Even though we do not support uint64 op kernels on mobile, it is inevitable to support uint64 tensors in order to enable TF uint64 ops via flex delegate. This CL enables the uint64 tensor type in MLIR converter only. PiperOrigin-RevId: 342939673 Change-Id: I24f422040f82cad7affce4b921361f79e8a51730
144 lines
4.7 KiB
C++
144 lines
4.7 KiB
C++
/* Copyright 2017 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/lite/c/common.h"
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#include <gtest/gtest.h>
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namespace tflite {
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// NOTE: this tests only the TfLiteIntArray part of context.
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// most of common.h is provided in the context of using it with
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// interpreter.h and interpreter.cc, so interpreter_test.cc tests context
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// structures more thoroughly.
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TEST(IntArray, TestIntArrayCreate) {
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TfLiteIntArray* a = TfLiteIntArrayCreate(0);
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TfLiteIntArray* b = TfLiteIntArrayCreate(3);
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TfLiteIntArrayFree(a);
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TfLiteIntArrayFree(b);
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}
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TEST(IntArray, TestIntArrayCopy) {
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TfLiteIntArray* a = TfLiteIntArrayCreate(2);
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a->data[0] = 22;
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a->data[1] = 24;
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TfLiteIntArray* b = TfLiteIntArrayCopy(a);
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ASSERT_NE(a, b);
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ASSERT_EQ(a->size, b->size);
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ASSERT_EQ(a->data[0], b->data[0]);
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ASSERT_EQ(a->data[1], b->data[1]);
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TfLiteIntArrayFree(a);
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TfLiteIntArrayFree(b);
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}
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TEST(IntArray, TestIntArrayEqual) {
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TfLiteIntArray* a = TfLiteIntArrayCreate(1);
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a->data[0] = 1;
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TfLiteIntArray* b = TfLiteIntArrayCreate(2);
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b->data[0] = 5;
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b->data[1] = 6;
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TfLiteIntArray* c = TfLiteIntArrayCreate(2);
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c->data[0] = 5;
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c->data[1] = 6;
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TfLiteIntArray* d = TfLiteIntArrayCreate(2);
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d->data[0] = 6;
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d->data[1] = 6;
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ASSERT_FALSE(TfLiteIntArrayEqual(a, b));
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ASSERT_TRUE(TfLiteIntArrayEqual(b, c));
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ASSERT_TRUE(TfLiteIntArrayEqual(b, b));
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ASSERT_FALSE(TfLiteIntArrayEqual(c, d));
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TfLiteIntArrayFree(a);
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TfLiteIntArrayFree(b);
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TfLiteIntArrayFree(c);
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TfLiteIntArrayFree(d);
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}
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TEST(FloatArray, TestFloatArrayCreate) {
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TfLiteFloatArray* a = TfLiteFloatArrayCreate(0);
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TfLiteFloatArray* b = TfLiteFloatArrayCreate(3);
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TfLiteFloatArrayFree(a);
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TfLiteFloatArrayFree(b);
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}
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TEST(Types, TestTypeNames) {
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auto type_name = [](TfLiteType t) {
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return std::string(TfLiteTypeGetName(t));
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};
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EXPECT_EQ(type_name(kTfLiteNoType), "NOTYPE");
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EXPECT_EQ(type_name(kTfLiteFloat64), "FLOAT64");
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EXPECT_EQ(type_name(kTfLiteFloat32), "FLOAT32");
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EXPECT_EQ(type_name(kTfLiteFloat16), "FLOAT16");
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EXPECT_EQ(type_name(kTfLiteInt16), "INT16");
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EXPECT_EQ(type_name(kTfLiteInt32), "INT32");
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EXPECT_EQ(type_name(kTfLiteUInt8), "UINT8");
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EXPECT_EQ(type_name(kTfLiteUInt64), "UINT64");
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EXPECT_EQ(type_name(kTfLiteInt8), "INT8");
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EXPECT_EQ(type_name(kTfLiteInt64), "INT64");
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EXPECT_EQ(type_name(kTfLiteBool), "BOOL");
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EXPECT_EQ(type_name(kTfLiteComplex64), "COMPLEX64");
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EXPECT_EQ(type_name(kTfLiteComplex128), "COMPLEX128");
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EXPECT_EQ(type_name(kTfLiteString), "STRING");
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}
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TEST(Quantization, TestQuantizationFree) {
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TfLiteTensor t;
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// Set these values, otherwise TfLiteTensorFree has uninitialized values.
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t.allocation_type = kTfLiteArenaRw;
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t.dims = nullptr;
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t.dims_signature = nullptr;
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t.quantization.type = kTfLiteAffineQuantization;
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t.sparsity = nullptr;
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auto* params = reinterpret_cast<TfLiteAffineQuantization*>(
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malloc(sizeof(TfLiteAffineQuantization)));
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params->scale = TfLiteFloatArrayCreate(3);
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params->zero_point = TfLiteIntArrayCreate(3);
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t.quantization.params = reinterpret_cast<void*>(params);
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TfLiteTensorFree(&t);
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}
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TEST(Sparsity, TestSparsityFree) {
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TfLiteTensor t = {};
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// Set these values, otherwise TfLiteTensorFree has uninitialized values.
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t.allocation_type = kTfLiteArenaRw;
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t.dims = nullptr;
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t.dims_signature = nullptr;
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// A dummy CSR sparse matrix.
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t.sparsity = static_cast<TfLiteSparsity*>(malloc(sizeof(TfLiteSparsity)));
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t.sparsity->traversal_order = TfLiteIntArrayCreate(2);
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t.sparsity->block_map = nullptr;
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t.sparsity->dim_metadata = static_cast<TfLiteDimensionMetadata*>(
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malloc(sizeof(TfLiteDimensionMetadata) * 2));
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t.sparsity->dim_metadata_size = 2;
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t.sparsity->dim_metadata[0].format = kTfLiteDimDense;
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t.sparsity->dim_metadata[0].dense_size = 4;
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t.sparsity->dim_metadata[1].format = kTfLiteDimSparseCSR;
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t.sparsity->dim_metadata[1].array_segments = TfLiteIntArrayCreate(2);
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t.sparsity->dim_metadata[1].array_indices = TfLiteIntArrayCreate(3);
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TfLiteTensorFree(&t);
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}
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} // namespace tflite
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int main(int argc, char** argv) {
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::testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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