Source code for hidet.graph.transforms

# Licensed 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.
from typing import List
from hidet.graph.flow_graph import FlowGraph

from .base import GraphPass, PassContext, logger
from .instruments import GraphPassInstrument, SaveGraphInstrument, ProfileInstrument
from .subgraph_rewrite import subgraph_rewrite_pass
from .automatic_mix_precision import automatic_mix_precision_pass
from .resolve_variant import resolve_variant_pass
from .fuse_operator import fuse_operator_pass
from .eliminate_barrier import eliminate_barrier_pass
from .selective_quantize import selective_quantize_pass
from .conv_channel_last import conv_channel_last_pass

from .resolve_variant import ResolveRule, register_resolve_rule, get_resolve_chain
from .graph_patterns import TensorPattern, OperatorPattern, SubgraphRewriteRule, register_rewrite_rule, op_pattern
from .graph_patterns import registered_rewrite_rules, clear_registered_rewrite_rules

from .graph_patterns import quant


def optimize(graph: FlowGraph) -> FlowGraph:
    """Optimize a flow graph.

    This function applies a sequence of predefined graph-level passes to a :class:`~hidet.graph.FlowGraph` to
    conduct optimizations and graph transformations.

    .. tip::

        Some graph passes provide options to config, please refer to :class:`hidet.graph.PassContext` for more
        information on graph pass configuration.

    Parameters
    ----------
    graph: FlowGraph
        The flow graph to be optimized.

    Returns
    -------
    ret: FlowGraph
        The optimized flow graph.
    """
    ctx = PassContext.current()
    passes = [
        conv_channel_last_pass(),
        subgraph_rewrite_pass(),
        automatic_mix_precision_pass(),
        selective_quantize_pass(),
        resolve_variant_pass(),
        fuse_operator_pass(),
        eliminate_barrier_pass(),
    ]
    for inst in ctx.instruments:
        inst.before_all_passes(graph)
    for optimize_pass in passes:
        graph = optimize_pass(graph)
    for inst in reversed(ctx.instruments):
        inst.after_all_passes(graph)
    return graph.update_nodes()