# Copyright (c) 2020 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 paddle.framework import core
from paddle.utils import unique_name

from ..common import OP_ROLE_KEY, OpRole, is_optimizer_op, is_update_op

__all__ = []


class PlaceType:
    # sync with memcpy op, maybe not a good design
    CPU = 0
    CUDA = 1
    CUDA_PINNED = 2
    XPU = 3  # unsupport for now
    NPU = 4
    NPU_PINNED = 5

    @staticmethod
    def default_device():
        if core.is_compiled_with_cuda():
            return PlaceType.CUDA
        elif core.is_compiled_with_npu():
            return PlaceType.NPU
        return PlaceType.CPU

    @staticmethod
    def default_pinned():
        if core.is_compiled_with_cuda():
            return PlaceType.CUDA_PINNED
        elif core.is_compiled_with_npu():
            return PlaceType.NPU_PINNED
        return PlaceType.CPU


class OffloadHelper:
    cpu_place_type = 0
    cuda_place_type = PlaceType.default_device()
    cuda_pinned_place_type = PlaceType.default_pinned()

    def __init__(self, mp_ring_id=None, dp_ring_id=None):
        self.mp_ring_id = mp_ring_id
        self.dp_ring_id = dp_ring_id

    def _insert_cast_op(self, block, idx, src_name, dst_name):
        src_var = block.var(src_name)
        if not block.has_var(dst_name):
            block.create_var(
                name=dst_name,
                shape=src_var.shape,
                dtype=core.VarDesc.VarType.FP16,
                persistable=True,
            )
        dst_var = block.var(dst_name)
        assert dst_var.dtype == core.VarDesc.VarType.FP16
        block._insert_op_without_sync(
            idx,
            type='cast',
            inputs={'X': src_var},
            outputs={'Out': dst_var},
            attrs={
                'in_dtype': src_var.dtype,
                'out_dtype': dst_var.dtype,
                OP_ROLE_KEY: OpRole.Optimize,
            },
        )

    def _insert_broadcast_op(self, block, idx, param_name):
        rings = []

        if self.dp_ring_id is not None:
            rings.append(self.dp_ring_id)

        # need sync non distributed param in mp group
        if self.mp_ring_id is not None:
            param = block.var(param_name)
            if not hasattr(param, 'is_distributed') or not param.is_distributed:
                rings.append(self.mp_ring_id)

        # the insert op order is: mp, dp
        for ring in rings:
            block._insert_op_without_sync(
                idx,
                type="c_broadcast",
                inputs={'X': param_name},
                outputs={'Out': param_name},
                attrs={
                    'ring_id': ring,
                    'root': 0,
                    'use_calc_stream': True,
                    OP_ROLE_KEY: OpRole.Forward,
                },
            )

    def _insert_memcpy_op(self, block, idx, src_name, dst_name, dst_place_type):
        src_var = block.var(src_name)
        dst_var = block.var(dst_name)
        block._insert_op_without_sync(
            idx,
            type='memcpy',
            inputs={'X': src_var},
            outputs={'Out': dst_var},
            attrs={
                'dst_place_type': dst_place_type,
                OP_ROLE_KEY: OpRole.Optimize,
            },
        )

    def _insert_fetch_op(self, block, idx, src_name, dst_name):
        self._insert_memcpy_op(
            block, idx, src_name, dst_name, OffloadHelper.cuda_place_type
        )

    def _insert_offload_op(self, block, idx, src_name, dst_name):
        self._insert_memcpy_op(
            block, idx, src_name, dst_name, OffloadHelper.cuda_pinned_place_type
        )

    def _get_offload_var_name(self, name):
        return unique_name.generate(name + '@offload')

    def _create_offload_var(self, var_name, offload_var_name, blocks):
        for block in blocks:
            var = block.var(var_name)
            var.persistable = False
            offload_var = block.create_var(
                name=offload_var_name,
                shape=var.shape,
                dtype=var.dtype,
                persistable=True,
            )

    def offload_fp32param(self, block, startup_block, offload=True):
        """
        (p_fp16) = cast(p)
        (p_fp16_recompute) = cast(p)
        (pout,) = adam(p)
        ===========================>
        rename(p_fp16_recompute, p_fp16)

        (p,) = prefetch(p@offload)
        (pout,) = adam(p)
        (p_fp16) = cast(p)
        (p@offload) = memcpy(p)
        """
        param_to_idx = {}
        param_to_fp16 = {}
        # recompute_var which need rename to fp16_param
        fp16_param_to_recompute = {}
        recompute_to_fp16 = {}

        def remove_param(input_name):
            param_to_idx.pop(input_name)
            if input_name in param_to_fp16:
                fp16_param = param_to_fp16.pop(input_name)
                if fp16_param in fp16_param_to_recompute:
                    recompute = fp16_param_to_recompute.pop(fp16_param)
                    recompute_to_fp16.pop(recompute)

        # step1: record param
        for idx, op in reversed(list(enumerate(block.ops))):
            if is_update_op(op):
                param = op.desc.input("Param")[0]
                param_to_idx[param] = idx

        # step2: remove param which can't offload and
        #        record param->fp16param, fp16param->recompute_var
        for idx, op in enumerate(block.ops):
            if is_optimizer_op(op):
                break
            # TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast
            if not offload and op.type == 'coalesce_tensor':
                continue
            for input_name in op.desc.input_arg_names():
                if input_name not in param_to_idx:
                    continue

                # param which will be used by fp32 op
                if op.type != 'cast':
                    remove_param(input_name)
                    continue

                # param is only used by cast op,
                # which to cast fp32_param to fp16_param
                output_name = op.output_arg_names[0]
                if 'cast_fp16' not in output_name:
                    remove_param(input_name)
                    continue

                if 'subprog' not in output_name:
                    assert output_name == input_name + '.cast_fp16'
                    assert (
                        input_name not in param_to_fp16
                    ), "There must be only one cast op from fp32 param to fp16 param."
                    param_to_fp16[input_name] = output_name
                else:
                    # fp16-->recompute_var
                    assert (
                        input_name in param_to_fp16
                    ), "param must first be cast to fp16"
                    fp16_param = param_to_fp16[input_name]
                    fp16_param_to_recompute[fp16_param] = output_name
                    recompute_to_fp16[output_name] = fp16_param

        param_name_to_offload_name = {}
        # step3: main_block add offload, cast op
        # change recompute to fp16, remove cast(param) to fp16
        for idx, op in reversed(list(enumerate(block.ops))):
            if is_update_op(op):
                param = op.desc.input("Param")[0]
                if param not in param_to_idx:
                    continue
                # step3.1: create offload_var
                offload_var_name = self._get_offload_var_name(param)
                param_name_to_offload_name[param] = offload_var_name
                if offload:
                    self._create_offload_var(
                        param, offload_var_name, [block, startup_block]
                    )

                    # step3.2: insert cast op and offload op
                    self._insert_offload_op(
                        block, idx + 1, param, offload_var_name
                    )

                assert param in param_to_fp16
                fp16_param_name = param_to_fp16[param]
                fp16_param_var = block.var(fp16_param_name)
                fp16_param_var.persistable = True
                self._insert_cast_op(
                    block, idx + 1, param, param_to_fp16[param]
                )

                if offload:
                    # step3.3: insert fetch op
                    self._insert_fetch_op(block, idx, offload_var_name, param)
                continue

            # step3.4: remove cast op
            if op.type == 'cast':
                input_name = op.desc.input_arg_names()[0]
                if input_name in param_to_idx:
                    block._remove_op(idx, sync=False)
                    continue

            # step3.5: change recompute_param to fp16_param
            for input_name in op.desc.input_arg_names():
                if input_name in recompute_to_fp16:
                    op._rename_input(input_name, recompute_to_fp16[input_name])
            for output_name in op.desc.output_arg_names():
                if output_name in recompute_to_fp16:
                    op._rename_output(
                        output_name, recompute_to_fp16[output_name]
                    )

        # step4: remove recompute_param
        for name in recompute_to_fp16.keys():
            block._remove_var(name, sync=False)

        # step5: startup_block add offload
        visited_vars = set()
        # FIXME(wangxi): should insert in idx, need move comm init to the head.
        insert_idx = len(startup_block.ops)
        for idx, op in reversed(list(enumerate(startup_block.ops))):
            for out_name in op.output_arg_names:
                if out_name in visited_vars:
                    continue

                if out_name in param_name_to_offload_name:
                    var_name = out_name
                    if offload:
                        offload_var_name = param_name_to_offload_name[var_name]
                        self._insert_offload_op(
                            startup_block,
                            insert_idx,
                            var_name,
                            offload_var_name,
                        )
                    self._insert_cast_op(
                        startup_block,
                        insert_idx,
                        var_name,
                        param_to_fp16[var_name],
                    )
                    # NOTE(wangxi): cast and offload should insert after broadcast param.
                    # the insert op order is: {mp, dp}broadcast, cast, offload
                    self._insert_broadcast_op(
                        startup_block, insert_idx, var_name
                    )

                visited_vars.add(out_name)

        block._sync_with_cpp()
        startup_block._sync_with_cpp()

    def cast_fp32param_in_optimize(self, block, startup_block):
        """
        (p_fp16) = cast(p)
        (p_fp16_recompute) = cast(p)
        (pout,) = adam(p)
        ===========================>
        rename(p_fp16_recompute, p_fp16)

        (pout,) = adam(p)
        (p_fp16) = cast(p)
        """
        self.offload_fp32param(block, startup_block, offload=False)

    def offload(self, block, startup_block):
        """
        (m1, m2) = prefetch(m1@offload, m2@offload)
        (m1out, m2out, pout) = adam(m1, m2, p)
        (m1@offload, m2@offload) = memcpy(m1, m2)
        """
        vars_name_to_offload_name = {}

        # main_block add offload
        for idx, op in reversed(list(enumerate(block.ops))):
            if not is_optimizer_op(op):
                break

            vars_name = []
            if op.type == "adam" or op.type == "adamw":
                # {Moment1Out = [''], Moment2Out = [''], ParamOut = ['']} =
                # adam(inputs={Moment1 = [''], Moment2 = [''], Param = ['']})
                vars_name.append(op.desc.input("Moment1")[0])
                vars_name.append(op.desc.input("Moment2")[0])
            elif op.type == 'momentum':
                pass
            elif op.type == 'lars':
                pass
            elif op.type == 'lamb':
                pass

            # step1: create and init offload_var
            for var_name in vars_name:
                assert var_name not in vars_name_to_offload_name

                offload_var_name = self._get_offload_var_name(var_name)
                vars_name_to_offload_name[var_name] = offload_var_name

                self._create_offload_var(
                    var_name, offload_var_name, [block, startup_block]
                )

            # step2: insert offload op
            for var_name in vars_name:
                offload_var_name = vars_name_to_offload_name[var_name]
                self._insert_offload_op(
                    block, idx + 1, var_name, offload_var_name
                )

            # step3: insert fetch op
            for var_name in vars_name:
                offload_var_name = vars_name_to_offload_name[var_name]
                self._insert_fetch_op(block, idx, offload_var_name, var_name)

        # startup_block add offload
        visited_vars = set()
        for idx, op in reversed(list(enumerate(startup_block.ops))):
            for out_name in op.output_arg_names:
                if out_name in visited_vars:
                    continue

                if out_name in vars_name_to_offload_name:
                    var_name = out_name
                    offload_var_name = vars_name_to_offload_name[var_name]
                    # insert offload op after var is generated
                    self._insert_offload_op(
                        startup_block, idx + 1, var_name, offload_var_name
                    )
                visited_vars.add(out_name)

        block._sync_with_cpp()
        startup_block._sync_with_cpp()

    def opt_sharding_cast_fp32param(
        self, block, startup_block, params, offload=False
    ):
        """
        (p_fp16) = cast(p)
        (p_fp16_recompute) = cast(p)
        (pout,) = adam(p)
        ===========================>
        rename(p_fp16_recompute, p_fp16)

        (pout,) = adam(p)
        (p_fp16) = cast(p)
        broadcast(p_fp16)
        """
        global_params = set()
        local_params = set()
        param_to_fp16 = {}
        # recompute_var which need rename to fp16_param
        fp16_param_to_recompute = {}
        recompute_to_fp16 = {}

        def remove_param(input_name):
            global_params.remove(input_name)
            if input_name in local_params:
                local_params.remove(input_name)
            if input_name in param_to_fp16:
                fp16_param = param_to_fp16.pop(input_name)
                if fp16_param in fp16_param_to_recompute:
                    recompute = fp16_param_to_recompute.pop(fp16_param)
                    recompute_to_fp16.pop(recompute)

        # step1: record param
        global_params = set(params)
        for idx, op in reversed(list(enumerate(block.ops))):
            if is_update_op(op):
                param = op.desc.input("Param")[0]
                local_params.add(param)

        # step2: remove param which can't offload and
        #        record param->fp16param, fp16param->recompute_var
        for idx, op in enumerate(block.ops):
            if is_optimizer_op(op):
                break
            # TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast
            if op.type == 'coalesce_tensor':
                continue
            for input_name in op.desc.input_arg_names():
                if input_name not in global_params:
                    continue

                # param which will be used by fp32 op
                if op.type != 'cast':
                    remove_param(input_name)
                    continue

                # param is only used by cast op,
                # which to cast fp32_param to fp16_param
                output_name = op.output_arg_names[0]
                if 'cast_fp16' not in output_name:
                    remove_param(input_name)
                    continue

                if 'subprog' not in output_name:
                    assert output_name == input_name + '.cast_fp16'
                    assert (
                        input_name not in param_to_fp16
                    ), "There must be only one cast op from fp32 param to fp16 param."
                    param_to_fp16[input_name] = output_name
                else:
                    # fp16-->recompute_var
                    assert (
                        input_name in param_to_fp16
                    ), "param must first be cast to fp16"
                    fp16_param = param_to_fp16[input_name]
                    fp16_param_to_recompute[fp16_param] = output_name
                    recompute_to_fp16[output_name] = fp16_param

        param_name_to_offload_name = {}
        # step3: main_block add offload, cast op
        # change recompute to fp16, remove cast(param) to fp16
        for idx, op in reversed(list(enumerate(block.ops))):
            if is_update_op(op):
                param = op.desc.input("Param")[0]
                if param not in global_params:
                    continue
                # step3.1: create offload_var
                offload_var_name = self._get_offload_var_name(param)
                param_name_to_offload_name[param] = offload_var_name
                if offload:
                    self._create_offload_var(
                        param, offload_var_name, [block, startup_block]
                    )

                    # step3.2: insert cast op and offload op
                    self._insert_offload_op(
                        block, idx + 1, param, offload_var_name
                    )

                assert param in param_to_fp16
                fp16_param_name = param_to_fp16[param]
                fp16_param_var = block.var(fp16_param_name)
                fp16_param_var.persistable = True
                self._insert_cast_op(
                    block, idx + 1, param, param_to_fp16[param]
                )

                if offload:
                    # step3.3: insert fetch op
                    self._insert_fetch_op(block, idx, offload_var_name, param)

                continue

            # step3.4: remove cast op
            if op.type == 'cast':
                input_name = op.desc.input_arg_names()[0]
                if input_name in global_params:
                    block._remove_op(idx, sync=False)
                    continue

            # step3.5: change recompute_param to fp16_param
            for input_name in op.desc.input_arg_names():
                if input_name in recompute_to_fp16:
                    op._rename_input(input_name, recompute_to_fp16[input_name])
            for output_name in op.desc.output_arg_names():
                if output_name in recompute_to_fp16:
                    op._rename_output(
                        output_name, recompute_to_fp16[output_name]
                    )

        # step4: remove recompute_param
        for name in recompute_to_fp16.keys():
            block._remove_var(name, sync=False)

        # step5: remove fp32 param which not need
        for idx, op in enumerate(block.ops):
            if op.type not in ['coalesce_tensor', 'c_broadcast']:
                continue
            for input_name in op.desc.input_arg_names():
                if input_name in param_to_fp16:
                    op._rename_input(input_name, param_to_fp16[input_name])
            for output_name in op.desc.output_arg_names():
                if output_name in param_to_fp16:
                    op._rename_output(output_name, param_to_fp16[output_name])

        for param in global_params:
            assert param in param_to_fp16
            fp16_param_name = param_to_fp16[param]
            fp16_param_var = block.var(fp16_param_name)
            fp16_param_var.persistable = True

            if param not in local_params:
                block._remove_var(param, sync=False)

        # step6: startup_block add offload
        visited_vars = set()
        insert_idx = len(startup_block.ops)
        for idx, op in reversed(list(enumerate(startup_block.ops))):
            for out_name in op.output_arg_names:
                if out_name in visited_vars:
                    continue

                if out_name in param_to_fp16:
                    var_name = out_name
                    if offload:
                        self._insert_offload_op(
                            startup_block,
                            idx + 1,
                            var_name,
                            param_name_to_offload_name[var_name],
                        )

                    self._insert_cast_op(
                        startup_block,
                        insert_idx,
                        var_name,
                        param_to_fp16[var_name],
                    )

                    # NOTE(wangxi): cast and offload should insert after broadcast param.
                    # the insert op order is: {mp, dp}broadcast, cast, offload
                    self._insert_broadcast_op(
                        startup_block, insert_idx, var_name
                    )

                    if var_name not in local_params:
                        param = startup_block.var(out_name)
                        param.persistable = False

                visited_vars.add(out_name)

        block._sync_with_cpp()
        startup_block._sync_with_cpp()
