# Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team.
#
# 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 torch

from megatron import get_args
from megatron.core import tensor_parallel
from megatron.model.enums import AttnMaskType
from megatron.model.module import MegatronModule
from megatron.model.utils import init_method_normal
from megatron.model.utils import scaled_init_method_normal

from .language_model import get_language_model
from .language_model import parallel_lm_logits


def post_language_model_processing(lm_output, labels, logit_weights,
                                   parallel_output, fp16_lm_cross_entropy):

    # Output. Format [s b h]
    output = parallel_lm_logits(lm_output, logit_weights, parallel_output)

    if labels is None:
        # [s b h] => [b s h]
        return output.transpose(0, 1).contiguous()
    else:
        # [b s] => [s b]
        labels = labels.transpose(0, 1).contiguous()
        if fp16_lm_cross_entropy:
            assert output.dtype == torch.half
            loss = tensor_parallel.vocab_parallel_cross_entropy(
                output, labels)
        else:
            loss = tensor_parallel.vocab_parallel_cross_entropy(
                output.float(), labels)

        # [s b] => [b, s]
        loss = loss.transpose(0, 1).contiguous()
        return loss


class GPTModel(MegatronModule):
    """GPT-2 Language model."""
    def __init__(self,
                 num_tokentypes=0,
                 parallel_output=True,
                 pre_process=True,
                 post_process=True):
        args = get_args()
        super(GPTModel, self).__init__(
            share_word_embeddings=not args.untie_embeddings_and_output_weights)

        self.parallel_output = parallel_output
        self.pre_process = pre_process
        self.post_process = post_process
        self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy
        self.untie_embeddings_and_output_weights =\
            args.untie_embeddings_and_output_weights

        self.language_model, self._language_model_key = get_language_model(
            num_tokentypes=num_tokentypes,
            add_pooler=False,
            encoder_attn_mask_type=AttnMaskType.causal,
            init_method=init_method_normal(args.init_method_std),
            scaled_init_method=scaled_init_method_normal(
                args.init_method_std, args.num_layers),
            pre_process=self.pre_process,
            post_process=self.post_process)

        if not args.untie_embeddings_and_output_weights:
            self.initialize_word_embeddings(init_method_normal)

    def set_input_tensor(self, input_tensor):
        """See megatron.model.transformer.set_input_tensor()"""
        self.language_model.set_input_tensor(input_tensor)

    def forward(self,
                input_ids,
                position_ids=None,
                attention_mask=None,
                labels=None,
                inference_params=None):

        lm_output = self.language_model(input_ids,
                                        position_ids,
                                        attention_mask,
                                        inference_params=inference_params)

        if self.post_process:
            return post_language_model_processing(
                lm_output, labels, self.language_model.output_layer.weight
                if self.untie_embeddings_and_output_weights else
                self.word_embeddings_weight(), self.parallel_output,
                self.fp16_lm_cross_entropy)
        else:
            return lm_output

    def state_dict_for_save_checkpoint(self, prefix='', keep_vars=False):

        state_dict_ = {}
        state_dict_[self._language_model_key] \
            = self.language_model.state_dict_for_save_checkpoint(
                prefix=prefix, keep_vars=keep_vars)
        # Save word_embeddings.
        if self.post_process and not\
                self.pre_process and not\
                self.untie_embeddings_and_output_weights:
            state_dict_[self._word_embeddings_for_head_key] \
                = self.word_embeddings.state_dict(prefix=prefix,
                                                  keep_vars=keep_vars)
        return state_dict_

    def load_state_dict(self, state_dict, strict=True):
        """Customized load."""

        # Load word_embeddings.
        if self.post_process and not\
                self.pre_process and not\
                self.untie_embeddings_and_output_weights:
            self.word_embeddings.load_state_dict(
                state_dict[self._word_embeddings_for_head_key], strict=strict)
        if self._language_model_key in state_dict:
            state_dict = state_dict[self._language_model_key]
        self.language_model.load_state_dict(state_dict, strict=strict)