memory_optimization_transpiler.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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 __future__ import print_function

from collections import defaultdict
from .. import core
from ... import compat as cpt
from ..framework import Program, default_main_program, Parameter
from ..backward import _rename_arg_
from functools import reduce
from six.moves import range

dtype_to_size = {
    core.VarDesc.VarType.FP16: 2,
    core.VarDesc.VarType.FP32: 4,
    core.VarDesc.VarType.FP64: 8,
    core.VarDesc.VarType.INT16: 2,
    core.VarDesc.VarType.INT32: 4,
    core.VarDesc.VarType.INT64: 8,
    core.VarDesc.VarType.BOOL: 1,
    core.VarDesc.VarType.UINT8: 1,
}

SUB_BLOCK_OPS = [
    "while", "while_grad", "parallel_do", "parallel_do_grad",
    "conditional_block", "conditional_block_grad"
]

SUB_BLOCK_PAIR = [("while", "while_grad"), ("parallel_do", "parallel_do_grad"),
                  ("conditional_block", "conditional_block_grad")]

PRINT_LOG = False


class ControlFlowGraph(object):
    def __init__(self, program, ops, forward_num, skip_opt):
        self._program = program
        self._ops = ops
        self._forward_num = forward_num
        self._successors = defaultdict(set)
        self._presuccessors = defaultdict(set)
        self._uses = defaultdict(set)
        self._defs = defaultdict(set)
        self._live_in = defaultdict(set)
        self._live_out = defaultdict(set)
        self._skip_opt = skip_opt
        self.pool = []

    def _add_connections(self, connections):
        """Populates _successors and _presuccessors for two neighbor nodes."""
        for node1, node2 in connections:
            self._add(node1, node2)

    def _add(self, node1, node2):
        self._successors[node1].add(node2)
        self._presuccessors[node2].add(node1)

    # TODO(panyx0718): We need to have a unified way of building intermediate
    # representation.
    def _build_graph(self):
        """Build a graph based on op sequence.
        """
        self.op_size = len(self._ops)
        op_node_connections = [(i, i + 1) for i in range(self.op_size - 1)]
        self._add_connections(op_node_connections)
        for i in range(self.op_size):
            self._uses[i].update(self._ops[i].input_arg_names())
            self._defs[i].update(self._ops[i].output_arg_names())
            self._live_in[i] = self._uses[i]

    def _update_graph(self, old_name, new_name, begin_idx=0):
        for i in range(begin_idx, self.op_size):
            if old_name in self._uses[i]:
                self._uses[i].remove(old_name)
                self._uses[i].add(new_name)
            if old_name in self._defs[i]:
                self._defs[i].remove(old_name)
                self._defs[i].add(new_name)
            if old_name in self._live_in[i]:
                self._live_in[i].remove(old_name)
                self._live_in[i].add(new_name)
            if old_name in self._live_out[i]:
                self._live_out[i].remove(old_name)
                self._live_out[i].add(new_name)

    def _dataflow_analyze(self):
        self._build_graph()
        live_in = defaultdict(set)
        worklist = list(range(len(self._ops) - 1, -1, -1))
        while worklist:
            i = worklist.pop(0)
            live_in[i] = set(self._live_in[i])
            for s in self._successors[i]:
                self._live_out[i] |= self._live_in[s]
            self._live_in[i] = self._uses[i] | (
                self._live_out[i] - self._defs[i])
            if live_in[i] != self._live_in[i]:
                for d in self._presuccessors[i]:
                    worklist.append(d)

    def _fill_pool(self, i, is_forward):
        block_desc = self._ops[i].block()
        in_diff, _ = self._get_diff(self._live_in[i], self._live_out[i])
        can_optimize = [
            x for x in in_diff
            if self._check_var_validity(block_desc, x, is_forward)
        ]
        if can_optimize:
            for var_name in can_optimize:
                cache = (var_name, self._find_var(block_desc, var_name,
                                                  is_forward).shape())
                if cache not in self.pool:
                    self.pool.append(cache)

    def _get_diff(self, a, b):
        u = a & b
        return a - u, b - u

    def _has_var(self, block_desc, var_name, is_forward):
        if is_forward:
            return block_desc.has_var(cpt.to_bytes(var_name))
        else:
            return block_desc.has_var_recursive(cpt.to_bytes(var_name))

    def _find_var(self, block_desc, var_name, is_forward):
        if is_forward:
            return block_desc.find_var(cpt.to_bytes(var_name))
        else:
            return block_desc.find_var_recursive(cpt.to_bytes(var_name))

    def _check_var_validity(self, block_desc, x, is_forward):
        if str(x) == "@EMPTY@":
            return False
        if not self._has_var(block_desc, x, is_forward):
            return False
        if self._find_var(block_desc, x, is_forward).persistable():
            return False
        if self._find_var(block_desc, x,
                          is_forward).type() != core.VarDesc.VarType.LOD_TENSOR:
            return False
        if x in self._skip_opt:
            return False
        if not self._find_var(block_desc, x, is_forward).shape():
            return False
        return True

    # TODO(panyx0718): This needs to be less hacky. It seems memory optimization
    # doesn't consider vars copied between cpu and gpu.
    def _update_skip_opt_set(self):
        for i in range(self.op_size):
            op = self._ops[i]
            if op.type() == "fill_constant" and op.attr("force_cpu") == True:
                self._skip_opt.update(op.output_arg_names())

    def release_memory(self, skip_opt_set=None):
        self._dataflow_analyze()
        self._update_skip_opt_set()
        if skip_opt_set:
            self._skip_opt.update(skip_opt_set)
        fwd_id = 0
        bwd_id = 0
        for i in range(self.op_size):
            op = self._ops[i]
            if op.type() in SUB_BLOCK_OPS:
                continue
            block_desc = op.block()
            is_forward = i < self._forward_num
            in_diff, out_diff = self._get_diff(self._live_in[i],
                                               self._live_out[i])
            can_optimize = [
                x for x in in_diff
                if self._check_var_validity(block_desc, x, is_forward)
            ]
            if can_optimize:
                index = i + fwd_id + 1 if is_forward else i - self._forward_num + bwd_id + 1
                delete_op = block_desc._insert_op(index)
                delete_op.set_type("delete_var")
                delete_op.set_input("X", can_optimize)
                if is_forward:
                    fwd_id += 1
                else:
                    bwd_id += 1

    def memory_optimize(self, skip_opt_set=None, level=0):
        def compare_shape(x_shape, cache_shape, opt_level):
            if opt_level == 0:
                return x_shape == cache_shape
            elif opt_level == 1:
                if (x_shape[0] == -1) ^ (cache_shape[0] == -1):
                    return False
                x_size = abs(reduce(lambda x, y: x * y, x_shape))
                cache_size = abs(reduce(lambda x, y: x * y, cache_shape))
                if x_size <= cache_size:
                    return True
            else:
                raise ValueError("only support opt_level 0 or 1.")
            return False

        self._dataflow_analyze()
        self._update_skip_opt_set()
        # update skip set to meet users' demand
        if skip_opt_set:
            self._skip_opt.update(skip_opt_set)
        for i in range(self.op_size):
            op = self._ops[i]
            if op.type() in SUB_BLOCK_OPS:
                continue
            block_desc = op.block()
            is_forward = i < self._forward_num
            if self.pool:
                defs_can_optimize = [
                    x for x in self._defs[i]
                    if self._check_var_validity(block_desc, x, is_forward)
                ]
                out_pair = [
                    (x, self._find_var(block_desc, x, is_forward).shape())
                    for x in defs_can_optimize
                ]
                for x, x_shape in out_pair:
                    # If x is both in uses and defs, it can not be optimized!
                    if x in self._uses[i]:
                        continue
                    for index, cache_pair in enumerate(self.pool):
                        cache_var = cache_pair[0]
                        cache_shape = cache_pair[1]
                        if not self._has_var(block_desc, cache_var, is_forward):
                            if PRINT_LOG:
                                print("cache %s not exists!" %
                                      (cpt.to_text(cache_var)))
                            continue
                        if x == cache_var:
                            if PRINT_LOG:
                                print("x : ", cpt.to_text(x), " cache : ",
                                      cpt.to_text(cache_var), " is same var!")
                            break

                        x_dtype = self._find_var(block_desc, x,
                                                 is_forward).dtype()
                        cache_dtype = self._find_var(block_desc, cache_var,
                                                     is_forward).dtype()

                        if not compare_shape(x_shape, cache_shape, level):
                            continue
                        # TODO(qijun): actually, we should compare
                        # dtype_to_size[x_dtype] and dtype_to_size[cache_dtype]
                        if x_dtype != cache_dtype:
                            continue

                        if PRINT_LOG:
                            print(("Hit Cache !!!! cache pool index "
                                   "is %d, var name is %s, "
                                   "cached var name is %s, "
                                   "var shape is %s ") % (index, x, cache_var,
                                                          str(cache_shape)))
                        self.pool.pop(index)
                        # Rename the var to the cache var already with
                        # memory allocated in order to reuse the memory.
                        _rename_arg_(self._ops, x, cache_var, begin_idx=i)
                        self._program.block(block_desc.id).var(cpt.to_text(
                            x)).desc = self._find_var(block_desc, cache_var,
                                                      is_forward)
                        self._update_graph(x, cache_var, begin_idx=i)
                        break
            self._fill_pool(i, is_forward)


def _process_sub_block_pair(pdesc, sub_block_pair):
    """Creates a list of tuple each of which tracks info of a subblock.

      Note: this function doesn't handle nested subblocks yet.
      TODO(panyx0718): assert if case nested subblocks happen.

    :param pdesc: ProgramDesc.
    :param sub_block_pair: A list op pairs. Each op pair is the forward
        op and backward op. The ops in the list are special that they contain
        a subblock of ops.
    :return: A list of tuples, each tuple is (all ops in a subblock pair
        including forward and backward, number of forward ops,
        all output args names of the ops in the subblock pairs).
    """
    ops_list = []
    block_desc = pdesc.block(0)
    op_size = block_desc.op_size()
    for fwd_op, bwd_op in sub_block_pair:
        sub_block_ids = []
        grad_sub_block_ids = []
        sub_block_id_pair = []
        sub_op_dict = {}
        for i in range(op_size):
            op = block_desc.op(i)
            if op.type() == fwd_op:
                sub_block_ids.append(op.attr("sub_block").id)
                sub_op_dict[op.attr("sub_block").id] = op
            elif op.type() == bwd_op:
                grad_sub_block_ids.append(op.attr("sub_block").id)
                sub_op_dict[op.attr("sub_block").id] = op

        # Find fwd_op/bwd_op block pair
        for grad_id in grad_sub_block_ids:
            fwd_id = pdesc.block(grad_id).get_forward_block_idx()
            if fwd_id in sub_block_ids:
                sub_block_id_pair.append((fwd_id, grad_id))
                sub_block_ids.remove(fwd_id)

        # Get fwd_op/bwd_op block ops
        for fwd_id, grad_id in sub_block_id_pair:
            sub_block_ops = []
            sub_block = pdesc.block(fwd_id)
            block_op_size = sub_block.op_size()
            for i in range(block_op_size):
                sub_block_ops.append(sub_block.op(i))

            grad_sub_block = pdesc.block(grad_id)
            grad_sub_block_op_size = grad_sub_block.op_size()
            for i in range(grad_sub_block_op_size):
                sub_block_ops.append(grad_sub_block.op(i))

            sub_op_output = set()
            sub_op_output.update(sub_op_dict[fwd_id].output_arg_names())
            sub_op_output.update(sub_op_dict[grad_id].output_arg_names())
            sub_op_output.update(sub_op_dict[fwd_id].input_arg_names())
            sub_op_output.update(sub_op_dict[grad_id].input_arg_names())
            ops_list.append((sub_block_ops, block_op_size, sub_op_output))

        # Process rest fwd_op block ops
        for fwd_id in sub_block_ids:
            sub_block_ops = []
            sub_block = pdesc.block(fwd_id)
            sub_block_op_size = sub_block.op_size()
            for i in range(sub_block_op_size):
                sub_block_ops.append(sub_block.op(i))
            sub_op_output = set()
            sub_op_output.update(sub_op_dict[fwd_id].output_arg_names())
            sub_op_output.update(sub_op_dict[fwd_id].input_arg_names())
            ops_list.append((sub_block_ops, sub_block_op_size, sub_op_output))
    return ops_list


def _get_cfgs(input_program):
    """Process each block and create ControlFlowGraph for each of them.

    :param input_program: Program object.
    :return: A list of ControlFlowGraph, each corresponds to a block.
    """
    ops_list = []
    pdesc = input_program.get_desc()
    block_desc = pdesc.block(0)
    op_size = block_desc.op_size()

    # Only process one level of nested subblock.
    ops_list.extend(_process_sub_block_pair(pdesc, SUB_BLOCK_PAIR))

    skip_opt_set = set()
    for _, _, skip_opt in ops_list:
        skip_opt_set.update(skip_opt)

    # Get global block ops
    ops_list.insert(
        0, ([block_desc.op(i) for i in range(op_size)], op_size, skip_opt_set))
    cfgs = [
        ControlFlowGraph(input_program, ops, forward_num, skip_opt)
        for ops, forward_num, skip_opt in ops_list
    ]
    return cfgs


def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
    """Optimize memory by reusing var memory.

      Note: it doesn't not support subblock nested in subblock.

    Args:
        input_program(str): Input Program
        skip_opt_set(set): vars wil be skipped in memory optimze
        print_log(bool): whether to print debug log.
        level(int): If level=0, reuse if the shape is completely equal, o
    Returns:
        None
    """
    if level != 0 and level != 1:
        raise ValueError("only support opt_level 0 or 1.")
    global PRINT_LOG
    PRINT_LOG = print_log
    cfgs = _get_cfgs(input_program)
    for cfg in cfgs:
        cfg.memory_optimize(skip_opt_set=skip_opt_set, level=level)


def release_memory(input_program, skip_opt_set=None):
    """
    Modify the input program and insert :code:`delete_op` to early drop not used
    variables. The modification will be performed inplace.

    Notes: This is an experimental API and could be removed in next few
    releases. Users should not use this API.

    Args:
        input_program(Program): The program will be inserted :code:`delete_op`.
        skip_opt_set(set): vars wil be skipped in memory optimze
    Returns:
        None
    """
    cfgs = _get_cfgs(input_program)
    for cfg in cfgs:
        cfg.release_memory(skip_opt_set=skip_opt_set)