# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # this module is named _tcgen05 to avoid name collision with cute.nvgpu.tcgen05 import cutlass from cutlass import Boolean, Float32, Int32, Uint32, Uint64, cute from cutlass._mlir import ir from cutlass._mlir.dialects import llvm, nvvm, vector from cutlass.cutlass_dsl import dsl_user_op NVVM_CTA_GROUP_MAP = [ None, nvvm.Tcgen05GroupKind.CTA_1, nvvm.Tcgen05GroupKind.CTA_2, ] LDST_MAP = { "32x32b": (nvvm.Tcgen05LdStShape.SHAPE_32X32B, 1), "16x128b": (nvvm.Tcgen05LdStShape.SHAPE_16X128B, 2), "16x256b": (nvvm.Tcgen05LdStShape.SHAPE_16X256B, 4), } def _make_tmem_llvm_ptr(addr, *, loc=None, ip=None): ptr_ty = llvm.PointerType.get(cute.AddressSpace.tmem.value) val = Int32(addr).ir_value(loc=loc, ip=ip) return llvm.inttoptr(ptr_ty, val, loc=loc, ip=ip) @dsl_user_op def alloc( taddr: cute.Pointer, cta_group: int = 1, *, loc=None, ip=None, ) -> None: nvvm.tcgen05_alloc( taddr.to_llvm_ptr(loc=loc, ip=ip), Uint32(512).ir_value(loc=loc, ip=ip), group=NVVM_CTA_GROUP_MAP[cta_group], loc=loc, ip=ip, ) @dsl_user_op def dealloc(cta_group: int = 1, *, loc=None, ip=None) -> None: nvvm.tcgen05_dealloc( _make_tmem_llvm_ptr(0, loc=loc, ip=ip), Int32(512).ir_value(loc=loc, ip=ip), group=NVVM_CTA_GROUP_MAP[cta_group], loc=loc, ip=ip, ) def make_bf16_idesc( MMA_M: int, MMA_N: int, *, negate_A: bool = False, negate_B: bool = False, transpose_A: bool = False, transpose_B: bool = False, ): idesc = Uint32( (1 << 4) | (1 << 7) | (1 << 10) | ((MMA_N >> 3) << 17) | ((MMA_M >> 4) << 24) ) idesc |= Uint32(negate_A) << 13 idesc |= Uint32(negate_B) << 14 idesc |= Uint32(transpose_A) << 15 idesc |= Uint32(transpose_B) << 16 return idesc def make_sdesc_128B_swizzle(LBO: int): SBO = 8 * 128 return Uint64((LBO >> 4 << 16) | (SBO >> 4 << 32) | (1 << 46) | (2 << 61)) @dsl_user_op def mma_f16( d_tmem, a_desc, b_desc, idesc, enable_input_d, cta_group: int = 1, *, loc=None, ip=None, ) -> None: with cute.arch.elect_one(): nvvm.tcgen05_mma( nvvm.Tcgen05MMAKind.F16, NVVM_CTA_GROUP_MAP[cta_group], _make_tmem_llvm_ptr(d_tmem, loc=loc, ip=ip), Uint64(a_desc).ir_value(loc=loc, ip=ip), Uint64(b_desc).ir_value(loc=loc, ip=ip), Int32(idesc).ir_value(loc=loc, ip=ip), Boolean(enable_input_d).ir_value(loc=loc, ip=ip), loc=loc, ip=ip, ) @dsl_user_op def mma_ts_f16( d_tmem, a_tmem, b_desc, idesc, enable_input_d, cta_group: int = 1, *, loc=None, ip=None, ) -> None: with cute.arch.elect_one(): nvvm.tcgen05_mma( nvvm.Tcgen05MMAKind.F16, NVVM_CTA_GROUP_MAP[cta_group], _make_tmem_llvm_ptr(d_tmem, loc=loc, ip=ip), _make_tmem_llvm_ptr(a_tmem, loc=loc, ip=ip), Uint64(b_desc).ir_value(loc=loc, ip=ip), Int32(idesc).ir_value(loc=loc, ip=ip), Boolean(enable_input_d).ir_value(loc=loc, ip=ip), loc=loc, ip=ip, ) @dsl_user_op def commit(mbar, cta_mask=None, cta_group: int = 1, *, loc=None, ip=None): mbar_llvm = mbar.to_llvm_ptr(loc=loc, ip=ip) group = NVVM_CTA_GROUP_MAP[cta_group] if cutlass.const_expr(cta_mask is not None): with cute.arch.elect_one(): nvvm.tcgen05_commit_arrive( mbar_llvm, multicast_mask=cta_mask.ir_value(loc=loc, ip=ip), group=group, loc=loc, ip=ip, ) else: with cute.arch.elect_one(): nvvm.tcgen05_commit_arrive(mbar_llvm, group=group, loc=loc, ip=ip) @dsl_user_op def ld(row, col, shape: str, num: int, *, loc=None, ip=None): nvvm_shape, regs_per_num = LDST_MAP[shape] num_regs = regs_per_num * num tmem = (Int32(row) << Int32(16)) | Int32(col) tmem_ptr = _make_tmem_llvm_ptr(tmem, loc=loc, ip=ip) if num_regs == 1: reg = nvvm.tcgen05_ld(Int32.mlir_type, nvvm_shape, tmem_ptr, loc=loc, ip=ip) reg_f32 = llvm.bitcast(Float32.mlir_type, reg, loc=loc, ip=ip) return Float32(reg_f32) else: vec_i32_ty = ir.VectorType.get([num_regs], Int32.mlir_type, loc=loc) vec_f32_ty = ir.VectorType.get([num_regs], Float32.mlir_type, loc=loc) regs = nvvm.tcgen05_ld(vec_i32_ty, nvvm_shape, tmem_ptr, loc=loc, ip=ip) regs_f32 = llvm.bitcast(vec_f32_ty, regs, loc=loc, ip=ip) return cute.TensorSSA(regs_f32, (num_regs,), Float32) @dsl_user_op def st(row, col, shape: str, num: int, vals, *, loc=None, ip=None) -> None: # if input is TensorSSA, convert to Tensor so we can bitcast if isinstance(vals, cute.TensorSSA): vals_ = cute.make_rmem_tensor_like(vals) vals_.store(vals) vals = vals_ # bitcast to Int32 vals = cute.recast_tensor(vals, Int32) nvvm_shape, regs_per_num = LDST_MAP[shape] num_regs = regs_per_num * num tmem = (Int32(row) << Int32(16)) | Int32(col) tmem_ptr = _make_tmem_llvm_ptr(tmem, loc=loc, ip=ip) if num_regs == 1: nvvm.tcgen05_st( nvvm_shape, tmem_ptr, vals[0].ir_value(loc=loc, ip=ip), loc=loc, ip=ip, ) else: vec_i32_ty = ir.VectorType.get([num_regs], Int32.mlir_type, loc=loc) val_vec = vector.from_elements( vec_i32_ty, [vals[i].ir_value(loc=loc, ip=ip) for i in range(num_regs)], loc=loc, ip=ip, ) nvvm.tcgen05_st(nvvm_shape, tmem_ptr, val_vec, loc=loc, ip=ip) @dsl_user_op def fence_after_thread_sync(*, loc=None, ip=None): nvvm.tcgen05_fence(nvvm.Tcgen05FenceKind.AFTER_THREAD_SYNC, loc=loc, ip=ip) @dsl_user_op def fence_before_thread_sync(*, loc=None, ip=None): nvvm.tcgen05_fence(nvvm.Tcgen05FenceKind.BEFORE_THREAD_SYNC, loc=loc, ip=ip) @dsl_user_op def wait_ld(*, loc=None, ip=None): nvvm.tcgen05_wait(nvvm.Tcgen05WaitKind.LOAD, loc=loc, ip=ip) @dsl_user_op def wait_st(*, loc=None, ip=None): nvvm.tcgen05_wait(nvvm.Tcgen05WaitKind.STORE, loc=loc, ip=ip)