//! StarCoder model implementation with quantization support.
//!
//! StarCoder is a large language model optimized for code generation.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Causal self-attention mechanism
//! - Multi-query attention (MQA)
//! - LayerNorm for normalization
//! - Absolute positional embeddings
//! - Support for 8-bit quantization
//!
//! References:
//! - [StarCoder Paper](https://arxiv.org/abs/2305.06161)
//! - [Model Card](https://huggingface.co/bigcode/starcoder)
//!

#![allow(unused)]
use candle::{DType, Device, Module, Result, Tensor, D};
use candle_nn::{layer_norm, linear_b, LayerNorm, Linear, VarBuilder};
use std::sync::Arc;

#[derive(Debug, Clone, serde::Deserialize)]
pub struct Config {
    vocab_size: usize,
    hidden_size: usize,
    intermediate_size: usize,
    num_hidden_layers: usize,
    num_attention_heads: usize,
    num_key_value_heads: usize,
    hidden_act: candle_nn::Activation,
    max_position_embeddings: usize,
    norm_epsilon: f64,
    rope_theta: f64,
    use_bias: bool,
    sliding_window: Option<usize>,
}

#[derive(Debug, Clone)]
struct RotaryEmbedding {
    sin: Tensor,
    cos: Tensor,
}

fn rotate_half(xs: &Tensor) -> Result<Tensor> {
    let last_dim = xs.dim(D::Minus1)?;
    let xs1 = xs.narrow(D::Minus1, 0, last_dim / 2)?;
    let xs2 = xs.narrow(D::Minus1, last_dim / 2, last_dim - last_dim / 2)?;
    Tensor::cat(&[&xs2.neg()?, &xs1], D::Minus1)
}

impl RotaryEmbedding {
    fn new(dtype: DType, cfg: &Config, dev: &Device) -> Result<Self> {
        let dim = cfg.hidden_size / cfg.num_attention_heads;
        let max_seq_len = cfg.max_position_embeddings;
        let inv_freq: Vec<_> = (0..dim)
            .step_by(2)
            .map(|i| 1f32 / cfg.rope_theta.powf(i as f64 / dim as f64) as f32)
            .collect();
        let inv_freq_len = inv_freq.len();
        let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
        let t = Tensor::arange(0u32, max_seq_len as u32, dev)?
            .to_dtype(dtype)?
            .reshape((max_seq_len, 1))?;
        let freqs = t.matmul(&inv_freq)?;
        let freqs = Tensor::cat(&[&freqs, &freqs], D::Minus1)?;
        Ok(Self {
            sin: freqs.sin()?,
            cos: freqs.cos()?,
        })
    }

    fn apply_rotary_emb_qkv(
        &self,
        q: &Tensor,
        k: &Tensor,
        seqlen_offset: usize,
    ) -> Result<(Tensor, Tensor)> {
        let (_b_sz, _h, seq_len, _n_embd) = q.dims4()?;
        let cos = self.cos.narrow(0, seqlen_offset, seq_len)?;
        let sin = self.sin.narrow(0, seqlen_offset, seq_len)?;
        let cos = cos.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
        let sin = sin.unsqueeze(0)?.unsqueeze(0)?; // (1, 1, seq_len, dim)
        let q_embed = (q.broadcast_mul(&cos)? + rotate_half(q)?.broadcast_mul(&sin))?;
        let k_embed = (k.broadcast_mul(&cos)? + rotate_half(k)?.broadcast_mul(&sin))?;
        Ok((q_embed, k_embed))
    }
}

#[derive(Debug, Clone)]
#[allow(clippy::upper_case_acronyms)]
struct MLP {
    c_fc: Linear,
    c_proj: Linear,
    act: candle_nn::Activation,
}

impl MLP {
    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
        let (h_size, i_size) = (cfg.hidden_size, cfg.intermediate_size);
        let c_fc = linear_b(h_size, i_size, cfg.use_bias, vb.pp("c_fc"))?;
        let c_proj = linear_b(i_size, h_size, cfg.use_bias, vb.pp("c_proj"))?;
        Ok(Self {
            c_fc,
            c_proj,
            act: cfg.hidden_act,
        })
    }
}

impl Module for MLP {
    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        xs.apply(&self.c_fc)?.apply(&self.act)?.apply(&self.c_proj)
    }
}

#[derive(Debug, Clone)]
struct Attention {
    q_proj: Linear,
    k_proj: Linear,
    v_proj: Linear,
    o_proj: Linear,
    num_heads: usize,
    num_kv_heads: usize,
    num_kv_groups: usize,
    head_dim: usize,
    hidden_size: usize,
    rotary_emb: Arc<RotaryEmbedding>,
    kv_cache: Option<(Tensor, Tensor)>,
}

impl Attention {
    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
        let hidden_sz = cfg.hidden_size;
        let num_heads = cfg.num_attention_heads;
        let num_kv_heads = cfg.num_key_value_heads;
        let num_kv_groups = num_heads / num_kv_heads;
        let head_dim = hidden_sz / num_heads;
        let b = cfg.use_bias;
        let q_proj = linear_b(hidden_sz, num_heads * head_dim, b, vb.pp("q_proj"))?;
        let k_proj = linear_b(hidden_sz, num_kv_heads * head_dim, b, vb.pp("k_proj"))?;
        let v_proj = linear_b(hidden_sz, num_kv_heads * head_dim, b, vb.pp("v_proj"))?;
        let o_proj = linear_b(num_heads * head_dim, hidden_sz, b, vb.pp("o_proj"))?;
        Ok(Self {
            q_proj,
            k_proj,
            v_proj,
            o_proj,
            num_heads,
            num_kv_heads,
            num_kv_groups,
            head_dim,
            hidden_size: hidden_sz,
            rotary_emb,
            kv_cache: None,
        })
    }

    fn forward(
        &mut self,
        xs: &Tensor,
        attention_mask: Option<&Tensor>,
        seqlen_offset: usize,
    ) -> Result<Tensor> {
        let (b_sz, q_len, _) = xs.dims3()?;

        let query_states = self.q_proj.forward(xs)?;
        let key_states = self.k_proj.forward(xs)?;
        let value_states = self.v_proj.forward(xs)?;

        let query_states = query_states
            .reshape((b_sz, q_len, self.num_heads, self.head_dim))?
            .transpose(1, 2)?;
        let key_states = key_states
            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
            .transpose(1, 2)?;
        let value_states = value_states
            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
            .transpose(1, 2)?;

        let (query_states, key_states) =
            self.rotary_emb
                .apply_rotary_emb_qkv(&query_states, &key_states, seqlen_offset)?;

        let (key_states, value_states) = match &self.kv_cache {
            None => (key_states, value_states),
            Some((prev_k, prev_v)) => {
                let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
                let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
                (key_states, value_states)
            }
        };
        self.kv_cache = Some((key_states.clone(), value_states.clone()));

        let key_states = crate::utils::repeat_kv(key_states, self.num_kv_groups)?;
        let value_states = crate::utils::repeat_kv(value_states, self.num_kv_groups)?;

        let scale = 1f64 / f64::sqrt(self.head_dim as f64);
        let attn_weights = (query_states.matmul(&key_states.transpose(2, 3)?)? * scale)?;

        let attn_weights = match attention_mask {
            None => attn_weights,
            Some(mask) => attn_weights.broadcast_add(mask)?,
        };
        let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
        let attn_output = attn_weights.matmul(&value_states)?;
        attn_output
            .transpose(1, 2)?
            .reshape((b_sz, q_len, self.hidden_size))?
            .apply(&self.o_proj)
    }

    fn clear_kv_cache(&mut self) {
        self.kv_cache = None
    }
}

#[derive(Debug, Clone)]
struct DecoderLayer {
    self_attn: Attention,
    mlp: MLP,
    input_layernorm: LayerNorm,
    post_attention_layernorm: LayerNorm,
}

impl DecoderLayer {
    fn new(rotary_emb: Arc<RotaryEmbedding>, cfg: &Config, vb: VarBuilder) -> Result<Self> {
        let self_attn = Attention::new(rotary_emb, cfg, vb.pp("self_attn"))?;
        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
        let input_layernorm =
            layer_norm(cfg.hidden_size, cfg.norm_epsilon, vb.pp("input_layernorm"))?;
        let post_attention_layernorm = layer_norm(
            cfg.hidden_size,
            cfg.norm_epsilon,
            vb.pp("post_attention_layernorm"),
        )?;
        Ok(Self {
            self_attn,
            mlp,
            input_layernorm,
            post_attention_layernorm,
        })
    }

    fn forward(
        &mut self,
        xs: &Tensor,
        attention_mask: Option<&Tensor>,
        seqlen_offset: usize,
    ) -> Result<Tensor> {
        let residual = xs;
        let xs = self.input_layernorm.forward(xs)?;
        let xs = self.self_attn.forward(&xs, attention_mask, seqlen_offset)?;
        let xs = (xs + residual)?;
        let residual = &xs;
        let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
        residual + xs
    }

    fn clear_kv_cache(&mut self) {
        self.self_attn.clear_kv_cache()
    }
}

#[derive(Debug, Clone)]
pub struct Model {
    embed_tokens: candle_nn::Embedding,
    layers: Vec<DecoderLayer>,
    norm: LayerNorm,
    lm_head: Linear,
    sliding_window: Option<usize>,
    device: Device,
    dtype: DType,
}

impl Model {
    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
        let vb_m = vb.pp("model");
        let embed_tokens =
            candle_nn::embedding(cfg.vocab_size, cfg.hidden_size, vb_m.pp("embed_tokens"))?;
        let rotary_emb = Arc::new(RotaryEmbedding::new(vb.dtype(), cfg, vb_m.device())?);
        let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
        let vb_l = vb_m.pp("layers");
        for layer_idx in 0..cfg.num_hidden_layers {
            let layer = DecoderLayer::new(rotary_emb.clone(), cfg, vb_l.pp(layer_idx))?;
            layers.push(layer)
        }
        let norm = layer_norm(cfg.hidden_size, cfg.norm_epsilon, vb_m.pp("norm"))?;
        let lm_head = candle_nn::Linear::new(embed_tokens.embeddings().clone(), None);
        Ok(Self {
            embed_tokens,
            layers,
            norm,
            lm_head,
            sliding_window: cfg.sliding_window,
            device: vb.device().clone(),
            dtype: vb.dtype(),
        })
    }

    fn prepare_decoder_attention_mask(
        &self,
        b_size: usize,
        tgt_len: usize,
        seqlen_offset: usize,
    ) -> Result<Tensor> {
        let sliding_window = self.sliding_window.unwrap_or(tgt_len + 42);
        let mask: Vec<_> = (0..tgt_len)
            .flat_map(|i| {
                (0..tgt_len).map(move |j| {
                    if i < j || j + sliding_window < i {
                        f32::NEG_INFINITY
                    } else {
                        0.
                    }
                })
            })
            .collect();
        let mask = Tensor::from_slice(&mask, (tgt_len, tgt_len), &self.device)?;
        let mask = if seqlen_offset > 0 {
            let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, &self.device)?;
            Tensor::cat(&[&mask0, &mask], D::Minus1)?
        } else {
            mask
        };
        mask.expand((b_size, 1, tgt_len, tgt_len + seqlen_offset))?
            .to_dtype(self.dtype)
    }

    pub fn forward(&mut self, input_ids: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
        let (b_size, seq_len) = input_ids.dims2()?;
        let attention_mask = if seq_len <= 1 {
            None
        } else {
            let mask = self.prepare_decoder_attention_mask(b_size, seq_len, seqlen_offset)?;
            Some(mask)
        };
        let mut xs = self.embed_tokens.forward(input_ids)?;
        for layer in self.layers.iter_mut() {
            xs = layer.forward(&xs, attention_mask.as_ref(), seqlen_offset)?
        }
        xs.narrow(1, seq_len - 1, 1)?
            .apply(&self.norm)?
            .apply(&self.lm_head)
    }

    pub fn clear_kv_cache(&mut self) {
        for layer in self.layers.iter_mut() {
            layer.clear_kv_cache()
        }
    }
}