| |
|
|
| import math |
| from typing import Type, Callable, Optional, Tuple |
|
|
| import cutlass |
| import cutlass.cute as cute |
|
|
| from cutlass import Float32, Int32 |
| from cutlass.cutlass_dsl import T, dsl_user_op |
| from cutlass._mlir.dialects import nvvm, llvm, arith, vector |
| from cutlass.cute.runtime import from_dlpack |
|
|
|
|
| def convert_from_dlpack(x, leading_dim, alignment=16, divisibility=1) -> cute.Tensor: |
| return ( |
| from_dlpack(x, assumed_align=alignment) |
| .mark_layout_dynamic(leading_dim=leading_dim) |
| .mark_compact_shape_dynamic( |
| mode=leading_dim, stride_order=x.dim_order(), divisibility=divisibility |
| ) |
| ) |
|
|
|
|
| def make_tiled_copy_A( |
| copy_atom: cute.CopyAtom, tiled_mma: cute.TiledMma, swapAB: cutlass.Constexpr[bool] = False |
| ) -> cute.TiledCopy: |
| if cutlass.const_expr(swapAB): |
| return cute.make_tiled_copy_B(copy_atom, tiled_mma) |
| else: |
| return cute.make_tiled_copy_A(copy_atom, tiled_mma) |
|
|
|
|
| def make_tiled_copy_B( |
| copy_atom: cute.CopyAtom, tiled_mma: cute.TiledMma, swapAB: cutlass.Constexpr[bool] = False |
| ) -> cute.TiledCopy: |
| if cutlass.const_expr(swapAB): |
| return cute.make_tiled_copy_A(copy_atom, tiled_mma) |
| else: |
| return cute.make_tiled_copy_B(copy_atom, tiled_mma) |
|
|
|
|
| def mma_make_fragment_A( |
| smem: cute.Tensor, thr_mma: cute.core.ThrMma, swapAB: cutlass.Constexpr[bool] = False |
| ) -> cute.Tensor: |
| if cutlass.const_expr(swapAB): |
| return mma_make_fragment_B(smem, thr_mma) |
| else: |
| return thr_mma.make_fragment_A(thr_mma.partition_A(smem)) |
|
|
|
|
| def mma_make_fragment_B( |
| smem: cute.Tensor, thr_mma: cute.core.ThrMma, swapAB: cutlass.Constexpr[bool] = False |
| ) -> cute.Tensor: |
| if cutlass.const_expr(swapAB): |
| return mma_make_fragment_A(smem, thr_mma) |
| else: |
| return thr_mma.make_fragment_B(thr_mma.partition_B(smem)) |
|
|
|
|
| def get_smem_store_atom( |
| arch: cutlass.Constexpr[int], element_type: Type[cute.Numeric] |
| ) -> cute.CopyAtom: |
| if cutlass.const_expr(arch < 90): |
| return cute.make_copy_atom( |
| cute.nvgpu.CopyUniversalOp(), |
| element_type, |
| num_bits_per_copy=2 * element_type.width, |
| ) |
| else: |
| return cute.make_copy_atom( |
| cute.nvgpu.warp.StMatrix8x8x16bOp(transpose=False, num_matrices=4), |
| element_type, |
| ) |
|
|
|
|
| @cute.jit |
| def warp_reduce( |
| val: cute.TensorSSA | cute.Numeric, |
| op: Callable, |
| width: cutlass.Constexpr[int] = cute.arch.WARP_SIZE, |
| ) -> cute.TensorSSA | cute.Numeric: |
| if cutlass.const_expr(isinstance(val, cute.TensorSSA)): |
| res = cute.make_fragment(val.shape, val.dtype) |
| res.store(val) |
| for i in cutlass.range_constexpr(cute.size(val.shape)): |
| res[i] = warp_reduce(res[i], op, width) |
| return res.load() |
| else: |
| for i in cutlass.range_constexpr(int(math.log2(width))): |
| val = op(val, cute.arch.shuffle_sync_bfly(val, offset=1 << i)) |
| return val |
|
|
|
|
| def convert_layout_acc_mn(acc_layout: cute.Layout) -> cute.Layout: |
| """ |
| For Sm80, convert ((2, 2), MMA_M, MMA_N, ...) to ((2, MMA_M), (2, MMA_N), ...). |
| For Sm90, convert ((2, 2, V), MMA_M, MMA_N, ...) to ((2, MMA_M), (2, V, MMA_N), ...). |
| """ |
| acc_layout_col_major = cute.make_layout(acc_layout.shape) |
| acc_layout_mn = cute.make_layout( |
| ( |
| (acc_layout_col_major.shape[0][1], acc_layout_col_major.shape[1]), |
| ( |
| acc_layout_col_major.shape[0][0], |
| *acc_layout_col_major.shape[0][2:], |
| acc_layout_col_major.shape[2], |
| ), |
| *acc_layout_col_major.shape[3:], |
| ), |
| stride=( |
| (acc_layout_col_major.stride[0][1], acc_layout_col_major.stride[1]), |
| ( |
| acc_layout_col_major.stride[0][0], |
| *acc_layout_col_major.stride[0][2:], |
| acc_layout_col_major.stride[2], |
| ), |
| *acc_layout_col_major.stride[3:], |
| ), |
| ) |
| return cute.composition(acc_layout, acc_layout_mn) |
|
|
|
|
| def make_acc_tensor_mn_view(acc: cute.Tensor) -> cute.Tensor: |
| return cute.make_tensor(acc.iterator, convert_layout_acc_mn(acc.layout)) |
|
|
|
|
| @cute.jit |
| def convert_layout_acc_frgA(acc_layout: cute.Layout) -> cute.Layout: |
| |
| |
| |
| |
| if cutlass.const_expr(cute.rank(acc_layout.shape[0]) == 3): |
| l = cute.logical_divide( |
| acc_layout, ((None, None, 2), None, None) |
| ) |
| rA_mma_view = cute.make_layout( |
| ( |
| (l.shape[0][0], l.shape[0][1], l.shape[0][2][0]), |
| l.shape[1], |
| (l.shape[0][2][1], l.shape[2]), |
| ), |
| stride=( |
| (l.stride[0][0], l.stride[0][1], l.stride[0][2][0]), |
| l.stride[1], |
| (l.stride[0][2][1], l.stride[2]), |
| ), |
| ) |
| else: |
| |
| l = cute.logical_divide(acc_layout, (None, None, 2)) |
| rA_mma_view = cute.make_layout( |
| ( |
| (l.shape[0], l.shape[2][0]), |
| l.shape[1], |
| l.shape[2][1], |
| ), |
| stride=( |
| (l.stride[0], l.stride[2][0]), |
| l.stride[1], |
| l.stride[2][1], |
| ), |
| ) |
| return rA_mma_view |
|
|
|
|
| def transpose_view(a: cute.Tensor) -> cute.Tensor: |
| """Transpose the first two dimensions of a tensor on smem.""" |
| shape = (a.shape[1], a.shape[0], *a.shape[2:]) |
| order = (1, 0, *range(2, cute.rank(a))) |
| return cute.composition(a, cute.make_ordered_layout(shape, order=order)) |
|
|
|
|
| @dsl_user_op |
| def exp2f_asm(a: float | Float32, *, loc=None, ip=None) -> Float32: |
| return Float32( |
| llvm.inline_asm( |
| T.f32(), |
| [Float32(a).ir_value(loc=loc, ip=ip)], |
| "ex2.approx.ftz.f32 $0, $1;", |
| "=f,f", |
| has_side_effects=False, |
| is_align_stack=False, |
| asm_dialect=llvm.AsmDialect.AD_ATT, |
| ) |
| ) |
|
|
|
|
| @cute.jit |
| def exp2f(x: cute.TensorSSA | Float32) -> cute.TensorSSA | Float32: |
| """exp2f calculation for both vector and scalar. |
| :param x: input value |
| :type x: cute.TensorSSA or Float32 |
| :return: exp2 value |
| :rtype: cute.TensorSSA or Float32 |
| """ |
| if cutlass.const_expr(isinstance(x, cute.TensorSSA)): |
| res = cute.make_fragment(x.shape, Float32) |
| res.store(x) |
| for i in cutlass.range_constexpr(cute.size(x.shape)): |
| res[i] = cute.arch.exp2(res[i]) |
| return res.load() |
| else: |
| return cute.arch.exp2(x) |
|
|
|
|
| @dsl_user_op |
| def log2f(a: float | Float32, *, loc=None, ip=None) -> Float32: |
| return Float32( |
| llvm.inline_asm( |
| T.f32(), |
| [Float32(a).ir_value(loc=loc, ip=ip)], |
| "lg2.approx.ftz.f32 $0, $1;", |
| "=f,f", |
| has_side_effects=False, |
| is_align_stack=False, |
| asm_dialect=llvm.AsmDialect.AD_ATT, |
| ) |
| ) |
|
|
| @dsl_user_op |
| def logf(a: float | Float32, *, loc=None, ip=None) -> Float32: |
| return log2f(a, loc=loc, ip=ip) * math.log(2.0) |
|
|
|
|
| @dsl_user_op |
| def fmax( |
| a: float | Float32, b: float | Float32, c: float | Float32 | None = None, *, loc=None, ip=None |
| ) -> Float32: |
| return Float32( |
| nvvm.fmax( |
| T.f32(), |
| Float32(a).ir_value(loc=loc, ip=ip), |
| Float32(b).ir_value(loc=loc, ip=ip), |
| c=Float32(c).ir_value(loc=loc, ip=ip) if c is not None else None, |
| loc=loc, |
| ip=ip, |
| ) |
| ) |
|
|
|
|
| @cute.jit |
| def fmax_reduce( |
| x: cute.TensorSSA, init_val: float | Float32 | None = None, arch: cutlass.Constexpr[int] = 80 |
| ) -> Float32: |
| if cutlass.const_expr(arch < 100 or cute.size(x.shape) % 8 != 0): |
| |
| |
| |
| res = cute.make_fragment(x.shape, Float32) |
| res.store(x) |
| |
| |
| |
| |
| |
| |
| local_max = [res[0], res[1], res[2], res[3]] |
| for i in cutlass.range_constexpr(4, cute.size(x.shape), 4): |
| local_max[0] = fmax(local_max[0], res[i + 0]) |
| local_max[1] = fmax(local_max[1], res[i + 1]) |
| local_max[2] = fmax(local_max[2], res[i + 2]) |
| local_max[3] = fmax(local_max[3], res[i + 3]) |
| local_max[0] = fmax(local_max[0], local_max[1]) |
| local_max[2] = fmax(local_max[2], local_max[3]) |
| local_max[0] = fmax(local_max[0], local_max[2]) |
| return local_max[0] if cutlass.const_expr(init_val is None) else fmax(local_max[0], init_val) |
| else: |
| |
| |
| res = cute.make_fragment(x.shape, Float32) |
| res.store(x) |
| local_max = [ |
| fmax(init_val, res[0], res[1]) |
| if cutlass.const_expr(init_val is not None) |
| else fmax(res[0], res[1]), |
| fmax(res[2], res[3]), |
| fmax(res[4], res[5]), |
| fmax(res[6], res[7]), |
| ] |
| for i in cutlass.range_constexpr(8, cute.size(x.shape), 8): |
| local_max[0] = fmax(local_max[0], res[i], res[i + 1]) |
| local_max[1] = fmax(local_max[1], res[i + 2], res[i + 3]) |
| local_max[2] = fmax(local_max[2], res[i + 4], res[i + 5]) |
| local_max[3] = fmax(local_max[3], res[i + 6], res[i + 7]) |
| local_max[0] = fmax(local_max[0], local_max[1]) |
| return fmax(local_max[0], local_max[2], local_max[3]) |
|
|
|
|
| @cute.jit |
| def fadd_reduce( |
| x: cute.TensorSSA, init_val: float | Float32 | None = None, arch: cutlass.Constexpr[int] = 80 |
| ) -> Float32: |
| if cutlass.const_expr(arch < 100 or cute.size(x.shape) % 8 != 0): |
| if cutlass.const_expr(init_val is None): |
| init_val = Float32.zero |
| return x.reduce(cute.ReductionOp.ADD, init_val, 0) |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| else: |
| res = cute.make_fragment(x.shape, Float32) |
| res.store(x) |
| local_sum_0 = ( |
| cute.arch.add_packed_f32x2((init_val, 0.0), (res[0], res[1])) |
| |
| if cutlass.const_expr(init_val is not None) |
| else (res[0], res[1]) |
| ) |
| local_sum = [local_sum_0, (res[2], res[3]), (res[4], res[5]), (res[6], res[7])] |
| for i in cutlass.range_constexpr(8, cute.size(x.shape), 8): |
| local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], (res[i + 0], res[i + 1])) |
| local_sum[1] = cute.arch.add_packed_f32x2(local_sum[1], (res[i + 2], res[i + 3])) |
| local_sum[2] = cute.arch.add_packed_f32x2(local_sum[2], (res[i + 4], res[i + 5])) |
| local_sum[3] = cute.arch.add_packed_f32x2(local_sum[3], (res[i + 6], res[i + 7])) |
| local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], local_sum[1]) |
| local_sum[2] = cute.arch.add_packed_f32x2(local_sum[2], local_sum[3]) |
| local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], local_sum[2]) |
| return local_sum[0][0] + local_sum[0][1] |
|
|
|
|
| @dsl_user_op |
| def atomic_add_fp32(a: float | Float32, gmem_ptr: cute.Pointer, *, loc=None, ip=None) -> None: |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| nvvm.atomicrmw( |
| res=T.f32(), op=nvvm.AtomicOpKind.FADD, ptr=gmem_ptr.llvm_ptr, a=Float32(a).ir_value() |
| ) |
|
|
|
|
| @dsl_user_op |
| def elem_pointer(x: cute.Tensor, coord: cute.Coord, *, loc=None, ip=None) -> cute.Pointer: |
| return x.iterator + cute.crd2idx(coord, x.layout, loc=loc, ip=ip) |
|
|
|
|
| @dsl_user_op |
| def elem_pointer_i64(x: cute.Tensor, coord: cute.Coord, *, loc=None, ip=None) -> cute.Pointer: |
| flat_coord_i64 = tuple(cutlass.Int64(c) for c in cute.flatten(coord)) |
| flat_stride = cute.flatten_to_tuple(x.stride) |
| assert len(flat_coord_i64) == len(flat_stride), "Coordinate and stride must have the same length" |
| offset = sum(c * s for c, s in zip(flat_coord_i64, flat_stride)) |
| return x.iterator + cute.crd2idx(coord, x.layout, loc=loc, ip=ip) |
|
|
|
|
| @cute.jit |
| def predicate_k(tAcA: cute.Tensor, limit: cutlass.Int32) -> cute.Tensor: |
| |
| tApA = cute.make_fragment( |
| cute.make_layout( |
| (cute.size(tAcA, mode=[0, 1]), cute.size(tAcA, mode=[1]), cute.size(tAcA, mode=[2])), |
| stride=(cute.size(tAcA, mode=[2]), 0, 1), |
| ), |
| cutlass.Boolean, |
| ) |
| for rest_v in cutlass.range_constexpr(tApA.shape[0]): |
| for rest_k in cutlass.range_constexpr(tApA.shape[2]): |
| tApA[rest_v, 0, rest_k] = cute.elem_less(tAcA[(0, rest_v), 0, rest_k][1], limit) |
| return tApA |
|
|
|
|
| @dsl_user_op |
| def cp_async_mbarrier_arrive_shared( |
| mbar_ptr: cute.Pointer, noinc: bool = False, *, loc=None, ip=None |
| ) -> None: |
| nvvm.cp_async_mbarrier_arrive_shared( |
| mbar_ptr.llvm_ptr, |
| noinc=noinc, |
| loc=loc, |
| ip=ip, |
| ) |
|
|
|
|
| def canonical_warp_group_idx(sync: bool = True) -> cutlass.Int32: |
| warp_group_idx = cute.arch.thread_idx()[0] // 128 |
| if cutlass.const_expr(sync): |
| warp_group_idx = cute.arch.make_warp_uniform(warp_group_idx) |
| return warp_group_idx |
|
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|
|
| @cute.jit |
| def shuffle_sync( |
| value: cute.Numeric, |
| offset: cute.typing.Int, |
| width: cutlass.Constexpr[int] = cute.arch.WARP_SIZE, |
| ) -> cute.Numeric: |
| assert value.width % 32 == 0, "value type must be a multiple of 32 bits" |
| |
| mask = cute.arch.WARP_SIZE - width |
| clamp = cute.arch.WARP_SIZE - 1 |
| mask_and_clamp = mask << 8 | clamp |
| val = cute.make_fragment(1, type(value)) |
| val[0] = value |
| val_i32 = cute.recast_tensor(val, cutlass.Int32) |
| for i in cutlass.range_constexpr(cute.size(val_i32)): |
| val_i32[i] = cute.arch.shuffle_sync(val_i32[i], offset, mask_and_clamp=mask_and_clamp) |
| return val[0] |
|
|
|
|
| @dsl_user_op |
| def shr_u32(val: cutlass.Uint32, shift: cutlass.Uint32, *, loc=None, ip=None) -> cutlass.Uint32: |
| return cutlass.Uint32( |
| llvm.inline_asm( |
| T.i32(), |
| [cutlass.Uint32(val).ir_value(loc=loc, ip=ip), cutlass.Uint32(shift).ir_value(loc=loc, ip=ip)], |
| "shr.s32 $0, $1, $2;", |
| "=r,r,r", |
| has_side_effects=False, |
| is_align_stack=False, |
| asm_dialect=llvm.AsmDialect.AD_ATT, |
| ) |
| ) |
|
|
|
|
| @cute.jit |
| def warp_prefix_sum(val: cutlass.Int32, lane: Optional[cutlass.Int32] = None) -> cutlass.Int32: |
| if cutlass.const_expr(lane is None): |
| lane = cute.arch.lane_idx() |
| |
| for i in cutlass.range_constexpr(int(math.log2(cute.arch.WARP_SIZE))): |
| offset = 1 << i |
| |
| partial_sum = cute.arch.shuffle_sync_up(val, offset=offset, mask_and_clamp=0) |
| if lane >= offset: |
| val += partial_sum |
| |
| return val |
|
|
|
|
| @dsl_user_op |
| def cvt_f16x2_f32(a: float | Float32, b: float | Float32, to_dtype: Type, *, loc=None, ip=None) -> cutlass.Int32: |
| assert to_dtype in [cutlass.BFloat16, cutlass.Float16], "to_dtype must be BFloat16 or Float16" |
| return cutlass.Int32( |
| llvm.inline_asm( |
| T.i32(), |
| [Float32(a).ir_value(loc=loc, ip=ip), Float32(b).ir_value(loc=loc, ip=ip)], |
| f"cvt.rn.{'bf16x2' if to_dtype is cutlass.BFloat16 else 'f16x2'}.f32 $0, $2, $1;", |
| "=r,f,f", |
| has_side_effects=False, |
| is_align_stack=False, |
| asm_dialect=llvm.AsmDialect.AD_ATT, |
| ) |
| ) |
|
|
|
|
| @cute.jit |
| def cvt_f16(src: cute.Tensor, dst: cute.Tensor): |
| assert cute.size(dst.shape) == cute.size(src.shape), "dst and src must have the same size" |
| assert cute.size(src.shape) % 2 == 0, "src must have an even number of elements" |
| assert dst.element_type in [cutlass.BFloat16, cutlass.Float16], "dst must be BFloat16 or Float16" |
| assert src.element_type is Float32, "src must be Float32" |
| dst_i32 = cute.recast_tensor(dst, cutlass.Int32) |
| assert cute.size(dst_i32.shape) * 2 == cute.size(src.shape) |
| for i in cutlass.range_constexpr(cute.size(dst_i32)): |
| dst_i32[i] = cvt_f16x2_f32(src[2 * i], src[2 * i + 1], dst.element_type) |
|
|
|
|
| @dsl_user_op |
| def e2e_asm2(x: Float32, y: Float32, *, loc=None, ip=None) -> Tuple[Float32, Float32]: |
| out_f32x2 = llvm.inline_asm( |
| llvm.StructType.get_literal([T.f32(), T.f32()]), |
| [Float32(x).ir_value(loc=loc, ip=ip), Float32(y, loc=loc, ip=ip).ir_value()], |
| "{\n\t" |
| ".reg .f32 f1, f2, f3, f4, f5, f6, f7;\n\t" |
| ".reg .b64 l1, l2, l3, l4, l5, l6, l7, l8, l9, l10;\n\t" |
| ".reg .s32 r1, r2, r3, r4, r5, r6, r7, r8;\n\t" |
| "max.ftz.f32 f1, $2, 0fC2FE0000;\n\t" |
| "max.ftz.f32 f2, $3, 0fC2FE0000;\n\t" |
| "mov.b64 l1, {f1, f2};\n\t" |
| "mov.f32 f3, 0f4B400000;\n\t" |
| "mov.b64 l2, {f3, f3};\n\t" |
| "add.rm.ftz.f32x2 l7, l1, l2;\n\t" |
| "sub.rn.ftz.f32x2 l8, l7, l2;\n\t" |
| "sub.rn.ftz.f32x2 l9, l1, l8;\n\t" |
| "mov.f32 f7, 0f3D9DF09D;\n\t" |
| "mov.b64 l6, {f7, f7};\n\t" |
| "mov.f32 f6, 0f3E6906A4;\n\t" |
| "mov.b64 l5, {f6, f6};\n\t" |
| "mov.f32 f5, 0f3F31F519;\n\t" |
| "mov.b64 l4, {f5, f5};\n\t" |
| "mov.f32 f4, 0f3F800000;\n\t" |
| "mov.b64 l3, {f4, f4};\n\t" |
| "fma.rn.ftz.f32x2 l10, l9, l6, l5;\n\t" |
| "fma.rn.ftz.f32x2 l10, l10, l9, l4;\n\t" |
| "fma.rn.ftz.f32x2 l10, l10, l9, l3;\n\t" |
| "mov.b64 {r1, r2}, l7;\n\t" |
| "mov.b64 {r3, r4}, l10;\n\t" |
| "shl.b32 r5, r1, 23;\n\t" |
| "add.s32 r7, r5, r3;\n\t" |
| "shl.b32 r6, r2, 23;\n\t" |
| "add.s32 r8, r6, r4;\n\t" |
| "mov.b32 $0, r7;\n\t" |
| "mov.b32 $1, r8;\n\t" |
| "}\n", |
| "=r,=r,f,f", |
| has_side_effects=False, |
| is_align_stack=False, |
| asm_dialect=llvm.AsmDialect.AD_ATT, |
| ) |
| out0 = Float32(llvm.extractvalue(T.f32(), out_f32x2, [0], loc=loc, ip=ip)) |
| out1 = Float32(llvm.extractvalue(T.f32(), out_f32x2, [1], loc=loc, ip=ip)) |
| return out0, out1 |
| @dsl_user_op |
| def domain_offset_aligned(coord: cute.Coord, tensor: cute.Tensor, *, loc=None, ip=None) -> cute.Tensor: |
| assert isinstance(tensor.iterator, cute.Pointer) |
| |
| new_ptr = cute.make_ptr( |
| tensor.element_type, |
| elem_pointer(tensor, coord).toint(), |
| tensor.memspace, |
| assumed_align=tensor.iterator.alignment, |
| ) |
| return cute.make_tensor(new_ptr, tensor.layout) |
|
|
|
|
| @dsl_user_op |
| def domain_offset_i64(coord: cute.Coord, tensor: cute.Tensor, *, loc=None, ip=None) -> cute.Tensor: |
| flat_coord_i64 = tuple(cutlass.Int64(c) for c in cute.flatten(coord)) |
| flat_stride = cute.flatten_to_tuple(tensor.stride) |
| assert len(flat_coord_i64) == len( |
| flat_stride |
| ), "Coordinate and stride must have the same length" |
| offset = sum(c * s for c, s in zip(flat_coord_i64, flat_stride)) |
| assert isinstance(tensor.iterator, cute.Pointer) |
| |
| new_ptr = cute.make_ptr( |
| tensor.element_type, |
| tensor.iterator.toint() + offset * tensor.element_type.width // 8, |
| tensor.memspace, |
| assumed_align=tensor.iterator.max_alignment, |
| ) |
| return cute.make_tensor(new_ptr, tensor.layout) |
|
|
|
|
| @dsl_user_op |
| def coord_offset_i64( |
| tensor: cute.Tensor, idx: cute.typing.Int, dim: int, *, loc=None, ip=None |
| ) -> cute.Tensor: |
| offset = cutlass.Int64(idx) * cute.size(tensor.stride[dim]) |
| assert isinstance(tensor.iterator, cute.Pointer) |
| |
| new_ptr = cute.make_ptr( |
| tensor.element_type, |
| tensor.iterator.toint() + offset * tensor.element_type.width // 8, |
| tensor.memspace, |
| assumed_align=tensor.iterator.max_alignment, |
| ) |
| new_layout = cute.slice_(tensor.layout, (*[None] * dim, 0, *[None] * (cute.rank(tensor) - dim - 1))) |
| return cute.make_tensor(new_ptr, new_layout) |
|
|
