1627 lines
60 KiB
C
1627 lines
60 KiB
C
#define _POSIX_C_SOURCE 200809L
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#include <dlfcn.h>
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#include <math.h>
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#include <stdarg.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <time.h>
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#if defined(__has_include)
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#if __has_include(<cublasLt.h>)
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#include <cublasLt.h>
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#define HAVE_CUBLASLT_HEADERS 1
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#else
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#define HAVE_CUBLASLT_HEADERS 0
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#endif
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#else
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#define HAVE_CUBLASLT_HEADERS 0
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#endif
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typedef int CUdevice;
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typedef uint64_t CUdeviceptr;
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typedef int CUresult;
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typedef void *CUcontext;
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typedef void *CUmodule;
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typedef void *CUfunction;
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typedef void *CUstream;
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#define CU_SUCCESS 0
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#define CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT 16
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#define CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR 75
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#define CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR 76
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#define MAX_STRESS_STREAMS 16
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#define MIN_PROFILE_BUDGET_BYTES ((size_t)4u * 1024u * 1024u)
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#define MIN_STREAM_BUDGET_BYTES ((size_t)64u * 1024u * 1024u)
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#define MAX_SINGLE_PRECISION_STREAMS 4
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#define MAX_SINGLE_PRECISION_PROFILE_BUDGET_BYTES ((size_t)2u * 1024u * 1024u * 1024u)
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static const char *ptx_source =
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".version 6.0\n"
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".target sm_30\n"
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".address_size 64\n"
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"\n"
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".visible .entry burn(\n"
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" .param .u64 data,\n"
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" .param .u32 words,\n"
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" .param .u32 rounds\n"
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")\n"
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"{\n"
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" .reg .pred %p<2>;\n"
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" .reg .b32 %r<8>;\n"
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" .reg .b64 %rd<5>;\n"
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"\n"
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" ld.param.u64 %rd1, [data];\n"
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" ld.param.u32 %r1, [words];\n"
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" ld.param.u32 %r2, [rounds];\n"
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" mov.u32 %r3, %ctaid.x;\n"
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" mov.u32 %r4, %ntid.x;\n"
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" mov.u32 %r5, %tid.x;\n"
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" mad.lo.s32 %r0, %r3, %r4, %r5;\n"
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" setp.ge.u32 %p0, %r0, %r1;\n"
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" @%p0 bra DONE;\n"
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" mul.wide.u32 %rd2, %r0, 4;\n"
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" add.s64 %rd3, %rd1, %rd2;\n"
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" ld.global.u32 %r6, [%rd3];\n"
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"LOOP:\n"
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" setp.eq.u32 %p1, %r2, 0;\n"
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" @%p1 bra STORE;\n"
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" mad.lo.u32 %r6, %r6, 1664525, 1013904223;\n"
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" sub.u32 %r2, %r2, 1;\n"
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" bra LOOP;\n"
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"STORE:\n"
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" st.global.u32 [%rd3], %r6;\n"
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"DONE:\n"
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" ret;\n"
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"}\n";
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typedef CUresult (*cuInit_fn)(unsigned int);
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typedef CUresult (*cuDeviceGetCount_fn)(int *);
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typedef CUresult (*cuDeviceGet_fn)(CUdevice *, int);
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typedef CUresult (*cuDeviceGetName_fn)(char *, int, CUdevice);
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typedef CUresult (*cuDeviceGetAttribute_fn)(int *, int, CUdevice);
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typedef CUresult (*cuCtxCreate_fn)(CUcontext *, unsigned int, CUdevice);
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typedef CUresult (*cuCtxDestroy_fn)(CUcontext);
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typedef CUresult (*cuCtxSynchronize_fn)(void);
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typedef CUresult (*cuMemAlloc_fn)(CUdeviceptr *, size_t);
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typedef CUresult (*cuMemFree_fn)(CUdeviceptr);
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typedef CUresult (*cuMemsetD8_fn)(CUdeviceptr, unsigned char, size_t);
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typedef CUresult (*cuMemcpyHtoD_fn)(CUdeviceptr, const void *, size_t);
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typedef CUresult (*cuMemcpyDtoH_fn)(void *, CUdeviceptr, size_t);
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typedef CUresult (*cuModuleLoadDataEx_fn)(CUmodule *, const void *, unsigned int, void *, void *);
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typedef CUresult (*cuModuleGetFunction_fn)(CUfunction *, CUmodule, const char *);
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typedef CUresult (*cuLaunchKernel_fn)(CUfunction,
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unsigned int,
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unsigned int,
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unsigned int,
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unsigned int,
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unsigned int,
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unsigned int,
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unsigned int,
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CUstream,
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void **,
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void **);
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typedef CUresult (*cuMemGetInfo_fn)(size_t *, size_t *);
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typedef CUresult (*cuStreamCreate_fn)(CUstream *, unsigned int);
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typedef CUresult (*cuStreamDestroy_fn)(CUstream);
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typedef CUresult (*cuGetErrorName_fn)(CUresult, const char **);
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typedef CUresult (*cuGetErrorString_fn)(CUresult, const char **);
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struct cuda_api {
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void *lib;
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cuInit_fn cuInit;
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cuDeviceGetCount_fn cuDeviceGetCount;
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cuDeviceGet_fn cuDeviceGet;
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cuDeviceGetName_fn cuDeviceGetName;
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cuDeviceGetAttribute_fn cuDeviceGetAttribute;
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cuCtxCreate_fn cuCtxCreate;
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cuCtxDestroy_fn cuCtxDestroy;
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cuCtxSynchronize_fn cuCtxSynchronize;
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cuMemAlloc_fn cuMemAlloc;
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cuMemFree_fn cuMemFree;
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cuMemsetD8_fn cuMemsetD8;
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cuMemcpyHtoD_fn cuMemcpyHtoD;
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cuMemcpyDtoH_fn cuMemcpyDtoH;
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cuModuleLoadDataEx_fn cuModuleLoadDataEx;
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cuModuleGetFunction_fn cuModuleGetFunction;
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cuLaunchKernel_fn cuLaunchKernel;
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cuMemGetInfo_fn cuMemGetInfo;
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cuStreamCreate_fn cuStreamCreate;
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cuStreamDestroy_fn cuStreamDestroy;
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cuGetErrorName_fn cuGetErrorName;
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cuGetErrorString_fn cuGetErrorString;
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};
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struct stress_report {
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char backend[32];
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char device[128];
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int cc_major;
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int cc_minor;
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int buffer_mb;
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int stream_count;
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unsigned long iterations;
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uint64_t checksum;
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char details[16384];
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};
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static int load_symbol(void *lib, const char *name, void **out) {
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*out = dlsym(lib, name);
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return *out != NULL;
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}
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static int load_cuda(struct cuda_api *api) {
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memset(api, 0, sizeof(*api));
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api->lib = dlopen("libcuda.so.1", RTLD_NOW | RTLD_LOCAL);
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if (!api->lib) {
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return 0;
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}
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if (!(
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load_symbol(api->lib, "cuInit", (void **)&api->cuInit) &&
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load_symbol(api->lib, "cuDeviceGetCount", (void **)&api->cuDeviceGetCount) &&
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load_symbol(api->lib, "cuDeviceGet", (void **)&api->cuDeviceGet) &&
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load_symbol(api->lib, "cuDeviceGetName", (void **)&api->cuDeviceGetName) &&
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load_symbol(api->lib, "cuDeviceGetAttribute", (void **)&api->cuDeviceGetAttribute) &&
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load_symbol(api->lib, "cuCtxCreate_v2", (void **)&api->cuCtxCreate) &&
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load_symbol(api->lib, "cuCtxDestroy_v2", (void **)&api->cuCtxDestroy) &&
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load_symbol(api->lib, "cuCtxSynchronize", (void **)&api->cuCtxSynchronize) &&
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load_symbol(api->lib, "cuMemAlloc_v2", (void **)&api->cuMemAlloc) &&
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load_symbol(api->lib, "cuMemFree_v2", (void **)&api->cuMemFree) &&
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load_symbol(api->lib, "cuMemsetD8_v2", (void **)&api->cuMemsetD8) &&
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load_symbol(api->lib, "cuMemcpyHtoD_v2", (void **)&api->cuMemcpyHtoD) &&
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load_symbol(api->lib, "cuMemcpyDtoH_v2", (void **)&api->cuMemcpyDtoH) &&
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load_symbol(api->lib, "cuModuleLoadDataEx", (void **)&api->cuModuleLoadDataEx) &&
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load_symbol(api->lib, "cuModuleGetFunction", (void **)&api->cuModuleGetFunction) &&
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load_symbol(api->lib, "cuLaunchKernel", (void **)&api->cuLaunchKernel))) {
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dlclose(api->lib);
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memset(api, 0, sizeof(*api));
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return 0;
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}
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load_symbol(api->lib, "cuMemGetInfo_v2", (void **)&api->cuMemGetInfo);
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load_symbol(api->lib, "cuStreamCreate", (void **)&api->cuStreamCreate);
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if (!load_symbol(api->lib, "cuStreamDestroy_v2", (void **)&api->cuStreamDestroy)) {
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load_symbol(api->lib, "cuStreamDestroy", (void **)&api->cuStreamDestroy);
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}
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return 1;
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}
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static const char *cu_error_name(struct cuda_api *api, CUresult rc) {
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const char *value = NULL;
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if (api->cuGetErrorName && api->cuGetErrorName(rc, &value) == CU_SUCCESS && value) {
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return value;
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}
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return "CUDA_ERROR";
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}
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static const char *cu_error_string(struct cuda_api *api, CUresult rc) {
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const char *value = NULL;
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if (api->cuGetErrorString && api->cuGetErrorString(rc, &value) == CU_SUCCESS && value) {
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return value;
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}
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return "unknown";
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}
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static int check_rc(struct cuda_api *api, const char *step, CUresult rc) {
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if (rc == CU_SUCCESS) {
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return 1;
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}
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fprintf(stderr, "%s failed: %s (%s)\n", step, cu_error_name(api, rc), cu_error_string(api, rc));
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return 0;
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}
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static double now_seconds(void) {
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struct timespec ts;
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clock_gettime(CLOCK_MONOTONIC, &ts);
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return (double)ts.tv_sec + ((double)ts.tv_nsec / 1000000000.0);
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}
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static size_t round_down_size(size_t value, size_t multiple) {
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if (multiple == 0 || value < multiple) {
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return value;
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}
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return value - (value % multiple);
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}
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static int query_compute_capability(struct cuda_api *api, CUdevice dev, int *major, int *minor) {
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int cc_major = 0;
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int cc_minor = 0;
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if (!check_rc(api,
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"cuDeviceGetAttribute(major)",
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api->cuDeviceGetAttribute(&cc_major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, dev))) {
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return 0;
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}
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if (!check_rc(api,
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"cuDeviceGetAttribute(minor)",
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api->cuDeviceGetAttribute(&cc_minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, dev))) {
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return 0;
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}
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*major = cc_major;
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*minor = cc_minor;
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return 1;
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}
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static int query_multiprocessor_count(struct cuda_api *api, CUdevice dev, int *count) {
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int mp_count = 0;
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if (!check_rc(api,
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"cuDeviceGetAttribute(multiprocessors)",
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api->cuDeviceGetAttribute(&mp_count, CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, dev))) {
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return 0;
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}
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*count = mp_count;
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return 1;
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}
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static size_t clamp_budget_to_free_memory(struct cuda_api *api, size_t requested_bytes) {
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size_t free_bytes = 0;
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size_t total_bytes = 0;
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size_t max_bytes = requested_bytes;
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if (!api->cuMemGetInfo) {
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return requested_bytes;
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}
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if (api->cuMemGetInfo(&free_bytes, &total_bytes) != CU_SUCCESS || free_bytes == 0) {
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return requested_bytes;
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}
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max_bytes = (free_bytes * 9u) / 10u;
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if (max_bytes < (size_t)4u * 1024u * 1024u) {
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max_bytes = (size_t)4u * 1024u * 1024u;
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}
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if (requested_bytes > max_bytes) {
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return max_bytes;
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}
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return requested_bytes;
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}
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static int choose_stream_count(int mp_count, int planned_profiles, size_t total_budget, int have_streams) {
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int stream_count = 1;
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if (!have_streams || mp_count <= 0 || planned_profiles <= 0) {
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return 1;
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}
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stream_count = mp_count / 8;
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if (stream_count < 2) {
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stream_count = 2;
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}
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if (stream_count > MAX_STRESS_STREAMS) {
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stream_count = MAX_STRESS_STREAMS;
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}
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while (stream_count > 1) {
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size_t per_stream_budget = total_budget / ((size_t)planned_profiles * (size_t)stream_count);
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if (per_stream_budget >= MIN_STREAM_BUDGET_BYTES) {
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break;
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}
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stream_count--;
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}
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return stream_count;
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}
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static size_t clamp_single_precision_profile_budget(size_t profile_budget_bytes) {
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if (profile_budget_bytes > MAX_SINGLE_PRECISION_PROFILE_BUDGET_BYTES) {
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return MAX_SINGLE_PRECISION_PROFILE_BUDGET_BYTES;
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}
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return profile_budget_bytes;
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}
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static void destroy_streams(struct cuda_api *api, CUstream *streams, int count) {
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if (!api->cuStreamDestroy) {
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return;
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}
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for (int i = 0; i < count; i++) {
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if (streams[i]) {
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api->cuStreamDestroy(streams[i]);
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streams[i] = NULL;
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}
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}
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}
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#if HAVE_CUBLASLT_HEADERS
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static void append_detail(char *buf, size_t cap, const char *fmt, ...) {
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size_t len = strlen(buf);
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if (len >= cap) {
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return;
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}
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va_list ap;
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va_start(ap, fmt);
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vsnprintf(buf + len, cap - len, fmt, ap);
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va_end(ap);
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}
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#endif
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static int run_ptx_fallback(struct cuda_api *api,
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CUdevice dev,
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const char *device_name,
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int cc_major,
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int cc_minor,
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int seconds,
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int size_mb,
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struct stress_report *report) {
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CUcontext ctx = NULL;
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CUmodule module = NULL;
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CUfunction kernel = NULL;
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uint32_t sample[256];
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CUdeviceptr device_mem[MAX_STRESS_STREAMS] = {0};
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CUstream streams[MAX_STRESS_STREAMS] = {0};
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uint32_t words[MAX_STRESS_STREAMS] = {0};
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uint32_t rounds[MAX_STRESS_STREAMS] = {0};
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void *params[MAX_STRESS_STREAMS][3];
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size_t bytes_per_stream[MAX_STRESS_STREAMS] = {0};
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unsigned long iterations = 0;
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int mp_count = 0;
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int stream_count = 1;
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memset(report, 0, sizeof(*report));
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snprintf(report->backend, sizeof(report->backend), "driver-ptx");
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snprintf(report->device, sizeof(report->device), "%s", device_name);
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report->cc_major = cc_major;
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report->cc_minor = cc_minor;
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report->buffer_mb = size_mb;
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if (!check_rc(api, "cuCtxCreate", api->cuCtxCreate(&ctx, 0, dev))) {
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return 0;
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}
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size_t requested_bytes = (size_t)size_mb * 1024u * 1024u;
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if (requested_bytes < MIN_PROFILE_BUDGET_BYTES) {
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requested_bytes = MIN_PROFILE_BUDGET_BYTES;
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}
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size_t total_bytes = clamp_budget_to_free_memory(api, requested_bytes);
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if (total_bytes < MIN_PROFILE_BUDGET_BYTES) {
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total_bytes = MIN_PROFILE_BUDGET_BYTES;
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}
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report->buffer_mb = (int)(total_bytes / (1024u * 1024u));
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if (query_multiprocessor_count(api, dev, &mp_count) &&
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api->cuStreamCreate &&
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api->cuStreamDestroy) {
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stream_count = choose_stream_count(mp_count, 1, total_bytes, 1);
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}
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if (stream_count > 1) {
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int created = 0;
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for (; created < stream_count; created++) {
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if (!check_rc(api, "cuStreamCreate", api->cuStreamCreate(&streams[created], 0))) {
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destroy_streams(api, streams, created);
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stream_count = 1;
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break;
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}
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}
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}
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report->stream_count = stream_count;
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|
|
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for (int lane = 0; lane < stream_count; lane++) {
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size_t slice = total_bytes / (size_t)stream_count;
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if (lane == stream_count - 1) {
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slice = total_bytes - ((size_t)lane * (total_bytes / (size_t)stream_count));
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}
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slice = round_down_size(slice, sizeof(uint32_t));
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if (slice < MIN_PROFILE_BUDGET_BYTES) {
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slice = MIN_PROFILE_BUDGET_BYTES;
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}
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bytes_per_stream[lane] = slice;
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words[lane] = (uint32_t)(slice / sizeof(uint32_t));
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|
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if (!check_rc(api, "cuMemAlloc", api->cuMemAlloc(&device_mem[lane], slice))) {
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goto fail;
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}
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if (!check_rc(api, "cuMemsetD8", api->cuMemsetD8(device_mem[lane], 0, slice))) {
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goto fail;
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}
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rounds[lane] = 2048;
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params[lane][0] = &device_mem[lane];
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params[lane][1] = &words[lane];
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params[lane][2] = &rounds[lane];
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}
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|
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if (!check_rc(api,
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"cuModuleLoadDataEx",
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api->cuModuleLoadDataEx(&module, ptx_source, 0, NULL, NULL))) {
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goto fail;
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}
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if (!check_rc(api, "cuModuleGetFunction", api->cuModuleGetFunction(&kernel, module, "burn"))) {
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goto fail;
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}
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|
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unsigned int threads = 256;
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|
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double deadline = now_seconds() + (double)seconds;
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double next_sync = now_seconds() + 1.0;
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while (now_seconds() < deadline) {
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int launched = 0;
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for (int lane = 0; lane < stream_count; lane++) {
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unsigned int blocks = (unsigned int)((words[lane] + threads - 1) / threads);
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if (!check_rc(api,
|
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"cuLaunchKernel",
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api->cuLaunchKernel(kernel,
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blocks,
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1,
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1,
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threads,
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1,
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1,
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0,
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streams[lane],
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params[lane],
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NULL))) {
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goto fail;
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}
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launched++;
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iterations++;
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}
|
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if (launched <= 0) {
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goto fail;
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}
|
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double now = now_seconds();
|
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if (now >= next_sync || now >= deadline) {
|
|
if (!check_rc(api, "cuCtxSynchronize", api->cuCtxSynchronize())) {
|
|
goto fail;
|
|
}
|
|
next_sync = now + 1.0;
|
|
}
|
|
}
|
|
api->cuCtxSynchronize();
|
|
|
|
if (!check_rc(api, "cuMemcpyDtoH", api->cuMemcpyDtoH(sample, device_mem[0], sizeof(sample)))) {
|
|
goto fail;
|
|
}
|
|
|
|
for (size_t i = 0; i < sizeof(sample) / sizeof(sample[0]); i++) {
|
|
report->checksum += sample[i];
|
|
}
|
|
report->iterations = iterations;
|
|
snprintf(report->details,
|
|
sizeof(report->details),
|
|
"fallback_int32=OK requested_mb=%d actual_mb=%d streams=%d per_stream_mb=%zu iterations=%lu\n",
|
|
size_mb,
|
|
report->buffer_mb,
|
|
report->stream_count,
|
|
bytes_per_stream[0] / (1024u * 1024u),
|
|
iterations);
|
|
|
|
for (int lane = 0; lane < stream_count; lane++) {
|
|
if (device_mem[lane]) {
|
|
api->cuMemFree(device_mem[lane]);
|
|
}
|
|
}
|
|
destroy_streams(api, streams, stream_count);
|
|
api->cuCtxDestroy(ctx);
|
|
return 1;
|
|
|
|
fail:
|
|
for (int lane = 0; lane < MAX_STRESS_STREAMS; lane++) {
|
|
if (device_mem[lane]) {
|
|
api->cuMemFree(device_mem[lane]);
|
|
}
|
|
}
|
|
destroy_streams(api, streams, MAX_STRESS_STREAMS);
|
|
if (ctx) {
|
|
api->cuCtxDestroy(ctx);
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
#if HAVE_CUBLASLT_HEADERS
|
|
typedef cublasStatus_t (*cublasLtCreate_fn)(cublasLtHandle_t *);
|
|
typedef cublasStatus_t (*cublasLtDestroy_fn)(cublasLtHandle_t);
|
|
typedef cublasStatus_t (*cublasLtMatmulDescCreate_fn)(cublasLtMatmulDesc_t *,
|
|
cublasComputeType_t,
|
|
cudaDataType_t);
|
|
typedef cublasStatus_t (*cublasLtMatmulDescDestroy_fn)(cublasLtMatmulDesc_t);
|
|
typedef cublasStatus_t (*cublasLtMatmulDescSetAttribute_fn)(cublasLtMatmulDesc_t,
|
|
cublasLtMatmulDescAttributes_t,
|
|
const void *,
|
|
size_t);
|
|
typedef cublasStatus_t (*cublasLtMatrixLayoutCreate_fn)(cublasLtMatrixLayout_t *,
|
|
cudaDataType_t,
|
|
uint64_t,
|
|
uint64_t,
|
|
int64_t);
|
|
typedef cublasStatus_t (*cublasLtMatrixLayoutDestroy_fn)(cublasLtMatrixLayout_t);
|
|
typedef cublasStatus_t (*cublasLtMatmulPreferenceCreate_fn)(cublasLtMatmulPreference_t *);
|
|
typedef cublasStatus_t (*cublasLtMatmulPreferenceDestroy_fn)(cublasLtMatmulPreference_t);
|
|
typedef cublasStatus_t (*cublasLtMatmulPreferenceSetAttribute_fn)(cublasLtMatmulPreference_t,
|
|
cublasLtMatmulPreferenceAttributes_t,
|
|
const void *,
|
|
size_t);
|
|
typedef cublasStatus_t (*cublasLtMatmulAlgoGetHeuristic_fn)(
|
|
cublasLtHandle_t,
|
|
cublasLtMatmulDesc_t,
|
|
cublasLtMatrixLayout_t,
|
|
cublasLtMatrixLayout_t,
|
|
cublasLtMatrixLayout_t,
|
|
cublasLtMatrixLayout_t,
|
|
cublasLtMatmulPreference_t,
|
|
int,
|
|
cublasLtMatmulHeuristicResult_t *,
|
|
int *);
|
|
typedef cublasStatus_t (*cublasLtMatmul_fn)(cublasLtHandle_t,
|
|
cublasLtMatmulDesc_t,
|
|
const void *,
|
|
const void *,
|
|
cublasLtMatrixLayout_t,
|
|
const void *,
|
|
cublasLtMatrixLayout_t,
|
|
const void *,
|
|
const void *,
|
|
cublasLtMatrixLayout_t,
|
|
void *,
|
|
cublasLtMatrixLayout_t,
|
|
const cublasLtMatmulAlgo_t *,
|
|
void *,
|
|
size_t,
|
|
cudaStream_t);
|
|
|
|
struct cublaslt_api {
|
|
void *lib;
|
|
cublasLtCreate_fn cublasLtCreate;
|
|
cublasLtDestroy_fn cublasLtDestroy;
|
|
cublasLtMatmulDescCreate_fn cublasLtMatmulDescCreate;
|
|
cublasLtMatmulDescDestroy_fn cublasLtMatmulDescDestroy;
|
|
cublasLtMatmulDescSetAttribute_fn cublasLtMatmulDescSetAttribute;
|
|
cublasLtMatrixLayoutCreate_fn cublasLtMatrixLayoutCreate;
|
|
cublasLtMatrixLayoutDestroy_fn cublasLtMatrixLayoutDestroy;
|
|
cublasLtMatmulPreferenceCreate_fn cublasLtMatmulPreferenceCreate;
|
|
cublasLtMatmulPreferenceDestroy_fn cublasLtMatmulPreferenceDestroy;
|
|
cublasLtMatmulPreferenceSetAttribute_fn cublasLtMatmulPreferenceSetAttribute;
|
|
cublasLtMatmulAlgoGetHeuristic_fn cublasLtMatmulAlgoGetHeuristic;
|
|
cublasLtMatmul_fn cublasLtMatmul;
|
|
};
|
|
|
|
struct profile_desc {
|
|
const char *name;
|
|
const char *block_label;
|
|
int min_cc;
|
|
int enabled;
|
|
int needs_scalar_scale;
|
|
int needs_block_scale;
|
|
int min_multiple;
|
|
cudaDataType_t a_type;
|
|
cudaDataType_t b_type;
|
|
cudaDataType_t c_type;
|
|
cudaDataType_t d_type;
|
|
cublasComputeType_t compute_type;
|
|
};
|
|
|
|
struct prepared_profile {
|
|
struct profile_desc desc;
|
|
CUstream stream;
|
|
cublasLtMatmulDesc_t op_desc;
|
|
cublasLtMatrixLayout_t a_layout;
|
|
cublasLtMatrixLayout_t b_layout;
|
|
cublasLtMatrixLayout_t c_layout;
|
|
cublasLtMatrixLayout_t d_layout;
|
|
cublasLtMatmulPreference_t preference;
|
|
cublasLtMatmulHeuristicResult_t heuristic;
|
|
CUdeviceptr a_dev;
|
|
CUdeviceptr b_dev;
|
|
CUdeviceptr c_dev;
|
|
CUdeviceptr d_dev;
|
|
CUdeviceptr a_scale_dev;
|
|
CUdeviceptr b_scale_dev;
|
|
CUdeviceptr workspace_dev;
|
|
size_t workspace_size;
|
|
uint64_t m;
|
|
uint64_t n;
|
|
uint64_t k;
|
|
unsigned long iterations;
|
|
int ready;
|
|
};
|
|
|
|
static const struct profile_desc k_profiles[] = {
|
|
{
|
|
"fp64",
|
|
"fp64",
|
|
80,
|
|
1,
|
|
0,
|
|
0,
|
|
8,
|
|
CUDA_R_64F,
|
|
CUDA_R_64F,
|
|
CUDA_R_64F,
|
|
CUDA_R_64F,
|
|
CUBLAS_COMPUTE_64F,
|
|
},
|
|
{
|
|
"fp32_tf32",
|
|
"fp32",
|
|
80,
|
|
1,
|
|
0,
|
|
0,
|
|
128,
|
|
CUDA_R_32F,
|
|
CUDA_R_32F,
|
|
CUDA_R_32F,
|
|
CUDA_R_32F,
|
|
CUBLAS_COMPUTE_32F_FAST_TF32,
|
|
},
|
|
{
|
|
"fp16_tensor",
|
|
"fp16",
|
|
80,
|
|
1,
|
|
0,
|
|
0,
|
|
128,
|
|
CUDA_R_16F,
|
|
CUDA_R_16F,
|
|
CUDA_R_16F,
|
|
CUDA_R_16F,
|
|
CUBLAS_COMPUTE_32F_FAST_16F,
|
|
},
|
|
{
|
|
"int8_tensor",
|
|
"int8",
|
|
75,
|
|
1,
|
|
0,
|
|
0,
|
|
128,
|
|
CUDA_R_8I,
|
|
CUDA_R_8I,
|
|
CUDA_R_32I,
|
|
CUDA_R_32I,
|
|
CUBLAS_COMPUTE_32I,
|
|
},
|
|
{
|
|
"fp8_e4m3",
|
|
"fp8",
|
|
89,
|
|
1,
|
|
1,
|
|
0,
|
|
128,
|
|
CUDA_R_8F_E4M3,
|
|
CUDA_R_8F_E4M3,
|
|
CUDA_R_16BF,
|
|
CUDA_R_16BF,
|
|
CUBLAS_COMPUTE_32F,
|
|
},
|
|
{
|
|
"fp8_e5m2",
|
|
"fp8",
|
|
89,
|
|
1,
|
|
1,
|
|
0,
|
|
128,
|
|
CUDA_R_8F_E5M2,
|
|
CUDA_R_8F_E5M2,
|
|
CUDA_R_16BF,
|
|
CUDA_R_16BF,
|
|
CUBLAS_COMPUTE_32F,
|
|
},
|
|
#if defined(CUDA_R_4F_E2M1) && defined(CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3)
|
|
{
|
|
"fp4_e2m1",
|
|
"fp4",
|
|
100,
|
|
1,
|
|
0,
|
|
1,
|
|
128,
|
|
CUDA_R_4F_E2M1,
|
|
CUDA_R_4F_E2M1,
|
|
CUDA_R_16BF,
|
|
CUDA_R_16BF,
|
|
CUBLAS_COMPUTE_32F,
|
|
},
|
|
#endif
|
|
};
|
|
|
|
#define PROFILE_COUNT ((int)(sizeof(k_profiles) / sizeof(k_profiles[0])))
|
|
|
|
static int load_cublaslt(struct cublaslt_api *api) {
|
|
memset(api, 0, sizeof(*api));
|
|
api->lib = dlopen("libcublasLt.so.13", RTLD_NOW | RTLD_LOCAL);
|
|
if (!api->lib) {
|
|
api->lib = dlopen("libcublasLt.so", RTLD_NOW | RTLD_LOCAL);
|
|
}
|
|
if (!api->lib) {
|
|
return 0;
|
|
}
|
|
return
|
|
load_symbol(api->lib, "cublasLtCreate", (void **)&api->cublasLtCreate) &&
|
|
load_symbol(api->lib, "cublasLtDestroy", (void **)&api->cublasLtDestroy) &&
|
|
load_symbol(api->lib, "cublasLtMatmulDescCreate", (void **)&api->cublasLtMatmulDescCreate) &&
|
|
load_symbol(api->lib, "cublasLtMatmulDescDestroy", (void **)&api->cublasLtMatmulDescDestroy) &&
|
|
load_symbol(api->lib,
|
|
"cublasLtMatmulDescSetAttribute",
|
|
(void **)&api->cublasLtMatmulDescSetAttribute) &&
|
|
load_symbol(api->lib, "cublasLtMatrixLayoutCreate", (void **)&api->cublasLtMatrixLayoutCreate) &&
|
|
load_symbol(api->lib, "cublasLtMatrixLayoutDestroy", (void **)&api->cublasLtMatrixLayoutDestroy) &&
|
|
load_symbol(api->lib,
|
|
"cublasLtMatmulPreferenceCreate",
|
|
(void **)&api->cublasLtMatmulPreferenceCreate) &&
|
|
load_symbol(api->lib,
|
|
"cublasLtMatmulPreferenceDestroy",
|
|
(void **)&api->cublasLtMatmulPreferenceDestroy) &&
|
|
load_symbol(api->lib,
|
|
"cublasLtMatmulPreferenceSetAttribute",
|
|
(void **)&api->cublasLtMatmulPreferenceSetAttribute) &&
|
|
load_symbol(api->lib,
|
|
"cublasLtMatmulAlgoGetHeuristic",
|
|
(void **)&api->cublasLtMatmulAlgoGetHeuristic) &&
|
|
load_symbol(api->lib, "cublasLtMatmul", (void **)&api->cublasLtMatmul);
|
|
}
|
|
|
|
static const char *cublas_status_text(cublasStatus_t status) {
|
|
switch (status) {
|
|
case CUBLAS_STATUS_SUCCESS:
|
|
return "CUBLAS_STATUS_SUCCESS";
|
|
case CUBLAS_STATUS_NOT_INITIALIZED:
|
|
return "CUBLAS_STATUS_NOT_INITIALIZED";
|
|
case CUBLAS_STATUS_ALLOC_FAILED:
|
|
return "CUBLAS_STATUS_ALLOC_FAILED";
|
|
case CUBLAS_STATUS_INVALID_VALUE:
|
|
return "CUBLAS_STATUS_INVALID_VALUE";
|
|
case CUBLAS_STATUS_ARCH_MISMATCH:
|
|
return "CUBLAS_STATUS_ARCH_MISMATCH";
|
|
case CUBLAS_STATUS_MAPPING_ERROR:
|
|
return "CUBLAS_STATUS_MAPPING_ERROR";
|
|
case CUBLAS_STATUS_EXECUTION_FAILED:
|
|
return "CUBLAS_STATUS_EXECUTION_FAILED";
|
|
case CUBLAS_STATUS_INTERNAL_ERROR:
|
|
return "CUBLAS_STATUS_INTERNAL_ERROR";
|
|
case CUBLAS_STATUS_NOT_SUPPORTED:
|
|
return "CUBLAS_STATUS_NOT_SUPPORTED";
|
|
default:
|
|
return "CUBLAS_STATUS_UNKNOWN";
|
|
}
|
|
}
|
|
|
|
static int check_cublas(const char *step, cublasStatus_t status) {
|
|
if (status == CUBLAS_STATUS_SUCCESS) {
|
|
return 1;
|
|
}
|
|
fprintf(stderr, "%s failed: %s (%d)\n", step, cublas_status_text(status), (int)status);
|
|
return 0;
|
|
}
|
|
|
|
static size_t bytes_for_elements(cudaDataType_t type, uint64_t elements) {
|
|
switch (type) {
|
|
case CUDA_R_32F:
|
|
case CUDA_R_32I:
|
|
return (size_t)(elements * 4u);
|
|
case CUDA_R_16F:
|
|
case CUDA_R_16BF:
|
|
return (size_t)(elements * 2u);
|
|
case CUDA_R_8I:
|
|
case CUDA_R_8F_E4M3:
|
|
case CUDA_R_8F_E5M2:
|
|
return (size_t)(elements);
|
|
#if defined(CUDA_R_4F_E2M1)
|
|
case CUDA_R_4F_E2M1:
|
|
return (size_t)((elements + 1u) / 2u);
|
|
#endif
|
|
default:
|
|
return (size_t)(elements * 4u);
|
|
}
|
|
}
|
|
|
|
static cudaDataType_t matmul_scale_type(const struct profile_desc *desc) {
|
|
if (desc->compute_type == CUBLAS_COMPUTE_32I) {
|
|
return CUDA_R_32I;
|
|
}
|
|
if (desc->compute_type == CUBLAS_COMPUTE_64F) {
|
|
return CUDA_R_64F;
|
|
}
|
|
return CUDA_R_32F;
|
|
}
|
|
|
|
static size_t fp4_scale_bytes(uint64_t rows, uint64_t cols) {
|
|
uint64_t row_tiles = (rows + 127u) / 128u;
|
|
uint64_t col_tiles = (cols + 63u) / 64u;
|
|
return (size_t)(row_tiles * col_tiles * 128u);
|
|
}
|
|
|
|
static uint64_t choose_square_dim(size_t budget_bytes, size_t bytes_per_cell, int multiple) {
|
|
double approx = sqrt((double)budget_bytes / (double)bytes_per_cell);
|
|
uint64_t dim = (uint64_t)approx;
|
|
if (dim < (uint64_t)multiple) {
|
|
dim = (uint64_t)multiple;
|
|
}
|
|
dim = (uint64_t)round_down_size((size_t)dim, (size_t)multiple);
|
|
if (dim < (uint64_t)multiple) {
|
|
dim = (uint64_t)multiple;
|
|
}
|
|
if (dim > 65536u) {
|
|
dim = 65536u;
|
|
}
|
|
return dim;
|
|
}
|
|
|
|
static int device_upload(struct cuda_api *cuda, CUdeviceptr dev, const void *src, size_t bytes) {
|
|
return check_rc(cuda, "cuMemcpyHtoD", cuda->cuMemcpyHtoD(dev, src, bytes));
|
|
}
|
|
|
|
static int alloc_filled(struct cuda_api *cuda, CUdeviceptr *ptr, size_t bytes, unsigned char pattern) {
|
|
if (!check_rc(cuda, "cuMemAlloc", cuda->cuMemAlloc(ptr, bytes))) {
|
|
return 0;
|
|
}
|
|
if (!check_rc(cuda, "cuMemsetD8", cuda->cuMemsetD8(*ptr, pattern, bytes))) {
|
|
cuda->cuMemFree(*ptr);
|
|
*ptr = 0;
|
|
return 0;
|
|
}
|
|
return 1;
|
|
}
|
|
|
|
static size_t profile_scale_bytes(const struct profile_desc *desc, uint64_t m, uint64_t n, uint64_t k) {
|
|
size_t bytes = 0;
|
|
if (desc->needs_scalar_scale) {
|
|
bytes += 2u * sizeof(float);
|
|
}
|
|
#if defined(CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3)
|
|
if (desc->needs_block_scale) {
|
|
bytes += fp4_scale_bytes(k, m);
|
|
bytes += fp4_scale_bytes(k, n);
|
|
}
|
|
#else
|
|
(void)m;
|
|
(void)n;
|
|
(void)k;
|
|
#endif
|
|
return bytes;
|
|
}
|
|
|
|
static void destroy_profile(struct cublaslt_api *cublas, struct cuda_api *cuda, struct prepared_profile *profile) {
|
|
if (profile->workspace_dev) {
|
|
cuda->cuMemFree(profile->workspace_dev);
|
|
}
|
|
if (profile->a_scale_dev) {
|
|
cuda->cuMemFree(profile->a_scale_dev);
|
|
}
|
|
if (profile->b_scale_dev) {
|
|
cuda->cuMemFree(profile->b_scale_dev);
|
|
}
|
|
if (profile->d_dev) {
|
|
cuda->cuMemFree(profile->d_dev);
|
|
}
|
|
if (profile->c_dev) {
|
|
cuda->cuMemFree(profile->c_dev);
|
|
}
|
|
if (profile->b_dev) {
|
|
cuda->cuMemFree(profile->b_dev);
|
|
}
|
|
if (profile->a_dev) {
|
|
cuda->cuMemFree(profile->a_dev);
|
|
}
|
|
if (profile->preference) {
|
|
cublas->cublasLtMatmulPreferenceDestroy(profile->preference);
|
|
}
|
|
if (profile->d_layout) {
|
|
cublas->cublasLtMatrixLayoutDestroy(profile->d_layout);
|
|
}
|
|
if (profile->c_layout) {
|
|
cublas->cublasLtMatrixLayoutDestroy(profile->c_layout);
|
|
}
|
|
if (profile->b_layout) {
|
|
cublas->cublasLtMatrixLayoutDestroy(profile->b_layout);
|
|
}
|
|
if (profile->a_layout) {
|
|
cublas->cublasLtMatrixLayoutDestroy(profile->a_layout);
|
|
}
|
|
if (profile->op_desc) {
|
|
cublas->cublasLtMatmulDescDestroy(profile->op_desc);
|
|
}
|
|
memset(profile, 0, sizeof(*profile));
|
|
}
|
|
|
|
static int prepare_profile(struct cublaslt_api *cublas,
|
|
cublasLtHandle_t handle,
|
|
struct cuda_api *cuda,
|
|
const struct profile_desc *desc,
|
|
CUstream stream,
|
|
size_t profile_budget_bytes,
|
|
struct prepared_profile *out) {
|
|
size_t bytes_per_cell = 0;
|
|
size_t attempt_budget = profile_budget_bytes;
|
|
|
|
bytes_per_cell += bytes_for_elements(desc->a_type, 1);
|
|
bytes_per_cell += bytes_for_elements(desc->b_type, 1);
|
|
bytes_per_cell += bytes_for_elements(desc->c_type, 1);
|
|
bytes_per_cell += bytes_for_elements(desc->d_type, 1);
|
|
if (bytes_per_cell == 0) {
|
|
return 0;
|
|
}
|
|
|
|
while (attempt_budget >= MIN_PROFILE_BUDGET_BYTES) {
|
|
memset(out, 0, sizeof(*out));
|
|
out->desc = *desc;
|
|
out->stream = stream;
|
|
|
|
uint64_t dim = choose_square_dim(attempt_budget, bytes_per_cell, desc->min_multiple);
|
|
out->m = dim;
|
|
out->n = dim;
|
|
out->k = dim;
|
|
|
|
size_t desired_workspace = attempt_budget / 8u;
|
|
if (desired_workspace > 32u * 1024u * 1024u) {
|
|
desired_workspace = 32u * 1024u * 1024u;
|
|
}
|
|
desired_workspace = round_down_size(desired_workspace, 256u);
|
|
|
|
size_t a_bytes = 0;
|
|
size_t b_bytes = 0;
|
|
size_t c_bytes = 0;
|
|
size_t d_bytes = 0;
|
|
size_t scale_bytes = 0;
|
|
while (1) {
|
|
a_bytes = bytes_for_elements(desc->a_type, out->k * out->m);
|
|
b_bytes = bytes_for_elements(desc->b_type, out->k * out->n);
|
|
c_bytes = bytes_for_elements(desc->c_type, out->m * out->n);
|
|
d_bytes = bytes_for_elements(desc->d_type, out->m * out->n);
|
|
scale_bytes = profile_scale_bytes(desc, out->m, out->n, out->k);
|
|
|
|
size_t matrix_bytes = a_bytes + b_bytes + c_bytes + d_bytes + scale_bytes;
|
|
if (matrix_bytes <= attempt_budget) {
|
|
size_t remaining = attempt_budget - matrix_bytes;
|
|
out->workspace_size = desired_workspace;
|
|
if (out->workspace_size > remaining) {
|
|
out->workspace_size = round_down_size(remaining, 256u);
|
|
}
|
|
break;
|
|
}
|
|
|
|
if (out->m <= (uint64_t)desc->min_multiple) {
|
|
break;
|
|
}
|
|
out->m -= (uint64_t)desc->min_multiple;
|
|
out->n = out->m;
|
|
out->k = out->m;
|
|
}
|
|
if (out->m < (uint64_t)desc->min_multiple) {
|
|
attempt_budget /= 2u;
|
|
continue;
|
|
}
|
|
|
|
if (!alloc_filled(cuda, &out->a_dev, a_bytes, 0x11) ||
|
|
!alloc_filled(cuda, &out->b_dev, b_bytes, 0x11) ||
|
|
!alloc_filled(cuda, &out->c_dev, c_bytes, 0x00) ||
|
|
!alloc_filled(cuda, &out->d_dev, d_bytes, 0x00)) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
|
|
cudaDataType_t scale_type = matmul_scale_type(desc);
|
|
if (!check_cublas("cublasLtMatmulDescCreate",
|
|
cublas->cublasLtMatmulDescCreate(&out->op_desc, desc->compute_type, scale_type))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
|
|
cublasOperation_t transa = CUBLAS_OP_T;
|
|
cublasOperation_t transb = CUBLAS_OP_N;
|
|
if (!check_cublas("set TRANSA",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_TRANSA,
|
|
&transa,
|
|
sizeof(transa))) ||
|
|
!check_cublas("set TRANSB",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_TRANSB,
|
|
&transb,
|
|
sizeof(transb)))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
|
|
if (desc->needs_scalar_scale) {
|
|
float one = 1.0f;
|
|
if (!alloc_filled(cuda, &out->a_scale_dev, sizeof(one), 0x00) ||
|
|
!alloc_filled(cuda, &out->b_scale_dev, sizeof(one), 0x00)) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
if (!device_upload(cuda, out->a_scale_dev, &one, sizeof(one)) ||
|
|
!device_upload(cuda, out->b_scale_dev, &one, sizeof(one))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
void *a_scale_ptr = (void *)(uintptr_t)out->a_scale_dev;
|
|
void *b_scale_ptr = (void *)(uintptr_t)out->b_scale_dev;
|
|
if (!check_cublas("set A scale ptr",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
|
|
&a_scale_ptr,
|
|
sizeof(a_scale_ptr))) ||
|
|
!check_cublas("set B scale ptr",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
|
|
&b_scale_ptr,
|
|
sizeof(b_scale_ptr)))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
#if defined(CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3)
|
|
if (desc->needs_block_scale) {
|
|
size_t a_scale_bytes = fp4_scale_bytes(out->k, out->m);
|
|
size_t b_scale_bytes = fp4_scale_bytes(out->k, out->n);
|
|
if (!alloc_filled(cuda, &out->a_scale_dev, a_scale_bytes, 0x11) ||
|
|
!alloc_filled(cuda, &out->b_scale_dev, b_scale_bytes, 0x11)) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
cublasLtMatmulMatrixScale_t scale_mode = CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3;
|
|
void *a_scale_ptr = (void *)(uintptr_t)out->a_scale_dev;
|
|
void *b_scale_ptr = (void *)(uintptr_t)out->b_scale_dev;
|
|
if (!check_cublas("set A scale mode",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_A_SCALE_MODE,
|
|
&scale_mode,
|
|
sizeof(scale_mode))) ||
|
|
!check_cublas("set B scale mode",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_B_SCALE_MODE,
|
|
&scale_mode,
|
|
sizeof(scale_mode))) ||
|
|
!check_cublas("set A block scale ptr",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
|
|
&a_scale_ptr,
|
|
sizeof(a_scale_ptr))) ||
|
|
!check_cublas("set B block scale ptr",
|
|
cublas->cublasLtMatmulDescSetAttribute(out->op_desc,
|
|
CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
|
|
&b_scale_ptr,
|
|
sizeof(b_scale_ptr)))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
}
|
|
#endif
|
|
|
|
if (!check_cublas("create A layout",
|
|
cublas->cublasLtMatrixLayoutCreate(&out->a_layout, desc->a_type, out->k, out->m, out->k)) ||
|
|
!check_cublas("create B layout",
|
|
cublas->cublasLtMatrixLayoutCreate(&out->b_layout, desc->b_type, out->k, out->n, out->k)) ||
|
|
!check_cublas("create C layout",
|
|
cublas->cublasLtMatrixLayoutCreate(&out->c_layout, desc->c_type, out->m, out->n, out->m)) ||
|
|
!check_cublas("create D layout",
|
|
cublas->cublasLtMatrixLayoutCreate(&out->d_layout, desc->d_type, out->m, out->n, out->m))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
|
|
if (!check_cublas("create preference", cublas->cublasLtMatmulPreferenceCreate(&out->preference))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
|
|
if (out->workspace_size > 0) {
|
|
if (!alloc_filled(cuda, &out->workspace_dev, out->workspace_size, 0x00)) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
if (!check_cublas("set workspace",
|
|
cublas->cublasLtMatmulPreferenceSetAttribute(
|
|
out->preference,
|
|
CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES,
|
|
&out->workspace_size,
|
|
sizeof(out->workspace_size)))) {
|
|
destroy_profile(cublas, cuda, out);
|
|
return 0;
|
|
}
|
|
|
|
int found = 0;
|
|
if (check_cublas("heuristic",
|
|
cublas->cublasLtMatmulAlgoGetHeuristic(handle,
|
|
out->op_desc,
|
|
out->a_layout,
|
|
out->b_layout,
|
|
out->c_layout,
|
|
out->d_layout,
|
|
out->preference,
|
|
1,
|
|
&out->heuristic,
|
|
&found)) &&
|
|
found > 0) {
|
|
out->ready = 1;
|
|
return 1;
|
|
}
|
|
|
|
destroy_profile(cublas, cuda, out);
|
|
attempt_budget = round_down_size(attempt_budget * 3u / 4u, 256u);
|
|
if (attempt_budget < MIN_PROFILE_BUDGET_BYTES) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static int run_cublas_profile(cublasLtHandle_t handle,
|
|
struct cublaslt_api *cublas,
|
|
struct prepared_profile *profile) {
|
|
int32_t alpha_i32 = 1;
|
|
int32_t beta_i32 = 0;
|
|
double alpha_f64 = 1.0;
|
|
double beta_f64 = 0.0;
|
|
float alpha = 1.0f;
|
|
float beta = 0.0f;
|
|
const void *alpha_ptr = α
|
|
const void *beta_ptr = β
|
|
if (profile->desc.compute_type == CUBLAS_COMPUTE_32I) {
|
|
alpha_ptr = &alpha_i32;
|
|
beta_ptr = &beta_i32;
|
|
} else if (profile->desc.compute_type == CUBLAS_COMPUTE_64F) {
|
|
alpha_ptr = &alpha_f64;
|
|
beta_ptr = &beta_f64;
|
|
}
|
|
return check_cublas(profile->desc.name,
|
|
cublas->cublasLtMatmul(handle,
|
|
profile->op_desc,
|
|
alpha_ptr,
|
|
(const void *)(uintptr_t)profile->a_dev,
|
|
profile->a_layout,
|
|
(const void *)(uintptr_t)profile->b_dev,
|
|
profile->b_layout,
|
|
beta_ptr,
|
|
(const void *)(uintptr_t)profile->c_dev,
|
|
profile->c_layout,
|
|
(void *)(uintptr_t)profile->d_dev,
|
|
profile->d_layout,
|
|
&profile->heuristic.algo,
|
|
(void *)(uintptr_t)profile->workspace_dev,
|
|
profile->workspace_size,
|
|
profile->stream));
|
|
}
|
|
|
|
static int run_cublaslt_stress(struct cuda_api *cuda,
|
|
CUdevice dev,
|
|
const char *device_name,
|
|
int cc_major,
|
|
int cc_minor,
|
|
int seconds,
|
|
int size_mb,
|
|
const char *precision_filter,
|
|
struct stress_report *report) {
|
|
struct cublaslt_api cublas;
|
|
struct prepared_profile prepared[MAX_STRESS_STREAMS * PROFILE_COUNT];
|
|
cublasLtHandle_t handle = NULL;
|
|
CUcontext ctx = NULL;
|
|
CUstream streams[MAX_STRESS_STREAMS] = {0};
|
|
uint16_t sample[256];
|
|
int cc = cc_major * 10 + cc_minor;
|
|
int planned = 0;
|
|
int active = 0;
|
|
int mp_count = 0;
|
|
int stream_count = 1;
|
|
int profile_count = PROFILE_COUNT;
|
|
int prepared_count = 0;
|
|
size_t requested_budget = 0;
|
|
size_t total_budget = 0;
|
|
size_t per_profile_budget = 0;
|
|
int budget_profiles = 0;
|
|
|
|
memset(report, 0, sizeof(*report));
|
|
snprintf(report->backend, sizeof(report->backend), "cublasLt");
|
|
snprintf(report->device, sizeof(report->device), "%s", device_name);
|
|
report->cc_major = cc_major;
|
|
report->cc_minor = cc_minor;
|
|
report->buffer_mb = size_mb;
|
|
|
|
if (!load_cublaslt(&cublas)) {
|
|
snprintf(report->details, sizeof(report->details), "cublasLt=unavailable\n");
|
|
return 0;
|
|
}
|
|
if (!check_rc(cuda, "cuCtxCreate", cuda->cuCtxCreate(&ctx, 0, dev))) {
|
|
return 0;
|
|
}
|
|
if (!check_cublas("cublasLtCreate", cublas.cublasLtCreate(&handle))) {
|
|
cuda->cuCtxDestroy(ctx);
|
|
return 0;
|
|
}
|
|
|
|
/* Count profiles matching the filter (for deciding what to run). */
|
|
for (size_t i = 0; i < sizeof(k_profiles) / sizeof(k_profiles[0]); i++) {
|
|
if (k_profiles[i].enabled && cc >= k_profiles[i].min_cc &&
|
|
(precision_filter == NULL || strcmp(k_profiles[i].block_label, precision_filter) == 0)) {
|
|
planned++;
|
|
}
|
|
}
|
|
if (planned <= 0) {
|
|
snprintf(report->details, sizeof(report->details), "cublasLt_profiles=unsupported\n");
|
|
cublas.cublasLtDestroy(handle);
|
|
cuda->cuCtxDestroy(ctx);
|
|
return 0;
|
|
}
|
|
|
|
/* Count all profiles active on this GPU regardless of filter.
|
|
* Mixed phases still divide budget across the full precision set, while
|
|
* single-precision benchmark phases dedicate budget only to active
|
|
* profiles matching precision_filter. */
|
|
int planned_total = 0;
|
|
for (size_t i = 0; i < sizeof(k_profiles) / sizeof(k_profiles[0]); i++) {
|
|
if (k_profiles[i].enabled && cc >= k_profiles[i].min_cc) {
|
|
planned_total++;
|
|
}
|
|
}
|
|
if (planned_total < planned) {
|
|
planned_total = planned;
|
|
}
|
|
budget_profiles = planned_total;
|
|
if (precision_filter != NULL) {
|
|
budget_profiles = planned;
|
|
}
|
|
if (budget_profiles <= 0) {
|
|
budget_profiles = planned_total;
|
|
}
|
|
|
|
requested_budget = (size_t)size_mb * 1024u * 1024u;
|
|
if (requested_budget < (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES) {
|
|
requested_budget = (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES;
|
|
}
|
|
total_budget = clamp_budget_to_free_memory(cuda, requested_budget);
|
|
if (total_budget < (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES) {
|
|
total_budget = (size_t)budget_profiles * MIN_PROFILE_BUDGET_BYTES;
|
|
}
|
|
if (query_multiprocessor_count(cuda, dev, &mp_count) &&
|
|
cuda->cuStreamCreate &&
|
|
cuda->cuStreamDestroy) {
|
|
stream_count = choose_stream_count(mp_count, budget_profiles, total_budget, 1);
|
|
}
|
|
if (precision_filter != NULL && stream_count > MAX_SINGLE_PRECISION_STREAMS) {
|
|
stream_count = MAX_SINGLE_PRECISION_STREAMS;
|
|
}
|
|
if (stream_count > 1) {
|
|
int created = 0;
|
|
for (; created < stream_count; created++) {
|
|
if (!check_rc(cuda, "cuStreamCreate", cuda->cuStreamCreate(&streams[created], 0))) {
|
|
destroy_streams(cuda, streams, created);
|
|
stream_count = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
report->stream_count = stream_count;
|
|
per_profile_budget = total_budget / ((size_t)budget_profiles * (size_t)stream_count);
|
|
if (per_profile_budget < MIN_PROFILE_BUDGET_BYTES) {
|
|
per_profile_budget = MIN_PROFILE_BUDGET_BYTES;
|
|
}
|
|
if (precision_filter != NULL) {
|
|
per_profile_budget = clamp_single_precision_profile_budget(per_profile_budget);
|
|
}
|
|
report->buffer_mb = (int)(total_budget / (1024u * 1024u));
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"requested_mb=%d actual_mb=%d streams=%d mp_count=%d budget_profiles=%d per_worker_mb=%zu\n",
|
|
size_mb,
|
|
report->buffer_mb,
|
|
report->stream_count,
|
|
mp_count,
|
|
budget_profiles,
|
|
per_profile_budget / (1024u * 1024u));
|
|
|
|
for (int i = 0; i < profile_count; i++) {
|
|
const struct profile_desc *desc = &k_profiles[i];
|
|
if (!(desc->enabled && cc >= desc->min_cc)) {
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"%s=SKIPPED cc<%d\n",
|
|
desc->name,
|
|
desc->min_cc);
|
|
continue;
|
|
}
|
|
if (precision_filter != NULL && strcmp(desc->block_label, precision_filter) != 0) {
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"%s=SKIPPED precision_filter\n",
|
|
desc->name);
|
|
continue;
|
|
}
|
|
for (int lane = 0; lane < stream_count; lane++) {
|
|
CUstream stream = streams[lane];
|
|
if (prepared_count >= (int)(sizeof(prepared) / sizeof(prepared[0]))) {
|
|
break;
|
|
}
|
|
if (prepare_profile(&cublas, handle, cuda, desc, stream, per_profile_budget, &prepared[prepared_count])) {
|
|
active++;
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"%s[%d]=READY dim=%llux%llux%llu block=%s stream=%d\n",
|
|
desc->name,
|
|
lane,
|
|
(unsigned long long)prepared[prepared_count].m,
|
|
(unsigned long long)prepared[prepared_count].n,
|
|
(unsigned long long)prepared[prepared_count].k,
|
|
desc->block_label,
|
|
lane);
|
|
prepared_count++;
|
|
} else {
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"%s[%d]=SKIPPED unsupported\n",
|
|
desc->name,
|
|
lane);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (active <= 0) {
|
|
cublas.cublasLtDestroy(handle);
|
|
destroy_streams(cuda, streams, stream_count);
|
|
cuda->cuCtxDestroy(ctx);
|
|
return 0;
|
|
}
|
|
|
|
/* Keep the GPU queue continuously full by submitting kernels without
|
|
* synchronizing after every wave. A sync barrier after each small batch
|
|
* creates CPU↔GPU ping-pong gaps that prevent full TDP utilisation,
|
|
* especially when individual kernels are short. Instead we sync at most
|
|
* once per second (for error detection) and once at the very end. */
|
|
double deadline = now_seconds() + (double)seconds;
|
|
double next_sync = now_seconds() + 1.0;
|
|
while (now_seconds() < deadline) {
|
|
int launched = 0;
|
|
for (int i = 0; i < prepared_count; i++) {
|
|
if (!prepared[i].ready) {
|
|
continue;
|
|
}
|
|
if (!run_cublas_profile(handle, &cublas, &prepared[i])) {
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"%s=FAILED runtime\n",
|
|
prepared[i].desc.name);
|
|
for (int j = 0; j < prepared_count; j++) {
|
|
destroy_profile(&cublas, cuda, &prepared[j]);
|
|
}
|
|
cublas.cublasLtDestroy(handle);
|
|
destroy_streams(cuda, streams, stream_count);
|
|
cuda->cuCtxDestroy(ctx);
|
|
return 0;
|
|
}
|
|
prepared[i].iterations++;
|
|
report->iterations++;
|
|
launched++;
|
|
}
|
|
if (launched <= 0) {
|
|
break;
|
|
}
|
|
double now = now_seconds();
|
|
if (now >= next_sync || now >= deadline) {
|
|
if (!check_rc(cuda, "cuCtxSynchronize", cuda->cuCtxSynchronize())) {
|
|
for (int i = 0; i < prepared_count; i++) {
|
|
destroy_profile(&cublas, cuda, &prepared[i]);
|
|
}
|
|
cublas.cublasLtDestroy(handle);
|
|
destroy_streams(cuda, streams, stream_count);
|
|
cuda->cuCtxDestroy(ctx);
|
|
return 0;
|
|
}
|
|
next_sync = now + 1.0;
|
|
}
|
|
}
|
|
/* Final drain — ensure all queued work finishes before we read results. */
|
|
cuda->cuCtxSynchronize();
|
|
|
|
for (int i = 0; i < prepared_count; i++) {
|
|
if (!prepared[i].ready) {
|
|
continue;
|
|
}
|
|
append_detail(report->details,
|
|
sizeof(report->details),
|
|
"%s_iterations=%lu\n",
|
|
prepared[i].desc.name,
|
|
prepared[i].iterations);
|
|
}
|
|
|
|
for (int i = 0; i < prepared_count; i++) {
|
|
if (prepared[i].ready) {
|
|
if (check_rc(cuda, "cuMemcpyDtoH", cuda->cuMemcpyDtoH(sample, prepared[i].d_dev, sizeof(sample)))) {
|
|
for (size_t j = 0; j < sizeof(sample) / sizeof(sample[0]); j++) {
|
|
report->checksum += sample[j];
|
|
}
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < prepared_count; i++) {
|
|
destroy_profile(&cublas, cuda, &prepared[i]);
|
|
}
|
|
cublas.cublasLtDestroy(handle);
|
|
destroy_streams(cuda, streams, stream_count);
|
|
cuda->cuCtxDestroy(ctx);
|
|
return 1;
|
|
}
|
|
#endif
|
|
|
|
static void print_stress_report(const struct stress_report *report, int device_index, int seconds) {
|
|
printf("device=%s\n", report->device);
|
|
printf("device_index=%d\n", device_index);
|
|
printf("compute_capability=%d.%d\n", report->cc_major, report->cc_minor);
|
|
printf("backend=%s\n", report->backend);
|
|
printf("duration_s=%d\n", seconds);
|
|
printf("buffer_mb=%d\n", report->buffer_mb);
|
|
printf("streams=%d\n", report->stream_count);
|
|
printf("iterations=%lu\n", report->iterations);
|
|
printf("checksum=%llu\n", (unsigned long long)report->checksum);
|
|
if (report->details[0] != '\0') {
|
|
printf("%s", report->details);
|
|
}
|
|
printf("status=OK\n");
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
int seconds = 5;
|
|
int size_mb = 64;
|
|
int device_index = 0;
|
|
const char *precision_filter = NULL; /* NULL = all; else block_label to match */
|
|
const char *precision_plan = NULL;
|
|
const char *precision_plan_seconds = NULL;
|
|
for (int i = 1; i < argc; i++) {
|
|
if ((strcmp(argv[i], "--seconds") == 0 || strcmp(argv[i], "-t") == 0) && i + 1 < argc) {
|
|
seconds = atoi(argv[++i]);
|
|
} else if ((strcmp(argv[i], "--size-mb") == 0 || strcmp(argv[i], "-m") == 0) && i + 1 < argc) {
|
|
size_mb = atoi(argv[++i]);
|
|
} else if ((strcmp(argv[i], "--device") == 0 || strcmp(argv[i], "-d") == 0) && i + 1 < argc) {
|
|
device_index = atoi(argv[++i]);
|
|
} else if (strcmp(argv[i], "--precision") == 0 && i + 1 < argc) {
|
|
precision_filter = argv[++i];
|
|
} else if (strcmp(argv[i], "--precision-plan") == 0 && i + 1 < argc) {
|
|
precision_plan = argv[++i];
|
|
} else if (strcmp(argv[i], "--precision-plan-seconds") == 0 && i + 1 < argc) {
|
|
precision_plan_seconds = argv[++i];
|
|
} else {
|
|
fprintf(stderr,
|
|
"usage: %s [--seconds N] [--size-mb N] [--device N] [--precision int8|fp8|fp16|fp32|fp64|fp4] [--precision-plan p1,p2,...,mixed] [--precision-plan-seconds s1,s2,...]\n",
|
|
argv[0]);
|
|
return 2;
|
|
}
|
|
}
|
|
if (seconds <= 0) {
|
|
seconds = 5;
|
|
}
|
|
if (size_mb <= 0) {
|
|
size_mb = 64;
|
|
}
|
|
if (device_index < 0) {
|
|
device_index = 0;
|
|
}
|
|
|
|
struct cuda_api cuda;
|
|
if (!load_cuda(&cuda)) {
|
|
fprintf(stderr, "failed to load libcuda.so.1 or required Driver API symbols\n");
|
|
return 1;
|
|
}
|
|
load_symbol(cuda.lib, "cuGetErrorName", (void **)&cuda.cuGetErrorName);
|
|
load_symbol(cuda.lib, "cuGetErrorString", (void **)&cuda.cuGetErrorString);
|
|
|
|
if (!check_rc(&cuda, "cuInit", cuda.cuInit(0))) {
|
|
return 1;
|
|
}
|
|
|
|
int count = 0;
|
|
if (!check_rc(&cuda, "cuDeviceGetCount", cuda.cuDeviceGetCount(&count))) {
|
|
return 1;
|
|
}
|
|
if (count <= 0) {
|
|
fprintf(stderr, "no CUDA devices found\n");
|
|
return 1;
|
|
}
|
|
|
|
if (device_index >= count) {
|
|
fprintf(stderr, "device index %d out of range (found %d CUDA device(s))\n", device_index, count);
|
|
return 1;
|
|
}
|
|
|
|
CUdevice dev = 0;
|
|
if (!check_rc(&cuda, "cuDeviceGet", cuda.cuDeviceGet(&dev, device_index))) {
|
|
return 1;
|
|
}
|
|
|
|
char name[128] = {0};
|
|
if (!check_rc(&cuda, "cuDeviceGetName", cuda.cuDeviceGetName(name, (int)sizeof(name), dev))) {
|
|
return 1;
|
|
}
|
|
|
|
int cc_major = 0;
|
|
int cc_minor = 0;
|
|
if (!query_compute_capability(&cuda, dev, &cc_major, &cc_minor)) {
|
|
return 1;
|
|
}
|
|
|
|
struct stress_report report;
|
|
int ok = 0;
|
|
|
|
#if HAVE_CUBLASLT_HEADERS
|
|
if (precision_plan != NULL && precision_plan[0] != '\0') {
|
|
char *plan_copy = strdup(precision_plan);
|
|
char *plan_seconds_copy = NULL;
|
|
int phase_seconds[32] = {0};
|
|
int phase_seconds_count = 0;
|
|
int phase_ok = 0;
|
|
if (plan_copy == NULL) {
|
|
fprintf(stderr, "failed to allocate precision plan buffer\n");
|
|
return 1;
|
|
}
|
|
if (precision_plan_seconds != NULL && precision_plan_seconds[0] != '\0') {
|
|
plan_seconds_copy = strdup(precision_plan_seconds);
|
|
if (plan_seconds_copy == NULL) {
|
|
free(plan_copy);
|
|
fprintf(stderr, "failed to allocate precision plan seconds buffer\n");
|
|
return 1;
|
|
}
|
|
for (char *sec_token = strtok(plan_seconds_copy, ",");
|
|
sec_token != NULL && phase_seconds_count < (int)(sizeof(phase_seconds) / sizeof(phase_seconds[0]));
|
|
sec_token = strtok(NULL, ",")) {
|
|
while (*sec_token == ' ' || *sec_token == '\t') {
|
|
sec_token++;
|
|
}
|
|
if (*sec_token == '\0') {
|
|
continue;
|
|
}
|
|
phase_seconds[phase_seconds_count++] = atoi(sec_token);
|
|
}
|
|
}
|
|
int phase_idx = 0;
|
|
for (char *token = strtok(plan_copy, ","); token != NULL; token = strtok(NULL, ","), phase_idx++) {
|
|
while (*token == ' ' || *token == '\t') {
|
|
token++;
|
|
}
|
|
if (*token == '\0') {
|
|
continue;
|
|
}
|
|
const char *phase_name = token;
|
|
const char *phase_filter = token;
|
|
if (strcmp(token, "mixed") == 0 || strcmp(token, "all") == 0) {
|
|
phase_filter = NULL;
|
|
}
|
|
int phase_duration = seconds;
|
|
if (phase_idx < phase_seconds_count && phase_seconds[phase_idx] > 0) {
|
|
phase_duration = phase_seconds[phase_idx];
|
|
}
|
|
printf("phase_begin=%s\n", phase_name);
|
|
fflush(stdout);
|
|
memset(&report, 0, sizeof(report));
|
|
ok = run_cublaslt_stress(&cuda, dev, name, cc_major, cc_minor, phase_duration, size_mb, phase_filter, &report);
|
|
if (ok) {
|
|
print_stress_report(&report, device_index, phase_duration);
|
|
phase_ok = 1;
|
|
} else {
|
|
printf("phase_error=%s\n", phase_name);
|
|
if (report.details[0] != '\0') {
|
|
printf("%s", report.details);
|
|
if (report.details[strlen(report.details) - 1] != '\n') {
|
|
printf("\n");
|
|
}
|
|
}
|
|
printf("status=FAILED\n");
|
|
}
|
|
printf("phase_end=%s\n", phase_name);
|
|
fflush(stdout);
|
|
}
|
|
free(plan_seconds_copy);
|
|
free(plan_copy);
|
|
return phase_ok ? 0 : 1;
|
|
}
|
|
ok = run_cublaslt_stress(&cuda, dev, name, cc_major, cc_minor, seconds, size_mb, precision_filter, &report);
|
|
#endif
|
|
if (!ok) {
|
|
if (precision_filter != NULL) {
|
|
fprintf(stderr,
|
|
"requested precision path unavailable: precision=%s device=%s cc=%d.%d\n",
|
|
precision_filter,
|
|
name,
|
|
cc_major,
|
|
cc_minor);
|
|
return 1;
|
|
}
|
|
int ptx_mb = size_mb;
|
|
if (!run_ptx_fallback(&cuda, dev, name, cc_major, cc_minor, seconds, ptx_mb, &report)) {
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
print_stress_report(&report, device_index, seconds);
|
|
return 0;
|
|
}
|