# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. 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.
"""Paddle Mixtral model"""
from __future__ import annotations

import math
import warnings
from functools import partial
from typing import Optional, Tuple

import paddle
import paddle.distributed.fleet.meta_parallel as mpu
import paddle.nn.functional as F
from paddle import Tensor, nn
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils import recompute

try:
    from paddle.incubate.nn.functional import fused_rotary_position_embedding
except ImportError:
    fused_rotary_position_embedding = None

try:
    from paddle.distributed.fleet.utils.sequence_parallel_utils import (
        GatherOp,
        ScatterOp,
        mark_as_sequence_parallel_parameter,
    )
except:
    pass

from ...utils.log import logger
from .. import linear_utils
from ..activations import ACT2FN
from ..conversion_utils import StateDictNameMapping, init_name_mappings
from ..linear_utils import Linear
from ..model_outputs import MoECausalLMOutputWithPast, MoEModelOutputWithPast
from ..model_utils import PretrainedModel, register_base_model
from .configuration import MixtralConfig

try:
    from paddle.nn.functional.flash_attention import flash_attention
except:
    flash_attention = None

__all__ = [
    "MixtralModel",
    "MixtralPretrainedModel",
    "MixtralForCausalLM",
    "MixtralPretrainingCriterion",
]


def load_balancing_loss_func(gate_logits, num_experts, top_k=2, attention_mask=None):
    """
    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Paddle.
    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
    experts is too unbalanced.
    Args:
        gate_logits (Union[`paddle.Tensor`, Tuple[paddle.Tensor]):
            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [batch_size X sequence_length, num_experts].
        num_experts (`int`):
            Number of experts.
        top_k (`int`):
            Number of top k experts to be considered for the loss computation.
        attention_mask (`paddle.Tensor`, None):
            The attention_mask used in forward function
            shape [batch_size X sequence_length] if not None.
    Returns:
        The auxiliary loss.
    """
    if gate_logits is None or not isinstance(gate_logits, tuple):
        return 0

    if isinstance(gate_logits, tuple):
        concatenated_gate_logits = paddle.concat(
            gate_logits, axis=0
        )  # [num_hidden_layers X batch_size X sequence_length, num_experts]

    routing_weights = F.softmax(concatenated_gate_logits, axis=-1)
    _, selected_experts = paddle.topk(routing_weights, top_k, axis=-1)
    expert_mask = F.one_hot(
        selected_experts, num_classes=num_experts
    )  # [num_hidden_layers X batch_size X sequence_length, top_k, num_experts]

    if attention_mask is None or len(attention_mask.shape) == 4:
        # Only intokens strategy has 4-D attention_mask, we currently do not support excluding padding tokens.
        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = paddle.mean(expert_mask.astype("float32"), axis=0)

        # Compute the average probability of routing to these experts
        router_prob_per_expert = paddle.mean(routing_weights, axis=0)
    else:
        # Exclude the load balancing loss of padding tokens.
        if len(attention_mask.shape) == 2:
            batch_size, sequence_length = attention_mask.shape
            num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)

            # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
            expert_attention_mask = (
                attention_mask[None, :, :, None, None]
                .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
                .reshape([-1, top_k, num_experts])
            )  # [num_hidden_layers * batch_size * sequence_length, top_k, num_experts]

            # Compute the percentage of tokens routed to each experts
            tokens_per_expert = paddle.sum(expert_mask.astype("float32") * expert_attention_mask, axis=0) / paddle.sum(
                expert_attention_mask, axis=0
            )

            # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
            router_per_expert_attention_mask = (
                attention_mask[None, :, :, None]
                .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
                .reshape([-1, num_experts])
            )

            # Compute the average probability of routing to these experts
            router_prob_per_expert = paddle.sum(
                routing_weights * router_per_expert_attention_mask, axis=0
            ) / paddle.sum(router_per_expert_attention_mask, axis=0)

    overall_loss = paddle.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
    return overall_loss * num_experts


def get_triangle_upper_mask(x, mask=None):
    if mask is not None:
        return mask
    # [bsz, n_head, q_len, kv_seq_len]
    shape = x.shape
    #  [bsz, 1, q_len, kv_seq_len]
    shape[1] = 1
    mask = paddle.full(shape, paddle.finfo(x.dtype).min, dtype=x.dtype)
    mask = paddle.triu(mask, diagonal=1)
    mask.stop_gradient = True
    return mask


def assign_kv_heads(num_kv_heads: int, num_gpus: int):
    # Initialize the assignment list
    """
    Assign kv heads to different GPUs in the Tensor Parallel Setup

    Examples:
        assign_kv_heads(num_kv_heads=1, num_gpus=2): [[0], [0]]
        assign_kv_heads(num_kv_heads=2, num_gpus=2): [[0], [1]]
        assign_kv_heads(num_kv_heads=4, num_gpus=2): [[0,1], [2,3]]
        assign_kv_heads(num_kv_heads=1, num_gpus=4): [[0],[0],[0],[0]]
        assign_kv_heads(num_kv_heads=2, num_gpus=4): [[0],[0],[1],[1]]
        assign_kv_heads(num_kv_heads=4, num_gpus=4): [[0],[1],[2],[3]]
    """
    assignment_list = [[] for _ in range(num_gpus)]
    # Case 1: more heads than cards
    if num_kv_heads > num_gpus:
        num_heads_per_card = num_kv_heads // num_gpus
        for i in range(num_gpus):
            for j in range(num_heads_per_card):
                assignment_list[i].append(i * num_heads_per_card + j)
    # Case 2: more cards than heads. each card get only 1 head.
    else:
        num_card_per_heads = num_gpus // num_kv_heads
        for i in range(num_kv_heads):
            for j in range(num_card_per_heads):
                assignment_list[i * num_card_per_heads + j].append(i)
    return assignment_list


def parallel_matmul(x: Tensor, y: Tensor, tensor_parallel_output=True):
    is_fleet_init = True
    tensor_parallel_degree = 1
    try:
        hcg = fleet.get_hybrid_communicate_group()
        model_parallel_group = hcg.get_model_parallel_group()
        tensor_parallel_degree = hcg.get_model_parallel_world_size()
    except:
        is_fleet_init = False

    if paddle.in_dynamic_mode():
        y_is_distributed = y.is_distributed
    else:
        y_is_distributed = tensor_parallel_degree > 1

    if is_fleet_init and tensor_parallel_degree > 1 and y_is_distributed:
        # if not running under distributed.launch, it will raise AttributeError: 'Fleet' object has no attribute '_hcg'
        input_parallel = paddle.distributed.collective._c_identity(x, group=model_parallel_group)
        logits = paddle.matmul(input_parallel, y, transpose_y=False)

        if tensor_parallel_output:
            return logits

        return paddle.distributed.collective._c_concat(logits, group=model_parallel_group)

    else:
        logits = paddle.matmul(x, y, transpose_y=False)
        return logits


def scaled_dot_product_attention(
    query_states,
    config,
    key_states,
    value_states,
    attention_mask,
    output_attentions,
    training=True,
    sequence_parallel=False,
):
    bsz, q_len, num_heads, head_dim = query_states.shape
    _, kv_seq_len, _, _ = value_states.shape

    if config.use_flash_attention and flash_attention:
        # Paddle Flash Attention input [ bz, seqlen, nhead, head_dim]
        # Torch Flash Attention input [ bz, nhead, seqlen, head_dim]

        version = paddle.version.full_version
        if version != "0.0.0" and version <= "2.5.2":
            attn_output, attn_weights = flash_attention(
                query_states,
                key_states,
                value_states,
                causal=True,
                return_softmax=output_attentions,
            )
        else:
            attn_output = F.scaled_dot_product_attention(
                query_states,
                key_states,
                value_states,
                attn_mask=attention_mask,
                is_causal=attention_mask is None,
                dropout_p=config.attention_dropout if training else 0.0,
                training=training,
            )
            attn_weights = None

        if sequence_parallel:
            attn_output = attn_output.reshape([bsz * q_len, head_dim * num_heads])
        else:
            attn_output = attn_output.reshape([bsz, q_len, head_dim * num_heads])
        return (attn_output, attn_weights) if output_attentions else attn_output
    else:
        #  [ bz, seqlen, nhead, head_dim] -> [bs, nhead, seq_len, head_dim]
        query_states = paddle.transpose(query_states, [0, 2, 1, 3])
        # merge with the next transpose
        key_states = paddle.transpose(key_states, [0, 2, 1, 3])
        value_states = paddle.transpose(value_states, [0, 2, 1, 3])

        # matmul and devide by sqrt(head_dim)
        attn_weights = paddle.matmul(query_states / math.sqrt(head_dim), key_states.transpose([0, 1, 3, 2]))

        if attn_weights.shape != [bsz, num_heads, q_len, kv_seq_len]:
            raise ValueError(
                f"Attention weights should be of shape {(bsz, num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.shape}"
            )

        if attention_mask is None:
            attention_mask = get_triangle_upper_mask(attn_weights)
        attention_mask = attention_mask.reshape([bsz, 1, q_len, kv_seq_len])
        if attention_mask.shape != [bsz, 1, q_len, kv_seq_len]:
            raise ValueError(
                f"Attention mask should be of shape {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.shape}"
            )

        attn_weights = attn_weights + attention_mask
        if not paddle.in_dynamic_mode():
            attn_weights = F.softmax(attn_weights, axis=-1, dtype="float32").astype(query_states.dtype)
        else:
            with paddle.amp.auto_cast(False):
                attn_weights = F.softmax(attn_weights, axis=-1, dtype="float32").astype(query_states.dtype)

        attn_weights = F.dropout(attn_weights, p=config.attention_dropout, training=training)

        attn_output = paddle.matmul(attn_weights, value_states)
        attn_output = attn_output.transpose([0, 2, 1, 3])

        if sequence_parallel:
            attn_output = attn_output.reshape([bsz * q_len, head_dim * num_heads])
        else:
            attn_output = attn_output.reshape([bsz, q_len, head_dim * num_heads])
        return (attn_output, attn_weights) if output_attentions else attn_output


def masked_fill(x, mask, value):
    y = paddle.full(x.shape, value, x.dtype)
    return paddle.where(mask.to("bool"), y, x)


def is_casual_mask(attention_mask):
    """
    Upper triangular of attention_mask equals to attention_mask is casual
    """
    return (paddle.triu(attention_mask) == attention_mask).all().item()


def _make_causal_mask(input_ids_shape, past_key_values_length):
    """
    Make causal mask used for self-attention
    """
    batch_size, target_length = input_ids_shape  # target_length: seq_len

    mask = paddle.tril(paddle.ones((target_length, target_length), dtype="bool"))

    if past_key_values_length > 0:
        # [tgt_len, tgt_len + past_len]
        mask = paddle.concat([paddle.ones([target_length, past_key_values_length], dtype="bool"), mask], axis=-1)

    # [bs, 1, tgt_len, tgt_len + past_len]
    return mask[None, None, :, :].expand([batch_size, 1, target_length, target_length + past_key_values_length])


def _expand_2d_mask(mask, dtype, tgt_length):
    """
    Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
    """
    batch_size, src_length = mask.shape[0], mask.shape[-1]
    tgt_length = tgt_length if tgt_length is not None else src_length

    mask = mask[:, None, None, :].astype("bool")
    mask.stop_gradient = True
    expanded_mask = mask.expand([batch_size, 1, tgt_length, src_length])

    return expanded_mask


class MixtralRMSNorm(nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.weight = paddle.create_parameter(
            shape=[self.hidden_size],
            dtype=paddle.get_default_dtype(),
            default_initializer=nn.initializer.Constant(1.0),
        )
        self.variance_epsilon = config.rms_norm_eps
        self.config = config

        if config.sequence_parallel:
            mark_as_sequence_parallel_parameter(self.weight)

    def forward(self, hidden_states):
        if paddle.in_dynamic_mode():
            with paddle.amp.auto_cast(False):
                hidden_states = hidden_states.astype("float32")
                variance = hidden_states.pow(2).mean(-1, keepdim=True)
                hidden_states = paddle.rsqrt(variance + self.variance_epsilon) * hidden_states
        else:
            hidden_states = hidden_states.astype("float32")
            variance = hidden_states.pow(2).mean(-1, keepdim=True)
            hidden_states = paddle.rsqrt(variance + self.variance_epsilon) * hidden_states

        if self.weight.dtype in [paddle.float16, paddle.bfloat16]:
            hidden_states = paddle.cast(hidden_states, self.weight.dtype)
        return hidden_states * self.weight


def repeat_kv(hidden_states: paddle.Tensor, n_rep: int) -> paddle.Tensor:
    """
    This is the equivalent of paddle.repeat_interleave(hidden_states, n_rep, axis=1). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, slen, num_key_value_heads, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states

    hidden_states = hidden_states.unsqueeze(-2).tile([1, 1, 1, n_rep, 1])
    return hidden_states.reshape([batch, slen, num_key_value_heads * n_rep, head_dim])


class MixtralRotaryEmbedding(nn.Layer):
    def __init__(self, dim, max_position_embeddings=2048, base=10000):
        super().__init__()
        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        # [dim / 2]
        self.inv_freq = 1.0 / (self.base ** (paddle.cast(paddle.arange(0, self.dim, 2), dtype="float32") / self.dim))
        self._set_cos_sin_cache(seq_len=max_position_embeddings)

    def _set_cos_sin_cache(self, seq_len):
        self.max_seq_len_cached = seq_len
        # [seq_len]
        t = paddle.arange(seq_len, dtype="float32")
        # [seq_len, dim/2]
        freqs = paddle.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        # [seq_len, dim]
        emb = paddle.concat([freqs, freqs], axis=-1)
        # [1, seqlen, 1, dim]
        self.cos_cached = emb.cos()[None, :, None, :]
        self.sin_cached = emb.sin()[None, :, None, :]

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len)
        cos = self.cos_cached[:, :seq_len, :, :]
        sin = self.sin_cached[:, :seq_len, :, :]
        return (
            cos.cast(x.dtype) if cos.dtype != x.dtype else cos,
            sin.cast(x.dtype) if sin.dtype != x.dtype else sin,
        )


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return paddle.concat([-x2, x1], axis=-1)  # shape is the same as x


def apply_rotary_pos_emb(q, k, cos, sin, position_ids):

    if position_ids is None:
        # Note: Only for MixtralForCausalLMPipe model pretraining
        cos = cos[:, : q.shape[1], :, :]  # [bs, seq_len, 1, dim]
        sin = sin[:, : q.shape[1], :, :]  # [bs, seq_len, 1, dim]
    else:
        cos = cos.squeeze(axis=[0, 2])  # [seq_len, dim]
        sin = sin.squeeze(axis=[0, 2])  # [seq_len, dim]
        cos = cos[position_ids].unsqueeze(2)  # [bs, seq_len, 1, dim]
        sin = sin[position_ids].unsqueeze(2)  # [bs, seq_len, 1, dim]
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class MixtralMLP(nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.tensor_parallel_degree = config.tensor_parallel_degree

        if config.sequence_parallel:
            ColumnParallelLinear = linear_utils.ColumnSequenceParallelLinear
            RowParallelLinear = linear_utils.RowSequenceParallelLinear
        else:
            ColumnParallelLinear = linear_utils.ColumnParallelLinear
            RowParallelLinear = linear_utils.RowParallelLinear

        if config.tensor_parallel_degree > 1:
            self.w1 = ColumnParallelLinear(
                self.hidden_size,
                self.intermediate_size,
                gather_output=False,
                has_bias=False,
            )
            self.w3 = ColumnParallelLinear(
                self.hidden_size,
                self.intermediate_size,
                gather_output=False,
                has_bias=False,
            )
            self.w2 = RowParallelLinear(
                self.intermediate_size,
                self.hidden_size,
                input_is_parallel=True,
                has_bias=False,
            )
        else:
            self.w1 = Linear(self.hidden_size, self.intermediate_size, bias_attr=False)
            self.w2 = Linear(self.intermediate_size, self.hidden_size, bias_attr=False)
            self.w3 = Linear(self.hidden_size, self.intermediate_size, bias_attr=False)

        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        x = self.act_fn(self.w1(x)) * self.w3(x)
        x = self.w2(x)
        return x


class MixtralSparseMoeBlock(nn.Layer):
    def __init__(self, config: MixtralConfig):
        super().__init__()
        self.hidden_dim = config.hidden_size
        self.ffn_dim = config.intermediate_size
        self.num_experts = config.num_local_experts
        self.top_k = config.num_experts_per_tok
        self.gate = Linear(self.hidden_dim, self.num_experts, bias_attr=False)
        self.experts = nn.LayerList([MixtralMLP(config) for _ in range(self.num_experts)])

    def forward(self, hidden_states):
        batch_size, seq_len, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.reshape([-1, hidden_dim])
        # router_logits: [batch_size * seq_len, num_experts]
        router_logits = self.gate(hidden_states)

        with paddle.amp.auto_cast(False):
            routing_weights = F.softmax(router_logits.astype("float32"), axis=1)
        routing_weights, selected_experts = paddle.topk(routing_weights, self.top_k, axis=-1)
        routing_weights /= routing_weights.sum(axis=-1, keepdim=True)
        # we cast back to input dtype
        routing_weights = routing_weights.astype(hidden_states.dtype)

        final_hidden_states = paddle.zeros(
            [batch_size * seq_len, hidden_dim],
            dtype=hidden_states.dtype,
        )

        # One hot encode the selected experts to create an expert mask
        # this will be used to easily index which expert is going to be sollicitated.
        # shape: [num_experts, top_k, batch_size * seq_len]
        expert_mask = F.one_hot(selected_experts, num_classes=self.num_experts).transpose([2, 1, 0])
        expert_mask = expert_mask.to("bool")
        # Loop over all available experts in the model and perform the computation on each expert.
        for expert_id in range(self.num_experts):
            expert_layer = self.experts[expert_id]
            idx, top_x = paddle.where(expert_mask[expert_id])

            if top_x.shape[0] == 0:
                continue

            current_state = paddle.gather(hidden_states, top_x.squeeze())
            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx].unsqueeze(-1)

            top_x = top_x.squeeze()
            if top_x.shape == []:
                top_x = paddle.to_tensor([top_x.item()])
            final_hidden_states.index_add_(top_x, 0, current_hidden_states.astype(hidden_states.dtype))

        final_hidden_states = final_hidden_states.reshape([batch_size, seq_len, hidden_dim])
        return final_hidden_states, router_logits


class MixtralAttention(nn.Layer):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: MixtralConfig, layerwise_recompute: bool = False):
        super().__init__()

        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads

        self.head_dim = self.hidden_size // config.num_attention_heads

        self.num_key_value_heads = config.num_key_value_heads
        assert config.num_attention_heads // config.num_key_value_heads
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.gqa_or_mqa = config.num_attention_heads != config.num_key_value_heads
        self.rope_theta = config.rope_theta
        self.max_position_embeddings = config.max_position_embeddings
        self.seq_length = config.seq_length
        self.sequence_parallel = config.sequence_parallel

        # Note that we will actually perform a recompute only if both enable_recompute and layerwise_recompute are set to True
        # Enable_recompute defaults to False and is controlled by Trainer
        self.enable_recompute = False
        self.layerwise_recompute = layerwise_recompute
        self.recompute_granularity = config.recompute_granularity
        if config.tensor_parallel_degree > 1:
            assert (
                self.num_heads % config.tensor_parallel_degree == 0
            ), f"num_heads: {self.num_heads}, tensor_parallel_degree: {config.tensor_parallel_degree}"
            self.num_heads = self.num_heads // config.tensor_parallel_degree

            assert (
                self.num_key_value_heads % config.tensor_parallel_degree == 0
            ), f"num_key_value_heads: {self.num_key_value_heads}, tensor_parallel_degree: {config.tensor_parallel_degree}"
            self.num_key_value_heads = self.num_key_value_heads // config.tensor_parallel_degree

        self.use_fused_rope = config.use_fused_rope
        if self.use_fused_rope:
            if "gpu" not in paddle.device.get_device() or fused_rotary_position_embedding is None:
                warnings.warn(
                    "Enable fuse rope in the config, but fuse rope is not available. "
                    "Will disable fuse rope. Try using latest gpu version of Paddle."
                )
                self.use_fused_rope = False

        if config.sequence_parallel:
            ColumnParallelLinear = linear_utils.ColumnSequenceParallelLinear
            RowParallelLinear = linear_utils.RowSequenceParallelLinear
        else:
            ColumnParallelLinear = linear_utils.ColumnParallelLinear
            RowParallelLinear = linear_utils.RowParallelLinear

        if config.tensor_parallel_degree > 1:
            self.q_proj = ColumnParallelLinear(
                self.hidden_size,
                self.hidden_size,
                has_bias=False,
                gather_output=False,
            )
            self.k_proj = ColumnParallelLinear(
                self.hidden_size,
                self.config.num_key_value_heads * self.head_dim,
                has_bias=False,
                gather_output=False,
            )
            self.v_proj = ColumnParallelLinear(
                self.hidden_size,
                self.config.num_key_value_heads * self.head_dim,
                has_bias=False,
                gather_output=False,
            )
        else:
            self.q_proj = Linear(
                self.hidden_size,
                self.hidden_size,
                bias_attr=False,
            )
            self.k_proj = Linear(
                self.hidden_size,
                self.config.num_key_value_heads * self.head_dim,
                bias_attr=False,
            )
            self.v_proj = Linear(
                self.hidden_size,
                self.config.num_key_value_heads * self.head_dim,
                bias_attr=False,
            )

        if config.tensor_parallel_degree > 1:
            self.o_proj = RowParallelLinear(
                self.hidden_size,
                self.hidden_size,
                has_bias=False,
                input_is_parallel=True,
            )
        else:
            self.o_proj = Linear(
                self.hidden_size,
                self.hidden_size,
                bias_attr=False,
            )

        self.rotary_emb = MixtralRotaryEmbedding(
            self.head_dim,
            max_position_embeddings=self.max_position_embeddings,
            base=self.rope_theta,
        )

        self.config = config

    def forward(
        self,
        hidden_states,
        position_ids: Optional[Tuple[paddle.Tensor]] = None,
        past_key_value: Optional[Tuple[paddle.Tensor]] = None,
        attention_mask: Optional[paddle.Tensor] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[paddle.Tensor, Optional[paddle.Tensor], Optional[Tuple[paddle.Tensor]]]:
        """Input shape: Batch x Time x Channel"""
        # [bs, seq_len, num_head * head_dim] -> [seq_len / n, bs, num_head * head_dim] (n is model parallelism)

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)
        if self.sequence_parallel:
            target_query_shape = [-1, self.seq_length, self.num_heads, self.head_dim]
            target_key_value_shape = [-1, self.seq_length, self.num_key_value_heads, self.head_dim]
        else:
            target_query_shape = [0, 0, self.num_heads, self.head_dim]
            target_key_value_shape = [0, 0, self.num_key_value_heads, self.head_dim]
        query_states = query_states.reshape(shape=target_query_shape)
        key_states = key_states.reshape(shape=target_key_value_shape)
        value_states = value_states.reshape(shape=target_key_value_shape)

        kv_seq_len = key_states.shape[-3]

        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-3]

        if self.use_fused_rope:
            assert past_key_value is None, "fuse rotary not support cache kv for now"
            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
            query_states, key_states, _ = fused_rotary_position_embedding(
                query_states,
                key_states,
                v=None,
                sin=sin,
                cos=cos,
                position_ids=position_ids,
                use_neox_rotary_style=False,
            )
        else:
            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        # [bs, seq_len, num_head, head_dim]
        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = paddle.concat([past_key_value[0], key_states], axis=1)
            value_states = paddle.concat([past_key_value[1], value_states], axis=1)

        past_key_value = (key_states, value_states) if use_cache else None

        # TODO(wj-Mcat): use broadcast strategy when n_kv_heads = 1
        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        has_gradient = not (query_states.stop_gradient and key_states.stop_gradient and value_states.stop_gradient)
        if (
            self.enable_recompute
            and self.layerwise_recompute
            and has_gradient
            and self.recompute_granularity == "core_attn"
        ):
            outputs = recompute(
                scaled_dot_product_attention,
                query_states,
                self.config,
                key_states,
                value_states,
                attention_mask,
                output_attentions,
                self.training,
                self.sequence_parallel,
                use_reentrant=self.config.recompute_use_reentrant,
            )
        else:
            outputs = scaled_dot_product_attention(
                query_states,
                self.config,
                key_states,
                value_states,
                attention_mask,
                output_attentions,
                self.training,
                self.sequence_parallel,
            )
        if output_attentions:
            attn_output, attn_weights = outputs
        else:
            attn_output = outputs

        # if sequence_parallel is true, out shape are [q_len / n, bs, num_head * head_dim]
        # else their shape are [bs, q_len, num_head * head_dim], n is mp parallelism.
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        outputs = (attn_output,)

        if output_attentions:
            outputs += (attn_weights,)

        if use_cache:
            outputs += (past_key_value,)

        if type(outputs) is tuple and len(outputs) == 1:
            outputs = outputs[0]

        return outputs


class MixtralDecoderLayer(nn.Layer):
    def __init__(self, config, layerwise_recompute: bool = False):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.self_attn = MixtralAttention(config, layerwise_recompute)
        self.block_sparse_moe = MixtralSparseMoeBlock(config)
        self.input_layernorm = MixtralRMSNorm(config)
        self.post_attention_layernorm = MixtralRMSNorm(config)
        self.sequence_parallel = config.sequence_parallel
        # Note that we will actually perform a recompute only if both enable_recompute and layerwise_recompute are set to True
        # Enable_recompute defaults to False and is controlled by Trainer
        self.enable_recompute = False
        self.layerwise_recompute = layerwise_recompute
        self.recompute_granularity = config.recompute_granularity

    def forward(
        self,
        hidden_states: paddle.Tensor,
        position_ids: Optional[Tuple[paddle.Tensor]] = None,
        attention_mask: Optional[paddle.Tensor] = None,
        output_attentions: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        past_key_value: Optional[Tuple[paddle.Tensor]] = None,
        use_cache: Optional[bool] = False,
    ) -> Tuple[paddle.Tensor, Optional[Tuple[paddle.Tensor, paddle.Tensor]]]:
        """
        Args:
            hidden_states (`paddle.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`paddle.Tensor`, *optional*): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_router_logits (`bool`, *optional*):
                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
                should not be returned during inference.
            use_cache (`bool`, *optional*):
                If set to `True`, `cache` key value states are returned and can be used to speed up decoding
                (see `cache`).
            cache (`Tuple(paddle.Tensor)`, *optional*): cached past key and value projection states
        """

        # [bs * seq_len, embed_dim] -> [seq_len * bs / n, embed_dim] (sequence_parallel)
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        has_gradient = not hidden_states.stop_gradient
        if (
            self.enable_recompute
            and self.layerwise_recompute
            and has_gradient
            and self.recompute_granularity == "full_attn"
        ):
            outputs = recompute(
                self.self_attn,
                hidden_states,
                position_ids,
                past_key_value,
                attention_mask,
                output_attentions,
                use_cache,
                use_reentrant=self.config.recompute_use_reentrant,
            )
        else:
            outputs = self.self_attn(
                hidden_states,
                position_ids,
                past_key_value,
                attention_mask,
                output_attentions,
                use_cache,
            )

        if type(outputs) is tuple:
            hidden_states = outputs[0]
        else:
            hidden_states = outputs

        if output_attentions:
            self_attn_weights = outputs[1]

        if use_cache:
            present_key_value = outputs[2 if output_attentions else 1]

        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states, router_logits = self.block_sparse_moe(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        if output_router_logits:
            outputs += (router_logits,)

        if type(outputs) is tuple and len(outputs) == 1:
            outputs = outputs[0]

        return outputs


class MixtralPretrainedModel(PretrainedModel):
    config_class = MixtralConfig
    base_model_prefix = "mixtral"
    _keys_to_ignore_on_load_unexpected = [r"self_attn.rotary_emb.inv_freq"]

    @classmethod
    def _get_name_mappings(cls, config: MixtralConfig) -> list[StateDictNameMapping]:
        mappings: list[StateDictNameMapping] = []
        model_mappings = [
            ["embed_tokens.weight"],
            ["norm.weight"],
        ]
        for layer_index in range(config.num_hidden_layers):
            layer_mappings = [
                [f"layers.{layer_index}.self_attn.q_proj.weight", None, "transpose"],
                [f"layers.{layer_index}.self_attn.k_proj.weight", None, "transpose"],
                [f"layers.{layer_index}.self_attn.v_proj.weight", None, "transpose"],
                [f"layers.{layer_index}.self_attn.o_proj.weight", None, "transpose"],
                [f"layers.{layer_index}.self_attn.rotary_emb.inv_freq"],
                [f"layers.{layer_index}.input_layernorm.weight"],
                [f"layers.{layer_index}.post_attention_layernorm.weight"],
            ]
            model_mappings.extend(layer_mappings)

            for expert_idx in range(config.num_local_experts):
                expert_mappings = [
                    [f"layers.{layer_index}.block_sparse_moe.experts.{expert_idx}.w1.weight", None, "transpose"],
                    [f"layers.{layer_index}.block_sparse_moe.experts.{expert_idx}.w2.weight", None, "transpose"],
                    [f"layers.{layer_index}.block_sparse_moe.experts.{expert_idx}.w3.weight", None, "transpose"],
                ]
                model_mappings.extend(expert_mappings)
            model_mappings.append([f"layers.{layer_index}.block_sparse_moe.gate.weight", None, "transpose"])

        init_name_mappings(mappings=model_mappings)
        # base-model prefix "MixtralModel"
        if "MixtralModel" not in config.architectures:
            for mapping in model_mappings:
                mapping[0] = "model." + mapping[0]
                mapping[1] = "mixtral." + mapping[1]
            model_mappings.append(["lm_head.weight", "lm_head.weight", "transpose"])

        mappings = [StateDictNameMapping(*mapping, index=index) for index, mapping in enumerate(model_mappings)]
        return mappings

    @classmethod
    def _get_tensor_parallel_mappings(cls, config: MixtralConfig, is_split=True):

        from paddlenlp.transformers.conversion_utils import split_or_merge_func

        fn = split_or_merge_func(
            is_split=is_split,
            tensor_parallel_degree=config.tensor_parallel_degree,
            tensor_parallel_rank=config.tensor_parallel_rank,
            num_attention_heads=config.num_attention_heads,
        )

        def get_tensor_parallel_split_mappings(num_layers, num_local_experts):
            final_actions = {}

            base_actions = {
                "lm_head.weight": partial(fn, is_column=True),
                # Row Linear
                "embed_tokens.weight": partial(fn, is_column=False),
                "layers.0.self_attn.o_proj.weight": partial(fn, is_column=False),
            }

            if not config.vocab_size % config.tensor_parallel_degree == 0:
                base_actions.pop("lm_head.weight")
                base_actions.pop("embed_tokens.weight")

            # Column Linear
            base_actions["layers.0.self_attn.q_proj.weight"] = partial(fn, is_column=True)
            # if we have enough num_key_value_heads to split, then split it.
            if config.num_key_value_heads % config.tensor_parallel_degree == 0:
                base_actions["layers.0.self_attn.k_proj.weight"] = partial(fn, is_column=True)
                base_actions["layers.0.self_attn.v_proj.weight"] = partial(fn, is_column=True)

            for key, action in base_actions.items():
                if "layers.0." in key:
                    for i in range(num_layers):
                        final_actions[key.replace("layers.0.", f"layers.{i}.")] = action
                final_actions[key] = action

            # Add tp split for expert params.
            base_actions = {
                "layers.0.block_sparse_moe.experts.0.w1.weight": partial(fn, is_column=True),
                "layers.0.block_sparse_moe.experts.0.w2.weight": partial(fn, is_column=False),
                "layers.0.block_sparse_moe.experts.0.w3.weight": partial(fn, is_column=True),
            }
            for key, action in base_actions.items():
                for i in range(num_layers):
                    newkey = key.replace("layers.0.", f"layers.{i}.")
                    for j in range(num_local_experts):
                        newkey2 = newkey.replace("experts.0.", f"experts.{j}.")
                        final_actions[newkey2] = action

            return final_actions

        mappings = get_tensor_parallel_split_mappings(config.num_hidden_layers, config.num_local_experts)

        return mappings

    def _init_weights(self, layer):
        """Initialization hook"""
        if self.config.tensor_parallel_degree > 1:
            rng_tracker = get_rng_state_tracker().rng_state
        if isinstance(
            layer,
            (
                nn.Linear,
                nn.Embedding,
                mpu.VocabParallelEmbedding,
                mpu.ColumnParallelLinear,
                mpu.RowParallelLinear,
                MixtralLMHead,
                linear_utils.ColumnSequenceParallelLinear,
                linear_utils.RowSequenceParallelLinear,
            ),
        ):
            # 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):
                if layer.weight.is_distributed:
                    with rng_tracker():
                        layer.weight.set_value(
                            paddle.tensor.normal(
                                mean=0.0,
                                std=self.config.initializer_range
                                if hasattr(self.config, "initializer_range")
                                else self.mixtral.config.initializer_range,
                                shape=layer.weight.shape,
                            )
                        )
                else:
                    layer.weight.set_value(
                        paddle.tensor.normal(
                            mean=0.0,
                            std=self.config.initializer_range
                            if hasattr(self.config, "initializer_range")
                            else self.mixtral.config.initializer_range,
                            shape=layer.weight.shape,
                        )
                    )
        # Layer.apply is DFS https://github.com/PaddlePaddle/Paddle/blob/a6f5021fcc58b21f4414bae6bf4731ef6971582c/python/paddle/nn/layer/layers.py#L527-L530
        # sublayer is init first
        # scale RowParallelLinear weight
        with paddle.no_grad():
            if isinstance(layer, MixtralMLP):
                factor = 1 / math.sqrt(2 * self.config.num_hidden_layers)
                layer.w2.weight.scale_(factor)
            if isinstance(layer, MixtralAttention):
                factor = 1 / math.sqrt(2 * self.config.num_hidden_layers)
                layer.o_proj.weight.scale_(factor)


@register_base_model
class MixtralModel(MixtralPretrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MixtralDecoderLayer`]
    Args:
        config: MixtralConfig
    """

    def __init__(self, config: MixtralConfig):
        super().__init__(config)
        self.vocab_size = config.vocab_size
        self.hidden_size = config.hidden_size
        self.sequence_parallel = config.sequence_parallel
        self.recompute_granularity = config.recompute_granularity
        self.no_recompute_layers = config.no_recompute_layers if config.no_recompute_layers is not None else []

        # Recompute defaults to False and is controlled by Trainer
        self.enable_recompute = False
        if config.tensor_parallel_degree > 1 and config.vocab_size % config.tensor_parallel_degree == 0:
            self.embed_tokens = mpu.VocabParallelEmbedding(
                self.vocab_size,
                self.hidden_size,
                weight_attr=paddle.ParamAttr(initializer=nn.initializer.XavierNormal()),
            )
        else:
            self.embed_tokens = nn.Embedding(
                self.vocab_size,
                self.hidden_size,
            )

        self.layers = nn.LayerList(
            [MixtralDecoderLayer(config, i not in self.no_recompute_layers) for i in range(config.num_hidden_layers)]
        )
        self.norm = MixtralRMSNorm(config)

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @staticmethod
    def _prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values_length, dtype):
        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            if len(attention_mask.shape) == 2:
                expanded_attn_mask = _expand_2d_mask(attention_mask, dtype, tgt_length=input_shape[-1])
                # For decoding phase in generation, seq_length = 1, we don't need to add causal mask
                if input_shape[-1] > 1:
                    combined_attention_mask = _make_causal_mask(
                        input_shape,
                        past_key_values_length=past_key_values_length,
                    )
                    expanded_attn_mask = expanded_attn_mask & combined_attention_mask
            # [bsz, seq_len, seq_len] -> [bsz, 1, seq_len, seq_len]
            elif len(attention_mask.shape) == 3:
                expanded_attn_mask = attention_mask.unsqueeze(1).astype("bool")
            # if attention_mask is already 4-D, do nothing
            else:
                expanded_attn_mask = attention_mask
        else:
            expanded_attn_mask = _make_causal_mask(
                input_shape,
                past_key_values_length=past_key_values_length,
            )
        # Convert bool attention_mask to float attention mask, which will be added to attention_scores later
        expanded_attn_mask = paddle.where(expanded_attn_mask.to("bool"), 0.0, paddle.finfo(dtype).min).astype(dtype)
        return expanded_attn_mask

    @paddle.jit.not_to_static
    def recompute_training_full(
        self,
        layer_module: nn.Layer,
        hidden_states: Tensor,
        position_ids: Optional[Tensor],
        attention_mask: Tensor,
        output_attentions: bool,
        output_router_logits: bool,
        past_key_value: Tensor,
        use_cache: bool,
    ):
        def create_custom_forward(module):
            def custom_forward(*inputs):
                return module(*inputs)

            return custom_forward

        hidden_states = recompute(
            create_custom_forward(layer_module),
            hidden_states,
            position_ids,
            attention_mask,
            output_attentions,
            output_router_logits,
            past_key_value,
            use_cache,
            use_reentrant=self.config.recompute_use_reentrant,
        )

        return hidden_states

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        use_cache=None,
        past_key_values=None,
        output_attentions=False,
        output_hidden_states=None,
        output_router_logits: Optional[bool] = None,
        return_dict=False,
        **kwargs,
    ):
        if self.sequence_parallel and use_cache:
            raise ValueError("We currently only support sequence parallel without cache.")

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        if past_key_values is None:
            past_key_values = tuple([None] * len(self.layers))
        # NOTE: to make cache can be clear in-time
        past_key_values = list(past_key_values)

        seq_length_with_past = seq_length
        cache_length = 0
        if past_key_values[0] is not None:
            cache_length = past_key_values[0][0].shape[1]
            seq_length_with_past += cache_length
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if self.sequence_parallel:
            # [bs, seq_len, num_head * head_dim] -> [bs * seq_len, num_head * head_dim]
            bs, seq_len, hidden_size = inputs_embeds.shape
            inputs_embeds = paddle.reshape_(inputs_embeds, [bs * seq_len, hidden_size])
            # [seq_len * bs / n, num_head * head_dim] (n is mp parallelism)
            inputs_embeds = ScatterOp.apply(inputs_embeds)

        # embed positions
        if attention_mask is None:
            # [bs, seq_len]
            attention_mask = paddle.ones((batch_size, seq_length_with_past), dtype=paddle.bool)

        if position_ids is None:
            position_ids = paddle.arange(seq_length, dtype="int64").expand((batch_size, seq_length))

        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, (batch_size, seq_length), cache_length, inputs_embeds.dtype
        )  # [bs, 1, seq_len, seq_len]
        if self.config.use_flash_attention:
            is_casual = is_casual_mask(attention_mask)
            if is_casual:
                attention_mask = None
        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_logits = () if output_router_logits else None
        next_decoder_cache = () if use_cache else None

        for idx, (decoder_layer) in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)
            past_key_value = past_key_values[idx] if past_key_values is not None else None

            has_gradient = not hidden_states.stop_gradient
            if (
                self.enable_recompute
                and idx not in self.no_recompute_layers
                and has_gradient
                and self.recompute_granularity == "full"
            ):
                layer_outputs = self.recompute_training_full(
                    decoder_layer,
                    hidden_states,
                    position_ids,
                    attention_mask,
                    output_attentions,
                    output_router_logits,
                    past_key_value,
                    use_cache,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    position_ids,
                    attention_mask,
                    output_attentions,
                    output_router_logits,
                    past_key_value,
                    use_cache,
                )

            # NOTE: clear outdate cache after it has been used for memory saving
            past_key_value = past_key_values[idx] = None
            if type(layer_outputs) is tuple:
                hidden_states = layer_outputs[0]
            else:
                hidden_states = layer_outputs

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_router_logits:
                all_router_logits += (layer_outputs[-1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
                if v is not None
            )
        return MoEModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            router_logits=all_router_logits,
        )


class MixtralPretrainingCriterion(paddle.nn.Layer):
    """
    Criterion for Mixtral.
    It calculates the final loss.
    """

    def __init__(self, config):

        super(MixtralPretrainingCriterion, self).__init__()
        self.ignore_index = getattr(config, "ignore_index", -100)
        self.config = config
        self.enable_parallel_cross_entropy = config.tensor_parallel_degree > 1 and config.tensor_parallel_output

        if self.enable_parallel_cross_entropy:  # and False: # and lm_head is distributed
            self.loss_func = mpu.ParallelCrossEntropy(ignore_index=self.ignore_index)
        else:
            self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none", ignore_index=self.ignore_index)

    def forward(self, prediction_scores, masked_lm_labels):
        if self.enable_parallel_cross_entropy:
            if prediction_scores.shape[-1] == self.config.vocab_size:
                warnings.warn(
                    f"enable_parallel_cross_entropy, the vocab_size should be splited: {prediction_scores.shape[-1]}, {self.config.vocab_size}"
                )
                self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none", ignore_index=self.ignore_index)

        with paddle.amp.auto_cast(False):
            masked_lm_loss = self.loss_func(prediction_scores.astype("float32"), masked_lm_labels.unsqueeze(2))

            # skip ignore_index which loss == 0
            masked_lm_loss = masked_lm_loss[masked_lm_loss > 0]
            loss = paddle.mean(masked_lm_loss)

        return loss


class MixtralLMHead(nn.Layer):
    def __init__(self, config: MixtralConfig):
        super(MixtralLMHead, self).__init__()
        self.config = config
        if config.tensor_parallel_degree > 1 and config.vocab_size % config.tensor_parallel_degree == 0:
            vocab_size = config.vocab_size // config.tensor_parallel_degree
        else:
            vocab_size = config.vocab_size

        if vocab_size != config.vocab_size:
            with get_rng_state_tracker().rng_state():
                self.weight = self.create_parameter(
                    shape=[config.hidden_size, vocab_size],
                    dtype=paddle.get_default_dtype(),
                )
        else:
            self.weight = self.create_parameter(
                shape=[config.hidden_size, vocab_size],
                dtype=paddle.get_default_dtype(),
            )
        # Must set distributed attr for Tensor Parallel !
        self.weight.is_distributed = True if (vocab_size != config.vocab_size) else False
        if self.weight.is_distributed:
            self.weight.split_axis = 1

    def forward(self, hidden_states, tensor_parallel_output=None):
        if self.config.sequence_parallel:
            hidden_states = GatherOp.apply(hidden_states)
            seq_length = self.config.seq_length
            hidden_states = paddle.reshape_(hidden_states, [-1, seq_length, self.config.hidden_size])

        if tensor_parallel_output is None:
            tensor_parallel_output = self.config.tensor_parallel_output

        logits = parallel_matmul(hidden_states, self.weight, tensor_parallel_output=tensor_parallel_output)
        return logits


class MixtralForCausalLM(MixtralPretrainedModel):
    enable_to_static_method = True

    def __init__(self, config):
        super().__init__(config)
        self.config = config

        self.mixtral = MixtralModel(config)
        self.lm_head = MixtralLMHead(config)
        self.criterion = MixtralPretrainingCriterion(config)
        self.router_aux_loss_coef = config.router_aux_loss_coef
        self.num_experts = config.num_local_experts
        self.num_experts_per_tok = config.num_experts_per_tok

        if config.sliding_window is not None:
            logger.warning("We do not support sliding window attention for now.")

    def get_input_embeddings(self):
        return self.mixtral.embed_tokens

    def set_input_embeddings(self, value):
        self.mixtral.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.mixtral = decoder

    def get_decoder(self):
        return self.mixtral

    def prepare_inputs_for_generation(
        self,
        input_ids,
        use_cache=False,
        past_key_values=None,
        inputs_embeds=None,
        output_router_logits=False,
        **kwargs
    ):
        batch_size, seq_length = input_ids.shape
        position_ids = kwargs.get("position_ids", paddle.arange(seq_length).expand((batch_size, seq_length)))
        attention_mask = kwargs.get("attention_mask", None)
        if past_key_values:
            input_ids = input_ids[:, -1].unsqueeze(axis=-1)
            position_ids = position_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
                "output_router_logits": output_router_logits,
            }
        )
        return model_inputs

    def _get_model_inputs_spec(self, dtype: str):
        return {
            "input_ids": paddle.static.InputSpec(shape=[None, None], dtype="int64"),
            "attention_mask": paddle.static.InputSpec(shape=[None, None], dtype="int64"),
            "position_ids": paddle.static.InputSpec(shape=[None, None], dtype="int64"),
        }

    @staticmethod
    def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
        # update cache
        if isinstance(outputs, tuple) and len(outputs) > 1 and not isinstance(outputs[1], paddle.Tensor):
            model_kwargs["past_key_values"] = outputs[1]

        if isinstance(outputs, MoECausalLMOutputWithPast) and "past_key_values" in outputs:
            model_kwargs["past_key_values"] = outputs.past_key_values

        # update position_ids
        if "position_ids" in model_kwargs and model_kwargs["position_ids"] is not None:
            position_ids = model_kwargs["position_ids"]
            model_kwargs["position_ids"] = paddle.concat([position_ids, position_ids[..., -1:] + 1], axis=-1)

        if not is_encoder_decoder and "attention_mask" in model_kwargs:
            attention_mask = model_kwargs["attention_mask"]
            model_kwargs["attention_mask"] = paddle.concat(
                [attention_mask, paddle.ones([attention_mask.shape[0], 1], dtype=attention_mask.dtype)], axis=-1
            )

        return model_kwargs

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        labels=None,
        use_cache=False,
        past_key_values=None,
        output_attentions=None,
        output_hidden_states=None,
        output_router_logits: Optional[bool] = None,
        return_dict=None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mixtral(
            input_ids,  # [bs, seq_len]
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            past_key_values=past_key_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]  # [bs, seq_len, dim]

        # if labels is None，means we need full output, instead of tensor_parallel_output
        # tensor_parallel_output is together with ParallelCrossEntropy
        tensor_parallel_output = self.config.tensor_parallel_output and self.config.tensor_parallel_degree > 1

        logits = self.lm_head(hidden_states, tensor_parallel_output=tensor_parallel_output)

        loss = None
        if labels is not None:
            loss = self.criterion(logits, labels)

        aux_loss = None
        if output_router_logits:
            aux_loss = load_balancing_loss_func(
                outputs.router_logits if return_dict else outputs[-1],
                self.num_experts,
                self.num_experts_per_tok,
                attention_mask,
            )
            if labels is not None:
                loss += self.router_aux_loss_coef * aux_loss

        if not return_dict:
            output = (logits,) + outputs[1:]
            if output_router_logits:
                output = (aux_loss,) + output
            return (loss,) + output if loss is not None else output

        return MoECausalLMOutputWithPast(
            loss=loss,
            aux_loss=aux_loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            router_logits=outputs.router_logits,
        )
