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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 "harness/compat.h"
#include "testBase.h"
#include "harness/testHarness.h"
#include "harness/typeWrappers.h"
#include "harness/conversions.h"
#include "harness/errorHelpers.h"
#include <float.h>
const char *crossKernelSource =
"__kernel void sample_test(__global float4 *sourceA, __global float4 *sourceB, __global float4 *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" destValues[tid] = cross( sourceA[tid], sourceB[tid] );\n"
"\n"
"}\n" ;
const char *crossKernelSourceV3 =
"__kernel void sample_test(__global float *sourceA, __global float *sourceB, __global float *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" vstore3( cross( vload3( tid, sourceA), vload3( tid, sourceB) ), tid, destValues );\n"
"\n"
"}\n";
const char *twoToFloatKernelPattern =
"__kernel void sample_test(__global float%s *sourceA, __global float%s *sourceB, __global float *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" destValues[tid] = %s( sourceA[tid], sourceB[tid] );\n"
"\n"
"}\n";
const char *twoToFloatKernelPatternV3 =
"__kernel void sample_test(__global float%s *sourceA, __global float%s *sourceB, __global float *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" destValues[tid] = %s( vload3( tid, (__global float*) sourceA), vload3( tid, (__global float*) sourceB) );\n"
"\n"
"}\n";
const char *oneToFloatKernelPattern =
"__kernel void sample_test(__global float%s *sourceA, __global float *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" destValues[tid] = %s( sourceA[tid] );\n"
"\n"
"}\n";
const char *oneToFloatKernelPatternV3 =
"__kernel void sample_test(__global float%s *sourceA, __global float *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" destValues[tid] = %s( vload3( tid, (__global float*) sourceA) );\n"
"\n"
"}\n";
const char *oneToOneKernelPattern =
"__kernel void sample_test(__global float%s *sourceA, __global float%s *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" destValues[tid] = %s( sourceA[tid] );\n"
"\n"
"}\n";
const char *oneToOneKernelPatternV3 =
"__kernel void sample_test(__global float%s *sourceA, __global float%s *destValues)\n"
"{\n"
" int tid = get_global_id(0);\n"
" vstore3( %s( vload3( tid, (__global float*) sourceA) ), tid, (__global float*) destValues );\n"
"\n"
"}\n";
#define TEST_SIZE (1 << 20)
double verifyFastDistance( float *srcA, float *srcB, size_t vecSize );
double verifyFastLength( float *srcA, size_t vecSize );
void vector2string( char *string, float *vector, size_t elements )
{
*string++ = '{';
*string++ = ' ';
string += sprintf( string, "%a", vector[0] );
size_t i;
for( i = 1; i < elements; i++ )
string += sprintf( string, ", %a", vector[i] );
*string++ = ' ';
*string++ = '}';
*string = '\0';
}
void fillWithTrickyNumbers( float *aVectors, float *bVectors, size_t vecSize )
{
static const cl_float trickyValues[] = { -FLT_EPSILON, FLT_EPSILON,
MAKE_HEX_FLOAT(0x1.0p63f, 0x1L, 63), MAKE_HEX_FLOAT(0x1.8p63f, 0x18L, 59), MAKE_HEX_FLOAT(0x1.0p64f, 0x1L, 64), MAKE_HEX_FLOAT(-0x1.0p63f, -0x1L, 63), MAKE_HEX_FLOAT(-0x1.8p-63f, -0x18L, -67), MAKE_HEX_FLOAT(-0x1.0p64f, -0x1L, 64),
MAKE_HEX_FLOAT(0x1.0p-63f, 0x1L, -63), MAKE_HEX_FLOAT(0x1.8p-63f, 0x18L, -67), MAKE_HEX_FLOAT(0x1.0p-64f, 0x1L, -64), MAKE_HEX_FLOAT(-0x1.0p-63f, -0x1L, -63), MAKE_HEX_FLOAT(-0x1.8p-63f, -0x18L, -67), MAKE_HEX_FLOAT(-0x1.0p-64f, -0x1L, -64),
FLT_MAX / 2.f, -FLT_MAX / 2.f, INFINITY, -INFINITY, 0.f, -0.f };
static const size_t trickyCount = sizeof( trickyValues ) / sizeof( trickyValues[0] );
static const size_t stride[4] = {1, trickyCount, trickyCount*trickyCount, trickyCount*trickyCount*trickyCount };
size_t i, j, k;
for( j = 0; j < vecSize; j++ )
for( k = 0; k < vecSize; k++ )
for( i = 0; i < trickyCount; i++ )
aVectors[ j + stride[j] * (i + k*trickyCount)*vecSize] = trickyValues[i];
if( bVectors )
{
size_t copySize = vecSize * vecSize * trickyCount;
memset( bVectors, 0, sizeof(float) * copySize );
memset( aVectors + copySize, 0, sizeof(float) * copySize );
memcpy( bVectors + copySize, aVectors, sizeof(float) * copySize );
}
}
void cross_product( const float *vecA, const float *vecB, float *outVector, float *errorTolerances, float ulpTolerance )
{
outVector[ 0 ] = ( vecA[ 1 ] * vecB[ 2 ] ) - ( vecA[ 2 ] * vecB[ 1 ] );
outVector[ 1 ] = ( vecA[ 2 ] * vecB[ 0 ] ) - ( vecA[ 0 ] * vecB[ 2 ] );
outVector[ 2 ] = ( vecA[ 0 ] * vecB[ 1 ] ) - ( vecA[ 1 ] * vecB[ 0 ] );
outVector[ 3 ] = 0.0f;
errorTolerances[ 0 ] = fmaxf( fabsf( vecA[ 1 ] ), fmaxf( fabsf( vecB[ 2 ] ), fmaxf( fabsf( vecA[ 2 ] ), fabsf( vecB[ 1 ] ) ) ) );
errorTolerances[ 1 ] = fmaxf( fabsf( vecA[ 2 ] ), fmaxf( fabsf( vecB[ 0 ] ), fmaxf( fabsf( vecA[ 0 ] ), fabsf( vecB[ 2 ] ) ) ) );
errorTolerances[ 2 ] = fmaxf( fabsf( vecA[ 0 ] ), fmaxf( fabsf( vecB[ 1 ] ), fmaxf( fabsf( vecA[ 1 ] ), fabsf( vecB[ 0 ] ) ) ) );
errorTolerances[ 0 ] = errorTolerances[ 0 ] * errorTolerances[ 0 ] * ( ulpTolerance * FLT_EPSILON ); // This gives us max squared times ulp tolerance, i.e. the worst-case expected variance we could expect from this result
errorTolerances[ 1 ] = errorTolerances[ 1 ] * errorTolerances[ 1 ] * ( ulpTolerance * FLT_EPSILON );
errorTolerances[ 2 ] = errorTolerances[ 2 ] * errorTolerances[ 2 ] * ( ulpTolerance * FLT_EPSILON );
}
int test_geom_cross(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements )
{
int vecsize;
RandomSeed seed(gRandomSeed);
/* Get the default rounding mode */
cl_device_fp_config defaultRoundingMode = get_default_rounding_mode(deviceID);
if( 0 == defaultRoundingMode )
return -1;
for(vecsize = 3; vecsize <= 4; ++vecsize)
{
clProgramWrapper program;
clKernelWrapper kernel;
clMemWrapper streams[3];
BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE * vecsize));
BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE * vecsize));
BufferOwningPtr<cl_float> C(malloc(sizeof(cl_float) * TEST_SIZE * vecsize));
cl_float testVector[4];
int error, i;
cl_float *inDataA = A;
cl_float *inDataB = B;
cl_float *outData = C;
size_t threads[1], localThreads[1];
/* Create kernels */
if( create_single_kernel_helper( context, &program, &kernel, 1, vecsize == 3 ? &crossKernelSourceV3 : &crossKernelSource, "sample_test" ) )
return -1;
/* Generate some streams. Note: deliberately do some random data in w to verify that it gets ignored */
for( i = 0; i < TEST_SIZE * vecsize; i++ )
{
inDataA[ i ] = get_random_float( -512.f, 512.f, seed );
inDataB[ i ] = get_random_float( -512.f, 512.f, seed );
}
fillWithTrickyNumbers( inDataA, inDataB, vecsize );
streams[0] = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * vecsize * TEST_SIZE,
inDataA, NULL);
if( streams[0] == NULL )
{
log_error("ERROR: Creating input array A failed!\n");
return -1;
}
streams[1] = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * vecsize * TEST_SIZE,
inDataB, NULL);
if( streams[1] == NULL )
{
log_error("ERROR: Creating input array B failed!\n");
return -1;
}
streams[2] =
clCreateBuffer(context, CL_MEM_READ_WRITE,
sizeof(cl_float) * vecsize * TEST_SIZE, NULL, NULL);
if( streams[2] == NULL )
{
log_error("ERROR: Creating output array failed!\n");
return -1;
}
/* Assign streams and execute */
for( i = 0; i < 3; i++ )
{
error = clSetKernelArg(kernel, i, sizeof( streams[i] ), &streams[i]);
test_error( error, "Unable to set indexed kernel arguments" );
}
/* Run the kernel */
threads[0] = TEST_SIZE;
error = get_max_common_work_group_size( context, kernel, threads[0], &localThreads[0] );
test_error( error, "Unable to get work group size to use" );
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads, localThreads, 0, NULL, NULL );
test_error( error, "Unable to execute test kernel" );
/* Now get the results */
error = clEnqueueReadBuffer( queue, streams[2], true, 0, sizeof( cl_float ) * TEST_SIZE * vecsize, outData, 0, NULL, NULL );
test_error( error, "Unable to read output array!" );
/* And verify! */
for( i = 0; i < TEST_SIZE; i++ )
{
float errorTolerances[ 4 ];
// On an embedded device w/ round-to-zero, 3 ulps is the worst-case tolerance for cross product
cross_product( inDataA + i * vecsize, inDataB + i * vecsize, testVector, errorTolerances, 3.f );
// RTZ devices accrue approximately double the amount of error per operation. Allow for that.
if( defaultRoundingMode == CL_FP_ROUND_TO_ZERO )
{
errorTolerances[0] *= 2.0f;
errorTolerances[1] *= 2.0f;
errorTolerances[2] *= 2.0f;
errorTolerances[3] *= 2.0f;
}
float errs[] = { fabsf( testVector[ 0 ] - outData[ i * vecsize + 0 ] ),
fabsf( testVector[ 1 ] - outData[ i * vecsize + 1 ] ),
fabsf( testVector[ 2 ] - outData[ i * vecsize + 2 ] ) };
if( errs[ 0 ] > errorTolerances[ 0 ] || errs[ 1 ] > errorTolerances[ 1 ] || errs[ 2 ] > errorTolerances[ 2 ] )
{
log_error( "ERROR: Data sample %d does not validate! Expected (%a,%a,%a,%a), got (%a,%a,%a,%a)\n",
i, testVector[0], testVector[1], testVector[2], testVector[3],
outData[i*vecsize], outData[i*vecsize+1], outData[i*vecsize+2], outData[i*vecsize+3] );
log_error( " Input: (%a %a %a) and (%a %a %a)\n",
inDataA[ i * vecsize + 0 ], inDataA[ i * vecsize + 1 ], inDataA[ i * vecsize + 2 ],
inDataB[ i * vecsize + 0 ], inDataB[ i * vecsize + 1 ], inDataB[ i * vecsize + 2 ] );
log_error( " Errors: (%a out of %a), (%a out of %a), (%a out of %a)\n",
errs[ 0 ], errorTolerances[ 0 ], errs[ 1 ], errorTolerances[ 1 ], errs[ 2 ], errorTolerances[ 2 ] );
log_error(" ulp %f\n", Ulp_Error( outData[ i * vecsize + 1 ], testVector[ 1 ] ) );
return -1;
}
}
} // for(vecsize=...
if(!is_extension_available(deviceID, "cl_khr_fp64")) {
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
return 0;
} else {
log_info("Testing doubles...\n");
return test_geom_cross_double( deviceID, context, queue, num_elements, seed);
}
}
float getMaxValue( float vecA[], float vecB[], size_t vecSize )
{
float a = fmaxf( fabsf( vecA[ 0 ] ), fabsf( vecB[ 0 ] ) );
for( size_t i = 1; i < vecSize; i++ )
a = fmaxf( fabsf( vecA[ i ] ), fmaxf( fabsf( vecB[ i ] ), a ) );
return a;
}
typedef double (*twoToFloatVerifyFn)( float *srcA, float *srcB, size_t vecSize );
int test_twoToFloat_kernel(cl_command_queue queue, cl_context context, const char *fnName,
size_t vecSize, twoToFloatVerifyFn verifyFn, float ulpLimit, MTdata d )
{
clProgramWrapper program;
clKernelWrapper kernel;
clMemWrapper streams[3];
int error;
size_t i, threads[1], localThreads[1];
char kernelSource[10240];
char *programPtr;
char sizeNames[][4] = { "", "2", "3", "4", "", "", "", "8", "", "", "", "", "", "", "", "16" };
int hasInfNan = 1;
cl_device_id device = NULL;
error = clGetCommandQueueInfo( queue, CL_QUEUE_DEVICE, sizeof( device ), &device, NULL );
test_error( error, "Unable to get command queue device" );
/* Check for embedded devices doing nutty stuff */
error = clGetDeviceInfo( device, CL_DEVICE_PROFILE, sizeof( kernelSource ), kernelSource, NULL );
test_error( error, "Unable to get device profile" );
if( 0 == strcmp( kernelSource, "EMBEDDED_PROFILE" ) )
{
cl_device_fp_config config = 0;
error = clGetDeviceInfo( device, CL_DEVICE_SINGLE_FP_CONFIG, sizeof( config ), &config, NULL );
test_error( error, "Unable to get CL_DEVICE_SINGLE_FP_CONFIG" );
if( CL_FP_ROUND_TO_ZERO == (config & (CL_FP_ROUND_TO_NEAREST|CL_FP_ROUND_TO_ZERO)))
ulpLimit *= 2.0f; // rtz operations average twice the accrued error of rte operations
if( 0 == (config & CL_FP_INF_NAN) )
hasInfNan = 0;
}
BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE * 4));
BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE * 4));
BufferOwningPtr<cl_float> C(malloc(sizeof(cl_float) * TEST_SIZE));
cl_float *inDataA = A;
cl_float *inDataB = B;
cl_float *outData = C;
/* Create the source */
sprintf( kernelSource, vecSize == 3 ? twoToFloatKernelPatternV3 : twoToFloatKernelPattern, sizeNames[vecSize-1], sizeNames[vecSize-1], fnName );
/* Create kernels */
programPtr = kernelSource;
if( create_single_kernel_helper( context, &program, &kernel, 1, (const char **)&programPtr, "sample_test" ) )
{
return -1;
}
/* Generate some streams */
for( i = 0; i < TEST_SIZE * vecSize; i++ )
{
inDataA[ i ] = get_random_float( -512.f, 512.f, d );
inDataB[ i ] = get_random_float( -512.f, 512.f, d );
}
fillWithTrickyNumbers( inDataA, inDataB, vecSize );
/* Clamp values to be in range for fast_ functions */
if( verifyFn == verifyFastDistance )
{
for( i = 0; i < TEST_SIZE * vecSize; i++ )
{
if( fabsf( inDataA[i] ) > MAKE_HEX_FLOAT(0x1.0p62f, 0x1L, 62) || fabsf( inDataA[i] ) < MAKE_HEX_FLOAT(0x1.0p-62f, 0x1L, -62) )
inDataA[ i ] = get_random_float( -512.f, 512.f, d );
if( fabsf( inDataB[i] ) > MAKE_HEX_FLOAT(0x1.0p62f, 0x1L, 62) || fabsf( inDataB[i] ) < MAKE_HEX_FLOAT(0x1.0p-62f, 0x1L, -62) )
inDataB[ i ] = get_random_float( -512.f, 512.f, d );
}
}
streams[0] =
clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * vecSize * TEST_SIZE, inDataA, NULL);
if( streams[0] == NULL )
{
log_error("ERROR: Creating input array A failed!\n");
return -1;
}
streams[1] =
clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * vecSize * TEST_SIZE, inDataB, NULL);
if( streams[1] == NULL )
{
log_error("ERROR: Creating input array B failed!\n");
return -1;
}
streams[2] = clCreateBuffer(context, CL_MEM_READ_WRITE,
sizeof(cl_float) * TEST_SIZE, NULL, NULL);
if( streams[2] == NULL )
{
log_error("ERROR: Creating output array failed!\n");
return -1;
}
/* Assign streams and execute */
for( i = 0; i < 3; i++ )
{
error = clSetKernelArg(kernel, (int)i, sizeof( streams[i] ), &streams[i]);
test_error( error, "Unable to set indexed kernel arguments" );
}
/* Run the kernel */
threads[0] = TEST_SIZE;
error = get_max_common_work_group_size( context, kernel, threads[0], &localThreads[0] );
test_error( error, "Unable to get work group size to use" );
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads, localThreads, 0, NULL, NULL );
test_error( error, "Unable to execute test kernel" );
/* Now get the results */
error = clEnqueueReadBuffer( queue, streams[2], true, 0, sizeof( cl_float ) * TEST_SIZE, outData, 0, NULL, NULL );
test_error( error, "Unable to read output array!" );
/* And verify! */
int skipCount = 0;
for( i = 0; i < TEST_SIZE; i++ )
{
cl_float *src1 = inDataA + i * vecSize;
cl_float *src2 = inDataB + i * vecSize;
double expected = verifyFn( src1, src2, vecSize );
if( (float) expected != outData[ i ] )
{
if( isnan(expected) && isnan( outData[i] ) )
continue;
if( ! hasInfNan )
{
size_t ii;
for( ii = 0; ii < vecSize; ii++ )
{
if( ! isfinite( src1[ii] ) || ! isfinite( src2[ii] ) )
{
skipCount++;
continue;
}
}
if( ! isfinite( (cl_float) expected ) )
{
skipCount++;
continue;
}
}
if( ulpLimit < 0 )
{
// Limit below zero means we need to test via a computed error (like cross product does)
float maxValue =
getMaxValue( inDataA + i * vecSize, inDataB + i * vecSize,vecSize );
// In this case (dot is the only one that gets here), the ulp is 2*vecSize - 1 (n + n-1 max # of errors)
float errorTolerance = maxValue * maxValue * ( 2.f * (float)vecSize - 1.f ) * FLT_EPSILON;
// Limit below zero means test via epsilon instead
double error =
fabs( (double)expected - (double)outData[ i ] );
if( error > errorTolerance )
{
log_error( "ERROR: Data sample %d at size %d does not validate! Expected (%a), got (%a), sources (%a and %a) error of %g against tolerance %g\n",
(int)i, (int)vecSize, expected,
outData[ i ],
inDataA[i*vecSize],
inDataB[i*vecSize],
(float)error,
(float)errorTolerance );
char vecA[1000], vecB[1000];
vector2string( vecA, inDataA +i * vecSize, vecSize );
vector2string( vecB, inDataB + i * vecSize, vecSize );
log_error( "\tvector A: %s, vector B: %s\n", vecA, vecB );
return -1;
}
}
else
{
float error = Ulp_Error( outData[ i ], expected );
if( fabsf(error) > ulpLimit )
{
log_error( "ERROR: Data sample %d at size %d does not validate! Expected (%a), got (%a), sources (%a and %a) ulp of %f\n",
(int)i, (int)vecSize, expected, outData[ i ], inDataA[i*vecSize], inDataB[i*vecSize], error );
char vecA[1000], vecB[1000];
vector2string( vecA, inDataA + i * vecSize, vecSize );
vector2string( vecB, inDataB + i * vecSize, vecSize );
log_error( "\tvector A: %s, vector B: %s\n", vecA, vecB );
return -1;
}
}
}
}
if( skipCount )
log_info( "Skipped %d tests out of %d because they contained Infs or NaNs\n\tEMBEDDED_PROFILE Device does not support CL_FP_INF_NAN\n", skipCount, TEST_SIZE );
return 0;
}
double verifyDot( float *srcA, float *srcB, size_t vecSize )
{
double total = 0.f;
for( unsigned int i = 0; i < vecSize; i++ )
total += (double)srcA[ i ] * (double)srcB[ i ];
return total;
}
int test_geom_dot(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed(gRandomSeed);
for( size = 0; sizes[ size ] != 0 ; size++ )
{
if( test_twoToFloat_kernel( queue, context, "dot", sizes[size], verifyDot, -1.0f /*magic value*/, seed ) != 0 )
{
log_error( " dot vector size %d FAILED\n", (int)sizes[ size ] );
retVal = -1;
}
}
if (retVal)
return retVal;
if(!is_extension_available(deviceID, "cl_khr_fp64"))
{
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
return 0;
}
log_info("Testing doubles...\n");
return test_geom_dot_double( deviceID, context, queue, num_elements, seed);
}
double verifyFastDistance( float *srcA, float *srcB, size_t vecSize )
{
double total = 0, value;
unsigned int i;
// We calculate the distance as a double, to try and make up for the fact that
// the GPU has better precision distance since it's a single op
for( i = 0; i < vecSize; i++ )
{
value = (double)srcA[i] - (double)srcB[i];
total += value * value;
}
return sqrt( total );
}
int test_geom_fast_distance(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed(gRandomSeed);
for( size = 0; sizes[ size ] != 0 ; size++ )
{
float maxUlps = 8192.0f + // error in sqrt
( 1.5f * (float) sizes[size] + // cumulative error for multiplications (a-b+0.5ulp)**2 = (a-b)**2 + a*0.5ulp + b*0.5 ulp + 0.5 ulp for multiplication
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
if( test_twoToFloat_kernel( queue, context, "fast_distance",
sizes[ size ], verifyFastDistance,
maxUlps, seed ) != 0 )
{
log_error( " fast_distance vector size %d FAILED\n",
(int)sizes[ size ] );
retVal = -1;
}
else
{
log_info( " fast_distance vector size %d passed\n",
(int)sizes[ size ] );
}
}
return retVal;
}
double verifyDistance( float *srcA, float *srcB, size_t vecSize )
{
double total = 0, value;
unsigned int i;
// We calculate the distance as a double, to try and make up for the fact that
// the GPU has better precision distance since it's a single op
for( i = 0; i < vecSize; i++ )
{
value = (double)srcA[i] - (double)srcB[i];
total += value * value;
}
return sqrt( total );
}
int test_geom_distance(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed(gRandomSeed );
for( size = 0; sizes[ size ] != 0 ; size++ )
{
float maxUlps = 3.0f + // error in sqrt
( 1.5f * (float) sizes[size] + // cumulative error for multiplications (a-b+0.5ulp)**2 = (a-b)**2 + a*0.5ulp + b*0.5 ulp + 0.5 ulp for multiplication
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
if( test_twoToFloat_kernel( queue, context, "distance", sizes[ size ], verifyDistance, maxUlps, seed ) != 0 )
{
log_error( " distance vector size %d FAILED\n",
(int)sizes[ size ] );
retVal = -1;
}
else
{
log_info( " distance vector size %d passed\n", (int)sizes[ size ] );
}
}
if (retVal)
return retVal;
if(!is_extension_available(deviceID, "cl_khr_fp64"))
{
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
return 0;
} else {
log_info("Testing doubles...\n");
return test_geom_distance_double( deviceID, context, queue, num_elements, seed);
}
}
typedef double (*oneToFloatVerifyFn)( float *srcA, size_t vecSize );
int test_oneToFloat_kernel(cl_command_queue queue, cl_context context, const char *fnName,
size_t vecSize, oneToFloatVerifyFn verifyFn, float ulpLimit, MTdata d )
{
clProgramWrapper program;
clKernelWrapper kernel;
clMemWrapper streams[2];
BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE * 4));
BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE));
int error;
size_t i, threads[1], localThreads[1];
char kernelSource[10240];
char *programPtr;
char sizeNames[][4] = { "", "2", "3", "4", "", "", "", "8", "", "", "", "", "", "", "", "16" };
cl_float *inDataA = A;
cl_float *outData = B;
/* Create the source */
sprintf( kernelSource, vecSize == 3? oneToFloatKernelPatternV3 : oneToFloatKernelPattern, sizeNames[vecSize-1], fnName );
/* Create kernels */
programPtr = kernelSource;
if( create_single_kernel_helper( context, &program, &kernel, 1, (const char **)&programPtr, "sample_test" ) )
{
return -1;
}
/* Generate some streams */
for( i = 0; i < TEST_SIZE * vecSize; i++ )
{
inDataA[ i ] = get_random_float( -512.f, 512.f, d );
}
fillWithTrickyNumbers( inDataA, NULL, vecSize );
/* Clamp values to be in range for fast_ functions */
if( verifyFn == verifyFastLength )
{
for( i = 0; i < TEST_SIZE * vecSize; i++ )
{
if( fabsf( inDataA[i] ) > MAKE_HEX_FLOAT(0x1.0p62f, 0x1L, 62) || fabsf( inDataA[i] ) < MAKE_HEX_FLOAT(0x1.0p-62f, 0x1L, -62) )
inDataA[ i ] = get_random_float( -512.f, 512.f, d );
}
}
streams[0] =
clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * vecSize * TEST_SIZE, inDataA, NULL);
if( streams[0] == NULL )
{
log_error("ERROR: Creating input array A failed!\n");
return -1;
}
streams[1] = clCreateBuffer(context, CL_MEM_READ_WRITE,
sizeof(cl_float) * TEST_SIZE, NULL, NULL);
if( streams[1] == NULL )
{
log_error("ERROR: Creating output array failed!\n");
return -1;
}
/* Assign streams and execute */
error = clSetKernelArg( kernel, 0, sizeof( streams[ 0 ] ), &streams[0] );
test_error( error, "Unable to set indexed kernel arguments" );
error = clSetKernelArg( kernel, 1, sizeof( streams[ 1 ] ), &streams[1] );
test_error( error, "Unable to set indexed kernel arguments" );
/* Run the kernel */
threads[0] = TEST_SIZE;
error = get_max_common_work_group_size( context, kernel, threads[0],
&localThreads[0] );
test_error( error, "Unable to get work group size to use" );
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads,
localThreads, 0, NULL, NULL );
test_error( error, "Unable to execute test kernel" );
/* Now get the results */
error = clEnqueueReadBuffer( queue, streams[1], true, 0,
sizeof( cl_float ) * TEST_SIZE, outData,
0, NULL, NULL );
test_error( error, "Unable to read output array!" );
/* And verify! */
for( i = 0; i < TEST_SIZE; i++ )
{
double expected = verifyFn( inDataA + i * vecSize, vecSize );
if( (float) expected != outData[ i ] )
{
float ulps = Ulp_Error( outData[i], expected );
if( fabsf( ulps ) <= ulpLimit )
continue;
// We have to special case NAN
if( isnan( outData[ i ] ) && isnan( expected ) )
continue;
if(! (fabsf(ulps) < ulpLimit) )
{
log_error( "ERROR: Data sample %d at size %d does not validate! Expected (%a), got (%a), source (%a), ulp %f\n",
(int)i, (int)vecSize, expected, outData[ i ], inDataA[i*vecSize], ulps );
char vecA[1000];
vector2string( vecA, inDataA + i *vecSize, vecSize );
log_error( "\tvector: %s", vecA );
return -1;
}
}
}
return 0;
}
double verifyLength( float *srcA, size_t vecSize )
{
double total = 0;
unsigned int i;
// We calculate the distance as a double, to try and make up for the fact that
// the GPU has better precision distance since it's a single op
for( i = 0; i < vecSize; i++ )
{
total += (double)srcA[i] * (double)srcA[i];
}
return sqrt( total );
}
int test_geom_length(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed( gRandomSeed );
for( size = 0; sizes[ size ] != 0 ; size++ )
{
float maxUlps = 3.0f + // error in sqrt
0.5f * // effect on e of taking sqrt( x + e )
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
if( test_oneToFloat_kernel( queue, context, "length", sizes[ size ], verifyLength, maxUlps, seed ) != 0 )
{
log_error( " length vector size %d FAILED\n", (int)sizes[ size ] );
retVal = -1;
}
else
{
log_info( " length vector vector size %d passed\n", (int)sizes[ size ] );
}
}
if (retVal)
return retVal;
if(!is_extension_available(deviceID, "cl_khr_fp64"))
{
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
return 0;
}
else
{
log_info("Testing doubles...\n");
return test_geom_length_double( deviceID, context, queue, num_elements, seed);
}
}
double verifyFastLength( float *srcA, size_t vecSize )
{
double total = 0;
unsigned int i;
// We calculate the distance as a double, to try and make up for the fact that
// the GPU has better precision distance since it's a single op
for( i = 0; i < vecSize; i++ )
{
total += (double)srcA[i] * (double)srcA[i];
}
return sqrt( total );
}
int test_geom_fast_length(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed(gRandomSeed);
for( size = 0; sizes[ size ] != 0 ; size++ )
{
float maxUlps = 8192.0f + // error in half_sqrt
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
if( test_oneToFloat_kernel( queue, context, "fast_length", sizes[ size ], verifyFastLength, maxUlps, seed ) != 0 )
{
log_error( " fast_length vector size %d FAILED\n", (int)sizes[ size ] );
retVal = -1;
}
else
{
log_info( " fast_length vector size %d passed\n", (int)sizes[ size ] );
}
}
return retVal;
}
typedef void (*oneToOneVerifyFn)( float *srcA, float *dstA, size_t vecSize );
int test_oneToOne_kernel(cl_command_queue queue, cl_context context, const char *fnName,
size_t vecSize, oneToOneVerifyFn verifyFn, float ulpLimit, int softball, MTdata d )
{
clProgramWrapper program;
clKernelWrapper kernel;
clMemWrapper streams[2];
BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE
* vecSize));
BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE
* vecSize));
int error;
size_t i, j, threads[1], localThreads[1];
char kernelSource[10240];
char *programPtr;
char sizeNames[][4] = { "", "2", "3", "4", "", "", "", "8", "", "", "", "", "", "", "", "16" };
cl_float *inDataA = A;
cl_float *outData = B;
float ulp_error = 0;
/* Create the source */
sprintf( kernelSource, vecSize == 3 ? oneToOneKernelPatternV3: oneToOneKernelPattern, sizeNames[vecSize-1], sizeNames[vecSize-1], fnName );
/* Create kernels */
programPtr = kernelSource;
if( create_single_kernel_helper( context, &program, &kernel, 1, (const char **)&programPtr, "sample_test" ) )
return -1;
/* Initialize data. First element always 0. */
memset( inDataA, 0, sizeof(cl_float) * vecSize );
if( 0 == strcmp( fnName, "fast_normalize" ))
{ // keep problematic cases out of the fast function
for( i = vecSize; i < TEST_SIZE * vecSize; i++ )
{
cl_float z = get_random_float( -MAKE_HEX_FLOAT( 0x1.0p60f, 1, 60), MAKE_HEX_FLOAT( 0x1.0p60f, 1, 60), d);
if( fabsf(z) < MAKE_HEX_FLOAT( 0x1.0p-60f, 1, -60) )
z = copysignf( 0.0f, z );
inDataA[i] = z;
}
}
else
{
for( i = vecSize; i < TEST_SIZE * vecSize; i++ )
inDataA[i] = any_float(d);
}
streams[0] =
clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
sizeof(cl_float) * vecSize * TEST_SIZE, inDataA, NULL);
if( streams[0] == NULL )
{
log_error("ERROR: Creating input array A failed!\n");
return -1;
}
streams[1] =
clCreateBuffer(context, CL_MEM_READ_WRITE,
sizeof(cl_float) * vecSize * TEST_SIZE, NULL, NULL);
if( streams[1] == NULL )
{
log_error("ERROR: Creating output array failed!\n");
return -1;
}
/* Assign streams and execute */
error = clSetKernelArg(kernel, 0, sizeof( streams[0] ), &streams[0] );
test_error( error, "Unable to set indexed kernel arguments" );
error = clSetKernelArg(kernel, 1, sizeof( streams[1] ), &streams[1] );
test_error( error, "Unable to set indexed kernel arguments" );
/* Run the kernel */
threads[0] = TEST_SIZE;
error = get_max_common_work_group_size( context, kernel, threads[0], &localThreads[0] );
test_error( error, "Unable to get work group size to use" );
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads, localThreads, 0, NULL, NULL );
test_error( error, "Unable to execute test kernel" );
/* Now get the results */
error = clEnqueueReadBuffer( queue, streams[1], true, 0, sizeof( cl_float ) * TEST_SIZE * vecSize, outData, 0, NULL, NULL );
test_error( error, "Unable to read output array!" );
/* And verify! */
for( i = 0; i < TEST_SIZE; i++ )
{
float expected[4];
int fail = 0;
verifyFn( inDataA + i * vecSize, expected, vecSize );
for( j = 0; j < vecSize; j++ )
{
// We have to special case NAN
if( isnan( outData[ i * vecSize + j ] )
&& isnan( expected[ j ] ) )
continue;
if( expected[j] != outData[ i * vecSize + j ] ) {
ulp_error = Ulp_Error( outData[i*vecSize+j], expected[ j ] );
if( fabsf(ulp_error) > ulpLimit ) {
fail = 1;
break;
}
}
}
// try again with subnormals flushed to zero if the platform flushes
if( fail && gFlushDenormsToZero )
{
float temp[4], expected2[4];
for( j = 0; j < vecSize; j++ )
{
if( IsFloatSubnormal(inDataA[i*vecSize+j] ) )
temp[j] = copysignf( 0.0f, inDataA[i*vecSize+j] );
else
temp[j] = inDataA[ i*vecSize +j];
}
verifyFn( temp, expected2, vecSize );
fail = 0;
for( j = 0; j < vecSize; j++ )
{
// We have to special case NAN
if( isnan( outData[ i * vecSize + j ] ) && isnan( expected[ j ] ) )
continue;
if( expected2[j] != outData[ i * vecSize + j ] )
{
ulp_error = Ulp_Error(outData[i*vecSize + j ], expected[ j ] );
if( fabsf(ulp_error) > ulpLimit )
{
if( IsFloatSubnormal(expected2[j]) )
{
expected2[j] = 0.0f;
if( expected2[j] != outData[i*vecSize + j ] )
{
ulp_error = Ulp_Error( outData[ i * vecSize + j ], expected[ j ] );
if( fabsf(ulp_error) > ulpLimit ) {
fail = 1;
break;
}
}
}
}
}
}
}
if( fail )
{
log_error( "ERROR: Data sample {%d,%d} at size %d does not validate! Expected %12.24f (%a), got %12.24f (%a), ulp %f\n",
(int)i, (int)j, (int)vecSize, expected[j], expected[j], outData[ i*vecSize+j], outData[ i*vecSize+j], ulp_error );
log_error( " Source: " );
for( size_t q = 0; q < vecSize; q++ )
log_error( "%g ", inDataA[ i * vecSize+q]);
log_error( "\n : " );
for( size_t q = 0; q < vecSize; q++ )
log_error( "%a ", inDataA[i*vecSize +q] );
log_error( "\n" );
log_error( " Result: " );
for( size_t q = 0; q < vecSize; q++ )
log_error( "%g ", outData[ i *vecSize + q ] );
log_error( "\n : " );
for( size_t q = 0; q < vecSize; q++ )
log_error( "%a ", outData[ i * vecSize + q ] );
log_error( "\n" );
log_error( " Expected: " );
for( size_t q = 0; q < vecSize; q++ )
log_error( "%g ", expected[ q ] );
log_error( "\n : " );
for( size_t q = 0; q < vecSize; q++ )
log_error( "%a ", expected[ q ] );
log_error( "\n" );
return -1;
}
}
return 0;
}
void verifyNormalize( float *srcA, float *dst, size_t vecSize )
{
double total = 0, value;
unsigned int i;
// We calculate everything as a double, to try and make up for the fact that
// the GPU has better precision distance since it's a single op
for( i = 0; i < vecSize; i++ )
total += (double)srcA[i] * (double)srcA[i];
if( total == 0.f )
{
// Special edge case: copy vector over without change
for( i = 0; i < vecSize; i++ )
dst[i] = srcA[i];
return;
}
// Deal with infinities
if( total == INFINITY )
{
total = 0.0f;
for( i = 0; i < vecSize; i++ )
{
if( fabsf( srcA[i]) == INFINITY )
dst[i] = copysignf( 1.0f, srcA[i] );
else
dst[i] = copysignf( 0.0f, srcA[i] );
total += (double)dst[i] * (double)dst[i];
}
srcA = dst;
}
value = sqrt( total );
for( i = 0; i < vecSize; i++ )
dst[i] = (float)( (double)srcA[i] / value );
}
int test_geom_normalize(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed(gRandomSeed);
for( size = 0; sizes[ size ] != 0 ; size++ )
{
float maxUlps = 2.5f + // error in rsqrt + error in multiply
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
if( test_oneToOne_kernel( queue, context, "normalize", sizes[ size ], verifyNormalize, maxUlps, 0, seed ) != 0 )
{
log_error( " normalized vector size %d FAILED\n", (int)sizes[ size ] );
retVal = -1;
}
else
{
log_info( " normalized vector size %d passed\n", (int)sizes[ size ] );
}
}
if (retVal)
return retVal;
if(!is_extension_available(deviceID, "cl_khr_fp64"))
{
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
return 0;
} else {
log_info("Testing doubles...\n");
return test_geom_normalize_double( deviceID, context, queue, num_elements, seed);
}
}
int test_geom_fast_normalize(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
{
size_t sizes[] = { 1, 2, 3, 4, 0 };
unsigned int size;
int retVal = 0;
RandomSeed seed( gRandomSeed );
for( size = 0; sizes[ size ] != 0 ; size++ )
{
float maxUlps = 8192.5f + // error in rsqrt + error in multiply
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
if( test_oneToOne_kernel( queue, context, "fast_normalize", sizes[ size ], verifyNormalize, maxUlps, 1, seed ) != 0 )
{
log_error( " fast_normalize vector size %d FAILED\n", (int)sizes[ size ] );
retVal = -1;
}
else
{
log_info( " fast_normalize vector size %d passed\n", (int)sizes[ size ] );
}
}
return retVal;
}