"""Full definition of a GPT NeoX Language Model, all of it in this single file.

Based on the nanoGPT implementation: https://github.com/karpathy/nanoGPT and

https://github.com/EleutherAI/gpt-neox/tree/main/megatron/model.

"""

import math

from typing import Any, Optional, Tuple

import torch

import torch.nn as nn

from typing_extensions import Self

from lit_gpt.config import Config

class GPT(nn.Module):

    def __init__(self, config: Config) -> None:

        super().__init__()

        assert config.padded_vocab_size is not None

        self.config = config

        self.lm_head = nn.Linear(config.n_embd, config.padded_vocab_size, bias=config.lm_head_bias)

        self.transformer = nn.ModuleDict(

            dict(

                wte=nn.Embedding(config.padded_vocab_size, config.n_embd),

                h=nn.ModuleList(Block(config) for _ in range(config.n_layer)),

                ln_f=config.norm_class(config.n_embd, eps=config.norm_eps),

            )

        )

        self.max_seq_length = self.config.block_size

        self.mask_cache: Optional[torch.Tensor] = None

    @property

    def max_seq_length(self) -> int:

        return self._max_seq_length

    @max_seq_length.setter

    def max_seq_length(self, value: int) -> None:

        """

        When doing inference, the sequences used might be shorter than the model's context length.

        This allows setting a smaller number to avoid allocating unused memory

        """

        if value > self.config.block_size:

            raise ValueError(f"Cannot attend to {value}, block size is only {self.config.block_size}")

        self._max_seq_length = value

        if not hasattr(self, "cos"):

            # first call

            cos, sin = self.rope_cache()

            self.register_buffer("cos", cos, persistent=False)

            self.register_buffer("sin", sin, persistent=False)

        elif value != self.cos.size(0):

            # override

            self.cos, self.sin = self.rope_cache(device=self.cos.device)

        # the mask and kv cache size will get updated on `set_kv_cache`. we cannot update it here because we don't know

        # if the kv cache is expected

    def reset_parameters(self) -> None:

        # Trigger resetting the rope-cache

        self.max_seq_length = self.config.block_size

    def _init_weights(self, module: nn.Module) -> None:

        """Meant to be used with `gpt.apply(gpt._init_weights)`."""

        if isinstance(module, nn.Linear):

            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

            if module.bias is not None:

                torch.nn.init.zeros_(module.bias)

        elif isinstance(module, nn.Embedding):

            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx: torch.Tensor, input_pos: Optional[torch.Tensor] = None) -> torch.Tensor:

        T = idx.size(1)

        if self.max_seq_length < T:

            raise ValueError(f"Cannot forward sequence of length {T}, max seq length is only {self.max_seq_length}.")

        if input_pos is not None:  # use the kv cache

            cos = self.cos.index_select(0, input_pos)

            sin = self.sin.index_select(0, input_pos)

            if self.mask_cache is None:

                raise TypeError("You need to call `gpt.set_kv_cache()`")

            mask = self.mask_cache.index_select(2, input_pos)

        else:

            cos = self.cos[:T]

            sin = self.sin[:T]

            mask = None

        x = self.transformer.wte(idx)  # token embeddings of shape (b, t, n_embd)

        for block in self.transformer.h:

            x = block(x, cos, sin, mask, input_pos)

        x = self.transformer.ln_f(x)

        return self.lm_head(x)  # (b, t, vocab_size)

    @classmethod

    def from_name(cls, name: str, **kwargs: Any) -> Self:

        return cls(Config.from_name(name, **kwargs))

    def rope_cache(self, device: Optional[torch.device] = None) -> Tuple[torch.Tensor, torch.Tensor]:

        return build_rope_cache(

            seq_len=self.max_seq_length,

            n_elem=self.config.rope_n_elem,

            device=device,

            condense_ratio=self.config.rope_condense_ratio,

            base=self.config.rope_base,

        )

    def set_kv_cache(

        self,

        batch_size: int,

        rope_cache_length: Optional[int] = None,

        device: Optional[torch.device] = None,

        dtype: Optional[torch.dtype] = None,

    ) -> None:

        if rope_cache_length is None:

            rope_cache_length = self.cos.size(-1)

        max_seq_length = self.max_seq_length

        # initialize the kv cache for all blocks

        for block in self.transformer.h:

            block.attn.kv_cache = block.attn.build_kv_cache(

                batch_size, max_seq_length, rope_cache_length, device, dtype

            )

        if self.mask_cache is None or self.mask_cache.size(3) != max_seq_length:

            # passing `attn_mask` to SDPA downgrades it to use the inefficient implementation. since we only need the mask

            # for the kv-cache support (only during inference), we only create it in that situation

            # this will be resolved by https://github.com/pytorch/pytorch/issues/96099

            ones = torch.ones((max_seq_length, max_seq_length), device=device, dtype=torch.bool)

            self.mask_cache = torch.tril(ones).unsqueeze(0).unsqueeze(0)

    def clear_kv_cache(self) -> None:

        self.mask_cache = None

        for block in self.transformer.h:

            block.attn.kv_cache = None

class Block(nn.Module):

    def __init__(self, config: Config) -> None:

        super().__init__()

        self.norm_1 = config.norm_class(config.n_embd, eps=config.norm_eps)

        self.attn = CausalSelfAttention(config)

        self.norm_2 = None if config.shared_attention_norm else config.norm_class(config.n_embd, eps=config.norm_eps)

        self.mlp = config.mlp_class(config)

        self.config = config

    def forward(

        self,

        x: torch.Tensor,

        cos: torch.Tensor,

        sin: torch.Tensor,

        mask: Optional[torch.Tensor] = None,

        input_pos: Optional[torch.Tensor] = None,

    ) -> torch.Tensor:

        n_1 = self.norm_1(x)

        h = self.attn(n_1, cos, sin, mask, input_pos)

        if self.config.parallel_residual:

            n_2 = n_1 if self.config.shared_attention_norm else self.norm_2(x)

            x = self.mlp(n_2) + h + x

        else:

            if self.config.shared_attention_norm:

                raise NotImplementedError(

                    "No checkpoint amongst the ones we support uses this configuration"

                    " (non-parallel residual and shared attention norm)."

                )

            x = h + x

            x = self.mlp(self.norm_2(x)) + x

        return x

class CausalSelfAttention(nn.Module):

    def __init__(self, config: Config) -> None:

        super().__init__()

        shape = (config.n_head + 2 * config.n_query_groups) * config.head_size

        # key, query, value projections for all heads, but in a batch

        self.attn = nn.Linear(config.n_embd, shape, bias=config.bias)

        # output projection

        self.proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)

        # disabled by default

        self.kv_cache: Optional[KVCache] = None

        self.config = config

    def forward(

        self,

        x: torch.Tensor,

        cos: torch.Tensor,

        sin: torch.Tensor,

        mask: Optional[torch.Tensor] = None,

        input_pos: Optional[torch.Tensor] = None,

    ) -> torch.Tensor:

        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)

        qkv = self.attn(x)

        # assemble into a number of query groups to support MHA, MQA and GQA together (see `config.n_query_groups`)

        q_per_kv = self.config.n_head // self.config.n_query_groups

        total_qkv = q_per_kv + 2  # each group has 1+ queries, 1 key, and 1 value

        qkv = qkv.view(B, T, self.config.n_query_groups, total_qkv, self.config.head_size)

        qkv = qkv.permute(0, 2, 3, 1, 4)  # (B, n_query_groups, total_qkv, T, hs)

        # split batched computation into three

        q, k, v = qkv.split((q_per_kv, 1, 1), dim=2)

        # maybe repeat k and v if for the non multi-head attention cases

        # training: flash attention requires it

        # inference: multi-query would require a full kv cache so avoid it to limit its memory usage

        if self.config.n_query_groups != self.config.n_head and (input_pos is None or self.config.n_query_groups != 1):

            k = k.expand(B, self.config.n_query_groups, q_per_kv, T, self.config.head_size)

            v = v.expand(B, self.config.n_query_groups, q_per_kv, T, self.config.head_size)

        q = q.reshape(B, -1, T, self.config.head_size)  # (B, nh_q, T, hs)

        k = k.reshape(B, -1, T, self.config.head_size)  # (B, nh_k, T, hs)

        v = v.reshape(B, -1, T, self.config.head_size)  # (B, nh_v, T, hs)

        q_roped = apply_rope(q[..., : self.config.rope_n_elem], cos, sin)

        k_roped = apply_rope(k[..., : self.config.rope_n_elem], cos, sin)

        q = torch.cat((q_roped, q[..., self.config.rope_n_elem :]), dim=-1)

        k = torch.cat((k_roped, k[..., self.config.rope_n_elem :]), dim=-1)

        if input_pos is not None:

            if not isinstance(self.kv_cache, KVCache):

                raise TypeError("You need to call `gpt.set_kv_cache()`")

            k, v = self.kv_cache(input_pos, k, v)

        y = self.scaled_dot_product_attention(q, k, v, mask)

        y = y.reshape(B, T, C)  # re-assemble all head outputs side by side

        # output projection

        return self.proj(y)

    def scaled_dot_product_attention(

        self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, mask: Optional[torch.Tensor] = None

    ) -> torch.Tensor:

        scale = 1.0 / math.sqrt(self.config.head_size)

        y = torch.nn.functional.scaled_dot_product_attention(

            q, k, v, attn_mask=mask, dropout_p=0.0, scale=scale, is_causal=mask is None

        )

        return y.transpose(1, 2)

    def build_kv_cache(

        self,

        batch_size: int,

        max_seq_length: int,

        rope_cache_length: Optional[int] = None,

        device: Optional[torch.device] = None,

        dtype: Optional[torch.dtype] = None,

    ) -> "KVCache":

        heads = 1 if self.config.n_query_groups == 1 else self.config.n_head

        v_shape = (batch_size, heads, max_seq_length, self.config.head_size)

        if rope_cache_length is None:

            if self.config.rotary_percentage != 1.0:

                raise TypeError("Please pass the `rope_cache_length=gpt.cos.size(-1)` value")

            k_shape = v_shape

        else:

            k_shape = (

                batch_size,

                heads,

                max_seq_length,

                rope_cache_length + self.config.head_size - self.config.rope_n_elem,

            )

        return KVCache(k_shape, v_shape, device=device, dtype=dtype)

class GptNeoxMLP(nn.Module):

    def __init__(self, config: Config) -> None:

        super().__init__()

        self.fc = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)

        self.proj = nn.Linear(config.intermediate_size, config.n_embd, bias=config.bias)

        self.config = config

    def forward(self, x: torch.Tensor) -> torch.Tensor:

        x = self.fc(x)

        x = torch.nn.functional.gelu(x, approximate=self.config.gelu_approximate)

        return self.proj(x)

class LLaMAMLP(nn.Module):

    def __init__(self, config: Config) -> None:

        super().__init__()

        self.fc_1 = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)

        self.fc_2 = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)

        self.proj = nn.Linear(config.intermediate_size, config.n_embd, bias=config.bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:

        x_fc_1 = self.fc_1(x)

        x_fc_2 = self.fc_2(x)

        x = torch.nn.functional.silu(x_fc_1) * x_fc_2

        return self.proj(x)

def build_rope_cache(

    seq_len: int, n_elem: int, device: Optional[torch.device] = None, base: int = 10000, condense_ratio: int = 1

) -> Tuple[torch.Tensor, torch.Tensor]:

    """Enhanced Transformer with Rotary Position Embedding.

    Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/

    transformers/rope/__init__.py. MIT License:

    https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.

    """

    # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$

    theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, device=device).float() / n_elem))

    # Create position indexes `[0, 1, ..., seq_len - 1]`

    seq_idx = torch.arange(seq_len, device=device) / condense_ratio

    # Calculate the product of position index and $\theta_i$

    idx_theta = torch.outer(seq_idx, theta).repeat(1, 2)

    return torch.cos(idx_theta), torch.sin(idx_theta)

def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:

    head_size = x.size(-1)

    x1 = x[..., : head_size // 2]  # (B, nh, T, hs/2)

    x2 = x[..., head_size // 2 :]  # (B, nh, T, hs/2)

    rotated = torch.cat((-x2, x1), dim=-1)  # (B, nh, T, hs)

    roped = (x * cos) + (rotated * sin)

    return roped.type_as(x)

class KVCache(nn.Module):

    def __init__(

        self,

        k_shape: Tuple[int, int, int, int],

        v_shape: Tuple[int, int, int, int],

        device: Optional[torch.device] = None,

        dtype: Optional[torch.dtype] = None,

    ) -> None:

        super().__init__()

        self.register_buffer("k", torch.zeros(k_shape, device=device, dtype=dtype), persistent=False)

        self.register_buffer("v", torch.zeros(v_shape, device=device, dtype=dtype), persistent=False)

    def forward(self, input_pos: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:

        # move the buffer to the activation dtype for when AMP is used

        self.k = self.k.to(k.dtype)

        self.v = self.v.to(v.dtype)

        # update the cache

        k = self.k.index_copy_(2, input_pos, k)

        v = self.v.index_copy_(2, input_pos, v)

        return k, v

    def reset_parameters(self) -> None:

        torch.nn.init.zeros_(self.k)

        torch.nn.init.zeros_(self.v)