Merge pull request #22874 from trevor-m:tmorris_tftrt_transpose_reshape
PiperOrigin-RevId: 217444920
This commit is contained in:
TensorFlower Gardener
commit
24294c6288
@ -326,6 +326,34 @@ tf_cuda_cc_test(
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]),
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)
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tf_cuda_cc_test(
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name = "convert_nodes_test",
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size = "medium",
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srcs = ["convert/convert_nodes_test.cc"],
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tags = [
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"no_cuda_on_cpu_tap",
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"no_windows",
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"nomac",
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],
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deps = [
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":trt_logging",
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":trt_conversion",
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":trt_plugins",
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"@com_google_googletest//:gtest",
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"//tensorflow/core:core_cpu",
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"//tensorflow/core:core_cpu_base",
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"//tensorflow/core:framework",
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"//tensorflow/core:lib",
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"//tensorflow/core:protos_all_cc",
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"//tensorflow/core:test",
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"//tensorflow/core:test_main",
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"//tensorflow/core:testlib",
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] + if_tensorrt([
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"@local_config_cuda//cuda:cuda_headers",
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"@local_config_tensorrt//:nv_infer",
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]),
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)
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# Library for the segmenting portion of TensorRT operation creation
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cc_library(
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name = "segment",
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@ -455,6 +483,7 @@ cuda_py_tests(
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"test/multi_connection_neighbor_engine_test.py",
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"test/neighboring_engine_test.py",
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"test/rank_two_test.py",
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"test/reshape_transpose_test.py",
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"test/vgg_block_nchw_test.py",
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"test/vgg_block_test.py",
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],
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@ -115,6 +115,8 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) {
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"Sqrt",
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"Abs",
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"Neg",
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"Transpose",
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"Reshape",
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#if NV_TENSORRT_MAJOR > 3
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"MatMul",
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"BatchMatMul",
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File diff suppressed because it is too large
Load Diff
@ -16,6 +16,7 @@ limitations under the License.
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#ifndef TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_
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#define TENSORFLOW_CONTRIB_TENSORRT_CONVERT_CONVERT_NODES_H_
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#include <list>
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#include <set>
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#include <string>
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#include <unordered_map>
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@ -26,6 +27,7 @@ limitations under the License.
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#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
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#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h"
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#include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h"
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#include "tensorflow/contrib/tensorrt/resources/trt_resources.h"
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#include "tensorflow/core/framework/graph.pb.h"
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#include "tensorflow/core/graph/graph.h"
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#include "tensorflow/core/grappler/costs/graph_properties.h"
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@ -33,6 +35,7 @@ limitations under the License.
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#if GOOGLE_CUDA
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#if GOOGLE_TENSORRT
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#include "tensorrt/include/NvInfer.h"
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namespace tensorflow {
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namespace tensorrt {
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@ -170,6 +173,162 @@ class OutputEdgeValidator {
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bool operator()(const tensorflow::Edge* out_edge) const;
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};
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////////////////////////////////////////////////////////////////////////////////
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// Classes/functions below are exposed for testing purposes only.
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////////////////////////////////////////////////////////////////////////////////
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string DebugString(const nvinfer1::Dims& dims);
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string DebugString(const nvinfer1::ITensor& tensor);
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int64_t TrtDimsNumElements(const nvinfer1::Dims& dims);
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// Class to convert TF weight to TRT weight.
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class TRT_ShapedWeights {
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public:
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TRT_ShapedWeights(tensorflow::DataType type, const void* values,
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nvinfer1::Dims shape);
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explicit TRT_ShapedWeights(tensorflow::DataType type);
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// TODO(aaroey): use rvalue reference.
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TRT_ShapedWeights(const TRT_ShapedWeights& rhs);
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nvinfer1::Weights GetWeightsForTRT() const;
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const void* GetValues() const { return values_; }
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int64_t count() const;
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size_t size_bytes() const;
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// Default converter
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operator nvinfer1::Weights() const { return GetWeightsForTRT(); }
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string DebugString() const;
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// TODO(aaroey): make these private.
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nvinfer1::Dims shape_; // Note: shape.type[] is not used.
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tensorflow::DataType type_;
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private:
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// TODO(aaroey): this should not be const as it's always from TRTWeightStore.
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const void* values_;
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friend bool operator==(const TRT_ShapedWeights& lhs,
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const TRT_ShapedWeights& rhs);
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};
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class TRT_TensorOrWeights {
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public:
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explicit TRT_TensorOrWeights(nvinfer1::ITensor* tensor);
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explicit TRT_TensorOrWeights(const TRT_ShapedWeights& weights);
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// TODO(aaroey): use rvalue reference.
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TRT_TensorOrWeights(const TRT_TensorOrWeights& rhs);
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bool is_tensor() const { return is_tensor_; }
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bool is_weights() const { return !is_tensor_; }
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nvinfer1::ITensor* tensor() {
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CHECK(is_tensor());
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return tensor_;
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}
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const nvinfer1::ITensor* tensor() const {
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CHECK(is_tensor());
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return tensor_;
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}
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TRT_ShapedWeights& weights() {
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CHECK(is_weights());
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return weights_;
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}
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const TRT_ShapedWeights& weights() const {
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CHECK(is_weights());
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return weights_;
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}
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// TODO(aaroey): rename to dims() to be consistent.
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nvinfer1::Dims shape() const;
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string DebugString() const;
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private:
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nvinfer1::ITensor* tensor_;
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TRT_ShapedWeights weights_;
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const bool is_tensor_;
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};
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// Class to convert TF nodes to TRT network.
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class Converter {
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public:
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Converter(nvinfer1::INetworkDefinition* trt_network, bool fp16,
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int max_batch_size);
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virtual ~Converter() {}
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nvinfer1::INetworkDefinition* network() { return trt_network_; }
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TRTWeightStore* weight_store() { return &weight_store_; }
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bool IsFP16() const { return fp16_; }
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int GetMaxBatchSize() const { return max_batch_size_; }
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TRT_ShapedWeights GetTempWeights(tensorflow::DataType type,
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const nvinfer1::Dims& dims);
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TRT_ShapedWeights GetTempWeightsLike(const TRT_ShapedWeights& weights) {
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return GetTempWeights(weights.type_, weights.shape_);
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}
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Status ConvertNode(const tensorflow::NodeDef& node_def);
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TRT_TensorOrWeights GetTensorOrWeights(const string& name);
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Status AddInputTensor(const string& name, nvinfer1::ITensor* tensor);
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Status TransposeTensor(nvinfer1::ITensor* input_tensor,
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const std::vector<int>& order_with_batch_dim,
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const nvinfer1::ITensor** output_tensor);
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// Converts input into tensor with shape specified by dims.
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Status PrepareTensorForShape(const TRT_TensorOrWeights& input,
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const nvinfer1::Dims& dims,
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const nvinfer1::ITensor** tensor);
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// Expose for testing purposes.
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Status GetInputs(const tensorflow::NodeDef& node_def,
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std::vector<TRT_TensorOrWeights>* inputs) const;
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private:
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using OpConverter =
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std::function<tensorflow::Status(Converter&, const tensorflow::NodeDef&,
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const std::vector<TRT_TensorOrWeights>&,
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std::vector<TRT_TensorOrWeights>*)>;
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void RegisterOpConverters();
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std::unordered_map<string, OpConverter> op_registry_;
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std::unordered_map<string, TRT_TensorOrWeights> trt_tensors_;
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OpConverter plugin_converter_;
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nvinfer1::INetworkDefinition* trt_network_;
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// TODO(aaroey): inline the definition of TRTWeightStore here, and add APIs to
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// operate the stored weights instead of operating it directly.
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TRTWeightStore weight_store_;
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bool fp16_;
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int max_batch_size_;
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friend class ConverterForTest;
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};
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} // namespace convert
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} // namespace tensorrt
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} // namespace tensorflow
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646
tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc
Normal file
646
tensorflow/contrib/tensorrt/convert/convert_nodes_test.cc
Normal file
@ -0,0 +1,646 @@
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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/contrib/tensorrt/convert/convert_nodes.h"
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#include <memory>
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#include <unordered_map>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "tensorflow/contrib/tensorrt/log/trt_logger.h"
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#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h"
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#include "tensorflow/core/framework/node_def.pb.h" // NOLINT
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/framework/tensor.pb.h" // NOLINT
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#include "tensorflow/core/framework/tensor_shape.h"
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#include "tensorflow/core/framework/tensor_testutil.h"
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#include "tensorflow/core/lib/core/status.h"
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#include "tensorflow/core/lib/core/status_test_util.h"
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#include "tensorflow/core/platform/test.h"
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#if GOOGLE_CUDA
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#if GOOGLE_TENSORRT
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#include "cuda/include/cuda.h"
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#include "cuda/include/cuda_runtime_api.h"
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#include "tensorrt/include/NvInfer.h"
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namespace tensorflow {
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namespace tensorrt {
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namespace convert {
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using ::testing::ElementsAre;
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void ExpectStatus(Status status, error::Code code, const char* substr) {
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EXPECT_EQ(code, status.code()) << status;
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EXPECT_THAT(status.error_message(), ::testing::HasSubstr(substr)) << status;
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}
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nvinfer1::Dims GetTestDims(const std::vector<int>& d) {
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nvinfer1::Dims dims;
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dims.nbDims = d.size();
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for (int i = 0; i < d.size(); ++i) {
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dims.d[i] = d[i];
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}
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return dims;
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}
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// Fake ITensor implementation for testing purposes.
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class FakeITensor : public nvinfer1::ITensor {
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public:
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FakeITensor() {}
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FakeITensor(const nvinfer1::Dims& dims, const string& name = "")
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: name_(name), dims_(dims) {}
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FakeITensor(const string& name, const std::vector<int>& dims)
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: name_(name), dims_(GetTestDims(dims)) {}
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void setName(const char* name) override { name_ = name; }
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const char* getName() const override { return name_.c_str(); }
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void setDimensions(nvinfer1::Dims dimensions) override { dims_ = dimensions; }
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nvinfer1::Dims getDimensions() const override { return dims_; }
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void setType(nvinfer1::DataType type) override { type_ = type; }
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nvinfer1::DataType getType() const override { return type_; }
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bool isNetworkInput() const override { return false; }
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bool isNetworkOutput() const override { return false; }
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void setBroadcastAcrossBatch(bool broadcastAcrossBatch) override {}
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bool getBroadcastAcrossBatch() const override { return false; }
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nvinfer1::TensorLocation getLocation() const override { return location_; }
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void setLocation(nvinfer1::TensorLocation location) override {
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location_ = location;
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}
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#if NV_TENSORRT_MAJOR >= 5
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bool setDynamicRange(float min, float max) override {}
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#endif
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private:
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string name_;
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nvinfer1::Dims dims_;
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nvinfer1::DataType type_;
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nvinfer1::TensorLocation location_;
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};
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bool Equals(const nvinfer1::Dims& lhs, const nvinfer1::Dims& rhs) {
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if (lhs.nbDims != rhs.nbDims) return false;
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for (int i = 0; i < lhs.nbDims; ++i) {
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if (lhs.d[i] != rhs.d[i]) return false;
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// We don't check the types in the tests.
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}
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return true;
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}
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bool operator==(const TRT_ShapedWeights& lhs, const TRT_ShapedWeights& rhs) {
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return Equals(lhs.shape_, rhs.shape_) && lhs.type_ == rhs.type_ &&
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lhs.values_ == rhs.values_;
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}
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TEST(TRT_ShapedWeights_Test, Basic) {
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{
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float raw_weights[10];
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TRT_ShapedWeights weights(DT_FLOAT, raw_weights, GetTestDims({2, 5}));
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nvinfer1::Weights trt_weights = weights.GetWeightsForTRT();
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EXPECT_EQ(nvinfer1::DataType::kFLOAT, trt_weights.type);
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EXPECT_EQ(static_cast<void*>(raw_weights), trt_weights.values);
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EXPECT_EQ(10, trt_weights.count);
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EXPECT_EQ(static_cast<void*>(raw_weights), weights.GetValues());
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EXPECT_EQ(10, weights.count());
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EXPECT_EQ(40, weights.size_bytes());
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}
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{
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int32 raw_weights = 0;
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TRT_ShapedWeights weights(DT_INT32, &raw_weights, GetTestDims({1, 1, 1}));
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nvinfer1::Weights trt_weights = weights.GetWeightsForTRT();
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EXPECT_EQ(nvinfer1::DataType::kINT32, trt_weights.type);
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EXPECT_EQ(static_cast<void*>(&raw_weights), trt_weights.values);
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EXPECT_EQ(1, trt_weights.count);
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EXPECT_EQ(static_cast<void*>(&raw_weights), weights.GetValues());
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EXPECT_EQ(1, weights.count());
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EXPECT_EQ(4, weights.size_bytes());
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}
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{
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TRT_ShapedWeights weights(DT_FLOAT);
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nvinfer1::Weights trt_weights = weights.GetWeightsForTRT();
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EXPECT_EQ(nvinfer1::DataType::kFLOAT, trt_weights.type);
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EXPECT_EQ(nullptr, trt_weights.values);
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EXPECT_EQ(0, trt_weights.count);
|
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|
||||
EXPECT_EQ(nullptr, weights.GetValues());
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EXPECT_EQ(0, weights.count());
|
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EXPECT_EQ(0, weights.size_bytes());
|
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}
|
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}
|
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|
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TEST(TRT_TensorOrWeights_Test, Basic) {
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{
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nvinfer1::Dims dims;
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dims.nbDims = 1;
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dims.d[0] = 1;
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FakeITensor itensor(dims);
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TRT_TensorOrWeights tw(&itensor);
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EXPECT_EQ(true, tw.is_tensor());
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EXPECT_EQ(false, tw.is_weights());
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EXPECT_EQ(&itensor, tw.tensor());
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EXPECT_TRUE(Equals(dims, tw.shape()))
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<< "- expected: " << DebugString(dims)
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<< "\n vs\n- actual: " << DebugString(tw.shape());
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||||
}
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||||
{
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TRT_ShapedWeights weights(DT_FLOAT);
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TRT_TensorOrWeights tw(weights);
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EXPECT_EQ(false, tw.is_tensor());
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EXPECT_EQ(true, tw.is_weights());
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EXPECT_EQ(weights, tw.weights());
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|
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nvinfer1::Dims dims;
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dims.nbDims = 0;
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EXPECT_TRUE(Equals(dims, tw.shape()))
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<< "- expected: " << DebugString(dims)
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<< "\n vs\n- actual: " << DebugString(tw.shape());
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}
|
||||
}
|
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|
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class ConverterForTest : public Converter {
|
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public:
|
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ConverterForTest()
|
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: Converter(nullptr, /*fp16=*/false, /*max_batch_size=*/1) {
|
||||
QCHECK_EQ(0, cudaStreamCreate(&stream_));
|
||||
Reset();
|
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}
|
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|
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~ConverterForTest() override { QCHECK_EQ(0, cudaStreamDestroy(stream_)); }
|
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|
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// Helper methods for testing purposes.
|
||||
|
||||
void AddOpConverter(const string& op_name, OpConverter op_converter) {
|
||||
op_registry_[op_name] = op_converter;
|
||||
}
|
||||
|
||||
void AddTensorOrWeights(const string& name, TRT_TensorOrWeights tw) {
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||||
ASSERT_TRUE(trt_tensors_.insert({name, tw}).second);
|
||||
}
|
||||
|
||||
void Reset() {
|
||||
// Clear the tensor map.
|
||||
trt_tensors_.clear();
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||||
// Reset the INetworkDefinition.
|
||||
engine_.reset(nullptr);
|
||||
network_.reset(nullptr);
|
||||
builder_.reset(nullptr);
|
||||
builder_.reset(nvinfer1::createInferBuilder(logger_));
|
||||
network_.reset(builder_->createNetwork());
|
||||
trt_network_ = network_.get();
|
||||
}
|
||||
|
||||
void BuildAndRun(const char* input_name, const std::vector<float>& input_data,
|
||||
const char* output_name, std::vector<float>* output_data) {
|
||||
// Mark the output tensor as TRT engine output.
|
||||
TRT_TensorOrWeights tensor = GetTensorOrWeights(output_name);
|
||||
tensor.tensor()->setName(output_name);
|
||||
network()->markOutput(*tensor.tensor());
|
||||
|
||||
// Build the TRT engine.
|
||||
QCHECK_EQ(nullptr, engine_.get());
|
||||
engine_.reset(builder_->buildCudaEngine(*network()));
|
||||
CHECK_NOTNULL(engine_.get());
|
||||
|
||||
// Execute the TRT engine.
|
||||
const int input_size = input_data.size() * sizeof(float);
|
||||
const int output_size = output_data->size() * sizeof(float);
|
||||
const int input_index = engine_->getBindingIndex(input_name);
|
||||
const int output_index = engine_->getBindingIndex(output_name);
|
||||
|
||||
ASSERT_EQ(engine_->getNbBindings(), 2);
|
||||
void* buffers[2];
|
||||
ASSERT_EQ(0, cudaMalloc(&buffers[input_index], input_size));
|
||||
ASSERT_EQ(0, cudaMalloc(&buffers[output_index], output_size));
|
||||
ASSERT_EQ(0, cudaMemcpyAsync(buffers[input_index], input_data.data(),
|
||||
input_size, cudaMemcpyHostToDevice, stream_));
|
||||
TrtUniquePtrType<nvinfer1::IExecutionContext> execution_context(
|
||||
engine_->createExecutionContext());
|
||||
execution_context->enqueue(1, buffers, stream_, nullptr);
|
||||
ASSERT_EQ(0, cudaMemcpyAsync(output_data->data(), buffers[output_index],
|
||||
output_size, cudaMemcpyDeviceToHost, stream_));
|
||||
cudaStreamSynchronize(stream_);
|
||||
ASSERT_EQ(0, cudaFree(buffers[input_index]));
|
||||
ASSERT_EQ(0, cudaFree(buffers[output_index]));
|
||||
}
|
||||
|
||||
private:
|
||||
Logger logger_;
|
||||
TrtUniquePtrType<nvinfer1::IBuilder> builder_;
|
||||
TrtUniquePtrType<nvinfer1::INetworkDefinition> network_;
|
||||
TrtUniquePtrType<nvinfer1::ICudaEngine> engine_;
|
||||
cudaStream_t stream_;
|
||||
};
|
||||
|
||||
class ConverterTest : public ::testing::Test {
|
||||
protected:
|
||||
nvinfer1::ITensor* AddTestTensor(const char* name,
|
||||
const std::vector<int>& dims) {
|
||||
nvinfer1::ITensor* tensor = converter_.network()->addInput(
|
||||
name, nvinfer1::DataType::kFLOAT, GetTestDims(dims));
|
||||
converter_.AddTensorOrWeights(name, TRT_TensorOrWeights{tensor});
|
||||
return tensor;
|
||||
}
|
||||
|
||||
template <typename CType>
|
||||
TRT_ShapedWeights AddTestWeights(const char* name, const DataType dtype,
|
||||
const std::vector<int>& dims,
|
||||
const std::vector<CType>& values) {
|
||||
const nvinfer1::Dims trt_dims = GetTestDims(dims);
|
||||
const int64_t num_elements = TrtDimsNumElements(trt_dims);
|
||||
QCHECK_EQ(num_elements, values.size())
|
||||
<< num_elements << " vs " << values.size();
|
||||
TRT_ShapedWeights weights(dtype);
|
||||
if (num_elements) {
|
||||
const int64_t size_bytes = DataTypeSize(dtype) * num_elements;
|
||||
QCHECK_EQ(size_bytes, sizeof(CType) * values.size())
|
||||
<< size_bytes << " vs " << sizeof(CType) * values.size();
|
||||
converter_.weight_store()->store_.push_back(
|
||||
std::vector<uint8_t>(size_bytes));
|
||||
void* dst =
|
||||
static_cast<void*>(converter_.weight_store()->store_.back().data());
|
||||
memcpy(dst, values.data(), size_bytes);
|
||||
weights = TRT_ShapedWeights(dtype, dst, trt_dims);
|
||||
}
|
||||
converter_.AddTensorOrWeights(name, TRT_TensorOrWeights{weights});
|
||||
return weights;
|
||||
}
|
||||
|
||||
NodeDef MakeNodeDef(const string& name, const string& op,
|
||||
const std::vector<string>& inputs) {
|
||||
NodeDef node_def;
|
||||
node_def.set_name(name);
|
||||
node_def.set_op(op);
|
||||
for (const string& input : inputs) {
|
||||
node_def.add_input(input);
|
||||
}
|
||||
return node_def;
|
||||
}
|
||||
|
||||
ConverterForTest converter_;
|
||||
};
|
||||
|
||||
TEST_F(ConverterTest, GetTempWeights) {
|
||||
TRT_ShapedWeights weights =
|
||||
converter_.GetTempWeights(DT_FLOAT, GetTestDims({2, 3}));
|
||||
|
||||
nvinfer1::Weights trt_weights = weights.GetWeightsForTRT();
|
||||
EXPECT_EQ(nvinfer1::DataType::kFLOAT, trt_weights.type);
|
||||
EXPECT_NE(nullptr, trt_weights.values);
|
||||
EXPECT_EQ(6, trt_weights.count);
|
||||
|
||||
EXPECT_NE(nullptr, weights.GetValues());
|
||||
EXPECT_EQ(6, weights.count());
|
||||
EXPECT_EQ(24, weights.size_bytes());
|
||||
|
||||
// TODO(aaroey): test the case where shape element count is 0.
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, GetInputs) {
|
||||
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");
|
||||
|
||||
FakeITensor input, input_1, input_2;
|
||||
TF_EXPECT_OK(converter_.AddInputTensor("input", &input));
|
||||
TF_EXPECT_OK(converter_.AddInputTensor("input:1", &input_1));
|
||||
TF_EXPECT_OK(converter_.AddInputTensor("weird_input:2:3:4", &input_2));
|
||||
|
||||
std::vector<TRT_TensorOrWeights> inputs;
|
||||
TF_EXPECT_OK(converter_.GetInputs(node_def, &inputs));
|
||||
EXPECT_EQ(4, inputs.size());
|
||||
EXPECT_EQ(&input, inputs[0].tensor());
|
||||
EXPECT_EQ(&input, inputs[1].tensor());
|
||||
EXPECT_EQ(&input_1, inputs[2].tensor());
|
||||
EXPECT_EQ(&input_2, inputs[3].tensor());
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, ConvertNode) {
|
||||
FakeITensor output_tensors[2];
|
||||
auto op_converter = [&output_tensors](
|
||||
Converter& ctx, const NodeDef& node_def,
|
||||
const std::vector<TRT_TensorOrWeights>& inputs,
|
||||
std::vector<TRT_TensorOrWeights>* outputs) -> Status {
|
||||
nvinfer1::Dims dims = inputs[0].tensor()->getDimensions();
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
dims.d[0] += 1;
|
||||
output_tensors[i].setDimensions(dims);
|
||||
outputs->push_back(TRT_TensorOrWeights(&output_tensors[i]));
|
||||
}
|
||||
return Status::OK();
|
||||
};
|
||||
converter_.AddOpConverter("MyOp", op_converter);
|
||||
|
||||
FakeITensor input_tensor("my_input", {12345});
|
||||
TF_EXPECT_OK(converter_.AddInputTensor("my_input", &input_tensor));
|
||||
|
||||
NodeDef node_def = MakeNodeDef("my_op", "MyOp", {"my_input"});
|
||||
TF_EXPECT_OK(converter_.ConvertNode(node_def));
|
||||
|
||||
TRT_TensorOrWeights actual_output_1 = converter_.GetTensorOrWeights("my_op");
|
||||
EXPECT_EQ(&output_tensors[0], actual_output_1.tensor());
|
||||
EXPECT_EQ(12346, actual_output_1.tensor()->getDimensions().d[0]);
|
||||
|
||||
TRT_TensorOrWeights actual_output_2 =
|
||||
converter_.GetTensorOrWeights("my_op:1");
|
||||
EXPECT_EQ(&output_tensors[1], actual_output_2.tensor());
|
||||
EXPECT_EQ(12347, actual_output_2.tensor()->getDimensions().d[0]);
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, TransposeTensor) {
|
||||
nvinfer1::ITensor* input_tensor = AddTestTensor("", {2, 3, 5});
|
||||
const nvinfer1::ITensor* output_tensor = nullptr;
|
||||
|
||||
// Rank doesn't match.
|
||||
ExpectStatus(
|
||||
converter_.TransposeTensor(input_tensor, {0, 1}, &output_tensor),
|
||||
error::INVALID_ARGUMENT,
|
||||
"Rank of perm for transpose does not match with that of the input");
|
||||
|
||||
// Transpose at batch dimension.
|
||||
ExpectStatus(
|
||||
converter_.TransposeTensor(input_tensor, {1, 0, 2, 3}, &output_tensor),
|
||||
error::UNIMPLEMENTED, "Transpose at batch dimension is not supported.");
|
||||
|
||||
// OK.
|
||||
TF_EXPECT_OK(
|
||||
converter_.TransposeTensor(input_tensor, {0, 3, 1, 2}, &output_tensor));
|
||||
EXPECT_TRUE(Equals(GetTestDims({5, 2, 3}), output_tensor->getDimensions()))
|
||||
<< DebugString(*output_tensor);
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, PrepareTensorForShape_Tensor) {
|
||||
nvinfer1::ITensor* input_tensor = AddTestTensor("", {2, 3, 5});
|
||||
TRT_TensorOrWeights tw(input_tensor);
|
||||
const nvinfer1::ITensor* output_tensor = nullptr;
|
||||
|
||||
// Shape size doesn't match.
|
||||
ExpectStatus(converter_.PrepareTensorForShape(tw, GetTestDims({2, 3, 6}),
|
||||
&output_tensor),
|
||||
error::INVALID_ARGUMENT, "Reshape shapes are not compatible.");
|
||||
|
||||
// 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 shape, ok.
|
||||
TF_EXPECT_OK(converter_.PrepareTensorForShape(tw, GetTestDims({-1, 2}),
|
||||
&output_tensor));
|
||||
EXPECT_TRUE(Equals(GetTestDims({15, 2}), output_tensor->getDimensions()))
|
||||
<< DebugString(*output_tensor);
|
||||
|
||||
// Regular shape.
|
||||
TF_EXPECT_OK(converter_.PrepareTensorForShape(tw, GetTestDims({10, 3}),
|
||||
&output_tensor));
|
||||
EXPECT_TRUE(Equals(GetTestDims({10, 3}), output_tensor->getDimensions()))
|
||||
<< DebugString(*output_tensor);
|
||||
}
|
||||
|
||||
#if NV_TENSORRT_MAJOR > 3
|
||||
TEST_F(ConverterTest, PrepareTensorForShape_Weights) {
|
||||
TRT_ShapedWeights weights =
|
||||
converter_.GetTempWeights(DT_FLOAT, GetTestDims({2, 3, 5}));
|
||||
TRT_TensorOrWeights tw(weights);
|
||||
const nvinfer1::ITensor* output_tensor = nullptr;
|
||||
TF_EXPECT_OK(converter_.PrepareTensorForShape(tw, GetTestDims({10, 3}),
|
||||
&output_tensor));
|
||||
EXPECT_TRUE(Equals(GetTestDims({10, 3}), output_tensor->getDimensions()))
|
||||
<< DebugString(*output_tensor);
|
||||
}
|
||||
#endif
|
||||
|
||||
template <DataType dtype, typename InputCType, typename OutputCType>
|
||||
void TestConvertConst(ConverterForTest* converter) {
|
||||
NodeDef node_def;
|
||||
node_def.set_name("my_const");
|
||||
node_def.set_op("Const");
|
||||
|
||||
auto reset_and_test = [&node_def, converter](
|
||||
const Tensor& tensor, const bool as_tensor_content,
|
||||
const std::vector<int>& expected_dims,
|
||||
const std::vector<OutputCType>& expected_value) {
|
||||
converter->Reset();
|
||||
|
||||
auto& attr = *node_def.mutable_attr();
|
||||
if (as_tensor_content) {
|
||||
tensor.AsProtoTensorContent(attr["value"].mutable_tensor());
|
||||
} else {
|
||||
tensor.AsProtoField(attr["value"].mutable_tensor());
|
||||
}
|
||||
TF_EXPECT_OK(converter->ConvertNode(node_def));
|
||||
TRT_TensorOrWeights output = converter->GetTensorOrWeights("my_const");
|
||||
EXPECT_TRUE(Equals(GetTestDims(expected_dims), output.weights().shape_))
|
||||
<< output.DebugString();
|
||||
ASSERT_EQ(expected_value.size(), output.weights().count())
|
||||
<< output.DebugString();
|
||||
const OutputCType* actual_values =
|
||||
static_cast<const OutputCType*>(output.weights().GetValues());
|
||||
for (int i = 0; i < expected_value.size(); ++i) {
|
||||
EXPECT_EQ(expected_value[i], actual_values[i]);
|
||||
}
|
||||
};
|
||||
|
||||
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.
|
||||
attr["value"].mutable_tensor()->set_dtype(dtype);
|
||||
Tensor t; // Empty tensor.
|
||||
reset_and_test(t, false, {}, {});
|
||||
}
|
||||
{
|
||||
Tensor t = ::tensorflow::test::AsScalar<InputCType>(12);
|
||||
reset_and_test(t, false, {1}, {12});
|
||||
reset_and_test(t, true, {1}, {12});
|
||||
}
|
||||
{
|
||||
Tensor t = ::tensorflow::test::AsTensor<InputCType>({1, 2});
|
||||
reset_and_test(t, false, {2}, {1, 2});
|
||||
reset_and_test(t, true, {2}, {1, 2});
|
||||
}
|
||||
{
|
||||
Tensor t = ::tensorflow::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});
|
||||
}
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, ConvertConst) {
|
||||
{
|
||||
converter_.Reset();
|
||||
NodeDef node_def = MakeNodeDef("my_const", "Const", {"input"});
|
||||
AddTestTensor("input", {1});
|
||||
ExpectStatus(
|
||||
converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Constant node is expected to have empty input list: my_const");
|
||||
}
|
||||
{
|
||||
converter_.Reset();
|
||||
NodeDef node_def = MakeNodeDef("my_const", "Const", {});
|
||||
(*node_def.mutable_attr())["dtype"].set_type(DT_DOUBLE);
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Unsupported data type");
|
||||
}
|
||||
|
||||
TestConvertConst<DT_FLOAT, float, float>(&converter_);
|
||||
TestConvertConst<DT_INT8, int8, int32>(&converter_);
|
||||
#if NV_TENSORRT_MAJOR > 3
|
||||
TestConvertConst<DT_INT32, int32, int32>(&converter_);
|
||||
#endif
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, ConvertTranspose) {
|
||||
{
|
||||
// Input list is empty, should fail.
|
||||
NodeDef node_def = MakeNodeDef("my_transpose", "Transpose", {});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Input expects tensor and weights, at my_transpose");
|
||||
}
|
||||
NodeDef node_def =
|
||||
MakeNodeDef("my_transpose", "Transpose", {"input", "weights"});
|
||||
{
|
||||
// Permutation is a tensor, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestTensor("weights", {3});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Input expects tensor and weights, at my_transpose");
|
||||
}
|
||||
{
|
||||
// Transpose at batch dimension, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {4}, {1, 0, 2, 3});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::UNIMPLEMENTED,
|
||||
"Transpose at batch dimension is not supported");
|
||||
}
|
||||
{
|
||||
// Permutation rank doesn't match, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {3}, {0, 1, 2});
|
||||
ExpectStatus(
|
||||
converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Rank of perm for transpose does not match with that of the input.");
|
||||
}
|
||||
{
|
||||
// Ok.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {4}, {0, 3, 1, 2});
|
||||
TF_EXPECT_OK(converter_.ConvertNode(node_def));
|
||||
TRT_TensorOrWeights output = converter_.GetTensorOrWeights("my_transpose");
|
||||
EXPECT_TRUE(output.is_tensor());
|
||||
EXPECT_TRUE(
|
||||
Equals(GetTestDims({3, 1, 2}), output.tensor()->getDimensions()))
|
||||
<< output.DebugString();
|
||||
|
||||
std::vector<float> output_data(6);
|
||||
converter_.BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_transpose",
|
||||
&output_data);
|
||||
EXPECT_THAT(output_data, ElementsAre(1, 4, 2, 5, 3, 6));
|
||||
}
|
||||
}
|
||||
|
||||
TEST_F(ConverterTest, ConvertReshape) {
|
||||
{
|
||||
// Input list is empty, should fail.
|
||||
NodeDef node_def = MakeNodeDef("my_reshape", "Reshape", {});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Input expects weights for shape, at my_reshape");
|
||||
}
|
||||
NodeDef node_def = MakeNodeDef("my_reshape", "Reshape", {"input", "weights"});
|
||||
{
|
||||
// Shape is a tensor, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestTensor("weights", {3});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::INVALID_ARGUMENT,
|
||||
"Input expects weights for shape, at my_reshape");
|
||||
}
|
||||
{
|
||||
// Reshape to scalar, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {}, {});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::UNIMPLEMENTED,
|
||||
"Reshape to shape=[] is not supported, at my_reshape");
|
||||
}
|
||||
{
|
||||
// Reshape at batch dimension, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {4}, {-1, 1, 1, 2});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::UNIMPLEMENTED,
|
||||
"Reshape on batch dimension is not supported, at my_reshape");
|
||||
}
|
||||
{
|
||||
// Reshape at batch dimension, should fail.
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {4}, {3, 1, 1, 2});
|
||||
ExpectStatus(converter_.ConvertNode(node_def), error::UNIMPLEMENTED,
|
||||
"Reshape on batch dimension is not supported, at my_reshape");
|
||||
}
|
||||
// Reshape on non batch dimensions, ok.
|
||||
for (int batch_dim : {-1, 1}) {
|
||||
converter_.Reset();
|
||||
AddTestTensor("input", {1, 2, 3});
|
||||
AddTestWeights<int32>("weights", DT_INT32, {4}, {batch_dim, 1, 3, 2});
|
||||
TF_EXPECT_OK(converter_.ConvertNode(node_def));
|
||||
TRT_TensorOrWeights output = converter_.GetTensorOrWeights("my_reshape");
|
||||
EXPECT_TRUE(output.is_tensor());
|
||||
EXPECT_TRUE(
|
||||
Equals(GetTestDims({1, 3, 2}), output.tensor()->getDimensions()))
|
||||
<< output.DebugString();
|
||||
|
||||
std::vector<float> output_data(6);
|
||||
converter_.BuildAndRun("input", {1, 2, 3, 4, 5, 6}, "my_reshape",
|
||||
&output_data);
|
||||
EXPECT_THAT(output_data, ElementsAre(1, 2, 3, 4, 5, 6));
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace convert
|
||||
} // namespace tensorrt
|
||||
} // namespace tensorflow
|
||||
|
||||
#endif // GOOGLE_TENSORRT
|
||||
#endif // GOOGLE_CUDA
|
||||
@ -136,6 +136,16 @@ class SimpleMultiEnginesTest(trt_test.TfTrtIntegrationTestBase):
|
||||
# - my_trt_op_1 should have ["weights","conv", "div"]
|
||||
return ["my_trt_op_0", "my_trt_op_1"]
|
||||
|
||||
def ShouldRunTest(self, run_params):
|
||||
# TODO(aaroey): LayoutOptimizer adds Transpose(Const, Const) to the graph
|
||||
# which breaks the conversion. We should fix it as:
|
||||
# - Detect the invalid NodeDef earlier before adding them to segment
|
||||
# - Let it able to change the RewriterConfig when calling
|
||||
# create_inference_graph().
|
||||
# It will be good to add debugging feature for Grappler to print the graph
|
||||
# after running each optimizer.
|
||||
return False
|
||||
|
||||
|
||||
class PartiallyConvertedTestA(trt_test.TfTrtIntegrationTestBase):
|
||||
|
||||
|
||||
@ -50,17 +50,22 @@ class BatchMatMulTest(trt_test.TfTrtIntegrationTestBase):
|
||||
w2 = array_ops.placeholder(dtype=dtype, shape=w2_dims, name=w2_name)
|
||||
with g.device("/GPU:0"):
|
||||
b = constant_op.constant(np.random.randn(12, 5, 12, 7), dtype=dtype)
|
||||
c = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype)
|
||||
d = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype)
|
||||
x1 = math_ops.matmul(inp, b)
|
||||
c = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype)
|
||||
x1 = x1 + c
|
||||
|
||||
x2 = math_ops.matmul(inp, w1)
|
||||
d = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype)
|
||||
x2 = x2 * d
|
||||
e = gen_array_ops.reshape(inp, [12, 40, 12])
|
||||
|
||||
e = self.trt_incompatible_op(inp)
|
||||
e = gen_array_ops.reshape(e, [12, 40, 12])
|
||||
x3 = math_ops.matmul(e, w2)
|
||||
f = constant_op.constant(np.random.randn(40, 1), dtype=dtype)
|
||||
x3 = x3 + f
|
||||
x3 = gen_array_ops.reshape(x3, [12, 5, 8, 7])
|
||||
x3 = self.trt_incompatible_op(x3)
|
||||
|
||||
out = x1 + x2 + x3
|
||||
array_ops.squeeze(out, name=output_name)
|
||||
return trt_test.TfTrtIntegrationTestParams(
|
||||
|
||||
@ -33,95 +33,100 @@ from tensorflow.python.platform import test
|
||||
|
||||
class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase):
|
||||
|
||||
def _ConstOp(self, shape):
|
||||
return constant_op.constant(np.random.randn(*shape), dtype=dtypes.float32)
|
||||
|
||||
def GetParams(self):
|
||||
"""Testing conversion of BiasAdd MatMul in TF-TRT conversion."""
|
||||
dtype = dtypes.float32
|
||||
input_name = "input"
|
||||
input_dims = [48, 12]
|
||||
input_matrix_rows = 4
|
||||
input_matrix_columns = 144
|
||||
input_dims = [input_matrix_rows, input_matrix_columns]
|
||||
output_name = "output"
|
||||
g = ops.Graph()
|
||||
with g.as_default():
|
||||
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
|
||||
x = array_ops.placeholder(
|
||||
dtype=dtypes.float32, shape=input_dims, name=input_name)
|
||||
|
||||
b = constant_op.constant(np.random.randn(12, 4), dtype=dtype)
|
||||
b = self._ConstOp((input_matrix_columns, 4))
|
||||
x1 = math_ops.matmul(x, b)
|
||||
b = constant_op.constant(np.random.randn(1, 4), dtype=dtype)
|
||||
b = self._ConstOp((1, 4))
|
||||
x1 = x1 + b
|
||||
|
||||
b = constant_op.constant(np.random.randn(48, 4), dtype=dtype)
|
||||
x2 = math_ops.matmul(x, b, transpose_a=True)
|
||||
x2 = gen_array_ops.reshape(x2, [48, 1])
|
||||
b = self._ConstOp((input_matrix_rows, 144))
|
||||
x2 = self.trt_incompatible_op(x)
|
||||
x2 = math_ops.matmul(x2, b, transpose_a=True)
|
||||
x2 = gen_array_ops.reshape(x2, [4, -1])
|
||||
x2 = self.trt_incompatible_op(x2)
|
||||
|
||||
b = constant_op.constant(np.random.randn(4, 12), dtype=dtype)
|
||||
b = self._ConstOp((4, input_matrix_columns))
|
||||
x3 = math_ops.matmul(x, b, transpose_b=True)
|
||||
|
||||
b = constant_op.constant(np.random.randn(16, 48), dtype=dtype)
|
||||
x4 = math_ops.matmul(x, b, transpose_b=True, transpose_a=True)
|
||||
x4 = gen_array_ops.reshape(x4, [48, 4])
|
||||
b = self._ConstOp((16, input_matrix_rows))
|
||||
x4 = self.trt_incompatible_op(x)
|
||||
x4 = math_ops.matmul(x4, b, transpose_b=True, transpose_a=True)
|
||||
x4 = gen_array_ops.reshape(x4, [4, -1])
|
||||
x4 = self.trt_incompatible_op(x4)
|
||||
|
||||
x5 = gen_array_ops.reshape(x, [4, 144])
|
||||
b = constant_op.constant(np.random.randn(144, 48), dtype=dtype)
|
||||
x5 = math_ops.matmul(x5, b)
|
||||
b = constant_op.constant(np.random.randn(48), dtype=dtype)
|
||||
b = self._ConstOp((input_matrix_columns, 48))
|
||||
x5 = math_ops.matmul(x, b)
|
||||
b = self._ConstOp((48,))
|
||||
x5 = nn.bias_add(x5, b)
|
||||
x5 = gen_array_ops.reshape(x5, [48, 4])
|
||||
x5 = gen_array_ops.reshape(x5, [4, -1])
|
||||
|
||||
x6 = gen_array_ops.reshape(x, [4, 12, 12])
|
||||
b = constant_op.constant(np.random.randn(12), dtype=dtype)
|
||||
b = self._ConstOp((12,))
|
||||
x6 = nn.bias_add(x6, b, data_format="NHWC")
|
||||
x6 = gen_array_ops.reshape(x6, [48, -1])
|
||||
x6 = gen_array_ops.reshape(x6, [4, -1])
|
||||
|
||||
x7 = gen_array_ops.reshape(x, [4, 12, 3, 4])
|
||||
b = constant_op.constant(np.random.randn(4), dtype=dtype)
|
||||
b = self._ConstOp((4,))
|
||||
x7 = nn.bias_add(x7, b, data_format="NHWC")
|
||||
x7 = gen_array_ops.reshape(x7, [48, -1])
|
||||
x7 = gen_array_ops.reshape(x7, [4, -1])
|
||||
|
||||
x8 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2])
|
||||
b = constant_op.constant(np.random.randn(2), dtype=dtype)
|
||||
b = self._ConstOp((2,))
|
||||
x8 = nn.bias_add(x8, b, data_format="NHWC")
|
||||
x8 = gen_array_ops.reshape(x8, [48, -1])
|
||||
x8 = gen_array_ops.reshape(x8, [4, -1])
|
||||
|
||||
x9 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2])
|
||||
b = constant_op.constant(np.random.randn(12), dtype=dtype)
|
||||
b = self._ConstOp((12,))
|
||||
x9 = nn.bias_add(x9, b, data_format="NCHW")
|
||||
x9 = gen_array_ops.reshape(x9, [48, -1])
|
||||
x9 = gen_array_ops.reshape(x9, [4, -1])
|
||||
|
||||
x10 = gen_array_ops.reshape(x, [4, 12, 3, 4])
|
||||
b = constant_op.constant(np.random.randn(12), dtype=dtype)
|
||||
b = self._ConstOp((12,))
|
||||
x10 = nn.bias_add(x10, b, data_format="NCHW")
|
||||
x10 = gen_array_ops.reshape(x10, [48, -1])
|
||||
x10 = gen_array_ops.reshape(x10, [4, -1])
|
||||
|
||||
x11 = gen_array_ops.reshape(x, [4, 12, 12])
|
||||
b = constant_op.constant(np.random.randn(12), dtype=dtype)
|
||||
b = self._ConstOp((12,))
|
||||
x11 = nn.bias_add(x11, b, data_format="NCHW")
|
||||
x11 = gen_array_ops.reshape(x11, [48, -1])
|
||||
x11 = gen_array_ops.reshape(x11, [4, -1])
|
||||
|
||||
out = array_ops.concat(
|
||||
[x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11], axis=-1)
|
||||
out = array_ops.concat([x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11],
|
||||
axis=-1)
|
||||
out = array_ops.squeeze(out, name=output_name)
|
||||
return trt_test.TfTrtIntegrationTestParams(
|
||||
gdef=g.as_graph_def(),
|
||||
input_names=[input_name],
|
||||
input_dims=[input_dims],
|
||||
output_names=[output_name],
|
||||
expected_output_dims=[(48, 89)])
|
||||
expected_output_dims=[(4, 6680)])
|
||||
|
||||
def GetConversionParams(self, run_params):
|
||||
"""Return a ConversionParams for test."""
|
||||
return super(BiasaddMatMulTest,
|
||||
self).GetConversionParams(run_params)._replace(
|
||||
max_batch_size=48, maximum_cached_engines=2)
|
||||
max_batch_size=4, maximum_cached_engines=2)
|
||||
|
||||
def _ValidEngines(self):
|
||||
"""Engines expected to build and run."""
|
||||
return [
|
||||
"my_trt_op_0", "my_trt_op_1", "my_trt_op_2", "my_trt_op_3",
|
||||
"my_trt_op_6", "my_trt_op_7", "my_trt_op_8", "my_trt_op_9"
|
||||
]
|
||||
return ["my_trt_op_0"]
|
||||
|
||||
def _InvalidEngines(self):
|
||||
"""Engines that will cause conversion error at building time."""
|
||||
return ["my_trt_op_4", "my_trt_op_5"]
|
||||
return ["my_trt_op_1", "my_trt_op_2"]
|
||||
|
||||
def ExpectedEnginesToBuild(self, run_params):
|
||||
"""Return the expected engines to build."""
|
||||
|
||||
@ -32,79 +32,34 @@ from tensorflow.python.platform import test
|
||||
|
||||
class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase):
|
||||
|
||||
def _ConstOp(self, shape):
|
||||
return constant_op.constant(np.random.randn(*shape), dtype=dtypes.float32)
|
||||
|
||||
def GetParams(self):
|
||||
"""Tests for scale & elementwise layers in TF-TRT."""
|
||||
dtype = dtypes.float32
|
||||
input_name = "input"
|
||||
input_dims = [10, 24, 24, 20]
|
||||
output_name = "output"
|
||||
g = ops.Graph()
|
||||
with g.as_default():
|
||||
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
|
||||
# scale
|
||||
a = constant_op.constant(np.random.randn(1), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# scale
|
||||
a = constant_op.constant(np.random.randn(1), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# scale
|
||||
a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# scale
|
||||
a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# scale
|
||||
a = constant_op.constant(np.random.randn(24, 24, 20), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# scale
|
||||
a = constant_op.constant(np.random.randn(24, 24, 20), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(20), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(20), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(1, 24, 1, 1), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(1, 24, 1, 1), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(1, 24, 24, 1), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(1, 24, 24, 1), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(1, 24, 24, 20), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(1, 24, 24, 20), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(24, 20), dtype=dtype)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
# elementwise
|
||||
a = constant_op.constant(np.random.randn(24, 20), dtype=dtype)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
x = array_ops.placeholder(
|
||||
dtype=dtypes.float32, shape=input_dims, name=input_name)
|
||||
for weights_shape in [
|
||||
(1,), # scale
|
||||
(24, 1, 1), # scale
|
||||
(24, 24, 20), # scale
|
||||
(20,), # elementwise
|
||||
(1, 24, 1, 1), # elementwise
|
||||
(1, 24, 24, 1), # elementwise
|
||||
(1, 24, 24, 20), # elementwise
|
||||
(24, 20), # elementwise
|
||||
]:
|
||||
a = self._ConstOp(weights_shape)
|
||||
f = x + a
|
||||
x = math_ops.sigmoid(f)
|
||||
a = self._ConstOp(weights_shape)
|
||||
f = a + x
|
||||
x = math_ops.sigmoid(f)
|
||||
gen_array_ops.reshape(x, [5, -1], name=output_name)
|
||||
return trt_test.TfTrtIntegrationTestParams(
|
||||
gdef=g.as_graph_def(),
|
||||
@ -115,24 +70,7 @@ class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase):
|
||||
|
||||
def ExpectedEnginesToBuild(self, run_params):
|
||||
"""Return the expected engines to build."""
|
||||
return [
|
||||
"my_trt_op_0",
|
||||
"my_trt_op_1",
|
||||
"my_trt_op_2",
|
||||
"my_trt_op_3",
|
||||
"my_trt_op_4",
|
||||
"my_trt_op_5",
|
||||
"my_trt_op_6",
|
||||
"my_trt_op_7",
|
||||
"my_trt_op_8",
|
||||
"my_trt_op_9",
|
||||
"my_trt_op_10",
|
||||
"my_trt_op_11",
|
||||
"my_trt_op_12",
|
||||
"my_trt_op_13",
|
||||
"my_trt_op_14",
|
||||
"my_trt_op_15",
|
||||
]
|
||||
return ["my_trt_op_%d" % i for i in range(16)]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
152
tensorflow/contrib/tensorrt/test/reshape_transpose_test.py
Normal file
152
tensorflow/contrib/tensorrt/test/reshape_transpose_test.py
Normal file
@ -0,0 +1,152 @@
|
||||
# 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.
|
||||
# ==============================================================================
|
||||
"""Basic tests for TF-TensorRT integration."""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.platform import test
|
||||
|
||||
|
||||
class ReshapeTest(trt_test.TfTrtIntegrationTestBase):
|
||||
|
||||
def GetParams(self):
|
||||
dtype = dtypes.float32
|
||||
input_name = "input"
|
||||
input_dims = [100, 24, 24, 2]
|
||||
output_name = "output"
|
||||
g = ops.Graph()
|
||||
with g.as_default():
|
||||
inp = array_ops.placeholder(
|
||||
dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
|
||||
outputs = []
|
||||
# Here we test two types of reshapes, one changes the batch dimension and
|
||||
# the other does not. Note that we're not able to test reshaping to
|
||||
# scalar, since TRT requires input tensor to be of rank at least 2, so a
|
||||
# reshape with scalar input will be filtered out of the segment before
|
||||
# conversion.
|
||||
with g.device("/GPU:0"):
|
||||
# These reshapes happen at batch dimension, thus conversion should fail.
|
||||
for shape in [[2, 50, 24, 24, 2], [-1, 50, 24, 24, 2],
|
||||
[2, 50, -1, 24, 2]]:
|
||||
incompatible_reshape = array_ops.reshape(inp, shape)
|
||||
reshape_back = array_ops.reshape(incompatible_reshape,
|
||||
[-1, 24, 24, 2])
|
||||
outputs.append(self.trt_incompatible_op(reshape_back))
|
||||
# Add another block with many reshapes that don't change the batch
|
||||
# dimension.
|
||||
compatible_reshape = array_ops.reshape(
|
||||
inp, [-1, 24 * 24, 2], name="reshape-0")
|
||||
compatible_reshape = array_ops.reshape(
|
||||
compatible_reshape, [100, 24, -1], name="reshape-1")
|
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compatible_reshape = array_ops.reshape(
|
||||
compatible_reshape, [100, 24 * 2, 24], name="reshape-2")
|
||||
compatible_reshape = array_ops.reshape(
|
||||
compatible_reshape, [-1, 24, 24 * 2], name="reshape-3")
|
||||
compatible_reshape = array_ops.reshape(
|
||||
compatible_reshape, [-1, 6, 4, 24, 2], name="reshape-4")
|
||||
compatible_reshape = array_ops.reshape(
|
||||
compatible_reshape, [-1, 6, 4, 6, 4, 2, 1], name="reshape-5")
|
||||
compatible_reshape = array_ops.reshape(
|
||||
compatible_reshape, [-1, 24, 24, 2], name="reshape-6")
|
||||
outputs.append(self.trt_incompatible_op(compatible_reshape))
|
||||
math_ops.add_n(outputs, name=output_name)
|
||||
return trt_test.TfTrtIntegrationTestParams(
|
||||
gdef=g.as_graph_def(),
|
||||
input_names=[input_name],
|
||||
input_dims=[input_dims],
|
||||
output_names=[output_name],
|
||||
expected_output_dims=[tuple(input_dims)])
|
||||
|
||||
def ExpectedEnginesToBuild(self, run_params):
|
||||
"""Return the expected engines to build."""
|
||||
return {
|
||||
"my_trt_op_3": ["reshape-%d" % i for i in range(7)] +
|
||||
["reshape-%d/shape" % i for i in range(7)]
|
||||
}
|
||||
|
||||
def ShouldRunTest(self, run_params):
|
||||
"""Whether to run the test."""
|
||||
return (not trt_test.IsQuantizationMode(run_params.precision_mode) and
|
||||
not run_params.dynamic_engine)
|
||||
|
||||
|
||||
class TransposeTest(trt_test.TfTrtIntegrationTestBase):
|
||||
|
||||
def GetParams(self):
|
||||
"""Create a graph containing single segment."""
|
||||
dtype = dtypes.float32
|
||||
input_name = "input"
|
||||
input_dims = [100, 24, 24, 2]
|
||||
output_name = "output"
|
||||
g = ops.Graph()
|
||||
with g.as_default():
|
||||
inp = array_ops.placeholder(
|
||||
dtype=dtype, shape=[None] + input_dims[1:], name=input_name)
|
||||
with g.device("/GPU:0"):
|
||||
# Add a block with compatible transposes.
|
||||
compatible_transpose = array_ops.transpose(
|
||||
inp, [0, 3, 1, 2], name="transpose-1")
|
||||
compatible_transpose = array_ops.transpose(
|
||||
compatible_transpose, [0, 2, 3, 1], name="transposeback")
|
||||
|
||||
# Add an incompatible op so the first block will not be in the same
|
||||
# subgraph where the following block belongs.
|
||||
bridge = self.trt_incompatible_op(compatible_transpose)
|
||||
|
||||
# Add a block with incompatible transposes.
|
||||
#
|
||||
# Note: by default Grappler will run the TRT optimizer twice. At the
|
||||
# first time it will group the two transpose ops below to same segment
|
||||
# then fail the conversion due to the expected batch dimension problem.
|
||||
# At the second time, since the input of bridge op is my_trt_op_0, it
|
||||
# will fail to do shape inference which then cause conversion to fail.
|
||||
# TODO(laigd): support shape inference, make TRT optimizer run only
|
||||
# once, and fix this.
|
||||
incompatible_transpose = array_ops.transpose(
|
||||
bridge, [2, 1, 0, 3], name="transpose-2")
|
||||
excluded_transpose = array_ops.transpose(
|
||||
incompatible_transpose, [0, 2, 3, 1], name="transpose-3")
|
||||
array_ops.identity(excluded_transpose, name=output_name)
|
||||
return trt_test.TfTrtIntegrationTestParams(
|
||||
gdef=g.as_graph_def(),
|
||||
input_names=[input_name],
|
||||
input_dims=[input_dims],
|
||||
output_names=[output_name],
|
||||
expected_output_dims=[(24, 100, 2, 24)])
|
||||
|
||||
def ExpectedEnginesToBuild(self, run_params):
|
||||
"""Return the expected engines to build."""
|
||||
return {
|
||||
"my_trt_op_0": [
|
||||
"transpose-1", "transpose-1/perm", "transposeback",
|
||||
"transposeback/perm"
|
||||
]
|
||||
}
|
||||
|
||||
def ShouldRunTest(self, run_params):
|
||||
"""Whether to run the test."""
|
||||
return (not trt_test.IsQuantizationMode(run_params.precision_mode) and
|
||||
not run_params.dynamic_engine)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test.main()
|
||||
Loading…
Reference in New Issue
Block a user