#   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.

import paddle
from paddle.framework import core
from paddle.utils import unique_name
from paddle.framework import ParamAttr
from paddle.fluid.framework import _varbase_creator
from paddle.nn.initializer import Constant
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.nn import functional as F
import logging
from paddle.fluid.log_helper import get_logger
from paddle import _legacy_C_ops
from paddle import in_dynamic_mode
from paddle.nn import Layer
from paddle.nn.quant.lsq import FakeQuantActLSQPlus, FakeQuantWeightLSQPlus

__all__ = [
    'FakeQuantAbsMax',
    'FakeQuantMovingAverageAbsMax',
    'FakeQuantChannelWiseAbsMax',
    'QuantizedConv2D',
    'QuantizedConv2DTranspose',
    'QuantizedLinear',
    'MovingAverageAbsMaxScale',
    'MAOutputScaleLayer',
    'FakeQuantMAOutputScaleLayer',
    'QuantStub',
    'QuantizedRowParallelLinear',
    'QuantizedColumnParallelLinear',
]

_logger = get_logger(__name__,
                     logging.INFO,
                     fmt='%(asctime)s-%(levelname)s: %(message)s')


class FakeQuantAbsMax(Layer):
    r"""
    FakeQuantAbsMax layer does the abs_max quant and then dequant.
    Its computational formula is described as below:

    :math:`scale = max(abs(X))`
    :math:`range = 2^{bit\_length - 1} - 1`
    :math:`Out = round(X / scale * range) * scale / range`
    """

    def __init__(self,
                 name=None,
                 quant_bits=8,
                 dtype='float32',
                 quant_on_weight=False,
                 reduce_type=None):
        super(FakeQuantAbsMax, self).__init__()
        self._quant_bits = quant_bits
        self._name = name
        self._reduce_type = reduce_type
        scale_prefix = "{}.scale".format(
            name) if name else 'quant_dequant.scale'
        self._scale_name = unique_name.generate(scale_prefix)
        if quant_on_weight:
            scale_attr = ParamAttr(name=self._scale_name,
                                   initializer=Constant(0.001),
                                   trainable=False)
            self._scale = self.create_parameter(shape=[1],
                                                attr=scale_attr,
                                                dtype=self._dtype)
            self._scale.stop_gradient = True
        else:
            self._scale = None

    def forward(self, input):
        if in_dynamic_mode():
            attrs = ('bit_length', self._quant_bits)
            quant_out = _varbase_creator(type=input.type,
                                         name="{}.quantized.dequantized".format(
                                             input.name),
                                         shape=input.shape,
                                         dtype=input.dtype,
                                         persistable=False)
            out_scale = self._scale
            if self._reduce_type == "max":
                paddle.distributed.all_reduce(
                    out_scale, op=paddle.distributed.ReduceOp.MAX)

            if not out_scale:
                out_scale = _varbase_creator(
                    type=core.VarDesc.VarType.LOD_TENSOR,
                    name=self._scale_name,
                    shape=[1],
                    dtype=self._dtype,
                    persistable=False)
                out_scale.stop_gradient = True
            out, _, = _legacy_C_ops.fake_quantize_dequantize_abs_max(
                input, quant_out, out_scale, *attrs)
            return out

        check_variable_and_dtype(input, 'input', ['float32'], "FakeQuantAbsMax")
        attrs = {'bit_length': self._quant_bits}
        inputs = {"X": [input]}
        quant_out = self._helper.create_variable(
            name="{}.quantized.dequantized".format(input.name),
            dtype=input.dtype,
            type=core.VarDesc.VarType.LOD_TENSOR,
            persistable=False,
            stop_gradient=False)
        out_scale = self._scale
        if not out_scale:
            out_scale = self._helper.create_variable(
                name=self._scale_name,
                dtype=self._dtype,
                type=core.VarDesc.VarType.LOD_TENSOR,
                persistable=False,
                stop_gradient=True)
        outputs = {"Out": [quant_out], "OutScale": [out_scale]}

        self._helper.append_op(type="fake_quantize_dequantize_abs_max",
                               inputs=inputs,
                               outputs=outputs,
                               attrs=attrs)

        return quant_out


class FakeQuantMovingAverageAbsMax(Layer):
    r"""
    FakeQuantMovingAverageAbsMax layer does the moving_average_abs_max quant and then dequant.
    Its computational formula is described as below:

    :math:`scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)`
    :math:`range = 2^{bit\_length - 1} - 1`
    :math:`Out = round(X / scale * range) * scale / range`
    """

    def __init__(self,
                 name=None,
                 moving_rate=0.9,
                 quant_bits=8,
                 dtype='float32',
                 reduce_type=None):
        super(FakeQuantMovingAverageAbsMax, self).__init__()
        self._moving_rate = moving_rate
        self._quant_bits = quant_bits
        self._reduce_type = reduce_type
        scale_prefix = "{}.scale".format(
            name) if name else 'quant_dequant.scale'
        scale_attr = ParamAttr(name=unique_name.generate(scale_prefix),
                               initializer=Constant(0.001),
                               trainable=False)
        self._scale = self.create_parameter(shape=[1],
                                            attr=scale_attr,
                                            dtype=dtype)
        self._scale.stop_gradient = True

        state_prefix = "{}.state".format(
            name) if name else 'quant_dequant.state'
        state_attr = ParamAttr(name=unique_name.generate(state_prefix),
                               initializer=Constant(1),
                               trainable=False)
        self._state = self.create_parameter(shape=[1],
                                            attr=state_attr,
                                            dtype=dtype)
        self._state.stop_gradient = True

        accum_prefix = "{}.accum".format(
            name) if name else 'quant_dequant.accum'
        accum_attr = ParamAttr(name=unique_name.generate(accum_prefix),
                               initializer=Constant(1),
                               trainable=False)
        self._accum = self.create_parameter(shape=[1],
                                            attr=accum_attr,
                                            dtype=dtype)
        self._accum.stop_gradient = True

    def forward(self, input):
        if in_dynamic_mode():
            attrs = ('moving_rate', self._moving_rate, 'bit_length',
                     self._quant_bits, 'is_test', not self.training)
            quant_out = _varbase_creator(type=input.type,
                                         name="{}.quantized.dequantized".format(
                                             input.name),
                                         shape=input.shape,
                                         dtype=input.dtype,
                                         persistable=False)
            if self._reduce_type == "max":
                paddle.distributed.all_reduce(
                    self._scale, op=paddle.distributed.ReduceOp.MAX)

            state = self._state if self.training else None
            accum = self._accum if self.training else None

            out, _, _, _ = _legacy_C_ops.fake_quantize_dequantize_moving_average_abs_max(
                input, self._scale, accum, state, quant_out, self._scale, state,
                accum, *attrs)

            return out

        check_variable_and_dtype(input, 'input', ['float32'],
                                 "FakeQuantMovingAverageAbsMax")
        attrs = {
            'moving_rate': self._moving_rate,
            'bit_length': self._quant_bits,
            'is_test': not self.training
        }
        inputs = {"X": [input], "InScale": [self._scale]}
        quant_out = self._helper.create_variable(
            name="{}.quantized.dequantized".format(input.name),
            dtype=input.dtype,
            type=core.VarDesc.VarType.LOD_TENSOR,
            persistable=False,
            stop_gradient=False)
        outputs = {"Out": [quant_out], "OutScale": [self._scale]}

        if self.training:
            inputs['InState'] = [self._state]
            inputs['InAccum'] = [self._accum]
            outputs['OutState'] = [self._state]
            outputs['OutAccum'] = [self._accum]

        self._helper.append_op(
            type="fake_quantize_dequantize_moving_average_abs_max",
            inputs=inputs,
            outputs=outputs,
            attrs=attrs)

        return quant_out


class FakeQuantChannelWiseAbsMax(Layer):

    def __init__(self,
                 name=None,
                 channel_num=None,
                 quant_bits=8,
                 quant_axis=0,
                 dtype='float32',
                 quant_on_weight=False,
                 reduce_type=None):
        assert quant_on_weight == True, "Channel_wise only can be used on weight quantization."
        super(FakeQuantChannelWiseAbsMax, self).__init__()
        self._quant_bits = quant_bits
        self._quant_axis = quant_axis
        self._dtype = dtype
        self._name = name
        self._channel_num = channel_num
        self._reduce_type = reduce_type
        scale_prefix = "{}.scale".format(
            name) if name else 'quant_dequant.scale'
        self._scale_name = unique_name.generate(scale_prefix)
        if quant_on_weight:
            scale_attr = ParamAttr(name=self._scale_name,
                                   initializer=Constant(0.0),
                                   trainable=False)
            self._scale = self.create_parameter(shape=[self._channel_num],
                                                attr=scale_attr,
                                                dtype=self._dtype)
            self._scale.stop_gradient = True
        else:
            self._scale = None

    def forward(self, input):
        if in_dynamic_mode():
            attrs = ('bit_length', self._quant_bits, 'quant_axis',
                     self._quant_axis)
            quant_out = _varbase_creator(type=input.type,
                                         name="{}.quantized.dequantized".format(
                                             input.name),
                                         shape=input.shape,
                                         dtype=input.dtype,
                                         persistable=False)

            out_scale = self._scale
            if self._reduce_type == "max":
                paddle.distributed.all_reduce(
                    out_scale, op=paddle.distributed.ReduceOp.MAX)
            if out_scale is None:
                out_scale = _varbase_creator(
                    type=core.VarDesc.VarType.LOD_TENSOR,
                    name=self._scale_name,
                    shape=[self._channel_num],
                    dtype=self._dtype,
                    persistable=False)
                out_scale.stop_gradient = True

            out, _, = _legacy_C_ops.fake_channel_wise_quantize_dequantize_abs_max(
                input, quant_out, out_scale, *attrs)
            return out

        check_variable_and_dtype(input, 'input', ['float32'],
                                 "FakeQuantChannelWiseAbsMax")
        attrs = {'bit_length': self._quant_bits, 'quant_axis': self._quant_axis}
        inputs = {"X": [input]}
        quant_out = self._helper.create_variable(
            name="{}.quantized.dequantized".format(input.name),
            dtype=input.dtype,
            type=core.VarDesc.VarType.LOD_TENSOR,
            persistable=False,
            stop_gradient=False)
        out_scale = self._scale
        if not out_scale:
            out_scale = self._helper.create_variable(
                name=self._scale_name,
                dtype=self._dtype,
                type=core.VarDesc.VarType.LOD_TENSOR,
                persistable=False,
                stop_gradient=True)
        outputs = {"Out": [quant_out], "OutScale": [out_scale]}

        self._helper.append_op(
            type="fake_channel_wise_quantize_dequantize_abs_max",
            inputs=inputs,
            outputs=outputs,
            attrs=attrs)

        return quant_out


class MovingAverageAbsMaxScale(Layer):

    def __init__(self,
                 name=None,
                 moving_rate=0.9,
                 dtype='float32',
                 reduce_type=None):
        r"""
        MovingAverageMaxScale layer is used to calculating the output quantization
        scale of Layer. Its computational formula is described as below:

        :math:`scale = (moving\_rate*accum+max(abs(x)))/(moving\_rate*state+1)`
        :math:`Out = X`
        """
        super(MovingAverageAbsMaxScale, self).__init__()
        self._moving_rate = moving_rate
        self._reduce_type = reduce_type
        scale_prefix = '{}.scale'.format(name) if name else 'outscale.scale'
        scale_name = unique_name.generate(scale_prefix)
        scale_attr = ParamAttr(name=scale_name,
                               initializer=Constant(0),
                               trainable=False)
        self._scale = self.create_parameter(shape=[1],
                                            attr=scale_attr,
                                            dtype=dtype)
        self._scale.stop_gradient = True

        state_prefix = "{}.state".format(name) if name else 'outscale.state'
        state_attr = ParamAttr(name=unique_name.generate(state_prefix),
                               initializer=Constant(0),
                               trainable=False)
        self._state = self.create_parameter(shape=[1],
                                            attr=state_attr,
                                            dtype=dtype)
        self._state.stop_gradient = True

        accum_prefix = "{}.accum".format(name) if name else 'outscale.accum'
        accum_attr = ParamAttr(name=unique_name.generate(accum_prefix),
                               initializer=Constant(0),
                               trainable=False)
        self._accum = self.create_parameter(shape=[1],
                                            attr=accum_attr,
                                            dtype=dtype)
        self._accum.stop_gradient = True

    def forward(self, input):
        if in_dynamic_mode():
            attrs = ('moving_rate', self._moving_rate, 'is_test',
                     not self.training)

            quant_out = _varbase_creator(type=input.type,
                                         name="{}.tmp".format(input.name),
                                         shape=input.shape,
                                         dtype=input.dtype,
                                         persistable=False)
            if self._reduce_type == "max":
                paddle.distributed.all_reduce(
                    self._scale, op=paddle.distributed.ReduceOp.MAX)

            state = self._state if self.training else None
            accum = self._accum if self.training else None

            out, _, _, _ = _legacy_C_ops.moving_average_abs_max_scale(
                input, accum, state, quant_out, self._scale, state, accum,
                *attrs)
            return out

        check_variable_and_dtype(input, 'input', ['float32', 'float64'],
                                 'MovingAverageAbsMaxScale')

        attrs = {'moving_rate': self._moving_rate, 'is_test': not self.training}
        inputs = {"X": [input]}
        quant_out = self._helper.create_variable(
            name="{}.tmp".format(input.name),
            dtype=input.dtype,
            type=core.VarDesc.VarType.LOD_TENSOR,
            persistable=False,
            stop_gradient=False)
        outputs = {"Out": [quant_out], "OutScale": [self._scale]}

        if self.training:
            inputs['InState'] = [self._state]
            inputs['InAccum'] = [self._accum]
            outputs['OutState'] = [self._state]
            outputs['OutAccum'] = [self._accum]

        self._helper.append_op(type="moving_average_abs_max_scale",
                               inputs=inputs,
                               outputs=outputs,
                               attrs=attrs)

        return quant_out


QuantStub = MovingAverageAbsMaxScale


class QuantizedConv2D(Layer):
    """
    The computational logic of QuantizedConv2D is the same with Conv2D.
    The only difference is that its inputs are all fake quantized.
    """

    def __init__(self,
                 layer,
                 weight_bits=8,
                 activation_bits=8,
                 moving_rate=0.9,
                 weight_quantize_type='abs_max',
                 activation_quantize_type='abs_max',
                 weight_pre_layer=None,
                 act_pre_layer=None,
                 weight_quant_layer=None,
                 act_quant_layer=None):
        super(QuantizedConv2D, self).__init__()
        # For Conv2D
        self._groups = getattr(layer, '_groups')
        self._stride = getattr(layer, '_stride')
        self._padding = getattr(layer, '_padding')
        self._padding_mode = getattr(layer, '_padding_mode')
        if self._padding_mode != 'zeros':
            self._reversed_padding_repeated_twice = getattr(
                layer, '_reversed_padding_repeated_twice')
        self._dilation = getattr(layer, '_dilation')
        self._data_format = getattr(layer, '_data_format')
        self.weight = getattr(layer, 'weight')
        self.bias = getattr(layer, 'bias')

        # For FakeQuant
        self._conv2d_quant_axis = 0
        if weight_quant_layer is not None:
            self._fake_quant_weight = weight_quant_layer()
        else:
            self._fake_quant_weight = _get_fake_quant_type(
                weight_quantize_type,
                name=self.weight.name,
                moving_rate=moving_rate,
                quant_bits=weight_bits,
                dtype=self._dtype,
                quant_on_weight=True,
                channel_num=self.weight.shape[self._conv2d_quant_axis],
                quant_axis=self._conv2d_quant_axis)
        if act_quant_layer is not None:
            self._fake_quant_input = act_quant_layer()
        else:
            self._fake_quant_input = _get_fake_quant_type(
                activation_quantize_type,
                name=layer.full_name(),
                moving_rate=moving_rate,
                quant_bits=activation_bits,
                dtype=self._dtype,
                quant_on_weight=False)

        self._act_preprocess = act_pre_layer(
        ) if act_pre_layer is not None else None
        self._weight_preprocess = weight_pre_layer(
        ) if weight_pre_layer is not None else None

    def forward(self, input):
        if self._act_preprocess is not None:
            input = self._act_preprocess(input)
        quant_input = self._fake_quant_input(input)

        weight = self.weight
        if self._weight_preprocess is not None:
            weight = self._weight_preprocess(self.weight)
        quant_weight = self._fake_quant_weight(weight)

        if self._padding_mode != 'zeros':
            quant_input = F.pad(quant_input,
                                self._reversed_padding_repeated_twice,
                                mode=self._padding_mode,
                                data_format=self._data_format)
            self._padding = 0

        return F.conv2d(quant_input,
                        quant_weight,
                        bias=self.bias,
                        padding=self._padding,
                        stride=self._stride,
                        dilation=self._dilation,
                        groups=self._groups,
                        data_format=self._data_format)


class QuantizedConv2DTranspose(Layer):
    """
    The computational logic of QuantizedConv2DTranspose is the same with Conv2DTranspose.
    The only difference is that its inputs are all fake quantized.

    Examples:
       .. code-block:: python
          import paddle
          import paddle.nn as nn
          from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
          x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
          conv = nn.Conv2DTranspose(4, 6, (3, 3))
          conv_quantized = QuantizedConv2DTranspose(conv)
          y_quantized = conv_quantized(x_var)
          y_var = conv(x_var)
          y_quantized_np = y_quantized.numpy()
          y_np = y_var.numpy()
          print(y_np.shape, y_quantized_np.shape)
          # (2, 6, 10, 10), (2, 6, 10, 10)
    """

    def __init__(self,
                 layer,
                 weight_bits=8,
                 activation_bits=8,
                 moving_rate=0.9,
                 weight_quantize_type='abs_max',
                 activation_quantize_type='abs_max',
                 weight_pre_layer=None,
                 act_pre_layer=None,
                 weight_quant_layer=None,
                 act_quant_layer=None):
        r"""
        Constructor.

        The arguments are the same as ImperativeQuantAware.
        """
        super(QuantizedConv2DTranspose, self).__init__()
        # For Conv2DTranspose
        self._groups = getattr(layer, '_groups')
        self._stride = getattr(layer, '_stride')
        self._padding = getattr(layer, '_padding')
        self._output_padding = getattr(layer, 'output_padding')
        self._dilation = getattr(layer, '_dilation')
        self._data_format = getattr(layer, '_data_format')
        self.weight = getattr(layer, 'weight')
        self.bias = getattr(layer, 'bias')
        # For FakeQuant
        self._conv2d_transpose_quant_axis = 1
        if weight_quant_layer is not None:
            self._fake_quant_weight = weight_quant_layer()
        else:
            self._fake_quant_weight = _get_fake_quant_type(
                weight_quantize_type,
                name=self.weight.name,
                moving_rate=moving_rate,
                quant_bits=weight_bits,
                dtype=self._dtype,
                quant_on_weight=True,
                channel_num=self.weight.shape[
                    self._conv2d_transpose_quant_axis],
                quant_axis=self._conv2d_transpose_quant_axis)
        if act_quant_layer is not None:
            self._fake_quant_input = act_quant_layer()
        else:
            self._fake_quant_input = _get_fake_quant_type(
                activation_quantize_type,
                name=layer.full_name(),
                moving_rate=moving_rate,
                quant_bits=activation_bits,
                dtype=self._dtype,
                quant_on_weight=False)

        self._act_preprocess = act_pre_layer(
        ) if act_pre_layer is not None else None
        self._weight_preprocess = weight_pre_layer(
        ) if weight_pre_layer is not None else None

    def forward(self, input, output_size=None):
        if self._act_preprocess is not None:
            input = self._act_preprocess(input)
        quant_input = self._fake_quant_input(input)

        weight = self.weight
        if self._weight_preprocess is not None:
            weight = self._weight_preprocess(self.weight)
        quant_weight = self._fake_quant_weight(weight)

        if output_size is None:
            output_padding = self._output_padding
        else:
            output_padding = 0

        return F.conv2d_transpose(quant_input,
                                  quant_weight,
                                  bias=self.bias,
                                  padding=self._padding,
                                  output_padding=output_padding,
                                  stride=self._stride,
                                  dilation=self._dilation,
                                  groups=self._groups,
                                  output_size=output_size,
                                  data_format=self._data_format)


class QuantizedLinear(Layer):
    """
    The computational logic of QuantizedLinear is the same with Linear.
    The only difference is that its inputs are all fake quantized.
    """

    def __init__(self,
                 layer,
                 weight_bits=8,
                 activation_bits=8,
                 moving_rate=0.9,
                 weight_quantize_type='abs_max',
                 activation_quantize_type='abs_max',
                 weight_pre_layer=None,
                 act_pre_layer=None,
                 weight_quant_layer=None,
                 act_quant_layer=None):
        super(QuantizedLinear, self).__init__()
        # For Linear
        self.weight = getattr(layer, 'weight')
        self.bias = getattr(layer, 'bias')
        self.name = getattr(layer, 'name')
        # For FakeQuant
        self._linear_quant_axis = 1

        if weight_quant_layer is not None:
            self._fake_quant_weight = weight_quant_layer()
        else:
            self._fake_quant_weight = _get_fake_quant_type(
                weight_quantize_type,
                name=self.weight.name,
                moving_rate=moving_rate,
                quant_bits=weight_bits,
                dtype=self._dtype,
                quant_on_weight=True,
                channel_num=self.weight.shape[self._linear_quant_axis],
                quant_axis=self._linear_quant_axis,
                quant_linear=True)

        if act_quant_layer is not None:
            self._fake_quant_input = act_quant_layer()
        else:
            self._fake_quant_input = _get_fake_quant_type(
                activation_quantize_type,
                name=layer.full_name(),
                moving_rate=moving_rate,
                quant_bits=activation_bits,
                dtype=self._dtype,
                quant_on_weight=False)

        self._act_preprocess = act_pre_layer(
        ) if act_pre_layer is not None else None
        self._weight_preprocess = weight_pre_layer(
        ) if weight_pre_layer is not None else None

    def forward(self, input):
        if self._act_preprocess is not None:
            input = self._act_preprocess(input)
        quant_input = self._fake_quant_input(input)

        weight = self.weight
        if self._weight_preprocess is not None:
            weight = self._weight_preprocess(self.weight)
        quant_weight = self._fake_quant_weight(weight)

        out = F.linear(x=quant_input,
                       weight=quant_weight,
                       bias=self.bias,
                       name=self.name)
        return out


class QuantizedColumnParallelLinear(Layer):

    def __init__(self,
                 layer,
                 weight_bits=8,
                 activation_bits=8,
                 moving_rate=0.9,
                 weight_quantize_type='abs_max',
                 activation_quantize_type='abs_max',
                 weight_pre_layer=None,
                 act_pre_layer=None,
                 weight_quant_layer=None,
                 act_quant_layer=None):
        super(QuantizedColumnParallelLinear, self).__init__()
        '''

        '''
        assert weight_quant_layer is None, "When quantizing ColumnParallelLinear, weight_quant_layer should be None."
        assert act_quant_layer is None, "When quantizing ColumnParallelLinear, act_quant_layer should be None."

        self.weight = getattr(layer, 'weight')
        self.bias = getattr(layer, 'bias')
        self.name = getattr(layer, '_name')
        # For FakeQuant
        self._linear_quant_axis = 1

        self.is_mp = getattr(layer, 'is_mp')
        self.model_parallel_group = getattr(layer, 'model_parallel_group')
        self.gather_output = getattr(layer, 'gather_output')

        self._fake_quant_weight = _get_fake_quant_type(
            weight_quantize_type,
            name=self.weight.name,
            moving_rate=moving_rate,
            quant_bits=weight_bits,
            dtype=self._dtype,
            quant_on_weight=True,
            channel_num=self.weight.shape[self._linear_quant_axis],
            quant_axis=self._linear_quant_axis,
            reduce_type='max'
            if paddle.distributed.get_world_size() > 1 else None)

        self._fake_quant_input = _get_fake_quant_type(
            activation_quantize_type,
            name=layer.full_name(),
            moving_rate=moving_rate,
            quant_bits=activation_bits,
            dtype=self._dtype,
            quant_on_weight=False,
            reduce_type=None)

        self._act_preprocess = act_pre_layer(
        ) if act_pre_layer is not None else None
        self._weight_preprocess = weight_pre_layer(
        ) if weight_pre_layer is not None else None

    def forward(self, input):
        if self.is_mp:
            input_parallel = paddle.distributed.collective._c_identity(
                input, group=self.model_parallel_group)
        else:
            input_parallel = input

        if self._act_preprocess is not None:
            input_parallel = self._act_preprocess(input_parallel)
        quant_input = self._fake_quant_input(input_parallel)

        weight = self.weight
        if self._weight_preprocess is not None:
            weight = self._weight_preprocess(self.weight)
        quant_weight = self._fake_quant_weight(weight)

        output_parallel = F.linear(x=quant_input,
                                   weight=quant_weight,
                                   bias=self.bias,
                                   name=self.name)

        if self.gather_output and self.is_mp:
            output = paddle.distributed.collective._c_concat(
                output_parallel, group=self.model_parallel_group)
        else:
            output = output_parallel
        return output


class QuantizedRowParallelLinear(Layer):

    def __init__(self,
                 layer,
                 weight_bits=8,
                 activation_bits=8,
                 moving_rate=0.9,
                 weight_quantize_type='abs_max',
                 activation_quantize_type='abs_max',
                 weight_pre_layer=None,
                 act_pre_layer=None,
                 weight_quant_layer=None,
                 act_quant_layer=None):
        super(QuantizedRowParallelLinear, self).__init__()
        assert weight_quant_layer is None, "When quantizing RowParallelLinear, weight_quant_layer cannot defined by yourself."
        assert act_quant_layer is None, "When quantizing RowParallelLinear, act_quant_layer cannot defined by yourself."

        # For Linear
        self.weight = getattr(layer, 'weight')
        self.bias = getattr(layer, 'bias')
        self.name = getattr(layer, '_name')
        # For FakeQuant
        self._linear_quant_axis = 1

        self.input_is_parallel = getattr(layer, 'input_is_parallel')
        self.is_mp = getattr(layer, 'is_mp')
        self.model_parallel_group = getattr(layer, 'model_parallel_group')

        self._fake_quant_weight = _get_fake_quant_type(
            weight_quantize_type,
            name=self.weight.name,
            moving_rate=moving_rate,
            quant_bits=weight_bits,
            dtype=self._dtype,
            quant_on_weight=True,
            channel_num=self.weight.shape[self._linear_quant_axis],
            quant_axis=self._linear_quant_axis,
            reduce_type='max'
            if paddle.distributed.get_world_size() > 1 else None)

        self._fake_quant_input = _get_fake_quant_type(
            activation_quantize_type,
            name=layer.full_name(),
            moving_rate=moving_rate,
            quant_bits=activation_bits,
            dtype=self._dtype,
            quant_on_weight=False,
            reduce_type='max'
            if paddle.distributed.get_world_size() > 1 else None)

        self._act_preprocess = act_pre_layer(
        ) if act_pre_layer is not None else None
        self._weight_preprocess = weight_pre_layer(
        ) if weight_pre_layer is not None else None

    def forward(self, input):
        if self.input_is_parallel or (not self.is_mp):
            input_parallel = input
        else:
            # split last dim
            input_parallel = paddle.distributed.collective._c_split(
                input, group=self.model_parallel_group)

        if self._act_preprocess is not None:
            input_parallel = self._act_preprocess(input_parallel)
        quant_input = self._fake_quant_input(input_parallel)

        weight = self.weight
        if self._weight_preprocess is not None:
            weight = self._weight_preprocess(self.weight)
        quant_weight = self._fake_quant_weight(weight)

        output_parallel = F.linear(x=quant_input,
                                   weight=quant_weight,
                                   name=self.name)
        if self.is_mp:
            output_ = paddle.distributed.collective._mp_allreduce(
                output_parallel,
                group=self.model_parallel_group,
                use_calc_stream=True,
                use_model_parallel=True)
        else:
            output_ = output_parallel
        output = output_ + self.bias if self.bias is not None else output_
        return output


class MAOutputScaleLayer(Layer):
    """
    Add MovingAverageMaxScale layer to the behind of the input layer.
    Calculate the scale (moving average abs max) for the output of the input layer.
    """

    def __init__(self,
                 layer=None,
                 moving_rate=0.9,
                 name=None,
                 dtype='float32',
                 reduce_type=None):
        r"""
        Construct
        """
        super(MAOutputScaleLayer, self).__init__()
        self._layer = layer
        if name is None:
            name = layer.full_name()
        self._ma_output_scale = \
            MovingAverageAbsMaxScale(name, moving_rate, dtype, reduce_type)

    def forward(self, *inputs, **kwargs):
        out = self._layer(*inputs, **kwargs)
        # TODO (jc): support the ops of several outputs
        if (isinstance(out, list) or isinstance(out, tuple)
                or isinstance(out, dict)):
            return out
        else:
            return self._ma_output_scale(out)


class FakeQuantMAOutputScaleLayer(Layer):
    """
    Add FakeQuantMovingAverageAbsMax layer to the behind of the input layer.
    """

    def __init__(self,
                 layer,
                 weight_bits=8,
                 activation_bits=8,
                 moving_rate=0.9,
                 name=None,
                 reduce_type=None,
                 *args,
                 **kwargs):

        super(FakeQuantMAOutputScaleLayer, self).__init__()
        self._layer = layer
        self._fake_quant_output = _get_fake_quant_type(
            'moving_average_abs_max',
            name=layer.full_name() if name is None else name,
            moving_rate=moving_rate,
            quant_bits=activation_bits,
            dtype=self._dtype,
            quant_on_weight=False,
            reduce_type=reduce_type)

    def forward(self, *inputs, **kwargs):
        out = self._layer(*inputs, **kwargs)
        # TODO (jc): support the ops of several outputs
        if (isinstance(out, list) or isinstance(out, tuple)) and len(out) > 1:
            return out
        else:
            return self._fake_quant_output(out)


def _get_fake_quant_type(quant_type, **kwargs):
    call_args = {
        "name": kwargs.get("name", None),
        "quant_bits": kwargs.get("quant_bits", 8),
        "dtype": kwargs.get("dtype", "float32"),
        "reduce_type": kwargs.get("reduce_type", None)
    }

    if quant_type == 'abs_max':
        call_args["quant_on_weight"] = kwargs.get("quant_on_weight", False)
    elif quant_type == 'moving_average_abs_max':
        call_args["moving_rate"] = kwargs.get("moving_rate", 0.9)
    elif quant_type == 'channel_wise_abs_max':
        call_args["quant_on_weight"] = kwargs.get("quant_on_weight", False)
        call_args["channel_num"] = kwargs.get("channel_num", None)
        call_args["quant_axis"] = kwargs.get("quant_axis", 0)
        assert call_args["channel_num"] is not None, (
            "You need to input channel_num"
            "when you use channel_wise_abs_max strategy.")
    elif quant_type == 'lsq_weight':
        call_args["all_postive"] = kwargs.get("all_postive", False)
        call_args["per_channel"] = False
        call_args["channel_num"] = 1
        call_args["quant_linear"] = kwargs.get("quant_linear", False)
    elif quant_type == 'channel_wise_lsq_weight':
        quant_type = 'lsq_weight'
        call_args["all_postive"] = kwargs.get("all_postive", False)
        call_args["per_channel"] = True
        call_args["channel_num"] = kwargs.get("channel_num", None)
        call_args["quant_linear"] = kwargs.get("quant_linear", False)
        assert call_args["channel_num"] is not None, (
            "You need to input channel_num"
            "when you use channel_wise_abs_max strategy.")
    elif quant_type == 'lsq_act':
        call_args["all_postive"] = kwargs.get("all_postive", False)
        call_args["symmetric"] = kwargs.get("symmetric", True)
    fake_quant_map = {
        'abs_max': FakeQuantAbsMax,
        'moving_average_abs_max': FakeQuantMovingAverageAbsMax,
        'channel_wise_abs_max': FakeQuantChannelWiseAbsMax,
        'lsq_weight': FakeQuantWeightLSQPlus,
        'lsq_act': FakeQuantActLSQPlus
    }

    return fake_quant_map[quant_type](**call_args)
