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OpenCL-CTS/test_conformance/math_brute_force/binary_float.cpp
Marco Antognini 01497c402e Reduce scope of variables (#1228) 
Make variables local to loops, with appropriate types. These variables
are not read after the loop without being reset first, so this patch
doesn't change behaviour.

These variables should now be used for one purpose only, making it
easier to reason about the code. This will make future refactoring
easier.

Signed-off-by: Marco Antognini <marco.antognini@arm.com>
2021-04-28 09:30:51 +01:00

990 lines
38 KiB
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//
// Copyright (c) 2017 The Khronos Group Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#include "function_list.h"
#include "test_functions.h"
#include "utility.h"
#include <cstring>
const float twoToMinus126 = MAKE_HEX_FLOAT(0x1p-126f, 1, -126);
static int BuildKernel(const char *name, int vectorSize, cl_uint kernel_count,
cl_kernel *k, cl_program *p, bool relaxedMode)
{
const char *c[] = { "__kernel void math_kernel",
sizeNames[vectorSize],
"( __global float",
sizeNames[vectorSize],
"* out, __global float",
sizeNames[vectorSize],
"* in1, __global float",
sizeNames[vectorSize],
"* in2 )\n"
"{\n"
" size_t i = get_global_id(0);\n"
" out[i] = ",
name,
"( in1[i], in2[i] );\n"
"}\n" };
const char *c3[] = {
"__kernel void math_kernel",
sizeNames[vectorSize],
"( __global float* out, __global float* in, __global float* in2)\n"
"{\n"
" size_t i = get_global_id(0);\n"
" if( i + 1 < get_global_size(0) )\n"
" {\n"
" float3 f0 = vload3( 0, in + 3 * i );\n"
" float3 f1 = vload3( 0, in2 + 3 * i );\n"
" f0 = ",
name,
"( f0, f1 );\n"
" vstore3( f0, 0, out + 3*i );\n"
" }\n"
" else\n"
" {\n"
" size_t parity = i & 1; // Figure out how many elements are "
"left over after BUFFER_SIZE % (3*sizeof(float)). Assume power of two "
"buffer size \n"
" float3 f0;\n"
" float3 f1;\n"
" switch( parity )\n"
" {\n"
" case 1:\n"
" f0 = (float3)( in[3*i], NAN, NAN ); \n"
" f1 = (float3)( in2[3*i], NAN, NAN ); \n"
" break;\n"
" case 0:\n"
" f0 = (float3)( in[3*i], in[3*i+1], NAN ); \n"
" f1 = (float3)( in2[3*i], in2[3*i+1], NAN ); \n"
" break;\n"
" }\n"
" f0 = ",
name,
"( f0, f1 );\n"
" switch( parity )\n"
" {\n"
" case 0:\n"
" out[3*i+1] = f0.y; \n"
" // fall through\n"
" case 1:\n"
" out[3*i] = f0.x; \n"
" break;\n"
" }\n"
" }\n"
"}\n"
};
const char **kern = c;
size_t kernSize = sizeof(c) / sizeof(c[0]);
if (sizeValues[vectorSize] == 3)
{
kern = c3;
kernSize = sizeof(c3) / sizeof(c3[0]);
}
char testName[32];
snprintf(testName, sizeof(testName) - 1, "math_kernel%s",
sizeNames[vectorSize]);
return MakeKernels(kern, (cl_uint)kernSize, testName, kernel_count, k, p,
relaxedMode);
}
typedef struct BuildKernelInfo
{
cl_uint offset; // the first vector size to build
cl_uint kernel_count;
cl_kernel **kernels;
cl_program *programs;
const char *nameInCode;
bool relaxedMode; // Whether to build with -cl-fast-relaxed-math.
} BuildKernelInfo;
static cl_int BuildKernelFn(cl_uint job_id, cl_uint thread_id UNUSED, void *p)
{
BuildKernelInfo *info = (BuildKernelInfo *)p;
cl_uint i = info->offset + job_id;
return BuildKernel(info->nameInCode, i, info->kernel_count,
info->kernels[i], info->programs + i, info->relaxedMode);
}
// Thread specific data for a worker thread
typedef struct ThreadInfo
{
cl_mem inBuf; // input buffer for the thread
cl_mem inBuf2; // input buffer for the thread
cl_mem outBuf[VECTOR_SIZE_COUNT]; // output buffers for the thread
float maxError; // max error value. Init to 0.
double
maxErrorValue; // position of the max error value (param 1). Init to 0.
double maxErrorValue2; // position of the max error value (param 2). Init
// to 0.
MTdata d;
cl_command_queue tQueue; // per thread command queue to improve performance
} ThreadInfo;
typedef struct TestInfo
{
size_t subBufferSize; // Size of the sub-buffer in elements
const Func *f; // A pointer to the function info
cl_program programs[VECTOR_SIZE_COUNT]; // programs for various vector sizes
cl_kernel
*k[VECTOR_SIZE_COUNT]; // arrays of thread-specific kernels for each
// worker thread: k[vector_size][thread_id]
ThreadInfo *
tinfo; // An array of thread specific information for each worker thread
cl_uint threadCount; // Number of worker threads
cl_uint jobCount; // Number of jobs
cl_uint step; // step between each chunk and the next.
cl_uint scale; // stride between individual test values
float ulps; // max_allowed ulps
int ftz; // non-zero if running in flush to zero mode
int isFDim;
int skipNanInf;
int isNextafter;
bool relaxedMode; // True if test is running in relaxed mode, false
// otherwise.
} TestInfo;
// A table of more difficult cases to get right
static const float specialValues[] = {
-NAN,
-INFINITY,
-FLT_MAX,
MAKE_HEX_FLOAT(-0x1.000002p64f, -0x1000002L, 40),
MAKE_HEX_FLOAT(-0x1.0p64f, -0x1L, 64),
MAKE_HEX_FLOAT(-0x1.fffffep63f, -0x1fffffeL, 39),
MAKE_HEX_FLOAT(-0x1.000002p63f, -0x1000002L, 39),
MAKE_HEX_FLOAT(-0x1.0p63f, -0x1L, 63),
MAKE_HEX_FLOAT(-0x1.fffffep62f, -0x1fffffeL, 38),
MAKE_HEX_FLOAT(-0x1.000002p32f, -0x1000002L, 8),
MAKE_HEX_FLOAT(-0x1.0p32f, -0x1L, 32),
MAKE_HEX_FLOAT(-0x1.fffffep31f, -0x1fffffeL, 7),
MAKE_HEX_FLOAT(-0x1.000002p31f, -0x1000002L, 7),
MAKE_HEX_FLOAT(-0x1.0p31f, -0x1L, 31),
MAKE_HEX_FLOAT(-0x1.fffffep30f, -0x1fffffeL, 6),
-1000.f,
-100.f,
-4.0f,
-3.5f,
-3.0f,
MAKE_HEX_FLOAT(-0x1.800002p1f, -0x1800002L, -23),
-2.5f,
MAKE_HEX_FLOAT(-0x1.7ffffep1f, -0x17ffffeL, -23),
-2.0f,
MAKE_HEX_FLOAT(-0x1.800002p0f, -0x1800002L, -24),
-1.5f,
MAKE_HEX_FLOAT(-0x1.7ffffep0f, -0x17ffffeL, -24),
MAKE_HEX_FLOAT(-0x1.000002p0f, -0x1000002L, -24),
-1.0f,
MAKE_HEX_FLOAT(-0x1.fffffep-1f, -0x1fffffeL, -25),
MAKE_HEX_FLOAT(-0x1.000002p-1f, -0x1000002L, -25),
-0.5f,
MAKE_HEX_FLOAT(-0x1.fffffep-2f, -0x1fffffeL, -26),
MAKE_HEX_FLOAT(-0x1.000002p-2f, -0x1000002L, -26),
-0.25f,
MAKE_HEX_FLOAT(-0x1.fffffep-3f, -0x1fffffeL, -27),
MAKE_HEX_FLOAT(-0x1.000002p-126f, -0x1000002L, -150),
-FLT_MIN,
MAKE_HEX_FLOAT(-0x0.fffffep-126f, -0x0fffffeL, -150),
MAKE_HEX_FLOAT(-0x0.000ffep-126f, -0x0000ffeL, -150),
MAKE_HEX_FLOAT(-0x0.0000fep-126f, -0x00000feL, -150),
MAKE_HEX_FLOAT(-0x0.00000ep-126f, -0x000000eL, -150),
MAKE_HEX_FLOAT(-0x0.00000cp-126f, -0x000000cL, -150),
MAKE_HEX_FLOAT(-0x0.00000ap-126f, -0x000000aL, -150),
MAKE_HEX_FLOAT(-0x0.000008p-126f, -0x0000008L, -150),
MAKE_HEX_FLOAT(-0x0.000006p-126f, -0x0000006L, -150),
MAKE_HEX_FLOAT(-0x0.000004p-126f, -0x0000004L, -150),
MAKE_HEX_FLOAT(-0x0.000002p-126f, -0x0000002L, -150),
-0.0f,
+NAN,
+INFINITY,
+FLT_MAX,
MAKE_HEX_FLOAT(+0x1.000002p64f, +0x1000002L, 40),
MAKE_HEX_FLOAT(+0x1.0p64f, +0x1L, 64),
MAKE_HEX_FLOAT(+0x1.fffffep63f, +0x1fffffeL, 39),
MAKE_HEX_FLOAT(+0x1.000002p63f, +0x1000002L, 39),
MAKE_HEX_FLOAT(+0x1.0p63f, +0x1L, 63),
MAKE_HEX_FLOAT(+0x1.fffffep62f, +0x1fffffeL, 38),
MAKE_HEX_FLOAT(+0x1.000002p32f, +0x1000002L, 8),
MAKE_HEX_FLOAT(+0x1.0p32f, +0x1L, 32),
MAKE_HEX_FLOAT(+0x1.fffffep31f, +0x1fffffeL, 7),
MAKE_HEX_FLOAT(+0x1.000002p31f, +0x1000002L, 7),
MAKE_HEX_FLOAT(+0x1.0p31f, +0x1L, 31),
MAKE_HEX_FLOAT(+0x1.fffffep30f, +0x1fffffeL, 6),
+1000.f,
+100.f,
+4.0f,
+3.5f,
+3.0f,
MAKE_HEX_FLOAT(+0x1.800002p1f, +0x1800002L, -23),
2.5f,
MAKE_HEX_FLOAT(+0x1.7ffffep1f, +0x17ffffeL, -23),
+2.0f,
MAKE_HEX_FLOAT(+0x1.800002p0f, +0x1800002L, -24),
1.5f,
MAKE_HEX_FLOAT(+0x1.7ffffep0f, +0x17ffffeL, -24),
MAKE_HEX_FLOAT(+0x1.000002p0f, +0x1000002L, -24),
+1.0f,
MAKE_HEX_FLOAT(+0x1.fffffep-1f, +0x1fffffeL, -25),
MAKE_HEX_FLOAT(+0x1.000002p-1f, +0x1000002L, -25),
+0.5f,
MAKE_HEX_FLOAT(+0x1.fffffep-2f, +0x1fffffeL, -26),
MAKE_HEX_FLOAT(+0x1.000002p-2f, +0x1000002L, -26),
+0.25f,
MAKE_HEX_FLOAT(+0x1.fffffep-3f, +0x1fffffeL, -27),
MAKE_HEX_FLOAT(0x1.000002p-126f, 0x1000002L, -150),
+FLT_MIN,
MAKE_HEX_FLOAT(+0x0.fffffep-126f, +0x0fffffeL, -150),
MAKE_HEX_FLOAT(+0x0.000ffep-126f, +0x0000ffeL, -150),
MAKE_HEX_FLOAT(+0x0.0000fep-126f, +0x00000feL, -150),
MAKE_HEX_FLOAT(+0x0.00000ep-126f, +0x000000eL, -150),
MAKE_HEX_FLOAT(+0x0.00000cp-126f, +0x000000cL, -150),
MAKE_HEX_FLOAT(+0x0.00000ap-126f, +0x000000aL, -150),
MAKE_HEX_FLOAT(+0x0.000008p-126f, +0x0000008L, -150),
MAKE_HEX_FLOAT(+0x0.000006p-126f, +0x0000006L, -150),
MAKE_HEX_FLOAT(+0x0.000004p-126f, +0x0000004L, -150),
MAKE_HEX_FLOAT(+0x0.000002p-126f, +0x0000002L, -150),
+0.0f,
};
static const size_t specialValuesCount =
sizeof(specialValues) / sizeof(specialValues[0]);
static cl_int Test(cl_uint job_id, cl_uint thread_id, void *data);
int TestFunc_Float_Float_Float(const Func *f, MTdata d, bool relaxedMode)
{
TestInfo test_info;
cl_int error;
float maxError = 0.0f;
double maxErrorVal = 0.0;
double maxErrorVal2 = 0.0;
int skipTestingRelaxed = 0;
logFunctionInfo(f->name, sizeof(cl_float), relaxedMode);
// Init test_info
memset(&test_info, 0, sizeof(test_info));
test_info.threadCount = GetThreadCount();
test_info.subBufferSize = BUFFER_SIZE
/ (sizeof(cl_float) * RoundUpToNextPowerOfTwo(test_info.threadCount));
test_info.scale = getTestScale(sizeof(cl_float));
test_info.step = (cl_uint)test_info.subBufferSize * test_info.scale;
if (test_info.step / test_info.subBufferSize != test_info.scale)
{
// there was overflow
test_info.jobCount = 1;
}
else
{
test_info.jobCount = (cl_uint)((1ULL << 32) / test_info.step);
}
test_info.f = f;
test_info.ulps = gIsEmbedded ? f->float_embedded_ulps : f->float_ulps;
test_info.ftz =
f->ftz || gForceFTZ || 0 == (CL_FP_DENORM & gFloatCapabilities);
test_info.relaxedMode = relaxedMode;
test_info.isFDim = 0 == strcmp("fdim", f->nameInCode);
test_info.skipNanInf = test_info.isFDim && !gInfNanSupport;
test_info.isNextafter = 0 == strcmp("nextafter", f->nameInCode);
// cl_kernels aren't thread safe, so we make one for each vector size for
// every thread
for (auto i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++)
{
size_t array_size = test_info.threadCount * sizeof(cl_kernel);
test_info.k[i] = (cl_kernel *)malloc(array_size);
if (NULL == test_info.k[i])
{
vlog_error("Error: Unable to allocate storage for kernels!\n");
error = CL_OUT_OF_HOST_MEMORY;
goto exit;
}
memset(test_info.k[i], 0, array_size);
}
test_info.tinfo =
(ThreadInfo *)malloc(test_info.threadCount * sizeof(*test_info.tinfo));
if (NULL == test_info.tinfo)
{
vlog_error(
"Error: Unable to allocate storage for thread specific data.\n");
error = CL_OUT_OF_HOST_MEMORY;
goto exit;
}
memset(test_info.tinfo, 0,
test_info.threadCount * sizeof(*test_info.tinfo));
for (cl_uint i = 0; i < test_info.threadCount; i++)
{
cl_buffer_region region = {
i * test_info.subBufferSize * sizeof(cl_float),
test_info.subBufferSize * sizeof(cl_float)
};
test_info.tinfo[i].inBuf =
clCreateSubBuffer(gInBuffer, CL_MEM_READ_ONLY,
CL_BUFFER_CREATE_TYPE_REGION, &region, &error);
if (error || NULL == test_info.tinfo[i].inBuf)
{
vlog_error("Error: Unable to create sub-buffer of gInBuffer for "
"region {%zd, %zd}\n",
region.origin, region.size);
goto exit;
}
test_info.tinfo[i].inBuf2 =
clCreateSubBuffer(gInBuffer2, CL_MEM_READ_ONLY,
CL_BUFFER_CREATE_TYPE_REGION, &region, &error);
if (error || NULL == test_info.tinfo[i].inBuf2)
{
vlog_error("Error: Unable to create sub-buffer of gInBuffer2 for "
"region {%zd, %zd}\n",
region.origin, region.size);
goto exit;
}
for (auto j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++)
{
test_info.tinfo[i].outBuf[j] = clCreateSubBuffer(
gOutBuffer[j], CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION,
&region, &error);
if (error || NULL == test_info.tinfo[i].outBuf[j])
{
vlog_error("Error: Unable to create sub-buffer of "
"gOutBuffer[%d] for region {%zd, %zd}\n",
(int)j, region.origin, region.size);
goto exit;
}
}
test_info.tinfo[i].tQueue =
clCreateCommandQueue(gContext, gDevice, 0, &error);
if (NULL == test_info.tinfo[i].tQueue || error)
{
vlog_error("clCreateCommandQueue failed. (%d)\n", error);
goto exit;
}
test_info.tinfo[i].d = init_genrand(genrand_int32(d));
}
// Init the kernels
{
BuildKernelInfo build_info = {
gMinVectorSizeIndex, test_info.threadCount, test_info.k,
test_info.programs, f->nameInCode, relaxedMode
};
if ((error = ThreadPool_Do(BuildKernelFn,
gMaxVectorSizeIndex - gMinVectorSizeIndex,
&build_info)))
goto exit;
}
// Run the kernels
if (!gSkipCorrectnessTesting)
{
error = ThreadPool_Do(Test, test_info.jobCount, &test_info);
// Accumulate the arithmetic errors
for (cl_uint i = 0; i < test_info.threadCount; i++)
{
if (test_info.tinfo[i].maxError > maxError)
{
maxError = test_info.tinfo[i].maxError;
maxErrorVal = test_info.tinfo[i].maxErrorValue;
maxErrorVal2 = test_info.tinfo[i].maxErrorValue2;
}
}
if (error) goto exit;
if (gWimpyMode)
vlog("Wimp pass");
else
vlog("passed");
vlog("\t%8.2f @ {%a, %a}", maxError, maxErrorVal, maxErrorVal2);
}
vlog("\n");
exit:
// Release
for (auto i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++)
{
clReleaseProgram(test_info.programs[i]);
if (test_info.k[i])
{
for (cl_uint j = 0; j < test_info.threadCount; j++)
clReleaseKernel(test_info.k[i][j]);
free(test_info.k[i]);
}
}
if (test_info.tinfo)
{
for (cl_uint i = 0; i < test_info.threadCount; i++)
{
free_mtdata(test_info.tinfo[i].d);
clReleaseMemObject(test_info.tinfo[i].inBuf);
clReleaseMemObject(test_info.tinfo[i].inBuf2);
for (auto j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++)
clReleaseMemObject(test_info.tinfo[i].outBuf[j]);
clReleaseCommandQueue(test_info.tinfo[i].tQueue);
}
free(test_info.tinfo);
}
return error;
}
static cl_int Test(cl_uint job_id, cl_uint thread_id, void *data)
{
const TestInfo *job = (const TestInfo *)data;
size_t buffer_elements = job->subBufferSize;
size_t buffer_size = buffer_elements * sizeof(cl_float);
cl_uint base = job_id * (cl_uint)job->step;
ThreadInfo *tinfo = job->tinfo + thread_id;
fptr func = job->f->func;
int ftz = job->ftz;
bool relaxedMode = job->relaxedMode;
float ulps = getAllowedUlpError(job->f, relaxedMode);
MTdata d = tinfo->d;
cl_int error;
cl_uchar *overflow = (cl_uchar *)malloc(buffer_size);
const char *name = job->f->name;
int isFDim = job->isFDim;
int skipNanInf = job->skipNanInf;
int isNextafter = job->isNextafter;
cl_uint *t = 0;
cl_float *r = 0;
cl_float *s = 0;
cl_float *s2 = 0;
cl_int copysign_test = 0;
RoundingMode oldRoundMode;
int skipVerification = 0;
if (relaxedMode)
{
func = job->f->rfunc;
if (strcmp(name, "pow") == 0 && gFastRelaxedDerived)
{
ulps = INFINITY;
skipVerification = 1;
}
}
// start the map of the output arrays
cl_event e[VECTOR_SIZE_COUNT];
cl_uint *out[VECTOR_SIZE_COUNT];
for (auto j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++)
{
out[j] = (cl_uint *)clEnqueueMapBuffer(
tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_WRITE, 0,
buffer_size, 0, NULL, e + j, &error);
if (error || NULL == out[j])
{
vlog_error("Error: clEnqueueMapBuffer %d failed! err: %d\n", j,
error);
return error;
}
}
// Get that moving
if ((error = clFlush(tinfo->tQueue))) vlog("clFlush failed\n");
// Init input array
cl_uint *p = (cl_uint *)gIn + thread_id * buffer_elements;
cl_uint *p2 = (cl_uint *)gIn2 + thread_id * buffer_elements;
cl_uint idx = 0;
int totalSpecialValueCount = specialValuesCount * specialValuesCount;
int lastSpecialJobIndex = (totalSpecialValueCount - 1) / buffer_elements;
if (job_id <= (cl_uint)lastSpecialJobIndex)
{ // test edge cases
float *fp = (float *)p;
float *fp2 = (float *)p2;
uint32_t x, y;
x = (job_id * buffer_elements) % specialValuesCount;
y = (job_id * buffer_elements) / specialValuesCount;
for (; idx < buffer_elements; idx++)
{
fp[idx] = specialValues[x];
fp2[idx] = specialValues[y];
++x;
if (x >= specialValuesCount)
{
x = 0;
y++;
if (y >= specialValuesCount) break;
}
}
}
// Init any remaining values.
for (; idx < buffer_elements; idx++)
{
p[idx] = genrand_int32(d);
p2[idx] = genrand_int32(d);
}
if ((error = clEnqueueWriteBuffer(tinfo->tQueue, tinfo->inBuf, CL_FALSE, 0,
buffer_size, p, 0, NULL, NULL)))
{
vlog_error("Error: clEnqueueWriteBuffer failed! err: %d\n", error);
goto exit;
}
if ((error = clEnqueueWriteBuffer(tinfo->tQueue, tinfo->inBuf2, CL_FALSE, 0,
buffer_size, p2, 0, NULL, NULL)))
{
vlog_error("Error: clEnqueueWriteBuffer failed! err: %d\n", error);
goto exit;
}
for (auto j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++)
{
// Wait for the map to finish
if ((error = clWaitForEvents(1, e + j)))
{
vlog_error("Error: clWaitForEvents failed! err: %d\n", error);
goto exit;
}
if ((error = clReleaseEvent(e[j])))
{
vlog_error("Error: clReleaseEvent failed! err: %d\n", error);
goto exit;
}
// Fill the result buffer with garbage, so that old results don't carry
// over
uint32_t pattern = 0xffffdead;
memset_pattern4(out[j], &pattern, buffer_size);
if ((error = clEnqueueUnmapMemObject(tinfo->tQueue, tinfo->outBuf[j],
out[j], 0, NULL, NULL)))
{
vlog_error("Error: clEnqueueMapBuffer failed! err: %d\n", error);
goto exit;
}
// run the kernel
size_t vectorCount =
(buffer_elements + sizeValues[j] - 1) / sizeValues[j];
cl_kernel kernel = job->k[j][thread_id]; // each worker thread has its
// own copy of the cl_kernel
cl_program program = job->programs[j];
if ((error = clSetKernelArg(kernel, 0, sizeof(tinfo->outBuf[j]),
&tinfo->outBuf[j])))
{
LogBuildError(program);
return error;
}
if ((error = clSetKernelArg(kernel, 1, sizeof(tinfo->inBuf),
&tinfo->inBuf)))
{
LogBuildError(program);
return error;
}
if ((error = clSetKernelArg(kernel, 2, sizeof(tinfo->inBuf2),
&tinfo->inBuf2)))
{
LogBuildError(program);
return error;
}
if ((error = clEnqueueNDRangeKernel(tinfo->tQueue, kernel, 1, NULL,
&vectorCount, NULL, 0, NULL, NULL)))
{
vlog_error("FAILED -- could not execute kernel\n");
goto exit;
}
}
// Get that moving
if ((error = clFlush(tinfo->tQueue))) vlog("clFlush 2 failed\n");
if (gSkipCorrectnessTesting)
{
if ((error = clFinish(tinfo->tQueue)))
{
vlog_error("Error: clFinish failed! err: %d\n", error);
goto exit;
}
free(overflow);
return CL_SUCCESS;
}
FPU_mode_type oldMode;
oldRoundMode = kRoundToNearestEven;
if (isFDim)
{
// Calculate the correctly rounded reference result
memset(&oldMode, 0, sizeof(oldMode));
if (ftz) ForceFTZ(&oldMode);
// Set the rounding mode to match the device
if (gIsInRTZMode) oldRoundMode = set_round(kRoundTowardZero, kfloat);
}
if (!strcmp(name, "copysign")) copysign_test = 1;
#define ref_func(s, s2) (copysign_test ? func.f_ff_f(s, s2) : func.f_ff(s, s2))
// Calculate the correctly rounded reference result
r = (float *)gOut_Ref + thread_id * buffer_elements;
s = (float *)gIn + thread_id * buffer_elements;
s2 = (float *)gIn2 + thread_id * buffer_elements;
if (skipNanInf)
{
for (size_t j = 0; j < buffer_elements; j++)
{
feclearexcept(FE_OVERFLOW);
r[j] = (float)ref_func(s[j], s2[j]);
overflow[j] =
FE_OVERFLOW == (FE_OVERFLOW & fetestexcept(FE_OVERFLOW));
}
}
else
{
for (size_t j = 0; j < buffer_elements; j++)
r[j] = (float)ref_func(s[j], s2[j]);
}
if (isFDim && ftz) RestoreFPState(&oldMode);
// Read the data back -- no need to wait for the first N-1 buffers but wait
// for the last buffer. This is an in order queue.
for (auto j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++)
{
cl_bool blocking = (j + 1 < gMaxVectorSizeIndex) ? CL_FALSE : CL_TRUE;
out[j] = (cl_uint *)clEnqueueMapBuffer(
tinfo->tQueue, tinfo->outBuf[j], blocking, CL_MAP_READ, 0,
buffer_size, 0, NULL, NULL, &error);
if (error || NULL == out[j])
{
vlog_error("Error: clEnqueueMapBuffer %d failed! err: %d\n", j,
error);
goto exit;
}
}
if (!skipVerification)
{
// Verify data
t = (cl_uint *)r;
for (size_t j = 0; j < buffer_elements; j++)
{
for (auto k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++)
{
cl_uint *q = out[k];
// If we aren't getting the correctly rounded result
if (t[j] != q[j])
{
float test = ((float *)q)[j];
double correct = ref_func(s[j], s2[j]);
// Per section 10 paragraph 6, accept any result if an input
// or output is a infinity or NaN or overflow As per
// OpenCL 2.0 spec, section 5.8.4.3, enabling
// fast-relaxed-math mode also enables -cl-finite-math-only
// optimization. This optimization allows to assume that
// arguments and results are not NaNs or +/-INFs. Hence,
// accept any result if inputs or results are NaNs or INFs.
if (relaxedMode || skipNanInf)
{
if (skipNanInf && overflow[j]) continue;
// Note: no double rounding here. Reference functions
// calculate in single precision.
if (IsFloatInfinity(correct) || IsFloatNaN(correct)
|| IsFloatInfinity(s2[j]) || IsFloatNaN(s2[j])
|| IsFloatInfinity(s[j]) || IsFloatNaN(s[j]))
continue;
}
float err = Ulp_Error(test, correct);
int fail = !(fabsf(err) <= ulps);
if (fail && ftz)
{
// retry per section 6.5.3.2
if (IsFloatResultSubnormal(correct, ulps))
{
fail = fail && (test != 0.0f);
if (!fail) err = 0.0f;
}
// nextafter on FTZ platforms may return the smallest
// normal float (2^-126) given a denormal or a zero
// as the first argument. The rationale here is that
// nextafter flushes the argument to zero and then
// returns the next representable number in the
// direction of the second argument, and since
// denorms are considered as zero, the smallest
// normal number is the next representable number.
// In which case, it should have the same sign as the
// second argument.
if (isNextafter)
{
if (IsFloatSubnormal(s[j]) || s[j] == 0.0f)
{
float value = copysignf(twoToMinus126, s2[j]);
fail = fail && (test != value);
if (!fail) err = 0.0f;
}
}
else
{
// retry per section 6.5.3.3
if (IsFloatSubnormal(s[j]))
{
double correct2, correct3;
float err2, err3;
if (skipNanInf) feclearexcept(FE_OVERFLOW);
correct2 = ref_func(0.0, s2[j]);
correct3 = ref_func(-0.0, s2[j]);
// Per section 10 paragraph 6, accept any result
// if an input or output is a infinity or NaN or
// overflow As per OpenCL 2.0 spec,
// section 5.8.4.3, enabling fast-relaxed-math
// mode also enables -cl-finite-math-only
// optimization. This optimization allows to
// assume that arguments and results are not
// NaNs or +/-INFs. Hence, accept any result if
// inputs or results are NaNs or INFs.
if (relaxedMode || skipNanInf)
{
if (fetestexcept(FE_OVERFLOW) && skipNanInf)
continue;
// Note: no double rounding here. Reference
// functions calculate in single precision.
if (IsFloatInfinity(correct2)
|| IsFloatNaN(correct2)
|| IsFloatInfinity(correct3)
|| IsFloatNaN(correct3))
continue;
}
err2 = Ulp_Error(test, correct2);
err3 = Ulp_Error(test, correct3);
fail = fail
&& ((!(fabsf(err2) <= ulps))
&& (!(fabsf(err3) <= ulps)));
if (fabsf(err2) < fabsf(err)) err = err2;
if (fabsf(err3) < fabsf(err)) err = err3;
// retry per section 6.5.3.4
if (IsFloatResultSubnormal(correct2, ulps)
|| IsFloatResultSubnormal(correct3, ulps))
{
fail = fail && (test != 0.0f);
if (!fail) err = 0.0f;
}
// try with both args as zero
if (IsFloatSubnormal(s2[j]))
{
double correct4, correct5;
float err4, err5;
if (skipNanInf) feclearexcept(FE_OVERFLOW);
correct2 = ref_func(0.0, 0.0);
correct3 = ref_func(-0.0, 0.0);
correct4 = ref_func(0.0, -0.0);
correct5 = ref_func(-0.0, -0.0);
// Per section 10 paragraph 6, accept any
// result if an input or output is a
// infinity or NaN or overflow As per
// OpenCL 2.0 spec, section 5.8.4.3,
// enabling fast-relaxed-math mode also
// enables -cl-finite-math-only
// optimization. This optimization allows to
// assume that arguments and results are not
// NaNs or +/-INFs. Hence, accept any result
// if inputs or results are NaNs or INFs.
if (relaxedMode || skipNanInf)
{
if (fetestexcept(FE_OVERFLOW)
&& skipNanInf)
continue;
// Note: no double rounding here.
// Reference functions calculate in
// single precision.
if (IsFloatInfinity(correct2)
|| IsFloatNaN(correct2)
|| IsFloatInfinity(correct3)
|| IsFloatNaN(correct3)
|| IsFloatInfinity(correct4)
|| IsFloatNaN(correct4)
|| IsFloatInfinity(correct5)
|| IsFloatNaN(correct5))
continue;
}
err2 = Ulp_Error(test, correct2);
err3 = Ulp_Error(test, correct3);
err4 = Ulp_Error(test, correct4);
err5 = Ulp_Error(test, correct5);
fail = fail
&& ((!(fabsf(err2) <= ulps))
&& (!(fabsf(err3) <= ulps))
&& (!(fabsf(err4) <= ulps))
&& (!(fabsf(err5) <= ulps)));
if (fabsf(err2) < fabsf(err)) err = err2;
if (fabsf(err3) < fabsf(err)) err = err3;
if (fabsf(err4) < fabsf(err)) err = err4;
if (fabsf(err5) < fabsf(err)) err = err5;
// retry per section 6.5.3.4
if (IsFloatResultSubnormal(correct2, ulps)
|| IsFloatResultSubnormal(correct3,
ulps)
|| IsFloatResultSubnormal(correct4,
ulps)
|| IsFloatResultSubnormal(correct5,
ulps))
{
fail = fail && (test != 0.0f);
if (!fail) err = 0.0f;
}
}
}
else if (IsFloatSubnormal(s2[j]))
{
double correct2, correct3;
float err2, err3;
if (skipNanInf) feclearexcept(FE_OVERFLOW);
correct2 = ref_func(s[j], 0.0);
correct3 = ref_func(s[j], -0.0);
// Per section 10 paragraph 6, accept any result
// if an input or output is a infinity or NaN or
// overflow As per OpenCL 2.0 spec,
// section 5.8.4.3, enabling fast-relaxed-math
// mode also enables -cl-finite-math-only
// optimization. This optimization allows to
// assume that arguments and results are not
// NaNs or +/-INFs. Hence, accept any result if
// inputs or results are NaNs or INFs.
if (relaxedMode || skipNanInf)
{
// Note: no double rounding here. Reference
// functions calculate in single precision.
if (overflow[j] && skipNanInf) continue;
if (IsFloatInfinity(correct2)
|| IsFloatNaN(correct2)
|| IsFloatInfinity(correct3)
|| IsFloatNaN(correct3))
continue;
}
err2 = Ulp_Error(test, correct2);
err3 = Ulp_Error(test, correct3);
fail = fail
&& ((!(fabsf(err2) <= ulps))
&& (!(fabsf(err3) <= ulps)));
if (fabsf(err2) < fabsf(err)) err = err2;
if (fabsf(err3) < fabsf(err)) err = err3;
// retry per section 6.5.3.4
if (IsFloatResultSubnormal(correct2, ulps)
|| IsFloatResultSubnormal(correct3, ulps))
{
fail = fail && (test != 0.0f);
if (!fail) err = 0.0f;
}
}
}
}
if (fabsf(err) > tinfo->maxError)
{
tinfo->maxError = fabsf(err);
tinfo->maxErrorValue = s[j];
tinfo->maxErrorValue2 = s2[j];
}
if (fail)
{
vlog_error(
"\nERROR: %s%s: %f ulp error at {%a (0x%x), %a "
"(0x%x)}: *%a vs. %a (0x%8.8x) at index: %d\n",
name, sizeNames[k], err, s[j], ((cl_uint *)s)[j],
s2[j], ((cl_uint *)s2)[j], r[j], test,
((cl_uint *)&test)[0], j);
error = -1;
goto exit;
}
}
}
}
}
if (isFDim && gIsInRTZMode) (void)set_round(oldRoundMode, kfloat);
for (auto j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++)
{
if ((error = clEnqueueUnmapMemObject(tinfo->tQueue, tinfo->outBuf[j],
out[j], 0, NULL, NULL)))
{
vlog_error("Error: clEnqueueUnmapMemObject %d failed 2! err: %d\n",
j, error);
return error;
}
}
if ((error = clFlush(tinfo->tQueue))) vlog("clFlush 3 failed\n");
if (0 == (base & 0x0fffffff))
{
if (gVerboseBruteForce)
{
vlog("base:%14u step:%10u scale:%10zu buf_elements:%10u ulps:%5.3f "
"ThreadCount:%2u\n",
base, job->step, job->scale, buffer_elements, job->ulps,
job->threadCount);
}
else
{
vlog(".");
}
fflush(stdout);
}
exit:
if (overflow) free(overflow);
return error;
}
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