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221 lines
6.2 KiB
Python
221 lines
6.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from https://github.com/vllm-project/vllm/blob/4868b542c9dfd166662eecc4bb8be3a36a3feaa2/vllm/cute_utils/_tcgen05.py
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# this module is named _tcgen05 to avoid name collision with cute.nvgpu.tcgen05
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import cutlass
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from cutlass import Boolean, Float32, Int32, Uint32, Uint64, cute
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from cutlass._mlir import ir
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from cutlass._mlir.dialects import llvm, nvvm, vector
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from cutlass.cutlass_dsl import dsl_user_op
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NVVM_CTA_GROUP_MAP = [
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None,
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nvvm.Tcgen05GroupKind.CTA_1,
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nvvm.Tcgen05GroupKind.CTA_2,
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]
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LDST_MAP = {
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"32x32b": (nvvm.Tcgen05LdStShape.SHAPE_32X32B, 1),
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"16x128b": (nvvm.Tcgen05LdStShape.SHAPE_16X128B, 2),
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"16x256b": (nvvm.Tcgen05LdStShape.SHAPE_16X256B, 4),
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}
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def _make_tmem_llvm_ptr(addr, *, loc=None, ip=None):
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ptr_ty = llvm.PointerType.get(cute.AddressSpace.tmem.value)
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val = Int32(addr).ir_value(loc=loc, ip=ip)
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return llvm.inttoptr(ptr_ty, val, loc=loc, ip=ip)
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@dsl_user_op
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def alloc(
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taddr: cute.Pointer,
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cta_group: int = 1,
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*,
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loc=None,
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ip=None,
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) -> None:
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nvvm.tcgen05_alloc(
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taddr.to_llvm_ptr(loc=loc, ip=ip),
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Uint32(512).ir_value(loc=loc, ip=ip),
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group=NVVM_CTA_GROUP_MAP[cta_group],
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loc=loc,
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ip=ip,
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)
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@dsl_user_op
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def dealloc(cta_group: int = 1, *, loc=None, ip=None) -> None:
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nvvm.tcgen05_dealloc(
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_make_tmem_llvm_ptr(0, loc=loc, ip=ip),
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Int32(512).ir_value(loc=loc, ip=ip),
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group=NVVM_CTA_GROUP_MAP[cta_group],
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loc=loc,
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ip=ip,
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)
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def make_bf16_idesc(
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MMA_M: int,
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MMA_N: int,
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*,
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negate_A: bool = False,
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negate_B: bool = False,
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transpose_A: bool = False,
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transpose_B: bool = False,
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):
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idesc = Uint32(
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(1 << 4) | (1 << 7) | (1 << 10) | ((MMA_N >> 3) << 17) | ((MMA_M >> 4) << 24)
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)
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idesc |= Uint32(negate_A) << 13
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idesc |= Uint32(negate_B) << 14
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idesc |= Uint32(transpose_A) << 15
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idesc |= Uint32(transpose_B) << 16
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return idesc
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def make_sdesc_128B_swizzle(LBO: int):
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SBO = 8 * 128
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return Uint64((LBO >> 4 << 16) | (SBO >> 4 << 32) | (1 << 46) | (2 << 61))
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@dsl_user_op
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def mma_f16(
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d_tmem,
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a_desc,
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b_desc,
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idesc,
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enable_input_d,
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cta_group: int = 1,
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*,
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loc=None,
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ip=None,
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) -> None:
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nvvm.tcgen05_mma(
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nvvm.Tcgen05MMAKind.F16,
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NVVM_CTA_GROUP_MAP[cta_group],
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_make_tmem_llvm_ptr(d_tmem, loc=loc, ip=ip),
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Uint64(a_desc).ir_value(loc=loc, ip=ip),
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Uint64(b_desc).ir_value(loc=loc, ip=ip),
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Int32(idesc).ir_value(loc=loc, ip=ip),
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Boolean(enable_input_d).ir_value(loc=loc, ip=ip),
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loc=loc,
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ip=ip,
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)
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@dsl_user_op
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def mma_ts_f16(
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d_tmem,
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a_tmem,
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b_desc,
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idesc,
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enable_input_d,
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cta_group: int = 1,
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*,
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loc=None,
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ip=None,
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) -> None:
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nvvm.tcgen05_mma(
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nvvm.Tcgen05MMAKind.F16,
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NVVM_CTA_GROUP_MAP[cta_group],
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_make_tmem_llvm_ptr(d_tmem, loc=loc, ip=ip),
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_make_tmem_llvm_ptr(a_tmem, loc=loc, ip=ip),
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Uint64(b_desc).ir_value(loc=loc, ip=ip),
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Int32(idesc).ir_value(loc=loc, ip=ip),
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Boolean(enable_input_d).ir_value(loc=loc, ip=ip),
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loc=loc,
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ip=ip,
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)
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@dsl_user_op
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def commit(mbar, cta_mask=None, cta_group: int = 1, *, loc=None, ip=None):
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mbar_llvm = mbar.to_llvm_ptr(loc=loc, ip=ip)
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group = NVVM_CTA_GROUP_MAP[cta_group]
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if cutlass.const_expr(cta_mask is not None):
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nvvm.tcgen05_commit_arrive(
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mbar_llvm,
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multicast_mask=cta_mask.ir_value(loc=loc, ip=ip),
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group=group,
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loc=loc,
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ip=ip,
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)
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else:
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nvvm.tcgen05_commit_arrive(mbar_llvm, group=group, loc=loc, ip=ip)
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@dsl_user_op
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def ld(row, col, shape: str, num: int, *, loc=None, ip=None):
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nvvm_shape, regs_per_num = LDST_MAP[shape]
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num_regs = regs_per_num * num
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tmem = (Int32(row) << Int32(16)) | Int32(col)
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tmem_ptr = _make_tmem_llvm_ptr(tmem, loc=loc, ip=ip)
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if num_regs == 1:
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reg = nvvm.tcgen05_ld(Int32.mlir_type, nvvm_shape, tmem_ptr, loc=loc, ip=ip)
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reg_f32 = llvm.bitcast(Float32.mlir_type, reg, loc=loc, ip=ip)
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return Float32(reg_f32)
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else:
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vec_i32_ty = ir.VectorType.get([num_regs], Int32.mlir_type, loc=loc)
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vec_f32_ty = ir.VectorType.get([num_regs], Float32.mlir_type, loc=loc)
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regs = nvvm.tcgen05_ld(vec_i32_ty, nvvm_shape, tmem_ptr, loc=loc, ip=ip)
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regs_f32 = llvm.bitcast(vec_f32_ty, regs, loc=loc, ip=ip)
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return cute.TensorSSA(regs_f32, (num_regs,), Float32)
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@dsl_user_op
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def st(row, col, shape: str, num: int, vals, *, loc=None, ip=None) -> None:
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# if input is TensorSSA, convert to Tensor so we can bitcast
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if isinstance(vals, cute.TensorSSA):
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vals_ = cute.make_rmem_tensor_like(vals)
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vals_.store(vals)
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vals = vals_
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# bitcast to Int32
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vals = cute.recast_tensor(vals, Int32)
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nvvm_shape, regs_per_num = LDST_MAP[shape]
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num_regs = regs_per_num * num
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tmem = (Int32(row) << Int32(16)) | Int32(col)
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tmem_ptr = _make_tmem_llvm_ptr(tmem, loc=loc, ip=ip)
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if num_regs == 1:
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nvvm.tcgen05_st(
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nvvm_shape,
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tmem_ptr,
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vals[0].ir_value(loc=loc, ip=ip),
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loc=loc,
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ip=ip,
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)
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else:
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vec_i32_ty = ir.VectorType.get([num_regs], Int32.mlir_type, loc=loc)
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val_vec = vector.from_elements(
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vec_i32_ty,
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[vals[i].ir_value(loc=loc, ip=ip) for i in range(num_regs)],
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loc=loc,
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ip=ip,
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)
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nvvm.tcgen05_st(nvvm_shape, tmem_ptr, val_vec, loc=loc, ip=ip)
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@dsl_user_op
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def fence_after_thread_sync(*, loc=None, ip=None):
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nvvm.tcgen05_fence(nvvm.Tcgen05FenceKind.AFTER_THREAD_SYNC, loc=loc, ip=ip)
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@dsl_user_op
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def fence_before_thread_sync(*, loc=None, ip=None):
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nvvm.tcgen05_fence(nvvm.Tcgen05FenceKind.BEFORE_THREAD_SYNC, loc=loc, ip=ip)
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@dsl_user_op
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def wait_ld(*, loc=None, ip=None):
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nvvm.tcgen05_wait(nvvm.Tcgen05WaitKind.LOAD, loc=loc, ip=ip)
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@dsl_user_op
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def wait_st(*, loc=None, ip=None):
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nvvm.tcgen05_wait(nvvm.Tcgen05WaitKind.STORE, loc=loc, ip=ip)
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