a5d4188849
Instead of using CheckFloatEq and CheckDoubleEq directly, I introduced a macro which first stores the expected result in a volatile variable. Here are some comments of previous CLs: The reason is same as the CL #31808 (issue 1430943002, X87: Change the test case for X87 float operations), please refer: https://codereview.chromium.org/1430943002/. Here is the key comments from CL #31808 Some new test cases use CheckFloatEq(...) and CheckDoubleEq(...) function for result check. When GCC compiling the CheckFloatEq() and CheckDoubleEq() function, those inlined functions has different behavior comparing with GCC ia32 build and x87 build. The major difference is sse float register still has single precision rounding semantic. While X87 register has no such rounding precsion semantic when directly use register value. The V8 turbofan JITTed has exactly same result in both X87 and IA32 port. So we add the following sentence to do type cast to keep the same precision for RunCallInt64ToFloat32/RunCallInt64ToFloat64. Such as: volatile double expect = static_cast<float>(*i). R=titzer@chromium.org, weiliang.lin@intel.com Review URL: https://codereview.chromium.org/1773513002 Cr-Commit-Position: refs/heads/master@{#34534}
349 lines
12 KiB
C++
349 lines
12 KiB
C++
// Copyright 2014 the V8 project authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file.
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#ifndef V8_CCTEST_COMPILER_VALUE_HELPER_H_
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#define V8_CCTEST_COMPILER_VALUE_HELPER_H_
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#include <stdint.h>
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#include "src/compiler/common-operator.h"
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#include "src/compiler/node.h"
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#include "src/compiler/node-matchers.h"
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#include "src/isolate.h"
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#include "src/objects.h"
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#include "test/cctest/cctest.h"
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namespace v8 {
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namespace internal {
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namespace compiler {
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// A collection of utilities related to numerical and heap values, including
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// example input values of various types, including int32_t, uint32_t, double,
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// etc.
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class ValueHelper {
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public:
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Isolate* isolate_;
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ValueHelper() : isolate_(CcTest::InitIsolateOnce()) {}
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void CheckFloat64Constant(double expected, Node* node) {
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CHECK_EQ(IrOpcode::kFloat64Constant, node->opcode());
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CHECK_EQ(expected, OpParameter<double>(node));
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}
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void CheckNumberConstant(double expected, Node* node) {
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CHECK_EQ(IrOpcode::kNumberConstant, node->opcode());
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CHECK_EQ(expected, OpParameter<double>(node));
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}
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void CheckInt32Constant(int32_t expected, Node* node) {
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CHECK_EQ(IrOpcode::kInt32Constant, node->opcode());
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CHECK_EQ(expected, OpParameter<int32_t>(node));
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}
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void CheckUint32Constant(int32_t expected, Node* node) {
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CHECK_EQ(IrOpcode::kInt32Constant, node->opcode());
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CHECK_EQ(expected, OpParameter<int32_t>(node));
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}
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void CheckHeapConstant(HeapObject* expected, Node* node) {
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CHECK_EQ(IrOpcode::kHeapConstant, node->opcode());
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CHECK_EQ(expected, *OpParameter<Handle<HeapObject>>(node));
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}
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void CheckTrue(Node* node) {
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CheckHeapConstant(isolate_->heap()->true_value(), node);
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}
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void CheckFalse(Node* node) {
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CheckHeapConstant(isolate_->heap()->false_value(), node);
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}
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static std::vector<float> float32_vector() {
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static const float nan = std::numeric_limits<float>::quiet_NaN();
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static const float kValues[] = {
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-std::numeric_limits<float>::infinity(),
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-2.70497e+38f,
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-1.4698e+37f,
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-1.22813e+35f,
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-1.20555e+35f,
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-1.34584e+34f,
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-1.0079e+32f,
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-6.49364e+26f,
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-3.06077e+25f,
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-1.46821e+25f,
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-1.17658e+23f,
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-1.9617e+22f,
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-2.7357e+20f,
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-9223372036854775808.0f, // INT64_MIN
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-1.48708e+13f,
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-1.89633e+12f,
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-4.66622e+11f,
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-2.22581e+11f,
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-1.45381e+10f,
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-1.3956e+09f,
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-1.32951e+09f,
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-1.30721e+09f,
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-1.19756e+09f,
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-9.26822e+08f,
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-6.35647e+08f,
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-4.00037e+08f,
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-1.81227e+08f,
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-5.09256e+07f,
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-964300.0f,
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-192446.0f,
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-28455.0f,
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-27194.0f,
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-26401.0f,
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-20575.0f,
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-17069.0f,
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-9167.0f,
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-960.178f,
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-113.0f,
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-62.0f,
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-15.0f,
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-7.0f,
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-1.0f,
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-0.0256635f,
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-4.60374e-07f,
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-3.63759e-10f,
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-4.30175e-14f,
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-5.27385e-15f,
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-1.48084e-15f,
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-1.05755e-19f,
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-3.2995e-21f,
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-1.67354e-23f,
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-1.11885e-23f,
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-1.78506e-30f,
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-5.07594e-31f,
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-3.65799e-31f,
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-1.43718e-34f,
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-1.27126e-38f,
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-0.0f,
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0.0f,
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1.17549e-38f,
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1.56657e-37f,
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4.08512e-29f,
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3.31357e-28f,
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6.25073e-22f,
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4.1723e-13f,
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1.44343e-09f,
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5.27004e-08f,
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9.48298e-08f,
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5.57888e-07f,
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4.89988e-05f,
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0.244326f,
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1.0f,
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12.4895f,
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19.0f,
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47.0f,
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106.0f,
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538.324f,
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564.536f,
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819.124f,
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7048.0f,
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12611.0f,
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19878.0f,
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20309.0f,
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797056.0f,
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1.77219e+09f,
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1.51116e+11f,
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4.18193e+13f,
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3.59167e+16f,
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9223372036854775807.0f, // INT64_MAX
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18446744073709551615.0f, // UINT64_MAX
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3.38211e+19f,
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2.67488e+20f,
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1.78831e+21f,
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9.20914e+21f,
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8.35654e+23f,
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1.4495e+24f,
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5.94015e+25f,
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4.43608e+30f,
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2.44502e+33f,
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2.61152e+33f,
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1.38178e+37f,
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1.71306e+37f,
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3.31899e+38f,
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3.40282e+38f,
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std::numeric_limits<float>::infinity(),
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nan,
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-nan,
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};
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return std::vector<float>(&kValues[0], &kValues[arraysize(kValues)]);
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}
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static std::vector<double> float64_vector() {
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static const double nan = std::numeric_limits<double>::quiet_NaN();
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static const double values[] = {-2e66,
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-9223373136366403584.0,
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-9223372036854775808.0, // INT64_MIN
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-2147483649.5,
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-2147483648.25,
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-2147483648.0,
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-2147483647.875,
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-2147483647.125,
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-2147483647.0,
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-999.75,
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-2e66,
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-1.75,
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-1.0,
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-0.5,
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-0.0,
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0.0,
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3e-88,
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0.125,
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0.25,
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0.375,
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0.5,
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1.0,
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1.25,
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2,
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3.1e7,
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5.125,
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6.25,
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888,
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982983.25,
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2147483647.0,
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2147483647.375,
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2147483647.75,
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2147483648.0,
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2147483648.25,
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2147483649.25,
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9223372036854775807.0, // INT64_MAX
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9223373136366403584.0,
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18446744073709551615.0, // UINT64_MAX
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2e66,
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V8_INFINITY,
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-V8_INFINITY,
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-nan,
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nan};
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return std::vector<double>(&values[0], &values[arraysize(values)]);
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}
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static const std::vector<int32_t> int32_vector() {
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std::vector<uint32_t> values = uint32_vector();
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return std::vector<int32_t>(values.begin(), values.end());
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}
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static const std::vector<uint32_t> uint32_vector() {
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static const uint32_t kValues[] = {
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0x00000000, 0x00000001, 0xffffffff, 0x1b09788b, 0x04c5fce8, 0xcc0de5bf,
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// This row is useful for testing lea optimizations on intel.
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0x00000002, 0x00000003, 0x00000004, 0x00000005, 0x00000008, 0x00000009,
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0x273a798e, 0x187937a3, 0xece3af83, 0x5495a16b, 0x0b668ecc, 0x11223344,
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0x0000009e, 0x00000043, 0x0000af73, 0x0000116b, 0x00658ecc, 0x002b3b4c,
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0x88776655, 0x70000000, 0x07200000, 0x7fffffff, 0x56123761, 0x7fffff00,
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0x761c4761, 0x80000000, 0x88888888, 0xa0000000, 0xdddddddd, 0xe0000000,
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0xeeeeeeee, 0xfffffffd, 0xf0000000, 0x007fffff, 0x003fffff, 0x001fffff,
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0x000fffff, 0x0007ffff, 0x0003ffff, 0x0001ffff, 0x0000ffff, 0x00007fff,
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0x00003fff, 0x00001fff, 0x00000fff, 0x000007ff, 0x000003ff, 0x000001ff};
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return std::vector<uint32_t>(&kValues[0], &kValues[arraysize(kValues)]);
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}
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static const std::vector<int64_t> int64_vector() {
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std::vector<uint64_t> values = uint64_vector();
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return std::vector<int64_t>(values.begin(), values.end());
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}
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static const std::vector<uint64_t> uint64_vector() {
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static const uint64_t kValues[] = {
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0x00000000, 0x00000001, 0xffffffff,
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0x1b09788b, 0x04c5fce8, 0xcc0de5bf,
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0x00000002, 0x00000003, 0x00000004,
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0x00000005, 0x00000008, 0x00000009,
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0xffffffffffffffff, 0xfffffffffffffffe, 0xfffffffffffffffd,
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0x0000000000000000, 0x0000000100000000, 0xffffffff00000000,
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0x1b09788b00000000, 0x04c5fce800000000, 0xcc0de5bf00000000,
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0x0000000200000000, 0x0000000300000000, 0x0000000400000000,
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0x0000000500000000, 0x0000000800000000, 0x0000000900000000,
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0x273a798e187937a3, 0xece3af835495a16b, 0x0b668ecc11223344,
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0x0000009e, 0x00000043, 0x0000af73,
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0x0000116b, 0x00658ecc, 0x002b3b4c,
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0x88776655, 0x70000000, 0x07200000,
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0x7fffffff, 0x56123761, 0x7fffff00,
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0x761c4761eeeeeeee, 0x80000000eeeeeeee, 0x88888888dddddddd,
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0xa0000000dddddddd, 0xddddddddaaaaaaaa, 0xe0000000aaaaaaaa,
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0xeeeeeeeeeeeeeeee, 0xfffffffdeeeeeeee, 0xf0000000dddddddd,
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0x007fffffdddddddd, 0x003fffffaaaaaaaa, 0x001fffffaaaaaaaa,
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0x000fffff, 0x0007ffff, 0x0003ffff,
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0x0001ffff, 0x0000ffff, 0x00007fff,
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0x00003fff, 0x00001fff, 0x00000fff,
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0x000007ff, 0x000003ff, 0x000001ff,
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0x00003fffffffffff, 0x00001fffffffffff, 0x00000fffffffffff,
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0x000007ffffffffff, 0x000003ffffffffff, 0x000001ffffffffff,
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0x8000008000000000, 0x8000008000000001, 0x8000000000000400,
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0x8000000000000401};
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return std::vector<uint64_t>(&kValues[0], &kValues[arraysize(kValues)]);
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}
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static const std::vector<double> nan_vector(size_t limit = 0) {
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static const double nan = std::numeric_limits<double>::quiet_NaN();
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static const double values[] = {-nan, -V8_INFINITY * -0.0,
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-V8_INFINITY * 0.0, V8_INFINITY * -0.0,
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V8_INFINITY * 0.0, nan};
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return std::vector<double>(&values[0], &values[arraysize(values)]);
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}
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static const std::vector<uint32_t> ror_vector() {
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static const uint32_t kValues[31] = {
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1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
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17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
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return std::vector<uint32_t>(&kValues[0], &kValues[arraysize(kValues)]);
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}
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};
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// Helper macros that can be used in FOR_INT32_INPUTS(i) { ... *i ... }
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// Watch out, these macros aren't hygenic; they pollute your scope. Thanks STL.
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#define FOR_INPUTS(ctype, itype, var) \
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std::vector<ctype> var##_vec = ValueHelper::itype##_vector(); \
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for (std::vector<ctype>::iterator var = var##_vec.begin(); \
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var != var##_vec.end(); ++var)
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#define FOR_INT32_INPUTS(var) FOR_INPUTS(int32_t, int32, var)
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#define FOR_UINT32_INPUTS(var) FOR_INPUTS(uint32_t, uint32, var)
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#define FOR_INT64_INPUTS(var) FOR_INPUTS(int64_t, int64, var)
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#define FOR_UINT64_INPUTS(var) FOR_INPUTS(uint64_t, uint64, var)
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#define FOR_FLOAT32_INPUTS(var) FOR_INPUTS(float, float32, var)
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#define FOR_FLOAT64_INPUTS(var) FOR_INPUTS(double, float64, var)
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#define FOR_INT32_SHIFTS(var) for (int32_t var = 0; var < 32; var++)
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#define FOR_UINT32_SHIFTS(var) for (uint32_t var = 0; var < 32; var++)
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// TODO(bmeurer): Drop this crap once we switch to GTest/Gmock.
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static inline void CheckFloatEq(volatile float x, volatile float y) {
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if (std::isnan(x)) {
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CHECK(std::isnan(y));
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} else {
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CHECK_EQ(x, y);
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}
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}
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#define CHECK_FLOAT_EQ(lhs, rhs) \
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do { \
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volatile float tmp = lhs; \
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CheckFloatEq(tmp, rhs); \
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} while (0)
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static inline void CheckDoubleEq(volatile double x, volatile double y) {
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if (std::isnan(x)) {
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CHECK(std::isnan(y));
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} else {
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CHECK_EQ(x, y);
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}
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}
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#define CHECK_DOUBLE_EQ(lhs, rhs) \
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do { \
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volatile double tmp = lhs; \
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CheckDoubleEq(tmp, rhs); \
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} while (0)
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} // namespace compiler
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} // namespace internal
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} // namespace v8
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#endif // V8_CCTEST_COMPILER_VALUE_HELPER_H_
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