# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 ..functional import ACT2FN
from ..module import Module
from ..quantization import QuantMode
from ..quantization.layers import FP8Linear, FP8RowLinear
from .linear import ColumnLinear, RowLinear


class MLP(Module):

    def __init__(self,
                 hidden_size,
                 ffn_hidden_size,
                 hidden_act,
                 bias=True,
                 dtype=None,
                 tp_group=None,
                 tp_size=1,
                 quant_mode=QuantMode(0),
                 instance_id: int = 0):
        super().__init__()
        if hidden_act not in ACT2FN:
            raise ValueError(
                'unsupported activation function: {}'.format(hidden_act))
        fc_output_size = 2 * ffn_hidden_size if hidden_act == 'swiglu' else ffn_hidden_size
        self.use_fp8_qdq = quant_mode.has_fp8_qdq()

        if self.use_fp8_qdq:
            self.fc = FP8Linear(hidden_size,
                                fc_output_size,
                                bias=bias,
                                dtype=dtype,
                                tp_group=tp_group,
                                tp_size=tp_size,
                                gather_output=False)
            self.proj = FP8RowLinear(ffn_hidden_size,
                                     hidden_size,
                                     bias=bias,
                                     dtype=dtype,
                                     tp_group=tp_group,
                                     tp_size=tp_size,
                                     instance_id=instance_id)
        else:
            self.fc = ColumnLinear(hidden_size,
                                   fc_output_size,
                                   bias=bias,
                                   dtype=dtype,
                                   tp_group=tp_group,
                                   tp_size=tp_size,
                                   gather_output=False)
            self.proj = RowLinear(ffn_hidden_size,
                                  hidden_size,
                                  bias=bias,
                                  dtype=dtype,
                                  tp_group=tp_group,
                                  tp_size=tp_size,
                                  instance_id=instance_id)
        self.hidden_act = hidden_act
        self.dtype = dtype

    def forward(self, hidden_states, workspace=None):
        inter = self.fc(hidden_states)
        inter = ACT2FN[self.hidden_act](inter)
        output = self.proj(inter, workspace)
        return output


class GatedMLP(MLP):

    def __init__(self,
                 hidden_size,
                 ffn_hidden_size,
                 hidden_act,
                 bias=True,
                 dtype=None,
                 tp_group=None,
                 tp_size=1,
                 quant_mode=QuantMode(0),
                 instance_id: int = 0):
        self.use_fp8_qdq = quant_mode.has_fp8_qdq()
        super().__init__(hidden_size,
                         ffn_hidden_size,
                         hidden_act,
                         bias=bias,
                         dtype=dtype,
                         tp_group=tp_group,
                         tp_size=tp_size,
                         quant_mode=quant_mode,
                         instance_id=instance_id)

        if self.use_fp8_qdq:
            self.gate = FP8Linear(hidden_size,
                                  ffn_hidden_size,
                                  bias=bias,
                                  dtype=dtype,
                                  tp_group=tp_group,
                                  tp_size=tp_size,
                                  gather_output=False)
        else:
            self.gate = ColumnLinear(hidden_size,
                                     ffn_hidden_size,
                                     bias=bias,
                                     dtype=dtype,
                                     tp_group=tp_group,
                                     tp_size=tp_size,
                                     gather_output=False)

    def forward(self, hidden_states, workspace=None):
        inter = self.fc(hidden_states)
        inter = ACT2FN[self.hidden_act](inter)
        gate = self.gate(hidden_states)
        output = self.proj(inter * gate, workspace)
        return output