venv/lib/python3.10/site-packages/bitblas/gpu/transpose.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 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