| // |
| // 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_flags)(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_flags)(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_flags)(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_flags)(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_flags)(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_flags)(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_flags)(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_flags)(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_flags)(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_flags)(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; |
| } |
| |
| |
| |
