# Copyright (c) 2022 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 collections

import paddle
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import (
    LayerDesc,
    PipelineLayer,
    SharedLayerDesc,
    get_rng_state_tracker,
)
from paddle.incubate.distributed.models import moe
from paddle.nn.layer.transformer import _convert_param_attr_to_list

from paddlenlp.transformers import PretrainedModel, register_base_model

MoeLayer = moe.MoELayer

__all__ = [
    "GPTModel",
    "GPTPretrainedModel",
    "GPTForPretraining",
    "GPTPretrainingCriterion",
    "GPTForGreedyGeneration",
    "GPTLMHeadModel",
]


class ExpertLayer(nn.Layer):
    def __init__(self, d_model, d_hidden, name=None, rank=0, windex=0, num_expert=1):
        super(ExpertLayer, self).__init__()

        self.htoh4 = nn.Linear(
            d_model,
            d_hidden,
            weight_attr=nn.initializer.KaimingUniform(),
            bias_attr=nn.initializer.Constant(value=0.0),
        )
        self.h4toh = nn.Linear(
            d_hidden,
            d_model,
            weight_attr=nn.initializer.KaimingUniform(),
            bias_attr=nn.initializer.Constant(value=0.0),
        )
        self.htoh4.weight.name = "expert_" + self.htoh4.weight.name
        self.h4toh.weight.name = "expert_" + self.h4toh.weight.name
        self.htoh4.bias.name = "expert_" + self.htoh4.bias.name
        self.h4toh.bias.name = "expert_" + self.h4toh.bias.name

    def forward(self, x):
        x = self.htoh4(x)
        x = F.gelu(x, approximate=True)
        x = self.h4toh(x)
        return x


def parallel_matmul(lm_output, logit_weights, parallel_output):
    hcg = fleet.get_hybrid_communicate_group()
    model_parallel_group = hcg.get_model_parallel_group()
    world_size = hcg.get_model_parallel_world_size()

    if world_size > 1:
        input_parallel = paddle.distributed.collective._c_identity(lm_output, group=model_parallel_group)

        logits = paddle.matmul(input_parallel, logit_weights, transpose_y=True)

        if parallel_output:
            return logits

        return paddle.distributed.collective._c_concat(logits, group=model_parallel_group)
    else:
        logits = paddle.matmul(lm_output, logit_weights, transpose_y=True)
        return logits


class MultiHeadAttention(nn.Layer):
    """
    Attention mapps queries and a set of key-value pairs to outputs, and
    Multi-Head Attention performs multiple parallel attention to jointly attending
    to information from different representation subspaces.

    """

    Cache = collections.namedtuple("Cache", ["k", "v"])
    StaticCache = collections.namedtuple("StaticCache", ["k", "v"])

    def __init__(
        self,
        embed_dim,
        num_heads,
        dropout=0.0,
        kdim=None,
        vdim=None,
        need_weights=False,
        weight_attr=None,
        bias_attr=None,
        fuse=True,
        num_partitions=1,
    ):
        super(MultiHeadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.need_weights = need_weights
        self.fuse = fuse

        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"

        assert self.num_heads % num_partitions == 0
        self.num_heads = self.num_heads // num_partitions

        if self.fuse:
            assert self.kdim == embed_dim, "embed_dim should be equal to kdim"
            assert self.vdim == embed_dim, "embed_dim should be equal to vidm"

            self.qkv_proj = fleet.meta_parallel.ColumnParallelLinear(
                embed_dim, 3 * embed_dim, weight_attr=weight_attr, has_bias=True, gather_output=False
            )
        else:
            self.q_proj = fleet.meta_parallel.ColumnParallelLinear(
                embed_dim, embed_dim, weight_attr=weight_attr, has_bias=True, gather_output=False
            )

            self.k_proj = fleet.meta_parallel.ColumnParallelLinear(
                self.kdim, embed_dim, weight_attr=weight_attr, has_bias=True, gather_output=False
            )

            self.v_proj = fleet.meta_parallel.ColumnParallelLinear(
                self.vdim, embed_dim, weight_attr=weight_attr, has_bias=True, gather_output=False
            )

        self.out_proj = fleet.meta_parallel.RowParallelLinear(
            embed_dim, embed_dim, weight_attr=weight_attr, has_bias=True, input_is_parallel=True
        )

    def _fuse_prepare_qkv(self, query):
        mix_layer = self.qkv_proj(query)
        mix_layer = paddle.reshape_(mix_layer, [0, 0, self.num_heads, 3 * self.head_dim])
        mix_layer = paddle.transpose(mix_layer, [0, 2, 1, 3])
        q, k, v = paddle.split(mix_layer, num_or_sections=3, axis=-1)
        return q, k, v

    def _prepare_qkv(self, query, key, value, use_cache=False, cache=None):
        r"""
        Prepares linear projected queries, keys and values for usage of subsequent
        multiple parallel attention. If `cache` is not None, using cached results
        to reduce redundant calculations.

        """
        q = self.q_proj(query)
        q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
        q = tensor.transpose(x=q, perm=[0, 2, 1, 3])

        if isinstance(cache, self.StaticCache):
            # for encoder-decoder attention in inference and has cached
            k, v = cache.k, cache.v
        else:
            k, v = self.compute_kv(key, value)

        if isinstance(cache, self.Cache):
            # for decoder self-attention in inference
            k = tensor.concat([cache.k, k], axis=2)
            v = tensor.concat([cache.v, v], axis=2)
        if use_cache is True:
            cache = self.Cache(k, v)

        return (q, k, v) if use_cache is False else (q, k, v, cache)

    def compute_kv(self, key, value):
        r"""
        Applies linear projection on input keys and values, then splits heads
        (reshape and transpose) to get keys and values from different representation
        subspaces. The results are used as key-values pairs for subsequent multiple
        parallel attention.

        It is part of calculations in multi-head attention, and is provided as
        a method to pre-compute and prefetch these results, thus we can use them
        to construct cache for inference.

        """
        k = self.k_proj(key)
        v = self.v_proj(value)
        k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
        k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
        v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
        v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
        return k, v

    def gen_cache(self, key, value=None, type=Cache):
        """
        Generates cache for `forward` usage in inference according to arguments.
        The generated cache is an instance of `MultiHeadAttention.Cache` or an
        instance of `MultiHeadAttention.StaticCache`.
        """
        if type == MultiHeadAttention.StaticCache:  # static_kv
            k, v = self.compute_kv(key, value)
            return self.StaticCache(k, v)
        elif value is None:  # incremental_state
            k = paddle.full(shape=[key.shape[0], self.num_heads, 0, self.head_dim], dtype=key.dtype, fill_value=0)
            v = paddle.full(shape=[key.shape[0], self.num_heads, 0, self.head_dim], dtype=key.dtype, fill_value=0)
            return self.Cache(k, v)
        else:
            # incremental_state with initial value, mainly for usage like UniLM
            return self.Cache(key, value)

    def forward(self, query, key, value, attn_mask=None, use_cache=False, cache=None):
        r"""
        Applies multi-head attention to map queries and a set of key-value pairs
        to outputs.
        """
        key = query if key is None else key
        value = query if value is None else value
        # compute q ,k ,v
        if use_cache is False:
            if self.fuse:
                q, k, v = self._fuse_prepare_qkv(query)
            else:
                q, k, v = self._prepare_qkv(query, key, value, use_cache, cache)
        else:
            q, k, v, cache = self._prepare_qkv(query, key, value, use_cache, cache)
        # scale dot product attention
        product = paddle.matmul(x=q, y=k, transpose_y=True) * (self.head_dim**-0.5)

        # if attn_mask is not None:
        # product = product + attn_mask
        # weights = F.softmax(product)

        weights = incubate.softmax_mask_fuse_upper_triangle(product)

        if self.dropout:
            with get_rng_state_tracker().rng_state("local_seed"):
                weights = F.dropout(weights, self.dropout, training=self.training, mode="upscale_in_train")

        out = tensor.matmul(weights, v)

        # combine heads
        out = tensor.transpose(out, perm=[0, 2, 1, 3])
        out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])

        # project to output
        out = self.out_proj(out)

        outs = [out]
        if self.need_weights:
            outs.append(weights)
        if use_cache:
            outs.append(cache)
        return out if len(outs) == 1 else tuple(outs)


class TransformerDecoder(nn.Layer):
    """
    TransformerDecoder is a stack of N decoder layers.
    """

    def __init__(self, decoder_layers, num_layers, norm=None, hidden_size=None):
        super(TransformerDecoder, self).__init__()

        self.num_layers = num_layers
        self.layers = decoder_layers
        self.norm = norm
        if norm == "LayerNorm":
            self.norm = nn.LayerNorm(hidden_size)
        elif norm is not None:
            raise ValueError("Only support LayerNorm")
        self.checkpoints = []

    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None, use_cache=False, cache=None):
        r"""
        Applies a stack of N Transformer decoder layers on inputs. If `norm` is
        provided, also applies layer normalization on the output of last decoder
        layer.
        """
        output = tgt
        new_caches = []
        self.checkpoints = []

        for i, mod in enumerate(self.layers):
            if cache is None:
                if use_cache:
                    output, new_cache = mod(output, memory, tgt_mask=tgt_mask, use_cache=use_cache, cache=cache)
                    new_caches.append(new_cache)
                else:
                    output = mod(output, memory, tgt_mask=tgt_mask, use_cache=use_cache, cache=cache)
            else:
                output, new_cache = mod(output, memory, tgt_mask=tgt_mask, use_cache=use_cache, cache=cache[i])
                new_caches.append(new_cache)
            self.checkpoints.append(output.name)

        if self.norm is not None:
            output = self.norm(output)
        return output if use_cache is False else (output, new_caches)

    def gen_cache(self, memory, do_zip=False):
        r"""
        Generates cache for `forward` usage. The generated cache is a list, and
        each element in it is a tuple( :code:`(incremental_cache, static_cache)` )
        produced by `TransformerDecoderLayer.gen_cache`. See `TransformerDecoderLayer.gen_cache`
        for more details. If `do_zip` is True, apply `zip` on these tuples to get
        a list with two elements.
        """
        cache = [layer.gen_cache(memory) for layer in self.layers]
        if do_zip:
            cache = list(zip(*cache))
        return cache


class TransformerDecoderLayer(nn.Layer):
    """
    The transformer decoder layer.

    It contains multiheadattention and some linear layers.
    """

    def __init__(
        self,
        d_model,
        nhead,
        dim_feedforward,
        dropout=0.1,
        activation="gelu",
        attn_dropout=None,
        act_dropout=None,
        normalize_before=True,
        weight_attr=None,
        bias_attr=None,
        num_partitions=1,
        expert_mode=False,
        num_experts=1,
        top_k=2,
        hcg=None,
        gate=None,
        recompute_interval=0,
        recompute_partition=False,
        recompute_offload=False,
    ):
        self._config = locals()
        self._config.pop("self")
        self._config.pop("__class__", None)  # py3

        super(TransformerDecoderLayer, self).__init__()
        attn_dropout = dropout if attn_dropout is None else attn_dropout
        act_dropout = dropout if act_dropout is None else act_dropout
        self.normalize_before = normalize_before
        self.recompute_interval = recompute_interval

        # moe config
        self.top_k = top_k
        self.num_experts = num_experts
        self.expert_mode = expert_mode
        self.hcg = hcg

        weight_attrs = _convert_param_attr_to_list(weight_attr, 3)
        bias_attrs = _convert_param_attr_to_list(bias_attr, 3)

        self.self_attn = MultiHeadAttention(
            d_model,
            nhead,
            dropout=attn_dropout,
            weight_attr=weight_attrs[0],
            bias_attr=bias_attrs[0],
            num_partitions=num_partitions,
        )

        if expert_mode:
            experts_list = nn.LayerList()
            for expi in range(num_experts):
                exp_layer = ExpertLayer(d_model, dim_feedforward // top_k, windex=expi, num_expert=num_experts)
                experts_list.append(exp_layer)

            moe_group = hcg.get_expert_parallel_group()
            mp_group = hcg.get_model_parallel_group()
            gate_config = {
                "type": "gshard",
                "top_k": top_k,
            }

            recompute_ctx = {"mp_group": mp_group, "offload": recompute_offload, "partition": recompute_partition}
            self.moe_mlp = MoeLayer(
                d_model=d_model,
                experts=experts_list,
                gate=gate_config,
                moe_group=moe_group,
                mp_group=mp_group,
                recompute_interval=self.recompute_interval,
                recompute_ctx=recompute_ctx,
            )
        else:
            self.linear1 = fleet.meta_parallel.ColumnParallelLinear(
                d_model, dim_feedforward, weight_attr=weight_attrs[2], gather_output=False, has_bias=True
            )

            self.linear2 = fleet.meta_parallel.RowParallelLinear(
                dim_feedforward, d_model, weight_attr=weight_attrs[2], input_is_parallel=True, has_bias=True
            )

        self.norm1 = nn.LayerNorm(d_model, epsilon=1e-5)
        self.norm2 = nn.LayerNorm(d_model, epsilon=1e-5)
        self.dropout1 = nn.Dropout(dropout, mode="upscale_in_train")
        self.dropout2 = nn.Dropout(act_dropout, mode="upscale_in_train")
        self.activation = getattr(F, activation)

    def forward(self, tgt, memory=None, tgt_mask=None, use_cache=False, cache=None):
        residual = tgt

        if self.normalize_before:
            tgt = self.norm1(tgt)

        if use_cache is False:
            tgt = self.self_attn(tgt, tgt, tgt, tgt_mask, use_cache, cache)
        else:
            tgt, incremental_cache = self.self_attn(tgt, tgt, tgt, tgt_mask, use_cache, cache)

        with get_rng_state_tracker().rng_state("global_seed"):
            tgt = residual + self.dropout1(tgt)

        if not self.normalize_before:
            tgt = self.norm1(tgt)

        residual = tgt
        if self.normalize_before:
            tgt = self.norm2(tgt)

        if self.expert_mode:
            tgt = self.moe_mlp(tgt)
        else:
            with get_rng_state_tracker().rng_state("global_seed"):
                tgt = self.dropout2(self.linear2(F.gelu(self.linear1(tgt), approximate=True)))

        tgt = residual + tgt

        if not self.normalize_before:
            tgt = self.norm2(tgt)

        return tgt if use_cache is False else (tgt, incremental_cache)

    def gen_cache(self, memory):
        incremental_cache = self.self_attn.gen_cache(memory, type=self.self_attn.Cache)
        return incremental_cache


class GPTEmbeddings(nn.Layer):
    """
    Include embeddings from word, position and token_type embeddings
    """

    def __init__(
        self,
        vocab_size,
        hidden_size=768,
        hidden_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        initializer_range=0.02,
    ):
        super(GPTEmbeddings, self).__init__()

        self.word_embeddings = fleet.meta_parallel.VocabParallelEmbedding(
            vocab_size,
            hidden_size,
            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)),
        )

        self.position_embeddings = nn.Embedding(
            max_position_embeddings,
            hidden_size,
            weight_attr=paddle.ParamAttr(
                name="pos_embeddings", initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)
            ),
        )

        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, input_ids, position_ids=None):
        if position_ids is None:
            ones = paddle.ones_like(input_ids, dtype="int64")
            seq_length = paddle.cumsum(ones, axis=-1)
            position_ids = seq_length - ones

        input_embedings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        embeddings = input_embedings + position_embeddings

        with get_rng_state_tracker().rng_state("global_seed"):
            embeddings = self.dropout(embeddings)

        return embeddings


class GPTPretrainedModel(PretrainedModel):
    """
    An abstract class for pretrained GPT models. It provides GPT related
    `model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
    `pretrained_init_configuration`, `base_model_prefix` for downloading and
    loading pretrained models. See `PretrainedModel` for more details.
    """

    model_config_file = "model_config.json"
    pretrained_init_configuration = {
        "gpt-cpm-large-cn": {  # 2.6B
            "vocab_size": 30000,
            "hidden_size": 2560,
            "num_hidden_layers": 32,
            "num_attention_heads": 32,
            "intermediate_size": 10240,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "pad_token_id": 0,
            "eos_token_id": 7,
            "bos_token_id": 0,
            "eol_token_id": 3,
            "num_partitions": 1,
        },
        "gpt-cpm-small-cn-distill": {  # 109M
            "vocab_size": 30000,
            "hidden_size": 768,
            "num_hidden_layers": 12,
            "num_attention_heads": 12,
            "intermediate_size": 3072,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "pad_token_id": 0,
            "eos_token_id": 7,
            "bos_token_id": 0,
            "eol_token_id": 3,
            "num_partitions": 1,
        },
        "gpt3-13B-en": {  # 13B
            "vocab_size": 50304,
            "hidden_size": 5120,
            "num_hidden_layers": 40,
            "num_attention_heads": 128,
            "intermediate_size": 20480,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "eos_token_id": 50256,
            "eol_token_id": 198,
            "num_partitions": 1,
        },
        "gpt3-1.3B-en": {  # 1.3B
            "vocab_size": 50304,
            "hidden_size": 2048,
            "num_hidden_layers": 24,
            "num_attention_heads": 16,
            "intermediate_size": 8192,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "eos_token_id": 50256,
            "eol_token_id": 198,
            "num_partitions": 1,
        },
        "gpt2-medium-en": {  # 345M
            "vocab_size": 50304,
            "hidden_size": 1024,
            "num_hidden_layers": 24,
            "num_attention_heads": 16,
            "intermediate_size": 4096,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "eos_token_id": 50256,
            "eol_token_id": 198,
            "num_partitions": 1,
        },
        "gpt2-en": {  # 117M
            "vocab_size": 50304,
            "hidden_size": 768,
            "num_hidden_layers": 12,
            "num_attention_heads": 12,
            "intermediate_size": 3072,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "eos_token_id": 50256,
            "eol_token_id": 198,
            "num_partitions": 1,
        },
        "gpt2-small-en": {  # config for CE
            "vocab_size": 50304,
            "hidden_size": 1024,  # 1024
            "num_hidden_layers": 8,  # 4
            "num_attention_heads": 16,
            "intermediate_size": 1024 * 4,  # 4096,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "attention_probs_dropout_prob": 0.1,
            "max_position_embeddings": 1024,
            "type_vocab_size": 1,  # no use
            "initializer_range": 0.02,
            "eos_token_id": 50256,
            "eol_token_id": 198,
            "num_partitions": 1,
        },
    }
    resource_files_names = {"model_state": "model_state.pdparams"}
    pretrained_resource_files_map = {
        "model_state": {
            "gpt-cpm-large-cn": "https://paddlenlp.bj.bcebos.com/models/transformers/gpt/gpt-cpm-large-cn.pdparams",
            "gpt-cpm-small-cn-distill": "https://paddlenlp.bj.bcebos.com/models/transformers/gpt/gpt-cpm-small-cn-distill.pdparams",
            "gpt2-medium-en": "https://paddlenlp.bj.bcebos.com/models/transformers/gpt/gpt2-medium-en.pdparams",
        }
    }
    base_model_prefix = "gpt"

    def _init_weights(self, layer):
        """Initialization hook"""
        # no hook
        return
        if isinstance(layer, (nn.Linear, nn.Embedding)):
            # In the dygraph mode, use the `set_value` to reset the parameter directly,
            # and reset the `state_dict` to update parameter in static mode.
            if isinstance(layer.weight, paddle.Tensor):
                layer.weight.set_value(
                    paddle.tensor.normal(
                        mean=0.0,
                        std=self.initializer_range
                        if hasattr(self, "initializer_range")
                        else self.gpt.config["initializer_range"],
                        shape=layer.weight.shape,
                    )
                )


@register_base_model
class GPTModel(GPTPretrainedModel):
    """
    The base model of gpt.
    """

    def __init__(
        self,
        vocab_size,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        initializer_range=0.02,
        pad_token_id=0,
        eos_token_id=7,
        bos_token_id=0,
        eol_token_id=3,
        num_partitions=1,
        expert_mode=False,
        num_experts=1,
        top_k=2,
        hcg=None,
        gate=None,
        recompute_interval=0,
        recompute_partition=False,
        recompute_offload=False,
    ):
        super(GPTModel, self).__init__()

        self.pad_token_id = pad_token_id
        self.initializer_range = initializer_range
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size

        self.embeddings = GPTEmbeddings(
            vocab_size,
            hidden_size,
            hidden_dropout_prob,
            max_position_embeddings,
            type_vocab_size,
            self.initializer_range,
        )

        decoder_layers = nn.LayerList()
        for i in range(num_hidden_layers):
            decoder_layers.append(
                TransformerDecoderLayer(
                    d_model=hidden_size,
                    nhead=num_attention_heads,
                    dim_feedforward=intermediate_size,
                    dropout=hidden_dropout_prob,
                    activation=hidden_act,
                    attn_dropout=attention_probs_dropout_prob,
                    act_dropout=hidden_dropout_prob,
                    weight_attr=paddle.ParamAttr(
                        initializer=nn.initializer.Normal(mean=0.0, std=self.initializer_range)
                    ),
                    bias_attr=None,
                    num_partitions=num_partitions,
                    expert_mode=expert_mode,
                    num_experts=num_experts,
                    top_k=top_k,
                    hcg=hcg,
                    gate=gate,
                    recompute_interval=recompute_interval,
                    recompute_partition=recompute_partition,
                    recompute_offload=recompute_offload,
                )
            )

        self.decoder = TransformerDecoder(decoder_layers, num_hidden_layers, norm="LayerNorm", hidden_size=hidden_size)

        self.checkpoints = []

    def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=False, cache=None):
        self.checkpoints = []
        if position_ids is None:
            past_length = 0
            if cache is not None:
                past_length = cache[0].k.shape[-2]
            position_ids = paddle.arange(past_length, input_ids.shape[-1] + past_length, dtype="int64")
            position_ids = position_ids.unsqueeze(0)
            # .expand_as(input_ids)
            position_ids = paddle.expand_as(position_ids, input_ids)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids)

        encoder_outputs = self.decoder(
            embedding_output,
            memory=None,
            # tgt_mask=attention_mask,
            tgt_mask=None,
            use_cache=use_cache,
            cache=cache,
        )
        self.checkpoints.extend(self.decoder.checkpoints)
        return encoder_outputs


class GPTForPretraining(GPTPretrainedModel):
    """
    The pretraining model of GPT.

    It returns some logits and cached_kvs.
    """

    def __init__(self, gpt):
        super(GPTForPretraining, self).__init__()
        self.gpt = gpt

    def forward(
        self, input_ids, position_ids=None, attention_mask=None, masked_positions=None, use_cache=False, cache=None
    ):
        outputs = self.gpt(
            input_ids, position_ids=position_ids, attention_mask=attention_mask, use_cache=use_cache, cache=cache
        )
        if use_cache:
            encoder_outputs, cached_kvs = outputs[:2]
        else:
            encoder_outputs = outputs

        logits = parallel_matmul(encoder_outputs, self.gpt.embeddings.word_embeddings.weight, True)

        if use_cache:
            return logits, cached_kvs
        else:
            return logits


class GPTPretrainingCriterion(paddle.nn.Layer):
    """
    Criterion for GPT.

    It calculates the final loss.
    """

    def __init__(self):
        super(GPTPretrainingCriterion, self).__init__()
        self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none")
        self.parallel_loss_func = fleet.meta_parallel.ParallelCrossEntropy()

    def forward(self, prediction_scores, masked_lm_labels, loss_mask):

        hcg = fleet.get_hybrid_communicate_group()
        mp_size = hcg.get_model_parallel_world_size()
        if mp_size > 1:
            masked_lm_loss = self.parallel_loss_func(prediction_scores, masked_lm_labels.unsqueeze(2))
        else:
            masked_lm_loss = self.loss_func(prediction_scores, masked_lm_labels.unsqueeze(2))

        loss_mask = loss_mask.reshape([-1])
        masked_lm_loss = paddle.sum(masked_lm_loss.reshape([-1]) * loss_mask)
        loss = masked_lm_loss / loss_mask.sum()
        return loss


class GPTForGreedyGeneration(GPTPretrainedModel):
    """
    The generate model for GPT-2.
    It use the greedy strategy and generate the next word with highest probability.
    """

    def __init__(self, gpt, max_predict_len):
        super(GPTForGreedyGeneration, self).__init__()
        self.gpt = gpt
        self.max_predict_len = paddle.to_tensor(max_predict_len, dtype="int32")

    def model(
        self, input_ids, position_ids=None, attention_mask=None, masked_positions=None, use_cache=False, cache=None
    ):
        outputs = self.gpt(
            input_ids, position_ids=position_ids, attention_mask=attention_mask, use_cache=use_cache, cache=cache
        )
        if use_cache:
            encoder_outputs, cached_kvs = outputs[:2]
        else:
            encoder_outputs = outputs
        logits = paddle.matmul(encoder_outputs, self.gpt.embeddings.word_embeddings.weight, transpose_y=True)

        if use_cache:
            return logits, cached_kvs
        else:
            return logits

    def forward(self, input_ids, end_id):
        output, cached_kvs = self.model(input_ids, use_cache=True, cache=None)
        src_ids = input_ids
        nid = paddle.argmax(output[:, -1, :], axis=-1).reshape([-1, 1])
        src_ids = paddle.concat([src_ids, nid], axis=1)
        cur_len = 0
        while cur_len < self.max_predict_len:
            output, cached_kvs = self.model(nid, use_cache=True, cache=cached_kvs)

            nid = paddle.argmax(output[:, -1, :], axis=-1).reshape([-1, 1])
            src_ids = paddle.concat([src_ids, nid], axis=1)
            cur_len += 1
            if paddle.max(nid) == end_id:
                break
        return src_ids


class GPTLMHead(nn.Layer):
    def __init__(self, hidden_size, vocab_size, embedding_weights=None):
        super(GPTLMHead, self).__init__()
        self.decoder_weight = (
            self.create_parameter(shape=[vocab_size, hidden_size], dtype=paddle.get_default_dtype(), is_bias=True)
            if embedding_weights is None
            else embedding_weights
        )

    def forward(self, hidden_states):
        logits = paddle.tensor.matmul(hidden_states, self.decoder_weight, transpose_y=True)
        return logits


class GPTLMHeadModel(GPTPretrainedModel):
    def __init__(self, gpt):
        super(GPTLMHeadModel, self).__init__()
        self.gpt = gpt
        self.lm_head = GPTLMHead(
            self.gpt.config["hidden_size"], self.gpt.config["vocab_size"], self.gpt.embeddings.word_embeddings.weight
        )

    def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=False, cache=None):
        outputs = self.gpt(
            input_ids, position_ids=position_ids, attention_mask=attention_mask, use_cache=use_cache, cache=cache
        )

        if use_cache:
            encoder_outputs, cached_kvs = outputs[:2]
        else:
            encoder_outputs = outputs

        logits = self.lm_head(encoder_outputs)

        if use_cache:
            return logits, cached_kvs
        else:
            return logits

    def prepare_inputs_for_generation(self, input_ids, use_cache=False, cache=None, **kwargs):
        # only last token for inputs_ids if cache is defined in kwargs
        position_ids = kwargs.get("position_ids", None)
        attention_mask = kwargs.get("attention_mask", None)
        if cache is not None:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            if position_ids is not None:
                position_ids = position_ids[:, -1].unsqueeze(-1)
            if attention_mask is not None:
                attention_mask = attention_mask[:, :, -1, :].unsqueeze(2)

        return {
            "input_ids": input_ids,
            "position_ids": position_ids,
            "attention_mask": attention_mask,
            "use_cache": use_cache,
            "cache": cache,
        }

    def __getattr__(self, name):
        try:
            return super().__getattr__(name)
        except AttributeError as e:
            try:
                return getattr(getattr(self, self.base_model_prefix), name)
            except AttributeError:
                try:
                    return getattr(self, self.base_model_prefix).config[name]
                except KeyError:
                    raise e


# these Layers is just for PipelineParallel


class GPTPretrainingCriterionPipe(GPTPretrainingCriterion):
    """Extends GPTPretrainingCriterion to meet the input standard."""

    def forward(self, prediction_scores, args):
        masked_lm_labels = args[0]
        loss_mask = args[1]
        loss = super().forward(prediction_scores, masked_lm_labels, loss_mask)
        return loss


class EmbeddingPipe(GPTEmbeddings):
    """Extends GPTEmbeddings to forward attention_mask through the pipeline."""

    @property
    def embedding_weight(self):
        return self.word_embeddings.weight

    def forward(self, input_ids):
        embeddings = super().forward(input_ids=input_ids, position_ids=None)
        return embeddings


class GPTForPretrainingPipe(PipelineLayer):
    """GPTForPretraining adapted for pipeline parallelism.

    The largest change is flattening the GPTModel class so we can express it as a
    sequence of layers including embedding, transformer layers, and output.
    """

    def __init__(
        self,
        vocab_size,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        initializer_range=0.02,
        pad_token_id=0,
        eos_token_id=7,
        bos_token_id=0,
        eol_token_id=3,
        num_partitions=1,
        topology=None,
        recompute_interval=0,
        expert_mode=False,
        num_experts=1,
        top_k=2,
        hcg=None,
    ):

        # forward desc
        self.descs = []

        self.descs.append(
            SharedLayerDesc(
                "embed",
                EmbeddingPipe,
                shared_weight_attr="embedding_weight",
                vocab_size=vocab_size,
                hidden_size=hidden_size,
                hidden_dropout_prob=hidden_dropout_prob,
                max_position_embeddings=max_position_embeddings,
                type_vocab_size=type_vocab_size,
                initializer_range=0.02,
            )
        )

        for _ in range(num_hidden_layers):
            self.descs.append(
                LayerDesc(
                    TransformerDecoderLayer,
                    d_model=hidden_size,
                    nhead=num_attention_heads,
                    dim_feedforward=intermediate_size,
                    dropout=hidden_dropout_prob,
                    activation=hidden_act,
                    attn_dropout=attention_probs_dropout_prob,
                    act_dropout=hidden_dropout_prob,
                    weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(mean=0.0, std=initializer_range)),
                    bias_attr=None,
                    num_partitions=num_partitions,
                    expert_mode=expert_mode,
                    num_experts=num_experts,
                    top_k=top_k,
                    hcg=hcg,
                )
            )

        self.descs.append(LayerDesc(nn.LayerNorm, normalized_shape=hidden_size))

        def _logits_helper(embedding, output):
            return parallel_matmul(output, embedding.embedding_weight, True)

        self.descs.append(
            SharedLayerDesc(
                "embed",
                EmbeddingPipe,
                forward_func=_logits_helper,
                shared_weight_attr="embedding_weight",
                vocab_size=vocab_size,
                hidden_size=hidden_size,
                hidden_dropout_prob=hidden_dropout_prob,
                max_position_embeddings=max_position_embeddings,
                type_vocab_size=type_vocab_size,
                initializer_range=0.02,
            )
        )

        super().__init__(
            layers=self.descs,
            loss_fn=GPTPretrainingCriterionPipe(),
            topology=topology,
            seg_method="layer:TransformerDecoderLayer",
            recompute_interval=recompute_interval,
            recompute_ctx={
                "mp_group": fleet.fleet._hcg.get_model_parallel_group(),
                "offload": False,
                "partition": False,
            },
        )
