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sgl-project--sglang/python/sglang/srt/layers/attention/cute_utils/_tcgen05.py
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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

221 lines
6.2 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://github.com/vllm-project/vllm/blob/4868b542c9dfd166662eecc4bb8be3a36a3feaa2/vllm/cute_utils/_tcgen05.py
# 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:
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:
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):
nvvm.tcgen05_commit_arrive(
mbar_llvm,
multicast_mask=cta_mask.ir_value(loc=loc, ip=ip),
group=group,
loc=loc,
ip=ip,
)
else:
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)