File size: 5,112 Bytes
a3f2e4d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
# 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 transpose.py in dlight.
"""Reduction rule for operators including softmax, layer norm, RMS norm, etc"""
from typing import List, Union
from tvm import arith, tir
from tvm.target import Target
from tvm.tir import Schedule
from tvm.tir.schedule import BlockRV
from ..base import (
detect_dominant_read,
normalize_prim_func,
try_inline_contiguous_spatial,
)
from .base import GPUScheduleRule
class Transpose(GPUScheduleRule):
"""Schedule rule for transpose"""
def is_transpose(self, sch: Schedule, block_rv: BlockRV):
block = sch.get(block_rv)
if isinstance(block.body, tir.BufferStore):
rhs = block.body.value
if isinstance(rhs, tir.BufferLoad):
lhs_indices = block.body.indices
rhs_indices = rhs.indices
if list(lhs_indices) != list(rhs_indices) and set(lhs_indices) == set(rhs_indices):
return True
return False
def apply( # pylint: disable=too-many-locals
self,
func: tir.PrimFunc,
target: Target,
_: bool,
) -> Union[None, tir.Schedule, List[tir.Schedule]]:
# pylint: disable=invalid-name
if not isinstance(func, tir.PrimFunc) or not self.is_target_available(target):
return None
if target.kind.name == "cuda":
len_tx = 16
len_ty = 8
unroll_depth = 256
else:
len_tx = 8
len_ty = 4
unroll_depth = 64
len_vec = 4
sch = tir.Schedule(func)
blocks = normalize_prim_func(sch)
transpose_block_idx = -1
for idx, block in reversed(list(enumerate(blocks))):
if self.is_transpose(sch, block.block_rv):
transpose_block_idx = idx
break
if not block.is_injective():
return None
if transpose_block_idx == -1:
return None
transpose_block = blocks[transpose_block_idx].block_rv
prologue = None # the optional decoding block
if transpose_block_idx > 0:
spatials = try_inline_contiguous_spatial(sch, blocks[: transpose_block_idx - 1])
assert len(spatials) == 0
prologue = blocks[transpose_block_idx - 1].block_rv
loops = sch.get_loops(transpose_block)
if len(loops) != 2:
# transpose with more than 2 axes is not supported
return None
c_factor = 1
if prologue is not None:
block_stmt = sch.get(prologue)
result = arith.normalize_to_iter_sum(
detect_dominant_read(block_stmt),
input_iters={i.var: i.dom.extent for i in block_stmt.iter_vars},
)
if len(result.args) > 0:
c_factor = int(result.args[0].lower_factor)
i, j = loops
i, vi = sch.split(i, factors=[None, c_factor], preserve_unit_iters=True)
bi, ti = sch.split(i, factors=[None, len_ty], preserve_unit_iters=True)
bj, tj = sch.split(j, factors=[None, len_tx], preserve_unit_iters=True)
sch.reorder(bi, bj, ti, tj, vi)
sch.bind(bi, "blockIdx.y")
sch.bind(bj, "blockIdx.x")
sch.bind(ti, "threadIdx.y")
sch.bind(tj, "threadIdx.x")
len_vec = min(len_vec, c_factor)
_, vi = sch.split(vi, factors=[None, len_vec])
if len_vec > 1:
sch.vectorize(vi)
cache_read = sch.cache_read(transpose_block, read_buffer_index=0, storage_scope="shared")
sch.compute_at(cache_read, bj)
loops = sch.get_loops(cache_read)[2:]
fused = sch.fuse(*loops)
_, ty, tx, v = sch.split(fused, factors=[None, len_ty, len_tx, c_factor])
sch.bind(ty, "threadIdx.y")
sch.bind(tx, "threadIdx.x")
sch.unroll(v)
sch.storage_align(block=cache_read, buffer_index=0, axis=0, factor=32, offset=1)
sch.annotate(bi, ann_key="pragma_auto_unroll_max_step", ann_val=unroll_depth)
sch.annotate(bi, ann_key="pragma_unroll_explicit", ann_val=1)
if prologue is not None:
sch.compute_inline(prologue)
return sch
|