""" Copyright (c) 2025 by FlashInfer team. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Fused RMSNorm + FP4 Quantization using CuTe-DSL ================================================ High-performance fused kernel for RMS normalization followed by FP4 quantization. This is an alternative backend to cuDNN, using CuTe-DSL for maximum flexibility and performance on SM100+ architectures. Supports both NVFP4 and MXFP4 quantization formats. """ import functools import math import operator from typing import Callable, Tuple import cutlass import cutlass.cute as cute import torch from cutlass import Float32, Int32, Int64, Uint32, Uint64, Uint8 from cutlass.cutlass_dsl import T, dsl_user_op from cutlass._mlir.dialects import llvm from ..api_logging import flashinfer_api # ============================================================================= # Constants # ============================================================================= FLOAT4_E2M1_MAX = 6.0 # Maximum value representable in FP4 E2M1 FLOAT8_E4M3_MAX = 448.0 # Maximum value representable in FP8 E4M3 SF_VEC_SIZE = 16 # Elements per scale factor block COPY_BITS = 128 # 128-bit vectorized loads # ============================================================================= # Architecture Detection # ============================================================================= @functools.lru_cache(maxsize=16) def get_sm_version(device: int | torch.device | str | None = None) -> int: """Get the SM version of a CUDA device. Args: device: CUDA device to query. Can be an int (device index), torch.device, device string (e.g., 'cuda:0'), or None to use current device. Returns: SM version as an integer (e.g., 100 for SM100). """ if not torch.cuda.is_available(): return 80 if device is None: device = torch.cuda.current_device() props = torch.cuda.get_device_properties(device) return props.major * 10 + props.minor # ============================================================================= # PTX Intrinsics - Cluster Operations # ============================================================================= @dsl_user_op def set_block_rank( smem_ptr: cute.Pointer, peer_cta_rank_in_cluster: Int32, *, loc=None, ip=None ) -> Int32: """Map smem pointer to address at another CTA rank in the cluster.""" smem_ptr_i32 = smem_ptr.toint(loc=loc, ip=ip).ir_value() return Int32( llvm.inline_asm( T.i32(), [smem_ptr_i32, peer_cta_rank_in_cluster.ir_value()], "mapa.shared::cluster.u32 $0, $1, $2;", "=r,r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def store_shared_remote( val: Float32, smem_ptr: cute.Pointer, mbar_ptr: cute.Pointer, peer_cta_rank_in_cluster: Int32, *, loc=None, ip=None, ) -> None: """Store Float32 value to shared memory on a remote CTA in the cluster.""" remote_smem_ptr_i32 = set_block_rank( smem_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip ).ir_value() remote_mbar_ptr_i32 = set_block_rank( mbar_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip ).ir_value() llvm.inline_asm( None, [remote_smem_ptr_i32, val.ir_value(loc=loc, ip=ip), remote_mbar_ptr_i32], "st.async.shared::cluster.mbarrier::complete_tx::bytes.f32 [$0], $1, [$2];", "r,f,r", has_side_effects=True, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) @dsl_user_op def elem_pointer(x: cute.Tensor, coord, *, loc=None, ip=None) -> cute.Pointer: """Get pointer to element at coordinate in tensor.""" return x.iterator + cute.crd2idx(coord, x.layout, loc=loc, ip=ip) # ============================================================================= # PTX Intrinsics - 128-bit Vectorized Global Loads # ============================================================================= @dsl_user_op def ld_global_v4_u32( base_ptr: Int64, *, loc=None, ip=None ) -> Tuple[Uint32, Uint32, Uint32, Uint32]: """Load 128 bits (4 x uint32) from global memory.""" result = llvm.inline_asm( llvm.StructType.get_literal([T.i32(), T.i32(), T.i32(), T.i32()]), [Int64(base_ptr).ir_value(loc=loc, ip=ip)], "ld.global.v4.u32 {$0, $1, $2, $3}, [$4];", "=r,=r,=r,=r,l", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, loc=loc, ip=ip, ) v0 = llvm.extractvalue(T.i32(), result, [0], loc=loc, ip=ip) v1 = llvm.extractvalue(T.i32(), result, [1], loc=loc, ip=ip) v2 = llvm.extractvalue(T.i32(), result, [2], loc=loc, ip=ip) v3 = llvm.extractvalue(T.i32(), result, [3], loc=loc, ip=ip) return Uint32(v0), Uint32(v1), Uint32(v2), Uint32(v3) @dsl_user_op def st_global_u64(base_ptr: Int64, value: Uint64, *, loc=None, ip=None): """Store 64 bits to global memory.""" llvm.inline_asm( None, [ Int64(base_ptr).ir_value(loc=loc, ip=ip), Uint64(value).ir_value(loc=loc, ip=ip), ], "st.global.u64 [$0], $1;", "l,l", has_side_effects=True, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) @dsl_user_op def get_ptr_as_int64(tensor: cute.Tensor, offset: Int32, *, loc=None, ip=None) -> Int64: """Get the memory address of tensor[offset] as Int64.""" elem_ptr = tensor.iterator + Int32(offset) ptr_int = llvm.ptrtoint(T.i64(), elem_ptr.llvm_ptr, loc=loc, ip=ip) return Int64(ptr_int) @dsl_user_op def rcp_approx_ftz(a: Float32, *, loc=None, ip=None) -> Float32: """Fast reciprocal using PTX rcp.approx.ftz.f32.""" return Float32( llvm.inline_asm( T.f32(), [Float32(a).ir_value(loc=loc, ip=ip)], "rcp.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 fmin_f32(a: Float32, b: Float32, *, loc=None, ip=None) -> Float32: """Compute min of two float32 values using PTX min.f32 (branchless clamping).""" return Float32( llvm.inline_asm( T.f32(), [Float32(a).ir_value(loc=loc, ip=ip), Float32(b).ir_value(loc=loc, ip=ip)], "min.f32 $0, $1, $2;", "=f,f,f", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) # ============================================================================= # Half2 SIMD Intrinsics # ============================================================================= @dsl_user_op def half2_mul(a: Uint32, b: Uint32, *, loc=None, ip=None) -> Uint32: """Multiply two Half2 values element-wise: (a.x*b.x, a.y*b.y).""" return Uint32( llvm.inline_asm( T.i32(), [Uint32(a).ir_value(loc=loc, ip=ip), Uint32(b).ir_value(loc=loc, ip=ip)], "mul.f16x2 $0, $1, $2;", "=r,r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def bfloat2_mul(a: Uint32, b: Uint32, *, loc=None, ip=None) -> Uint32: """Multiply two BFloat2 values element-wise: (a.x*b.x, a.y*b.y).""" return Uint32( llvm.inline_asm( T.i32(), [Uint32(a).ir_value(loc=loc, ip=ip), Uint32(b).ir_value(loc=loc, ip=ip)], "mul.bf16x2 $0, $1, $2;", "=r,r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) # ============================================================================= # Half2 SIMD Max-Abs Intrinsics # ============================================================================= @dsl_user_op def habs2(x: Uint32, *, loc=None, ip=None) -> Uint32: """Half2 absolute value - clears sign bits of both fp16 values.""" return Uint32( llvm.inline_asm( T.i32(), [Uint32(x).ir_value(loc=loc, ip=ip)], "and.b32 $0, $1, 0x7FFF7FFF;", "=r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def hmax2(a: Uint32, b: Uint32, *, loc=None, ip=None) -> Uint32: """Half2 max - element-wise max of 2 fp16 pairs.""" return Uint32( llvm.inline_asm( T.i32(), [Uint32(a).ir_value(loc=loc, ip=ip), Uint32(b).ir_value(loc=loc, ip=ip)], "max.f16x2 $0, $1, $2;", "=r,r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def hmax_to_f32(x: Uint32, *, loc=None, ip=None) -> Float32: """Extract max of 2 fp16 values in half2 as float32.""" return Float32( llvm.inline_asm( T.f32(), [Uint32(x).ir_value(loc=loc, ip=ip)], """ { .reg .b16 h0, h1; .reg .f32 f0, f1; mov.b32 {h0, h1}, $1; cvt.f32.f16 f0, h0; cvt.f32.f16 f1, h1; max.f32 $0, f0, f1; } """, "=f,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def bfloat2_habs2(x: Uint32, *, loc=None, ip=None) -> Uint32: """BFloat16x2 absolute value - clears sign bits of both bf16 values.""" return Uint32( llvm.inline_asm( T.i32(), [Uint32(x).ir_value(loc=loc, ip=ip)], "and.b32 $0, $1, 0x7FFF7FFF;", "=r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def bfloat2_hmax2(a: Uint32, b: Uint32, *, loc=None, ip=None) -> Uint32: """BFloat16x2 max - element-wise max of 2 bf16 pairs.""" return Uint32( llvm.inline_asm( T.i32(), [Uint32(a).ir_value(loc=loc, ip=ip), Uint32(b).ir_value(loc=loc, ip=ip)], "max.bf16x2 $0, $1, $2;", "=r,r,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def bfloat2_hmax_to_f32(x: Uint32, *, loc=None, ip=None) -> Float32: """Extract max of 2 bf16 values in bfloat2 as float32.""" return Float32( llvm.inline_asm( T.f32(), [Uint32(x).ir_value(loc=loc, ip=ip)], """ { .reg .b32 lo, hi; .reg .f32 f0, f1; and.b32 lo, $1, 0xFFFF; shr.b32 hi, $1, 16; shl.b32 lo, lo, 16; shl.b32 hi, hi, 16; mov.b32 f0, lo; mov.b32 f1, hi; max.f32 $0, f0, f1; } """, "=f,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) # ============================================================================= # Half2 to Float2 Scaled Conversion # ============================================================================= @dsl_user_op def half2_to_float2_scaled( h2: Uint32, scale: Float32, *, loc=None, ip=None ) -> Tuple[Float32, Float32]: """Convert half2 to float2 AND multiply by scale.""" result = llvm.inline_asm( llvm.StructType.get_literal([T.f32(), T.f32()]), [Uint32(h2).ir_value(loc=loc, ip=ip), Float32(scale).ir_value(loc=loc, ip=ip)], """ { .reg .b16 h0, h1; .reg .f32 f0, f1; mov.b32 {h0, h1}, $2; cvt.f32.f16 f0, h0; cvt.f32.f16 f1, h1; mul.f32 $0, f0, $3; mul.f32 $1, f1, $3; } """, "=f,=f,r,f", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, loc=loc, ip=ip, ) f0 = llvm.extractvalue(T.f32(), result, [0], loc=loc, ip=ip) f1 = llvm.extractvalue(T.f32(), result, [1], loc=loc, ip=ip) return Float32(f0), Float32(f1) @dsl_user_op def bfloat2_to_float2_scaled( bf2: Uint32, scale: Float32, *, loc=None, ip=None ) -> Tuple[Float32, Float32]: """Convert bfloat16x2 to float2 AND multiply by scale.""" result = llvm.inline_asm( llvm.StructType.get_literal([T.f32(), T.f32()]), [Uint32(bf2).ir_value(loc=loc, ip=ip), Float32(scale).ir_value(loc=loc, ip=ip)], """ { .reg .b32 lo, hi; .reg .f32 f0, f1; and.b32 lo, $2, 0xFFFF; shr.b32 hi, $2, 16; shl.b32 lo, lo, 16; shl.b32 hi, hi, 16; mov.b32 f0, lo; mov.b32 f1, hi; mul.f32 $0, f0, $3; mul.f32 $1, f1, $3; } """, "=f,=f,r,f", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, loc=loc, ip=ip, ) f0 = llvm.extractvalue(T.f32(), result, [0], loc=loc, ip=ip) f1 = llvm.extractvalue(T.f32(), result, [1], loc=loc, ip=ip) return Float32(f0), Float32(f1) # ============================================================================= # FP8 E4M3 Intrinsics # ============================================================================= @dsl_user_op def cvt_f32_to_e4m3(a: Float32, *, loc=None, ip=None) -> Uint32: """Convert float32 to E4M3 using native cvt.rn.satfinite.e4m3x2.f32.""" return Uint32( llvm.inline_asm( T.i32(), [Float32(a).ir_value(loc=loc, ip=ip)], """ { .reg .b16 fp8_pair; .reg .f32 zero; mov.f32 zero, 0f00000000; cvt.rn.satfinite.e4m3x2.f32 fp8_pair, zero, $1; cvt.u32.u16 $0, fp8_pair; } """, "=r,f", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def fp8_e4m3_to_f32_and_rcp(fp8_val: Uint32, *, loc=None, ip=None) -> Float32: """Convert FP8 E4M3 to float32 AND compute reciprocal.""" return Float32( llvm.inline_asm( T.f32(), [Uint32(fp8_val).ir_value(loc=loc, ip=ip)], """ { .reg .pred p_zero; .reg .u32 exp_u, mant_u; .reg .s32 exp_s; .reg .f32 exp_f, mant_f, fp8_float, result; setp.eq.u32 p_zero, $1, 0; and.b32 mant_u, $1, 7; shr.b32 exp_u, $1, 3; and.b32 exp_u, exp_u, 15; sub.s32 exp_s, exp_u, 7; cvt.rn.f32.s32 exp_f, exp_s; ex2.approx.f32 exp_f, exp_f; cvt.rn.f32.u32 mant_f, mant_u; fma.rn.f32 mant_f, mant_f, 0f3E000000, 0f3F800000; mul.f32 fp8_float, exp_f, mant_f; rcp.approx.ftz.f32 result, fp8_float; selp.f32 $0, 0f00000000, result, p_zero; } """, "=f,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) # ============================================================================= # UE8M0 Intrinsics (for MXFP4) # ============================================================================= @dsl_user_op def cvt_f32_to_ue8m0(max_val: Float32, *, loc=None, ip=None) -> Uint32: """ Convert float32 max value to UE8M0 scale factor. UE8M0 is unsigned 8-bit exponent-only format: - value = 2^(ue8m0 - 127) - ue8m0 = ceil(log2(max_val)) + 127 Uses lg2.approx.f32 for fast log2 approximation. Uses cvt.rpi (round towards positive infinity, i.e., ceiling). Returns value clamped to [0, 255]. """ return Uint32( llvm.inline_asm( T.i32(), [Float32(max_val).ir_value(loc=loc, ip=ip)], """ { .reg .pred p_zero, p_neg, p_ovf; .reg .f32 log2_val; .reg .s32 exp_int, result; // Check for zero/negative setp.le.f32 p_zero, $1, 0f00000000; // Compute ceil(log2(max_val)) using cvt.rpi (round towards +inf) lg2.approx.f32 log2_val, $1; cvt.rpi.s32.f32 exp_int, log2_val; // Add bias and clamp to [0, 255] add.s32 result, exp_int, 127; setp.lt.s32 p_neg, result, 0; setp.gt.s32 p_ovf, result, 255; selp.s32 result, 0, result, p_neg; selp.s32 result, 255, result, p_ovf; selp.s32 $0, 0, result, p_zero; } """, "=r,f", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) @dsl_user_op def ue8m0_to_output_scale(ue8m0_val: Uint32, *, loc=None, ip=None) -> Float32: """ Convert UE8M0 to output_scale for MXFP4 quantization. UE8M0 value = 2^(ue8m0 - 127) Returns 1 / 2^(ue8m0 - 127) = 2^(127 - ue8m0) """ return Float32( llvm.inline_asm( T.f32(), [Uint32(ue8m0_val).ir_value(loc=loc, ip=ip)], """ { .reg .pred p_zero; .reg .s32 neg_exp; .reg .f32 neg_exp_f, result; // Check for zero setp.eq.u32 p_zero, $1, 0; // Compute 2^(127 - ue8m0) = 1 / 2^(ue8m0 - 127) sub.s32 neg_exp, 127, $1; cvt.rn.f32.s32 neg_exp_f, neg_exp; ex2.approx.f32 result, neg_exp_f; selp.f32 $0, 0f00000000, result, p_zero; } """, "=f,r", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) # ============================================================================= # E2M1 Conversion # ============================================================================= @dsl_user_op def cvt_e2m1x8_f32( v0: Float32, v1: Float32, v2: Float32, v3: Float32, v4: Float32, v5: Float32, v6: Float32, v7: Float32, *, loc=None, ip=None, ) -> Uint32: """Convert eight float32 values to eight E2M1 (4-bit) values packed into uint32.""" return Uint32( llvm.inline_asm( T.i32(), [ Float32(v0).ir_value(loc=loc, ip=ip), Float32(v1).ir_value(loc=loc, ip=ip), Float32(v2).ir_value(loc=loc, ip=ip), Float32(v3).ir_value(loc=loc, ip=ip), Float32(v4).ir_value(loc=loc, ip=ip), Float32(v5).ir_value(loc=loc, ip=ip), Float32(v6).ir_value(loc=loc, ip=ip), Float32(v7).ir_value(loc=loc, ip=ip), ], """ { .reg .b8 byte0, byte1, byte2, byte3; cvt.rn.satfinite.e2m1x2.f32 byte0, $2, $1; cvt.rn.satfinite.e2m1x2.f32 byte1, $4, $3; cvt.rn.satfinite.e2m1x2.f32 byte2, $6, $5; cvt.rn.satfinite.e2m1x2.f32 byte3, $8, $7; mov.b32 $0, {byte0, byte1, byte2, byte3}; } """, "=r,f,f,f,f,f,f,f,f", has_side_effects=False, is_align_stack=False, asm_dialect=llvm.AsmDialect.AD_ATT, ) ) # ============================================================================= # Warp, Block, and Cluster Reduction Utilities # ============================================================================= @cute.jit def warp_reduce(val, op, width: cutlass.Constexpr[int] = 32): """Reduce across threads in a warp using butterfly shuffle.""" if cutlass.const_expr(isinstance(val, cute.TensorSSA)): res = cute.make_rmem_tensor(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 @cute.jit def block_reduce( val: Float32, op: Callable, reduction_buffer: cute.Tensor, init_val: Float32, ) -> Float32: """Block reduction across multiple warps using shared memory.""" lane_idx = cute.arch.lane_idx() warp_idx = cute.arch.warp_idx() warps_per_row = cute.size(reduction_buffer.shape[1]) row_idx = warp_idx // warps_per_row col_idx = warp_idx % warps_per_row if lane_idx == 0: reduction_buffer[row_idx, col_idx] = val cute.arch.barrier() block_reduce_val = init_val if lane_idx < warps_per_row: block_reduce_val = reduction_buffer[row_idx, lane_idx] return warp_reduce(block_reduce_val, op) @cute.jit def cluster_reduce( val: Float32, op: Callable, reduction_buffer: cute.Tensor, mbar_ptr: cute.Pointer, cluster_n: cutlass.Constexpr[int], init_val: Float32, ) -> Float32: """Cluster reduction across multiple CTAs using mbarrier.""" cta_rank_in_cluster = cute.arch.block_idx_in_cluster() lane_idx = cute.arch.lane_idx() warp_idx = cute.arch.warp_idx() rows_per_block = reduction_buffer.shape[0] warps_per_row = reduction_buffer.shape[1][0] row_idx = warp_idx // warps_per_row col_idx = warp_idx % warps_per_row # Warp 0 sets up mbarrier transaction count if warp_idx == 0: with cute.arch.elect_one(): num_warps = rows_per_block * warps_per_row expected_bytes = num_warps * cluster_n * 4 cute.arch.mbarrier_arrive_and_expect_tx(mbar_ptr, expected_bytes) # Each lane < cluster_n writes to a different CTA's shared memory if lane_idx < cluster_n: store_shared_remote( val, elem_pointer(reduction_buffer, (row_idx, (col_idx, cta_rank_in_cluster))), mbar_ptr, peer_cta_rank_in_cluster=lane_idx, ) # Wait for all cluster writes cute.arch.mbarrier_wait(mbar_ptr, phase=0) # Reduce across all values num_total = warps_per_row * cluster_n num_iter = cute.ceil_div(num_total, 32) block_reduce_val = init_val for i in cutlass.range_constexpr(num_iter): idx = lane_idx + i * 32 if idx < num_total: block_reduce_val = op(block_reduce_val, reduction_buffer[row_idx, idx]) return warp_reduce(block_reduce_val, op) @cute.jit def row_reduce( x: cute.TensorSSA, op: cute.ReductionOp, threads_per_row: cutlass.Constexpr[int], reduction_buffer: cute.Tensor, mbar_ptr, cluster_n: cutlass.Constexpr[int], init_val: Float32, ): """Row reduction with optional cluster support.""" local_val = x.reduce(op, init_val=init_val, reduction_profile=0) warp_op = { cute.ReductionOp.ADD: operator.add, cute.ReductionOp.MAX: cute.arch.fmax, }[op] warp_width = min(threads_per_row, 32) warp_val = warp_reduce(local_val, warp_op, width=warp_width) warps_per_row = max(threads_per_row // 32, 1) if cutlass.const_expr(warps_per_row > 1 or cluster_n > 1): if cutlass.const_expr(cluster_n == 1): return block_reduce(warp_val, warp_op, reduction_buffer, init_val) else: return cluster_reduce( warp_val, warp_op, reduction_buffer, mbar_ptr, cluster_n, init_val ) else: return warp_val # ============================================================================= # Predicate Utility # ============================================================================= @cute.jit def predicate_k(tXcX: cute.Tensor, limit: int) -> cute.Tensor: """Create predicate tensor for bounds checking.""" tXpX = cute.make_rmem_tensor( cute.make_layout( ( cute.size(tXcX, mode=[0, 1]), cute.size(tXcX, mode=[1]), cute.size(tXcX, mode=[2]), ), stride=(cute.size(tXcX, mode=[2]), 0, 1), ), cutlass.Boolean, ) for rest_v in cutlass.range_constexpr(tXpX.shape[0]): for rest_k in cutlass.range_constexpr(tXpX.shape[2]): tXpX[rest_v, 0, rest_k] = cute.elem_less( tXcX[(0, rest_v), 0, rest_k][1], limit ) return tXpX # ============================================================================= # CuTe-DSL Kernel Class # ============================================================================= class RMSNormFP4QuantKernel: """ Fused RMSNorm + FP4 Quantization Kernel. Key optimizations: 1. Half2/BFloat2 SIMD for max-abs computation 2. Branchless scale clamping via fmin_f32 3. Cluster synchronization for large H dimensions 4. Direct 128-bit vectorized global loads """ def __init__( self, dtype: cutlass.Numeric, H: int, block_size: int, output_swizzled: bool, is_fp16: bool, sm_version: int | None = None, scale_format: str | None = None, # "e4m3" or "ue8m0", None = auto ): self.dtype = dtype self.H = H self.block_size = block_size self.output_swizzled = output_swizzled self.is_fp16 = is_fp16 self.sm_version = sm_version if sm_version is not None else get_sm_version() # Auto-select scale format based on block_size if not specified if scale_format is None: self.scale_format = "ue8m0" if block_size == 32 else "e4m3" else: self.scale_format = scale_format # Validate configuration assert block_size in (16, 32), f"block_size must be 16 or 32, got {block_size}" assert self.scale_format in ("e4m3", "ue8m0"), ( "scale_format must be 'e4m3' or 'ue8m0'" ) # Compute cluster size self.cluster_n = self._compute_cluster_n(H, dtype, self.sm_version) # H per CTA for cluster case self.H_per_cta = H // self.cluster_n # Compute thread configuration self.threads_per_row = self._compute_threads_per_row(self.H_per_cta) self.num_threads = self._compute_num_threads(self.H_per_cta) self.rows_per_block = self.num_threads // self.threads_per_row self.warps_per_row = max(self.threads_per_row // 32, 1) # Vectorization parameters elem_bytes = dtype.width // 8 self.vec_size = COPY_BITS // 8 // elem_bytes self.num_vec_blocks = max( 1, (self.H_per_cta // self.vec_size + self.threads_per_row - 1) // self.threads_per_row, ) self.cols_per_tile = self.vec_size * self.num_vec_blocks * self.threads_per_row # Scale factor block count (full H, not per CTA) self.num_sf_blocks_per_row = H // block_size # Swizzle parameters if output_swizzled: num_col_vecs = H // block_size self.num_k_tiles = (num_col_vecs + 3) // 4 self.k_tile_stride = 512 @staticmethod def _compute_cluster_n(H: int, dtype: cutlass.Numeric, sm_version: int) -> int: """Compute optimal cluster size based on H and device shared memory. Dynamically determines the minimum cluster_n that fits within the device's shared memory limit, making it compatible with different GPU architectures (e.g., SM100 with 228KB vs SM120 with 128KB). """ if sm_version < 90: return 1 props = torch.cuda.get_device_properties(torch.cuda.current_device()) max_smem_bytes = props.shared_memory_per_block_optin elem_size = dtype.width // 8 for cluster_n in [1, 2, 4, 8, 16]: if H % cluster_n != 0: continue smem_needed = RMSNormFP4QuantKernel._estimate_smem_bytes( H, cluster_n, elem_size ) if smem_needed <= max_smem_bytes: return cluster_n return 16 @staticmethod def _compute_threads_per_row(H_per_cta: int) -> int: """Compute optimal threads per row based on H per CTA.""" if H_per_cta <= 64: return 8 elif H_per_cta <= 128: return 16 elif H_per_cta <= 3072: return 32 elif H_per_cta <= 6144: return 64 elif H_per_cta <= 16384: return 128 else: return 256 @staticmethod def _compute_num_threads(H_per_cta: int) -> int: """Compute total threads per block based on H per CTA.""" return 128 if H_per_cta <= 16384 else 256 @staticmethod def _estimate_smem_bytes(H: int, cluster_n: int, elem_size: int) -> int: """Estimate shared memory bytes needed for given configuration. This is used to dynamically determine cluster_n based on device shared memory limits. """ H_per_cta = H // cluster_n threads_per_row = RMSNormFP4QuantKernel._compute_threads_per_row(H_per_cta) num_threads = RMSNormFP4QuantKernel._compute_num_threads(H_per_cta) rows_per_block = num_threads // threads_per_row warps_per_row = max(threads_per_row // 32, 1) vec_size = COPY_BITS // 8 // elem_size num_vec_blocks = max( 1, (H_per_cta // vec_size + threads_per_row - 1) // threads_per_row ) cols_per_tile = vec_size * num_vec_blocks * threads_per_row tile_bytes = rows_per_block * cols_per_tile * elem_size if cluster_n == 1: # 2 tiles: sX, sW + reduction buffer return 2 * tile_bytes + rows_per_block * warps_per_row * 4 else: # 1 tile: sX + larger reduction buffer + mbarrier return tile_bytes + rows_per_block * warps_per_row * cluster_n * 4 + 8 @staticmethod def _make_tv_layout( threads_per_row: int, rows_per_block: int, vec_size: int, num_vec_blocks: int, ) -> tuple: """Create Thread-Value layout for coalesced vectorized memory access.""" shape = ( (threads_per_row, rows_per_block), (vec_size, num_vec_blocks), ) stride = ( (vec_size * rows_per_block, 1), (rows_per_block, rows_per_block * vec_size * threads_per_row), ) return shape, stride def _smem_size_in_bytes(self) -> int: """Calculate shared memory requirement.""" # Input tile tile_bytes = self.rows_per_block * self.cols_per_tile * (self.dtype.width // 8) # Reduction buffer (with cluster support) if self.cluster_n == 1: reduction_bytes = self.rows_per_block * self.warps_per_row * 4 else: reduction_bytes = ( self.rows_per_block * self.warps_per_row * self.cluster_n * 4 ) # mbarrier (for cluster mode) mbar_bytes = 8 if self.cluster_n > 1 else 0 return tile_bytes + reduction_bytes + mbar_bytes @cute.jit def __call__( self, mX: cute.Tensor, mW: cute.Tensor, mY: cute.Tensor, mS: cute.Tensor, mGlobalScale: cute.Tensor, M: Int32, eps: Float32, stream, ): """Host function to launch the kernel. Takes tensors directly via TVM-FFI. - mX: Input tensor, shape (M, H), row-major - mW: Weight tensor, shape (H,) - mY: Output FP4 tensor, shape (M, H // 2), row-major (packed) - mS: Scale factor tensor, shape depends on swizzle mode - mGlobalScale: Global scale tensor, shape (1,), float32 """ # Create TV layout tv_shape, tv_stride = self._make_tv_layout( self.threads_per_row, self.rows_per_block, self.vec_size, self.num_vec_blocks, ) tv_layout = cute.make_layout(tv_shape, stride=tv_stride) tiler_mn = (self.rows_per_block, self.cols_per_tile) # Launch with cluster support self.kernel(mX, mW, mY, mS, mGlobalScale, M, eps, tv_layout, tiler_mn).launch( grid=[cute.ceil_div(M, self.rows_per_block), self.cluster_n, 1], block=[self.num_threads, 1, 1], cluster=[1, self.cluster_n, 1] if cutlass.const_expr(self.cluster_n > 1) else None, smem=self._smem_size_in_bytes(), stream=stream, ) @cute.kernel def kernel( self, mX: cute.Tensor, mW: cute.Tensor, mY: cute.Tensor, mS: cute.Tensor, mGlobalScale: cute.Tensor, M: Int32, eps: Float32, tv_layout: cute.Layout, tiler_mn: cute.Shape, ): """Device kernel with cluster synchronization for large H. mGlobalScale contains the global scale value. The kernel reads it and computes 1/global_scale, which is multiplied with rstd to apply: y = x * rstd * w / global_scale = rmsnorm(x, w) / global_scale """ tidx, _, _ = cute.arch.thread_idx() bidx, _, _ = cute.arch.block_idx() H = self.H block_size = self.block_size num_sf_blocks_per_row = self.num_sf_blocks_per_row is_fp16 = self.is_fp16 cluster_n = self.cluster_n # Get cluster position if cutlass.const_expr(cluster_n > 1): cluster_y = cute.arch.block_idx()[1] else: cluster_y = cutlass.const_expr(0) threads_per_row = tv_layout.shape[0][0] warps_per_row = max(threads_per_row // 32, 1) rows_per_block = tiler_mn[0] # Thread position within row lane_in_row = tidx % threads_per_row row_in_block = tidx // threads_per_row # Precompute fp4_max_rcp = rcp_approx_ftz(Float32(FLOAT4_E2M1_MAX)) # ================================================================== # Allocate shared memory # ================================================================== smem = cutlass.utils.SmemAllocator() # Shared memory tile for input (for sum-of-squares) sX = smem.allocate_tensor( mX.element_type, cute.make_ordered_layout(tiler_mn, order=(1, 0)), byte_alignment=16, ) # Reduction buffer (with cluster support) if cutlass.const_expr(cluster_n == 1): reduction_buffer = smem.allocate_tensor( Float32, cute.make_layout((rows_per_block, warps_per_row)), byte_alignment=4, ) mbar_ptr = None else: reduction_buffer = smem.allocate_tensor( Float32, cute.make_layout((rows_per_block, (warps_per_row, cluster_n))), byte_alignment=4, ) mbar_ptr = smem.allocate_array(Int64, num_elems=1) # ================================================================== # Initialize cluster # ================================================================== if cutlass.const_expr(cluster_n > 1): if tidx == 0: cute.arch.mbarrier_init(mbar_ptr, 1) cute.arch.mbarrier_init_fence() cute.arch.cluster_arrive_relaxed() cute.arch.cluster_wait() # ================================================================== # Create identity tensor for coordinate tracking # ================================================================== idX = cute.make_identity_tensor(mX.shape) # Slice for this block's rows (and cluster CTA's slice of columns) gX = cute.local_tile(mX, tiler_mn, (bidx, cluster_y)) cX = cute.local_tile(idX, tiler_mn, (bidx, cluster_y)) # Expand weight to 2D for tiled copy mW_expanded_layout = cute.prepend( mW.layout, cute.make_layout((tiler_mn[0],), stride=(0,)) ) mW_2d = cute.make_tensor(mW.iterator, mW_expanded_layout) _ = cute.local_tile( mW_2d, tiler_mn, (0, cluster_y) ) # gW unused but call needed # ================================================================== # Create TiledCopy for sum-of-squares phase # ================================================================== copy_atom_load_async = cute.make_copy_atom( cute.nvgpu.cpasync.CopyG2SOp(), mX.element_type, num_bits_per_copy=COPY_BITS, ) tiled_copy_load = cute.make_tiled_copy( copy_atom_load_async, tv_layout, tiler_mn ) thr_copy_X = tiled_copy_load.get_slice(tidx) # Partition tensors tXgX = thr_copy_X.partition_S(gX) tXsX = thr_copy_X.partition_D(sX) tXcX = thr_copy_X.partition_S(cX) # Register fragments tXrX = cute.make_fragment_like(tXgX) # ================================================================== # Bounds checking # ================================================================== tXpX = predicate_k(tXcX, limit=H) row_coord = tXcX[(0, 0), 0, 0] row_in_bounds = row_coord[0] < M # ================================================================== # Phase 1: Async copy global → shared (for sum-of-squares) # ================================================================== if row_in_bounds: cute.copy(copy_atom_load_async, tXgX, tXsX, pred=tXpX) cute.arch.cp_async_commit_group() cute.arch.cp_async_wait_group(0) # ================================================================== # Phase 2: Compute sum of squares with cluster reduction # ================================================================== cute.autovec_copy(tXsX, tXrX) x = tXrX.load().to(Float32) # Sum of squares x_sq = x * x sum_sq = row_reduce( x_sq, cute.ReductionOp.ADD, threads_per_row, reduction_buffer, mbar_ptr, cluster_n, Float32(0.0), ) # rstd = 1 / sqrt(mean(x²) + eps) mean_sq = sum_sq / H # Use full H, not H_per_cta rstd = cute.math.rsqrt(mean_sq + eps, fastmath=True) # Read global_scale from device memory (CUDA graph compatible) # Note: global_scale is incorporated into the block scale, NOT applied to input global_scale_val = mGlobalScale[0] # Sync after reduction if cutlass.const_expr(cluster_n > 1): cute.arch.cluster_arrive_relaxed() cute.arch.cluster_wait() else: cute.arch.barrier() # ================================================================== # Phase 3: RMSNorm + Quantize with Vectorized Global Loads # ================================================================== # Get actual row index actual_row_idx = bidx * rows_per_block + row_in_block if actual_row_idx < M: # Process SF blocks assigned to this thread num_sf_per_thread = ( num_sf_blocks_per_row + threads_per_row - 1 ) // threads_per_row for sf_iter in range(num_sf_per_thread): sf_idx = lane_in_row + sf_iter * threads_per_row if sf_idx < num_sf_blocks_per_row: block_start = sf_idx * block_size # ======================================================= # Load elements from GLOBAL MEMORY as Half2 # ======================================================= if cutlass.const_expr(block_size == 16): # Get pointers for X and W x_ptr0 = get_ptr_as_int64(mX, actual_row_idx * H + block_start) x_ptr1 = get_ptr_as_int64( mX, actual_row_idx * H + block_start + Int32(8) ) w_ptr0 = get_ptr_as_int64(mW, block_start) w_ptr1 = get_ptr_as_int64(mW, block_start + Int32(8)) # Load 16 elements of X as 8 Half2 pairs x0, x1, x2, x3 = ld_global_v4_u32(x_ptr0) x4, x5, x6, x7 = ld_global_v4_u32(x_ptr1) # Load 16 elements of W as 8 Half2 pairs w0, w1, w2, w3 = ld_global_v4_u32(w_ptr0) w4, w5, w6, w7 = ld_global_v4_u32(w_ptr1) # Multiply x * w in Half2/BFloat2 if cutlass.const_expr(is_fp16): xw0 = half2_mul(x0, w0) xw1 = half2_mul(x1, w1) xw2 = half2_mul(x2, w2) xw3 = half2_mul(x3, w3) xw4 = half2_mul(x4, w4) xw5 = half2_mul(x5, w5) xw6 = half2_mul(x6, w6) xw7 = half2_mul(x7, w7) # Half2 SIMD max-abs tree reduction abs0_h2 = habs2(xw0) abs1_h2 = habs2(xw1) abs2_h2 = habs2(xw2) abs3_h2 = habs2(xw3) abs4_h2 = habs2(xw4) abs5_h2 = habs2(xw5) abs6_h2 = habs2(xw6) abs7_h2 = habs2(xw7) max01_h2 = hmax2(abs0_h2, abs1_h2) max23_h2 = hmax2(abs2_h2, abs3_h2) max45_h2 = hmax2(abs4_h2, abs5_h2) max67_h2 = hmax2(abs6_h2, abs7_h2) max0123_h2 = hmax2(max01_h2, max23_h2) max4567_h2 = hmax2(max45_h2, max67_h2) max_xw_h2 = hmax2(max0123_h2, max4567_h2) max_xw = hmax_to_f32(max_xw_h2) max_abs = max_xw * rstd # Convert to Float32 for quantization y0, y1 = half2_to_float2_scaled(xw0, rstd) y2, y3 = half2_to_float2_scaled(xw1, rstd) y4, y5 = half2_to_float2_scaled(xw2, rstd) y6, y7 = half2_to_float2_scaled(xw3, rstd) y8, y9 = half2_to_float2_scaled(xw4, rstd) y10, y11 = half2_to_float2_scaled(xw5, rstd) y12, y13 = half2_to_float2_scaled(xw6, rstd) y14, y15 = half2_to_float2_scaled(xw7, rstd) else: xw0 = bfloat2_mul(x0, w0) xw1 = bfloat2_mul(x1, w1) xw2 = bfloat2_mul(x2, w2) xw3 = bfloat2_mul(x3, w3) xw4 = bfloat2_mul(x4, w4) xw5 = bfloat2_mul(x5, w5) xw6 = bfloat2_mul(x6, w6) xw7 = bfloat2_mul(x7, w7) # BFloat2 SIMD max-abs tree reduction abs0_h2 = bfloat2_habs2(xw0) abs1_h2 = bfloat2_habs2(xw1) abs2_h2 = bfloat2_habs2(xw2) abs3_h2 = bfloat2_habs2(xw3) abs4_h2 = bfloat2_habs2(xw4) abs5_h2 = bfloat2_habs2(xw5) abs6_h2 = bfloat2_habs2(xw6) abs7_h2 = bfloat2_habs2(xw7) max01_h2 = bfloat2_hmax2(abs0_h2, abs1_h2) max23_h2 = bfloat2_hmax2(abs2_h2, abs3_h2) max45_h2 = bfloat2_hmax2(abs4_h2, abs5_h2) max67_h2 = bfloat2_hmax2(abs6_h2, abs7_h2) max0123_h2 = bfloat2_hmax2(max01_h2, max23_h2) max4567_h2 = bfloat2_hmax2(max45_h2, max67_h2) max_xw_h2 = bfloat2_hmax2(max0123_h2, max4567_h2) max_xw = bfloat2_hmax_to_f32(max_xw_h2) max_abs = max_xw * rstd # Convert to Float32 for quantization y0, y1 = bfloat2_to_float2_scaled(xw0, rstd) y2, y3 = bfloat2_to_float2_scaled(xw1, rstd) y4, y5 = bfloat2_to_float2_scaled(xw2, rstd) y6, y7 = bfloat2_to_float2_scaled(xw3, rstd) y8, y9 = bfloat2_to_float2_scaled(xw4, rstd) y10, y11 = bfloat2_to_float2_scaled(xw5, rstd) y12, y13 = bfloat2_to_float2_scaled(xw6, rstd) y14, y15 = bfloat2_to_float2_scaled(xw7, rstd) # ======================================================= # Compute scale factor (FP8 E4M3) - Branchless clamping # global_scale is incorporated into block scale (not input) # Formula: scale = global_scale * max_abs / FP4_MAX # ======================================================= scale_float = global_scale_val * max_abs * fp4_max_rcp scale_float = fmin_f32(scale_float, Float32(FLOAT8_E4M3_MAX)) scale_fp8_u32 = cvt_f32_to_e4m3(scale_float) scale_fp8 = Uint8(scale_fp8_u32 & Uint32(0xFF)) # inv_scale = global_scale / scale_float # This cancels the global_scale in scale_float, giving ~6/max_abs inv_scale = ( fp8_e4m3_to_f32_and_rcp(scale_fp8_u32) * global_scale_val ) # ======================================================= # Store scale factor # ======================================================= if cutlass.const_expr(self.output_swizzled): inner_k_idx = sf_idx % Int32(4) inner_m_idx = (actual_row_idx % Int32(128)) // Int32(32) outer_m_idx = actual_row_idx % Int32(32) k_tile_idx = sf_idx // Int32(4) m_tile_idx = actual_row_idx // Int32(128) m_tile_stride = self.num_k_tiles * self.k_tile_stride swizzled_offset = ( m_tile_idx * m_tile_stride + k_tile_idx * self.k_tile_stride + outer_m_idx * Int32(16) + inner_m_idx * Int32(4) + inner_k_idx ) mS[swizzled_offset] = scale_fp8 else: mS[actual_row_idx, sf_idx] = scale_fp8 # ======================================================= # Quantize and pack FP4 values # ======================================================= q0 = y0 * inv_scale q1 = y1 * inv_scale q2 = y2 * inv_scale q3 = y3 * inv_scale q4 = y4 * inv_scale q5 = y5 * inv_scale q6 = y6 * inv_scale q7 = y7 * inv_scale q8 = y8 * inv_scale q9 = y9 * inv_scale q10 = y10 * inv_scale q11 = y11 * inv_scale q12 = y12 * inv_scale q13 = y13 * inv_scale q14 = y14 * inv_scale q15 = y15 * inv_scale packed_lo = cvt_e2m1x8_f32(q0, q1, q2, q3, q4, q5, q6, q7) packed_hi = cvt_e2m1x8_f32(q8, q9, q10, q11, q12, q13, q14, q15) packed64 = (Uint64(packed_hi) << Uint64(32)) | Uint64(packed_lo) out_offset = block_start // 2 out_ptr = get_ptr_as_int64( mY, actual_row_idx * (H // 2) + out_offset ) st_global_u64(out_ptr, packed64) else: # block_size == 32: Process in 2 chunks of 16 x_ptr0_c0 = get_ptr_as_int64( mX, actual_row_idx * H + block_start ) x_ptr1_c0 = get_ptr_as_int64( mX, actual_row_idx * H + block_start + Int32(8) ) w_ptr0_c0 = get_ptr_as_int64(mW, block_start) w_ptr1_c0 = get_ptr_as_int64(mW, block_start + Int32(8)) x_ptr0_c1 = get_ptr_as_int64( mX, actual_row_idx * H + block_start + Int32(16) ) x_ptr1_c1 = get_ptr_as_int64( mX, actual_row_idx * H + block_start + Int32(24) ) w_ptr0_c1 = get_ptr_as_int64(mW, block_start + Int32(16)) w_ptr1_c1 = get_ptr_as_int64(mW, block_start + Int32(24)) x0_c0, x1_c0, x2_c0, x3_c0 = ld_global_v4_u32(x_ptr0_c0) x4_c0, x5_c0, x6_c0, x7_c0 = ld_global_v4_u32(x_ptr1_c0) w0_c0, w1_c0, w2_c0, w3_c0 = ld_global_v4_u32(w_ptr0_c0) w4_c0, w5_c0, w6_c0, w7_c0 = ld_global_v4_u32(w_ptr1_c0) x0_c1, x1_c1, x2_c1, x3_c1 = ld_global_v4_u32(x_ptr0_c1) x4_c1, x5_c1, x6_c1, x7_c1 = ld_global_v4_u32(x_ptr1_c1) w0_c1, w1_c1, w2_c1, w3_c1 = ld_global_v4_u32(w_ptr0_c1) w4_c1, w5_c1, w6_c1, w7_c1 = ld_global_v4_u32(w_ptr1_c1) if cutlass.const_expr(is_fp16): xw0_c0 = half2_mul(x0_c0, w0_c0) xw1_c0 = half2_mul(x1_c0, w1_c0) xw2_c0 = half2_mul(x2_c0, w2_c0) xw3_c0 = half2_mul(x3_c0, w3_c0) xw4_c0 = half2_mul(x4_c0, w4_c0) xw5_c0 = half2_mul(x5_c0, w5_c0) xw6_c0 = half2_mul(x6_c0, w6_c0) xw7_c0 = half2_mul(x7_c0, w7_c0) xw0_c1 = half2_mul(x0_c1, w0_c1) xw1_c1 = half2_mul(x1_c1, w1_c1) xw2_c1 = half2_mul(x2_c1, w2_c1) xw3_c1 = half2_mul(x3_c1, w3_c1) xw4_c1 = half2_mul(x4_c1, w4_c1) xw5_c1 = half2_mul(x5_c1, w5_c1) xw6_c1 = half2_mul(x6_c1, w6_c1) xw7_c1 = half2_mul(x7_c1, w7_c1) abs0_c0_h2 = habs2(xw0_c0) abs1_c0_h2 = habs2(xw1_c0) abs2_c0_h2 = habs2(xw2_c0) abs3_c0_h2 = habs2(xw3_c0) abs4_c0_h2 = habs2(xw4_c0) abs5_c0_h2 = habs2(xw5_c0) abs6_c0_h2 = habs2(xw6_c0) abs7_c0_h2 = habs2(xw7_c0) abs0_c1_h2 = habs2(xw0_c1) abs1_c1_h2 = habs2(xw1_c1) abs2_c1_h2 = habs2(xw2_c1) abs3_c1_h2 = habs2(xw3_c1) abs4_c1_h2 = habs2(xw4_c1) abs5_c1_h2 = habs2(xw5_c1) abs6_c1_h2 = habs2(xw6_c1) abs7_c1_h2 = habs2(xw7_c1) max01_c0_h2 = hmax2(abs0_c0_h2, abs1_c0_h2) max23_c0_h2 = hmax2(abs2_c0_h2, abs3_c0_h2) max45_c0_h2 = hmax2(abs4_c0_h2, abs5_c0_h2) max67_c0_h2 = hmax2(abs6_c0_h2, abs7_c0_h2) max0123_c0_h2 = hmax2(max01_c0_h2, max23_c0_h2) max4567_c0_h2 = hmax2(max45_c0_h2, max67_c0_h2) max_c0_h2 = hmax2(max0123_c0_h2, max4567_c0_h2) max01_c1_h2 = hmax2(abs0_c1_h2, abs1_c1_h2) max23_c1_h2 = hmax2(abs2_c1_h2, abs3_c1_h2) max45_c1_h2 = hmax2(abs4_c1_h2, abs5_c1_h2) max67_c1_h2 = hmax2(abs6_c1_h2, abs7_c1_h2) max0123_c1_h2 = hmax2(max01_c1_h2, max23_c1_h2) max4567_c1_h2 = hmax2(max45_c1_h2, max67_c1_h2) max_c1_h2 = hmax2(max0123_c1_h2, max4567_c1_h2) max_xw_h2 = hmax2(max_c0_h2, max_c1_h2) max_xw = hmax_to_f32(max_xw_h2) max_abs = max_xw * rstd y0_c0, y1_c0 = half2_to_float2_scaled(xw0_c0, rstd) y2_c0, y3_c0 = half2_to_float2_scaled(xw1_c0, rstd) y4_c0, y5_c0 = half2_to_float2_scaled(xw2_c0, rstd) y6_c0, y7_c0 = half2_to_float2_scaled(xw3_c0, rstd) y8_c0, y9_c0 = half2_to_float2_scaled(xw4_c0, rstd) y10_c0, y11_c0 = half2_to_float2_scaled(xw5_c0, rstd) y12_c0, y13_c0 = half2_to_float2_scaled(xw6_c0, rstd) y14_c0, y15_c0 = half2_to_float2_scaled(xw7_c0, rstd) y0_c1, y1_c1 = half2_to_float2_scaled(xw0_c1, rstd) y2_c1, y3_c1 = half2_to_float2_scaled(xw1_c1, rstd) y4_c1, y5_c1 = half2_to_float2_scaled(xw2_c1, rstd) y6_c1, y7_c1 = half2_to_float2_scaled(xw3_c1, rstd) y8_c1, y9_c1 = half2_to_float2_scaled(xw4_c1, rstd) y10_c1, y11_c1 = half2_to_float2_scaled(xw5_c1, rstd) y12_c1, y13_c1 = half2_to_float2_scaled(xw6_c1, rstd) y14_c1, y15_c1 = half2_to_float2_scaled(xw7_c1, rstd) else: xw0_c0 = bfloat2_mul(x0_c0, w0_c0) xw1_c0 = bfloat2_mul(x1_c0, w1_c0) xw2_c0 = bfloat2_mul(x2_c0, w2_c0) xw3_c0 = bfloat2_mul(x3_c0, w3_c0) xw4_c0 = bfloat2_mul(x4_c0, w4_c0) xw5_c0 = bfloat2_mul(x5_c0, w5_c0) xw6_c0 = bfloat2_mul(x6_c0, w6_c0) xw7_c0 = bfloat2_mul(x7_c0, w7_c0) xw0_c1 = bfloat2_mul(x0_c1, w0_c1) xw1_c1 = bfloat2_mul(x1_c1, w1_c1) xw2_c1 = bfloat2_mul(x2_c1, w2_c1) xw3_c1 = bfloat2_mul(x3_c1, w3_c1) xw4_c1 = bfloat2_mul(x4_c1, w4_c1) xw5_c1 = bfloat2_mul(x5_c1, w5_c1) xw6_c1 = bfloat2_mul(x6_c1, w6_c1) xw7_c1 = bfloat2_mul(x7_c1, w7_c1) abs0_c0_h2 = bfloat2_habs2(xw0_c0) abs1_c0_h2 = bfloat2_habs2(xw1_c0) abs2_c0_h2 = bfloat2_habs2(xw2_c0) abs3_c0_h2 = bfloat2_habs2(xw3_c0) abs4_c0_h2 = bfloat2_habs2(xw4_c0) abs5_c0_h2 = bfloat2_habs2(xw5_c0) abs6_c0_h2 = bfloat2_habs2(xw6_c0) abs7_c0_h2 = bfloat2_habs2(xw7_c0) abs0_c1_h2 = bfloat2_habs2(xw0_c1) abs1_c1_h2 = bfloat2_habs2(xw1_c1) abs2_c1_h2 = bfloat2_habs2(xw2_c1) abs3_c1_h2 = bfloat2_habs2(xw3_c1) abs4_c1_h2 = bfloat2_habs2(xw4_c1) abs5_c1_h2 = bfloat2_habs2(xw5_c1) abs6_c1_h2 = bfloat2_habs2(xw6_c1) abs7_c1_h2 = bfloat2_habs2(xw7_c1) max01_c0_h2 = bfloat2_hmax2(abs0_c0_h2, abs1_c0_h2) max23_c0_h2 = bfloat2_hmax2(abs2_c0_h2, abs3_c0_h2) max45_c0_h2 = bfloat2_hmax2(abs4_c0_h2, abs5_c0_h2) max67_c0_h2 = bfloat2_hmax2(abs6_c0_h2, abs7_c0_h2) max0123_c0_h2 = bfloat2_hmax2(max01_c0_h2, max23_c0_h2) max4567_c0_h2 = bfloat2_hmax2(max45_c0_h2, max67_c0_h2) max_c0_h2 = bfloat2_hmax2(max0123_c0_h2, max4567_c0_h2) max01_c1_h2 = bfloat2_hmax2(abs0_c1_h2, abs1_c1_h2) max23_c1_h2 = bfloat2_hmax2(abs2_c1_h2, abs3_c1_h2) max45_c1_h2 = bfloat2_hmax2(abs4_c1_h2, abs5_c1_h2) max67_c1_h2 = bfloat2_hmax2(abs6_c1_h2, abs7_c1_h2) max0123_c1_h2 = bfloat2_hmax2(max01_c1_h2, max23_c1_h2) max4567_c1_h2 = bfloat2_hmax2(max45_c1_h2, max67_c1_h2) max_c1_h2 = bfloat2_hmax2(max0123_c1_h2, max4567_c1_h2) max_xw_h2 = bfloat2_hmax2(max_c0_h2, max_c1_h2) max_xw = bfloat2_hmax_to_f32(max_xw_h2) max_abs = max_xw * rstd y0_c0, y1_c0 = bfloat2_to_float2_scaled(xw0_c0, rstd) y2_c0, y3_c0 = bfloat2_to_float2_scaled(xw1_c0, rstd) y4_c0, y5_c0 = bfloat2_to_float2_scaled(xw2_c0, rstd) y6_c0, y7_c0 = bfloat2_to_float2_scaled(xw3_c0, rstd) y8_c0, y9_c0 = bfloat2_to_float2_scaled(xw4_c0, rstd) y10_c0, y11_c0 = bfloat2_to_float2_scaled(xw5_c0, rstd) y12_c0, y13_c0 = bfloat2_to_float2_scaled(xw6_c0, rstd) y14_c0, y15_c0 = bfloat2_to_float2_scaled(xw7_c0, rstd) y0_c1, y1_c1 = bfloat2_to_float2_scaled(xw0_c1, rstd) y2_c1, y3_c1 = bfloat2_to_float2_scaled(xw1_c1, rstd) y4_c1, y5_c1 = bfloat2_to_float2_scaled(xw2_c1, rstd) y6_c1, y7_c1 = bfloat2_to_float2_scaled(xw3_c1, rstd) y8_c1, y9_c1 = bfloat2_to_float2_scaled(xw4_c1, rstd) y10_c1, y11_c1 = bfloat2_to_float2_scaled(xw5_c1, rstd) y12_c1, y13_c1 = bfloat2_to_float2_scaled(xw6_c1, rstd) y14_c1, y15_c1 = bfloat2_to_float2_scaled(xw7_c1, rstd) # Compute scale factor (E4M3 or UE8M0 based on scale_format) # For E4M3: global_scale is incorporated into block scale # For UE8M0 (MXFP4): global_scale is not used if cutlass.const_expr(self.scale_format == "ue8m0"): scale_float = max_abs * fp4_max_rcp scale_ue8m0 = cvt_f32_to_ue8m0(scale_float) scale_u8 = Uint8(scale_ue8m0 & Uint32(0xFF)) inv_scale = ue8m0_to_output_scale(scale_ue8m0) else: # E4M3: scale = global_scale * max_abs / FP4_MAX scale_float = global_scale_val * max_abs * fp4_max_rcp scale_float = fmin_f32( scale_float, Float32(FLOAT8_E4M3_MAX) ) scale_fp8_u32 = cvt_f32_to_e4m3(scale_float) scale_u8 = Uint8(scale_fp8_u32 & Uint32(0xFF)) # inv_scale = global_scale / scale_float # This cancels the global_scale in scale_float inv_scale = ( fp8_e4m3_to_f32_and_rcp(scale_fp8_u32) * global_scale_val ) if cutlass.const_expr(self.output_swizzled): inner_k_idx = sf_idx % Int32(4) inner_m_idx = (actual_row_idx % Int32(128)) // Int32(32) outer_m_idx = actual_row_idx % Int32(32) k_tile_idx = sf_idx // Int32(4) m_tile_idx = actual_row_idx // Int32(128) m_tile_stride = self.num_k_tiles * self.k_tile_stride swizzled_offset = ( m_tile_idx * m_tile_stride + k_tile_idx * self.k_tile_stride + outer_m_idx * Int32(16) + inner_m_idx * Int32(4) + inner_k_idx ) mS[swizzled_offset] = scale_u8 else: mS[actual_row_idx, sf_idx] = scale_u8 # Quantize and store chunk 0 q0 = y0_c0 * inv_scale q1 = y1_c0 * inv_scale q2 = y2_c0 * inv_scale q3 = y3_c0 * inv_scale q4 = y4_c0 * inv_scale q5 = y5_c0 * inv_scale q6 = y6_c0 * inv_scale q7 = y7_c0 * inv_scale q8 = y8_c0 * inv_scale q9 = y9_c0 * inv_scale q10 = y10_c0 * inv_scale q11 = y11_c0 * inv_scale q12 = y12_c0 * inv_scale q13 = y13_c0 * inv_scale q14 = y14_c0 * inv_scale q15 = y15_c0 * inv_scale packed_lo = cvt_e2m1x8_f32(q0, q1, q2, q3, q4, q5, q6, q7) packed_hi = cvt_e2m1x8_f32(q8, q9, q10, q11, q12, q13, q14, q15) packed64 = (Uint64(packed_hi) << Uint64(32)) | Uint64(packed_lo) out_offset = block_start // 2 out_ptr = get_ptr_as_int64( mY, actual_row_idx * (H // 2) + out_offset ) st_global_u64(out_ptr, packed64) # Quantize and store chunk 1 q0 = y0_c1 * inv_scale q1 = y1_c1 * inv_scale q2 = y2_c1 * inv_scale q3 = y3_c1 * inv_scale q4 = y4_c1 * inv_scale q5 = y5_c1 * inv_scale q6 = y6_c1 * inv_scale q7 = y7_c1 * inv_scale q8 = y8_c1 * inv_scale q9 = y9_c1 * inv_scale q10 = y10_c1 * inv_scale q11 = y11_c1 * inv_scale q12 = y12_c1 * inv_scale q13 = y13_c1 * inv_scale q14 = y14_c1 * inv_scale q15 = y15_c1 * inv_scale packed_lo = cvt_e2m1x8_f32(q0, q1, q2, q3, q4, q5, q6, q7) packed_hi = cvt_e2m1x8_f32(q8, q9, q10, q11, q12, q13, q14, q15) packed64 = (Uint64(packed_hi) << Uint64(32)) | Uint64(packed_lo) out_offset = (block_start + 16) // 2 out_ptr = get_ptr_as_int64( mY, actual_row_idx * (H // 2) + out_offset ) st_global_u64(out_ptr, packed64) # ============================================================================= # PyTorch API Functions - Streamlined with Pointer-based Compilation # ============================================================================= @functools.cache def _get_compiled_kernel( hidden_size: int, block_size: int, is_fp16: bool, sm_version: int, scale_format: str, is_sf_swizzled_layout: bool, ) -> Callable: """ Get a compiled kernel closure that takes torch.Tensor directly. Uses TVM-FFI for efficient tensor passing without manual pointer construction. """ cutlass_dtype = cutlass.Float16 if is_fp16 else cutlass.BFloat16 # Create kernel instance kernel_obj = RMSNormFP4QuantKernel( dtype=cutlass_dtype, H=hidden_size, block_size=block_size, output_swizzled=is_sf_swizzled_layout, is_fp16=is_fp16, sm_version=sm_version, scale_format=scale_format, ) # Use symbolic size for dynamic M dimension sym_m = cute.sym_int() # Create fake tensors for compilation with TVM-FFI # Use stride_order=(1, 0) for row-major layout and assumed_align=128 for async copy x_fake = cute.runtime.make_fake_compact_tensor( cutlass_dtype, (sym_m, hidden_size), stride_order=(1, 0), assumed_align=128 ) w_fake = cute.runtime.make_fake_compact_tensor( cutlass_dtype, (hidden_size,), assumed_align=128 ) y_fake = cute.runtime.make_fake_compact_tensor( cutlass.Uint8, (sym_m, hidden_size // 2), stride_order=(1, 0), assumed_align=128 ) # Scale factor tensor layout depends on swizzle mode if is_sf_swizzled_layout: # For swizzled mode, use 1D layout - the swizzle pattern is computed in kernel # Size is: num_m_tiles * num_k_tiles * 512, which is independent of M # Use a separate symbolic variable for this size sym_swizzled_size = cute.sym_int() s_fake = cute.runtime.make_fake_compact_tensor( cutlass.Uint8, (sym_swizzled_size,), assumed_align=128 ) else: # For non-swizzled mode, use 2D row-major layout s_fake = cute.runtime.make_fake_compact_tensor( cutlass.Uint8, (sym_m, hidden_size // block_size), stride_order=(1, 0), assumed_align=128, ) # Create fake stream that uses environment stream at runtime stream_fake = cute.runtime.make_fake_stream(use_tvm_ffi_env_stream=True) # Global scale fake tensor (shape [1], float32) global_scale_fake = cute.runtime.make_fake_compact_tensor( cutlass.Float32, (1,), assumed_align=4 ) # Compile with TVM-FFI enabled compiled_kernel = cute.compile( kernel_obj, x_fake, w_fake, y_fake, s_fake, global_scale_fake, Int32(1), # Dummy M Float32(1e-6), # Dummy eps stream_fake, options="--enable-tvm-ffi", ) def tensor_api( x: torch.Tensor, w: torch.Tensor, y: torch.Tensor, s: torch.Tensor, global_scale: torch.Tensor, M: int, eps: float, ) -> None: """Runtime API that passes torch tensors directly via TVM-FFI.""" nonlocal compiled_kernel # For swizzled mode, flatten the scale tensor to 1D s_tensor = s.flatten() if is_sf_swizzled_layout else s.contiguous() # View y as uint8 since kernel expects uint8 but caller may pass float4_e2m1fn_x2 y_uint8 = y.view(torch.uint8) compiled_kernel( x, w, y_uint8, s_tensor, global_scale, Int32(M), Float32(eps), ) return tensor_api @flashinfer_api def rmsnorm_fp4quant( input: torch.Tensor, weight: torch.Tensor, y_fp4: torch.Tensor | None = None, block_scale: torch.Tensor | None = None, global_scale: torch.Tensor | None = None, eps: float = 1e-6, block_size: int = 16, scale_format: str | None = None, is_sf_swizzled_layout: bool = False, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Fused RMS normalization with FP4 quantization using CuTe-DSL. Computes: ``y = RMSNorm(input) * weight``, optionally applies global scaling (``y = y / global_scale``), then quantizes ``y`` to FP4. Parameters ---------- input : torch.Tensor Input tensor, shape ``(batch_size, hidden_size)`` or ``(batch_size, seq_len, hidden_size)``. Must be ``torch.float16`` or ``torch.bfloat16``. weight : torch.Tensor Weight tensor for RMSNorm, shape ``(hidden_size,)``. Must have the same dtype as input. y_fp4 : torch.Tensor, optional Output tensor for quantized values in FP4_E2M1 format with dtype ``torch.float4_e2m1fn_x2``. Shape must be ``(batch_size, hidden_size // 2)`` or matching 3D input. If ``None``, will be allocated automatically. block_scale : torch.Tensor, optional Output tensor for per-block scale factors. - If ``is_sf_swizzled_layout=False`` (default): row-major layout with shape ``(batch_size, hidden_size // block_size)`` or matching 3D input. - If ``is_sf_swizzled_layout=True``: swizzled layout for efficient tensor core access, with shape ``(batch_size * hidden_size // block_size,)`` flattened. The swizzle pattern uses 128x4 tiles where scales are arranged as: ``[m_tile][k_tile][outer_m (32)][inner_m (4)][inner_k (4)]``. Dtype should be ``torch.float8_e4m3fn`` for E4M3 format or ``torch.uint8`` for UE8M0 format. If ``None``, will be allocated automatically. global_scale : torch.Tensor, optional Global scale factor tensor of shape ``(1,)`` with dtype ``torch.float32``. If provided, the RMSNorm output is divided by this value before quantization: ``y = rmsnorm(x, w) / global_scale``. This is used for NVFP4 format where a pre-computed global scale lifts per-block scales into optimal dynamic range. If ``None``, no global scaling is applied (equivalent to global_scale=1.0). eps : float Epsilon for numerical stability in RMSNorm. Default is ``1e-6``. block_size : int Number of elements per quantization block. Default is ``16``. - ``16``: NVFP4 format with E4M3 scale factors - ``32``: MXFP4 format with UE8M0 scale factors scale_format : str, optional Scale factor format: ``"e4m3"`` or ``"ue8m0"``. If ``None``, auto-selects based on ``block_size``: ``"e4m3"`` for block_size=16, ``"ue8m0"`` for block_size=32. is_sf_swizzled_layout : bool If ``True``, output scale factors in swizzled layout optimized for tensor core GEMM operations. The swizzle uses 128x4 tiles with the pattern: ``[m_tile_idx * k_tiles * 512 + k_tile_idx * 512 + outer_m * 16 + inner_m * 4 + inner_k]`` where ``outer_m = row % 32``, ``inner_m = (row % 128) // 32``, etc. Default is ``False`` (row-major layout). Returns ------- Tuple[torch.Tensor, torch.Tensor] A tuple of ``(y_fp4, block_scale)``: - ``y_fp4``: Quantized FP4 values packed as uint8. - ``block_scale``: Per-block scale factors. Notes ----- - Requires SM100+ (Blackwell) for FP4 quantization PTX intrinsics. - For block_size=16 (NVFP4): uses E4M3 scale factors (max value 448.0). - For block_size=32 (MXFP4): uses UE8M0 scale factors (power-of-2 scales). - FP4 E2M1 format has a max representable value of 6.0. """ # Handle 2D vs 3D input is_3d = input.dim() == 3 if is_3d: B, S, H = input.shape input_2d = input.view(B * S, H).contiguous() else: input_2d = input batch_size, hidden_size = input_2d.shape dtype = input.dtype assert hidden_size % block_size == 0, "hidden_size must be divisible by block_size" assert hidden_size >= 64, "hidden_size must be >= 64" assert block_size in [16, 32], "block_size must be 16 or 32" # Determine data type and scale format is_fp16 = dtype == torch.float16 actual_scale_format = ( scale_format if scale_format else ("ue8m0" if block_size == 32 else "e4m3") ) sm_version = get_sm_version(input.device) # Allocate output tensors if not provided if y_fp4 is None: if is_3d: y_fp4 = torch.empty( (B, S, hidden_size // 2), dtype=torch.float4_e2m1fn_x2, device=input.device, ) else: y_fp4 = torch.empty( (batch_size, hidden_size // 2), dtype=torch.float4_e2m1fn_x2, device=input.device, ) if block_scale is None: # Determine scale dtype based on format scale_dtype = ( torch.uint8 if actual_scale_format == "ue8m0" else torch.float8_e4m3fn ) num_sf_blocks_per_row = hidden_size // block_size if is_sf_swizzled_layout: # Swizzled layout: flattened with 128x4 tile pattern num_m_tiles = (batch_size + 127) // 128 num_k_tiles = (num_sf_blocks_per_row + 3) // 4 k_tile_stride = 512 swizzled_size = num_m_tiles * num_k_tiles * k_tile_stride block_scale = torch.empty( (swizzled_size,), dtype=scale_dtype, device=input.device ) else: if is_3d: block_scale = torch.empty( (B, S, num_sf_blocks_per_row), dtype=scale_dtype, device=input.device, ) else: block_scale = torch.empty( (batch_size, num_sf_blocks_per_row), dtype=scale_dtype, device=input.device, ) # Get 2D views for kernel if is_3d: y_fp4_2d = y_fp4.view(B * S, -1) block_scale_2d = ( block_scale.view(B * S, -1) if not is_sf_swizzled_layout else block_scale ) else: y_fp4_2d = y_fp4 block_scale_2d = block_scale # Create global_scale tensor if not provided (1.0 = no scaling) if global_scale is None: global_scale = torch.ones(1, dtype=torch.float32, device=input.device) # Get cached tensor_api and call it directly tensor_api = _get_compiled_kernel( hidden_size, block_size, is_fp16, sm_version, actual_scale_format, is_sf_swizzled_layout, ) tensor_api( input_2d.contiguous(), weight.contiguous(), y_fp4_2d, block_scale_2d.view(torch.uint8), global_scale.contiguous(), batch_size, eps, ) return y_fp4, block_scale __all__ = [ "RMSNormFP4QuantKernel", "get_sm_version", "rmsnorm_fp4quant", ]