef CreateFusedBatchNormOp(DataType tf_type, std::string data_format,
bool is_training, float epsilon) {
Scope s = Scope::NewRootScope();
auto x = ops::Placeholder(s.WithOpName("x"), tf_type);
auto scale = ops::Placeholder(s.WithOpName("scale"), tf_type);
auto offset = ops::Placeholder(s.WithOpName("offset"), tf_type);
auto mean = ops::Placeholder(s.WithOpName("mean"), tf_type);
auto variance = ops::Placeholder(s.WithOpName("variance"), tf_type);
typename T::Attrs attrs;
attrs.data_format_ = data_format;
attrs.is_training_ = is_training;
if (epsilon > 0) {
attrs.epsilon_ = epsilon;
} else {
EXPECT_GE(epsilon, 0);
}
return T(s.WithOpName("my_batchnorm"), x, scale, offset, mean, variance,
attrs)
.operation.node()
->def();
}
TEST_P(OpConverter_FP32_Test, ConvertFusedBatchNorm) {
using OpFunc = std::function<NodeDef(DataType, std::string, bool, float)>;
std::vector<OpFunc> get_node_def_vec{
CreateFusedBatchNormOp<ops::FusedBatchNorm>,
CreateFusedBatchNormOp<ops::FusedBatchNormV2>,
CreateFusedBatchNormOp<ops::FusedBatchNormV3>};
struct TestParam {
std::string data_format;
int tensor_input_idx; // Index of an input that will be provided as tensor.
bool is_training;
float epsilon;
Status conversion_status;
bool keep_channel_unknown;
};
struct NodeInput {
std::string name;
std::vector<int> dims;
std::vector<float> val;
};
std::vector<NodeInput> node_input{
{"x", {2, 3, 2, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}},
{"scale", {3}, {7, 8, 9}},
{"offset", {3}, {10, 20, 30}},
{"mean", {3}, {1, 2, 3}},
{"variance", {3}, {4, 5, 6}}};
std::vector<float> expected_output{10.0, 13.495633, 23.574135, 27.148273,
37.342354, 41.013527, 30.9738, 34.469433,
45.018955, 48.59309, 59.369415, 63.04059};
for (auto get_node_def : get_node_def_vec) {
NodeDef tmp_node_def = get_node_def(tf_type_, "NCHW", true, 0);
std::string op_name = tmp_node_def.op();
std::vector<TestParam> test_param{
{"NHWC", 0, false, 0,
errors::Unimplemented(StrCat(
op_name, " only supports data_format=NCHW, at my_batchnorm"))},
{"NCHW", 0, true, 0,
errors::Unimplemented(StrCat(
op_name, " only supports is_training=false, at my_batchnorm"))},
{"NCHW", 1, false, 0,
errors::Unimplemented(StrCat("The input \"scale\" for ", op_name,
" must be a constant, at my_batchnorm"))},
{"NCHW", 2, false, 0,
errors::Unimplemented(StrCat("The input \"offset\" for ", op_name,
" must be a constant, at my_batchnorm"))},
{"NCHW", 3, false, 0,
errors::Unimplemented(StrCat("The input \"mean\" for ", op_name,
" must be a constant, at my_batchnorm"))},
{"NCHW", 4, false, 0,
errors::Unimplemented(StrCat("The input \"variance\" for ", op_name,
" must be a constant, at my_batchnorm"))},
{"NCHW", 0, false, 0.01}}; // The last one is the only test that runs.
if (trt_mode_ == TrtTestMode::kDynamicShape) {
test_param.push_back(
{"NCHW", 0, false, 0.01,
errors::InvalidArgument(
"Channel dimension must be static, at my_batchnorm"),
true});
}
for (auto p : test_param) {
Reset();
NodeDef node_def =
get_node_def(tf_type_, p.data_format, p.is_training, p.epsilon);
for (int i = 0; i < node_input.size(); i++) {
if (i == 0 || i == p.tensor_input_idx) {
// The first input (x) is always added as a tensor, and it hase shape
// NCHW. The other inputs are per channel values (1D, size C).
//
// In implicit batch mode, it is not possible to add any of the 1D
// inputs as a tensor: the first dim is always treated as batch dim in
// implicit batch mode, and that has to agree for all tensors. We have
// two input tensors with shapes NCHW and C and in general N != C.
// The converter already picked up N from the fist input, and reports
// an error when we try to add any other tensors with not matching
// first dim.
//
// This restriction does not apply in explicit batch mode: the tensors
// can have different first dim. The converter still expects that only
// the first arg is a tensor. TODO(tfeher) Check if one can relax this
// restriction.
Status expected_status =
(i != 0 && trt_mode_ == TrtTestMode::kImplicitBatch)
? errors::InvalidArgument(
StrCat("Batch size doesn't match for tensor ",
node_input[i].name,
": Provided batch size does not match "
"converter batch size: 3 vs 2"))
: Status::OK();
std::vector<int> partial_input_shape;
if (i == 0 && trt_mode_ == TrtTestMode::kDynamicShape &&
!p.keep_channel_unknown) {
// keep channel dim static (known)
partial_input_shape.resize(4, -1);
partial_input_shape[1] = node_input[i].dims[1];
}
AddTestTensor(node_input[i].name, node_input[i].dims, tf_type_,
node_input[i].val, partial_input_shape,
expected_status);
} else {
AddTestWeights(node_input[i].name, node_input[i].dims,
node_input[i].val, tf_type_);
}
}
TestOpConverter("my_batchnorm", node_def, node_input[0].dims,
p.conversion_status, Status::OK(),
ArrayFloatNear(expected_output));
}
}
}
TEST_P(OpConverter_FP32_Test, ConvertTranspose) {
// Get the NodeDef for Transpose.
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32);
auto transpose = ops::Transpose(s.WithOpName("my_transpose"), input, weights);
const NodeDef& node_def = transpose.operation.node()->def();
std::vector<TestParamBase> test_params = {
// For the first test we leave param empty. This signals to use a
// input as weight which will be invalid
TestParamBase{{3, 1, 2, 1},
{},
{},
{},
Status(error::UNIMPLEMENTED,
"The input \"perm\" for Transpose must be a "
"constant, at my_transpose")},
TestParamBase{{1, 1, 2, 3},
{},
{},
{0, 1, 2},
Status(error::INVALID_ARGUMENT,
"Rank of perm for transpose does not match with "
"that of the input.")},
// Transpose batch dim
TestParamBase{
{1, 1, 2, 3},
{},
{3, 2, 1, 1},
{3, 2, 1, 0},
(trt_mode_ == TrtTestMode::kImplicitBatch)
? Status(error::UNIMPLEMENTED,
"Transpose at batch dimension is not supported")
: Status::OK()},
TestParamBase{{1, 1, 2, 3}, {}, {1, 3, 1, 2}, {0, 3, 1, 2}},
};
if (trt_mode_ == TrtTestMode::kDynamicShape) {
// Dynamic shape tests where some shapes are known
test_params.push_back(TestParamBase{
{1, 1, 2, 3}, {-1, 1, 2, -1}, {1, 3, 1, 2}, {0, 3, 1, 2}});
}
std::vector<float> expected_values{1, 4, 2, 5, 3, 6};
for (auto p : test_params) {
SCOPED_TRACE(p);
Reset();
AddTestTensor("input", p.input_dims, {1, 2, 3, 4, 5, 6},
p.partial_input_dims);
if (p.param.empty()) {
AddTestTensor("weights", {3});
} else {
AddTestWeights<int32>("weights", {static_cast<int>(p.param.size())},
p.param);
}
TestOpConverter("my_transpose", node_def, p.expected_output_dims, p.status,
p.runtime_status, ElementsAreArray(expected_values));
}
}
TEST_P(OpConverter_FP32_Test, ConvertReshape) {
// Get the NodeDef for Reshape.
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32);
auto reshape = ops::Reshape(s.WithOpName("my_reshape"), input, weights);
const NodeDef& node_def = reshape.operation.node()->def();
if (trt_mode_ == TrtTestMode::kImplicitBatch) {
// Shape is a tensor, should fail in implicit batch mode.
Reset();
AddTestTensor("input", {3, 2, 1});
AddTestTensor("weights", {3});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"The input \"shape\" for Reshape must be a constant in implicit batch "
"mode, at my_reshape");
} else if (!IS_TRT_VERSION_GE(7, 1, 3, 0)) {
// Shape is a tensor, should fail before TRT 7.1.3 even in explicit batch /
// dynamic shape mode.
Reset();
AddTestTensor("input", {3, 2, 1});
AddTestTensor("weights", {3});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Non constant shape input tensor for Reshape requires minimum TRT "
"7.1.3");
}
Status reshape_from_scalar_status =
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Internal(
"Failed to convert input input to a TRT_TensorOrWeights: "
"Scalar input tensor is not supported since the first "
"dimension is treated as batch dimension by TRT")
: Status::OK();
Status add_scalar_tensor_status =
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Internal(
"Scalars cannot be represented in implicit batch mode")
: Status::OK();
Status reshape_to_scalar_status =
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Unimplemented(
"Reshape to shape=[] is not supported, at my_reshape")
: Status::OK();
Status reshape_batch_status =
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Unimplemented(
"Reshape on batch dimension is not supported, at my_reshape")
: Status::OK();
struct TestParams {
std::vector<int> tensor_dims;
std::vector<int> shape;
std::vector<int> expected_shape;
Status conversion_status;
Status runtime_status;
std::vector<int> shape_prof; // needed concrete values if shape == -1.
Status add_test_tensor_status;
};
std::vector<TestParams> params = {
// Reshape scalar to tensor, should fail in implicit batch mode.
TestParams{{},
{1, 1},
{},
reshape_from_scalar_status,
{},
{},
add_scalar_tensor_status},
// Reshape tensor to scalar, should fail in implicit batch mode.
// - In explicit batch mode if shape is set as weight it works.
// - In explicit batch mode && using shape as tensor input it should
// fail. In that case we set the expected conversion status in the
// test loop.
TestParams{{1, 1}, {}, {}, reshape_to_scalar_status},
// Reshape at batch dimension, should fail in implicit batch mode.
TestParams{{1, 1, 2, 3}, {3, 1, 1, 2}, {}, reshape_batch_status},
TestParams{{2, 1, 2, 3}, {-1, 1, 4}, {3, 1, 4}, reshape_batch_status},
// Tests that should succeed in every trt_mode.
TestParams{{1, 1, 2, 3}, {-1, 1, 3, 2}, {1, 1, 3, 2}},
TestParams{{1, 1, 2, 3}, {1, 1, -1}, {1, 1, 6}},
TestParams{{1, 1, 2, 3}, {1, 1, 3, 2}},
TestParams{{2, 1, 2, 3}, {2, 1, 3, 2}},
TestParams{{1, 1, 1}, {1}},
TestParams{{1}, {1, 1}},
TestParams{{2, 1, 1}, {2}},
TestParams{{2}, {2, 1}},
};
if (trt_mode_ == TrtTestMode::kImplicitBatch) {
// Reshape tensor with zero rank using an empty shape tensor, should fail in
// implicit batch mode. In explicit batch mode this is an identity operation
// and does not add a reshape layer therefore we do not test it.
params.push_back(TestParams{{},
{},
{},
reshape_from_scalar_status,
{},
{},
add_scalar_tensor_status});
}
// Testing the methods for representing the reshape shape for IShuffleLayer:
// as a weight (true) or as a tensor (false).
std::vector<bool> shape_input_options(1, true);
if (trt_mode_ != TrtTestMode::kImplicitBatch &&
IS_TRT_VERSION_GE(7, 1, 3, 0)) {
shape_input_options.push_back(false);
}
for (auto p : params) {
for (auto shape_as_weight : shape_input_options) {
std::ostringstream oss;
oss << "shape " << PrintToString(p.shape);
SCOPED_TRACE(StrCat(oss.str(), shape_as_weight ? " weight" : " tensor"));
if (!shape_as_weight && p.shape.empty()) {
p.conversion_status = errors::Unimplemented(
"Reshape with dynamic input requires 1D input tensor, at "
"my_reshape");
}
Reset();
const int n_elements =
std::accumulate(p.tensor_dims.begin(), p.tensor_dims.end(), 1,
std::multiplies<int>());
std::vector<float> input_vec(n_elements);
std::iota(input_vec.begin(), input_vec.end(), 1);
AddTestTensor("input", p.tensor_dims, tf_type_, input_vec, {},
p.add_test_tensor_status);
if (shape_as_weight) {
AddTestWeights<int32>("weights", {static_cast<int>(p.shape.size())},
p.shape);
} else {
std::vector<int32> dims;
std::vector<int32> values{p.shape};
if (!p.shape.empty()) {
dims.push_back(p.shape.size());
} else {
// If the shape is empty we use a dummy value to ensure that
// AddTestTensor creates the corresponding entry in InputOutputData.
values.push_back(1);
}
AddTestTensor("weights", dims, DT_INT32, values, dims);
}
std::vector<int> expected_shape =
p.expected_shape.empty() ? p.shape : p.expected_shape;
VLOG(2) << "Calling TestOpConverter";
TestOpConverter("my_reshape", node_def, expected_shape,
p.conversion_status, p.runtime_status,
ElementsAreArray(input_vec));
}
}
}
TEST_P(OpConverter_FP32_Test, ConvertShape) {
// Get the NodeDef for Shape op.
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto shape = ops::Shape(s.WithOpName("my_shape"), input);
const NodeDef& node_def = shape.operation.node()->def();
Status conversion_status =
(trt_mode_ == TrtTestMode::kImplicitBatch)
? errors::Unimplemented(
"Shape is only supported for explicit batch mode.")
: Status::OK();
std::vector<TestParamBase> test_params = {
// TODO(b/166274212): Enable the test parameter for TensorRT 7.1.3.
#if !IS_TRT_VERSION_GE(7, 1, 3, 0)
TestParamBase{{1, 2, 3}, {}, {3}, {}, conversion_status},
#endif
// Add input as weight (we use non empty param ({1}) to trigger this).
TestParamBase{{1, 2, 3}, {}, {3}, {1}, conversion_status},
};
auto input_is_weight = [](const TestParamBase p) { return !p.param.empty(); };
for (auto p : test_params) {
SCOPED_TRACE(p);
Reset();
// The number of elements of the input tensor. We leave it 0 in case we do
// not need to add an input tensor. This happens in explicit batch mode: the
// shape is known at conversion time and therefore the shape is added to the
// network as a constant layer. In this case the single node network that
// we use for the unit test have no actual input tensor when it is converted
// to a TensorRT network.
int n_elements = 0;
if (input_is_weight(p) || trt_mode_ != TrtTestMode::kExplicitBatch) {
// Calculate the number of elements for adding input data.
n_elements = std::accumulate(p.input_dims.begin(), p.input_dims.end(), 1,
std::multiplies<int>());
}
std::vector<float> input_val(n_elements, 1);
if (!input_is_weight(p)) {
AddTestTensor("input", p.input_dims, input_val);
} else {
AddTestWeights("input", p.input_dims, input_val, tf_type_);
}
TestOpConverter("my_shape", node_def, p.expected_output_dims, p.status,
p.runtime_status, ElementsAreArray(p.input_dims),
{DT_INT32});
}
}
struct MatMulTestParams {
std::vector<int> shape_a;
std::vector<int> values_a;
bool transpose_a;
std::vector<int> shape_b;
std::vector<int> values_b;
bool transpose_b;
std::vector<int> expected_shape;
std::vector<int> expected_output;
};
// Helper function for testing MatMul and BatchMatMul. get_matmul is a function
// used to generate the node. It accepts (DataType, transpose_a, transpose_b) as
// parameters.
void TestMatMulHelper(
ParameterizedOpConverterTestBase* test,
const std::function<NodeDef(DataType, bool, bool)>& get_matmul,
const std::vector<MatMulTestParams>& params) {
{
// Unsupported data type.
test->Reset();
NodeDef node_def = get_matmul(DT_INT32, false, false);
test->AddTestTensor("input", {1, 2}, DT_INT32, {});
test->AddTestWeights<int32>("weights", {2, 1}, {3, 5});
test->RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
StrCat("Data type int32 is not supported for ", node_def.op(),
", must be one of [float, half], at my_matmul"));
}
// FC conversion depends on whether the last dim of A is known or not. In
// Dynamic shape mode, we will check whether A is handled correctly if it has
// a partially known input shape (last dim known).
std::vector<bool> a_test_partial_shape_values{false};
if (test->get_trt_mode() == TrtTestMode::kDynamicShape) {
a_test_partial_shape_values.push_back(true);
}
for (auto p : params) {
for (bool a_is_tensor : {true, false}) {
for (bool b_is_tensor : {true, false}) {
for (bool a_partial_shape : a_test_partial_shape_values) {
if (a_partial_shape && !a_is_tensor) {
// Only tensors can have partial shape.
continue;
}
if (!a_is_tensor && !b_is_tensor) {
// Skip test when both args are weights. We do not convert this
// since const folding eliminates this case.
continue;
}
SCOPED_TRACE(StrCat("A", p.transpose_a ? ".T" : "", " is ",
a_is_tensor ? "tensor" : "weight", ", B",
p.transpose_b ? ".T" : "", " is ",
b_is_tensor ? "tensor " : "weight, rank A ",
p.shape_a.size(), ", rank B ", p.shape_b.size()));
test->Reset();
NodeDef node_def =
get_matmul(test->get_tf_type(), p.transpose_a, p.transpose_b);
const bool is_batch_matmul = node_def.op() == "BatchMatMul";
if (a_is_tensor) {
if (a_partial_shape) {
// Prepare a partial shape for A where only the last dim is known.
std::vector<int> partial_shape(p.shape_a.size(), -1);
int k = p.shape_a.size() - 1;
partial_shape.at(k) = p.shape_a.at(k);
test->AddTestTensor("input", p.shape_a, test->get_tf_type(),
p.values_a, partial_shape);
} else {
test->AddTestTensor("input", p.shape_a, p.values_a);
}
} else {
test->AddTestWeights("input", p.shape_a, p.values_a,
test->get_tf_type());
}
if (b_is_tensor) {
if (a_is_tensor && p.shape_a[0] != p.shape_b[0] &&
test->get_trt_mode() == TrtTestMode::kImplicitBatch) {
VLOG(2) << "Skipping test with inpcompatible batch dimensions";
continue;
}
test->AddTestTensor("weights", p.shape_b, p.values_b);
} else {
test->AddTestWeights("weights", p.shape_b, p.values_b,
test->get_tf_type());
}
Status conversion_status = Status::OK();
if (test->get_trt_mode() == TrtTestMode::kImplicitBatch) {
// Implicit batch mode has several restriction. We change conversion
// status accordingly.
if (is_batch_matmul) {
if (a_is_tensor && p.shape_a.size() < p.shape_b.size()) {
conversion_status = errors::InvalidArgument(
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims ",
p.shape_a.size(), " vs broadcast #dims ", p.shape_b.size(),
")");
}
if (b_is_tensor && p.shape_b.size() < p.shape_a.size()) {
conversion_status = errors::InvalidArgument(
"Broadcasting beyond batch dimension is not supported "
"(tensor #dims ",
p.shape_b.size(), " vs broadcast #dims ", p.shape_a.size(),
")");
}
if ((!a_is_tensor || !b_is_tensor) && p.shape_a[0] != 1) {
conversion_status = errors::Unimplemented(
"TensorRT does not support batched constants in implicit "
"batch mode.");
}
} else if ((a_is_tensor && p.shape_a.size() <= 2 &&
(p.transpose_a || b_is_tensor)) ||
(b_is_tensor && p.shape_b.size() <= 2)) {
conversion_status = errors::InvalidArgument(
"MatMul with 2D tensors requires explicit batch mode, or that"
" tensor A is not transposed and B is a constant tensor.");
}
}
test->TestOpConverter("my_matmul", node_def, p.expected_shape,
conversion_status, Status::OK(),
ElementsAreArray(p.expected_output));
if (!conversion_status.ok()) {
VLOG(2) << "Converted with status " << conversion_status;
}
VLOG(2) << "== Finished test iteration ==";
}
}
}
}
}
template <typename LayerType>
void CheckAddedLayers(OpConverterTest* test, bool expect_found) {
bool layer_found = false;
for (int i = 0; i < test->converter_->network()->getNbLayers(); i++) {
nvinfer1::ILayer* layer = test->converter_->network()->getLayer(i);
if (dynamic_cast<LayerType*>(layer)) {
layer_found = true;
}
}
EXPECT_EQ(expect_found, layer_found);
}
std::vector<MatMulTestParams> GetMatMulTestParams() {
std::vector<MatMulTestParams> params{
// clang-format off
MatMulTestParams{{2, 2}, {0, 1, 2, 3}, false, // A (shape, val, T?)
{2, 2}, {0, 1, 2, 3}, false, // B (shape, val, T?)
{2, 2}, {2, 3, 6, 11}}, // result (shape, val)
MatMulTestParams{{2, 2}, {0, 1, 2, 3}, false,
{2, 2}, {0, 1, 2, 3}, true,
{2, 2}, {1, 3, 3, 13}},
MatMulTestParams{{2, 2}, {0, 1, 2, 3}, true,
{2, 2}, {0, 1, 2, 3}, false,
{2, 2}, {4, 6, 6, 10}},
MatMulTestParams{{2, 2}, {0, 1, 2, 3}, true,
{2, 2}, {0, 1, 2, 3}, true,
{2, 2}, {2, 6, 3, 11}},
MatMulTestParams{{2, 3}, {0, 1, 2, 3, 4, 5}, false,
{2, 3}, {1, 2, 3, 4, 5, 6}, true,
{2, 2}, {8, 17, 26, 62}},
MatMulTestParams{{2, 3}, {0, 1, 2, 3, 4, 5}, true,
{2, 3}, {1, 2, 3, 4, 5, 6}, false,
{3, 3}, {12, 15, 18, 17, 22, 27, 22, 29, 36}},
MatMulTestParams{{3, 2}, {0, 1, 2, 3, 4, 5}, false,
{2, 3}, {1, 2, 3, 4, 5, 6}, false,
{3, 3}, {4, 5, 6, 14, 19, 24, 24, 33, 42}},
MatMulTestParams{{3, 2}, {0, 1, 2, 3, 4, 5}, true,
{2, 3}, {1, 2, 3, 4, 5, 6}, true,
{2, 2}, {16, 34, 22, 49}},
// clang-format on
};
return params;
}
TEST_P(OpConverter_FP32_Test, ConvertMatMul) {
// Get the NodeDef for MatMul.
auto get_matmul_nodedef = [](DataType dtype, bool transpose_a,
bool transpose_b) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), dtype);
auto weights = ops::Placeholder(s.WithOpName("weights"), dtype);
const auto matmul_attrs =
ops::MatMul::TransposeA(transpose_a).TransposeB(transpose_b);
auto matmul =
ops::MatMul(s.WithOpName("my_matmul"), input, weights, matmul_attrs);
return matmul.operation.node()->def();
};
TestMatMulHelper(this, get_matmul_nodedef, GetMatMulTestParams());
}
TEST_P(OpConverter_FP32_Test, ConvertBatchMatMul) {
// Get the NodeDef for BatchMatMul.
auto get_batch_matmul_nodedef = [](DataType dtype, bool transpose_a,
bool transpose_b) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), dtype);
auto weights = ops::Placeholder(s.WithOpName("weights"), dtype);
const auto matmul_attrs =
ops::BatchMatMul::AdjX(transpose_a).AdjY(transpose_b);
auto matmul = ops::BatchMatMul(s.WithOpName("my_matmul"), input, weights,
matmul_attrs);
return matmul.operation.node()->def();
};
// We derive test data from the MatMul test params by adding extra leading
// dimensions.
std::vector<MatMulTestParams> params_2d = GetMatMulTestParams();
std::vector<MatMulTestParams> params;
params.reserve(params_2d.size() * 3 + 1);
auto insert_ones = [](std::vector<int> v, int n) {
std::vector<int> ones(n, 1);
ones.insert(ones.end(), v.begin(), v.end());
return ones;
};
// Add a leading 1 dimension to A, B and result.
std::transform(params_2d.begin(), params_2d.end(), std::back_inserter(params),
[](MatMulTestParams p) {
p.shape_a.insert(p.shape_a.begin(), 1);
p.shape_b.insert(p.shape_b.begin(), 1);
p.expected_shape.insert(p.expected_shape.begin(), 1);
return p;
});
// Test with N > 1: weights cannot be batched in implicit batch mode.
// clang-format off
params.push_back(
MatMulTestParams{{2, 2, 2}, {0, 1, 2, 3, 0, 1, 2, 3}, false, // A
{2, 2, 2}, {0, 1, 2, 3, 0, 1, 2, 3}, false, // B
{2, 2, 2}, {2, 3, 6, 11, 2, 3, 6, 11}} // result
);
params.push_back(
MatMulTestParams{{2, 2, 3}, {0, 1, 2, 3, 4, 5, 0, 1, 2, 3, 4, 5},
false,
{2, 2, 3}, {1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6}, true,
{2, 2, 2}, {8, 17, 26, 62, 8, 17, 26, 62}});
// clang-format on
// Add two leading 1 dimensions to A, B and result.
std::transform(params_2d.begin(), params_2d.end(), std::back_inserter(params),
[insert_ones](MatMulTestParams p) {
p.shape_a = insert_ones(p.shape_a, 2);
p.shape_b = insert_ones(p.shape_b, 2);
p.expected_shape = insert_ones(p.expected_shape, 2);
return p;
});
// Test broadcast: add two leading 1 dimensions to A, but not to B.
std::transform(params_2d.begin(), params_2d.end(), std::back_inserter(params),
[insert_ones](MatMulTestParams p) {
p.shape_a = insert_ones(p.shape_a, 2);
p.expected_shape = insert_ones(p.expected_shape, 2);
return p;
});
// Test broadcast: add a leading 1 dimension to A and two leading 1s to B.
// Broadcasting A need a dynamic brodacast which will be incompatible with
// FC layer.
std::transform(params_2d.begin(), params_2d.end(), std::back_inserter(params),
[insert_ones](MatMulTestParams p) {
p.shape_a = insert_ones(p.shape_a, 1);
p.shape_b = insert_ones(p.shape_b, 2);
p.expected_shape = insert_ones(p.expected_shape, 2);
return p;
});
// Test with N > 1: since weights cannot be batched in implicit batch mode.
// We tests with batch size 2.
std::transform(params_2d.begin(), params_2d.end(), std::back_inserter(params),
[insert_ones](MatMulTestParams p) {
p.shape_a.insert(p.shape_a.begin(), 2);
p.values_a.reserve(p.values_a.size() * 2);
p.values_a.insert(p.values_a.end(), p.values_a.begin(),
p.values_a.end());
p.shape_b.insert(p.shape_b.begin(), 2);
p.values_b.reserve(p.values_b.size() * 2);
p.values_b.insert(p.values_b.end(), p.values_b.begin(),
p.values_b.end());
p.expected_shape.insert(p.expected_shape.begin(), 2);
p.expected_output.reserve(p.expected_output.size() * 2);
p.expected_output.insert(p.expected_output.end(),
p.expected_output.begin(),
p.expected_output.end());
return p;
});
// 4D tensor where the second "batch dim" is not 1
params.push_back(MatMulTestParams{
{1, 2, 4, 5},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39},
false, // A
{1, 2, 3, 5},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30},
true, // B
{1, 2, 4, 3},
{40, 90, 140, 115, 290, 465, 190, 490,
790, 265, 690, 1115, 1990, 2540, 3090, 2440,
3115, 3790, 2890, 3690, 4490, 3340, 4265, 5190}}); // result
TestMatMulHelper(this, get_batch_matmul_nodedef, params);
}
#if IS_TRT_VERSION_GE(7, 1, 3, 0)
TEST_P(OpConverter_FP32_Test, ConvertEinsum) {
// Get the NodeDef for Einsum.
auto get_einsum_nodedef = [](DataType dtype, std::string eq,
int n_inputs = 2) -> NodeDef {
Scope s = Scope::NewRootScope();
auto a = ops::Placeholder(s.WithOpName("input_a"), dtype);
std::vector<Input> input_vec{a};
if (n_inputs > 1) {
auto b = ops::Placeholder(s.WithOpName("input_b"), dtype);
input_vec.push_back(b);
}
InputList inputs(input_vec);
auto einsum = ops::Einsum(s.WithOpName("my_einsum"), inputs, eq);
return einsum.operation.node()->def();
};
if (trt_mode_ == TrtTestMode::kImplicitBatch) {
Reset();
NodeDef node_def = get_einsum_nodedef(tf_type_, "ab,cb->ac");
AddTestTensor("input_a", {2, 3});
AddTestTensor("input_b", {2, 3});
TestOpConverter(
"my_einsum", node_def, {2, 2},
errors::Unimplemented("Einsum converter requires dynamic shape mode"),
Status::OK(), ElementsAreArray({13, 16, 40, 52}));
// No further tests.
return;
}
struct TestParams {
std::string equation;
std::vector<int> shape_a;
std::vector<int> values_a;
std::vector<int> shape_b;
std::vector<int> values_b;
std::vector<int> expected_shape;
std::vector<int> expected_output;
Status conv_status;
};
Status unimplemented_eq =
errors::Unimplemented("No conversion for einsum equation.");
std::vector<TestParams> params {
// Dot product.
TestParams{"i,i->", {2}, {2, 3}, {2}, {1, 2}, {1}, {8}, unimplemented_eq},
// Outer product.
TestParams{"i,k->ik",
{2},
{1, 2},
{3},
{1, 2, 3},
{2, 3},
{1, 2, 3, 2, 4, 6},
unimplemented_eq},
// Transpose.
TestParams{"ik->ki", {2, 3}, {0, 1, 2, 3, 4, 5}, {},
{}, {3, 2}, {0, 3, 1, 4, 2, 5}, unimplemented_eq},
// Diag.
TestParams{"ii->i",
{3, 3},
{0, 1, 2, 3, 4, 5, 6, 7, 8},
{},
{},
{3},
{0, 4, 8},
unimplemented_eq},
// Trace.
TestParams{
"ii", {3, 3}, {0, 1, 2, 3, 4, 5, 6, 7, 8}, {}, {}, {},
{12}, unimplemented_eq},
// MatMul with reduction.
TestParams{"abbc,dc->ad",
{1, 2, 2, 3},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
{2, 3},
{1, 2, 3, 4, 5, 6},
{2, 3},
{1, 2, 3, 2, 4, 6},
unimplemented_eq},
// Ellipsis with broadcast.
TestParams{"...ik,...jk->...ij",
{1, 3, 1, 4},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
{2, 1, 1, 4},
{1, 2, 3, 4, 5, 6, 7, 8},
{2, 3, 1, 1},
{20, 60, 100, 44, 148, 252},
unimplemented_eq},
// MatMul and Batched MatMul.
TestParams{"ab,bc->ac", {2, 3}, {0, 1, 2, 3, 4, 5}, {3, 2},
{1, 2, 3, 4, 5, 6}, {2, 2}, {13, 16, 40, 52}},
TestParams{"abc,cde->abde",
{1, 2, 3},
{0, 1, 2, 3, 4, 5},
{3, 2, 2},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
{1, 2, 2, 2},
{23, 26, 29, 32, 68, 80, 92, 104}},
TestParams{"abcd,cde->abe",
{1, 2, 2, 3},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
{2, 3, 2},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
{1, 2, 2},
{125, 140, 341, 392}},
TestParams{"abc,cd->abd", {1, 2, 3}, {0, 1, 2, 3, 4, 5}, {3, 2},
{1, 2, 3, 4, 5, 6}, {1, 2, 2}, {13, 16, 40, 52}},
TestParams{"acbe,aecd->abcd",
{1, 2, 3, 4},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
{1, 4, 2, 3},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24},
{1, 3, 2, 3},
{90, 96, 102, 732, 786, 840, 250, 272, 294, 940, 1010, 1080,
410, 448, 486, 1148, 1234, 1320}},
TestParams{
"aecd,abcd->acbe",
{1, 2, 3, 4},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23},
{1, 2, 3, 4},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24},
{1, 3, 2, 2},
{20, 140, 92, 788, 148, 460, 412, 1300, 404, 908, 860, 1940}},
TestParams{"acd,dce->ae",
{1, 2, 3},
{0, 1, 2, 3, 4, 5},
{3, 2, 2},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
{1, 2},
{115, 130}},
TestParams{"abcd,bace->bade",
{2, 3, 2, 1},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
{3, 2, 2, 1},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12},
{3, 2, 1, 1},
{2, 46, 28, 128, 86, 242}},
#if !IS_TRT_VERSION_GE(8, 0, 0, 0)
// Deactivating buggy test case for TRT8 per nvbug 3322485.
TestParams{"cebfad,fageb->abcdg",
{1, 1, 3, 3, 2, 2},
{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35},
{3, 2, 2, 1, 3},
{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36},
{2, 3, 1, 2, 2},
{252, 288, 291, 336, 768, 912, 810, 963,
1356, 1608, 1401, 1662, 438, 492, 495, 558,
1176, 1338, 1236, 1407, 1986, 2256, 2049, 2328}},
#endif
};
for (auto p : params) {
for (bool a_is_tensor : {true, false}) {
for (bool b_is_tensor : {true, false}) {
if (!a_is_tensor && !b_is_tensor) {
// Skip test when both args are weights. We do not convert this
// since const folding eliminates this case.
continue;
}
Reset();
int n_inputs = p.shape_b.empty() ? 1 : 2;
NodeDef node_def = get_einsum_nodedef(tf_type_, p.equation, n_inputs);
if (a_is_tensor) {
AddTestTensor("input_a", p.shape_a, p.values_a);
} else {
AddTestWeights("input_a", p.shape_a, p.values_a, tf_type_);
}
if (!p.shape_b.empty()) {
if (b_is_tensor) {
AddTestTensor("input_b", p.shape_b, p.values_b);
} else {
AddTestWeights("input_b", p.shape_b, p.values_b, tf_type_);
}
}
TestOpConverter("my_einsum", node_def, p.expected_shape, p.conv_status,
Status::OK(), ElementsAreArray(p.expected_output));
}
}
}
}
#endif // IS_TRT_VERSION_GE(7, 1, 3, 0)
TEST_P(OpConverter_FP32_FP16_Test, ConvertBiasAdd) {
// Note that kINT32 is not supported by IScaleLayer, so we don't test
// DT_INT32 type here. DT_FLOAT and DT_HALF are tested.
// Get the NodeDef for BiasAdd.
auto get_biasadd_nodedef = [](const string& data_format,
DataType tf_type) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
auto weights = ops::Placeholder(s.WithOpName("weights"), tf_type);
const auto biasadd_attrs = ops::BiasAdd::DataFormat(data_format);
auto biasadd =
ops::BiasAdd(s.WithOpName("my_biasadd"), input, weights, biasadd_attrs);
return biasadd.operation.node()->def();
};
for (const string& data_format : {"NHWC", "NCHW"}) {
for (const int trt_input_rank : {1, 2, 3, 4}) {
Reset();
NodeDef node_def = get_biasadd_nodedef(data_format, tf_type_);
// Add input, dims_array will be like {2, 1, ..., 1, 3}
std::vector<int32> dims_array(trt_input_rank + 1, 1);
if (trt_input_rank == 1) {
dims_array[1] = (data_format == "NHWC" ? 3 : 2);
} else {
dims_array[1] = 2;
dims_array[trt_input_rank] = 3;
}
const int num_input = TrtTensorDimsNumElements(CreateDims(dims_array));
ASSERT_EQ(trt_input_rank > 1 ? 6 : (data_format == "NHWC" ? 3 : 2),
num_input);
std::vector<float> input_data(num_input, 0);
AddTestTensor("input", dims_array, input_data);
const int channel_size = (data_format == "NHWC" ? 3 : 2);
std::vector<float> bias(channel_size);
for (int i = 0; i < channel_size; ++i) {
bias[i] = i + 1; // bias will be {1, 2, 3, ...}
}
AddTestWeights("weights", {channel_size}, bias, tf_type_);
// Build and run the engine.
std::vector<float> output_data;
if (trt_input_rank == 1) {
if (data_format == "NHWC") {
output_data = {1, 2, 3};
} else {
output_data = {1, 2};
}
} else {
if (data_format == "NHWC") {
output_data = {1, 2, 3, 1, 2, 3};
} else {
output_data = {1, 1, 1, 2, 2, 2};
}
}
TestOpConverter("my_biasadd", node_def, dims_array, Status::OK(),
Status::OK(), ElementsAreArray(output_data));
}
}
}
template <typename OpType>
NodeDef GetBinaryOpNodeDef(DataType dtype) {
Scope s = Scope::NewRootScope();
auto input_l = ops::Placeholder(s.WithOpName("input1"), dtype);
auto input_r = ops::Placeholder(s.WithOpName("input2"), dtype);
auto op = OpType(s.WithOpName("my_binary"), input_l, input_r);
return op.operation.node()->def();
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertBinary) {
{
AttrValue dtype;
dtype.set_type(tf_type_);
// Both inputs are weights.
Reset();
NodeDef node_def =
MakeNodeDef("my_add", "Add", {"weights1", "weights2"}, {{"T", dtype}});
AddTestWeights<float>("weights1", {1}, {1});
AddTestWeights<float>("weights2", {1}, {1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"Constant folding is falled back to TensorFlow, binary op received "
"both input as constant at: my_add");
}
using OpFunc = std::function<NodeDef(DataType)>;
std::map<std::string, std::pair<OpFunc, std::vector<float>>> op_test_info;
#define ADD_OP(name, op, v1, v2, v3, v4, v5, v6, v7, v8) \
op_test_info[name] = \
std::make_pair(GetBinaryOpNodeDef<op>, \
std::vector<float>(v1, v2, v3, v4, v5, v6, v7, v8))
ADD_OP("Add", ops::Add, {5, 8, 6, 9, 5, 8, 6, 9});
ADD_OP("AddV2", ops::AddV2, {5, 8, 6, 9, 5, 8, 6, 9});
ADD_OP("Sub", ops::Sub, {1, 4, 0, 3, 1, 4, 0, 3});
ADD_OP("Mul", ops::Mul, {6, 12, 9, 18, 6, 12, 9, 18});
ADD_OP("Div", ops::Div, {1.5, 3, 1, 2, 1.5, 3, 1, 2});
ADD_OP("RealDiv", ops::RealDiv, {1.5, 3, 1, 2, 1.5, 3, 1, 2});
ADD_OP("FloorDiv", ops::FloorDiv, {1, 3, 1, 2, 1, 3, 1, 2});
ADD_OP("Minimum", ops::Minimum, {2, 2, 3, 3, 2, 2, 3, 3});
ADD_OP("Maximum", ops::Maximum, {3, 6, 3, 6, 3, 6, 3, 6});
ADD_OP("Pow", ops::Pow, {9, 36, 27, 216, 9, 36, 27, 216});
#undef ADD_OP
// Add all ops supported by ConvertBinary.
auto* supported_ops = BinaryOperationMap();
// Test combinations of tensor vs weight inputs (except when both inputs are
// weights).
for (const bool operand_1_is_tensor : {true, false}) {
for (const bool operand_2_is_tensor : {true, false}) {
if (!operand_1_is_tensor && !operand_2_is_tensor) continue;
for (auto& iter : *supported_ops) {
string op_name = iter.first;
SCOPED_TRACE(StrCat(op_name, "_", operand_1_is_tensor ? "T" : "W",
operand_2_is_tensor ? "T" : "W"));
Reset();
if (!op_test_info.count(op_name)) {
FAIL() << "Binary op test map does not contain op " << op_name;
}
NodeDef node_def = op_test_info[op_name].first(tf_type_);
std::vector<std::string> input_names;
std::vector<std::vector<int>> input_dims;
std::vector<std::vector<float>> input_values;
if (operand_1_is_tensor) {
AddTestTensor("input1", {2, 1, 2}, {3, 6, 3, 6});
} else {
AddTestWeights("input1", {1, 2}, std::vector<float>{3, 6}, tf_type_);
}
if (operand_2_is_tensor) {
AddTestTensor("input2", {2, 2, 1}, {2, 3, 2, 3});
} else {
AddTestWeights("input2", {2, 1}, std::vector<float>{2, 3}, tf_type_);
}
TestOpConverter("my_binary", node_def, {2, 2, 2}, Status::OK(),
Status::OK(),
ElementsAreArray(op_test_info[op_name].second));
}
}
}
}
NodeDef GetAddNNodeDef(const std::vector<string>& input_names, DataType dtype) {
Scope s = Scope::NewRootScope();
OutputList inputs;
for (const string& name : input_names) {
inputs.push_back(ops::Placeholder(s.WithOpName(name), dtype));
}
auto op = ops::AddN(s.WithOpName("my_addn"), inputs);
return op.operation.node()->def();
}
struct AddNTestParams {
std::vector<float> input_values;
std::vector<string> input_names;
std::vector<int> dimensions;
std::vector<float> expected_output;
Status status;
};
void TestAddN(ParameterizedOpConverterTestBase* test, AddNTestParams& p) {
// All inputs are tensors.
test->Reset();
const NodeDef node_def = GetAddNNodeDef(p.input_names, test->get_tf_type());
if (p.input_values.size() % p.input_names.size() != 0) {
LOG(ERROR) << "The number of input values: `" << p.input_values.size()
<< "` is not a multiple of the number of inputs: `"
<< p.input_names.size() << "`";
ASSERT_TRUE(false);
}
DataVec input_data;
int input_offset = 0;
const int window_size = p.input_values.size() / p.input_names.size();
for (const string& name : p.input_names) {
std::vector<float>::const_iterator start_pos =
p.input_values.begin() + input_offset;
std::vector<float>::const_iterator end_pos = start_pos + window_size;
std::vector<float> sub_input_val(start_pos, end_pos);
input_offset += window_size;
test->AddTestTensor(name, p.dimensions, test->get_tf_type(), sub_input_val);
}
test->TestOpConverter("my_addn", node_def, p.dimensions,
/*expected_conversion_status=*/p.status,
/*expected_runtime_status=*/p.status,
/*matcher=*/ElementsAreArray(p.expected_output),
/*out_tf_types=*/{test->get_tf_type()});
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertAddN) {
{
// Weights with batch dim that is not 1.
Reset();
const NodeDef node_def = GetAddNNodeDef({"tensor", "weights"}, tf_type_);
AddTestTensor("tensor", /*dims=*/{1, 2});
AddTestWeights<float>("weights", {2, 1, 2}, {0, 1, 2, 3});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Weights input to AddN is required to have batch dimension 1.");
}
const std::vector<float> common_input = CreateVectorIota<float>(6);
std::vector<AddNTestParams> params = {
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2", "inp3"},
/*dimensions=*/{1, 1, 2, 1, 1},
/*expected_output=*/{6, 9},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2"},
/*dimensions=*/{1, 1, 3, 1, 1},
/*expected_output=*/{3, 5, 7},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2", "inp3"},
/*dimensions=*/{1, 2, 1, 1},
/*expected_output=*/{6, 9},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2"},
/*dimensions=*/{1, 1, 3, 1},
/*expected_output=*/{3, 5, 7},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2", "inp3"},
/*dimensions=*/{1, 2, 1},
/*expected_output=*/{6, 9},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2"},
/*dimensions=*/{1, 1, 3},
/*expected_output=*/{3, 5, 7},
/*status=*/Status::OK()},
{/*input_value=*/common_input,
/*input_names=*/{"inp1", "inp2", "inp3"},
/*dimensions=*/{2, 1},
/*expected_output=*/{6, 9},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2"},
/*dimensions=*/{1, 3},
/*expected_output=*/{3, 5, 7},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2", "inp3"},
/*dimensions=*/{2},
/*expected_output=*/{6, 9},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2"},
/*dimensions=*/{3},
/*expected_output=*/{3, 5, 7},
/*status=*/Status::OK()},
{/*input_values=*/common_input,
/*input_names=*/{"inp1", "inp2", "inp3", "inp4", "inp5", "inp6"},
/*dimensions=*/{1},
/*expected_output=*/{15},
/*status=*/Status::OK()},
};
for (auto p : params) {
TestAddN(this, p);
}
}
TEST_P(OpConverter_FP32_Test, ConvertQuantize) {
{
// FakeQuantWithMinMaxArgs attributes are empty, should fail.
Reset(TrtPrecisionMode::INT8);
NodeDef node_def =
MakeNodeDef("my_quantize", "FakeQuantWithMinMaxArgs", {"input"});
AddTestTensor("input", {1, 2, 3});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Min or max attribute not found for FakeQuantWithMinMaxArgs "
"at my_quantize");
}
{
// FakeQuantWithMinMaxArgs ranges set via attributes, ok.
Reset(TrtPrecisionMode::INT8);
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT);
auto quantize_attrs = ops::FakeQuantWithMinMaxArgs::Min(-6.0f).Max(6.0f);
auto quantize = ops::FakeQuantWithMinMaxArgs(s.WithOpName("my_quantize"),
input, quantize_attrs);
const NodeDef& node_def = quantize.operation.node()->def();
AddTestTensor("input", {1, 2, 3});
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()->trt_tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()->trt_tensor()]);
}
{
// FakeQuantWithMinMaxVars ranges set via inputs, ok.
Reset(TrtPrecisionMode::INT8);
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT);
auto weights_min = ops::Placeholder(s.WithOpName("weights_min"), DT_FLOAT);
auto weights_max = ops::Placeholder(s.WithOpName("weights_max"), DT_FLOAT);
auto quantize = ops::FakeQuantWithMinMaxVars(
s.WithOpName("my_quantize"), input, weights_min, weights_max);
const NodeDef& node_def = quantize.operation.node()->def();
AddTestTensor("input", {1, 2, 3});
AddTestWeights<float>("weights_min", {1}, {-6.0f});
AddTestWeights<float>("weights_max", {1}, {6.0f});
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()->trt_tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()->trt_tensor()]);
}
{
// QuantizeAndDequantizeV2 ranges set via inputs, ok.
Reset(TrtPrecisionMode::INT8);
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT);
auto weights_min = ops::Placeholder(s.WithOpName("weights_min"), DT_FLOAT);
auto weights_max = ops::Placeholder(s.WithOpName("weights_max"), DT_FLOAT);
auto quantize = ops::QuantizeAndDequantizeV2(
s.WithOpName("my_quantize"), input, weights_min, weights_max);
const NodeDef& node_def = quantize.operation.node()->def();
AddTestTensor("input", {1, 2, 3});
AddTestWeights<float>("weights_min", {1}, {-6.0f});
AddTestWeights<float>("weights_max", {1}, {6.0f});
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()->trt_tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()->trt_tensor()]);
}
{
// QuantizeAndDequantizeV2 Range inputs are tensors, should fail.
Reset(TrtPrecisionMode::INT8);
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT);
auto weights_min = ops::Placeholder(s.WithOpName("weights_min"), DT_FLOAT);
auto weights_max = ops::Placeholder(s.WithOpName("weights_max"), DT_FLOAT);
auto quantize = ops::QuantizeAndDequantizeV2(
s.WithOpName("my_quantize"), input, weights_min, weights_max);
const NodeDef& node_def = quantize.operation.node()->def();
AddTestTensor("input", {1, 2, 3});
AddTestTensor("weights_min", {1});
AddTestTensor("weights_max", {1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"input_min\" for QuantizeAndDequantizeV2 must be a constant"
", at my_quantize");
}
{
// QuantizeAndDequantizeV3 ranges set via inputs, ok.
Reset(TrtPrecisionMode::INT8);
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT);
auto weights_min = ops::Placeholder(s.WithOpName("weights_min"), DT_FLOAT);
auto weights_max = ops::Placeholder(s.WithOpName("weights_max"), DT_FLOAT);
auto num_bits = ops::Placeholder(s.WithOpName("num_bits"), DT_INT32);
auto quantize = ops::QuantizeAndDequantizeV3(
s.WithOpName("my_quantize"), input, weights_min, weights_max, num_bits);
const NodeDef& node_def = quantize.operation.node()->def();
AddTestTensor("input", {1, 2, 3});
AddTestWeights<float>("weights_min", {1}, {-6.0f});
AddTestWeights<float>("weights_max", {1}, {6.0f});
AddTestWeights<int>("num_bits", {1}, {8});
RunValidationAndConversion(node_def);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_quantize", &output));
ASSERT_TRUE(output.is_tensor());
auto ranges = quantization_ranges();
EXPECT_EQ(1, ranges.count(output.tensor()->trt_tensor()));
EXPECT_EQ(6.0f, ranges[output.tensor()->trt_tensor()]);
}
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertSquare) {
{
// Input is weights, should fail.
Reset();
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto square = ops::Square(s.WithOpName("my_square"), input);
NodeDef node_def = square.operation.node()->def();
AddTestWeights("input", {1, 2, 3}, {1, 2, 3, 4, -5, 6}, tf_type_);
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"x\" for Square must be a tensor, at my_square");
}
Reset();
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto square = ops::Square(s.WithOpName("my_square"), input);
NodeDef node_def = square.operation.node()->def();
const int num_inputs = 20;
std::vector<float> inputs(num_inputs);
std::vector<float> expected_outputs(num_inputs);
for (int i = 0; i < num_inputs; ++i) {
const float value = (i - 9);
inputs[i] = value;
expected_outputs[i] = value * value;
}
AddTestTensor("input", {1, 1, 20}, tf_type_, inputs);
TestOpConverter("my_square", node_def, {1, 1, 20}, Status::OK(), Status::OK(),
ArrayFloatNear(expected_outputs, 0));
}
#if IS_TRT_VERSION_GE(7, 1, 3, 0)
TEST_P(OpConverter_FP32_Test, ConvertCombinedNMS) {
// Get the NodeDef for CombinedNMS.
auto get_nms_nodedef = [](DataType tf_type, bool clip_boxes = true,
bool pad_per_class = false) -> NodeDef {
Scope s = Scope::NewRootScope();
auto boxes_tensor = ops::Placeholder(s.WithOpName("boxes"), tf_type);
auto scores_tensor = ops::Placeholder(s.WithOpName("scores"), tf_type);
auto max_output_size_per_class =
ops::Placeholder(s.WithOpName("max_output_size_per_class"), DT_INT32);
auto max_total_size =
ops::Placeholder(s.WithOpName("max_total_size"), DT_INT32);
auto iou_threshold =
ops::Placeholder(s.WithOpName("iou_threshold"), tf_type);
auto score_threshold =
ops::Placeholder(s.WithOpName("score_threshold"), tf_type);
auto nms_attrs = ops::CombinedNonMaxSuppression::Attrs()
.PadPerClass(pad_per_class)
.ClipBoxes(clip_boxes);
auto nms_op = ops::CombinedNonMaxSuppression(
s.WithOpName("my_nms"), boxes_tensor, scores_tensor,
max_output_size_per_class, max_total_size, iou_threshold,
score_threshold, nms_attrs);
return nms_op.operation.node()->def();
};
struct TestParams {
const std::string description;
const std::vector<int32> boxes_tensor_dims;
const std::vector<int32> scores_tensor_dims;
const std::vector<float> boxes_values;
const std::vector<float> scores_values;
const int32 max_output_size_per_class;
const int32 max_total_size;
const float iou_threshold;
const float score_threshold;
bool pad_per_class;
bool clip_boxes;
const std::vector<std::vector<int32>> expected_output_dims;
const std::vector<float> exp_boxes;
const std::vector<float> exp_scores;
const std::vector<float> exp_classes;
const std::vector<float> exp_num_detections;
Status conversion_status;
Status runtime_status;
};
Status conv_status =
trt_mode_ == TrtTestMode::kDynamicShape
? errors::Unimplemented(
"TensorRT BatchedNMS Plugin requires input with static shape")
: Status::OK();
std::vector<TestParams> params = {
// TODO(aaroey): there is a bug in TRT's CombinedNonMaxSuppression
// implementation that, the extra output classes that are outside of the
// range specified by valid_detections[i] are not zeros but -1s.
TestParams{
"Test 1: Original test",
{1, 1, 3, 4}, // boxes dims
{1, 1, 3}, // scores dims
{0, 0, 0.3, 0.4, 0, 0, 0.3, 0.4, 0, 0, 0.3, 0.4}, // boxes values
{0.4, 0.7, 0.3}, // scores values
3, // max_output_size_per_class
2, // max_total_size
.5f, // IOU threshold
0, // score_threshold
false, // pad_per_class
true, // clip_boxes
{{1, 2, 4}, // expected_nmsed_boxes_dims
{1, 2}, // expected_nmsed_scores_dims
{1, 2}, // expected_nmsed_classes_dims
{1}}, // expected_valid_detections_dims
{0, 0, 0.3, 0.4, 0, 0, 0.3, 0.4}, // exp_boxes_values
{0.7, 0.4}, // exp_scores
{1, 0}, // exp_classes
{2}, // exp_num_detections
conv_status},
// Test with clip_boxes = False
TestParams{
"Test 2: clip_boxes",
{1, 5, 1, 4}, // boxes dims
{1, 5, 1}, // scores dims
// boxes values:
{0, 0, 5, 10, 0, 4, 5, 14, 8, 0, 12, 4, 6, 2, 10, 6, 8, 9, 11, 12},
{5, 4, 3, 2, 1}, // scores values
4, // max_output_size_per_class
4, // max_total_size
0.1, // IOU threshold
0, // score threshold
false, // pad_per_class
false, // clip_boxes
{{1, 4, 4}, // expected nmsed_boxes_dims
{1, 4}, // expected nmsed_scores_dims
{1, 4}, // expected_nmsed_classes_dims
{1}}, // expected_valid_detections_dims
// exp_boxes_values:
{0, 0, 5, 10, 8, 0, 12, 4, 8, 9, 11, 12, 0, 0, 0, 0},
{5, 3, 1, 0}, // exp_scores
{0, 0, 0, -1}, // exp_classes
{3}, // exp_num_detections
conv_status},
// Test with clip_boxes = False, and nonzero score threshold
TestParams{
"Test 3: score threshold",
{1, 5, 1, 4}, // boxes dims
{1, 5, 1}, // scores dims
// boxes values:
{0, 0, 5, 10, 0, 4, 5, 14, 8, 0, 12, 4, 6, 2, 10, 6, 8, 9, 11, 12},
{5, 4, 3, 2, 1}, // scores values
4, // max_output_size_per_class
4, // max_total_size
0.1, // IOU threshold
2, // score threshold
false, // pad_per_class
false, // clip_boxes
{{1, 4, 4}, // expected nmsed_boxes_dims
{1, 4}, // expected nmsed_scores_dims
{1, 4}, // expected_nmsed_classes_dims
{1}}, // expected_valid_detections_dims
// exp_boxes_values:
{0, 0, 5, 10, 8, 0, 12, 4, 0, 0, 0, 0, 0, 0, 0, 0},
{5, 3, 0, 0}, // exp_scores
{0, 0, -1, -1}, // exp_classes
{2}, // exp_num_detections
conv_status},
// Test where the boxes are defined as with max value first for the box
// coordinates. This test fails before TRT 7.1.3.
TestParams{
"Test 4: max coord first",
{1, 5, 1, 4}, // boxes dims
{1, 5, 1}, // scores dims
// boxes values:
{5, 10, 0, 0, 5, 14, 0, 4, 12, 4, 8, 0, 10, 6, 6, 2, 11, 12, 8, 9},
{5, 4, 3, 2, 1}, // scores values
4, // max_output_size_per_class
4, // max_total_size
0.1, // IOU threshold
0, // score threshold
false, // pad_per_class
false, // clip_boxes
{{1, 4, 4}, // expected nmsed_boxes_dims
{1, 4}, // expected nmsed_scores_dims
{1, 4}, // expected_nmsed_classes_dims
{1}}, // expected_valid_detections_dims
// exp_boxes_values:
{5, 10, 0, 0, 12, 4, 8, 0, 11, 12, 8, 9, 0, 0, 0, 0},
{5, 3, 1, 0}, // exp_scores
{0, 0, 0, -1}, // exp_classes
{3}, // exp_num_detections
conv_status},
TestParams{"Test 5: TopK error",
{1, 5000, 1, 4}, // boxes dims
{1, 5000, 1}, // scores dims
{}, // boxes values:
{}, // scores values
4, // max_output_size_per_class
4, // max_total_size
0.1, // IOU threshold
0, // score threshold
false, // pad_per_class
false, // clip_boxes
{}, // expected_valid_detections_dims
{}, // exp_boxes_values
{}, // exp_scores
{}, // exp_classes
{}, // exp_num_detections
conv_status.ok()
? errors::InvalidArgument(
"TRT NMS plugin allow top_k<=4096, where top_k = "
"max(num_boxes, max_total_size). You can override "
"this by setting TF_TRT_ALLOW_NMS_TOPK_OVERRIDE=1 "
"environment variable, but this can result in a "
"loss of accuracy.")
: conv_status},
};
for (auto p : params) {
Reset();
SCOPED_TRACE(p.description);
AddTestTensor("boxes", p.boxes_tensor_dims, p.boxes_values);
AddTestTensor("scores", p.scores_tensor_dims, p.scores_values);
AddTestWeights<int32>("max_output_size_per_class", {1},
{p.max_output_size_per_class});
AddTestWeights<int32>("max_total_size", {1}, {p.max_total_size});
AddTestWeights<float>("iou_threshold", {1}, {p.iou_threshold}, tf_type_);
AddTestWeights<float>("score_threshold", {1}, {p.score_threshold},
tf_type_);
auto node_def = get_nms_nodedef(tf_type_, p.clip_boxes, p.pad_per_class);
TestOpConverterMultiOut("my_nms", node_def, p.expected_output_dims,
p.conversion_status, p.runtime_status,
{
ElementsAreArray(p.exp_boxes),
ElementsAreArray(p.exp_scores),
ElementsAreArray(p.exp_classes),
ElementsAreArray(p.exp_num_detections),
},
{tf_type_, tf_type_, tf_type_, DT_INT32});
}
}
#endif // IS_TRT_VERSION_GE(7, 1, 3, 0)
template <typename T>
NodeDef CreateUnaryOp(DataType tf_type) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
return T(s.WithOpName("my_unary"), input).operation.node()->def();
}
constexpr float kLeakyReluAlpha = 0.2f;
template <>
NodeDef CreateUnaryOp<ops::internal::LeakyRelu>(DataType tf_type) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
return ops::internal::LeakyRelu(
s.WithOpName("my_unary"), input,
ops::internal::LeakyRelu::Alpha(kLeakyReluAlpha))
.operation.node()
->def();
}
TEST_P(OpConverter_FP32_Test, ConvertActivation) {
{
// Input is weights, should fail.
Reset();
const NodeDef& node_def = CreateUnaryOp<ops::Relu>(tf_type_);
AddTestWeights<int32>("input", {1, 2, 3}, {-3, -2, -1, 0, 1, 2});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"input\" for Relu must be a tensor, at my_unary");
}
constexpr float kSeluAlpha = 1.7580993408473768599402175208123f;
constexpr float kSeluScale = 1.0507009873554804934193349852946f;
using OpFunc = std::function<NodeDef(DataType)>;
using ValFunc = float (*)(float);
std::map<std::string, std::pair<OpFunc, ValFunc>> op_map;
#define ADD_OP(name, op, compute) \
op_map[name] = std::make_pair(CreateUnaryOp<op>, compute)
ADD_OP("LeakyRelu", ops::internal::LeakyRelu,
[](float x) { return (x > 0.0f) ? x : x * kLeakyReluAlpha; });
ADD_OP("Relu", ops::Relu, [](float x) { return (x > 0.0f) ? x : 0.0f; });
ADD_OP("Relu6", ops::Relu6,
[](float x) { return std::min(std::max(x, 0.0f), 6.0f); });
ADD_OP("Sigmoid", ops::Sigmoid,
[](float x) { return 1.0f / (1.0f + std::exp(-x)); });
ADD_OP("Tanh", ops::Tanh, static_cast<ValFunc>(std::tanh));
ADD_OP("Elu", ops::Elu,
[](float x) { return (x > 0.0f) ? x : std::exp(x) - 1; });
ADD_OP("Selu", ops::Selu, [](float x) {
return (x > 0.0f) ? kSeluScale * x
: kSeluScale * kSeluAlpha * (std::exp(x) - 1);
});
ADD_OP("Softsign", ops::Softsign,
[](float x) { return x / (std::abs(x) + 1); });
ADD_OP("Softplus", ops::Softplus,
[](float x) { return std::log(std::exp(x) + 1); });
#undef ADD_OP
// Get list of ops to test.
std::vector<string> ops_to_test;
// Add all ops supported by ConvertActivation.
auto* map = ActivationTypeMap();
ops_to_test.reserve(map->size());
for (auto& pair : *map) {
ops_to_test.push_back(pair.first);
}
// Add other activation ops to test.
ops_to_test.push_back("Relu6");
ops_to_test.push_back("LeakyRelu");
auto p = TestParamBase{
{1, 1, 2, 3}, // input dims
{}, // input partial dims
{1, 1, 2, 3}, // expected output dims
};
// Ok.
for (const string& op_name : ops_to_test) {
if (!op_map.count(op_name)) {
FAIL() << "Activation op test map does not contain op " << op_name;
}
Reset();
NodeDef node_def = op_map[op_name].first(tf_type_);
const std::vector<float> input = {-100, -2, -1, 0, 1, 88};
AddTestTensor("input", p.input_dims, input);
// std::exp in Softplus will overflow for input > 88
std::vector<float> output_values;
std::transform(input.begin(), input.end(),
std::back_inserter(output_values), op_map[op_name].second);
TestOpConverter("my_unary", node_def, p.expected_output_dims, Status::OK(),
Status::OK(),
#if IS_TRT_VERSION_GE(8, 0, 0, 0)
// NVBug # 3322482 - Known bug with TRT 8.0 on specific GPU
// architectures
ArrayFloatNear(output_values, 1e-4, false)
#else
ArrayFloatNear(output_values, 0, false)
#endif
);
TRT_TensorOrWeights output;
TF_EXPECT_OK(GetTensorOrWeights("my_unary", &output));
}
}
TEST_P(OpConverter_FP32_Test, ConvertExpandDims) {
// Get the NodeDef for ExpandDims.
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32);
auto expanddims =
ops::ExpandDims(s.WithOpName("my_expanddims"), input, weights);
const NodeDef& node_def = expanddims.operation.node()->def();
{
// Input is weights, should fail.
Reset();
AddTestWeights<int32>("input", {1, 2, 3}, {1, 2, 3, 4, 5, 6});
AddTestWeights<int32>("weights", {1}, {1});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"input\" for ExpandDims must be a "
"tensor, at my_expanddims");
}
{
// Axis is a tensor, should fail.
Reset();
AddTestTensor("input", {3, 2, 1});
AddTestTensor("weights", {3});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"axis\" for ExpandDims must be a "
"constant, at my_expanddims");
}
std::vector<TestParamBase> test_params = {
TestParamBase{{1, 1, 2, 3},
{},
{1, 1, 1, 2, 3},
{0},
trt_mode_ == TrtTestMode::kImplicitBatch
? Status(error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the "
"batch dimension, at my_expanddims")
: Status::OK()},
TestParamBase{{1, 1, 2, 3},
{},
{1, 1, 1, 2, 3},
{-5},
trt_mode_ == TrtTestMode::kImplicitBatch
? Status(error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the "
"batch dimension, at my_expanddims")
: Status::OK()},
TestParamBase{{1, 1, 2, 3},
{},
{},
{5},
Status(error::INVALID_ARGUMENT,
"Axis value of 5 is out of bounds, must be in range"
" [-5, 5), at my_expanddims")},
TestParamBase{{1, 1, 2, 3},
{},
{},
{-6},
Status(error::INVALID_ARGUMENT,
"Axis value of -6 is out of bounds, must be in range"
" [-5, 5), at my_expanddims")},
TestParamBase{{1, 2, 3}, {}, {1, 1, 2, 3}, {1}},
TestParamBase{{1, 2, 3}, {}, {1, 1, 2, 3}, {-3}},
TestParamBase{{1, 2, 3}, {}, {1, 2, 3, 1}, {3}},
TestParamBase{{1, 2, 3}, {}, {1, 2, 3, 1}, {-1}},
TestParamBase{{1, 2, 3}, {}, {1, 2, 1, 3}, {2}},
TestParamBase{{1, 2, 3}, {}, {1, 2, 1, 3}, {-2}},
TestParamBase{{1, 6}, {}, {1, 1, 6}, {1}},
TestParamBase{{1, 6}, {}, {1, 6, 1}, {-1}},
};
for (auto p : test_params) {
Reset();
AddTestTensor("input", p.input_dims, {1, 2, 3, 4, 5, 6});
AddTestWeights<int32>("weights", {1}, {p.param[0]});
TestOpConverter("my_expanddims", node_def, p.expected_output_dims, p.status,
p.runtime_status, ElementsAreArray({1, 2, 3, 4, 5, 6}));
}
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertSoftmax) {
// Get the NodeDef for SoftMax.
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("logits"), tf_type_);
auto softmax = ops::Softmax(s.WithOpName("my_softmax"), input);
const NodeDef& node_def = softmax.operation.node()->def();
struct TestParams {
std::vector<int> input_dims;
std::vector<float> expected_values;
};
std::vector<TestParams> test_params = {
TestParams{{2, 3},
{0.09003057, 0.24472848, 0.66524094, 0.09003057, 0.24472848,
0.66524094}},
TestParams{{6, 1}, {1, 1, 1, 1, 1, 1}}, // works with std input
TestParams{{1, 6}, // this works with arange(1,7) input
{0.00426978, 0.01160646, 0.03154963, 0.08576079, 0.23312202,
0.6336913}},
};
std::vector<float> input_values{1, 2, 3, 4, 5, 6};
for (auto p : test_params) {
Reset();
AddTestTensor("logits", p.input_dims, input_values);
TestOpConverter("my_softmax", node_def, p.input_dims, Status::OK(),
Status::OK(), ArrayFloatNear(p.expected_values, 1e-3));
}
}
TEST_P(OpConverter_FP32_Test, ConvertSqueeze) {
const bool use_implicit_batch = (trt_mode_ == TrtTestMode::kImplicitBatch);
// Get the NodeDef for Squeeze.
auto get_squeeze_nodedef = [](std::vector<int> axes,
DataType tf_type) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
if (!axes.empty()) {
ops::Squeeze::Attrs squeeze_attrs;
squeeze_attrs.axis_ = gtl::ArraySlice<int>(axes); // non-absl ok
auto squeeze =
ops::Squeeze(s.WithOpName("my_squeeze"), input, squeeze_attrs);
return squeeze.operation.node()->def();
} else {
auto squeeze = ops::Squeeze(s.WithOpName("my_squeeze"), input);
return squeeze.operation.node()->def();
}
};
std::vector<TestParamBase> test_params = {
TestParamBase{
{1, 2, 1, 3}, // input dims
{}, // input partial dims
{2, 3}, // expected output dims
{}, // axis
trt_mode_ == TrtTestMode::kExplicitBatch
? Status::OK()
: Status{error::UNIMPLEMENTED,
"Squeeze is not implemented for empty squeeze_dims, at "
"my_squeeze"}},
TestParamBase{{1, 2, 1, 3},
{},
{2, 1, 3},
{0},
use_implicit_batch
? Status{error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the "
"batch dimension, at my_squeeze"}
: Status::OK()},
TestParamBase{{1, 2, 1, 3},
{},
{2, 1, 3},
{-4},
use_implicit_batch
? Status{error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the "
"batch dimension, at my_squeeze"}
: Status::OK()},
TestParamBase{
{1, 1, 2, 3},
{},
{},
{4},
Status{error::INVALID_ARGUMENT,
"Axis value of 4 is out of bounds, must be in range [-4, 4), "
"at my_squeeze"}},
TestParamBase{
{1, 1, 2, 3},
{},
{},
{-5},
Status{error::INVALID_ARGUMENT,
"Axis value of -5 is out of bounds, must be in range [-4, 4), "
"at my_squeeze"}},
TestParamBase{{1, 1, 2, 3}, {}, {1, 2, 3}, {1}},
TestParamBase{{1, 1, 2, 3}, {}, {1, 2, 3}, {-3}},
TestParamBase{{1, 2, 3, 1}, {}, {1, 2, 3}, {3}},
TestParamBase{{1, 2, 3, 1}, {}, {1, 2, 3}, {-1}},
TestParamBase{{1, 1, 2, 1, 3, 1}, {}, {1, 2, 3}, {1, 3, 5}},
TestParamBase{{1, 1, 2, 1, 3, 1}, {}, {1, 2, 3}, {3, 1, 5}},
TestParamBase{{1, 1, 2, 1, 3, 1}, {}, {1, 2, 3}, {-1, -3, -5}},
TestParamBase{{1, 1, 2, 1, 3, 1}, {}, {1, 2, 3}, {1, -3, 5}},
TestParamBase{{1, 1, 6}, {}, {1, 6}, {1}},
TestParamBase{{1, 6, 1}, {}, {1, 6}, {2}},
};
auto squeeze_non_singleton = TestParamBase{
{1, 1, 2, 3},
{},
{},
{2},
Status{error::INVALID_ARGUMENT,
"Dimension 2 with size 2 cannot be squeezed because it must be "
"size 1, at my_squeeze"}};
if (trt_mode_ == TrtTestMode::kDynamicShape) {
// In this test we try to squeeze axis=2 which has size > 1. In dynamic
// shape mode the converter sees only -1, so it cannot catch this error.
squeeze_non_singleton.status = Status::OK(); // conversion status
squeeze_non_singleton.runtime_status =
errors::InvalidArgument("Negative number of dimensions -1");
// Dynamic shape tests with partially known input shape
test_params.push_back(TestParamBase{{2, 1, 3}, {2, -1, 3}, {2, 3}, {1}});
test_params.push_back(TestParamBase{{2, 1, 3}, {2, 1, -1}, {2, 3}, {1}});
}
test_params.push_back(squeeze_non_singleton);
for (TestParamBase p : test_params) {
SCOPED_TRACE(p);
Reset();
NodeDef node_def = get_squeeze_nodedef(p.param, tf_type_);
AddTestTensor("input", p.input_dims, {1, 2, 3, 4, 5, 6},
p.partial_input_dims);
TestOpConverter("my_squeeze", node_def, p.expected_output_dims, p.status,
p.runtime_status, ElementsAreArray({1, 2, 3, 4, 5, 6}));
}
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertStridedSlice) {
// Get nodedef for StridedSlice layer.
auto get_strided_slice_nodedef =
[](DataType tf_type, int64 begin_mask = 0, int64 end_mask = 0,
int64 ellipsis_mask = 0, int64 new_axis_mask = 0,
int64 shrink_axis_mask = 0) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
auto begin = ops::Placeholder(s.WithOpName("begin"), DT_INT32);
auto end = ops::Placeholder(s.WithOpName("end"), DT_INT32);
auto strides = ops::Placeholder(s.WithOpName("strides"), DT_INT32);
ops::StridedSlice::Attrs attrs = ops::StridedSlice::Attrs()
.BeginMask(begin_mask)
.EndMask(end_mask)
.EllipsisMask(ellipsis_mask)
.NewAxisMask(new_axis_mask)
.ShrinkAxisMask(shrink_axis_mask);
auto strided_slice = ops::StridedSlice(s.WithOpName("my_strided_slice"),
input, begin, end, strides, attrs);
return strided_slice.operation.node()->def();
};
{
// Input is weights, should fail.
Reset();
NodeDef node_def = get_strided_slice_nodedef(tf_type_);
AddTestWeights<int32>("input", {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6});
AddTestWeights<int32>("begin", {4}, {0, 0, 0, 0});
AddTestWeights<int32>("end", {4}, {1, 1, 2, 3});
AddTestWeights<int32>("strides", {4}, {1, 1, 1, 1});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"input\" for StridedSlice must "
"be a tensor, at my_strided_slice");
}
{
// Begin, end, strides are tensors, should fail.
Reset();
NodeDef node_def = get_strided_slice_nodedef(tf_type_);
AddTestTensor("input", {4, 1, 1, 1});
AddTestTensor("begin", {4});
AddTestTensor("end", {4});
AddTestTensor("strides", {4});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"begin\" for StridedSlice must be a constant, at "
"my_strided_slice");
}
struct TestParams {
std::vector<int> input_dims;
std::vector<int> begin;
std::vector<int> end;
std::vector<int> strides;
int begin_mask;
int end_mask;
int ellipsis_mask;
int new_axis_mask;
int shrink_axis_mask;
std::vector<int> expected_output_dims;
std::vector<float> expected_output;
Status conversion_status;
Status runtime_status;
std::vector<int> partial_input_dims;
};
auto get_mask = [](const std::vector<int>& mask) {
int result = 0;
for (int i = 0; i < mask.size(); i++) {
if (mask[i]) result += (1 << i);
}
return result;
};
// Same input is used for all tests.
const std::vector<float> ok_input = {1, 2, 3, 4, 5, 6};
Status batch_conv_status =
(trt_mode_ == TrtTestMode::kImplicitBatch)
? errors::Unimplemented(
"TensorRT does not allow modifications to "
"the batch dimension, at my_strided_slice")
: Status::OK();
std::vector<TestParams> params = {
// Modify batch dim, should fail in implicit batch mode.
TestParams{
/*input_dims=*/{2, 1, 1, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{1, 1, 1, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({0, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{1, 2},
batch_conv_status,
},
// Unknown batch size without end_mask.
TestParams{
/*input_dims=*/{2, 1, 1, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{1, 1, 1, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({0, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{1, 2},
batch_conv_status,
Status::OK(),
{-1, 1, 1, 3},
},
// Unknown batch size but using end_mask, ok.
TestParams{
/*input_dims=*/{2, 1, 1, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 1, 1, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({1, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{2, 1, 1, 2},
/*expected_output=*/{1, 2, 4, 5},
Status::OK(),
Status::OK(),
{-1, 1, 1, 3},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 2, 0},
/*end=*/{1, 1, 0, 3},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/0,
/*end_mask=*/0,
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{},
/*expected_output=*/{},
errors::InvalidArgument("\"size\" cannot be negative for "
"StridedSlice"),
},
// 2D Crop.
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 0, 1, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{1, 2},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 1, 1},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{5, 6},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 1, 1},
/*end=*/{0, 1, 2, 3},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{5, 6},
},
// 2D crop with negative stride
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 1, 2},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, 1, -1, -1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{6, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 1, 1},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, 1, -1, -1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 2},
/*expected_output=*/{5, 4, 2, 1},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, 1, -1, -1},
/*begin_mask=*/get_mask({0, 0, 1, 1}),
/*end_mask=*/get_mask({1, 1, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{6, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, -1, -1, -1},
/*begin_mask=*/get_mask({1, 1, 1, 1}),
/*end_mask=*/get_mask({1, 1, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/{6, 5, 4, 3, 2, 1},
},
// 2D Crop, with transpose.
TestParams{
/*input_dims=*/{1, 2, 3, 1},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 1, 2, 1},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{1, 2},
},
TestParams{
/*input_dims=*/{1, 2, 3, 1},
/*begin=*/{0, 1, 1, 0},
/*end=*/{0, 2, 3, 1},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{5, 6},
},
TestParams{
/*input_dims=*/{1, 2, 1, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 1, 1, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{1, 2},
},
TestParams{
/*input_dims=*/{1, 2, 1, 3},
/*begin=*/{0, 1, 0, 1},
/*end=*/{0, 2, 1, 3},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 2},
/*expected_output=*/{5, 6},
},
// 2D Crop, with reshape.
TestParams{
/*input_dims=*/{1, 2, 3},
/*begin=*/{0, 0, 0},
/*end=*/{0, 1, 2},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2},
/*expected_output=*/{1, 2},
},
TestParams{
/*input_dims=*/{1, 2, 3},
/*begin=*/{0, 1, 1},
/*end=*/{0, 0, 0},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2},
/*expected_output=*/{5, 6},
},
// 1D Crop.
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 0, 0, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 2},
/*expected_output=*/{1, 2, 4, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 1, 0},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 1, 3},
/*expected_output=*/{4, 5, 6},
},
// 1D Crop, with transpose.
TestParams{
/*input_dims=*/{1, 2, 3, 1},
/*begin=*/{0, 0, 0, 0},
/*end=*/{0, 1, 0, 0},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 3, 1},
/*expected_output=*/{1, 2, 3},
},
TestParams{
/*input_dims=*/{1, 2, 3, 1},
/*begin=*/{0, 1, 0, 0},
/*end=*/{0, 0, 0, 0},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 3, 1},
/*expected_output=*/{4, 5, 6},
},
// 1D Crop, with reshape.
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 0},
/*end=*/{0, 3},
/*strides=*/{1, 1},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3},
/*expected_output=*/{1, 2, 3},
},
TestParams{
/*input_dims=*/{1, 1, 6},
/*begin=*/{0, 0, 2},
/*end=*/{0, 0, 5},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0}),
/*end_mask=*/get_mask({1, 1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 3},
/*expected_output=*/{3, 4, 5},
},
TestParams{
/*input_dims=*/{1, 6, 1},
/*begin=*/{0, 2, 0},
/*end=*/{0, 5, 0},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3, 1},
/*expected_output=*/{3, 4, 5},
},
// Negative axis.
TestParams{
/*input_dims=*/{1, 6, 1},
/*begin=*/{0, -6, 0},
/*end=*/{0, -3, 0},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3, 1},
/*expected_output=*/{1, 2, 3},
},
TestParams{
/*input_dims=*/{1, 6, 1},
/*begin=*/{0, 0, 0},
/*end=*/{0, -1, 0},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 5, 1},
/*expected_output=*/{1, 2, 3, 4, 5},
},
// Clamp out of bounds begin and end.
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, -9999, -9},
/*end=*/{0, 1, 1000, 4},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/{1, 2, 3, 4, 5, 6},
},
// Strides
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 0},
/*end=*/{0, 5},
/*strides=*/{1, 2},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3},
/*expected_output=*/{1, 3, 5},
},
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 0},
/*end=*/{0, 6},
/*strides=*/{1, 2},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3},
/*expected_output=*/{1, 3, 5},
},
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 1},
/*end=*/{0, 6},
/*strides=*/{1, 2},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3},
/*expected_output=*/{2, 4, 6},
},
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 2},
/*end=*/{0, 6},
/*strides=*/{1, 3},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 2},
/*expected_output=*/{3, 6},
},
// Negative non -1 strides
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 5},
/*end=*/{0, 0},
/*strides=*/{1, -2},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 1}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3},
/*expected_output=*/{6, 4, 2},
},
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 5},
/*end=*/{0, 0},
/*strides=*/{1, -2},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 3},
/*expected_output=*/{6, 4, 2},
},
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 5},
/*end=*/{0, 1},
/*strides=*/{1, -3},
/*begin_mask=*/get_mask({0, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 2},
/*expected_output=*/{6, 3},
},
// ellipsis_mask
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 1},
/*end=*/{0, 2},
/*strides=*/{1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({0, 0, 0, 0}),
/*ellipsis_mask=*/get_mask({1, 0, 0, 0}),
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{2, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 1},
/*end=*/{0, 0, 2},
/*strides=*/{1, 1, 1},
/*begin_mask=*/get_mask({1, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/get_mask({0, 1, 0, 0}),
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{2, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 1},
/*end=*/{0, 1, 2, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({0, 0, 0, 0}),
/*ellipsis_mask=*/get_mask({1, 0, 0, 0}),
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{2, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 1},
/*end=*/{1, 1, 2, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({0, 0, 0, 0}),
/*ellipsis_mask=*/get_mask({0, 1, 0, 0}),
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{2, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 0, 1},
/*end=*/{0, 1, 1, 2, 2},
/*strides=*/{1, 1, 1, 1, 1},
/*begin_mask=*/get_mask({0, 0, 0, 0}),
/*end_mask=*/get_mask({0, 0, 0, 0}),
/*ellipsis_mask=*/get_mask({1, 0, 0, 0}),
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/0,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{2, 5},
},
// shrink_axis_mask
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 1},
/*end=*/{0, 0, 0, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({1, 1, 1, 0}),
/*end_mask=*/get_mask({1, 1, 1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/get_mask({0, 0, 0, 1}),
/*expected_output_dims=*/{1, 1, 2},
/*expected_output=*/{2, 5},
},
TestParams{
/*input_dims=*/{1, 1, 2, 3},
/*begin=*/{0, 0, 0, 1},
/*end=*/{0, 1, 2, 2},
/*strides=*/{1, 1, 1, 1},
/*begin_mask=*/get_mask({1, 0, 0, 0}),
/*end_mask=*/get_mask({1, 0, 0, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/get_mask({0, 1, 0, 1}),
/*expected_output_dims=*/{1, 2},
/*expected_output=*/{2, 5},
},
TestParams{
/*input_dims=*/{1, 6},
/*begin=*/{0, 0},
/*end=*/{0, 1},
/*strides=*/{1, 1},
/*begin_mask=*/get_mask({1, 0}),
/*end_mask=*/get_mask({1, 0}),
/*ellipsis_mask=*/0,
/*new_axis_mask=*/0,
/*shrink_axis_mask=*/get_mask({0, 1}),
/*expected_output_dims=*/{1},
/*expected_output=*/{1},
},
};
for (auto p : params) {
if (trt_mode_ == TrtTestMode::kDynamicShape ||
(trt_mode_ == TrtTestMode::kExplicitBatch &&
!HasStaticShape(p.partial_input_dims))) {
p.conversion_status = errors::Unimplemented(
"Strided slice op not implemented for dynamic shape input");
}
Reset();
NodeDef node_def = get_strided_slice_nodedef(
tf_type_, p.begin_mask, p.end_mask, p.ellipsis_mask, p.new_axis_mask,
p.shrink_axis_mask);
VLOG(2) << "Preparing test case with dims " << DebugString(p.input_dims);
if (p.partial_input_dims.empty()) {
AddTestTensor("input", p.input_dims, ok_input);
} else {
AddTestTensor("input", p.input_dims, tf_type_, ok_input,
p.partial_input_dims);
}
VLOG(2) << "Adding weights begin: " << DebugString(p.begin)
<< ", end: " << DebugString(p.end)
<< ", strides: " << DebugString(p.strides);
AddTestWeights<int32>("begin", {static_cast<int>(p.begin.size())}, p.begin);
AddTestWeights<int32>("end", {static_cast<int>(p.end.size())}, p.end);
AddTestWeights<int32>("strides", {static_cast<int>(p.strides.size())},
p.strides);
TestOpConverter("my_strided_slice", node_def, p.expected_output_dims,
p.conversion_status, p.runtime_status,
ElementsAreArray(p.expected_output));
}
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertSlice) {
// Get nodedef for Slice layer.
auto get_slice_nodedef = [](DataType tf_type) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
auto begin = ops::Placeholder(s.WithOpName("begin"), DT_INT32);
auto size = ops::Placeholder(s.WithOpName("size"), DT_INT32);
auto slice = ops::Slice(s.WithOpName("my_slice"), input, begin, size);
return slice.operation.node()->def();
};
struct TestParams {
std::vector<int> input_dims;
std::vector<int> begin;
std::vector<int> size;
std::vector<int> expected_output_dims;
std::vector<int> expected_output;
Status conversion_status;
Status runtime_status;
};
Status conv_dynamic =
trt_mode_ == TrtTestMode::kDynamicShape
? errors::Unimplemented(
"Strided slice op not implemented for dynamic shape input")
: Status::OK();
Status conv_dynamic2 =
trt_mode_ == TrtTestMode::kDynamicShape
? errors::Unimplemented(
"Input dims must be defined for size = -1, at my_slice")
: Status::OK();
std::vector<TestParams> params = {
// Begin is below bounds, should fail.
TestParams{
{1, 1, 2, 3},
{0, 0, -1, 0},
{1, 1, 2, 3},
{},
{},
trt_mode_ == TrtTestMode::kDynamicShape
? conv_dynamic
: errors::InvalidArgument("\"begin\" for dimension 2 in Slice "
"is out of range, at my_slice")},
// Batch dimension is modified, should fail in implicit batch mode.
TestParams{
{2, 1, 1, 3},
{0, 0, 0, 0},
{1, 1, 1, 3},
{1, 1, 1, 3},
{1, 2, 3},
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Unimplemented("TensorRT does not allow modifications"
" to the batch dimension, at my_slice")
: Status::OK()},
// Dynamic batch size but using size[0] of -1, ok.
TestParams{{1, 1, 2, 3},
{0, 0, 0, 0},
{-1, 1, 2, 2},
{1, 1, 2, 2},
{1, 2, 4, 5},
conv_dynamic2},
// OK test: but converter fails in dynamic shape mode
TestParams{{1, 1, 2, 3},
{0, 0, 0, 0},
{-1, -1, -1, -1},
{1, 1, 2, 3},
{1, 2, 3, 4, 5, 6},
conv_dynamic2},
TestParams{{1, 1, 2, 3},
{0, 0, 0, 0},
{1, 1, 2, 3},
{1, 1, 2, 3},
{1, 2, 3, 4, 5, 6}},
TestParams{{1, 1, 2, 3},
{0, 0, 0, 0},
{1, -1, 2, 2},
{1, 1, 2, 2},
{1, 2, 4, 5},
conv_dynamic2},
TestParams{{1, 6}, {0, 1}, {1, 5}, {1, 5}, {2, 3, 4, 5, 6}},
TestParams{{1, 6}, {0, 1}, {-1, 3}, {1, 3}, {2, 3, 4}, conv_dynamic2},
//
// In dynamic shape mode we do not know the input shape during
// conversion, therfore we cannot check out of bound access.
TestParams{
{1, 1, 2, 3},
{0, 0, 3, 0},
{1, 1, 2, 3},
{},
{},
trt_mode_ == TrtTestMode::kDynamicShape
? Status::OK()
: errors::InvalidArgument("\"begin\" for dimension 2 in Slice "
"is out of range, at my_slice"),
errors::Internal("Internal: Failed to build TensorRT engine")},
TestParams{{1, 1, 2, 3},
{0, 0, 0, 0},
{1, 1, 2, -2},
{},
{},
errors::InvalidArgument("Invalid size value at my_slice")},
TestParams{
{1, 1, 2, 3},
{0, 0, 0, 0},
{1, 1, 3, 2},
{},
{},
trt_mode_ == TrtTestMode::kDynamicShape
? Status::OK()
: errors::InvalidArgument("\"begin\" + \"size\" for dimension "
"2 in Slice is out of range, at "
"my_slice"),
errors::Internal("Internal: Failed to build TensorRT engine")},
};
for (auto p : params) {
Reset();
NodeDef node_def = get_slice_nodedef(tf_type_);
AddTestTensor("input", p.input_dims, {1, 2, 3, 4, 5, 6});
AddTestWeights<int32>("begin", {static_cast<int>(p.begin.size())}, p.begin);
AddTestWeights<int32>("size", {static_cast<int>(p.size.size())}, p.size);
TestOpConverter("my_slice", node_def, p.expected_output_dims,
p.conversion_status, p.runtime_status,
ElementsAreArray(p.expected_output));
}
}
TEST_P(OpConverter_FP32_Test, ConvertConv2D) {
// Get nodedef for Conv2D layer.
DataType tf_type = tf_type_;
auto get_conv2d_nodedef =
[tf_type](std::vector<int> strides = {1, 1, 1, 1},
string padding = "SAME", string data_format = "NCHW",
std::vector<int> dilations = {1, 1, 1, 1}) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
auto filter = ops::Placeholder(s.WithOpName("weights"), tf_type);
ops::Conv2D::Attrs attrs =
ops::Conv2D::Attrs().DataFormat(data_format).Dilations(dilations);
auto conv2d = ops::Conv2D(s.WithOpName("my_conv2d"), input, filter, strides,
padding, attrs);
return conv2d.operation.node()->def();
};
{
// Input is weights, should fail.
Reset();
NodeDef node_def = get_conv2d_nodedef();
AddTestWeights<float>("input", {1, 2, 3}, {1, 2, 3, 4, 5, 6});
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"input\" for Conv2D must be a tensor, at my_conv2d");
}
{
// Filter is tensor, should fail.
Reset();
NodeDef node_def = get_conv2d_nodedef();
AddTestTensor("input", {3, 1, 2, 1});
AddTestTensor("weights", {3, 3, 1, 1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"filter\" for Conv2D must be a constant, at my_conv2d");
}
{
// Filter is not 4D, should fail.
Reset();
NodeDef node_def = get_conv2d_nodedef();
AddTestTensor("input", {1, 1, 2, 3});
AddTestWeights<float>("weights", {3, 3, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Conv2D expects kernel of dimension 4, at my_conv2d");
}
{
// Dilations is not 4D, should fail.
Reset();
NodeDef node_def =
get_conv2d_nodedef({1, 1, 1, 1}, "SAME", "NCHW", {1, 1, 1});
AddTestTensor("input", {1, 1, 2, 3});
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Convolution dilations field must specify 4 dimensions, at my_conv2d");
}
{
// Dilation value is not 1 for channel, should fail.
Reset();
NodeDef node_def =
get_conv2d_nodedef({1, 1, 1, 1}, "SAME", "NCHW", {1, 2, 1, 1});
AddTestTensor("input", {1, 1, 2, 3});
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Dilation rate must be 1 for batch and channel "
"dimensions, at my_conv2d");
}
{
// Dilation value is not 1 for channel (NHWC), should fail.
Reset();
NodeDef node_def =
get_conv2d_nodedef({1, 1, 1, 1}, "SAME", "NHWC", {1, 1, 1, 2});
AddTestTensor("input", {1, 2, 3, 1});
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Dilation rate must be 1 for batch and channel "
"dimensions, at my_conv2d");
}
{
// Strides is not 4D, should fail.
Reset();
NodeDef node_def =
get_conv2d_nodedef({1, 1, 1}, "SAME", "NCHW", {1, 1, 1, 1});
AddTestTensor("input", {1, 1, 2, 3});
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Convolution strides field must specify 4 dimensions, at my_conv2d");
}
{
// Stride value is not 1 for channel, should fail.
Reset();
NodeDef node_def =
get_conv2d_nodedef({1, 2, 1, 1}, "SAME", "NCHW", {1, 1, 1, 1});
AddTestTensor("input", {1, 1, 2, 3});
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"Stride must be 1 for batch and channel dimensions, at my_conv2d");
}
if (trt_mode_ == TrtTestMode::kDynamicShape) {
Reset();
NodeDef node_def = get_conv2d_nodedef();
// Channel dim unknown, should fail.
nvinfer1::DataType trt_type;
TF_ASSERT_OK(TfTypeToTrtType(tf_type_, &trt_type));
AddTestTensorWithTFDims("input", {-1, -1, -1, -1}, trt_type);
AddTestWeights<float>("weights", {1, 2, 1, 1}, {-1, 1});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Channel dimension must be static, at my_conv2d");
}
struct TestParams {
std::vector<int> input_dims;
std::vector<float> input;
std::vector<int> filter_dims;
std::vector<float> filter;
std::vector<int> strides;
string padding;
string data_format;
std::vector<int> dilations;
std::vector<int> expected_output_dims;
std::vector<float> expected_output;
};
// Ok.
std::vector<TestParams> ok_params = {
// Basic
TestParams{/*input_dims=*/{1, 1, 2, 3},
/*input=*/{0, 1, 2, 3, 3, 4},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 2},
/*expected_output=*/{1, 1, 0, 1}},
// SAME padding (Asymmetric)
TestParams{/*input_dims=*/{1, 1, 2, 3},
/*input=*/{0, 1, 2, 3, 3, 4},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 1},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/{1, 1, -2, 0, 1, -4}},
// SAME padding (Symmetric)
TestParams{/*input_dims=*/{1, 1, 2, 3},
/*input=*/{0, 1, 2, 3, 3, 4},
/*filter_dims=*/{1, 3, 1, 1},
/*filter=*/{-1, 0, 1},
/*strides=*/{1, 1, 1, 1},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/{1, 2, -1, 3, 1, -3}},
// NHWC
TestParams{/*input_dims=*/{1, 2, 3, 1},
/*input=*/{0, 1, 2, 3, 3, 4},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NHWC",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 2, 2, 1},
/*expected_output=*/{1, 1, 0, 1}},
// Dilated
TestParams{/*input_dims=*/{1, 1, 2, 3},
/*input=*/{0, 1, 2, 3, 3, 4},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 2},
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_output=*/{2, 1}},
// Strided
TestParams{/*input_dims=*/{1, 1, 2, 4},
/*input=*/{0, 1, 2, 2, 3, 4, 4, 7},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 2},
/*padding=*/"VALID",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 2},
/*expected_output=*/{1, 0, 1, 3}},
};
for (int i = 0; i < ok_params.size(); i++) {
Reset();
NodeDef node_def =
get_conv2d_nodedef(ok_params[i].strides, ok_params[i].padding,
ok_params[i].data_format, ok_params[i].dilations);
std::vector<int> partial_input_shape;
if (trt_mode_ == TrtTestMode::kDynamicShape) {
// The channel dim cannot have unknown size, fix that.
partial_input_shape.resize(ok_params[i].input_dims.size(), -1);
int channel_id = (ok_params[i].data_format == "NCHW") ? 1 : 3;
partial_input_shape[channel_id] = ok_params[i].input_dims[channel_id];
}
AddTestTensor("input", ok_params[i].input_dims, tf_type_,
ok_params[i].input, partial_input_shape);
AddTestWeights<float>("weights", ok_params[i].filter_dims,
ok_params[i].filter);
TestOpConverter("my_conv2d", node_def, ok_params[i].expected_output_dims,
Status::OK(), Status::OK(),
ElementsAreArray(ok_params[i].expected_output));
}
}
TEST_P(OpConverter_FP32_Test, ConvertConv2DBackpropInput) {
// Get nodedef for Conv2D layer.
auto get_conv2d_backprop_input_nodedef =
[](DataType tf_type, std::vector<int> strides = {1, 1, 1, 1},
string padding = "SAME", string data_format = "NCHW",
std::vector<int> dilations = {1, 1, 1, 1}) -> NodeDef {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
auto filter = ops::Placeholder(s.WithOpName("weights"), tf_type);
auto input_sizes = ops::Placeholder(s.WithOpName("input_sizes"), DT_INT32);
ops::Conv2DBackpropInput::Attrs attrs = ops::Conv2DBackpropInput::Attrs()
.DataFormat(data_format)
.Dilations(dilations);
auto conv2d = ops::Conv2DBackpropInput(
s.WithOpName("my_conv2d_backprop_input"), input_sizes, filter, input,
strides, padding, attrs);
return conv2d.operation.node()->def();
};
struct TestParams {
std::vector<int> input_dims;
std::vector<float> input;
std::vector<int> filter_dims;
std::vector<float> filter;
std::vector<int> strides;
string padding;
string data_format;
std::vector<int> dilations;
std::vector<int> expected_output_dims;
std::vector<float> expected_output;
Status conversion_status;
// For dynamic shape mode, we must use the partial_input_dims for
// creating the test tensor if any of the input_dims are -1.
std::vector<int> partial_input_dims;
};
// Ok.
std::vector<TestParams> params = {
// Transpose Strided
TestParams{/*input_dims=*/{1, 1, 2, 2},
/*input=*/{0, 1, 2, 3},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 2},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 4},
/*expected_output=*/{0, 0, -1, 1, -2, 2, -3, 3}},
// Transpose Strided NHWC
TestParams{/*input_dims=*/{1, 2, 2, 1},
/*input=*/{0, 1, 2, 3},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 2, 1},
/*padding=*/"SAME",
/*data_format=*/"NHWC",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 2, 4, 1},
/*expected_output=*/{0, 0, -1, 1, -2, 2, -3, 3}},
// Transpose Strided NHWC with VALID padding
TestParams{/*input_dims=*/{1, 3, 1, 1},
/*input=*/{0, 1, 2},
/*filter_dims=*/{2, 1, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 2, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NHWC",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 7, 1, 1},
/*expected_output=*/{0, 0, -1, 1, -2, 2, 0}},
TestParams{/*input_dims=*/{1, 1, 2, 2},
/*input=*/{0, 1, 2, 3},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 2},
/*padding=*/"EXPLICIT",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 4},
/*expected_output=*/{0, 0, -1, 1, -2, 2, -3, 3},
errors::Unimplemented("EXPLICIT padding type not "
"implemented, only VALID and SAME are"
" supported")},
// Dilation + Conv2DBackpropInput, should fail.
TestParams{/*input_dims=*/{1, 1, 2, 2},
/*input=*/{0, 1, 2, 3},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 1},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 2},
{1, 1, 2, 2},
{},
errors::Unimplemented("Dilation with Conv2DBackpropInput "
"(conv2d_transpose) is not supported, "
"at my_conv2d_backprop_input")},
};
if (trt_mode_ == TrtTestMode::kDynamicShape) {
params.push_back(TestParams{
/*input_dims=*/{1, 1, 2, 2},
/*input=*/{0, 1, 2, 3},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 2},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 4},
/*expected_output=*/{0, 0, -1, 1, -2, 2, -3, 3},
errors::InvalidArgument(
"Channel dimension must be static, at my_conv2d_backprop_input"),
/*partial input dims=*/{1, -1, 2, 2}});
// Test dynamic batch dimension.
params.push_back(
TestParams{/*input_dims=*/{2, 1, 2, 2},
/*input=*/
// clang-format off
{0, 1, 2, 3,
3, 2, 1, 0},
// clang-format on
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 2},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{2, 1, 2, 4},
/*expected_output=*/
// clang-format off
{ 0, 0, -1, 1, -2, 2, -3, 3,
-3, 3, -2, 2, -1, 1, 0, 0},
// clang-format on
/*conversion_status=*/Status::OK(),
/*partial input dims=*/{-1, 1, 2, 2}});
// Test dynamic height and width.
params.push_back(TestParams{
/*input_dims=*/{1, 1, 2, 2},
/*input=*/{0, 1, 2, 3},
/*filter_dims=*/{1, 2, 1, 1},
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 2},
/*padding=*/"SAME",
/*data_format=*/"NCHW",
/*dilations=*/{1, 1, 1, 1},
/*expected_output_dims=*/{1, 1, 2, 4},
/*expected_output=*/
{0, 0, -1, 1, -2, 2, -3, 3},
/*conversion_status=*/
errors::Unimplemented(
"Conv2dBackpropInput does not support input with unknown spatial "
"shape"),
/*partial input dims=*/{1, 1, -1, -1}});
}
for (auto p : params) {
for (int input_sizes_length : {2, 4}) {
Reset();
NodeDef node_def = get_conv2d_backprop_input_nodedef(
tf_type_, p.strides, p.padding, p.data_format, p.dilations);
switch (trt_mode_) {
case TrtTestMode::kImplicitBatch: {
AddTestTensor("input", p.input_dims, p.input);
break;
}
case TrtTestMode::kExplicitBatch: {
AddTestTensor("input", p.input_dims, p.input);
break;
}
case TrtTestMode::kDynamicShape: {
AddTestTensor("input", p.input_dims, tf_type_, p.input,
p.partial_input_dims.size() > 0 ? p.partial_input_dims
: p.input_dims);
break;
}
default: {
ASSERT_TRUE(false) << "unknown test mode";
}
}
AddTestWeights<float>("weights", p.filter_dims, p.filter, tf_type_);
if (input_sizes_length == 4) {
AddTestWeights<int>("input_sizes", {4}, p.expected_output_dims);
} else {
std::vector<int> tf_input_sizes(2);
// Remove the channel and batch dimensions.
if (p.data_format == "NHWC") {
std::copy(p.expected_output_dims.begin() + 1,
p.expected_output_dims.end() - 1, tf_input_sizes.begin());
} else {
std::copy(p.expected_output_dims.begin() + 2,
p.expected_output_dims.end(), tf_input_sizes.begin());
}
QCHECK_EQ(2, tf_input_sizes.size());
AddTestWeights<int>("input_sizes", {2}, tf_input_sizes);
}
TestOpConverter("my_conv2d_backprop_input", node_def,
p.expected_output_dims, p.conversion_status, Status::OK(),
ElementsAreArray(p.expected_output));
}
}
}
// Get the NodeDef for Pack.
NodeDef GetConv3DNodeDef(std::vector<int> strides = {1, 1, 1, 1, 1},
string padding = "SAME", string data_format = "NCDHW",
std::vector<int> dilations = {1, 1, 1, 1, 1},
bool is_conv3d_backprop_input = false) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_FLOAT);
auto filter = ops::Placeholder(s.WithOpName("weights"), DT_FLOAT);
if (is_conv3d_backprop_input) {
auto input_sizes = ops::Placeholder(s.WithOpName("input_sizes"), DT_INT32);
ops::Conv3DBackpropInputV2::Attrs attrs =
ops::Conv3DBackpropInputV2::Attrs()
.DataFormat(data_format)
.Dilations(dilations);
auto conv3d =
ops::Conv3DBackpropInputV2(s.WithOpName("my_conv3d"), input_sizes,
filter, input, strides, padding, attrs);
return conv3d.operation.node()->def();
} else {
ops::Conv3D::Attrs attrs =
ops::Conv3D::Attrs().DataFormat(data_format).Dilations(dilations);
auto conv3d = ops::Conv3D(s.WithOpName("my_conv3d"), input, filter, strides,
padding, attrs);
return conv3d.operation.node()->def();
}
}
struct Conv3DTestParams {
std::vector<int> input_dims;
std::vector<float> input;
std::vector<int> filter_dims;
std::vector<float> filter;
std::vector<int> strides;
string padding;
string data_format;
std::vector<int> dilations;
bool is_conv3d_backprop;
std::vector<int> expected_output_dims;
std::vector<float> expected_output;
bool allow_dynamic_channel_dim;
Status validation_status;
};
void TestConv3D(ParameterizedOpConverterTestBase* test, Conv3DTestParams& p) {
test->Reset();
NodeDef node_def = GetConv3DNodeDef(p.strides, p.padding, p.data_format,
p.dilations, p.is_conv3d_backprop);
std::vector<int> partial_input_shape;
if (!p.allow_dynamic_channel_dim &&
test->get_trt_mode() == TrtTestMode::kDynamicShape) {
// The channel dim cannot have unknown size, fix that.
partial_input_shape.resize(p.input_dims.size(), -1);
int channel_id = (p.data_format == "NCDHW") ? 1 : 4;
partial_input_shape[channel_id] = p.input_dims[channel_id];
}
test->AddTestTensor("input", p.input_dims, test->get_tf_type(), p.input,
partial_input_shape);
test->AddTestWeights<float>("weights", p.filter_dims, p.filter);
if (p.is_conv3d_backprop) {
test->AddTestWeights<float>("input_sizes",
{static_cast<int>(p.expected_output.size())},
p.expected_output);
}
test->TestOpConverter("my_conv3d", node_def, p.expected_output_dims,
/*expected_conversion_status=*/p.validation_status,
/*expected_runtime_status=*/Status::OK(),
/*matcher=*/ElementsAreArray(p.expected_output),
/*out_tf_types=*/{test->get_tf_type()});
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertConv3D) {
{
// Input is weights, should fail.
Reset();
NodeDef node_def = GetConv3DNodeDef();
AddTestWeights<float>("input", {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6});
AddTestWeights<float>("weights", {1, 3, 3, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"input\" for Conv3D must be a tensor, at my_conv3d");
}
{
// Filter is tensor, should fail.
Reset();
NodeDef node_def = GetConv3DNodeDef();
AddTestTensor("input", {1, 1, 2, 3}, tf_type_, CreateVectorIota<float>(6));
AddTestTensor("weights", {1, 3, 3, 1}, tf_type_,
CreateVectorIota<float>(9));
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"filter\" for Conv3D must be a constant, at my_conv3d");
}
{
// Filter is not 5D, should fail.
Reset();
NodeDef node_def = GetConv3DNodeDef();
AddTestTensor("input", {1, 1, 2, 3}, tf_type_, CreateVectorIota<float>(6));
AddTestWeights<float>("weights", {3, 3, 1, 1}, {1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Conv3D expects kernel of dimension 5, at my_conv3d");
}
{
// Dilations is not 5D, should fail.
Reset();
NodeDef node_def =
GetConv3DNodeDef({1, 1, 1, 1, 1}, "SAME", "NCDHW", {1, 1, 1, 1});
AddTestTensor("input", {1, 1, 2, 3}, tf_type_, CreateVectorIota<float>(6));
AddTestWeights<float>(
"weights", {3, 3, 1, 1, 1},
{1, 2, 3, 4, 5, 6, 7, 8, 9}); // Dimensions, then values
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Convolution dilations field must specify 5 dimensions, at my_conv3d");
}
{
// Dilation value is not 1 for channel, should fail.
Reset();
NodeDef node_def =
GetConv3DNodeDef({1, 1, 1, 1, 1}, "SAME", "NCDHW", {1, 2, 1, 1, 1});
AddTestTensor("input", {1, 1, 2, 3}, tf_type_, CreateVectorIota<float>(6));
AddTestWeights<float>("weights", {3, 3, 1, 1, 1},
{1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Dilation rate must be 1 for batch and channel "
"dimensions, at my_conv3d");
}
{
// Dilation value is not 1 for channel (NDHWC), should fail.
Reset();
NodeDef node_def =
GetConv3DNodeDef({1, 1, 1, 1, 1}, "SAME", "NDHWC", {1, 1, 1, 1, 2});
AddTestTensor("input", {1, 2, 3, 1}, tf_type_, CreateVectorIota<float>(6));
AddTestWeights<float>("weights", {3, 3, 1, 1, 1},
{1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Dilation rate must be 1 for batch and channel "
"dimensions, at my_conv3d");
}
{
// Dilation + Conv3DBackpropInputV2, should fail.
Reset();
NodeDef node_def = GetConv3DNodeDef({1, 1, 1, 1, 1}, "SAME", "NDHWC",
{1, 1, 2, 1, 1}, true);
AddTestTensor("input", {1, 2, 3, 1}, tf_type_, CreateVectorIota<float>(6));
AddTestWeights<float>("weights", {3, 3, 1, 1, 1},
{1, 2, 3, 4, 5, 6, 7, 8, 9});
AddTestWeights<int>("input_sizes", {4}, {1, 2, 3, 1});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Dilation with Conv3DBackpropInputV2 "
"(conv3d_transpose) is not supported, "
"at my_conv3d");
}
{
// Asymmetric+ Conv3DBackpropInputV2, should fail.
Reset();
NodeDef node_def = GetConv3DNodeDef({1, 1, 1, 1, 1}, "SAME", "NDHWC",
{1, 1, 1, 1, 1}, true);
AddTestTensor("input", {1, 2, 2, 2}, tf_type_, CreateVectorIota<float>(8));
AddTestWeights<float>("weights", {1, 1, 2, 1, 1}, {1, 1});
AddTestWeights<int>("input_sizes", {8}, {1, 2, 3, 4, 5, 6, 7, 8});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Asymmetric padding with Conv3DBackpropInputV2 "
"(conv3d_transpose) is not supported, at "
"my_conv3d");
}
{
// Strides is not 5D, should fail.
Reset();
NodeDef node_def =
GetConv3DNodeDef({1, 1, 1, 1, 1, 1}, "SAME", "NCDHW", {1, 1, 1, 1, 1});
AddTestTensor("input", {1, 2, 2, 2}, tf_type_, CreateVectorIota<float>(8));
AddTestWeights<float>("weights", {1, 1, 2, 1, 1}, {1, 1});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Convolution strides field must specify 5 dimensions, at my_conv3d");
}
{
// Stride value is not 1 for channel, should fail.
Reset();
NodeDef node_def =
GetConv3DNodeDef({1, 2, 1, 1, 1}, "SAME", "NCDHW", {1, 1, 1, 1, 1});
AddTestTensor("input", {1, 1, 2, 3}, tf_type_, CreateVectorIota<float>(6));
AddTestWeights<float>("weights", {3, 3, 1, 1, 1},
{1, 2, 3, 4, 5, 6, 7, 8, 9});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"Stride must be 1 for batch and channel dimensions, at my_conv3d");
}
// Start here
std::vector<Conv3DTestParams> ok_params = {
// Basic - just 1x1 conv - input = output
{/*input_dims=*/{1, 1, 3, 3, 3}, // CDHW
/*input=*/{1, 2, 15, 3, 6, -3, 22, 1, 88, 56, 36, 1, 1, 105,
1, 16, -28, 1, 42, 9, 3, 1, 7, 1, 11, 61, 5},
/*filter_dims=*/{1, 1, 1, 1, 1}, // DRSCK
/*filter=*/{1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 3, 3, 3},
/*expected_output=*/{1, 2, 15, 3, 6, -3, 22, 1, 88,
56, 36, 1, 1, 105, 1, 16, -28, 1,
42, 9, 3, 1, 7, 1, 11, 61, 5},
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
// Basic - 2x1 filter
{/*input_dims=*/{1, 1, 3, 3, 3}, // CDHW
/*input=*/{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6},
/*filter_dims=*/{2, 1, 1, 1, 1}, // DRSCK
/*filter=*/{1, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 2, 3, 3},
/*expected_output=*/
{2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 7},
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
// SAME padding (Asymmetric)
{/*input_dims=*/{1, 1, 2, 3, 2}, // CDHW
/*input=*/{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
/*filter_dims=*/{2, 1, 1, 1, 1}, // DRSCK
/*filter=*/{-1, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"SAME",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 2, 3, 2},
// Diff in first 2 depths is const 6.
/*expected_output=*/{6, 6, 6, 6, 6, 6, -6, -7, -8, -9, -10, -11},
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
// SAME padding (Symmetric)
{/*input_dims=*/{1, 1, 2, 3, 2}, // CDHW
/*input=*/{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
/*filter_dims=*/{3, 1, 1, 1, 1}, // DRSCK
/*filter=*/{-1, 0, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"SAME",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 2, 3, 2},
// Swaps front two depths, negates
/*expected_output=*/{6, 7, 8, 9, 10, 11, 0, -1, -2, -3, -4, -5},
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()
},
// NDHWC (multi-channel)
{/*input_dims=*/{1, 2, 3, 2, 2}, // DHWC
/*input=*/{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11},
/*filter_dims=*/{2, 1, 1, 2, 1}, // DRSCK
/*filter=*/{-1, 1, 1, -1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NDHWC",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 3, 2, 1},
/*expected_output=*/{0, 0, 0, 0, 0, 0}, // Filters oppose each-other
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
// Dilated
{/*input_dims=*/{1, 1, 3, 3, 3}, // CDHW
/*input=*/{1, 1, 1, 1, 1, 1, 1, 1, 1, -10, -10, -10, -10, -10,
-10, -10, -10, -10, 7, 7, 7, 7, 7, 7, 7, 7, 7},
/*filter_dims=*/{2, 1, 1, 1, 1}, // DRSCK
/*filter=*/{1, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 2, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 1, 3, 3},
// Only front depth is valid, skips neg values
/*expected_output=*/{8, 8, 8, 8, 8, 8, 8, 8, 8},
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
// Strided
{/*input_dims=*/{1, 1, 3, 3, 3},
/*input=*/{1, 0, 2, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0,
0, 0, 0, 0, 5, 0, 6, 0, 0, 0, 7, 0, 8},
/*filter_dims=*/{1, 1, 1, 1, 1},
/*filter=*/{1},
/*strides=*/{1, 1, 2, 2, 2},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{1, 1, 2, 2, 2},
// Should only pick up the corners
/*expected_output=*/{1, 2, 3, 4, 5, 6, 7, 8},
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
// Transpose Strided
{/*input_dims=*/{1, 1, 2, 2, 2}, // CDHW
/*input=*/{1, 2, 3, 4, 5, 6, 7, 8},
/*filter_dims=*/{1, 1, 1, 1, 1},
/*filter=*/{1},
/*strides=*/{1, 1, 2, 2, 2},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/true,
/*expected_output_dims=*/{1, 1, 3, 3, 3},
/*expected_output=*/{1, 0, 2, 0, 0, 0, 3, 0, 4, // Cube expands and
0, 0, 0, 0, 0, 0, 0, 0, 0, // fills center
5, 0, 6, 0, 0, 0, 7, 0, 8}, // with zeroes
/*allow_dynamic_channel_dim=*/false,
/*validation_status=*/Status::OK()},
};
if (trt_mode_ == TrtTestMode::kDynamicShape) {
ok_params.reserve(ok_params.size() + 2);
const std::vector<float> common_input = CreateVectorIota<float>(3 * 3 * 3);
// NCDHW - Dynamic Channel - Should fail in kDynamicShape
ok_params.push_back(Conv3DTestParams{
/*input_dims=*/{1, 1, 3, 3, 3},
/*input=*/common_input,
/*filter_dims=*/{1, 1, 1, 1, 1},
/*filter=*/{1},
/*strides=*/{1, 1, 2, 2, 2},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{}, // ignore, will fail anyway
/*expected_output=*/{}, // ignore, will fail anyway
/*allow_dynamic_channel_dim=*/true,
/*validation_status=*/
Status{error::INVALID_ARGUMENT,
"Channel dimension must be static, at my_conv3d"}});
// NDHWC - Dynamic Channel - Should fail in kDynamicShape
ok_params.push_back(Conv3DTestParams{
/*input_dims=*/{1, 3, 3, 3, 1},
/*input=*/common_input,
/*filter_dims=*/{1, 1, 1, 1, 1},
/*filter=*/{1},
/*strides=*/{1, 2, 2, 2, 1},
/*padding=*/"VALID",
/*data_format=*/"NDHWC",
/*dilations=*/{1, 1, 1, 1, 1},
/*is_conv3d_backprop=*/false,
/*expected_output_dims=*/{}, // ignore, will fail anyway
/*expected_output=*/{}, // ignore, will fail anyway
/*allow_dynamic_channel_dim=*/true,
/*validation_status=*/
Status{error::INVALID_ARGUMENT,
"Channel dimension must be static, at my_conv3d"}});
}
for (auto p : ok_params) {
TestConv3D(this, p);
}
}
template <typename T>
NodeDef CreatePoolOp(DataType tf_type, std::vector<int> ksize,
std::vector<int> strides, string padding,
string data_format) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
typename T::Attrs attrs;
attrs.data_format_ = data_format;
return T(s.WithOpName("my_pool"), input, ksize, strides, padding, attrs)
.operation.node()
->def();
}
TEST_P(OpConverter_FP32_Test, ConvertPool) {
// Get nodedef for MaxPool and AvgPool layers (2D or 3D).
auto get_pool_nodedef =
[](DataType tf_type, int nDim, std::vector<int> ksize = {},
std::vector<int> strides = {}, string padding = "SAME",
string data_format = "", const bool is_max_pooling = true) -> NodeDef {
if (ksize.empty()) {
ksize = nDim == 2 ? std::vector<int>{1, 1, 1, 1}
: std::vector<int>{1, 1, 1, 1, 1};
}
if (strides.empty()) {
strides = nDim == 2 ? std::vector<int>{1, 1, 1, 1}
: std::vector<int>{1, 1, 1, 1, 1};
}
if (data_format == "") {
data_format = nDim == 2 ? "NCHW" : "NCDHW";
}
if (is_max_pooling) {
if (nDim == 3) {
return CreatePoolOp<ops::MaxPool3D>(tf_type, ksize, strides, padding,
data_format);
} else {
return CreatePoolOp<ops::MaxPool>(tf_type, ksize, strides, padding,
data_format);
}
} else {
if (nDim == 3) {
return CreatePoolOp<ops::AvgPool3D>(tf_type, ksize, strides, padding,
data_format);
} else {
return CreatePoolOp<ops::AvgPool>(tf_type, ksize, strides, padding,
data_format);
}
}
};
std::vector<int> test_nDims{2, 3};
for (int nDim : test_nDims) {
// Input is weights, should fail.
Reset();
NodeDef node_def = get_pool_nodedef(tf_type_, nDim);
AddTestWeights<float>("input", {1, 1, 1, 2, 3}, {1, 2, 3, 4, 5, 6});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
StrCat("The input \"input\" for ", node_def.op(),
" must be a tensor, at my_pool"));
}
struct TestParams {
std::vector<int> input_dims;
std::vector<float> input;
std::vector<int> ksize;
std::vector<int> strides;
string padding;
string data_format;
std::vector<int> expected_output_dims;
// The expected outputs for the following operations: MaxPool2D, AvgPool2D,
// MaxPool3D, AvgPool3D
std::vector<std::vector<float>> expected_outputs;
};
// We use common_input as the input to test both 2D and 3D pooling operations,
// to simplify TestParams. For 2D operations, only the first 1/3 of the values
// are used.
const std::vector<float> common_input{-4, 2, 15, 3, 6, -3, 22, 1, 88,
56, 36, 1, 1, 105, 1, 16, -28, 1,
42, 9, 3, 1, 7, 1, 11, 61, 5};
// The output of 2D ops for the case where the op is equivalent to the
// identity op.
const std::vector<float> common_2d_output{-4, 2, 15, 3, 6, -3, 22, 1, 88};
std::vector<TestParams> ok_params = {
// Basic - just 1x1 max pooling - input = output
TestParams{
/*input_dims=*/{1, 1, 3, 3, 3},
/*input=*/common_input,
/*ksize=*/{1, 1, 1, 1, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*expected_output_dims=*/{1, 1, 3, 3, 3},
/*expected_outputs=*/
{common_2d_output, common_2d_output, common_input, common_input}},
// Basic - just 1x1 max pooling - input = output, SAME padding
TestParams{
/*input_dims=*/{1, 1, 3, 3, 3},
/*input=*/common_input,
/*ksize=*/{1, 1, 1, 1, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"SAME",
/*data_format=*/"NCDHW",
/*expected_output_dims=*/{1, 1, 3, 3, 3},
/*expected_outputs=*/
{common_2d_output, common_2d_output, common_input, common_input}},
// 3x3 pooling NCDHW
TestParams{/*input_dims=*/{1, 1, 3, 3, 3},
/*input=*/common_input,
/*ksize=*/{1, 1, 3, 3, 3},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*expected_output_dims=*/{1, 1, 1, 1, 1},
/*expected_outputs=*/{{88}, {14.444445}, {105}, {17}}},
// 3x3 pooling, NDHWC
TestParams{/*input_dims=*/{1, 3, 3, 3, 1},
/*input=*/common_input,
/*ksize=*/{1, 3, 3, 3, 1},
/*strides=*/{1, 1, 1, 1, 1},
/*padding=*/"VALID",
/*data_format=*/"NDHWC",
/*expected_output_dims=*/{1, 1, 1, 1, 1},
/*expected_outputs=*/{{88}, {14.444445}, {105}, {17}}},
// Strided
TestParams{/*input_dims=*/{1, 1, 3, 3, 3},
/*input=*/{1, 0, 2, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0,
0, 0, 0, 0, 5, 0, 6, 0, 0, 0, 7, 0, 8},
/*ksize=*/{1, 1, 1, 1, 1},
/*strides=*/{1, 1, 2, 2, 2},
/*padding=*/"VALID",
/*data_format=*/"NCDHW",
/*expected_output_dims=*/{1, 1, 2, 2, 2},
/*expected_outputs=*/
{{1, 2, 3, 4}, // Should only pick up the corners
{1, 2, 3, 4},
{1, 2, 3, 4, 5, 6, 7, 8},
{1, 2, 3, 4, 5, 6, 7, 8}}},
};
for (auto p : ok_params) {
int test_counter = 0;
for (int nDim : test_nDims) {
auto input = p.input;
auto input_dims = p.input_dims;
auto ksize = p.ksize;
auto strides = p.strides;
auto expected_output_dims = p.expected_output_dims;
std::string data_format = p.data_format;
if (nDim == 2) {
input.resize(9);
data_format = p.data_format == "NDHWC" ? "NHWC" : "NCHW";
// Remove one of the spatial dimensions
input_dims.erase(input_dims.begin() + 2);
ksize.erase(ksize.begin() + 2);
strides.erase(strides.begin() + 2);
expected_output_dims.erase(expected_output_dims.begin() + 2);
}
for (bool is_max_pooling : {true, false}) {
Reset();
NodeDef node_def =
get_pool_nodedef(tf_type_, nDim, ksize, strides, p.padding,
data_format, is_max_pooling);
AddTestTensor("input", input_dims, input);
TestOpConverter("my_pool", node_def, expected_output_dims, Status::OK(),
Status::OK(),
ElementsAreArray(p.expected_outputs.at(test_counter)));
test_counter++;
}
}
}
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertTopK) {
// Get the NodeDef for TopKV2.
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type_);
auto weights = ops::Placeholder(s.WithOpName("weights"), DT_INT32);
auto topk = ops::TopK(s.WithOpName("my_topk"), input, weights);
const NodeDef& node_def = topk.operation.node()->def();
{
// K is a tensor, should fail.
Reset();
AddTestTensor("input", {1, 1, 2, 3});
AddTestTensor("weights", {1}, DT_INT32, {});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"k\" for TopKV2 must be a constant, at my_topk");
}
{
// Ok.
Reset();
AddTestTensor("input", {1, 1, 2, 5}, {-9, 3, 5, 1, 6, -5, 7, 1, 0, -1});
AddTestWeights<int32>("weights", {1}, {2});
std::vector<std::vector<int>> expected_output_dims{{1, 1, 2, 2},
{1, 1, 2, 2}};
TestOpConverterMultiOut("my_topk", node_def, expected_output_dims,
Status::OK(), Status::OK(),
{ElementsAre(6, 5, 7, 1), ElementsAre(4, 2, 1, 2)},
{tf_type_, DT_INT32});
}
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertGather) {
// Get the NodeDef for GatherV2.
Scope s = Scope::NewRootScope();
auto params = ops::Placeholder(s.WithOpName("params"), tf_type_);
auto indices = ops::Placeholder(s.WithOpName("indices"), DT_INT32);
auto axis = ops::Placeholder(s.WithOpName("axis"), DT_INT32);
auto gather = ops::GatherV2(s.WithOpName("my_gather"), params, indices, axis);
const NodeDef& node_def = gather.operation.node()->def();
{
// Axis is a tensor, should fail.
Reset();
AddTestTensor("params", {1, 1, 2, 3}, tf_type_, {});
AddTestTensor("indices", {1, 2}, DT_INT32, {});
AddTestTensor("axis", {1}, DT_INT32, {});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"axis\" for GatherV2 must be a constant, at my_gather");
}
{
// Axis is out of bounds, should fail.
Reset();
AddTestTensor("params", {1, 1, 2, 3});
AddTestTensor("indices", {1, 2}, DT_INT32, {});
AddTestWeights<int32>("axis", {1}, {4});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Axis value of 4 is out of bounds, must be in "
"range [-4, 4), at my_gather");
}
struct TestParams {
// TF shape of the input 'params' (including batch dimension).
std::vector<int> params_shape;
// TF shape of the input 'indices' (including batch dimension).
std::vector<int> indices_shape;
std::vector<int> indices;
int axis;
// Expected TF shape of the output (including batch dimension).
std::vector<int> expected_output_shape;
std::vector<int> expected_output;
bool params_is_tensor;
Status status;
Status runtime_status;
Status add_index_status;
};
// Input is the same {1, 2, 3, 4, 5, 6} for all cases.
const std::vector<int> params_input = {1, 2, 3, 4, 5, 6};
std::vector<TestParams> test_params = {
// Axis is batch dimension, should fail in implicit batch mode.
TestParams{/*params_shape=*/{2, 1, 1, 3},
/*indices_shape=*/{2},
/*indices=*/{1, 0},
/*axis=*/0,
/*expected_output_shape=*/{2, 1, 1, 3},
/*expected_output=*/{4, 5, 6, 1, 2, 3},
/*params_is_tensor=*/true,
trt_mode_ == TrtTestMode::kImplicitBatch
? Status{error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the"
" batch dimension, at my_gather"}
: Status::OK()},
// Batch size of indices is not 1 when params is a tensor.
TestParams{/*params_shape=*/{2, 1, 3},
/*indices_shape=*/{2, 1},
/*indices=*/{2, 0},
/*axis=*/2,
/*expected_output_shape=*/{2, 1, 2, 1},
/*expected_output=*/{3, 1, 6, 4},
/*params_is_tensor=*/true,
trt_mode_ == TrtTestMode::kImplicitBatch
? Status{error::UNIMPLEMENTED,
"Indices must have a batch size of 1 when params"
" is a tensor."}
: Status::OK()},
// Axis is not zero when params is a weight, should fail in implicit batch
// mode.
TestParams{/*params_shape=*/{2, 1, 3},
/*indices_shape=*/{2},
/*indices=*/{1, 2},
/*axis=*/2,
/*expected_output_shape=*/{2, 1, 2},
/*expected_output=*/{2, 3, 5, 6},
/*params_is_tensor=*/false,
trt_mode_ == TrtTestMode::kImplicitBatch
? Status{error::UNIMPLEMENTED,
"The input axis must be zero when params is a"
" weight."}
: Status::OK()},
// Params with only batch dimension.
TestParams{/*params_shape=*/{6},
/*indices_shape=*/{2},
/*indices=*/{1, 3},
/*axis=*/0,
/*expected_output_shape=*/{2},
/*expected_output=*/{2, 4},
/*params_is_tensor=*/true,
trt_mode_ == TrtTestMode::kImplicitBatch // conversion_status
? Status{error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the "
"batch dimension, at my_gather"}
: Status::OK(),
Status::OK(), // runtime_status
trt_mode_ == TrtTestMode::kImplicitBatch // add_index_status
? Status{error::INVALID_ARGUMENT,
"Batch size doesn't match for tensor indices: "
"Provided batch size does not match converter "
"batch size: 2 vs 6"}
: Status::OK()},
// Vector indices, and output rank is rank(params).
TestParams{
/*params_shape=*/{1, 1, 2, 3},
/*indices_shape=*/{1},
/*indices=*/{0},
/*axis=*/3,
/*expected_output_shape=*/{1, 1, 2, 1},
/*expected_output=*/{1, 4},
/*params_is_tensor=*/true,
},
TestParams{
/*params_shape=*/{1, 1, 2, 3},
/*indices_shape=*/{1},
/*indices=*/{1},
/*axis=*/2,
/*expected_output_shape=*/{1, 1, 1, 3},
/*expected_output=*/{4, 5, 6},
/*params_is_tensor=*/true,
},
// Indices with rank>1, and output rank is rank(params) + rank(indices) -
// 1
TestParams{
/*params_shape=*/{1, 1, 2, 3},
/*indices_shape=*/{1, 1},
/*indices=*/{0},
/*axis=*/3,
/*expected_output_shape=*/{1, 1, 2, 1, 1},
/*expected_output=*/{1, 4},
/*params_is_tensor=*/true,
},
TestParams{
/*params_shape=*/{1, 1, 2, 3},
/*indices_shape=*/{1, 1},
/*indices=*/{1},
/*axis=*/3,
/*expected_output_shape=*/{1, 1, 2, 1, 1},
/*expected_output=*/{2, 5},
/*params_is_tensor=*/true,
},
TestParams{
/*params_shape=*/{1, 1, 2, 3},
/*indices_shape=*/{1, 1},
/*indices=*/{2},
/*axis=*/-1,
/*expected_output_shape=*/{1, 1, 2, 1, 1},
/*expected_output=*/{3, 6},
/*params_is_tensor=*/true,
},
TestParams{
/*params_shape=*/{1, 1, 2, 3},
/*indices_shape=*/{1, 3},
/*indices=*/{2, 0, 1},
/*axis=*/3,
/*expected_output_shape=*/{1, 1, 2, 1, 3},
/*expected_output=*/{3, 1, 2, 6, 4, 5},
/*params_is_tensor=*/true,
},
TestParams{
/*params_shape=*/{1, 3, 2},
/*indices_shape=*/{1, 2, 2},
/*indices=*/{0, 0, 1, 0},
/*axis=*/2,
/*expected_output_shape=*/{1, 3, 1, 2, 2},
/*expected_output=*/{1, 1, 2, 1, 3, 3, 4, 3, 5, 5, 6, 5},
/*params_is_tensor=*/true,
},
TestParams{
/*params_shape=*/{1, 2, 3},
/*indices_shape=*/{1},
/*indices=*/{0},
/*axis=*/0,
/*expected_output_shape=*/{1, 2, 3},
/*expected_output=*/{1, 2, 3, 4, 5, 6},
/*params_is_tensor=*/false,
},
TestParams{
/*params_shape=*/{3, 2},
/*indices_shape=*/{1, 2},
/*indices=*/{0, 1},
/*axis=*/0,
/*expected_output_shape=*/{1, 2, 2},
/*expected_output=*/{1, 2, 3, 4},
/*params_is_tensor=*/false,
},
TestParams{
/*params_shape=*/{2, 3},
/*indices_shape=*/{1, 1, 2},
/*indices=*/{0, 1},
/*axis=*/0,
/*expected_output_shape=*/{1, 1, 2, 3},
/*expected_output=*/{1, 2, 3, 4, 5, 6},
/*params_is_tensor=*/false,
},
TestParams{
/*params_shape=*/{3, 2},
/*indices_shape=*/{2, 2},
/*indices=*/{0, 2, 1, 0},
/*axis=*/0,
/*expected_output_shape=*/{2, 2, 2},
/*expected_output=*/{1, 2, 5, 6, 3, 4, 1, 2},
/*params_is_tensor=*/false,
},
};
for (auto p : test_params) {
Reset();
if (p.params_is_tensor) {
AddTestTensor("params", p.params_shape, params_input);
} else {
AddTestWeights("params", p.params_shape, params_input, tf_type_);
}
AddTestTensor("indices", p.indices_shape, DT_INT32, p.indices, {},
p.add_index_status);
AddTestWeights<int32>("axis", {1}, {p.axis});
TestOpConverter("my_gather", node_def, p.expected_output_shape, p.status,
p.runtime_status, ElementsAreArray(p.expected_output));
}
}
template <typename OpType>
NodeDef CreateReduceOp(DataType tf_type, bool keep_dims) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), tf_type);
auto axis = ops::Placeholder(s.WithOpName("axis"), DT_INT32);
typename OpType::Attrs op_attrs;
op_attrs.keep_dims_ = keep_dims;
auto op = OpType(s.WithOpName("my_reduce"), input, axis, op_attrs);
return op.operation.node()->def();
}
// Applies reduction op on sub-sequences of input
// output[i] = reduce(input[m * i : m * (i +1)])
std::vector<float> CalcReduce(string op_name, std::vector<float> input, int m,
float (*op)(float, float), float init) {
std::vector<float> output(input.size() / m);
for (int i = 0; i < output.size(); i++) {
auto begin = input.begin() + i * m;
auto end = input.begin() + (i + 1) * m;
output[i] = std::accumulate(begin, end, init, op);
if (op_name == "Mean") {
output[i] /= m;
}
}
return output;
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertReduce) {
{
// Input is weights, should fail.
Reset();
const NodeDef node_def = CreateReduceOp<ops::Sum>(tf_type_, false);
AddTestWeights<float>("input", {1, 2, 3}, {-3, -2, -1, 0, 1, 2});
AddTestWeights<int32>("axis", {1}, {1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"input\" for Sum must be a tensor, at my_reduce");
}
{
// Axis is weights, should fail.
Reset();
const NodeDef node_def = CreateReduceOp<ops::Sum>(tf_type_, false);
AddTestTensor("input", {1, 2, 3}, {-3, -2, -1, 0, 1, 2});
AddTestTensor("axis", {1}, DT_INT32, {1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"axis\" for Sum must be a constant, at my_reduce");
}
using OpFunc = std::function<NodeDef(DataType, bool)>;
using ValFunc = float (*)(float, float);
struct ReduceTestDescriptor {
string name;
OpFunc get_node;
ValFunc val_func;
float init_val;
};
std::vector<ReduceTestDescriptor> op_test_info{
{"Sum", CreateReduceOp<ops::Sum>, [](float x, float y) { return x + y; },
0},
{"Prod", CreateReduceOp<ops::Prod>,
[](float x, float y) { return x * y; }, 1},
{"Mean", CreateReduceOp<ops::Mean>,
[](float x, float y) { return x + y; }, 0},
{"Min", CreateReduceOp<ops::Min>,
[](float x, float y) { return y < x ? y : x; }, 1000},
{"Max", CreateReduceOp<ops::Max>,
[](float x, float y) { return x < y ? y : x; }, -1000}};
std::vector<float> input_values{1, 2, 3, 4, 5, 6};
struct TestParams {
std::vector<int> input_dims;
std::vector<float> input_values;
// Helper array contains the same elements as input but permuted in a way
// that the reduction can be calculated over contiguous elements using
// CalcReduce
std::vector<float> helper_array;
std::vector<int> axis;
int stride; // product of input_dims along axis
Status conversion_status;
};
std::vector<TestParams> params{
// Out of range tests
TestParams{{2, 3, 1}, input_values, input_values, {3}, 3},
TestParams{{2, 3, 1}, input_values, input_values, {-4}, 3},
// Ok tests
TestParams{{2, 3, 1}, input_values, {1, 4, 2, 5, 3, 6}, {0}, 2},
TestParams{{2, 3, 1}, input_values, input_values, {1}, 3},
TestParams{{2, 3, 1}, input_values, input_values, {2}, 1},
TestParams{{2, 3, 1}, input_values, input_values, {0, 1}, 6},
// Ok tests with negative axis values
TestParams{{2, 3, 1}, input_values, {1, 4, 2, 5, 3, 6}, {-3}, 2},
TestParams{{2, 3, 1}, input_values, input_values, {-2}, 3},
TestParams{{2, 3, 1}, input_values, input_values, {-1}, 1},
TestParams{{2, 3, 1}, input_values, input_values, {-3, 1}, 6},
};
for (bool keep_dims : {false, true}) {
for (auto& op : op_test_info) {
VLOG(2) << "Processing " << op.name << " with keep_dims=" << keep_dims;
for (auto p : params) {
SCOPED_TRACE(StrCat(op.name, keep_dims ? " & keep_dims" : ""));
Reset();
NodeDef node_def = op.get_node(tf_type_, keep_dims);
AddTestTensor("input", p.input_dims, p.input_values);
AddTestWeights<int32>("axis", {static_cast<int>(p.axis.size())},
p.axis);
std::vector<int> expected_output_dims(p.input_dims);
// Set expected output dim and conversion error messages
for (int ax : p.axis) {
int rank = p.input_dims.size();
if (ax >= rank || ax < -rank) {
p.conversion_status =
errors::InvalidArgument("Axis value of ", ax,
" is out of bounds, must be in "
"range [",
-rank, ", ", rank, "), at my_reduce");
} else {
int ax_positive = ax >= 0 ? ax : ax + rank;
// Zero marks elements that we will remove later.
expected_output_dims[ax_positive] = keep_dims ? 1 : 0;
if (trt_mode_ == TrtTestMode::kImplicitBatch &&
(ax == 0 || ax == -rank)) {
p.conversion_status = errors::Unimplemented(
"TensorRT does not allow manipulation of the batch "
"dimension, at my_reduce");
}
}
}
expected_output_dims.erase(std::remove(expected_output_dims.begin(),
expected_output_dims.end(), 0),
expected_output_dims.end());
VLOG(2) << "out dims "
<< absl::StrCat("[", absl::StrJoin(expected_output_dims, ","),
"]");
std::vector<float> expected_values = CalcReduce(
op.name, p.helper_array, p.stride, op.val_func, op.init_val);
if (tf_type_ == DT_INT32) {
// We need to floor the float values in the `expected_values` vector.
std::for_each(expected_values.begin(), expected_values.end(),
[](float& _n) { _n = std::floor(_n); }
);
}
TestOpConverter("my_reduce", node_def, expected_output_dims,
p.conversion_status, Status::OK(),
ArrayFloatNear(expected_values));
}
}
}
}
NodeDef CreateCastOp(DataType tf_type) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), DT_HALF);
return ops::Cast(s.WithOpName("my_unary"), input, DT_FLOAT)
.operation.node()
->def();
}
TEST_P(OpConverter_FP32_Test, ConvertUnary) {
{
// Input is weights, should fail.
Reset();
const NodeDef node_def = CreateUnaryOp<ops::Neg>(tf_type_);
AddTestWeights<float>("input", {1, 2, 3}, {-3, -2, -1, 0, 1, 2});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"x\" for Neg must be a tensor, at my_unary");
}
using OpFunc = std::function<NodeDef(DataType)>;
using ValFunc = float (*)(float);
std::map<std::string, std::pair<OpFunc, ValFunc>> op_map;
#define ADD_OP(name, op, compute) \
op_map[name] = \
std::make_pair(CreateUnaryOp<op>, static_cast<ValFunc>(compute))
ADD_OP("Abs", ops::Abs, std::abs);
ADD_OP("Acos", ops::Acos, std::acos);
ADD_OP("Acosh", ops::Acosh, std::acosh);
ADD_OP("Asin", ops::Asin, std::asin);
ADD_OP("Asinh", ops::Asinh, std::asinh);
ADD_OP("Atan", ops::Atan, std::atan);
ADD_OP("Atanh", ops::Atanh, std::atanh);
op_map["Cast"] = std::make_pair(CreateCastOp, [](float x) { return x; });
ADD_OP("Ceil", ops::Ceil, std::ceil);
ADD_OP("Cos", ops::Cos, std::cos);
ADD_OP("Cosh", ops::Cosh, std::cosh);
ADD_OP("Exp", ops::Exp, std::exp);
ADD_OP("Erf", ops::Erf, std::erf);
ADD_OP("Floor", ops::Floor, std::floor);
ADD_OP("Log", ops::Log, std::log);
ADD_OP("Neg", ops::Neg, [](float x) { return -x; });
ADD_OP("Reciprocal", ops::Reciprocal, [](float x) { return 1.0f / x; });
ADD_OP("Rsqrt", ops::Rsqrt, [](float x) { return 1.0f / std::sqrt(x); });
ADD_OP("Sin", ops::Sin, std::sin);
ADD_OP("Sinh", ops::Sinh, std::sinh);
ADD_OP("Sqrt", ops::Sqrt, std::sqrt);
ADD_OP("Tan", ops::Tan, std::tan);
#undef ADD_OP
// Get list of ops to test.
std::vector<string> ops_to_test;
// Add all ops supported by ConvertUnary.
auto* map = UnaryOperationMap();
ops_to_test.reserve(map->size());
for (auto& pair : *map) {
ops_to_test.push_back(pair.first);
}
// Add other unary ops to test.
ops_to_test.push_back("Rsqrt");
// Prepare test parameters
auto p = TestParamBase{
{1, 1, 2, 3}, // input dims
{}, // input partial dims
{1, 1, 2, 3}, // expected output dims
};
for (const string& op_name : ops_to_test) {
SCOPED_TRACE(op_name);
Reset();
if (!op_map.count(op_name)) {
FAIL() << "Unary op test map does not contain op " << op_name;
}
NodeDef node_def = op_map[op_name].first(tf_type_);
// TODO(bixia): we assume this test is only instantiated for DT_FLOAT for
// now. Need to find a better way to express input and output types.
//
// TODO(tfeher): improve tests by defining an expected output data type and
// check that. Currently only the shape and values of the output are
// checked.
DataType input_tf_type = op_name == "Cast" ? DT_HALF : tf_type_;
std::vector<float> input_values{-0.9f, 0.6f, 0.0f, -3.5f, 100.0f, 2.9f};
AddTestTensor("input", p.input_dims, input_tf_type, input_values);
std::vector<float> output;
std::transform(input_values.begin(), input_values.end(),
std::back_inserter(output), op_map[op_name].second);
TestOpConverter("my_unary", node_def, p.expected_output_dims, Status::OK(),
p.runtime_status, ArrayFloatNear(output, 0.0001, true));
}
}
// Get the NodeDef for ConcatV2.
// TODO(hinsu): Consider switching this to static function.
auto get_concat_nodedef = [](DataType dtype, int num_inputs) -> NodeDef {
Scope s = Scope::NewRootScope();
std::vector<Input> values;
for (int i = 0; i < num_inputs; ++i) {
const string input_name = StrCat("values_", i);
values.push_back(ops::Placeholder(s.WithOpName(input_name), dtype));
}
auto axis = ops::Placeholder(s.WithOpName("axis"), DT_INT32);
auto concat = ops::Concat(s.WithOpName("my_concat"),
absl::Span<const Input>(values), axis);
return concat.operation.node()->def();
};
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertConcat) {
{
// Axis is a tensor, should fail.
Reset();
NodeDef node_def = get_concat_nodedef(tf_type_, 2);
AddTestTensor("values_0", {1, 1, 2, 3});
AddTestTensor("values_1", {1, 1, 2, 3});
AddTestTensor("axis", {1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"axis\" for ConcatV2 must be a constant, at my_concat");
}
{
// Axis is out of bounds, should fail.
Reset();
NodeDef node_def = get_concat_nodedef(tf_type_, 2);
AddTestTensor("values_0", {1, 1, 2, 3});
AddTestTensor("values_1", {1, 1, 2, 3});
AddTestWeights<int32>("axis", {1}, {4});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Axis value of 4 is out of bounds, must be in "
"range [-4, 4), at my_concat");
}
{
// Inputs have inconsistent ranks, should fail.
Reset();
NodeDef node_def = get_concat_nodedef(tf_type_, 2);
AddTestTensor("values_0", {1, 1, 2, 3});
AddTestTensor("values_1", {1, 1, 6});
AddTestWeights<int32>("axis", {1}, {1});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Received inputs with inconsistent rank, at my_concat");
}
struct TestParams {
std::vector<std::vector<int>> input_shapes;
std::vector<std::vector<int>> input_values;
int axis;
std::vector<int> expected_output_dims;
std::vector<int> expected_output;
Status conversion_status;
Status run_status;
bool input_as_weight;
};
const std::vector<std::vector<int>> common_input{CreateVectorIota<int>(6),
CreateVectorIota<int>(6, 6)};
std::vector<TestParams> params = {
{
/*input_shapes=*/{{1, 1, 2, 3}, {1, 1, 2, 3}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{1, 2, 2, 3},
/*expected_output=*/CreateVectorIota<int>(12),
},
{
/*input_shapes=*/{{1, 1, 2, 3}, {1, 1, 2, 3}},
/*input_values=*/common_input,
/*axis=*/2,
/*expected_output_dims=*/{1, 1, 4, 3},
/*expected_output=*/CreateVectorIota<int>(12),
},
{
/*input_shapes=*/{{1, 1, 2, 3}, {1, 1, 2, 3}},
/*input_values=*/common_input,
/*axis=*/3,
/*expected_output_dims=*/{1, 1, 2, 6},
/*expected_output=*/
{0, 1, 2, 6, 7, 8, 3, 4, 5, 9, 10, 11},
},
{
/*input_shapes=*/{{1, 1}, {1, 2}, {1, 3}, {1, 1}, {1, 1}, {1, 2}},
/*input_values=*/
{{1}, {2, 3}, {4, 5, 6}, {7}, {8}, {9, 10}},
/*axis=*/1,
/*expected_output_dims=*/{1, 10},
/*expected_output=*/
CreateVectorIota<int>(10, /*start_value=*/1),
},
{
// An input is a weight
/*input_shapes=*/{{1, 1, 2, 3}, {1, 1, 2, 3}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{1, 2, 2, 3},
/*expected_output=*/CreateVectorIota<int>(12),
/*conversion_status=*/
errors::Unimplemented("The input \"values_1\" for ConcatV2 "
"must be a tensor, at my_concat"),
/*run_status=*/Status::OK(),
/*input_as_weight=*/true,
},
{
// Axis is batch dimension, should fail in implicit batch mode.
/*input_shapes=*/{{1, 1, 2, 3}, {1, 1, 2, 3}},
/*input_values=*/common_input,
/*axis=*/0,
/*expected_output_dims=*/{2, 1, 2, 3},
/*expected_output=*/CreateVectorIota<int>(12),
/*conversion_status=*/trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Unimplemented(
"TensorRT does not allow manipulation of the "
"batch dimension, at my_concat")
: Status::OK(),
},
{
// Inconsistent input shape, runtime error in dynamic shape mode.
/*input_shapes=*/{{1, 1, 2, 3}, {1, 1, 3, 2}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{2, 1, 2, 3},
/*expected_output=*/CreateVectorIota<int>(12),
trt_mode_ != TrtTestMode::kDynamicShape
? errors::InvalidArgument(
"Received inputs with inconsistent shape, at my_concat")
: Status::OK(),
errors::InvalidArgument(""),
}};
for (auto p : params) {
Reset();
const int num_inputs = p.input_shapes.size();
EXPECT_EQ(num_inputs, p.input_values.size());
NodeDef node_def = get_concat_nodedef(tf_type_, num_inputs);
// Create inputs.
for (int j = 0; j < num_inputs; ++j) {
string name = StrCat("values_", j);
if (j == 1 && p.input_as_weight) {
AddTestWeights(name, p.input_shapes[j], p.input_values[j], tf_type_);
} else {
AddTestTensor(name, p.input_shapes[j], p.input_values[j]);
}
}
AddTestWeights<int32>("axis", {1}, {p.axis});
TestOpConverter("my_concat", node_def, p.expected_output_dims,
p.conversion_status, p.run_status,
ElementsAreArray(p.expected_output));
}
}
// Get the NodeDef for Split.
auto get_split_nodedef = [](DataType dtype, int num_split) -> NodeDef {
Scope s = Scope::NewRootScope();
auto axis = ops::Placeholder(s.WithOpName("axis"), DT_INT32);
auto value = ops::Placeholder(s.WithOpName("value"), dtype);
auto split = ops::Split(s.WithOpName("my_split"), axis, value, num_split);
return split.operation.node()->def();
};
template <DataType dtype>
void TestConvertSplit(OpConverterTest* test) {
typedef typename EnumToDataType<dtype>::Type CType;
struct TestParams {
std::vector<int> input_shape;
std::vector<CType> value;
int axis;
int num_split;
std::vector<int> expected_output_dims;
std::vector<std::vector<CType>> expected_outputs;
};
const std::vector<CType> common_input = CreateVectorIota<CType>(6);
std::vector<TestParams> ok_params = {
// Identity (num_split = 1)
{/*input_shape=*/{1, 2, 3}, /*value=*/common_input, /*axis=*/1,
/*num_split=*/1, /*expected_output_dims=*/{1, 2, 3},
/*expected_outputs=*/{CreateVectorIota<CType>(6)}},
{/*input_shape=*/{1, 2, 3},
/*value=*/common_input,
/*axis=*/3,
/*num_split=*/3,
/*expected_output_dims=*/{1, 2, 1},
/*expected_outputs=*/
{{CType(0), CType(3)}, {CType(1), CType(4)}, {CType(2), CType(5)}}},
{/*input_shape=*/{1, 6},
/*value=*/common_input,
/*axis=*/2,
/*num_split=*/6,
/*expected_output_dims=*/{1, 1},
/*expected_outputs=*/
{{CType(0)},
{CType(1)},
{CType(2)},
{CType(3)},
{CType(4)},
{CType(5)}}},
{/*input_shape=*/{1, 6},
/*value=*/common_input,
/*axis=*/-1,
/*num_split=*/2,
/*expected_output_dims=*/{1, 3},
/*expected_outputs=*/
{CreateVectorIota<CType>(3), CreateVectorIota<CType>(3, CType(3))}},
};
for (int i = 0; i < ok_params.size(); ++i) {
test->Reset();
NodeDef node_def = get_split_nodedef(dtype, ok_params[i].num_split);
// Create inputs.
test->AddTestWeights<int32>("axis", {1}, {ok_params[i].axis});
nvinfer1::DataType trt_type;
TF_ASSERT_OK(TfTypeToTrtType(dtype, &trt_type));
test->AddTestTensor("value", ok_params[i].input_shape, 1, trt_type);
// Convert.
test->RunValidationAndConversion(node_def);
// Get output tensors and verify output dims.
EXPECT_EQ(ok_params[i].expected_outputs.size(), ok_params[i].num_split);
std::vector<TRT_TensorOrWeights> outputs(ok_params[i].num_split);
DataVec output_data;
for (int j = 0; j < outputs.size(); ++j) {
const string name = j == 0 ? StrCat("my_split") : StrCat("my_split:", j);
TF_EXPECT_OK(test->GetTensorOrWeights(name, &outputs[j]));
EXPECT_TRUE(outputs[j].is_tensor());
EXPECT_THAT(outputs[j].tensor()->getDimensions(),
DimsAreArray(ok_params[i].expected_output_dims));
// Create buffer to store output.
output_data.push_back(
{name, test->ConstructTensor<CType>(
ok_params[i].expected_outputs[j].size())});
}
// Verify output values are correct.
const DataVec input_data{
{"value", test->AsTensor<CType>(ok_params[i].value)}};
TF_EXPECT_OK(test->BuildAndRun(input_data, &output_data));
for (int j = 0; j < outputs.size(); ++j) {
EXPECT_THAT(GetSpanForData<CType>(output_data[j]),
ElementsAreArray(ok_params[i].expected_outputs[j]));
}
}
}
TEST_F(OpConverterTest, ConvertSplit) {
{
// Axis is a tensor, should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 1);
AddTestTensor("axis", {1});
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"axis\" for Split must be a constant, at my_split");
}
{
// Axis is out of bounds, should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 1);
AddTestWeights<int32>("axis", {1}, {4});
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Axis value of 4 is out of bounds, must be in "
"range [-4, 4), at my_split");
}
{
// Axis is out of bounds (negative), should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 1);
AddTestWeights<int32>("axis", {1}, {-5});
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Axis value of -5 is out of bounds, must be in "
"range [-4, 4), at my_split");
}
{
// Axis is batch dimension, should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 1);
AddTestWeights<int32>("axis", {1}, {0});
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the "
"batch dimension, at my_split");
}
{
// Value is a weight, should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 1);
AddTestWeights<int32>("axis", {1}, {1});
AddTestWeights<float>("value", {1, 2, 3}, {1, 2, 3, 4, 5, 6});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"value\" for Split must be a tensor, at my_split");
}
{
// Dim is not evenly divisibly by num_split, should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 2);
AddTestWeights<int32>("axis", {1}, {3});
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Dimension 3 of size 3 is not evenly divisible by 2, at my_split");
}
{
// num_split > dim size, should fail.
Reset();
NodeDef node_def = get_split_nodedef(DT_FLOAT, 4);
AddTestWeights<int32>("axis", {1}, {3});
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Dimension 3 of size 3 is not evenly divisible by 4, at my_split");
}
TestConvertSplit<DT_FLOAT>(this);
TestConvertSplit<DT_HALF>(this);
TestConvertSplit<DT_INT32>(this);
}
// Get the NodeDef for Unpack (Unstack in TF API).
auto get_unpack_nodedef = [](DataType dtype, int num, int axis) -> NodeDef {
Scope s = Scope::NewRootScope();
auto value = ops::Placeholder(s.WithOpName("value"), dtype);
auto unstack_attrs = ops::Unstack::Axis(axis);
auto unstack =
ops::Unstack(s.WithOpName("my_unpack"), value, num, unstack_attrs);
return unstack.operation.node()->def();
};
struct UnpackTestParams {
std::vector<int> input_shape;
std::vector<float> input_value;
int axis;
int num;
std::vector<int> expected_output_dims;
std::vector<std::vector<float>> expected_outputs;
Status run_status;
};
void TestConvertUnpack(ParameterizedOpConverterTestBase* test,
UnpackTestParams& p) {
test->Reset();
NodeDef node_def = get_unpack_nodedef(test->get_tf_type(), p.num, p.axis);
// Create inputs.
test->AddTestTensor("value", p.input_shape, test->get_tf_type(),
p.input_value);
std::vector<Matcher<std::vector<float>>> matcher_vec;
std::vector<DataType> datatype_vec;
std::vector<std::vector<int>> expected_output_dims;
for (int j = 0; j < p.expected_outputs.size(); ++j) {
matcher_vec.push_back(ElementsAreArray(p.expected_outputs[j]));
datatype_vec.push_back(test->get_tf_type());
expected_output_dims.push_back(p.expected_output_dims);
}
test->TestOpConverterMultiOut(/*name=*/"my_unpack",
/*node_def=*/node_def,
/*expected_output_dims=*/expected_output_dims,
/*expected_conversion_status=*/p.run_status,
/*expected_runtime_status=*/p.run_status,
/*matcher=*/matcher_vec,
/*out_tf_type=*/datatype_vec);
}
// TODO: Reactivate when INT32 Segfault fixed
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertUnpack) {
// We need to skip error testing for Dynamic Shape mode, as it is impossible
// to convert Unpack in Dynamic Shape Mode.
if (trt_mode_ != TrtTestMode::kDynamicShape) {
{
// Value is weights, should fail.
Reset();
NodeDef node_def = get_unpack_nodedef(tf_type_, /*num=*/3, /*axis=*/3);
AddTestWeights<float>("value", {1, 1, 2, 3}, {1, 2, 3, 4, 5, 6});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"value\" for Unpack must be a tensor, at my_unpack");
}
{
// Axis is out of bounds, should fail.
Reset();
NodeDef node_def = get_unpack_nodedef(tf_type_, /*num=*/1, /*axis=*/4);
AddTestTensor("value", {1, 1, 2, 3});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Axis value of 4 is out of bounds, must be in "
"range [-4, 4), at my_unpack");
}
{
// Axis is out of bounds (negative), should fail.
Reset();
NodeDef node_def = get_unpack_nodedef(tf_type_, /*num=*/1, /*axis=*/-5);
AddTestTensor("value", {1, 1, 2, 3});
RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Axis value of -5 is out of bounds, must be "
"in range [-4, 4), at my_unpack");
}
{
if (trt_mode_ != TrtTestMode::kExplicitBatch) {
// Axis is batch dimension, should fail.
Reset();
NodeDef node_def = get_unpack_nodedef(tf_type_, /*num=*/1, /*axis=*/0);
AddTestTensor("value", {1, 2, 3});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of "
"the batch dimension, at my_unpack");
}
}
{
// Dim size does not match num, should fail.
Reset();
NodeDef node_def = get_unpack_nodedef(tf_type_, /*num=*/5, /*axis=*/2);
AddTestTensor("value", {1, 1, 6});
RunValidationAndConversion(
node_def, error::INVALID_ARGUMENT,
"Dimension 2 has size 6 which is not equal to num of 5, at "
"my_unpack");
}
{
// Output would be TF scalar, should fail.
Reset();
NodeDef node_def = get_unpack_nodedef(tf_type_, /*num=*/1, /*axis=*/0);
AddTestTensor("value", {}, tf_type_, {}, {},
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Internal("Scalars cannot be represented in "
"implicit batch mode")
: Status::OK());
if (trt_mode_ == TrtTestMode::kImplicitBatch) {
RunValidationAndConversion(
node_def, error::INTERNAL,
"Failed to convert input value to a TRT_TensorOrWeights: Scalar "
"input tensor is not supported since the first dimension is "
"treated "
"as batch dimension by TRT");
} else {
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Input \"value\" for Unpack must be rank 2 "
"or greater, at my_unpack");
}
}
}
const std::vector<float> common_input = CreateVectorIota<float>(6);
Status run_status = trt_mode_ == TrtTestMode::kDynamicShape
? errors::Unimplemented(
"Strided slice op not implemented for dynamic "
"shape input")
: Status::OK();
std::vector<UnpackTestParams> params = {
{/*input_shape=*/{1, 1, 2, 1, 3, 1},
/*input_value=*/common_input,
/*axis=*/4,
/*num=*/3,
/*expected_output_dims=*/{1, 1, 2, 1, 1},
/*expected_outputs=*/{{0, 3}, {1, 4}, {2, 5}},
/*run_status=*/run_status},
{/*input_shape=*/{1, 1, 2, 1, 3},
/*input_value=*/common_input,
/*axis=*/4,
/*num=*/3,
/*expected_output_dims=*/{1, 1, 2, 1},
/*expected_outputs=*/{{0, 3}, {1, 4}, {2, 5}},
/*run_status=*/run_status},
{/*input_shape=*/{1, 1, 2, 3},
/*input_value=*/common_input,
/*axis=*/1,
/*num=*/1,
/*expected_output_dims=*/{1, 2, 3},
/*expected_outputs=*/{CreateVectorIota<float>(6)},
/*run_status=*/run_status},
{/*input_shape=*/{1, 6, 1},
/*input_value=*/common_input,
/*axis=*/-2,
/*num=*/6,
/*expected_output_dims=*/{1, 1},
/*expected_outputs=*/{{0}, {1}, {2}, {3}, {4}, {5}},
/*run_status=*/run_status},
{/*input_shape=*/{1, 6},
/*input_value=*/common_input,
/*axis=*/1,
/*num=*/6,
/*expected_output_dims=*/{1},
/*expected_outputs=*/{{0}, {1}, {2}, {3}, {4}, {5}},
/*run_status=*/run_status},
};
for (auto p : params) {
TestConvertUnpack(this, p);
}
}
// Get the NodeDef for Pack.
NodeDef GetPackNodeDef(DataType dtype, int num_inputs, int axis) {
Scope s = Scope::NewRootScope();
std::vector<Input> values;
for (int i = 0; i < num_inputs; ++i) {
const string input_name = StrCat("values_", i);
values.push_back(ops::Placeholder(s.WithOpName(input_name), dtype));
}
// Pack op is renamed to Stack in APIs.
auto pack =
ops::Stack(s.WithOpName("my_pack"), absl::Span<const Input>(values),
ops::Stack::Axis(axis));
return pack.operation.node()->def();
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertPack) {
struct TestParams {
std::vector<std::vector<int>> input_shapes;
std::vector<std::vector<int>> partial_input_shapes;
std::vector<std::vector<float>> input_values;
int axis;
std::vector<int> expected_output_dims;
std::vector<float> expected_output;
Status conversion_status;
Status runtime_status;
bool input_1_is_weight;
};
const std::vector<std::vector<float>> common_input{
CreateVectorIota<float>(6),
CreateVectorIota<float>(6, /*start_value=*/6)};
std::vector<TestParams> params = {
// Second input is weight, should fail in implicit batch mode
{/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{1, 2, 2, 3},
/*expected_output=*/CreateVectorIota<float>(12),
trt_mode_ == TrtTestMode::kImplicitBatch
? Status{error::UNIMPLEMENTED,
"The input \"values_1\" for Pack must be a tensor, at "
"my_pack"}
: Status::OK(),
/*runtime_status*/ Status::OK(),
/*weight_input*/ true},
// Axis is out of bounds, should fail.
{
/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/-5,
/*expected_output_dims=*/{},
/*expected_output=*/{},
Status{error::INVALID_ARGUMENT,
"Axis value of -5 is out of bounds, must be in"
" range [-4, 4), at my_pack"},
},
// Axis is batch dimension, should fail in implicit batch mode.
{/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/-4,
/*expected_output_dims=*/{2, 1, 2, 3},
/*expected_output=*/CreateVectorIota<float>(12),
trt_mode_ == TrtTestMode::kImplicitBatch
? Status{error::UNIMPLEMENTED,
"TensorRT does not allow manipulation of the batch "
"dimension, at my_pack"}
: Status::OK()},
// Inconsistent rank, should fail.
{
/*input_shapes=*/{{1, 2, 3}, {1, 6}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{},
/*expected_output=*/{},
Status{error::INVALID_ARGUMENT,
"Received inputs with inconsistent rank, at my_pack"},
},
{
/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{1, 2, 2, 3},
/*expected_output=*/CreateVectorIota<float>(12),
},
{
/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/2,
/*expected_output_dims=*/{1, 2, 2, 3},
/*expected_output=*/
{0, 1, 2, 6, 7, 8, 3, 4, 5, 9, 10, 11},
},
{
/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/3,
/*expected_output_dims=*/{1, 2, 3, 2},
/*expected_output=*/
{0, 6, 1, 7, 2, 8, 3, 9, 4, 10, 5, 11},
},
{
/*input_shapes=*/{{1, 2, 3}},
/*partial_input_shapes=*/{{}},
/*input_values=*/{CreateVectorIota<float>(6)},
/*axis=*/1,
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/CreateVectorIota<float>(6),
},
{
/*input_shapes=*/{{1, 2, 3}},
/*partial_input_shapes=*/{{}},
/*input_values=*/{CreateVectorIota<float>(6)},
/*axis=*/2,
/*expected_output_dims=*/{1, 2, 1, 3},
/*expected_output=*/CreateVectorIota<float>(6),
},
};
// Inputs have inconsistent shapes, should fail.
if (trt_mode_ != TrtTestMode::kDynamicShape) {
params.push_back(TestParams{
/*input_shapes=*/{{1, 2, 3}, {1, 3, 2}},
/*partial_input_shapes=*/{{}, {}},
/*input_values=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{},
/*expected_output=*/CreateVectorIota<float>(12),
Status{error::INVALID_ARGUMENT,
"Received inputs with inconsistent shape, at my_pack"}});
} else {
// In dynamic shape mode we cannot catch inconsistent shapes at conversion
// time, only during runtime. But TensorRT does not raise a proper runtime
// error, instead it aborts the program with the following message:
// Assertion failed: t->start.d[i] + t->extent.d[i] <= r.dims.d[i]
// ../builder/cudnnBuilderGraph.cpp:862
// Aborting...
// TODO(tfeher) Add dynamic shapes test once TRT handles shape error
// decently
}
if (trt_mode_ == TrtTestMode::kDynamicShape) {
// Test with mixed dynamic / static shape input tensors
params.push_back(
TestParams{/*input_shapes=*/{{1, 2, 3}, {1, 2, 3}},
/*partial_input_shapes=*/{{-1, -1, -1}, {1, 2, 3}},
/*input_values=*/common_input,
/*axis=*/2,
/*expected_output_dims=*/{1, 2, 2, 3},
/*expected_output=*/
{0, 1, 2, 6, 7, 8, 3, 4, 5, 9, 10, 11}});
}
for (auto p : params) {
Reset();
const int num_inputs = p.input_shapes.size();
EXPECT_EQ(num_inputs, p.input_values.size());
NodeDef node_def = GetPackNodeDef(tf_type_, num_inputs, p.axis);
// Create inputs.
for (int j = 0; j < num_inputs; ++j) {
if (j == 1 && p.input_1_is_weight) {
AddTestWeights(StrCat("values_", j), p.input_shapes[j],
p.input_values[j], tf_type_);
} else {
AddTestTensor(StrCat("values_", j), p.input_shapes[j], tf_type_,
p.input_values[j], p.partial_input_shapes[j]);
}
}
TestOpConverter("my_pack", node_def, p.expected_output_dims,
p.conversion_status, p.runtime_status,
ElementsAreArray(p.expected_output));
}
}
// Get the NodeDef for ArgMin or ArgMax.
template <typename OpType>
NodeDef GetArgMinMaxNodeDef(DataType input_dtype, DataType output_dtype) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), input_dtype);
auto dimension = ops::Placeholder(s.WithOpName("dimension"), DT_INT32);
auto attrs = OpType::OutputType(output_dtype);
auto arg = OpType(s.WithOpName("my_arg"), input, dimension, attrs);
return arg.operation.node()->def();
}
struct ArgMinMaxTestParams {
std::vector<int> input_shape;
std::vector<float> input_value;
int axis;
std::vector<int> expected_output_dims;
std::vector<int> expected_argmax_output;
std::vector<int> expected_argmin_output;
Status status;
};
template <typename OpType>
void TestConvertArgMinMax(ParameterizedOpConverterTestBase* test,
DataType _tf_type, ArgMinMaxTestParams& p) {
test->Reset();
NodeDef node_def = GetArgMinMaxNodeDef<OpType>(_tf_type,
/*output_dtype=*/DT_INT32);
std::vector<int> expected_out;
if (node_def.op() == "ArgMax") {
expected_out = p.expected_argmax_output;
} else if (node_def.op() == "ArgMin") {
expected_out = p.expected_argmin_output;
} else {
ASSERT_TRUE(false);
}
test->AddTestTensor("input", p.input_shape, _tf_type, p.input_value);
test->AddTestWeights("dimension", {1}, {p.axis}, DT_INT32);
test->TestOpConverter("my_arg", node_def, p.expected_output_dims,
/*expected_conversion_status=*/p.status,
/*expected_runtime_status=*/Status::OK(),
/*matcher=*/ElementsAreArray(expected_out), {DT_INT32});
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertArgMinMax) {
{
// Dimension is a tensor, should fail.
Reset();
NodeDef node_def =
GetArgMinMaxNodeDef<ops::ArgMax>(tf_type_,
/*output_dtype=*/DT_INT32);
AddTestTensor("input", {1, 2, 3});
AddTestTensor("dimension", {1});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"dimension\" for ArgMax must be a constant, at my_arg");
}
{
// Output type is INT64, should fail.
Reset();
NodeDef node_def =
GetArgMinMaxNodeDef<ops::ArgMax>(tf_type_,
/*output_dtype=*/DT_INT64);
AddTestTensor("input", {1, 2, 3});
AddTestWeights("dimension", {1}, {3}, DT_INT32);
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"Output type int64 is not supported, at my_arg");
}
const std::vector<float> common_input = CreateVectorIota<float>(6);
std::vector<ArgMinMaxTestParams> params = {
{/*input_shape=*/{2, 3},
/*input_value=*/common_input,
/*axis=*/0,
/*expected_output_dims=*/{3},
/*expected_argmax_output=*/{1, 1, 1},
/*expected_argmin_output=*/{0, 0, 0},
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::Unimplemented("TensorRT does not allow manipulation of "
"the batch dimension, at my_arg")
: Status::OK()},
{
/*input_shape=*/{1, 6},
/*input_value=*/common_input,
/*axis=*/1,
/*expected_output_dims=*/{1},
/*expected_argmax_output=*/{5},
/*expected_argmin_output=*/{0},
},
{
/*input_shape=*/{1, 10},
/*input_value=*/
{-5.0f, 3.0f, 5.0f, 1.0f, 6.0f, -9.0f, 7.0f, 1.0f, 0.0f, -1.0f},
/*axis=*/-1,
/*expected_output_dims=*/{1},
/*expected_argmax_output=*/{6},
/*expected_argmin_output=*/{5},
},
{
/*input_shape=*/{1, 2, 3},
/*input_value=*/common_input,
/*axis=*/2,
/*expected_output_dims=*/{1, 2},
/*expected_argmax_output=*/{2, 2},
/*expected_argmin_output=*/{0, 0},
},
{
/*input_shape=*/{1, 2, 3},
/*input_value=*/common_input,
/*axis=*/-2,
/*expected_output_dims=*/{1, 3},
/*expected_argmax_output=*/{1, 1, 1},
/*expected_argmin_output=*/{0, 0, 0},
},
{
/*input_shape=*/{1, 2, 1, 3},
/*input_value=*/common_input,
/*axis=*/3,
/*expected_output_dims=*/{1, 2, 1},
/*expected_argmax_output=*/{2, 2},
/*expected_argmin_output=*/{0, 0},
},
{
/*input_shape=*/{1, 2, 1, 3},
/*input_value=*/common_input,
/*axis=*/-3,
/*expected_output_dims=*/{1, 1, 3},
/*expected_argmax_output=*/{1, 1, 1},
/*expected_argmin_output=*/{0, 0, 0},
},
{/*input_shape=*/{1, 2, 1, 1, 3},
/*input_value=*/common_input,
/*axis=*/4,
/*expected_output_dims=*/{1, 2, 1, 1},
/*expected_argmax_output=*/{2, 2},
/*expected_argmin_output=*/{0, 0},
#if !IS_TRT_VERSION_GE(7, 0, 0, 11)
errors::Unimplemented("op is not able to support tensors with 4+"
" dimensions (excluding batch size)")
#else
Status::OK()
#endif
},
{/*input_shape=*/{1, 2, 1, 1, 3},
/*input_value=*/common_input,
/*axis=*/-4,
/*expected_output_dims=*/{1, 1, 1, 3},
/*expected_argmax_output=*/{1, 1, 1},
/*expected_argmin_output=*/{0, 0, 0},
#if !IS_TRT_VERSION_GE(7, 0, 0, 11)
errors::Unimplemented("op is not able to support tensors with 4+"
" dimensions (excluding batch size)")
#else
Status::OK()
#endif
},
};
for (auto p : params) {
TestConvertArgMinMax<ops::ArgMin>(this, tf_type_, p);
TestConvertArgMinMax<ops::ArgMax>(this, tf_type_, p);
}
}
// Get the NodeDef for DepthToSpace or SpaceToSpace.
template <typename OpType>
NodeDef GetDepthSpaceShuffleNodeDef(DataType dtype, int block_size,
string data_format) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), dtype);
auto attrs = OpType::DataFormat(data_format);
auto shuffle = OpType(s.WithOpName("my_shuffle"), input, block_size, attrs);
return shuffle.operation.node()->def();
}
struct DepthSpaceShuffleTestParams {
std::vector<int> input_dims;
std::vector<int> input_value;
int block_size;
string data_format;
std::vector<int> expected_output_dims;
std::vector<int> expected_output;
};
template <typename OpType>
void TestConvertDepthSpaceShuffle(
ParameterizedOpConverterTestBase* test,
const std::vector<DepthSpaceShuffleTestParams>& params) {
Status status = Status::OK();
{
// Input is a weight, should fail.
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<ops::DepthToSpace>(
test->get_tf_type(), 2, "NCHW");
test->AddTestWeights<float>("input", {1, 4, 1, 1}, {1, 2, 3, 4});
test->RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
StrCat("The input \"input\" for ", node_def.op(),
" must be a tensor, at my_shuffle"));
}
{
// Input rank != 4
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<ops::DepthToSpace>(
test->get_tf_type(), 2, "NCHW");
test->AddTestTensor("input", {1, 16, 32});
test->RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
StrCat("The input to ", node_def.op(),
" must be rank 4, at "
"my_shuffle"));
}
{
// Unsupported format, should fail.
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<ops::DepthToSpace>(
test->get_tf_type(), 2, "NCHW_VECT_C");
test->AddTestTensor("input", {1, 16, 32, 32});
test->RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"Data format NCHW_VECT_C is not supported, at my_shuffle");
}
if (test->get_trt_mode() != TrtTestMode::kDynamicShape) {
// In dynamic shape mode, we cannot check input dimension values at
// conversion time therefore we cannot confirm block_size vs input dim
// consistency. We rely on the user to provide a valid TF graph. Otherwise
// TRT will fail with a runtime error.
if (std::is_same<OpType, ops::DepthToSpace>::value) {
// Channels not divisible by block_size, should fail.
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<ops::DepthToSpace>(
test->get_tf_type(), 3, "NCHW");
test->AddTestTensor("input", {1, 16, 32, 32});
test->RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Number of channels must be divisible by"
" block_size*block_size, at my_shuffle");
} else {
{ // Width not divisible by block_size, should fail.
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<ops::SpaceToDepth>(
test->get_tf_type(), 3, "NCHW");
test->AddTestTensor("input", {1, 16, 9, 32});
test->RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Width and height must be divisible by"
" block_size, at my_shuffle");
}
{
// Height not divisible by block_size, should fail.
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<ops::SpaceToDepth>(
test->get_tf_type(), 3, "NCHW");
test->AddTestTensor("input", {1, 16, 32, 9});
test->RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
"Width and height must be divisible by"
" block_size, at my_shuffle");
}
}
}
for (auto p : params) {
test->Reset();
NodeDef node_def = GetDepthSpaceShuffleNodeDef<OpType>(
test->get_tf_type(), p.block_size, p.data_format);
test->AddTestTensor("input", p.input_dims, p.input_value);
test->TestOpConverter("my_shuffle", node_def, p.expected_output_dims,
status, Status::OK(),
ElementsAreArray(p.expected_output));
}
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertDepthToSpace) {
const std::vector<int> common_input = CreateVectorIota<int>(16);
std::vector<DepthSpaceShuffleTestParams> params = {
{
/*input_shape=*/{1, 4, 2, 2},
/*input_value=*/common_input,
/*block_size=*/2,
/*data_format=*/"NCHW",
/*expected_output_dims=*/{1, 1, 4, 4},
/*expected_output=*/
{0, 4, 1, 5, 8, 12, 9, 13, 2, 6, 3, 7, 10, 14, 11, 15},
},
{
/*input_shape=*/{1, 2, 2, 4},
/*input_value=*/common_input,
/*block_size=*/2,
/*data_format=*/"NHWC",
/*expected_output_dims=*/{1, 4, 4, 1},
/*expected_output=*/
{0, 1, 4, 5, 2, 3, 6, 7, 8, 9, 12, 13, 10, 11, 14, 15},
},
{
/*input_shape=*/{1, 16, 1, 1},
/*input_value=*/common_input,
/*block_size=*/4,
/*data_format=*/"NCHW",
/*expected_output_dims=*/{1, 1, 4, 4},
/*expected_output=*/CreateVectorIota<int>(16),
},
{
/*input_shape=*/{1, 2, 2, 8},
/*input_value=*/CreateVectorIota<int>(32),
/*block_size=*/2,
/*data_format=*/"NHWC",
/*expected_output_dims=*/{1, 4, 4, 2},
/*expected_output=*/{0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6,
7, 12, 13, 14, 15, 16, 17, 18, 19, 24, 25,
26, 27, 20, 21, 22, 23, 28, 29, 30, 31},
}};
TestConvertDepthSpaceShuffle<ops::DepthToSpace>(this, params);
}
TEST_P(OpConverter_FP32_FP16_INT32_Test, ConvertSpaceToDepth) {
const std::vector<int> common_input = CreateVectorIota<int>(16);
std::vector<DepthSpaceShuffleTestParams> params = {
{
/*input_shape=*/{1, 1, 4, 4},
/*input_value=*/common_input,
/*block_size=*/2,
/*data_format=*/"NCHW",
/*expected_output_dims=*/{1, 4, 2, 2},
/*expected_output=*/
{0, 2, 8, 10, 1, 3, 9, 11, 4, 6, 12, 14, 5, 7, 13, 15},
},
{
/*input_shape=*/{1, 4, 4, 1},
/*input_value=*/common_input,
/*block_size=*/2,
/*data_format=*/"NHWC",
/*expected_output_dims=*/{1, 2, 2, 4},
/*expected_output=*/
{0, 1, 4, 5, 2, 3, 6, 7, 8, 9, 12, 13, 10, 11, 14, 15},
},
{
/*input_shape=*/{1, 1, 4, 4},
/*input_value=*/common_input,
/*block_size=*/4,
/*data_format=*/"NCHW",
/*expected_output_dims=*/{1, 16, 1, 1},
/*expected_output=*/CreateVectorIota<int>(16),
},
{
/*input_shape=*/{1, 4, 4, 2},
/*input_value=*/CreateVectorIota<int>(32),
/*block_size=*/2,
/*data_format=*/"NHWC",
/*expected_output_dims=*/{1, 2, 2, 8},
/*expected_output=*/{0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6,
7, 12, 13, 14, 15, 16, 17, 18, 19, 24, 25,
26, 27, 20, 21, 22, 23, 28, 29, 30, 31},
},
};
TestConvertDepthSpaceShuffle<ops::SpaceToDepth>(this, params);
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertClipByValue) {
Scope s = Scope::NewRootScope();
auto t = ops::Placeholder(s.WithOpName("t"), tf_type_);
auto clip_value_min =
ops::Placeholder(s.WithOpName("clip_value_min"), tf_type_);
auto clip_value_max =
ops::Placeholder(s.WithOpName("clip_value_max"), tf_type_);
auto clip = ops::ClipByValue(s.WithOpName("my_clip"), t, clip_value_min,
clip_value_max);
const NodeDef& node_def = clip.operation.node()->def();
nvinfer1::DataType trt_type_;
TF_ASSERT_OK(TfTypeToTrtType(tf_type_, &trt_type_));
{
// Input is a weight, should fail.
Reset();
AddTestWeights("t", {1, 2, 3}, {1, 2, 3, 4, 5, 6}, tf_type_);
AddTestWeights("clip_value_min", {1}, {1}, tf_type_);
AddTestWeights("clip_value_max", {1}, {5}, tf_type_);
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"t\" for ClipByValue must be a "
"tensor, at my_clip");
}
{
// Clip min is a tensor, should fail.
Reset();
AddTestTensor("t", {1, 2, 3});
AddTestTensor("clip_value_min", {1});
AddTestWeights("clip_value_max", {1}, {1}, tf_type_);
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"clip_value_min\" for ClipByValue "
"must be a constant, at my_clip");
}
{
// Clip max is a tensor, should fail.
Reset();
AddTestTensor("t", {1, 2, 3});
AddTestWeights("clip_value_min", {1}, {1}, tf_type_);
AddTestTensor("clip_value_max", {1});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"clip_value_max\" for ClipByValue "
"must be a constant, at my_clip");
}
struct TestParams {
std::vector<int> dims;
int clip_value_min;
int clip_value_max;
std::vector<float> expected_output;
};
const std::vector<float> common_input = CreateVectorIota<float>(6);
std::vector<TestParams> params = {{
/*dims=*/{6},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 6},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 2, 3},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 2, 3, 1},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 1, 3, 1, 2},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{1, 1, 3, 1, 2, 1},
/*clip_value_min=*/2,
/*clip_value_max=*/4,
/*expected_output=*/{2, 2, 2, 3, 4, 4},
},
{
/*dims=*/{2, 1, 3},
/*clip_value_min=*/-1,
/*clip_value_max=*/8,
/*expected_output=*/common_input,
}};
for (auto p : params) {
Reset();
AddTestTensor("t", p.dims, tf_type_, common_input);
AddTestWeights("clip_value_min", {1}, {p.clip_value_min}, tf_type_);
AddTestWeights("clip_value_max", {1}, {p.clip_value_max}, tf_type_);
TestOpConverter("my_clip", node_def, p.dims,
/*expected_conversion_status=*/Status::OK(),
/*expected_runtime_status=*/Status::OK(),
/*matcher=*/ElementsAreArray(p.expected_output));
}
}
// Get the NodeDef for SquaredDifference.
NodeDef GetSquaredDifferenceNodeDef(DataType dtype) {
Scope s = Scope::NewRootScope();
auto x = ops::Placeholder(s.WithOpName("x"), dtype);
auto y = ops::Placeholder(s.WithOpName("y"), dtype);
auto squared_diff =
ops::SquaredDifference(s.WithOpName("my_squared_diff"), x, y);
return squared_diff.operation.node()->def();
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertSquaredDifference) {
{
// Input is a weight, should fail.
Reset();
NodeDef node_def = GetSquaredDifferenceNodeDef(tf_type_);
AddTestWeights<float>("x", {1, 2, 3}, {1, 2, 3, 4, 5, 6});
AddTestTensor("y", {1, 1, 2, 3});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"x\" for SquaredDifference must be "
"a tensor, at my_squared_diff");
}
struct TestParams {
std::vector<int> dims_x;
std::vector<int> dims_y;
std::vector<float> value_x;
std::vector<float> value_y;
std::vector<int> expected_output_dims;
std::vector<float> expected_output;
Status status;
Status runtime_status;
};
const std::vector<float> common_input = CreateVectorIota<float>(6);
std::vector<TestParams> params = {
{/*dims_x=*/{1, 2, 3},
/*dims_y=*/{1, 7, 5},
/*value_x=*/common_input,
/*value_y=*/std::vector<float>(7 * 5, 0),
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/common_input,
trt_mode_ == TrtTestMode::kDynamicShape
? Status::OK()
: errors::InvalidArgument("Infeasible broadcast scheme"),
errors::Internal(
"Binding index out of range. This can happen if profile is not set, "
"or the network is invalid for the current profile.")},
{
/*dims_x=*/{1, 1, 2, 3},
/*dims_y=*/{1, 1, 2, 3},
/*value_x=*/common_input,
/*value_y=*/{0, -1, 3, 0, 10, -7},
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/{0, 4, 1, 9, 36, 144},
},
{
/*dims_x=*/{1, 1, 2, 3},
/*dims_y=*/{1, 1, 1, 3},
/*value_x=*/common_input,
/*value_y=*/{0, 1, 2},
/*expected_output_dims=*/{1, 1, 2, 3},
/*expected_output=*/{0, 0, 0, 9, 9, 9},
},
};
for (auto p : params) {
Reset();
NodeDef node_def = GetSquaredDifferenceNodeDef(tf_type_);
AddTestTensor("x", p.dims_x, p.value_x);
AddTestTensor("y", p.dims_y, p.value_y);
TestOpConverter("my_squared_diff", node_def, p.expected_output_dims,
p.status, p.runtime_status,
ElementsAreArray(p.expected_output));
}
}
template <typename OpType>
NodeDef MakeResizeNodeDef(DataType dtype, bool align_corners) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), dtype);
auto size = ops::Placeholder(s.WithOpName("size"), DT_INT32);
auto attrs = typename OpType::Attrs().AlignCorners(align_corners);
auto resize = OpType(s.WithOpName("my_resize"), input, size, attrs);
return resize.operation.node()->def();
}
struct ResizeTestParams {
std::vector<int> input_dims;
std::vector<int> output_resize_dims;
std::vector<float> input_value;
bool size_as_tensor;
bool align_corners;
std::vector<int> expected_output_dims;
std::vector<float> expected_nearest_output_values;
std::vector<float> expected_bilinear_output_values;
Status status;
};
template <typename OpType>
void TestConvertResize(ParameterizedOpConverterTestBase* test,
ResizeTestParams& p) {
test->Reset();
// Create resize node.
NodeDef node_def =
MakeResizeNodeDef<OpType>(test->get_tf_type(), p.align_corners);
test->AddTestTensor("input", p.input_dims, test->get_tf_type(),
p.input_value);
// Create output size.
if (p.size_as_tensor) {
std::vector<int32> size_dims{2};
std::vector<int32> size_values{p.output_resize_dims};
test->AddTestTensor("size", size_dims, DT_INT32, size_values, size_dims);
} else {
test->AddTestWeights("size", {2}, p.output_resize_dims, DT_INT32);
}
std::vector<float> expected_out;
if (node_def.op() == "ResizeBilinear") {
expected_out = p.expected_bilinear_output_values;
} else if (node_def.op() == "ResizeNearestNeighbor") {
expected_out = p.expected_nearest_output_values;
} else {
ASSERT_TRUE(false);
}
test->TestOpConverter("my_resize", node_def, p.expected_output_dims,
/*expected_conversion_status=*/p.status,
/*expected_runtime_status=*/p.status,
/*matcher=*/ElementsAreArray(expected_out),
/*out_tf_types=*/{DT_FLOAT});
}
TEST_P(OpConverter_FP32_FP16_Test, ConvertResize) {
{
// First input is weight, should fail.
Reset();
NodeDef node_def = MakeResizeNodeDef<ops::ResizeBilinear>(tf_type_,
/*align_corners=*/
true);
AddTestWeights<float>("input", {1, 2}, {1, 2});
AddTestWeights<int>("size", {1, 2}, {1, 2});
RunValidationAndConversion(
node_def, error::UNIMPLEMENTED,
"The input \"input\" for ResizeBilinear must be a "
"tensor, at my_resize");
}
std::vector<ResizeTestParams> params{
{/*input_dims=*/{1, 1, 2, 1}, // N, H, W, C
/*output_resize_dims=*/{2, 3}, // H_out, W_out
/*input_values=*/{2.0f, -1.0f},
/*size_as_tensor=*/false,
/*align_corners=*/false,
/*expected_output_dims=*/{1, 2, 3, 1}, // N, H, W, C
/*expected_nearest_output_values=*/
{2.0f, 2.0f, -1.0f, 2.0f, 2.0f, -1.0f},
/*expected_bilinear_output_values=*/
{2.0f, 0.f, -1.0f, 2.0f, 0.f, -1.0f},
/*status=*/Status::OK()},
{/*input_dims=*/{1, 1, 2, 1}, // N, H, W, C
/*output_resize_dims=*/{2, 3}, // H_out, W_out
/*input_values=*/{2.0f, -1.0f},
/*size_as_tensor=*/false,
/*align_corners=*/true,
/*expected_output_dims=*/{1, 2, 3, 1}, // N, H, W, C
/*expected_nearest_output_values=*/
{2.0f, 2.0f, -1.0f, 2.0f, 2.0f, -1.0f},
/*expected_bilinear_output_values=*/
{2.0f, 0.5f, -1.0f, 2.0f, 0.5f, -1.0f},
/*status=*/Status::OK()}};
if (trt_mode_ != TrtTestMode::kImplicitBatch) {
// Size as a tensor is not supported in implicit batch mode.
params.push_back({/*input_dims=*/{1, 1, 2, 1}, // N, H, W, C
/*output_resize_dims=*/{2, 3}, // H_out, W_out
/*input_values=*/{2.0f, -1.0f},
/*size_as_tensor=*/true,
/*align_corners=*/true,
/*expected_output_dims=*/{1, 2, 3, 1}, // N, H, W, C
/*expected_nearest_output_values=*/
{2.0f, 2.0f, -1.0f, 2.0f, 2.0f, -1.0f},
/*expected_bilinear_output_values=*/
{2.0f, 0.5f, -1.0f, 2.0f, 0.5f, -1.0f},
/*status=*/Status::OK()});
}
for (auto p : params) {
TestConvertResize<ops::ResizeNearestNeighbor>(this, p);
// This use case is not supported as of TRT version 7.1
#if IS_TRT_VERSION_GE(7, 1, 0, 0)
if (!p.align_corners) {
p.status = errors::InvalidArgument(
"Cannot Convert Bilinear Resize when align_corners=False");
}
#endif
TestConvertResize<ops::ResizeBilinear>(this, p);
}
}
NodeDef MakePadNodeDef(std::string name, DataType dtype) {
Scope s = Scope::NewRootScope();
auto input = ops::Placeholder(s.WithOpName("input"), dtype);
auto padding = ops::Placeholder(s.WithOpName("padding"), DT_INT32);
auto pad = ops::Pad(s.WithOpName(name), input, padding);
return pad.operation.node()->def();
}
struct PadTestParams {
std::vector<int> input_dims;
std::vector<int> pad_dims;
std::vector<int> pad_values;
std::vector<float> input_values;
std::vector<int> expected_output_dims;
std::vector<float> expected_output_values;
Status status;
};
TEST_P(OpConverter_FP32_FP16_Test, ConvertPad) {
{
// First input is weight, should fail.
Reset();
NodeDef node_def = MakePadNodeDef("my_pad", tf_type_);
AddTestWeights("input", {1, 2}, {1, 2}, tf_type_);
AddTestWeights<int>("padding", {1, 2}, {1, 2});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"tensor\" for Pad must be a "
"tensor");
}
{
// padding is a tensor, should fail.
Reset();
NodeDef node_def = MakePadNodeDef("my_pad", tf_type_);
AddTestTensor("input", {1, 2});
AddTestTensor("padding", {1, 2});
RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
"The input \"paddings\" for Pad must be a "
"constant");
}
{
// Make sure that ranges are inferred across a Pad.
Reset();
NodeDef node_def = MakePadNodeDef("my_pad", tf_type_);
AddTestTensor("input", {1, 1, 2, 1});
AddTestWeights<int>("padding", {4, 2}, {0, 0, 1, 0, 0, 1, 0, 0});
TRT_TensorOrWeights input;
TRT_TensorOrWeights output;
RunValidationAndConversion(node_def);
TF_EXPECT_OK(GetTensorOrWeights("input", &input));
TF_EXPECT_OK(GetTensorOrWeights("my_pad", &output));
ITensorProxyPtr input_tensor = input.tensor();
converter_->ProvideQuantizationRange(&input_tensor, -5.0f, 5.0f);
auto ranges = quantization_ranges();
EXPECT_EQ(5.0f, ranges[input.tensor()->trt_tensor()]);
}
std::vector<PadTestParams> params{
// 1 padding dim
{
/*input_dims=*/{1, 1, 3, 2}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 0, 0, 0, 1, 0, 0},
/*input_values=*/{1, 2, 3, 4, 5, 6},
/*expected_output_dims=*/{1, 1, 4, 2}, // N, H, W, C
/*expected_output_values=*/
{1, 2, 3, 4, 5, 6, 0, 0},
},
{
/*input_dims=*/{1, 1, 3, 2}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 0, 0, 0, 0, 0, 1},
/*input_values=*/{1, 2, 3, 4, 5, 6},
/*expected_output_dims=*/{1, 1, 3, 3}, // N, H, W, C
/*expected_output_values=*/
{1, 2, 0, 3, 4, 0, 5, 6, 0},
},
{
/*input_dims=*/{1, 1, 3, 2}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 1, 0, 0, 0, 0, 0},
/*input_values=*/{1, 2, 3, 4, 5, 6},
/*expected_output_dims=*/{1, 2, 3, 2}, // N, H, W, C
/*expected_output_values=*/
{0, 0, 0, 0, 0, 0, 1, 2, 3, 4, 5, 6},
},
// 2 padding dims
{
/*input_dims=*/{1, 1, 2, 1}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 1, 0, 0, 1, 0, 0},
/*input_values=*/{2.0f, -1.0f},
/*expected_output_dims=*/{1, 2, 3, 1}, // N, H, W, C
/*expected_output_values=*/
{0.0, 0.0, 0.0, 2.0f, -1.0f, 0.0},
},
PadTestParams{
/*input_dims=*/{1, 1, 2, 2}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 1, 0, 0, 1, 0, 0},
/*input_values=*/{2, -1, 3., 4},
/*expected_output_dims=*/{1, 2, 3, 2}, // N, H, W, C
/*expected_output_values=*/
{0, 0, 0, 0, 0, 0, 2, -1, 3, 4, 0, 0},
},
PadTestParams{
/*input_dims=*/{1, 1, 2, 1, 2}, // N, C, H, W, D
/*pad_dims=*/{5, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 1, 0, 0, 1, 0, 0, 0, 0},
/*input_values=*/{2, -1, 3., 4},
/*expected_output_dims=*/{1, 2, 3, 1, 2}, // N, H, W, C
/*expected_output_values=*/
{0, 0, 0, 0, 0, 0, 2, -1, 3, 4, 0, 0},
},
PadTestParams{
/*input_dims=*/{1, 1, 2, 1, 2}, // N, C, H, W, D
/*pad_dims=*/{5, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 0, 1, 0, 0, 1, 1, 0, 0},
/*input_values=*/{2, -1, 3., 4},
/*expected_output_dims=*/{1, 2, 2, 3, 2}, // N, H, W, C
/*expected_output_values=*/
{0., 0., 2., -1., 0., 0., 0., 0., 3., 4., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0},
},
PadTestParams{
/*input_dims=*/{1, 1, 2, 1}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {1, 0, 0, 0, 0, 1, 0, 0},
/*input_values=*/{2.0f, -1.0f},
/*expected_output_dims=*/{2, 1, 3, 1}, // N, H, W, C
/*expected_output_values=*/{0.0, 0.0, 0.0, 2.0f, -1.0f, 0.0},
trt_mode_ == TrtTestMode::kImplicitBatch
? errors::InvalidArgument("Padding layer does not support "
"padding on batch dimension")
: Status::OK()},
PadTestParams{
/*input_dims=*/{1, 1, 2, 1}, // N, H, W, C
/*pad_dims=*/{4, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 1, 0, 0, 1, 1, 1},
/*input_values=*/{2.0f, -1.0f},
/*expected_output_dims=*/{}, // N, H, W, C
/*expected_output_values=*/{},
errors::InvalidArgument("Padding layer does not support padding on "
"> 2")},
PadTestParams{
/*input_dims=*/{1, 2, 2}, // N, H, W
/*pad_dims=*/{3, 2}, // #dims, {pad_before, pad_after}
/*pad_values*/ {0, 0, 1, 0, 0, 1},
/*input_values=*/{2, -1, 3., 4},
/*expected_output_dims=*/{1, 3, 3}, // N, H, W, C
/*expected_output_values=*/
{0., 0., 0., 2., -1., 0., 3., 4., 0.},
errors::InvalidArgument("Convertpad requires at least 4D input, at "
"my_pad")}};
for (auto p : params) {
Reset();
// Create pad node.
NodeDef node_def = MakePadNodeDef("my_pad", tf_type_);
// Create input tensor.
AddTestTensor("input", p.input_dims, p.input_values);
// Create output size.
AddTestWeights<int32>("padding", p.pad_dims, p.pad_values);
TestOpConverter("my_pad", node_def, p.expected_output_dims, p.status,
p.status, ElementsAreArray(p.expected_output_values));
}
}
} // namespace convert
} // namespace tensorrt
} // namespace tensorflow
#endif // GOOGLE_CUDA && GOOGLE_TENSORRT