~idSSKrSSKrSSKrSSKrSSKrSSKJr SSKJr SSKJ r SSK J r SSK J r SSKJr \R R#\R R%\55r\R R)\S5/r\R R)\S 5rS /rSq\R2"5S 5r\R2"5S 5r"S S\5rSrSSSSSS.rSSSSSS.r Sr!\""\!5r#Sr$\%"S\&"S555r'S\'S'S\'S'S\'S'Sr(S r)"S!S"\5r*"S#S$\5r+g)%N)Path)knobs)compile_module_from_src) _allocation) GPUTarget) GPUDriverincludelib libcuda.so.1c ([RR=n(aU/$[R"SS/5R SS9nUR 5Vs/sHnSU;dM UR5SPM nnUVs/sH"n[RRU5PM$ nn[R"S5nU(alU(deURS5Vs/sHIn[RR[RRUS55(dMGUPMK nnS nU(aUS [U5-- nUS - nO US - nUS - n[SU55(dU5eU$s snfs snfs snf)Nz/sbin/ldconfigz-pignore)errorsr LD_LIBRARY_PATH:zlibcuda.so cannot found! z!Possible files are located at %s.z:Please create a symlink of libcuda.so to any of the files.zlibcuda_dirs..(s/Sddrww~~bggll4@AAdsA A )rnvidia libcuda_path subprocess check_outputdecode splitlinessplitrrdirnamegetenvrrstrany) env_libcuda_pathlibslinelocslocdirsenv_ld_library_pathdirmsgs r libcuda_dirsr/sd <<4444 !!  " "$4d#; < C C8 C TD*.): U):nPT>T DJJL ):D U,0 1DSBGGOOC DD 1))$564288=s=PRPWPWP\P\]`bpPqAr=s &C  2SY>> KK MM >> SdS S SXUXX S K V 1ts F*F)F )AF3Fc$[/[5Q$N) libdevice_dirr/r library_dirsr5,s  +LN ++r4c.^\rSrSrU4SjrSrSrU=r$) CudaUtils6cn>[US5(d[[U]U5UlUR$)Ninstance)hasattrsuperr7__new__r:)cls __class__s rr=CudaUtils.__new__8s-sJ'' C8=CL||r4cr[[[RR [ S55R 5S[5[[S9nURq URUl URUl URUl URUlURUlg)Nzdriver.c cuda_utilssrcnamer5 include_dirs libraries)rrrrrr" read_textr5rFrG PyCUtensorMap load_binaryget_device_propertiescuOccupancyMaxActiveClustersset_printf_fifo_sizefill_tma_descriptor)selfmods r__init__CudaUtils.__init__=s%RWW\\':67AAC%%  )) ??%(%>%>",/,L,L)$'$<$<!#&#:#: r4)rLrNrKrJrM)__name__ __module__ __qualname____firstlineno__r=rQ__static_attributes__ __classcell__r?s@rr7r76s ;;r4r7cUSS:XagURS5(ag0SS_SS_S S _S S _S S_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_SS_U$)Nr* CUdeviceptr tensordesc CUtensorMapi1int8_ti8i16int16_ti32int32_ti64int64_tu1uint8_tu8u16uint16_tu32uint32_tu64uint64_tfp16doublebf16fp32f32fp64 nvTmaDesc) startswith)tys r ty_to_cpprzSs !u| }}\""  h  h  y  y   y   i   i  z  z  z        x    ]! " #  r4rlrnrp)rqrsrtrurv pack_fp16 pack_bf16 pack_fp32 pack_fp64 iiiKKppOOOOOOc|^^^^U4SjnU4SjmU4SjmU4SjmU"UR55n[U5VVs0sHupVXV_M nnnSRUR5Vs/sH nT"U5PM sn5n[U-n /n UR5H n T"X5 M [U 5VVs0sHupVXV_M nnn[ U5S:a)SSRSUR 555-OSn /n UR 5HUupWUS :XaM U[ ;aU R[ US U35 M6U R[U5S U35 MW SRU 5n/nUR 5H~upWUSS :XaURS US 35 M%U[ ;aURSUS35 MFUS:XaURSU35 MbUS :wdMjURSU35 M [[ U55nSnUR 5VVs/sHupWUSS :XdMSUSUSUSUS3 PM! nnnUR 5VVs/sHupWUS:XdM SUSUSUS3PM nnnUR 5VVs/sH-upWU[ ;dM[ USUS[USUS3PM/ nnnUR 5VVs/sHupWUS :wdM SU3PM nnnURS 5 URS!5 S"[ U5S:aSU-OSS#SRU5S$URUR 5VVs/sHupWT"U5SUS%3PM snn5S&U S'U S(URU5SURU5SURU5S)[ U5S:aSSRU5-OSS*3nU$s snnfs snfs snnfs snnfs snnfs snnfs snnfs snnf)+Nc>/nSnUGHKn[U[5(Ga URS5(Ga T (aT UOSnUS- n[R"SU5nUR S5nUR S5nUR S5S-nUcLURSU-5 [SU-5Hn URS5 M URS 5 OURS 5 [U5Hn URS 5 M [U5Hn URS5 M GM:URU5 GMN T (aU[T 5:XdeU$) Nrr]ztensordesc<([^[>]*)\[([^]]*)\],r[rfr_rwrd) isinstancer$rxrematchgroupcountappendrangelen) signatureoutputtensordesc_idxsigmetardtypeshapendim_tensordesc_metas r_expand_signature(make_launcher.._expand_signatures5C#s##|(D(D:I~6t!#!CSI A A{{3'!+<MM#+. #1t8_ e,-MM$'MM+.tAMM%(%tAMM%(% c"9<#nO8L&LLL r4cv>[U[5(aUH nT"X!5 M gURU5 gr1)rtupler)rrx_flatten_signatures rr)make_launcher.._flatten_signatures0 c5 ! !"1- MM# r4c>[U[5(a!SR[TU55nSUS3$USS:XagUS;ag[ U5$)Nr[]rr[z PyObject* constexprrw)rrrmaprz)ryval_extracted_types rr&make_launcher.._extracted_typesW b% ((334Cse1:  a5C< + +}r4c >[U[5(a!SR[TU55nSUS3$USS:XagUS;agUR S5(agS S S S S SSSSSS. [ U5$)N()rr[Orr]dlbhiLBHIK) rrlongr`rcrergrirlrnrp)rrrrrxrz)ryr format_ofs rr make_launcher..format_ofs b% ''#i,-Cse1:  a5C< + + == & &   B-  r4rrz, c30# UH upSU3v M g7f)z&_argNr3)rrrys rr make_launcher..s L:K5:Ksrz argr[ptr_infoz.dev_ptr_arg_storagerwz*tma_ptrz zDevicePtrInfo ptr_infoz = getPointer(_argz); if (!ptr_infoz.valid) return NULL;zCUtensorMap* tma_ptrz = getTmaDesc(_argz); if (!tma_ptrz) return NULL;z _argz _storage = z(_argz);z&argz&global_scratchz&profile_scratcha #include "cuda.h" #include #include #include #define PY_SSIZE_T_CLEAN #include typedef struct { PyObject_HEAD; _Alignas(128) CUtensorMap tensorMap; } PyCUtensorMapObject; static inline void gpuAssert(CUresult code, const char *file, int line) { if (code != CUDA_SUCCESS) { const char* prefix = "Triton Error [CUDA]: "; const char* str; cuGetErrorString(code, &str); char err[1024] = {0}; strcat(err, prefix); strcat(err, str); PyGILState_STATE gil_state; gil_state = PyGILState_Ensure(); PyErr_SetString(PyExc_RuntimeError, err); PyGILState_Release(gil_state); } } #define CUDA_CHECK(ans) { gpuAssert((ans), __FILE__, __LINE__); } typedef CUresult (*cuLaunchKernelEx_t)(const CUlaunchConfig* config, CUfunction f, void** kernelParams, void** extra); static cuLaunchKernelEx_t getLaunchKernelExHandle() { // Open the shared library void* handle = dlopen("libcuda.so.1", RTLD_LAZY); if (!handle) { PyErr_SetString(PyExc_RuntimeError, "Failed to open libcuda.so.1"); return NULL; } // Clear any existing error dlerror(); cuLaunchKernelEx_t cuLaunchKernelExHandle = (cuLaunchKernelEx_t)dlsym(handle, "cuLaunchKernelEx"); // Check for errors const char *dlsym_error = dlerror(); if (dlsym_error) { PyErr_SetString(PyExc_RuntimeError, "Failed to retrieve cuLaunchKernelEx from libcuda.so.1"); return NULL; } return cuLaunchKernelExHandle; } static void _launch(int gridX, int gridY, int gridZ, int num_warps, int num_ctas, int launch_cooperative_grid, int launch_pdl, int shared_memory, CUstream stream, CUfunction function, CUdeviceptr global_scratch, CUdeviceptr profile_scratchz) { void *params[] = { au }; if (gridX*gridY*gridZ > 0) { // 4 attributes that we can currently pass maximum CUlaunchAttribute launchAttr[4]; static cuLaunchKernelEx_t cuLaunchKernelExHandle = NULL; if (cuLaunchKernelExHandle == NULL) { cuLaunchKernelExHandle = getLaunchKernelExHandle(); } CUlaunchConfig config; config.gridDimX = gridX * num_ctas; config.gridDimY = gridY; config.gridDimZ = gridZ; config.blockDimX = 32 * num_warps; config.blockDimY = 1; config.blockDimZ = 1; config.sharedMemBytes = shared_memory; config.hStream = stream; config.attrs = launchAttr; int num_attrs = 0; if (launch_pdl != 0) { CUlaunchAttribute pdlAttr = { .id = CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION, .value = 1}; launchAttr[num_attrs] = pdlAttr; ++num_attrs; } if (launch_cooperative_grid != 0) { CUlaunchAttribute coopAttr = { .id = CU_LAUNCH_ATTRIBUTE_COOPERATIVE, .value = 1}; launchAttr[num_attrs] = coopAttr; ++num_attrs; } if (num_ctas != 1) { CUlaunchAttribute clusterAttr = {}; clusterAttr.id = CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION; clusterAttr.value.clusterDim.x = num_ctas; clusterAttr.value.clusterDim.y = 1; clusterAttr.value.clusterDim.z = 1; launchAttr[num_attrs] = clusterAttr; ++num_attrs; CUlaunchAttribute clusterSchedulingAttr = {}; clusterSchedulingAttr.id = CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE; clusterSchedulingAttr.value.clusterSchedulingPolicyPreference = CU_CLUSTER_SCHEDULING_POLICY_SPREAD; launchAttr[num_attrs] = clusterSchedulingAttr; ++num_attrs; } // num_ctas == 16 is non-portable. Does work for H100 and B200 tho config.numAttrs = num_attrs; if (num_ctas == 16) { CUDA_CHECK(cuFuncSetAttribute( function, CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED, 1 )); } CUDA_CHECK(cuLaunchKernelExHandle(&config, function, params, 0)); } } typedef struct _DevicePtrInfo { CUdeviceptr dev_ptr; bool valid; } DevicePtrInfo; static PyObject* data_ptr_str = NULL; static PyObject* py_tensor_map_type = NULL; static inline DevicePtrInfo getPointer(PyObject *obj, int idx) { DevicePtrInfo ptr_info; ptr_info.dev_ptr = 0; ptr_info.valid = true; if (PyLong_Check(obj)) { ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(obj); return ptr_info; } if (obj == Py_None) { // valid nullptr return ptr_info; } PyObject *ret = PyObject_CallMethodNoArgs(obj, data_ptr_str); if (!ret) { PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method"); ptr_info.valid = false; goto cleanup; } if (!PyLong_Check(ret)) { PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int"); ptr_info.valid = false; goto cleanup; } ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(ret); if(!ptr_info.dev_ptr) return ptr_info; uint64_t dev_ptr; int status = cuPointerGetAttribute(&dev_ptr, CU_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr); if (status == CUDA_ERROR_INVALID_VALUE) { PyErr_Format(PyExc_ValueError, "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx); ptr_info.valid = false; } else if (status != CUDA_SUCCESS) { CUDA_CHECK(status); // Catch any other cuda API errors ptr_info.valid = false; } ptr_info.dev_ptr = dev_ptr; cleanup: Py_XDECREF(ret); return ptr_info; } static inline CUtensorMap* getTmaDesc(PyObject *obj) { if (sizeof(CUtensorMap*) != 8) { PyErr_SetString(PyExc_SystemError, "getTmaDesc() requires 64-bit compilation"); return NULL; } if (Py_TYPE(obj) != (PyTypeObject*)py_tensor_map_type) { PyErr_Format(PyExc_TypeError, "object must be of type PyCUtensorMap, got %s", Py_TYPE(obj)->tp_name); return NULL; } CUtensorMap* map = &((PyCUtensorMapObject*)obj)->tensorMap; uintptr_t align_128 = (uintptr_t)map & (128 - 1); if (align_128 != 0) { PyErr_Format(PyExc_ValueError, "CUtensorMap must be aligned to 128B, but got (&map) mod 128 = %ld", align_128); return NULL; } return map; } static void ensureCudaContext() { CUcontext pctx; CUDA_CHECK(cuCtxGetCurrent(&pctx)); if (!pctx) { // Ensure device context. CUdevice device; CUDA_CHECK(cuDeviceGet(&device, 0)); CUDA_CHECK(cuDevicePrimaryCtxRetain(&pctx, device)); CUDA_CHECK(cuCtxSetCurrent(pctx)); } } static uint16_t pack_fp16(double f) { uint16_t result; // from https://github.com/python/pythoncapi-compat #if 0x030600B1 <= PY_VERSION_HEX && PY_VERSION_HEX <= 0x030B00A1 && !defined(PYPY_VERSION) _PyFloat_Pack2(f, (unsigned char*)&result, 1); #else PyFloat_Pack2(f, (unsigned char*)&result, 1); #endif return result; } static uint16_t pack_bf16(double f) { float f32 = (float)f; uint32_t u32 = *(uint32_t*)&f32; return (uint16_t)(u32 >> 16); } static uint32_t pack_fp32(double f) { float f32 = (float)f; return *(uint32_t*)&f32; } static uint64_t pack_fp64(double f) { return *(uint64_t*)&f; } static PyObject* launch(PyObject* self, PyObject* args) { // ensure cuda context is valid before calling any CUDA APIs, e.g. before getPointer calls cuPointerGetAttributes ensureCudaContext(); int gridX, gridY, gridZ; uint64_t _stream; uint64_t _function; int launch_cooperative_grid; int launch_pdl; PyObject *launch_enter_hook = NULL; PyObject *launch_exit_hook = NULL; PyObject *kernel_metadata = NULL; PyObject *launch_metadata = NULL; PyObject *global_scratch_obj = NULL; PyObject *profile_scratch_obj = NULL; ;z if(!PyArg_ParseTuple(args, "aM", &gridX, &gridY, &gridZ, &_stream, &_function, &launch_cooperative_grid, &launch_pdl, &global_scratch_obj, &profile_scratch_obj, &kernel_metadata, &launch_metadata, &launch_enter_hook, &launch_exit_hooka)) { return NULL; } int num_warps, num_ctas, shared_memory; if (!PyArg_ParseTuple(kernel_metadata, "iii", &num_warps, &num_ctas, &shared_memory)) { PyErr_SetString(PyExc_TypeError, "kernel_metadata must be a tuple"); return NULL; } // extract launch metadata if (launch_enter_hook != Py_None){ PyObject* ret = PyObject_CallOneArg(launch_enter_hook, launch_metadata); if (!ret) return NULL; Py_DECREF(ret); } CUdeviceptr global_scratch = 0; if (global_scratch_obj != Py_None) { DevicePtrInfo global_scratch_info = getPointer(global_scratch_obj, -1); if (!global_scratch_info.valid) { return NULL; } global_scratch = global_scratch_info.dev_ptr; } CUdeviceptr profile_scratch = 0; if (profile_scratch_obj != Py_None) { DevicePtrInfo profile_scratch_info = getPointer(profile_scratch_obj, -1); if (!profile_scratch_info.valid) { return NULL; } profile_scratch = profile_scratch_info.dev_ptr; } // raise exception asap z Py_BEGIN_ALLOW_THREADS; _launch(gridX, gridY, gridZ, num_warps, num_ctas, launch_cooperative_grid, launch_pdl, shared_memory, (CUstream)_stream, (CUfunction)_function, global_scratch, profile_scratchap); Py_END_ALLOW_THREADS; if (PyErr_Occurred()) { return NULL; } if(launch_exit_hook != Py_None){ PyObject* ret = PyObject_CallOneArg(launch_exit_hook, launch_metadata); if (!ret) return NULL; Py_DECREF(ret); } Py_RETURN_NONE; } static PyMethodDef ModuleMethods[] = { {"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}, {NULL, NULL, 0, NULL} // sentinel }; static struct PyModuleDef ModuleDef = { PyModuleDef_HEAD_INIT, "__triton_launcher", NULL, //documentation -1, //size ModuleMethods }; PyMODINIT_FUNC PyInit___triton_launcher(void) { data_ptr_str = PyUnicode_InternFromString("data_ptr"); if(data_ptr_str == NULL) { return NULL; } PyObject* driver_mod = PyImport_ImportModule("triton.backends.nvidia.driver"); if (driver_mod == NULL) { return NULL; } py_tensor_map_type = PyObject_GetAttrString(driver_mod, "PyCUtensorMap"); if (py_tensor_map_type == NULL) { return NULL; } PyObject *m = PyModule_Create(&ModuleDef); if(m == NULL) { return NULL; } PyModule_AddFunctions(m, ModuleMethods); return m; } ) values enumerater_BASE_ARGS_FORMATritemsFLOAT_STORAGE_TYPErrzrFLOAT_PACK_FUNCTION) constantsrrrexpand_signaturersry args_formatformatflat_signaturer args_list arg_decl_list arg_declsinternal_args_listparamsnewline ptr_decls tma_declsfloat_storage_declsrDrrrs ` @@@r make_launcherrs%N.))9)9);<"+,<"=>"=$!"=I>''93C3C3EF3ER9R=3EFGK  ,FN!3/""+N";<";$!";I "FG__&&EA a5C< f #5aS1#=MaSPde& foetetevev\a\]   X qc!3A3oaSWev __&&EA # # Z b ! 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