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# 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
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