# Copyright (c) 2023 NVIDIA Corporation & Affiliates. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY # CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE # SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. from __future__ import annotations from ..runtime import driver def generate_cu_signature(constants, signature, ids): # CUtensorMap*s are always the last arguments num_regular_signatures = max(signature.keys()) + 1 if len(signature) > 0 else 0 if ids["ids_of_tensormaps"] is not None: for i, _ in enumerate(ids["ids_of_tensormaps"]): signature[num_regular_signatures + i] = '*CUtensorMap' return signature, num_regular_signatures def dummy_tensormaps_info(n=2): ret = [] for i in range(n): ret.append(InfoFromBackendForTensorMap(dummy=True)) return ret def parse_tma_info(infos, ids_of_folded_args): ret = [] for info in infos: e = InfoFromBackendForTensorMap(infos=info) e.ids_of_folded_args = ids_of_folded_args ret.append(e) return ret def get_tma_mapping(tensormaps_info): ret = {} if tensormaps_info is not None: for i, e in enumerate(tensormaps_info): ret.update(e.get_address_tma_mapping()) else: ret = None return ret def get_ids_of_tensormaps(tensormaps_info): ret = None # order is not relevant if tensormaps_info is not None: ret = [e.get_id_of_tensormap() for e in tensormaps_info] return ret # decouple information for tensormap from backend # please ignore the naming style, xx_yy is compiler.py style, xxYy is to comply with cuda tensormap style # mixing style is for readability class InfoFromBackendForTensorMap: N = 2 n = 0 ntma = 0 def __init__(self, infos=None, dummy=False): self.dummy = dummy self.ids_of_folded_args = () if not dummy and not isinstance(infos, dict): self._extract_info_from_backend(infos) elif not dummy and isinstance(infos, dict): self._extract_info_from_dict(infos) elif dummy: self._dummy() def _dummy(self): assert InfoFromBackendForTensorMap.n < InfoFromBackendForTensorMap.N if InfoFromBackendForTensorMap.n == 0: self.tensorDataType = driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"] self.tensorRank = 4 self.globalAddressArgIdx = 0 self.globalStridesArgIdx = [7, 6, -1, -1] self.globalDimsArgIdx = [5, 3, -1, -1] self.boxDims = [16, 64, 1, 1] self.elementStrides = [1, 1, 1, 1] self.interleave = driver.utils.CUtensorMapInterleave["CU_TENSOR_MAP_INTERLEAVE_NONE"] self.swizzle = driver.utils.CUtensorMapSwizzle["CU_TENSOR_MAP_SWIZZLE_32B"] self.l2Promotion = driver.utils.CUtensorMapL2promotion["CU_TENSOR_MAP_L2_PROMOTION_L2_128B"] self.TMADescArgIdx = 11 self.oobFill = driver.utils.CUtensorMapFloatOOBfill["CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE"] InfoFromBackendForTensorMap.n += 1 return if InfoFromBackendForTensorMap.n == 1: self.tensorDataType = driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"] self.tensorRank = 4 self.globalAddressArgIdx = 1 self.globalStridesArgIdx = [7, 6, -1, -1] self.globalDimsArgIdx = [5, 3, -1, -1] self.boxDims = [16, 64, 1, 1] self.elementStrides = [1, 1, 1, 1] self.interleave = driver.utils.CUtensorMapInterleave["CU_TENSOR_MAP_INTERLEAVE_NONE"] self.swizzle = driver.utils.CUtensorMapSwizzle["CU_TENSOR_MAP_SWIZZLE_32B"] self.l2Promotion = driver.utils.CUtensorMapL2promotion["CU_TENSOR_MAP_L2_PROMOTION_L2_128B"] self.TMADescArgIdx = 12 self.oobFill = driver.utils.CUtensorMapFloatOOBfill["CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE"] InfoFromBackendForTensorMap.n += 1 return def _extract_info_from_backend(self, infos): self.tensorDataType = infos.tensorDataType self.tensorRank = infos.tensorRank self.globalAddressArgIdx = infos.globalAddressArgIdx self.globalStridesArgIdx = infos.globalStridesArgIdx self.globalDimsArgIdx = infos.globalDimsArgIdx self.boxDims = infos.boxDims self.elementStrides = infos.elementStrides self.interleave = infos.interleave self.swizzle = infos.swizzle self.l2Promotion = infos.l2Promotion self.oobFill = infos.oobFill self.TMADescArgIdx = infos.TMADescArgIdx # dict could be from cached metadata json def _extract_info_from_dict(self, infos: dict): self.tensorDataType = infos['tensorDataType'] self.tensorRank = infos['tensorRank'] self.globalAddressArgIdx = infos['globalAddressArgIdx'] self.globalStridesArgIdx = infos['globalStridesArgIdx'] self.globalDimsArgIdx = infos['globalDimsArgIdx'] self.boxDims = infos['boxDims'] self.elementStrides = infos['elementStrides'] self.interleave = infos['interleave'] self.swizzle = infos['swizzle'] self.l2Promotion = infos['l2Promotion'] self.oobFill = infos['oobFill'] self.TMADescArgIdx = infos['TMADescArgIdx'] def get_address_tma_mapping(self): return {self.globalAddressArgIdx: self.TMADescArgIdx + len(self.ids_of_folded_args)} def get_id_of_tensormap(self): return self.TMADescArgIdx + len(self.ids_of_folded_args) def getTMADescArgIdx(self): return self.TMADescArgIdx # dtype:cuda.CUtensorMapDataType | int def bytes_from_type(self, dtype): return { driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT8"]: 1, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT16"]: 2, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT32"]: 4, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_INT32"]: 4, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_UINT64"]: 8, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_INT64"]: 8, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT16"]: 2, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT32"]: 4, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT64"]: 8, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_BFLOAT16"]: 2, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_FLOAT32_FTZ"]: 4, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_TFLOAT32"]: 4, driver.utils.CUtensorMapDataType["CU_TENSOR_MAP_DATA_TYPE_TFLOAT32_FTZ"]: 4 }[dtype] def getTensorMapDataType(self): return self.tensorDataType def getInterleave(self): return self.interleave def getSwizzle(self): return self.swizzle def getL2Promotion(self): return self.l2Promotion def getOobFill(self): return self.oobFill def getTensorRank(self): return self.tensorRank def getBoxDims(self): return self.boxDims def getElementStrides(self): return self.elementStrides def getGlobalAddress(self, args): idx = self.getOriginArgIdx(self.globalAddressArgIdx, args) return args[idx] # args, captured kernel args in runtime def getGlobalDims(self, args): shape = [] for e in self.globalDimsArgIdx: t = 1 # < 0 means folded arg or constant (-1 - value) # -1 means extended dim which is 1, -2 means folded arg with constant 1 (-1 - value) if e == -1: t = 1 elif e < 0 and e != -1: t = -e - 1 else: idx = self.getOriginArgIdx(e, args) t = args[idx] shape.append(t) return shape def getGlobalStrides(self, args): t_globalDims = [int(e) for e in self.getGlobalDims(args)] t_globalStridesArgIdx = self.globalStridesArgIdx.copy() strides_in_elements = [] # todo: get all stride from backend even in extended mode for i in range(self.tensorRank): t = 1 if t_globalStridesArgIdx[i] == -1: for ii in range(i): t *= t_globalDims[ii] # -2 means the sride in arguments is folded constant 1, we don't use 1 because it can not be distinguished from index 1 elif t_globalStridesArgIdx[i] < 0: t = -1 - t_globalStridesArgIdx[i] else: new_idx = self.getOriginArgIdx(t_globalStridesArgIdx[i], args) t = args[new_idx] strides_in_elements.append(t) strides_in_elements = strides_in_elements[1:] strides_in_bytes = [e * self.bytes_from_type(self.tensorDataType) for e in strides_in_elements] return strides_in_bytes def getOriginArgIdx(self, idx, args): if self.ids_of_folded_args: ids_before_folding_arg = [i for i in range(len(args)) if i not in self.ids_of_folded_args] return ids_before_folding_arg[idx] else: return idx def tensormap(self, args): return driver.utils.cuTensorMapEncodeTiled( self.getTensorMapDataType(), self.getTensorRank(), self.getGlobalAddress(args), self.getGlobalDims(args), self.getGlobalStrides(args), self.getBoxDims(), self.getElementStrides(), self.getInterleave(), self.getSwizzle(), self.getL2Promotion(), self.getOobFill(), ) # make hashable to use as partial key in cache def __hash__(self): return hash((self.ids_of_folded_args, self.globalAddressArgIdx, tuple(self.globalDimsArgIdx), tuple(self.globalStridesArgIdx), self.tensorDataType, self.tensorRank, tuple(self.boxDims), tuple(self.elementStrides), self.interleave, self.swizzle, self.l2Promotion, self.oobFill)) def __eq__(self, other): if not isinstance(other, self.__class__): return False return (self.ids_of_folded_args, self.globalAddressArgIdx, self.globalDimsArgIdx, self.globalStridesArgIdx, self.tensorDataType, self.tensorRank, self.boxDims, self.elementStrides, self.interleave, self.swizzle, self.l2Promotion, self.oobFill) == (other.ids_of_folded_args, other.globalAddressArgIdx, other.globalDimsArgIdx, other.globalStridesArgIdx, other.tensorDataType, other.tensorRank, other.boxDims, other.elementStrides, other.interleave, other.swizzle, other.l2Promotion, other.oobFill) class TensorMapManager: def __init__(self): self.tensormaps_device = {} def __getitem__(self, key: tuple): if key in self.tensormaps_device: return int(self.tensormaps_device[key]) else: (e, args) = key t_tensormap = e.tensormap(args) TENSORMAP_SIZE_IN_BYTES = 128 t_tensormap_device = driver.utils.cuMemAlloc(TENSORMAP_SIZE_IN_BYTES) driver.utils.cuMemcpyHtoD(t_tensormap_device, t_tensormap, TENSORMAP_SIZE_IN_BYTES) self.tensormaps_device[key] = t_tensormap_device return int(self.tensormaps_device[key]) def __del__(self): for _, v in self.tensormaps_device.items(): driver.utils.cuMemFree(v)