# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Optional

import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
import time

def find_multiple(n: int, k: int) -> int:
    if n % k == 0:
        return n
    return n + k - (n % k)

class AdaptiveLayerNorm(nn.Module):
    r"""Adaptive Layer Normalization"""

    def __init__(self, d_model, norm) -> None:
        super(AdaptiveLayerNorm, self).__init__()
        self.project_layer = nn.Linear(d_model, 2 * d_model)
        self.norm = norm
        self.d_model = d_model
        self.eps = self.norm.eps

    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
        if embedding is None:
            return self.norm(input)
        weight, bias = torch.split(
            self.project_layer(embedding),
            split_size_or_sections=self.d_model,
            dim=-1,
        )
        return weight * self.norm(input) + bias


@dataclass
class ModelArgs:
    block_size: int = 2048
    vocab_size: int = 32000
    n_layer: int = 32
    n_head: int = 32
    dim: int = 4096
    intermediate_size: int = None
    n_local_heads: int = -1
    head_dim: int = 64
    rope_base: float = 10000
    norm_eps: float = 1e-5
    has_cross_attention: bool = False
    context_dim: int = 0
    is_causal: bool = False
    dropout_rate: float = 0.1
    attn_dropout_rate: float = 0.1

    def __post_init__(self):
        if self.n_local_heads == -1:
            self.n_local_heads = self.n_head
        if self.intermediate_size is None:
            hidden_dim = 4 * self.dim
            n_hidden = int(2 * hidden_dim / 3)
            self.intermediate_size = find_multiple(n_hidden, 256)
        # self.head_dim = self.dim // self.n_head

class Transformer(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.config = config

        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
        self.norm = AdaptiveLayerNorm(config.dim, RMSNorm(config.dim, eps=config.norm_eps))

        self.max_batch_size = -1
        self.max_seq_length = config.block_size
        freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.head_dim,
                                              self.config.rope_base)
        self.register_buffer("freqs_cis", freqs_cis)

        causal_mask = torch.tril(
            torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool)
        )
        self.register_buffer("causal_mask", causal_mask)

    def forward(self,
                x: Tensor,
                c: Tensor,
                input_pos: Optional[Tensor] = None,
                mask: Optional[Tensor] = None,
                context: Optional[Tensor] = None,
                context_input_pos: Optional[Tensor] = None,
                cross_attention_mask: Optional[Tensor] = None,
                ) -> Tensor:
        if mask is None:
            mask = self.causal_mask[:x.size(1), :x.size(1)]
        else:
            mask = mask[..., input_pos]
        freqs_cis = self.freqs_cis[input_pos]
        if context is not None:
            context_freqs_cis = self.freqs_cis[context_input_pos]
        else:
            context_freqs_cis = None
        skip_in_x_list = []
        for i, layer in enumerate(self.layers):
            x = layer(x, c, freqs_cis, mask, context, context_freqs_cis, cross_attention_mask)
        x = self.norm(x, c)
        return x


class TransformerBlock(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.attention = Attention(config)
        self.feed_forward = FeedForward(config)
        self.ffn_norm = AdaptiveLayerNorm(config.dim, RMSNorm(config.dim, eps=config.norm_eps))
        self.attention_norm = AdaptiveLayerNorm(config.dim, RMSNorm(config.dim, eps=config.norm_eps))

        if config.has_cross_attention:
            self.has_cross_attention = True
            self.cross_attention = Attention(config, is_cross_attention=True)
            self.cross_attention_norm = AdaptiveLayerNorm(config.dim, RMSNorm(config.dim, eps=config.norm_eps))
        else:
            self.has_cross_attention = False

    def forward(self,
                x: Tensor,
                c: Tensor,
                freqs_cis: Tensor,
                mask: Tensor,
                context: Optional[Tensor] = None,
                context_freqs_cis: Optional[Tensor] = None,
                cross_attention_mask: Optional[Tensor] = None,
                ) -> Tensor:
        #time_attn_start = time.time()
        h = x + self.attention(self.attention_norm(x, c), freqs_cis, mask)
        #print(f"time take for attention of sequence length {x.shape[1]} is {time.time() - time_attn_start}")
        if self.has_cross_attention:
            h = h + self.cross_attention(self.cross_attention_norm(h, c), freqs_cis, cross_attention_mask, context, context_freqs_cis)
        out = h + self.feed_forward(self.ffn_norm(h, c))
        return out


class Attention(nn.Module):
    def __init__(self, config: ModelArgs, is_cross_attention: bool = False):
        super().__init__()
        assert config.dim % config.n_head == 0

        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
        # key, query, value projections for all heads, but in a batch
        if is_cross_attention:
            self.wq = nn.Linear(config.dim, config.n_head * config.head_dim, bias=False)
            self.wkv = nn.Linear(config.context_dim, 2 * config.n_local_heads * config.head_dim, bias=False)
        else:
            self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
        self.wo = nn.Linear(config.head_dim * config.n_head, config.dim, bias=False)
        self.kv_cache = None

        self.n_head = config.n_head
        self.head_dim = config.head_dim
        self.n_local_heads = config.n_local_heads
        self.dim = config.dim
        self.attn_dropout_rate = config.attn_dropout_rate

    def forward(self,
                x: Tensor,
                freqs_cis: Tensor,
                mask: Tensor,
                context: Optional[Tensor] = None,
                context_freqs_cis: Optional[Tensor] = None,
                ) -> Tensor:
        bsz, seqlen, _ = x.shape

        kv_size = self.n_local_heads * self.head_dim
        if context is None:
            q, k, v = self.wqkv(x).split([kv_size, kv_size, kv_size], dim=-1)
            context_seqlen = seqlen
        else:
            q = self.wq(x)
            k, v = self.wkv(context).split([kv_size, kv_size], dim=-1)
            context_seqlen = context.shape[1]

        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
        k = k.view(bsz, context_seqlen, self.n_local_heads, self.head_dim)
        v = v.view(bsz, context_seqlen, self.n_local_heads, self.head_dim)

        q = apply_rotary_emb(q, freqs_cis)
        k = apply_rotary_emb(k, context_freqs_cis if context_freqs_cis is not None else freqs_cis)

        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))

        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=self.attn_dropout_rate if self.training else 0.0)

        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.head_dim * self.n_head)

        y = self.wo(y)
        return y


class FeedForward(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=False)
        self.w3 = nn.Linear(config.dim, config.intermediate_size, bias=False)
        self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)

    def forward(self, x: Tensor) -> Tensor:
        return self.w2(self.dropout(F.silu(self.w1(x)) * self.w3(x)))


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)

    def forward(self, x: Tensor) -> Tensor:
        output = self._norm(x.float()).type_as(x)
        return output * self.weight


def precompute_freqs_cis(
        seq_len: int, n_elem: int, base: int = 10000,
        dtype: torch.dtype = torch.bfloat16
) -> Tensor:
    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
    t = torch.arange(seq_len, device=freqs.device)
    freqs = torch.outer(t, freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
    return cache.to(dtype=dtype)


def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
    freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2)
    x_out2 = torch.stack(
        [
            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
        ],
        -1,
    )

    x_out2 = x_out2.flatten(3)
    return x_out2.type_as(x)