VTimeLLM/flash-attention/flash_attn/cute/softmax.py

# Copyright (c) 2025, Tri Dao.

import math
import operator
from typing import Tuple

import cutlass
import cutlass.cute as cute
from cutlass import Float32

import flash_attn.cute.utils as utils


class Softmax:
    def __init__(
        self,
        scale_log2: Float32,
        num_rows: cutlass.Constexpr[int],
        arch: cutlass.Constexpr[int] = 80,
    ):
        self.scale_log2 = scale_log2
        self.row_max = cute.make_fragment(num_rows, Float32)
        self.row_sum = cute.make_fragment_like(self.row_max)
        self.arch = arch

    def reset(self) -> None:
        self.row_max.fill(-Float32.inf)
        self.row_sum.fill(0.0)

    def _compute_row_max(
        self, acc_S_row: cute.TensorSSA, init_val: float | Float32 | None = None
    ) -> Float32:
        return utils.fmax_reduce(acc_S_row, init_val, arch=self.arch)

    def _compute_row_sum(
        self, acc_S_row_exp: cute.TensorSSA, init_val: float | Float32 | None = None
    ) -> Float32:
        return utils.fadd_reduce(acc_S_row_exp, init_val, arch=self.arch)

    @cute.jit
    def online_softmax(
        self,
        acc_S: cute.Tensor,
        is_first: cutlass.Constexpr[bool] = False,
        check_inf: cutlass.Constexpr[bool] = True,
    ) -> cute.Tensor:
        """Apply online softmax and return the row_scale to rescale O.

        :param acc_S: acc_S tensor
        :type acc_S: cute.Tensor
        :param is_first: is first n_block
        :type is_first: cutlass.Constexpr
        """
        # Change acc_S to M,N layout view.
        acc_S_mn = utils.make_acc_tensor_mn_view(acc_S)
        row_scale = cute.make_fragment_like(self.row_max, Float32)
        # Each iteration processes one row of acc_S
        for r in cutlass.range(cute.size(self.row_max), unroll_full=True):
            acc_S_row = acc_S_mn[r, None].load()  # (n_block_size)
            row_max_cur = self._compute_row_max(
                acc_S_row,
                init_val=self.row_max[r] if cutlass.const_expr(not is_first) else None,
            )
            row_max_cur = utils.warp_reduce(row_max_cur, cute.arch.fmax, width=4)
            if cutlass.const_expr(check_inf):
                row_max_cur = 0.0 if row_max_cur == -Float32.inf else row_max_cur
            if cutlass.const_expr(is_first):
                row_max_cur_scaled = row_max_cur * self.scale_log2
                acc_S_row_exp = utils.exp2f(acc_S_row * self.scale_log2 - row_max_cur_scaled)
                acc_S_row_sum = self._compute_row_sum(acc_S_row_exp)
                row_scale[r] = 1.0
            else:
                row_max_prev = self.row_max[r]
                row_max_cur_scaled = row_max_cur * self.scale_log2
                acc_S_row_exp = utils.exp2f(acc_S_row * self.scale_log2 - row_max_cur_scaled)
                # row_scale[r] = utils.exp2f(row_max_prev * self.scale_log2 - row_max_cur_scaled)
                row_scale[r] = utils.exp2f((row_max_prev - row_max_cur) * self.scale_log2)
                acc_S_row_sum = (
                    self._compute_row_sum(acc_S_row_exp, init_val=self.row_sum[r] * row_scale[r])
                )
            self.row_max[r] = row_max_cur
            self.row_sum[r] = acc_S_row_sum
            acc_S_mn[r, None].store(acc_S_row_exp)
        return row_scale

    @cute.jit
    def finalize(self, final_scale: Float32 = 1.0, sink_val: Float32 | cute.Tensor | None = None) -> cute.Tensor:
        """Finalize the online softmax by computing the scale and logsumexp."""
        if cutlass.const_expr(sink_val is not None and isinstance(sink_val, cute.Tensor)):
            assert cute.size(sink_val) == cute.size(self.row_sum)
        # quad reduction for row_sum as we didn't do it during each iteration of online softmax
        self.row_sum.store(utils.warp_reduce(self.row_sum.load(), operator.add, width=4))
        row_scale = cute.make_fragment_like(self.row_max, Float32)
        for r in cutlass.range(cute.size(self.row_sum), unroll_full=True):
            if cutlass.const_expr(sink_val is not None):
                sink_val_cur = sink_val if not isinstance(sink_val, cute.Tensor) else sink_val[r]
                LOG2_E = math.log2(math.e)
                self.row_sum[r] += utils.exp2f(sink_val_cur * LOG2_E - self.row_max[r] * self.scale_log2)
            # if row_sum is zero or nan, set acc_O_mn_row to 1.0
            acc_O_mn_row_is_zero_or_nan = (
                self.row_sum[r] == 0.0 or self.row_sum[r] != self.row_sum[r]
            )
            row_scale[r] = (
                cute.arch.rcp_approx(self.row_sum[r] if not acc_O_mn_row_is_zero_or_nan else 1.0)
            ) * final_scale
            row_sum_cur = self.row_sum[r]
            LN2 = math.log(2.0)
            self.row_sum[r] = (
                (self.row_max[r] * self.scale_log2 + utils.log2f(row_sum_cur)) * LN2
                if not acc_O_mn_row_is_zero_or_nan
                else -Float32.inf
            )
        return row_scale

    @cute.jit
    def rescale_O(self, acc_O: cute.Tensor, row_scale: cute.Tensor) -> None:
        """Scale each row of acc_O by the given scale tensor.
        :param acc_O: input tensor
        :type acc_O: cute.Tensor
        :param row_scale: row_scale tensor
        :type row_scale: cute.Tensor
        """
        acc_O_mn = utils.make_acc_tensor_mn_view(acc_O)
        assert cute.size(row_scale) == cute.size(acc_O_mn, mode=[0])
        for r in cutlass.range(cute.size(row_scale), unroll_full=True):
            acc_O_mn[r, None].store(acc_O_mn[r, None].load() * row_scale[r])


class SoftmaxSm100(Softmax):
    def __init__(self, scale_log2: Float32, rescale_threshold: cutlass.Constexpr[float] = 0.0):
        super().__init__(scale_log2, num_rows=1, arch=100)
        self.rescale_threshold = rescale_threshold

    @cute.jit
    def update_row_max(self, acc_S_row: cute.TensorSSA, is_first: int) -> Tuple[Float32, Float32]:
        if cutlass.const_expr(is_first):
            row_max_new = self._compute_row_max(acc_S_row)
            row_max_safe = row_max_new if row_max_new != -cutlass.Float32.inf else 0.0
            acc_scale = 0.0
        else:
            row_max_old = self.row_max[0]
            row_max_new = self._compute_row_max(acc_S_row, init_val=row_max_old)
            row_max_safe = row_max_new if row_max_new != -cutlass.Float32.inf else 0.0
            acc_scale_ = (row_max_old - row_max_safe) * self.scale_log2
            acc_scale = utils.exp2f(acc_scale_)
            if cutlass.const_expr(self.rescale_threshold > 0.0):
                if acc_scale_ >= -self.rescale_threshold:
                    row_max_new = row_max_old
                    row_max_safe = row_max_old
                    acc_scale = 1.0
        self.row_max[0] = row_max_new
        return row_max_safe, acc_scale

    def update_row_sum(
        self, acc_S_row_exp: cute.TensorSSA, row_scale: Float32, is_first: int = False
    ) -> None:
        init_val = self.row_sum[0] * row_scale if cutlass.const_expr(not is_first) else None
        # self.row_sum[0] = self._compute_row_sum(acc_S_row_exp, init_val=self.row_sum[0] * row_scale)
        self.row_sum[0] = self._compute_row_sum(acc_S_row_exp, init_val=init_val)
        # tmp = self._compute_row_sum(acc_S_row_exp)
        # self.row_sum[0] = self.row_sum[0] * row_scale + tmp

    @cute.jit
    def scale_subtract_rowmax(
        self,
        acc_S_row: cute.Tensor,
        row_max: Float32,
    ):
        assert cute.size(acc_S_row.shape) % 2 == 0, "acc_S_row must have an even number of elements"
        row_max_scaled = row_max * self.scale_log2
        for i in cutlass.range(0, cute.size(acc_S_row.shape), 2, unroll_full=True):
            acc_S_row[i], acc_S_row[i + 1] = cute.arch.fma_packed_f32x2(
                (acc_S_row[i], acc_S_row[i + 1]),
                (self.scale_log2, self.scale_log2),
                (-row_max_scaled, -row_max_scaled),
            )

    @cute.jit
    def apply_exp2_convert(
        self,
        acc_S_row: cute.Tensor,
        acc_S_row_converted: cute.Tensor,
        e2e: cutlass.Constexpr[bool] = False,
        e2e_freq: cutlass.Constexpr[int] = 16,
        e2e_res: cutlass.Constexpr[int] = 4,
        e2e_frg_limit: cutlass.Constexpr[int] = 1,
    ):
        assert cute.size(acc_S_row.shape) % 2 == 0, "acc_S_row must have an even number of elements"
        frg_tile = 32
        assert frg_tile % 2 == 0
        frg_cnt = cute.size(acc_S_row) // frg_tile
        assert cute.size(acc_S_row) % frg_tile == 0
        acc_S_row_frg = cute.logical_divide(acc_S_row, cute.make_layout(frg_tile))
        acc_S_row_converted_frg = cute.logical_divide(
            acc_S_row_converted, cute.make_layout(frg_tile)
        )
        for j in cutlass.range_constexpr(frg_cnt):
            for k in cutlass.range_constexpr(0, cute.size(acc_S_row_frg, mode=[0]), 2):
                # acc_S_row_frg[k, j] = utils.exp2f(acc_S_row_frg[k, j])
                # acc_S_row_frg[k + 1, j] = utils.exp2f(acc_S_row_frg[k + 1, j])
                if cutlass.const_expr(not e2e):
                    acc_S_row_frg[k, j] = cute.arch.exp2(acc_S_row_frg[k, j])
                    acc_S_row_frg[k + 1, j] = cute.arch.exp2(acc_S_row_frg[k + 1, j])
                else:
                    if cutlass.const_expr(k % e2e_freq < e2e_freq - e2e_res or j >= frg_cnt - e2e_frg_limit):
                        acc_S_row_frg[k, j] = cute.arch.exp2(acc_S_row_frg[k, j])
                        acc_S_row_frg[k + 1, j] = cute.arch.exp2(acc_S_row_frg[k + 1, j])
                    else:
                        acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j] = utils.e2e_asm2(acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j])
            acc_S_row_converted_frg[None, j].store(
                acc_S_row_frg[None, j].load().to(acc_S_row_converted.element_type)
            )

    @cute.jit
    def scale_apply_exp2_convert(
        self,
        acc_S_row: cute.Tensor,
        row_max: Float32,
        acc_S_row_converted: cute.Tensor,
    ):
        assert cute.size(acc_S_row.shape) % 2 == 0, "acc_S_row must have an even number of elements"
        minus_row_max_scaled = -row_max * self.scale_log2
        for i in cutlass.range_constexpr(0, cute.size(acc_S_row.shape), 2):
            acc_S_row[i], acc_S_row[i + 1] = cute.arch.fma_packed_f32x2(
                (acc_S_row[i], acc_S_row[i + 1]),
                (self.scale_log2, self.scale_log2),
                (minus_row_max_scaled, minus_row_max_scaled),
            )

        # for i in cutlass.range_constexpr(0, cute.size(acc_S_row.shape), 2):
        #     acc_S_row[i], acc_S_row[i + 1] = cute.arch.fma_packed_f32x2(
        #         (acc_S_row[i], acc_S_row[i + 1]),
        #         (self.scale_log2, self.scale_log2),
        #         (minus_row_max_scaled, minus_row_max_scaled),
        #     )
        #     acc_S_row[i] = cute.arch.exp2(acc_S_row[i])
        #     acc_S_row[i + 1] = cute.arch.exp2(acc_S_row[i + 1])

        frg_tile = 32
        assert frg_tile % 2 == 0
        frg_cnt = cute.size(acc_S_row) // frg_tile
        assert cute.size(acc_S_row) % frg_tile == 0
        acc_S_row_frg = cute.logical_divide(acc_S_row, cute.make_layout(frg_tile))
        acc_S_row_converted_frg = cute.logical_divide(
            acc_S_row_converted, cute.make_layout(frg_tile)
        )
        for j in cutlass.range_constexpr(frg_cnt):
            for k in cutlass.range_constexpr(0, cute.size(acc_S_row_frg, mode=[0]), 2):
                # acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j] = (
                #     cute.arch.fma_packed_f32x2(
                #         (acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j]),
                #         (self.scale_log2, self.scale_log2),
                #         (minus_row_max_scaled, minus_row_max_scaled),
                #     )
                # )
                # acc_S_row_frg[k, j] = utils.exp2f(acc_S_row_frg[k, j])
                # acc_S_row_frg[k + 1, j] = utils.exp2f(acc_S_row_frg[k + 1, j])
                acc_S_row_frg[k, j] = cute.arch.exp2(acc_S_row_frg[k, j])
                acc_S_row_frg[k + 1, j] = cute.arch.exp2(acc_S_row_frg[k + 1, j])
            acc_S_row_converted_frg[None, j].store(
                acc_S_row_frg[None, j].load().to(acc_S_row_converted.element_type)
            )