| // |
| // 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> |
| |
| #if defined(__APPLE__) |
| #include <sys/time.h> |
| #endif |
| |
| 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], |
| "* in )\n" |
| "{\n" |
| " size_t i = get_global_id(0);\n" |
| " out[i] = ", |
| name, |
| "( in[i] );\n" |
| "}\n" }; |
| |
| const char *c3[] = { |
| "__kernel void math_kernel", |
| sizeNames[vectorSize], |
| "( __global float* out, __global float* in)\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" |
| " f0 = ", |
| name, |
| "( f0 );\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" |
| " switch( parity )\n" |
| " {\n" |
| " case 1:\n" |
| " f0 = (float3)( in[3*i], NAN, NAN ); \n" |
| " break;\n" |
| " case 0:\n" |
| " f0 = (float3)( in[3*i], in[3*i+1], NAN ); \n" |
| " break;\n" |
| " }\n" |
| " f0 = ", |
| name, |
| "( f0 );\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 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. Init to 0. |
| 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 isRangeLimited; // 1 if the function is only to be evaluated over a |
| // range |
| float half_sin_cos_tan_limit; |
| bool relaxedMode; // True if test is running in relaxed mode, false |
| // otherwise. |
| } TestInfo; |
| |
| static cl_int Test(cl_uint job_id, cl_uint thread_id, void *data); |
| |
| int TestFunc_Float_Float(const Func *f, MTdata d, bool relaxedMode) |
| { |
| TestInfo test_info; |
| cl_int error; |
| size_t i, j; |
| float maxError = 0.0f; |
| double maxErrorVal = 0.0; |
| int skipTestingRelaxed = (relaxedMode && strcmp(f->name, "tan") == 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)); |
| |
| if (gWimpyMode) |
| { |
| test_info.subBufferSize = gWimpyBufferSize |
| / (sizeof(cl_float) |
| * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| } |
| |
| 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; |
| // cl_kernels aren't thread safe, so we make one for each vector size for |
| // every thread |
| for (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 (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, ®ion, &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; |
| } |
| |
| for (j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++) |
| { |
| test_info.tinfo[i].outBuf[j] = clCreateSubBuffer( |
| gOutBuffer[j], CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, |
| ®ion, &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; |
| } |
| } |
| |
| // Check for special cases for unary float |
| test_info.isRangeLimited = 0; |
| test_info.half_sin_cos_tan_limit = 0; |
| if (0 == strcmp(f->name, "half_sin") || 0 == strcmp(f->name, "half_cos")) |
| { |
| test_info.isRangeLimited = 1; |
| test_info.half_sin_cos_tan_limit = 1.0f |
| + test_info.ulps |
| * (FLT_EPSILON / 2.0f); // out of range results from finite |
| // inputs must be in [-1,1] |
| } |
| else if (0 == strcmp(f->name, "half_tan")) |
| { |
| test_info.isRangeLimited = 1; |
| test_info.half_sin_cos_tan_limit = |
| INFINITY; // out of range resut from finite inputs must be numeric |
| } |
| |
| // 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 || skipTestingRelaxed) |
| { |
| error = ThreadPool_Do(Test, test_info.jobCount, &test_info); |
| |
| // Accumulate the arithmetic errors |
| for (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; |
| } |
| } |
| |
| if (error) goto exit; |
| |
| if (gWimpyMode) |
| vlog("Wimp pass"); |
| else |
| vlog("passed"); |
| |
| if (skipTestingRelaxed) |
| { |
| vlog(" (rlx skip correctness testing)\n"); |
| goto exit; |
| } |
| } |
| |
| if (gMeasureTimes) |
| { |
| // Init input array |
| uint32_t *p = (uint32_t *)gIn; |
| if (strstr(f->name, "exp") || strstr(f->name, "sin") |
| || strstr(f->name, "cos") || strstr(f->name, "tan")) |
| for (j = 0; j < BUFFER_SIZE / sizeof(float); j++) |
| ((float *)p)[j] = (float)genrand_real1(d); |
| else if (strstr(f->name, "log")) |
| for (j = 0; j < BUFFER_SIZE / sizeof(float); j++) |
| p[j] = genrand_int32(d) & 0x7fffffff; |
| else |
| for (j = 0; j < BUFFER_SIZE / sizeof(float); j++) |
| p[j] = genrand_int32(d); |
| if ((error = clEnqueueWriteBuffer(gQueue, gInBuffer, CL_FALSE, 0, |
| BUFFER_SIZE, gIn, 0, NULL, NULL))) |
| { |
| vlog_error("\n*** Error %d in clEnqueueWriteBuffer ***\n", error); |
| return error; |
| } |
| |
| |
| // Run the kernels |
| for (j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++) |
| { |
| size_t vectorSize = sizeof(cl_float) * sizeValues[j]; |
| size_t localCount = (BUFFER_SIZE + vectorSize - 1) |
| / vectorSize; // BUFFER_SIZE / vectorSize rounded up |
| if ((error = clSetKernelArg(test_info.k[j][0], 0, |
| sizeof(gOutBuffer[j]), &gOutBuffer[j]))) |
| { |
| LogBuildError(test_info.programs[j]); |
| goto exit; |
| } |
| if ((error = clSetKernelArg(test_info.k[j][0], 1, sizeof(gInBuffer), |
| &gInBuffer))) |
| { |
| LogBuildError(test_info.programs[j]); |
| goto exit; |
| } |
| |
| double sum = 0.0; |
| double bestTime = INFINITY; |
| for (i = 0; i < PERF_LOOP_COUNT; i++) |
| { |
| uint64_t startTime = GetTime(); |
| if ((error = clEnqueueNDRangeKernel(gQueue, test_info.k[j][0], |
| 1, NULL, &localCount, NULL, |
| 0, NULL, NULL))) |
| { |
| vlog_error("FAILED -- could not execute kernel\n"); |
| goto exit; |
| } |
| |
| // Make sure OpenCL is done |
| if ((error = clFinish(gQueue))) |
| { |
| vlog_error("Error %d at clFinish\n", error); |
| goto exit; |
| } |
| |
| uint64_t endTime = GetTime(); |
| double time = SubtractTime(endTime, startTime); |
| sum += time; |
| if (time < bestTime) bestTime = time; |
| } |
| |
| if (gReportAverageTimes) bestTime = sum / PERF_LOOP_COUNT; |
| double clocksPerOp = bestTime * (double)gDeviceFrequency |
| * gComputeDevices * gSimdSize * 1e6 |
| / (BUFFER_SIZE / sizeof(float)); |
| vlog_perf(clocksPerOp, LOWER_IS_BETTER, "clocks / element", "%sf%s", |
| f->name, sizeNames[j]); |
| } |
| } |
| |
| if (!gSkipCorrectnessTesting) vlog("\t%8.2f @ %a", maxError, maxErrorVal); |
| vlog("\n"); |
| |
| exit: |
| // Release |
| for (i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++) |
| { |
| clReleaseProgram(test_info.programs[i]); |
| if (test_info.k[i]) |
| { |
| for (j = 0; j < test_info.threadCount; j++) |
| clReleaseKernel(test_info.k[i][j]); |
| |
| free(test_info.k[i]); |
| } |
| } |
| if (test_info.tinfo) |
| { |
| for (i = 0; i < test_info.threadCount; i++) |
| { |
| clReleaseMemObject(test_info.tinfo[i].inBuf); |
| for (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 scale = job->scale; |
| cl_uint base = job_id * (cl_uint)job->step; |
| ThreadInfo *tinfo = job->tinfo + thread_id; |
| fptr func = job->f->func; |
| const char *fname = job->f->name; |
| bool relaxedMode = job->relaxedMode; |
| float ulps = getAllowedUlpError(job->f, relaxedMode); |
| if (relaxedMode) |
| { |
| func = job->f->rfunc; |
| } |
| |
| cl_uint j, k; |
| cl_int error; |
| |
| int isRangeLimited = job->isRangeLimited; |
| float half_sin_cos_tan_limit = job->half_sin_cos_tan_limit; |
| int ftz = job->ftz; |
| |
| // start the map of the output arrays |
| cl_event e[VECTOR_SIZE_COUNT]; |
| cl_uint *out[VECTOR_SIZE_COUNT]; |
| for (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"); |
| |
| // Write the new values to the input array |
| cl_uint *p = (cl_uint *)gIn + thread_id * buffer_elements; |
| for (j = 0; j < buffer_elements; j++) |
| { |
| p[j] = base + j * scale; |
| if (relaxedMode) |
| { |
| float p_j = *(float *)&p[j]; |
| if (strcmp(fname, "sin") == 0 |
| || strcmp(fname, "cos") |
| == 0) // the domain of the function is [-pi,pi] |
| { |
| if (fabs(p_j) > M_PI) ((float *)p)[j] = NAN; |
| } |
| |
| if (strcmp(fname, "reciprocal") == 0) |
| { |
| const float l_limit = HEX_FLT(+, 1, 0, -, 126); |
| const float u_limit = HEX_FLT(+, 1, 0, +, 126); |
| |
| if (fabs(p_j) < l_limit |
| || fabs(p_j) > u_limit) // the domain of the function is |
| // [2^-126,2^126] |
| ((float *)p)[j] = NAN; |
| } |
| } |
| } |
| |
| 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); |
| return error; |
| } |
| |
| for (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); |
| return error; |
| } |
| if ((error = clReleaseEvent(e[j]))) |
| { |
| vlog_error("Error: clReleaseEvent failed! err: %d\n", error); |
| return error; |
| } |
| |
| // 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); |
| return error; |
| } |
| |
| // 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 = clEnqueueNDRangeKernel(tinfo->tQueue, kernel, 1, NULL, |
| &vectorCount, NULL, 0, NULL, NULL))) |
| { |
| vlog_error("FAILED -- could not execute kernel\n"); |
| return error; |
| } |
| } |
| |
| // Get that moving |
| if ((error = clFlush(tinfo->tQueue))) vlog("clFlush 2 failed\n"); |
| |
| if (gSkipCorrectnessTesting) return CL_SUCCESS; |
| |
| // Calculate the correctly rounded reference result |
| float *r = (float *)gOut_Ref + thread_id * buffer_elements; |
| float *s = (float *)p; |
| for (j = 0; j < buffer_elements; j++) r[j] = (float)func.f_f(s[j]); |
| |
| // Read the data back -- no need to wait for the first N-1 buffers. This is |
| // an in order queue. |
| for (j = gMinVectorSizeIndex; j + 1 < gMaxVectorSizeIndex; j++) |
| { |
| out[j] = (cl_uint *)clEnqueueMapBuffer( |
| tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, 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); |
| return error; |
| } |
| } |
| |
| // Wait for the last buffer |
| out[j] = (uint32_t *)clEnqueueMapBuffer(tinfo->tQueue, tinfo->outBuf[j], |
| CL_TRUE, 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); |
| return error; |
| } |
| |
| // Verify data |
| uint32_t *t = (uint32_t *)r; |
| for (j = 0; j < buffer_elements; j++) |
| { |
| for (k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++) |
| { |
| uint32_t *q = out[k]; |
| |
| // If we aren't getting the correctly rounded result |
| if (t[j] != q[j]) |
| { |
| float test = ((float *)q)[j]; |
| double correct = func.f_f(s[j]); |
| float err = Ulp_Error(test, correct); |
| float abs_error = Abs_Error(test, correct); |
| int fail = 0; |
| int use_abs_error = 0; |
| |
| // it is possible for the output to not match the reference |
| // result but for Ulp_Error to be zero, for example -1.#QNAN |
| // vs. 1.#QNAN. In such cases there is no failure |
| if (err == 0.0f) |
| { |
| fail = 0; |
| } |
| else if (relaxedMode) |
| { |
| if (strcmp(fname, "sin") == 0 || strcmp(fname, "cos") == 0) |
| { |
| fail = !(fabsf(abs_error) <= ulps); |
| use_abs_error = 1; |
| } |
| if (strcmp(fname, "sinpi") == 0 |
| || strcmp(fname, "cospi") == 0) |
| { |
| if (s[j] >= -1.0 && s[j] <= 1.0) |
| { |
| fail = !(fabsf(abs_error) <= ulps); |
| use_abs_error = 1; |
| } |
| } |
| |
| if (strcmp(fname, "reciprocal") == 0) |
| { |
| fail = !(fabsf(err) <= ulps); |
| } |
| |
| if (strcmp(fname, "exp") == 0 || strcmp(fname, "exp2") == 0) |
| { |
| float exp_error = ulps; |
| |
| if (!gIsEmbedded) |
| { |
| exp_error += floor(fabs(2 * s[j])); |
| } |
| |
| fail = !(fabsf(err) <= exp_error); |
| ulps = exp_error; |
| } |
| if (strcmp(fname, "tan") == 0) |
| { |
| |
| if (!gFastRelaxedDerived) |
| { |
| fail = !(fabsf(err) <= ulps); |
| } |
| // Else fast math derived implementation does not |
| // require ULP verification |
| } |
| if (strcmp(fname, "exp10") == 0) |
| { |
| if (!gFastRelaxedDerived) |
| { |
| fail = !(fabsf(err) <= ulps); |
| } |
| // Else fast math derived implementation does not |
| // require ULP verification |
| } |
| if (strcmp(fname, "log") == 0 || strcmp(fname, "log2") == 0 |
| || strcmp(fname, "log10") == 0) |
| { |
| if (s[j] >= 0.5 && s[j] <= 2) |
| { |
| fail = !(fabsf(abs_error) <= ulps); |
| } |
| else |
| { |
| ulps = gIsEmbedded ? job->f->float_embedded_ulps |
| : job->f->float_ulps; |
| fail = !(fabsf(err) <= ulps); |
| } |
| } |
| |
| |
| // fast-relaxed implies finite-only |
| if (IsFloatInfinity(correct) || IsFloatNaN(correct) |
| || IsFloatInfinity(s[j]) || IsFloatNaN(s[j])) |
| { |
| fail = 0; |
| err = 0; |
| } |
| } |
| else |
| { |
| fail = !(fabsf(err) <= ulps); |
| } |
| |
| // half_sin/cos/tan are only valid between +-2**16, Inf, NaN |
| if (isRangeLimited |
| && fabsf(s[j]) > MAKE_HEX_FLOAT(0x1.0p16f, 0x1L, 16) |
| && fabsf(s[j]) < INFINITY) |
| { |
| if (fabsf(test) <= half_sin_cos_tan_limit) |
| { |
| err = 0; |
| fail = 0; |
| } |
| } |
| |
| if (fail) |
| { |
| if (ftz) |
| { |
| typedef int (*CheckForSubnormal)( |
| double, float); // If we are in fast relaxed math, |
| // we have a different calculation |
| // for the subnormal threshold. |
| CheckForSubnormal isFloatResultSubnormalPtr; |
| |
| if (relaxedMode) |
| { |
| isFloatResultSubnormalPtr = |
| &IsFloatResultSubnormalAbsError; |
| } |
| else |
| { |
| isFloatResultSubnormalPtr = &IsFloatResultSubnormal; |
| } |
| // retry per section 6.5.3.2 |
| if ((*isFloatResultSubnormalPtr)(correct, ulps)) |
| { |
| fail = fail && (test != 0.0f); |
| if (!fail) err = 0.0f; |
| } |
| |
| // retry per section 6.5.3.3 |
| if (IsFloatSubnormal(s[j])) |
| { |
| double correct2 = func.f_f(0.0); |
| double correct3 = func.f_f(-0.0); |
| float err2; |
| float err3; |
| if (use_abs_error) |
| { |
| err2 = Abs_Error(test, correct2); |
| err3 = Abs_Error(test, correct3); |
| } |
| else |
| { |
| 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 ((*isFloatResultSubnormalPtr)(correct2, ulps) |
| || (*isFloatResultSubnormalPtr)(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]; |
| } |
| if (fail) |
| { |
| vlog_error("\nERROR: %s%s: %f ulp error at %a (0x%8.8x): " |
| "*%a vs. %a\n", |
| job->f->name, sizeNames[k], err, ((float *)s)[j], |
| ((uint32_t *)s)[j], ((float *)t)[j], test); |
| return -1; |
| } |
| } |
| } |
| } |
| |
| for (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:%10u buf_elements:%10zd ulps:%5.3f " |
| "ThreadCount:%2u\n", |
| base, job->step, job->scale, buffer_elements, job->ulps, |
| job->threadCount); |
| } |
| else |
| { |
| vlog("."); |
| } |
| fflush(stdout); |
| } |
| |
| return CL_SUCCESS; |
| } |
