venv/lib/python3.10/site-packages/bitblas/gpu/reduction.py

# Copyright 2018 The apache/tvm Authors. All Rights Reserved.
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
# 
# Modifications Copyright (c) Microsoft.
# The code below is mostly copied from apache/tvm reduction.py in dlight.
"""A rule for reduction. """
from typing import List, Optional, Tuple, Union

from tvm import arith, ir, tir
from tvm.target import Target

from ..base import (
    BlockInfo,
    normalize_prim_func,
    try_inline_contiguous_spatial,
    detect_dominant_read,
    is_broadcast_epilogue,
)
from . import utils
from .base import GPUScheduleRule


def _get_reduction_expr(block: tir.Block) -> Optional[tir.PrimExpr]:
    # Detect and return `Y` in `X[...] = X[...] + Y`
    buffer_store = block.body
    if not isinstance(buffer_store, tir.BufferStore):
        return None
    if not isinstance(buffer_store.value, tir.Add):
        return None
    if not ir.structural_equal(
        buffer_store.value.a,
        tir.BufferLoad(buffer_store.buffer, block.body.indices),
        map_free_vars=True,
    ):
        return None
    return buffer_store.value.b


class Reduction(GPUScheduleRule):
    """A rule for Reduction."""

    def apply(  # pylint: disable=too-many-locals,too-many-branches,too-many-return-statements
        self,
        func: tir.PrimFunc,
        target: Target,
        _: bool,
    ) -> Union[None, tir.Schedule, List[tir.Schedule]]:
        if not isinstance(func, tir.PrimFunc) or not self.is_target_available(target):
            return None
        sch = tir.Schedule(func)
        block_infos = normalize_prim_func(sch)
        if block_infos is None:
            return None
        block_infos = try_inline_contiguous_spatial(sch, block_infos)
        if len(block_infos) == 1:
            epilogue = None
        elif len(block_infos) == 2:
            epilogue = block_infos[1]
            if not epilogue.is_injective():
                return None
        else:
            return None

        block_info = block_infos[0]
        block = block_info.block_rv
        block_stmt = sch.get(block)

        # Step 1. Check reduction block
        if (
            (not block_info.is_reduction())
            or len(block_stmt.writes) != 1
            or _get_reduction_expr(block_stmt) is None
        ):
            return None
        # Step 2. Normalize the block, merge spatial and reduction iters
        is_inner_reduction, c_factor, loop_order, s_split_index = self._normalize(
            sch,
            block_info,
            arith.normalize_to_iter_sum(
                detect_dominant_read(block_stmt),
                input_iters={i.var: i.dom for i in block_stmt.iter_vars},
            ),
        )
        if is_inner_reduction is None and c_factor is None:
            return None
        # Step 3. Do the scheduling
        if is_inner_reduction:
            self._sch_inner_reduction(
                sch, target, block, c_factor, epilogue, loop_order, s_split_index
            )
        else:
            self._sch_inner_spatial(
                sch, target, block, block_info, c_factor, epilogue, loop_order, s_split_index
            )
        return sch

    def _normalize(  # pylint: disable=too-many-branches
        self,
        sch: tir.Schedule,
        block_info: BlockInfo,
        access: arith.IterSumExpr,
    ) -> Tuple[Optional[bool], Optional[int]]:
        if access.base != 0:
            return None, None, None, None
        iter_to_info = {i.var: i for i in block_info.iters}
        s_loops, r_loops, c_loops, c_factor = [], [], [], None
        s_split_loop, s_split_index = None, None
        for split_expr in access.args:
            var = split_expr.source.source
            info = iter_to_info.pop(var)
            loop = info.loop_rv
            is_inner_reduction = info.kind == "R"
            if split_expr.lower_factor > 1:
                if c_loops:
                    return None, None, None, None
                s_split_loop = loop
                s_split_index = len(s_loops)
                loop, c_loop = sch.split(loop, factors=[None, split_expr.lower_factor])
                c_loops.append(c_loop)
                if not is_inner_reduction:
                    c_factor = split_expr.lower_factor
            if is_inner_reduction:
                r_loops.append(loop)
            else:
                s_loops.append(loop)

        if iter_to_info:
            for var, info in iter_to_info.items():
                if info.kind == "S" and info.dom.extent == 1:
                    s_loops.append(info.loop_rv)
                else:
                    return None, None, None, None

        loop_order = {}
        s_block_var_loops = []
        for i in block_info.iters:
            if i.loop_rv in s_loops or i.loop_rv == s_split_loop:
                s_block_var_loops.append(i.loop_rv)

        for i in range(len(s_block_var_loops)):
            for j in range(len(s_loops)):
                if s_block_var_loops[i] == s_loops[j]:
                    loop_order[i] = j
                    break
                if s_block_var_loops[i] == s_split_loop:
                    loop_order[i] = s_split_index
                    break

        assert s_loops
        assert r_loops
        if len(s_loops) != len([i for i in block_info.iters if i.kind == "S"]):
            return None, None
        if not c_loops:
            c_loops = [sch.add_unit_loop(block_info.block_rv)]
        sch.reorder(*s_loops, *r_loops, *c_loops)
        sch.fuse(*s_loops)
        sch.fuse(*r_loops)
        return is_inner_reduction, c_factor, loop_order, s_split_index

    def _sch_inner_reduction(  # pylint: disable=too-many-arguments
        self,
        sch: tir.Schedule,
        target: Target,
        block: tir.schedule.BlockRV,
        unroll_spatial_factor: Optional[int],
        epilogue_info: Optional[BlockInfo],
        loop_order,
        s_split_index,
    ):
        # pylint: disable=invalid-name
        _, r, _ = sch.get_loops(block)
        (len_tx,) = utils.suggest_threads_per_block(  # pylint: disable=unbalanced-tuple-unpacking
            target, [sch.get(r)]
        )

        _, tx = sch.split(r, factors=[None, len_tx])
        # Schedule the RF block
        rf = sch.rfactor(tx, 0)
        bx, r, tx, _ = sch.get_loops(rf)
        sch.reorder(bx, tx, r)
        sch.bind(bx, "blockIdx.x")
        sch.bind(tx, "threadIdx.x")
        sch.annotate(tx, ann_key="pragma_auto_unroll_max_step", ann_val=256)
        sch.annotate(tx, ann_key="pragma_unroll_explicit", ann_val=1)
        sch.set_scope(rf, 0, "local")
        sch.decompose_reduction(rf, r)
        # Schedule the write back block
        sch.reverse_compute_at(block, bx, preserve_unit_loops=True)
        _, tx, *s = sch.get_loops(block)

        if unroll_spatial_factor:
            assert len(s) == len(loop_order)
            new_order_s = [s[loop_order[i]] for i in range(len(s))]
            sch.reorder(*new_order_s)
            new_order_s[s_split_index], c = sch.split(
                new_order_s[s_split_index], factors=[None, unroll_spatial_factor]
            )
            sch.reorder(*new_order_s, c)
            s = sch.fuse(*new_order_s)
            sch.reorder(s, tx, c)
        else:
            s = sch.fuse(*s)
            sch.reorder(s, tx)
        sch.bind(tx, "threadIdx.x")
        # Schedule epilogue
        if epilogue_info is not None:
            epilogue = epilogue_info.block_rv
            sch.reverse_compute_at(epilogue, bx)
            if is_broadcast_epilogue(sch, block, epilogue):
                sch.set_scope(block, 0, "shared")
                _, *s = sch.get_loops(epilogue)  # pylint: disable=invalid-name
                _, tx = sch.split(sch.fuse(*s), factors=[None, len_tx])
                sch.bind(tx, "threadIdx.x")
            else:
                sch.set_scope(block, 0, "local")
        # pylint: enable=invalid-name

    def _sch_inner_spatial(
        self,
        sch: tir.Schedule,
        _: Target,
        block: tir.schedule.BlockRV,
        block_info: BlockInfo,
        unroll_spatial_factor: Optional[int],
        epilogue_info: Optional[BlockInfo],
        loop_order,
        s_split_index,
    ):
        # pylint: disable=invalid-name
        s, r, _ = sch.get_loops(block)
        len_tx, len_ty = 16, 16
        s_factor = [i.dom.extent for i in block_info.iters if i.kind == "S"][-1]
        # get perfect spatial factor, spatial factor should be divide the innermost spatial loop so
        # that the block after r_factor and be reversed compute at the original scope
        while len_tx > 1:
            if s_factor % len_tx == 0:
                break
            len_tx -= 1
        _, _ = sch.split(s, factors=[None, len_tx])
        _, ty = sch.split(r, factors=[None, len_ty])
        # Schedule the RF block
        rf = sch.rfactor(ty, 0)
        bx, tx, r, ty, _ = sch.get_loops(rf)
        sch.reorder(bx, tx, ty, r)
        sch.bind(tx, "threadIdx.x")
        sch.bind(ty, "threadIdx.y")
        sch.bind(bx, "blockIdx.x")
        sch.set_scope(rf, 0, "local")
        sch.decompose_reduction(rf, r)
        # Schedule the write back block
        sch.reverse_compute_at(block, bx, preserve_unit_loops=True)
        _, r, *s = sch.get_loops(block)
        if unroll_spatial_factor:
            assert len(s) == len(loop_order)
            new_order_s = [s[loop_order[i]] for i in range(len(s))]
            sch.reorder(*new_order_s)
            new_order_s[s_split_index], c = sch.split(
                new_order_s[s_split_index], factors=[None, unroll_spatial_factor]
            )
            sch.reorder(*new_order_s, c)
            s = sch.fuse(*new_order_s)
            sch.reorder(s, c, r)
        else:
            s = sch.fuse(*s)
            sch.reorder(s, r)
        sch.bind(s, "threadIdx.x")
        sch.bind(r, "threadIdx.y")

        # Schedule epilogue
        if epilogue_info is not None:
            epilogue = epilogue_info.block_rv
            sch.reverse_compute_at(epilogue, bx)
            if is_broadcast_epilogue(sch, block, epilogue):
                sch.set_scope(block, 0, "shared")
                _, *s = sch.get_loops(epilogue)  # pylint: disable=invalid-name
                _, tx, ty = sch.split(sch.fuse(*s), factors=[None, len_tx, len_ty])
                sch.bind(tx, "threadIdx.x")
                sch.bind(ty, "threadIdx.y")
            else:
                # The epilogue is element-wise without broadcasting.
                # Thus the remaining spatial part should be bind to tx.
                sch.set_scope(block, 0, "local")
                _, *s = sch.get_loops(epilogue)  # pylint: disable=invalid-name
                tx, _ = sch.split(sch.fuse(*s), factors=[len_tx, None])
                sch.bind(tx, "threadIdx.x")
        # pylint: enable=invalid-name